Brain Connectivity Patterns Research Articles

Distinct and opposite profiles of connectivity during self-reference task and rest in youth at clinical high risk for psychosis.

Self-reference is impaired in psychotic disorders such as schizophrenia, associated with disability, and closely related to characteristic patterns of aberrant brain connectivity. However, at present, it is unclear whether self-reference is impacted in pathogenesis of the disorder. Alterations in connectivity during a self-reference task or resting-state in the psychosis risk (i.e., prodromal) period may yield important clues for biomarker development, as well as for novel treatment targets. This study examined a task-based and resting-state functional magnetic resonance imaging in individuals at clinical high risk (CHR) for psychosis (n = 22) and healthy control unaffected peers (n = 20). The self-reference task comprised three task conditions where subjects were asked if an adjective was relevant to themselves (self), a designated other individual (other), or to evaluate the word's spelling (letter). Connectivity analyses examined medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC), regions commonly found in conjunction analyses of self-reference, during both the self-reference task and rest. In task connectivity analyses, CHR individuals exhibited decreased mPFC-PCC connectivity when compared to controls. In resting-state analyses, CHR participants showed greater mPFC-PCC connectivity. Taken together, results suggest that psychosis-like alterations in mPFC-PCC connectivity is present prior to psychosis onset across both task and rest.

Physical brain connectomics

Brain connectivity and structure-function relationships are analyzed from a physical perspective in place of common graph-theoretic and statistical approaches that overwhelmingly ignore the brain's physical structure and geometry. Field theory is used to define connectivity tensors in terms of bare and dressed propagators, and discretized representations are implemented that respect the physical nature and dimensionality of the quantities involved, retain the correct continuum limit, and enable diagrammatic analysis. Eigenfunction analysis is used to simultaneously characterize and probe patterns of brain connectivity and activity, in place of statistical or phenomenological patterns. Physically based measures that characterize the connectivity are then developed in coordinate and spectral domains; some of which generalize or rectify graph-theoretic measures to implement correct dimensionality and continuum limits, and some replace graph-theoretic quantities. Traditional graph-based measures are shown to be highly prone to artifacts introduced by discretization and threshold, often because essential physical constraints have not been imposed, dimensionality has not been included, and/or distinctions between scalar, vector, and tensor quantities have not been considered. The results can replace them in ways that converge correctly and measure properties of brain structure, rather than of its discretization, and thus potentially enable physical interpretation of the many phenomenological results in the literature. Geometric effects are shown to dominate in determining many brain properties and care must be taken not to interpret geometric differences as differences in intrinsic neural connectivity. The results demonstrate the need to use systematic physical methods to analyze the brain and the potential of such methods to obtain new insights from data, make new predictions for experimental test, and go beyond phenomenological classification to dynamics and mechanisms.

Aberrant functional connectivity of neural circuits associated with social and sensorimotor deficits in young children with autism spectrum disorder.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by atypical functional integration of brain regions. The vast majority of neuroimaging studies of ASD have focused on older children, adolescents, and adults with the disorder. Very little work has explored whole-brain functional connectivity of young children with ASD. Here, we collected resting-state functional magnetic resonance imaging data from 58 young children (mean age 4.98 years; 29 with ASD; 29 matched healthy controls [HC]). All children were under sedation during scanning. A functional "connectedness" method was first used to seek for brain regions showing atypical functional connectivity (FC) in children with ASD. Then, a recurrent-seek strategy was applied to reveal atypical FC circuits in ASD children. FC matrices between regions-of-interest (ROIs) were compared between ASD and HC. Finally, a support vector regression (SVR) method was used to assess the relationship between the FC circuits and ASD symptom severity. Two atypical FC circuits comprising 23 ROIs in ASD were revealed: one predominantly comprised brain regions involved with social cognition showing under-connectivity in ASD; the other predominantly comprised sensory-motor and visual brain regions showing over-connectivity in ASD. The SVR analysis showed that the two FC circuits were separately related to social deficits and restricted behavior scores. These findings indicate disrupted FC of neural circuits involved in the social and sensorimotor processes in young children with ASD. The finding of the atypical FC patterns in young children with ASD underscores the utility of studying younger children with the disorder, and highlights nuanced patterns of brain connectivity underlying behavior closer to disorder onset. Autism Research 2018, 11: 1643-1652. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Autism spectrum disorder (ASD) is an early-onset neurodevelopmental disorder. Understanding brain functional alterations at early ages is important for understanding biological mechanisms of ASD. Here, we found two atypical brain functional circuits in young children with ASD that were related to social and sensorimotor function. These results show how atypical patterns of brain functional connectivity in young children with of ASD may underlie core symptoms of the disorder.

