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Altered resting-state functional network connectivity is associated with suicide attempt in young depressed patients.

Sun, 12/08/2019 - 21:55
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Altered resting-state functional network connectivity is associated with suicide attempt in young depressed patients.

Psychiatry Res. 2019 Nov 27;:112713

Authors: Cao J, Ai M, Chen X, Chen J, Wang W, Kuang L

Abstract
The purpose of this study was to investigate the changes in resting-state brain functional network connectivity (FNC) in young depressed patients with and without suicidal behavior, and the relationship between FNC and suicidal attempts in depressed youths using resting-state fMRI (RS-fMRI). We conducted independent component analysis (ICA) to identify intrinsically connected neural networks and analyze the alterations of intra- and inter-network connectivity using FNC analysis in 35 depressed youth with suicidal attempts (SU group), 18 patients without suicidal attempts (NSU group) and 47 healthy controls (HC), and investigate brain-behavior associations between the FNC coefficients and clinical behavior in the SU group. SU group showed significantly decreased internetwork connectivity between anterior default mode network (aDMN) and salience network (SN), as well as the right frontal-parietal network (rFPN). However, the internetwork connectivity between the SN and rFPN in SU group was higher than that in NSU group. Moreover, decreased aDMN-rFPN connectivity was negatively correlated with BHS scores, and the differences in SN-rFPN and aDMN-pDMN connectivity were negatively associated with the HAMD score in the SU group. Our findings may provide new insights into the patterns of functional organization in the brain of suicidal depressed patients.

PMID: 31810745 [PubMed - as supplied by publisher]

Modulation of simultaneously collected hemodynamic and electrophysiological functional connectivity by ketamine and midazolam.

Sat, 12/07/2019 - 21:53
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Modulation of simultaneously collected hemodynamic and electrophysiological functional connectivity by ketamine and midazolam.

Hum Brain Mapp. 2019 Dec 06;:

Authors: Forsyth A, McMillan R, Campbell D, Malpas G, Maxwell E, Sleigh J, Dukart J, Hipp J, Muthukumaraswamy SD

Abstract
The pharmacological modulation of functional connectivity in the brain may underlie therapeutic efficacy for several neurological and psychiatric disorders. Functional magnetic resonance imaging (fMRI) provides a noninvasive method of assessing this modulation, however, the indirect nature of the blood-oxygen level dependent signal restricts the discrimination of neural from physiological contributions. Here we followed two approaches to assess the validity of fMRI functional connectivity in developing drug biomarkers, using simultaneous electroencephalography (EEG)/fMRI in a placebo-controlled, three-way crossover design with ketamine and midazolam. First, we compared seven different preprocessing pipelines to determine their impact on the connectivity of common resting-state networks. Independent components analysis (ICA)-denoising resulted in stronger reductions in connectivity after ketamine, and weaker increases after midazolam, than pipelines employing physiological noise modelling or averaged signals from cerebrospinal fluid or white matter. This suggests that pipeline decisions should reflect a drug's unique noise structure, and if this is unknown then accepting possible signal loss when choosing extensive ICA denoising pipelines could engender more confidence in the remaining results. We then compared the temporal correlation structure of fMRI to that derived from two connectivity metrics of EEG, which provides a direct measure of neural activity. While electrophysiological estimates based on the power envelope were more closely aligned to BOLD signal connectivity than those based on phase consistency, no significant relationship between the change in electrophysiological and hemodynamic correlation structures was found, implying caution should be used when making cross-modal comparisons of pharmacologically-modulated functional connectivity.

PMID: 31808268 [PubMed - as supplied by publisher]

Diagnosing autism spectrum disorder using brain entropy: A fast entropy method.

Sat, 12/07/2019 - 21:53
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Diagnosing autism spectrum disorder using brain entropy: A fast entropy method.

