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Predicting visual working memory with multimodal magnetic resonance imaging.
- Source :
-
Human brain mapping [Hum Brain Mapp] 2021 Apr 01; Vol. 42 (5), pp. 1446-1462. Date of Electronic Publication: 2020 Dec 05. - Publication Year :
- 2021
-
Abstract
- The indispensability of visual working memory (VWM) in human daily life suggests its importance in higher cognitive functions and neurological diseases. However, despite the extensive research efforts, most findings on the neural basis of VWM are limited to a unimodal context (either structure or function) and have low generalization. To address the above issues, this study proposed the usage of multimodal neuroimaging in combination with machine learning to reveal the neural mechanism of VWM across a large cohort (N = 547). Specifically, multimodal magnetic resonance imaging features extracted from voxel-wise amplitude of low-frequency fluctuations, gray matter volume, and fractional anisotropy were used to build an individual VWM capacity prediction model through a machine learning pipeline, including the steps of feature selection, relevance vector regression, cross-validation, and model fusion. The resulting model exhibited promising predictive performance on VWM (r = .402, p < .001), and identified features within the subcortical-cerebellum network, default mode network, motor network, corpus callosum, anterior corona radiata, and external capsule as significant predictors. The main results were then compared with those obtained on emotional regulation and fluid intelligence using the same pipeline, confirming the specificity of our findings. Moreover, the main results maintained well under different cross-validation regimes and preprocess strategies. These findings, while providing richer evidence for the importance of multimodality in understanding cognitive functions, offer a solid and general foundation for comprehensively understanding the VWM process from the top down.<br /> (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)
- Subjects :
- Adolescent
Adult
Aged
Aged, 80 and over
Emotional Regulation physiology
Female
Humans
Intelligence physiology
Machine Learning
Male
Middle Aged
Models, Theoretical
Multimodal Imaging
Nerve Net diagnostic imaging
Young Adult
Cerebral Cortex anatomy & histology
Cerebral Cortex diagnostic imaging
Cerebral Cortex physiology
Magnetic Resonance Imaging methods
Memory, Short-Term physiology
Nerve Net physiology
Neuroimaging methods
Visual Perception physiology
White Matter anatomy & histology
White Matter diagnostic imaging
White Matter physiology
Subjects
Details
- Language :
- English
- ISSN :
- 1097-0193
- Volume :
- 42
- Issue :
- 5
- Database :
- MEDLINE
- Journal :
- Human brain mapping
- Publication Type :
- Academic Journal
- Accession number :
- 33277955
- Full Text :
- https://doi.org/10.1002/hbm.25305