Alterations Of Large-scale Brain Networks Research Articles

Task-based modulation of functional connectivity of dorsal attention network in adult-ADHD

Recent studies have prompted a shift in the understanding of attention deficit hyperactivity disorder (ADHD) from models positing dysfunction of individual brain areas to those that assume alterations in large-scale brain networks. Despite this shift, the underlying neural mechanism of ADHD in the adult population remains uncertain. With functional magnetic resonance imaging (fMRI), this study examined brain connectivity of dorsal and ventral attention networks. Adults with and without ADHD completed a Go/No-Go task inside the scanner and the functional connectivity of attention networks was analysed. The generalized psychophysiological interaction analysis indicated differences involving the dorsal attention network. For the ADHD group, an interaction effect revealed altered dorsal attention-default mode network connectivity modulation, particularly between the right frontal eye field and posterior cingulate gyrus. We conclude that dorsal attention network dysfunction may be involved in sustained attention deficits in adult-ADHD. This study sheds light into network-level alterations contributing to the understanding of adult-ADHD, which may be a potential avenue for future research and clinical interventions.

Association between level of anxiety and degree of psychosomatic features in medical students at a private university in Northern Peru

Introduction. University students experience multiple academic and social activities that cause enormous anxiety and stress, affecting them psychologically and physically. In this case, the psychosomatic symptoms, resulting from stress and anxiety, are part of a physical ailment and their emergence can be linked to the setting and time when they occur. Objective. To determine the association between anxiety levels and the degree of psychosomatic features (PF). Method. A study with a cross-sectional analytical design was developed, which included a total of 352 medical students from the city of Piura, Peru, to whom the PHQ-15 test was administered through Google Forms. Those with a previous psychiatric diagnosis were excluded. Results. It was found that PF are influenced by being female (RPa = 1.45, CI 95% = [1.23, 1.71], p ≤ .001), and having mild (RPa = 1.11, CI 95% = [1.20, 1.63], p ≤ .001) and moderate anxiety levels (RPa = 1.7, CI 95% = [1.24, 2.34], p = .001). Discussion and conclusion. The presence of a mental condition is necessary for the presence of PF. These stressors create selective alterations of large-scale brain networks involved in the cognitive control, regulation and processing of emotions, stress, and somatic-visceral perception. The study concludes that psychosomatic features are found in approximately 75% of medical students and anxiety in approximately 15%. Mild and moderate anxiety is an influential factor in psychosomatic features, as is being female.

Aberrant Brain Network Integration and Segregation in Diabetic Peripheral Neuropathy Revealed by Structural Connectomics.

Diabetic peripheral neuropathy (DPN) is one of the most common forms of peripheral neuropathy, and its incidence has been increasing. Mounting evidence has shown that patients with DPN have been associated with widespread alterations in the structure, function and connectivity of the brain, suggesting possible alterations in large-scale brain networks. Using structural covariance networks as well as advanced graph-theory-based computational approaches, we investigated the topological abnormalities of large-scale brain networks for a relatively large sample of patients with DPN (N = 67) compared to matched healthy controls (HCs; N = 88). Compared with HCs, the structural covariance networks of patients with DPN showed an increased characteristic path length, clustering coefficient, sigma, transitivity, and modularity, suggestive of inefficient global integration and increased local segregation. These findings may improve our understanding of the pathophysiological mechanisms underlying alterations in the central nervous system of patients with DPN from the perspective of large-scale structural brain networks.

Structural Alterations in Large-scale Brain Networks and Their Relationship with Sleep Disturbances in the Adolescent Population

Although sleep disturbances are highly prevalent in adolescents, neuroimaging evidence on the effects of sleep disturbances on their developing brains remains limited. Therefore, we explored gray matter volumes (GMVs) at the whole-brain level and investigated their relationship to sleep disturbances in a sample of Korean adolescents in the general population. We recruited participants from one middle school and high school. All participants and their legal guardians gave informed consent before participating in our study. We used component 5 of the Pittsburgh Sleep Quality Index to measure sleep disturbances and conducted a voxel-based morphometry-DARTEL procedure to measure GMVs. We performed partial correlation analyses to examine whether the GMVs were associated with sleep disturbances. A total of 56 adolescents participated in this study. Our results revealed that GMVs in multiple global regions were negatively correlated with sleep disturbances. Moreover, most of these identified regions belong to large-scale brain networks categorized by functional neuroimaging studies. We found an association between regional GMVs in multiple global regions involved in large-scale networks and the severity of sleep disturbances in the adolescent population. Based on this evidence and previous neuroimaging evidence, we suggest that structural alterations in the networks may be linked to sleep disturbances.

Demystifying anhedonia in childhood with large-scale networks

Children with anhedonia show specific alterations in large-scale brain networks.

Language Network Function in Young Children Born Very Preterm.

Language deficits are reported in preterm born children across development. Recent neuroimaging studies have found functional alterations in large-scale brain networks underlying these language deficits, but the early childhood development of the language network has not been investigated. Here, we compared intrinsic language network connectivity in 4-year-old children born VPT and term-born controls, using defined language regions (Broca’s area, Wernicke’s areas, and their homologues in the right hemisphere). Resting-state functional magnetic resonance imaging (fMRI) was obtained, and the group differences in whole-brain connectivity were examined from each seed as well as correlations with language outcomes. We found significantly decreased functional connectivity in almost all language regions in children born VPT compared to their term controls. Notably, Broca’s area homologue in the right hemisphere emerged as a functional hub of decreased connectivity in VPT group, specifically to bilateral inferior frontal and supramarginal gyri; connectivity strength between Broca’s area homologue with the right supramarginal and the left inferior frontal gyri was associated with better language outcomes at 4 years of age. Wernicke’s area and its homologue also showed decreased inter-hemispheric connections to bilateral supramarginal gyri in the VPT group. Decreased intra- and inter-hemispheric connectivity among primary language regions suggests immature and altered function in the language network in children born VPT.

