Aggressive behaviors are highly prevalent among psychiatric patients, presenting a major obstacle to patient care. In addition to suffering, these symptoms constitute a leading cause for institutionalization Gouveia et al., 2021a; Brentani et al., 2013; Gouveia et al., 2019. Standard treatments for aggressive behaviors involve behavioral and pharmacological therapies that mainly act on the dopaminergic and serotonergic systems (e.g. serotonin reuptake inhibitors, antipsychotics) Gouveia et al., 2021a; Brentani et al., 2013; Gouveia et al., 2019⁠. Despite their efficacy, a substantial proportion (30%) of patients fail to respond and are considered to be treatment refractory Adler et al., 2015; Gouveia et al., 2020; Gouveia et al., 2021b. For these patients, neuromodulation therapies, such as deep brain stimulation (DBS), have been investigated Gouveia et al., 2019; Gouveia et al., 2021c; Contreras Lopez et al., 2021; Benedetti-Isaac et al., 2015; Torres et al., 2020; Micieli et al., 2017; López Ríos et al., 2023. DBS is a neurosurgical therapy in which implanted electrodes are used to adjustably deliver electrical current to specific brain targets Jakobs et al., 2019. It is an established therapy for Parkinson’s Disease, dystonia, essential tremor, epilepsy Salanova et al., 2021 and obsessive-compulsive disorder Hamani et al., 2014; Li et al., 2020, showing promising results for the treatment of several other neuropsychiatric disorders, including Alzheimer’s disease Germann et al., 2021a; Lozano et al., 2016, depression, anorexia nervosa, addiction, and posttraumatic stress disorder Elias et al., 2021; Lozano et al., 2008; De Vloo et al., 2021; Elias et al., 2022; Hamani et al., 2022; Davidson et al., 2022.

To date, DBS for refractory aggressive behavior has primarily targeted the posterior hypothalamic region Gouveia et al., 2019; Gouveia et al., 2021c; Contreras Lopez et al., 2021; Benedetti-Isaac et al., 2015; Torres et al., 2020; Micieli et al., 2017; López Ríos et al., 2023. The hypothalamus is a diencephalic structure with well-established roles in the control of homeostasis and motivated behaviors and is a key area in a broader neurocircuitry regulating aggressive behavior that also involves the orbitofrontal cortex, hippocampus, amygdala and periaqueductal gray Dudás, 2013; Blair, 2016; Miczek et al., 2007. Along the anterior-posterior axis, the hypothalamus can be divided into three regions (i.e. supraoptic or anterior, tuber cinereum or medial and supramamillary or posterior) with distinct cell types, projections and functions Dudás, 2013; Beattie et al., 1930. While the anterior region is mainly involved in thermoregulation and the control of circadian rhythms, the medial region regulates feeding and sexual behavior and plays a critical role in several endocrine and autonomic processes Dudás, 2013; Hoff, 1950; Flament-Durand, 1980. The posterior hypothalamus (pHyp) is an ergotropic area involved in the generation of sympathetic responses Hess, 1945. It provides robust projections to the midbrain and reticular formation via the hypothalamotegmental tract, thus being critical in the regulation of wakefulness and stress responses Beattie et al., 1930; Lechan and Toni, 2016; Saper and Lowell, 2014. Indeed, lesions (e.g. hamartomas and gliomas) located at the pHyp have been reported to cause apathetic and somnolent symptoms, while the selective neurosurgical ablation of a small portion of the pHyp has been successfully used to treat patients with severe treatment-resistant aggressive behavior (for a detailed review, see Gouveia et al., 2019; Sano et al., 1966; Sano and Mayanagi, 1988).

More recently, DBS of the pHyp (pHyp-DBS) has also been shown to reduce aggressive symptoms in humans, with variable outcomes Gouveia et al., 2021c; Contreras Lopez et al., 2021; Benedetti-Isaac et al., 2015; Torres et al., 2020; Franzini et al., 2010; Torres et al., 2013; López Ríos et al., 2023. At present, only 21patients diagnosed with autism spectrum disorder (ASD), intellectual disability, obsessive-compulsive disorder, epilepsy and schizophrenia with ages ranging from 10 to 51 years have been reported, mostly as individual cases or small case series. In these patients, long-term improvement (up to several years of follow-up) in aggressive behaviors was in the order of 38–100% compared to baseline Gouveia et al., 2021c; Contreras Lopez et al., 2021; Benedetti-Isaac et al., 2015; Torres et al., 2020; Micieli et al., 2017; López Ríos et al., 2023. Although case studies and small series provide valuable insight into the safety and therapeutic impact of pHyp-DBS in individual patients, they do not allow the characterization of clinical phenotypes, optimal stimulation targets or brain networks underlying treatment.

In this work, we gathered data from the largest international multi-centre dataset of patients treated to date with pHyp-DBS for aggressive behaviors to retrospectively investigate possible neurobiological mechanisms of action. Combining a well-documented electrode localization and volume of activated tissue (VAT) modeling pipeline (https://www.lead-dbs.org/) with probabilistic sweet-spot mapping Elias et al., 2021; Dembek et al., 2017, normative connectomics Fox, 2018; Elias et al., 2020; Germann et al., 2021b, and transcriptomics analysis Gouveia et al., 2021a; Mroczek et al., 2021 (https://alleninstitute.org/), we delineated a potentially ‘optimized’ surgical target and identified the brain networks and underlying neurobiological processes that might underpin successful pHyp-DBS. Demographic data, pre-and post-operative magnetic resonance imaging (MRI) and computed tomography (CT) scans were obtained from each participant for DBS lead localization, followed by an estimation of the VAT and the determination of the most effective region of stimulation. The VATs were further processed for connectomic analyses to investigate the structural (i.e. fiber tracts) and functional (i.e. brain areas) maps associated with symptom improvement. Using demographics and individual functional connectivity, we tested a predictive model of improvement following pHyp-DBS. Finally, we investigated genes with a spatial pattern of distribution similar to the functional connectivity map to explore associated biological processes. A graphical summary of the methodology used in this study can be found in Figure 1 and in Figure 1—figure supplement 1.

Figure 1 with 1 supplement see all

Download asset Open asset

In this work, we performed an integrated imaging analysis of a large, international multi-center dataset of patients treated with pHyp-DBS for severe refractory aggressive behavior. Overall, the response rate for pHyp-DBS was high, with 91% (30 out of 33) of patients qualifying as treatment responders. In addition, 64% of patients presented symptom improvement greater than 81% (Figure 2), and the average improvement across the whole pHyp-DBS cohort was 75%. These results are in line with previous work showing that symptom improvement following hypothalamic neurosurgery for the treatment of aggressive behavior is in the order of 90% Gouveia et al., 2019. In our series, four patients were implanted with directional DBS electrodes. Outcomes in patients implanted with these systems or conventional electrodes were found to be comparable Contreras Lopez et al., 2021. While analysis derived from retrospective clinical data is more prone to potential bias, this study included a collection of previously published prospective trials, where each center standardized the timeframe for data collection and the instruments used to evaluate symptom improvement.

Although we did not observe a difference in the main diagnosis between the pediatric and adult populations, the former presented the highest percentage of symptom improvement following pHyp-DBS treatment. Considering that the brain only fully achieves maturity in early adulthood Gallagher et al., 2019, it is possible that treating dysfunctional circuitries while they are still being developed could induce beneficial and long-lasting plastic changes to restore functional normality. DBS is primarily used for the treatment of neurological and psychiatric disorders in adults and is emerging as an effective and safe therapy for neurological diseases in children Lipsman et al., 2010 such as childhood dystonia Elkaim et al., 2018, drug-resistant epilepsy Yan et al., 2018 and Gilles de la Tourette syndrome Coulombe et al., 2018.

Using patients’ pre and postoperative imaging, we performed a Voxel Efficacy Mapping investigation and were able to determine a specific area that, when stimulated, is associated with an improvement greater than 90% located in the more posterior-inferior-lateral aspect of the posterior hypothalamic area (coordinates are provided in Talairach-Tournoux space and MNI152 space in the Results section). In humans, DBS and lesions of the posterior hypothalamus have been consistently shown to reduce aggressive symptoms (for a detailed review, see Gouveia et al., 2019). One of the most commonly targeted areas in those series lies along the midpoint of the line between the anterior and posterior commissures (AC-PC line), in a region anterior to the rostral end of the aqueduct, posterior to the anterior border of the mammillary body and superior to the red nucleus Sano and Mayanagi, 1988 (Figure 3—figure supplement 1 shows the location of the sweet-spot in relation to the red nucleus and subthalamic nucleus).

Our connectomics analyses provided insight into the structural and functional networks that underlie the clinical efficacy of pHyp-DBS. As no patient-specific functional or diffusion-weighted imaging scans were available, these analyses were performed using high-resolution, high signal-to-noise ratio images derived from healthy individuals that create an average atlas of the functional and connectivity profiles of the human brain Gouveia et al., 2021c; Germann et al., 2021a; Elias et al., 2021; Germann et al., 2021b; Wang et al., 2021; Elias, 2022. The analysis of functional and structural connectivity via normative datasets is an opportunity to investigate possible underlying mechanisms based on the current knowledge of the typical human brain. Unlike patient-specific studies, where functional MRI and diffusion-weighted images are acquired from a single patient, normative analyses rely on high-quality atlases derived from very large cohorts of healthy subjects (around 1000 brain scans). Thus, these exploratory data-driven approaches are intended to investigate the pattern of brain activity and structural connectivity related to a specific region of interest (i.e. individual VAT) and correlate these data with clinical outcomes to simulate the presumed brain changes associated with DBS treatment. This approach allows for the expansion of the investigation to a large, previously inaccessible cohort of patients whose individual data are available, generating knowledge that can help optimize treatment in future patient populations. Although normative connectivity data may not fully capture patient- or pathology-specific variations, they have been previously shown to generate results that are comparable to patient-specific imaging data Elias, 2022; Wang et al., 2021.

Structural connectivity analysis in our study implicated fiber tracts involved in somatosensation (e.g. medial lemniscus and spinothalamic tract), emotional regulation (e.g. amygdalofugal pathway and medial forebrain bundle), and motor signaling (e.g. motor projections and central tegmental tract) in the amelioration of aggressive behavior. Previous studies have shown a significant correlation between aggressive behavior and motor agitation Gouveia et al., 2020; Gouveia et al., 2021b, with a concomitant reduction in both following neuromodulatory treatments targeting the posterior hypothalamus Gouveia et al., 2021c; López Ríos et al., 2023. Indeed, patients with neurodevelopmental disorders, especially those presenting with self-injurious behavior, are known to frequently have altered sensory perception (including reduced pain sensitivity), which can contribute to the chronicity and severity of the behaviour Gouveia et al., 2021a; Koenig et al., 2017. Furthermore, self-injurious behaviors in this patient population are considered to be a type of repetitive/stereotypic behavior that results in physical injury to the patient’s own body Hagopian et al., 2015; Yan et al., 2022, highlighting the relevance of the motor system in the network subserving aggressive behavior. As such, modulation of somatosensory and motor pathways – perhaps leading to increased pain perception or awareness, and reduction in repetitive/stereotypic behaviors – could help reduce self-injurious behaviors.

More generally, aggressive behaviors and motor agitation are believed to occur as a result of a decreased tolerance to provocative stimuli caused by reduced serotonergic transmission in the prefrontal cortex, resulting in an ineffective top-down inhibitory control over a consequently hyper-activated amygdala. The amygdala, in turn, sends alert signs to the periaqueductal gray and hypothalamus for motor activation and hormonal production, preparing the organism for a response Gouveia et al., 2021a; Gouveia et al., 2019; Gouveia et al., 2020; Gouveia et al., 2021b; Blair, 2016. Thus, it is conceivable that stimulation of fiber tracts involved in emotional regulation and motor outputs could mediate clinical improvement by reducing motor agitation, improving concentration and learning, and increasing tolerance to provocative external stimuli. The functional connectivity mapping showed that the successful VATs were functionally connected to several key areas within the neurocircuitry of aggressive behavior, including the amygdala, nucleus accumbens, hypothalamus, periaqueductal gray, and cingulate cortex. To further explore these results, we investigated the relationship between the functional connectivity of each of these brain structures and individual treatment outcomes and observed that functional connectivity with the PAG is the most relevant. In addition, when modeling the functional connectivity of any two brain areas identified in the functional connectivity analysis, we observed that the most relevant connections involved the PAG and limbic structures believed to be at the core of the neurocircuitry of aggressive behavior, such as the amygdala, anterior cingulate cortex, and nucleus accumbens. In line with our findings, previous studies have shown positive outcomes following ablative and neuromodulation treatments targeting the amygdala, hypothalamus, and nucleus accumbens for the control of aggressive behavior Gouveia et al., 2021a; Gouveia et al., 2019; Gouveia et al., 2021b; Yan et al., 2022. Moreover, PAG DBS – primarily performed to treat neuropathic pain Knotkova et al., 2021 – has been reported to induce changes in mood and anxiety Gray et al., 2014.

Another aspect detected by the functional connectivity analysis was the involvement of areas responsible for the production of serotonin (i.e. raphe nuclei) and dopamine (i.e. substantia nigra). Both these monoamines are thought to be directly involved in the initiation and maintenance of aggressive behaviors due to their role in facilitating top-down inhibitory controls, regulating mood, and mediating social behaviors Gouveia et al., 2019; Gouveia et al., 2021c; Miczek et al., 2002. Antipsychotics (both typical and atypical) and selective serotonin reuptake inhibitors – the most commonly used drugs to control of aggressive behaviors – act on the dopaminergic and serotonergic neurotransmission systems Gouveia et al., 2021a; Gouveia et al., 2019. In a mouse model of escalated aggression, pHyp-DBS increased the density of serotonin receptors type 1A (5-HT1_A) in the orbitofrontal cortex and amygdala, while pretreatment with a 5-HT1_A antagonist blocked the anti-aggressive effect of stimulation Gouveia et al., 2023.

To further explore the dataset pooled in our study, we performed a spatial imaging transcriptomic analysis. Our goal was to investigate spatial correlations between gene expression patterns (i.e. microarray data from the Allen Human Brain Atlas) and imaging characteristics (i.e. functional connectivity map derived from the patient’s VATs) and possible molecular characteristics associated with symptom improvement (for a review on imaging transcriptomics see Fornito et al., 2019). Imaging transcriptomics is a powerful non-invasive method to measure neural phenotypes and investigate neurobiological mechanisms of disease or treatments Arnatkeviciute et al., 2022. Genes whose spatial pattern correlates with brain changes in imaging transcriptomics are also found in post-mortem and large-scale GWAS studies of specific patient populations Arnatkeviciute et al., 2022. In fact, several studies have demonstrated associations between the spatial distribution of genes and neurodevelopmental changes Xie et al., 2020; Romero-Garcia et al., 2019 as well as psychiatric disorders Romero-Garcia et al., 2020. In this study, we identified a number of genes with similar spatial distribution to brain regions that showed clinically important functional connectivity with the stimulation target. The biological processes associated with these genes were investigated using several gene ontology analysis tools. The Jensen Tissues library revealed a list of brain structures classically associated with aggressive behavior, including those associated with the limbic system (e.g. amygdala and hypothalamus) and those involved in higher-order cognitive centers (e.g. prefrontal cortex). As described above, dysregulation in the top-down inhibitory control of the prefrontal cortex over the limbic system is thought to subserve aggressive behaviors. Additionally, the KEGG 2021 Human Library identified genes implicated in the oxytocin signaling pathway, a peptide hormone critically implicated in aggressive behavior Neumann, 2008. It has been shown that intranasal administration of oxytocin increases human aggressive responses when subjects are evaluated in a social orientation paradigm Ne’eman et al., 2016. Moreover, single-nucleotide polymorphisms in the oxytocin gene have been associated with childhood-onset aggressive behaviors Malik et al., 2012 and with aggressive behaviors in men intoxicated with alcohol Johansson et al., 2012.

