Age-related differences in the functional topography of the locus coeruleus and their implications for cognitive and affective functions

The central noradrenergic system comprises projections to widespread cortical and subcortical areas of the brain, with important neuromodulatory roles in sleep and arousal, executive function, mood, emotion regulation, and memory (Samuels and Szabadi, 2008a). Most of the noradrenergic transmission in the brain originates from the locus coeruleus (LC), a bilateral nucleus in the dorsolateral pons. The LC has been implicated in aging-related cognitive changes and neurodegenerative conditions, such as Alzheimer’s disease (AD) or Parkinson’s disease (PD) (Betts et al., 2019b). Interestingly, different conditions seem to affect different parts of the LC (Madelung et al., 2022; Ye et al., 2022), and several studies show that subregions of the LC might be involved in different cognitive functions and psychiatric symptoms (Hämmerer et al., 2018; Ciampa et al., 2022). Specifically, the rostral part is associated with memory and emotion regulation, being often affected in AD (Hämmerer et al., 2018; Ciampa et al., 2022; Betts et al., 2019a; Theofilas et al., 2017), whereas the integrity of the caudal part has been linked to more general cognitive processes in PD (Ye et al., 2022; Doppler et al., 2021).

However, a dichotomous functional division model of the LC might be oversimplistic, and technical difficulties concerning imaging of the LC (such as low spatial resolution of functional imaging techniques, inconvenient anatomical location, or noise from adjacent cerebrospinal fluid spaces; Engels-Domínguez et al., 2023) could hide a more complex underlying functional organization. Additionally, multiple overlapping axes of functional organization might be present that can confound current traditional cluster-based models that seek to divide the LC into subregions (Haak and Beckmann, 2020; Poe et al., 2020). Similar issues have been raised regarding other subcortical nuclei, where researchers started to unravel this complexity by modeling voxel-wise functional organization with a gradient-based approach. For example, several studies reported that the hippocampus is organized by a gradient of connectivity profiles and microstructural properties along its longitudinal axis (Przeździk et al., 2019). These spatial characteristics of hippocampal gradients explained individual differences in behavior better than a cluster-based model, and therefore provided a closer approximation to the true organization profile of this region. Other studies investigated gradients of functional connectivity in the striatum in healthy individuals and patients with PD. Oldehinkel et al. found that, apart from the primary functional connectivity gradient, a second axis of connectopic organization was present in the striatum that closely resembled the spatial distribution of dopamine transporters across different medication and symptomatology stages, providing a non-invasive marker for striatal dopamine transporter density in PD (Oldehinkel et al., 2022).

To our knowledge, so far, no studies have investigated the functional gradients of the LC. This is important because this region modulates various behavioral functions and widespread brain regions in a very precise manner (Liu et al., 2020). Furthermore, studies using fMRI to describe connectivity in the LC report heterogeneous results – possibly because the localization methods, the studied populations, and preprocessing pipelines often differ (Liebe et al., 2020; Song et al., 2021; Liebe et al., 2022; Zhang et al., 2016) – and a similarity gradient-based approach might be better suited to characterize its functional organization by mitigating the effect of heterogeneous individual differences in connectivity and imaging acquisition-related noise, since it is less affected by, for example, region of interest (ROI) inaccuracies (Haak et al., 2018). The aims of our study were to: (1) identify the functional gradients of the LC and reproduce them across different cohorts and image resolutions, using a 3T (the Cambridge Centre for Ageing and Neuroscience or CamCAN cohort) and a 7T (the Human Brain Connectome or HCP 7T cohort) datasets; (2) characterize how the functional gradients of the LC change over the course of aging in the CamCAN dataset, a large cohort of 618 individuals with resting-state fMRI between 18 and 88 years of age; (3) identify LC projections to cortical and subcortical areas; and (4) detect relevant associations between LC gradients with cognitive, emotional/affective, and physiological functions that decline with increasing age.

We show that the LC exhibits a basic rostro-caudal functional gradient that is reproducible across different cohorts and age ranges. The LC functional gradient is driven by different patterns of connections to cortical and subcortical areas that are relevant to understand the pathophysiological mechanisms of neurodegenerative disorders. Moreover, we demonstrate that individual differences in the finer spatial layout of the rostro-caudal gradient are related to age, and functions ascribed to the noradrenergic system correlate with gradient spatial characteristics when corrected for the effects of age. Our findings provide an alternative model of LC functional organization that might explain why distinct functional alterations of the LC could lead to different cognitive and psychiatric disorders.

In this study, we show that functional connectivity in the LC is organized along a rostro-caudal gradient. This functional gradient was reproduced in two independent cohorts, and was driven by differential connections to the medial temporal lobe, hippocampus, amygdalae, striatum, medial parietal cortex, and anterior cingulate (from the rostral part) and the visual, sensorimotor cortices and thalamus (from the caudal part). Importantly, we show that spatial variation and rostral-caudal balance showed age-related changes: specifically, a loss of rostral-like connectivity, a more clustered functional organization over the entire LC, and an increased asymmetry were associated with older age. Moreover, these differences in the spatial characteristics of the LC gradient were associated with poorer emotional memory, emotion regulation, higher anxiety, as well as depression scores, independently of age, sex, and education. These findings highlight the essential role played by the LC in regulating memory, emotion, and psychiatric symptoms through its heterogenous patterns of functional connections projecting to different cortical and subcortical brain areas.

A rostral-caudal division has already been reported in the LC by studies using structural imaging and LC-contrast MRI sequences (Dahl et al., 2019). This division seems to be driven by a heterogeneous distribution of cellular, molecular, and connectivity-related characteristics along the LC. Specifically, LC-contrast MRI sequences such as magnetization transfer imaging seem to be associated with neuronal integrity in the LC, although the biological sources of this contrast are still not fully understood (Keren et al., 2015). A post-mortem study showed that noradrenergic neural somas in the LC show a specific distribution characterized by a dense collection of cell bodies in the central LC that disperses in both caudal and rostral directions, with a lower number of these noradrenergic cells in the caudal part compared to the rostral part (Fernandes et al., 2012). Using a gradient-based approach applied to functional imaging, we reproduced this structural division, revealing for the first time a rostro-caudal functional organization consistent across cohorts with different acquisition parameters.