Comput Methods Programs Biomed. 2019 Nov 27;:105240

Authors: Zhang L, Wang XH, Li L

Abstract
BACKGROUND AND OBJECTIVE: Previous resting-state fMRI-based diagnostic models for autism spectrum disorder (ASD) were based on traditional linear features. The complexity of the ASD brain remains unexplored.
METHODS: To increase our understanding of the nonlinear neural mechanisms in ASD, entropy (i.e., approximate entropy (ApEn) and sample entropy (SampEn)) method was used to analyze the resting-state fMRI datasets collected from 21 ASD patients and 26 typically developing (TD) individuals. Here, a fast entropy algorithm was proposed through matrix computation. We combined entropy with a support-vector machine and selected "important entropy" as features to diagnose ASD. The classification performance of the fast entropy method was compared to the state-of-the-art functional connectivity (FC) method.
RESULTS: The area under the receiver operating characteristic curve based on FC was 0.62. The areas under the receiver operating characteristic curves based on ApEn and SampEn were 0.79 and 0.89, respectively. The results showed that the proposed fast entropy method was more efficacious than the FC method. In addition, lower entropy was found in the ASD patients. The ApEn of the left postcentral gyrus (rs = -0.556, p = 0.009) and the SampEn of the right lingual gyrus (rs = -0.526, p = 0.014) were both significantly negatively related to Autism Diagnostic Observation Schedule total scores in the ASD patients. The proposed algorithm for entropy computation was faster than the traditional entropy method.
CONCLUSIONS: Our study provides a new perspective to better understand the neural mechanisms of ASD. Brain entropy based on a fast algorithm was applied to distinguish ASD patients from TD individuals. ApEn and SampEn could be potential biomarkers in ASD investigations.

PMID: 31806393 [PubMed - as supplied by publisher]

Resting-state functional connectivity alterations in periventricular nodular heterotopia related epilepsy.

Fri, 12/06/2019 - 21:52
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Resting-state functional connectivity alterations in periventricular nodular heterotopia related epilepsy.

Sci Rep. 2019 Dec 05;9(1):18473

Authors: Liu W, Hu X, An D, Zhou D, Gong Q

Abstract
Periventricular nodular heterotopia (PNH) is a neural migration disorder which often presents clinically with seizures. However, the underlying functional neural basis of PNH is still unclear. We aimed to explore the underlying pathological mechanism of PNH by combining both whole brain functional connectivity (FC) and seed-based FC analyses. We utilized resting-state fMRI to measure functional connectivity strength (FCS) in 38 patients with PNH-related epilepsy and 38 control subjects. The regions with FCS alterations were selected as seeds in the following FC analyses. Pearson correlation analyses were performed to explore associations between these functional neural correlates and clinical features. In comparison with controls, PNH patients showed lower FCS in bilateral insula (P < 0.05, family wise error (FWE) correction), higher FC in the default mode network and lower FC in the fronto-limbic-cerebellar circuits (P < 0.05, FWE correction). Pearson correlation analyses revealed that FCS in bilateral insula was negatively correlated with the epilepsy duration (P < 0.05); medial prefronto-insular connectivity was negatively correlated with Hamilton Anxiety Scale (P < 0.05) and cerebellar-insular connectivity was also negatively correlated with Hamilton Depression Scale (P < 0.05). Using the resting-state FCS analytical approach, we identified significant insular hypoactivation in PNH patients, which suggests that the insula might represent the cortical hub of the whole-brain networks in this condition. Additionally, disruption of resting state FC in large-scale neural networks pointed to a connectivity-based neuropathological process in PNH.

PMID: 31804610 [PubMed - in process]

Functional Parcellation of Individual Cerebral Cortex Based on Functional MRI.

Fri, 12/06/2019 - 21:52
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Functional Parcellation of Individual Cerebral Cortex Based on Functional MRI.

Neuroinformatics. 2019 Dec 04;:

Authors: Zhao J, Tang C, Nie J

Abstract
The human brain atlas assists us to enhance our scientific understanding of brain structure and function. The typical anatomical atlases are mainly based on brain morphometry which cannot ensure the consistency of structure and function, and are also hard to cover individual functional differences especially in cerebral cortex. Thus, in recent years, functional atlases for individuals have captured great attention, since they are essential not only for identifying the unique functional organization of individual brains, but also to explore individual variations in behaviors. In this study, a novel approach was proposed to accurately parcellate the whole cerebral cortex at the individual level using resting-state functional magnetic resonance image (rs-fMRI). To examine the functional homogeneity in parcellation, a new evaluation criterion, similarity of cluster (SC) coefficient, was proposed. The parcellation results demonstrated the high consistency between two resting-state sessions (Dice >0.72). The most consistent parcellation appeared in the frontal cortex and the least consistent parcellation appeared in the occipital cortex. The functional homogeneity of subregions was high in frontal cortex and insula whereas low in precentral gyrus. According to SC value, the optimal clustering number was about 1600 per hemisphere. Identification accuracy was 100% between two rs-fMRI sessions, and it was also above 0.97 for rest-task and task-task sessions.

PMID: 31802355 [PubMed - as supplied by publisher]

Global brain signal in awake rats.

Fri, 12/06/2019 - 21:52
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Global brain signal in awake rats.