A large number of genes identified by the Jensen Compartments and KEGG 2021 Human Libraries are part of biological processes associated with neuronal communication and plasticity, thus possibly linked to the intrinsic mechanism of action of DBS Hamani and Temel, 2012; Florence et al., 2016. As a neuromodulation therapy, DBS induces long-lasting changes in cellular and molecular aspects of neurons belonging to dysfunctional neurocircuitries in order to restore functional normality Jakobs et al., 2019; Karas et al., 2013; Lozano et al., 2019. Although its neurobiological mechanisms are not fully understood, DBS is believed to exert its effects by altering the cellular membrane potential, resulting in either an increase or a decrease in action potentials Jakobs et al., 2019; Karas et al., 2013; Lozano et al., 2019. These changes are then propagated throughout neural networks, changing neurotransmitter dynamics, protein expression, and membrane receptor availability Jakobs et al., 2019; Karas et al., 2013; Lozano et al., 2019. In fact, a previous study using imaging transcriptomics to investigate brain changes after functional neurosurgery of the amygdala in patients with refractory aggressive behavior has also identified several genes related to neuronal communication and plasticity Gouveia et al., 2021a. Preclinical studies have also found significant increases in neural precursor cells, plasticity, and precursor cell markers in animals implanted with DBS, along with significant reductions in the number of activated microglia and astrocytes Vedam-Mai et al., 2016; Leplus et al., 2019; Chan et al., 2018; Bambico et al., 2015; Encinas et al., 2011. This is in line with the cell type investigation, where a significant reduction in the ratio of genes per cell type was observed for astrocytes and microglia when compared to the expected distribution. Both astrocytes and microglia are highly implicated in mechanisms of neuroinflammation Carson et al., 2006, which in turn is associated with several psychiatric disorders Najjar et al., 2013. The reduction of neuroinflammatory markers (such as glial activation and interleukin levels) has been described in several preclinical DBS studies and is thought to be involved in the mechanism of action of DBS Chan et al., 2018; Chen et al., 2020; Amorim et al., 2015.

Some limitations of this study are of note; with the exception of the analysis of the most efficacious location of stimulation (i.e. voxel efficacy map), which is fully derived from patient data, the remaining exploratory analyses were performed using normative datasets. This methodology was chosen as neither patient-specific functional nor diffusion-weighted imaging was available. Sophisticated imaging acquisitions, such as the ones necessary for the connectivity analysis, are not frequently acquired in patients with implanted DBS, as many devices have not been considered safe for MRI use. Additionally, it is frequent for health centers to have a ‘no-MRI’ policy for patients with implanted DBS, only allowing for the acquisition of CTs following surgery. It is important to highlight that previous studies showed that normative datasets could be used to create probabilistic models of optimal connectivity associated with patients’ outcomes that are meaningful to predict outcomes in patient-specific connectivity data Wang et al., 2021. Similarly, individual brain genetic profiles were not available for this patient population. The imaging transcriptomic analysis performed here was based on the Allen Human Brain Atlas, which is derived from post-mortem biopsies of multiple adult control brains Hawrylycz et al., 2012; Arnatkeviciute et al., 2019; Arnatkeviciute et al., 2023; Markello et al., 2021, and has been shown to be relevant to large-scale GWAS studies of specific patient populations Arnatkeviciute et al., 2022.

The structural connectivity analysis performed here provides a detailed overview of the relevant tracts that are related to symptom alleviation in this patient population. However, for precise segregation of these fiber tracts and the determination of those that are necessary for symptom improvement, it would be necessary to perform preclinical experiments to selectively activate/inactivate specific fiber tracts and investigate the impact of these changes on behavior and network dynamics. Nevertheless, understanding how the location of the most efficacious area of stimulation correlates with these tracts is important for refining the surgical planning of future patients, as one could use individual patient tractography to determine the optimal location of the electrodes based on how it would impact individual fiber tracts.

The codes for electrode localization, modelling of the volume of activated tissue, and imaging connectomics (i.e. functional and structural connectivity) are freely available in Lead-DBS (https://www.lead-dbs.org/). The codes, along with the Allen Human Brain Atlas (AHBA) microarray dataset, for the analysis of spatial transcriptomics are freely available in abagen (https://abagen.readthedocs.io/en/stable/). Along with the codes, the websites for these two toolboxes provide manuals describing the step-by-step procedure for successful analysis. The dataset accompanying this study is freely available at Zenodo (https://doi.org/10.5281/zenodo.7344268).

1. 1. Gouveia FV
   2. Jurgen G

   (2022) Zenodo

   Dataset accompanying this study.

   https://doi.org/10.5281/zenodo.7344268

1. 1. Adler BA
   2. Wink LK
   3. Early M
   4. Shaffer R
   5. Minshawi N
   6. McDougle CJ
   7. Erickson CA

   (2015) Drug-refractory aggression, self-injurious behavior, and severe tantrums in autism spectrum disorders: a chart review study

   Autism 19:102–106.

   https://doi.org/10.1177/1362361314524641

   • PubMed
   • Google Scholar
2. 1. Amorim BO
   2. Covolan L
   3. Ferreira E
   4. Brito JG
   5. Nunes DP
   6. de Morais DG
   7. Nobrega JN
   8. Rodrigues AM
   9. deAlmeida ACG
   10. Hamani C

   (2015) Deep brain stimulation induces Antiapoptotic and anti-inflammatory effects in epileptic rats

   Journal of Neuroinflammation 12:162.

   https://doi.org/10.1186/s12974-015-0384-7

   • PubMed
   • Google Scholar
3. 1. Amorim L
   2. Gouveia FV
   3. Germann J
   4. Zambori D
   5. Morais R
   6. Sato FM
   7. Fongaro C
   8. Portolese J
   9. Brentani H
   10. Martinez R

   (2018) Oxytocin and Gynecomastia: correlation or causality

   Cureus 10:e2661.

   https://doi.org/10.7759/cureus.2661

   • Google Scholar
4. 1. Arnatkeviciute A
   2. Fulcher BD
   3. Fornito A

   (2019) A practical guide to linking brain-wide gene expression and neuroimaging data

   NeuroImage 189:353–367.

   https://doi.org/10.1016/j.neuroimage.2019.01.011

   • PubMed
   • Google Scholar
5. Preprint

   1. Arnatkeviciute A
   2. Fulcher BD
   3. Bellgrove MA
   4. Fornito A

   (2022) Imaging transcriptomics of brain disorders

   PsyArXiv.

   https://doi.org/10.1016/j.bpsgos.2021.10.002

   • Google Scholar
6. 1. Arnatkeviciute A
   2. Markello RD
   3. Fulcher BD
   4. Misic B
   5. Fornito A

   (2023) Toward best practices for imaging Transcriptomics of the human brain

   Biological Psychiatry 93:391–404.

   https://doi.org/10.1016/j.biopsych.2022.10.016

   • PubMed
   • Google Scholar
7. 1. Avants BB
   2. Tustison NJ
   3. Song G
   4. Cook PA
   5. Klein A
   6. Gee JC

   (2011) A reproducible evaluation of ants similarity metric performance in brain image registration

   NeuroImage 54:2033–2044.

   https://doi.org/10.1016/j.neuroimage.2010.09.025

   • PubMed
   • Google Scholar
8. 1. Bambico FR
   2. Bregman T
   3. Diwan M
   4. Li J
   5. Darvish-Ghane S
   6. Li Z
   7. Laver B
   8. Amorim BO
   9. Covolan L
   10. Nobrega JN
   11. Hamani C

   (2015) Neuroplasticity-dependent and -Independent mechanisms of chronic deep brain stimulation in stressed rats

   Translational Psychiatry 5:e674.

   https://doi.org/10.1038/tp.2015.166

   • PubMed
   • Google Scholar
9. 1. Beattie J
   2. Brow GR
   3. Long CNH

   (1930) Physiological and anatomical evidence for the existence of nerve tracts connecting the hypothalamus with spinal sympathetic centres

   Proceedings of the Royal Society of London. Series B, Containing Papers of a Biological Character 106:253–275.

   https://doi.org/10.1098/rspb.1930.0027

   • Google Scholar
10. 1. Benedetti-Isaac JC
    2. Torres-Zambrano M
    3. Vargas-Toscano A
    4. Perea-Castro E
    5. Alcalá-Cerra G
    6. Furlanetti LL
    7. Reithmeier T
    8. Tierney TS
    9. Anastasopoulos C
    10. Fonoff ET
    11. Contreras Lopez WO

    (2015) Seizure frequency reduction after Posteromedial hypothalamus deep brain stimulation in drug-resistant epilepsy associated with intractable aggressive behavior

    Epilepsia 56:1152–1161.

    https://doi.org/10.1111/epi.13025

    • PubMed
    • Google Scholar
11. 1. Blair RJR

    (2016) The Neurobiology of impulsive aggression

    Journal of Child and Adolescent Psychopharmacology 26:4–9.

    https://doi.org/10.1089/cap.2015.0088

    • Google Scholar
12. 1. Brentani H
    2. Paula CSD
    3. Bordini D
    4. Rolim D
    5. Sato F
    6. Portolese J
    7. Pacifico MC
    8. McCracken JT

    (2013) Autism spectrum disorders: an overview on diagnosis and treatment

    Revista Brasileira de Psiquiatria 35 Suppl 1:S62–S72.

    https://doi.org/10.1590/1516-4446-2013-S104

    • PubMed
    • Google Scholar
13. 1. Carson MJ
    2. Thrash JC
    3. Walter B

    (2006) The cellular response in Neuroinflammation: the role of leukocytes, Microglia and Astrocytes in neuronal death and survival

    Clinical Neuroscience Research 6:237–245.

    https://doi.org/10.1016/j.cnr.2006.09.004

    • PubMed
    • Google Scholar
14. 1. Chan HH
    2. Wathen CA
    3. Mathews ND
    4. Hogue O
    5. Modic JP
    6. Kundalia R
    7. Wyant C
    8. Park H-J
    9. Najm IM
    10. Trapp BD
    11. Machado AG
    12. Baker KB

    (2018) Lateral cerebellar nucleus stimulation promotes motor recovery and suppresses Neuroinflammation in a fluid percussion injury rodent model

    Brain Stimulation 11:1356–1367.

    https://doi.org/10.1016/j.brs.2018.07.051

    • PubMed
    • Google Scholar
15. 1. Chen Y
    2. Zhu G
    3. Liu D
    4. Zhang X
    5. Liu Y
    6. Yuan T
    7. Du T
    8. Zhang J

    (2020) Subthalamic nucleus deep brain stimulation suppresses Neuroinflammation by Fractalkine pathway in Parkinson’s disease rat model

    Brain, Behavior, and Immunity 90:16–25.

    https://doi.org/10.1016/j.bbi.2020.07.035

    • Google Scholar
16. 1. Coblentz A
    2. Elias GJB
    3. Boutet A
    4. Germann J
    5. Algarni M
    6. Oliveira LM
    7. Neudorfer C
    8. Widjaja E
    9. Ibrahim GM
    10. Kalia SK
    11. Jain M
    12. Lozano AM
    13. Fasano A

    (2021) Mapping efficacious deep brain stimulation for pediatric dystonia

    Journal of Neurosurgery. Pediatrics 27:346–356.

    https://doi.org/10.3171/2020.7.PEDS20322

    • PubMed
    • Google Scholar
17. 1. Contreras Lopez WO
    2. Navarro PA
    3. Gouveia FV
    4. Fonoff ET
    5. Lebrun I
    6. Auada AVV
    7. Lopes Alho EJ
    8. Martinez RCR

    (2021) Directional deep brain stimulation of the Posteromedial hypothalamus for refractory intermittent explosive disorder: A case series using a novel Neurostimulation device and intraoperative Microdialysis

    World Neurosurgery 155:e19–e33.

    https://doi.org/10.1016/j.wneu.2021.07.086

    • PubMed
    • Google Scholar
18. 1. Coulombe M-A
    2. Elkaim LM
    3. Alotaibi NM
    4. Gorman DA
    5. Weil AG
    6. Fallah A
    7. Kalia SK
    8. Lipsman N
    9. Lozano AM
    10. Ibrahim GM

    (2018) Deep brain stimulation for Gilles de la Tourette syndrome in children and youth: a meta-analysis with individual participant data

    Journal of Neurosurgery. Pediatrics 23:236–246.

    https://doi.org/10.3171/2018.7.PEDS18300

    • PubMed
    • Google Scholar
19. 1. Davidson B
    2. Giacobbe P
    3. George TP
    4. Nestor SM
    5. Rabin JS
    6. Goubran M
    7. Nyman AJ
    8. Baskaran A
    9. Meng Y
    10. Pople CB
    11. Graham SJ
    12. Tam F
    13. Hamani C
    14. Lipsman N

    (2022) Deep brain stimulation of the nucleus accumbens in the treatment of severe alcohol use disorder: a phase I pilot trial

    Molecular Psychiatry 27:3992–4000.

    https://doi.org/10.1038/s41380-022-01677-6

    • PubMed
    • Google Scholar
20. 1. Dembek TA
    2. Barbe MT
    3. Åström M
    4. Hoevels M
    5. Visser-Vandewalle V
    6. Fink GR
    7. Timmermann L

    (2017) Probabilistic mapping of deep brain stimulation effects in essential tremor

    NeuroImage. Clinical 13:164–173.

    https://doi.org/10.1016/j.nicl.2016.11.019

    • PubMed
    • Google Scholar
21. 1. Dembek TA
    2. Roediger J
    3. Horn A
    4. Reker P
    5. Oehrn C
    6. Dafsari HS
    7. Li N
    8. Kühn AA
    9. Fink GR
    10. Visser-Vandewalle V
    11. Barbe MT
    12. Timmermann L

    (2019) Probabilistic sweet spots predict motor outcome for deep brain stimulation in Parkinson disease

    Annals of Neurology 86:527–538.

    https://doi.org/10.1002/ana.25567

    • PubMed
    • Google Scholar
22. 1. De Vloo P
    2. Lam E
    3. Elias GJ
    4. Boutet A
    5. Sutandar K
    6. Giacobbe P
    7. Woodside DB
    8. Lipsman N
    9. Lozano A

    (2021) Long-term follow-up of deep brain stimulation for anorexia Nervosa

    Journal of Neurology, Neurosurgery, and Psychiatry 92:1135–1136.

    https://doi.org/10.1136/jnnp-2020-325711

    • PubMed
    • Google Scholar
23. Book

    1. Dudás B

    (2013)

    The Human Hypothalamus: Anatomy, Functions and Disorders

    Nova Science Publishers.

