Abstract
There has been increasing interest in using neuroimaging measures to predict psychiatric disorders. However, predictions usually rely on large brain networks and large disorder heterogeneity. Thus, they lack both anatomical and behavioural specificity, preventing the advancement of targeted interventions. Here we address both challenges. First, using resting-state functional magnetic resonance imaging, we parcellated the amygdala, a region implicated in mood disorders, into seven nuclei. Next, a questionnaire factor analysis provided subclinical mental health dimensions frequently altered in anxious-depressive individuals, such as negative emotions and sleep problems. Finally, for each behavioural dimension, we identified the most predictive resting-state functional connectivity between individual amygdala nuclei and highly specific regions of interest, such as the dorsal raphe nucleus in the brainstem or medial frontal cortical regions. Connectivity in circumscribed amygdala networks predicted behaviours in an independent dataset. Our results reveal specific relations between mental health dimensions and connectivity in precise subcortical networks.
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Data availability
All data used in the present study are available for download from the Human Connectome Project (www.humanconnectome.org). Users must apply for access and agree to the HCP data use terms (for details see https://www.humanconnectome.org/study/hcp-young-adult/data-use-terms). Here we used both Open Access and Restricted data. The masks of all ROIs used in this study as well as all individual amygdala nuclei generated are available in the OSF repository at https://doi.org/10.17605/OSF.IO/EGM2R.
Code availability
Code that allows users with HCP data access to replicate analyses is provided in the OSF repository at https://doi.org/10.17605/OSF.IO/EGM2R. This includes code to plot all figures presented in the manuscript. Intermediate analysis outputs can be made available to registered HCP users. Please see the README file in the Scripts folder for further detail.
References
Bijsterbosch, J. D. et al. Stratification of MDD and GAD patients by resting state brain connectivity predicts cognitive bias. NeuroImage Clin. 19, 425–433 (2018).
Reggente, N. et al. Multivariate resting-state functional connectivity predicts response to cognitive behavioral therapy in obsessive–compulsive disorder. Proc. Natl Acad. Sci. USA 115, 2222–2227 (2018).
Takagi, Y. et al. A neural marker of obsessive-compulsive disorder from whole-brain functional connectivity. Sci. Rep. 7, 7538 (2017).
Zeng, L.-L. et al. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain J. Neurol. 135, 1498–1507 (2012).
Aggleton, J. P. The contribution of the amygdala to normal and abnormal emotional states. Trends Neurosci. 16, 328–333 (1993).
Drevets, W. C. Neuroimaging abnormalities in the amygdala in mood disorders. Ann. N. Y. Acad. Sci. 985, 420–444 (2003).
Kennedy, D. P. & Adolphs, R. The social brain in psychiatric and neurological disorders. Trends Cogn. Sci. 16, 559–572 (2012).
Murray, E. A., Wise, S. P. & Drevets, W. C. Localization of dysfunction in major depressive disorder: prefrontal cortex and amygdala. Biol. Psychiatry 69, e43–e54 (2011).
Phelps, E. A. & LeDoux, J. E. Contributions of the amygdala to emotion processing: from animal models to human behavior. Neuron 48, 175–187 (2005).
Adolphs, R. The biology of fear. Curr. Biol. 23, R79–R93 (2013).
Bernardi, S. & Salzman, C. D. The contribution of nonhuman primate research to the understanding of emotion and cognition and its clinical relevance. Proc. Natl Acad. Sci. USA 116, 26305–26312 (2019).
Dal Monte, O., Costa, V. D., Noble, P. L., Murray, E. A. & Averbeck, B. B. Amygdala lesions in rhesus macaques decrease attention to threat. Nat. Commun. 6, 10161 (2015).
Drevets, W. C. et al. Subgenual prefrontal cortex abnormalities in mood disorders. Nature 386, 824–827 (1997).
Chang, S. W. C. et al. Neural mechanisms of social decision-making in the primate amygdala. Proc. Natl Acad. Sci. USA 112, 16012–16017 (2015).
Murray, E. A. & Wise, S. P. Interactions between orbital prefrontal cortex and amygdala: advanced cognition, learned responses and instinctive behaviors. Curr. Opin. Neurobiol. 20, 212–220 (2010).
Amaral, D. G., Price, J. L., Pitkanen, A. & Carmichael, S. T. in The Amygdala: Neurobiological Aspects of Emotion, Memory, and Mental Dysfunction (ed Aggleton, J. P.) 1–66 (Wiley-Liss, 1992).
