Effective connectivity during episodic memory retrieval in schizophrenia participants before and after antipsychotic medication

The subcortical brain regions influence the cortical areas during resting-state: an fMRI study

Introduction

Numerous modes or patterns of neural activity can be seen in the brain of individuals during the resting state. However, those functions do not persist long, and they are continuously altering in the brain. We have hypothesized that the brain activations during the resting state should themselves be responsible for this alteration of the activities.

Methods

Using the resting-state fMRI data of 63 healthy young individuals, we estimated the causality effects of each resting-state activation map on all other networks. The resting-state networks were identified, their causality effects on the other components were extracted, the networks with the top 20% of the causality were chosen, and the networks which were under the influence of those causal networks were also identified.

Results

Our results showed that the influence of each activation component over other components is different. The brain areas which showed the highest causality coefficients were subcortical regions, such as the brain stem, thalamus, and amygdala. On the other hand, nearly all the areas which were mostly under the causal effects were cortical regions.

Discussion

In summary, our results suggest that subcortical brain areas exert a higher influence on cortical regions during the resting state, which could help in a better understanding the dynamic nature of brain functions.

Exploring Brain Effective Connectivity Networks Through Spatiotemporal Graph Convolutional Models

Learning brain effective connectivity networks (ECN) from functional magnetic resonance imaging (fMRI) data has gained much attention in recent years. With the successful applications of deep learning in numerous fields, several brain ECN learning methods based on deep learning have been reported in the literature. However, current methods ignore the deep temporal features of fMRI data and fail to fully employ the spatial topological relationship between brain regions. In this article, we propose a novel method for learning brain ECN based on spatiotemporal graph convolutional models (STGCM), named STGCMEC, in which we first adopt the temporal convolutional network to extract the deep temporal features of fMRI data and utilize the graph convolutional network to update the spatial features of each brain region by aggregating information from neighborhoods, which makes the features of brain regions more discriminative. Then, based on such features of brain regions, we design a joint loss function to guide STGCMEC to learn the brain ECN, which includes a task prediction loss and a graph regularization loss. The experimental results on a simulated dataset and a real Alzheimer's disease neuroimaging initiative (ADNI) dataset show that the proposed STGCMEC is able to better learn brain ECN compared with some state-of-the-art methods.

Hippocampal Hyperconnectivity to the Visual Cortex Predicts Treatment Response

BACKGROUND

Converging lines of evidence point to hippocampal dysfunction in psychosis spectrum disorders, including altered functional connectivity. Evidence also suggests that antipsychotic medications can modulate hippocampal dysfunction. The goal of this project was to identify patterns of hippocampal connectivity predictive of response to antipsychotic treatment in 2 cohorts of patients with a psychosis spectrum disorder, one medication-naïve and the other one unmedicated.

HYPOTHESIS

We hypothesized that we would identify reliable patterns of hippocampal connectivity in the 2 cohorts that were predictive of treatment response and that medications would modulate abnormal hippocampal connectivity after 6 weeks of treatment.

STUDY DESIGN

We used a prospective design to collect resting-state fMRI scans prior to antipsychotic treatment and after 6 weeks of treatment with risperidone, a commonly used antipsychotic medication, in both cohorts. We enrolled 44 medication-naïve first-episode psychosis patients (FEP) and 39 unmedicated patients with schizophrenia (SZ).

STUDY RESULTS

In both patient cohorts, we observed a similar pattern where greater hippocampal connectivity to regions of the occipital cortex was predictive of treatment response. Lower hippocampal connectivity of the frontal pole, orbitofrontal cortex, subcallosal area, and medial prefrontal cortex was predictive of treatment response in unmedicated SZ, but not in the medication-naïve cohort. Furthermore, greater reduction in hippocampal connectivity to the visual cortex with treatment was associated with better clinical response.

CONCLUSIONS

Our results suggest that greater connectivity between the hippocampus and occipital cortex is not only predictive of better treatment response, but that antipsychotic medications have a modulatory effect by reducing hyperconnectivity.

Brain effective connectivity and functional connectivity as markers of lifespan vascular exposures in middle-aged adults: The Bogalusa Heart Study

Introduction

Effective connectivity (EC), the causal influence that functional activity in a source brain location exerts over functional activity in a target brain location, has the potential to provide different information about brain network dynamics than functional connectivity (FC), which quantifies activity synchrony between locations. However, head-to-head comparisons between EC and FC from either task-based or resting-state functional MRI (fMRI) data are rare, especially in terms of how they associate with salient aspects of brain health.

Methods

In this study, 100 cognitively-healthy participants in the Bogalusa Heart Study aged 54.2 ± 4.3years completed Stroop task-based fMRI, resting-state fMRI. EC and FC among 24 regions of interest (ROIs) previously identified as involved in Stroop task execution (EC-task and FC-task) and among 33 default mode network ROIs (EC-rest and FC-rest) were calculated from task-based and resting-state fMRI using deep stacking networks and Pearson correlation. The EC and FC measures were thresholded to generate directed and undirected graphs, from which standard graph metrics were calculated. Linear regression models related graph metrics to demographic, cardiometabolic risk factors, and cognitive function measures.

Results

Women and whites (compared to men and African Americans) had better EC-task metrics, and better EC-task metrics associated with lower blood pressure, white matter hyperintensity volume, and higher vocabulary score (maximum value of p = 0.043). Women had better FC-task metrics, and better FC-task metrics associated with APOE-ε4 3–3 genotype and better hemoglobin-A1c, white matter hyperintensity volume and digit span backwards score (maximum value of p = 0.047). Better EC rest metrics associated with lower age, non-drinker status, and better BMI, white matter hyperintensity volume, logical memory II total score, and word reading score (maximum value of p = 0.044). Women and non-drinkers had better FC-rest metrics (value of p = 0.004).

Discussion

In a diverse, cognitively healthy, middle-aged community sample, EC and FC based graph metrics from task-based fMRI data, and EC based graph metrics from resting-state fMRI data, were associated with recognized indicators of brain health in differing ways. Future studies of brain health should consider taking both task-based and resting-state fMRI scans and measuring both EC and FC analyses to get a more complete picture of functional networks relevant to brain health.

Longitudinal changes in neural gain and its relationship to cognitive control trajectory in young adults with early psychosis

The mixed cognitive outcomes in early psychosis (EP) have important implications for recovery. In this longitudinal study, we asked whether baseline differences in the cognitive control system (CCS) in EP participants would revert toward a normative trajectory seen in healthy controls (HC). Thirty EP and 30 HC undertook functional MRI at baseline using the multi-source interference task-a paradigm that selectively introduces stimulus conflict-and 19 in each group repeated the task at 12 months. Activation of the left superior parietal cortex normalized over time for the EP group, relative to HC, coincident with improvements in reaction time and social-occupational functioning. To examine these group and timepoint differences, we used dynamic causal modeling to infer changes in effective connectivity between regions underlying the MSIT task execution, namely visual, anterior insula, anterior cingulate, and superior parietal cortical regions. To resolve stimulus conflict, EP participants transitioned from an indirect to a direct neuromodulation of sensory input to the anterior insula over timepoints, though not as strongly as HC participants. Stronger direct nonlinear modulation of the anterior insula by the superior parietal cortex at follow-up was associated with improved task performance. Overall, normalization of the CCS through adoption of more direct processing of complex sensory input to the anterior insula, was observed in EP after 12 months of treatment. Such processing of complex sensory input reflects a computational principle called gain control, which appears to track changes in cognitive trajectory within the EP group.

Stress-Induced Changes in Effective Connectivity During Regulation of the Emotional Response to Threat

Background: Stress-related disruption of emotion regulation appears to involve the prefrontal cortex (PFC) and amygdala. However, the interactions between brain regions that mediate stress-induced changes in emotion regulation remain unclear. The present study builds upon prior work that assessed stress-induced changes in the neurobehavioral response to threat by investigating effective connectivity between these brain regions. Methods: Participants completed the Montreal Imaging Stress Task followed by a Pavlovian fear conditioning procedure during functional magnetic resonance imaging. Stress ratings and psychophysiological responses were used to assess stress reactivity. Effective connectivity during fear conditioning was identified using multivariate autoregressive modeling. Effective connectivity values were calculated during threat presentations that were either predictable (preceded by a warning cue) or unpredictable (no warning cue). Results: A neural hub within the dorsomedial PFC (dmPFC) showed greater effective connectivity to other PFC regions, inferior parietal lobule, insula, and amygdala during predictable than unpredictable threat. The dmPFC also showed greater connectivity to different dorsolateral PFC and amygdala regions during unpredictable than predictable threat. Stress ratings varied with connectivity differences from the dmPFC to the amygdala. Connectivity from dmPFC to amygdala was greater in general during unpredictable than predictable threat, however, this connectivity increased during predictable compared with unpredictable threat as stress reactivity increased. Conclusions: Our findings suggest that acute stress disrupts connectivity underlying top-down emotion regulation of the threat response. Furthermore, increased connectivity between the dmPFC and amygdala may play a critical role in stress-induced changes in the emotional response to threat. Impact statement The present study builds upon prior work that assessed stress-induced changes in the human neurobehavioral response to threat by demonstrating that increased top-down connectivity from the dorsomedial prefrontal cortex to the amygdala varies with individual differences in stress reactivity. These findings provide novel evidence in humans of stress-induced disruption of a specific top-down corticolimbic circuit during active emotion regulation processes, which may play a causal role in the long-term effects of chronic or excessive stress exposure.

