Multivariate Associations Among Behavioral, Clinical, and Multimodal Imaging Phenotypes in Patients With Psychosis

Linked patterns of symptoms and cognitive covariation with functional brain controllability in major depressive disorder

BACKGROUND

Controllability analysis is an approach developed for evaluating the ability of a brain region to modulate function in other regions, which has been found to be altered in major depressive disorder (MDD). Both depressive symptoms and cognitive impairments are prominent features of MDD, but the case-control differences of controllability between MDD and controls can not fully interpret the contribution of both clinical symptoms and cognition to brain controllability and linked patterns among them in MDD.

METHODS

Sparse canonical correlation analysis was used to investigate the associations between resting-state functional brain controllability at the network level and clinical symptoms and cognition in 99 first-episode medication-naïve patients with MDD.

FINDINGS

Average controllability was significantly correlated with clinical features. The average controllability of the dorsal attention network (DAN) and visual network had the highest correlations with clinical variables. Among clinical variables, depressed mood, suicidal ideation and behaviour, impaired work and activities, and gastrointestinal symptoms were significantly negatively associated with average controllability, and reduced cognitive flexibility was associated with reduced average controllability.

INTERPRETATION

These findings highlight the importance of brain regions in modulating activity across brain networks in MDD, given their associations with symptoms and cognitive impairments observed in our study. Disrupted control of brain reconfiguration of DAN and visual network during their state transitions may represent a core brain mechanism for the behavioural impairments observed in MDD.

FUNDING

National Natural Science Foundation of China (82001795 and 82027808), National Key R&D Program (2022YFC2009900), and Sichuan Science and Technology Program (2024NSFSC0653).

Multivariate associations between neuroanatomy and cognition in unmedicated and medicated individuals with schizophrenia

Previous studies that focused on univariate correlations between neuroanatomy and cognition in schizophrenia identified some inconsistent findings. Moreover, antipsychotic medication may impact the brain-behavior profiles in affected individuals. It remains unclear whether unmedicated and medicated individuals with schizophrenia would share common neuroanatomy-cognition associations. Therefore, we aimed to investigate multivariate neuroanatomy-cognition relationships in both groups. A sample of 59 drug-naïve individuals with first-episode schizophrenia (FES) and a sample of 115 antipsychotic-treated individuals with schizophrenia were finally included. Multivariate modeling was conducted in the two patient samples between multiple cognitive domains and neuroanatomic features, such as cortical thickness (CT), cortical surface area (CSA), and subcortical volume (SV). We observed distinct multivariate correlational patterns between the two samples of individuals with schizophrenia. In the FES sample, better performance in token motor, symbol coding, and verbal fluency tests was associated with greater thalamic volumes but lower CT in the prefrontal and anterior cingulate cortices. Two significant multivariate correlations were identified in antipsychotic-treated individuals: 1) worse verbal memory performance was related to smaller volumes for the most subcortical structures and smaller CSA mainly in the temporal regions and inferior parietal lobule; 2) a lower symbol coding test score was correlated with smaller CSA in the right parahippocampal gyrus but greater volume in the right caudate. These multivariate patterns were sample-specific and not confounded by imaging quality, illness duration, antipsychotic dose, or psychopathological symptoms. Our findings may help to understand the neurobiological basis of cognitive impairments and the development of cognition-targeted interventions.

Multivariate brain-behaviour associations in psychiatric disorders

Mapping brain-behaviour associations is paramount to understand and treat psychiatric disorders. Standard approaches involve investigating the association between one brain and one behavioural variable (univariate) or multiple variables against one brain/behaviour feature ('single' multivariate). Recently, large multimodal datasets have propelled a new wave of studies that leverage on 'doubly' multivariate approaches capable of parsing the multifaceted nature of both brain and behaviour simultaneously. Within this movement, canonical correlation analysis (CCA) and partial least squares (PLS) emerge as the most popular techniques. Both seek to capture shared information between brain and behaviour in the form of latent variables. We provide an overview of these methods, review the literature in psychiatric disorders, and discuss the main challenges from a predictive modelling perspective. We identified 39 studies across four diagnostic groups: attention deficit and hyperactive disorder (ADHD, k = 4, N = 569), autism spectrum disorders (ASD, k = 6, N = 1731), major depressive disorder (MDD, k = 5, N = 938), psychosis spectrum disorders (PSD, k = 13, N = 1150) and one transdiagnostic group (TD, k = 11, N = 5731). Most studies (67%) used CCA and focused on the association between either brain morphology, resting-state functional connectivity or fractional anisotropy against symptoms and/or cognition. There were three main findings. First, most diagnoses shared a link between clinical/cognitive symptoms and two brain measures, namely frontal morphology/brain activity and white matter association fibres (tracts between cortical areas in the same hemisphere). Second, typically less investigated behavioural variables in multivariate models such as physical health (e.g., BMI, drug use) and clinical history (e.g., childhood trauma) were identified as important features. Finally, most studies were at risk of bias due to low sample size/feature ratio and/or in-sample testing only. We highlight the importance of carefully mitigating these sources of bias with an exemplar application of CCA.

A framework for a brain-derived nosology of psychiatric disorders

Current psychiatric diagnoses are not defined by neurobiological measures which hinders the development of therapies targeting mechanisms underlying mental illness 1,2 . Research confined to diagnostic boundaries yields heterogeneous biological results, whereas transdiagnostic studies often investigate individual symptoms in isolation. There is currently no paradigm available to comprehensively investigate the relationship between different clinical symptoms, individual disorders, and the underlying neurobiological mechanisms. Here, we propose a framework that groups clinical symptoms derived from ICD-10/DSM-V according to shared brain mechanisms defined by brain structure, function, and connectivity. The reassembly of existing ICD-10/DSM-5 symptoms reveal six cross-diagnostic psychopathology scores related to mania symptoms, depressive symptoms, anxiety symptoms, stress symptoms, eating pathology, and fear symptoms. They were consistently associated with multimodal neuroimaging components in the training sample of young adults aged 23, the independent test sample aged 23, participants aged 14 and 19 years, and in psychiatric patients. The identification of symptom groups of mental illness robustly defined by precisely characterized brain mechanisms enables the development of a psychiatric nosology based upon quantifiable neurobiological measures. As the identified symptom groups align well with existing diagnostic categories, our framework is directly applicable to clinical research and patient care.

Homotopic functional connectivity disruptions in schizophrenia and their associated gene expression

It has been revealed that abnormal voxel-mirrored homotopic connectivity (VMHC) is present in patients with schizophrenia, yet there are inconsistencies in the relevant findings. Moreover, little is known about their association with brain gene expression profiles. In this study, transcription-neuroimaging association analyses using gene expression data from Allen Human Brain Atlas and case-control VMHC differences from both the discovery (meta-analysis, including 9 studies with a total of 386 patients and 357 controls) and replication (separate group-level comparisons within two datasets, including a total of 258 patients and 287 controls) phases were performed to identify genes associated with VMHC alterations. Enrichment analyses were conducted to characterize the biological functions and specific expression of identified genes, and Neurosynth decoding analysis was performed to examine the correlation between cognitive-related processes and VMHC alterations in schizophrenia. In the discovery and replication phases, patients with schizophrenia exhibited consistent VMHC changes compared to controls, which were correlated with a series of cognitive-related processes; meta-regression analysis revealed that illness duration was negatively correlated with VMHC abnormalities in the cerebellum and postcentral/precentral gyrus. The abnormal VMHC patterns were stably correlated with 1287 genes enriched for fundamental biological processes like regulation of cell communication, nervous system development, and cell communication. In addition, these genes were overexpressed in astrocytes and immune cells, enriched in extensive cortical regions and wide developmental time windows. The present findings may contribute to a more comprehensive understanding of the molecular mechanisms underlying VMHC alterations in patients with schizophrenia.

Covariation of preadult environmental exposures, adult brain imaging phenotypes, and adult personality traits

Exposure to preadult environmental exposures may have long-lasting effects on mental health by affecting the maturation of the brain and personality, two traits that interact throughout the developmental process. However, environment-brain-personality covariation patterns and their mediation relationships remain unclear. In 4297 healthy participants (aged 18-30 years), we combined sparse multiple canonical correlation analysis with independent component analysis to identify the three-way covariation patterns of 59 preadult environmental exposures, 760 adult brain imaging phenotypes, and five personality traits, and found two robust environment-brain-personality covariation models with sex specificity. One model linked greater stress and less support to weaker functional connectivity and activity in the default mode network, stronger activity in subcortical nuclei, greater thickness and volume in the occipital, parietal and temporal cortices, and lower agreeableness, consciousness and extraversion as well as higher neuroticism. The other model linked higher urbanicity and better socioeconomic status to stronger functional connectivity and activity in the sensorimotor network, smaller volume and surface area and weaker functional connectivity and activity in the medial prefrontal cortex, lower white matter integrity, and higher openness to experience. We also conducted mediation analyses to explore the potential bidirectional mediation relationships between adult brain imaging phenotypes and personality traits with the influence of preadult environmental exposures and found both environment-brain-personality and environment-personality-brain pathways. We finally performed moderated mediation analyses to test the potential interactions between macro- and microenvironmental exposures and found that one category of exposure moderated the mediation pathways of another category of exposure. These results improve our understanding of the effects of preadult environmental exposures on the adult brain and personality traits and may facilitate the design of targeted interventions to improve mental health by reducing the impact of adverse environmental exposures.