Personality traits are related with dynamic functional connectivity in major depression disorder: A resting-state analysis

BackgroundMajor depressive disorder (MDD) is one of the most well-known psychiatric disorders, which can be destructive for its damage to people's normal cognitive, emotional and social functions. Personality refers to the unique and stable character of thinking and behavior style of an individual, which has long been thought as a key influence factor for MDD. Although some knowledge about the common neural basic between MDD and personality traits has been acquired, there are few studies exploring dynamic neural mechanism behind them, which changes brain connectivity pattern rapidly to adapt to the environment over time. MethodsIn this study, the emerging dynamic functional network connectivity (DFNC) method was used in resting-state fMRI data to find the differences between healthy group (N = 107) and MDD group (N = 109) in state-based dynamic measures, and the correlations between these measures and personality traits (extraversion and neuroticism in Eysenck Personality Questionnaire, EPQ) were explored. ResultsThe results showed that MDD was significantly less than the health control group in dwell time and fraction time of state 4, which was positively correlated with extraversion score and negatively correlated with neuroticism score. Further exploration on state 4 showed that it had low modularity, hyper-connectedness of sensory-related regions and DMN, and weak connections between cortex and subcortical areas, which suggested that the absence of this state in MDD might represent a decrease in activity and positive emotions. ConclusionWe found the dynamic functional connectivity mechanism underlying MDD, confirmed our hypothesis that there existed the interacted relationship between trait, disease and the brain's dynamic characteristic, and suggested some reference for treatment of depression.

Intersubject similarity of personality is associated with intersubject similarity of brain connectivity patterns

Personality is a central high-level psychological concept that defines individual human beings and has been associated with a variety of real-world outcomes (e.g., mental health and academic performance). Using 2 h, high resolution, functional magnetic resonance imaging (fMRI) resting state data of 984 (primary dataset N = 801, hold-out dataset N = 183) participants from the Human Connectome Project (HCP), we investigated the relationship between personality (five-factor model, FFM) and intrinsic whole-brain functional connectome. We found a pattern of functional brain connectivity (“global personality network”) related to personality traits. Consistent with the heritability of personality traits, the connectivity strength of this global personality network is also heritable (more similar between monozygotic twin pairs compared to the dizygotic twin pairs). Validated by both the repeated family-based 10-fold cross-validation and hold-out dataset, our intersubject network similarity analysis allowed us to identify participants' pairs with similar personality profiles. Across all the identified pairs of participants, we found a positive correlation between the network similarity and personality similarity, supporting our “similar brain, similar personality” hypothesis. Furthermore, the global personality network can be used to predict the individual subject's responses in the personality questionnaire on an item level. In sum, based on individual brain connectivity pattern, we could predict different facets of personality, and this prediction is not based on localized regions, but rather relies on the individual connectivity pattern in large-scale brain networks.

Aberrant Structural Brain Connectivity in Adolescents with Attentional Problems Who Were Born Prematurely.

