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Human brain state dynamics are highly reproducible and associated with neural and behavioral features [1]

['Kangjoo Lee', 'Department Of Psychiatry', 'Yale University School Of Medicine', 'New Haven', 'Connecticut', 'United States Of America', 'Jie Lisa Ji', 'Clara Fonteneau', 'Lucie Berkovitch', 'Saclay Cea Centre']

Date: 2024-10

Neural activity and behavior vary within an individual (states) and between individuals (traits). However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatiotemporal features of co-activation patterns at the single-subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, encompassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of state-trait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.

Competing interests: K.L. consults for Manifest Technologies. A.A. and J.D.M. hold equity with Neumora Therapeutics (formerly BlackThorn Therapeutics), Manifest Technologies, and are co-inventors on the following patents: Anticevic A, Murray JD, Ji JL: Systems and Methods for Neuro-Behavioral Relationships in Dimensional Geometric Embedding(N-BRIDGE), PCT International Application No.PCT/US2119/022110, filed March 13, 2019 and Murray JD, Anticevic A, Martin WJ: Methods and tools for detecting, diagnosing, predicting, prognosticating, or treating a neurobehavioral phenotype in a subject, U.S. Application No.16/149,903, filed on October 2, 664 2018, U.S. Application for PCT International Application No.18/054, 009 filed on October 2, 2018. J.L.J. is an employee of Manifest Technologies, has previously worked for Neumora, and is a co-inventor on the following patent: Anticevic A, Murray JD, Ji JL: Systems and Methods for Neuro-Behavioral Relationships in Dimensional Geometric Embedding (N-BRIDGE), PCT International Application No.PCT/US2119/022110, filed March 13, 2019. C.F. consults for Manifest Technologies and formerly consulted for RBNC (formerly BlackThorn Therapeutics). G.R. consults for and holds equity in Neumora and Manifest Technologies. L.P. is an employee of Manifest Technologies. J.H.K. holds equity in Biohaven Pharmaceuticals, Biohaven Pharmaceuticals Medical Sciences, Clearmind Medicine, EpiVario, Neumora Therapeutics, Tempero Bio, Terran Biosciences, Tetricus, and Spring Care. J.H.K. consults for AE Research Foundation, Aptinyx, Biohaven Pharmaceuticals, Biogen, Bionomics, Limited (Australia), BioXcel Therapeutics, Boehringer Ingelheim International, Cerevel Therapeutics, Clearmind Medicine, Cybin IRL, Delix Therapeutics, Eisai, Enveric Biosciences, Epiodyne, EpiVario, Evidera, Freedom Biosciences, Janssen Research & Development, Jazz Pharmaceuticals, Leal Therapeutics, Neumora Therapeutics, Neurocrine Biosciences, Novartis Pharmaceuticals Corporation, Otsuka America Pharmaceutical, Perception Neuroscience, Praxis Precision Medicines, PsychoGenics, Spring Care, Sunovion Pharmaceuticals, Takeda Industries, Tempero Bio, Terran Biosciences, and Tetricus. All other co-authors declare no competing interests.

Funding: This work was supported by 5P50AA012870-22 SP-National Institute on Alcohol Abuse and Alcoholism (NIAAA)/NIH/DHHS ( https://www.niaaa.nih.gov/ ) for J.H.K. and National Institute of Mental Health (NIMH)/NIH/DHHS ( https://www.nimh.nih.gov/ ) grants 5U01MH121766-03 SP for A.A and P50MH109429 for J.D.M. L.B. was supported by the Fondation Bettencourt Schueller ( https://www.fondationbs.org/en ) and the Philippe Foundation ( https://www.philippefoundation.org/ ). G.R. was supported by the Slovenian Research Agency (ARRS) grants P3-0338, J7-8275, J5-4590 ( http://www.aris-rs.si/en/ ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability: All relevant raw data files are available from the Human Connectome Project (HCP) S1200 data database ( https://www.humanconnectome.org/study/hcp-young-adult/document/1200-subjects-data-release ). The datasets underlying the results in this work are provided in Supporting Information files. The codes used in this study are available on Zenodo: Lee, K. (2024). pyCAP codes: Human brain state dynamics are highly reproducible and associated with neural and behavioral features. Zenodo. https://doi.org/10.5281/zenodo.13251562 .

Here, we test the hypothesis that there is a reproducible CAP feature set that reflects both state and trait brain dynamics and that this feature set relates to individual phenotypes across multiple behavioral domains. To address this, we studied rs-fMRI and behavioral data obtained from 337 healthy young adults with no family relation in the individual Human Connectome Project (HCP) S1200 data [ 45 ]. To optimize neural features accounting for CAP variation within and between subjects, we develop a three-axes model of state-trait brain dynamics using moment-to-moment changes in brain CAPs. We identify 3 reproducible CAPs that can be quantified at the single-subject level, exhibiting recurrent snapshots of resting-state network spatial profiles and individual-specific temporal profiles. By analyzing spatiotemporal state-trait dynamics of CAP patterns, the data revealed groups of individuals that consistently exhibit behaviorally relevant CAP characteristics. These results suggest that a critical step toward the development of reproducible brain–behavioral models may involve initial mapping of neural features that can robustly and reproducibly capture combined trait (between-subject variability) and state (within-subject variability) variance in neural features.