Brain Struct Funct. 2019 Dec 04;:

Authors: Ma Y, Ma Z, Liang Z, Neuberger T, Zhang N

Abstract
Although often used as a nuisance in resting-state functional magnetic resonance imaging (rsfMRI), the global brain signal in humans and anesthetized animals has important neural basis. However, our knowledge of the global signal in awake rodents is sparse. To bridge this gap, we systematically analyzed rsfMRI data acquired with a conventional single-echo (SE) echo planar imaging (EPI) sequence in awake rats. The spatial pattern of rsfMRI frames during peaks of the global signal exhibited prominent co-activations in the thalamo-cortical and hippocampo-cortical networks, as well as in the basal forebrain, hinting that these neural networks might contribute to the global brain signal in awake rodents. To validate this concept, we acquired rsfMRI data using a multi-echo (ME) EPI sequence and removed non-neural components in the rsfMRI signal. Consistent co-activation patterns were obtained in extensively de-noised ME-rsfMRI data, corroborating the finding from SE-rsfMRI data. Furthermore, during rsfMRI experiments, we simultaneously recorded neural spiking activities in the hippocampus using GCaMP-based fiber photometry. The hippocampal calcium activity exhibited significant correspondence with the global rsfMRI signal. These data collectively suggest that the global rsfMRI signal contains significant neural components that involve coordinated activities in the thalamo-cortical and hippocampo-cortical networks. These results provide important insight into the neural substrate of the global brain signal in awake rodents.

PMID: 31802256 [PubMed - as supplied by publisher]

Altered resting-state functional connectivity in young children at familial high risk for psychotic illness: A preliminary study.

Fri, 12/06/2019 - 21:52
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Altered resting-state functional connectivity in young children at familial high risk for psychotic illness: A preliminary study.

Schizophr Res. 2019 Dec 02;:

Authors: Anteraper SA, Collin G, Guell X, Schneidert T, Molokotos E, Henriksen MT, Mesholam-Gately R, Thermenos HW, Seidman LJ, Keshavan MS, Gabrieli JDE, Whitfield-Gabrieli S

Abstract
Multiple lines of evidence suggest that illness development in schizophrenia and other psychotic disorders predates the first psychotic episode by many years. In this study, we examined a sample of 15 pre-adolescent children, ages 7 through 12 years, who are at familial high-risk (FHR) because they have a parent or sibling with a history of schizophrenia or related psychotic disorder. Using multi-voxel pattern analysis (MVPA), a data-driven fMRI analysis, we assessed whole-brain differences in functional connectivity in the FHR sample as compared to an age- and sex-matched control (CON) group of 15 children without a family history of psychosis. MVPA analysis yielded a single cluster in right posterior superior temporal gyrus (pSTG/BA 22) showing significant group-differences in functional connectivity. Post-hoc characterization of this cluster through seed-to-voxel analysis revealed mostly reduced functional connectivity of the pSTG seed to a set of language and default mode network (DMN) associated brain regions including Heschl's gyrus, inferior temporal gyrus extending into fusiform gyrus, (para)hippocampus, thalamus, and a cerebellar cluster encompassing mainly Crus I/II. A height-threshold of whole-brain p < .001 (two-sided), and FDR-corrected cluster-threshold of p < .05 (non-parametric statistics) was used for post-hoc characterization. These findings suggest that abnormalities in functional communication in a network encompassing right STG and associated brain regions are present before adolescence in at-risk children and may be a risk marker for psychosis. Subsequent changes in this functional network across development may contribute to either disease manifestation or resilience in children with a familial vulnerability for psychosis.

PMID: 31801673 [PubMed - as supplied by publisher]

Intrinsic functional connectivity, CSF biomarker profiles and their relation to cognitive function in mild cognitive impairment.

Fri, 12/06/2019 - 21:52
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Intrinsic functional connectivity, CSF biomarker profiles and their relation to cognitive function in mild cognitive impairment.

Acta Neuropsychiatr. 2019 Dec 05;:1-24

Authors: Matura S, Köhler J, Reif A, Fusser F, Karakaya T, Scheibe M, Ehret F, Hartmann D, Kang JS, Mayer C, Prvulovic D, Pantel J

Abstract
Mild cognitive impairment (MCI) often precedes Alzheimer's Disease (AD) and in a high proportion of individuals affected by MCI there are already neuropathological processes ongoing that become more evident when patients progress to AD. Accordingly, there is a need for reliable biomarkers to distinguish between normal aging and incipient AD. Recent research suggests that, in addition to established biomarkers such as CSF Aß42, total tau and hyperphosphorylated tau, resting state connectivity established by fMRI might also be a feasible biomarker for prodromal stages of AD. In order to explore this possibility, we investigated resting state functional connectivity as well as CSF biomarker profiles in patients with mild cognitive impairment (n = 30; age 66.43 ± 7.06 years) and cognitively healthy controls (n = 38; age 66.89 ± 7.12 years). CSF Aß42, total Tau and hyperphosphorylated tau concentrations were correlated with measures of cognitive performance (immediate and delayed recall, global cognition, processing speed). Moreover, MCI related alterations in intrinsic functional connectivity within the Default Mode Network (DMN) were investigated using functional resting state MRI (rs-fMRI). As expected, MCI patients showed decreased CSF Aß42 and increased total Tau concentrations. These alterations were associated with cognitive performance. However, there were no differences between MCI patients and cognitively healthy controls regarding intrinsic functional connectivity. In conclusion, our results indicate, that CSF protein profiles seem to be more closely related to cognitive decline than alterations in resting state activity. Thus, resting state connectivity might not be a reliable biomarker for early stages of AD.