    • Google Scholar
24. 1. Duffley G
    2. Anderson DN
    3. Vorwerk J
    4. Dorval AD
    5. Butson CR

    (2019) Evaluation of Methodologies for computing the deep brain stimulation volume of tissue activated

    Journal of Neural Engineering 16:066024.

    https://doi.org/10.1088/1741-2552/ab3c95

    • PubMed
    • Google Scholar
25. 1. Eisenstein SA
    2. Koller JM
    3. Black KD
    4. Campbell MC
    5. Lugar HM
    6. Ushe M
    7. Tabbal SD
    8. Karimi M
    9. Hershey T
    10. Perlmutter JS
    11. Black KJ

    (2014) Functional anatomy of Subthalamic nucleus stimulation in Parkinson disease

    Annals of Neurology 76:279–295.

    https://doi.org/10.1002/ana.24204

    • PubMed
    • Google Scholar
26. 1. Elias GJB
    2. Giacobbe P
    3. Boutet A
    4. Germann J
    5. Beyn ME
    6. Gramer RM
    7. Pancholi A
    8. Joel SE
    9. Lozano AM

    (2020) Probing the circuitry of panic with deep brain stimulation: Connectomic analysis and review of the literature

    Brain Stimulation 13:10–14.

    https://doi.org/10.1016/j.brs.2019.09.010

    • Google Scholar
27. 1. Elias GJB
    2. Boutet A
    3. Joel SE
    4. Germann J
    5. Gwun D
    6. Neudorfer C
    7. Gramer RM
    8. Algarni M
    9. Paramanandam V
    10. Prasad S
    11. Beyn ME
    12. Horn A
    13. Madhavan R
    14. Ranjan M
    15. Lozano CS
    16. Kühn AA
    17. Ashe J
    18. Kucharczyk W
    19. Munhoz RP
    20. Giacobbe P
    21. Kennedy SH
    22. Woodside DB
    23. Kalia SK
    24. Fasano A
    25. Hodaie M
    26. Lozano AM

    (2021) Probabilistic mapping of deep brain stimulation: insights from 15 years of therapy

    Annals of Neurology 89:426–443.

    https://doi.org/10.1002/ana.25975

    • PubMed
    • Google Scholar
28. Book

    1. Elias GJB

    (2022)

    Normative Connectomes and their use in DBS

    In: Horn A, editors. In Connectomic Deep Brain Stimulation. Elsevier. pp. 245–274.

    • Google Scholar
29. 1. Elias GJB
    2. Germann J
    3. Boutet A
    4. Pancholi A
    5. Beyn ME
    6. Bhatia K
    7. Neudorfer C
    8. Loh A
    9. Rizvi SJ
    10. Bhat V
    11. Giacobbe P
    12. Woodside DB
    13. Kennedy SH
    14. Lozano AM

    (2022) Structuro-functional Surrogates of response to Subcallosal cingulate deep brain stimulation for depression

    Brain 145:362–377.

    https://doi.org/10.1093/brain/awab284

    • Google Scholar
30. 1. Elkaim LM
    2. De Vloo P
    3. Kalia SK
    4. Lozano AM
    5. Ibrahim GM

    (2018) Deep brain stimulation for childhood dystonia: Current evidence and emerging practice

    Expert Review of Neurotherapeutics 18:773–784.

    https://doi.org/10.1080/14737175.2018.1523721

    • PubMed
    • Google Scholar
31. 1. Encinas JM
    2. Hamani C
    3. Lozano AM
    4. Enikolopov G

    (2011) Neurogenic hippocampal targets of deep brain stimulation

    The Journal of Comparative Neurology 519:6–20.

    https://doi.org/10.1002/cne.22503

    • PubMed
    • Google Scholar
32. 1. Ewert S
    2. Plettig P
    3. Li N
    4. Chakravarty MM
    5. Collins DL
    6. Herrington TM
    7. Kühn AA
    8. Horn A

    (2018) Toward defining deep brain stimulation targets in MNI space: A subcortical Atlas based on Multimodal MRI, Histology and structural Connectivity

    NeuroImage 170:271–282.

    https://doi.org/10.1016/j.neuroimage.2017.05.015

    • PubMed
    • Google Scholar
33. 1. Flament-Durand J

    (1980)

    The hypothalamus: anatomy and functions

    Acta Psychiatrica Belgica 80:364–375.

    • PubMed
    • Google Scholar
34. 1. Florence G
    2. Sameshima K
    3. Fonoff ET
    4. Hamani C

    (2016) Deep brain stimulation: more complex than the inhibition of cells and Excitation of fibers

    The Neuroscientist 22:332–345.

    https://doi.org/10.1177/1073858415591964

    • PubMed
    • Google Scholar
35. 1. Fornito A
    2. Arnatkevičiūtė A
    3. Fulcher BD

    (2019) Bridging the gap between Connectome and Transcriptome

    Trends in Cognitive Sciences 23:34–50.

    https://doi.org/10.1016/j.tics.2018.10.005

    • PubMed
    • Google Scholar
36. 1. Fox MD
    2. Buckner RL
    3. Liu H
    4. Chakravarty MM
    5. Lozano AM
    6. Pascual-Leone A

    (2014) Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases

    PNAS 111:E4367–E4375.

    https://doi.org/10.1073/pnas.1405003111

    • PubMed
    • Google Scholar
37. 1. Fox MD

    (2018) Mapping symptoms to brain networks with the human Connectome

    The New England Journal of Medicine 379:2237–2245.

    https://doi.org/10.1056/NEJMra1706158

    • PubMed
    • Google Scholar
38. 1. Franzini A
    2. Messina G
    3. Cordella R
    4. Marras C
    5. Broggi G

    (2010) Deep brain stimulation of the Posteromedial hypothalamus: indications, long-term results, and neurophysiological considerations

    Neurosurgical Focus 29:E13.

    https://doi.org/10.3171/2010.5.FOCUS1094

    • PubMed
    • Google Scholar
39. Book

    1. Gallagher A
    2. Bulteau C
    3. Cohen D
    4. Michaud JL

    (2019)

    Neurocognitive Development: Normative Development

    Elsevier Science.

    • Google Scholar
40. 1. Germann J
    2. Elias GJB
    3. Boutet A
    4. Narang K
    5. Neudorfer C
    6. Horn A
    7. Loh A
    8. Deeb W
    9. Salvato B
    10. Almeida L
    11. Foote KD
    12. Rosenberg PB
    13. Tang-Wai DF
    14. Wolk DA
    15. Burke AD
    16. Salloway S
    17. Sabbagh MN
    18. Chakravarty MM
    19. Smith GS
    20. Lyketsos CG
    21. Okun MS
    22. Lozano AM

    (2021a) Brain structures and networks responsible for stimulation-induced memory flashbacks during Forniceal deep brain stimulation for Alzheimer’s disease

    Alzheimer’s & Dementia 17:777–787.

    https://doi.org/10.1002/alz.12238

    • PubMed
    • Google Scholar
41. 1. Germann J
    2. Elias GJB
    3. Neudorfer C
    4. Boutet A
    5. Chow CT
    6. Wong EHY
    7. Parmar R
    8. Gouveia FV
    9. Loh A
    10. Giacobbe P
    11. Kim SJ
    12. Jung HH
    13. Bhat V
    14. Kucharczyk W
    15. Chang JW
    16. Lozano AM

    (2021b) Potential optimization of focused ultrasound Capsulotomy for obsessive compulsive disorder

    Brain 144:3529–3540.

    https://doi.org/10.1093/brain/awab232

    • PubMed
    • Google Scholar
42. 1. Gouveia FV
    2. Hamani C
    3. Fonoff ET
    4. Brentani H
    5. Alho EJL
    6. de Morais RMCB
    7. de Souza AL
    8. Rigonatti SP
    9. Martinez RCR

    (2019) Amygdala and hypothalamus: historical overview with focus on aggression

    Neurosurgery 85:11–30.

    https://doi.org/10.1093/neuros/nyy635

    • PubMed
    • Google Scholar
43. 1. Gouveia FV
    2. Germann J
    3. Devenyi GA
    4. Morais RMCB
    5. Santos APM
    6. Fonoff ET
    7. Hamani C
    8. Brentani H
    9. Chakravarty MM
    10. Martinez RCR

    (2020) Refractoriness of aggressive behaviour to pharmacological treatment: cortical thickness analysis in autism spectrum disorder

    BJPsych Open 6:e96.

    https://doi.org/10.1192/bjo.2020.91

    • PubMed
    • Google Scholar
44. 1. Gouveia FV
    2. Germann J
    3. de Morais R
    4. Fonoff ET
    5. Hamani C
    6. Alho EJ
    7. Brentani H
    8. Martins AP
    9. Devenyi G
    10. Patel R
    11. Steele C
    12. Gramer R
    13. Chakravarty M
    14. Martinez RCR

    (2021a) Longitudinal changes after amygdala surgery for intractable aggressive behavior: clinical, imaging Genetics, and deformation-based Morphometry study-A case series

    Neurosurgery 88:E158–E169.

    https://doi.org/10.1093/neuros/nyaa378

    • PubMed
    • Google Scholar
45. 1. Gouveia FV
    2. Germann J
    3. Devenyi GA
    4. Fonoff ET
    5. Morais RMCB
    6. Brentani H
    7. Chakravarty MM
    8. Martinez RCR

    (2021b) Bilateral amygdala radio-frequency ablation for refractory aggressive behavior alters local cortical thickness to a pattern found in non-refractory patients

    Frontiers in Human Neuroscience 15:653631.

    https://doi.org/10.3389/fnhum.2021.653631

    • PubMed
    • Google Scholar
46. 1. Gouveia FV
    2. Germann J
    3. Elias GJB
    4. Hamani C
    5. Fonoff ET
    6. Martinez RCR

    (2021c) Case report: 5 years follow-up on posterior hypothalamus deep brain stimulation for intractable aggressive behaviour associated with drug-resistant epilepsy

    Brain Stimulation 14:1201–1204.

    https://doi.org/10.1016/j.brs.2021.07.062

    • Google Scholar
47. Preprint

    1. Gouveia FV
    2. Diwan M
    3. Martinez RC
    4. Giacobbe P
    5. Lipsman N
    6. Hamani C

    (2023) Reduction of Aggressive Behaviour Following Hypothalamic Deep Brain Stimulation: Involvement of 5-HT _1A and Testosterone

    bioRxiv.

    https://doi.org/10.1101/2023.03.20.533520

    • Google Scholar
48. 1. Gray AM
    2. Pounds-Cornish E
    3. Eccles FJR
    4. Aziz TZ
    5. Green AL
    6. Scott RB

    (2014) Deep brain stimulation as a treatment for neuropathic pain: a longitudinal study addressing neuropsychological outcomes

    The Journal of Pain 15:283–292.

    https://doi.org/10.1016/j.jpain.2013.11.003

    • PubMed
    • Google Scholar
49. 1. Hagopian LP
    2. Rooker GW
    3. Zarcone JR

    (2015) Delineating subtypes of self-injurious behavior maintained by automatic reinforcement

    Journal of Applied Behavior Analysis 48:523–543.

    https://doi.org/10.1002/jaba.236

    • PubMed
    • Google Scholar
50. 1. Hamani C
    2. Temel Y

    (2012) Deep brain stimulation for psychiatric disease: contributions and validity of animal models

    Science Translational Medicine 4:142rv8.

    https://doi.org/10.1126/scitranslmed.3003722

    • PubMed
    • Google Scholar
51. 1. Hamani C
    2. Pilitsis J
    3. Rughani AI
    4. Rosenow JM
    5. Patil PG
    6. Slavin KS
    7. Abosch A
    8. Eskandar E
    9. Mitchell LS
    10. Kalkanis S
    11. American Society for Stereotactic and Functional Neurosurgery
    12. Congress of Neurological Surgeons
    13. CNS and American Association of Neurological Surgeons

    (2014) Deep brain stimulation for obsessive-compulsive disorder: Systematic review and evidence-based guideline sponsored by the American society for stereotactic and functional Neurosurgery and the Congress of neurological Surgeons (CNS) and endorsed by the CNS and American Association of neurological Surgeons

    Neurosurgery 75:327–333.

    https://doi.org/10.1227/NEU.0000000000000499

    • PubMed
    • Google Scholar
52. 1. Hamani C
    2. Davidson B
    3. Corchs F
    4. Abrahao A
    5. Nestor SM
    6. Rabin JS
    7. Nyman AJ
    8. Phung L
    9. Goubran M
    10. Levitt A
    11. Talakoub O
    12. Giacobbe P
    13. Lipsman N

    (2022) Deep brain stimulation of the Subgenual Cingulum and Uncinate Fasciculus for the treatment of Posttraumatic stress disorder

    Science Advances 8:eadc9970.

    https://doi.org/10.1126/sciadv.adc9970

    • PubMed
    • Google Scholar
53. 1. Hawrylycz MJ
    2. Lein ES
    3. Guillozet-Bongaarts AL
    4. Shen EH
    5. Ng L
    6. Miller JA
    7. van de Lagemaat LN
    8. Smith KA
    9. Ebbert A
    10. Riley ZL
    11. Abajian C
    12. Beckmann CF
    13. Bernard A
    14. Bertagnolli D
    15. Boe AF
    16. Cartagena PM
    17. Chakravarty MM
    18. Chapin M
    19. Chong J
    20. Dalley RA
    21. David Daly B
    22. Dang C
    23. Datta S
    24. Dee N
    25. Dolbeare TA
    26. Faber V
    27. Feng D
    28. Fowler DR
    29. Goldy J
    30. Gregor BW
    31. Haradon Z
    32. Haynor DR
    33. Hohmann JG
    34. Horvath S
    35. Howard RE
    36. Jeromin A
    37. Jochim JM
    38. Kinnunen M
    39. Lau C
    40. Lazarz ET
    41. Lee C
    42. Lemon TA
    43. Li L
    44. Li Y
    45. Morris JA
    46. Overly CC
    47. Parker PD
    48. Parry SE
    49. Reding M
    50. Royall JJ
    51. Schulkin J
    52. Sequeira PA
    53. Slaughterbeck CR
    54. Smith SC
    55. Sodt AJ
    56. Sunkin SM
    57. Swanson BE
    58. Vawter MP
    59. Williams D
    60. Wohnoutka P
    61. Zielke HR
    62. Geschwind DH
    63. Hof PR
    64. Smith SM
    65. Koch C
    66. Grant SGN
    67. Jones AR

    (2012) An Anatomically comprehensive Atlas of the adult human brain Transcriptome

