Parsing Heterogeneity in the Brain Connectivity of Depressed and Healthy Adults During Positive Mood

Functional connectivity subtypes during a positive mood induction: Predicting clinical response in a randomized controlled trial of ketamine for treatment-resistant depression

Ketamine has shown promise in rapidly improving symptoms of depression and most notably treatment-resistant depression (TRD). However, given the heterogeneity of TRD, biobehavioral markers of treatment response are necessary for the personalized prescription of intravenous ketamine. Heterogeneity in depression can be manifested in discrete patterns of functional connectivity (FC) in default mode, ventral affective, and cognitive control networks. This study employed a data-driven approach to parse FC during positive mood processing to characterize subgroups of patients with TRD prior to infusion and determine whether these connectivity-based subgroups could predict subsequent antidepressant response to ketamine compared to saline infusion. 152 adult patients with TRD completed a baseline assessment of FC during positive mood processing and were randomly assigned to either ketamine or saline infusion. The assessment utilized Subgroup-Group Iterative Multiple Model Estimation to recover directed connectivity maps and applied Walktrap algorithm to determine data-driven subgroups. Depression severity was assessed pre- and 24-hr postinfusion. Two connectivity-based subgroups were identified: Subgroup A (n = 110) and Subgroup B (n = 42). We observed that treatment response was moderated by an infusion type by subgroup interaction (p = .040). For patients receiving ketamine, subgroup did not predict treatment response (β = -.326, p = .499). However, subgroup predicted response for saline patients. Subgroup B individuals, relative to A, were more likely to be saline responders at 24-hr postinfusion (β = -2.146, p = .007). Thus, while ketamine improved depressive symptoms uniformly across both subgroups, this heterogeneity was a predictor of placebo response. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Parsing the heterogeneity of depression: a data-driven subgroup derived from cognitive function

Background

Increasing evidences suggests that depression is a heterogeneous clinical syndrome. Cognitive deficits in depression are associated with poor psychosocial functioning and worse response to conventional antidepressants. However, a consistent profile of neurocognitive abnormalities in depression remains unclear.

Objective

We used data-driven parsing of cognitive performance to reveal subgroups present across depressed individuals and then investigate the change pattern of cognitive subgroups across the course in follow-up.

Method

We assessed cognition in 163 patients with depression using The Chinese Brief Cognitive Test(C-BCT) and the scores were compared with those of 196 healthy controls (HCs). 58 patients were reassessed after 8 weeks. We used K-means cluster analysis to identify cognitive subgroups, and compared clinical variables among these subgroups. A linear mixed-effects model, incorporating time and group (with interaction term: time × group) as fixed effects, was used to assess cognitive changes over time. Stepwise logistic regression analysis was conducted to identify risk factors associated with these subgroups.

Results

Two distinct neurocognitive subgroups were identified: (1) a cognitive-impaired subgroup with global impairment across all domains assessed by the C-BCT, and (2) a cognitive-preserved subgroup, exhibited intact cognitive function, with performance well within the healthy range. The cognitive-impaired subgroup presented with more severe baseline symptoms, including depressed mood, guilt, suicidality, and poorer work performance. Significant group × time interactions were observed in the Trail Making Test Part A (TMT-A) and Continuous Performance Test (CPT), but not in Symbol Coding or Digit Span tests. Despite partial improvement in TMT-A and CPT tests, the cognitive-impaired subgroup's scores remained lower than those of the cognitive-preserved subgroup across all tests at the study endpoint. Multiple regression analysis indicated that longer illness duration, lower educational levels, and antipsychotic medication use may be risk factors for cognitive impairment.

Conclusion

This study identifies distinguishable cognitive subgroups in acute depression, thereby confirming the presence of cognitive heterogeneity. The cognitive-impaired subgroup exhibits distinct symptoms and persistent cognitive deficits even after treatment. Screening for cognitive dysfunction may facilitate more targeted interventions.

Clinical Trial Registration

https://www.chictr.org, identifier ChiCTR2400092796.

Bridging animal models and humans: neuroimaging as intermediate phenotypes linking genetic or stress factors to anhedonia

BACKGROUND

Intermediate phenotypes, such as characteristic neuroimaging patterns, offer unique insights into the genetic and stress-related underpinnings of neuropsychiatric disorders like depression. This study aimed to identify neuroimaging intermediate phenotypes associated with depression, bridging etiological factors to behavioral manifestations and connecting insights from animal models to diverse clinical populations.

METHODS

We analyzed datasets from both rodents and humans. The rodent studies included a genetic model (P11 knockout) and an environmental stress model (chronic unpredictable mild stress), while the human data comprised 748 participants from three cohorts. Using the amplitude of low-frequency fluctuations, we identified neuroimaging patterns in rodent models. We then applied a machine-learning approach to cluster neuroimaging subtypes of depression. To assess the genetic predispositions and stress-related changes associated with these subtypes, we analyzed genotype and metabolite data. Linear regression was employed to determine which neuroimaging features predicted core depression symptoms across species.

RESULTS

The genetic and environmental stress models exhibited distinct neuroimaging patterns in subcortical and sensorimotor regions. Consistent patterns emerged in two neuroimaging subtypes identified across three independent depressed cohorts. The subtype resembling P11 knockout demonstrated higher genetic susceptibility, with enriched expression of risk genes in brain tissues and abnormal metabolites linked to tryptophan metabolism. In contrast, the stress animal-like subtype did not show changes in genetic risk scores but exhibited enriched risk gene expression in somatic and endocrine tissues, along with mitochondrial dysfunction in the antioxidant stress system. Notably, these distinct subcortical-sensorimotor neuroimaging patterns predicted anhedonia, a core symptom of depression, in both rodent models and depressed subtypes.

CONCLUSIONS

This cross-species validation suggests that these neuroimaging patterns may serve as robust intermediate phenotypes, linking etiology to anhedonia and facilitating the translation of findings from animal models to humans with depression and other psychiatric disorders.

Individual differences in functional connectivity during suppression of imagined threat

Functional magnetic resonance imaging studies typically rely on between-person analyses. To examine individual differences in functional connectivity, we used Group Iterative Multiple Model Estimation and its subgrouping function to analyze functional magnetic resonance imaging data of 54 participants who were suppressing imagined future threat. A two-stage random-effects meta-analytic approach was employed to examine individual differences. In addition to generalizable connections between brain regions, we identified individual differences in personalized models suggesting different pathways through which individuals suppress future threat. Two subgroups with distinct connectivity patterns emerged: One subgroup (n = 29; 53.70%), characterized by an additional lagged connection from the right to the left posterior cingulate cortex, exhibited comparatively higher anxiety and less brain connectivity, whereas the other subgroup (n = 25; 46.30%), showing an additional connection from the left posterior cingulate cortex to the ventromedial prefrontal cortex, was associated with lower anxiety levels and greater connectivity. This study points to individual differences in functional connectivity during emotion regulation.

The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research

Despite the advantage of neuroimaging-based machine learning (ML) models as pivotal tools for investigating brain-behavior relationships in neuropsychiatric studies, these data-driven predictive approaches have yet to yield substantial, clinically actionable insights for mental health care. A notable impediment lies in the inadequate accommodation of most ML research to the natural heterogeneity within large samples. Although commonly thought of as individual-level analyses, many ML algorithms are unimodal and homogeneous and thus incapable of capturing the potentially heterogeneous relationships between biology and psychopathology. We review the current landscape of computational research targeting population heterogeneity and argue that there is a need to expand from brain subtyping and behavioral phenotyping to analyses that focus on heterogeneity at the relational level. To this end, we review and suggest several existing ML models with the capacity to discern how external environmental and sociodemographic factors moderate the brain-behavior mapping function in a data-driven fashion. These heterogeneous ML models hold promise for enhancing the discovery of individualized brain-behavior associations and advancing precision psychiatry.

Evaluating Discrete Time Methods for Subgrouping Continuous Processes

Rapid developments over the last several decades have brought increased focus and attention to the role of time scales and heterogeneity in the modeling of human processes. To address these emerging questions, subgrouping methods developed in the discrete-time framework-such as the vector autoregression (VAR)-have undergone widespread development to identify shared nomothetic trends from idiographic modeling results. Given the dependence of VAR-based parameters on the measurement intervals of the data, we sought to clarify the strengths and limitations of these methods in recovering subgroup dynamics under different measurement intervals. Building on the work of Molenaar and collaborators for subgrouping individual time-series by means of the subgrouped chain graphical VAR (scgVAR) and the subgrouping option in the group iterative multiple model estimation (S-GIMME), we present results from a Monte Carlo study aimed at addressing the implications of identifying subgroups using these discrete-time methods when applied to continuous-time data. Results indicate that discrete-time subgrouping methods perform well at recovering true subgroups when the measurement intervals are large enough to capture the full range of a system's dynamics, either via lagged or contemporaneous effects. Further implications and limitations are discussed therein.

Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample

BACKGROUND

Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder, but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with more extensive clinical phenotyping. The goals of the current study were to evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, single-site sample.

METHODS

We used regularized canonical correlation analysis to identify data-driven brain-behavior dimensions that explain individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting-state functional magnetic resonance imaging data for 328 patients with major depressive disorder and 461 healthy control participants. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes.

RESULTS

The optimal regularized canonical correlation analysis model yielded 3 robust and generalizable brain-behavior dimensions that explained individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified 4 depression subtypes, each with distinct clinical symptom profiles, abnormal resting-state functional connectivity patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation.

CONCLUSIONS

Our results define dimensional and categorical solutions to parsing neurobiological heterogeneity in major depressive disorder that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that regularized canonical correlation analysis and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.

Findings of PTSD-specific deficits in default mode network strength following a mild experimental stressor

Reductions in default mode (DMN) connectivity strength have been reported in posttraumatic stress disorder (PTSD). However, the specificity of DMN connectivity deficits in PTSD compared to major depressive disorder (MDD), and the sensitivity of these alterations to acute stressors are not yet known. 52 participants with a primary diagnosis of PTSD (n = 28) or MDD (n = 24) completed resting-state functional magnetic resonance imaging immediately before and after a mild affective stressor. A 2 × 2 design was conducted to determine the effects of group, stress, and group*stress on DMN connectivity strength. Exploratory analyses were completed to identify the brain region(s) underlying the DMN alterations. There was significant group*stress interaction (p = 0.03), reflecting stress-induced reduction in DMN strength in PTSD (p = 0.02), but not MDD (p = 0.50). Nodal exploration of connectivity strength in the DMN identified regions of the ventromedial prefrontal cortex and the precuneus potentially contributing to DMN connectivity deficits. The findings indicate the possibility of distinct, disease-specific, patterns of connectivity strength reduction in the DMN in PTSD, especially following an experimental stressor. The identified dynamic shift in functional connectivity, which was perhaps induced by the stressor task, underscores the potential utility of the DMN connectivity and raises the question whether these disruptions may be inversely affected by antidepressants known to treat both MDD and PTSD psychopathology.

Subgrouping with Chain Graphical VAR Models

Recent years have seen the emergence of an "idio-thetic" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel "idio-thetic" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach-the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.

Identifying two distinct neuroanatomical subtypes of first-episode depression using heterogeneity through discriminative analysis

BACKGROUND

Neurobiological heterogeneity in depression remains largely unknown, leading to inconsistent neuroimaging findings.

METHODS

Here, we adopted a novel proposed machine learning method ground on gray matter volumes (GMVs) to investigate neuroanatomical subtypes of first-episode treatment-naïve depression. GMVs were obtained from high-resolution T1-weighted images of 195 patients with first-episode, treatment-naïve depression and 78 matched healthy controls (HCs). Then we explored distinct subtypes of depression by employing heterogeneity through discriminative analysis (HYDRA) with regional GMVs as features.

RESULTS

Two prominently divergent subtypes of first-episode depression were identified, exhibiting opposite structural alterations compared with HCs but no different demographic features. Subtype 1 presented widespread increased GMVs mainly located in frontal, parietal, temporal cortex and partially located in limbic system. Subtype 2 presented widespread decreased GMVs mainly located in thalamus, cerebellum, limbic system and partially located in frontal, parietal, temporal cortex. Subtype 2 had smaller TIV and longer illness duration than Subtype 1. And TIV in Subtype 1 was positively correlated with age of onset while not in Subtype 2, probably implying the different potential neuropathological mechanisms.

LIMITATIONS

Despite results obtained in this study were validated by employing another brain atlas, the conclusions were acquired from a single dataset.

CONCLUSIONS

This study revealed two distinguishing neuroanatomical subtypes of first-episode depression, which provides new insights into underlying biological mechanisms of the heterogeneity in depression and might be helpful for accurate clinical diagnosis and future treatment.

Neural connectivity moderators and mechanisms of ketamine treatment among treatment-resistant depressed patients: a randomized controlled trial

BACKGROUND

Intravenous (IV) ketamine has emerged as a rapid and effective treatment for TRD. However, the specific neural mechanisms of ketamine's effects in humans remains unclear. Although neuroplasticity is implicated as a mechanism of action in animal models, relatively few randomized controlled trials (RCTs) in TRD patients have examined ketamine's impact on functional connectivity, a posited functional marker of neuroplasticity-particularly in the context of a mood-induction paradigm (termed miFC).

METHODS

152 adults with TRD (63% female; 37% male) were randomly allocated to receive a single infusion of ketamine or saline in a 2:1 ratio. We examined changes in connectivity (from baseline to 24-h post-infusion) that differed by treatment, and whether clinical treatment response at 24-h post-infusion was uniquely related (among patients allocated to ketamine relative to saline) to (1) pre-treatment connectivity and (2) changes in connectivity. We examined both miFC and rsFC, using prefrontal cortex and limbic seed regions. We also conducted a multiverse analysis to examine findings most robust against analytic decisions.

FINDINGS

Across both miFC and rsFC, ketamine was associated with greater in prefrontal/limbic connectivity compared to saline, and lower baseline connectivity of limbic and prefrontal regions predicted greater treatment response in patients receiving ketamine. Greater connectivity increases in participants receiving ketamine was uniquely related to greater treatment response. In addition, certain findings were identified as being reproducible against different analytic decisions in multiverse analyses.

INTERPRETATION

Our findings identify specific neural connectivity patterns impacted by ketamine and were uniquely related to outcomes following ketamine (relative to saline). These findings generally support prominent neuroplasticity models of ketamine's therapeutic efficacy. These findings lay new groundwork for understanding how to enhance and optimize ketamine treatments and develop novel rapid-acting treatments for depression.

FUNDING

This research was supported by NIH grant R01MH113857 and by the Clinical and Translational Sciences Institute at the University of Pittsburgh (UL1-TR-001857).

Trying to name what doesn't change: Neural nonresponse to Cognitive Therapy for depression

BACKGROUND

Theoretical models of neural mechanisms underlying Cognitive Behavior Therapy (CBT) for major depressive disorder (MDD) propose that psychotherapy changes neural functioning of prefrontal cortical structures associated with cognitive-control processes (DeRubeis, Siegle, & Hollon, ); however, MDD is persistent and characterized by long-lasting vulnerabilities to recurrence after intervention, suggesting that underlying neural mechanisms of MDD remain despite treatment. It follows that identification of treatment-resistant aberrant neural processes in MDD may inform clinical and research efforts targeting sustained remission. Thus, we sought to identify brain regions showing aberrant neural functioning in MDD that either (1) fail to exhibit substantive change (nonresponse) or (2) exhibit functional changes (response) following CBT.

METHODS

To identify treatment-resistant neural processes (as well as neural processes exhibiting change after treatment), we collected functional magnetic resonance imaging (fMRI) data of MDD patients (n = 58) before and after CBT as well as never-depressed controls (n = 35) before and after a similar amount of time. We evaluated fMRI data using conjunction analyses, which utilized several contrast-based criteria to characterize brain regions showing both differences between patients and controls at baseline and nonresponse or response to CBT.

RESULTS

Findings revealed nonresponse in a cerebellar region and response in prefrontal and parietal regions.

CONCLUSIONS

Results are consistent with prior theoretical models of CBT's direct effect on cortical regulatory processes but expand on them with identification of additional regions (and associated neural systems) of response and nonresponse to CBT.