One approach that captures both trait and state neural characteristics is the analysis of co-activation patterns (CAPs) for rs-fMRI [ 33 ]. This analysis focuses on moment-to-moment changes in the whole brain blood oxygenation level dependent (BOLD) signals at each time point, providing a method to quantify the spatial patterns of co-activation across people and individual variation in patterns of neural temporal organization [ 33 ]. Several studies have reported similar average CAP patterns in healthy human adults [ 33 ], which also show some notable sex differences [ 34 ] and are impacted by proceeding task conditions [ 35 ]. Alterations of spatial and temporal organizations of CAPs (e.g., the number of time-frames occupied by a CAP state) were found across different levels of consciousness [ 36 ], schizophrenia [ 37 ], pre-psychosis [ 38 ], depression [ 39 , 40 ], and bipolar disorders [ 41 , 42 ]. All of these studies characterized group-level effects between patients and healthy controls with a fixed number of CAPs across groups, often capturing a parsimonious snapshot of brain dynamics by selecting a small number of time points associated with high-amplitude signals in preselected (i.e., seed) regions. While these studies have provided insights that CAPs contain rich information, they are systematically omitting full range of BOLD fluctuations. Put differently, few studies have leveraged the entire BOLD signal range to define CAPs [ 7 ]. Moreover, no study to our knowledge has investigated the properties of within and between-subject variability across a reproducible set of CAPs that harness the entire BOLD signal fluctuation range [ 43 , 44 ]. Finally, no study has in turn quantified how individual differences in CAP properties map onto complex behavior.

A recent meta-analysis of 3 large consortia data sets (N = 38,863 in total) has shown that brain–behavior associations in the general population have small effect sizes (e.g., |r| < 0.2) using data from thousands of individuals, when correlating neural measures from structural MRI, rs-fMRI, and task fMRI activation to behavioral measures including cognitive ability or psychopathology [ 27 ]. While large sample sizes are key for discovering and replicating small brain–behavior relationships on average [ 27 ], these recent advances leave the open question that there may be strong brain–behavioral effects that can be seen with quantitative approaches that consider time-varying signal dynamics [ 28 – 30 ]. Still, the application of state-related quantitative approaches in fMRI remain underutilized for characterizing reproducible inter-individual differences in brain–behavioral relationships [ 31 , 32 ]. Furthermore, combining state-related and trait-related information from rs-fMRI signals may provide convergent information about individual brain–behavior associations. To this end, we tested the hypothesis that reproducible neural-behavioral mapping may be achieved by quantifying combined state and trait information from time-varying rs-fMRI signals across the brain.

Historically, rs-fMRI studies have quantified neural traits (e.g., stationary functional connectivity characterizing a subject) to study how they vary across people in relation to a given behavioral trait (e.g., fluid intelligence or a set of clinical symptoms) [ 21 – 23 ]. The analyses of neural state dynamics or time-varying rs-fMRI connectivity can be used to understand individual differences [ 24 ]. Evaluating moment-to-moment changes in neural activity can provide information about latent brain states associated with task-switching and decision-making in working memory [ 25 ]. Using dimension reduction of task fMRI data across multiple cognitive tasks, Shine and colleagues suggested that execution of diverse cognitive tasks and individual differences in fluid intelligence can be described using a dynamic flow along a low-dimensional manifold of global brain activity [ 26 ]. There is a knowledge gap regarding how combined state and trait variation of spontaneous brain dynamics map onto individual variation in complex behavioral phenotypes.

Spontaneous fluctuations of brain activity measured at rest (i.e., resting state functional Magnetic Resonance Imaging (rs-fMRI)) are embedded in time and space, exhibiting rich spatial-temporal information that varies within (state) and between (trait) individuals. The joint properties of state-trait rs-fMRI signal variation remains poorly understood, constituting a critical knowledge gap. An individual’s mental state at any given time of rs-fMRI may be influenced by many intrinsic (e.g., metabolic) [ 8 , 9 ] or extrinsic (e.g., medications) factors that directly affect the circuit activity underlying complex behavior [ 10 – 18 ]. On the other hand, there might be other dimensions that contribute to variability in large neuroimaging data sets and undermine their ability to identify clear brain–behavior relationships. One of these dimensions may be time-varying signal dynamics. For example, personality theories posit that traits are characterized as patterns of thoughts, feeling, and behavior that generalize across similar situations within individuals and differ between individuals, whereas behavioral states reflect patterns that vary over time and situations [ 19 , 20 ].

The field of functional Magnetic Resonance Imaging (fMRI) has attempted to characterize the functional organization of the human brain and how it relates to individual differences [ 1 , 2 ]. These emerging methods can identify low-dimensional representations of neural traits (i.e., subject-specific) [ 3 , 4 ] or states (i.e., varying over time within a subject) [ 5 – 7 ] which may be predictive of behavioral phenotypes. This growing body of work suggests that fMRI may hold great potential for characterizing how complex neural signals map onto human behavioral variation.

Results

Three brain co-activation patterns are reproducibily found in healthy subjects at rest The analysis of moment-to-moment changes in CAPs assumes a single neural state (i.e., CAP state) per each fMRI time frame and identifies a set of CAPs recurring over time and across subjects by spatial clustering of fMRI time frames [7,33]. We identify a reproducible set of CAPs from 4 runs of rs-fMRI data (15-min/run) obtained over 2 days from 337 healthy young adults (ages 22 to 37 years, 180 females) using a shuffled split-half resampling strategy across 1,000 permutations. Here, we used the entire BOLD signal fluctuation range for CAP estimations, without sparse time point sampling. In each permutation, we randomly split the sample (N = 337) into 2, each involving the equal number of nonoverlapping subjects (n = 168, respectively, randomly excluding a subject) (Figs 1A and S1). To analyze CAPs at a low dimension space and to reduce the computational burden of CAP analysis that treats every 3D time frame in the clustering process (e.g., 4,000 time frames/subject), we used the Cole-Anticevic Brain Network Parcellation (CAB-NP) that involves 718 cortical surface and subcortical volumetric parcels [46]. We averaged the preprocessed BOLD signals in the voxels belonging to each parcel [47]. Therefore, within each split, a 4,000 x 718 array of individual rs-fMRI data are temporally concatenated across subjects. The time frames are clustered based on spatial similarity using K-means clustering, where the number of clusters (k) is estimated for each split using the elbow method varying k from 2 to 15 (see the estimated Silhouette scores from the K-means clustering solutions in S2 Fig). Finally, a CAP was obtained by averaging the time frames within each cluster with respect to each parcel. PPT PowerPoint slide

