A Connectome-wide Functional Signature of Transdiagnostic Risk for Mental Illness

Intrinsic functional brain connectivity in adolescent anxiety: Associations with behavioral phenotypes and cross-syndrome network features

BACKGROUND

Considerable research has mapped the human brain networks implicated in anxiety. Yet, less is known about the intrinsic features of the brain implicated in adolescent anxiety and their generalizability to affective and behavioral problems. To this end, we investigated the intrinsic functional connectomes associated with anxiety, their associations with behavioral phenotypes of clinical interest, and the cross-syndrome overlap between the anxiety network and other affective syndromes in an adolescent sample.

METHODS

We used the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) dataset which comprises 203 clinical and healthy adolescents aged 14-17. Participants underwent a resting-state magnetic resonance imaging scan and completed the Child Behavior Checklist (CBCL) and Behavioral Inhibition/Activation System scale. Using network-based statistics, we identified functional networks associated with anxiety and other behavioral syndromes. The anxiety network strengths were then correlated with behavioral measures.

RESULTS

A significant resting-state functional network associated with anxiety was identified, largely characterized by hyperconnectivity between the somatomotor and both the default mode network and subcortical regions. Network strengths derived from the anxiety network were significantly correlated to various behavioral syndromes, including internalizing and externalizing tendencies. Cross-syndrome overlapping edges were also observed in networks of internalizing disorders, more prominently post-traumatic stress syndromes.

CONCLUSIONS

Our results revealed the functional connectomes characteristic of anxiety in adolescents. This resting-state functional network was also predictive of and shared similar features with behavioral syndromes typically associated with anxiety-related disorders, providing evidence that the high comorbidity of anxiety with other clinical conditions may have a neurobiological basis.

Individualized prediction of multi-domain intelligence quotient in bipolar disorder patients using resting-state functional connectivity

BACKGROUND

Although accumulating studies have explored the neural underpinnings of intelligence quotient (IQ) in patients with bipolar disorder (BD), these studies utilized a classification/comparison scheme that emphasized differences between BD and healthy controls at a group level. The present study aimed to infer BD patients' IQ scores at the individual level using a prediction model.

METHODS

We applied a cross-validated Connectome-based Predictive Modeling (CPM) framework using resting-state fMRI functional connectivity (FCs) to predict BD patients' IQ scores, including verbal IQ (VIQ), performance IQ (PIQ), and full-scale IQ (FSIQ). For each IQ domain, we selected the FCs that contributed to the predictions and described their distribution across eight widely-recognized functional networks. Moreover, we further explored the overlapping patterns of the contributed FCs for different IQ domains.

RESULTS

The CPM achieved statistically significant prediction performance for three IQ domains in BD patients. Regarding the contributed FCs, we observed a widespread distribution of internetwork FCs across somatomotor, visual, dorsal attention, and ventral attention networks, demonstrating their correspondence with aberrant FCs correlated to cognition deficits in BD patients. A convergent pattern in terms of contributed FCs for different IQ domains was observed, as evidenced by the shared-FCs with a leftward hemispheric dominance.

CONCLUSIONS

The present study preliminarily explored the feasibility of inferring individual IQ scores in BD patients using the FCs-based CPM framework. It is a step toward the development of applicable techniques for quantitative and objective cognitive assessment in BD patients and contributes novel insights into understanding the complex neural mechanisms underlying different IQ domains.

Common and disease-specific patterns of functional connectivity and topology alterations across unipolar and bipolar disorder during depressive episodes: a transdiagnostic study

Bipolar disorder (BD) and unipolar depression (UD) are defined as distinct diagnostic categories. However, due to some common clinical and pathophysiological features, it is a clinical challenge to distinguish them, especially in the early stages of BD. This study aimed to explore the common and disease-specific connectivity patterns in BD and UD. This study was constructed over 181 BD, 265 UD and 204 healthy controls. In addition, an independent group of 90 patients initially diagnosed with major depressive disorder at the baseline and then transferred to BD with the episodes of mania/hypomania during follow-up, was identified as initial depressive episode BD (IDE-BD). All participants completed resting-state functional magnetic resonance imaging (R-fMRI) at recruitment. Both network-based analysis and graph theory analysis were applied. Both BD and UD showed decreased functional connectivity (FC) in the whole brain network. The shared aberrant network across groups of patients with depressive episode (BD, IDE-BD and UD) mainly involves the visual network (VN), somatomotor networks (SMN) and default mode network (DMN). Analysis of the topological properties over the three networks showed that decreased clustering coefficient was found in BD, IDE-BD and UD, however, decreased shortest path length and increased global efficiency were only found in BD and IDE-BD but not in UD. The study indicate that VN, SMN, and DMN, which involve stimuli reception and abstraction, emotion processing, and guiding external movements, are common abnormalities in affective disorders. The network separation dysfunction in these networks is shared by BD and UD, however, the network integration dysfunction is specific to BD. The aberrant network integration functions in BD and IDE-BD might be valuable diagnostic biomarkers.

A systematic review of structural neuroimaging markers of psychotherapeutic and pharmacological treatment for obsessive-compulsive disorder

Identifying individual difference factors associated with treatment response and putative mechanisms of therapeutic change may improve treatment for Obsessive Compulsive Disorder (OCD). Our systematic review of structural neuroimaging markers (i.e., morphometry, structural connectivity) of psychotherapy and medication treatment response for OCD identified 26 eligible publications from 20 studies (average study total n=54 ± 41.6 [range: 11-175]; OCD group n=29 ± 19) in child, adolescent, and adult samples evaluating baseline brain structure correlates of treatment response as well as treatment-related changes in brain structure. Findings were inconsistent across studies; significant associations within the anterior cingulate cortex (3/5 regional, 2/8 whole brain studies) and orbitofrontal cortex (5/10 regional, 2/7 whole brain studies) were most common, but laterality and directionality were not always consistent. Structural neuroimaging markers of treatment response do not currently hold clinical utility. Given increasing evidence that associations between complex behavior and brain structure are characterized by small, but potentially meaningful, effects, much larger samples are likely needed. Multivariate approaches (e.g., machine learning) may also improve the clinical predictive utility of neuroimaging data.

Mapping the neurodevelopmental predictors of psychopathology

Neuroimaging research has uncovered a multitude of neural abnormalities associated with psychopathology, but few prediction-based studies have been conducted during adolescence, and even fewer used neurobiological features that were extracted across multiple neuroimaging modalities. This gap in the literature is critical, as deriving accurate brain-based models of psychopathology is an essential step towards understanding key neural mechanisms and identifying high-risk individuals. As such, we trained adaptive tree-boosting algorithms on multimodal neuroimaging features from the Lifespan Human Connectome Developmental (HCP-D) sample that contained 956 participants between the ages of 8 to 22 years old. Our feature space consisted of 1037 anatomical, 1090 functional, and 192 diffusion MRI features, which were used to derive models that separately predicted internalizing symptoms, externalizing symptoms, and the general psychopathology factor. We found that multimodal models were the most accurate, but all brain-based models of psychopathology yielded out-of-sample predictions that were weakly correlated with actual symptoms (r2 < 0.15). White matter microstructural properties, including orientation dispersion indices and intracellular volume fractions, were the most predictive of general psychopathology, followed by cortical thickness and functional connectivity. Spatially, the most predictive features of general psychopathology were primarily localized within the default mode and dorsal attention networks. These results were mostly consistent across all dimensions of psychopathology, except orientation dispersion indices and the default mode network were not as heavily weighted in the prediction of internalizing and externalizing symptoms. Taken with prior literature, it appears that neurobiological features are an important part of the equation for predicting psychopathology but relying exclusively on neural markers is clearly not sufficient, especially among adolescent samples with subclinical symptoms. Consequently, risk factor models of psychopathology may benefit from incorporating additional sources of information that have also been shown to explain individual differences, such as psychosocial factors, environmental stressors, and genetic vulnerabilities.

A comparative machine learning study of schizophrenia biomarkers derived from functional connectivity

Functional connectivity holds promise as a biomarker of schizophrenia. Yet, the high dimensionality of predictive models trained on functional connectomes, combined with small sample sizes in clinical research, increases the risk of overfitting. Recently, low-dimensional representations of the connectome such as macroscale cortical gradients and gradient dispersion have been proposed, with studies noting consistent gradient and dispersion differences in psychiatric conditions. However, it is unknown which of these derived measures has the highest predictive capacity and how they compare to raw functional connectivity specifically in the case of schizophrenia. Our study evaluates which connectome features derived from resting state functional MRI - functional connectivity, gradients, or gradient dispersion - best identify schizophrenia. To this end, we leveraged data of 936 individuals from three large open-access datasets: COBRE, LA5c, and SRPBS-1600. We developed a pipeline which allows us to aggregate over a million different features and assess their predictive potential in a single, computationally efficient experiment. We selected top 1% of features with the largest permutation feature importance and trained 13 classifiers on them using 10-fold cross-validation. Our findings indicate that functional connectivity outperforms its low-dimensional derivatives such as cortical gradients and gradient dispersion in identifying schizophrenia (Mann-Whitney test conducted on test accuracy: connectivity vs. 1st gradient: U = 142, p < 0.003; connectivity vs. neighborhood dispersion: U = 141, p = 0.004). Additionally, we demonstrated that the edges which contribute the most to classification performance are the ones connecting primary sensory regions. Functional connectivity within the primary sensory regions showed the highest discrimination capabilities between subjects with schizophrenia and neurotypical controls. These findings along with the feature selection pipeline proposed here will facilitate future inquiries into the prediction of schizophrenia subtypes and transdiagnostic phenomena.