Differences in structural brain connectivity that underlie inattention have been previously investigated in adolescents with attention deficit/hyperactivity disorder, but not in the context of premature birth, which is often associated with attentional problems. The purpose of this study was to identify the neural correlates of attentional problems in adolescents born prematurely and determine neonatal predictors of those neural correlates and attention problems. The study included 24 adolescents (12.5 ± 1.8 years of age; 12 girls, 12 boys) who were born prematurely and underwent MR imaging of the brain and cognitive assessment, both shortly after birth and as adolescents. Structural connectivity was assessed at adolescence using diffusion tensor imaging and tractography. Of the 24 subjects, 12 had attention deficits. A set of axonal pathways connecting the frontal, parietal, temporal, and occipital lobes had significantly lower fractional anisotropy in subjects with attentional problems. The temporoparietal connection between the left precuneus and left middle temporal gyrus was the most significantly underconnected interlobar axonal pathway. Low birth weight and ventriculomegaly, but not white matter injury or intraventricular hemorrhage on neonatal MR imaging, predicted temporoparietal hypoconnectivity in adolescence. However, neither birth weight nor other neonatal characteristics were associated with attention deficits directly. We identified an aberrant structural brain connectivity pattern, involving temporoparietal hypoconnectivity, in prematurely born adolescents with attentional problems. We also identified birth weight as a potential neonatal predictor of the temporoparietal hypoconnectivity. These findings add to our understanding of the neural basis and etiology of inattention in adolescents after premature birth.

Neural Mechanisms Linking Emotion with Cardiovascular Disease.

The present review discusses brain circuits that are engaged by negative emotions and possibly linked to cardiovascular disease risk. It describes recent human brain imaging studies that relate activity in these brain circuits to emotional processes, peripheral physiology, preclinical pathophysiology, as well as clinical outcomes. Negative emotions and the regulation of negative emotions reliably engage several brain regions that cross-sectional and longitudinal brain imaging studies have associated with CVD risk markers and outcomes. These brain regions include the amygdala, anterior cingulate cortex, medial prefrontal cortex, and insula. Other studies have applied advanced statistical techniques to characterize multivariate patterns of brain activity and brain connectivity that associate with negative emotion and CVD-relevant peripheral physiology. Brain imaging studies on emotion and cardiovascular disease risk are expanding our understanding of the brain-body bases of psychosocial and behavioral risk for cardiovascular disease.

Characteristic patterns of inter- and intra-hemispheric metabolic connectivity in patients with stable and progressive mild cognitive impairment and Alzheimer\u2019s disease

The change in hypometabolism affects the regional links in the brain network. Here, to understand the underlying brain metabolic network deficits during the early stage and disease evolution of AD (Alzheimer disease), we applied correlation analysis to identify the metabolic connectivity patterns using 18F-FDG PET data for NC (normal control), sMCI (stable MCI), pMCI (progressive MCI) and AD, and explore the inter- and intra-hemispheric connectivity between anatomically-defined brain regions. Regions extracted from 90 anatomical structures were used to construct the matrix for measuring the inter- and intra-hemispheric connectivity. The brain connectivity patterns from the metabolic network show a decreasing trend of inter- and intra-hemispheric connections for NC, sMCI, pMCI and AD. Connection of temporal to the frontal or occipital regions is a characteristic pattern for conversion of NC to MCI, and the density of links in the parietal-occipital network is a differential pattern between sMCI and pMCI. The reduction pattern of inter and intra-hemispheric brain connectivity in the metabolic network depends on the disease stages, and is with a decreasing trend with respect to disease severity. Both frontal-occipital and parietal-occipital connectivity patterns in the metabolic network using 18F-FDG PET are the key feature for differentiating disease groups in AD.

Age, Disease, and Their Interaction Effects on Intrinsic Connectivity of Children and Adolescents in Autism Spectrum Disorder Using Functional Connectomics.

