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There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro-connectome, particularly in the context of data fusion.

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children, usually categorized as three subtypes, predominant inattention (ADHD-I), predominant hyperactivity-impulsivity (ADHD-HI), and a combined subtype (ADHD-C). Yet, common and unique abnormalities of electroencephalogram (EEG) across different subtypes remain poorly understood. Here, we leveraged microstate characteristics and power features to investigate temporal and frequency abnormalities in ADHD and its subtypes using high-density EEG on 161 participants (54 ADHD-Is and 53 ADHD-Cs and 54 healthy controls). Four EEG microstates were identified. The coverage of salience network (state C) were decreased in ADHD compared to HC (p = 1.46e-3), while the duration and contribution of frontal–parietal network (state D) were increased (p = 1.57e-3; p = 1.26e-4). Frequency power analysis also indicated that higher delta power in the fronto-central area (p = 6.75e-4) and higher power of theta/beta ratio in the bilateral fronto-temporal area (p = 3.05e-3) were observed in ADHD. By contrast, remarkable subtype differences were found primarily on the visual network (state B), of which ADHD-C have higher occurrence and coverage than ADHD-I (p = 9.35e-5; p = 1.51e-8), suggesting that children with ADHD-C might exhibit impulsivity of opening their eyes in an eye-closed experiment, leading to hyper-activated visual network. Moreover, the top discriminative features selected from support vector machine model with recursive feature elimination (SVM-RFE) well replicated the above results, which achieved an accuracy of 72.7% and 73.8% separately in classifying ADHD and two subtypes. To conclude, this study highlights EEG microstate dynamics and frequency features may serve as sensitive measurements to detect the subtle differences in ADHD and its subtypes, providing a new window for better diagnosis of ADHD. [ABSTRACT FROM AUTHOR]

Background: Grip strength is a widely used and well-validated measure of overall health that is increasingly understood to index risk for psychiatric illness and neurodegeneration in older adults. However, existing work has not examined how grip strength relates to a comprehensive set of mental health outcomes, which can detect early signs of cognitive decline. Furthermore, whether brain structure mediates associations between grip strength and cognition remains unknown.Methods: Based on cross-sectional and longitudinal data from over 40,000 participants in the UK Biobank, this study investigated the behavioral and neural correlates of handgrip strength using a linear mixed effect model and mediation analysis.Results: In cross-sectional analysis, we found that greater grip strength was associated with better cognitive functioning, higher life satisfaction, greater subjective well-being, and reduced depression and anxiety symptoms while controlling for numerous demographic, anthropometric, and socioeconomic confounders. Further, grip strength of females showed stronger associations with most behavioral outcomes than males. In longitudinal analysis, baseline grip strength was related to cognitive performance at ~9 years follow-up, while the reverse effect was much weaker. Further, baseline neuroticism, health, and financial satisfaction were longitudinally associated with subsequent grip strength. The results revealed widespread associations between stronger grip strength and increased grey matter volume, especially in subcortical regions and temporal cortices. Moreover, grey matter volume of these regions also correlated with better mental health and considerably mediated their relationship with grip strength.Conclusions: Overall, using the largest population-scale neuroimaging dataset currently available, our findings provide the most well-powered characterization of interplay between grip strength, mental health, and brain structure, which may facilitate the discovery of possible interventions to mitigate cognitive decline during aging. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance imaging (fMRI) is a powerful technique with the potential to estimate individual variations in behavioral and cognitive traits. Joint learning of multiple datasets can utilize their complementary information so as to improve learning performance, but it also gives rise to the challenge for data fusion to effectively integrate brain patterns elicited by multiple fMRI data. However, most of the current data fusion methods analyze each single dataset separately and further infer the relationship among them, which fail to utilize the multidimensional structure inherent across modalities and may ignore complex but important interactions. To address this issue, we propose a novel sparse tensor decomposition method to integrate multiple task-stimulus (paradigm) fMRI data. Seeing each paradigm fMRI as one modality, our proposed method considers the relationships across subjects and modalities simultaneously. In specific, a third-order tensor is first modeled by using the functional network connectivity (FNC) of subjects in multiple fMRI paradigms. A novel sparse tensor decomposition with the regularization terms is designed to factorize the tensor into a series of rank-one components, which can extract the shared components across modalities as the embedded features. The L2,1-norm regularizer (i.e., group sparsity) is enforced to select a few common features among multiple subjects. Validation of the proposed method is performed on realistic three paradigm fMRI datasets from the Philadelphia Neurodevelopmental Cohort (PNC) study, for the study of the relationship between the FNC and human cognitive abilities. Experimental results show our method outperforms several other competing methods in the prediction of individuals with different cognitive behaviors via the wide range achievement test (WRAT). Furthermore, our method discovers the FNC related to the cognitive behaviors, such as the connectivity associated with the default mode network (DMN) for three paradigms, and the connectivity between DMN and visual (VIS) domains within the emotion task. [ABSTRACT FROM AUTHOR]

Objective: Recently, functional magnetic resonance imaging (fMRI)-derived brain functional connectivity (FC) patterns have been used as fingerprints to predict individual differences in phenotypic measures, and cognitive dysfunction associated with brain diseases. In these applications, how to accurately estimate FC patterns is crucial yet technically challenging. Methods: In this article, we propose a correlation guided graph learning (CGGL) method to estimate FC patterns for establishing brain-behavior relationships. Different from the existing graph learning methods which only consider the graph structure across brain regions-of-interest (ROIs), our proposed CGGL takes into account both the temporal correlation of ROIs across time points, and the graph structure across ROIs. The resulting FC patterns reflect substantial inter-individual variations related to the behavioral measure of interest. Results: We validate the effectiveness of our proposed CGGL on the Philadelphia Neurodevelopmental Cohort data for separately predicting three behavioral measures based on resting-state fMRI. Experimental results demonstrate that the proposed CGGL outperforms other competing FC pattern estimation methods. Conclusion: Our method increases the predictive power of the constructed FC patterns when establishing brain-behavior relationships, and gains meaningful insights into relevant biological mechanisms. Significance: The proposed CGGL offers a more powerful, and reliable method to estimate FC patterns, which can be used as fingerprints in many brain network studies. [ABSTRACT FROM AUTHOR]

