Changes in white matter functional networks during wakefulness and sleep

Yang, Yang; Wang, Shilei; Liu, Jiayi; Zou, Guangyuan; Jiang, Jun; Jiang, Binghu; Cao, Wentian; Zou, Qihong

doi:10.1002/hbm.25961

Cited by 6 publications

(5 citation statements)

References 67 publications

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“…Our analysis revealed 40 nodes derived as spatially unique ICs that were identified using a group-ICA approach. The spatial distributions of the ICs are consistent with those in previous works identified based on either ICA (Huang Y. et al, 2020) or K-means clustering (Peer et al, 2017;Wang et al, 2022;Yang et al, 2022). In most of those studies, the nodes that act in concert were further grouped into subgroups based on their spatial distance to GM, namely, superficial, middle, and deep layers.…”

Section: Discussionsupporting

confidence: 86%

Changes in white matter functional networks across late adulthood

Gao

Lawless

et al. 2023

Front. Aging Neurosci.

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IntroductionThe aging brain is characterized by decreases in not only neuronal density but also reductions in myelinated white matter (WM) fibers that provide the essential foundation for communication between cortical regions. Age-related degeneration of WM has been previously characterized by histopathology as well as T2 FLAIR and diffusion MRI. Recent studies have consistently shown that BOLD (blood oxygenation level dependent) effects in WM are robustly detectable, are modulated by neural activities, and thus represent a complementary window into the functional organization of the brain. However, there have been no previous systematic studies of whether or how WM BOLD signals vary with normal aging. We therefore performed a comprehensive quantification of WM BOLD signals across scales to evaluate their potential as indicators of functional changes that arise with aging.MethodsBy using spatial independent component analysis (ICA) of BOLD signals acquired in a resting state, WM voxels were grouped into spatially distinct functional units. The functional connectivities (FCs) within and among those units were measured and their relationships with aging were assessed. On a larger spatial scale, a graph was reconstructed based on the pair-wise connectivities among units, modeling the WM as a complex network and producing a set of graph-theoretical metrics.ResultsThe spectral powers that reflect the intensities of BOLD signals were found to be significantly affected by aging across more than half of the WM units. The functional connectivities (FCs) within and among those units were found to decrease significantly with aging. We observed a widespread reduction of graph-theoretical metrics, suggesting a decrease in the ability to exchange information between remote WM regions with aging.DiscussionOur findings converge to support the notion that WM BOLD signals in specific regions, and their interactions with other regions, have the potential to serve as imaging markers of aging.

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Section: Discussionsupporting

confidence: 86%

Changes in white matter functional networks across late adulthood

Gao

Lawless

et al. 2023

Front. Aging Neurosci.

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“…For both good sleepers and poor sleepers, middle layer WM-FNs showed a distinct activity pattern with maximal amplitude at ~0.07Hz, whereas superficial and deep WM-FNs showed maximal amplitude at ~0.01Hz. Such a distinct pattern was also found in previous studies on WM BOLD signals, 23 , 31 , 37 ascribed to specific functions of the middle networks or the heterogeneous characteristics of the BOLD hemodynamic response function in WM. Nonetheless, there was no confirmative explanation for this phenomenon, which should be investigated in future studies.…”

Section: Discussionsupporting

confidence: 83%

“…Voxels identified in >60% of the subjects were selected to generate a group-level WM mask in order to keep as many valuable voxels due to individual differences in WM. 23 , 31 A group-level GM mask was reconstructed in a similar manner. Then the group-level WM and GM masks were compared with fMRI data; voxels having functional data of less than 80% of the subjects were removed.…”

Section: Methodsmentioning

confidence: 99%

“… 30 Power spectrum analysis has found that fluctuation amplitudes of WM functional networks (FNs) change with sleep/wake states. 31 Yet the relationship between frequency characteristics in WM and sleep quality has not been investigated. To this end, WM-FNs were obtained using clustering analysis based on WM BOLD-fMRI signals, and functional connectivity within WM-FNs and between WM- and GM-FNs were calculated.…”