Hippocampal Dysconnectivity and Altered Glutamatergic Modulation of the Default Mode Network: A Combined Resting-State Connectivity and Magnetic Resonance Spectroscopy Study in Schizophrenia

BACKGROUND

Converging lines of evidence point to hippocampal dysfunction in schizophrenia. It is thought that hippocampal dysfunction spreads across hippocampal subfields and to cortical regions by way of long-range efferent projections. Importantly, abnormalities in the excitation/inhibition balance could impair the long-range modulation of neural networks. The goal of this project was twofold. First, we sought to identify replicable patterns of hippocampal dysconnectivity in patients with a psychosis spectrum disorder. Second, we aimed to investigate a putative link between glutamatergic metabolism and hippocampal connectivity alterations.

METHODS

We evaluated resting-state hippocampal functional connectivity alterations in two cohorts of patients with a psychosis spectrum disorder. The first cohort consisted of 55 medication-naïve patients with first-episode psychosis and 41 matched healthy control subjects, and the second cohort consisted of 42 unmedicated patients with schizophrenia and 41 matched control subjects. We also acquired measurements of glutamate + glutamine in the left hippocampus using magnetic resonance spectroscopy for 42 patients with first-episode psychosis and 37 healthy control subjects from our first cohort.

RESULTS

We observed a pattern of hippocampal functional hypoconnectivity to regions of the default mode network and hyperconnectivity to the lateral occipital cortex in both cohorts. We also show that in healthy control subjects, greater hippocampal glutamate + glutamine levels predicted greater hippocampal functional connectivity to the anterior default mode network. Furthermore, this relationship was reversed in medication-naïve subjects with first-episode psychosis.

CONCLUSIONS

These results suggest that an alteration in the relationship between glutamate and functional connectivity may disrupt the dynamic of major neural networks.

Mnemonic Discrimination Deficits in First-Episode Psychosis and a Ketamine Model Suggest Dentate Gyrus Pathology Linked to NMDA Receptor Hypofunction

BACKGROUND

Converging evidence from neuroimaging and postmortem studies suggests that hippocampal subfields are differentially affected in schizophrenia. Recent studies report dentate gyrus dysfunction in chronic schizophrenia, but the underlying mechanisms remain to be elucidated. Here, we sought to examine if this deficit is already present in first-episode psychosis and if NMDA receptor hypofunction, a putative central pathophysiological mechanism in schizophrenia, experimentally induced by ketamine, would result in a similar abnormality.

METHODS

We applied a mnemonic discrimination task selectively taxing pattern separation in two experiments: 1) a group of 23 patients with first-episode psychosis and 23 matched healthy volunteers and 2) a group of 19 healthy volunteers before and during a ketamine challenge (0.27 mg/kg over 10 min, then 0.25 mg/kg/hour for 50 min, 0.01 mL/s). We calculated response bias-corrected pattern separation and recognition scores. We also examined the relationships between task performance and symptom severity as well as ketamine levels.

RESULTS

We reported a deficit in pattern separation performance in patients with first-episode psychosis compared with healthy volunteers (p = .04) and in volunteers during the ketamine challenge compared with baseline (p = .003). Pattern recognition was lower in patients with first-episode psychosis than in control subjects (p < .01). Exploratory analyses revealed no correlation between task performance and Repeatable Battery for the Assessment of Neuropsychological Status total scores or positive symptoms in patients with first-episode psychosis or with ketamine serum levels.

CONCLUSIONS

We observed a mnemonic discrimination deficit in both datasets. Our findings suggest a tentative mechanistic link between dentate gyrus dysfunction in first-episode psychosis and NMDA receptor hypofunction.

Functioning Problems in Persons with Schizophrenia in the Russian Context

Assessing functionality in schizophrenia from a biopsychosocial perspective is essential to generate treatments that respond to the needs of the individual in his/her context. This research aims to assess the prevalence of functioning problems and their association with socio-demographic and clinical variables in a sample of Russian individuals with schizophrenia, using the International Classification of Functioning, Disability, and Health as a framework. An empirical cross-sectional study assessed the functioning of 40 individuals with schizophrenia using the International Classification of Functioning, Disability, and Health Core Set for schizophrenia. For the Body functions component, the highest prevalence of problems was found in b144 Memory functions (75%) and b140 Attention functions (70%). In the Activities and participation component, the greatest limitations were in d770 Intimate relationships (79.3%) and d240 Handling stress and other psychological demands (82.5%). In the Environmental factors, the most frequent problems were in e110 Products or substances for personal consumption (25%) and e460 Societal attitudes (22.5%); when scored as facilitators, the highest rated categories were e125 Products and technology for communication (100%) and e165 Assets (100%). These results may guide the design of specific treatments for these individuals and serve as a starting point for further studies in similar contexts and in other regions in Russia.

Altered directed connectivity during processing of predictive stimuli in psychiatric patient populations

OBJECTIVES

The study investigated the role of top-down versus bottom-up connectivity, during the processing of predictive information, in three different psychiatric disorders.

METHODS

Electroencephalography (EEG) was recorded during the performance of a task, which evaluates the ability to use predictive information in order to facilitate predictable versus random target detection. We evaluated EEG event-related directed connectivity, in patients with schizophrenia (SZ), major depressive disorder (MDD), and autism spectrum disorder (ASD), compared with healthy age-matched controls. Directed connectivity was evaluated using phase transfer entropy.

RESULTS

We showed that top-down frontal-parietal connectivity was weaker in SZ (theta and beta bands) and ASD (alpha band) compared to control subjects, during the processing of stimuli consisting of the predictive sequence. In SZ patients, top-down connectivity was also attenuated, during the processing of predictive targets in the beta frequency band. In contrast, compared with controls, MDD patients displayed an increased top-down flow of information, during the processing of predicted targets (alpha band).

CONCLUSIONS

The findings suggest that top-down frontal-parietal connectivity is altered differentially across three major psychiatric disorders, specifically during the processing of predictive stimuli.

SIGNIFICANCE

Altered top-down connectivity may contribute to the specific prediction deficits observed in each of the patient populations.

An Integrated Account of Expert Perspectives on Functioning in Schizophrenia

An integrated and interdisciplinary care system for individuals with schizophrenia is essential, which implies the need for a tool that assesses the difficulties and contextual factors of relevance to their functioning, and facilitates coordinated working across the different professions involved in their care. The International Classification of Functioning, Disability and Health Core Sets (ICF-CS) cover these requirements. This study aimed to evaluate the content validity of the ICF-CSs for schizophrenia from the perspective of experts. Six three-round Delphi studies were conducted with expert panels from different professional backgrounds which have played a significant role in the treatment of individuals with schizophrenia (psychiatry, psychology, nursing, occupational therapy, social work and physiotherapy). In total, 790 experts from 85 different countries participated in the first round. In total, 90 ICF categories and 28 Personal factors reached expert consensus (reached consensus from four or more professional perspectives). All the categories in the brief version of the ICF-CS for schizophrenia reached consensus from all the professional perspectives considered. As for the comprehensive version, 89.7% of its categories reached expert consensus. The results support the worldwide content validity of the ICF-CSs for schizophrenia from an expert perspective and underline the importance of assessing functioning by considering all the components implied.