In addition to distinct cellular distributions, spatial heterogeneity in the LC is also driven by projections to different brain areas along its longitudinal axis. Tracer studies show that the rostral-dorsal part of the LC projects to neocortical regions, the hippocampus, medial temporal lobe, and amygdala, whereas the caudal-ventral part sends its main efferent projections to the cerebellum and medulla (Samuels and Szabadi, 2008b). Here, we provide an in vivo account of LC functional projections, revealing that the connections most reproducible across cohorts, mainly from the rostral part, are in reasonable agreement with anatomical connectivity studies, and these connections drive the gradient of functional connectivity throughout the LC. However, in addition to tracer studies, we found that the caudal part also shows connectivity to primary sensorimotor and visual cortical regions, as well as the thalami. Some of these regions, especially the thalami, were reported to show connectivity with the LC in previous fMRI studies (Liebe et al., 2020). Regarding primary sensorimotor and visual regions, it is possible that downstream multisynaptic connections in these regions reflect the cerebellar projections of the caudal LC, since the cerebellum is known to have strong reciprocal connections to multiple thalamic nuclei and primary sensorimotor cortical areas (Caligiore et al., 2017). It is important to note that the fMRI acquisition in the CamCAN cohort did not cover the entire cerebellum and upper cervical spinal cord, which are known targets of projections in the caudal LC, therefore we did not investigate them in this study.

Since in vivo investigation of the LC is hindered by its small size and location, the functional implications of the heterogeneous structure and connectivity of the LC have not been fully explored. Electrophysiological studies report non-overlapping spontaneous activation dynamics from different sites in the LC that associate with and drive different cortical states, suggesting the presence of distinct neural ensembles in the LC that regulate multiple cortical states, instead of a homogeneous arousal-related release of noradrenaline across the cortex (Noei et al., 2022). Although fMRI provides a comprehensive view of LC function due to this nucleus’ extensive and diffuse cortical projections, this technique is limited by low spatial resolution, which could explain why previous resting-state fMRI studies focused on the LC obtained inconsistent results (Liebe et al., 2020; Song et al., 2021). The high connectivity heterogeneity of the LC shown in our study indicates that using the LC as a single, homogeneous ROI might not be the best approach to assess connectivity in this region. We show that a gradient-based approach is able to uncover the functional organization of the LC, and that it is relevant to LC-related behavioral measures. Specifically, we found that the LC functional connectivity gradient shows age-related differences, and its spatial features correlated with several behavioral measures that were previously linked to the noradrenergic system. Functional connectivity of the LC has been demonstrated to depend on age, especially connections to the medial temporal lobe and amygdala projecting from the rostral part of this region (Song et al., 2021). In line with this, we observed a loss of rostral-like connectivity with increasing age. Other in vivo MRI studies also reported that most age-related structural changes seem to be confined to the rostral part of the LC (Liu et al., 2020; Betts et al., 2017; Liu et al., 2019), and can be linked to age-related alterations in cortical thickness (Bachman et al., 2021). These changes possibly stem from morphological alterations that affect neuromelanin-containing neurons in the rostral part, leading to a reduction in cell size and potentially cell loss, which might be associated with alterations in their projections as well (Chan-Palay and Asan, 1989). Another possible explanation is that, with advancing age, misfolded protein aggregates build up in the brain that may or may not cause manifest neurodegeneration later on Farrell et al., 2022. Recent research identified the LC as an early site for tau deposition, where accumulation of pathological proteins increases the risk for neurodegenerative conditions like AD (Jacobs et al., 2021). Recent histological studies reported that the distribution of pathological proteins (especially tau neurofibrillary tangles) is non-uniform in the LC, preferentially affecting the rostral-dorsal part, irrespective of Braak stages (Gilvesy et al., 2022). Early morphological changes in tau-containing LC neurons included gradual dendritic atrophy and changes in axonal morphology indicative of impaired axonal transport, which might contribute to earlier loss of connectivity in the rostral LC section during aging. Our results support these age-related effects on LC integrity and provide a functional imaging marker to assess accompanying regional changes in LC connectivity in vivo.

Independently of age, an old-like functional gradient was also associated with worse performance on emotional memory and emotion regulation scores, consistent with previous studies showing that lower rostral LC integrity is associated with worse memory performance in general (Dahl et al., 2019). A possible explanation for this is the relationship between pathological amyloid-β buildup and decreased functional connectivity of the LC to the hippocampus and amygdala during the encoding of salient events (Prokopiou et al., 2022). These connections were more characteristic of the rostral part of the LC in our study, where poorer memory performance was associated with a loss of rostral-like connectivity. Other studies further detail the association between LC integrity and memory, showing that the link between LC integrity and memory performance is more emphasized when participants try to recall aversive effects, which reflects the role of the noradrenergic system in the formation of emotionally charged memories (Hämmerer et al., 2018). Furthermore, the LC functional gradient was associated with emotion regulation, a process that is strongly associated with fear learning and development of anxiety, which have been linked to the noradrenergic system (Uematsu et al., 2017). The fact that the LC functional gradient reflected emotional memory performance and emotion regulation in our study independently of age points to the selectivity of the LC gradient in explaining these behavioral measures and its viability as a marker for emotional processing. Interestingly, we did not find an association between the LC functional gradient and executive functions or sleep quality. Although more detailed studies are needed, it is possible that the association between executive function and the LC is mostly mediated by age. Regarding the PSQI sleep quality indices, this questionnaire is a subjective assessment of sleep quality and more objective studies are needed to examine the effects of LC functional gradients on sleep architecture.

