The complexity of spontaneous brain activity changes in schizophrenia, bipolar disorder, and ADHD was examined using different variations of entropy

Adult attention deficit/hyperactivity disorder (ADHD), schizophrenia (SCHZ), and bipolar disorder (BP) have common symptoms and differences, and the underlying neural mechanisms are still unclear. This article will thoroughly discuss the differences between ADHD, BP, and SCHZ (31 healthy control and...

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Veröffentlicht in:Human brain mapping 2023-01, Vol.44 (1), p.94-118
Hauptverfasser: Guan, Sihai, Wan, Dongyu, Zhao, Rong, Canario, Edgar, Meng, Chun, Biswal, Bharat B.
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Wan, Dongyu
Zhao, Rong
Canario, Edgar
Meng, Chun
Biswal, Bharat B.
description Adult attention deficit/hyperactivity disorder (ADHD), schizophrenia (SCHZ), and bipolar disorder (BP) have common symptoms and differences, and the underlying neural mechanisms are still unclear. This article will thoroughly discuss the differences between ADHD, BP, and SCHZ (31 healthy control and 31 ADHD; 34 healthy control and 34 BP; 42 healthy control and 42 SCHZ) relative to healthy subjects in combination with three atlases (et al., the Brainnetome atlas, the Dosenbach atlas, the Power atlas) and seven entropies (et al., approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn), fuzzy entropy (FuEn), differential entropy (DiffEn), range entropy (RaEn), and dispersion entropy (DispEn)), as well as the prominent significant brain regions, in the hope of giving information that is more suitable for analyzing different diseases' entropy. First, the reliability (et al., intraclass correlation coefficient [ICC]) of seven kinds of entropy is calculated and analyzed by using the MSC dataset (10 subjects and 100 sessions in total) and simulation data; then, seven types of entropy and multiscale entropy expanded based on seven kinds of entropy are used to explore the differences and brain regions of ADHD, BP, and SCHZ relative to healthy subjects; and finally, by verifying the classification performance of the seven information entropies on ADHD, BP, and SCHZ, the effectiveness of the seven entropy methods is evaluated through these three methods. The core brain regions that affect the classification are given, and DiffEn performed best on ADHD, SaEn for BP, and RaEn for SCHZ. The flowchart of the complexity of spontaneous brain activity by using multi‐types entropies. The raw rs‐fMRI images of the MSC dataset and UCLA dataset underwent preprocessing, time‐series extraction and then were used to compute entropy values using seven entropy methods. The entropy analysis was conducted based on whole‐brain gray matter voxels. Test–retest reliability was finally examined by using the intraclass correlation coefficient (ICC). Besides, altered complexity of spontaneous brain activity and classification results were explored.
doi_str_mv 10.1002/hbm.26129
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This article will thoroughly discuss the differences between ADHD, BP, and SCHZ (31 healthy control and 31 ADHD; 34 healthy control and 34 BP; 42 healthy control and 42 SCHZ) relative to healthy subjects in combination with three atlases (et al., the Brainnetome atlas, the Dosenbach atlas, the Power atlas) and seven entropies (et al., approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn), fuzzy entropy (FuEn), differential entropy (DiffEn), range entropy (RaEn), and dispersion entropy (DispEn)), as well as the prominent significant brain regions, in the hope of giving information that is more suitable for analyzing different diseases' entropy. First, the reliability (et al., intraclass correlation coefficient [ICC]) of seven kinds of entropy is calculated and analyzed by using the MSC dataset (10 subjects and 100 sessions in total) and simulation data; then, seven types of entropy and multiscale entropy expanded based on seven kinds of entropy are used to explore the differences and brain regions of ADHD, BP, and SCHZ relative to healthy subjects; and finally, by verifying the classification performance of the seven information entropies on ADHD, BP, and SCHZ, the effectiveness of the seven entropy methods is evaluated through these three methods. The core brain regions that affect the classification are given, and DiffEn performed best on ADHD, SaEn for BP, and RaEn for SCHZ. The flowchart of the complexity of spontaneous brain activity by using multi‐types entropies. The raw rs‐fMRI images of the MSC dataset and UCLA dataset underwent preprocessing, time‐series extraction and then were used to compute entropy values using seven entropy methods. The entropy analysis was conducted based on whole‐brain gray matter voxels. Test–retest reliability was finally examined by using the intraclass correlation coefficient (ICC). Besides, altered complexity of spontaneous brain activity and classification results were explored.</abstract><cop>Hoboken, USA</cop><pub>John Wiley &amp; Sons, Inc</pub><pmid>36358029</pmid><doi>10.1002/hbm.26129</doi><tpages>25</tpages><orcidid>https://orcid.org/0000-0002-3710-3500</orcidid><orcidid>https://orcid.org/0000-0003-0145-6627</orcidid><oa>free_for_read</oa></addata></record>
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subjects (multiscale)entropies
ADHD/BP/SCHZ
Adult
Attention Deficit Disorder with Hyperactivity - diagnostic imaging
Attention deficit hyperactivity disorder
Bipolar disorder
Bipolar Disorder - diagnostic imaging
Brain
Brain - diagnostic imaging
Classification
Consortia
Correlation coefficient
Correlation coefficients
Datasets
different atlases
Entropy
Humans
Magnetic resonance imaging
Mathematical analysis
Mental disorders
Methods
Permutations
Random variables
Reliability analysis
Reproducibility of Results
resting‐state fMRI
Schizophrenia
Schizophrenia - diagnostic imaging
Signs and symptoms
test–retest reliability
Young adults
title The complexity of spontaneous brain activity changes in schizophrenia, bipolar disorder, and ADHD was examined using different variations of entropy
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