PMID: 31801648 [PubMed - as supplied by publisher]

Estimation of Brain Functional Connectivity in Patients with Mild Cognitive Impairment.

Fri, 12/06/2019 - 21:52
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Estimation of Brain Functional Connectivity in Patients with Mild Cognitive Impairment.

Brain Sci. 2019 Nov 30;9(12):

Authors: Farràs-Permanyer L, Mancho-Fora N, Montalà-Flaquer M, Gudayol-Ferré E, Gallardo-Moreno GB, Zarabozo-Hurtado D, Villuendas-González E, Peró-Cebollero M, Guàrdia-Olmos J

Abstract
Mild cognitive impairment is defined as greater cognitive decline than expected for a person at a particular age and is sometimes considered a stage between healthy aging and Alzheimer's disease or other dementia syndromes. It is known that functional connectivity patterns change in people with this diagnosis. We studied functional connectivity patterns and functional segregation in a resting-state fMRI paradigm comparing 10 MCI patients and 10 healthy controls matched by education level, age and sex. Ninety ROIs from the automated anatomical labeling (AAL) atlas were selected for functional connectivity analysis. A correlation matrix was created for each group, and a third matrix with the correlation coefficient differences between the two matrices was created. Functional segregation was analyzed with the 3-cycle method, which is novel in studies of this topic. Finally, cluster analyses were also performed. Our results showed that the two correlation matrices were visually similar but had many differences related to different cognitive functions. Differences were especially apparent in the anterior default mode network (DMN), while the visual resting-state network (RSN) showed no differences between groups. Differences in connectivity patterns in the anterior DMN should be studied more extensively to fully understand its role in the differentiation of healthy aging and an MCI diagnosis.

PMID: 31801260 [PubMed]

Pharmaco-fMRI: A Tool to Predict the Response to Antiepileptic Drugs in Epilepsy.

Fri, 12/06/2019 - 12:52
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Pharmaco-fMRI: A Tool to Predict the Response to Antiepileptic Drugs in Epilepsy.

Front Neurol. 2019;10:1203

Authors: Xiao F, Koepp MJ, Zhou D

Abstract
Pharmacological treatment with antiepileptic medications (AEDs) in epilepsy is associated with a variety of neurocognitive side effects. However, the mechanisms underlying these side effects, and why certain brain anatomies are more affected still remain poorly understood. Advanced functional magnetic resonance imaging (fMRI) methods, such as pharmaco-fMRI, can investigate medication-related effects on brain activities using task and resting state fMRI and showing reproducible activation and deactivation patterns. This methodological approach has been used successfully to complement neuropsychological studies of AEDs. Here we review pharmaco-fMRI studies in people with epilepsy targeting the most-widely prescribed AEDs. Pharmco-fMRI has advanced our understanding of the impact of AEDs on specific brain networks and thus may provide potential biomarkers to move beyond the current "trial and error" approach when commencing anti-epileptic medication.

PMID: 31798524 [PubMed]

Distinct Disruptive Patterns of Default Mode Subnetwork Connectivity Across the Spectrum of Preclinical Alzheimer's Disease.

Fri, 12/06/2019 - 12:52
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Distinct Disruptive Patterns of Default Mode Subnetwork Connectivity Across the Spectrum of Preclinical Alzheimer's Disease.