    Nature 489:391–399.

    https://doi.org/10.1038/nature11405

    • PubMed
    • Google Scholar
54. Report

    1. Hess WR

    (1945) Von DEN Höheren Zentren des Vegetativen Funktionssystemes

    Von DEN.

    https://doi.org/10.5169/SEALS-306728

    • Google Scholar
55. 1. Hoff H

    (1950) The hypothalamus, its anatomy, physiology and pathology

    Acta Neuroveg 1:123–144.

    https://doi.org/10.1007/BF01226522

    • Google Scholar
56. 1. Horn A
    2. Kühn AA

    (2015) Lead-DBS: A Toolbox for deep brain stimulation electrode Localizations and Visualizations

    NeuroImage 107:127–135.

    https://doi.org/10.1016/j.neuroimage.2014.12.002

    • Google Scholar
57. 1. Jakobs M
    2. Fomenko A
    3. Lozano AM
    4. Kiening KL

    (2019) Cellular, molecular, and clinical mechanisms of action of deep brain stimulation-a systematic review on established indications and outlook on future developments

    EMBO Molecular Medicine 11:e9575.

    https://doi.org/10.15252/emmm.201809575

    • PubMed
    • Google Scholar
58. 1. Johansson A
    2. Bergman H
    3. Corander J
    4. Waldman ID
    5. Karrani N
    6. Salo B
    7. Jern P
    8. Ålgars M
    9. Sandnabba K
    10. Santtila P
    11. Westberg L

    (2012) Alcohol and aggressive behavior in men--moderating effects of oxytocin receptor gene (OXTR) Polymorphisms

    Genes, Brain and Behavior 11:214–221.

    https://doi.org/10.1111/j.1601-183X.2011.00744.x

    • Google Scholar
59. 1. Karas PJ
    2. Mikell CB
    3. Christian E
    4. Liker MA
    5. Sheth SA

    (2013) Deep brain stimulation: a mechanistic and clinical update

    Neurosurgical Focus 35:E1.

    https://doi.org/10.3171/2013.9.FOCUS13383

    • PubMed
    • Google Scholar
60. 1. Knotkova H
    2. Hamani C
    3. Sivanesan E
    4. Le Beuffe MFE
    5. Moon JY
    6. Cohen SP
    7. Huntoon MA

    (2021) Neuromodulation for chronic pain

    Lancet 397:2111–2124.

    https://doi.org/10.1016/S0140-6736(21)00794-7

    • PubMed
    • Google Scholar
61. 1. Koenig J
    2. Rinnewitz L
    3. Warth M
    4. Hillecke TK
    5. Brunner R
    6. Resch F
    7. Kaess M

    (2017) Psychobiological response to pain in female adolescents with Nonsuicidal self-injury

    Journal of Psychiatry & Neuroscience 42:189–199.

    https://doi.org/10.1503/jpn.160074

    • PubMed
    • Google Scholar
62. 1. Kuleshov MV
    2. Jones MR
    3. Rouillard AD
    4. Fernandez NF
    5. Duan Q
    6. Wang Z
    7. Koplev S
    8. Jenkins SL
    9. Jagodnik KM
    10. Lachmann A
    11. McDermott MG
    12. Monteiro CD
    13. Gundersen GW
    14. Ma’ayan A

    (2016) Enrichr: a comprehensive Gene set enrichment analysis web server 2016 update

    Nucleic Acids Research 44:W90–W97.

    https://doi.org/10.1093/nar/gkw377

    • PubMed
    • Google Scholar
63. 1. Lacadie CM
    2. Fulbright RK
    3. Rajeevan N
    4. Constable RT
    5. Papademetris X

    (2008) More accurate Talairach coordinates for neuroimaging using non-linear registration

    NeuroImage 42:717–725.

    https://doi.org/10.1016/j.neuroimage.2008.04.240

    • PubMed
    • Google Scholar
64. 1. Lechan RM
    2. Toni R

    (2016)

    Functional Anatomy of the Hypothalamus and Pituitary

    Endotext, Functional Anatomy of the Hypothalamus and Pituitary, MDText.com, Inc.

    • Google Scholar
65. 1. Leplus A
    2. Lauritzen I
    3. Melon C
    4. Kerkerian-Le Goff L
    5. Fontaine D
    6. Checler F

    (2019) Chronic Fornix deep brain stimulation in a transgenic Alzheimer’s rat model reduces Amyloid burden, inflammation, and neuronal loss

    Brain Structure & Function 224:363–372.

    https://doi.org/10.1007/s00429-018-1779-x

    • PubMed
    • Google Scholar
66. 1. Li N
    2. Baldermann JC
    3. Kibleur A
    4. Treu S
    5. Akram H
    6. Elias GJB
    7. Boutet A
    8. Lozano AM
    9. Al-Fatly B
    10. Strange B
    11. Barcia JA
    12. Zrinzo L
    13. Joyce E
    14. Chabardes S
    15. Visser-Vandewalle V
    16. Polosan M
    17. Kuhn J
    18. Kühn AA
    19. Horn A

    (2020) A unified Connectomic target for deep brain stimulation in obsessive-compulsive disorder

    Nature Communications 11:3364.

    https://doi.org/10.1038/s41467-020-16734-3

    • Google Scholar
67. 1. Lipsman N
    2. Ellis M
    3. Lozano AM

    (2010) Current and future indications for deep brain stimulation in pediatric populations

    Neurosurgical Focus 29:E2.

    https://doi.org/10.3171/2010.5.FOCUS1095

    • Google Scholar
68. 1. López Ríos AL
    2. Germann J
    3. Hutchison WD
    4. Botero Posada LF
    5. Ahunca Velasquez LF
    6. Garcia Jimenez FA
    7. Gloria Escobar JM
    8. Chacon Ruiz Martinez R
    9. Hamani C
    10. Lebrun I
    11. Auada AVV
    12. Restrepo Bravo CA
    13. Gouveia FV

    (2023) Long-term follow-up on bilateral posterior hypothalamic deep brain stimulation for treating refractory aggressive behavior in a patient with CRI Du chat syndrome: analysis of clinical data

    Stereotactic and Functional Neurosurgery 100:275–281.

    https://doi.org/10.1159/000526871

    • Google Scholar
69. 1. Lozano AM
    2. Mayberg HS
    3. Giacobbe P
    4. Hamani C
    5. Craddock RC
    6. Kennedy SH

    (2008) Subcallosal cingulate gyrus deep brain stimulation for treatment-resistant depression

    Biological Psychiatry 64:461–467.

    https://doi.org/10.1016/j.biopsych.2008.05.034

    • PubMed
    • Google Scholar
70. 1. Lozano AM
    2. Fosdick L
    3. Chakravarty MM
    4. Leoutsakos J-M
    5. Munro C
    6. Oh E
    7. Drake KE
    8. Lyman CH
    9. Rosenberg PB
    10. Anderson WS
    11. Tang-Wai DF
    12. Pendergrass JC
    13. Salloway S
    14. Asaad WF
    15. Ponce FA
    16. Burke A
    17. Sabbagh M
    18. Wolk DA
    19. Baltuch G
    20. Okun MS
    21. Foote KD
    22. McAndrews MP
    23. Giacobbe P
    24. Targum SD
    25. Lyketsos CG
    26. Smith GS

    (2016) A phase II study of Fornix deep brain stimulation in mild Alzheimer’s disease

    Journal of Alzheimer’s Disease 54:777–787.

    https://doi.org/10.3233/JAD-160017

    • PubMed
    • Google Scholar
71. 1. Lozano AM
    2. Lipsman N
    3. Bergman H
    4. Brown P
    5. Chabardes S
    6. Chang JW
    7. Matthews K
    8. McIntyre CC
    9. Schlaepfer TE
    10. Schulder M
    11. Temel Y
    12. Volkmann J
    13. Krauss JK

    (2019) Deep brain stimulation: Current challenges and future directions

    Nature Reviews. Neurology 15:148–160.

    https://doi.org/10.1038/s41582-018-0128-2

    • PubMed
    • Google Scholar
72. 1. Malik AI
    2. Zai CC
    3. Abu Z
    4. Nowrouzi B
    5. Beitchman JH

    (2012) The role of oxytocin and oxytocin receptor gene variants in childhood-onset aggression

    Genes, Brain, and Behavior 11:545–551.

    https://doi.org/10.1111/j.1601-183X.2012.00776.x

    • PubMed
    • Google Scholar
73. 1. Mansouri AM
    2. Germann J
    3. Boutet A
    4. Elias GJB
    5. Mithani K
    6. Chow CT
    7. Karmur B
    8. Ibrahim GM
    9. McAndrews MP
    10. Lozano AM
    11. Zadeh G
    12. Valiante TA

    (2020) Identification of neural networks preferentially engaged by epileptogenic mass lesions through lesion network mapping analysis

    Scientific Reports 10:10989.

    https://doi.org/10.1038/s41598-020-67626-x

    • PubMed
    • Google Scholar
74. 1. Markello RD
    2. Arnatkeviciute A
    3. Poline J-B
    4. Fulcher BD
    5. Fornito A
    6. Misic B

    (2021) Standardizing workflows in imaging transcriptomics with the abagen toolbox

    eLife 10:e72129.

    https://doi.org/10.7554/eLife.72129

    • PubMed
    • Google Scholar
75. 1. Micieli R
    2. Rios ALL
    3. Aguilar RP
    4. Posada LFB
    5. Hutchison WD

    (2017) Single-unit analysis of the human posterior hypothalamus and red nucleus during deep brain stimulation for Aggressivity

    Journal of Neurosurgery 126:1158–1164.

    https://doi.org/10.3171/2016.4.JNS141704

    • PubMed
    • Google Scholar
76. 1. Miczek KA
    2. Fish EW
    3. De Bold JF
    4. De Almeida RMM

    (2002) Social and neural determinants of aggressive behavior: Pharmacotherapeutic targets at serotonin, dopamine and gamma-aminobutyric acid systems

    Psychopharmacology 163:434–458.

    https://doi.org/10.1007/s00213-002-1139-6

    • PubMed
    • Google Scholar
77. 1. Miczek KA
    2. de Almeida RMM
    3. Kravitz EA
    4. Rissman EF
    5. de Boer SF
    6. Raine A

    (2007) Neurobiology of escalated aggression and violence

    The Journal of Neuroscience 27:11803–11806.

    https://doi.org/10.1523/JNEUROSCI.3500-07.2007

    • PubMed
    • Google Scholar
78. Software

    1. Mouse Imaging Centre, Hospital for Sick Children, Toronto Canada

    (2022) Rminc, version edfa0f4

    GitHub.

    https://github.com/Mouse-Imaging-Centre/RMINC
79. 1. Mroczek M
    2. Desouky A
    3. Sirry W

    (2021) Imaging Transcriptomics in neurodegenerative diseases

    Journal of Neuroimaging 31:244–250.

    https://doi.org/10.1111/jon.12827

    • PubMed
    • Google Scholar
80. 1. Najjar S
    2. Pearlman DM
    3. Alper K
    4. Najjar A
    5. Devinsky O

    (2013) Neuroinflammation and psychiatric illness

    Journal of Neuroinflammation 10:43.

    https://doi.org/10.1186/1742-2094-10-43

    • PubMed
    • Google Scholar
81. 1. Ne’eman R
    2. Perach-Barzilay N
    3. Fischer-Shofty M
    4. Atias A
    5. Shamay-Tsoory SG

    (2016) Intranasal administration of oxytocin increases human aggressive behavior

    Hormones and Behavior 80:125–131.

    https://doi.org/10.1016/j.yhbeh.2016.01.015

    • PubMed
    • Google Scholar
82. 1. Neumann ID

    (2008) Brain oxytocin: a key regulator of emotional and social Behaviours in both females and males

    Journal of Neuroendocrinology 20:858–865.

    https://doi.org/10.1111/j.1365-2826.2008.01726.x

    • PubMed
    • Google Scholar
83. 1. Nieuwenhuys R
    2. Geeraedts LM
    3. Veening JG

    (1982) The medial forebrain bundle of the rat. I. general introduction

    The Journal of Comparative Neurology 206:49–81.

    https://doi.org/10.1002/cne.902060106

    • PubMed
    • Google Scholar
84. Book

    1. Nieuwenhuys R
    2. Voogd J
    3. van C

    (2007)

    The Human Central Nervous System: A Synopsis and Atlas

    Springer.

    • Google Scholar
85. Book

    1. Penny WD
    2. Friston KJ
    3. Ashburner JT
    4. Kiebel SJ
    5. Nichols TE

    (2011)

    Statistical Parametric Mapping: The Analysis of Functional Brain Images

    Elsevier.

    • Google Scholar
86. 1. Romero-Garcia R
    2. Warrier V
    3. Bullmore ET
    4. Baron-Cohen S
    5. Bethlehem RAI

    (2019) Synaptic and Transcriptionally downregulated genes are associated with cortical thickness differences in autism

    Molecular Psychiatry 24:1053–1064.

    https://doi.org/10.1038/s41380-018-0023-7

    • PubMed
    • Google Scholar
87. 1. Romero-Garcia R
    2. Seidlitz J
    3. Whitaker KJ
    4. Morgan SE
    5. Fonagy P
    6. Dolan RJ
    7. Jones PB
    8. Goodyer IM
    9. Suckling J
    10. Vértes PE
    11. Bullmore ET
    12. NSPN Consortium

    (2020) Schizotypy-related Magnetization of cortex in healthy adolescence is Colocated with expression of schizophrenia-related genes

    Biological Psychiatry 88:248–259.

    https://doi.org/10.1016/j.biopsych.2019.12.005

    • PubMed
    • Google Scholar
88. 1. Salanova V
    2. Sperling MR
    3. Gross RE
    4. Irwin CP
    5. Vollhaber JA
    6. Giftakis JE
    7. Fisher RS
    8. SANTÉ Study Group

    (2021) The SANTé study at 10 years of follow-up: effectiveness, safety, and sudden unexpected death in epilepsy

    Epilepsia 62:1306–1317.

    https://doi.org/10.1111/epi.16895

    • PubMed
    • Google Scholar
89. 1. Sano K
    2. Yoshioka M
    3. Ogashiwa M
    4. Ishijima B
    5. Ohye C

    (1966) Postero-medial Hypothalamotomy in the treatment of aggressive behaviors

    Confinia Neurologica 27:164–167.

    https://doi.org/10.1159/000103949

    • PubMed
    • Google Scholar
90. 1. Sano K
    2. Mayanagi Y

    (1988) Posteromedial Hypothalamotomy in the treatment of violent, aggressive behaviour

    Acta Neurochirurgica. Supplementum 44:145–151.

    https://doi.org/10.1007/978-3-7091-9005-0_28

    • PubMed
    • Google Scholar
91. 1. Saper CB
    2. Lowell BB

    (2014) The hypothalamus

    Current Biology 24:R1111–R1116.