Effective Connectivity of the Hippocampus Can Differentiate Patients with Schizophrenia from Healthy Controls: A Spectral DCM Approach

We applied spectral dynamic causal modelling (Friston et al. in Neuroimage 94:396–407. 10.1016/j.neuroimage.2013.12.009, 2014) to analyze the effective connectivity differences between the nodes of three resting state networks (i.e. default mode network, salience network and dorsal attention network) in a dataset of 31 male healthy controls (HC) and 25 male patients with a diagnosis of schizophrenia (SZ). Patients showed increased directed connectivity from the left hippocampus (LHC) to the: dorsal anterior cingulate cortex (DACC), right anterior insula (RAI), left frontal eye fields and the bilateral inferior parietal sulcus (LIPS & RIPS), as well as increased connectivity from the right hippocampus (RHC) to the: bilateral anterior insula (LAI & RAI), right frontal eye fields and RIPS. In SZ, negative symptoms predicted the connectivity strengths from the LHC to: the DACC, the left inferior parietal sulcus (LIPAR) and the RHC, while positive symptoms predicted the connectivity strengths from the LHC to the LIPAR and from the RHC to the LHC. These results reinforce the crucial role of hippocampus dysconnectivity in SZ pathology and its potential as a biomarker of disease severity.

Narrative identity in the psychosis spectrum: A systematic review and developmental model

Individuals with schizophrenia-spectrum disorders face profound challenges as they attempt to maintain identity through the course of illness. Narrative identity-the study of internalized, evolving life stories-provides a rich theoretical and empirical perspective on these challenges. Based on evidence from a systematic review of narrative identity in the psychosis spectrum (30 studies, combined N = 3859), we argue that the narrative identities of individuals with schizophrenia-spectrum disorders are distinguished by three features: disjointed structure, a focus on suffering, and detached narration. Psychotic disorders typically begin to emerge during adolescence and emerging adulthood, which are formative developmental stages for narrative identity, so it is particularly informative to understand identity disturbances from a developmental perspective. We propose a developmental model in which a focus on suffering emerges in childhood; disjointed structure emerges in middle and late adolescence; and detached narration emerges before or around the time of a first psychotic episode. Further research with imminent risk and early course psychosis populations would be needed to test these predictions. The disrupted life stories of individuals on the psychosis spectrum provide multiple rich avenues for further research to understand narrative self-disturbances.

Functional connectivity abnormalities of the long-axis hippocampal subregions in schizophrenia during episodic memory

Past evidence suggests that hippocampal subregions, namely the anterior and posterior parts, may be engaged in distinct networks underlying the memory functions which may be altered in patients with schizophrenia. However, of the very few studies that have investigated the hippocampal longitudinal axis subdivisions functional connectivity in patients with schizophrenia, the majority was based on resting-state data, and yet, none aimed to examine these during an episodic memory task. A total of 41 patients with schizophrenia and 45 healthy controls were recruited for a magnetic resonance imaging protocol in which they performed an explicit memory task. Seed-based functional connectivity analysis was employed to assess connectivity abnormalities between hippocampal subregions and voxel-wise connectivity targets in patients with schizophrenia. We observed a significantly reduced connectivity between the posterior hippocampus and regions from the default mode network, but increased connectivity with the primary visual cortex, in patients with schizophrenia compared to healthy subjects. Increased connectivity between the anterior hippocampus and anterior temporal regions also characterized patients with schizophrenia. In the current study, we provided evidence and support for studying hippocampal subdivisions along the longitudinal axis in schizophrenia. Our results suggest that the abnormalities in hippocampal subregions functional connectivity reflect deficits in episodic memory that may be implicated in the pathophysiology of schizophrenia.

Neuroimaging as a Window Into the Pathophysiological Mechanisms of Schizophrenia

Schizophrenia is a complex neuropsychiatric disorder with a diverse clinical phenotype that has a substantial personal and public health burden. To advance the mechanistic understanding of the illness, neuroimaging can be utilized to capture different aspects of brain pathology in vivo, including brain structural integrity deficits, functional dysconnectivity, and altered neurotransmitter systems. In this review, we consider a number of key scientific questions relevant in the context of neuroimaging studies aimed at unraveling the pathophysiology of schizophrenia and take the opportunity to reflect on our progress toward advancing the mechanistic understanding of the illness. Our data is congruent with the idea that the brain is fundamentally affected in the illness, where widespread structural gray and white matter involvement, functionally abnormal cortical and subcortical information processing, and neurometabolic dysregulation are present in patients. Importantly, certain brain circuits appear preferentially affected and subtle abnormalities are already evident in first episode psychosis patients. We also demonstrated that brain circuitry alterations are clinically relevant by showing that these pathological signatures can be leveraged for predicting subsequent response to antipsychotic treatment. Interestingly, dopamine D2 receptor blockers alleviate neural abnormalities to some extent. Taken together, it is highly unlikely that the pathogenesis of schizophrenia is uniform, it is more plausible that there may be multiple different etiologies that converge to the behavioral phenotype of schizophrenia. Our data underscore that mechanistically oriented neuroimaging studies must take non-specific factors such as antipsychotic drug exposure or illness chronicity into consideration when interpreting disease signatures, as a clear characterization of primary pathophysiological processes is an imperative prerequisite for rational drug development and for alleviating disease burden in our patients.

Neural Signatures of Memory Encoding in Schizophrenia Are Modulated by Antipsychotic Treatment

There is no pharmacological treatment to remediate cognitive impairment in schizophrenia (SZ). It is imperative to characterize underlying pathologies of memory processing in order to effectively develop new treatments. In this longitudinal study, we combined functional magnetic resonance imaging during a memory encoding task with proton MR spectroscopy to measure hippocampal glutamate + glutamine (Glx). Seventeen SZ were scanned while unmedicated and after 6 weeks of treatment with risperidone and compared to a group of matched healthy controls (HC) scanned 6 weeks apart. Unmedicated patients showed reduced blood oxygen level dependent (BOLD) response in several regions, including the hippocampus, and greater BOLD response in regions of the default mode network (DMN) during correct memory encoding. Post hoc contrasts from significant group by time interactions indicated reduced hippocampal BOLD response at baseline with subsequent increase following treatment. Hippocampal Glx was not different between groups at baseline, but at week 6, hippocampal Glx was significantly lower in SZ compared to HC. Finally, in unmedicated SZ, higher hippocampal Glx predicted less deactivation of the BOLD response in regions of the DMN. Using 2 brain imaging modalities allowed us to concurrently investigate different mechanisms involved in memory encoding dysfunction in SZ. Hippocampal pathology during memory encoding stems from decreased hippocampal recruitment and faulty deactivation of the DMN, and hippocampal recruitment during encoding can be modulated by antipsychotic treatment. High Glx in unmedicated patients predicted less deactivation of the DMN; these results suggest a mechanism by which faulty DMN deactivation, a hallmark of pathological findings in SZ, is achieved.

Baduanjin mind-body exercise improves logical memory in long-term hospitalized patients with schizophrenia: A randomized controlled trial

Neurocognitive impairment is one of the core symptoms in schizophrenia and poses a great challenge to effective treatment. Sixty-one long-term hospitalized patients with schizophrenia were recruited and randomly assigned to two groups: Baduanjin exercise and brisk walking. Patients in the Baduanjin group received 24 weeks of Baduanjin training (5 days/week, 40 min/day), while patients in the brisk walking group received 24 weeks of brisk walking (5 days/week, 40 min/day). Scores on the Wechsler Memory Scale, Digit Symbol Substitution Test (DSST), and the positive and negative syndrome scale were used to evaluate the logical memory (LM), processing speed, and clinical symptoms of all participants, while the score of Trail Making Test-A (TMT-A) was applied to assess the visual attention and graphomotor speed, at baseline and the 16th week and 24th week of intervention. The one-way repeated measures analysis of variance (ANOVA) was used to test the differences in neurocognitive changes between the two groups. Repeated measures ANOVA showed significant differences between the two groups in the LM immediate (F = 6.21, p = 0.003) and LM delayed (F=5.60, p = 0.005) scores, but not in the completion times of TMT-A (F=.22, p = 0.806) or DSST scores (F=0.97, p = 0.328). A significant effect of time was also detected in the LM immediate (F=10.24, p = 0.000) and LM delayed (F=4.93, p = 0.009) scores and in the completion time of the TMT-A (F=33.10, p = 0.000), but not in the DSST scores (F=2.12, p = 0.122). Baduanjin exercise could improve logical memory in the long-term hospitalized patients with schizophrenia.