Dimensional subtyping of first-episode drug-naïve major depressive disorder: A multisite resting-state fMRI study

Major depressive disorder (MDD) is a heterogeneous syndrome, and understanding its neural mechanisms is crucial for the advancement of personalized medicine. However, conventional subtyping studies often categorize MDD patients into a single subgroup, neglecting the continuous interindividual variations. This implies a pressing need for a dimensional approach. 230 first-episode drug-naïve MDD patients and 395 healthy controls were obtained from 5 sites via the Rest-meta-MDD project. A Bayesian model was used to decompose the resting-state functional connectivity (RSFC) into multiple distinct RSFC patterns (refer to as "factors"), and each individual was allowed to express multiple factors to varying degrees (dimensional subtyping). The associations between demographic and clinical variables with the identified factors were calculated. We identified three latent factors with distinct but partially overlapping hypo- and hyper-RSFC patterns. Most participants co-expressed multiple latent factors. All factors shared abnormal RSFC involving the default mode network and frontoparietal network, but the directionality partially differed across factors. All factors were not significantly associated with demographic and clinical variables. These findings shed light on the interindividual variability in MDD and could form the basis for developing novel therapeutic approaches that capitalize on the heterogeneity of MDD.

Transdiagnostic MRI markers of psychopathology following traumatic brain injury: a systematic review and network meta-analysis protocol

INTRODUCTION

Psychopathology following traumatic brain injury (TBI) is a common and debilitating consequence that is often associated with reduced functional and psychosocial outcomes. There is a lack of evidence regarding the neural underpinnings of psychopathology following TBI, and whether there may be transdiagnostic neural markers that are shared across traditional psychiatric diagnoses. The aim of this systematic review and meta-analysis is to examine the association of MRI-derived markers of brain structure and function with both transdiagnostic and specific psychopathology following moderate-severe TBI.

METHODS AND ANALYSIS

A systematic literature search of Embase (1974-2022), Ovid MEDLINE (1946-2022) and PsycINFO (1806-2022) will be conducted. Publications in English that investigate MRI correlates of psychopathology characterised by formal diagnoses or symptoms of psychopathology in closed moderate-severe TBI populations over 16 years of age will be included. Publications will be excluded that: (a) evaluate non-MRI neuroimaging techniques (CT, positron emission tomography, magnetoencephalography, electroencephalogram); (b) comprise primarily a paediatric cohort; (c) comprise primarily penetrating TBI. Eligible studies will be assessed against a modified Joanna Briggs Institute Critical Appraisal Instrument and data will be extracted by two independent reviewers. A descriptive analysis of MRI findings will be provided based on qualitative synthesis of data extracted. Quantitative analyses will include a meta-analysis and a network meta-analysis where there are sufficient data available.

ETHICS AND DISSEMINATION

Ethics approval is not required for the present study as there will be no original data collected. We intend to disseminate the results through publication to a high-quality peer-reviewed journal and conference presentations on completion.

PROSPERO REGISTRATION NUMBER

CRD42022358358.

Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification

As a complex dynamic system, the brain exhibits spatially organized recurring patterns of activity over time. Coactivation patterns (CAPs), which analyzes data from each single frame, have been utilized to detect transient brain activity states recently. However, previous CAP analyses have been conducted at the group level, which might neglect meaningful individual differences. Here, we estimated individual CAP states at both subject- and scan-level based on a densely sampled dataset: Midnight Scan Club. We used differential identifiability, which measures the gap between intra- and inter-subject similarity, to evaluate individual differences. We found individual CAPs at the subject-level achieved the best fingerprinting ability by maintaining high intra-subject similarity and enlarging inter-subject differences, and brain regions of association networks mainly contributed to the identifiability. On the other hand, scan-level CAP states were unstable across scans for the same participant. Expectedly, we found subject-specific CAPs became more reliable and discriminative with more data (i.e., longer duration). As the acquisition time of each participant is limited in practice, our results recommend a data collection strategy that collects more scans with appropriate duration (e.g., 12 ~ 15 min/scan) to obtain more reliable subject-specific CAPs, when total acquisition time is fixed (e.g., 150 min). In summary, this work has constructed reliable subject-specific CAP states with meaningful individual differences, and recommended an appropriate data collection strategy, which can guide subsequent investigations into individualized brain dynamics.

Resolving heterogeneity in depression using individualized structural covariance network analysis

BACKGROUND

Elucidating individual aberrance is a critical first step toward precision medicine for heterogeneous disorders such as depression. The neuropathology of depression is related to abnormal inter-regional structural covariance indicating a brain maturational disruption. However, most studies focus on group-level structural covariance aberrance and ignore the interindividual heterogeneity. For that reason, we aimed to identify individualized structural covariance aberrance with the help of individualized differential structural covariance network (IDSCN) analysis.

METHODS

T1-weighted anatomical images of 195 first-episode untreated patients with depression and matched healthy controls (n = 78) were acquired. We obtained IDSCN for each patient and identified subtypes of depression based on shared differential edges.

RESULTS

As a result, patients with depression demonstrated tremendous heterogeneity in the distribution of differential structural covariance edges. Despite this heterogeneity, altered edges within subcortical-cerebellum network were often shared by most of the patients. Two robust neuroanatomical subtypes were identified. Specifically, patients in subtype 1 often shared decreased motor network-related edges. Patients in subtype 2 often shared decreased subcortical-cerebellum network-related edges. Functional annotation further revealed that differential edges in subtype 2 were mainly implicated in reward/motivation-related functional terms.

CONCLUSIONS

In conclusion, we investigated individualized differential structural covariance and identified that decreased edges within subcortical-cerebellum network are often shared by patients with depression. The identified two subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of depression.

Characterizing heterogeneity in early adolescent reward networks and individualized associations with behavioral and clinical outcomes

Associations between connectivity networks and behavioral outcomes such as depression are typically examined by comparing average networks between known groups. However, neural heterogeneity within groups may limit the ability to make inferences at the individual level as qualitatively distinct processes across individuals may be obscured in group averages. This study characterizes the heterogeneity of effective connectivity reward networks among 103 early adolescents and examines associations between individualized features and multiple behavioral and clinical outcomes. To characterize network heterogeneity, we used extended unified structural equation modeling to identify effective connectivity networks for each individual and an aggregate network. We found that an aggregate reward network was a poor representation of individuals, with most individual-level networks sharing less than 50% of the group-level network paths. We then used Group Iterative Multiple Model Estimation to identify a group-level network, subgroups of individuals with similar networks, and individual-level networks. We identified three subgroups that appear to reflect differences in network maturity, but this solution had modest validity. Finally, we found numerous associations between individual-specific connectivity features and behavioral reward functioning and risk for substance use disorders. We suggest that accounting for heterogeneity is necessary to use connectivity networks for inferences precise to the individual.

Blind Subgrouping of Task-based fMRI

Significant heterogeneity in network structures reflecting individuals' dynamic processes can exist within subgroups of people (e.g., diagnostic category, gender). This makes it difficult to make inferences regarding these predefined subgroups. For this reason, researchers sometimes wish to identify subsets of individuals who have similarities in their dynamic processes regardless of any predefined category. This requires unsupervised classification of individuals based on similarities in their dynamic processes, or equivalently, in this case, similarities in their network structures of edges. The present paper tests a recently developed algorithm, S-GIMME, that takes into account heterogeneity across individuals with the aim of providing subgroup membership and precise information about the specific network structures that differentiate subgroups. The algorithm has previously provided robust and accurate classification when evaluated with large-scale simulation studies but has not yet been validated on empirical data. Here, we investigate S-GIMME's ability to differentiate, in a purely data-driven manner, between brain states explicitly induced through different tasks in a new fMRI dataset. The results provide new evidence that the algorithm was able to resolve, in an unsupervised data-driven manner, the differences between different active brain states in empirical fMRI data to segregate individuals and arrive at subgroup-specific network structures of edges. The ability to arrive at subgroups that correspond to empirically designed fMRI task conditions, with no biasing or priors, suggests this data-driven approach can be a powerful addition to existing methods for unsupervised classification of individuals based on their dynamic processes.

