Examining Functional Resting-State Connectivity in Psychosis and Its Subgroups in the Bipolar-Schizophrenia Network on Intermediate Phenotypes Cohort

Peripheral inflammatory subgroup differences in anterior Default Mode network and multiplex functional network topology are associated with cognition in psychosis

INTRODUCTION

High-inflammation subgroups of patients with psychosis demonstrate cognitive deficits and neuroanatomical alterations. Systemic inflammation assessed using IL-6 and C-reactive protein may alter functional connectivity within and between resting-state networks, but the cognitive and clinical implications of these alterations remain unknown. We aim to determine the relationships of elevated peripheral inflammation subgroups with resting-state functional networks and cognition in psychosis spectrum disorders.

METHODS

Serum and resting-state fMRI were collected from psychosis probands (schizophrenia, schizoaffective, psychotic bipolar disorder) and healthy controls (HC) from the B-SNIP1 (Chicago site) study who were stratified into inflammatory subgroups based on factor and cluster analyses of 13 cytokines (HC Low n = 32, Proband Low n = 65, Proband High n = 29). Nine resting-state networks derived from independent component analysis were used to assess functional and multilayer connectivity. Inter-network connectivity was measured using Fisher z-transformation of correlation coefficients. Network organization was assessed by investigating networks of positive and negative connections separately, as well as investigating multilayer networks using both positive and negative connections. Cognition was assessed using the Brief Assessment of Cognition in Schizophrenia. Linear regressions, Spearman correlations, permutations tests and multiple comparison corrections were used for analyses in R.

RESULTS

Anterior default mode network (DMNa) connectivity was significantly reduced in the Proband High compared to Proband Low (Cohen's d = -0.74, p = 0.002) and HC Low (d = -0.85, p = 0.0008) groups. Inter-network connectivity between the DMNa and the right-frontoparietal networks was lower in Proband High compared to Proband Low (d = -0.66, p = 0.004) group. Compared to Proband Low, the Proband High group had lower negative (d = 0.54, p = 0.021) and positive network (d = 0.49, p = 0.042) clustering coefficient, and lower multiplex network participation coefficient (d = -0.57, p = 0.014). Network findings in high inflammation subgroups correlate with worse verbal fluency, verbal memory, symbol coding, and overall cognition.

CONCLUSION

These results expand on our understanding of the potential effects of peripheral inflammatory signatures and/or subgroups on network dysfunction in psychosis and how they relate to worse cognitive performance. Additionally, the novel multiplex approach taken in this study demonstrated how inflammation may disrupt the brain's ability to maintain healthy co-activation patterns between the resting-state networks while inhibiting certain connections between them.

Neuroimaging based biotypes for precision diagnosis and prognosis in cancer-related cognitive impairment

Cancer related cognitive impairment (CRCI) is commonly associated with cancer and its treatments, yet the present binary diagnostic approach fails to capture the full spectrum of this syndrome. Cognitive function is highly complex and exists on a continuum that is poorly characterized by dichotomous categories. Advanced statistical methodologies applied to symptom assessments have demonstrated that there are multiple subclasses of CRCI. However, studies suggest that relying on symptom assessments alone may fail to account for significant differences in the neural mechanisms that underlie a specific cognitive phenotype. Treatment plans that address the specific physiologic mechanisms involved in an individual patient's condition is the heart of precision medicine. In this narrative review, we discuss how biotyping, a precision medicine framework being utilized in other mental disorders, could be applied to CRCI. Specifically, we discuss how neuroimaging can be used to determine biotypes of CRCI, which allow for increased precision in prediction and diagnosis of CRCI via biologic mechanistic data. Biotypes may also provide more precise clinical endpoints for intervention trials. Biotyping could be made more feasible with proxy imaging technologies or liquid biomarkers. Large cross-sectional phenotyping studies are needed in addition to evaluation of longitudinal trajectories, and data sharing/pooling is highly feasible with currently available digital infrastructures.