Interpretable and integrative deep learning for discovering brain-behaviour associations

Recent advances highlight the limitations of classification strategies in machine learning that rely on a single data source for understanding, diagnosing and predicting psychiatric syndromes. Moreover, approaches based solely on clinician labels often fail to capture the complexity and variability of these conditions. Recent research underlines the importance of considering multiple dimensions that span across different psychiatric syndromes. These developments have led to more comprehensive approaches to studying psychiatric conditions that incorporate diverse data sources such as imaging, genetics, and symptom reports. Multi-view unsupervised learning frameworks, particularly deep learning models, present promising solutions for integrating and analysing complex datasets. Such models contain generative capabilities which facilitate the exploration of relationships between different data views. In this study, we propose a robust framework for interpreting these models that combines digital avatars with stability selection to assess these relationships. We apply this framework to the Healthy Brain Network cohort which includes clinical behavioural scores and brain imaging features, uncovering a consistent set of brain-behaviour interactions. These associations link cortical measurements obtained from structural MRI with clinical reports evaluating psychiatric symptoms. Our framework effectively identifies relevant and stable associations, even with incomplete datasets, while isolating variability of interest from confounding factors.

Hierarchical Neurocognitive Model of Externalizing and Internalizing Comorbidity

Mounting evidence suggests hierarchical psychopathology factors underlying psychiatric comorbidity. However, the exact neurobiological characterizations of these multilevel factors remain elusive. In this study, leveraging the brain-behavior predictive framework with a 10-year longitudinal imaging-genetic cohort (IMAGEN, ages 14, 19 and 23, N = 1,750), we constructed two neural factors underlying externalizing and internalizing symptoms, which were reproducible across six clinical and population-based datasets (ABCD, STRATIFY/ESTRA, ABIDE II, ADHD-200 and XiNan, from age 10 to age 36, N = 3,765). These two neural factors exhibit distinct neural configurations: hyperconnectivity in impulsivity-related circuits for the externalizing symptoms and hypoconnectivity in goal-directed circuits for the internalizing symptoms. Both factors also differ in their cognitive-behavior relevance, genetic substrates and developmental profiles. Together with previous studies, these findings propose a hierarchical neurocognitive spectral model of comorbid mental illnesses from preadolescence to adulthood: a general neuropsychopathological (NP) factor (manifested as inefficient executive control) and two stratified factors for externalizing (deficient inhibition control) and internalizing (impaired goal-directed function) symptoms, respectively. These holistic insights are crucial for the development of stratified therapeutic interventions for mental disorders.

Transdiagnostic features of inflammatory markers and executive function across psychiatric disorders

Executive dysfunction and dysregulated inflammation are found in patients with different psychiatric disorders. However, whether there are different associations between inflammatory markers and executive performance in patients with different psychiatric diagnoses is unknown. Our study aims were (1) to compare peripheral cytokine expression and executive function in patients with bipolar disorder (BD), substance use disorder (SUD), and schizophrenia (SCZ), and in healthy controls (HC) and (2) to explore the potential association between inflammatory cytokines and executive function in different patient groups and HC. Participants with BD (n = 816), SUD (opioid use disorder and/or methamphetamine use disorder, n = 518), SCZ (n = 146), and HC (n = 186) were recruited. Plasma cytokine levels [tumor necrosis factor (TNF)-α, interleukin (IL)-8 (only measured in 8 SCZ patients), transforming growth factor (TGF)-β1 (not measured in SCZ patients)], C-reactive protein (CRP), brain-derived neurotrophic factor (BDNF) levels, and executive function [Wisconsin Card Sorting Test (WCST) and Continuous Performance Test (CPT)] were assessed. We found that all patient groups had worse executive performance and higher inflammatory cytokine levels than the HC group. SCZ patients had the worst executive performance, while SUD patients had the highest inflammatory cytokine levels. Increased plasma IL-8, CRP, and TNF-α levels were specifically associated with worse executive function in BD, SUD, and SCZ patients (P = 0.009, 0.04, and 0.03, respectively). We concluded that dysregulated inflammation might be a transdiagnostic feature among different psychiatric disorders and associated with executive dysfunction. Further studies to investigate the causal relationship and mechanisms between inflammation and executive dysfunction may be needed.

Multimodal neural correlates of childhood psychopathology

Complex structural and functional changes occurring in typical and atypical development necessitate multidimensional approaches to better understand the risk of developing psychopathology. Here, we simultaneously examined structural and functional brain network patterns in relation to dimensions of psychopathology in the Adolescent Brain Cognitive Development (ABCD) dataset. Several components were identified, recapitulating the psychopathology hierarchy, with the general psychopathology (p) factor explaining most covariance with multimodal imaging features, while the internalizing, externalizing, and neurodevelopmental dimensions were each associated with distinct morphological and functional connectivity signatures. Connectivity signatures associated with the p factor and neurodevelopmental dimensions followed the sensory-to-transmodal axis of cortical organization, which is related to the emergence of complex cognition and risk for psychopathology. Results were consistent in two separate data subsamples and robust to variations in analytical parameters. Although model parameters yielded statistically significant brain-behavior associations in unseen data, generalizability of the model was rather limited for all three latent components (r change from within- to out-of-sample statistics: LC1within = 0.36, LC1out = 0.03; LC2within = 0.34, LC2out = 0.05; LC3within = 0.35, LC3out = 0.07). Our findings help in better understanding biological mechanisms underpinning dimensions of psychopathology, and could provide brain-based vulnerability markers.

Deficient salience and default mode functional integration in high worry-proneness subject: a connectome-wide association study

Worry has been conceptualized as a relatively uncontrollable chain of thought that increases the risk of mental problems, such as anxiety disorders. Here, we examined the link between individual variation in the functional connectome and worry proneness, which remains unclear. A total of 32 high worry-proneness (HWP) subjects and 25 low worry-proneness (LWP) subjects were recruited. We conducted multivariate distance-based matrix regression to identify phenotypic relationships in high-dimensional brain resting-state functional connectivity data from HWP subjects. Multiple hub regions, including key brain nodes of the salience network (SN) and default mode network (DMN), were identified in HWP subjects. Follow-up analyses revealed that a high worry-proneness score was dominated by functional connectivity between the SN and the DMN. Moreover, HWP subjects showed hypoconnectivity between the cerebellum and the SN and DMN compared with LWP subjects. This cross-sectional study could not fully measure the causal relationships between changes in functional networks and worry proneness in healthy subjects. Functional changes in the cerebellum-cortical region might affect the modulation of external stimuli processing. Together, our results provide new insight into the role of key networks, including the SN, DMN and cerebellum, in understanding the potential mechanism underlying the high worry dimension in healthy subjects.

Generalizable and replicable brain-based predictions of cognitive functioning across common psychiatric illness

A primary aim of computational psychiatry is to establish predictive models linking individual differences in brain functioning with symptoms. In particular, cognitive impairments are transdiagnostic, treatment resistant, and associated with poor outcomes. Recent work suggests that thousands of participants may be necessary for the accurate and reliable prediction of cognition, questioning the utility of most patient collection efforts. Here, using a transfer learning framework, we train a model on functional neuroimaging data from the UK Biobank to predict cognitive functioning in three transdiagnostic samples (ns = 101 to 224). We demonstrate prediction performance in all three samples comparable to that reported in larger prediction studies and a boost of up to 116% relative to classical models trained directly in the smaller samples. Critically, the model generalizes across datasets, maintaining performance when trained and tested across independent samples. This work establishes that predictive models derived in large population-level datasets can boost the prediction of cognition across clinical studies.

The hierarchical taxonomy of psychopathology and the search for neurobiological substrates of mental illness: A systematic review and roadmap for future research

Understanding the neurobiological mechanisms involved in psychopathology has been hindered by the limitations of categorical nosologies. The Hierarchical Taxonomy of Psychopathology (HiTOP) is an alternative dimensional system for characterizing psychopathology, derived from quantitative studies of covariation among diagnoses and symptoms. HiTOP provides more promising targets for clinical neuroscience than traditional psychiatric diagnoses and can facilitate cumulative integration of existing research. We systematically reviewed 164 human neuroimaging studies with sample sizes of 194 or greater that have investigated dimensions of psychopathology classified within HiTOP. Replicated results were identified for constructs at five different levels of the hierarchy, including the overarching p-factor, the externalizing superspectrum, the thought disorder and internalizing spectra, the distress subfactor, and the depression symptom dimension. Our review highlights the potential of dimensional clinical neuroscience research and the usefulness of HiTOP while also suggesting limitations of existing work in this relatively young field. We discuss how HiTOP can be integrated synergistically with neuroscience-oriented, transdiagnostic frameworks developed by the National Institutes of Health, including the Research Domain Criteria, Addictions Neuroclinical Assessment, and the National Institute on Drug Abuse's Phenotyping Assessment Battery, and how researchers can use HiTOP to accelerate clinical neuroscience research in humans and other species. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Functional brain networks in clinical high-risk for bipolar disorder and psychosis