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TIFF original image Download: Fig 1. A reproducible set of CAPs in the whole-brain rs-fMRI involve recurring mixed representations of canonical resting-state networks. (A) Analysis overview. In each permutation, 337 subjects are randomly split into 2 equal-sized groups. Within each split, a parcel-by-time array of rs-fMRI data is temporally concatenated across subjects. Time frames are clustered based on spatial similarity using K-means clustering. The number of clusters (k) is estimated for each split. Each CAP is obtained as the centroid of each cluster (S1 Fig). (B) Individual’s statistical preference toward a specific number of CAPs (k) is reproducible. In each split, an individual’s preference toward a specific number was quantified using the number of permutations that resulted in a specific solution (e.g., 4 CAPs or 5 CAPs) across 1,000 permutations. Specifically, we compute the difference (occurrence of k = 5)—(occurrence of k = 4) for each subject (Methods). (C) Spatial correlation of the 5-CAP basis set (left) and between the 4-CAP basis set and the 5-CAP basis set (right). r values were rounded to the nearest 2 decimal digits. (D) CAB-NP [46]. (E) Spatial topography of 5 basis CAPs. (F) Spatial similarity of the 5 basis CAPs to canonical resting-state networks, predefined using the CAB-NP parcellation in (D). CAB-NP, Cole-Anticevic Brain Network Parcellation; CAP, co-activation pattern; rs-fMRI, resting state functional Magnetic Resonance Imaging. https://doi.org/10.1371/journal.pbio.3002808.g001 We first found that there are individual differences in the number of reproducible brain states. Specifically, in both splits, the estimated number of CAPs was either 4 or 5, each exhibiting an ≈50% occurrence rate across permutations (S3A and S3B Fig). However, interestingly, the co-occurrence of the same number of CAPs in both splits was rare (<6%) (S3C Fig). In other words, a half of the sample produced 5 CAPs, while the other half produced 4 CAPs. Because each of 2 nonoverlapping halves contain a distinct subset of samples, we hypothesized that individual difference in the number of reproducible brain states plays a role in the observed between-split differences. To test this hypothesis, we quantified the individual’s preference toward a specific number of CAPs by comparing the probability of estimating 4 CAPs or 5 CAPs. The probability of estimating k CAPs was quantified using the occurrence of k solution estimations in a split across permutations (see Methods). Indeed, there was a highly reproducible tendency for individual subjects to occupy either 4 or 5 CAPs (Fig 1B). Together, these results suggest the presence of a CAP state that is reproducibly found in a subset of subjects but not in others. To identify reproducible spatial topography of CAPs for further analyses, we generated 2 sets of basis CAPs independently: the 4-CAP and the 5-CAP basis sets (S4 Fig). The 4-CAP basis set was obtained by applying agglomerative hierarchical clustering to the CAPs collected from only the permutations that resulted in the estimation of 4 CAPs. Then, a basis CAP was generated by averaging the CAPs belonging to each cluster, and the value in each parcel of the basis CAP was normalized to z-scores using the mean and standard deviation across 718 parcels (S4 Fig). The 5-CAP basis set was also obtained using the CAPs collected from the permutations resulting in 5-CAP solutions. We found that the 4-CAP basis set consisted of 2 pairs of anti-correlated CAPs (I+ and I-, II+ and II-), and the 5-CAP basis set consisted of the same 2 pairs of anti-correlated CAPs and 1 additional CAP (III) (Fig 1C). The patterns of these basis CAPs were consistent between 2 splits (S5 Fig). The number (I, II, and III) and sign (+ and −) of CAPs were labeled arbitrarily. Overall, we found 3 CAPs recurring over time and across healthy subjects in rs-fMRI.

Patterns of whole-brain co-activation are recurrent snapshots of mixed resting-state networks As expected, the spatial patterns of 3 CAPs were associated with well-known rs-fMRI networks (Fig 1E and 1F). CAP I involved a strong bi-polarity between the default mode and frontoparietal networks versus the dorsal attention, cingulo-opercular, somatomotor, and secondary visual networks. Here, bi-polarity stands for positive versus negative cosine similarity of each CAP with distinct resting-state networks (CAP+ versus CAP−). CAP II exhibited a weaker bi-polarity between the primary visual, orbito-affective, default mode, and frontoparietal networks versus the dorsal attention, somatomotor, and secondary visual networks. CAP III showed a strong bi-polarity between the default mode, somatomotor, and secondary visual networks versus the frontoparietal, dorsal attention, and cingulo-opercular networks. Considering that resting-state networks are identified based on the co-fluctuations of signals in distributed brain regions, our results show that these CAPs represent recurring snapshots of the diverse signal co-fluctuations among regions involved in different functional networks at each time frame.

CAP III is reproducibly found in some individuals but not in others Our result in Fig 1B suggests that there are individual differences in the number of reproducible brain states. Because CAPs are estimated using data from a group of subjects, the contribution of a single subject to this estimation is relatively small. In addition, it remains unknown whether the spatial topography of estimated CAPs are reproducible across permutations. To address these, we investigated three questions: (i) whenever 4 CAPs are estimated from a split data, are their spatial patterns reproducible across the permutations; (ii) whenever 5 CAPs are estimated from a split data, are their spatial patterns reproducible across the permutations; and (iii) is there a specific CAP state that is reproducibly missing in 4-CAP solutions when compared to the 5-CAP solutions. All 718 cortical and subcortical parcels were included in this and following analyses throughout this article. First, we calculated the marginal distribution of spatial correlation values (r(EC i , BC j )) between the CAPs estimated from each split data (Estimated CAP; EC i , i = 1,.., 4 or 5) and a given basis CAP (Basis CAP; BC) (Fig 2A). Note that these predefined basis CAPs are the group-average and permutation-average CAPs obtained using the agglomerative hierarchical clustering of all CAPs across permutations (Fig 1E). In each permutation, each EC i was labeled according to the maximum rank correlation with the given basis CAP. As a result, the marginal distribution of r values showed that the spatial patterns of 4-CAP solutions and 5-CAP solutions were strongly reproducible (S6 Fig). The CAPs estimated from each split were highly correlated with at least one of the basis CAPs, demonstrating a 1-on-1 matching for all CAPs. In addition, CAP III was reproducibly found in one split but not in another split across permutations (Figs 2 and S7). Together, this analysis demonstrates that the presence or absence of CAP III is not a random artifact but actually associated with reproducible neural dynamics of individuals. PPT PowerPoint slide