Reduced white matter microstructure in bipolar disorder with and without psychosis

OBJECTIVES

Affective and psychotic features overlap considerably in bipolar I disorder, complicating efforts to determine its etiology and develop targeted treatments. In order to clarify whether mechanisms are similar or divergent for bipolar disorder with psychosis (BDP) and bipolar disorder with no psychosis (BDNP), neurobiological profiles for both the groups must first be established. This study examines white matter structure in the BDP and BDNP groups, in an effort to identify portions of white matter that may differ between the bipolar and healthy groups or between the bipolar subgroups themselves.

METHODS

Diffusion-weighted imaging data were acquired from participants with BDP (n = 45), BDNP (n = 40), and healthy comparisons (HC) (n = 66). Fractional anisotropy (FA), radial diffusivity (RD), and spin distribution function (SDF) values indexing white matter diffusivity or spin density were calculated and compared between the groups.

RESULTS

In comparisons between both the bipolar groups and HC, FA (FDR < 0.00001) and RD (FDR = 0.0037) differed minimally, in localized portions of the left cingulum and corpus callosum, while reductions in SDF (FDR = 0.0002) were more widespread. The bipolar subgroups did not differ from each other on FA, RD, or SDF metrics.

CONCLUSIONS

Together, these results demonstrate a novel profile of white matter differences in bipolar disorder and suggest that this white matter pathology is associated with the affective disturbance common to those with bipolar disorder rather than the psychotic features unique to some. The white matter alterations identified in this study may provide substrates for future studies examining specific mechanisms that target affective domains of illness.

Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping

Background: Psychiatric disorders have been historically classified using symptom information alone. Recently, there has been a dramatic increase in research interest not only in identifying the mechanisms underlying defined pathologies but also in redefining their etiology. This is particularly relevant for the field of personalized medicine, which searches for data-driven approaches to improve diagnosis, prognosis, and treatment selection for individual patients. Methods: This review aims to provide a high-level overview of the rapidly growing field of functional magnetic resonance imaging (fMRI) from the perspective of unsupervised machine learning applications for disease subtyping. Following the PRISMA guidelines for protocol reproducibility, we searched the PubMed database for articles describing functional MRI applications used to obtain, interpret, or validate psychiatric disease subtypes. We also employed the active learning framework ASReview to prioritize publications in a machine learning-guided way. Results: From the 20 studies that met the inclusion criteria, five used functional MRI data to interpret symptom-derived disease clusters, four used it to interpret clusters derived from biomarker data other than fMRI itself, and 11 applied clustering techniques involving fMRI directly. Major depression disorder and schizophrenia were the two most frequently studied pathologies (35% and 30% of the retrieved studies, respectively), followed by ADHD (15%), psychosis as a whole (10%), autism disorder (5%), and the consequences of early exposure to violence (5%). Conclusions: The increased interest in personalized medicine and data-driven disease subtyping also extends to psychiatric disorders. However, to date, this subfield is at an incipient exploratory stage, and all retrieved studies were mostly proofs of principle where further validation and increased sample sizes are craved for. Whereas results for all explored diseases are inconsistent, we believe this reflects the need for concerted, multisite data collection efforts with a strong focus on measuring the generalizability of results. Finally, whereas functional MRI is the best way of measuring brain function available to date, its low signal-to-noise ratio and elevated monetary cost make it a poor clinical alternative. Even with technology progressing and costs decreasing, this might incentivize the search for more accessible, clinically ready functional proxies in the future.

