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Estimating Brain Functional Networks Based on Adaptively-Weighted fMRI Signals for MCI Identification

Brain functional network (BFN) analysis is becoming a crucial way to explore the inherent organized pattern of the brain and reveal potential biomarkers for diagnosing neurological or psychological disorders. In so doing, a well-estimated BFN is of great concern. In practice, however, noises or arti...

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Autores principales: Chen, Huihui, Zhang, Yining, Zhang, Limei, Qiao, Lishan, Shen, Dinggang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7874154/
https://www.ncbi.nlm.nih.gov/pubmed/33584242
http://dx.doi.org/10.3389/fnagi.2020.595322
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author Chen, Huihui
Zhang, Yining
Zhang, Limei
Qiao, Lishan
Shen, Dinggang
author_facet Chen, Huihui
Zhang, Yining
Zhang, Limei
Qiao, Lishan
Shen, Dinggang
author_sort Chen, Huihui
collection PubMed
description Brain functional network (BFN) analysis is becoming a crucial way to explore the inherent organized pattern of the brain and reveal potential biomarkers for diagnosing neurological or psychological disorders. In so doing, a well-estimated BFN is of great concern. In practice, however, noises or artifacts involved in the observed data (i.e., fMRI time series in this paper) generally lead to a poor estimation of BFN, and thus a complex preprocessing pipeline is often used to improve the quality of the data prior to BFN estimation. One of the popular preprocessing steps is data-scrubbing that aims at removing “bad” volumes from the fMRI time series according to the amplitude of the head motion. Despite its helpfulness in general, this traditional scrubbing scheme cannot guarantee that the removed volumes are necessarily unhelpful, since such a step is fully independent to the subsequent BFN estimation task. Moreover, the removal of volumes would reduce the statistical power, and different numbers of volumes are generally scrubbed for different subjects, resulting in an inconsistency or bias in the estimated BFNs. To address these issues, we develop a new learning framework that conducts BFN estimation and data-scrubbing simultaneously by an alternating optimization algorithm. The newly developed algorithm adaptively weights volumes (instead of removing them directly) for the task of BFN estimation. As a result, the proposed method can not only reduce the difficulty of threshold selection involved in the traditional scrubbing scheme, but also provide a more flexible framework that scrubs the data in the subsequent FBN estimation model. Finally, we validate the proposed method by identifying subjects with mild cognitive impairment (MCI) from normal controls based on the estimated BFNs, achieving an 80.22% classification accuracy, which significantly improves the baseline methods.
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spelling pubmed-78741542021-02-11 Estimating Brain Functional Networks Based on Adaptively-Weighted fMRI Signals for MCI Identification Chen, Huihui Zhang, Yining Zhang, Limei Qiao, Lishan Shen, Dinggang Front Aging Neurosci Neuroscience Brain functional network (BFN) analysis is becoming a crucial way to explore the inherent organized pattern of the brain and reveal potential biomarkers for diagnosing neurological or psychological disorders. In so doing, a well-estimated BFN is of great concern. In practice, however, noises or artifacts involved in the observed data (i.e., fMRI time series in this paper) generally lead to a poor estimation of BFN, and thus a complex preprocessing pipeline is often used to improve the quality of the data prior to BFN estimation. One of the popular preprocessing steps is data-scrubbing that aims at removing “bad” volumes from the fMRI time series according to the amplitude of the head motion. Despite its helpfulness in general, this traditional scrubbing scheme cannot guarantee that the removed volumes are necessarily unhelpful, since such a step is fully independent to the subsequent BFN estimation task. Moreover, the removal of volumes would reduce the statistical power, and different numbers of volumes are generally scrubbed for different subjects, resulting in an inconsistency or bias in the estimated BFNs. To address these issues, we develop a new learning framework that conducts BFN estimation and data-scrubbing simultaneously by an alternating optimization algorithm. The newly developed algorithm adaptively weights volumes (instead of removing them directly) for the task of BFN estimation. As a result, the proposed method can not only reduce the difficulty of threshold selection involved in the traditional scrubbing scheme, but also provide a more flexible framework that scrubs the data in the subsequent FBN estimation model. Finally, we validate the proposed method by identifying subjects with mild cognitive impairment (MCI) from normal controls based on the estimated BFNs, achieving an 80.22% classification accuracy, which significantly improves the baseline methods. Frontiers Media S.A. 2021-01-14 /pmc/articles/PMC7874154/ /pubmed/33584242 http://dx.doi.org/10.3389/fnagi.2020.595322 Text en Copyright © 2021 Chen, Zhang, Zhang, Qiao and Shen. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Chen, Huihui
Zhang, Yining
Zhang, Limei
Qiao, Lishan
Shen, Dinggang
Estimating Brain Functional Networks Based on Adaptively-Weighted fMRI Signals for MCI Identification
title Estimating Brain Functional Networks Based on Adaptively-Weighted fMRI Signals for MCI Identification
title_full Estimating Brain Functional Networks Based on Adaptively-Weighted fMRI Signals for MCI Identification
title_fullStr Estimating Brain Functional Networks Based on Adaptively-Weighted fMRI Signals for MCI Identification
title_full_unstemmed Estimating Brain Functional Networks Based on Adaptively-Weighted fMRI Signals for MCI Identification
title_short Estimating Brain Functional Networks Based on Adaptively-Weighted fMRI Signals for MCI Identification
title_sort estimating brain functional networks based on adaptively-weighted fmri signals for mci identification
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7874154/
https://www.ncbi.nlm.nih.gov/pubmed/33584242
http://dx.doi.org/10.3389/fnagi.2020.595322
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