Front Aging Neurosci. 2019;11:307

Authors: Xue C, Yuan B, Yue Y, Xu J, Wang S, Wu M, Ji N, Zhou X, Zhao Y, Rao J, Yang W, Xiao C, Chen J

Abstract
Background: The early progression continuum of Alzheimer's disease (AD) has been considered to advance through subjective cognitive decline (SCD), non-amnestic mild cognitive impairment (naMCI), and amnestic mild cognitive impairment (aMCI). Altered functional connectivity (FC) in the default mode network (DMN) is regarded as a hallmark of AD. Furthermore, the DMN can be divided into two subnetworks, the anterior and posterior subnetworks. However, little is known about distinct disruptive patterns in the subsystems of the DMN across the preclinical AD spectrum. This study investigated the connectivity patterns of anterior DMN (aDMN) and posterior DMN (pDMN) across the preclinical AD spectrum. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) was used to investigate the FC in the DMN subnetworks in 20 healthy controls (HC), eight SCD, 11 naMCI, and 28 aMCI patients. Moreover, a correlation analysis was used to examine associations between the altered connectivity of the DMN subnetworks and the neurocognitive performance. Results: Compared to the HC, SCD patients showed increased FC in the bilateral superior frontal gyrus (SFG), naMCI patients showed increased FC in the left inferior parietal lobule (IPL), and aMCI patients showed increased FC in the bilateral IPL in the aDMN; while SCD patients showed decreased FC in the precuneus, naMCI patients showed increased FC in the left middle temporal gyrus (MTG), and aMCI patients also showed increased FC in the right middle frontal gyrus (MFG) in the pDMN. Notably, the FC between the ventromedial prefrontal cortex (vmPFC) and the left MFG and the IPL in the aDMN was associated with episodic memory in the SCD and aMCI groups. Interestingly, the FC between the posterior cingulated cortex (PCC) and several regions in the pDMN was associated with other cognitive functions in the SCD and naMCI groups. Conclusions: This study demonstrates that the three preclinical stages of AD exhibit distinct FC alternations in the DMN subnetworks. Furthermore, the patient group data showed that the altered FC involves cognitive function. These findings can provide novel insights for tailored clinical intervention across the preclinical AD spectrum.

PMID: 31798440 [PubMed]

Optimized Configuration of Functional Brain Network for Processing Semantic Audiovisual Stimuli Underlying the Modulation of Attention: A Graph-Based Study.

Fri, 12/06/2019 - 12:52
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Optimized Configuration of Functional Brain Network for Processing Semantic Audiovisual Stimuli Underlying the Modulation of Attention: A Graph-Based Study.

Front Integr Neurosci. 2019;13:67

Authors: Xi Y, Li Q, Zhang M, Liu L, Li G, Lin W, Wu J

Abstract
Semantic audiovisual stimuli have a facilitatory effect on behavioral performance and influence the integration of multisensory inputs across sensory modalities. Many neuroimaging and electrophysiological studies investigated the neural mechanisms of multisensory semantic processing and reported that attention modulates the response to multisensory semantic inputs. In the present study, we designed an functional magnetic resonance imaging (fMRI) experiment of semantic discrimination using the unimodal auditory, unimodal visual and bimodal audiovisual stimuli with semantic information. By manipulating the stimuli present on attended and unattended position, we recorded the task-related fMRI data corresponding to the unimodal auditory, unimodal visual and bimodal audiovisual stimuli in attended and unattended conditions. We also recorded the fMRI data in resting state. Then the fMRI method was used together with a graph theoretical analysis to construct the functional brain networks in task-related and resting states and quantitatively characterize the topological network properties. The aim of our present study is to explore the characteristics of functional brain networks that process semantic audiovisual stimuli in attended and unattended conditions, revealing the neural mechanism of multisensory processing and the modulation of attention. The behavioral results showed that the audiovisual stimulus presented simultaneously promoted the performance of semantic discrimination task. And the analyses of network properties showed that compared with the resting-state condition, the functional networks of processing semantic audiovisual stimuli (both in attended and unattended conditions) had greater small-worldness, global efficiency, and lower clustering coefficient, characteristic path length, global efficiency and hierarchy. In addition, the hubs were concentrated in the bilateral temporal lobes, especially in the anterior temporal lobes (ATLs), which were positively correlated to reaction time (RT). Moreover, attention significantly altered the degree of small-worldness and the distribution of hubs in the functional network for processing semantic audiovisual stimuli. Our findings suggest that the topological structure of the functional brain network for processing semantic audiovisual stimulus is modulated by attention, and has the characteristics of high efficiency and low wiring cost, which maintains an optimized balance between functional segregation and integration for multisensory processing efficiently.

PMID: 31798426 [PubMed]

Connectivity strength, time lag structure and the epilepsy network in resting-state fMRI.

Fri, 12/06/2019 - 12:52
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Connectivity strength, time lag structure and the epilepsy network in resting-state fMRI.