Abnormal connectivity in the brain has been linked to the pathophysiology of severe mental illnesses, including bipolar disorder and schizophrenia. The current study aimed to investigate large-scale functional networks and global network metrics in clinical high-risk for bipolardisorder (CHR-BD, n = 25), clinical high-risk for psychosis (CHR-P, n = 30), and healthy controls (HCs, n = 19). Help-seeking youth at CHR-BD and CHR-P were recruited from the early intervention program at Dokuz Eylul University, Izmir, Turkey. Resting-state functional magnetic resonance imaging scans were obtained from youth at CHR-BD, CHR-P, and HCs. Graph theoretical analysis and network-based statistics were employed to construct and examine the topological features of the whole-brain metrics and large-scale functional networks. Connectivity was increased (i) between the visual and default mode, (ii) between the visual and salience, (iii) between the visual and cingulo-opercular networks, and decreased (i) within the default mode and (ii) between the default mode and fronto-parietal networks in the CHR-P compared to HCs. Decreased global efficiency was found in CHR-P compared to CHR-BD. Functional networks were not different between CHR-BD and HCs. Global efficiency was negatively correlated with subthreshold positive symptoms and thought disorder in the high-risk groups. The current results suggest disrupted networks in CHR-P compared to HCs and CHR-BD. Moreover, transdiagnostic psychosis features are linked to functional brain networks in the at-risk groups. However, given the small, medicated sample, results are exploratory and hypothesis-generating.

Polygenic Risk Underlies Youth Psychopathology and Personalized Functional Brain Network Topography

Importance

Functional brain networks are associated with both behavior and genetic factors. To uncover clinically translatable mechanisms of psychopathology, it is critical to define how the spatial organization of these networks relates to genetic risk during development.

Objective

To determine the relationship between transdiagnostic polygenic risk scores (PRSs), personalized functional brain networks (PFNs), and overall psychopathology (p-factor) during early adolescence.

Design

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing longitudinal cohort study of 21 collection sites across the United States. Here, we conduct a cross-sectional analysis of ABCD baseline data, collected 2017-2018.

Setting

The ABCD Study ® is a multi-site community-based study.

Participants

The sample is largely recruited through school systems. Exclusion criteria included severe sensory, intellectual, medical, or neurological issues that interfere with protocol and scanner contraindications. Split-half subsets were used for cross-validation, matched on age, ethnicity, family structure, handedness, parental education, site, sex, and anesthesia exposure.

Exposures

Polygenic risk scores of transdiagnostic genetic factors F1 (PRS-F1) and F2 (PRS-F2) derived from adults in Psychiatric Genomic Consortium and UK Biobanks datasets. PRS-F1 indexes liability for common psychiatric symptoms and disorders related to mood disturbance; PRS-F2 indexes liability for rarer forms of mental illness characterized by mania and psychosis.

Main Outcomes and Measures

(1) P-factor derived from bifactor models of youth- and parent-reported mental health assessments. (2) Person-specific functional brain network topography derived from functional magnetic resonance imaging (fMRI) scans.

Results

Total participants included 11,873 youths ages 9-10 years old; 5,678 (47.8%) were female, and the mean (SD) age was 9.92 (0.62) years. PFN topography was found to be heritable (N=7,459, 57.06% of vertices h 2 p FDR <0.05, mean h 2 =0.35). PRS-F1 was associated with p-factor (N=5,815, r=0.12, 95% CI [0.09-0.15], p<0.001). Interindividual differences in functional network topography were associated with p-factor (N=7,459, mean r=0.12), PRS-F1 (N=3,982, mean r=0.05), and PRS-F2 (N=3,982, mean r=0.08). Cortical maps of p-factor and PRS-F1 regression coefficients were highly correlated (r=0.7, p=0.003).

Conclusions and Relevance

Polygenic risk for transdiagnostic adulthood psychopathology is associated with both p-factor and heritable PFN topography during early adolescence. These results advance our understanding of the developmental drivers of psychopathology.

Family Environment Modulates Linkage of Transdiagnostic Psychiatric Phenotypes and Dissociable Brain Features in the Developing Brain

BACKGROUND

Family environment has long been known for shaping brain function and psychiatric phenotypes, especially during childhood and adolescence. Accumulating neuroimaging evidence suggests that across different psychiatric disorders, common phenotypes may share common neural bases, indicating latent brain-behavior relationships beyond diagnostic categories. However, the influence of family environment on the brain-behavior relationship from a transdiagnostic perspective remains unknown.

METHODS

We included a community-based sample of 699 participants (ages 5-22 years) and applied partial least squares regression analysis to determine latent brain-behavior relationships from whole-brain functional connectivity and comprehensive phenotypic measures. Comparisons were made between diagnostic and nondiagnostic groups to help interpret the latent brain-behavior relationships. A moderation model was introduced to examine the potential moderating role of family factors in the estimated brain-behavior associations.

RESULTS

Four significant latent brain-behavior pairs were identified that reflected the relationship of dissociable brain network and general behavioral problems, cognitive and language skills, externalizing problems, and social dysfunction, respectively. The group comparisons exhibited interpretable variations across different diagnostic groups. A warm family environment was found to moderate the brain-behavior relationship of core symptoms in internalizing disorders. However, in neurodevelopmental disorders, family factors were not found to moderate the brain-behavior relationship of core symptoms, but they were found to affect the brain-behavior relationship in other domains.

CONCLUSIONS

Our findings leveraged a transdiagnostic analysis to investigate the moderating effects of family factors on brain-behavior associations, emphasizing the different roles that family factors play during this developmental period across distinct diagnostic groups.

Posterior Cerebellar Resting-State Functional Hypoconnectivity: A Neural Marker of Schizophrenia Across Different Stages of Treatment Response

BACKGROUND

Identifying stable and consistent resting-state functional connectivity patterns across illness trajectories has the potential to be considered fundamental to the pathophysiology of schizophrenia. We aimed to identify consistent resting-state functional connectivity patterns across heterogeneous schizophrenia groups defined based on treatment response.

METHODS

In phase 1, we used a cross-sectional case-control design to characterize and compare stable independent component networks from resting-state functional magnetic resonance imaging scans of antipsychotic-naïve participants with first-episode schizophrenia (n = 54) and healthy participants (n = 43); we also examined associations with symptoms, cognition, and disability. In phase 2, we examined the stability (and replicability) of our phase 1 results in 4 groups (N = 105) representing a cross-sequential gradation of schizophrenia based on treatment response: risperidone responders, clozapine responders, clozapine nonresponders, and clozapine nonresponders following electroconvulsive therapy. Hypothesis-free whole-brain within- and between-network connectivity were examined.

RESULTS

Phase 1 identified posterior and anterior cerebellar hypoconnectivity and limbic hyperconnectivity in schizophrenia at a familywise error rate-corrected cluster significance threshold of p < .01. These network aberrations had unique associations with positive symptoms, cognition, and disability. During phase 2, we replicated the phase 1 results while comparing each of the 4 schizophrenia groups to the healthy participants. The participants in 2 longitudinal subdatasets did not demonstrate a significant change in these network aberrations following risperidone or electroconvulsive therapy. Posterior cerebellar hypoconnectivity (with thalamus and cingulate) emerged as the most consistent finding; it was replicated across different stages of treatment response (Cohen's d range -0.95 to -1.44), reproduced using different preprocessing techniques, and not confounded by educational attainment.

CONCLUSIONS

Posterior cerebellar-thalamo-cingulate hypoconnectivity is a consistent and stable state-independent neural marker of schizophrenia.

Childhood maltreatment and transdiagnostic connectivity of the default-mode network: The importance of duration of exposure

Childhood maltreatment (CM) has been demonstrated to be associated with changes in resting-state functional connectivity of the default-mode network (DMN) across various mental disorders. Growing evidence regarding severity of CM is available but transdiagnostic research considering the role of both severity and duration of CM for DMN connectivity at rest is still scarce. We recruited a sample of participants with varying levels of CM suffering from three disorders in which a history of CM is frequently found, namely, post-traumatic stress disorder, major depressive disorder, or somatic symptom disorder, as well as healthy volunteers to examine DMN connectivity in a transdiagnostic sample. We expected to find changes in inter-network connectivity of the DMN related to higher self-reported levels of CM severity and duration. Resting-state functional magnetic resonance imaging scans of 128 participants were analyzed focusing on regions of interest (ROI-to-ROI approach) and whole-brain Seed-to-Voxel analyses with retrospectively assessed CM as predictor in a regression model. Changes in connectivity between nodes of the DMN and the visual network were identified to be associated with CM duration but not severity. CM duration showed associations with increased connectivity of the precuneus and visual regions, as well as sensory-motor regions. The observed changes in connectivity could be interpreted as an impairment of information transfer between the transmodal DMN and unimodal visual and sensory-motor regions with impairment increasing with duration of exposure to CM.

