Dysregulated resting state functional connectivity and obesity: A systematic review

Neuroscience & Biobehavioral Reviews, Journal Year: 2021, Volume and Issue: 131, P. 270 - 292

Published: Aug. 20, 2021

Language: Английский

---

Diagnosis of Alzheimer’s disease via resting-state EEG: integration of spectrum, complexity, and synchronization signal features

Frontiers in Aging Neuroscience, Journal Year: 2023, Volume and Issue: 15

Published: Nov. 7, 2023

Alzheimer's disease (AD) is the most common neurogenerative disorder, making up 70% of total dementia cases with a prevalence more than 55 million people. Electroencephalogram (EEG) has become suitable, accurate, and highly sensitive biomarker for identification diagnosis AD.In this study, public database EEG resting state-closed eye recordings containing 36 AD subjects 29 normal was used. And then, three types signal features resting-state EEG, i.e., spectrum, complexity, synchronization, were performed by applying various processing statistical methods, to obtain 18 each epoch. Next, supervised machine learning classification algorithms decision trees, random forests, support vector (SVM) compared in categorizing processed leave-one-person-out cross-validation.The results showed that cases, major change characteristics an slowing, reduced decrease synchrony. The proposed methodology achieved relatively high accuracy 95.65, 95.86, 88.54% between SVM, respectively, showing integration synchronization signals can enhance performance identifying subjects.This study recommended aiding AD.

Language: Английский

---

A biodegradable and self-deployable electronic tent electrode for brain cortex interfacing

Nature Electronics, Journal Year: 2024, Volume and Issue: 7(9), P. 815 - 828

Published: Aug. 5, 2024

Language: Английский

---

Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease

Scientific Reports, Journal Year: 2018, Volume and Issue: 8(1)

Published: July 27, 2018

Graph analysis has become a popular approach to study structural brain networks in neurodegenerative disorders such as Alzheimer's disease (AD). However, reported results across similar studies are often not consistent. In this paper we investigated the stability of graph measures clustering, path length, global efficiency and transitivity cohort AD (N = 293) control subjects 293). More specifically, studied effect that group size composition, choice neuroanatomical atlas, cortical measure (thickness or volume) have on binary weighted network properties relate them magnitude differences between groups subjects. Our showed specific composition heavily influenced properties, particularly for with less than 150 Weighted generally required fewer stabilize all assessed robust significant differences, consistent atlases measures. these were driven by average correlation strength, which implies limitation capturing more complex features networks. graphs, only found when using thickness define edges. The findings two atlases, but no volumes. merits future investigations suggest should be preferred if Further, studying small cohorts complemented analyzing smaller, subsampled reduce risk spurious.

Language: Английский

---

Attentional Connectivity-based Prediction of Autism Using Heterogeneous rs-fMRI Data from CC200 Atlas

Experimental Neurobiology, Journal Year: 2020, Volume and Issue: 29(1), P. 27 - 37

Published: Feb. 29, 2020

Autism spectrum disorder (ASD) is a developmental syndrome characterized by obvious drawbacks in sociality and communication. It has crucial significance to exactly discern the individuals with ASD typical controls (TC). Previous imaging studies on ASD/TC identification have made remarkable progress exploration of objective as well biomarkers associated ASD. However, glaring deficiency manifested investigation solely homogeneous small datasets. Thus, we attempted unveil some replicable robust neural patterns autism using heterogeneous multi-site brain dataset from ABIDE (Autism Brain Imaging Data Exchange). Experiments were carried out an attention mechanism based Extra-Trees algorithm, taking study object connectivity measured resting-state functional magnetic resonance (fMRI) data CC200 atlas. With cross-validation strategy, our proposed method resulted mean classification accuracy 72.2% (sensitivity=68.6%, specificity=75.4%). raised precision prediction about 2% specificity 3.2% comparison most competitive reported effort. Connectivity analysis optimal model highlighted informative regions strongly involved social cognition interaction, lower correlation between anterior posterior default mode network (DMN) autistic than controls. This observation concordant previous studies, which enables effectively identify risk