Zangemeister, L., Grabenhorst, F. & Schultz, W. Neural basis for economic saving strategies in human amygdala-prefrontal reward circuits. Curr. Biol. 26, 3004–3013 (2016).
Rudebeck, P. H., Rich, E. L. & Mayberg, H. S. From bed to bench side: reverse translation to optimize neuromodulation for mood disorders. Proc. Natl Acad. Sci. USA 116, 26288–26296 (2019).
Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview. NeuroImage 80, 62–79 (2013).
Insel, T. et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167, 748–751 (2010).
Haro, J. M. et al. ROAMER: roadmap for mental health research in Europe. Int. J. Methods Psychiatr. Res. 23, 1–14 (2014).
Abivardi, A. & Bach, D. R. Deconstructing white matter connectivity of human amygdala nuclei with thalamus and cortex subdivisions in vivo. Hum. Brain Mapp. 38, 3927–3940 (2017).
Bzdok, D., Laird, A. R., Zilles, K., Fox, P. T. & Eickhoff, S. B. An investigation of the structural, connectional, and functional subspecialization in the human amygdala. Hum. Brain Mapp. 34, 3247–3266 (2013).
Solano-Castiella, E. et al. Parcellation of human amygdala in vivo using ultra high field structural MRI. NeuroImage 58, 741–748 (2011).
Smith, S. M. et al. Resting-state fMRI in the Human Connectome Project. NeuroImage 80, 144–168 (2013).
Fudge, J. L., Kunishio, K., Walsh, P., Richard, C. & Haber, S. N. Amygdaloid projections to ventromedial striatal subterritories in the primate. Neuroscience 110, 257–275 (2002).
Zikopoulos, B., John, Y. J., García-Cabezas, M. Á., Bunce, J. G. & Barbas, H. The intercalated nuclear complex of the primate amygdala. Neuroscience 330, 267–290 (2016).
Mai, J. K., Majtanik, M. & Paxinos, G. Atlas of the Human Brain (Academic Press, 2015).
Saygin, Z. M. et al. High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas. NeuroImage 155, 370–382 (2017).
Ding, S.-L. et al. Comprehensive cellular-resolution atlas of the adult human brain. J. Comp. Neurol. 524, 3127–3481 (2016).
Averbeck, B. B. & Costa, V. D. Motivational neural circuits underlying reinforcement learning. Nat. Neurosci. 20, 505–512 (2017).
Keuken, M. C. et al. Quantifying inter-individual anatomical variability in the subcortex using 7 T structural MRI. NeuroImage 94, 40–46 (2014).
Faull, O. K., Jenkinson, M., Ezra, M. & Pattinson, K. T. Conditioned respiratory threat in the subdivisions of the human periaqueductal gray. eLife 5, e12047 (2016).
Betts, M. J., Cardenas-Blanco, A., Kanowski, M., Jessen, F. & Düzel, E. In vivo MRI assessment of the human locus coeruleus along its rostrocaudal extent in young and older adults. NeuroImage 163, 150–159 (2017).
Folloni, D. et al. Dichotomous organization of amygdala/temporal-prefrontal bundles in both humans and monkeys. eLife 8, e47175 (2019).
Neubert, F.-X., Mars, R. B., Sallet, J. & Rushworth, M. F. S. Connectivity reveals relationship of brain areas for reward-guided learning and decision making in human and monkey frontal cortex. Proc. Natl Acad. Sci. USA 112, E2695–E2704 (2015).
Aggleton, J. P., Burton, M. J. & Passingham, R. E. Cortical and subcortical afferents to the amygdala of the rhesus monkey (Macaca mulatta). Brain Res. 190, 347–368 (1980).
Ironside, M. et al. Effect of prefrontal cortex stimulation on regulation of amygdala response to threat in individuals with trait anxiety: a randomized clinical trial. JAMA Psychiatry https://doi.org/10.1001/jamapsychiatry.2018.2172 (2018).
Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).
O’Reilly, J. X. et al. Causal effect of disconnection lesions on interhemispheric functional connectivity in rhesus monkeys. Proc. Natl Acad. Sci. USA 110, 13982–13987 (2013).
Carmichael, S. T. & Price, J. L. Sensory and premotor connections of the orbital and medial prefrontal cortex of macaque monkeys. J. Comp. Neurol. 363, 642–664 (1995).