Identifying and validating subtypes within major psychiatric disorders based on frontal-posterior functional imbalance via deep learning

Converging evidence increasingly implicates shared etiologic and pathophysiological characteristics among major psychiatric disorders (MPDs), such as schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). Examining the neurobiology of the psychotic-affective spectrum may greatly advance biological determination of psychiatric diagnosis, which is critical for the development of more effective treatments. In this study, ensemble clustering was developed to identify subtypes within a trans-diagnostic sample of MPDs. Whole brain amplitude of low-frequency fluctuations (ALFF) was used to extract the low-dimensional features for clustering in a total of 944 participants: 581 psychiatric patients (193 with SZ, 171 with BD, and 217 with MDD) and 363 healthy controls (HC). We identified two subtypes with differentiating patterns of functional imbalance between frontal and posterior brain regions, as compared to HC: (1) Archetypal MPDs (60% of MPDs) had increased frontal and decreased posterior ALFF, and decreased cortical thickness and white matter integrity in multiple brain regions that were associated with increased polygenic risk scores and enriched risk gene expression in brain tissues; (2) Atypical MPDs (40% of MPDs) had decreased frontal and increased posterior ALFF with no associated alterations in validity measures. Medicated Archetypal MPDs had lower symptom severity than their unmedicated counterparts; whereas medicated and unmedicated Atypical MPDs had no differences in symptom scores. Our findings suggest that frontal versus posterior functional imbalance as measured by ALFF is a novel putative trans-diagnostic biomarker differentiating subtypes of MPDs that could have implications for precision medicine.

Cognitive Impairment and Diminished Neural Responses Constitute a Biomarker Signature of Negative Symptoms in Psychosis

The treatment of negative symptoms (NS) in psychosis represents an urgent unmet medical need given the significant functional impairment it contributes to psychosis syndromes. The lack of progress in treating NS is impacted by the lack of known pathophysiology or associated quantitative biomarkers, which could provide tools for research. This current analysis investigated potential associations between NS and an extensive battery of behavioral and brain-based biomarkers in 932 psychosis probands from the B-SNIP database. The current analyses examined associations between PANSS-defined NS and (1) cognition, (2) pro-/anti-saccades, (3) evoked and resting-state electroencephalography (EEG), (4) resting-state fMRI, and (5) tractography. Canonical correlation analyses yielded symptom-biomarker constructs separately for each biomarker modality. Biomarker modalities were integrated using canonical discriminant analysis to summarize the symptom-biomarker relationships into a "biomarker signature" for NS. Finally, distinct biomarker profiles for 2 NS domains ("diminished expression" vs "avolition/apathy") were computed using step-wise linear regression. NS were associated with cognitive impairment, diminished EEG response amplitudes, deviant resting-state activity, and oculomotor abnormalities. While a connection between NS and poor cognition has been established, association to neurophysiology is novel, suggesting directions for future mechanistic studies. Each biomarker modality was related to NS in distinct and complex ways, giving NS a rich, interconnected fingerprint and suggesting that any one biomarker modality may not adequately capture the full spectrum of symptomology.

Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis

It is difficult to distinguish schizophrenia (SZ), schizoaffective disorder (SAD), and bipolar disorder with psychosis (BPP) as their clinical diagnoses rely on symptoms that overlap. In this paper, we investigate if there is biological evidence to support the symptom-based clinical categories by looking across the three disorders using dynamic connectivity measures, and provide meaningful characteristics on which brain functional connectivity measures are commonly or uniquely impaired. Large-sample functional magnetic resonance image (fMRI) datasets from 623 subjects including 238 healthy controls (HCs), 113 SZ patients, 132 SAD patients, and 140 BPP patients were analyzed. First, we computed whole-brain dynamic functional connectivity (DFC) using a sliding-window technique, and then extracted the individual connectivity states by applying our previously proposed decomposition-based DFC analysis method. Next, with the features from the dominant connectivity state, we assessed the clinical categories by performing both four-group (SZ, SAD, BPP and healthy control groups) and pair-wise classification using a support vector machine within cross-validation. Furthermore, we comprehensively summarized the shared and unique connectivity alterations among the disorders. In terms of the classification performance, our method achieved 69% in the four-group classification and >80% in the between-group classifications for the mean overall accuracy; and yielded 66% in the four-group classification and >80% in the between-group classifications for the mean balanced accuracy. Through summarizing the features that were automatically selected in the classifications, we found that among the three symptom-related disorders, their disorder-common impairments primarily included the decreased connectivity strength between thalamus and cerebellum and the increased strength between postcentral gyrus and thalamus. The disorder-unique changes included more various brain regions, mainly in the temporal and frontal gyrus. Our work demonstrates that dynamic functional connectivity provides biological evidence that both common and unique impairments exist in psychosis sub-groups.