Disordered directional brain network interactions during learning dynamics in schizophrenia revealed by multivariate autoregressive models

Directional network interactions underpin normative brain function in key domains including associative learning. Schizophrenia (SCZ) is characterized by altered learning dynamics, yet dysfunctional directional functional connectivity (dFC) evoked during learning is rarely assessed. Here, nonlinear learning dynamics were induced using a paradigm alternating between conditions (Encoding and Retrieval). Evoked fMRI time series data were modeled using multivariate autoregressive (MVAR) models, to discover dysfunctional direction interactions between brain network constituents during learning stages (Early vs. Late), and conditions. A functionally derived subnetwork of coactivated (healthy controls [HC] ∩ SCZ] nodes was identified. MVAR models quantified directional interactions between pairs of nodes, and coefficients were evaluated for intergroup differences (HC ≠ SCZ). In exploratory analyses, we quantified statistical effects of neuroleptic dosage on performance and MVAR measures. During Early Encoding, SCZ showed reduced dFC within a frontal–hippocampal–fusiform network, though during Late Encoding reduced dFC was associated with pathways toward the dorsolateral prefrontal cortex (dlPFC). During Early Retrieval, SCZ showed increased dFC in pathways to and from the dorsal anterior cingulate cortex, though during Late Retrieval, patients showed increased dFC in pathways toward the dlPFC, but decreased dFC in pathways from the dlPFC. These discoveries constitute novel extensions of our understanding of task‐evoked dysconnection in schizophrenia and motivate understanding of the directional aspect of the dysconnection in schizophrenia. Disordered directionality should be investigated using computational psychiatric approaches that complement the MVAR method used in our work.

Long-Term Aberrations To Cerebellar Endocannabinoids Induced By Early-Life Stress

Emerging evidence points to the role of the endocannabinoid system in long-term stress-induced neural remodeling with studies on stress-induced endocannabinoid dysregulation focusing on cerebral changes that are temporally proximal to stressors. Little is known about temporally distal and sex-specific effects, especially in cerebellum, which is vulnerable to early developmental stress and is dense with cannabinoid receptors. Following limited bedding at postnatal days 2-9, adult (postnatal day 70) cerebellar and hippocampal endocannabinoids, related lipids, and mRNA were assessed, and behavioral performance evaluated. Regional and sex-specific effects were present at baseline and following early-life stress. Limited bedding impaired peripherally-measured basal corticosterone in adult males only. In the CNS, early-life stress (1) decreased 2-arachidonoyl glycerol and arachidonic acid in the cerebellar interpositus nucleus in males only; (2) decreased 2-arachidonoyl glycerol in females only in cerebellar Crus I; and (3) increased dorsal hippocampus prostaglandins in males only. Cerebellar interpositus transcriptomics revealed substantial sex effects, with minimal stress effects. Stress did impair novel object recognition in both sexes and social preference in females. Accordingly, the cerebellar endocannabinoid system exhibits robust sex-specific differences, malleable through early-life stress, suggesting the role of endocannabinoids and stress to sexual differentiation of the brain and cerebellar-related dysfunctions.

Multi-Level Clustering of Dynamic Directional Brain Network Patterns and Their Behavioral Relevance

Dynamic functional connectivity (DFC) obtained from resting state functional magnetic resonance imaging (fMRI) data has been shown to provide novel insights into brain function which may be obscured by static functional connectivity (SFC). Further, DFC, and by implication how different brain regions may engage or disengage with each other over time, has been shown to be behaviorally relevant and more predictive than SFC of behavioral performance and/or diagnostic status. DFC is not a directional entity and may capture neural synchronization. However, directional interactions between different brain regions is another putative mechanism by which neural populations communicate. Accordingly, static effective connectivity (SEC) has been explored as a means of characterizing such directional interactions. But investigation of its dynamic counterpart, i.e., dynamic effective connectivity (DEC), is still in its infancy. Of particular note are methodological insufficiencies in identifying DEC configurations that are reproducible across time and subjects as well as a lack of understanding of the behavioral relevance of DEC obtained from resting state fMRI. In order to address these issues, we employed a dynamic multivariate autoregressive (MVAR) model to estimate DEC. The method was first validated using simulations and then applied to resting state fMRI data obtained in-house (N = 21), wherein we performed dynamic clustering of DEC matrices across multiple levels [using adaptive evolutionary clustering (AEC)] – spatial location, time, and subjects. We observed a small number of directional brain network configurations alternating between each other over time in a quasi-stable manner akin to brain microstates. The dominant and consistent DEC network patterns involved several regions including inferior and mid temporal cortex, motor and parietal cortex, occipital cortex, as well as part of frontal cortex. The functional relevance of these DEC states were determined using meta-analyses and pertained mainly to memory and emotion, but also involved execution and language. Finally, a larger cohort of resting-state fMRI and behavioral data from the Human Connectome Project (HCP) (N = 232, Q1–Q3 release) was used to demonstrate that metrics derived from DEC can explain larger variance in 70 behaviors across different domains (alertness, cognition, emotion, and personality traits) compared to SEC in healthy individuals.

Deterioration from healthy to mild cognitive impairment and Alzheimer's disease mirrored in corresponding loss of centrality in directed brain networks

OBJECTIVE

It is important to identify brain-based biomarkers that progressively deteriorate from healthy to mild cognitive impairment (MCI) to Alzheimer's disease (AD). Cortical thickness, amyloid-ß deposition, and graph measures derived from functional connectivity (FC) networks obtained using functional MRI (fMRI) have been previously identified as potential biomarkers. Specifically, in the latter case, betweenness centrality (BC), a nodal graph measure quantifying information flow, is reduced in both AD and MCI. However, all such reports have utilized BC calculated from undirected networks that characterize synchronization rather than information flow, which is better characterized using directed networks.

METHODS

Therefore, we estimated BC from directed networks using Granger causality (GC) on resting-state fMRI data (N = 132) to compare the following populations (p < 0.05, FDR corrected for multiple comparisons): normal control (NC), early MCI (EMCI), late MCI (LMCI) and AD. We used an additional metric called middleman power (MP), which not only characterizes nodal information flow as in BC, but also measures nodal power critical for information flow in the entire network.

RESULTS

MP detected more brain regions than BC that progressively deteriorated from NC to EMCI to LMCI to AD, as well as exhibited significant associations with behavioral measures. Additionally, graph measures obtained from conventional FC networks could not identify a single node, underscoring the relevance of GC.

CONCLUSION

Our findings demonstrate the superiority of MP over BC as well as GC over FC in our case. MP obtained from GC networks could serve as a potential biomarker for progressive deterioration of MCI and AD.

Effective connectivity within a triple network brain system discriminates schizophrenia spectrum disorders from psychotic bipolar disorder at the single-subject level

OBJECTIVE

Schizophrenia spectrum disorders (SSD) and psychotic bipolar disorder share a number of genetic and neurobiological features, despite a divergence in clinical course and outcome trajectories. We studied the diagnostic classification potential that can be achieved on the basis of the structure and connectivity within a triple network system (the default mode, salience and central executive network) in patients with SSD and psychotic bipolar disorder.

METHODS

Directed static connectivity and its dynamic variance was estimated among 8 nodes of the three large-scale networks. Multivariate autoregressive models of deconvolved resting state functional magnetic resonance imaging time series were obtained from 57 patients (38 with SSD and 19 with bipolar disorder and psychosis). We used 2/3 of the patients for training and validation of the classifier and the remaining 1/3 as an independent hold-out test data for performance estimation.

RESULTS

A high level of discrimination between bipolar disorder with psychosis and SSD (combined balanced accuracy = 96.2%; class accuracies 100% for bipolar and 92.3% for SSD) was achieved when effective connectivity and morphometry of the triple network nodes was combined with symptom scores. Patients with SSD were discriminated from patients with bipolar disorder and psychosis as showing higher clinical severity of disorganization and higher variability in the effective connectivity between salience and executive networks.

CONCLUSIONS

Our results support the view that the study of network-level connectivity patterns can not only clarify the pathophysiology of SSD but also provide a measure of excellent clinical utility to identify discrete diagnostic/prognostic groups among individuals with psychosis.

Learning Brain Effective Connectivity Network Structure Using Ant Colony Optimization Combining With Voxel Activation Information

Learning brain effective connectivity (EC) networks from functional magnetic resonance imaging (fMRI) data has become a new hot topic in the neuroinformatics field. However, how to accurately and efficiently learn brain EC networks is still a challenging problem. In this paper, we propose a new algorithm to learn the brain EC network structure using ant colony optimization (ACO) algorithm combining with voxel activation information, named as VACOEC. First, VACOEC uses the voxel activation information to measure the independence between each pair of brain regions and effectively restricts the space of candidate solutions, which makes many unnecessary searches of ants be avoided. Then, by combining the global score increase of a solution with the voxel activation information, a new heuristic function is designed to guide the process of ACO to search for the optimal solution. The experimental results on simulated datasets show that the proposed method can accurately and efficiently identify the directions of the brain EC networks. Moreover, the experimental results on real-world data show that patients with Alzheimers disease (AD) exhibit decreased effective connectivity not only in the intra-network within the default mode network (DMN) and salience network (SN), but also in the inter-network between DMN and SN, compared with normal control (NC) subjects. The experimental results demonstrate that VACOEC is promising for practical applications in the neuroimaging studies of geriatric subjects and neurological patients.