Multivariate analytical approaches for investigating brain-behavior relationships

Background

Many studies of brain-behavior relationships rely on univariate approaches where each variable of interest is tested independently, which does not allow for the simultaneous investigation of multiple correlated variables. Alternatively, multivariate approaches allow for examining relationships between psychopathology and neural substrates simultaneously. There are multiple multivariate methods to choose from that each have assumptions which can affect the results; however, many studies employ one method without a clear justification for its selection. Additionally, there are few studies illustrating how differences between methods manifest in examining brain-behavior relationships. The purpose of this study was to exemplify how the choice of multivariate approach can change brain-behavior interpretations.

Method

We used data from 9,027 9- to 10-year-old children from the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®) to examine brain-behavior relationships with three commonly used multivariate approaches: canonical correlation analysis (CCA), partial least squares correlation (PLSC), and partial least squares regression (PLSR). We examined the associations between psychopathology dimensions including general psychopathology, attention-deficit/hyperactivity symptoms, conduct problems, and internalizing symptoms with regional brain volumes.

Results

The results of CCA, PLSC, and PLSR showed both consistencies and differences in the relationship between psychopathology symptoms and brain structure. The leading significant component yielded by each method demonstrated similar patterns of associations between regional brain volumes and psychopathology symptoms. However, the additional significant components yielded by each method demonstrated differential brain-behavior patterns that were not consistent across methods.

Conclusion

Here we show that CCA, PLSC, and PLSR yield slightly different interpretations regarding the relationship between child psychopathology and brain volume. In demonstrating the divergence between these approaches, we exemplify the importance of carefully considering the method's underlying assumptions when choosing a multivariate approach to delineate brain-behavior relationships.

Ameliorative patterns of grey matter in patients with first-episode and treatment-naïve schizophrenia

BACKGROUND

Grey matter (GM) reduction is a consistent observation in established late stages of schizophrenia, but patients in the untreated early stages of illness display an increase as well as a decrease in GM distribution relative to healthy controls (HC). The relative excess of GM may indicate putative compensatory responses, though to date its relevance is unclear.

METHODS

343 first-episode treatment-naïve patients with schizophrenia (FES) and 342 HC were recruited. Multivariate source-based morphometry was performed to identify covarying 'networks' of grey matter concentration (GMC). Neurocognitive scores using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and symptom burden using the Positive and Negative Symptoms Scale (PANSS) were obtained. Bivariate linear relationships between GMC and cognition/symptoms were studied.

RESULTS

Compared to healthy subjects, FES had prominently lower GMC in two components; the first consists of the anterior insula, inferior frontal gyrus, anterior cingulate and the second component with the superior temporal gyrus, precuneus, inferior/superior parietal lobule, cuneus, and lingual gyrus. Higher GMC was seen in adjacent areas of the middle and superior temporal gyrus, middle frontal gyrus, inferior parietal cortex and putamen. Greater GMC of this component was associated with lower duration of untreated psychosis, less severe positive symptoms and better performance on cognitive tests.

CONCLUSIONS

In untreated stages of schizophrenia, both a distributed lower and higher GMC is observable. While the higher GMC is relatively modest, it occurs across frontoparietal, temporal and subcortical regions in association with reduced illness burden suggesting a compensatory role for higher GMC in the early stages of schizophrenia.

Association Between the Fronto-Limbic Network and Cognitive and Emotional Functioning in Individuals With Bipolar Disorder: A Systematic Review and Meta-analysis

Importance

Individuals with bipolar disorder (BD) experience cognitive and emotional dysfunctions. Various brain circuits are implicated in BD but have not been investigated in a meta-analysis of functional magnetic resonance imaging (fMRI) studies.

Objective

To investigate the brain functioning of individuals with BD compared with healthy control individuals in the domains of emotion processing, reward processing, and working memory.

Data Sources

All fMRI experiments on BD published before March 2020, as identified in a literature search of PubMed, Embase, Web of Science, Cochrane Library, PsycInfo, Emcare, Academic Search Premier, and ScienceDirect. The literature search was conducted on February 21, 2017, and March 2, 2020, and data were analyzed from January 2021 to January 2022.

Study Selection

fMRI experiments comparing adult individuals with BD and healthy control individuals were selected if they reported whole-brain results, including a task assessing at least 1 of the domains. In total, 2320 studies were screened, and 253 full-text articles were evaluated.

Data Extraction and Synthesis

A total of 49 studies were included after selection procedure. Coordinates reporting significant activation differences between individuals with BD and healthy control individuals were extracted. Differences in brain region activity were tested using the activation likelihood estimation method.

Main Outcomes and Measures

A whole-brain meta-analysis evaluated whether reported differences in brain activation in response to stimuli in 3 cognitive domains between individuals with BD and healthy control individuals were different.

Results

The study population included 999 individuals with BD (551 [55.2%] female) and 1027 healthy control individuals (532 [51.8%] female). Compared with healthy control individuals, individuals with BD showed amygdala and hippocampal hyperactivity and hypoactivation in the inferior frontal gyrus during emotion processing (20 studies; 324 individuals with BD and 369 healthy control individuals), hyperactivation in the orbitofrontal cortex during reward processing (9 studies; 195 individuals with BD and 213 healthy control individuals), and hyperactivation in the ventromedial prefrontal cortex and subgenual anterior cingulate cortex during working memory (20 studies; 530 individuals with BD and 417 healthy control individuals). Limbic hyperactivation was only found during euthymia in the emotion and reward processing domains; abnormalities in frontal cortex activity were also found in individuals with BD with mania and depression.

Conclusions and Relevance

This systematic review and meta-analysis revealed evidence for activity disturbances in key brain areas involved in cognitive and emotion processing in individuals with BD. Most of the regions are part of the fronto-limbic network. The results suggest that aberrations in the fronto-limbic network, present in both euthymic and symptomatic individuals, may be underlying cognitive and emotional dysfunctions in BD.

Multivariate brain-based dimensions of child psychiatric problems: degrees of generalizability

Multivariate machine learning techniques are a promising set of tools for identifying complex brain-behavior associations. However, failure to replicate results from these methods across samples has hampered their clinical relevance. This study aimed to delineate dimensions of brain functional connectivity that are associated with child psychiatric symptoms in two large and independent cohorts: the Adolescent Brain Cognitive Development (ABCD) Study and the Generation R Study (total n =8,605). Using sparse canonical correlations analysis, we identified three brain-behavior dimensions in ABCD: attention problems, aggression and rule-breaking behaviors, and withdrawn behaviors. Importantly, out-of-sample generalizability of these dimensions was consistently observed in ABCD, suggesting robust multivariate brain-behavior associations. Despite this, out-of-study generalizability in Generation R was limited. These results highlight that the degree of generalizability can vary depending on the external validation methods employed as well as the datasets used, emphasizing that biomarkers will remain elusive until models generalize better in true external settings.

Linking negative affect, personality and social conditions to structural brain development during the transition from late adolescent to young adulthood

BACKGROUND

The transition from late adolescence to early adulthood is a period that experiences a surge of life changes and brain reorganization caused by internal and external factors, including negative affect, personality, and social conditions.

METHODS

Non-imaging phenotype and structural brain variables were available on 497 healthy participants (279 females and 218 males) between 17 and 22 years old. We used sparse canonical correlation analysis (sCCA) on the high-dimensional and longitudinal data to extract modes with maximum covariation between structural brain changes and negative affect, personality, and social conditions.

RESULTS

Separate sCCAs for cortical volume, cortical thickness, cortical surface area and subcortical volume confirmed that each imaging phenotype was correlated with non-imaging features (sCCA |r| range: 0.21-0.38, all p_FDR < 0.01). Bilateral superior frontal, left caudal anterior cingulate and bilateral caudate had the highest canonical cross-loadings (|ρ| = 0.15-0.32). In longitudinal data analysis, scan-interval, negative affect, and enthusiasm had the highest association with structural brain changes (|ρ| = 0.07-0.38); at baseline, intellect and politeness were associated with individual variability in the structural brain (|ρ| = 0.10-0.25).

LIMITATIONS

The present study used non-imaging variables only at baseline, making it impossible to explore the relationship between changing behavior and structural brain development.

CONCLUSIONS

Individual structural brain changes are associated with multiple factors. In addition to time-dependent variables, we find that negative affect, enthusiasm and social support play a numerically weak but significant role in structural brain development during the transition from late adolescence to young adulthood.