MULTIMODAL NEURAL CORRELATES OF CHILDHOOD PSYCHOPATHOLOGY

Complex structural and functional changes occurring in typical and atypical development necessitate multidimensional approaches to better understand the risk of developing psychopathology. Here, we simultaneously examined structural and functional brain network patterns in relation to dimensions of psychopathology in the Adolescent Brain Cognitive Development dataset. Several components were identified, recapitulating the psychopathology hierarchy, with the general psychopathology (p) factor explaining most covariance with multimodal imaging features, while the internalizing, externalizing, and neurodevelopmental dimensions were each associated with distinct morphological and functional connectivity signatures. Connectivity signatures associated with the p factor and neurodevelopmental dimensions followed the sensory-to-transmodal axis of cortical organization, which is related to the emergence of complex cognition and risk for psychopathology. Results were consistent in two separate data subsamples, supporting generalizability, and robust to variations in analytical parameters. Our findings help in better understanding biological mechanisms underpinning dimensions of psychopathology, and could provide brain-based vulnerability markers.

A connectome-wide association study of altered functional connectivity in schizophrenia based on resting-state fMRI

BACKGROUND

Aberrant resting-state functional connectivity is a neuropathological feature of schizophrenia (SCZ). Prior investigations into functional connectivity abnormalities have primarily employed seed-based connectivity analysis, necessitating predefined seed locations. To address this limitation, a data-driven multivariate method known as connectome-wide association study (CWAS) has been proposed for exploring whole-brain functional connectivity.

METHODS

We conducted a CWAS analysis involving 46 patients with SCZ and 40 age- and sex-matched healthy controls. Multivariate distance matrix regression (MDMR) was utilized to identify key nodes in the brain. Subsequently, we conducted a follow-up seed-based connectivity analysis to elucidate specific connectivity patterns between regions of interest (ROIs). Additionally, we explored the spatial correlation between changes in functional connectivity and underlying molecular architectures by examining correlations between neurotransmitter/transporter distribution densities and functional connectivity.

RESULTS

MDMR revealed the right medial frontal gyrus and the left calcarine sulcus as two key nodes. Follow-up analysis unveiled hypoconnectivity between the right medial frontal superior gyrus and the right fusiform gyrus, as well as hypoconnectivity between the left calcarine sulcus and the right lingual gyrus in SCZ. Notably, a significant association between functional connectivity strength and positive symptom severity was identified. Furthermore, altered functional connectivity patterns suggested potential dysfunctions in the dopamine, serotonin, and gamma-aminobutyric acid systems.

CONCLUSIONS

This study elucidated reduced functional connectivity both within and between the medial frontal regions and the occipital cortex in patients with SCZ. Moreover, it indicated potential alterations in molecular architecture, thereby expanding current knowledge regarding neurobiological changes associated with SCZ.

Adaptive-to-maladaptive gradient of emotion regulation tendencies are embedded in the functional-structural hybrid connectome

BACKGROUND

Emotion regulation tendencies are well-known transdiagnostic markers of psychopathology, but their neurobiological foundations have mostly been examined within the theoretical framework of cortical-subcortical interactions.

METHODS

We explored the connectome-wide neural correlates of emotion regulation tendencies using functional and diffusion magnetic resonance images of healthy young adults (N = 99; age 20-30; 28 females). We first tested the importance of considering both the functional and structural connectome through intersubject representational similarity analyses. Then, we employed a canonical correlation analysis between the functional-structural hybrid connectome and 23 emotion regulation strategies. Lastly, we sought to externally validate the results on a transdiagnostic adolescent sample (N = 93; age 11-19; 34 females).

RESULTS

First, interindividual similarity of emotion regulation profiles was significantly correlated with interindividual similarity of the functional-structural hybrid connectome, more so than either the functional or structural connectome. Canonical correlation analysis revealed that an adaptive-to-maladaptive gradient of emotion regulation tendencies mapped onto a specific configuration of covariance within the functional-structural hybrid connectome, which primarily involved functional connections in the motor network and the visual networks as well as structural connections in the default mode network and the subcortical-cerebellar network. In the transdiagnostic adolescent dataset, stronger functional signatures of the found network were associated with higher general positive affect through more frequent use of adaptive coping strategies.

CONCLUSIONS

Taken together, our study illustrates a gradient of emotion regulation tendencies that is best captured when simultaneously considering the functional and structural connections across the whole brain.

Brain Functional Connectome Defines a Transdiagnostic Dimension Shared by Cognitive Function and Psychopathology in Preadolescents

BACKGROUND

Cognitive function and general psychopathology are two important classes of human behavior dimensions that are individually related to mental disorders across diagnostic categories. However, whether these two transdiagnostic dimensions are linked to common or distinct brain networks that convey resilience or risk for the development of psychiatric disorders remains unclear.

METHODS

The current study is a longitudinal investigation with 11,875 youths from the Adolescent Brain Cognitive Development (ABCD) Study at ages 9 to 10 years at the onset of the study. A machine learning approach based on canonical correlation analysis was used to identify latent dimensional associations of the resting-state functional connectome with multidomain behavioral assessments including cognitive functions and psychopathological measures. For the latent resting-state functional connectivity factor showing a robust behavioral association, its ability to predict psychiatric disorders was assessed using 2-year follow-up data, and its genetic association was evaluated using twin data from the same cohort.

RESULTS

A latent functional connectome pattern was identified that showed a strong and generalizable association with the multidomain behavioral assessments (5-fold cross-validation: ρ = 0.68-0.73 for the training set [n = 5096]; ρ = 0.56-0.58 for the test set [n = 1476]). This functional connectome pattern was highly heritable (h2 = 74.42%, 95% CI: 56.76%-85.42%), exhibited a dose-response relationship with the cumulative number of psychiatric disorders assessed concurrently and at 2 years post-magnetic resonance imaging scan, and predicted the transition of diagnosis across disorders over the 2-year follow-up period.

CONCLUSIONS

These findings provide preliminary evidence for a transdiagnostic connectome-based measure that underlies individual differences in the development of psychiatric disorders during early adolescence.

Label-based meta-analysis of functional brain dysconnectivity across mood and psychotic disorders

BACKGROUND

Resting-state functional magnetic resonance imaging (rsfMRI) studies have revealed patterns of functional brain dysconnectivity in psychiatric disorders such as major depression disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ). Although these disorders have been mostly studied in isolation, there is mounting evidence of shared neurobiological alterations across them.

METHODS

To uncover the nature of the relatedness between these psychiatric disorders, we conducted an innovative meta-analysis of dysconnectivity findings reported separately in MDD, BD and SZ. Rather than relying on a classical voxel level coordinate-based approach, our procedure extracted relevant neuroanatomical labels from text data and examined findings at the whole brain network level. Data were drawn from 428 rsfMRI studies investigating MDD (158 studies, 7429 patients/7414 controls), BD (81 studies, 3330 patients/4096 patients) and/or SZ (223 studies, 11,168 patients/11,754 controls). Permutation testing revealed commonalities and differences in hypoconnectivity and hyperconnectivity patterns across disorders.

RESULTS

Hypoconnectivity and hyperconnectivity patterns of higher-order cognitive (default-mode, fronto-parietal, cingulo-opercular) networks were similarly observed across the three disorders. By contrast, dysconnectivity of lower-order (somatomotor, visual, auditory) networks in some cases differed between disorders, notably dissociating SZ from BD and MDD.

CONCLUSIONS

Findings suggest that functional brain dysconnectivity of higher-order cognitive networks is largely transdiagnostic in nature while that of lower-order networks may best discriminate between mood and psychotic disorders, thus emphasizing the relevance of motor and sensory networks to psychiatric neuroscience.

No relations between executive functions and dimensional models of psychopathology or is time the missing link?

Impaired executive functions (EF) have been found within various mental disorders (e.g., attention deficit hyperactivity disorder, autism spectrum disorder, schizophrenia spectrum disorders) as described in DSM-5. However, although impaired EF has been observed within several categories of mental disorders, empirical research on direct relations between EF and broader dimension of psychopathology is still scarce. Therefore, in the current investigation we examined relations between three EF performance tasks and self-reported dimensions of psychopathology (i.e., the internalizing, externalizing, and thought disorder spectra) in a combined dataset of patients with a broad range of mental disorders (N = 440). Despite previously reported results that indicate impaired EF in several categories of mental disorders, in this study no direct relations were found between EF performance tasks and self-reported broader dimensions of psychopathology. These results indicate that relations between EF and psychopathology could be more complex and non-linear in nature. We evaluate the need for integration of EF and dimensional models of psychopathology and reflect on EF as a possible transdiagnostic factor of psychopathology.

Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes

BACKGROUND

Major depressive disorder (MDD) is complicated by population heterogeneity, motivating the investigation of biotypes through imaging-derived phenotypes. However, neuromorphic heterogeneity in MDD remains unclear, and how the correlated gene expression (CGE) connectome constrains these neuromorphic anomalies in MDD biotypes has not yet been studied.

METHODS

Here, we related cortical thickness deviations in MDD biotypes to a pattern of CGE connectome. Cortical thickness was estimated from 3-dimensional T1-weighted magnetic resonance images in 2 independent cohorts (discovery cohort: N = 425; replication cohort: N = 217). The transcriptional activity was measured according to Allen Human Brain Atlas. A density peak-based clustering algorithm was used to identify MDD biotypes.