The dynamics of the human brain span multiple spatial scales, from connectivity associated with a specific region/network to the global organization, each representing different brain mechanisms. Yet brain reconfigurations at different spatial scales are seldom explored and whether they are associated with the neural aspects of brain disorders is far from understood. In this study, we introduced a dynamic measure called step‐wise functional network reconfiguration (sFNR) to characterize how brain configuration rewires at different spatial scales. We applied sFNR to two independent datasets, one includes 160 healthy controls (HCs) and 151 patients with schizophrenia (SZ) and the other one includes 314 HCs and 255 individuals with autism spectrum disorder (ASD). We found that both SZ and ASD have increased whole‐brain sFNR and sFNR between cerebellar and subcortical/sensorimotor domains. At the ICN level, the abnormalities in SZ are mainly located in ICNs within subcortical, sensory, and cerebellar domains, while the abnormalities in ASD are more widespread across domains. Interestingly, the overlap SZ‐ASD abnormality in sFNR between cerebellar and sensorimotor domains was correlated with the reasoning‐problem‐solving performance in SZ (r = −.1652, p =.0058) as well as the Autism Diagnostic Observation Schedule in ASD (r =.1853, p =.0077). Our findings suggest that dynamic reconfiguration deficits may represent a key intersecting point for SZ and ASD. The investigation of brain dynamics at different spatial scales can provide comprehensive insights into the functional reconfiguration, which might advance our knowledge of cognitive decline and other pathophysiology in brain disorders. [ABSTRACT FROM AUTHOR]

Motor abnormalities in schizophrenia spectrum disorders (SSD) have increasingly attracted scientific interest in the past years. However, the neural mechanisms underlying parkinsonism in SSD are unclear. The present multimodal magnetic resonance imaging (MRI) study examined SSD patients with and without parkinsonism, as defined by a Simpson and Angus Scale (SAS) total score of ≥4 (SAS group, n = 22) or <4 (non-SAS group, n = 22). Parallel independent component analysis (p-ICA) was used to examine the covarying components among gray matter volume maps computed from structural MRI (sMRI) and fractional amplitude of low-frequency fluctuations (fALFF) maps computed from resting-state functional MRI (rs-fMRI) patient data. We found a significant correlation (P =.020, false discovery rate [FDR] corrected) between an sMRI component and an rs-fMRI component, which also significantly differed between the SAS and non-SAS group (P =.042, z = −2.04). The rs-fMRI component comprised the cortical sensorimotor network, and the sMRI component included predominantly a frontothalamic/cerebellar network. Across the patient sample, correlations adjusted for the Positive and Negative Syndrome Scale (PANSS) total scores showed a significant relationship between tremor score and loadings of the cortical sensorimotor network, as well as between glabella-salivation score, frontothalamic/cerebellar and cortical sensorimotor network loadings. These data provide novel insights into neural mechanisms of parkinsonism in SSD. Aberrant bottom-up modulation of cortical motor regions may account for these specific motor symptoms, at least in patients with SSD. [ABSTRACT FROM AUTHOR]

Background/aims: Nausea is a major complaint of gastroparesis (GP), and the pathophysiology of this condition is poorly understood. Therefore, this study utilized fMRI to investigate the possible central nervous system (CNS) mechanisms of nausea in 10 GP patients versus 8 healthy controls (HCs).Methods: Nausea severity was assessed on a 0-10 scale and presented as mean ± SD. Nausea was increased from baseline utilizing up to 30 min of visual stimulation (VS). Functional network connectivity was measured with fMRI at baseline and after 30 min of VS. fMRI data were preprocessed using statistical parametric mapping software. Thirty-four independent components were identified as meaningful resting-state networks (RSNs) by group independent component analysis. The Functional Network Connectivity (FNC) among 5 RSNs considered important in CNS nausea mechanisms was calculated as the Pearson's pairwise correlation.Results: Baseline nausea score in GP patients was 2.7 ± 2.0 and increased to 7.0 ± 1.5 after stimulation (P < 0.01). In HCs nausea scores did not increase from baseline after stimulus (0.3 ± 0.5). When comparing GP patients to HCs after VS, a significant reduction (P < 0.001) in bilateral insula network connectivity compared to the right insula network was detected. No significant differences in connectivity were noted among the other RSNs. Additionally, the average gray matter volume was non-significantly reduced in the insula in GP patients compared to HC.Conclusions: The insula connectivity network is impaired in nauseated GP patients. This phenomenon could explain the susceptibility of GP patients to nausea or may have resulted from a state of chronic nausea. [ABSTRACT FROM AUTHOR]

Catatonia is a nosologically unspecific syndrome, which subsumes a plethora of mostly complex affective, motor, and behavioral phenomena. Although catatonia frequently occurs in schizophrenia spectrum disorders (SSD), specific patterns of abnormal brain structure and function underlying catatonia are unclear at present. Here, we used a multivariate data fusion technique for multimodal magnetic resonance imaging (MRI) data to investigate patterns of aberrant intrinsic neural activity (INA) and gray matter volume (GMV) in SSD patients with and without catatonia. Resting-state functional MRI and structural MRI data were collected from 87 right-handed SSD patients. Catatonic symptoms were examined on the Northoff Catatonia Rating Scale (NCRS). A multivariate analysis approach was used to examine co-altered patterns of INA and GMV. Following a categorical approach, we found predominantly frontothalamic and corticostriatal abnormalities in SSD patients with catatonia (NCRS total score ≥ 3; n = 24) when compared to SSD patients without catatonia (NCRS total score = 0; n = 22) matched for age, gender, education, and medication. Corticostriatal network was associated with NCRS affective scores. Following a dimensional approach, 33 SSD patients with catatonia according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision were identified. NCRS behavioral scores were associated with a joint structural and functional system that predominantly included cerebellar and prefrontal/cortical motor regions. NCRS affective scores were associated with frontoparietal INA. This study provides novel neuromechanistic insights into catatonia in SSD suggesting co-altered structure/function-interactions in neural systems subserving coordinated visuospatial functions and motor behavior. [ABSTRACT FROM AUTHOR]