Brain connectivity analysis has provided crucial insights to pinpoint the differences between autistic and typically developing (TD) children during development. The aim of this study is to investigate the functional connectomics of autism spectrum disorder (ASD) versus TD and underpin the effects of development, disease, and their interactions on the observed atypical brain connectivity patterns. Resting-state functional magnetic resonance imaging (rs-fMRI) from the Autism Brain Imaging Data Exchange (ABIDE) data set, which is stratified into two cohorts: children (9-12 years) and adolescents (13-16 years), is used for the analysis. Differences in various graph theoretical network measures are calculated between ASD and TD in each group. Furthermore, two-factor analysis of variance test is used to study the effect of age, disease, and their interaction on the network measures and the network edges. Furthermore, the differences in connection strength between TD and ASD subjects are assessed using network-based statistics. The results showed that ASD exhibits increased functional integration at the expense of decreased functional segregation. In ASD adolescents, there is a significant decrease in modularity suggesting a less robust modular organization, and an increase in participation coefficient suggesting more random integration and widely distributed connection edges. Furthermore, there is significant hypoconnectivity observed in the adolescent group especially in the default mode network, while the children group shows both hyper- and hypoconnectivity. This study lends support to a model of global atypical connections and further identifies functional networks and areas that are independently affected by age, disease, and their interaction.

Estimation of Functional Dependence in High-Dimensional Spike-Field Activity.

Behavior is encoded across spatiotemoral scales of brain activity, from small-scale spikes to large-scale local field potentials (LFP). Identifying the functional dependence between spikes and LFP networks during behavior can help understand neural encoding and improve future neurotechnologies, but is difficult to achieve. First, spikes and LFP have different statistical characteristics (binary spikes vs. continuous LFPs) and time-scales. Second, given the prohibitively large number of spike channels and LFP features recorded in today's experiments, learning dependencies between all recorded signals is challenging and prone to overfitting. To solve this challenge, we present a model-based approach to estimate the functional dependence between high-dimensional field features and neuronal spikes. We model the binary time-series of spikes for each neuron as a point process dependent on the behavioral states and LFP features across the network. Given the prohibitively large number of possible spike-LFP dependency parameters, we first employ an Ll-regularization technique to learn the point process model during both supervised and unsupervised learning to ease detection of significant dependency parameters. We then use the Akaike information criterion (AIC) to enforce model sparsity by incorporating only a minimum number of non-zero dependency parameters into the point process model based on a trade-off between model complexity and its prediction power. Using extensive numerical simulations, we show that our method (i) can correctly identify the functional dependencies and thus improve the prediction of spiking activity and (ii) can improve the prediction of spiking activity with significantly fewer number of parameters compared to when regularization is not enforced. Our approach may serve as a tool to investigate brain connectivity patterns across spatiotemporal scales.

Hypoglycemia Unawareness in T1DM Is Associated with Altered Brain Default Mode Network Connectivity

The impact of hypoglycemia awareness (HA) on brain resting state connectivity remains unclear. To examine the impact of HA on the default mode network (DMN), a functional network in the brain that is active during wakeful rest and associated with introspection, 12 T1DM individuals with hypo unawareness (T1DM-UW) (by Clarke score) (7F/5M, age 44±12 years, BMI 26.4±4.2 kg/m2, HbA1c 7.1±0.7) and 15 T1DM-Aware (AW) (10F/5M, age 30±7, BMI 24.5±3.1, HbA1C 7.1±0.9) individuals underwent resting state BOLD fMRI scanning to assess the DMN during a two-step euglycemic (Eu)-hypoglycemic (Hypo) clamp (90-60 mg/dl). T1DM-AW individuals exhibited a hypoglycemia-induced decrease in DMN connectivity (P<0.001). In contrast, T1DM-UW individuals showed no difference in connectivity between conditions. Moreover, the degree of DMN inhibition correlated inversely with plasma cortisol (r=-0.432, p=0.031), norepinephrine (r=-0.40, p=0.038) and positively with hypoglycemia symptom scores (r=0.40, p=0.037). These findings suggest that, unlike T1DM-AW individuals, hypoglycemia fails to elicit changes in the DMN amongst T1DM-UA individuals. Furthermore, the change in DMN connectivity is associated with measures of physiological and interoceptive responses to stress. These findings highlight the need for future studies to investigate whether avoidance of hypoglycemia can restore brain connectivity patterns. Disclosure D. Groskreutz: None. J. Hwang: None. D. Seo: None. C. Lacadie: None. L. Parikh: None. R. Belfort-DeAguiar: Research Support; Self; GlaxoSmithKline plc.. D. Scheinost: None. R. Sinha: None. T. Constable: None. R. Sherwin: Other Relationship; Self; QuintilesIMS, MannKind Corporation. Research Support; Self; Regeneron Pharmaceuticals, Inc.. Other Relationship; Self; ICON plc..