Neuroimage Clin. 2019 Oct 23;24:102035

Authors: Bandt SK, Besson P, Ridley B, Pizzo F, Carron R, Regis J, Bartolomei F, Ranjeva JP, Guye M

Abstract
The relationship between the epilepsy network, intrinsic brain networks and hypersynchrony in epilepsy remains incompletely understood. To converge upon a synthesized understanding of these features, we studied two elements of functional connectivity in epilepsy: correlation and time lag structure using resting state fMRI data from both SEEG-defined epileptic brain regions and whole-brain fMRI analysis. Functional connectivity (FC) was analyzed in 15 patients with epilepsy and 36 controls. Correlation strength and time lag were selected to investigate the magnitude of and temporal interdependency across brain regions. Zone-based analysis was carried out investigating directed correlation strength and time lag between both SEEG-defined nodes of the epilepsy network and between the epileptogenic zone and all other brain regions. Findings were compared between patients and controls and against a functional atlas. FC analysis on the nodal and whole brain levels identifies consistent patterns of altered correlation strength and altered time lag architecture in epilepsy patients compared to controls. These patterns include 1) broadly distributed increased strength of correlation between the seizure onset node and the remainder of the brain, 2) decreased time lag within the seizure onset node, and 3) globally increased time lag throughout all regions of the brain not involved in seizure onset or propagation. Comparing the topographic distribution of findings against a functional atlas, all resting state networks were involved to a variable degree. These local and whole brain findings presented here lead us to propose the network steal hypothesis as a possible mechanistic explanation for the non-seizure clinical manifestations of epilepsy.

PMID: 31795065 [PubMed - as supplied by publisher]

Diverse functional connectivity patterns of resting-state brain networks associated with good and poor hand outcomes following stroke.

Fri, 12/06/2019 - 12:52
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Diverse functional connectivity patterns of resting-state brain networks associated with good and poor hand outcomes following stroke.

Neuroimage Clin. 2019 Nov 20;24:102065

Authors: Hong W, Lin Q, Cui Z, Liu F, Xu R, Tang C

Abstract
Motor stroke has been characterized by disruptions in multiple large-scale functional brain networks. However, it remains unclear whether stroke patients with good hand outcomes show different connectivity profiles within and between networks from those with poor hand outcomes. In this cross-sectional study, we recruited 52 chronic subcortical stroke patients [illness duration (mean ± SD): 16 ± 16.2 months] and 52 healthy controls from the local hospital and community from June 2010 to August 2016. We first performed independent component analysis (ICA) on resting-state fMRI data to extract fifteen resting-state networks. Then, we compared the functional connectivity within and between networks across 52 healthy controls, 26 patients with a partially paralyzed hand (PPH), and 26 patients with a completely paralyzed hand (CPH). Compared to the patients with a PPH, the patients with a CPH showed increased connectivity in the contralesional sensorimotor cortex within the contralesional sensorimotor network; the increased connectivity was negatively correlated with the performance of the paretic hand. Moreover, the patients with a CPH, compared to those with a PPH, showed decreased strengths of connectivity between the ipsilesional sensorimotor network and both the dorsal sensorimotor network and ventral visual network; the decreased strengths of connectivity were positively correlated with the performance of the paretic hand. Collectively, our findings suggest that stroke patients with different hand outcomes show distinct functional reorganization patterns in large-scale brain networks. These findings shed light on the network-level neuromechanisms that help explain why stroke survivors in the chronic stage show different hand outcomes.

PMID: 31795061 [PubMed - as supplied by publisher]

Abnormal dynamic properties of functional connectivity in disorders of consciousness.

Fri, 12/06/2019 - 12:52
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Abnormal dynamic properties of functional connectivity in disorders of consciousness.

Neuroimage Clin. 2019 Nov 05;24:102071

Authors: Cao B, Chen Y, Yu R, Chen L, Chen P, Weng Y, Chen Q, Song J, Xie Q, Huang R

Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to research abnormal functional connectivity (FC) in patients with disorders of consciousness (DOC). However, most studies assumed steady spatial-temporal signal interactions between distinct brain regions during the scan period. The aim of this study was to explore abnormal dynamic functional connectivity (dFC) in DOC patients. After excluding 26 patients' data that failed to meet the requirements of imaging quality, we retained 19 DOC patients (12 with unresponsive wakefulness syndrome and 7 in a minimally conscious state, diagnosed with the Coma Recovery Scale-Revised [CRS-R]) for the dFC analysis. We used the sliding windows approach to construct dFC matrices. Then these matrices were clustered into distinct states using the k-means clustering algorithm. We found that the DOC patients showed decreased dFC in the sensory and somatomotor networks compared with the healthy controls. There were also significant differences in temporal properties, the mean dwell time (MDT) and the number of transitions (NT), between the DOC patients and the healthy controls. In addition, we also used a hidden Markov model (HMM) to test the robustness of the results. With the connectome-based predictive modeling (CPM) approach, we found that the properties of abnormal dynamic network can be used to predict the CRS-R scores of the patients after severe brain injury. These findings may contribute to a better understanding of the abnormal brain networks in DOC patients.