Comparing the stability and reproducibility of brain-behaviour relationships found using Canonical Correlation Analysis and Partial Least Squares within the ABCD Sample

Introduction

Canonical Correlation Analysis (CCA) and Partial Least Squares Correlation (PLS) detect associations between two data matrices based on computing a linear combination between the two matrices (called latent variables; LVs). These LVs maximize correlation (CCA) and covariance (PLS). These different maximization criteria may render one approach more stable and reproducible than the other when working with brain and behavioural data at the population-level. This study compared the LVs which emerged from CCA and PLS analyses of brain-behaviour relationships from the Adolescent Brain Cognitive Development (ABCD) dataset and examined their stability and reproducibility.

Methods

Structural T1-weighted imaging and behavioural data were accessed from the baseline Adolescent Brain Cognitive Development dataset (N > 9000, ages = 9-11 years). The brain matrix consisted of cortical thickness estimates in different cortical regions. The behavioural matrix consisted of 11 subscale scores from the parent-reported Child Behavioral Checklist (CBCL) or 7 cognitive performance measures from the NIH Toolbox. CCA and PLS models were separately applied to the brain-CBCL analysis and brain-cognition analysis. A permutation test was used to assess whether identified LVs were statistically significant. A series of resampling statistical methods were used to assess stability and reproducibility of the LVs.

Results

When examining the relationship between cortical thickness and CBCL scores, the first LV was found to be significant across both CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV1 from the CCA model found that covariation of CBCL scores was linked to covariation of cortical thickness. LV1 from the PLS model identified decreased cortical thickness linked to lower CBCL scores. There was limited evidence of stability or reproducibility of LV1 for both CCA and PLS. When examining the relationship between cortical thickness and cognitive performance, there were 6 significant LVs for both CCA and PLS (p < .01). The first LV showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001).

Conclusion

CCA and PLS identify different brain-behaviour relationships with limited stability and reproducibility when examining the relationship between cortical thickness and parent-reported behavioural measures. However, both methods identified relatively similar brain-behaviour relationships that were stable and reproducible when examining the relationship between cortical thickness and cognitive performance. The results of the current study suggest that stability and reproducibility of brain-behaviour relationships identified by CCA and PLS are influenced by characteristics of the analyzed sample and the included behavioural measurements when applied to a large pediatric dataset.

Resting-state brain functional alterations and their genetic mechanisms in drug-naive first-episode psychosis

Extensive research has established the presence of resting-state brain functional damage in psychosis. However, the genetic mechanisms of such disease phenotype are yet to be unveiled. We investigated resting-state brain functional alterations in patients with drug-naive first-episode psychosis (DFP) by performing a neuroimaging meta-analysis of 8 original studies comprising 500 patients and 469 controls. Combined with the Allen Human Brain Atlas, we further conducted transcriptome-neuroimaging spatial correlations to identify genes whose expression levels were linked to brain functional alterations in DFP, followed by a range of gene functional characteristic analyses. Meta-analysis revealed a mixture of increased and decreased brain function in widespread areas including the default-mode, visual, motor, striatal, and cerebellar systems in DFP. Moreover, these brain functional alterations were spatially associated with the expression of 1662 genes, which were enriched for molecular functions, cellular components, and biological processes of the cerebral cortex, as well as psychiatric disorders including schizophrenia. Specific expression analyses demonstrated that these genes were specifically expressed in the brain tissue, in cortical neurons and immune cells, and during nearly all developmental periods. Concurrently, the genes could construct a protein-protein interaction network supported by hub genes and were linked to multiple behavioral domains including emotion, attention, perception, and motor. Our findings provide empirical evidence for the notion that brain functional damage in DFP involves a complex interaction of polygenes with various functional characteristics.

On the complex and dimensional relationship of maternal posttraumatic stress disorder during early childhood and child outcomes at school-age

BACKGROUND

Several studies have shown associations between maternal interpersonal violence-related posttraumatic stress disorder (PTSD), child mental health problems, and impaired socioemotional development. However, the existing literature lacks evidence linking constellations of risk factors such as maternal interpersonal-violence-related PTSD, psychopathology, and interactive behavior with toddlers and outcome measures at school-age.

METHODS

This study involved a prospective, longitudinal investigation of 62 mothers and examined the relationship between maternal variables measured when children were in early childhood (mean age 27 months), and child outcomes when children were school-age (age mean = 83.2 months) while retaining a focus on the context of maternal PTSD. To identify and weigh associated dimensions comparatively, we employed sparse canonical correlation analysis (sCCA) aimed at associating dimensions of a dataset of 20 maternal variables in early childhood with that of more than 20 child outcome variables (i.e., child psychopathology, life-events, and socioemotional skills) at school-age.

RESULTS

Phase 1 variables with the highest weights were those of maternal psychopathology: PTSD, depressive and dissociative symptoms, and self-report of parental stress. The highest weighted Phase 2 child outcome measures were those of child psychopathology: PTSD, anxiety, and depressive symptoms as well as peer bullying and victimization.

CONCLUSIONS

sCCA revealed that trauma-related concepts in mothers were significantly and reliably associated with child psychopathology and other indicators of risk for intergenerational transmission of violence and victimization. The results highlight the dimensional and multifaceted nature-both for mothers as well as children-of the intergenerational transmission of violence and associated psychopathology.

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.

Canonical Correlation Analysis and Partial Least Squares for Identifying Brain-Behavior Associations: A Tutorial and a Comparative Study

Canonical correlation analysis (CCA) and partial least squares (PLS) are powerful multivariate methods for capturing associations across 2 modalities of data (e.g., brain and behavior). However, when the sample size is similar to or smaller than the number of variables in the data, standard CCA and PLS models may overfit, i.e., find spurious associations that generalize poorly to new data. Dimensionality reduction and regularized extensions of CCA and PLS have been proposed to address this problem, yet most studies using these approaches have some limitations. This work gives a theoretical and practical introduction into the most common CCA/PLS models and their regularized variants. We examine the limitations of standard CCA and PLS when the sample size is similar to or smaller than the number of variables. We discuss how dimensionality reduction and regularization techniques address this problem and explain their main advantages and disadvantages. We highlight crucial aspects of the CCA/PLS analysis framework, including optimizing the hyperparameters of the model and testing the identified associations for statistical significance. We apply the described CCA/PLS models to simulated data and real data from the Human Connectome Project and Alzheimer's Disease Neuroimaging Initiative (both of n > 500). We use both low- and high-dimensionality versions of these data (i.e., ratios between sample size and variables in the range of ∼1-10 and ∼0.1-0.01, respectively) to demonstrate the impact of data dimensionality on the models. Finally, we summarize the key lessons of the tutorial.

Gray matter reduction in bilateral insula mediating adverse psychiatric effects of body mass index in schizophrenia

BACKGROUND

Both schizophrenia (SZ) and overweight/obesity (OWB) have shown some structural alterations in similar brain regions. As higher body mass index (BMI) often contributes to worse psychiatric outcomes in SZ, this study was designed to examine the effects of OWB on gray matter volume (GMV) in patients with SZ.

METHODS

Two hundred fifty subjects were included and stratified into four groups (n = 69, SZ patients with OWB, SZ-OWB; n = 74, SZ patients with normal weight, SZ-NW; n = 54, healthy controls with OWB, HC-OWB; and n = 53, HC with NW, HC-NW). All participants were scanned using high-resolution T1-weighted sequence. The whole-brain voxel-based morphometry was applied to examine the GMV alterations, and a 2 × 2 full factorial analysis of variance was performed to identify the main effects of diagnosis (SZ vs HC), BMI (NW vs OWB) factors, and their interactions. Further, the post hoc analysis was conducted to compare the pairwise differences in GMV alterations.

RESULTS

The main effects of diagnosis were located in right hippocampus, bilateral insula, rectus, median cingulate/paracingulate gyri and thalamus (SZ < HC); while the main effects of BMI were displayed in right amygdala, left hippocampus, bilateral insula, left lingual gyrus, and right superior temporal gyrus (OWB < NW). There were no significant diagnosis-by-BMI interaction effects in the present study, but the results showed that both SZ and OWB were additively associated with lower GMV in bilateral insula. Moreover, mediation analyses revealed the indirect effect of BMI on negative symptom via GMV reduction in bilateral insula.

CONCLUSION

This study further supports that higher BMI is associated with lower GMV, which may increase the risk of unfavourable disease courses in SZ.

The EEG multiverse of schizophrenia

Research on schizophrenia typically focuses on one paradigm for which clear-cut differences between patients and controls are established. Great efforts are made to understand the underlying genetical, neurophysiological, and cognitive mechanisms, which eventually may explain the clinical outcome. One tacit assumption of these "deep rooting" approaches is that paradigms tap into common and representative aspects of the disorder. Here, we analyzed the resting-state electroencephalogram (EEG) of 121 schizophrenia patients and 75 controls. Using multiple signal processing methods, we extracted 194 EEG features. Sixty-nine out of the 194 EEG features showed a significant difference between patients and controls, indicating that these features detect an important aspect of schizophrenia. Surprisingly, the correlations between these features were very low. We discuss several explanations to our results and propose that complementing "deep" with "shallow" rooting approaches might help in understanding the underlying mechanisms of the disorder.