Encoding the local connectivity patterns of fMRI for cognitive task and state classification.

In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher vectors (FV), vector of locally aggregated descriptors (VLAD) and bag-of-words (BoW) methods. We first obtain local descriptors, called mesh arc descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. Then, we extract a dictionary of relationships, called brain connectivity dictionary by fitting a generative Gaussian mixture model (GMM) to a set of MADs, and selecting codewords at the mean of each component of the mixture. Codewords represent connectivity patterns among anatomical regions. We also encode MADs by VLAD and BoW methods using k-Means clustering. We classify cognitive tasks using the Human Connectome Project (HCP) task fMRI dataset and cognitive states using the Emotional Memory Retrieval (EMR). We train support vector machines (SVMs) using the encoded MADs. Results demonstrate that, FV encoding of MADs can be successfully employed for classification of cognitive tasks, and outperform VLAD and BoW representations. Moreover, we identify the significant Gaussians in mixture models by computing energy of their corresponding FV parts, and analyze their effect on classification accuracy. Finally, we suggest a new method to visualize the codewords of the learned brain connectivity dictionary.

2288 Neural correlates of face processing in autism spectrum disorder: A quantitative meta-analysis of current literature and future directions

OBJECTIVES/SPECIFIC AIMS: Autism spectrum disorder (ASD) affects 1 in 68 people and includes restricted, repetitive behavior, and social communication deficits. Aspects of face processing (i.e., identity, emotion perception) are impaired in some with ASD. Neuroimaging studies have shown aberrant patterns of brain activation and connectivity of face processing regions. However, small sample sizes and inconsistent results have hindered clinical utility of these findings. The study aims to establish consistent patterns of brain responses to faces in ASD and provide directions for future research. METHODS/STUDY POPULATION: Neuroimaging studies were identified through a multi-database search according to PRISMA guidelines. In total, 23 studies were retained for a sample size of 383 healthy controls and 345 ASD. Peak coordinates were extracted for activation likelihood estimation (ALE) in GingerALE v2.3.6. Follow-up ALE analyses investigated directed Versus undirected gaze, static Versus dynamic, emotional Versus neutral, and familiar Versus unfamiliar faces. RESULTS/ANTICIPATED RESULTS: Faces produced bilateral activation of the fusiform gyrus (FG) in healthy controls (−42 −52 −20; 22 −74 −12, p<0.05, FDR) and left FG activation in ASD (−42 −54 −16, p<0.05, FDR). Activation in both groups was lateral to the mid-fusiform sulcus. Follow-up results pending. DISCUSSION/SIGNIFICANCE OF IMPACT: Reduced right FG activation to faces may inform biomarker or response to intervention studies. Mid-fusiform sulcus proved a reliable predictor of functional divides should be investigated on a subject-specific level.