The influence of autoregressive relation strength and search strategy on directionality recovery in group iterative multiple model estimation

Unified structural equation modeling (uSEM) implemented in the group iterative multiple model estimation (GIMME) framework has recently been widely used for characterizing within-person network dynamics of behavioral and functional neuroimaging variables. Previous studies have established that GIMME accurately recovers the presence of relations between variables. However, recovery of relation directionality is less consistent, which is concerning given the importance of directionality estimates for many research questions. There is evidence that strong autoregressive relations may aid directionality recovery and indirect evidence that a novel version of GIMME allowing for multiple solutions could improve recovery when such relations are weak, but it remains unclear how these strategies perform under a range of study conditions. Using comprehensive simulations that varied the strength of autoregressive relations among other factors, this study evaluated the directionality recovery of two GIMME search strategies: (a) estimating autoregressive relations by default in the null model (GIMME-AR) and (b) generating multiple solution paths (GIMME-MS). Both strategies recovered directionality best-and were roughly equivalent in performance-when autoregressive relations were strong (e.g., β = .60). When they were weak (β ≤ .10), GIMME-MS displayed an advantage, although overall directionality recovery was modest. Analyses of empirical data in which autoregressive relations were characteristically strong (resting state functional MRI) versus weak (daily diary) mirrored simulation results and confirmed that these strategies can disagree on directionality when autoregressive relations are weak. Findings have important implications for psychological and neuroimaging applications of uSEM/GIMME and suggest specific scenarios in which researchers might or might not be confident in directionality results. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Resting-state connectivity subtype of comorbid PTSD and alcohol use disorder moderates improvement from integrated prolonged exposure therapy in Veterans

BACKGROUND

Posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) are highly comorbid and are associated with significant functional impairment and inconsistent treatment outcomes. Data-driven subtyping of this clinically heterogeneous patient population and the associated underlying neural mechanisms are highly needed to identify who will benefit from psychotherapy.

METHODS

In 53 comorbid PTSD/AUD patients, resting-state functional magnetic resonance imaging was collected prior to undergoing individual psychotherapy. We used a data-driven approach to subgroup patients based on directed connectivity profiles. Connectivity subgroups were compared on clinical measures of PTSD severity and heavy alcohol use collected at pre- and post-treatment.

RESULTS

We identified a subgroup of patients associated with improvement in PTSD symptoms from integrated-prolonged exposure therapy. This subgroup was characterized by lower insula to inferior parietal cortex (IPC) connectivity, higher pregenual anterior cingulate cortex (pgACC) to posterior midcingulate cortex connectivity and a unique pgACC to IPC path. We did not observe any connectivity subgroup that uniquely benefited from integrated-coping skills or subgroups associated with change in alcohol consumption.

CONCLUSIONS

Data-driven approaches to characterize PTSD/AUD subtypes have the potential to identify brain network profiles that are implicated in the benefit from psychological interventions - setting the stage for future research that targets these brain circuit communication patterns to boost treatment efficacy.

Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research

Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.

Group-level comparison of brain connectivity networks

BACKGROUND

Functional connectivity (FC) studies are often performed to discern different patterns of brain connectivity networks between healthy and patient groups. Since many neuropsychiatric disorders are related to the change in these patterns, accurate modelling of FC data can provide useful information about disease pathologies. However, analysing functional connectivity data faces several challenges, including the correlations of the connectivity edges associated with network topological characteristics, the large number of parameters in the covariance matrix, and taking into account the heterogeneity across subjects.

METHODS

This study provides a new statistical approach to compare the FC networks between subgroups that consider the network topological structure of brain regions and subject heterogeneity.

RESULTS

The power based on the heterogeneity structure of identity scaled in a sample size of 25 exhibited values greater than 0.90 without influencing the degree of correlation, heterogeneity, and the number of regions. This index had values above 0.80 in the small sample size and high correlation. In most scenarios, the type I error was close to 0.05. Moreover, the application of this model on real data related to autism was also investigated, which indicated no significant difference in FC networks between healthy and patient individuals.

CONCLUSIONS

The results from simulation data indicated that the proposed model has high power and near-nominal type I error rates in most scenarios.

Understanding Pain and Trauma Symptoms in Veterans From Resting-State Connectivity: Unsupervised Modeling

Trauma and posttraumatic stress are highly comorbid with chronic pain and are often antecedents to developing chronic pain conditions. Pain and trauma are associated with greater utilization of medical services, greater use of psychiatric medication, and increased total cost of treatment. Despite the high overlap in the clinic, the neural mechanisms of pain and trauma are often studied separately. In this study, resting-state functional magnetic resonance imaging (rs-fMRI) scans were completed among a diagnostically heterogeneous sample of veterans with a range of back pain and trauma symptoms. Using Group Iterative Multiple Model Estimation (GIMME), an effective functional connectivity analysis, we explored an unsupervised model deriving subgroups based on path similarity in a priori defined regions of interest (ROIs) from brain regions implicated in the experience of pain and trauma. Three subgroups were identified by patterns in functional connection and differed significantly on several psychological measures despite similar demographic and diagnostic characteristics. The first subgroup was highly connected overall, was characterized by functional connectivity from the nucleus accumbens (NAc), the anterior cingulate cortex (ACC), and the posterior cingulate cortex (PCC) to the insula and scored low on pain and trauma symptoms. The second subgroup did not significantly differ from the first subgroup on pain and trauma measures but was characterized by functional connectivity from the ACC and NAc to the thalamus and from ACC to PCC. The third subgroup was characterized by functional connectivity from the thalamus and PCC to NAc and scored high on pain and trauma symptoms. Our results suggest that, despite demographic and diagnostic similarities, there may be neurobiologically dissociable biotypes with different mechanisms for managing pain and trauma. These findings may have implications for the determination of appropriate biotype-specific interventions that target these neurological systems.

Person-specific connectivity mapping uncovers differences of bilingual language experience on brain bases of attention in children

Bilingualism influences children's cognition, yet bilinguals vary greatly in their dual-language experiences. To uncover sources of variation in bilingual and monolingual brain function, the present study used standard analysis and innovative person-specific connectivity models combined with a data-driven grouping algorithm. Children (ages 7-9; N = 52) completed a visuo-spatial attention task while undergoing functional near-infrared spectroscopy neuroimaging. Both bilingual and monolingual groups performed similarly, and engaged bilateral frontal and parietal regions. However, bilinguals showed greater brain activity than monolinguals in left frontal and parietal regions. Connectivity models revealed two empirically-derived subgroups. One subgroup was composed of monolinguals and bilinguals who were more English dominant, and showed left frontal-parietal connections. The other was composed of bilinguals who were balanced in their dual-language abilities and showed left frontal lobe connections. The findings inform how individual variation in early language experiences influences children's emerging cortical networks for executive function, and reveal efficacy of data-driven approaches.

Toward personalized medicine in connectomic deep brain stimulation

At the group-level, deep brain stimulation leads to significant therapeutic benefit in a multitude of neurological and neuropsychiatric disorders. At the single-patient level, however, symptoms may sometimes persist despite "optimal" electrode placement at established treatment coordinates. This may be partly explained by limitations of disease-centric strategies that are unable to account for heterogeneous phenotypes and comorbidities observed in clinical practice. Instead, tailoring electrode placement and programming to individual patients' symptom profiles may increase the fraction of top-responding patients. Here, we propose a three-step, circuit-based framework with the aim of developing patient-specific treatment targets that address the unique symptom constellation prevalent in each patient. First, we describe how a symptom network target library could be established by mapping beneficial or undesirable DBS effects to distinct circuits based on (retrospective) group-level data. Second, we suggest ways of matching the resulting symptom networks to circuits defined in the individual patient (template matching). Third, we introduce network blending as a strategy to calculate optimal stimulation targets and parameters by selecting and weighting a set of symptom-specific networks based on the symptom profile and subjective priorities of the individual patient. We integrate the approach with published literature and conclude by discussing limitations and future challenges.

Early Life Stress and Neurodevelopment in Adolescence: Implications for Risk and Adaptation

An alarming high proportion of youth experience at least one kind of stressor in childhood and/or adolescence. Exposure to early life stress is associated with increased risk for psychopathology, accelerated biological aging, and poor physical health; however, it is important to recognize that not all youth who experience such stress go on to develop difficulties. In fact, resilience, or positive adaptation in the face of adversity, is relatively common. Individual differences in vulnerability or resilience to the effects of early stress may be represented in the brain as specific patterns, profiles, or signatures of neural activation, structure, and connectivity (i.e., neurophenotypes). Whereas neurophenotypes of risk that reflect the deleterious effects of early stress on the developing brain are likely to exacerbate negative outcomes in youth, neurophenotypes of resilience may reduce the risk of experiencing these negative outcomes and instead promote positive functioning. In this chapter we describe our perspective concerning the neurobiological mechanisms and moderators of risk and resilience in adolescence following early life stress and integrate our own work into this framework. We present findings suggesting that exposure to stress in childhood and adolescence is associated with functional and structural alterations in neurobiological systems that are important for social-affective processing and for cognitive control. While some of these neurobiological alterations increase risk for psychopathology, they may also help to limit adolescents' sensitivity to subsequent negative experiences. We also discuss person-centered strategies that we believe can advance our understanding of risk and resilience to early stress in adolescents. Finally, we describe ways in which the field can broaden its focus to include a consideration of other types of environmental factors, such as environmental pollutants, in affecting both risk and resilience to stress-related health difficulties in youth.