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TIFF original image Download: Fig 2. The spatial patterns of the CAPs estimated across split-half permutations are reproducible, demonstrating the consistent absence of a specific spatial pattern (CAP III) in one split but not in another split across permutations. (A) Proof of concept. In this figure, we demonstrate “whenever 4 CAPs are estimated from a split data, are their spatial patterns reproducible across the permutations,” and “if there is a specific CAP state that is reproducibly missing in 4-CAP solutions when compared to the 5-CAP solutions.” To address these, first, among 1,000 permutations, we only take permutations that resulted in 4-CAP solutions using the elbow method, which was 502 permutations in this data. The remaining 498 permutations mostly resulted in 5-CAP solutions, and rarely 6- or 7-CAP solutions as shown in (S3 Fig). Spatial similarity (r, correlation coefficient) is computed between each of the estimated CAPs (EC; denoted as a, b, c, and d) and a given basis CAP (BC). In this example, we select BC 1 from the 5-CAP basis set. r values were rounded to the nearest 2 decimal digits for visualization. Finally, we obtain the marginal distribution of r values between BC 1 and the estimated CAPs across 502 permutations. (B) The CAP III is reproducibly found in the 5-CAP solutions and not in the 4-CAP solutions across permutations. We repeated the spatial similarity analysis for the 4 CAPs estimated from each split-half data, when compared to the 5-CAP basis set. In each permutation, each estimated CAP was labeled according to the maximum rank correlation with the basis CAPs. Data-points (r-values) estimated from the CAPs with a same label were coded using the same color. The marginal distributions of r between all estimated CAPs and each BC from the 5-CAP basis set are illustrated using kernel density estimation. Results obtained from the split 1 data are shown in (B) and replicated in the split 2 data (see S7 Fig). Note that all 718 cortical and subcortical parcels were included in this analysis. For simplicity, subcortical regions of CAPs are not visualized. CAP, co-activation pattern. https://doi.org/10.1371/journal.pbio.3002808.g002

Spatial alignment of individual time frames to basis CAPs To find an optimal number of clusters or CAPs that are commonly found across individuals, we used an approach that considers a trade-off between the number of clusters and within-cluster similarity by combining the silhouette criteria and elbow method (S2 Fig). To evaluate the extent of the contribution of individual co-activation patterns to the observed CAP variability, we analyzed all fMRI time frames obtained from 337 subjects after scrubbing. For each split, we computed the spatial alignment of individual 3D fMRI time frames to the 5 basis CAPs (cluster centroids estimated by K-means clustering) using Pearson’s correlation, identifying a basis CAP yielding the highest correlation with each time frame. As a result, the mean and standard deviation of the maximum correlation were 0.22±0.11 (S8 Fig), indicating the substantial variability in resting state human brain dynamics. Notably, the group-level spatial topography of CAPs, estimated by averaging the time frames within each cluster, remained consistent across permutations (Fig 2), enabling us to investigate individual differences in their temporal dynamics.

Reproducible state-trait neural features at the single-subject level We identified 3 CAPs that reflect brain-wide motifs of time-varying neural activity. Here, we demonstrate a reproducible estimation of spatial CAP features at the single-subject level. The CAP analysis involves the assignment of individual time frames to one of the estimated CAPs using the K-means clustering process (Fig 3A). The CAPs estimated in each split were labeled using the maximum ranked correlation with the pre-identified 5-CAP basis set (S6 Fig). In turn, this frame-wise identification of CAP states allows the estimation of temporal profiles of CAP states for individual subjects. We demonstrate that reproducible state and trait features of neural dynamics can be quantified using several key parameters of CAP temporal characteristics (see Fig 3A). PPT PowerPoint slide

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TIFF original image Download: Fig 3. Resting state brain CAPs have distinct between and within-subject variance of temporal characteristics and test-retest reliability, as revealed by the 3-axes representation of neural trait variance space. (A) Analysis overview. In each split-half data from each permutation per day, FO, within-subject mean of dwell time (Mean DT) and within-subject standard deviation of dwell time (Var DT) are estimated for each CAP state. (B) Stability of individual mean DT of CAP I+ across permutations and across 2 days. Individual subjects were rank-ordered from top to bottom using the split 1 data from day 1. While the estimated mean DT values spanned from 0 to 6, the data set exhibited sparse occurrences in the distribution tails. To enhance visual clarity across rows (subjects), a saturated colormap was employed. For an alternative representation of the same data using an unsaturated colormap, refer to S9 Fig. We also found that individual Var DT and individual FO for these CAPs are reproducible across permutations and 2 days (S9 Fig). (C) Days 1 and 2 reliability of FO (top), Mean DT (middle), and Var DT (bottom) in each CAP state were quantified by the intraclass correlation coefficient using two-way random effect models (ICC(2,1)). When computing ICC for CAP III, permutations resulting in the absence of CAP III was not considered, because the values of temporal metrics are zero for both days. (D) Test-retest reliability of neural measures between 2 days of scan. (Top) For CAP I+ state, we show scatter plots of individual FO, within-subject mean and variance of DT between days 1 and 2. Linear fitting line (red) is shown for each scatter plot. r-value is measured by Pearson’s correlation coefficient and considered significant when the corresponding two-sided p-value is less than 0.001. (Bottom) For the remaining 4 CAP states, the same scatter plot analysis was repeated (S10 Fig). We summarize the estimated r-values from all CAPs in the bar plot. (E) CAPs on the neural trait variance space. Relative variance (coefficient of variance) of each CAP measure was computed across subjects: individual FO (x-axis), Mean DT (y-axis), and Var DT (z-axis). The three-axes representation allows for unifying and optimizing the variations of temporal CAP characteristics and distinct patterns of temporal organizations of brain activity. Note that all 718 cortical and subcortical parcels were included in this analysis. See subcortical regions of CAPs in Fig 1E. The data used to generate the results can be found in S1 Data. CAP, co-activation pattern; DT, dwell time; FO, fractional occupancy. https://doi.org/10.1371/journal.pbio.3002808.g003