Brain gray matter network organization in psychotic disorders

Abnormal neuroanatomic brain networks have been reported in schizophrenia, but their characterization across patients with psychotic disorders, and their potential alterations in nonpsychotic relatives, remain to be clarified. Participants recruited by the Bipolar and Schizophrenia Network for Intermediate Phenotypes consortium included 326 probands with psychotic disorders (107 with schizophrenia (SZ), 87 with schizoaffective disorder (SAD), 132 with psychotic bipolar disorder (BD)), 315 of their nonpsychotic first-degree relatives and 202 healthy controls. Single-subject gray matter graphs were extracted from structural MRI scans, and whole-brain neuroanatomic organization was compared across the participant groups. Compared with healthy controls, psychotic probands showed decreased nodal efficiency mainly in bilateral superior temporal regions. These regions had altered morphological relationships primarily with frontal lobe regions, and their network-level alterations were associated with positive symptoms of psychosis. Nonpsychotic relatives showed lower nodal centrality metrics in the prefrontal cortex and subcortical regions, and higher nodal centrality metrics in the left cingulate cortex and left thalamus. Diagnosis-specific analysis indicated that individuals with SZ had lower nodal efficiency in bilateral superior temporal regions than controls, probands with SAD only exhibited lower nodal efficiency in the left superior and middle temporal gyrus, and individuals with psychotic BD did not show significant differences from healthy controls. Our findings provide novel evidence of clinically relevant disruptions in the anatomic association of the superior temporal lobe with other regions of whole-brain networks in patients with psychotic disorders, but not in their unaffected relatives, suggesting that it is a disease-related trait. Network disorganization primarily involving frontal lobe and subcortical regions in nonpsychotic relatives may be related to familial illness risk.

Intrinsic neural activity differences in psychosis biotypes: Findings from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortium

Intro

The Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) proposed "Biotypes," subgroups of psychosis cases with neuro-cognitive homology. Neural activity unbound to stimulus processing (nonspecific or intrinsic activity) was important for differentiating Biotypes, with Biotype-2 characterized by high nonspecific neural activity. A precise estimate of intrinsic activity (IA) was not included in the initial Biotypes characterization. This report hypothesizes intrinsic activity is a critical differentiating feature for psychosis Biotypes.

Method

Participants were recruited at B-SNIP sites and included probands with psychosis (schizophrenia, schizoaffective disorder, bipolar I disorder), their first-degree biological relatives, and healthy persons (N = 1338). Probands were also sub-grouped by psychosis Biotype. 10-sec inter-stimulus intervals during an auditory paired-stimuli task were used to quantify intrinsic activity from 64 EEG sensors. Single-trial power and connectivity measures at empirically derived frequency bands were quantified. Multivariate discriminant and correlational analyses were used to summarize variables that efficiently and maximally differentiated groups by conventional diagnoses and Biotypes and to determine their relationship to clinical and social functioning.

Results

Biotype-1 consistently exhibited low IA, and Biotype 2 exhibited high IA relative to healthy persons across power frequency bands (delta/theta, alpha, beta, gamma) and alpha band connectivity estimates. DSM groups did not differ from healthy persons on any IA measure.

Discussion

Psychosis Biotypes, but not DSM syndromes, were differentiated by intrinsic activity; Biotype-2 was uniquely characterized by an accentuation of this measure. Neurobiologically defined psychosis subgroups may facilitate the use of intrinsic activity in translation models aimed at developing effective treatments for psychosisrelevant deviations in neural modulation.