Increasing robustness of pairwise methods for effective connectivity in magnetic resonance imaging by using fractional moment series of BOLD signal distributions

Estimating causal interactions in the brain from functional magnetic resonance imaging (fMRI) data remains a challenging task. Multiple studies have demonstrated that all current approaches to determine direction of connectivity perform poorly when applied to synthetic fMRI datasets. Recent advances in this field include methods for pairwise inference, which involve creating a sparse connectome in the first step, and then using a classifier in order to determine the directionality of connection between every pair of nodes in the second step. In this work, we introduce an advance to the second step of this procedure, by building a classifier based on fractional moments of the BOLD distribution combined into cumulants. The classifier is trained on datasets generated under the dynamic causal modeling (DCM) generative model. The directionality is inferred based on statistical dependencies between the two-node time series, for example, by assigning a causal link from time series of low variance to time series of high variance. Our approach outperforms or performs as well as other methods for effective connectivity when applied to the benchmark datasets. Crucially, it is also more resilient to confounding effects such as differential noise level across different areas of the connectome.

Dynamics of Segregation and Integration in Directional Brain Networks: Illustration in Soldiers With PTSD and Neurotrauma

Brain functioning relies on various segregated/specialized neural regions functioning as an integrated-interconnected network (i.e., metastability). Various psychiatric and neurologic disorders are associated with aberrant functioning of these brain networks. In this study, we present a novel framework integrating the strength and temporal variability of metastability in brain networks. We demonstrate that this approach provides novel mechanistic insights which enables better imaging-based predictions. Using whole-brain resting-state fMRI and a graph-theoretic framework, we integrated strength and temporal-variability of complex-network properties derived from effective connectivity networks, obtained from 87 U.S. Army soldiers consisting of healthy combat controls (n = 28), posttraumatic stress disorder (PTSD; n = 17), and PTSD with comorbid mild-traumatic brain injury (mTBI; n = 42). We identified prefrontal dysregulation of key subcortical and visual regions in PTSD/mTBI, with all network properties exhibiting lower variability over time, indicative of poorer flexibility. Larger impairment in the prefrontal-subcortical pathway but not prefrontal-visual pathway differentiated comorbid PTSD/mTBI from the PTSD group. Network properties of the prefrontal-subcortical pathway also had significant association (R ² = 0.56) with symptom severity and neurocognitive performance; and were also found to possess high predictive ability (81.4% accuracy in classifying the disorders, explaining 66-72% variance in symptoms), identified through machine learning. Our framework explained 13% more variance in behaviors compared to the conventional framework. These novel insights and better predictions were made possible by our novel framework using static and time-varying network properties in our three-group scenario, advancing the mechanistic understanding of PTSD and comorbid mTBI. Our contribution has wide-ranging applications for network-level characterization of healthy brains as well as mental disorders.

An international survey of Psychiatric-Mental-Health Nurses on the content validity of the International Classification of Functioning, Disability and Health Core Sets for Schizophrenia

The International Classification of Functioning, Disability and Health (ICF) Core Sets for schizophrenia describe the key problems in functioning that are experienced by individuals with this disorder. This study examines the content validity of these Core Sets and aims to identify the most frequent problems faced by people with schizophrenia, considering for this analysis the perspective of Psychiatric-Mental-Health Nurses. The study complied with the COREQ checklist for qualitative studies. A total of 101 nurses from 30 countries covering all six World Health Organization regions participated in a Delphi study. Their responses in Round 1 were linked to ICF categories, retaining those reported by at least 5% of participants. In Round 2, they were asked to rate the relevance of each of these categories to the nursing care of patients with schizophrenia. This process was repeated in Round 3. A total of 2327 concepts were extracted in Round 1 and linked to ICF categories. Following the analysis, 125 categories and 31 personal factors were presented to the experts in rounds 2 and 3. Consensus (defined as agreement ≥75%) was reached for 97 of these categories and 29 personal factors. These categories corresponded to all those (N = 25) in the Brief Core Set and 87 of the 97 categories of the Comprehensive Core Set for schizophrenia. Ten new categories emerged. The Delphi process identified the problems in functioning that nurses encounter when treating individuals with schizophrenia, and the results supported the content validity of the Core Sets. We conclude that these Core Sets offer a comprehensive framework for structuring clinical information and guiding the treatment process.

A longitudinal magnetic resonance spectroscopy study investigating effects of risperidone in the anterior cingulate cortex and hippocampus in schizophrenia

Magnetic Resonance Spectroscopy is a popular approach to probe brain chemistry in schizophrenia (SZ), but no consensus exists as to the extent of alterations. This may be attributable to differential effects of populations studied, brain regions examined, or antipsychotic medication effects. Here, we measured neurometabolites in the anterior cingulate cortex (ACC) and hippocampus, two structurally dissimilar brain regions implicated in the SZ pathophysiology. We enrolled 61 SZ with the goal to scan them before and after six weeks of treatment with risperidone. We also scanned 31 matched healthy controls twice, six weeks apart. Using mixed effect repeated measures linear models to examine the effect of group and time on metabolite levels in each voxel, we report an increase in hippocampal glutamate + glutamine (Glx) in SZ compared to controls (p = 0.043), but no effect of antipsychotic medication (p = 0.330). In the ACC, we did not find metabolite alterations or antipsychotic medication related changes after six weeks of treatment with risperidone. The coefficients for the discriminant function (differentiating SZ from HC) in the ACC were greatest for NAA (-0.83), and in the hippocampus for Glx (0.76), the same metabolites were associated with greater treatment response in patients at trend level. Taken together, our data extends the existing literature by demonstrating regionally distinct metabolite alterations in the same patient group and suggests that antipsychotic medications may have limited effects on metabolite levels in these regions.

Bypassing the volume conduction effect by multilayer neural network for effective connectivity estimation

Differentiation of real interactions between different brain regions from spurious ones has been a challenge in neuroimaging researches. While using electroencephalographic data, those spurious interactions are mostly caused by the volume conduction (VC) effect between the recording sites. In this study, we address the problem by jointly modeling the causal relationships among brain regions and the mixing effects of volume conduction. The VC effect is formulated with a time-invariant linear equation, and the causal relationships between the brain regions are modeled with a nonlinear multivariate autoregressive process. These two models are simultaneously implemented by a multilayer neural network. The internal hidden layers represent the interactions among the regions, while the external layers are devoted for the relationship between the source activities and observed EEG measurements at the scalp. The causal interactions are estimated by the causality coefficient measure, which is based on the information (weights and parameters) embedded in the network. The proposed method is verified using various simulated data. It is then applied to the real EEG signals collected from a memory retrieval test. The results showed that the method is able to eliminate the volume conduction interferences and consequently leads to higher accuracy in identification of true causal interactions. Graphical abstract The proposed network structure used to simultaneously model the volume conduction and source interactions. By this special structure, we first move from the sensor space to the source space at the first layer. Then, within internal hidden layers, the interactions between the sources are represented in the form of a general (nonlinear) multivariate autoregressive (nMVAR) model. Finally, we return from the source space to the sensor space at the last layer of the network. The proposed method bypasses the effect of volume conduction and causes more accurate connectivity estimation.

Altered Brain Activation During Memory Retrieval Precedes and Predicts Conversion to Psychosis in Individuals at Clinical High Risk

Memory deficits are a hallmark of psychotic disorders such as schizophrenia. However, whether the neural dysfunction underlying these deficits is present before the onset of illness and potentially predicts conversion to psychosis is unclear. In this study, we investigated brain functional alterations during memory processing in a sample of 155 individuals at clinical high risk (including 18 subjects who later converted to full psychosis) and 108 healthy controls drawn from the second phase of the North American Prodrome Longitudinal Study (NAPLS-2). All participants underwent functional magnetic resonance imaging with a paired-associate memory paradigm at the point of recruitment and were clinically followed up for approximately 2 years. We found that at baseline, subjects at high risk showed significantly higher activation during memory retrieval in the prefrontal, parietal, and bilateral temporal cortices (PFWE < .035). This effect was more pronounced in converters than nonconverters and was particularly manifested in unmedicated subjects (P < .001). The hyperactivation was significantly correlated with retrieval reaction time during scan in converters (P = .009) but not in nonconverters and controls, suggesting an exaggerated retrieval effort. These findings suggest that hyperactivation during memory retrieval may mark processes associated with conversion to psychosis, and such measures have potential as biomarkers for psychosis prediction.