    https://doi.org/10.1016/j.cub.2014.10.023

    • PubMed
    • Google Scholar
92. 1. Seidlitz J
    2. Nadig A
    3. Liu S
    4. Bethlehem RAI
    5. Vértes PE
    6. Morgan SE
    7. Váša F
    8. Romero-Garcia R
    9. Lalonde FM
    10. Clasen LS
    11. Blumenthal JD
    12. Paquola C
    13. Bernhardt B
    14. Wagstyl K
    15. Polioudakis D
    16. de la Torre-Ubieta L
    17. Geschwind DH
    18. Han JC
    19. Lee NR
    20. Murphy DG
    21. Bullmore ET
    22. Raznahan A

    (2020) Transcriptomic and cellular decoding of regional brain vulnerability to Neurogenetic disorders

    Nature Communications 11:5936.

    https://doi.org/10.1038/s41467-020-19362-z

    • PubMed
    • Google Scholar
93. 1. Shen EH
    2. Overly CC
    3. Jones AR

    (2012) The Allen human brain Atlas: comprehensive gene expression mapping of the human brain

    Trends in Neurosciences 35:711–714.

    https://doi.org/10.1016/j.tins.2012.09.005

    • PubMed
    • Google Scholar
94. 1. Sunkin SM
    2. Ng L
    3. Lau C
    4. Dolbeare T
    5. Gilbert TL
    6. Thompson CL
    7. Hawrylycz M
    8. Dang C

    (2013) Allen brain Atlas: an integrated Spatio-temporal portal for exploring the central nervous system

    Nucleic Acids Research 41:D996–D1008.

    https://doi.org/10.1093/nar/gks1042

    • PubMed
    • Google Scholar
95. 1. Torres CV
    2. Sola RG
    3. Pastor J
    4. Pedrosa M
    5. Navas M
    6. García-Navarrete E
    7. Ezquiaga E
    8. García-Camba E

    (2013) Long-term results of Posteromedial hypothalamic deep brain stimulation for patients with resistant aggressiveness

    Journal of Neurosurgery 119:277–287.

    https://doi.org/10.3171/2013.4.JNS121639

    • PubMed
    • Google Scholar
96. 1. Torres CV
    2. Blasco G
    3. Navas García M
    4. Ezquiaga E
    5. Pastor J
    6. Vega-Zelaya L
    7. Pulido Rivas P
    8. Pérez Rodrigo S
    9. Manzanares R

    (2020) Deep brain stimulation for aggressiveness: long-term follow-up and Tractography study of the stimulated brain areas

    Journal of Neurosurgery 1:1–10.

    https://doi.org/10.3171/2019.11.JNS192608

    • PubMed
    • Google Scholar
97. 1. Vedam-Mai V
    2. Baradaran-Shoraka M
    3. Reynolds BA
    4. Okun MS

    (2016) Tissue response to deep brain stimulation and Microlesion: A comparative study

    Neuromodulation 19:451–458.

    https://doi.org/10.1111/ner.12406

    • PubMed
    • Google Scholar
98. 1. Wang Q
    2. Akram H
    3. Muthuraman M
    4. Gonzalez-Escamilla G
    5. Sheth SA
    6. Oxenford S
    7. Yeh F-C
    8. Groppa S
    9. Vanegas-Arroyave N
    10. Zrinzo L
    11. Li N
    12. Kühn A
    13. Horn A

    (2021) Normative vs. patient-specific brain Connectivity in deep brain stimulation

    NeuroImage 224:117307.

    https://doi.org/10.1016/j.neuroimage.2020.117307

    • Google Scholar
99. 1. Xie Y
    2. Zhang X
    3. Liu F
    4. Qin W
    5. Fu J
    6. Xue K
    7. Yu C

    (2020) Brain mRNA expression associated with cortical volume alterations in autism spectrum disorder

    Cell Reports 32:108137.

    https://doi.org/10.1016/j.celrep.2020.108137

    • PubMed
    • Google Scholar
100. 1. Yan H
     2. Toyota E
     3. Anderson M
     4. Abel TJ
     5. Donner E
     6. Kalia SK
     7. Drake J
     8. Rutka JT
     9. Ibrahim GM

     (2018) A systematic review of deep brain stimulation for the treatment of drug-resistant epilepsy in childhood

     Journal of Neurosurgery 23:274–284.

     https://doi.org/10.3171/2018.9.PEDS18417

     • Google Scholar
101. 1. Yan H
     2. Elkaim LM
     3. Venetucci Gouveia F
     4. Huber JF
     5. Germann J
     6. Loh A
     7. Benedetti-Isaac JC
     8. Doshi PK
     9. Torres CV
     10. Segar DJ
     11. Elias GJB
     12. Boutet A
     13. Cosgrove GR
     14. Fasano A
     15. Lozano AM
     16. Kulkarni AV
     17. Ibrahim GM

     (2022) Deep brain stimulation for extreme behaviors associated with autism spectrum disorder Converges on a common pathway: a systematic review and Connectomic analysis

     Journal of Neurosurgery 1:1–10.

     https://doi.org/10.3171/2021.11.JNS21928

     • PubMed
     • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "Networks and genes modulated by posterior hypothalamic stimulation in patients with aggressive behaviours: Analysis of probabilistic mapping, normative connectomics, and atlas-derived transcriptomics of the largest international multi-centre dataset" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Michael Frank as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1. The validation of findings is heterogeneous and inconsistent across analysis pipelines. While the authors performed non-parametric permutation testing during sweet-spot mapping, structural and functional connectivity were validated using a 'four-fold consistency analysis'. The latter consists of a visual representation of streamlines and peak intensities after randomly dividing data into four groups, the findings were not validated quantitatively. If possible, the authors should apply permutation analysis in alignment with sweet-spot mapping and demonstrate the predictive ability of their identified networks in a LOO or k-fold cross-validation paradigm as carried out by similar studies. Given that the data has been derived from multiple centers, the prediction of left-out cohorts based on models generated by the remaining cohorts could be another means of validation. If validation is not possible, the authors should clearly state the limitations of their approach.

2. In addition to a 'four-fold consistency analysis', functional connectivity was evaluated using LOOCV in a-priori-identified ROIs. Their network analysis, however, revealed a far more extensive network encompassing cortical, subcortical, and cerebellar structures. To avoid selection bias the authors should incorporate identified structures into their analysis and apply appropriate means of validation.

3. Functional connectivity mapping: how were R-maps generated? The authors mention that patient-specific R-maps were p-thresholded and corrected for multiple comparisons, but it is not clear how group-level maps were generated. How did the authors perform regression on these maps? Were voxels that did not survive thresholding excluded?

4. The authors determined that age was a significant prédictor of the outcome, but it is unclear whether certain age groups presented with distinct etiologies underlying their aggressiveness. For example, aggression in epilepsy may show a better response to DBS as opposed to schizophrenia. How does patient outcome change when stratifying according to etiology? How does model performance change when controlling for etiology? The authors should include the etiology of aggressiveness in Table 1.

5. Stimulation parameters. The authors report average pulse widths of 219 µs and 142µs respectively, which is up to 4-fold higher as compared to DBS settings used conventionally in movement disorders and will significantly alter the volume of activated tissue. Did the authors account for the drastic increases in pulse width during VAT modeling?

6. Imaging transcriptomics. The methods described lack detail: How did the authors account for differences in expression across donors, samples, and regions during preprocessing of the Allen Human Brain Atlas? How was expression data collapsed into regions of interest? Did the authors apply any normalization? Recent publications have introduced reproducible workflows for processing and preparing the AHBA expression data for analysis that is publicly available.

7. 'genes with similar patterns of spatial distribution to the TFCE map were compiled in an extensive list'. It is unclear why authors used TFCE maps for spatial transcriptomics as opposed to the functional connectivity map featured in Figure 5. How was similarity measured between the TFCE map and the AHBA? How were candidate genes identified? Please provide a more comprehensive description of the analysis pipeline.

8. What do the bar plots in Figure 7 (left) represent? P-values? The authors should label the axes to make this clear to the reader.

9. Interprétation of imaging transcriptomics: The authors identify a therapeutic circuit associated with deep brain stimulation of the posterior hypothalamic area, however, it is unclear how to reconcile genes associated with hormones, inflammation, and plasticity in this context. The authors mention and discuss genes implicated in hormonal processing, specifically oxytocin. The results provided in Figure 7, however, do not support this finding and it is unclear how the authors identified genes linked to oxytocin. In addition, the authors identified reductions in the number of microglia and astrocytes, while oligodendrocytes were overexpressed relative to the expected distribution of genes per cell type. These findings were attributed to DBS effects, however, both connectomic and transcriptomic data are acquired from healthy subjects, which suggests a physiological deficit/enrichment in a therapeutic circuit. How do the authors interpret findings given that no electrode implantation and stimulation were performed?

10. Data availability. Code used for data processing should be made openly available or shared as source data along with the Figures that were generated using the code. Sweet-spot, structural, and functional connectivity maps should be shared openly.

11. Methods: Given the small number of patients over 5 centers, the authors should better address the similarities and differences of data collection across the sites.

12. Results: Clinically relevant bundles included somatosensory, emotions, and motor connections. This is pretty much a summary of connections through the hypothalamus and surrounding regions. Do the authors suggest that the effectiveness of DBS in this region is this nonspecific?

13. Indeed, it appears that the motor system is most involved based on the tractography. This is consistent with figure 3 in which the 'sweet spot' appears to be lateral to the hypothalamus and more centered in the red nucleus/STN region (Figures 1 and 3).

14. The emphasis on connections on monoamine connections (P. 7) is unwarranted, given the stronger connections to motor regions. Moreover, both DBS and tractography favor myelinated fibers over unmyelinated ones. The monoamines are unmyelinated. Finally, the MFB is a relatively small bundle compared to others in the region that carry motor and sensory information to and from the brainstem. The resolution of tractography is unlikely to distinguish the MFB and favor other fiber tracts.

15. In this regard, the paragraph on P. 9, "functional connectivity…" is confusing. (My understanding is that the authors are linking connections to the amygdala, etc.) via the monoamines. Is that correct? If so, that's a discussion point and not well supported by the connectivity data.

16. Other comments: The introduction lacks clear text referring to the organization and connections of the hypothalamus based on classic literature.

17. Examples. First, the authors described the division into 3 regions: anterior, medial, and posterior (reference is the author's article in neurosurgery). Then, without reference to these three regions, describe preclinical studies of the lateral and ventromedial hypothalamus. How do these two areas relate to the three regions?

Essential revisions:

1) The validation of findings is heterogeneous and inconsistent across analysis pipelines. While the authors performed non-parametric permutation testing during sweet-spot mapping, structural and functional connectivity were validated using a 'four-fold consistency analysis'. The latter consists of a visual representation of streamlines and peak intensities after randomly dividing data into four groups, the findings were not validated quantitatively. If possible, the authors should apply permutation analysis in alignment with sweet-spot mapping and demonstrate the predictive ability of their identified networks in a LOO or k-fold cross-validation paradigm as carried out by similar studies. Given that the data has been derived from multiple centers, the prediction of left-out cohorts based on models generated by the remaining cohorts could be another means of validation. If validation is not possible, the authors should clearly state the limitations of their approach.

We agree with the reviewer, and have now improved the validation of our connectomics analyses and removed the four-fold consistency analysis. As for the functional connectivity analysis, we performed a 1000 permutation test (p<0.05), which resulted in an extremely similar map, with the same main brain areas being detected in the corrected and uncorrected maps. Regarding the structural connectivity analysis, we used False Discovery Rate (FDR) correction at a significant level of p<0.001, as it is not feasible to perform a 1000 permutation test with this data. The structural connectome is composed of 12 million fibres, and every single permutation takes approximately 4 hours to be completed using our most powerful computational system. To perform 1000 permutations, it would take at least 4000 hours (i.e. 167 days or 5.5 months) of uninterrupted analysis to complete the test. However, it is important to highlight that an FDR correction at the level of p<0.001 is an extremely stringent method. This means that of the 23,000 fibres detected as being touched by the VATs, only 23 could be incorrect, while the remaining 22,977 are correct. Here again, we observed many similarities between the uncorrected and corrected maps, with the main anatomical structures being detected in both.

We replaced the brain slices in Figures 4 and 5 to demonstrate the new corrected maps and added the information to the Methods section as follows:

Methods, section: Imaging Connectomics Analyses – Structural and Functional Connectivity Mapping. pg 15, 2nd paragraph:

“For generating group-level maps, a voxel-wise linear regression analysis investigating the relationship between the functional connectivity of the VATs and the individual clinical outcome was then performed, followed by non-parametric permutation analysis. The individual improvement scores were randomly assigned to a functional connectivity map, and the analysis was repeated 1000 times. At each voxel, p_permute<0.05 was used as a significance threshold for the final functional connectivity analysis. Figure 1—figure supplement 1 illustrates this process.”

Methods, section: Imaging Connectomics Analyses – Structural and Functional Connectivity Mapping. pg 17, 1st paragraph:

“The results were corrected for multiple comparisons using False Discovery Rate (FDR) at q_FDRcor<0.001.”

2) In addition to a 'four-fold consistency analysis', functional connectivity was evaluated using LOOCV in a-priori-identified ROIs. Their network analysis, however, revealed a far more extensive network encompassing cortical, subcortical, and cerebellar structures. To avoid selection bias the authors should incorporate identified structures into their analysis and apply appropriate means of validation.

We would like to thank the reviewer for this valuable suggestion. We originally did not explore the various significant areas but performed a more focused analysis intended to demonstrate that regions of the known ‘aggression network’ are indeed implicated in our findings. We performed a new analysis exploring the correlation between symptom improvement and the functional connectivity of all the areas described in Figure 5 (i.e. functional connectivity map). To this aim, we extracted individual connectivity values from the peak within each significant region and performed the same additive linear model as before, incorporating the functional connectivity of each area, as well as patient age, to estimate individual symptom improvement. In addition, we performed a complete exploratory analysis considering the connectivity of any 2 brain structures and age. The resulting matrix shows to what extent functional connectivity to any two areas can be used to estimate clinical outcomes. Interestingly, this new analysis revealed the Periaqueductal Grey matter (PAG) to be the most important functionally connected area when investigated alone or in combination with brain structures critically involved in the regulation of emotional responses, namely the amygdala, anterior cingulate cortex, bed nucleus of the stria terminalis, nucleus accumbens, orbitofrontal cortex and fusiform gyrus. Also, PAG significance was retained during leave-one-out cross-validation (LOOCV). We added a new Table 2 and Supplementary File 1 showing the results of these analyses, revised Figure 6 and adjusted the text to describe the new analysis and results in the Results, Methods and Discussion sections, as follows:

Results, section: Estimation of Clinical Outcome, pg 8, 2nd paragraph:

“To investigate whether individual functional connectivity to particular hubs within the neurocircuitry of aggressive behaviour could be used to estimate symptom improvement following pHyp-DBS, additive linear models were created. For this, we extracted individual connectivity values from the peak within each brain area where functional connectivity with the VATs was found to be significantly related to outcomes at the group level (as described in the Normative Connectomics Analyses, Figure 6A). The best-performing parsimonious model, which incorporated patient age as well as individual VAT functional connectivity, revealed the Periaqueductal Grey Matter (PAG) to be the only structure that, together with age, significantly predicted more than half of the variance in individual symptom improvement (R=0.72, R2=0.52, p=1.86e-05, Figure 6B, Table 2), retaining significance during leave-one-out cross-validation (LOOCV) (R=0.65, R2=0.42, p=4.406e-5; Figure 6C). Additionally, we investigated if functional connectivity of the VATs with any 2 brain areas, in addition to patient age, would improve the estimation of clinical outcome (Supplementary File 1). Indeed, connectivity with PAG and limbic structures, namely the amygdala (R=0.75, R2=0.57, p=4.20e-07), anterior cingulate cortex (rostral: R=0.76, R2=0.58, p=2.75e-07; dorsal: R=0.75, R2=0.57, p=3.96e-07), bed nucleus of the stria terminallis ( R=0.76, R2=0.58, p=2.75e07), left nucleus accumbens (R=0.76, R2=0.58, p=8.51e-07), right orbitofrontal cortex (R=0.76, R2=0.58, p=2.75e-07) and right fusiform gyrus (R=0.75, R2=0.56, p=4.67e-07) was superior in predicting outcome.”