Furthermore, participants with higher anxiety and depression ratings exhibited a more asymmetric functional gradient, with the same parameters affected as in our results concerning aging. This is in line with previous studies reporting a role for the LC in the generation of anxiety and depression (Morris et al., 2020a). The LC modulates stress-related arousal through its ascending norepinephrinergic projections, and chronic stress involved in the pathogenesis of clinical anxiety amplifies tonic activity in the LC in response to subsequent stressors in animal models (Jedema et al., 2008). Additionally, chronic stress is associated with differences in dendritic morphology in the prefrontal cortex of rats, which might impact efferent projections from the LC (Liston et al., 2006). Anxiety symptoms also seem to come with degenerative or maladaptive structural changes within the LC, since this condition was associated with morphological differences of the LC on ultra-high field MRI in animal (Omoluabi et al., 2021) and human studies (Morris et al., 2020b). Although the relevance of the LC in depression is mostly tied to its role in the development of anxiety and therefore stress-induced depression (Weiss et al., 1994), there are several studies that report specific alterations of the LC in for example major depression disorder (MDD). A task fMRI study found decreased LC functional connectivity during a visual oddball task in patients with MDD (Del Cerro et al., 2020). Apart from functional alterations, the LC also shows reduced structural integrity in patients with MDD (Guinea-Izquierdo et al., 2021), as well as molecular changes that manifest in lower levels of neuronal nitric oxide synthase, an intracellular mediator of glutamatergic signaling, in the LC (Karolewicz et al., 2004). Regarding lateralized alterations of the LC functional gradient in participants with elevated anxiety and depression scores, asymmetry can be physiological to a point, since a lateralized signal distribution of the LC was observed in healthy adults in previous studies (Betts et al., 2017). Although future studies are needed to confirm this, it is possible that a more asymmetric LC functional gradient accompanying higher HADS scores reflects lateralized amygdala response alterations reported in previous studies of patients with anxiety disorders with accompanying plastic changes in LC connectivity (Evans et al., 2008). However, it is important to note that the HADS score represents a general assessment of a person’s anxiety and depression levels, and more specific and detailed studies will be needed to elucidate how LC functional gradients are associated with anxiety and depressive symptoms.

Interestingly, we observed lateralized changes in the LC gradients both in connection with aging and cognitive performance. Generally, the LC connects to several highly lateralized cortical networks, for example, the salience and frontoparietal attention networks, that might result in an asymmetric plasticity in the LC. Neurodegenerative disorders seem to affect the left LC more both in terms of integrity and connectivity. For example, more widespread loss of connectivity between the left LC and resting-state networks was found in PD patients, with a correlation between left LC-executive control network connectivity and cognition (Sun et al., 2023). Another study found decreased connectivity between the left LC and the parahippocampal gyrus in mild cognitive impairment (Jacobs et al., 2015). Furthermore, increased left LC contrast was associated with aging and PD as well (Ye et al., 2022; Betts et al., 2017). However, the biological basis for this is elusive, as post-mortem studies generally find the bilateral LC symmetric and mostly report pathological changes in the rostral and middle LC (Beardmore et al., 2021). In our case, a possible interpretation is that with the loss of rostral-like connectivity, previously rostral-like areas lose their specific connections and become more similar to the caudal part in terms of connectivity. In this study, since we did not investigate the cerebellum and the spinal cord, the typical caudal connectivity profile is more non-specific, since some of its dominant connections are not assessed.

This study has some limitations. Although we reproduced the functional gradients in a dataset with higher spatial resolution of 7T, the spatial resolution and coverage of 3T functional MRI scans is a limiting factor warranting further studies, as well as the fact that physiological data (such as pupil size tracking or respiratory monitoring) was not acquired during the 3T scans. A further technical limitation is that connectopic mapping requires a measure of spatial smoothness for gradient calculation, therefore we used a smoothing kernel of 3 mm FWHM and constrained it to the LC mask in order to avoid signal leakage. Also, the dataset used to investigate age-related changes is cross-sectional, and longitudinal studies need to be conducted in order to confirm the age-related differences we observed here. Because of the small ROI size and the limited temporal resolution of the 3T dataset, we employed a group-based approach to calculate the gradients, however, this limits the assessment of gradient reproducibility on the individual level, which should be explored in further studies. Furthermore, although the behavioral significance of functional gradients is increasingly recognized in correlational studies, further research is required to obtain more information about their underlying physiological role.

In conclusion, in this study we show that functional connectivity is organized into a gradient along the longitudinal axis of the LC. This functional gradient was reproducible across cohorts and different fMRI protocols, and showed relevant associations with age, memory, emotion, as well as anxiety and depression ratings. Therefore, our study provides an in vivo account of functional heterogeneity and organization in the LC, as well as a possible imaging marker for LC-related behavioral measures of memory and psychiatric symptoms.

All imaging and behavioural data used in this study is openly available at https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/ for the Cam-CAN cohort and at db.humanconnectome.org for the Human Connectome Project cohort.

1. 1. Avants BB
   2. Tustison NJ
   3. Stauffer M
   4. Song G
   5. Wu B
   6. Gee JC

   (2014) The Insight ToolKit image registration framework

   Frontiers in Neuroinformatics 8:44.

   https://doi.org/10.3389/fninf.2014.00044

   • PubMed
   • Google Scholar
2. 1. Bachman SL
   2. Dahl MJ
   3. Werkle-Bergner M
   4. Düzel S
   5. Forlim CG
   6. Lindenberger U
   7. Kühn S
   8. Mather M

   (2021) Locus coeruleus MRI contrast is associated with cortical thickness in older adults

   Neurobiology of Aging 100:72–82.

   https://doi.org/10.1016/j.neurobiolaging.2020.12.019

   • PubMed
   • Google Scholar
3. 1. Beardmore R
   2. Hou R
   3. Darekar A
   4. Holmes C
   5. Boche D

   (2021) The locus coeruleus in aging and alzheimer’s disease: A postmortem and brain imaging review

   Journal of Alzheimer’s Disease 83:5–22.

   https://doi.org/10.3233/JAD-210191

   • PubMed
   • Google Scholar
4. 1. Belkin M
   2. Niyogi P

   (2003) Laplacian eigenmaps for dimensionality reduction and data representation

   Neural Computation 15:1373–1396.

   https://doi.org/10.1162/089976603321780317

   • Google Scholar
5. 1. Betts MJ
   2. Cardenas-Blanco A
   3. Kanowski M
   4. Jessen F
   5. Düzel E

   (2017) In vivo MRI assessment of the human locus coeruleus along its rostrocaudal extent in young and older adults