Section: Introductionmentioning

confidence: 99%

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Functional Connectivity of White Matter and Its Association with Sleep Quality

Jiang¹,

Cai²,

Zhang³

2023

NSS

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Purpose Functional magnetic resonance imaging (fMRI) has been widely adopted to investigate the neural activity in gray matter (GM) in the field of sleep research, but the neural activity in white matter (WM) has received much less attention. The current study set out to test our hypothesis that WM functional abnormality is associated with poor sleep quality. Participants and Methods K-means clustering analysis was performed on 78 healthy adults drawn from the Human Connectome Project dataset to extract stable WM functional networks (WM-FNs) and GM-FNs. The differences in functional connectivity within WM-FNs and between WM- and GM-FNs, as well as the power spectrum between good sleep quality group (Pittsburgh Sleep Quality Index (PSQI) <6, daytime dysfunction = 0) and poor sleep quality group (PSQI >6, daytime dysfunction >0) were examined between groups with good and poor sleep quality. Additionally, linear relationships between sleep quality and altered functional characteristics of WM-FNs were evaluated. Results Functional connectivity between middle and superficial WM-FNs, short- and long-range functional connectivity between WM- and GM-FNs were decreased in poor sleepers and negatively correlated with PSQI score. The mean amplitudes of right sensorimotor WM networks at whole, high and low frequency bands were higher in poor sleepers and were positively correlated with PSQI score. Conclusion WM functional abnormality is associated with poor sleep quality. The neurobiological mechanisms that underlie the functional alterations of WM-FNs in poor sleepers need to be investigated in future studies.

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“…Imaging in such cases is characterized by topological organization disorder of functionally connected brain groups, which may lead to decreased cognitive, emotional, and memory function ( Fasiello et al, 2022 ). Further, sleep disorders are associated with various changes in magnetic resonance functional brain imaging, such as gray matter volume ( Paulekiene et al, 2022 ), cortical thickness ( Babu Henry Samuel et al, 2022 ), and white matter function ( Bai et al, 2022 ; Yang et al, 2022 ). These indexes reflect changes in brain structure, function, and metabolism, and provide a qualitative and quantitative basis for the study of sleep disorders, and guidance for disease prediction and future treatment target selection, thus may provide suggestions as to how to relieve patients’ pain and provide early diagnosis and treatment of related diseases.…”

Section: Discussionmentioning

confidence: 99%

A bibliometric analysis of the application of imaging in sleep in neurodegenerative disease

Jiang²,

Wen³

et al. 2023

Front. Aging Neurosci.

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ObjectiveThe purpose of this study was to examine the current state of the application of imaging in sleep research in degenerative disease, as well as hotspots and trends.Materials and methodsA search was conducted on the Web of Science Core Collection (WoSCC) between 1 September 2012, and 31 August 2022 for literature related to sleep imaging. This study analyzed 7,679 articles published in this field over the past 10 years, using CiteSpace to analyze tendencies, countries, institutions, authors, and hotspots.ResultsThere were 7,679 articles on the application of imaging to sleep research published by 566 institutions located in 135 countries in 1,428 journals; the number of articles was increasing on a yearly basis. According to keyword analysis, the research direction of the application of imaging in sleep research focused on the effects of degenerative diseases on sleep, such as Parkinson’s disease, Alzheimer’s disease, and small vessel disease. A literature evaluation found that Parkinson’s disease, insomnia, sleep quality, and rapid eye movement sleep behavior disorder were the top research trends in this field.ConclusionA growing body of research has focused on sleep disorders caused by degenerative diseases. In the application of imaging to sleep research, magnetic resonance functional brain imaging represents a reliable research method. In the future, more aging-related diseases may be the subject of sleep-related research, and imaging could provide convenient and reliable evidence in this respect.

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Changes in white matter functional networks during wakefulness and sleep

Cited by 6 publications

References 67 publications

Changes in white matter functional networks across late adulthood

Changes in white matter functional networks across late adulthood

Functional Connectivity of White Matter and Its Association with Sleep Quality

A bibliometric analysis of the application of imaging in sleep in neurodegenerative disease

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