Background: The underlying molecular mechanisms associated with alcohol use disorder (AUD) risk have only been partially revealed using traditional approaches such as univariate genomewide association and linkage‐based analyses. We therefore aimed to identify gene clusters related to Electroencephalograms (EEG) neurobiological phenotypes distinctive to individuals with AUD using a multivariate approach. Methods: The current project adopted a bimultivariate data‐driven approach, parallel independent component analysis (para‐ICA), to derive and explore significant genotype–phenotype associations in a case–control subset of the Collaborative Study on the Genetics of Alcoholism (COGA) dataset. Para‐ICA subjects comprised N = 799 self‐reported European Americans (367 controls and 432 AUD cases), recruited from COGA, who had undergone resting EEG and genotyping. Both EEG and genomewide single nucleotide polymorphism (SNP) data were preprocessed prior to being subjected to para‐ICA in order to derive genotype–phenotype relationships. Results: From the data, 4 EEG frequency and 4 SNP components were estimated, with 2 significantly correlated EEG–genetic relationship pairs. The first such pair primarily represented theta activity, negatively correlated with a genetic cluster enriched for (but not limited to) ontologies/disease processes representing cell signaling, neurogenesis, transmembrane drug transportation, alcoholism, and lipid/cholesterol metabolism. The second component pair represented mainly alpha activity, positively correlated with a genetic cluster with ontologies similarly enriched as the first component. Disease‐related enrichments for this component revealed heart and autoimmune disorders as top hits. Loading coefficients for both the alpha and theta components were significantly reduced in cases compared to controls. Conclusions: Our data suggest plausible multifactorial genetic components, primarily enriched for neuronal/synaptic signaling/transmission, immunity, and neurogenesis, mediating low‐frequency alpha and theta abnormalities in alcohol addiction. [ABSTRACT FROM AUTHOR]

Schizophrenia (SZ) is a chronic and severe mental disorder that affects how a person thinks, feels, and behaves. It has been proposed that this disorder is related to disrupted brain connectivity, which has been verified by many studies. With the development of functional magnetic resonance imaging (fMRI), further exploration of brain connectivity was made possible. Region-based networks are commonly used for mapping brain connectivity. However, they fail to illustrate the connectivity within regions of interest (ROIs) and lose precise location information. Voxel-based networks provide higher precision, but are difficult to construct and interpret due to the high dimensionality of the data. In this paper, we adopt a novel high-dimensional Gaussian graphical model – $\psi$ -learning method, which can help ease computational burden and provide more accurate inference for the underlying networks. This method has been proven to be an equivalent measure of the partial correlation coefficient and, thus, is flexible for network comparison through statistical tests. The fMRI data we used were collected by the mind clinical imaging consortium using an auditory task in which there are 92 SZ patients and 116 healthy controls. We compared the networks at three different scales by using global measurements, community structure, and edge-wise comparisons within the networks. Our results reveal, at the highest voxel resolution, sets of distinct aberrant patterns for the SZ patients, and more precise local structures are provided within ROIs for further investigation. [ABSTRACT FROM AUTHOR]

Brain functional connectivity has been shown to change over time during resting state fMRI experiments. Close examination of temporal changes have revealed a small set of whole-brain connectivity patterns called dynamic states. Dynamic functional network connectivity (dFNC) studies have demonstrated that it is possible to replicate the dynamic states across several resting state experiments. However, estimation of states and their temporal dynamicity still suffers from noisy and imperfect estimations. In regular dFNC implementations, states are estimated by comparing connectivity patterns through the data without considering time, in other words only zero order changes are examined. In this work we propose a method that includes first order variations of dFNC in the searching scheme of dynamic connectivity patterns. Our approach, referred to as temporal variation of functional network connectivity (tvFNC), estimates the derivative of dFNC, and then searches for reoccurring patterns of concurrent dFNC states and their derivatives. The tvFNC method is first validated using a simulated dataset and then applied to a resting-state fMRI sample including healthy controls (HC) and schizophrenia (SZ) patients and compared to the standard dFNC approach. Our dynamic approach reveals extra patterns in the connectivity derivatives complementing the already reported state patterns. State derivatives consist of additional information about increment and decrement of connectivity among brain networks not observed by the original dFNC method. The tvFNC shows more sensitivity than regular dFNC by uncovering additional FNC differences between the HC and SZ groups in each state. In summary, the tvFNC method provides a new and enhanced approach to examine time-varying functional connectivity. [ABSTRACT FROM AUTHOR]

Differences in cognitive performance between males and females are well-described, most commonly in certain spatial and language tasks. Sex-related differences in cognition are relevant to the study of the neurotypical brain and to neuropsychiatric disorders, which exhibit prominent disparities in the incidence, prevalence and severity of symptoms between men and women. While structural dimorphism in the human brain is well-described, controversy exists regarding the existence and degree of sex-related differences in brain function. We analyzed resting-state functional MRI from 650 neurotypical young adults matched for age and sex to determine the degree of sexual dimorphism present in intrinsic functional networks. Multilevel modeling was pursued to create 8-, 24-, and 51-network models of whole-brain data to quantify sex-related effects in network activity with increasing resolution. We determined that sexual dimorphism is present in the majority of intrinsic brain networks and affects ∼0.5–2% of brain locations surveyed in the three whole-brain network models. It is particularly common in task-positive control networks and is pervasive among default mode networks. The size of sex-related effects varied by network but can be moderate or even large in size. Female > male effects were on average larger, but male > female effects spread across greater network territory. Using a novel methodology, we mapped dimorphic locations to meta-analytic association test maps derived from task fMRI, demonstrating that the neurocognitive footprint of intrinsic neural correlates is much larger in males. All results were replicated in a motion-matched sub-sample. Our findings argue that sex is an important biological variable in human brain function and suggest that observed differences in neurocognitive performance have identifiable intrinsic neural correlates. [ABSTRACT FROM AUTHOR]

Abstract Frontotemporal Dementia (FTD) is preceded by a long period of subtle brain changes, occurring in the absence of overt cognitive symptoms, that need to be still fully characterized. Dynamic network analysis based on resting-state magnetic resonance imaging (rs-fMRI) is a potentially powerful tool for the study of preclinical FTD. In the present study, we employed a "chronnectome" approach (recurring, time-varying patterns of connectivity) to evaluate measures of dynamic connectivity in 472 at-risk FTD subjects from the Genetic Frontotemporal dementia research Initiative (GENFI) cohort. We considered 249 subjects with FTD-related pathogenetic mutations and 223 mutation non-carriers (HC). Dynamic connectivity was evaluated using independent component analysis and sliding-time window correlation to rs-fMRI data, and meta-state measures of global brain flexibility were extracted. Results show that presymptomatic FTD exhibits diminished dynamic fluidity, visiting less meta-states, shifting less often across them, and travelling through a narrowed meta-state distance, as compared to HC. Dynamic connectivity changes characterize preclinical FTD, arguing for the desynchronization of the inner fluctuations of the brain. These changes antedate clinical symptoms, and might represent an early signature of FTD to be used as a biomarker in clinical trials. Highlights • Frontotemporal Dementia is preceded by a long period of subtle brain changes. • Time-varying dynamic connectivity can unveiled underappreciated brain details. • Presymptomatic Frontotemporal Dementia exhibits a reduced dynamic fluidity. • Frontotemporal Dementia showed a selective vulnerability of specific brain regions. • At the very early stage Frontotemporal Dementia is affecting brain as global system. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of estimating multiple sparse, co-activated brain regions from functional magnetic resonance imaging (fMRI) observations belonging to different classes. More precisely, we propose a method to analyze similarities and differences in functional connectivity between children and young adults. Often, analysis is conducted on each class separately, and differences across classes are identified with an additional postprocessing step using adequate statistical tools. Here, we propose to rely on a generalized fused Lasso penalty, which allows us to make use of the entire data set in order to estimate connectivity patterns that are either shared across classes, or specific to a given group. By using the entire population during the estimation, we hope to increase the power of our analysis. The proposed model falls in the category of population-wise matrix decomposition, and a simple and efficient alternating direction method of multipliers algorithm is introduced to solve the associated optimization problem. After validating our approach on simulated data, experiments are performed on resting-state fMRI imaging from the Philadelphia neurodevelopmental cohort data set, comprised of normally developing children from ages 8 to 21. Developmental differences were observed in various brain regions, as a total of three class-specific resting-state components were identified. Statistical analysis of the estimated subject-specific features, as well as classification results (based on age groups, up to 81% accuracy, $n = 583$ samples) related to these components demonstrate that the proposed method is able to properly extract meaningful shared and class-specific sub-networks. [ABSTRACT FROM AUTHOR]