PMID: 31795053 [PubMed - as supplied by publisher]

Psychopathy is associated with shifts in the organization of neural networks in a large incarcerated male sample.

Fri, 12/06/2019 - 12:52
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Psychopathy is associated with shifts in the organization of neural networks in a large incarcerated male sample.

Neuroimage Clin. 2019 Nov 09;24:102083

Authors: Tillem S, Harenski K, Harenski C, Decety J, Kosson D, Kiehl KA, Baskin-Sommers A

Abstract
Psychopathy is a personality disorder defined by antisocial behavior paired with callousness, low empathy, and low interpersonal emotions. Psychopathic individuals reliably display complex atypicalities in emotion and attention processing that are evident when examining task performance, activation within specific neural regions, and connections between regions. Recent advances in neuroimaging methods, namely graph analysis, attempt to unpack this type of processing complexity by evaluating the overall organization of neural networks. Graph analysis has been used to better understand neural functioning in several clinical disorders but has not yet been used in the study of psychopathy. The present study applies a minimum spanning tree graph analysis to resting-state fMRI data collected from male inmates assessed for psychopathy with the Hare Psychopathy Checklist-Revised (n = 847). Minimum spanning tree analysis provides several metrics of neural organization optimality (i.e., the effectiveness, efficiency, and robustness of neural network organization). Results show that inmates higher in psychopathy exhibit a more efficiently organized dorsal attention network (β = =0.101, pcorrected = =0.018). Additionally, subcortical structures (e.g., amygdala, caudate, and hippocampus) act as less of a central hub in the global flow of information in inmates higher in psychopathy (β = =-0.104, pcorrected = =0.048). There were no significant effects of psychopathy on neural network organization in the default or salience networks. Together, these shifts in neural organization suggest that the brains of inmates higher in psychopathy are organized in a fundamentally different way than other individuals.

PMID: 31795050 [PubMed - as supplied by publisher]

Disrupted functional and structural connectivity within default mode network contribute to WMH-related cognitive impairment.

Fri, 12/06/2019 - 12:52
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Disrupted functional and structural connectivity within default mode network contribute to WMH-related cognitive impairment.

Neuroimage Clin. 2019 Nov 12;24:102088

Authors: Chen X, Huang L, Ye Q, Yang D, Qin R, Luo C, Li M, Zhang B, Xu Y

Abstract
AIMS: The prevalence of white matter hyperintensities (WMH) rises dramatically with aging. Both the progression of WMH and changing patterns of default mode network (DMN) have been proven to be closely associated with cognitive function. The present study hypothesized that changes in functional connectivity and structural connectivity of DMN contributed to WMH related cognitive impairment.
METHODS: A total of 116 subjects were enrolled from the Cerebral Small Vessel Disease Register in Drum Tower Hospital of Nanjing University, and were distributed across three categories according to Fazekas rating scale: WMH I (n = 57), WMH II (n = 34), and WMH III(n = 25). All participants underwent neuropsychological tests and multimodal MRI scans, including diffusion tensor imaging and resting-state fMRI imaging. The alterations of functional connectivity and structural connectivity within the DMN were further explored.
RESULTS: Age and hypertension were risk factors for WMH progression. Subjects with a higher WMH burden displayed higher DMN functional connectivity in the medial frontal gyrus, while lower DMN functional connectivity in the thalamus. After adjusting for aging, gender, and education, the increased DMN functional connectivity in the medial frontal gyrus, and the increased mean diffusivity of the white matter tracts between the hippocampus and posterior cingulate cortex were independent indicators of worse performance in memory. Moreover, the decreased DMN functional connectivity in the thalamus and increased mean diffusivity of the white matter tracts between the thalamus and posterior cingulate cortex were independent risk factors for a slower processing speed.
CONCLUSION: The changes in functional connectivity and structural connectivity within the DMN attributed to WMH progression were responsible for the development of cognitive impairment.

PMID: 31795048 [PubMed - as supplied by publisher]

Decreased functional connectivity of the insula within the salience network as an indicator for prospective insufficient response to antidepressants.

Fri, 12/06/2019 - 12:52
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Decreased functional connectivity of the insula within the salience network as an indicator for prospective insufficient response to antidepressants.