   NeuroImage 163:150–159.

   https://doi.org/10.1016/j.neuroimage.2017.09.042

   • PubMed
   • Google Scholar
6. 1. Betts MJ
   2. Cardenas-Blanco A
   3. Kanowski M
   4. Spottke A
   5. Teipel SJ
   6. Kilimann I
   7. Jessen F
   8. Düzel E

   (2019a) Locus coeruleus MRI contrast is reduced in Alzheimer’s disease dementia and correlates with CSF Aβ levels

   Alzheimer’s & Dementia 11:281–285.

   https://doi.org/10.1016/j.dadm.2019.02.001

   • PubMed
   • Google Scholar
7. 1. Betts MJ
   2. Kirilina E
   3. Otaduy MCG
   4. Ivanov D
   5. Acosta-Cabronero J
   6. Callaghan MF
   7. Lambert C
   8. Cardenas-Blanco A
   9. Pine K
   10. Passamonti L
   11. Loane C
   12. Keuken MC
   13. Trujillo P
   14. Lüsebrink F
   15. Mattern H
   16. Liu KY
   17. Priovoulos N
   18. Fliessbach K
   19. Dahl MJ
   20. Maaß A
   21. Madelung CF
   22. Meder D
   23. Ehrenberg AJ
   24. Speck O
   25. Weiskopf N
   26. Dolan R
   27. Inglis B
   28. Tosun D
   29. Morawski M
   30. Zucca FA
   31. Siebner HR
   32. Mather M
   33. Uludag K
   34. Heinsen H
   35. Poser BA
   36. Howard R
   37. Zecca L
   38. Rowe JB
   39. Grinberg LT
   40. Jacobs HIL
   41. Düzel E
   42. Hämmerer D

   (2019b) Locus coeruleus imaging as a biomarker for noradrenergic dysfunction in neurodegenerative diseases

   Brain 142:2558–2571.

   https://doi.org/10.1093/brain/awz193

   • PubMed
   • Google Scholar
8. 1. Buysse DJ
   2. Reynolds CF
   3. Monk TH
   4. Berman SR
   5. Kupfer DJ

   (1989) The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research

   Psychiatry Research 28:193–213.

   https://doi.org/10.1016/0165-1781(89)90047-4

   • PubMed
   • Google Scholar
9. 1. Caligiore D
   2. Pezzulo G
   3. Baldassarre G
   4. Bostan AC
   5. Strick PL
   6. Doya K
   7. Helmich RC
   8. Dirkx M
   9. Houk J
   10. Jörntell H
   11. Lago-Rodriguez A
   12. Galea JM
   13. Miall RC
   14. Popa T
   15. Kishore A
   16. Verschure PFMJ
   17. Zucca R
   18. Herreros I

   (2017) Consensus paper: Towards a systems-level view of cerebellar function: The interplay between cerebellum, basal ganglia, and cortex

   Cerebellum 16:203–229.

   https://doi.org/10.1007/s12311-016-0763-3

   • PubMed
   • Google Scholar
10. 1. Chan-Palay V
    2. Asan E

    (1989) Quantitation of catecholamine neurons in the locus coeruleus in human brains of normal young and older adults and in depression

    The Journal of Comparative Neurology 287:357–372.

    https://doi.org/10.1002/cne.902870307

    • PubMed
    • Google Scholar
11. 1. Ciampa CJ
    2. Parent JH
    3. Harrison TM
    4. Fain RM
    5. Betts MJ
    6. Maass A
    7. Winer JR
    8. Baker SL
    9. Janabi M
    10. Furman DJ
    11. D’Esposito M
    12. Jagust WJ
    13. Berry AS

    (2022) Associations among locus coeruleus catecholamines, tau pathology, and memory in aging

    Neuropsychopharmacology 47:1106–1113.

    https://doi.org/10.1038/s41386-022-01269-6

    • PubMed
    • Google Scholar
12. 1. Cohen AL
    2. Fair DA
    3. Dosenbach NUF
    4. Miezin FM
    5. Dierker D
    6. Van Essen DC
    7. Schlaggar BL
    8. Petersen SE

    (2008) Defining functional areas in individual human brains using resting functional connectivity MRI

    NeuroImage 41:45–57.

    https://doi.org/10.1016/j.neuroimage.2008.01.066

    • PubMed
    • Google Scholar
13. 1. Dahl MJ
    2. Mather M
    3. Düzel S
    4. Bodammer NC
    5. Lindenberger U
    6. Kühn S
    7. Werkle-Bergner M

    (2019) Rostral locus coeruleus integrity is associated with better memory performance in older adults

    Nature Human Behaviour 3:1203–1214.

    https://doi.org/10.1038/s41562-019-0715-2

    • PubMed
    • Google Scholar
14. 1. Dahl MJ
    2. Mather M
    3. Werkle-Bergner M
    4. Kennedy BL
    5. Guzman S
    6. Hurth K
    7. Miller CA
    8. Qiao Y
    9. Shi Y
    10. Chui HC
    11. Ringman JM

    (2022) Locus coeruleus integrity is related to tau burden and memory loss in autosomal-dominant Alzheimer’s disease

    Neurobiology of Aging 112:39–54.

    https://doi.org/10.1016/j.neurobiolaging.2021.11.006

    • PubMed
    • Google Scholar
15. 1. Dale AM
    2. Fischl B
    3. Sereno MI

    (1999) Cortical Surface-Based Analysis

    NeuroImage 9:179–194.

    https://doi.org/10.1006/nimg.1998.0395

    • PubMed
    • Google Scholar
16. 1. Del Cerro I
    2. Martínez-Zalacaín I
    3. Guinea-Izquierdo A
    4. Gascón-Bayarri J
    5. Viñas-Diez V
    6. Urretavizcaya M
    7. Naval-Baudin P
    8. Aguilera C
    9. Reñé-Ramírez R
    10. Ferrer I
    11. Menchón JM
    12. Soria V
    13. Soriano-Mas C

    (2020) Locus coeruleus connectivity alterations in late-life major depressive disorder during a visual oddball task

    NeuroImage. Clinical 28:102482.