Background: We have previously shown increased resting-state functional connectivity (rsFC) in the frontoparietal network (FPN) and the default mode network (DMN) in patients with acute anorexia nervosa. Based on these findings we investigated within-network rsFC in patients recovered from anorexia nervosa to examine whether these abnormalities are a state or trait marker of the disease. To extend the understanding of functional connectivity in patients with anorexia nervosa, we also estimated rsFC between large-scale networks. Methods: Girls and women recovered from anorexia nervosa and pair-wise, age- and sex-matched healthy controls underwent a resting-state fMRI scan. Using independent component analyses (ICA), we isolated the FPN, DMN and salience network. We used standard comparisons as well as a hypothesis-based approach to test the findings of our previous rsFC study in this recovered cohort. Temporal correlations between network time-course pairs were computed to investigate functional network connectivity (FNC). Results: Thirty-one patients recovered from anorexia nervosa and 31 controls participated in our study. Standard group comparisons revealed reduced rsFC between the dorsolateral prefrontal cortex (dlPFC) and the FPN in the recovered group. Using a hypothesis-based approach we extended the previous finding of increased rsFC between the angular gyrus and the FPN in patients recovered from anorexia nervosa. No group differences in FNC were revealed. Limitations: The study design did not allow us to conclude that the difference found in rsFC constitutes a scar effect of the disease. Conclusion: This study suggests that some abnormal rsFC patterns found in patients recovered from anorexia nervosa normalize after long-term weight restoration, while distorted rsFC in the FPN, a network that has been associated with cognitive control, may constitute a trait marker of the disorder. [ABSTRACT FROM AUTHOR]

Objectives: One of the most prominent features of schizophrenia is relatively lower general cognitive ability (GCA). An emerging approach to understanding the roots of variation in GCA relies on network properties of the brain. In this multi-center study, we determined global characteristics of brain networks using graph theory and related these to GCA in healthy controls and individuals with schizophrenia. Methods: Participants (N=116 controls, 80 patients with schizophrenia) were recruited from four sites. GCA was represented by the first principal component of a large battery of neurocognitive tests. Graph metrics were derived from diffusion-weighted imaging. Results: The global metrics of longer characteristic path length and reduced overall connectivity predicted lower GCA across groups, and group differences were noted for both variables. Measures of clustering, efficiency, and modularity did not differ across groups or predict GCA. Follow-up analyses investigated three topological types of connectivity—connections among high degree “rich club” nodes, “feeder” connections to these rich club nodes, and “local” connections not involving the rich club. Rich club and local connectivity predicted performance across groups. In a subsample (N=101 controls, 56 patients), a genetic measure reflecting mutation load, based on rare copy number deletions, was associated with longer characteristic path length. Conclusions: Results highlight the importance of characteristic path lengths and rich club connectivity for GCA and provide no evidence for group differences in the relationships between graph metrics and GCA. (JINS, 2016, 22, 240–249) [ABSTRACT FROM AUTHOR]

Analyses of gray matter concentration (GMC) deficits in patients with schizophrenia (Sz) have identified robust changes throughout the cortex. We assessed the relationships between diagnosis, overall symptom severity, and patterns of gray matter in the largest aggregated structural imaging dataset to date. We performed both sourcebased morphometry (SBM) and voxel-based morphometry (VBM) analyses on GMC images from 784 Sz and 936 controls (Ct) across 23 scanning sites in Europe and the United States. After correcting for age, gender, site, and diagnosis by site interactions, SBM analyses showed 9 patterns of diagnostic differences. They comprised separate cortical, subcortical, and cerebellar regions. Seven patterns showed greater GMC in Ct than Sz, while 2 (brainstem and cerebellum) showed greater GMC for Sz. The greatest GMC deficit was in a single pattern comprising regions in the superior temporal gyrus, inferior frontal gyrus, and medial frontal cortex, which replicated over analyses of data subsets. VBM analyses identified overall cortical GMC loss and one small cluster of increased GMC in Sz, which overlapped with the SBM brainstem component. We found no significant association between the component loadings and symptom severity in either analysis. This mega-analysis confirms that the commonly found GMC loss in Sz in the anterior temporal lobe, insula, and medial frontal lobe form a single, consistent spatial pattern even in such a diverse dataset. The separation of GMC loss into robust, repeatable spatial patterns across multiple datasets paves the way for the application of these methods to identify subtle genetic and clinical cohort effects. [ABSTRACT FROM AUTHOR]

Considering the diverse clinical presentation and likely polygenic etiology of schizophrenia, this investigation examined the effect of polygenic risk on a well-established intermediate phenotype for schizophrenia. We hypothesized that a measure of cumulative genetic risk based on additive effects of many genetic susceptibility loci for schizophrenia would predict prefrontal cortical inefficiency during working memory, a brain-based biomarker for the disorder. The present study combined imaging, genetic and behavioral data obtained by the Mind Clinical Imaging Consortium study of schizophrenia (n = 255). For each participant, we derived a polygenic risk score (PGRS), which was based on over 600 nominally significant single nucleotide polymorphisms, associated with schizophrenia in a separate discovery sample comprising 3322 schizophrenia patients and 3587 control participants. Increased polygenic risk for schizophrenia was associated with neural inefficiency in the left dorsolateral prefrontal cortex after covarying for the effects of acquisition site, diagnosis, and population stratification. We also provide additional supporting evidence for our original findings using scores based on results from the Psychiatric Genomics Consortium study. Gene ontology analysis of the PGRS highlighted genetic loci involved in brain development and several other processes possibly contributing to disease etiology. Our study permits new insights into the additive effect of hundreds of genetic susceptibility loci on a brain-based intermediate phenotype for schizophrenia. The combined impact of many common genetic variants of small effect are likely to better reveal etiologic mechanisms of the disorder than the study of single common genetic variants. [ABSTRACT FROM PUBLISHER]