RESULTS

We found that patients with MDD were clustered into 2 replicated biotypes based on single-patient regional deviations from healthy control participants across 2 datasets. Biotype 1 mainly exhibited cortical thinning across the brain, whereas biotype 2 mainly showed cortical thickening in the brain. Using brainwide gene expression data, we found that deviations of transcriptionally connected neighbors predicted regional deviation for both biotypes. Furthermore, putative CGE-informed epicenters of biotype 1 were concentrated on the cognitive control circuit, whereas biotype 2 epicenters were located in the social perception circuit. The patterns of epicenter likelihood were separately associated with depression- and anxiety-response maps, suggesting that epicenters of MDD biotypes may be associated with clinical efficacies.

CONCLUSIONS

Our findings linked the CGE connectome and neuromorphic deviations to identify distinct epicenters in MDD biotypes, providing insight into how microscale gene expressions informed MDD biotypes.

Testing the sensitivity of diagnosis-derived patterns in functional brain networks to symptom burden in a Norwegian youth sample

Aberrant brain network development represents a putative aetiological component in mental disorders, which typically emerge during childhood and adolescence. Previous studies have identified resting-state functional connectivity (RSFC) patterns reflecting psychopathology, but the generalisability to other samples and politico-cultural contexts has not been established. We investigated whether a previously identified cross-diagnostic case-control and autism spectrum disorder (ASD)-specific pattern of RSFC (discovery sample; aged 5-21 from New York City, USA; n = 1666) could be validated in a Norwegian convenience-based youth sample (validation sample; aged 9-25 from Oslo, Norway; n = 531). As a test of generalisability, we investigated if these diagnosis-derived RSFC patterns were sensitive to levels of symptom burden in both samples, based on an independent measure of symptom burden. Both the cross-diagnostic and ASD-specific RSFC pattern were validated across samples. Connectivity patterns were significantly associated with thematically appropriate symptom dimensions in the discovery sample. In the validation sample, the ASD-specific RSFC pattern showed a weak, inverse relationship with symptoms of conduct problems, hyperactivity and prosociality, while the cross-diagnostic pattern was not significantly linked to symptoms. Diagnosis-derived connectivity patterns in a developmental clinical US sample were validated in a convenience sample of Norwegian youth, however, they were not associated with mental health symptoms.

Transdiagnostic biomarkers of mental illness across the lifespan: A systematic review examining the genetic and neural correlates of latent transdiagnostic dimensions of psychopathology in the general population

This systematic review synthesizes evidence from research investigating the biological correlates of latent transdiagnostic dimensions of psychopathology (e.g., the p-factor, internalizing, externalizing) across the lifespan. Eligibility criteria captured genomic and neuroimaging studies investigating general and/or specific dimensions in general population samples across all age groups. MEDLINE, Embase, and PsycINFO were searched for relevant studies published up to March 2023 and 46 studies were selected for inclusion. The results revealed several biological correlates consistently associated with transdiagnostic dimensions of psychopathology, including polygenic scores for ADHD and neuroticism, global surface area and global gray matter volume. Shared and unique associations between symptom dimensions are highlighted, as are potential age-specific differences in biological associations. Findings are interpreted with reference to key methodological differences across studies. The included studies provide compelling evidence that the general dimension of psychopathology reflects common underlying genetic and neurobiological vulnerabilities that are shared across diverse manifestations of mental illness. Substantive interpretations of general psychopathology in the context of genetic and neurobiological evidence are discussed.

Quantifying the contribution of subject and group factors in brain activation

Research in neuroscience often assumes universal neural mechanisms, but increasing evidence points toward sizeable individual differences in brain activations. What remains unclear is the extent of the idiosyncrasy and whether different types of analyses are associated with different levels of idiosyncrasy. Here we develop a new method for addressing these questions. The method consists of computing the within-subject reliability and subject-to-group similarity of brain activations and submitting these values to a computational model that quantifies the relative strength of group- and subject-level factors. We apply this method to a perceptual decision-making task (n = 50) and find that activations related to task, reaction time, and confidence are influenced equally strongly by group- and subject-level factors. Both group- and subject-level factors are dwarfed by a noise factor, though higher levels of smoothing increases their contributions relative to noise. Overall, our method allows for the quantification of group- and subject-level factors of brain activations and thus provides a more detailed understanding of the idiosyncrasy levels in brain activations.

Robust hierarchically organized whole-brain patterns of dysconnectivity in schizophrenia spectrum disorders observed after personalized intrinsic network topography

BACKGROUND

Spatial patterns of brain functional connectivity can vary substantially at the individual level. Applying cortical surface-based approaches with individualized rather than group templates may accelerate the discovery of biological markers related to psychiatric disorders. We investigated cortico-subcortical networks from multi-cohort data in people with schizophrenia spectrum disorders (SSDs) and healthy controls (HC) using individualized connectivity profiles.

METHODS

We utilized resting-state and anatomical MRI data from n = 406 participants (n = 203 SSD, n = 203 HC) from four cohorts. Functional timeseries were extracted from previously defined intrinsic network subregions of the striatum, thalamus, and cerebellum as well as 80 cortical regions of interest, representing six intrinsic networks using (1) volume-based approaches, (2) a surface-based group atlas approaches, and (3) Personalized Intrinsic Network Topography (PINT).

RESULTS

The correlations between all cortical networks and the expected subregions of the striatum, cerebellum, and thalamus were increased using a surface-based approach (Cohen's D volume vs. surface 0.27-1.00, all p < 10-6 ) and further increased after PINT (Cohen's D surface vs. PINT 0.18-0.96, all p < 10-4 ). In SSD versus HC comparisons, we observed robust patterns of dysconnectivity that were strengthened using a surface-based approach and PINT (Number of differing pairwise-correlations: volume: 404, surface: 570, PINT: 628, FDR corrected).

CONCLUSION

Surface-based and individualized approaches can more sensitively delineate cortical network dysconnectivity differences in people with SSDs. These robust patterns of dysconnectivity were visibly organized in accordance with the cortical hierarchy, as predicted by computational models.

Reduced volume of the left cerebellar lobule VIIb and its increased connectivity within the cerebellum predict more general psychopathology one year later via worse cognitive flexibility in children

Predicting the risk for general psychopathology (the p factor) requires the examination of multiple factors ranging from brain to cognitive skills. While an increasing number of findings have reported the roles of the cerebral cortex and executive functions, it is much less clear whether and how the cerebellum and cognitive flexibility (a core component of executive function) may be associated with the risk for general psychopathology. Based on the data from more than 400 children aged 6-12 in the Children School Functions and Brain Development (CBD) Project, this study examined whether the left cerebellar lobule VIIb and its connectivity within the cerebellum may prospectively predict the risk for general psychopathology one year later and whether cognitive flexibility may mediate such predictions in school-age children. The reduced gray matter volume in the left cerebellar lobule VIIb and the increased connectivity of this region to the left cerebellar lobule VI prospectively predicted the risk for general psychopathology and was partially mediated by worse cognitive flexibility. Deficits in cognitive flexibility may play an important role in linking cerebellar structure and function to the risk for general psychopathology.

Prefrontal-cerebellar dynamics during post-success and post-error cognitive controls in major psychiatric disorders

BACKGROUND

Difficulty in cognitive adjustment after a conflict or error is a hallmark for many psychiatric disorders, yet the underlying neural correlates are not fully understood. We have previously shown that post-success and post-error cognitive controls are associated with distinct mechanisms particularly related to the prefrontal-cerebellar circuit, raising the possibility that altered dynamic interactions in this circuit may underlie mental illness.

METHODS

This study included 136 patients with three diagnosed disorders [48 schizophrenia (SZ), 49 bipolar disorder (BD), 39 attention deficit hyperactivity disorder (ADHD)] and 89 healthy controls who completed a stop-signal task during fMRI scans. Brain activations for concurrent, post-success, and post-error cognitive controls were analyzed and compared between groups. Dynamic causal modeling was applied to investigate prefrontal-cerebellar effective connectivity patterns during post-success and post-error processing.

RESULTS

No significant group differences were observed for brain activations and overall effective connectivity structures during post-success and post-error conditions. However, significant group differences were shown for the modulational effect on top-down connectivity from the prefrontal cortex to the cerebellum during post-error trials (pFWE = 0.02), which was driven by reduced modulations in both SZ and ADHD. During post-success trials, there were significantly decreased modulational effect on bottom-up connectivity from the cerebellum to the prefrontal cortex in ADHD (pFWE = 0.04) and decreased driving input to the cerebellum in SZ (pFWE = 0.04).

CONCLUSIONS

These findings suggest that patients with SZ and ADHD are associated with insufficient neural modulation on the prefrontal-cerebellar circuit during post-success and post-error cognitive processing, a phenomenon that may underlie cognitive deficits in these disorders.

Delineating disorder-general and disorder-specific dimensions of psychopathology from functional brain networks in a developmental clinical sample

The interplay between functional brain network maturation and psychopathology during development remains elusive. To establish the structure of psychopathology and its neurobiological mechanisms, mapping of both shared and unique functional connectivity patterns across developmental clinical populations is needed. We investigated shared associations between resting-state functional connectivity and psychopathology in children and adolescents aged 5-21 (n = 1689). Specifically, we used partial least squares (PLS) to identify latent variables (LV) between connectivity and both symptom scores and diagnostic information. We also investigated associations between connectivity and each diagnosis specifically, controlling for other diagnosis categories. PLS identified five significant LVs between connectivity and symptoms, mapping onto the psychopathology hierarchy. The first LV resembled a general psychopathology factor, followed by dimensions of internalising- externalising, neurodevelopment, somatic complaints, and thought problems. Another PLS with diagnostic data revealed one significant LV, resembling a cross-diagnostic case-control pattern. The diagnosis-specific PLS identified a unique connectivity pattern for autism spectrum disorder (ASD). All LVs were associated with distinct patterns of functional connectivity. These dimensions largely replicated in an independent sample (n = 420) from the same dataset, as well as to an independent cohort (n = 3504). This suggests that covariance in developmental functional brain networks supports transdiagnostic dimensions of psychopathology.