Neuroimage Clin. 2019 Nov 06;24:102064

Authors: Geugies H, Opmeer EM, Marsman JBC, Figueroa CA, van Tol MJ, Schmaal L, van der Wee NJA, Aleman A, Penninx BWJH, Veltman DJ, Schoevers RA, Ruhé HG

Abstract
Insufficient response to treatment is the main cause of prolonged suffering from major depressive disorder (MDD). Early identification of insufficient response could result in faster and more targeted treatment strategies to reduce suffering. We therefore explored whether baseline alterations within and between resting state functional connectivity networks could serve as markers of insufficient response to antidepressant treatment in two years of follow-up. We selected MDD patients (N = 17) from the NEtherlands Study of Depression and Anxiety (NESDA), who received ≥ two antidepressants, indicative for insufficient response, during the two year follow-up, a group of MDD patients who received only one antidepressant (N = 32) and a healthy control group (N = 19) matched on clinical characteristics and demographics. An independent component analysis (ICA) of baseline resting-state scans was conducted after which functional connectivity within the components was compared between groups. We observed lower connectivity of the right insula within the salience network in the group with ≥ two antidepressants compared to the group with one antidepressant. No difference in connectivity was found between the patient groups and healthy control group. Given the suggested role of the right insula in switching between task-positive mode (activation during attention-demanding tasks) and task-negative mode (activation during the absence of any task), we explored whether right insula activation differed during switching between these two modes. We observed that in the ≥2 antidepressant group, the right insula was less active compared to the group with one antidepressant, when switching from task-positive to task-negative mode than the other way around. These findings imply that lower right insula connectivity within the salience network may serve as an indicator for prospective insufficient response to antidepressants. This result, supplemented by the diminished insula activation when switching between task and rest related networks, could indicate an underlying mechanism that, if not sufficiently targeted by current antidepressants, could lead to insufficient response. When replicated, these findings may contribute to the identification of biomarkers for early detection of insufficient response.

PMID: 31795046 [PubMed - as supplied by publisher]

Large-scale Granger causal brain network based on resting-state fMRI data.

Thu, 12/05/2019 - 00:46
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Large-scale Granger causal brain network based on resting-state fMRI data.

Neuroscience. 2019 Nov 30;:

Authors: Wang X, Wang R, Li F, Lin Q, Zhao X, Hu Z

Abstract
The causal connections among small-scale regions based on resting-state fMRI data have been extensively studied and a lot of achievements have been demonstrated. However, the causal connection among large-scale regions was seldom discussed. In this paper, we applied global Granger causality analysis to construct the causal connections in the whole-brain network among 103 healthy subjects (33M/66F, ages 20-23) based on a resting-state fMRI dataset. We further explored four large-scale cognitive networks which have been widely known: central executive network (CEN), default mode network (DMN), dorsal attention network (DAN) and salience network (SN). These four cognitive networks are particularly important for understanding higher cognitive functions and dysfunction. Based on the above research, Out-In degree were introduced to identify the driving and driven hubs. Studying the driving and driven hub of brain network is of great significance for assessing the functional mechanism of the brain network. There were 817 directed edges identified as significant among the 8010 possible causal connections; seven driving hubs and ten driven hubs were identified in the whole-brain network. In CEN, dorsolateral prefrontal cortex (DlPFC) and superior parietal cortex (SPC) were the driven and driving hubs, respectively; in DMN, they were posterior cingulate cortex (PCC) and medial prefrontal cortex (MPFC); in DAN, they were frontal eye fields (FEF) and intraparietal sulcus (IPS); and in SN, they were frontoinsular cortex (FIC) and medial frontal cortex (MFC). These findings may provide insights into our understanding of human brain function mechanisms and the diagnosis of brain diseases.

PMID: 31794821 [PubMed - as supplied by publisher]

Functional Overlaps Exist in Neurological and Psychiatric Disorders: A Proof from Brain Network Analysis.

Thu, 12/05/2019 - 00:46
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Functional Overlaps Exist in Neurological and Psychiatric Disorders: A Proof from Brain Network Analysis.

Neuroscience. 2019 Nov 30;:

Authors: Ma K, Yu J, Shao W, Xu X, Zhang Z, Zhang D

Abstract
Psychopath and neuropath often exhibit similar symptoms in clinical functional performances. However, few studies ever demonstrate the existence of overlapped brain functional mechanism between neurological and psychiatric disorders. Accordingly, in this paper, we have made an attempt to verify the existence of functional overlaps among neurological and psychiatric disorders through brain network analysis. Specifically, our findings suggest that functional overlaps exist in mild cognitive impairment (MCI), Alzheimer's disease (AD) and major depressive disorder (MDD), as well as in epilepsy, attention deficit hyperactivity disorder (ADHD) and schizophrenia. In these overlapped functions, we also find that the brain regions of neuropsychopathic disorders exhibit different cooperative patterns at different levels of brain activities. For example, strong-strong cooperative patterns were observed at high levels of brain activities in epilepsy, ADHD and schizophrenia.

PMID: 31794696 [PubMed - as supplied by publisher]