    https://doi.org/10.1016/j.nicl.2020.102482

    • PubMed
    • Google Scholar
17. 1. Doppler CEJ
    2. Kinnerup MB
    3. Brune C
    4. Farrher E
    5. Betts M
    6. Fedorova TD
    7. Schaldemose JL
    8. Knudsen K
    9. Ismail R
    10. Seger AD
    11. Hansen AK
    12. Stær K
    13. Fink GR
    14. Brooks DJ
    15. Nahimi A
    16. Borghammer P
    17. Sommerauer M

    (2021) Regional locus coeruleus degeneration is uncoupled from noradrenergic terminal loss in Parkinson’s disease

    Brain 144:2732–2744.

    https://doi.org/10.1093/brain/awab236

    • PubMed
    • Google Scholar
18. 1. Engels-Domínguez N
    2. Koops EA
    3. Prokopiou PC
    4. Van Egroo M
    5. Schneider C
    6. Riphagen JM
    7. Singhal T
    8. Jacobs HIL

    (2023) State-of-the-art imaging of neuromodulatory subcortical systems in aging and Alzheimer’s disease: Challenges and opportunities

    Neuroscience and Biobehavioral Reviews 144:104998.

    https://doi.org/10.1016/j.neubiorev.2022.104998

    • PubMed
    • Google Scholar
19. 1. Evans KC
    2. Wright CI
    3. Wedig MM
    4. Gold AL
    5. Pollack MH
    6. Rauch SL

    (2008) A functional MRI study of amygdala responses to angry schematic faces in social anxiety disorder

    Depression and Anxiety 25:496–505.

    https://doi.org/10.1002/da.20347

    • PubMed
    • Google Scholar
20. 1. Farrell ME
    2. Papp KV
    3. Buckley RF
    4. Jacobs HIL
    5. Schultz AP
    6. Properzi MJ
    7. Vannini P
    8. Hanseeuw BJ
    9. Rentz DM
    10. Johnson KA
    11. Sperling RA

    (2022) Association of emerging β-amyloid and tau pathology with early cognitive changes in clinically normal older adults

    Neurology 98:e1512–e1524.

    https://doi.org/10.1212/WNL.0000000000200137

    • PubMed
    • Google Scholar
21. 1. Fernandes P
    2. Regala J
    3. Correia F
    4. Gonçalves-Ferreira AJ

    (2012) The human locus coeruleus 3-D stereotactic anatomy

    Surgical and Radiologic Anatomy 34:879–885.

    https://doi.org/10.1007/s00276-012-0979-y

    • PubMed
    • Google Scholar
22. 1. Friston KJ
    2. Williams S
    3. Howard R
    4. Frackowiak RS
    5. Turner R

    (1996) Movement-related effects in fMRI time-series

    Magnetic Resonance in Medicine 35:346–355.

    https://doi.org/10.1002/mrm.1910350312

    • PubMed
    • Google Scholar
23. 1. Gilvesy A
    2. Husen E
    3. Magloczky Z
    4. Mihaly O
    5. Hortobágyi T
    6. Kanatani S
    7. Heinsen H
    8. Renier N
    9. Hökfelt T
    10. Mulder J
    11. Uhlen M
    12. Kovacs GG
    13. Adori C

    (2022) Spatiotemporal characterization of cellular tau pathology in the human locus coeruleus-pericoerulear complex by three-dimensional imaging

    Acta Neuropathologica 144:651–676.

    https://doi.org/10.1007/s00401-022-02477-6

    • PubMed
    • Google Scholar
24. 1. Glasser MF
    2. Sotiropoulos SN
    3. Wilson JA
    4. Coalson TS
    5. Fischl B
    6. Andersson JL
    7. Xu J
    8. Jbabdi S
    9. Webster M
    10. Polimeni JR
    11. Van Essen DC
    12. Jenkinson M
    13. WU-Minn HCP Consortium

    (2013) The minimal preprocessing pipelines for the Human Connectome Project

    NeuroImage 80:105–124.

    https://doi.org/10.1016/j.neuroimage.2013.04.127

    • PubMed
    • Google Scholar
25. 1. Glasser MF
    2. Coalson TS
    3. Robinson EC
    4. Hacker CD
    5. Harwell J
    6. Yacoub E
    7. Ugurbil K
    8. Andersson J
    9. Beckmann CF
    10. Jenkinson M
    11. Smith SM
    12. Van Essen DC

    (2016) A multi-modal parcellation of human cerebral cortex

    Nature 536:171–178.

    https://doi.org/10.1038/nature18933

    • PubMed
    • Google Scholar
26. 1. Guinea-Izquierdo A
    2. Giménez M
    3. Martínez-Zalacaín I
    4. Del Cerro I
    5. Canal-Noguer P
    6. Blasco G
    7. Gascón J
    8. Reñé R
    9. Rico I
    10. Camins A
    11. Aguilera C
    12. Urretavizcaya M
    13. Ferrer I
    14. Menchón JM
    15. Soria V
    16. Soriano-Mas C

    (2021) Lower Locus Coeruleus MRI intensity in patients with late-life major depression

    PeerJ 9:e10828.

    https://doi.org/10.7717/peerj.10828

    • PubMed
    • Google Scholar
27. 1. Haak KV
    2. Marquand AF
    3. Beckmann CF

    (2018) Connectopic mapping with resting-state fMRI

    NeuroImage 170:83–94.

    https://doi.org/10.1016/j.neuroimage.2017.06.075

    • PubMed
    • Google Scholar
28. 1. Haak KV
    2. Beckmann CF

    (2020) Understanding brain organisation in the face of functional heterogeneity and functional multiplicity

    NeuroImage 220:117061.

    https://doi.org/10.1016/j.neuroimage.2020.117061

    • PubMed
    • Google Scholar
29. 1. Hämmerer D
    2. Callaghan MF
    3. Hopkins A
    4. Kosciessa J
    5. Betts M
    6. Cardenas-Blanco A
    7. Kanowski M
    8. Weiskopf N
    9. Dayan P
    10. Dolan RJ
    11. Düzel E

    (2018) Locus coeruleus integrity in old age is selectively related to memories linked with salient negative events