Structural equation modeling of treatment-related changes in neural connectivity for youth with PTSD

BACKGROUND

Previous studies suggest that improvement in symptoms of posttraumatic stress disorder (PTSD) is accompanied by changes in neural connectivity, however, few studies have investigated directional (effective) connectivity. The current study assesses treatment-related changes in effective connectivity in youth with PTSD undergoing Trauma-Focused Cognitive Behavioral Therapy (TF-CBT).

METHODS

Functional MRI scans before and after 16 weeks of TF-CBT for 20 youth with PTSD, or the same time interval for 20 healthy controls (HC) were included in the analysis. Structural equation modeling was used to model group differences in directional connectivity at baseline, and changes in connectivity from pre- to post-treatment.

RESULTS

At baseline, the PTSD group, relative to the HC group, had significantly greater connectivity in the path from dorsal cingulate to anterior cingulate and from dorsal cingulate to posterior cingulate corticies. From pre- to post-treatment, connectivity in these paths decreased significantly in the PTSD group, as did connectivity from right hippocampus to left superior temporal gyrus. Connectivity from the left amygdala to the lateral orbital frontal cortex was significantly lower in PTSD vs HC at baseline, but did not change from pre- to post-treatment.

CONCLUSION

Although based on a small sample, these results converge with previous studies in suggesting a central role for the dorsal cingulate cortex in PTSD symptoms. The direction of this connectivity suggests that the dorsal cingulate is the source of modulation of anterior and posterior cingulate cortex during trauma-focused cognitive behavioral therapy.

Neurobiological Clusters Are Associated With Trajectories of Overall Psychopathology in Youth

BACKGROUND

Integrating multiple neuroimaging modalities to identify clusters of individuals and then associating these clusters with psychopathology is a promising approach for understanding neurobiological mechanisms that underlie psychopathology and the extent to which these features are associated with clinical symptoms.

METHODS

We leveraged neuroimaging data from T1-weighted, diffusion-weighted, and resting-state functional magnetic resonance images from the Adolescent Brain Cognitive Development (ABCD) Study (N = 8035) and used similarity network fusion and spectral clustering to identify subgroups of participants. We examined neuroimaging measures as a function of clustering profiles using 1, 2, or 3 imaging modalities (i.e., data combinations), calculated the stability of the clustering assignment in each respective data combination, and compared the consistency of clusters across different data combinations. We then compared the extent to which clusters were associated with overall psychopathology at the baseline assessment and at 2 yearly follow-up visits.

RESULTS

Each data combination resulted in optimal clusters ranging from 2 to 4 subgroups for each data combination. Clusters were stable across subsampling of the ABCD Study cohort. Widespread structural measures (surface area, fractional anisotropy, and mean diffusivity) were important features contributing to clustering across different data combinations. Five of the seven data combinations were associated with overall psychopathology, both at baseline and over time (d = 0.08-0.41). Generally, lower global cortical volume and surface area, widespread reduced fractional anisotropy, and increased radial diffusivity were associated with increased overall psychopathology.

CONCLUSIONS

Profiles constructed from neuroimaging data combinations are associated with concurrent and future psychopathology trajectories.

Common and unique alterations of functional connectivity in major depressive disorder and bipolar disorder

OBJECTIVE

Major depressive disorder (MDD) and bipolar disorder (BD) are considered whole-brain disorders with some common clinical and neurobiological features. It is important to investigate neural mechanisms to distinguish between the two disorders. However, few studies have explored the functional dysconnectivity between the two disorders from the whole brain level.

METHODS

In this study, 117 patients with MDD, 65 patients with BD, and 116 healthy controls completed resting-state functional magnetic resonance imaging (R-fMRI) scans. Both edge-based network construction and large-scale network analyses were applied.

RESULTS

Results found that both the BD and MDD groups showed decreased FC in the whole brain network. The shared aberrant network across patients involves the visual network (VN), sensorimotor network (SMN), dorsal attention network (DAN), and ventral attention network (VAN), which is related to the processing of external stimuli. The default mode network (DMN) and the limbic network (LN) abnormalities were only found in patients with MDD. Furthermore, results showed the highest decrease in edges of patients with MDD in between-network FC in SMN-VN, whereas in VAN-VN of patients with BD.

CONCLUSIONS

Our findings indicated that both MDD and BD are extensive abnormal brain network diseases, mainly aberrant in those brain networks correlated to the processing of external stimuli, especially the attention network. Specific altered functional connectivity also was found in MDD and BD groups, respectively. These results may provide possible trait markers to distinguish the two disorders.

A shared neural basis underlying psychiatric comorbidity

Recent studies proposed a general psychopathology factor underlying common comorbidities among psychiatric disorders. However, its neurobiological mechanisms and generalizability remain elusive. In this study, we used a large longitudinal neuroimaging cohort from adolescence to young adulthood (IMAGEN) to define a neuropsychopathological (NP) factor across externalizing and internalizing symptoms using multitask connectomes. We demonstrate that this NP factor might represent a unified, genetically determined, delayed development of the prefrontal cortex that further leads to poor executive function. We also show this NP factor to be reproducible in multiple developmental periods, from preadolescence to early adulthood, and generalizable to the resting-state connectome and clinical samples (the ADHD-200 Sample and the Stratify Project). In conclusion, we identify a reproducible and general neural basis underlying symptoms of multiple mental health disorders, bridging multidimensional evidence from behavioral, neuroimaging and genetic substrates. These findings may help to develop new therapeutic interventions for psychiatric comorbidities.

Associations with youth psychotic-like experiences over time: Evidence for trans-symptom and specific cognitive and neural risk factors

The current study examined whether impairments in cognitive and neural factors at baseline (ages 9-10) predict initial levels or changes in psychotic-like experiences (PLEs) and whether such impairments generalize to other psychopathology symptoms (i.e., internalizing and externalizing symptoms). Using unique longitudinal Adolescent Brain Cognitive Development Study data, the study examined three time points from ages 9 to 13. Univariate latent growth models examined associations between baseline cognitive and neural metrics with symptom measures using discovery (n = 5,926) and replication (n = 5,952) data sets. For symptom measures (i.e., PLEs, internalizing, externalizing), we examined mean initial levels (i.e., intercepts) and changes over time (i.e., slopes). Predictors included neuropsychological test performance, global structural MRI, and several a priori within-network resting-state functional connectivity metrics. Results showed a pattern whereby baseline cognitive and brain metric impairments showed the strongest associations with PLEs over time. Lower cognitive, volume, surface area, and cingulo-opercular within-network connectivity metrics showed associations with increased PLEs and higher initial levels of externalizing and internalizing symptoms. Several metrics were uniquely associated with PLEs, including lower cortical thickness with higher initial PLEs and lower default mode network connectivity with increased PLEs slopes. Neural and cognitive impairments in middle childhood were broadly associated with increased PLEs over time, and showed stronger associations with PLEs compared with other psychopathology symptoms. The current study also identified markers potentially uniquely associated with PLEs (e.g., cortical thickness). Impairments in broad cognitive metrics, brain volume and surface area, and a network associated with information integration may represent risk factors for general psychopathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Canalization and plasticity in psychopathology

This theoretical article revives a classical bridging construct, canalization, to describe a new model of a general factor of psychopathology. To achieve this, we have distinguished between two types of plasticity, an early one that we call 'TEMP' for 'Temperature or Entropy Mediated Plasticity', and another, we call 'canalization', which is close to Hebbian plasticity. These two forms of plasticity can be most easily distinguished by their relationship to 'precision' or inverse variance; TEMP relates to increased model variance or decreased precision, whereas the opposite is true for canalization. TEMP also subsumes increased learning rate, (Ising) temperature and entropy. Dictionary definitions of 'plasticity' describe it as the property of being easily shaped or molded; TEMP is the better match for this. Importantly, we propose that 'pathological' phenotypes develop via mechanisms of canalization or increased model precision, as a defensive response to adversity and associated distress or dysphoria. Our model states that canalization entrenches in psychopathology, narrowing the phenotypic state-space as the agent develops expertise in their pathology. We suggest that TEMP - combined with gently guiding psychological support - can counter canalization. We address questions of whether and when canalization is adaptive versus maladaptive, furnish our model with references to basic and human neuroscience, and offer concrete experiments and measures to test its main hypotheses and implications. This article is part of the Special Issue on "National Institutes of Health Psilocybin Research Speaker Series".