A Computational Neural Model for Mapping Degenerate Neural Architectures

Degeneracy in biological systems refers to a many-to-one mapping between physical structures and their functional (including psychological) outcomes. Despite the ubiquity of the phenomenon, traditional analytical tools for modeling degeneracy in neuroscience are extremely limited. In this study, we generated synthetic datasets to describe three situations of degeneracy in fMRI data to demonstrate the limitations of the current univariate approach. We describe a novel computational approach for the analysis referred to as neural topographic factor analysis (NTFA). NTFA is designed to capture variations in neural activity across task conditions and participants. The advantage of this discovery-oriented approach is to reveal whether and how experimental trials and participants cluster into task conditions and participant groups. We applied NTFA on simulated data, revealing the appropriate degeneracy assumption in all three situations and demonstrating NTFA's utility in uncovering degeneracy. Lastly, we discussed the importance of testing degeneracy in fMRI data and the implications of applying NTFA to do so.

Predicting treatment selections for individuals with major depressive disorder according to functional connectivity subgroups

BACKGROUND

Major depressive disorder (MDD) is a highly prevalent and disabling disease. Currently, patients' treatment choices depend on their clinical symptoms observed by clinicians, which are subjective. Rich evidence suggests that different functional networks' dysfunctions correspond to different intervention preferences. Here, we aimed to develop a prediction model based on data-driven subgroups to provide treatment recommendations.

METHODS

All 630 participants enrolled from four sites underwent functional magnetic resonances imaging at baseline. In the discovery dataset (n=228), we firstly identified MDD subgroups by the hierarchical clustering method using the canonical variates of resting-state functional connectivity (FC) through canonical correlation analyses. The demographic, symptom improvement and FC were compared among subgroups. The preference intervention for each subgroup was also determined. Next, we predicted the individual treatment strategy. Specifically, a patient was assigned into predefined subgroups based on FC similarities and then his/her treatment strategy was determined by the subgroups' preferred interventions.

RESULTS

Three subgroups with specific treatment recommendations were emerged including: (1) a selective serotonin reuptake inhibitors-oriented subgroup with early improvements in working and activities. (2) a stimulation-oriented subgroup with more alleviation in suicide. (3) a selective serotonin noradrenaline reuptake inhibitors-oriented subgroup with more alleviation in hypochondriasis. Through cross-dataset testing respectively conducted on three testing datasets, results showed an overall accuracy of 72.83%.

CONCLUSIONS

Our works revealed the correspondences between subgroups and their treatment preferences and predicted individual treatment strategy based on such correspondences. Our model has the potential to support psychiatrists in early clinical decision making for better treatment outcomes.

Effects of escitalopram therapy on resting-state functional connectivity of subsystems of the default mode network in unmedicated patients with major depressive disorder

Antidepressants are often the first-line medications prescribed for patients with major depressive disorder (MDD). Given the critical role of the default mode network (DMN) in the physiopathology of MDD, the current study aimed to investigate the effects of antidepressants on the resting-state functional connectivity (rsFC) within and between the DMN subsystems. We collected resting-state functional magnetic resonance imaging (rs-fMRI) data from 36 unmedicated MDD patients at baseline and after escitalopram treatment for 12 weeks. The rs-fMRI data were also collected from 61 matched healthy controls at the time point with the same interval. Then, we decomposed the DMN into three subsystems based on a template from previous studies and computed the rsFC within and between the three subsystems. Finally, repeated measures analysis of covariance was conducted to identify the main effect of group and time and their interaction effect. We found that the significantly reduced within-subsystem rsFC in the DMN core subsystem in patients with MDD at baseline was increased after escitalopram treatment and became comparable with that in the healthy controls, whereas the reduced within-subsystem rsFC persisted in the DMN dorsal medial prefrontal cortex (dMPFC) and medial temporal subsystems in patients with MDD following escitalopram treatment. In addition, the reduced between-subsystem rsFC between the core and dMPFC subsystem showed a similar trend of change after treatment in patients with MDD. Moreover, our main results were confirmed using the DMN regions from another brain atlas. In the current study, we found different effects of escitalopram on the rsFC of the DMN subsystems. These findings deepened our understanding of the neuronal basis of antidepressants' effect on brain function in patients with MDD. The trial name: appropriate technology study of MDD diagnosis and treatment based on objective indicators and measurement. URL: http://www.chictr.org.cn/showproj.aspx?proj=21377 . Registration number: ChiCTR-OOC-17012566.

Classification of emotion categories based on functional connectivity patterns of the human brain

Neurophysiological and psychological models posit that emotions depend on connections across wide-spread corticolimbic circuits. While previous studies using pattern recognition on neuroimaging data have shown differences between various discrete emotions in brain activity patterns, less is known about the differences in functional connectivity. Thus, we employed multivariate pattern analysis on functional magnetic resonance imaging data (i) to develop a pipeline for applying pattern recognition in functional connectivity data, and (ii) to test whether connectivity patterns differ across emotion categories. Six emotions (anger, fear, disgust, happiness, sadness, and surprise) and a neutral state were induced in 16 participants using one-minute-long emotional narratives with natural prosody while brain activity was measured with functional magnetic resonance imaging (fMRI). We computed emotion-wise connectivity matrices both for whole-brain connections and for 10 previously defined functionally connected brain subnetworks and trained an across-participant classifier to categorize the emotional states based on whole-brain data and for each subnetwork separately. The whole-brain classifier performed above chance level with all emotions except sadness, suggesting that different emotions are characterized by differences in large-scale connectivity patterns. When focusing on the connectivity within the 10 subnetworks, classification was successful within the default mode system and for all emotions. We thus show preliminary evidence for consistently different sustained functional connectivity patterns for instances of emotion categories particularly within the default mode system.

Neural connectivity biotypes: associations with internalizing problems throughout adolescence

BACKGROUND

Neurophysiological patterns may distinguish which youth are at risk for the well-documented increase in internalizing symptoms during adolescence. Adolescents with internalizing problems exhibit altered resting-state functional connectivity (RSFC) of brain regions involved in socio-affective processing. Whether connectivity-based biotypes differentiate adolescents' levels of internalizing problems remains unknown.

METHOD

Sixty-eight adolescents (37 females) reported on their internalizing problems at ages 14, 16, and 18 years. A resting-state functional neuroimaging scan was collected at age 16. Time-series data of 15 internalizing-relevant brain regions were entered into the Subgroup-Group Iterative Multi-Model Estimation program to identify subgroups based on RSFC maps. Associations between internalizing problems and connectivity-based biotypes were tested with regression analyses.

RESULTS

Two connectivity-based biotypes were found: a Diffusely-connected biotype (N = 46), with long-range fronto-parietal paths, and a Hyper-connected biotype (N = 22), with paths between subcortical and medial frontal areas (e.g. affective and default-mode network regions). Higher levels of past (age 14) internalizing problems predicted a greater likelihood of belonging to the Hyper-connected biotype at age 16. The Hyper-connected biotype showed higher levels of concurrent problems (age 16) and future (age 18) internalizing problems.

CONCLUSIONS

Differential patterns of RSFC among socio-affective brain regions were predicted by earlier internalizing problems and predicted future internalizing problems in adolescence. Measuring connectivity-based biotypes in adolescence may offer insight into which youth face an elevated risk for internalizing disorders during this critical developmental period.

Comparative analysis of default mode networks in major psychiatric disorders using resting-state EEG

Default mode network (DMN) is a set of functional brain structures coherently activated when individuals are in resting-state. In this study, we constructed multi-frequency band resting-state EEG-based DMN functional network models for major psychiatric disorders to easily compare their pathophysiological characteristics. Phase-locking values (PLVs) were evaluated to quantify functional connectivity; global and nodal clustering coefficients (CCs) were evaluated to quantify global and local connectivity patterns of DMN nodes, respectively. DMNs of patients with post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD), panic disorder, major depressive disorder (MDD), bipolar disorder, schizophrenia (SZ), mild cognitive impairment (MCI), and Alzheimer's disease (AD) were constructed relative to their demographically-matched healthy control groups. Overall DMN patterns were then visualized and compared with each other. In global CCs, SZ and AD showed hyper-clustering in the theta band; OCD, MCI, and AD showed hypo-clustering in the low-alpha band; OCD and MDD showed hypo-clustering and hyper-clustering in low-beta, and high-beta bands, respectively. In local CCs, disease-specific patterns were observed. In the PLVs, lowered theta-band functional connectivity between the left lingual gyrus and the left hippocampus was frequently observed. Our comprehensive comparisons suggest EEG-based DMN as a useful vehicle for understanding altered brain networks of major psychiatric disorders.