Data-Driven Clustering Reveals a Link Between Symptoms and Functional Brain Connectivity in Depression

BackgroundDepression is a complex disorder with large interindividual variability in symptom profiles that often occur alongside symptoms of other psychiatric domains, such as anxiety. A dimensional and symptom-based approach may help refine the characterization of depressive and anxiety disorders and thus aid in establishing robust biomarkers. We use resting-state functional magnetic resonance imaging to assess the brain functional connectivity correlates of a symptom-based clustering of individuals. MethodsWe assessed symptoms using the Beck Depression and Beck Anxiety Inventories in individuals with or without a history of depression (N = 1084) and high-dimensional data clustering to form subgroups based on symptom profiles. We compared dynamic and static functional connectivity between subgroups in a subset of the total sample (n = 252). ResultsWe identified five subgroups with distinct symptom profiles, which cut across diagnostic boundaries with different total severity, symptom patterns, and centrality. For instance, inability to relax, fear of the worst, and feelings of guilt were among the most severe symptoms in subgroups 1, 2, and 3, respectively. The distribution of individuals was 32%, 25%, 22%, 10%, and 11% in subgroups 1 to 5, respectively. These subgroups showed evidence of differential static brain-connectivity patterns, in particular comprising a frontotemporal network. In contrast, we found no significant associations with clinical sum scores, dynamic functional connectivity, or global connectivity. ConclusionsAdding to the pursuit of individual-based treatment, subtyping based on a dimensional conceptualization and unique constellations of anxiety and depression symptoms is supported by distinct patterns of static functional connectivity in the brain.

Sensory threshold neuromuscular electrical stimulation fosters motor imagery performance

Motor imagery (MI) has been largely studied as a way to enhance motor learning and to restore motor functions. Although it is agreed that users should emphasize kinesthetic imagery during MI, recordings of MI brain patterns are not sufficiently reliable for many subjects. It has been suggested that the usage of somatosensory feedback would be more suitable than standardly used visual feedback to enhance MI brain patterns. However, somatosensory feedback should not interfere with the recorded MI brain pattern. In this study we propose a novel feedback modality to guide subjects during MI based on sensory threshold neuromuscular electrical stimulation (St-NMES). St-NMES depolarizes sensory and motor axons without eliciting any muscular contraction. We hypothesize that St-NMES does not induce detectable ERD brain patterns and fosters MI performance. Twelve novice subjects were included in a cross-over design study. We recorded their EEG, comparing St-NMES with visual feedback during MI or resting tasks. We found that St-NMES not only induced significantly larger desynchronization over sensorimotor areas (p<0.05) but also significantly enhanced MI brain connectivity patterns. Moreover, classification accuracy and stability were significantly higher with St-NMES. Importantly, St-NMES alone did not induce detectable artifacts, but rather the changes in the detected patterns were due to an increased MI performance. Our findings indicate that St-NMES is a promising feedback in order to foster MI performance and cold be used for BMI online applications.

F69. Salience Network Connectivity in Children With Nonverbal Learning Disability or Autism Spectrum Disorder

NonVerbal Learning Disability (NVLD) is characterized by deficits in spatial reasoning, executive functioning, fine-motor skills, math, and social functioning. Due to a number of clinical similarities, there is debate as to whether NVLD should be classified as a sub-type of Autism Spectrum Disorder (ASD) or as a discrete diagnosis. Herein, we use resting state fMRI (rsfMRI) to assess whether the social deficits seen in NVLD and ASD arise from different patterns of brain connectivity.

113. Brain Lesions Associated With Depression are Characterized by a Unique Pattern of Brain Connectivity

Focal brain lesions can lend insight into the causal neuroanatomical substrate of depression; however, studies of lesion location alone have led to inconsistent results.