Methods, section: Statistical Analysis, pg 18, 2nd paragraph:

“Additive linear models were used to test our ability to estimate the percentage of improvement from baseline using age and the functional connectivity of all brain areas functionally connected with the VATs.”

Discussion, pg 11, 2nd paragraph:

“The functional connectivity mapping showed that the successful VATs were functionally connected to several key areas within the neurocircuitry of aggressive behaviour, including the amygdala, nucleus accumbens, hypothalamus, periaqueductal gray, and cingulate cortex. To further explore these results, we investigated the relationship between the functional connectivity of each of these brain structures and individual treatment outcomes and observed that functional connectivity with the PAG is the most relevant. In addition, when modelling the functional connectivity of any two brain areas identified in the functional connectivity analysis, we observed that the most relevant connections involved the PAG and limbic structures believed to be at the core of the neurocircuitry of aggressive behaviour, such as the amygdala, anterior cingulate cortex, and nucleus accumbens. In line with our findings, previous studies have shown positive outcomes following ablative and neuromodulation treatments targeting the amygdala, hypothalamus, and nucleus accumbens for the control of aggressive behaviour 1,3,6,67. Moreover, PAG DBS – primarily performed to treat neuropathic pain 68 – has been reported to modulate mood and anxiety 69.”

3) Functional connectivity mapping: how were R-maps generated? The authors mention that patient-specific R-maps were p-thresholded and corrected for multiple comparisons, but it is not clear how group-level maps were generated. How did the authors perform regression on these maps? Were voxels that did not survive thresholding excluded?

This is a multiple-step analysis. First, it is necessary to localize the electrodes in each patient’s brain and estimate the volume of activated tissue (VAT) with the stimulation parameters associated with a symptomatic improvement. The VATs are then used as seeds for the next steps, during which we investigate how much functional influence the VTAs have on the other areas of the brain (i.e. individual r-map). This is done by correlating the BOLD time course of the VATs seed with the BOLD time course of all other voxels in the brain. The individual r-maps are then corrected for multiple comparisons to exclude voxels with potentially spurious correlations, resulting in an individual r-map that only included voxels surviving Bonferroni correction at the level of p<0.05. Finally, to create group-level maps, a voxel-wise linear regression analysis is performed to investigate for each voxel of the map if more or less influence (corrected individual r-map with the functional connectivity of the patient’s VAT) is more or less related to the clinical outcome (i.e. individual improvement). The last step is permutation correction resulting in a significant group-level functional connectivity map (p_permute<0.05). We improved the text in the methods section to better describe the generation of the group-level maps and added a new Figure 1—figure supplement 1 illustrating this analysis.

Methods, section: Imaging Connectomics Analyses – Structural and Functional Connectivity Mapping, pg 16, 2nd paragraph:

“For generating group-level maps, a voxel-wise linear regression analysis investigating the relationship between the functional connectivity of the VATs and the individual clinical outcome was then performed, followed by non-parametric permutation analysis. The individual improvement scores were randomly assigned to a functional connectivity map, and the analysis was repeated 1000 times. At each voxel, p_permute<0.05 was used as a significance threshold for the final functional connectivity analysis.Figure 1—figure supplement 1 illustrates this process.”

4) The authors determined that age was a significant predictor of the outcome, but it is unclear whether certain age groups presented with distinct etiologies underlying their aggressiveness. For example, aggression in epilepsy may show a better response to DBS as opposed to schizophrenia. How does patient outcome change when stratifying according to etiology? How does model performance change when controlling for etiology? The authors should include the etiology of aggressiveness in Table 1.

This is a very interesting point. We observed a similar distribution between the pediatric and adult populations in relation to the most common etiologies. Epilepsy was the most frequent diagnosis in both populations (pediatric: 50%, adult: 62%), followed by autism spectrum disorder (pediatric: 34%, adult: 24%). The remaining etiologies were mostly composed of single cases. The percentage of intellectual disability was also similar in pediatric and adult populations. Severe deficits were observed in 75% of pediatric and 85% of adult patients, while moderate disability was present in 25% of pediatric and 15% of adult patients. The different diagnoses presented by the patient are reported in the legend of Table 1. This strategy was adopted, rather than presenting specific diagnoses in each patient’s row to preserve anonymity. In the end of Table 1, the diagnoses are reported from more to less frequent. We have added a sentence to the Results and Discussion sections as follows:

Results, section: Patients Included, pg 5, 3rd paragraph:

“The most frequent diagnoses were epilepsy (pediatric: 50%, adult: 62%) and ASD (pediatric: 34%, adult: 24%). Intellectual disability was observed in all cases. This was described as severe in 82% of patients (pediatric: 75%, adult: 85%) and moderate in 18% of patients (pediatric: 25%, adult: 15%). Table 1 presents demographic data.“

Discussion, pg 9, 3rd paragraph:

“Although we did not observe a difference in the main diagnosis between the pediatric and adult populations, the former presented the highest percentage of symptom improvement following pHyp-DBS treatment.”

5) Stimulation parameters. The authors report average pulse widths of 219 µs and 142µs respectively, which is up to 4-fold higher as compared to DBS settings used conventionally in movement disorders and will significantly alter the volume of activated tissue. Did the authors account for the drastic increases in pulse width during VAT modeling?

We thank the reviewer for raising this important point about the volume of activated tissue (VAT) modelled and the unusual pulse width observed in some patients in this cohort. These patients presented sympathetic side effects of stimulation when DBS was set with higher frequencies (e.g. increased heart rate and blood pressure). The final parameters selected were the ones that would lead to clinical benefits without generating side effects.

A multitude of models exists for estimating VATs, ranging from advanced axon cable models – the gold standard, which simulate axon membrane dynamics and require patient-specific diffusion-weighted imaging and tremendous computing power ¹ – to simple heuristics-based models that estimate the rough extent of a VAT based on stimulation parameters without constructing an actual spatial model ^2–4. The model employed in the current paper (and a number of previous publications by our group ^5–10) is the FieldTripSimBio ‘E-field norm’ finite element method (FEM) model. This model, which was first described by Horn et al. ¹¹ and is freely available in Lead-DBS (https://www.lead-dbs.org/), strikes a balance between the sophisticated axon cable models and the simpler heuristic models. In particular, this model constructs an electric field (E-field, by applying an electric field strength threshold, or activation threshold) and calculates a VAT associated with specific voltage settings and contact configurations, taking into account the conductivity of surrounding brain tissue and electrode components. Notably, studies comparing VAT modelling techniques ¹² showed that ‘E-field norm’ FEM models closely approximate (<0.1 mm difference) the gold standard axon cable models in terms of the size of VATs constructed for monopolar stimulation settings. However, it should be acknowledged that the FieldTripSimBio model in Lead-DBS does not allow the user to specifically enter values for stimulation pulse width, instead employing a standard activation/electric field strength threshold (0.2 V/mm) that reflects a combination of commonly modelled axon diameters (roughly 3.5 μm) and commonly used pulse width values (i.e., 60-90 μs). This threshold is based on work by researchers such as Astrom et al. ¹³ and reflects a ‘middle ground’ value reflective of the fact that any VAT model will necessarily be an imperfect approximation of how electrical stimulation interfaces with brain tissue, depending heavily on aspects such as the diameter of local axons. Nonetheless, it is understood that increased pulse width does meaningfully increase the effective range of stimulation (thus translating to a larger VAT) by lowering the activation threshold of nearby axons ¹².

Given that our patient cohort included a small number of patients who were stimulated with notably higher pulse widths than the pulse width value assumed by our model (90 μs), it is reasonable to consider whether we underestimated the VAT in these patients. To address this, we experimented by modelling these patients’ VATs using a simpler heuristic model ² that does allow specific pulse width values to be selected by the user; specifically, we computed a range of VATs for these patients using varied pulse width values (ranging from 90 μs up to their actual pulse width values). Not surprisingly, this endeavour did yield larger VATs when higher pulse width values were used: on average, the absolute difference in VAT diameter between 90 μs and 450 μs (the largest pulse width observed in this cohort) versions of these patients’ VATs was 2 mm. To check whether this difference could have potentially impacted our results, we next repeated our probabilistic mapping analysis using altered VATs (specifically, VATs that were enlarged by 2 mm in diameter) for the patients with notably higher pulse width values. This new repeat analysis yielded a very similar average map to the original analysis: the overall map pattern and location/values of the peak corresponding to the most efficacious area for maximal symptom alleviation remaining unaltered, and only a few voxels on the periphery of the map changing in value by a couple of percentage points. As such, this new supplementary analysis indicates that our results are not meaningfully altered by the unusual pulse width observed in these patients.

We improved the Methods section of the manuscript and added a new Figure 3—figure supplement 2 illustrating both voxel efficacy maps.

Methods, section: Electrode localization and Volume of Activated Tissue (VAT) modelling, pg 15, 1st paragraph:

“Considering that increased pulse width increases the effective range of stimulation (thus translating to a larger VAT) by lowering the activation threshold of nearby axons 97, and given that our patient cohort included a small number of patients who received higher pulse widths than the ones assumed by the FieldTripSimBio model in Lead-DBS, we performed a supplementary analysis to investigate changes in VAT volumes by also modelling the pulse width observed in these patients. To this aim, we used a simpler heuristic model 38 that takes pulse width into account to compute additional VATs using pulse width values ranging from 90μs up to the patient’s actual pulse width values. As expected, this analysis yielded larger VATs when higher pulse width values were used, with an absolute difference in VAT diameter between 90 μs and 450 μs (the highest pulse width observed in this cohort) of 2 mm. Then, we investigated whether or not these larger VATs could have potentially impacted our results by performing a new probabilistic mapping analysis using the newly generated VATs (specifically, the largest VATs that were enlarged by 2 mm in diameter) for the patients with higher pulse width values. This new analysis yielded a very similar average map to the original analysis, with the overall map pattern and location/values of the peak corresponding to the most efficacious area for maximal symptom alleviation remaining unaltered with only a few voxels on the periphery of the map changing in value by a couple of percentage points (Figure 3—figure supplement 2). As such, this supplementary analysis indicates that our results were not meaningfully altered by the unusual pulse width observed in these patients.”

6 & 7. Imaging transcriptomics. The methods described lack detail: How did the authors account for differences in expression across donors, samples, and regions during preprocessing of the Allen Human Brain Atlas? How was expression data collapsed into regions of interest? Did the authors apply any normalization? Recent publications have introduced reproducible workflows for processing and preparing the AHBA expression data for analysis that is publicly available. 'genes with similar patterns of spatial distribution to the TFCE map were compiled in an extensive list'. It is unclear why authors used TFCE maps for spatial transcriptomics as opposed to the functional connectivity map featured in Figure 5. How was similarity measured between the TFCE map and the AHBA? How were candidate genes identified? Please provide a more comprehensive description of the analysis pipeline.

We would like to apologize for the short description of this analysis. We performed a gene set analysis using the abagen toolbox (https://abagen.readthedocs.io/en/stable/index.html) to investigate genes whose spatial pattern is similar to the pattern observed for clinically relevant functional connectivity. For this analysis, we used the Allen Human Brain Atlas (https://alleninstitute.org/) microarray data describing the cortical, subcortical, brainstem and cerebellar localization of over 20,000 genes in the human brain (3702 anatomical locations from 6 neurotypical adult brains) ^14–17, along with a cell-specific aggregate gene set ¹⁸. These data are provided preprocessed, with gene expression values, normalized across all donors’ brains, and registered to standard MNI space, allowing for the direct comparison between the spatial pattern of gene expression and the functional connectivity map (https://human.brain-map.org/microarray/search) ¹⁵. The TFCE maps were used to create clusters of clinically relevant functional connectivity that have a spatial extent that overlaps with the anatomical locations where microarray data was obtained. We parcellated both datasets (results of functional connectivity analysis and Allen Gene Atlas) according to the Harvard-Oxford brain atlas and correlated the spatial distribution of gene expression with the spatial distribution of the results of the functional connectivity mapping. The resulting list of candidate genes was used as input in gene ontology tools to investigate the associated biological processes and cell types. It is important to highlight that this process involves 2 corrections for multiple comparisons using FDR at q<0.005; one correction occurs at the level of the gene list to include only the most significant genes in the gene ontology analysis; and a second correction at the level of the gene ontology analysis to consider only the most significant biological processes. We improved the Methods section as follows:

Results, section: Imaging Transcriptomics – Gene set analysis, pg 8, 3rd paragraph:

“Finally, to investigate neural phenotypes and possible neurobiological mechanisms of treatments, we performed imaging transcriptomics analysis using the abagen toolbox (https://abagen.readthedocs.io/en/stable/index.html) and the human gene expression data from the Allen Human Brain Atlas (https://alleninstitute.org/). We investigated genes with a spatial distribution of expression that resembled the pattern of brain regions with clinically relevant (following TFCE correction) functional connectivity to the stimulation locus (qFDRcor<0.0001).”