Background Altered functional connectivity in critical networks has been associated with chronic alcohol abuse. In turn, changes in connectivity in executive control networks (ECNs) may undermine the ability to control alcohol consumption. It was hypothesized that network connectivity would be reduced in individuals with problematic alcohol use ( ALC) compared with controls and that diminished network connectivity would be associated with greater failure to control drinking. Methods Resting-state functional magnetic resonance imaging was analyzed to identify 14 previously identified intrinsic connectivity networks ( ICNs) using a priori regions of interest in cases ranging from binge drinkers to those with severe alcohol use disorder, as well as control subjects. Analyses tested for differences in network connectivity strength between 255 ALC cases and 87 age- and gender-matched controls. Further, structural equation analysis, using 383 ALC cases, tested whether functional connectivity strength mediated the relationship between years of regular drinking and alcohol problems. Results The age- and gender-matched analysis showed that ALC had significantly lower network connectivity strength than controls in the left executive control ( LECN), basal ganglia, and primary visual networks. For all ALC, LECN connectivity strength is negatively correlated with failed control and alcohol disorder severity. Edges connecting parietal regions with dorsolateral prefrontal, middle frontal, and temporal regions within the LECN drove these relationships. A positive association between years of drinking and severity of alcohol problems was mediated by reduced ECN connectivity. Conclusions This study reports relationships between network strength and problematic alcohol use, suggesting that chronic drinking negatively impacts brain connectivity, specifically in the LECN. Altered functional connectivity, related to chronic alcohol abuse, may contribute to the etiology of alcohol dependence and relapse. [ABSTRACT FROM AUTHOR]

The lack of consistency of genetic associations in highly heritable mental illnesses, such as schizophrenia, remains a challenge in molecular psychiatry. Because clinical phenotypes for psychiatric disorders are often ill defined, considerable effort has been made to relate genetic polymorphisms to underlying physiological aspects of schizophrenia (so called intermediate phenotypes), that may be more reliable. Given the polygenic etiology of schizophrenia, the aim of this work was to form a measure of cumulative genetic risk and study its effect on neural activity during working memory (WM) using functional magnetic resonance imaging. Neural activity during the Sternberg Item Recognition Paradigm was measured in 79 schizophrenia patients and 99 healthy controls. Participants were genotyped, and a genetic risk score (GRS), which combined the additive effects of 41 single-nucleotide polymorphisms (SNPs) from 34 risk genes for schizophrenia, was calculated. These risk SNPs were chosen according to the continuously updated meta-analysis of genetic studies on schizophrenia available at www.schizophreniaresearchforum.org. We found a positive relationship between GRS and left dorsolateral prefrontal cortex inefficiency during WM processing. GRS was not correlated with age, performance, intelligence, or medication effects and did not differ between acquisition sites, gender, or diagnostic groups. Our study suggests that cumulative genetic risk, combining the impact of many genes with small effects, is associated with a known brain-based intermediate phenotype for schizophrenia. The GRS approach could provide an advantage over studying single genes in studies focusing on the genetic basis of polygenic conditions such as neuropsychiatric disorders. [ABSTRACT FROM PUBLISHER]

Identification of factors that predict recurrent antisocial behavior is integral to the social sciences, criminal justice procedures, and the effective treatment of high-risk individuals. Here we show that error-related brain activity elicited during performance of an in- hibitory task prospectively predicted subsequent rearrest among adult offenders within 4 y of release (N = 96). The odds that an offender with relatively low anterior cingulate activity would be rearrested were approximately double that of an offender with high activity in this region, holding constant other observed risk factors. These results suggest a potential neurocognitive biomarker for persistent antisocial behavior. [ABSTRACT FROM AUTHOR]

The article discusses a study aimed at providing a clearer examination of neural mechanisms of impaired decision making specific to hoarding disorder (HD). Patients with primary HD were recruited and compared with patients with obsessive-compulsive disorder (OCD) and healthy control subjects (HCs). It suggests that in patients with HD, anxiety, indecisiveness and sadness were related to inferior frontal gyrus activation and that the endowment effect may be informative for studying hoarding.

Since the discovery of functional connectivity in fMRI data (i.e., temporal correlations between spatially distinct regions of the brain) there has been a considerable amount of work in this field. One important focus has been on the analysis of brain connectivity using the concept of networks instead of regions. Approximately ten years ago, two important research areas grew out of this concept. First, a network proposed to be “a default mode of brain function” since dubbed the default mode network was proposed by Raichle. Secondly, multisubject or group independent component analysis (ICA) provided a data-driven approach to study properties of brain networks, including the default mode network. In this paper, we provide a focused review of how ICA has contributed to the study of intrinsic networks. We discuss some methodological considerations for group ICA and highlight multiple analytic approaches for studying brain networks. We also show examples of some of the differences observed in the default mode and resting networks in the diseased brain. In summary, we are in exciting times and still just beginning to reap the benefits of the richness of functional brain networks as well as available analytic approaches. [ABSTRACT FROM PUBLISHER]

The anterior cingulate and several other prefrontal and parietal brain regions are implicated in error processing and cognitive control. The effects of different doses of alcohol on activity within these brain regions during a functional magnetic resonance imaging (fMRI) task where errors are frequently committed have not been fully explored. This study examined the impact of a placebo [breath alcohol concentration (BrAC) = 0.00%], moderate (BrAC = 0.05%), and high (BrAC = 0.10%) doses of alcohol on brain hemodynamic activity during a functional MRI (fMRI) Go/No-Go task in 38 healthy volunteers. Alcohol increased reaction time and false alarm errors in a dose-dependent manner. fMRI analyses showed alcohol decreased activity in anterior cingulate, lateral prefrontal cortex, insula, and parietal lobe regions during false alarm responses to No-Go stimuli. These findings indicate that brain regions implicated in error processing are affected by alcohol and might provide a neural basis for alcohol's effects on behavioral performance. [ABSTRACT FROM AUTHOR]