A transdiagnostic network for psychiatric illness derived from atrophy and lesions

Psychiatric disorders share neurobiology and frequently co-occur. This neurobiological and clinical overlap highlights opportunities for transdiagnostic treatments. In this study, we used coordinate and lesion network mapping to test for a shared brain network across psychiatric disorders. In our meta-analysis of 193 studies, atrophy coordinates across six psychiatric disorders mapped to a common brain network defined by positive connectivity to anterior cingulate and insula, and by negative connectivity to posterior parietal and lateral occipital cortex. This network was robust to leave-one-diagnosis-out cross-validation and specific to atrophy coordinates from psychiatric versus neurodegenerative disorders (72 studies). In 194 patients with penetrating head trauma, lesion damage to this network correlated with the number of post-lesion psychiatric diagnoses. Neurosurgical ablation targets for psychiatric illness (four targets) also aligned with the network. This convergent brain network for psychiatric illness may partially explain high rates of psychiatric comorbidity and could highlight neuromodulation targets for patients with more than one psychiatric disorder.

A double-edged hormone: The moderating role of personality and attachment on oxytocin's treatment facilitation effect

BACKGROUND

Studies exploring the potential augmenting effect of oxytocin for patients with mental disorders have thus far reported mixed effects. However, oxytocin's effect may differ across patients with different interpersonal characteristics. This study aimed to examine the moderating role of attachment and personality traits on the effect of oxytocin administration on the therapeutic working alliance and symptomatic change, among hospitalized patients with severe mental illness.

METHODS

Patients (N = 87) were randomly assigned to receive oxytocin or placebo, as an add-on to psychotherapy for a period of four weeks, in two inpatient units. Therapeutic alliance and symptomatic change were measured weekly, and personality and attachment were assessed at pre- and post-intervention.

RESULTS

Oxytocin administration was significantly associated with improvement of depression (B=2.12, SE=0.82, t = 2.56, p = .012), and suicidal ideation (B=0.03, SE=0.01, t = 2.44, p = .016) for patients low in openness and extraversion, respectively. Nonetheless, oxytocin administration was also significantly associated with a deterioration in the working alliance for patients high in extraversion (B=-0.11, SE=0.04, t = -2.73, p = .007), low in neuroticism (B=0.08, SE=0.03, t = 2.01, p = .047) and low in agreeableness (B=0.11, SE=0.04, t = 2.76, p = .007).

CONCLUSIONS

Oxytocin may act as a double-edged sword when it comes to its effect on treatment process and outcome. Future studies should focus on routes to characterize patients who might benefit the most from such augmentation.

CLINICAL TRIAL REGISTRATION

Pre-registration in clinicaltrials.com: NCT03566069; Israel Ministry of Health: MOH_2017-12-05_002003.

A Neurobiological Profile Underlying Comorbidity Load and Prospective Increases in Dysphoria in a Focal Fear Sample

BACKGROUND

Knowledge of the neural mechanisms underlying increased disease burden in anxiety disorders that is unaccounted for by individual categorical diagnoses could lead to improved clinical care. Here, we tested the utility of a joint functional magnetic resonance imaging-electroencephalography neurobiological profile characterized by overvaluation of negative stimuli (amygdala) in combination with blunted elaborated processing of these same stimuli (the late positive potential [LPP], an event-related potential) in predicting increased psychopathology across a 2-year period in people with anxiety disorders.

METHODS

One hundred ten participants (64 female, 45 male, 1 other) including 78 participants with phobias who varied in the extent of their internalizing comorbidity and 32 participants who were free from psychopathology viewed negative and neutral pictures during separate functional magnetic resonance imaging blood oxygen level-dependent and electroencephalogram recordings. Dysphoria was assessed at baseline and 2 years later.

RESULTS

Participants with both heightened amygdala activation and blunted LPPs to negative pictures showed the greatest increases in dysphoria 2 years later. Cross-sectionally, participants with higher comorbidity load (≥2 additional diagnoses, n = 34) showed increased amygdala activation to negative pictures compared with participants with lower comorbidity load (≤1 additional diagnosis, n = 44) and compared with participants free from psychopathology. In addition, high comorbid participants showed reduced LPPs to negative pictures compared with low comorbid participants.

CONCLUSIONS

Heightened amygdala in response to negative stimuli in combination with blunted LPPs could indicate overvaluation of threatening stimuli in the absence of elaborated processing that might otherwise help regulate threat responding. This brain profile could underlie the worsening and maintenance of internalizing psychopathology over time.

Compressed sensorimotor-to-transmodal hierarchical organization in schizophrenia

BACKGROUND

Schizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks.

METHODS

We applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state fMRI to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients v. 122 controls).

RESULTS

We demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal-parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and fronto-parietal and ventral attention regions along the classical sensation-to-cognition continuum (voxel-level corrected, p < 0.05).

CONCLUSIONS

The compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascading impairments from the disruption of the somatosensory-motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.

Visual processing deficits in patients with schizophrenia spectrum and bipolar disorders and associations with psychotic symptoms, and intellectual abilities

Objective

Low-level sensory disruption is hypothesized as a precursor to clinical and cognitive symptoms in severe mental disorders. We compared visual discrimination performance in patients with schizophrenia spectrum disorder or bipolar disorder with healthy controls, and investigated associations with clinical symptoms and IQ.

Methods

Patients with schizophrenia spectrum disorder (n = 32), bipolar disorder (n = 55) and healthy controls (n = 152) completed a computerized visual discrimination task. Participants responded whether the latter of two consecutive grids had higher or lower spatial frequency, and discrimination thresholds were estimated using an adaptive maximum likelihood procedure. Case-control differences in threshold were assessed using linear regression, F-test and post-hoc pair-wise comparisons. Linear models were used to test for associations between visual discrimination threshold and psychotic symptoms derived from the PANSS and IQ assessed using the Matrix Reasoning and Vocabulary subtests from the Wechsler Abbreviated Scale of Intelligence (WASI).

Results

Robust regression revealed a significant main effect of diagnosis on discrimination threshold (robust F = 6.76, p = .001). Post-hoc comparisons revealed that patients with a schizophrenia spectrum disorder (mean = 14%, SD = 0.08) had higher thresholds compared to healthy controls (mean = 10.8%, SD = 0.07, β = 0.35, t = 3.4, p = .002), as did patients with bipolar disorder (12.23%, SD = 0.07, β = 0.21, t = 2.42, p = .04). There was no significant difference between bipolar disorder and schizophrenia (β = -0.14, t = -1.2, p = .45). Linear models revealed negative associations between IQ and threshold across all participants when controlling for diagnostic group (β = -0.3, t = -3.43, p = .0007). This association was found within healthy controls (t = -3.72, p = .0003) and patients with bipolar disorder (t = -2.53, p = .015), and no significant group by IQ interaction on threshold (F = 0.044, p = .97). There were no significant associations between PANSS domain scores and discrimination threshold.

Conclusion

Patients with schizophrenia spectrum or bipolar disorders exhibited higher visual discrimination thresholds than healthy controls, supporting early visual deficits among patients with severe mental illness. Discrimination threshold was negatively associated with IQ among healthy controls and bipolar disorder patients. These findings elucidate perception-related disease mechanisms in severe mental illness, which warrants replication in independent samples.

General psychopathology factor (p-factor) prediction using resting-state functional connectivity and a scanner-generalization neural network

The general psychopathology factor (p-factor) represents shared variance across mental disorders based on psychopathologic symptoms. The Adolescent Brain Cognitive Development (ABCD) Study offers an unprecedented opportunity to investigate functional networks (FNs) from functional magnetic resonance imaging (fMRI) associated with the psychopathology of an adolescent cohort (n > 10,000). However, the heterogeneities associated with the use of multiple sites and multiple scanners in the ABCD Study need to be overcome to improve the prediction of the p-factor using fMRI. We proposed a scanner-generalization neural network (SGNN) to predict the individual p-factor by systematically reducing the scanner effect for resting-state functional connectivity (RSFC). We included 6905 adolescents from 18 sites whose fMRI data were collected using either Siemens or GE scanners. The p-factor was estimated based on the Child Behavior Checklist (CBCL) scores available in the ABCD study using exploratory factor analysis. We evaluated the Pearson's correlation coefficients (CCs) for p-factor prediction via leave-one/two-site-out cross-validation (LOSOCV/LTSOCV) and identified important FNs from the weight features (WFs) of the SGNN. The CCs were higher for the SGNN than for alternative models when using both LOSOCV (0.1631 ± 0.0673 for the SGNN vs. 0.1497 ± 0.0710 for kernel ridge regression [KRR]; p < 0.05 from a two-tailed paired t-test) and LTSOCV (0.1469 ± 0.0381 for the SGNN vs. 0.1394 ± 0.0359 for KRR; p = 0.01). It was found that (a) the default-mode and dorsal attention FNs were important for p-factor prediction, and (b) the intra-visual FN was important for scanner generalization. We demonstrated the efficacy of our novel SGNN model for p-factor prediction while simultaneously eliminating scanner-related confounding effects for RSFC.

Cingulate-Prefrontal Connectivity During Dynamic Cognitive Control Mediates Association Between p Factor and Adaptive Functioning in a Transdiagnostic Pediatric Sample

BACKGROUND

Covariation among psychiatric symptoms is being actively pursued for transdiagnostic dimensions of psychopathology with predictive utility. A superordinate dimension, the p factor, reflects overall psychopathology burden and has support from genetic and neuroimaging correlates. However, the neurocognitive correlates that link an elevated p factor to maladaptive outcomes are unknown. We tested the mediating potential of dynamic adjustments in cognitive control rooted in functional connections anchored by the dorsal anterior cingulate cortex (dACC) in a transdiagnostic pediatric sample.