Freedman, L. J., Insel, T. R. & Smith, Y. Subcortical projections of area 25 (subgenual cortex) of the macaque monkey. J. Comp. Neurol. 421, 172–188 (2000).
Sharma, K. K., Kelly, E. A., Pfeifer, C. W. & Fudge, J. L. Translating fear circuitry: amygdala projections to subgenual and perigenual anterior cingulate in the macaque. Cereb. Cortex 30, 550–562 (2020).
Harrison, O. K., Guell, X., Klein-Flügge, M. C. & Barry, R. L. Structural and resting state functional connectivity beyond the cortex. NeuroImage 240, 118379 (2021).
Drysdale, A. T. et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med. 23, 28–38 (2017).
Gillan, C. M., Kosinski, M., Whelan, R., Phelps, E. A. & Daw, N. D. Characterizing a psychiatric symptom dimension related to deficits in goal-directed control. eLife 5, e11305 (2016).
Rouault, M., Seow, T., Gillan, C. M. & Fleming, S. M. Psychiatric symptom dimensions are associated with dissociable shifts in metacognition but not task performance. Biol. Psychiatry 84, 443–451 (2018).
Williams, L. M. Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: a theoretical review of the evidence and future directions for clinical translation. Depress. Anxiety 34, 9–24 (2017).
Holtzheimer, P. E. et al. Subcallosal cingulate deep brain stimulation for treatment-resistant depression: a multisite, randomised, sham-controlled trial. Lancet Psychiatry 4, 839–849 (2017).
Dandekar, M. P., Fenoy, A. J., Carvalho, A. F., Soares, J. C. & Quevedo, J. Deep brain stimulation for treatment-resistant depression: an integrative review of preclinical and clinical findings and translational implications. Mol. Psychiatry 23, 1094–1112 (2018).
Riva-Posse, P. et al. A connectomic approach for subcallosal cingulate deep brain stimulation surgery: prospective targeting in treatment-resistant depression. Mol. Psychiatry 23, 843–849 (2018).
Johansen-Berg, H. et al. Anatomical connectivity of the subgenual cingulate region targeted with deep brain stimulation for treatment-resistant depression. Cereb. Cortex 18, 1374–1383 (2008).
Dichter, G. S., Gibbs, D. & Smoski, M. J. A systematic review of relations between resting-state functional-MRI and treatment response in major depressive disorder. J. Affect. Disord. 172, 8–17 (2015).
Salomons, T. V. et al. Resting-state cortico-thalamic-striatal connectivity predicts response to dorsomedial prefrontal rTMS in major depressive disorder. Neuropsychopharmacology 39, 488–498 (2014).
Furtado, C. P. et al. An investigation of medial temporal lobe changes and cognition following antidepressant response: a prospective rTMS study. Brain Stimul. 6, 346–354 (2013).
Fox, M. D., Buckner, R. L., White, M. P., Greicius, M. D. & Pascual-Leone, A. Efficacy of transcranial magnetic stimulation targets for depression is related to intrinsic functional connectivity with the subgenual cingulate. Biol. Psychiatry 72, 595–603 (2012).
Verhagen, L. et al. Offline impact of transcranial focused ultrasound on cortical activation in primates. eLife 8, (2019).
Bongioanni, A. et al. Activation and disruption of a neural mechanism for novel choice in monkeys. Nature https://doi.org/10.1038/s41586-020-03115-5 (2021).
Blackmore, J., Shrivastava, S., Sallet, J., Butler, C. R. & Cleveland, R. O. Ultrasound neuromodulation: a review of results, mechanisms and safety. Ultrasound Med. Biol. 45, 1509–1536 (2019).
Folloni, D. et al. Manipulation of subcortical and deep cortical activity in the primate brain using transcranial focused ultrasound stimulation. Neuron 101, 1109–1116.e5 (2019).
Browning, M., Behrens, T. E., Jocham, G., O’Reilly, J. X. & Bishop, S. J. Anxious individuals have difficulty learning the causal statistics of aversive environments. Nat. Neurosci. 18, 590–596 (2015).
Scholl, J. & Klein-Flügge, M. Understanding psychiatric disorder by capturing ecologically relevant features of learning and decision-making. Behav. Brain Res. 355, 56–75 (2018).
Klein-Flügge, M. C., Bongioanni, A. & Rushworth, M. F. S. Medial and orbital frontal cortex in decision-making and flexible behavior. Neuron https://doi.org/10.1016/j.neuron.2022.05.022 (2022).