Nonsocial and social cognition in schizophrenia: current evidence and future directions

Cognitive impairment in schizophrenia involves a broad array of nonsocial and social cognitive domains. It is a core feature of the illness, and one with substantial implications for treatment and prognosis. Our understanding of the causes, consequences and interventions for cognitive impairment in schizophrenia has grown substantially in recent years. Here we review a range of topics, including: a) the types of nonsocial cognitive, social cognitive, and perceptual deficits in schizophrenia; b) how deficits in schizophrenia are similar or different from those in other disorders; c) cognitive impairments in the prodromal period and over the lifespan in schizophrenia; d) neuroimaging of the neural substrates of nonsocial and social cognition, and e) relationships of nonsocial and social cognition to functional outcome. The paper also reviews the considerable efforts that have been directed to improve cognitive impairments in schizophrenia through novel psychopharmacology, cognitive remediation, social cognitive training, and alternative approaches. In the final section, we consider areas that are emerging and have the potential to provide future insights, including the interface of motivation and cognition, the influence of childhood adversity, metacognition, the role of neuroinflammation, computational modelling, the application of remote digital technology, and novel methods to evaluate brain network organization. The study of cognitive impairment has provided a way to approach, examine and comprehend a wide range of features of schizophrenia, and it may ultimately affect how we define and diagnose this complex disorder.

Impaired Effective Connectivity During a Cerebellar-Mediated Sensorimotor Synchronization Task in Schizophrenia

Prominent conceptual models characterize schizophrenia as a dysconnectivity syndrome, with recent research focusing on the contributions of the cerebellum in this framework. The present study examined the role of the cerebellum and its effective connectivity to the cerebrum during sensorimotor synchronization in schizophrenia. Specifically, the role of the cerebellum in temporally coordinating cerebral motor activity was examined through path analysis. Thirty-one individuals diagnosed with schizophrenia and 40 healthy controls completed a finger-tapping fMRI task including tone-paced synchronization and self-paced continuation tapping at a 500 ms intertap interval (ITI). Behavioral data revealed shorter and more variable ITIs during self-paced continuation, greater clock (vs motor) variance, and greater force of tapping in the schizophrenia group. In a whole-brain analysis, groups showed robust activation of the cerebellum during self-paced continuation but not during tone-paced synchronization. However, effective connectivity analysis revealed decreased connectivity in individuals with schizophrenia between the cerebellum and primary motor cortex but increased connectivity between cerebellum and thalamus during self-paced continuation compared with healthy controls. These findings in schizophrenia indicate diminished temporal coordination of cerebral motor activity by cerebellum during the continuation tapping portion of sensorimotor synchronization. Taken together with the behavioral finding of greater temporal variability in schizophrenia, these effective connectivity results are consistent with structural and temporal models of dysconnectivity in the disorder.

Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging (fMRI) has been used to relate neuronal network activity to cognitive processing and behavior. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, Linear Non-Gaussian Acyclic Models, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.

Aberrant Middle Prefrontal-Motor Cortex Connectivity Mediates Motor Inhibitory Biomarker in Schizophrenia

BACKGROUND

Inhibitory deficits in motor cortex in schizophrenia have been well demonstrated using short-interval intracortical inhibition (SICI) by transcranial magnetic stimulation. However, it remains unknown whether these deficits originate from dysfunction of motor cortex itself or reflect abnormal modulations of motor cortex by other schizophrenia-related brain areas.

METHODS

The study was completed by 24 patients with schizophrenia spectrum disorders and 30 healthy control subjects. SICI was obtained by delivering transcranial magnetic stimulation over the left motor cortex. Resting-state functional magnetic resonance imaging and diffusion tensor imaging fractional anisotropy were used to measure functional connectivity (FC) and white matter microstructures, respectively. Stimulation sites for SICI at motor cortex were used as the seeds to obtain whole-brain FC maps. Clinical symptoms were assessed with the Brief Psychiatric Rating Scale.

RESULTS

In schizophrenia, left prefrontal cortex-motor cortex FC was inversely associated with SICI but positively associated with the underlying white matter microstructure at the left corona radiata and also associated with overall symptoms (all corrected p < .05). Mediation analysis showed that the prefrontal-motor cortex FC significantly mediated the corona radiata white matter effects on SICI (p = .007).

CONCLUSIONS

Higher resting-state left prefrontal-motor cortex FC, accompanied by a higher fractional anisotropy of left corona radiata, predicted fewer inhibitory deficits, suggesting that the inhibitory deficits in motor cortex in schizophrenia may in part be mediated by a top-down prefrontal influence. SICI may serve as a robust biomarker indexing inhibitory dysfunction at anatomic as well as circuitry levels in schizophrenia.

Altered Effective Connectivity in the Default Network of the Brains of First-Episode, Drug-Naïve Schizophrenia Patients With Auditory Verbal Hallucinations

Although the default mode network (DMN) is known to be abnormal in schizophrenia (SZ) patients with auditory verbal hallucinations (AVHs), it is still unclear whether AVHs that occur in SZ are associated with certain information flow in the DMN. This study collected resting-state functional magnetic resonance imaging data from 28 first-episode, drug-naïve SZ patients with AVHs, 20 SZ patients without AVHs, and 38 healthy controls. We used Granger causality analysis (GCA) to examine effective connectivity (EC) of two hub regions [posterior cingulate cortex (PCC) and anteromedial prefrontal cortex (aMPFC)] within the DMN. We used two-sample t-tests to compare the difference in EC between the two patient groups, and used Spearman correlation analysis to characterize the relationship between imaging findings and clinical assessments. The GCA revealed that, compared with the non-AVHs group, EC decreased from aMPFC to left inferior temporal gyrus (ITG) and from PCC to left cerebellum posterior lobe, ITG, and right middle frontal gyrus in SZ patients with AVHs. We also found significant correlations between clinical assessments and mean strengths of connectivity from aMPFC to left ITG and from PCC to left ITG. Moreover, receiver operating characteristic analysis revealed that the above-mentioned effective connectivities had a diagnostic value for distinguishing SZ patients with AVHs from non-AVHs patients. These findings suggest that AVHs in SZ patients may be associated with the aberrant information flows of the DMN, and the left ITG may probably serve as a potential biomarker for the neural mechanisms underlying AVHs in SZ patients.

Excavatolide-B Enhances Contextual Memory Retrieval via Repressing the Delayed Rectifier Potassium Current in the Hippocampus

Memory retrieval dysfunction is a symptom of schizophrenia, autism spectrum disorder (ASD), and absence epilepsy (AE), as well as an early sign of Alzheimer’s disease. To date, few drugs have been reported to enhance memory retrieval. Here, we found that a coral-derived natural product, excavatolide-B (Exc-B), enhances contextual memory retrieval in both wild-type and Ca_v3.2^−/− mice via repressing the delayed rectifier potassium current, thus lowering the threshold for action potential initiation and enhancing induction of long-term potentiation (LTP). The human CACNA1H gene encodes a T-type calcium channel (Ca_v3.2), and its mutation is associated with schizophrenia, ASD, and AE, which are all characterized by abnormal memory function. Our previous publication demonstrated that Ca_v3.2^−/− mice exhibit impaired contextual-associated memory retrieval, whilst their retrieval of spatial memory and auditory cued memory remain intact. The effect of Exc-B on enhancing the retrieval of context-associated memory provides a hope for novel drug development.

Directional brain networks underlying OM chanting

OM chanting is an ancient technique of Indian meditation. OM chanting is associated with an experience of relaxation, changes in autonomic balance and deactivation of limbic brain regions. While functional localization is important, how brain regions interact with each other has been shown to underlie various brain functions. Therefore, in this study, we tested the hypothesis that there is reduced communication between deactivated regions during OM chanting. In order to do so, we employed multivariate autoregressive model (MVAR) based Granger causality to obtain directional connectivity between deactivated regions. fMRI scans of 12 right handed healthy volunteers (9 Men) from a previously published study was used in which participants performed OM chanting and a control condition in a block design. We found that outputs from insula, anterior cingulate and orbitofrontal cortices were significantly reduced in OM condition. Of interest is the reduction of outputs from these regions to limbic area amygdala. Modulation of brain regions involved in emotion processing and implicated in major depressive disorder (MDD) raises a potential possibility of OM chanting in the treatment of MDD.

Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning

Many brain-based disorders are traditionally diagnosed based on clinical interviews and behavioral assessments, which are recognized to be largely imperfect. Therefore, it is necessary to establish neuroimaging-based biomarkers to improve diagnostic precision. Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising technique for the characterization and classification of varying disorders. However, most of these classification methods are supervised, i.e., they require a priori clinical labels to guide classification. In this study, we adopted various unsupervised clustering methods using static and dynamic rs-fMRI connectivity measures to investigate whether the clinical diagnostic grouping of different disorders is grounded in underlying neurobiological and phenotypic clusters. In order to do so, we derived a general analysis pipeline for identifying different brain-based disorders using genetic algorithm-based feature selection, and unsupervised clustering methods on four different datasets; three of them-ADNI, ADHD-200, and ABIDE-which are publicly available, and a fourth one-PTSD and PCS-which was acquired in-house. Using these datasets, the effectiveness of the proposed pipeline was verified on different disorders: Attention Deficit Hyperactivity Disorder (ADHD), Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), Post-Traumatic Stress Disorder (PTSD), and Post-Concussion Syndrome (PCS). For ADHD and AD, highest similarity was achieved between connectivity and phenotypic clusters, whereas for ASD and PTSD/PCS, highest similarity was achieved between connectivity and clinical diagnostic clusters. For multi-site data (ABIDE and ADHD-200), we report site-specific results. We also reported the effect of elimination of outlier subjects for all four datasets. Overall, our results suggest that neurobiological and phenotypic biomarkers could potentially be used as an aid by the clinician, in additional to currently available clinical diagnostic standards, to improve diagnostic precision. Data and source code used in this work is publicly available at https://github.com/xinyuzhao/identification-of-brain-based-disorders.git.

Zinc Nanoparticles Enhance Brain Connectivity in the Canine Olfactory Network: Evidence From an fMRI Study in Unrestrained Awake Dogs

Prior functional Magnetic Resonance Imaging (fMRI) studies have indicated increased neural activation when zinc nanoparticles are added to odorants in canines. Here we demonstrate that zinc nanoparticles up-regulate directional brain connectivity in parts of the canine olfactory network. This provides an explanation for previously reported enhancement in the odor detection capability of the dogs in the presence of zinc nanoparticles. In this study, we obtained fMRI data from awake and unrestrained dogs while they were being exposed to odorants with and without zinc nanoparticles, zinc nanoparticles suspended in water vapor, as well as just water vapor alone. We obtained directional connectivity between the brain regions of the olfactory network that were significantly stronger for the condition of odorant + zinc nanoparticles compared to just odorants, water vapor + zinc nanoparticles and water vapor alone. We observed significant strengthening of the paths of the canine olfactory network in the presence of zinc nanoparticles. This result indicates that zinc nanoparticles could potentially be used to increase canine detection capabilities in the environments of very low concentrations of the odorants, which would have otherwise been undetected.

Aberrant Dynamic Connectivity for Fear Processing in Anorexia Nervosa and Body Dysmorphic Disorder

Anorexia nervosa (AN) and body dysmorphic disorder (BDD) share distorted perceptions of appearance with extreme negative emotion, yet the neural phenotypes of emotion processing remain underexplored in them, and they have never been directly compared. We sought to determine if shared and disorder-specific fronto-limbic connectivity patterns characterize these disorders. FMRI data was obtained from three unmedicated groups: BDD (n = 32), weight-restored AN (n = 25), and healthy controls (HC; n = 37), while they viewed fearful faces and rated their own degree of fearfulness in response. We performed dynamic effective connectivity modeling with medial prefrontal cortex (mPFC), rostral anterior cingulate cortex (rACC), and amygdala as regions-of-interest (ROI), and assessed associations between connectivity and clinical variables. HCs exhibited significant within-group bidirectional mPFC-amygdala connectivity, which increased across the blocks, whereas BDD participants exhibited only significant mPFC-to-amygdala connectivity (P < 0.05, family-wise error corrected). In contrast, participants with AN lacked significant prefrontal-amygdala connectivity in either direction. AN showed significantly weaker mPFC-to-amygdala connectivity compared to HCs (P = 0.0015) and BDD (P = 0.0050). The mPFC-to-amygdala connectivity was associated with greater subjective fear ratings (R2 = 0.11, P = 0.0016), eating disorder symptoms (R2 = 0.33, P = 0.0029), and anxiety (R2 = 0.29, P = 0.0055) intensity scores. Our findings, which suggest a complex nosological relationship, have implications for understanding emotion regulation circuitry in these related psychiatric disorders, and may have relevance for current and novel therapeutic approaches.

The association between visual creativity and cortical thickness in healthy adults

Creativity is necessary to human survival, human prosperity, civilization and well-being. Visual creativity is an important part of creativity and is the ability to create products of novel and useful visual forms, playing important role in many fields such as art, painting and sculpture. There have been several neuroimaging studies exploring the neural basis of visual creativity. However, to date, little is known about the relationship between cortical structure and visual creativity as measured by the Torrance Tests of Creative Thinking. Here, we investigated the association between cortical thickness and visual creativity in a large sample of 310 healthy adults. We used multiple regression to analyze the correlation between cortical thickness and visual creativity, adjusting for gender, age and general intelligence. The results showed that visual creativity was significantly negatively correlated with cortical thickness in the left middle frontal gyrus (MFG), right inferior frontal gyrus (IFG), right supplementary motor cortex (SMA) and the left insula. These observations have implications for understanding that a thinner prefrontal cortex (PFC) (e.g. IFG, MFG), SMA and insula correspond to higher visual creative performance, presumably due to their role in executive attention, cognitive control, motor planning and dynamic switching.

Nicotine and caffeine modulate haloperidol-induced changes in postsynaptic density transcripts expression: Translational insights in psychosis therapy and treatment resistance

Caffeine and nicotine are widely used by schizophrenia patients and may worsen psychosis and affect antipsychotic therapies. However, they have also been accounted as augmentation strategies in treatment-resistant schizophrenia. Despite both substances are known to modulate dopamine and glutamate transmission, little is known about the molecular changes induced by these compounds in association to antipsychotics, mostly at the level of the postsynaptic density (PSD), a site of dopamine-glutamate interplay. Here we investigated whether caffeine and nicotine, alone or combined with haloperidol, elicited significant changes in the levels of both transcripts and proteins of the PSD members Homer1 and Arc, which have been implicated in synaptic plasticity, schizophrenia pathophysiology, and antipsychotics molecular action. Homer1a mRNA expression was significantly reduced by caffeine and nicotine, alone or combined with haloperidol, compared to haloperidol. Haloperidol induced significantly higher Arc mRNA levels than both caffeine and caffeine plus haloperidol in the striatum. Arc mRNA expression was significantly higher by nicotine plus haloperidol vs. haloperidol in the cortex, while in striatum gene expression by nicotine was significantly lower than that by both haloperidol and nicotine plus haloperidol. Both Homer1a and Arc protein levels were significantly increased by caffeine, nicotine, and nicotine plus haloperidol. Homer1b mRNA expression was significantly increased by nicotine and nicotine plus haloperidol, while protein levels were unaffected. Locomotor activity was not significantly affected by caffeine, while it was reduced by nicotine. These data indicate that both caffeine and nicotine trigger relevant molecular changes in PSD sites when given in association with haloperidol.

RETRACTED: Mnemonic Discrimination Deficits in First-Episode Psychosis and a Ketamine Model Suggests Dentate Gyrus Pathology Linked to N-Methyl-D-Aspartate Receptor Hypofunction

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). Retraction notice to: “Mnemonic Discrimination Deficits in First-Episode Psychosis and a Ketamine Model Suggests Dentate Gyrus Pathology Linked to N-Methyl-D-Aspartate Receptor Hypofunction” by Nina Vanessa Kraguljac, Matthew Carle, Michael A. Frölich, Steve Tran, Michael A. Yassa, David Matthew White, Abhishek Reddy, and Adrienne Carol Lahti (Biol Psychiatry Cogn Neurosci Neuroimaging 2018; 3:231-238); https://doi.org/10.1016/j.bpsc.2017.02.005. This article has been retracted at the request of Cameron S. Carter, M.D., Editor of Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, with agreement from all authors. The authors discovered an error in the calculation of the response bias–corrected pattern recognition score in this article, which has significantly changed the results for experiment 1. Specifically, the authors discovered that the response bias corrected pattern recognition score was erroneously computed as P(‘old’|target) minus P(‘old’|lure) rather than P(‘old’|target) minus P(‘old’|foil). After re-running statistical analyses with the correct values, the authors found a significant difference in the response bias–corrected pattern recognition score in healthy volunteers (HV) compared with first-episode psychosis (FEP) patients (HV: 84.13 ± 10.96; FEP: 63.70 ± 21.83; t = 4.01; p < .01) in experiment 1. This finding is not consistent with the original report, where the authors reported no group differences in bias-corrected pattern recognition scores (originally reported values: t = 0.93, p = .36). The authors again found no significant correlations between pattern completion scores and BPRS total, positive, or negative symptom scores or RBANS scores, consistent with the original report. In experiment 2, bias-corrected pattern recognition scores did not differ between the saline and ketamine conditions (saline: 78.29 ± 28.04; ketamine: 73.59 ± 18.94; t = 0.81; p = 0.43), which is consistent with the original report (originally reported values: t = −0.69, p = .50). Contrary to the original report, task performance during the saline and ketamine infusions was no longer correlated at trend level for pattern recognition. Repeat analyses showed no correlations between pattern recognition scores during the ketamine challenge and BPRS total, positive, and negative symptom scores, or ketamine plasma levels at either time point, consistent with the original report. The authors have verified that bias-corrected pattern separation scores were calculated correctly for both experiments in the initial report. This error affects the abstract, the results, Figure 1, and discussion of the manuscript. The authors voluntarily informed the Journal of this honest error upon its discovery. Because of the extent and nature of the changes to the paper, the editors and authors concluded that, to ensure maximum clarity and transparency, the only course of action was to retract this version of the paper. The authors are revising the paper, which the Journal will re-review and consider further for publication.

Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma

Brain connectivity studies report group differences in pairwise connection strengths. While informative, such results are difficult to interpret since our understanding of the brain relies on region-based properties, rather than on connection information. Given that large disruptions in the brain are often caused by a few pivotal sources, we propose a novel framework to identify the sources of functional disruption from effective connectivity networks. Our approach integrates static and time-varying effective connectivity modeling in a probabilistic framework, to identify aberrant foci and the corresponding aberrant connectomics network. Using resting-state fMRI, we illustrate the utility of this novel approach in U.S. Army soldiers (N = 87) with posttraumatic stress disorder (PTSD), mild traumatic brain injury (mTBI) and combat controls. Additionally, we employed machine-learning classification to identify those significant connectivity features that possessed high predictive ability. We identified three disrupted foci (middle frontal gyrus [MFG], insula, hippocampus), and an aberrant prefrontal-subcortical-parietal network of information flow. We found the MFG to be the pivotal focus of network disruption, with aberrant strength and temporal-variability of effective connectivity to the insula, amygdala and hippocampus. These connectivities also possessed high predictive ability (giving a classification accuracy of 81%); and they exhibited significant associations with symptom severity and neurocognitive functioning. In summary, dysregulation originating in the MFG caused elevated and temporally less-variable connectivity in subcortical regions, followed by a similar effect on parietal memory-related regions. This mechanism likely contributes to the reduced control over traumatic memories leading to re-experiencing, hyperarousal and flashbacks observed in soldiers with PTSD and mTBI. Hum Brain Mapp 39:264-287, 2018. © 2017 Wiley Periodicals, Inc.

Dynamic brain connectivity is a better predictor of PTSD than static connectivity

Using resting-state functional magnetic resonance imaging, we test the hypothesis that subjects with post-traumatic stress disorder (PTSD) are characterized by reduced temporal variability of brain connectivity compared to matched healthy controls. Specifically, we test whether PTSD is characterized by elevated static connectivity, coupled with decreased temporal variability of those connections, with the latter providing greater sensitivity toward the pathology than the former. Static functional connectivity (FC; nondirectional zero-lag correlation) and static effective connectivity (EC; directional time-lagged relationships) were obtained over the entire brain using conventional models. Dynamic FC and dynamic EC were estimated by letting the conventional models to vary as a function of time. Statistical separation and discriminability of these metrics between the groups and their ability to accurately predict the diagnostic label of a novel subject were ascertained using separate support vector machine classifiers. Our findings support our hypothesis that PTSD subjects have stronger static connectivity, but reduced temporal variability of connectivity. Further, machine learning classification accuracy obtained with dynamic FC and dynamic EC was significantly higher than that obtained with static FC and static EC, respectively. Furthermore, results also indicate that the ease with which brain regions engage or disengage with other regions may be more sensitive to underlying pathology than the strength with which they are engaged. Future studies must examine whether this is true only in the case of PTSD or is a general organizing principle in the human brain. Hum Brain Mapp 38:4479-4496, 2017. © 2017 Wiley Periodicals, Inc.

Aberrant Hyperconnectivity in the Motor System at Rest Is Linked to Motor Abnormalities in Schizophrenia Spectrum Disorders

Motor abnormalities are frequently observed in schizophrenia and structural alterations of the motor system have been reported. The association of aberrant motor network function, however, has not been tested. We hypothesized that abnormal functional connectivity would be related to the degree of motor abnormalities in schizophrenia. In 90 subjects (46 patients) we obtained resting stated functional magnetic resonance imaging (fMRI) for 8 minutes 40 seconds at 3T. Participants further completed a motor battery on the scanning day. Regions of interest (ROI) were cortical motor areas, basal ganglia, thalamus and motor cerebellum. We computed ROI-to-ROI functional connectivity. Principal component analyses of motor behavioral data produced 4 factors (primary motor, catatonia and dyskinesia, coordination, and spontaneous motor activity). Motor factors were correlated with connectivity values. Schizophrenia was characterized by hyperconnectivity in 3 main areas: motor cortices to thalamus, motor cortices to cerebellum, and prefrontal cortex to the subthalamic nucleus. In patients, thalamocortical hyperconnectivity was linked to catatonia and dyskinesia, whereas aberrant connectivity between rostral anterior cingulate and caudate was linked to the primary motor factor. Likewise, connectivity between motor cortex and cerebellum correlated with spontaneous motor activity. Therefore, altered functional connectivity suggests a specific intrinsic and tonic neural abnormality in the motor system in schizophrenia. Furthermore, altered neural activity at rest was linked to motor abnormalities on the behavioral level. Thus, aberrant resting state connectivity may indicate a system out of balance, which produces characteristic behavioral alterations.

The impact of hemodynamic variability and signal mixing on the identifiability of effective connectivity structures in BOLD fMRI

PURPOSE

Multiple computational studies have demonstrated that essentially all current analytical approaches to determine effective connectivity perform poorly when applied to synthetic functional Magnetic Resonance Imaging (fMRI) datasets. In this study, we take a theoretical approach to investigate the potential factors facilitating and hindering effective connectivity research in fMRI.

MATERIALS AND METHODS

In this work, we perform a simulation study with use of Dynamic Causal Modeling generative model in order to gain new insights on the influence of factors such as the slow hemodynamic response, mixed signals in the network and short time series, on the effective connectivity estimation in fMRI studies.

RESULTS

First, we perform a Linear Discriminant Analysis study and find that not the hemodynamics itself but mixed signals in the neuronal networks are detrimental to the signatures of distinct connectivity patterns. This result suggests that for statistical methods (which do not involve lagged signals), deconvolving the BOLD responses is not necessary, but at the same time, functional parcellation into Regions of Interest (ROIs) is essential. Second, we study the impact of hemodynamic variability on the inference with use of lagged methods. We find that the local hemodynamic variability provide with an upper bound on the success rate of the lagged methods. Furthermore, we demonstrate that upsampling the data to TRs lower than the TRs in state-of-the-art datasets does not influence the performance of the lagged methods.

CONCLUSIONS

Factors such as background scale-free noise and hemodynamic variability have a major impact on the performance of methods for effective connectivity research in functional Magnetic Resonance Imaging.

Neural substrates of context- and person-dependent altruistic punishment

Human altruistic behaviors are heterogeneous across both contexts and people, whereas the neural signatures underlying the heterogeneity remain to be elucidated. To address this issue, we examined the neural signatures underlying the context- and person-dependent altruistic punishment, conjoining event-related fMRI with both task-based and resting-state functional connectivity (RSFC). Acting as an impartial third party, participants decided how to punish norm violators either alone or in the presence of putative others. We found that the presence of others decreased altruistic punishment due to diffusion of responsibility. Those behavioral effects paralleled altered neural responses in the dorsal anterior cingulate cortex (dACC) and putamen. Further, we identified modulation of responsibility diffusion on task-based functional connectivity of dACC with the brain regions implicated in reward processing (i.e., posterior cingulate cortex and amygdala/orbital frontal cortex). Finally, the RSFC results revealed that (i) increased intrinsic connectivity strengths of the putamen with temporoparietal junction and dorsolateral PFC were associated with attenuated responsibility diffusion in altruistic punishment and (ii) increased putamen-dorsomedial PFC connectivity strengths were associated with reduced responsibility diffusion in self-reported responsibility. Taken together, our findings elucidate the context- and person-dependent altruistic behaviors as well as associated neural substrates and thus provide a potential neurocognitive mechanism of heterogeneous human altruistic behaviors. Hum Brain Mapp 38:5535-5550, 2017. © 2017 Wiley Periodicals, Inc.