Multi-modal analysis of functional connectivity and cerebral blood flow reveals shared and unique effects of propofol in large-scale brain networks

Anesthesia-induced changes in functional connectivity and cerebral blow flow (CBF) in large-scale brain networks have emerged as key markers of reduced consciousness. However, studies of functional connectivity disagree on which large-scale networks are altered or preserved during anesthesia, making it difficult to find a consensus amount studies. Additionally, pharmacological alterations in CBF could amplify or occlude changes in connectivity due to the shared variance between CBF and connectivity. Here, we used data-driven connectivity methods and multi-modal imaging to investigate shared and unique neural correlates of reduced consciousness for connectivity in large-scale brain networks. Rs-fMRI and CBF data were collected from the same subjects during an awake and deep sedation condition induced by propofol. We measured whole-brain connectivity using the intrinsic connectivity distribution (ICD), a method not reliant on pre-defined seed regions, networks of interest, or connectivity thresholds. The shared and unique variance between connectivity and CBF were investigated. Finally, to account for shared variance, we present a novel extension to ICD that incorporates cerebral blood flow (CBF) as a scaling factor in the calculation of global connectivity, labeled CBF-adjusted ICD). We observed altered connectivity in multiple large-scale brain networks including the default mode (DMN), salience, visual, and motor networks and reduced CBF in the DMN, frontoparietal network, and thalamus. Regional connectivity and CBF were significantly correlated during both the awake and propofol condition. Nevertheless changes in connectivity and CBF between the awake and deep sedation condition were only significantly correlated in a subsystem of the DMN, suggesting that, while there is significant shared variance between the modalities, changes due to propofol are relatively unique. Similar, but less significant, results were observed in the CBF-adjusted ICD analysis, providing additional evidence that connectivity differences were not fully explained by CBF. In conclusion, these results provide further evidence of alterations in large-scale brain networks are associated with reduced consciousness and suggest that different modalities capture unique aspects of these large scale changes.

Abnormal resting-state functional connectivity of the left caudate nucleus in obsessive-compulsive disorder

Altered brain activities in the cortico-striato-thalamocortical (CSTC) circuitry are implicated in the pathophysiology of obsessive-compulsive disorder (OCD). However, whether the underlying changes occur only within this circuitry or in large-scale networks is still not thoroughly understood. This study performed voxel-based functional connectivity analysis on resting-state functional magnetic resonance imaging (fMRI) data from thirty OCD patients and thirty healthy controls to investigate whole-brain intrinsic functional connectivity patterns in OCD. Relative to the healthy controls, OCD patients showed decreased functional connectivity within the CSTC circuitry but increased functional connectivity in other brain regions. Furthermore, decreased left caudate nucleus-thalamus connectivity within the CSTC circuitry was positively correlated with the illness duration of OCD. This study provides additional evidence that CSTC circuitry may play an essential role and alteration of large-scale brain networks may be involved in the pathophysiology of OCD.

Imaging Functional and Structural Brain Connectomics in Attention-Deficit/Hyperactivity Disorder

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopment disorders in childhood. Clinically, the core symptoms of this disorder include inattention, hyperactivity, and impulsivity. Previous studies have documented that these behavior deficits in ADHD children are associated with not only regional brain abnormalities but also changes in functional and structural connectivity among regions. In the past several years, our understanding of how ADHD affects the brain's connectivity has been greatly advanced by mapping topological alterations of large-scale brain networks (i.e., connectomes) using noninvasive neurophysiological and neuroimaging techniques (e.g., electroencephalograph, functional MRI, and diffusion MRI) in combination with graph theoretical approaches. In this review, we summarize the recent progresses of functional and structural brain connectomics in ADHD, focusing on graphic analysis of large-scale brain systems. Convergent evidence suggests that children with ADHD had abnormal small-world properties in both functional and structural brain networks characterized by higher local clustering and lower global integrity, suggesting a disorder-related shift of network topology toward regular configurations. Moreover, ADHD children showed the redistribution of regional nodes and connectivity involving the default-mode, attention, and sensorimotor systems. Importantly, these ADHD-associated alterations significantly correlated with behavior disturbances (e.g., inattention and hyperactivity/impulsivity symptoms) and exhibited differential patterns between clinical subtypes. Together, these connectome-based studies highlight brain network dysfunction in ADHD, thus opening up a new window into our understanding of the pathophysiological mechanisms of this disorder. These works might also have important implications on the development of imaging-based biomarkers for clinical diagnosis and treatment evaluation in ADHD.

Resting-state networks in the macaque at 7 T

Assessment of brain connectivity has revealed that the structure and dynamics of large-scale network organization are altered in multiple disease states suggesting their use as diagnostic or prognostic indicators. Further investigation into the underlying mechanisms, organization, and alteration of large-scale brain networks requires a homologous animal model that would allow neurophysiological recordings and experimental manipulations. The current study presents a comprehensive assessment of macaque resting-state networks based on evaluation of intrinsic low-frequency fluctuations of the blood oxygen-level-dependent signal using group independent component analysis. Networks were found underlying multiple levels of sensory, motor, and cognitive processing. The results demonstrate that macaques share remarkable homologous network organization with humans, thereby providing strong support for their use as an animal model in the study of normal and abnormal brain connectivity as well as aiding the interpretation of electrophysiological recordings within the context of large-scale brain networks.