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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Vydané v:	Frontiers in aging neuroscience Ročník 12; s. 595322
Hlavní autori:	Chen, Huihui, Zhang, Yining, Zhang, Limei, Qiao, Lishan, Shen, Dinggang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland Frontiers Research Foundation 14.01.2021 Frontiers Media S.A
Predmet:	Algorithms Alzheimer's disease Brain mapping Cognitive ability Functional magnetic resonance imaging index terms-brain functional network Methods mild cognitive impairment Neurological diseases Neuroscience Pearson's correlation scrubbing sparse re presentation Time series Pearson's correlation mild cognitive impairment scrubbing sparse re presentation functional magnetic resonance imaging index terms-brain functional network
ISSN:	1663-4365, 1663-4365
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Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Reviewed by: Qi Zhu, Nanjing University of Aeronautics and Astronautics, China; Xiang Li, Massachusetts General Hospital and Harvard Medical School, United States; Weikai Li, Nanjing University of Aeronautics and Astronautics, China Edited by: Mark Stecker, Independent Researcher, Fresno, United States These authors share first authorship
ISSN:	1663-4365 1663-4365
DOI:	10.3389/fnagi.2020.595322