Kolling, N., Scholl, J., Chekroud, A., Trier, H. A. & Rushworth, M. F. S. Prospection, perseverance, and insight in sequential behavior. Neuron 99, 1069–1082.e7 (2018).
Beliveau, V. et al. A high-resolution in vivo atlas of the human brain’s serotonin system. J. Neurosci. 37, 120–128 (2017).
Saulin, A., Savli, M. & Lanzenberger, R. Serotonin and molecular neuroimaging in humans using PET. Amino Acids 42, 2039–2057 (2012).
Palomero-Gallagher, N., Amunts, K. & Zilles, K. in Brain Mapping (ed Toga, A. W.) 261–275 (Academic Press, 2015).
van den Brink, R. L., Pfeffer, T. & Donner, T. H. Brainstem modulation of large-scale intrinsic cortical activity correlations. Front. Hum. Neurosci. 13, 340 (2019).
Zilles, K. et al. Architectonics of the human cerebral cortex and transmitter receptor fingerprints: reconciling functional neuroanatomy and neurochemistry. Eur. Neuropsychopharmacol. 12, 587–599 (2002).
Almeida, J. R. C. et al. Reduced gray matter volume in ventral prefrontal cortex but not amygdala in bipolar disorder: significant effects of gender and trait anxiety. Psychiatry Res. 171, 54–68 (2009).
Klein-Flügge, M. C., Wittmann, M. K., Shpektor, A., Jensen, D. E. A. & Rushworth, M. F. S. Multiple associative structures created by reinforcement and incidental statistical learning mechanisms. Nat. Commun. 10, 4835 (2019).
Wittmann, M. K. et al. Global reward state affects learning and activity in raphe nucleus and anterior insula in monkeys. Nat. Commun. 11, 3771 (2020).
Schildkraut, J. J. The catecholamine hypothesis of affective disorders: a review of supporting evidence. Am. J. Psychiatry 122, 509–522 (1965).
Oishi, Y. et al. Slow-wave sleep is controlled by a subset of nucleus accumbens core neurons in mice. Nat. Commun. 8, 734 (2017).
Uğurbil, K. et al. Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project. NeuroImage 80, 80–104 (2013).
Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105–124 (2013).
Brooks, J. C. W. et al. Physiological noise modelling for spinal functional magnetic resonance imaging studies. NeuroImage 39, 680–692 (2008).
Smith, S. M., Hyvärinen, A., Varoquaux, G., Miller, K. L. & Beckmann, C. F. Group-PCA for very large fMRI datasets. Neuroimage 101, 738–749 (2014).
Gershon, R. C. et al. NIH Toolbox for assessment of neurological and behavioral function. Neurology 80, S2–S6 (2013).
Salsman, J. M. et al. Emotion assessment using the NIH Toolbox. Neurology 80, S76–S86 (2013).
Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R. & Kupfer, D. J. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 28, 193–213 (1989).
Heine, S. J. & Buchtel, E. E. Personality: the universal and the culturally specific. Annu. Rev. Psychol. 60, 369–394 (2009).
McCrae, R. R. & Costa, P. T. Jr. A contemplated revision of the NEO Five-Factor Inventory. Pers. Individ. Dif. 36, 587–596 (2004).
Raîche, G., Walls, T. A., Magis, D., Riopel, M. & Blais, J.-G. Non-graphical solutions for Cattell’s Scree test. Methodology 9, 23–29 (2013).
Smith, S. M. et al. A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat. Neurosci. 18, 1565–1567 (2015).
Acknowledgements
Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research, and by the McDonnell Center for Systems Neuroscience at Washington University. M.C.K.-F. was funded by a Sir Henry Wellcome and Henry Dale Fellowship (103184/Z/13/Z and 223263/Z/21/Z), M.F.S.R. was funded by an MRC grant (MR/P024955/1) and a Wellcome Senior Investigator Award (WT100973AIA). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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M.C.K.-F. and M.F.S.R. designed the study, M.C.K.-F., D.E.A.J. and M.F.S.R. conceived the analyses, M.C.K.-F. and D.E.A.J. wrote the analysis code, L.V., Y.T. and S.M.S. provided analysis advice, L.P. helped with data preprocessing, and all authors wrote the manuscript.