Definitions Fractional occupancy (FO(s, i)): the total number of time frames (or MRI time of repetition; TR) that a subject s spends in CAP state i per day, normalized by the total number of time frames spent in any CAP state by subject s per day. FO is a relative measure (%TR), such that the sum of FO of all CAP states is 1 within a subject per day. FO reflects between-subject variance (trait variance) of CAP dynamics. Dwell time (DT(s, i, c)): the number of time-frames (#TR) of a time-consecutive segment c occupying the same CAP state i within a subject s per day. Within-subject mean of DT (Mean DT(s, i)): the mean of estimated values of DT for all time-consecutive segments during which CAP i is occupied by subject s per day. Within-subject variance of DT (Var DT(s, i)): the standard deviation of estimated values of DT from all time-consecutive segments occupying a CAP i within a subject s per day. DT measures involve both trait (between-subject) and state (within-subject) components of neural dynamics. The quantification of these CAP measures was performed for each split data per permutation. To evaluate day-to-day variability of CAP dynamics, we computed these measures for each day separately. In summary, we estimated FO, mean DT, and var DT for each CAP per subject. This allowed us to average the estimated neural measures across permutations, providing a summary statistic of neural measures for each CAP for each subject per day. These statistics are statistically reproducible at the single-subject level, as shown in Fig 3B [48–50]. Care is needed when interpreting the results, because stable individual-specific properties of state dynamics such as mean DT in this study can also be considered as traits. In this study, we are interested in testing the hypothesis that there is a reproducible general motif of individual differences in neural co-activation dynamics, where individuals differently occupy (or project onto). While previous work in [26] focused on a low-dimensional manifold of spatiotemporal neural activity by applying principal component analysis (PCA) of rs-fMRI signal volumes, we aim to identify a low-dimensional feature space that characterizes state and trait properties of the temporal organizations of brain states. To do this, we first demonstrate that state-trait CAP dynamics are reproducible at the single-subject level across permutations, whereas within-subject between-day reliability was lower than between-permutation reliability on a same day (Figs 3B, 3C and S10). First, we measured the test-retest reliability of the neural measures using a linear regression (Fig 3D). For each CAP, we found a moderate low correlation (r≤0.5) of individual neural measures between day 1 and day 2 (Fig 3D). CAP I+ showed the highest between-day reliability and CAP III was the lowest. See S10 Fig for the scatter plots from the other 4 CAP states. When calculating the mean and SD of correlation across all CAPs, the between-day correlation is 0.41±0.07 for FO, 0.41±0.06 for mean DT, and 0.38±0.07 for var DT. Secondly, we computed the intraclass correlation coefficients using two-way random effect models (ICC(2,1)) for each split in each permutation. Therefore, for each CAP, we measure 2,000 ICC values across 1,000 permutations. The average ICC across all CAPs are 0.39±0.06 (mean ± standard deviation) for FO, 0.39±0.05 for Mean DT, and 0.34±0.06 for Var DT. These state-trait neural measures show fair test-retest (day-to-day) reliability, when compared to the meta-analytic estimate of average ICC (0.29±0.03, mean ± standard error) across other studies reported using edge-level functional connectivity [51]. Within-subject variance of FO across 5 CAPs are shown in S12 Fig across permutations. Together, these results show day-to-day variability (state) in CAP dynamics within individuals and highly reproducible between-subject (trait) variability within each day.

Joint analysis of state and trait neural variations We propose an analytic framework of joint state and trait neural variations, taking the test-retest (or day-to-day) reliability of neural features into account. Importantly, this framework allows us to visualize how CAP properties that vary within a person (state) also vary between people (trait). In Fig 3E, we illustrate a three-axes representation of state and trait variance components of spatiotemporal CAP dynamics. For each CAP, we estimate the normalized inter-subject variance (coefficient of variance) of 3 neural features. Then, the 5 CAP states (CAPs I+/−, II+/−, and III) are projected on this space. Interestingly, we found that CAP II exhibits the highest relative between-subject variation (i.e., trait) across all 3 measures, the FO, mean DT, and var DT. Conversely, CAP III exhibits lower between-subject variance but higher within-subject variance than CAP II (as seen in the distance between the measures on 2 different days; see Fig 3E). Indeed, the proposed joint analysis of state-trait neural variations provides a rich landscape of within-person and between-person variance of neural co-activations.

