Sparse Multivariate Analysis Reveals Dissociable White Matter Networks for Cognitive and Motor Processing Speed.

Overview

1. Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 610054, China; (S.Y.); (C.L.); (M.L.B.V.)
2. China-Cuba Belt and Road Joint Laboratory on Neurotechnology and Brain-Apparatus Communication, University of Electronic Science and Technology of China, Chengdu 611731, China; (E.M.-M.); (L.G.-G.)
3. GuangDong Mecable Communication Fiber Optical Cable Co., Ltd., Dongguan 523689, China
4. Cuban Neurosciences Center, Havana 11300, Cuba

Abstract

Highlights: What are the main findings? • Two distinct white matter networks were identified: one related to complex cognitive processing and another linked to motor response consistency. • EEG alpha peak frequency did not show a significant relationship with reaction time performance.

What are the implications of the main findings? • Cognitive and motor aspects of processing speed rely on different brain systems rather than a single mechanism. • Brain structure may be more informative than the main frequency of resting-state EEG oscillations for understanding individual differences in processing speed.

Abstract: Background: Reaction time (RT) is a fundamental measure of information processing speed in cognitive neuroscience and is influenced by both structural and functional brain properties. While prior studies have independently linked white matter microstructure and EEG alpha oscillations to cognitive performance, their joint contribution to distinct aspects of RT remains unclear. This study aims to investigate whether multimodal data can dissociate neural systems underlying cognitive and motor components of processing speed. Methods: We analyzed diffusion tensor imaging, resting-state individual EEG alpha peak frequency (IAF), demographic variables, and behavioral RT measures from a GO/NO-GO paradigm in 24 healthy adults from the Cuban Human Brain Mapping Project. Behavioral metrics included the mean, standard deviation and skewness of reaction times for simple and complex tasks. Sparse multiple canonical correlation analysis was applied to identify multivariate associations across modalities. Results: Two significant latent dimensions were identified. The first dimension linked bilateral fronto-temporal association tracts (SLF, IFOF, UNC) with complex RT performance, reflecting higher-order cognitive processing. The second dimension associated motor and interhemispheric tracts (CGC, CST, ILF, forceps major and minor) with intra-individual asymmetric variability (skewness) across tasks, indicating a motor-execution consistency system. IAF did not significantly contribute to either dimension. Sex showed strong associations with both components. Conclusions: Distinct white matter networks were associated with separable cognitive and motor aspects of processing speed, while resting-state alpha frequency did not show stable contributions with behavioral variability in this sample. IAF showed minimal contribution within the identified sparse multivariate dimensions. These findings highlight the importance of multimodal and multivariate approaches for understanding and potentially disentangling complex brain–behavior relationships.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• doi:10.7303/syn22324937, at the source; found in “Data Availability Statement”

Data Availability Statement

The dataset included BIDS files, the in-house programs, the psychological (WAIS-III, MMSE and reaction time), and the demographic and handedness data (∗.csv) available at https://www.synapse.org/. See reference [3]. You can visualize the data at https://doi.org/10.7303/syn22324937. To download them you need to be registered at the synapse.org website. All the datasets have also been stored in the McGill Centre for Integrative Neuroscience (MCIN) network. The dataset will be available by request at https://chbmp-open.loris.ca.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 7 keywords, 1 funder, 44 references.

Cite

This paper

Yasir, S., Fidele, N., Martinez-Montes, E., Galan-Garcia, L., Luo, C., Bringas Vega, M. L., & Valdes-Sosa, P. A. (2026). Sparse Multivariate Analysis Reveals Dissociable White Matter Networks for Cognitive and Motor Processing Speed. Brain sciences, 16(5), 533. https://doi.org/10.3390/brainsci16050533

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