Multi-centre analysis of networks and genes modulated by hypothalamic stimulation in patients with aggressive behaviours

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.

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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).

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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

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