Synaptic development and elimination are normal neurodevelopmental processes, which if altered could contribute to various neuropsychiatric disorders. 31 P- 1 H magnetic resonance spectroscopic imaging (MRSI) and structural magnetic resonance imaging (MRI) exams were conducted on 105 healthy children ages 6-18 years old to identify neuromolecular indices of synaptic development and elimination. Over the age range studied, age-related changes in high-energy phosphate (phosphocreatine), membrane phospholipid metabolism (precursors and breakdown products), and percent gray matter volume were found. These neuromolecular and structural indices of synaptic development and elimination are associated with development of several cognitive domains. Monitoring of these molecular markers is essential for devising treatment strategies for neurodevelopmental disorders. [ABSTRACT FROM AUTHOR]

Abstract: The recall of an object from features is a specific operation in semantic memory in which the thalamus and pre-supplementary motor area (pre-SMA) are integrally involved. Other higher-order semantic cortices are also likely to be involved. We used the object-recall-from-features paradigm, with more sensitive scanning techniques and larger sample size, to replicate and extend our previous results. Eighteen right-handed healthy participants performed an object-recall task and an association semantic task, while undergoing functional magnetic resonance imaging. During object-recall, subjects determined whether words pairs describing object features combined to recall an object; during the association task they decided if two words were related. Of brain areas specifically involved in object recall, in addition to the thalamus and pre-SMA, other regions included the left dorsolateral prefrontal cortex, inferior parietal lobule, and middle temporal gyrus, and bilateral rostral anterior cingulate and inferior frontal gyri. These regions are involved in semantic processing, verbal working memory and response-conflict detection and monitoring. The thalamus likely helps to coordinate activity of these different brain areas. Understanding the circuit that normally mediates this process is relevant for schizophrenia, where many regions in this circuit are functionally abnormal and semantic memory is impaired. [Copyright &y& Elsevier]

Abstract: Evidence from neuroimaging studies suggests that the right hemisphere of the human brain might be more specialized for attention than the left hemisphere. However, differences between right and left hemisphere in the magnitude of hemodynamic activity (i.e., ‘functional asymmetry’) rarely have been explicitly examined in previous neuroimaging studies of attention. This study used a new voxel-based comparison method to examine hemispheric differences in the amplitude of the hemodynamic response in response to infrequent target, infrequent novel, and frequent standard stimuli during an event-related fMRI auditory oddball task in 100 healthy adult participants. Processing of low probability task-relevant target stimuli, or ‘oddballs’, and low probability task-irrelevant novel stimuli is believed to engage in orienting and attentional processes. It was hypothesized that greater right-hemisphere activation compared to left would be observed to infrequent target and novel stimuli. Consistent with predictions, greater right hemisphere than left frontal, temporal, and parietal lobe activity was observed for target detection and novelty processing. Moreover, asymmetry effects did not differ with respect to age or gender of the participants. The results (1) support the proposal that the right hemisphere is differentially engaged in processing salient stimuli and (2) demonstrate the successful use of a new voxel-based laterality analysis technique for fMRI data. [Copyright &y& Elsevier]

Abstract: Independent component analysis (ICA) is an approach for decomposing fMRI data into spatially independent maps and time courses. We have recently proposed a method for ICA of multisubject data; in the current paper, an extension is proposed for allowing ICA group comparisons. This method is applied to data from experiments designed to stimulate visual cortex, motor cortex or both visual and motor cortices. Several intergroup and intragroup metrics are proposed for assessing the utility of the components for comparisons of group ICA data. The proposed method may prove to be useful in answering questions requiring multigroup comparisons when a flexible modeling approach is desired. [Copyright &y& Elsevier]

Although visual processing impairments have been explored in schizophrenia (SZ), their underlying neurobiology of the visual processing impairments has not been widely studied. Also, while some research has hinted at differences in information transfer and flow in SZ, there are few investigations of the dynamics of functional connectivity within visual networks. In this study, we analyzed resting-state fMRI data of the visual sensory network (VSN) in 160 healthy control (HC) subjects and 151 SZ subjects. We estimated 9 independent components within the VSN. Then, we calculated the dynamic functional network connectivity (dFNC) using the Pearson correlation. Next, using k-means clustering, we partitioned the dFNCs into five distinct states, and then we calculated the portion of time each subject spent in each state, which we termed the occupancy rate (OCR). Using OCR, we compared HC with SZ subjects and investigated the link between OCR and visual learning in SZ subjects. Besides, we compared the VSN functional connectivity of SZ and HC subjects in each state. We found that this network is indeed highly dynamic. Each state represents a unique connectivity pattern of fluctuations in VSN FNC, and all states showed significant disruption in SZ. Overall, HC showed stronger connectivity within the VSN in states. SZ subjects spent more time in a state in which the connectivity between the middle temporal gyrus and other regions of VNS is highly negative. Besides, OCR in a state with strong positive connectivity between the middle temporal gyrus and other regions correlated significantly with visual learning scores in SZ. [ABSTRACT FROM AUTHOR]

Recent studies have shown a critical role of the gastrointestinal microbiome in brain and behavior via the complex gut–microbiome–brain axis. However, the influence of the oral microbiome in neurological processes is much less studied, especially in response to the stimuli, such as smoking, within the oral microenvironment. Additionally, given the complex structural and functional networks in brain, our knowledge about the relationship between microbiome and brain function through specific brain circuits is still very limited. In this pilot study, we leveraged next generation sequencing for microbiome and functional neuroimaging technique to enable the delineation of microbiome-brain network links as well as their relationship to cigarette smoking. Thirty smokers and 30 age- and sex-matched nonsmokers were recruited for 16S sequencing of their oral microbial community. Among them, 56 subjects were scanned by resting-state functional magnetic resonance imaging to derive brain functional networks. Statistical analyses were performed to demonstrate the influence of smoking on the oral microbial composition, functional network connectivity, and the associations between microbial shifts and functional network connectivity alternations. Compared to nonsmokers, we found a significant decrease of beta diversity (P = 6 × 10−3) in smokers and identified several classes (Betaproteobacteria, Spirochaetia, Synergistia, and Mollicutes) with significant alterations in microbial abundance. Pathway analysis on the predicted KEGG pathways shows that the microbiota with altered abundance are mainly involved in pathways related to cell processes, DNA repair, immune system, and neurotransmitters signaling. One brain functional network connectivity component was identified to have a significant difference between smokers and nonsmokers (P = 0.032), mainly including connectivity between brain default network and other task-positive networks. This brain functional component was also significantly associated with smoking related microbiota, suggesting a correlated cross-individual pattern between smoking-induced oral microbiome dysbiosis and brain functional connectivity alternation, possibly involving immunological and neurotransmitter signaling pathways. This work is the first attempt to link oral microbiome and brain functional networks, and provides support for future work in characterizing the role of oral microbiome in mediating smoking effects on brain activity. • Link the microbiome-brain network in relation to smoking. • Decreased beta diversity, abundance changes in taxa and pathways by smoking. • Alternations of connectivity between DMN and task-positive networks in smokers. • Oral microbiome-brain association involving immune and neurotransmitter pathways. [ABSTRACT FROM AUTHOR]