    PNAS 115:2228–2233.

    https://doi.org/10.1073/pnas.1712268115

    • PubMed
    • Google Scholar
30. 1. Huertas I
    2. Oldehinkel M
    3. van Oort ESB
    4. Garcia-Solis D
    5. Mir P
    6. Beckmann CF
    7. Marquand AF

    (2017) A Bayesian spatial model for neuroimaging data based on biologically informed basis functions

    NeuroImage 161:134–148.

    https://doi.org/10.1016/j.neuroimage.2017.08.009

    • PubMed
    • Google Scholar
31. 1. Jacobs HIL
    2. Wiese S
    3. van de Ven V
    4. Gronenschild E
    5. Verhey FRJ
    6. Matthews PM

    (2015) Relevance of parahippocampal-locus coeruleus connectivity to memory in early dementia

    Neurobiology of Aging 36:618–626.

    https://doi.org/10.1016/j.neurobiolaging.2014.10.041

    • PubMed
    • Google Scholar
32. 1. Jacobs HIL
    2. Becker JA
    3. Kwong K
    4. Engels-Domínguez N
    5. Prokopiou PC
    6. Papp KV
    7. Properzi M
    8. Hampton OL
    9. d’Oleire Uquillas F
    10. Sanchez JS
    11. Rentz DM
    12. El Fakhri G
    13. Normandin MD
    14. Price JC
    15. Bennett DA
    16. Sperling RA
    17. Johnson KA

    (2021) In vivo and neuropathology data support locus coeruleus integrity as indicator of Alzheimer’s disease pathology and cognitive decline

    Science Translational Medicine 13:eabj2511.

    https://doi.org/10.1126/scitranslmed.abj2511

    • PubMed
    • Google Scholar
33. 1. Jedema HP
    2. Gold SJ
    3. Gonzalez-Burgos G
    4. Sved AF
    5. Tobe BJ
    6. Wensel T
    7. Grace AA

    (2008) Chronic cold exposure increases RGS7 expression and decreases alpha(2)-autoreceptor-mediated inhibition of noradrenergic locus coeruleus neurons

    The European Journal of Neuroscience 27:2433–2443.

    https://doi.org/10.1111/j.1460-9568.2008.06208.x

    • PubMed
    • Google Scholar
34. 1. Karolewicz B
    2. Szebeni K
    3. Stockmeier CA
    4. Konick L
    5. Overholser JC
    6. Jurjus G
    7. Roth BL
    8. Ordway GA

    (2004) Low nNOS protein in the locus coeruleus in major depression

    Journal of Neurochemistry 91:1057–1066.

    https://doi.org/10.1111/j.1471-4159.2004.02792.x

    • PubMed
    • Google Scholar
35. 1. Keren NI
    2. Taheri S
    3. Vazey EM
    4. Morgan PS
    5. Granholm ACE
    6. Aston-Jones GS
    7. Eckert MA

    (2015) Histologic validation of locus coeruleus MRI contrast in post-mortem tissue

    NeuroImage 113:235–245.

    https://doi.org/10.1016/j.neuroimage.2015.03.020

    • PubMed
    • Google Scholar
36. 1. Liebe T
    2. Kaufmann J
    3. Li M
    4. Skalej M
    5. Wagner G
    6. Walter M

    (2020) In vivo anatomical mapping of human locus coeruleus functional connectivity at 3 T MRI

    Human Brain Mapping 41:2136–2151.

    https://doi.org/10.1002/hbm.24935

    • PubMed
    • Google Scholar
37. 1. Liebe T
    2. Kaufmann J
    3. Hämmerer D
    4. Betts M
    5. Walter M

    (2022) In vivo tractography of human locus coeruleus-relation to 7T resting state fMRI, psychological measures and single subject validity

    Molecular Psychiatry 27:4984–4993.

    https://doi.org/10.1038/s41380-022-01761-x

    • PubMed
    • Google Scholar
38. 1. Liston C
    2. Miller MM
    3. Goldwater DS
    4. Radley JJ
    5. Rocher AB
    6. Hof PR
    7. Morrison JH
    8. McEwen BS

    (2006) Stress-induced alterations in prefrontal cortical dendritic morphology predict selective impairments in perceptual attentional set-shifting

    The Journal of Neuroscience 26:7870–7874.

    https://doi.org/10.1523/JNEUROSCI.1184-06.2006

    • PubMed
    • Google Scholar
39. 1. Liu KY
    2. Acosta-Cabronero J
    3. Cardenas-Blanco A
    4. Loane C
    5. Berry AJ
    6. Betts MJ
    7. Kievit RA
    8. Henson RN
    9. Düzel E
    10. Cam-CAN
    11. Howard R
    12. Hämmerer D

    (2019) In vivo visualization of age-related differences in the locus coeruleus

    Neurobiology of Aging 74:101–111.

    https://doi.org/10.1016/j.neurobiolaging.2018.10.014

    • PubMed
    • Google Scholar
40. 1. Liu KY
    2. Kievit RA
    3. Tsvetanov KA
    4. Betts MJ
    5. Düzel E
    6. Rowe JB
    7. Cam-CAN
    8. Howard R
    9. Hämmerer D

    (2020) Noradrenergic-dependent functions are associated with age-related locus coeruleus signal intensity differences

    Nature Communications 11:1712.

    https://doi.org/10.1038/s41467-020-15410-w

    • PubMed
    • Google Scholar
41. 1. Madelung CF
    2. Meder D
    3. Fuglsang SA
    4. Marques MM
    5. Boer VO
    6. Madsen KH
    7. Petersen ET
    8. Hejl AM
    9. Løkkegaard A
    10. Siebner HR

    (2022) Locus coeruleus shows a spatial pattern of structural disintegration in parkinson’s disease

    Movement Disorders 37:479–489.

    https://doi.org/10.1002/mds.28945

    • PubMed
    • Google Scholar
42. 1. Marquand AF
    2. Haak KV
    3. Beckmann CF

    (2017) Functional corticostriatal connection topographies predict goal directed behaviour in humans

    Nature Human Behaviour 1:0146.