The relationship between performance in a theory of mind task and intrinsic functional connectivity in youth with early onset psychosis

Psychotic disorders are characterized by theory of mind (ToM) impairment. Although ToM undergoes maturational changes throughout adolescence, there is a lack of studies examining ToM performance and its brain functional correlates in individuals with an early onset of psychosis (EOP; onset prior to age 18), and its relationship with age. Twenty-seven individuals with EOP were compared with 41 healthy volunteers using the "Reading-the-Mind-in-the-Eyes" Test, as a measure of ToM performance. A resting-state functional MRI scan was also acquired, in which the default mode network was used to identify areas relevant to ToM processing employing independent component analysis. Group effects revealed worse ToM performance and less intrinsic functional connectivity in the medial prefrontal cortex in EOP relative to healthy volunteers. Group by age interaction revealed age-positive associations in ToM task performance and in intrinsic connectivity in the medial prefrontal cortex in healthy volunteers, which were not present in EOP. Differences in ToM performance were partially mediated by intrinsic functional connectivity in the medial prefrontal cortex. Poorer ToM performance in EOP, coupled with less medial prefrontal cortex connectivity, could be associated with the impact of psychosis during a critical period of development of the social brain, limiting normative age-related maturation.

Imaging-Based Subtyping for Psychiatric Syndromes

Despite considerable research evidence demonstrating significant neurobiological alterations in psychiatric disorders, incorporating neuroimaging approaches into clinical practice remains challenging. There is an urgent need for biologically validated psychiatric disease constructs that can inform diagnostic algorithms and targeted treatment development. In this article, we present a conceptual review of the most robust and impactful findings from studies that use neuroimaging methods in efforts to define distinct disease subtypes, while emphasizing cross-diagnostic and dimensional approaches. In addition, we discuss current challenges in psychoradiology and outline potential future strategies for clinically applicable translation.

Transdiagnostic Multimodal Neuroimaging in Psychosis: Structural, Resting-State, and Task Magnetic Resonance Imaging Correlates of Cognitive Control

BACKGROUND

Disorders with psychotic features, including schizophrenia and some bipolar disorders, are associated with impairments in regulation of goal-directed behavior, termed cognitive control. Cognitive control-related neural alterations have been studied in psychosis. However, studies are typically unimodal, and relationships across modalities of brain function and structure remain unclear. Thus, we performed transdiagnostic multimodal analyses to examine cognitive control-related neural variation in psychosis.

METHODS

Structural, resting, and working memory task imaging for 31 control participants, 27 participants with bipolar disorder, and 23 participants with schizophrenia were collected and processed identically to the Human Connectome Project, enabling identification of relationships with prior multimodal work. Two cognitive control-related independent components (ICs) derived from the Human Connectome Project using multiset canonical correlation analysis with joint IC analysis were used to predict performance in psychosis. De novo multiset canonical correlation analysis with joint IC analysis was performed, and the results were correlated with cognitive control.

RESULTS

A priori working memory and cortical thickness maps significantly predicted cognitive control in psychosis. De novo multiset canonical correlation analysis with joint IC analysis identified an IC correlated with cognitive control that also discriminated groups. Structural contributions included insular and cingulate regions; task contributions included precentral, posterior parietal, cingulate, and visual regions; and resting-state contributions highlighted canonical network organization. Follow-up analyses suggested that correlations with cognitive control were primarily influenced by participants with schizophrenia.

CONCLUSIONS

A priori and de novo imaging replicably identified a set of interrelated patterns across modalities and the healthy-to-psychosis spectrum, suggesting robustness of these features. Relationships between imaging and cognitive control performance suggest that shared symptomatology may be key to identifying transdiagnostic relationships in psychosis.