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Extended data
Extended Data Fig. 1 Additional preprocessing to account for physiological noise.
a, Physiological noise correction steps: The minimally preprocessed HCP data was additionally corrected for physiological noise to improve the signal in temporal lobe and brainstem regions, the key areas for this study. All other data clean-up steps usually applied to generate fully preprocessed HCP data, specifically fix-denoising and motion correction, were applied at the same time. b, Signal to noise improvements: Illustration of the signal-to-noise improvements gained from this additional preprocessing step compared to standard full HCP preprocessing (in a subset of 100 participants). Top: Mean temporal signal to noise ratio (tSNR) obtained following our preprocessing pipeline; Bottom: Difference in tSNR between the preprocessing with and without physiological noise correction. The ratio of tSNRs (physio – noPhysio) / (physio + noPhysio) is illustrated. This shows tSNR gains in medial temporal lobe and medial prefrontal cortex but particularly subcortical and brainstem structures. c, tSNR improvements relative to no physiological noise correction in several ROIs for (a) respiratory, (b) respiratory + respiratory volume, (c) cardiac, (d) all three (PNM): The mean tSNR difference achieved with subsets of the physiological noise regressors is shown compared to the baseline of not performing any physiological noise correction. Improvements are illustrated for several regions of interest (ROIs) including amygdala, dorsal raphe (RN_DR), locus coeruleus (LC), and areas 25 and d32 in medial PFC. The regressors used were either just respiration, both respiration and respiratory volume over time (RVT), just cardiac activity, or all of the above (which is what was ultimately used in the main analysis). This shows that subcortical ROIs benefited more from physiological noise correction, with greatest improvements in brainstem nuclei, and that respiratory and cardiac regressors contributed about equally to the improvement, with the greatest improvements achieved when including all noise regressors. Error bars denote SEM; n = 19 participants (individual datapoints shown).
Extended Data Fig. 2 Replication of average amygdala functional connectivity.
Average amygdala connectivity: The group connectome shown for the original n = 200 3 T young-adult HCP participants presented in Fig. 1a was replicated in two other cohorts containing n = 200 non-overlapping 3T-HCP participants and n = 98 non-overlapping 7T-HCP participants. In the 3 T data, we used an improved pre-processing pipeline to correct for physiological noise, as before. This was not possible in the 7 T data where physiological noise regressors were not available. However, the 7 T resting-state data has improved signal-to-noise in subcortical regions due to the higher field strength. Scale bar denotes Pearson’s correlation coefficient (functional connectivity), corrected for global absolute connectivity strength.
Extended Data Fig. 3 Amygdala parcellation at different clustering depths.
a, Generation of group average dense connectome for amygdala parcellation: Summary of the additional processing steps required to compute a group average connectome from the 200 individual concatenated resting-state fMRI (rs-fMRI) time-series. The group connectome, restricted to connectivity between amygdala voxels and the whole brain, formed the basis for the amygdala parcellation. b, Amygdala parcellation step by step: Individual steps of the hierarchical clustering algorithm led to increasing subdivisions of the amygdala. All steps leading up to our final parcellation (depth 12), and a few additional clustering steps beyond it (up to depth 15), are shown. Hierarchical clustering was performed on absolute functional connectivity values. Note, for example, the central nuclei splitting off in step 9 (left) and 12 (right). The 12 cluster solution had five unique clusters in each hemisphere and two connected clusters (same color = same cluster). For subsequent analyses, the corresponding clusters in each hemisphere were joined, resulting in a total of seven clusters.
Extended Data Fig. 4 Replication of amygdala parcellation.
a, For comparison, the parcellation of the amygdala obtained in the original n = 200 3 T participants is shown for the n = 200 3 T replication sample and the n = 98 (all non-overlapping) 7 T participants (compare Fig. 1b). This shows that the key subdivisions of the amygdala were replicated in these two additional parcellations. b, Visualization of the cluster centroids from a coronal (left) and sagittal (right) point of view illustrates the similarity of the three parcellations (diamond: original 3 T parcellation; square: 3 T replication; circle: 7 T replication). c, Similarity of parcellations compared to null with contiguous symmetrical clusters: To quantify the similarity between the parcellations, two metrics are reported: the mean distance of the centroids and the % of overlapping voxels (that is, voxels with identical labels). Null distributions respect the size and symmetry of the original parcellations but shuffle the location of the nuclei in a way that yields contiguous but non-overlapping clusters. This shows that the parcellations (left: comparison with 3 T replication; right: with 7 T replication) are more similar than expected by chance (one-sided p-values from nonparametric test using permutation null distribution). Importantly, however, throughout the manuscript, we use the original 3 T parcellation across all analyses. In addition, the choice of parcellation (which is based on the mean group connectivity) is orthogonal to the key findings reported in the manuscript which relate to interindividual variation that is ignored when generating the parcellation.