Derivation and utility of schizophrenia polygenic risk associated multimodal MRI frontotemporal network

Schizophrenia is a highly heritable psychiatric disorder characterized by widespread functional and structural brain abnormalities. However, previous association studies between MRI and polygenic risk were mostly ROI-based single modality analyses, rather than identifying brain-based multimodal predictive biomarkers. Based on schizophrenia polygenic risk scores (PRS) from healthy white people within the UK Biobank dataset (N = 22,459), we discovered a robust PRS-associated brain pattern with smaller gray matter volume and decreased functional activation in frontotemporal cortex, which distinguished schizophrenia from controls with >83% accuracy, and predicted cognition and symptoms across 4 independent schizophrenia cohorts. Further multi-disease comparisons demonstrated that these identified frontotemporal alterations were most severe in schizophrenia and schizo-affective patients, milder in bipolar disorder, and indistinguishable from controls in autism, depression and attention-deficit hyperactivity disorder. These findings indicate the potential of the identified PRS-associated multimodal frontotemporal network to serve as a trans-diagnostic gene intermediated brain biomarker specific to schizophrenia.

Associations between BMI and brain structures involved in food intake regulation in first-episode schizophrenia spectrum disorders and healthy controls

Structural brain differences have been described in first-episode schizophrenia spectrum disorders (FES), and often overlap with those evident in the metabolic syndrome (MetS). We examined the associations between body mass index (BMI) and brain structures involved in food intake regulation in minimally treated FES patients (n = 117) compared to healthy controls (n = 117). The effects of FES diagnosis, BMI and their interactions on our selected prefrontal cortical thickness and subcortical gray matter volume regions of interest (ROIs) were investigated with hierarchical multivariate regressions, followed by post-hoc regressions for the individual ROIs. In a secondary analysis, we examined the relationships of other MetS risk factors and psychopathology with the brain ROIs. Both illness and BMI significantly predicted the grouped prefrontal cortical thickness ROIs, whereas only BMI predicted the grouped subcortical volume ROIs. For the individual ROIs, schizophrenia diagnosis predicted thinner left and right frontal pole and right lateral OFC thickness, and increased BMI predicted thinner left and right caudal ACC thickness. There were no significant main or interaction effects for diagnosis and BMI on any of the individual subcortical volume ROIs. Secondary analyses suggest associations between several brain ROIs and individual MetS risk factors, but not with psychopathology. Our findings indicate differential, independent effects for FES diagnosis and BMI on brain structures. Limited evidence suggests that the BMI effects are more prominent in FES. Exploratory analyses suggest associations between other MetS risk factors and some brain ROIs.

Alcohol use history increases the likelihood of suicide behavior among male chronic patients with schizophrenia in a Chinese population

AIMS

This study was designed to detect the association between the history of alcohol drinking and suicidality in schizophrenia (SCZ) inpatients in a Chinese population.

METHODS

We recruited 616 male SCZ inpatients and collected demographic and clinical data. Five-factor model of the Positive and Negative Syndrome Scale (PANSS) was used to assess their psychopathological symptoms.

RESULTS

Our results showed that 31.33% of SCZ patients had a history of alcohol drinking. They had higher rates of lifetime suicide attempt and suicidal ideation than those without a history of alcohol drinking. Moreover, patients with a history of drinking were more likely to attempt suicide (14.51% vs. 7.09%; χ²  = 7.70, df = 1, p = 0.006), with an odds ratio (OR) of 2.22 and have suicidal ideation (29.02% vs. 17.49%; χ²  = 9.89, df = 1, p = 0.002), with an OR of 1.93. In addition, patients who used to drink alcohol were more likely to be smokers and had more severe positive and depressive symptoms (all p < 0.05).

CONCLUSIONS

Our study indicates that history of alcohol drinking may increase the prevalence of lifetime suicide attempt and suicidal ideation in male patients with chronic SCZ. Moreover, the history of alcohol drinking may be associated with some demographic data and clinical symptoms.

Linking interindividual variability in brain structure to behaviour

What are the brain structural correlates of interindividual differences in behaviour? More than a decade ago, advances in structural MRI opened promising new avenues to address this question. The initial wave of research then progressively led to substantial conceptual and methodological shifts, and a replication crisis unveiled the limitations of traditional approaches, which involved searching for associations between local measurements of neuroanatomy and behavioural variables in small samples of healthy individuals. Given these methodological issues and growing scepticism regarding the idea of one-to-one mapping of psychological constructs to brain regions, new perspectives emerged. These not only embrace the multivariate nature of brain structure-behaviour relationships and promote generalizability but also embrace the representation of the relationships between brain structure and behavioural data by latent dimensions of interindividual variability. Here, we examine the past and present of the study of brain structure-behaviour associations in healthy populations and address current challenges and open questions for future investigations.

Clinical insight and cognitive functioning as mediators in the relationships between symptoms of psychosis, depression, and suicide ideation in first-episode psychosis

First-episode psychosis (FEP) is a particularly high-risk period for suicide. Literature suggests poor cognitive functioning may serve as a protective factor, while investigations of clinical insight reveal a complex relationship with suicide outcomes. This study examined the mediating role of cognition and clinical insight in the relationships between positive and negative symptoms, depression, and subsequent suicide ideation among individuals in FEP. Data were obtained from the Recovery After an Initial Schizophrenia Episode project. Participants (n = 404) included adolescents and adults in FEP between the ages of 15 and 40. Measurement utilized the Calgary Depression Rating Scale, Positive and Negative Syndrome Scale, and Brief Assessment of Cognition in Schizophrenia. Structural equation modeling was used to examine the mediation model. The likelihood of experiencing suicide ideation was significantly decreased when working memory was stronger (b = -0.034, SE = 0.02, OR = 0.967, p < .05), and significantly increased when clinical insight was stronger (b = 0.191, SE = 0.08, OR = 1.21, p < .01), positive symptoms were greater (b = 0.422, SE = 0.20, OR = 1.52, p < .05) and depressive symptoms were greater (b = 0.545, SE = 0.15, OR = 1.70, p < .001). Clinical insight and working memory functioned as mediators in the relationships between depression, positive symptoms, negative symptoms, and suicide ideation. Findings suggest it is essential that clinicians have awareness of insight being a risk factor for suicide ideation and balance therapeutic efforts to strengthen clinical insight and cognition in psychosocial treatments with suicide risk assessment and prevention methods.

Joint structural-functional magnetic resonance imaging features are associated with diagnosis and real-world functioning in patients with schizophrenia

OBJECTIVE

Earlier evidence suggested that structural-functional covariation in schizophrenia patients (SCZ) is associated with cognition, a predictor of functioning. Moreover, studies suggested that functional brain abnormalities of schizophrenia may be related with structural network features. However, only few studies have investigated the relationship between structural-functional covariation and both diagnosis and functioning in SCZ. We hypothesized that structural-functional covariation networks associated with diagnosis are related to real-world functioning in SCZ.

METHODS

We performed joint Independent Component Analysis on T1 images and resting-state fMRI-based Degree Centrality (DC) maps from 89 SCZ and 285 controls. Structural-functional covariation networks in which we found a main effect of diagnosis underwent correlation analysis to investigate their relationship with functioning. Covariation networks showing a significant association with both diagnosis and functioning underwent univariate analysis to better characterize group-level differences at the spatial level.

RESULTS

A structural-functional covariation network characterized by frontal, temporal, parietal and thalamic structural estimates significantly covaried with temporo-parietal resting-state DC. Compared with controls, SCZ had reduced structural-functional covariation within this network (p_FDR = 0.005). The same measure correlated positively with both social and occupational functioning (both p_FDR = 0.042). Univariate analyses revealed grey matter deviations in SCZ compared with controls within this structural-functional network in hippocampus, cerebellum, thalamus, orbito-frontal cortex, and insula. No group differences were found in DC.

CONCLUSIONS

Findings support the existence of a phenotypical association between group-level differences and inter-individual heterogeneity of functional deficits in SCZ. Given that only the joint structural/functional analysis revealed this association, structural-functional covariation may be a potentially relevant schizophrenia phenotype.