Neural feature reduction captures general motifs of individual variation An important and interesting question would be whether neural features with distinct patterns of state-trait variation can provide vital information about individual differences. Put differently, we are interested in studying if there is a set of neural features that can be commonly found across a number of healthy subjects that have a reproducible set of neural co-activation properties, which can in turn be related to behavioral phenotypes. To address this question, we first collected 30 neural features estimated for each individual: 3 neural measures (FO, mean DT, and var DT) × 5 CAPs (I+, I-, II+, II-, and III) × 2 days. We performed the agglomerative hierarchical clustering of a subject-by-feature (3.37×30) matrix (Fig 4A). We determined the number of clusters using a distance cut-off value of 70% of the final merge in the dendrogram (Fig 4B). As a result, we found 3 subgroups (A, B, and C), each consisting of 163, 127, and 47 individuals (Fig 4C). PPT PowerPoint slide

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TIFF original image Download: Fig 4. Identification of subgroups in healthy subjects exhibiting distinct neural state-trait variances. Three subgroups of healthy subjects in the HCP data (A, B, and C) are identified using the agglomerative hierarchical clustering of 30 individual neural state-trait features, which are estimated from temporal CAP characteristics (fractional occupancy, FO; within-subject mean of dwell time, mean DT; within-subject variance of dwell time, var DT). (A) For each subject, 30 neural features estimated from 5 CAPs and 2 days are collected. For each CAP, each neural feature was obtained by averaging the values estimated across permutations. Each data-point in the 3-axis scatter plots indicate a subject. Individual neural features were obtained by averaging the feature values across permutations within subject for each day. (B) Agglomerative hierarchical clustering is performed on the feature matrix. In the dendrogram, 3 clusters are found using a distance cut-off value of 7% of the final merge. In addition, to estimate the principal geometry of this state-trait feature space identifying subgroups, we applied PCA to the feature matrix. (C) Clustered subjects are embedded onto a 3D space using PCA. (D) Variance explained (%) by each neural PC. (E) Similarity of individual neural features between positive and negative CAPs. An example of CAPs I+ and I- are shown. See S11 Fig for all results (0.9±0.04, mean ± SD). (F) Loadings of each neural feature on the first 3 neural PCs. In each radar plot, 3 lines indicating FO (colored in slateblue), Mean DT (steelblue), and Var DT (turquoise) are shown for 5 CAPs. Feature loadings from days 1 (top) and 2 (bottom) are shown separately for an easier interpretation, while the neural PCs were obtained using neural features from both days as shown in (A). (G) The loadings of neural features on each PC are reliable between days. For each neural PC, Pearson’s correlation coefficient (r) was computed between 2 vectors of feature loadings collected from days 1 and 2. Neural PC 3 reflects the contribution of within-subject (between-day) variance in temporal CAP profiles. CAP, co-activation pattern; DT, dwell time; FO, fractional occupancy; HCP, Human Connectome Project; PCA, principal component analysis. https://doi.org/10.1371/journal.pbio.3002808.g004 To further study if there is a low-dimensional geometry of neural state-trait variation capturing individual differences, we applied PCA to the subject-by-feature matrix. Clearly, the 3 subgroups identified using hierarchical clustering were distributed in the low-dimensional space represented by the first 3 neural PCs, which explain 33.5%, 23.9%, and 16% of variance, respectively (Fig 4D). Notably, subgroup A shows higher scores on neural PC 1 than the other groups, and subgroup C shows higher scores on neural PC 2 than subgroup B (Fig 4C). Our further analysis of feature loadings on these PCs revealed a unique and reduced feature set of neural variation, each representing CAP-specific (PC 1) and general (PC 2) neural state-trait variations, which also exhibit day-to-day variability (PC 3). In addition, we found that each pair of positive and negative CAP patterns (states I+ and I-, states II+ and II-) exhibit similar temporal CAP profiles (Figs 4E and S11). Specifically, the neural PC 1 is characterized by distinct temporal profiles on CAPs I/III versus CAP II. It includes higher loadings of FO, mean DT and var DT at CAPs I/III and lower loadings of DT measures at CAP II (Fig 4F). Note that the FO is a relative measure (#TR) such that the sum of FO at all CAP states is 1, whereas the DT measures are absolute (#TR). This indicates that individuals exhibiting high scores on neural PC 1 occupy CAPs I and III for a relatively longer time, whereas individuals with low PC 1 scores occupy CAP II state for a longer time. Regarding CAP II, the FO exhibits a more pronounced negative loading on neural PC 1 compared to the dwell time measures (mean DT and var DT). On the other hand, the neural PC 2 highlights a general pattern of state persistence (high within-subject mean DT and high within-subject variance of DT), while also exhibiting a weak CAP-specific effect on FO (lower loadings of the FO at CAPs I/III and higher loadings of FO at CAP II) (Fig 4F). In addition, in neural PC 2, the DT measures of CAP II showed higher loadings than FO. A lengthy dwell time indicates that an individual occupies a state for an extended duration before transitioning to another CAP, suggesting strong state persistence. In contrast to the neural PCs 1 and 2 that showed strong between-day reliability, neural PC 3 showed a strong negative correlation between days |r| > 0.9; Fig 4G). In particular, neural PC 3 captures a specific component of day-to-day variability: the CAP-specific patterns observed in neural PC 1 can undergo systematic changes between days (e.g., sign-flipped feature loadings in Fig 4F). Together, our results demonstrate that both state and trait variance of spatiotemporal CAP dynamics involve pivotal information for identifying individual differences. Specifically, we identified 3 neural PCs that establish a low-dimensional, general motif of state and trait neural co-activation variation. The third principal component of individual variation involved information about day-to-day variability in neural co-activation, suggesting that patterns of within-subject variations can be uniquely individualized. This can be, in turn, considered as trait-like patterns providing additional information about individual neuro-phenotypes. While trait variations (neural PC 1 and 2) are dominantly loaded on the general motif of individual differences, the observed state variance at the time scale of days (neural PC 3) also contributes to this low-dimensional feature space, therefore reflecting neuro-phenotypes. The assessment of individual distributions of each neural measure supported these findings (S13 Fig). In addition, we found that the FO of CAPs I and II have overall a higher mean and variability than the FO of CAP III. We observed the same patterns in the mean DT and var DT (S13 Fig). Indeed, our analyses combining the hierarchical clustering and PCA of individual neural feature sets revealed 3 subgroups exhibiting distinct patterns of neural variations.