Functional connectivity in distinct cognitive subtypes in psychosis

BACKGROUND

Cognitive dysfunction is common in psychotic disorders, and may reflect underlying pathophysiology. However, substantial cognitive heterogeneity exists both within and between diagnostic categories, creating challenges for studying the neurobiology of cognitive dysfunction in patients. The aim of this study was to identify patients with psychosis with intact versus impaired cognitive profiles, and to examine resting state functional connectivity between patient groups and compared to healthy controls to determine the extent to which patterns of connectivity are overlapping or distinct.

METHODS

Participants with affective or non-affective psychosis (n=120) and healthy controls (n=31) were administered the MATRICS Consensus Cognitive Battery, clinical and community functioning assessments, and an fMRI scan to measure resting state functional connectivity (RSFC). Cognitive composite scores were used to identify groups of patients with and without cognitive dysfunction. RSFC was compared between groups of patients and healthy controls, controlling for demographic and clinical variables.

RESULTS

Both cognitively intact and cognitively impaired patients showed decreased intrinsic connectivity compared to controls in frontoparietal control (FPN) and motor networks. Patients with cognitive impairment showed additional reductions in FPN connectivity compared to patients with intact cognition, particularly in subnetwork A.

CONCLUSIONS

We leveraged the heterogeneity in cognitive ability among patients with psychosis to disentangle the relative contributions of cognitive dysfunction and presence of an underlying psychotic illness using resting state functional connectivity. These findings suggest at least partially separable effects of presence of a psychotic disorder and neurocognitive impairment contributing to network dysconnectivity in psychosis.

Peripheral oxytocin and vasopressin modulates regional brain activity differently in men and women with schizophrenia

BACKGROUND

Oxytocin (OT) and arginine vasopressin (AVP) exert sexually dimorphic effects on cognition and emotion processing. Abnormalities in these hormones are observed in schizophrenia and may contribute to multiple established sex differences associated with the disorder. Here we examined sex-dependent hormone associations with resting brain activity and their clinical associations in schizophrenia patients.

METHODS

OT and AVP serum concentrations were assayed in 35 individuals with schizophrenia (23 men) and 60 controls (24 men) from the Chicago BSNIP study site. Regional cerebral function was assessed with resting state fMRI by measuring the amplitude of low-frequency fluctuations (ALFF) which are believed to reflect intrinsic spontaneous neuronal activity.

RESULTS

In female patients, lower OT levels were associated with lower ALFF in frontal and cerebellar cortices (p's < 0.05) and in female controls AVP levels were inversely associated with ALFF in the frontal cortex (p = 0.01). In male patients, lower OT levels were associated with lower ALFF in the posterior cingulate and lower AVP levels were associated with lower ALFF in frontal cortex (p's < 0.05). In male controls, lower OT levels were associated with lower ALFF in frontal cortex and higher ALFF in the thalamus (p's < 0.05). There were some inverse ALFF-behavior associations in patients.

CONCLUSIONS

Alterations in peripheral hormone levels are associated with resting brain physiology in a sex-dependent manner in schizophrenia. These effects may contribute to sex differences in psychiatric symptom severity and course of illness in schizophrenia.

Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging

Brain functional imaging data, especially functional magnetic resonance imaging (fMRI) data, have been employed to reflect functional integration of the brain. Alteration in brain functional connectivity (FC) is expected to provide potential biomarkers for classifying or predicting brain disorders. In this paper, we present a comprehensive review in order to provide guidance about the available brain FC measures and typical classification strategies. We survey the state-of-the-art FC analysis methods including widely used static functional connectivity (SFC) and more recently proposed dynamic functional connectivity (DFC). Temporal correlations among regions of interest (ROIs), data-driven spatial network and functional network connectivity (FNC) are often computed to reflect SFC from different angles. SFC can be extended to DFC using a sliding-window framework, and intrinsic connectivity states along the time-varying connectivity patterns are typically extracted using clustering or decomposition approaches. We also briefly summarize window-less DFC approaches. Subsequently, we highlight various strategies for feature selection including the filter, wrapper and embedded methods. In terms of model building, we include traditional classifiers as well as more recently applied deep learning methods. Moreover, we review representative applications with remarkable classification accuracy for psychosis and mood disorders, neurodevelopmental disorder, and neurological disorders using fMRI data. Schizophrenia, bipolar disorder, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease and mild cognitive impairment (MCI) are discussed. Finally, challenges in the field are pointed out with respect to the inaccurate diagnosis labeling, the abundant number of possible features and the difficulty in validation. Some suggestions for future work are also provided.

