Dataset Information

Functional Network-Based Statistics Reveal Abnormal Resting-State Functional Connectivity in Minimal Hepatic Encephalopathy.

ABSTRACT: Purpose: Whole-brain functional network analysis is an emerging methodology for exploring the mechanisms underlying hepatic encephalopathy (HE). This study aimed to identify the brain subnetwork that is significantly altered within the functional connectome in minimal HE (MHE), the earliest stage of HE. Materials and Methods: The study enrolled 19 cirrhotic patients with MHE and 19 controls who underwent the resting-state functional magnetic resonance imaging and cognitive assessment based on the Psychometric Hepatic Encephalopathy Score (PHES). A whole-brain functional connectivity (FC) matrix was calculated for each subject. Then, network-based statistical analyses of the functional connectome were used to perform group comparisons, and correlation analyses were conducted to identify the relationships between FC alterations and cognitive performance. Results: MHE patients showed significant reduction of positive FC within a subnetwork that predominantly involved the regions of the default-mode network, such as the bilateral posterior cingulate gyrus, bilateral medial prefrontal cortex, bilateral hippocampus and parahippocampal gyrus, bilateral angular gyrus, and left lateral temporal cortex. Meanwhile, MHE patients showed significant reduction of negative FC between default-mode network regions (such as the bilateral posterior cingulate gyrus, medial prefrontal cortex, and angular gyrus) and the regions involved in the somatosensory network (i.e., bilateral precentral and postcentral gyri) and the language network (i.e., the bilateral Rolandic operculum). The correlations of FC within the default-mode subnetwork and PHES results were noted. Conclusion: Default-mode network dysfunction may be one of the core issues in the pathophysiology of MHE. Our findings support the notion that HE is a neurological disease related to intrinsic brain network disruption.

SUBMITTER: Zhan C

PROVIDER: S-EPMC6362410 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Functional Network-Based Statistics Reveal Abnormal Resting-State Functional Connectivity in Minimal Hepatic Encephalopathy.

Zhan Chuanyin C Chen Hua-Jun HJ Gao Yong-Qing YQ Zou Tian-Xiu TX

Frontiers in neurology 20190129

<b>Purpose:</b> Whole-brain functional network analysis is an emerging methodology for exploring the mechanisms underlying hepatic encephalopathy (HE). This study aimed to identify the brain subnetwork that is significantly altered within the functional connectome in minimal HE (MHE), the earliest stage of HE. <b>Materials and Methods:</b> The study enrolled 19 cirrhotic patients with MHE and 19 controls who underwent the resting-state functional magnetic resonance imaging and cognitive assessme ...[more]

PMID: 30761070

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Project description:IMPORTANCE:Major depressive disorder (MDD) frequently emerges during adolescence and can lead to persistent illness, disability, and suicide. The maturational changes that take place in the brain during adolescence underscore the importance of examining neurobiological mechanisms during this time of early illness. However, neural mechanisms of depression in adolescents have been understudied. Research has implicated the amygdala in emotion processing in mood disorders, and adult depression studies have suggested amygdala-frontal connectivity deficits. Resting-state functional magnetic resonance imaging is an advanced tool that can be used to probe neural networks and identify brain-behavior relationships. OBJECTIVE:To examine amygdala resting-state functional connectivity (RSFC) in adolescents with and without MDD using resting-state functional magnetic resonance imaging as well as how amygdala RSFC relates to a broad range of symptom dimensions. DESIGN, SETTING, AND PARTICIPANTS:A cross-sectional resting-state functional magnetic resonance imaging study was conducted within a depression research program at an academic medical center. Participants included 41 adolescents and young adults aged 12 to 19 years with MDD and 29 healthy adolescents (frequency matched on age and sex) with no psychiatric diagnoses. MAIN OUTCOMES AND MEASURES:Using a whole-brain functional connectivity approach, we examined the correlation of spontaneous fluctuation of the blood oxygen level-dependent signal of each voxel in the whole brain with that of the amygdala. RESULTS:Adolescents with MDD showed lower positive RSFC between the amygdala and hippocampus, parahippocampus, and brainstem (z >2.3, corrected P < .05); this connectivity was inversely correlated with general depression (R?=?-.523, P?=?.01), dysphoria (R?=?-.455, P?=?.05), and lassitude (R?=?-.449, P?=?.05) and was positively correlated with well-being (R?=?.470, P?=?.03). Patients also demonstrated greater (positive) amygdala-precuneus RSFC (z >2.3, corrected P < .05) in contrast to negative amygdala-precuneus RSFC in the adolescents serving as controls. CONCLUSIONS AND RELEVANCE:Impaired amygdala-hippocampal/brainstem and amygdala-precuneus RSFC have not previously been highlighted in depression and may be unique to adolescent MDD. These circuits are important for different aspects of memory and self-processing and for modulation of physiologic responses to emotion. The findings suggest potential mechanisms underlying both mood and vegetative symptoms, potentially via impaired processing of memories and visceral signals that spontaneously arise during rest, contributing to the persistent symptoms experienced by adolescents with depression.

Project description:Background and purposeEpilepsy is considered a disorder of neural networks. The aims of this study were to assess functional connectivity within resting-state networks and functional network connectivity across resting-state networks by use of resting-state fMRI in children with frontal lobe epilepsy and to relate changes in resting-state networks with neuropsychological function.Materials and methodsFifteen patients with frontal lobe epilepsy and normal MR imaging and 14 healthy control subjects were recruited. Spatial independent component analysis was used to identify the resting-state networks, including frontal, attention, default mode network, sensorimotor, visual, and auditory networks. The Z-maps of resting-state networks were compared between patients and control subjects. The relation between abnormal connectivity and neuropsychological function was assessed. Correlations from all pair-wise combinations of independent components were performed for each group and compared between groups.ResultsThe frontal network was the only network that showed reduced connectivity in patients relative to control subjects. The remaining 5 networks demonstrated both reduced and increased functional connectivity within resting-state networks in patients. There was a weak association between connectivity in frontal network and executive function (P = .029) and a significant association between sensorimotor network and fine motor function (P = .004). Control subjects had 79 pair-wise independent components that showed significant temporal coherence across all resting-state networks except for default mode network-auditory network. Patients had 66 pairs of independent components that showed significant temporal coherence across all resting-state networks. Group comparison showed reduced functional network connectivity between default mode network-attention, frontal-sensorimotor, and frontal-visual networks and increased functional network connectivity between frontal-attention, default mode network-sensorimotor, and frontal-visual networks in patients relative to control subjects.ConclusionsWe found abnormal functional connectivity within and across resting-state networks in children with frontal lobe epilepsy. Impairment in functional connectivity was associated with impaired neuropsychological function.

Dataset Information

Functional Network-Based Statistics Reveal Abnormal Resting-State Functional Connectivity in Minimal Hepatic Encephalopathy.

Publications

Functional Network-Based Statistics Reveal Abnormal Resting-State Functional Connectivity in Minimal Hepatic Encephalopathy.

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