Principal variations of neural state-trait features co-vary with principal variations of behavioral phenotypes Next, we were interested in studying to what extent individual variability quantified on this low-dimensional neural feature space was linked to variations of individual human behavior. We employed a similar dimension reduction strategy to estimate the behavioral principal components (PCs) that provide low-dimensional geometries across multiple behavioral domains where people occupy differently. This way, we can associate how individual subjects are distributed in 2 feature spaces respectively and how such patterns relate to each other. To estimate the geometry of principal variations in behavioral phenotypes, we performed PCA on 262 variables across 15 behavioral domains from the HCP S1200 unrestricted and restricted behavioral data: alertness (1–2), cognition (3–39), emotion (40–63), personality (64–68), emotion task performances (69–74), gambling task performances (75–86), language task performances (87–94), relational task performances (95–100), social task performances (101–113), working memory task performances (114–167), psychiatric dimensions (168–189), alcohol use (190–222), tobacco use (223–252), illicit drug use (253–258), and marijuana use (259–262) (Fig 5A). Find the list of behavioral variables in S14 Fig. Before performing PCA, several variables reflecting the reaction time (RT) in tasks were converted to 1/RT for a better interpretation of PC geometry. PPT PowerPoint slide

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TIFF original image Download: Fig 5. Principal variations of neural state-trait features co-vary with the principal variations of behavioral phenotypes, highlighting individual life function outcomes associated with emotion regulation, cognitive function, and alcohol and substance use. (A) Correlation structure between 262 behavioral variables, which were obtained from the HCP S1200 unrestricted and restricted data. Colorbars along each axis of the correlation matrix indicate color-codes for the category of each variable. Categories were defined from the HCP data dictionary available online (HCP_S1200_DataDictionary_April_20_2018.csv). Variables measuring RT from tasks were transformed into 1/RT to account for the fact that a shorter response time indicates better task performance. See S14 Fig for the list of all behavioral variables. (B) The first PC explained 11.2% of variance. The first 15 PCs explaining ~50% of variance were considered in further analysis. (C) Across 1,000 permutations for split-half resampling, we compared if the geometry of estimated PCs in 2 splits are consistent. Pearson’s correlation coefficient (r) was computed for each pair of behavioral PCs. (D) Rank-ordered loadings of each behavioral variable on the first principal component (PCA). Each data point indicates a behavioral variables. PCA was performed for all 262 variables in (A). 39 subcategories shown on the y-axis were also defined using the HCP data dictionary. Several subcategories belonging to the same category are coded using the same color as in (A). The data used to generate the results can be found in S2 Data. HCP, Human Connectome Project; PCA, principal component analysis; RT, reaction time. https://doi.org/10.1371/journal.pbio.3002808.g005 After performing PCA, the significance of derived PCs was evaluated using permutation testing. Specifically, PCA was performed for each permutation where the order of subjects was randomly shuffled, which in turn provided a null model [23]. As a result, we found 27 PCs that accounted for a proportion of variance that exceeded chance (p<0.05 across 10,000 permutations). Subsequently, we considered the first 15 PCs, which collectively explained approximately 50% of the total variance, for further analyses. Reproducibility of these 15 PCs was evaluated using a split-half permutation approach, where we randomly splitted 337 subjects into 2 equal sized groups (n = 168) and applied PCA for each split. Then, the similarity (Pearson’s correlation) of PC geometry between the n-th PCs estimated from 2 split-halves was computed for each permutation, where n is the ranked order of each PC based on explained variance. As a result, we found that the first behavioral PC (PC 1) explaining 11.2% of variance (Fig 5B) was highly reproducible, exhibiting the similarity (r = 0.9±0.03, mean ± SD across 1,000 permutations) of PC geometry between the first PCs estimated from 2 split-halves (Fig 5C). The behavioral PC 1 highlighted individual life function outcomes associated with cognitive function, emotion regulation, and alcohol and substance use (Figs 5D and 6A). The variables of working memory task performances have the highest loadings on the behavioral PC 1, followed by the emotion, relational, languages, gambling task performances, fluid intelligence, self-regulation/impulsivity, and episodic memory. In contrast, variables associated with alcohol and substance use (e.g., short-term tobacco use) and psychiatric dimensions (e.g., self-report measures of positive and negative affect, stress, anxiety, depression, and social support) exhibited the lowest, negative loadings on the behavioral PC 1. Behavioral PC 2 highlighted items associated with emotion, personality and psychiatric life functions (Fig 6B). Behavioral PC 3 highlighted substance use, showing notable high loadings of alcohol consumption habit-related items on this PC (Fig 6C). PPT PowerPoint slide