Resting state functional connectivity subtypes predict discrete patterns of cognitive-affective functioning across levels of analysis among patients with treatment-resistant depression

Resting state functional connectivity (RSFC) in ventral affective (VAN), default mode (DMN) and cognitive control (CCN) networks may partially underlie heterogeneity in depression. The current study used data-driven parsing of RSFC to identify subgroups of patients with treatment-resistant depression (TRD; n = 70) and determine if subgroups generalized to transdiagnostic measures of cognitive-affective functioning relevant to depression (indexed across self-report, behavioral, and molecular levels of analysis). RSFC paths within key networks were characterized using Subgroup-Group Iterative Multiple Model Estimation. Three connectivity-based subgroups emerged: Subgroup A, the largest subset and containing the fewest pathways; Subgroup B, containing unique bidirectional VAN/DMN negative feedback; and Subgroup C, containing the most pathways. Compared to other subgroups, subgroup B was characterized by lower self-reported positive affect and subgroup C by higher self-reported positive affect, greater variability in induced positive affect, worse response inhibition, and reduced striatal tissue iron concentration. RSFC-based categorization revealed three TRD subtypes associated with discrete aberrations in transdiagnostic cognitive-affective functioning that were largely unified across levels of analysis and were maintained after accounting for the variability captured by a disorder-specific measure of depressive symptoms. Findings advance understanding of transdiagnostic brain-behavior heterogeneity in TRD and may inform novel treatment targets for this population.

Individual Differences in the Affective Response to Pandemic-related Stressors in COVID-19 Healthcare Workers

Background

We investigated the evolving prevalence of mood and anxiety symptoms among healthcare workers from May, 2020 to January, 2021; risk factors for adverse outcomes; and characteristic modes of affective responses to pandemic-related stressors.

Methods

2,307 healthcare workers (78.9% female, modal age 25-34) participated in an online survey assessing depression (Patient Health Questionnaire [PHQ-9]) and anxiety symptoms (Generalized Anxiety Disorder scale [GAD-7]), demographic variables, and self-reported impact of pandemic-related stressors. 334 subjects were reassessed ∼6 months later.

Results

The prevalence of clinically significant depression and anxiety was 45.3% and 43.3%, respectively, and a majority (59.9%-62.9%) of those individuals had persistent significant symptoms at 6-month follow-up. Younger age, female gender, and specific occupations (support staff > nurses > physicians) were associated with increased depressive and anxiety symptoms. The most important risk factors were social isolation and fear of contracting COVID-19. The prevalence of clinically significant mood and anxiety symptoms increased by 39.8% from May, 2020 to January, 2021. PHQ-9 and GAD-7 scores were highly correlated and associated with nearly identical risk factors, suggesting that they are not capturing independent constructs in this sample. Principal components analysis identified seven orthogonal symptom domains with unique risk factors.

Conclusions

Clinically significant mood and anxiety symptoms are highly prevalent and persistent among healthcare workers, and are associated with numerous risk factors, the strongest of which are related to pandemic stressors and potentially modifiable. Interventions aimed at reducing social isolation and mitigating the impact of fear of infection warrant further study.

Identifying Subgroups of Major Depressive Disorder Using Brain Structural Covariance Networks and Mapping of Associated Clinical and Cognitive Variables

Background

Identifying data-driven subtypes of major depressive disorder (MDD) holds promise for parsing the heterogeneity of MDD in a neurobiologically informed way. However, limited studies have used brain structural covariance networks (SCNs) for subtyping MDD.

Methods

This study included 145 unmedicated patients with MDD and 206 demographically matched healthy control subjects, who underwent a structural magnetic resonance imaging scan and a comprehensive neurocognitive battery. Patterns of structural covariance were identified using source-based morphometry across both patients with MDD and healthy control subjects. K-means clustering algorithms were applied on dysregulated structural networks in MDD to identify potential MDD subtypes. Finally, clinical and neurocognitive measures were compared between identified subgroups to elucidate the profile of these MDD subtypes.

Results

Source-based morphometry across all individuals identified 28 whole-brain SCNs that encompassed the prefrontal, anterior cingulate, and orbitofrontal cortices; basal ganglia; and cerebellar, visual, and motor regions. Compared with healthy control subjects, individuals with MDD showed lower structural network integrity in three networks including default mode, ventromedial prefrontal cortical, and salience networks. Clustering analysis revealed two MDD subtypes based on the patterns of structural network abnormalities in these three networks. Further profiling revealed that patients in subtype 1 had younger age of onset and more symptom severity as well as greater deficits in cognitive performance than patients in subtype 2.

Conclusions

Overall, we identified two MDD subtypes based on SCNs that differed in their clinical and cognitive profile. Our results represent a proof-of-concept framework for leveraging these large-scale SCNs to parse heterogeneity in MDD.

Path and Directionality Discovery in Individual Dynamic Models: A Regularized Unified Structural Equation Modeling Approach for Hybrid Vector Autoregression

There recently has been growing interest in the study of psychological and neurological processes at an individual level. One goal in such endeavors is to construct person-specific dynamic assessments using time series techniques such as Vector Autoregressive (VAR) models. However, two problems exist with current VAR specifications: (1) VAR models are restricted in that contemporaneous relations are typically modeled either as undirected relations among residuals or directed relations among observed variables, but not both; (2) current estimation frameworks are limited by the reliance on stepwise model building procedures. This study adopts a new modeling approach. We first extended the current unified SEM (uSEM) framework, a widely used structural VAR model, to a hybrid representation (i.e., "huSEM") to include both undirected and directed contemporaneous effects, and then replaced the stepwise modeling with a LASSO-type regularization for a global search of the optimal sparse model. Our simulation study showed that regularized huSEM performed uniformly the best over alternative VAR representations and/or modeling approaches, with respect to accurately recovering the presence and directionality of hybrid relations and reliably removing false relations when the data are generated to have two types of contemporaneous relations. The present study to our knowledge is the first application of the recently developed regularized SEM technique to the estimation of huSEM, which points to a promising future for statistical learning in psychometric models.

Abnormal neural activities in adults and youths with major depressive disorder during emotional processing: a meta-analysis

BACKGROUND

Abnormal neural activities during emotional processing have been found in both adults and youths with major depressive disorder. However, findings were inconsistent in each group and cannot be compared to each other.

METHODS

We first identified neuroimaging experiments that revealed abnormal neural activities during emotional processing in patients with major depressive disorder published from January 1997 to January 2019. Then we conducted voxel-wise meta-analyses on adult and youth patients separately and compared the two age groups using direct meta-comparison.

RESULTS

Fifty-four studies comprising 1141 patients and 1242 healthy controls were identified. Both adult and youth patients showed abnormal neural activities in anterior cingulate cortex, insula, superior and middle temporal gyrus, and occipital cortex compared to healthy controls. However, hyperactivities in the superior and middle frontal gyrus, amygdala, and hippocampus were only observed in adult patients, while hyperactivity in the striatum was only found in youth patients compared to controls. In addition, compared with youths, adult patients exhibited significantly greater abnormal activities in insula, middle frontal gyrus, and hippocampus, and significantly lower abnormal activities in middle temporal gyrus, middle occipital gyrus, lingual gyrus, and striatum.

CONCLUSIONS

The common alterations confirmed the negative processing bias in major depressive disorder. Both adult and youth patients were suggested to have disturbed emotional perception, appraisal, and reactivity. However, adult patients might be more subject to the impaired appraisal and reactivity processes, while youth patients were more subject to the impaired perception process. These findings help us understand the progressive pathophysiology of major depressive disorder.

Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography

The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.

Functional connectivity between the amygdala and subgenual cingulate gyrus predicts the antidepressant effects of ketamine in patients with treatment-resistant depression

Aim

Approximately one‐third of patients with major depressive disorder develop treatment‐resistant depression. One‐third of patients with treatment‐resistant depression demonstrate resistance to ketamine, which is a novel antidepressant effective for this disorder. The objective of this study was to examine the utility of resting‐state functional magnetic resonance imaging for the prediction of treatment response to ketamine in treatment‐resistant depression.

Methods

An exploratory seed‐based resting‐state functional magnetic resonance imaging analysis was performed to examine baseline resting‐state functional connectivity differences between ketamine responders and nonresponders before treatment with multiple intravenous ketamine infusions.

