Functional Brain Network Estimation With Time Series Self-Scrubbing

Functional brain network (FBN) is becoming an increasingly important measurement for exploring cerebral mechanisms and mining informative biomarkers that assist diagnosis of some neurodegenerative disorders. Despite its effectiveness to discover valuable hidden patterns in the human brain, the estim...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE journal of biomedical and health informatics Jg. 23; H. 6; S. 2494 - 2504
Hauptverfasser:	Li, Weikai, Qiao, Lishan, Zhang, Limei, Wang, Zhengxia, Shen, Dinggang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.11.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Biomarkers Brain Brain - diagnostic imaging Brain - physiology Brain mapping Brain Mapping - methods Cognitive ability Cognitive Dysfunction - diagnostic imaging Correlation Data points Databases, Factual Estimation Functional brain network Functional magnetic resonance imaging Head movement Humans Image Interpretation, Computer-Assisted - methods Informatics Magnetic Resonance Imaging - methods mild cognitive impairment (MCI) Neurodegenerative diseases Optimization Pipelines Rest - physiology resting-state functional magnetic resonance imaging (rs-fMRI) Time series Time series analysis Washing
ISSN:	2168-2194, 2168-2208, 2168-2208
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	Functional brain network (FBN) is becoming an increasingly important measurement for exploring cerebral mechanisms and mining informative biomarkers that assist diagnosis of some neurodegenerative disorders. Despite its effectiveness to discover valuable hidden patterns in the human brain, the estimated FBNs are often heavily influenced by the quality of the observed data (e.g., blood oxygen level dependent signal series). In practice, a preprocessing pipeline is usually employed for improving data quality. With this in mind, some data points (volumes or time course in the time series) are still not clean enough, due to artifacts including spurious resting-state processes (head movement, mind-wandering). Therefore, not all volumes in the fMRI time series can contribute to the subsequent FBN estimation. To address this issue, we propose a novel FBN estimation method by introducing a latent variable as an indicator of the data quality, and develop an alternating optimization algorithm for jointly scrubbing the data and estimating FBN simultaneously. To further illustrate the effectiveness of the proposed method, we conduct experiments on two public datasets to identify subjects with mild cognitive impairment from normal controls based on the estimated FBNs, and achieve improved accuracies than the baseline methods.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2168-2194 2168-2208 2168-2208
DOI:	10.1109/JBHI.2019.2893880