    https://doi.org/10.1038/s41562-017-0146

    • PubMed
    • Google Scholar
43. 1. Morris LS
    2. McCall JG
    3. Charney DS
    4. Murrough JW

    (2020a) The role of the locus coeruleus in the generation of pathological anxiety

    Brain and Neuroscience Advances 4:2398212820930321.

    https://doi.org/10.1177/2398212820930321

    • PubMed
    • Google Scholar
44. 1. Morris LS
    2. Tan A
    3. Smith DA
    4. Grehl M
    5. Han-Huang K
    6. Naidich TP
    7. Charney DS
    8. Balchandani P
    9. Kundu P
    10. Murrough JW

    (2020b) Sub-millimeter variation in human locus coeruleus is associated with dimensional measures of psychopathology: An in vivo ultra-high field 7-Tesla MRI study

    NeuroImage. Clinical 25:102148.

    https://doi.org/10.1016/j.nicl.2019.102148

    • PubMed
    • Google Scholar
45. 1. Ngo GN
    2. Haak KV
    3. Beckmann CF
    4. Menon RS

    (2021) Mesoscale hierarchical organization of primary somatosensory cortex captured by resting-state-fMRI in humans

    NeuroImage 235:118031.

    https://doi.org/10.1016/j.neuroimage.2021.118031

    • PubMed
    • Google Scholar
46. 1. Noei S
    2. Zouridis IS
    3. Logothetis NK
    4. Panzeri S
    5. Totah NK

    (2022) Distinct ensembles in the noradrenergic locus coeruleus are associated with diverse cortical states

    PNAS 119:e2116507119.

    https://doi.org/10.1073/pnas.2116507119

    • PubMed
    • Google Scholar
47. 1. Oldehinkel M
    2. Llera A
    3. Faber M
    4. Huertas I
    5. Buitelaar JK
    6. Bloem BR
    7. Marquand AF
    8. Helmich RC
    9. Haak KV
    10. Beckmann CF

    (2022) Mapping dopaminergic projections in the human brain with resting-state fMRI

    eLife 11:e71846.

    https://doi.org/10.7554/eLife.71846

    • PubMed
    • Google Scholar
48. 1. Omoluabi T
    2. Torraville SE
    3. Maziar A
    4. Ghosh A
    5. Power KD
    6. Reinhardt C
    7. Harley CW
    8. Yuan Q

    (2021) Novelty-like activation of locus coeruleus protects against deleterious human pretangle tau effects while stress-inducing activation worsens its effects

    Alzheimer’s & Dementia 7:e12231.

    https://doi.org/10.1002/trc2.12231

    • PubMed
    • Google Scholar
49. 1. Poe GR
    2. Foote S
    3. Eschenko O
    4. Johansen JP
    5. Bouret S
    6. Aston-Jones G
    7. Harley CW
    8. Manahan-Vaughan D
    9. Weinshenker D
    10. Valentino R
    11. Berridge C
    12. Chandler DJ
    13. Waterhouse B
    14. Sara SJ

    (2020) Locus coeruleus: a new look at the blue spot

    Nature Reviews Neuroscience 21:644–659.

    https://doi.org/10.1038/s41583-020-0360-9

    • PubMed
    • Google Scholar
50. 1. Prokopiou PC
    2. Engels-Domínguez N
    3. Papp KV
    4. Scott MR
    5. Schultz AP
    6. Schneider C
    7. Farrell ME
    8. Buckley RF
    9. Quiroz YT
    10. El Fakhri G
    11. Rentz DM
    12. Sperling RA
    13. Johnson KA
    14. Jacobs HIL

    (2022) Lower novelty-related locus coeruleus function is associated with Aβ-related cognitive decline in clinically healthy individuals

    Nature Communications 13:1571.

    https://doi.org/10.1038/s41467-022-28986-2

    • PubMed
    • Google Scholar
51. 1. Przeździk I
    2. Faber M
    3. Fernández G
    4. Beckmann CF
    5. Haak KV

    (2019) The functional organisation of the hippocampus along its long axis is gradual and predicts recollection

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 119:324–335.

    https://doi.org/10.1016/j.cortex.2019.04.015

    • PubMed
    • Google Scholar
52. 1. Samuels ER
    2. Szabadi E

    (2008a) Its roles in the regulation of arousal and autonomic function part I: principles of functional organisation

    Current Neuropharmacology 6:254–285.

    https://doi.org/10.2174/157015908785777193

    • PubMed
    • Google Scholar
53. 1. Samuels ER
    2. Szabadi E

    (2008b) Functional neuroanatomy of the noradrenergic locus coeruleus: its roles in the regulation of arousal and autonomic function part I: principles of functional organisation

    Current Neuropharmacology 6:235–253.

    https://doi.org/10.2174/157015908785777229

    • PubMed
    • Google Scholar
54. 1. Song I
    2. Neal J
    3. Lee TH

    (2021) Age-related intrinsic functional connectivity changes of locus coeruleus from childhood to older adults

    Brain Sciences 11:1485.

    https://doi.org/10.3390/brainsci11111485

    • PubMed
    • Google Scholar
55. 1. Sun J
    2. Ma J
    3. Gao L
    4. Wang J
    5. Zhang D
    6. Chen L
    7. Fang J
    8. Feng T
    9. Wu T

    (2023) Disruption of locus coeruleus-related functional networks in Parkinson’s disease

    NPJ Parkinson’s Disease 9:81.

    https://doi.org/10.1038/s41531-023-00532-x

    • PubMed
    • Google Scholar
56. 1. Taylor JR
    2. Williams N
    3. Cusack R
    4. Auer T
    5. Shafto MA
    6. Dixon M
    7. Tyler LK
    8. Henson RN

    (2017) The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample

    NeuroImage 144:262–269.

    https://doi.org/10.1016/j.neuroimage.2015.09.018

    • PubMed
    • Google Scholar
57. 1. Theofilas P
    2. Ehrenberg AJ
    3. Dunlop S
    4. Di Lorenzo Alho AT
    5. Nguy A
    6. Leite REP
    7. Rodriguez RD
    8. Mejia MB
    9. Suemoto CK
    10. Ferretti-Rebustini R
    11. Polichiso L
    12. Nascimento CF
    13. Seeley WW
    14. Nitrini R
    15. Pasqualucci CA
    16. Jacob Filho W
    17. Rueb U
    18. Neuhaus J
    19. Heinsen H
    20. Grinberg LT