Extended Data Fig. 5 Replication of amygdala nuclei mean functional connectivity.
Strength of functional coupling (group average): The average amygdala nuclei to ROI functional connectivity, in all cases extracted based on the amygdala nuclei from the original n = 200 3 T parcellation, replicates across cohorts (top: original, bottom left: replication 3 T, bottom right: replication 7 T; compare Fig. 2b), as confirmed in the strong correlation between these patterns (top right). Scale bar denotes Pearson’s r corrected for global absolute mean connectivity.
Extended Data Fig. 7 Replication of factor analysis.
Factor loadings show behavioural factor analysis replicability: The factor analysis computed to generate mental health dimensions in our original n = 200 3 T participants (left) replicated in all n = 1206 HCP participants (2nd column) and the full set of n = 400 3 T and n = 98 7 T participants used in this manuscript (3rd and 4th column). Correlation coefficients and p-values refer to the similarity (two-sided t-test) with the original pattern shown on the left.
Extended Data Fig. 8 Detailed description of contributing anatomical networks.
a, Baseline average connectivity: Edges where functional connectivity was on average negative (<0.2, left), modulatory/zero (middle), or positive (>0.2, right; see also Fig. 2b and Extended Data Fig. 5) in the group of all 3 T participants are shown to aid interpretation of fingerprints. b, Anatomical fingerprint for smallest network at p < 0.1 (left), p < 0.05 (middle), peak (right): In addition to the smallest network of connections that reached significant out-of-sample predictions (using 3T-regression weights to predict 7 T behavioural dimensions) shown in Fig. 5c, here we show the smallest trend-wise significant (p < 0.1) and significant (p < 0.05 as in Fig. 5c) network as well as the network associated with the peak prediction (compare Fig. 5a); for precise p-value calculation in each case, see Methods and Results. All conventions are as in Fig. 5: anatomical fingerprints show ROIs on the circumference (dark = subcortical), amygdala nuclei in the centre (lines are colour-coded); line width denotes the size of the absolute 3 T regression coefficient; line style denotes its sign (continuous = positive; dashed=negative). In addition, here, a surrounding line is black if baseline functional connectivity in this edge is positive and grey if it is negative (defined as in a). Integer number indicates the number of edges shown; scatterplots underneath fingerprints show the associated out-of-sample 7 T prediction.
Extended Data Fig. 9 Whole versus amygdala nuclei predictions.
a, For comparison, the out-of-sample prediction achieved using increasing numbers of edges for all behaviours and the nuclei-version shown in Fig. 5a (here grey) is shown next to the out-of-sample predictions achieved using increasing numbers of whole-amygdala edges (coloured) which despite containing the same voxels in total performs worse than the nuclei version for all four mental health dimensions. b, Anatomical fingerprints associated with the peak out-of-sample prediction possible using whole-amygdala functional connectivity in a.
Extended Data Fig. 10 Amygdala functional connectivity relates better to dimensional behaviours than DSM scores.
a, b Predicting depression scores instead of dimensional markers: Out-of-sample prediction of 7 T participant’s DSM scores, similar to ASR scores shown in Fig. 7, is (a) not significant and (b) less accurate than three out of four of our dimensional behaviours (one-sided correlation to assess positive relationship). c, Overall predictions are worse for DSM compared to dimensional scores when using smaller sets of edges (bars shown in Fig. 5a for dimensional behaviours are overlaid as coloured lines for comparison); plotting conventions as in Fig. 5. d, Anatomical network at peak prediction: fingerprint shows the network of edges contributing to the prediction of DSM scores at the peak prediction (5 edges). e, Anatomical network at peak prediction: Similarly, anatomical fingerprint shows the network of edges contributing to the prediction of ASR scores at the peak prediction (35 edges, still only trend-wise significant) and its associated prediction; one-sided correlation to assess positive relationships.
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Klein-Flügge, M.C., Jensen, D.E.A., Takagi, Y. et al. Relationship between nuclei-specific amygdala connectivity and mental health dimensions in humans. Nat Hum Behav 6, 1705–1722 (2022). https://doi.org/10.1038/s41562-022-01434-3
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DOI: https://doi.org/10.1038/s41562-022-01434-3
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