METHODS

A multiple mediation model tested the association between the p factor (derived by principal component analysis of Child Behavior Checklist syndrome scales) and outcome measured with the Vineland Adaptive Behavior Scale-II in 89 children ages 8 to 13 years (23 female) with a variety of primary neurodevelopmental diagnoses who underwent functional magnetic resonance imaging during a socioaffective Stroop-like task with eye gaze as distractor. Mediators included functional connectivity of frontoparietal- and salience network-affiliated dACC seeds during conflict adaptation.

RESULTS

Higher p factor scores were related to worse adaptive functioning. This effect was partially mediated by conflict adaptation-dependent functional connectivity between the frontoparietal network-affiliated dACC seed and the right dorsolateral prefrontal cortex. Post hoc follow-up indicated that the p factor was related to all Vineland Adaptive Behaviors Scale-II domains; the association was strongest for socialization followed by daily living skills and then communication. Mediation results remained significant for socialization only.

CONCLUSIONS

Higher psychopathology burden was associated with worse adaptive functioning in early adolescence. This association was mediated by weaker dACC-dorsolateral prefrontal cortex functional connectivity underlying modulation of cognitive control in response to contextual contingencies. Our results contribute to the identification of transdiagnostic and developmentally relevant neurocognitive endophenotypes of psychopathology.

Dissociable salience and default mode network modulation in generalized anxiety disorder: a connectome-wide association study

Generalized anxiety disorder (GAD) is a common anxiety disorder experiencing psychological and somatic symptoms. Here, we explored the link between the individual variation in functional connectome and anxiety symptoms, especially psychological and somatic dimensions, which remains unknown. In a sample of 118 GAD patients and matched 85 healthy controls (HCs), we used multivariate distance-based matrix regression to examine the relationship between resting-state functional connectivity (FC) and the severity of anxiety. We identified multiple hub regions belonging to salience network (SN) and default mode network (DMN) where dysconnectivity associated with anxiety symptoms (P < 0.05, false discovery rate [FDR]-corrected). Follow-up analyses revealed that patient's psychological anxiety was dominated by the hyper-connectivity within DMN, whereas the somatic anxiety could be modulated by hyper-connectivity within SN and DMN. Moreover, hypo-connectivity between SN and DMN were related to both anxiety dimensions. Furthermore, GAD patients showed significant network-level FC changes compared with HCs (P < 0.01, FDR-corrected). Finally, we found the connectivity of DMN could predict the individual psychological symptom in an independent GAD sample. Together, our work emphasizes the potential dissociable roles of SN and DMN in the pathophysiology of GAD's anxiety symptoms, which may be crucial in providing a promising neuroimaging biomarker for novel personalized treatment strategies.

Meta-analysis of structural evidence for the Hierarchical Taxonomy of Psychopathology (HiTOP) model

BACKGROUND

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a classification system that seeks to organize psychopathology using quantitative evidence - yet the current model was established by narrative review. This meta-analysis provides a quantitative synthesis of literature on transdiagnostic dimensions of psychopathology to evaluate the validity of the HiTOP framework.

METHODS

Published studies estimating factor-analytic models from diagnostic and statistical manual of mental disorders (DSM) diagnoses were screened. A total of 120,596 participants from 35 studies assessing 23 DSM diagnoses were included in the meta-analytic models. Data were pooled into a meta-analytic correlation matrix using a random effects model. Exploratory factor analyses were conducted using the pooled correlation matrix. A hierarchical structure was estimated by extracting one to five factors representing levels of the HiTOP framework, then calculating congruence coefficients between factors at sequential levels.

RESULTS

Five transdiagnostic dimensions fit the DSM diagnoses well (comparative fit index = 0.92, root mean square error of approximation = 0.07, and standardized root-mean-square residual = 0.03). Most diagnoses had factor loadings >|0.30| on the expected factors, and congruence coefficients between factors indicated a hierarchical structure consistent with the HiTOP framework.

CONCLUSIONS

A model closely resembling the HiTOP framework fit the data well and placement of DSM diagnoses within transdiagnostic dimensions were largely confirmed, supporting it as valid structure for conceptualizing and organizing psychopathology. Results also suggest transdiagnostic research should (1) use traits, narrow symptoms, and dimensional measures of psychopathology instead of DSM diagnoses, (2) assess a broader array of constructs, and (3) increase focus on understudied pathologies.

The relationship between cognitive functioning and psychopathology in patients with psychiatric disorders: a transdiagnostic network analysis

BACKGROUND

Patients with psychiatric disorders often experience cognitive dysfunction, but the precise relationship between cognitive deficits and psychopathology remains unclear. We investigated the relationships between domains of cognitive functioning and psychopathology in a transdiagnostic sample using a data-driven approach.

METHODS

Cross-sectional network analyses were conducted to investigate the relationships between domains of psychopathology and cognitive functioning and detect clusters in the network. This naturalistic transdiagnostic sample consists of 1016 psychiatric patients who have a variety of psychiatric diagnoses, such as depressive disorders, anxiety disorders, obsessive-compulsive and related disorders, and schizophrenia spectrum and other psychotic disorders. Psychopathology symptoms were assessed using various questionnaires. Core cognitive domains were assessed with a battery of automated tests.

RESULTS

Network analysis detected three clusters that we labelled: general psychopathology, substance use, and cognition. Depressive and anxiety symptoms, verbal memory, and visual attention were the most central nodes in the network. Most associations between cognitive functioning and symptoms were negative, i.e. increased symptom severity was associated with worse cognitive functioning. Cannabis use, (subclinical) psychotic experiences, and anhedonia had the strongest total negative relationships with cognitive variables.

CONCLUSIONS

Cognitive functioning and psychopathology are independent but related dimensions, which interact in a transdiagnostic manner. Depression, anxiety, verbal memory, and visual attention are especially relevant in this network and can be considered independent transdiagnostic targets for research and treatment in psychiatry. Moreover, future research on cognitive functioning in psychopathology should take a transdiagnostic approach, focusing on symptom-specific interactions with cognitive domains rather than investigating cognitive functioning within diagnostic categories.

Linking Individual Differences in Personalized Functional Network Topography to Psychopathology in Youth

BACKGROUND

The spatial layout of large-scale functional brain networks differs between individuals and is particularly variable in the association cortex, implicated in a broad range of psychiatric disorders. However, it remains unknown whether this variation in functional topography is related to major dimensions of psychopathology in youth.

METHODS

The authors studied 790 youths ages 8 to 23 years who had 27 minutes of high-quality functional magnetic resonance imaging data as part of the Philadelphia Neurodevelopmental Cohort. Four correlated dimensions were estimated using a confirmatory correlated traits factor analysis on 112 item-level clinical symptoms, and one overall psychopathology factor with 4 orthogonal dimensions were extracted using a confirmatory factor analysis. Spatially regularized nonnegative matrix factorization was used to identify 17 individual-specific functional networks for each participant. Partial least square regression with split-half cross-validation was conducted to evaluate to what extent the topography of personalized functional networks encodes major dimensions of psychopathology.

RESULTS

Personalized functional network topography significantly predicted unseen individuals' major dimensions of psychopathology, including fear, psychosis, externalizing, and anxious-misery. Reduced representation of association networks was among the most important features for the prediction of all 4 dimensions. Further analysis revealed that personalized functional network topography predicted overall psychopathology (r = 0.16, permutation testing p < .001), which drove prediction of the 4 correlated dimensions.

CONCLUSIONS

These results suggest that individual differences in functional network topography in association networks is related to overall psychopathology in youth. Such results underscore the importance of considering functional neuroanatomy for personalized diagnostics and therapeutics in psychiatry.

Differentiated nomological networks of internalizing, externalizing, and the general factor of psychopathology ('p factor') in emerging adolescence in the ABCD study

BACKGROUND

Structural models of psychopathology consistently identify internalizing (INT) and externalizing (EXT) specific factors as well as a superordinate factor that captures their shared variance, the p factor. Questions remain, however, about the meaning of these data-driven dimensions and the interpretability and distinguishability of the larger nomological networks in which they are embedded.

METHODS

The sample consisted of 10 645 youth aged 9-10 years participating in the multisite Adolescent Brain and Cognitive Development (ABCD) Study. p, INT, and EXT were modeled using the parent-rated Child Behavior Checklist (CBCL). Patterns of associations were examined with variables drawn from diverse domains including demographics, psychopathology, temperament, family history of substance use and psychopathology, school and family environment, and cognitive ability, using instruments based on youth-, parent-, and teacher-report, and behavioral task performance.

RESULTS

p exhibited a broad pattern of statistically significant associations with risk variables across all domains assessed, including temperament, neurocognition, and social adversity. The specific factors exhibited more domain-specific patterns of associations, with INT exhibiting greater fear/distress and EXT exhibiting greater impulsivity.

CONCLUSIONS

In this largest study of hierarchical models of psychopathology to date, we found that p, INT, and EXT exhibit well-differentiated nomological networks that are interpretable in terms of neurocognition, impulsivity, fear/distress, and social adversity. These networks were, in contrast, obscured when relying on the a priori Internalizing and Externalizing dimensions of the CBCL scales. Our findings add to the evidence for the validity of p, INT, and EXT as theoretically and empirically meaningful broad psychopathology liabilities.