    (2017) Locus coeruleus volume and cell population changes during Alzheimer’s disease progression: A stereological study in human postmortem brains with potential implication for early-stage biomarker discovery

    Alzheimer’s & Dementia 13:236–246.

    https://doi.org/10.1016/j.jalz.2016.06.2362

    • PubMed
    • Google Scholar
58. 1. Tian Y
    2. Margulies DS
    3. Breakspear M
    4. Zalesky A

    (2020) Topographic organization of the human subcortex unveiled with functional connectivity gradients

    Nature Neuroscience 23:1421–1432.

    https://doi.org/10.1038/s41593-020-00711-6

    • PubMed
    • Google Scholar
59. 1. Tona KD
    2. Keuken MC
    3. de Rover M
    4. Lakke E
    5. Forstmann BU
    6. Nieuwenhuis S
    7. van Osch MJP

    (2017) In vivo visualization of the locus coeruleus in humans: quantifying the test-retest reliability

    Brain Structure & Function 222:4203–4217.

    https://doi.org/10.1007/s00429-017-1464-5

    • PubMed
    • Google Scholar
60. 1. T Vu A
    2. Jamison K
    3. Glasser MF
    4. Smith SM
    5. Coalson T
    6. Moeller S
    7. Auerbach EJ
    8. Uğurbil K
    9. Yacoub E

    (2017) Tradeoffs in pushing the spatial resolution of fMRI for the 7T Human Connectome Project

    NeuroImage 154:23–32.

    https://doi.org/10.1016/j.neuroimage.2016.11.049

    • PubMed
    • Google Scholar
61. 1. Uematsu A
    2. Tan BZ
    3. Ycu EA
    4. Cuevas JS
    5. Koivumaa J
    6. Junyent F
    7. Kremer EJ
    8. Witten IB
    9. Deisseroth K
    10. Johansen JP

    (2017) Modular organization of the brainstem noradrenaline system coordinates opposing learning states

    Nature Neuroscience 20:1602–1611.

    https://doi.org/10.1038/nn.4642

    • PubMed
    • Google Scholar
62. 1. von der Gablentz J
    2. Tempelmann C
    3. Münte TF
    4. Heldmann M

    (2015) Performance monitoring and behavioral adaptation during task switching: an fMRI study

    Neuroscience 285:227–235.

    https://doi.org/10.1016/j.neuroscience.2014.11.024

    • PubMed
    • Google Scholar
63. 1. Weiss JM
    2. Stout JC
    3. Aaron MF
    4. Quan N
    5. Owens MJ
    6. Butler PD
    7. Nemeroff CB

    (1994) Depression and anxiety: role of the locus coeruleus and corticotropin-releasing factor

    Brain Research Bulletin 35:561–572.

    https://doi.org/10.1016/0361-9230(94)90170-8

    • PubMed
    • Google Scholar
64. 1. Ye R
    2. O’Callaghan C
    3. Rua C
    4. Hezemans FH
    5. Holland N
    6. Malpetti M
    7. Jones PS
    8. Barker RA
    9. Williams-Gray CH
    10. Robbins TW
    11. Passamonti L
    12. Rowe J

    (2022) Locus coeruleus integrity from 7 T MRI relates to apathy and cognition in Parkinsonian disorders

    Movement Disorders 37:1663–1672.

    https://doi.org/10.1002/mds.29072

    • PubMed
    • Google Scholar
65. 1. Zhang S
    2. Hu S
    3. Chao HH
    4. Li CSR

    (2016) Resting-state functional connectivity of the locus coeruleus in humans: In comparison with the ventral tegmental area/substantia nigra pars compacta and the effects of age

    Cerebral Cortex 26:3413–3427.

    https://doi.org/10.1093/cercor/bhv172

    • PubMed
    • Google Scholar
66. 1. Zigmond AS
    2. Snaith RP

    (1983) The hospital anxiety and depression scale

    Acta Psychiatrica Scandinavica 67:361–370.

    https://doi.org/10.1111/j.1600-0447.1983.tb09716.x

    • PubMed
    • Google Scholar

Author details

1. Dániel Veréb

   Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden

   Contribution

   Conceptualization, Data curation, Formal analysis, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   daniel.vereb@ki.se

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2077-5252

2. Mite Mijalkov

   Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Anna Canal-Garcia

   Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Yu-Wei Chang

   Department of Physics, Goteborg University, Goteborg, Sweden

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

5. Emiliano Gomez-Ruiz

   Department of Physics, Goteborg University, Goteborg, Sweden

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Blanca Zufiria Gerboles

   Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Miia Kivipelto

   1. Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden
   2. University of Eastern Finland, Kuopio, Finland

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

8. Per Svenningsson

   1. University of Eastern Finland, Kuopio, Finland
   2. Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

9. Henrik Zetterberg

   1. Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
   2. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
   3. Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
   4. UK Dementia Research Institute at UCL, London, United Kingdom
   5. Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
   6. Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, United States

   Contribution

   Writing – review and editing

   Competing interests

   Henrik Zetterberg has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work)

10. Giovanni Volpe

    Department of Physics, Goteborg University, Goteborg, Sweden

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5057-1846

11. Matthew Betts

    1. Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University Magdeburg, Magdeburg, Germany
    2. German Center for Neurodegenerative Diseases (DZNE), Otto-von-Guericke University Magdeburg, Magdeburg, Germany
    3. Center for Behavioral Brain Sciences, University of Magdeburg, Magdeburg, Germany

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

12. Heidi IL Jacobs

    1. Maastricht University, Maastricht, Netherlands
    2. Massachusetts General Hospital, Boston, United States

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

13. Joana B Pereira

    1. Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden
    2. Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden

    Contribution

    Conceptualization, Methodology, Writing – original draft, Writing – review and editing

    For correspondence

    joana.pereira@ki.se

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4604-2711

• 1,844

  views

• 162

  downloads

• 12

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.