Multivariate patterns of brain-behavior associations across the adult lifespan

The nature of brain-behavior covariations with increasing age is poorly understood. In the current study, we used a multivariate approach to investigate the covariation between behavioral-health variables and brain features across adulthood. We recruited healthy adults aged 20–73 years-old (29 younger, mean age = 25.6 years; 30 older, mean age = 62.5 years), and collected structural and functional MRI (s/fMRI) during a resting-state and three tasks. From the sMRI, we extracted cortical thickness and subcortical volumes; from the fMRI, we extracted activation peaks and functional network connectivity (FNC) for each task. We conducted canonical correlation analyses between behavioral-health variables and the sMRI, or the fMRI variables, across all participants. We found significant covariations for both types of neuroimaging phenotypes (ps = 0.0004) across all individuals, with cognitive capacity and age being the largest opposite contributors. We further identified different variables contributing to the models across phenotypes and age groups. Particularly, we found behavior was associated with different neuroimaging patterns between the younger and older groups. Higher cognitive capacity was supported by activation and FNC within the executive networks in the younger adults, while it was supported by the visual networks’ FNC in the older adults. This study highlights how the brain-behavior covariations vary across adulthood and provides further support that cognitive performance relies on regional recruitment that differs between older and younger individuals.

Mechanisms underlying dorsolateral prefrontal cortex contributions to cognitive dysfunction in schizophrenia

Kraepelin, in his early descriptions of schizophrenia (SZ), characterized the illness as having "an orchestra without a conductor." Kraepelin further speculated that this "conductor" was situated in the frontal lobes. Findings from multiple studies over the following decades have clearly implicated pathology of the dorsolateral prefrontal cortex (DLPFC) as playing a central role in the pathophysiology of SZ, particularly with regard to key cognitive features such as deficits in working memory and cognitive control. Following an overview of the cognitive mechanisms associated with DLPFC function and how they are altered in SZ, we review evidence from an array of neuroscientific approaches addressing how these cognitive impairments may reflect the underlying pathophysiology of the illness. Specifically, we present evidence suggesting that alterations of the DLPFC in SZ are evident across a range of spatial and temporal resolutions: from its cellular and molecular architecture, to its gross structural and functional integrity, and from millisecond to longer timescales. We then present an integrative model based upon how microscale changes in neuronal signaling in the DLPFC can influence synchronized patterns of neural activity to produce macrocircuit-level alterations in DLPFC activation that ultimately influence cognition and behavior. We conclude with a discussion of initial efforts aimed at targeting DLPFC function in SZ, the clinical implications of those efforts, and potential avenues for future development.

Dissociation Pattern in Default-Mode Network Homogeneity in Drug-Naive Bipolar Disorder

Default mode network (DMN) plays a key role in the pathophysiology of in bipolar disorder (BD). However, the homogeneity of this network in BD is still poorly understood. This study aimed to investigate abnormalities in the NH of the DMN at rest and the correlation between the NH of DMN and clinical variables in patients with BD. Forty drug-naive patients with BD and thirty-seven healthy control subjects participated in the study. Network homogeneity (NH) and independent component analysis (ICA) methods were used for data analysis. Support vector machines (SVM) method was used to analyze NH in different brain regions. Compared with healthy controls, significantly increased NH in the left superior medial prefrontal cortex (MPFC) and decreased NH in the right posterior cingulate cortex (PCC) and bilateral precuneus were found in patients with BD. NH in the right PCC was positively correlated with the verbal fluency test and verbal function total scores. NH in the left superior MPFC was negatively correlated with triglyceride (TG). NH in the right PCC was positively correlated with TG but negatively correlated with high-density lipoprotein cholesterol (HDL-C). NH in the bilateral precuneus was positively correlated with cholesterol and low-density lipoprotein cholesterol (LDL-C). In addition, NH in the left superior MPFC showed high sensitivity (80.00%), specificity (71.43%), and accuracy (75.61%) in the SVM results. These findings contribute new evidence of the participation of the altered NH of the DMN in the pathophysiology of BD.

Mapping brain-behavior space relationships along the psychosis spectrum

Difficulties in advancing effective patient-specific therapies for psychiatric disorders highlight a need to develop a stable neurobiologically grounded mapping between neural and symptom variation. This gap is particularly acute for psychosis-spectrum disorders (PSD). Here, in a sample of 436 PSD patients spanning several diagnoses, we derived and replicated a dimensionality-reduced symptom space across hallmark psychopathology symptoms and cognitive deficits. In turn, these symptom axes mapped onto distinct, reproducible brain maps. Critically, we found that multivariate brain-behavior mapping techniques (e.g. canonical correlation analysis) do not produce stable results with current sample sizes. However, we show that a univariate brain-behavioral space (BBS) can resolve stable individualized prediction. Finally, we show a proof-of-principle framework for relating personalized BBS metrics with molecular targets via serotonin and glutamate receptor manipulations and neural gene expression maps derived from the Allen Human Brain Atlas. Collectively, these results highlight a stable and data-driven BBS mapping across PSD, which offers an actionable path that can be iteratively optimized for personalized clinical biomarker endpoints.

Social optimism biases are associated with cortical thickness

Optimism biases denote the tendency to see future desirable events as being more likely to happen to oneself than undesirable events. Such biases are important for mental health and may extend to other individuals or social groups (social optimism biases). However, little is known about whether social optimism biases relate to brain structure. Using sparse canonical correlation analysis, we associated cortical thickness (assessed by magnetic resonance imaging) with measures of social and personal optimism bias, trait optimism and related concepts. We identified a defensive self-enhancement dimension that associated significantly and reliably with the cortical thickness of the insula and inferior frontal cortex. This self-enhancement dimension included unfavorable biases toward unpopular out-groups and indicators of personal optimism and pessimism. A shared biological substrate underlying future expectancies that subserves the promotion of the self and the denigration of unpopular out-groups may render society-wide efforts to counteract stereotyping particularly difficult: such efforts may hinder the establishment of adaptive personal optimism biases.

Reduced Segregation Between Cognitive and Emotional Processes in Cannabis Dependence

Addiction is characterized by an erosion of cognitive control toward drug taking that is accentuated by negative emotional states. Here we tested the hypothesis that enhanced interference on cognitive control reflects a loss of segregation between cognition and emotion in addiction. We analyzed Human Connectome Project data from 1206 young adults, including 89 with cannabis dependence (CD). Two composite factors, one for cognition and one for emotion, were derived using principal component (PC) analyses. Component scores for these PCs were significantly associated in the CD group, such that negative emotionality correlated with poor cognition. However, the corresponding component scores were uncorrelated in matched controls and nondependent recreational cannabis users (n = 87). In CD, but not controls or recreational users, functional magnetic resonance imaging activations to emotional stimuli (angry/fearful faces > shapes) correlated with activations to cognitive demand (working memory; 2-back > 0-back). Canonical correlation analyses linked individual differences in cognitive and emotional component scores with brain activations. In CD, there was substantial overlap between cognitive and emotional brain-behavior associations, but in controls, associations were more restricted to the cognitive domain. These findings support our hypothesis of impaired segregation between cognitive and emotional processes in CD that might contribute to poor cognitive control under conditions of increased emotional demand.

Resting-state functional hypoconnectivity of amygdala in clinical high risk state and first-episode schizophrenia

Resting-state functional hypoconnectivity of the amygdala with several brain regions has been identified in patients with schizophrenia. However, little is known about it in individuals at clinical high risk state. Treatment-seeking, drug-naive young adults were recruited for the study. The participants included 33 adults at Clinical High Risk (CHRs), 31 adults with first-episode schizophrenia (FSZs), and 37 age-, gender-, and education-matched healthy controls. All the participants were subjected to resting-state functional magnetic resonance imaging scans. Seed-based voxel-wise amygdala/whole-brain functional connectivity (FC) was calculated and compared. In the CHR group, the right amygdala showed decreased FC with clusters located in the left orbital, right temporal, insular, and bilateral frontal and cingulate areas. In the FSZ group, the right amygdala showed decreased FC with clusters located in the right temporal, insular, cingulate, and frontal areas. Exactly 30% of the voxels showing decreased FC in the FSZ group coincided with those in the CHR group. No difference in FC was identified between the CHR and FSZ groups. Voxel-wise FC values with the left or right amygdala in the bilateral occipital cortex were negatively correlated with the PANSS total score in the FSZ group. Resting-state functional hypoconnectivity of the amygdala is a valuable risk phenotype of schizophrenia, and its distribution, rather than degree, distinguishes CHR state from schizophrenia. This particular hypoconnectivity in CHRs and FSZs is relatively independent of the symptomatology and may reflect a dysfunctional dopamine system.

Association between obesity and white matter microstructure impairments in patients with schizophrenia: A whole-brain magnetic resonance imaging study

AIM

We aimed to examine the possible association of obesity (body mass index [BMI] ≥ 30) with symptoms, psychotropic medication, and whole-brain structure in patients with schizophrenia.

METHODS

Participants were 65 patients diagnosed with schizophrenia (mean age: 37.2 ± 11.3 years, 32 females). All participants were Japanese and right-handed. Symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS) and Pittsburgh Sleep Quality Index (PSQI). Voxel based morphometry (VBM) and diffusion tensor imaging (DTI) were performed to analyze the association of obesity with gray and white matter structures, respectively.