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TIFF original image Download: Fig 6. Principal variations of neural state-trait features co-vary with the principal variations of behavioral phenotypes, highlighting individual life function outcomes associated with emotion regulation, cognitive function, and alcohol and substance use. (A) The geometry of behavioral PC 1 (black, left circle) reflects the difference in group-average behavioral variables (standardized behavioral data, right circle) between subgroups A (yellow) and B (green). Subgroup C is not shown because no significant group differences are found in (D). (B) The geometry of behavioral PC 2. (C) The geometry of behavioral PC 3. (D) Comparison of individual PC 1 scores between subgroups identified using neural state-trait measures (Fig 4). Two-sample two-sided t tests were performed between subgroups for each behavioral PC. p BON : Bonferroni corrected p-values. (E) Multiple linear regression model of 3 neural PC 1 with 2 covariates (age and sex) showed that the neural PC 1 was associated with the behavioral PC 1 (Partial R2 = 0.023, β 1 = 0.26, SE = 0.09, t = 2.8, p = 0.006), where multiple R2 = 0.041, adjusted R2 = 0.026, F(5,331) = 2.814, p-value = 0.017 for the full model. (F–H) Reproducibility analysis of the prediction of individual behavioral PC scores from neural PCs. In each permutation, PCA was performed for the neural and behavioral data from subjects in a random half of the entire sample (N = 168). Parameters of multiple linear regression models with three neural PC 1 with 2 covariates (age and sex) were estimated to evaluate the predictability of each behavioral PC. p BON : Bonferroni corrected p-values from F-tests. The data used to generate the results can be found in S2 and S3 Data. PCA, principal component analysis. https://doi.org/10.1371/journal.pbio.3002808.g006 To assess the association between the principal variation of behavioral variables and the principal variations of neural features, we first compared the distribution of individual scores on 15 behavioral PCs between the subgroups, identified using the neural features (Fig 4). Individuals classified as subgroup A (n = 163) exhibited significantly higher scores on behavioral PC 1 compared to subgroup B (n = 127) (p BON <0.05, t = 3.05, two-sample two-sided t tests) (Fig 6E). When comparing the individual scores of behavioral PC 1 between sex, we found no relationship. We did not observe any behavioral relevance of neural state-trait dynamics in identifying subgroup C (n = 47). In addition, behavioral PC 3 showed a strong sex effect (p BON <0.005). Next, we studied if individual scores on the behavioral PC 1 are associated with individual scores on the 3 neural PCs using the multiple linear regression model (behavioral PC 1 ~ neural PC 1 + neural PC 2 + neural PC 3 + age + sex). The neural PC 1 was associated with the behavioral PC 1 (partial R2 = 0.023, β 1 = 0.26, SE = 0.09, t = 2.8, p = 0.005), where the multiple R2 = 0.041, adjusted R2 = 0.026, F(5, 331) = 2.814 and p-value = 0.017 for the full model for predicting the behavioral PC 1 (Fig 6E). The neural PCs 2 and 3 and age did not show any association. Sex exhibited a weak association with the behavioral PC 1 (partial R2 = 0.016, β 1 = −1.44, SE = 0.61, t = −2.34, p = 0.02).

Reproducibility and cross-validation of low-dimensional neuro-behavioral association To further evaluate the reproducibility of the neuro-behavioral association in our low-dimensional space found in Fig 6E, we first performed the same multiple linear regression approach on a split data (random N = 168) across 1,000 permutations. Null data were generated by shuffling individual subjects in each behavioral PC data (S15 Fig). For predicting behavioral PC 1, the resulting partial R2 values were strongly reproducible across permutations (partial R2 = 0.025±0.017 for neural PC1, p BON <e−10, F-test; overall R2 = 0.056±0.024), as shown in Fig 6F. Similar to the analysis using the entire data, sex was also a reproducible predictor of behavioral PC 1 (partial R2 = 0.02±0.016, p BON <e−10, F-test). Secondly, we used the multiple linear regression model trained from each split 1 data for predicting individual behavioral scores in the corresponding split 2 data across permutations (S16 Fig). While the overall prediction performance was relatively low, it was highly reproducible and significantly different from null data analysis (R2 = 0.011±0.013, p < e– 10, F-test). Next, we repeated the same analyses for predicting behavioral PCs 2 and 3. Neural PCs 1 and 2 showed reproducible association with behavioral PC 2 that highlights emotion, personality, and psychiatric life functions, whereas age and sex showed larger predictive power (Fig 6G). For predicting behavioral PC 2, the estimated partial R2 was 0.008±0.01 for neural PC2, 0.018±0.014 for age, and 0.013±0.019 for sex (Fig 6G). On the other hand, neural PC 2 was predictive of behavioral PC 3 highlighting a strong association with alcohol consumption habits and sex differences (Fig 6H). For predicting behavioral PC 3, the estimated partial R2 was 0.016±0.014 for neural PC2 and 0.059±0.036 for sex, whereas the overall R2 for the full model was 0.098±0.041 (Fig 6H).

Impact of CAP III on the principal neuro-behavioral relationships It remains unclear whether and how the presence of CAP III impacts the temporal CAP profiles of other CAPs and how it relates to individual differences in behavior. To address these, we studied the relationship of CAP III to the 3 neural PCs (Fig 4) and the first behavioral PC (Fig 5). Specifically, to quantify the probability of CAP III occurrence, we compared the probability to have 5 CAPs involving CAP III and the probability to have 4 CAPs without involving CAP III. We found that subgroup C had a high probability of CAP III occurrence, when compared to other subgroups (Fig 7A). Individuals that have a high probability of CAP III occurrence present low scores of neural PC 1 (r = −0.26, p<0.001) and high scores of neural PC 2 (r = 0.24, p<0.001; Fig 7B and 7C). There was no relationship to individual scores of neural PC 3 (Fig 7D). There was a weak negative correlation between the probability of CAP III occurrence and individual scores of behavioral PC 1 (r = −0.18, p<0.005; Fig 7E). We found no correlation between the probability of CAP III occurrence and behavioral PCs 2 and 3. These results together indicate the association of spatiotemporal properties of CAP III with the neural PCs and the behavioral PC 1. PPT PowerPoint slide

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TIFF original image Download: Fig 7. The probability of CAP III occurrence is associated with the neural and behavioral PCs. (A) The probability of CAP III occurrence (x-axis) for each individual, which can be interpreted as an individual’s preference to have CAP III, was evaluated by the difference in the occurrence of 4 CAPs versus 5 CAPs, as described in Fig 1B. For each subject, we computed the number of permutations (occurrence out of 1,000 permutations) when 4 CAPs were estimated and the number of permutations for the same subject to be involved when 5 CAPs were estimated. Then, for each subject, we compared the difference in the occurrence (Δ Occurrence = Occurrence (k = 5)–Occurrence (k = 4)) from each split. Then, for each individual, the Δ Occurrence was averaged over 2 splits. Finally, the within-subject average Δ Occurrence was normalized across subjects to z-scores. Individuals were color-coded by subgroups defined using the hierarchical clustering of 30 neural features (Fig 4). (B–D) Scatter plots of individuals’ preference to have CAP III with respect to the individual scores on the neural PC 1 (B), neural PC 2 (C), neural PC 3 (D), and behavioral PC 1 (E). The data used to generate the results can be found in S4 Data. CAP, co-activation pattern. https://doi.org/10.1371/journal.pbio.3002808.g007

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