Discussion, page 12, 2nd paragraph:

“To further explore the dataset pooled in our study, we performed a spatial imaging transcriptomic analysis. Our goal was to investigate spatial correlations between gene expression patterns (i.e. microarray data from the Allen Human Brain Atlas) and imaging characteristics (i.e. functional connectivity map derived from the patient’s VATs) and possible molecular characteristics associated with symptom improvement (for a review on imaging transcriptomics see Fomito et al., 2019 70)”

Methods, section Imaging Transcriptomics – Gene Set Analysis, pg. 17, 2nd paragraph:

“We performed a gene set analysis using the abagen toolbox (https://abagen.readthedocs.io/en/stable/index.html) to investigate genes whose spatial pattern distribution is similar to the pattern of clinically relevant functional connectivity. For this analysis, we used the Allen Human Brain Atlas (https://alleninstitute.org/) microarray data describing the cortical, subcortical, brainstem and cerebellar localization of over 20,000 genes in the human brain (3702 anatomical locations from 6 neurotypical adult brains) 89–92, along with a cell-specific aggregate gene set 49. These data are provided preprocessed, with gene expression values normalized across all donors’ brains, and registered to standard MNI space allowing for the direct comparison between the spatial pattern of gene expression and the functional connectivity map (https://human.brain-map.org/microarray/search) 90. To achieve this spatial mapping, we first applied Threshold Free Cluster Enhancement (TFCE) to the functional connectivity map to generate clusters of significant functional connectivity with a spatial extent that overlaps with the anatomical locations where microarray data was obtained. Then, based on the Harvard-Oxford Atlas (http://www.cma.mgh.harvard.edu/), a regional parcellation covering 48 cortical and 21 subcortical areas in each hemisphere (a total of 138 parcellations) was applied to both datasets. A direct correlation between the spatial distribution of gene expression and functional connectivity analysis in each specific brain area was performed and corrected for multiple comparisons using FDR at qFDAcor<0.005, resulting in a gene list composed of 1734 genes associated with biological processes and ratios of genes per cell type. This list was used as input for gene ontology analyses using the EnRichr Gene Ontology tool (https://maayanlab.cloud/Enrichr/), along with the Jensen Gene Ontology enrichment tool (https://jensenlab.org/resources/proteomics/) and the Kyoto Encyclopedia of Genes and Genomes (KEGG pathway enrichment; https://www.genome.jp/kegg/kegg1b.html), resulting in an extensive list of associated biological terms that were corrected for multiple comparisons using FDR at q_FDAcor<0.005.”

(8 & 9) What do the bar plots in Figure 7 (left) represent? P-values? The authors should label the axes to make this clear to the reader. Interprétation of imaging transcriptomics: The authors identify a therapeutic circuit associated with deep brain stimulation of the posterior hypothalamic area, however, it is unclear how to reconcile genes associated with hormones, inflammation, and plasticity in this context. The authors mention and discuss genes implicated in hormonal processing, specifically oxytocin. The results provided in Figure 7, however, do not support this finding and it is unclear how the authors identified genes linked to oxytocin. In addition, the authors identified reductions in the number of microglia and astrocytes, while oligodendrocytes were overexpressed relative to the expected distribution of genes per cell type. These findings were attributed to DBS effects, however, both connectomic and transcriptomic data are acquired from healthy subjects, which suggests a physiological deficit/enrichment in a therapeutic circuit. How do the authors interpret findings given that no electrode implantation and stimulation were performed?

The analysis of normative datasets (functional and structural connectomics and spatial transcriptomics) is based on the idea of understanding underlying mechanisms of treatment considering our current knowledge of the average human brain. Unlike patient-specific studies where imaging is acquired from a single patient or genetic profiles are extracted from tissue samples, these normative analyses rely on high-quality “atlases” derived from healthy subjects, and, in the case of the functional and structural connectivity, atlases that are calculated from very large cohorts of subjects (around 1000 brain scans). Thus, imaging connectomics investigates the pattern of brain activity and structural connectivity related to a specific area of the brain (in this case, the volume of tissue activated (VATs) with DBS) and correlates the acquired data with clinical outcomes to shed light on potential mechanisms of action. Similarly, the spatial transcriptomic analysis was used to identify spatial correlations between gene expression patterns and brain characteristics detected by MRI ¹⁹ (in this case, the spatial pattern of functional connectivity) and investigate possible genetic underlying mechanisms. It is important to highlight that previous studies have shown that normative analyses yield results that are similar to the ones observed in patient-specific data ^20–22. In the case of imaging connectomics, normative datasets have been used to create probabilistic models of optimal connectivity associated with patients’ outcomes that are meaningful to predict outcomes in patient-specific connectivity data ²¹. Thus, these exploratory data-driven approaches strive to simulate the presumed fingerprint that a particular patient’s individualized DBS intervention might modulate and allows for the investigation of possible mechanisms of action in a large, previously inaccessible cohort of patients whose individual data are available. We improved the Discussion section of the manuscript. We also would like to apologize for the previous Figure 7, which was incorrect. We included the label for the bar plots in the left panel to facilitate the reading of the graph and added the missing result from the KEGG 2021 Human Library that shows the oxytocin signalling pathway.

Discussion, pg 10, 2nd paragraph:

“The analysis of functional and structural connectivity via normative datasets is an opportunity to investigate possible underlying mechanisms based on the current knowledge of the typical human brain. Unlike patient-specific studies, where functional MRI and diffusion-weighted images are acquired from a single patient, normative analyses rely on high-quality atlases derived from very large cohorts of healthy subjects (around 1000 brain scans). Thus, these exploratory data-driven approaches are intended to investigate the pattern of brain activity and structural connectivity related to a specific region of interest (i.e. individual VAT) and correlate these data with clinical outcomes to simulate the presumed brain changes associated with DBS treatment. This approach allows for the expansion of the investigation to a large, previously inaccessible cohort of patients whose individual data are available, generating knowledge that can help optimize treatment in future patient populations. Although normative connectivity data may not fully capture patient- or pathology-specific variations, they have been previously shown to generate results that are comparable to patient-specific imaging data 62,63.”

Discussion, pg 11, 2nd paragraph:

“The functional connectivity mapping showed that the successful VATs were functionally connected to several key areas within the neurocircuitry of aggressive behaviour, including the amygdala, nucleus accumbens, hypothalamus, periaqueductal gray, and cingulate cortex. To further explore these results, we investigated the relationship between the functional connectivity of each of these brain structures and individual treatment outcomes and observed that functional connectivity with the PAG is the most relevant. In addition, when modelling the functional connectivity of any two brain areas identified in the functional connectivity analysis, we observed that the most relevant connections involved the PAG and limbic structures believed to be at the core of the neurocircuitry of aggressive behaviour, such as the amygdala, anterior cingulate cortex, and nucleus accumbens. In line with our findings, previous studies have shown positive outcomes following ablative and neuromodulation treatments targeting the amygdala, hypothalamus, and nucleus accumbens for the control of aggressive behaviour 1,3,6,67. Moreover, PAG DBS – primarily performed to treat neuropathic pain 68 – has been reported to modulate mood and anxiety 69.

Discussion, pg 12, 3rd paragraph:

“A large number of genes identified by the Jensen Compartments and KEGG 2021 Human Libraries are part of biological processes associated with neuronal communication and plasticity, thus possibly linked to the intrinsic mechanism of action of DBS 76,77. As a neuromodulation therapy, DBS induces long-lasting changes in cellular and molecular aspects of neurons belonging to dysfunctional neurocircuitries in order to restore functional normality 12,78,79. Although its neurobiological mechanisms are not fully understood, DBS is believed to exert its effects by altering the cellular membrane potential, resulting in either an increase or a decrease in action potentials 12,78,79. These changes are then propagated throughout neural networks, changing neurotransmitter dynamics, protein expression, and membrane receptor availability 12,78,79. In fact, a previous study using imaging transcriptomics to investigate brain changes after functional neurosurgery of the amygdala in patients with refractory aggressive behaviour has also identified several genes related to neuronal communication and plasticity 1. Preclinical studies have also found significant increases in neural precursor cells, plasticity, and precursor cell markers in animals implanted with DBS, along with significant reductions in the number of activated microglia and astrocytes 80–84. This is in line with the cell type investigation, where a significant reduction in the ratio of genes per cell type was observed for astrocytes and microglia when compared to the expected distribution. Both astrocytes and microglia are highly implicated in mechanisms of neuroinflammation 85, which in turn is associated with several psychiatric disorders 86. The reduction of neuroinflammatory markers (such as glial activation and interleukin levels) has been described in several preclinical DBS studies and is thought to be involved in the mechanism of action of DBS 82,87,88.”

10) Data availability. Code used for data processing should be made openly available or shared as source data along with the Figures that were generated using the code. Sweet-spot, structural, and functional connectivity maps should be shared openly.

All tools and codes necessary for localizing the electrodes, the estimation of the volume of activated tissues, and imaging connectomics analyses are freely available in Lead-DBS (https://www.lead-dbs.org/), a toolbox designed for DBS electrode reconstructions and computer simulations based on postoperative imaging. All codes for spatial transcriptomics are freely available in abagen (https://abagen.readthedocs.io/en/stable/), a toolbox designed for analyzing the Allen Brain Atlas genetics data. Along with the codes, the websites for these tools provide manuals describing the step-by-step procedure for successful analysis. The datasets are freely available at Zenodo (doi: 10.5281/zenodo.7344268). We improved our Data Availability Statement to address this concern.

Data availability statement, pg 18, 4th paragraph:

“The codes for electrode localization, modelling of the volume of activated tissue, and imaging connectomics (i.e. functional and structural connectivity) are freely available in Lead-DBS (https://www.lead-dbs.org/). The codes, along with the Allen Human Brain Atlas (AHBA) microarray dataset, for the analysis of spatial transcriptomics are freely available in abagen (https://abagen.readthedocs.io/en/stable/). Along with the codes, the websites for these two toolboxes provide manuals describing the step-by-step procedure for successful analysis. The dataset accompanying this study is freely available at Zenodo (doi: 10.5281/zenodo.7344268).”

11) Methods: Given the small number of patients over 5 centers, the authors should better address the similarities and differences of data collection across the sites.

We added the similarities and differences across sites in the first paragraph of the Results section, as follows:

Results, section Patients Included, pg 6, 1st paragraph:

“In all centres, patients were evaluated by a multidisciplinary healthcare team that reviewed all clinical and medication history. Patients were only considered for surgery when a consensus was reached that they indeed presented severe medication-resistant aggressive behaviour (i.e., persistent severe symptomatology despite using multiple medications at well-established doses and duration). Whole-brain T1-weighted MRI was acquired preoperatively for surgical planning (1.5T MRI used in 14 cases at 3 centres, and 3T MRI used in 19 cases at 2 centres). Postoperative brain MRI and/or CT were obtained for electrode localization. Aggressive behaviour was assessed using standard questionnaires conducted by a neuropsychologist and answered by the parents/caregivers in all centres (i.e., Overt Aggressive Behaviour [OAS] used in 3 centres; Modified Overt Aggressive Behaviour [MOAS] used in 1 centre; Inventory for Client and Agency Planning [ICAP] used in 1 centre). Treatment response is reported as the percentage of improvement at the last follow-up relative to baseline (preoperative). Patients presenting more than 30% improvement were considered to be treatment responders.”

12) Results: Clinically relevant bundles included somatosensory, emotions, and motor connections. This is pretty much a summary of connections through the hypothalamus and surrounding regions. Do the authors suggest that the effectiveness of DBS in this region is this nonspecific?

This is a very interesting point. As the VATs are located in the posterior hypothalamus, the only fibres that could be touched by the VATs are the ones that cross the posterior hypothalamus. However, not all the fibres that cross the posterior hypothalamus are relevant for symptom improvement, as our statistical analysis only includes the most relevant fibre tracts. Understanding how symptom improvement and the location of the most efficacious stimulation region correlate with these tracts is important to refine surgical planning, as one could use individual patient tractography before surgery to determine the optimal location of the electrode based on individual fibre tracts. It is worth mentioning, however, that this analysis is excellent for segregating the fibre tracts as relevant or not relevant, but it is not capable of tearing apart the system to determine which of those are necessary for symptom alleviation. This could be done in preclinical settings using opto-or-chemogenetics to selectively inactivate specific fibre tracts and investigate the impact on behaviour and network dynamics. Thus, the structural connectivity analysis presented here contributes to the body of knowledge on the network of aggressive behaviour and provides clinically relevant data that can be useful to improve future patient outcomes. We added a sentence to the Discussion-limitations section of the manuscript addressing this concern, as follows:

Discussion, pg 13, 3rd paragraph:

“The structural connectivity analysis performed here provides a detailed overview of the relevant tracts that are related to symptom alleviation in this patient population. However, for precise segregation of these fibre tracts and the determination of those that are necessary for symptom improvement, it would be necessary to perform preclinical experiments to selectively activate/inactivate specific fibre tracts and investigate the impact of these changes on behaviour and network dynamics. Nevertheless, understanding how the location of the most efficacious area of stimulation correlates with these tracts is important for refining the surgical planning of future patients, as one could use individual patient tractography to determine the optimal location of the electrodes based on how it would impact individual fibre tracts.”

13) Indeed, it appears that the motor system is most involved based on the tractography. This is consistent with figure 3 in which the 'sweet spot' appears to be lateral to the hypothalamus and more centered in the red nucleus/STN region (Figures 1 and 3).

Although the structural connectivity mapping revealed tracts involved in motor and sensory information, it also showed tracts known to be involved in the regulation of emotions, such as the MFB, the Amygdalofugal Pathway and the ALIC. As discussed above, it is not possible to determine that motor projections are stronger or more relevant than others as we do not have the means to segregate the influence of each of these tracts in symptom improvement. However, we agree with the reviewer that the engagement of the motor system is indeed highly relevant for the reduction of aggressive behaviours, as we have previously shown that aggressive behaviour is highly correlated with motor agitation ^23,24. Additionally, in the context of ASD, self-injury behaviour is defined as a type of repetitive/stereotypic behaviour that results in physical injury to the patient’s own body. We added a sentence to the Discussion section highlighting the relevance of the motor system. The sweet spot is indeed located at the posterior-inferior-lateral region of the posterior hypothalamic area, reaching the most superior part of the red nucleus, however, not including the STN. It is important to highlight that the voxel-efficacy mapping only shows voxels that are associated with a minimum of 50% symptom improvement following treatment, thus, the areas not touching the red nucleus are also associated with excellent symptom alleviation. We added a new Figure 3—figure supplement 1, to show the localization of these areas in relation to the STN and red nucleus.

Discussion, pg 10, 3rd paragraph:

“Structural connectivity analysis in our study implicated fibre tracts involved in somatosensation (e.g. medial lemniscus and spinothalamic tract), emotional regulation (e.g., amygdalofugal pathway and medial forebrain bundle), and motor signalling (e.g., motor projections and central tegmental tract) in the amelioration of aggressive behaviour. Previous studies have shown a significant correlation between aggressive behaviour and motor agitation 5,64, with a concomitant reduction in both following neuromodulatory treatments targeting the posterior hypothalamus 7,37. Indeed, patients with neurodevelopmental disorders, especially those presenting with self-injurious behaviour, are known to frequently have altered sensory perception (including reduced pain sensitivity), which can contribute to the chronicity and severity of the behaviour 1,65. Furthermore, self-injurious behaviours in this patient population are considered to be a type of repetitive/stereotypic behaviour that results in physical injury to the patient’s own body 66,67, highlighting the relevance of the motor system in the network subserving aggressive behaviour. As such, modulation of somatosensory and motor pathways – perhaps leading to increased pain perception or awareness, and reduction in repetitive/stereotypic behaviours – could help reduce self-injurious behaviours. ”

Discussion section, pg. 10, 1st paragraph:

“One of the most commonly targeted areas in those series lies along the midpoint of the line between the anterior and posterior commissures (AC-PC line), in a region anterior to the rostral end of the aqueduct, posterior to the anterior border of the mammillary body and superior to the red nucleus 3,34 (Figure 3—figure supplement 1 shows the location of the sweet-spot in relation to the red nucleus and subthalamic nucleus)”

(14 & 15) The emphasis on connections on monoamine connections (P. 7) is unwarranted, given the stronger connections to motor regions. Moreover, both DBS and tractography favor myelinated fibers over unmyelinated ones. The monoamines are unmyelinated. Finally, the MFB is a relatively small bundle compared to others in the region that carry motor and sensory information to and from the brainstem. The resolution of tractography is unlikely to distinguish the MFB and favor other fiber tracts. In this regard, the paragraph on P. 9, "functional connectivity…" is confusing. (My understanding is that the authors are linking connections to the amygdala, etc.) via the monoamines. Is that correct? If so, that's a discussion point and not well supported by the connectivity data.