RESULTS

There was no significant difference in PANSS scores between obese and non-obese patients, while the PSQI score was significantly higher in the former than in the latter (p < 0.05). The daily dose of typical antipsychotics was significantly higher in obese patients than in non-obese patients (p < 0.001). In VBM, there was no significant difference in gray matter volume between obese and non-obese patients. In DTI, fractional anisotropy values in the corpus callosum, corona radiata, corticospinal tract, superior longitudinal fasciculus, and posterior thalamic radiations were significantly lower in obese patients than in non-obese patients (corrected p < 0.05). Axial diffusivity was significantly lower while radial and mean diffusivities values were significantly higher in obese patients than in non-obese patients (corrected p < 0.05) in similar but more restricted brain regions.

CONCLUSION

Our results suggest that obesity is related to sleep disturbances, daily dose of typical antipsychotics, and regional white matter microstructure impairments in patients with schizophrenia.

Covariation between spontaneous neural activity in the insula and affective temperaments is related to sleep disturbance in individuals with major depressive disorder

BACKGROUND

Affective temperaments have been considered antecedents of major depressive disorder (MDD). However, little is known about how the covariation between alterations in brain activity and distinct affective temperaments work collaboratively to contribute to MDD. Here, we focus on the insular cortex, a critical hub for the integration of subjective feelings, emotions, and motivations, to examine the neural correlates of affective temperaments and their relationship to depressive symptom dimensions.

METHODS

Twenty-nine medication-free patients with MDD and 58 healthy controls underwent magnetic resonance imaging scanning and completed the Temperament Evaluation of Memphis, Pisa, Paris and San Diego (TEMPS). Patients also received assessments of the Hamilton Depression Rating Scale (HDRS). We used multivariate analyses of partial least squares regression and partial correlation analyses to explore the associations among the insular activity, affective temperaments, and depressive symptom dimensions.

RESULTS

A profile (linear combination) of increased fractional amplitude of low-frequency fluctuations (fALFF) of the anterior insular subregions (left dorsal agranular-dysgranular insula and right ventral agranuar insula) was positively associated with an affective-temperament (depressive, irritable, anxious, and less hyperthymic) profile. The covariation between the insula-fALFF profile and the affective-temperament profile was significantly correlated with the sleep disturbance dimension (especially the middle and late insomnia scores) in the medication-free MDD patients.

CONCLUSIONS

The resting-state spontaneous activity of the anterior insula and affective temperaments collaboratively contribute to sleep disturbances in medication-free MDD patients. The approach used in this study provides a practical way to explore the relationship of multivariate measures in investigating the etiology of mental disorders.

Reduced network integration in default mode and executive networks is associated with social and personal optimism biases

An optimism bias refers to the belief in good things happening to oneself in the future with a higher likelihood than is justified. Social optimism biases extend this concept to groups that one identifies with. Previous literature has found that both personal and social optimism biases are linked to brain structure and task-related brain function. Less is known about whether optimism biases are also expressed in resting-state functional connectivity (RSFC). Forty-two participants completed questionnaires on dispositional personal optimism (which is not necessarily unjustified) and comparative optimism (i.e., whether we see our own future as being rosier than a comparison person's future) and underwent a resting-state functional magnetic resonance imaging scan. They further undertook an imaginative soccer task in order to assess both their personal and social optimism bias. We tested associations of these data with RSFC within and between 13 networks, using sparse canonical correlation analyses (sCCAs). We found that the primary sCCA component was positively connected to personal and social optimism bias and negatively connected to dispositional personal pessimism. This component was associated with (a) reduced integration of the default mode network, (b) reduced integration of the central executive and salience networks, and (c) reduced segregation between the default mode network and the central executive network. Our finding that optimism biases are linked to RSFC indicates that they may be rooted in neurobiology that exists outside of concurrent tasks. This poses questions as to what the limits of the malleability of such biases may be.

Associations between cognition and white matter microstructure in first-episode antipsychotic-naïve patients with schizophrenia and healthy controls: A multivariate pattern analysis

BACKGROUND

Cognitive functions have been associated with white matter (WM) microstructure in schizophrenia, but most studies are limited by examining only select cognitive measures and single WM tracts in chronic, medicated patients. It is unclear if the cognition-WM relationship differs between antipsychotic-naïve patients with schizophrenia and healthy controls, as differential associations have not been directly examined. Here we examine if there are differential patterns of associations between cognition and WM microstructure in first-episode antipsychotic-naïve patients with schizophrenia and healthy controls, and we characterize reliable contributors to the pattern of associations across multiple cognitive domains and WM regions, in order to elucidate white matter contribution to the neural underpinnings of cognitive deficits.

METHODS

Thirty-six first-episode antipsychotic-naïve patients with schizophrenia and 52 matched healthy controls underwent cognitive tests and diffusion-weighted imaging on a 3T Magnetic Resonance Imaging scanner. Using a multivariate partial least squares correlation analysis, we included 14 cognitive variables and mean fractional anisotropy values of 48 WM regions.

RESULTS

Initial analyses showed significant group differences in both measures of WM and cognition. There was no group interaction effect in the pattern of associations between cognition and WM microstructure. The combined analysis of patients and controls lead to a significant pattern of associations (omnibus test p = .015). Thirty-four regions and seven cognitive functions contributed reliably to the associations.

CONCLUSIONS

The lack of an interaction effect suggests similar associations in first-episode antipsychotic-naïve patients with schizophrenia and healthy controls. This, together with the differences in both WM and cognitive measurements, supports the involvement of WM in cognitive deficits in schizophrenia. Our findings add to the field by showing a coherent picture of the overall pattern of association between cognition and WM. These findings increase our understanding of the impact of WM on cognition, contributing to the search for neuromarkers of cognitive deficits in schizophrenia.

Conditional canonical correlation estimation based on covariates with random forests

MOTIVATION

Investigating the relationships between two sets of variables helps to understand their interactions and can be done with canonical correlation analysis (CCA). However, the correlation between the two sets can sometimes depend on a third set of covariates, often subject-related ones such as age, gender, or other clinical measures. In this case, applying CCA to the whole population is not optimal and methods to estimate conditional CCA, given the covariates, can be useful.

RESULTS

We propose a new method called Random Forest with Canonical Correlation Analysis (RFCCA) to estimate the conditional canonical correlations between two sets of variables given subject-related covariates. The individual trees in the forest are built with a splitting rule specifically designed to partition the data to maximize the canonical correlation heterogeneity between child nodes. We also propose a significance test to detect the global effect of the covariates on the relationship between two sets of variables. The performance of the proposed method and the global significance test is evaluated through simulation studies that show it provides accurate canonical correlation estimations and well-controlled Type-1 error. We also show an application of the proposed method with EEG data.

AVAILABILITY

RFCCA is implemented in a freely available R package on CRAN (https://CRAN.R-project.org/package=RFCCA).

SUPPLEMENTARY INFORMATION

Supplementary material are available at Bioinformatics online.

Cellular and extracellular white matter alterations indicate conversion to psychosis among individuals at clinical high-risk for psychosis

OBJECTIVES

It is important to find biomarkers associated with transition to illness in individuals at clinical high-risk for psychosis (CHR). Here, we use free-water imaging, an advanced diffusion MRI technique, to identify white matter alterations in the brains of CHR subjects who subsequently develop psychosis (CHR-P) compared to those who do not (CHR-NP).

METHODS

Twenty-four healthy controls (HC) and 30 CHR individuals, 8 of whom converted to schizophrenia after a mean follow-up of 15.16 months, received baseline MRI scans. Maps of fractional anisotropy (FA), FA of cellular tissue (FA_T), and extracellular free-water (FW) were extracted using tract-based spatial statistics after which voxel-wise non-parametric group statistics and correlations with symptom severity were performed.

RESULTS

There were no significant differences between HCs and the combined CHR group. However, prior to conversion, CHR-P showed widespread lower FA compared to CHR-NP (p_FWE < 0.05). FA changes in CHR-P were associated with significantly lower FA_T and higher FW, compared to CHR-NP. Positive symptoms correlated significantly with diffusion parameters in similar regions as those discriminating CHR-P from CHR-NP.

CONCLUSIONS

Our study suggests that cellular (FA_T) and extracellular (FW) white matter alterations are associated with positive symptom severity and indicate an elevated illness risk among CHR individuals.

Multivariate Patterns of Brain-Behavior-Environment Associations in the Adolescent Brain and Cognitive Development Study

BACKGROUND

Adolescence is a critical developmental stage. A key challenge is to characterize how variation in adolescent brain organization relates to psychosocial and environmental influences.

METHODS

We used canonical correlation analysis to discover distinct patterns of covariation between measures of brain organization (brain morphometry, intracortical myelination, white matter integrity, and resting-state functional connectivity) and individual, psychosocial, and environmental factors in a nationally representative U.S. sample of 9623 individuals (aged 9-10 years, 49% female) participating in the Adolescent Brain and Cognitive Development (ABCD) study.

