Relationship between nuclei-specific amygdala connectivity and mental health dimensions in humans

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.

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 3T 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 3T data, we used an improved pre-processing pipeline to correct for physiological noise, as before. This was not possible in the 7T data where physiological noise regressors were not available. However, the 7T 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 restingstate 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 3T participants is shown for the n=200 3T replication sample and the n=98 (all nonoverlapping) 7T 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 3T parcellation; square: 3T replication; circle: 7T 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 (i.e., 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 3T replication; right: with 7T replication) are more similar than expected by chance (onesided p-values from nonparametric test using permutation null distribution). Importantly, however, throughout the manuscript, we use the original 3T 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 3T parcellation, replicates across cohorts (top: original, bottom left: replication 3T, bottom right: replication 7T; 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. 6. Distribution of behavioural scores and extracted latent behaviours

a, Distribution of all behavioural markers included in the factor analysis shown in Fig 3 across the 200 HCP participants. For a full description of each score see Table 1 and Methods. b, Distribution of the latent behaviours generated from the factor analysis.

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 3T participants (left) replicated in all n=1206 HCP participants (2^nd column) and the full set of n=400 3T and n=98 7T participants used in this manuscript (3^rd and 4^th 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 3b) in the group of all 3T 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-ofsample predictions (using 3T-regression weights to predict 7T 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 3T 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 7T 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 wholeamygdala 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 7T 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; onesided correlation to assess positive relationships.

Figure 1. Average amygdala functional connectivity and definition of amygdala clusters

a, Average amygdala connectivity: A group connectome was generated from resting-state fMRI (rs-fMRI) data of n=200 3T young-adult HCP participants using an improved pre-processing pipeline to correct for physiological noise (Extended Data Fig 1). The average functional connectivity of all amygdala voxels to the rest of the brain, corrected for global absolute connectivity strength, shows patterns that would be expected from tracer studies, for example strong connectivity of the amygdalae with subgenual ACC, hypothalamus, and ventral striatum. Scale bar denotes Pearson’s r. This pattern was replicated in two independent datasets containing n=200 additional 3T-HCP participants and n=98 7T-HCP participants (Extended Data Fig 2). b, Amygdala clusters: Hierarchical clustering was performed on the similarities between the whole-brain functional connectivity patterns of different amygdala voxels to identify amygdala subdivisions sharing connectivity profiles. Seven subdivisions were identified (left: horizontal; middle: coronal; right: saggital view), showing strong symmetry across hemispheres and strong resemblance with subdivisions identified from histology and high-resolution post-mortem structural neuroimaging. The parcellations obtained in two independent datasets closely reproduced these nuclei subdivisions (Extended Data Fig 4), but for consistency, the parcellation shown here was used for all analyses reported in this study.

Figure 2. Amygdala nuclei and their profile of functional connectivity to regions of interest

a, Naming of nuclei: Labels assigned to the seven amygdala subdivisions obtained from hierarchical clustering: Ce = central nucleus, CoN = cortical nuclei, B = basal, AB/BM = auxiliary basal/basomedial, LaV/BL = lateral (ventral part) containing aspects of basolateral, LaI = lateral (intermediate part), LaD = lateral (dorsal part). b, Strength of functional coupling (group average): Average resting-state functional connectivity from the seven nuclei to 28 regions of interest (ROIs) defined a priori based on their known connectivity with the amygdala and potential role in regulating emotions and mental well-being. This highlights strong functional connectivity of subgenual cortex (area 25) to the entire amygdala, but particularly to basal subdivisions, in line with tracer work. Similar profiles are observed for posterior OFC (pOFC) and the subgenual portion of area 32 (s32). By contrast, subcortical and brainstem regions most strongly connect with the central nucleus as expected. The mean functional connectivity between ROIs and nuclei was replicated in two datasets (Extended Data Fig 5). Sale bar denotes Pearson’s r. c, Regions of interest (ROIs): Masks of all ROIs used in this study. For details on their definition, please refer to the Methods. NAc=Nucleus Accumbens; BNST=bed nucleus of the stria terminalis; vl/dPAG=ventrolateral/dorsal periaqueductal grey; SN=substantia nigra; RN_DR/RN_MR=dorsal and median raphe nuclei; LC=locus coeruleus. Definitions of cortical regions were taken from Glasser et al., 2016.

Figure 3. Latent behavioural dimensions capture distinct aspects of mental well-being

a, Factor loadings onto behavioural scores: A factor analysis conducted based on 33 behavioural scores (Table 1) available as part of HCP revealed four factors. The loadings for each factor are shown in different colors, corresponding to the four rows. The highest five contributing behavioural scores are shown in order of their contribution (absolute loading) on the right. This shows that the four factors capture quite distinct dimensions of participants’ mental well-being which we summarized as ‘Social and life satisfaction’, ‘Negative emotions’, ‘Sleep’ (problems), ‘Anger and rejection’. The four factors replicated when the factor analysis was performed on all 1206 HCP participants (see Methods), or only the subset of 3T and 7T participants included here (Extended Data Fig 7). b, Correlations between factor loadings (scale bar denotes Pearson’s r).