Autocorrelation (AC) in fMRI time-series is a well-known phenomenon, typically attributed to colored noise and therefore removed from the data. We hypothesize that AC reflects systematic and meaningful signal fluctuations that may be tied to neural activity and provide evidence to support this hypothesis. Each fMRI time-series is modeled as an autoregressive process from which the autocorrelation is quantified. Then, autocorrelation during resting-state fMRI and auditory oddball (AOD) task in schizophrenia and healthy volunteers is examined. During resting-state, AC was higher in the visual cortex while during AOD task, frontal part of the brain exhibited higher AC in both groups. AC values were significantly lower in specific brain regions in schizophrenia patients (such as thalamus during resting-state) compared to healthy controls in two independent datasets. Moreover, AC values had significant negative correlation with patients' symptoms. AC differences discriminated patients from healthy controls with high accuracy (resting-state). Contrary to most prior works, the results suggest AC shows meaningful patterns that are discriminative between patients and controls. Our results are in line with recent works attributing autocorrelation to feedback loop of brain's regulatory circuit. Autoconnectivity is cognitive state dependent (resting-state vs. task) and mental state dependent (healthy vs. schizophrenia). The concept of autoconnectivity resembles a recurrent neural network and provides a new perspective of functional integration in the brain. These findings may have important implications for understanding of brain function in health and disease as well as for analysis of fMRI time-series. [ABSTRACT FROM AUTHOR]

New techniques to investigate functional network connectivity in resting state functional magnetic resonance imaging data have recently emerged. One novel approach, called meta-state analysis, goes beyond the mere cross-correlation of time courses of distinct brain areas and explores temporal dynamism in more detail, allowing for connectivity states to overlap in time and capturing global dynamic behavior. Previous studies have shown that patients with chronic schizophrenia exhibit reduced neural dynamism compared to healthy controls, but it is not known whether these alterations extend to earlier phases of the illness. In this study, we analyzed individuals at clinical high-risk (CHR, n = 53) for developing psychosis, patients in an early stage of schizophrenia (ESZ, n = 58), and healthy controls (HC, n = 70). ESZ individuals exhibit reduced neural dynamism across all domains compared to HC. CHR individuals also show reduced neural dynamism but only in 2 out of 4 domains investigated. Overall, meta-state analysis adds information about dynamic fluidity of functional connectivity. [ABSTRACT FROM AUTHOR]

Cannabis and alcohol are believed to have widespread effects on the brain. Although adolescents are at increased risk for substance use, the adolescent brain may also be particularly vulnerable to the effects of drug exposure due to its rapid maturation. Here, we examined the association between cannabis and alcohol use duration and resting-state functional connectivity in a large sample of male juvenile delinquents. The present sample was drawn from the Southwest Advanced Neuroimaging Cohort, Youth sample, and from a youth detention facility in Wisconsin. All participants were scanned at the maximum-security facilities using The Mind Research Network's 1.5T Avanto SQ Mobile MRI scanner. Information on cannabis and alcohol regular use duration was collected using self-report. Resting-state networks were computed using group independent component analysis in 201 participants. Associations with cannabis and alcohol use were assessed using Mancova analyses controlling for age, IQ, smoking and psychopathy scores in the complete case sample of 180 male juvenile delinquents. No associations between alcohol or cannabis use and network spatial maps were found. Longer cannabis use was associated with decreased low frequency power of the default mode network, the executive control networks (ECNs), and several sensory networks, and with decreased functional network connectivity. Duration of alcohol use was associated with decreased low frequency power of the right frontoparietal network, salience network, dorsal attention network, and several sensory networks. Our findings suggest that adolescent cannabis and alcohol use are associated with widespread differences in resting-state time course power spectra, which may persist even after abstinence. [ABSTRACT FROM AUTHOR]

Background: Differentiating bipolar disorder (BD) from major depressive disorder (MDD) often poses a major clinical challenge, and optimal clinical care can be hindered by misdiagnoses. This study investigated the differences between BD and MDD in resting-state functional network connectivity (FNC) using a data-driven image analysis method.Methods: In this study, fMRI data were collected from unmedicated subjects including 13 BD, 40 MDD and 33 healthy controls (HC). The FNC was calculated between functional brain networks derived from fMRI using group independent component analysis (ICA). Group comparisons were performed on connectivity strengths and other graph measures of FNC matrices.Results: Statistical tests showed that, compared to MDD, the FNC in BD was characterized by more closely connected and more efficient topological structures as assessed by graph theory. The differences were found at both the whole-brain-level and the functional-network-level in prefrontal networks located in the dorsolateral/ventrolateral prefrontal cortex (DLPFC, VLPFC) and anterior cingulate cortex (ACC). Furthermore, interconnected structures in these networks in both patient groups were negatively associated with symptom severity on depression rating scales.Limitations: As patients were unmedicated, the sample sizes were relatively small, although they were comparable to those in previous fMRI studies comparing BD and MDD.Conclusions: Our results suggest that the differences in FNC of the PFC reflect distinct pathophysiological mechanisms in BD and MDD. Such findings ultimately may elucidate the neural pathways in which distinct functional changes can give rise to the clinical differences observed between these syndromes. [ABSTRACT FROM AUTHOR]

Default mode network (DMN) has been reported altered in schizophrenia (SZ) using static connectivity analysis. However, the studies on dynamic characteristics of DMN in SZ are still limited. In this work, we compare dynamic connectivity within DMN between 82 healthy controls (HC) and 82 SZ patients using resting-state fMRI. Firstly, dynamic DMN was computed using a sliding time window method for each subject. Then, the overall connectivity strengths were compared between two groups. Furthermore, we estimated functional connectivity states using K-means clustering, and then investigated group differences with respect to the connectivity strengths in states, the dwell time in each state, and the transition times between states. Finally, graph metrics of time-varying connectivity patterns and connectivity states were assessed. Results suggest that measured by the overall connectivity, HC showed stronger inter-subsystem interaction than patients. Compared to HC, patients spent more time in the states with nodes sparsely connected. For each state, SZ patients presented relatively weaker connectivity strengths mainly in inter-subsystem. Patients also exhibited lower values in averaged node strength, clustering coefficient, global efficiency, and local efficiency than HC. In summary, our findings indicate that SZ show impaired interaction among DMN subsystems, with a reduced central role for posterior cingulate cortex (PCC) and anterior medial prefrontal cortex (aMPFC) hubs as well as weaker interaction between dorsal medial prefrontal cortex (dMPFC) subsystem and medial temporal lobe (MTL) subsystem. For SZ, decreased integration of DMN may be associated with impaired ability in making self-other distinctions and coordinating present mental states with episodic decisions about future. [ABSTRACT FROM AUTHOR]