RESULTS

These analyses identified 14 reliable modes of brain-behavior-environment covariation (canonical r_discovery = .21 to .49, canonical r_test = .10 to .39, p_{false discovery rate corrected} < .0001). Across modes, neighborhood environment, parental characteristics, quality of family life, perinatal history, cardiometabolic health, cognition, and psychopathology had the most consistent and replicable associations with multiple measures of brain organization; positive and negative exposures converged to form patterns of psychosocial advantage or adversity. These showed modality-general, respectively positive or negative, associations with brain structure and function with little evidence of regional specificity. Nested within these cross-modal patterns were more specific associations between prefrontal measures of morphometry, intracortical myelination, and functional connectivity with affective psychopathology, cognition, and family environment.

CONCLUSIONS

We identified clusters of exposures that showed consistent modality-general associations with global measures of brain organization. These findings underscore the importance of understanding the complex and intertwined influences on brain organization and mental function during development and have the potential to inform public health policies aiming toward interventions to improve mental well-being.

Exploring the Effects of Temperament on Gray Matter Volume of Frontal Cortex in Patients with Mood Disorders

BACKGROUND

Patients with bipolar disorder (BD) and patients with major depressive disorder (MDD) have relatively specific temperament and structural abnormalities of brain regions related to emotion and cognition. However, the effects of temperament factors on the structure of frontal and temporal cortex is still unclear. The aims of this study were to explore the differences and relationships between temperament characteristics and the gray matter volume of frontal and temporal cortex in patients with BD or MDD.

METHODS

T1-weighted magnetic resonance imaging (MRI) data, demographic and clinical information were obtained from 279 depressed patients (90 patients with BD, 189 patients with MDD) and 162 healthy controls (HC). Temperament was assessed with the Chinese short version of Temperament Evaluation of Memphis, Pisa and San Diego - Auto questionnaire (TEMPS-A). The Desikan-Killiany atlas was used for yielding gray matter volume by FreeSurfer 6.0 software suite. A total of 22 frontal and temporal regions were chosen as regions of interest (ROIs).

RESULTS

Compared with patients with MDD, patients with BD had higher TEMPS-A total scores and scores on cyclothymic, irritable and hyperthymic subscales. The gray matter volume in bilateral rostral middle frontal gyrus (RMFG), left temporal pole and right superior frontal gyrus were reduced in patients with BD. Patients with MDD only had lower gray matter volume in bilateral temporal pole. In the pooled patients, there were negative associations between hyperthymia and gray matter volume in right RMFG.

CONCLUSION

Patients with BD and MDD had different temperament characteristics. The prominent temperament subscales in patients with BD were cyclothymia, irritable and hyperthymia. Patients with greater hyperthymia had lower gray matter volume in right frontal gyrus. Temperament may reflect an endophenotype in patients with mood disorders, especially in BD.

Linked patterns of biological and environmental covariation with brain structure in adolescence: a population-based longitudinal study

Adolescence is a period of major brain reorganization shaped by biologically timed and by environmental factors. We sought to discover linked patterns of covariation between brain structural development and a wide array of these factors by leveraging data from the IMAGEN study, a longitudinal population-based cohort of adolescents. Brain structural measures and a comprehensive array of non-imaging features (relating to demographic, anthropometric, and psychosocial characteristics) were available on 1476 IMAGEN participants aged 14 years and from a subsample reassessed at age 19 years (n = 714). We applied sparse canonical correlation analyses (sCCA) to the cross-sectional and longitudinal data to extract modes with maximum covariation between neuroimaging and non-imaging measures. Separate sCCAs for cortical thickness, cortical surface area and subcortical volumes confirmed that each imaging phenotype was correlated with non-imaging features (sCCA r range: 0.30-0.65, all P_FDR < 0.001). Total intracranial volume and global measures of cortical thickness and surface area had the highest canonical cross-loadings (|ρ| = 0.31-0.61). Age, physical growth and sex had the highest association with adolescent brain structure (|ρ| = 0.24-0.62); at baseline, further significant positive associations were noted for cognitive measures while negative associations were observed at both time points for prenatal parental smoking, life events, and negative affect and substance use in youth (|ρ| = 0.10-0.23). Sex, physical growth and age are the dominant influences on adolescent brain development. We highlight the persistent negative influences of prenatal parental smoking and youth substance use as they are modifiable and of relevance for public health initiatives.

Multimodal MRI assessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup

Multi‐institutional brain imaging studies have emerged to resolve conflicting results among individual studies. However, adjusting multiple variables at the technical and cohort levels is challenging. Therefore, it is important to explore approaches that provide meaningful results from relatively small samples at institutional levels. We studied 87 first episode psychosis (FEP) patients and 62 healthy subjects by combining supervised integrated factor analysis (SIFA) with a novel pipeline for automated structure‐based analysis, an efficient and comprehensive method for dimensional data reduction that our group recently established. We integrated multiple MRI features (volume, DTI indices, resting state fMRI—rsfMRI) in the whole brain of each participant in an unbiased manner. The automated structure‐based analysis showed widespread DTI abnormalities in FEP and rs‐fMRI differences between FEP and healthy subjects mostly centered in thalamus. The combination of multiple modalities with SIFA was more efficient than the use of single modalities to stratify a subgroup of FEP (individuals with schizophrenia or schizoaffective disorder) that had more robust deficits from the overall FEP group. The information from multiple MRI modalities and analytical methods highlighted the thalamus as significantly abnormal in FEP. This study serves as a proof‐of‐concept for the potential of this methodology to reveal disease underpins and to stratify populations into more homogeneous sub‐groups.

Antipsychotic ffects on ortical Morphology in chizophrenia and ipolar isorders

Background: Previous studies of atypical antipsychotic effects on cortical structures in schizophrenia (SZ) and bipolar disorder (BD) have findings that vary between the short and long term. In particular, there has not been a study exploring the effects of atypical antipsychotics on age-related cortical structural changes in SZ and BD. This study aimed to determine whether mid- to long-term atypical antipsychotic treatment (mean duration = 20 months) is associated with cortical structural changes and whether age-related cortical structural changes are affected by atypical antipsychotics.

Methods: Structural magnetic resonance imaging images were obtained from 445 participants consisting of 88 medicated patients (67 with SZ, 21 with BD), 84 unmedicated patients (50 with SZ, 34 with BD), and 273 healthy controls (HC). Surface-based analyses were employed to detect differences in thickness and area among the three groups. We examined the age-related effects of atypical antipsychotics after excluding the potential effects of illness duration.

Results: Significant differences in cortical thickness were observed in the frontal, temporal, parietal, and insular areas and the isthmus of the cingulate gyrus. The medicated group showed greater cortical thinning in these regions than the unmediated group and HC; furthermore, there were age-related differences in the effects of atypical antipsychotics, and these effects did not relate to illness duration. Moreover, cortical thinning was significantly correlated with lower symptom scores and Wisconsin Card Sorting Test (WCST) deficits in patients. After false discovery rate correction, cortical thinning in the right middle temporal gyrus in patients was significantly positively correlated with lower HAMD scores. The unmedicated group showed only greater frontotemporal thickness than the HC group.

Conclusion: Mid- to long-term atypical antipsychotic use may adversely affect cortical thickness over the course of treatment and ageing and may also result in worsening cognitive function.

Personalized estimates of morphometric similarity in bipolar disorder and schizophrenia

Bipolar disorder and schizophrenia are associated with brain morphometry alterations. This study investigates inter-individual variability in brain structural profiles, both within diagnostic groups and between patients and healthy individuals. Brain morphometric measures from three independent samples of patients with schizophrenia (n = 168), bipolar disorder (n = 122), and healthy individuals (n = 180) were modeled as single vectors to generated individualized profiles of subcortical volumes and regional cortical thickness. These profiles were then used to compute a person-based similarity index (PBSI) for subcortical volumes and for regional cortical thickness, to quantify the within-group similarity of the morphometric profile of each individual to that of the other participants in the same diagnostic group. There was no effect of diagnosis on the PBSI for subcortical volumes. In contrast, compared to healthy individuals, the PBSI for cortical thickness was lower in patients with schizophrenia (effect size = 0.4, p ≤ 0.0002), but not in patients with bipolar disorder. The results were robust and reproducible across samples. We conclude that disease mechanisms for these disorders produce modest inter-individual variations in brain morphometry that should be considered in future studies attempting to cluster patients in subgroups.

Person-based similarity in brain structure and functional connectivity in bipolar disorder

BACKGROUND

Bipolar disorder shows significant variability in clinical presentation. Here we adopt a personalized approach to quantify the brain structural and functional similarity of each individual patient to other patients and to healthy individuals.