Results

Fifteen patients with treatment‐resistant depression received multiple intravenous subanesthetic (0.5 mg/kg/40 minutes) ketamine infusions, and nine were identified as responders. The exploratory resting‐state functional magnetic resonance imaging analysis identified a cluster of significant baseline resting‐state functional connectivity differences associating ketamine response between the amygdala and subgenual anterior cingulate gyrus in the right hemisphere. Using anatomical region of interest analysis of the resting‐state functional connectivity, ketamine response was predicted with 88.9% sensitivity and 100% specificity. The resting‐state functional connectivity of significant group differences between responders and nonresponders retained throughout the treatment were considered a trait‐like feature of heterogeneity in treatment‐resistant depression.

Conclusion

This study suggests the possible clinical utility of resting‐state functional magnetic resonance imaging for predicting the antidepressant effects of ketamine in treatment‐resistant depression patients and implicated resting‐state functional connectivity alterations to determine the trait‐like pathophysiology underlying treatment response heterogeneity in treatment‐resistant depression.

This study illustrates that the alteration in the RSFC within the right AN in TRD patients reflects the antidepressant response to ketamine at baseline. The alteration remained throughout the 2‐week treatment with multiple ketamine infusions and seemed to reflect the trait‐like features underlying treatment heterogeneity in TRD. By employing an anatomical ROI of the sc/sgACC, the present study also suggests the possible clinical utility of the rsfMRI to predict the treatment response to ketamine in TRD patients.

Penalized model-based clustering of fMRI data

Functional magnetic resonance imaging (fMRI) data have become increasingly available and are useful for describing functional connectivity (FC), the relatedness of neuronal activity in regions of the brain. This FC of the brain provides insight into certain neurodegenerative diseases and psychiatric disorders, and thus is of clinical importance. To help inform physicians regarding patient diagnoses, unsupervised clustering of subjects based on FC is desired, allowing the data to inform us of groupings of patients based on shared features of connectivity. Since heterogeneity in FC is present even between patients within the same group, it is important to allow subject-level differences in connectivity, while still pooling information across patients within each group to describe group-level FC. To this end, we propose a random covariance clustering model (RCCM) to concurrently cluster subjects based on their FC networks, estimate the unique FC networks of each subject, and to infer shared network features. Although current methods exist for estimating FC or clustering subjects using fMRI data, our novel contribution is to cluster or group subjects based on similar FC of the brain while simultaneously providing group- and subject-level FC network estimates. The competitive performance of RCCM relative to other methods is demonstrated through simulations in various settings, achieving both improved clustering of subjects and estimation of FC networks. Utility of the proposed method is demonstrated with application to a resting-state fMRI data set collected on 43 healthy controls and 61 participants diagnosed with schizophrenia.

Brain Networks Connectivity in Mild to Moderate Depression: Resting State fMRI Study with Implications to Nonpharmacological Treatment

Network mechanisms of depression development and especially of improvement from nonpharmacological treatment remain understudied. The current study is aimed at examining brain networks functional connectivity in depressed patients and its dynamics in nonpharmacological treatment. Resting state fMRI data of 21 healthy adults and 51 patients with mild or moderate depression were analyzed with spatial independent component analysis; then, correlations between time series of the components were calculated and compared between-group (study 1). Baseline and repeated-measure data of 14 treated (psychotherapy or fMRI neurofeedback) and 15 untreated depressed participants were similarly analyzed and correlated with changes in depression scores (study 2). Aside from diverse findings, studies 1 and 2 both revealed changes in within-default mode network (DMN) and DMN to executive control network (ECN) connections. Connectivity in one pair, initially lower in depression, decreased in no treatment group and was inversely correlated with Montgomery-Asberg depression score change in treatment group. Weak baseline connectivity in this pair also predicted improvement on Montgomery-Asberg scale in both treatment and no treatment groups. Coupling of another pair, initially stronger in depression, increased in therapy though was unrelated to improvement. The results demonstrate possible role of within-DMN and DMN-ECN functional connectivity in depression treatment and suggest that neural mechanisms of nonpharmacological treatment action may be unrelated to normalization of initially disrupted connectivity.

Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics

Depression is a heterogeneous and etiologically complex psychiatric syndrome, not a unitary disease entity, encompassing a broad spectrum of psychopathology arising from distinct pathophysiological mechanisms. Motivated by a need to advance our understanding of these mechanisms and develop new treatment strategies, there is a renewed interest in investigating the neurobiological basis of heterogeneity in depression and rethinking our approach to diagnosis for research purposes. Large-scale genome-wide association studies have now identified multiple genetic risk variants implicating excitatory neurotransmission and synapse function and underscoring a highly polygenic inheritance pattern that may be another important contributor to heterogeneity in depression. Here, we review various sources of phenotypic heterogeneity and approaches to defining and studying depression subtypes, including symptom-based subtypes and biology-based approaches to decomposing the depression syndrome. We review "dimensional," "categorical," and "hybrid" approaches to parsing phenotypic heterogeneity in depression and defining subtypes using functional neuroimaging. Next, we review recent progress in neuroimaging genetics (correlating neuroimaging patterns of brain function with genetic data) and its potential utility for generating testable hypotheses concerning molecular and circuit-level mechanisms. We discuss how genetic variants and transcriptomic profiles may confer risk for depression by modulating brain structure and function. We conclude by highlighting several promising areas for future research into the neurobiological underpinnings of heterogeneity, including efforts to understand sexually dimorphic mechanisms, the longitudinal dynamics of depressive episodes, and strategies for developing personalized treatments and facilitating clinical decision-making.

Task MRI-Based Functional Brain Network of Major Depression

This chapter will focus on task magnetic resonance imaging (MRI) to understand the biological mechanisms and pathophysiology of brain in major depressive disorder (MDD), which would have minor alterations in the brain function. Therefore, the functional study, such as task MRI functional connectivity, would play a crucial role to explore the brain function in MDD. Different kinds of tasks would determine the alterations in functional connectivity in task MRI studies of MDD. The emotion-related tasks are linked with alterations in anterior cingulate cortex, insula, and default mode network. The emotional memory task is linked with amygdala-hippocampus alterations. The reward-related task would be related to the reward circuit alterations, such as fronto-straital. The cognitive-related tasks would be associated with frontal-related functional connectivity alterations, such as the dorsolateral prefrontal cortex, anterior cingulate cortex, and other frontal regions. The visuo-sensory characteristics of tasks might be associated with the parieto-occipital alterations. The frontolimbic regions might be major components of task MRI-based functional connectivity in MDD. However, different scenarios and tasks would influence the representations of results.

Repeated measurement of implicit self-associations in clinical depression: Psychometric, neural, and computational properties

Implicit self-associations are theorized to be rigidly and excessively negative in affective disorders like depression. Such information processing patterns may be useful as an approach to parsing heterogeneous etiologies, substrates, and treatment outcomes within the broad syndrome of depression. However, there is a lack of sufficient data on the psychometric, neural, and computational substrates of Implicit Association Test (IAT) performance in patient populations. In a treatment-seeking, clinically depressed sample (n = 122), we administered five variants of the IAT-a dominant paradigm used in hundreds of studies of implicit cognition to date-at two repeated sessions (outside and inside a functional MRI scanner). We examined reliability, clinical correlates, and neural and computational substrates of IAT performance. IAT scores showed adequate (.67-.81) split-half reliability and convergent validity with one another and with relevant explicit symptom measures. Test-retest correlations (in vs. outside the functional MRI scanner) were present but modest (.15-.55). In depressed patients, on average, negative implicit self-representations appeared to be weaker or less efficiently processed relative to positive self-representations; elicited greater recruitment of frontoparietal task network regions; and, according to computational modeling of trial-by-trial data, were driven primarily by differential efficiency of information accumulation for negative and positive attributes. Greater degree of discrepancy between implicit and explicit self-worth predicted depression severity. Overall, these IATs show potential utility in understanding heterogeneous substrates of depression but leave substantial room for improvement. The observed clinical, neural, and computational correlates of implicit self-associations offer novel insights into a simple computer-administered task in a clinical population and point toward heterogeneous depression mechanisms and treatment targets. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Biotypes of major depressive disorder: Neuroimaging evidence from resting-state default mode network patterns

BACKGROUND

Major depressive disorder (MDD) is heterogeneous disorder associated with aberrant functional connectivity within the default mode network (DMN). This study focused on data-driven identification and validation of potential DMN-pattern-based MDD subtypes to parse heterogeneity of the disorder.