We would like to apologize for not being clear in the discussion of our findings. Although functional and structural connectivity maps are related, they provide different means of exploring distinct aspects of the connectivity profile of each VAT. While the structural connectivity map may help to explain symptom improvement via direct fibre modulation (i.e. fibres that touch vs fibres that do not touch the VAT), the functional connectivity map investigates the functional dynamics of the network via BOLD signals (functional MRI). In this manuscript, we showed the functional connectivity (not fibre tracts) of the VATs with areas known to regulate monoamine production, such as the Raphe nuclei and the Substantia Nigra. Both serotonin and dopamine are critically involved in the control of aggressive behaviours being the target of the main classes of medications used to treat aggressive behaviours in different patient populations. We improved our Discussion section to address this concern as follows:

Discussion, pg 11, 2nd paragraph:

“The functional connectivity mapping showed that the successful VATs were functionally connected to several key areas within the neurocircuitry of aggressive behaviour, including the amygdala, nucleus accumbens, hypothalamus, periaqueductal gray, and cingulate cortex. To further explore these results, we investigated the relationship between the functional connectivity of each of these brain structures and individual treatment outcomes and observed that functional connectivity with the PAG is the most relevant. In addition, when modelling the functional connectivity of any two brain areas identified in the functional connectivity analysis, we observed that the most relevant connections involved the PAG and limbic structures believed to be at the core of the neurocircuitry of aggressive behaviour, such as the amygdala, anterior cingulate cortex, and nucleus accumbens. In line with our findings, previous studies have shown positive outcomes following ablative and neuromodulation treatments targeting the amygdala, hypothalamus, and nucleus accumbens for the control of aggressive behaviour 1,3,6,67. Moreover, PAG DBS – primarily performed to treat neuropathic pain 68 – has been reported to modulate mood and anxiety 69.

Another aspect detected by the functional connectivity analysis was the involvement of areas responsible for the production of serotonin (i.e., raphe nuclei) and dopamine (i.e., substantia nigra). Both these monoamines are thought to be directly involved in the initiation and maintenance of aggressive behaviours due to their role in facilitating top-down inhibitory controls, regulating mood, and mediating social behaviours 3,7,70. Antipsychotics (both typical and atypical) and selective serotonin reuptake inhibitors – the most commonly used drugs to control of aggressive behaviours – act on the dopaminergic and serotonergic neurotransmission systems 1,3. In a mouse model of escalated aggression, pHyp-DBS increased the density of serotonin receptors type 1A (5-HT1A) in the orbitofrontal cortex and amygdala, while pretreatment with a 5-HT1A antagonist countered the anti-aggressive effect of stimulation 71.”

16 & 17) Other comments: The introduction lacks clear text referring to the organization and connections of the hypothalamus based on classic literature. Examples. First, the authors described the division into 3 regions: anterior, medial, and posterior (reference is the author's article in neurosurgery. Then, without reference to these three regions, describe preclinical studies of the lateral and ventromedial hypothalamus. How do these two areas relate to the three regions?

We improved the Introduction section to better describe the anterior-posterior subdivision of the hypothalamus, as follows:

Introduction, pg 4, 2nd paragraph:

“The hypothalamus is a diencephalic structure with well-established roles in the control of homeostasis and motivated behaviours and is a key area in a broader neurocircuitry regulating aggressive behaviour that also involves the orbitofrontal cortex, hippocampus, amygdala and periaqueductal gray 24–26. Along the anterior-posterior axis, the hypothalamus can be divided into three regions (i.e. supraoptic or anterior, tuber cinereum or medial and supramamillary or posterior) with distinct cell types, projections and functions 24,27. While the anterior region is mainly involved in thermoregulation and the control of circadian rhythms, the medial region regulates feeding and sexual behaviour and plays a critical role in several endocrine and autonomic processes 24,28,29. The posterior hypothalamus (pHyp) is an ergotropic area involved in the generation of sympathetic responses 30. It provides robust projections to the midbrain and reticular formation via the hypothalamotegmental tract, thus being critical in the regulation of wakefulness and stress responses 27,31,32. Indeed, lesions (e.g., hamartomas and gliomas) located at the pHyp have been reported to cause apathetic and somnolent symptoms, while the selective neurosurgical ablation of a small portion of the pHyp has been successfully used to treat patients with severe treatment-resistant aggressive behaviour (for a detailed review, see Gouveia et al. 2019 3,33,34). More recently, DBS of the pHyp (pHyp-DBS) has also been shown to reduce aggressive symptoms in humans, with variable outcomes 7–10,35–37.”

References:

1. Gunalan, K., Howell, B. & McIntyre, C. C. Quantifying axonal responses in patient-specific models of subthalamic deep brain stimulation. Neuroimage 172, 263–277 (2018).

2. Dembek, T. A. et al. Probabilistic mapping of deep brain stimulation effects in essential tremor. Neuroimage Clin 13, 164–173 (2017).

3. Kuncel, A. M., Cooper, S. E. & Grill, W. M. A method to estimate the spatial extent of activation in thalamic deep brain stimulation. Clin. Neurophysiol. 119, 2148–2158 (2008).

4. Mädler, B. & Coenen, V. A. Explaining clinical effects of deep brain stimulation through simplified target-specific modeling of the volume of activated tissue. AJNR Am. J. Neuroradiol. 33, 1072–1080 (2012).

5. Elias, G. J. B. et al. Probabilistic Mapping of Deep Brain Stimulation: Insights from 15 Years of Therapy. Ann. Neurol. 89, 426–443 (2021).

6. Germann, J. et al. Brain structures and networks responsible for stimulation-induced memory flashbacks during forniceal deep brain stimulation for Alzheimer’s disease. Alzheimers. Dement. 17, 777–787 (2021).

7. Elias, G. J. B. et al. Mapping the network underpinnings of central poststroke pain and analgesic neuromodulation. Pain 161, 2805–2819 (2020).

8. Gouveia, F. V. et al. Case report: 5 Years follow-up on posterior hypothalamus deep brain stimulation for intractable aggressive behaviour associated with drug-resistant epilepsy. Brain Stimul. 14, 1201–1204 (2021).

9. Coblentz, A. et al. Mapping efficacious deep brain stimulation for pediatric dystonia. J. Neurosurg. Pediatr. 27, 346–356 (2021).

10. M Oliveira, L. et al. Probabilistic characterisation of deep brain stimulation in patients with tardive syndromes. J. Neurol. Neurosurg. Psychiatry (2021) doi:10.1136/jnnp-2020-324270.

11. Horn, A. et al. Connectivity Predicts deep brain stimulation outcome in Parkinson disease. Ann. Neurol. 82, 67–78 (2017).

12. Duffley, G., Anderson, D. N., Vorwerk, J., Dorval, A. D. & Butson, C. R. Evaluation of methodologies for computing the deep brain stimulation volume of tissue activated. J. Neural Eng. 16, 066024 (2019).

13. Astrom, M., Diczfalusy, E., Martens, H. & Wardell, K. Relationship between neural activation and electric field distribution during deep brain stimulation. IEEE Trans. Biomed. Eng. 62, 664–672 (2015).

14. Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).

15. Arnatkeviciute, A., Fulcher, B. D. & Fornito, A. A practical guide to linking brain-wide gene expression and neuroimaging data. Neuroimage 189, 353–367 (2019).

16. Arnatkeviciute, A., Markello, R. D., Fulcher, B. D., Misic, B. & Fornito, A. Toward Best Practices for Imaging Transcriptomics of the Human Brain. Biol. Psychiatry 93, 391–404 (2023).

17. Markello, R. D. et al. Standardizing workflows in imaging transcriptomics with the abagen toolbox. eLife 10, (2021).

18. Seidlitz, J. et al. Transcriptomic and cellular decoding of regional brain vulnerability to neurogenetic disorders. Nat. Commun. 11, 3358 (2020).

19. Fornito, A., Arnatkevičiūtė, A. & Fulcher, B. D. Bridging the Gap between Connectome and Transcriptome. Trends Cogn. Sci. 23, 34–50 (2019).

20. Arnatkeviciute, A., Fulcher, B. D., Bellgrove, M. A. & Fornito, A. Imaging Transcriptomics of Brain Disorders. Biol Psychiatry Glob Open Sci 2, 319–331 (2022).

21. Wang, Q. et al. Normative vs. patient-specific brain connectivity in deep brain stimulation. Neuroimage 224, 117307 (2021).

22. Elias, G. J. B. et al. Normative connectomes and their use in DBS. in Connectomic Deep Brain Stimulation 245–274 (Elsevier, 2022).

23. Gouveia, F. V. et al. Bilateral Amygdala Radio-Frequency Ablation for Refractory Aggressive Behavior Alters Local Cortical Thickness to a Pattern Found in Non-refractory Patients. Front. Hum. Neurosci. 15, 653631 (2021).

24. Venetucci Gouveia, F. et al. Case report: 5 Years follow-up on posterior hypothalamus deep brain stimulation for intractable aggressive behaviour associated with drug-resistant epilepsy. Brain Stimul. (2021) doi:10.1016/j.brs.2021.07.062.

25. Gouveia, F. V. et al. Amygdala and Hypothalamus: Historical Overview With Focus on Aggression. Neurosurgery 85, 11–30 (2019).

26. Gouveia, F. V. et al. Longitudinal Changes After Amygdala Surgery for Intractable Aggressive Behavior: Clinical, Imaging Genetics, and Deformation-Based Morphometry Study-A Case Series. Neurosurgery 88, E158–E169 (2021).

27. Yan, H. et al. Deep brain stimulation for extreme behaviors associated with autism spectrum disorder converges on a common pathway: a systematic review and connectomic analysis. J. Neurosurg. 1–10 (2022).

28. Knotkova, H. et al. Neuromodulation for chronic pain. Lancet 397, 2111–2124 (2021).

29. Gray, A. M. et al. Deep brain stimulation as a treatment for neuropathic pain: a longitudinal study addressing neuropsychological outcomes. J. Pain 15, 283–292 (2014).

30. Miczek, K. A., Fish, E. W., De Bold, J. F. & De Almeida, R. M. M. Social and neural determinants of aggressive behavior: pharmacotherapeutic targets at serotonin, dopamine and γ-aminobutyric acid systems. Psychopharmacology 163, 434–458 (2002).

31. Gouveia, F. V. et al. Reduction of aggressive behaviour following hypothalamic deep brain stimulation: involvement of 5-HT1Aand testosterone. bioRxiv (2023) doi:10.1101/2023.03.20.533520.

Author details

1. Flavia Venetucci Gouveia

   1. Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, Canada
   2. Sunnybrook Research Institute, Toronto, Canada
   3. Division of Neuroscience, Sírio-Libanês Hospital, São Paulo, Brazil

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Jurgen Germann

   For correspondence

   fvenetucci@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0029-1269

2. Jurgen Germann

   1. Division of Neurosurgery, Department of Surgery, University Health Network, Toronto, Canada
   2. Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Flavia Venetucci Gouveia

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0995-8226

3. Gavin JB Elias

   1. Division of Neurosurgery, Department of Surgery, University Health Network, Toronto, Canada
   2. Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada

   Contribution

   Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Alexandre Boutet

   1. Division of Neurosurgery, Department of Surgery, University Health Network, Toronto, Canada
   2. Joint Department of Medical Imaging, University of Toronto, Toronto, Canada

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

5. Aaron Loh

   1. Division of Neurosurgery, Department of Surgery, University Health Network, Toronto, Canada
   2. Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

6. Adriana Lucia Lopez Rios

   1. Department of Functional and Stereotactic Neurosurgery, University Hospital San Vicente Fundación, São Paulo, Brazil
   2. Department of Functional and Stereotactic Neurosurgery, San Vicente Fundación, Barranquilla, Colombia

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Cristina Torres Diaz

   Department of Neurosurgery, University Hospital La Princesa, Madrid, Spain

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

8. William Omar Contreras Lopez

   1. Nemod Research Group, Universidad Autónoma de Bucaramanga, Bucaramanga, Colombia
   2. Division of Functional Neurosurgery, Department of Neurosurgery, FOSCAL Clinic, Bucaramanga, Colombia

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

9. Raquel Chacon Ruiz Martinez

   1. Division of Neuroscience, Sírio-Libanês Hospital, São Paulo, Brazil
   2. LIM 23, Institute of Psychiatry, School of Medicine, University of São Paulo, São Paulo, Brazil

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

10. Erich Talamoni Fonoff

    Department of Neurology, Integrated Clinic of Neuroscience, School of Medicine, University of São Paulo, São Paulo, Brazil

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

11. Juan Carlos Benedetti-Isaac

    Stereotactic and Functional Neurosurgery Division of the International Misericordia Clinic, Barranquilla, Canada

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

12. Peter Giacobbe

    1. Sunnybrook Research Institute, Toronto, Canada
    2. Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, Toronto, Canada
    3. Department of Psychiatry, University of Toronto, Toronto, Canada

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

13. Pablo M Arango Pava

    Servicio de Neuocirugia Funcional y Esterotaxia, Clinica Comuneros Bucaramanga, Clinica Desa y Clinica Dime Neurocardiovascular de Cali; Clinica Nueva del Lago, Bucaramanga, Colombia

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

14. Han Yan

    1. Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
    2. Division of Neurosurgery, The Hospital for Sick Children, Toronto, Canada

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

15. George M Ibrahim

    1. Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
    2. Division of Neurosurgery, The Hospital for Sick Children, Toronto, Canada
    3. Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
    4. Institute of Medical Science, University of Toronto, Toronto, Canada

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

16. Nir Lipsman

    1. Sunnybrook Research Institute, Toronto, Canada
    2. Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
    3. Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, Toronto, Canada

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

17. Andres Lozano

    1. Division of Neurosurgery, Department of Surgery, University Health Network, Toronto, Canada
    2. Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

18. Clement Hamani

    1. Sunnybrook Research Institute, Toronto, Canada
    2. Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
    3. Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, Toronto, Canada

    Contribution

    Conceptualization, Funding acquisition, Writing – original draft, Writing – review and editing

    For correspondence

    clement.hamani@sunnybrook.ca

    Competing interests

    No competing interests declared

• 574

  Page views

• 131

  Downloads

• 0

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.