Figure 4. Nuclei-specific amygdala functional connectivity shows consistent relationships with interindividual variation in mental health dimensions

a, Two types of replication: Relationships between interindividual variation in nuclei-specific amygdala functional connectivity and mental health dimensions were examined in two HCP datasets containing n=393 3T and n=97 non-overlapping 7T participants (following outlier rejection). Despite challenges with neuroimaging signals in subcortical regions, relationships were robust and replicable, as established in two ways: (1) Across-dataset replication (left): the similarity of robust regression coefficients capturing the relationship between resting-state functional connectivity for each ‘edge’ (e.g., Ce to NAc) and each of four mental health dimensions was greater than expected by chance across datasets (null distribution generated using shuffled behavioural scores; n=10,000 iterations). (2) Within-subject replication (right): robust regression coefficients estimated on half of the resting-state data (runs 1+2 versus 3+4, from separate sessions) also showed greater-than-chance similarity (one-sided p-values from nonparametric test using a permutation null distribution). b, Similarity of regression weights across 3T and 7T datasets: Visualization of obtained robust regression coefficients for each edge, mental health dimension (columns) and dataset (rows) illustrates their similarity across cohorts. c, d, For each edge, its contribution rDiff to the across-dataset (c) and within-subject (d) similarity was computed as the difference between the correlation achieved when excluding this edge (195 values) and when including all 196 edges (28 ROIs x 7 nuclei). Visual inspection of rDiff values highlights strong similarities between rDiff values in the two replications (C vs D), clear differences between the four behavioural dimensions, and anatomical specificity – e.g., the importance of cortical connections with B and LaD nuclei for predicting life satisfaction, for connections with NAc, other subcortical regions and medial frontal area p32 for predicting sleep, and functional connectivity with the cortical nuclei (CoN) for predicting anger. e, Out-of-sample 7T prediction using 3T-weights: Regression coefficients estimated from the 3T-participants applied to 7T-functional connectivity values to predict 7T-mental health dimensions showed significant out-of-sample predictions for all mental health dimensions except sleep problems (independent one-sided correlations to assess positive relationships).

Figure 5. Functional connectivity in smaller sets of specific amygdala nuclei connections is predictive of interindividual variation in mental health dimensions.

a, Size and nature of amygdala nuclei networks predictive of mental health dimensions: Predictions achieved with subsets of edges between 1 and 196: robust regression coefficients estimated from 3T-participants were applied to 7T-functional connectivity values to predict interindividual differences in mental health dimensions in the held-out 7T data (as done in Fig 4e using all 196 edges). Prediction accuracies are shown as the correlation between true and predicted mental health dimensional scores in the 7T participants, but were only statistically evaluated at the peak (grey arrow: ‘global peak’) and to derive the smallest number of edges that reached a significant out-of-sample prediction (black arrow: ‘smallest sig’; see Methods for generation of null distribution using permutation; for precise p-values in each case, see Results); coloured bars show accuracy when including edges in order of their absolute regression coefficient in the 3T data; black curve indicates performance using the same number of edges but included in random order (n=10,000 shuffles; error bars denote SEM); black line at r=0.168 indicates threshold for significance at p<0.05 purely for visualization (grey line: p<0.1); second row shows the same but zoomed in on the first 70 edges. For all behavioural dimensions, smaller sets of amygdala nuclei functional connectivity values achieve a significant out-of-sample prediction. In general, except for life satisfaction, using the top 3T edges is better than a random selection of the same number of edges. b, c Illustration of the prediction (scatterplot, b) and associated anatomical network (contributing edges shown as fingerprints, c) for the smallest number of edges that achieved a significant out-of-sample prediction (indicated using a black arrow in a). Fingerprints shows ROIs on the circumference (dark=subcortical), amygdala nuclei in the centre (colour-coded); line width denotes the size of the absolute 3T regression coefficient; line style denotes its sign (continuous=positive; dashed=negative).

Figure 6. Parcellating the amygdala improves the accuracy for predicting interindividual differences in mental health dimensions

a, Sensitivity of whole-amygdala as opposed to nuclei-specific amygdala coupling: Out-of-sample predictions achieved when considering the functional connectivity of the whole amygdala with our 28 a priori ROIs, instead of nuclei-specific functional connectivity, are less precise, but still significant for two of the four mental health dimensions (negative emotions + anger; independent one-sided correlations to assess positive relationships). b, Across-dataset: To control for differences in the number of predictors for nuclei-specific and whole-amygdala functional connectivity (28 vs 196), out-of-sample 7T predictions (shown in Fig 4a and 6a) were evaluated based on their own null distribution. Nuclei-specific predictions (top) were still superior to whole-amygdala predictions (bottom) for all four mental health dimensions (coloured bars indicate Pearson’s r overlaid on the null distribution; for statistics see main text); this was also true when looking at peak predictions achieved using smaller sets of edges (Extended Data Fig 9). c,d, As in Fig 5, c illustration of the prediction (scatterplot, c) and associated contributing edges (fingerprint, d) are shown for the smallest set of edges that reached significance (sleep never reached significance; life satisfaction was significant when using fewer than 28 edges) which highlights clear differences between mental health dimension (correlations are shown for illustration using the smallest network that reaches significance in a one-sided correlation, if applicable).

Figure 7. Amygdala functional connectivity relates better to dimensional behaviours than overall depression scores

a, b Predicting depression score instead of dimensional markers: Out-of-sample prediction of 7T participant’s ASR_AnxD scores, using nuclei-specific functional connectivity and regression weights estimated from 3T participants as in Fig 4e, is (a) not significant (for DSM, see Extended Data Fig 10) and (b) less accurate than three out of four of our dimensional behaviours (independent one-sided correlations to assess positive relationships). c, Overall predictions are worse for ASR 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 Fig5.