METHODS

Brain morphometric and resting-state functional connectivity measures from two independent samples of patients with bipolar disorder and healthy individuals (total number of participants=215) were modeled as single vectors to generated individualized morphometric and connectivity profiles. These profiles were then used to compute a person-based similarity indices which quantified the similarity in neuroimaging profiles amongst patients and between patients and health individuals.

RESULTS

The morphometric and connectivity profiles of patients showed within-diagnosis similarity which was comparable to that observed in healthy individuals. They also showed minimal deviance from those of healthy individuals; the correlation between the profiles of patients and healthy individuals was high (range: 0.71-0.94, p<10^-5). The degree of similarity between imaging profiles was associated with IQ (for cortical thickness) and age (functional integration) rather than clinical variables. Patients who were prescribed lithium, compared to those who were not, showed greater similarity to healthy individuals in terms of network integration (t = 2.2, p = 0.03).

LIMITATIONS

We focused on patients with Bipolar disorder, type I only.

CONCLUSIONS

High inter-individual similarity in neuroimaging profiles was observed amongst patients with bipolar disorder and between patients and healthy individuals. We infer that brain alterations associated with bipolar disorder may be nested within the normal biological diversity consistent with the high prevalence of mood symptoms in the general population.

Multivariate neuroanatomical correlates of behavioral and psychological symptoms in dementia and the moderating role of education

Neuropsychiatric symptoms are commonly observed as brain pathology progresses with dementia. Behavioral and affective disturbances underly the distinct neuroanatomical basis of typical symptoms of cognitive impairment; however it remains unclear whether enriched intellectual experience, such as educational attainment, can mitigate the effect of brain structural patterns on neuropsychiatric symptom severity. We utilized the Open Access Series of Imaging Studies (OASIS-3) dataset, which includes brain structural MRI and behavioral symptom evaluation. We included 904 older adults who were mostly cognitively normal, clinically diagnosed with very mild to moderate Alzheimer's disease, or other types of dementia. Canonical correlation analysis was used to identify the patterns of multivariate association between the gray matter structure and neuropsychiatric symptom severity. First, we identified two canonical modes capturing the distinct neuroanatomical basis of common and mood-specific factors of neuropsychiatric symptoms. The first common pattern reflected a smaller volume in the amygdala and adjacent temporal regional thickness. The second mood-specific pattern reflected patterns in lateral and orbital prefrontal regional thickness. In the external correlational analysis, the two canonical correlations reflected global brain volume and white matter lesions; however, the second pattern was not associated with functional impairments or cognitive function. Moreover, older adults with higher education showed an attenuated severity of behavioral symptoms, even with the presence of a brain structural pattern. Our findings suggest that educational attainment, as a proxy of cognitive reserve, can mitigate the severity of behavioral and affective symptoms of dementia.

A technical review of canonical correlation analysis for neuroscience applications

Collecting comprehensive data sets of the same subject has become a standard in neuroscience research and uncovering multivariate relationships among collected data sets have gained significant attentions in recent years. Canonical correlation analysis (CCA) is one of the powerful multivariate tools to jointly investigate relationships among multiple data sets, which can uncover disease or environmental effects in various modalities simultaneously and characterize changes during development, aging, and disease progressions comprehensively. In the past 10 years, despite an increasing number of studies have utilized CCA in multivariate analysis, simple conventional CCA dominates these applications. Multiple CCA-variant techniques have been proposed to improve the model performance; however, the complicated multivariate formulations and not well-known capabilities have delayed their wide applications. Therefore, in this study, a comprehensive review of CCA and its variant techniques is provided. Detailed technical formulation with analytical and numerical solutions, current applications in neuroscience research, and advantages and limitations of each CCA-related technique are discussed. Finally, a general guideline in how to select the most appropriate CCA-related technique based on the properties of available data sets and particularly targeted neuroscience questions is provided.

Linking language features to clinical symptoms and multimodal imaging in individuals at clinical high risk for psychosis

BACKGROUND

Abnormalities in the semantic and syntactic organization of speech have been reported in individuals at clinical high-risk (CHR) for psychosis. The current study seeks to examine whether such abnormalities are associated with changes in brain structure and functional connectivity in CHR individuals.

METHODS

Automated natural language processing analysis was applied to speech samples obtained from 46 CHR and 22 healthy individuals. Brain structural and resting-state functional imaging data were also acquired from all participants. Sparse canonical correlation analysis (sCCA) was used to ascertain patterns of covariation between linguistic features, clinical symptoms, and measures of brain morphometry and functional connectivity related to the language network.

RESULTS

In CHR individuals, we found a significant mode of covariation between linguistic and clinical features (r = 0.73; p = 0.003), with negative symptoms and bizarre thinking covarying mostly with measures of syntactic complexity. In the entire sample, separate sCCAs identified a single mode of covariation linking linguistic features with brain morphometry (r = 0.65; p = 0.05) and resting-state network connectivity (r = 0.63; p = 0.01). In both models, semantic and syntactic features covaried with brain structural and functional connectivity measures of the language network. However, the contribution of diagnosis to both models was negligible.

CONCLUSIONS

Syntactic complexity appeared sensitive to prodromal symptoms in CHR individuals while the patterns of brain-language covariation seemed preserved. Further studies in larger samples are required to establish the reproducibility of these findings.

Latent Clinical-Anatomical Dimensions of Schizophrenia

Widespread structural brain abnormalities have been consistently reported in schizophrenia, but their relation to the heterogeneous clinical manifestations remains unknown. In particular, it is unclear whether anatomical abnormalities in discrete regions give rise to discrete symptoms or whether distributed abnormalities give rise to the broad clinical profile associated with schizophrenia. Here, we apply a multivariate data-driven approach to investigate covariance patterns between multiple-symptom domains and distributed brain abnormalities in schizophrenia. Structural magnetic resonance imaging and clinical data were derived from one discovery sample (133 patients and 113 controls) and one independent validation sample (108 patients and 69 controls). Disease-related voxel-wise brain abnormalities were estimated using deformation-based morphometry. Partial least-squares analysis was used to comprehensively map clinical, neuropsychological, and demographic data onto distributed deformation in a single multivariate model. The analysis identified 3 latent clinical-anatomical dimensions that collectively accounted for 55% of the covariance between clinical data and brain deformation. The first latent clinical-anatomical dimension was replicated in an independent sample, encompassing cognitive impairments, negative symptom severity, and brain abnormalities within the default mode and visual networks. This cognitive-negative dimension was associated with low socioeconomic status and was represented across multiple races. Altogether, we identified a continuous cognitive-negative dimension of schizophrenia, centered on 2 intrinsic networks. By simultaneously taking into account both clinical manifestations and neuroanatomical abnormalities, the present results open new avenues for multi-omic stratification and biotyping of individuals with schizophrenia.

Reconfiguration of Dynamic Functional Connectivity in Sensory and Perceptual System in Schizophrenia

Schizophrenia is thought as a self-disorder with dysfunctional brain connectivity. This self-disorder is often attributed to high-order cognitive impairment. Yet due to the frequent report of sensorial and perceptual deficits, it has been hypothesized that self-disorder in schizophrenia is dysfunctional communication between sensory and cognitive processes. To further verify this assumption, the present study comprehensively examined dynamic reconfigurations of resting-state functional connectivity (rsFC) in schizophrenia at voxel level, region level, and network levels (102 patients vs. 124 controls). We found patients who show consistently increased rsFC variability in sensory and perceptual system, including visual network, sensorimotor network, attention network, and thalamus at all the three levels. However, decreased variability in high-order networks, such as default mode network and frontal-parietal network were only consistently observed at region and network levels. Taken together, these findings highlighted the rudimentary role of elevated instability of information communication in sensory and perceptual system and attenuated whole-brain integration of high-order network in schizophrenia, which provided novel neural evidence to support the hypothesis of disrupted perceptual and cognitive function in schizophrenia. The foci of effects also highlighted that targeting perceptual deficits can be regarded as the key to enhance our understanding of pathophysiology in schizophrenia and promote new treatment intervention.

Approaches to Defining Common and Dissociable Neurobiological Deficits Associated With Psychopathology in Youth

Psychiatric disorders show high rates of comorbidity and nonspecificity of presenting clinical symptoms, while demonstrating substantial heterogeneity within diagnostic categories. Notably, many of these psychiatric disorders first manifest in youth. We review progress and next steps in efforts to parse heterogeneity in psychiatric symptoms in youths by identifying abnormalities within neural circuits. To address this fundamental challenge in psychiatry, a number of methods have been proposed. We provide an overview of these methods, broadly organized into dimensional versus categorical approaches and single-view versus multiview approaches. Dimensional approaches including factor analysis and canonical correlation analysis aim to capture dimensional associations between psychopathology and brain measures across a continuous spectrum from health to disease. In contrast, categorical approaches, such as clustering and community detection, aim to identify subtypes of individuals within a class of symptoms or brain features. We highlight several studies that apply these methods to samples of youths and discuss issues to consider when using these approaches. Finally, we end by highlighting avenues for future research.