Both methylation and brain volume patterns hold important biological information for the development and prognosis of schizophrenia (SZ). A combined study to probe the association between them provides a new perspective to understanding SZ. Genomic methylation of peripheral blood and regional brain volumes derived from magnetic resonance imaging were analyzed using parallel independent component analyses in this study. Nine methylation components and five brain volumetric components were extracted for 94 SZ patients and 106 healthy controls. After controlling for age, sex, race, and substance use, a component comprised primarily of bilateral cerebellar volumes was significantly correlated to a methylation component from 14 CpG sites in 13 genes. Both patients and healthy controls demonstrated similar associations, but patients had significantly smaller cerebellar volumes and dysmethylation in the associated epigenetic component compared to controls. The 13 genes are enriched in cellular growth and proliferation with some genes involved in neuronal growth and cerebellum development (GATA4, ADRA1D, EPHA3, and KCNK10), and these genes are prominently associated with neurological and psychological disorders. Such findings suggest that the methylation pattern of the genes coding for cellular growth may influence the cerebellar development through regulating gene expression, and the alteration in the methylation of these genes in SZ patients may contribute to the cerebellar volume reduction observed in patients. [ABSTRACT FROM AUTHOR]

• Combine the group data with limited samples can improve the quality of directed acyclic graph estimation. • Lower global and local efficiency of the directed network found in schizophrenia patients. • Directed network induced features can better differentiate schizophrenia patients from healthy controls. Functional connectivity (FC) between brain region has been widely studied and linked with cognition and behavior of an individual. FC is usually defined as the correlation or partial correlation of fMRI blood oxygen level-dependent (BOLD) signals between two brain regions. Although FC has been effective to understand brain organization, it cannot reveal the direction of interactions. Many directed acyclic graph (DAG) based methods have been applied to study the directed interactions but their performance was limited by the small sample size while high dimensionality of the available data. By enforcing group regularization and utilizing samples from both case and control groups, we propose a joint DAG model to estimate the directed FC. We first demonstrate that the proposed model is efficient and accurate through a series of simulation studies. We then apply it to the case-control study of schizophrenia (SZ) with data collected from the MIND Clinical Imaging Consortium (MCIC). We have successfully identified decreased functional integration, disrupted hub structures and characteristic edges (CtEs) in SZ patients. Those findings have been confirmed by previous studies with some identified to be potential markers for SZ patients. A comparison of the results between the directed FC and undirected FC showed substantial differences in the selected features. In addition, we used the identified features based on directed FC for the classification of SZ patients and achieved better accuracy than using undirected FC or raw features, demonstrating the advantage of using directed FC for brain network analysis. [ABSTRACT FROM AUTHOR]

Abstract: Functional connectivity of the brain has been studied by analyzing correlation differences in time courses among seed voxels or regions with other voxels of the brain in healthy individuals as well as in patients with brain disorders. The spatial extent of strongly temporally coherent brain regions co-activated during rest has also been examined using independent component analysis (ICA). However, the weaker temporal relationships among ICA component time courses, which we operationally define as a measure of functional network connectivity (FNC), have not yet been studied. In this study, we propose an approach for evaluating FNC and apply it to functional magnetic resonance imaging (fMRI) data collected from persons with schizophrenia and healthy controls. We examined the connectivity and latency among ICA component time courses to test the hypothesis that patients with schizophrenia would show increased functional connectivity and increased lag among resting state networks compared to controls. Resting state fMRI data were collected and the inter-relationships among seven selected resting state networks (identified using group ICA) were evaluated by correlating each subject''s ICA time courses with one another. Patients showed higher correlation than controls among most of the dominant resting state networks. Patients also had slightly more variability in functional connectivity than controls. We present a novel approach for quantifying functional connectivity among brain networks identified with spatial ICA. Significant differences between patient and control connectivity in different networks were revealed possibly reflecting deficiencies in cortical processing in patients. [Copyright &y& Elsevier]

Abstract: This study provides the first description of neural network dynamics associated with response inhibition in healthy adolescents and adults. Functional and effective connectivity analyses of whole brain hemodynamic activity elicited during performance of a Go/No-Go task were used to identify functionally integrated neural networks and characterize their causal interactions. Three response inhibition circuits formed a hierarchical, inter-dependent system wherein thalamic modulation of input to premotor cortex by fronto-striatal regions led to response suppression. Adolescents differed from adults in the degree of network engagement, regional fronto-striatal-thalamic connectivity, and network dynamics. We identify and characterize several age-related differences in the function of neural circuits that are associated with behavioral performance changes across adolescent development. [Copyright &y& Elsevier]

Visuospatial processing is a cognitive function that is critical to navigating one's surroundings and begins to develop during infancy. Extensive research has examined visuospatial processing in adults, but far less work has investigated how visuospatial processing and the underlying neurophysiology changes from childhood to early adolescence, which is a critical period of human development that is marked by the onset of puberty. In the current study, we examined behavioral performance and the oscillatory dynamics serving visuospatial processing using magnetoencephalography (MEG) in a cohort of 70 children and young adolescents aged 8–15 years. All participants performed a visuospatial processing task during MEG, and the resulting oscillatory responses were imaged using a beamformer and probed for developmental and sex-related differences. Our findings indicated that reaction time on the task was negatively correlated with age, and that the amplitude of theta oscillations in the medial occipital cortices increased with age. Significant sex-by-age interactions were also detected, with female participants exhibiting increased theta oscillatory activity in the right prefrontal cortex with increasing age, while male participants exhibited theta increases in the left parietal lobe/left precuneus and left supplementary motor area with increasing age. These data indicate that different nodes of the visuospatial processing network develop earlier in males compared to females (and vice versa) in this age range, which may have major implications for the developmental trajectory of behavioral performance and executive function more generally during the transition through puberty. [ABSTRACT FROM AUTHOR]