Variability in Resting State Network and Functional Network Connectivity Associated With Schizophrenia Genetic Risk: A Pilot Study

Imaging genetics posits a valuable strategy for elucidating genetic influences on brain abnormalities in psychiatric disorders. However, association analysis between 2D genetic data (subject × genetic variable) and 3D first-level functional magnetic resonance imaging (fMRI) data (subject × voxel × time) has been challenging given the asymmetry in data dimension. A summary feature needs to be derived for the imaging modality to compute inter-modality association at subject level. In this work, we propose to use variability in resting state networks (RSNs) and functional network connectivity (FNC) as potential features for purpose of association analysis. We conducted a pilot study to investigate the proposed features in a dataset of 171 healthy controls and 134 patients with schizophrenia (SZ). We computed variability in RSN and FNC in a group independent component analysis framework and tested three types of variability metrics, namely Euclidean distance, Pearson correlation and Kullback-Leibler (KL) divergence. Euclidean distance and Pearson correlation metrics more effectively discriminated controls from patients than KL divergence. The group differences observed with variability in RSN and FNC were highly consistent, indicating patients presenting increased deviation from the cohort-common pattern of RSN and FNC than controls. The variability in RSN and FNC showed significant associations with network global efficiency, the more the deviation, the lower the efficiency. Furthermore, the RSN and FNC variability were found to associate with individual SZ risk SNPs as well as cumulative polygenic risk score for SZ. Collectively the current findings provide preliminary evidence for variability in RSN and FNC being promising imaging features that may find applications as biomarkers and in imaging genetic association analysis.

Identifying dynamic functional connectivity biomarkers using GIG-ICA: Application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder

Functional magnetic resonance imaging (fMRI) studies have shown altered brain dynamic functional connectivity (DFC) in mental disorders. Here, we aim to explore DFC across a spectrum of symptomatically-related disorders including bipolar disorder with psychosis (BPP), schizoaffective disorder (SAD), and schizophrenia (SZ). We introduce a group information guided independent component analysis procedure to estimate both group-level and subject-specific connectivity states from DFC. Using resting-state fMRI data of 238 healthy controls (HCs), 140 BPP, 132 SAD, and 113 SZ patients, we identified measures differentiating groups from the whole-brain DFC and traditional static functional connectivity (SFC), separately. Results show that DFC provided more informative measures than SFC. Diagnosis-related connectivity states were evident using DFC analysis. For the dominant state consistent across groups, we found 22 instances of hypoconnectivity (with decreasing trends from HC to BPP to SAD to SZ) mainly involving post-central, frontal, and cerebellar cortices as well as 34 examples of hyperconnectivity (with increasing trends HC through SZ) primarily involving thalamus and temporal cortices. Hypoconnectivities/hyperconnectivities also showed negative/positive correlations, respectively, with clinical symptom scores. Specifically, hypoconnectivities linking postcentral and frontal gyri were significantly negatively correlated with the PANSS positive/negative scores. For frontal connectivities, BPP resembled HC while SAD and SZ were more similar. Three connectivities involving the left cerebellar crus differentiated SZ from other groups and one connection linking frontal and fusiform cortices showed a SAD-unique change. In summary, our method is promising for assessing DFC and may yield imaging biomarkers for quantifying the dimension of psychosis. Hum Brain Mapp 38:2683-2708, 2017. © 2017 Wiley Periodicals, Inc.