METHODS

The sample comprised 1397 participants including 690 patients with MDD and 707 healthy controls (HC) registered from multiple sites based on the REST-meta-MDD Project in China. Baseline resting-state functional magnetic resonance imaging (rs-fMRI) data was recorded for each participant. Discriminative features were selected from DMN between patients and HC. Patient subgroups were defined by K-means and principle component analysis in the multi-site datasets and validated in an independent single-site dataset. Statistical significance of resultant clustering were confirmed. Demographic and clinical variables were compared between identified patient subgroups.

RESULTS

Two MDD subgroups with differing functional connectivity profiles of DMN were identified in the multi-site datasets, and relatively stable in different validation samples. The predominant dysfunctional connectivity profiles were detected among superior frontal cortex, ventral medial prefrontal cortex, posterior cingulate cortex and precuneus, whereas one subgroup exhibited increases of connectivity (hyperDMN MDD) and another subgroup showed decreases of connectivity (hypoDMN MDD). The hyperDMN subgroup in the discovery dataset had age-related severity of depressive symptoms. Patient subgroups had comparable demographic and clinical symptom variables.

CONCLUSIONS

Findings suggest the existence of two neural subtypes of MDD associated with different dysfunctional DMN connectivity patterns, which may provide useful evidence for parsing heterogeneity of depression and be valuable to inform the search for personalized treatment strategies.

Association of Childhood Violence Exposure With Adolescent Neural Network Density

Importance

Adverse childhood experiences are a public health issue with negative sequelae that persist throughout life. Current theories suggest that adverse childhood experiences reflect underlying dimensions (eg, violence exposure and social deprivation) with distinct neural mechanisms; however, research findings have been inconsistent, likely owing to variability in how the environment interacts with the brain.

Objective

To examine whether dimensional exposure to childhood adversity is associated with person-specific patterns in adolescent resting-state functional connectivity (rsFC), defined as synchronized activity across brain regions when not engaged in a task.

Design, Setting, and Participants

A sparse network approach in a large sample with substantial representation of understudied, underserved African American youth was used to conduct an observational, population-based longitudinal cohort study. A total of 183 adolescents aged 15 to 17 years from Detroit, Michigan; Toledo, Ohio; and Chicago, Illinois, who participated in the Fragile Families and Child Wellbeing Study were eligible for inclusion. Environmental data from birth to adolescence were collected via telephone and in-person interviews, and neuroimaging data collected at a university lab. The study was conducted from February 1, 1998, to April 26, 2017, and data analysis was performed from January 3, 2019, to May 22, 2020.

Exposures

Composite variables representing violence exposure and social deprivation created from primary caregiver reports on children at ages 3, 5, and 9 years.

Main Outcomes and Measures

Resting-state functional connectivity person-specific network metrics (data-driven subgroup membership, density, and node degree) focused on connectivity among a priori regions of interest in 2 resting-state networks (salience network and default mode) assessed with functional magnetic resonance imaging.

Results

Of the 183 eligible adolescents, 175 individuals (98 girls [56%]) were included in the analysis; mean (SD) age was 15.88 (0.53) years and 127 participants (73%) were African American. Adolescents with high violence exposure were 3.06 times more likely (95% CI, 1.17-8.92) to be in a subgroup characterized by high heterogeneity (few shared connections) and low network density (sparsity). Childhood violence exposure, but not social deprivation, was associated with reduced rsFC density (β = -0.25; 95% CI, -0.41 to -0.05; P = .005), with fewer salience network connections (β = -0.26; 95% CI, -0.43 to -0.08; P = .005) and salience network-default mode connections (β = -0.20; 95% CI, -0.38 to -0.03; P = .02). Violence exposure was associated with node degree of right anterior insula (β = -0.29; 95% CI, -0.47 to -0.12; P = .001) and left inferior parietal lobule (β = -0.26; 95% CI, -0.44 to -0.09; P = .003).

Conclusions and Relevance

The findings of this study suggest that childhood violence exposure is associated with adolescent neural network sparsity. A community-detection algorithm, blinded to child adversity, grouped youth exposed to heightened violence based only on patterns of rsFC. The findings may have implications for understanding how dimensions of adverse childhood experiences impact individualized neural development.

Connections that characterize callousness: Affective features of psychopathy are associated with personalized patterns of resting-state network connectivity

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There was significant heterogeneity in participants’ neural networks.

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Psychopathy associated with default mode-central executive network connectivity.

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Associations were specific to affective psychopathic traits.

Background

Psychopathic traits are hypothesized to be associated with dysfunction across three resting-state networks: the default mode (DMN), salience (SN), and central executive (CEN). Past work has not considered heterogeneity in the neural networks of individuals who display psychopathic traits, which is likely critical in understanding the etiology of psychopathy and could underlie different symptom presentations. Thus, this study maps person-specific resting state networks and links connectivity patterns to features of psychopathy.

Methods

We examined resting-state functional connectivity among eight regions of interest in the DMN, SN, and CEN using a person-specific, sparse network mapping approach (Group Iterative Multiple Model Estimation) in a community sample of 22-year-old men from low-income, urban families (N = 123). Associations were examined between a dimensional measure of psychopathic traits and network density (i.e., number of connections within and between networks).

Results

There was significant heterogeneity in neural networks of participants, which were characterized by person-specific connections and no common connections across the sample. Psychopathic traits, particularly affective traits, were associated with connection density between the DMN and CEN, such that greater density was associated with elevated psychopathic traits.

Discussion

Findings emphasize that neural networks underlying psychopathy are highly individualized. However, individuals with high levels of psychopathic traits had increased density in connections between the DMN and CEN, networks that have been linked with self-referential thinking and executive functioning. Taken together, the results highlight the utility of person-specific approaches in modeling neural networks underlying psychopathic traits, which could ultimately inform personalized prevention and intervention strategies.

BRAIN Initiative: Cutting-Edge Tools and Resources for the Community

The overarching goal of the NIH BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative is to advance the understanding of healthy and diseased brain circuit function through technological innovation. Core principles for this goal include the validation and dissemination of the myriad innovative technologies, tools, methods, and resources emerging from BRAIN-funded research. Innovators, BRAIN funding agencies, and non-Federal partners are working together to develop strategies for making these products usable, available, and accessible to the scientific community. Here, we describe several early strategies for supporting the dissemination of BRAIN technologies. We aim to invigorate a dialogue with the neuroscience research and funding community, interdisciplinary collaborators, and trainees about the existing and future opportunities for cultivating groundbreaking research products into mature, integrated, and adaptable research systems. Along with the accompanying Society for Neuroscience 2019 Mini-Symposium, "BRAIN Initiative: Cutting-Edge Tools and Resources for the Community," we spotlight the work of several BRAIN investigator teams who are making progress toward providing tools, technologies, and services for the neuroscience community. These tools access neural circuits at multiple levels of analysis, from subcellular composition to brain-wide network connectivity, including the following: integrated systems for EM- and florescence-based connectomics, advances in immunolabeling capabilities, and resources for recording and analyzing functional connectivity. Investigators describe how the resources they provide to the community will contribute to achieving the goals of the NIH BRAIN Initiative. Finally, in addition to celebrating the contributions of these BRAIN-funded investigators, the Mini-Symposium will illustrate the broader diversity of BRAIN Initiative investments in cutting-edge technologies and resources.

Causes and Consequences of Diagnostic Heterogeneity in Depression: Paths to Discovering Novel Biological Depression Subtypes

Depression is a highly heterogeneous syndrome that bears only modest correlations with its biological substrates, motivating a renewed interest in rethinking our approach to diagnosing depression for research purposes and new efforts to discover subtypes of depression anchored in biology. Here, we review the major causes of diagnostic heterogeneity in depression, with consideration of both clinical symptoms and behaviors (symptomatology and trajectory of depressive episodes) and biology (genetics and sexually dimorphic factors). Next, we discuss the promise of using data-driven strategies to discover novel subtypes of depression based on functional neuroimaging measures, including dimensional, categorical, and hybrid approaches to parsing diagnostic heterogeneity and understanding its biological basis. The merits of using resting-state functional magnetic resonance imaging functional connectivity techniques for subtyping are considered along with a set of technical challenges and potential solutions. We conclude by identifying promising future directions for defining neurobiologically informed depression subtypes and leveraging them in the future for predicting treatment outcomes and informing clinical decision making.