Language: Английский

---

Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy

Frontiers in Computational Neuroscience, Journal Year: 2020, Volume and Issue: 13

Published: Jan. 10, 2020

People living with schizophrenia (SCZ) experience severe brain network deterioration. The is constantly fizzling nonlinear causal activities measured by electroencephalogram (EEG) and despite the variety of effective connectivity methods, only few approaches can quantify direct interactions. To circumvent this problem, we are motivated to quantitatively measure multivariate transfer entropy (MTE) which has been demonstrated be able capture both linear relationships effectively. In work, propose construct EEG MTE further compare its performance Granger analysis (GCA) Bivariate (BVTE). simulation results show that outperformed GCA BVTE under varied signal-to-noise conditions, edges recovered, sensitivity, specificity. Moreover, applications P300 task healthy controls (HC) SCZ patients clearly deteriorated interactions SCZ, compared HC. provides a novel tool potentially deepen our knowledge deterioration SCZ.

Language: Английский

---

Classification of Autism Spectrum Disorder From EEG-Based Functional Brain Connectivity Analysis

Neural Computation, Journal Year: 2021, Volume and Issue: 33(7), P. 1914 - 1941

Published: April 22, 2021

Autism is a psychiatric condition that typically diagnosed with behavioral assessment methods. Recent years have seen rise in the number of children autism. Since this could serious health and socioeconomic consequences, it imperative to investigate how develop strategies for an early diagnosis might pave way adequate intervention. In study, phase-based functional brain connectivity derived from electroencephalogram (EEG) machine learning framework was used classify autism typical experimentally obtained data set 12 spectrum disorder (ASD) children. Specifically, networks quantitatively been characterized by graph-theoretic parameters computed three proposed approaches based on standard phase-locking value, which were as features environment. Our study successfully classified between two groups approximately 95.8% accuracy, 100% sensitivity, 92% specificity through trial-averaged value (PLV) approach cubic support vector (SVM). This work has also shown significant changes ASD revealed at theta band using aggregated features. Therefore, findings offer insight into potential use tool classifying

Language: Английский

---

A survey of brain network analysis by electroencephalographic signals

Cognitive Neurodynamics, Journal Year: 2021, Volume and Issue: 16(1), P. 17 - 41

Published: June 14, 2021

Language: Английский

---

Biomarkers of non-communicable chronic disease: an update on contemporary methods

PeerJ, Journal Year: 2022, Volume and Issue: 10, P. e12977 - e12977

Published: Feb. 24, 2022

Chronic diseases constitute a major global burden with significant impact on health systems, economies, and quality of life. include broad range that can be communicable or non-communicable. are often associated modifications normal physiological levels various analytes routinely measured in serum other body fluids, as well pathological findings, such chronic inflammation, oxidative stress, mitochondrial dysfunction. Identification at-risk populations, early diagnosis, prediction prognosis play role preventing reducing the diseases. Biomarkers tools used by professionals to aid identification management diagnostic, predictive, prognostic. Several individual grouped biomarkers have been successfully diagnosis certain diseases, however, it is generally accepted more sophisticated approach link interpret involved disease necessary improve our current procedures. In order ensure comprehensive unbiased coverage literature, first primary frame manuscript (title, headings subheadings) was drafted authors working this paper. Second, based components preliminary skeleton search literature performed using PubMed Google Scholar engines. Multiple keywords related topic were used. Out screened papers, only 190 which most relevant, recent articles selected cover relation etiological mechanisms different recently finally advances applications multivariate statistical clinically applied tool for discussed. Recently, analysis has employed promising prospect. A brief discussion common highlighted review. The use diagnostic algorithms might show way novel criteria enhanced effectiveness inpatients one numerous non-communicable new relevant better patients according risk progression, sickness, fatality ongoing. It important determine whether newly identified purely associations real underlying pathophysiological processes. Use could great importance regard.

Language: Английский

---

Epilepsy as a manifestation of a multistate network of oscillatory systems

Neurobiology of Disease, Journal Year: 2019, Volume and Issue: 130, P. 104488 - 104488

Published: June 8, 2019

Language: Английский

---