Regional homogeneity and functional connectivity patterns in major depressive disorder, cognitive vulnerability to depression and healthy subjects

BackgroundCognitive vulnerability to depression (CVD) is a high risk for depressive disorder. Recent studies focus on individuals with CVD to determine the neural basis of major depressive disorder (MDD) neuropathology. However, whether CVD showed specific or similar brain functional activity and connectivity patterns, compared to MDD, remain largely unknown. MethodsHere, using resting-state functional magnetic resonance imaging in subjects with CVD, healthy controls (HC) and MDD, regional homogeneity (ReHo) and resting-state functional connectivity (R-FC) analyses were conducted to assess local synchronization and changes in functional connectivity patterns. ResultsSignificant ReHo differences were found in right posterior lobe of cerebellum (PLC), left lingual gyrus (LG) and precuneus. Compared to HC, CVD subjects showed increased ReHo in the PLC, which was similar to the difference found between MDD and HC. Compared to MDD patients, CVD subjects showed decreased ReHo in PLC, LG, and precuneus. R-FC analyses found increased functional connections between LG and left inferior parietal lobule, posterior cingulate cortex, and dorsolateral prefrontal cortex in CVD compared to both HC and MDD. Moreover, Regional mean ReHo values were positively correlated with Center for Epidemiologic Studies Depression Scale scores. ConclusionThese analyses revealed that PLC and functional connections between LG and left inferior parietal lobule, posterior cingulate cortex, and dorsolateral prefrontal cortex may be a potential marker for CVD.

F169. BRAIN CONNECTIVITY DURING PSYCHOLOGICAL STRESS IN PATIENTS WITH SCHIZOPHRENIA

BackgroundIt is commonly accepted that in most patients with schizophrenia external factors act on genetic predisposition to produce active psychotic symptoms. In fact, we showed that patients with schizophrenia have an abnormal brain activation and peripheral autonomic response to psychological stress. We sought to characterize the brain connectivity networks of such response in schizophrenia.MethodsWe studied the pattern of brain connectivity in relation to mental arithmetic stress paradigm in 21 patients and 21 healthy subjects aged 18 to 50 years, using 3T-fMRI. A period of 6 minutes of resting state acquisition (PRE) were followed by a block design with three 1-minute CONTROL task (one digit sum), 1 minute STRESS task (two digit subtraction) and 1 minute rest after task (POST). Pairwise Pearson correlations were calculated between 90 regions of interest. Data were analyzed with MATLAB and SPSS software.ResultsPatients with schizophrenia showed a lower connectivity network between fronto-temporal limbic areas compared with control subjects during control and stress task.Moreover, we observed a great variability of link density during resting state in patients but not in controls, and it diminishes in response to task.DiscussionPatients present abnormalities in networks related to stress response showing an alteration in fronto-temporal connectivity, and a poor and random modulation of these networks at rest. Current and previous findings suggest abnormal fronto-temporal connectivity that ultimately would lead to psychotic symptoms emergency in response to an environmental stressor and, even, could be related to hypervigilance and misattribution feeding into the paranoid cognition characteristic of patients with schizophrenia.

Individual differences in decision making competence revealed by multivariate fMRI.

While an extensive literature in decision neuroscience has elucidated the neurobiological foundations of decision making, prior research has focused primarily on group-level effects in a sample population. Due to the presence of inherent differences between individuals' cognitive abilities, it is also important to examine the neural correlates of decision making that explain interindividual variability in cognitive performance. This study therefore investigated how individual differences in decision making competence, as measured by the Adult Decision Making Competence (A-DMC) battery, are related to functional brain connectivity patterns derived from resting-state fMRI data in a sample of 304 healthy participants. We examined connectome-wide associations, identifying regions within frontal, parietal, temporal, and occipital cortex that demonstrated significant associations with decision making competence. We then assessed whether the functional interactions between brain regions sensitive to decision making competence and seven intrinsic connectivity networks (ICNs) were predictive of specific facets of decision making assessed by subtests of the A-DMC battery. Our findings suggest that individual differences in specific facets of decision making competence are mediated by ICNs that support executive, social, and perceptual processes, and motivate an integrative framework for understanding the neural basis of individual differences in decision making competence.