White-matter functional networks changes in patients with schizophrenia

105

Parkinson's disease (PD) is a neurodegenerative disorder with dysfunction in cortices as well as white matter (WM) tracts. While the changes to WM structure have been extensively investigated in PD, the nature of the functional changes to WM remains unknown. In this study, the regional activity and functional connectivity of WM were compared between PD patients (n = 57) and matched healthy controls (n = 52), based on multimodel magnetic resonance imaging data sets. By tract‐based spatial statistical analyses of regional activity, patients showed decreased structural‐functional coupling in the left corticospinal tract compared to controls. This tract also displayed abnormally increased functional connectivity within the left post‐central gyrus and left putamen in PD patients. At the network level, the WM functional network showed small‐worldness in both controls and PD patients, yet it was abnormally increased in the latter group. Based on the features of the WM functional connectome, previously un‐evaluated individuals could be classified with fair accuracy (73%) and area under the curve of the receiver operating characteristics (75%). These neuroimaging findings provide direct evidence for WM functional changes in PD, which is crucial to understand the functional role of fiber tracts in the pathology of neural circuits.

Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Regional and network properties of white matter function in Parkinson's disease

Cuiping

Liu

et al. 2018

105

“…The last potential area of interest is CSF/WM regression in studies examining the FC of WM. Recent studies suggest the potential of this method to study brain function in healthy subjects (Ding et al, ; Peer et al, ) and also under pathological conditions (Jiang et al ). The specific form of nuisance regression may differ from our reported results since we focused on GM denoising only.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies—Quantifying noise removal and neural signal preservation

Bartoň

Mareček

Krajcovicova

et al. 2018

This study examines the impact of using different cerebrospinal fluid (CSF) and white matter (WM) nuisance signals for data‐driven filtering of functional magnetic resonance imaging (fMRI) data as a cleanup method before analyzing intrinsic brain fluctuations. The routinely used temporal signal‐to‐noise ratio metric is inappropriate for assessing fMRI filtering suitability, as it evaluates only the reduction of data variability and does not assess the preservation of signals of interest. We defined a new metric that evaluates the preservation of selected neural signal correlates, and we compared its performance with a recently published signal–noise separation metric. These two methods provided converging evidence of the unfavorable impact of commonly used filtering approaches that exploit higher numbers of principal components from CSF and WM compartments (typically 5 + 5 for CSF and WM, respectively). When using only the principal components as nuisance signals, using a lower number of signals results in a better performance (i.e., 1 + 1 performed best). However, there was evidence that this routinely used approach consisting of 1 + 1 principal components may not be optimal for filtering resting‐state (RS) fMRI data, especially when RETROICOR filtering is applied during the data preprocessing. The evaluation of task data indicated the appropriateness of 1 + 1 principal components, but when RETROICOR was applied, there was a change in the optimal filtering strategy. The suggested change for extracting WM (and also CSF in RETROICOR‐corrected RS data) is using local signals instead of extracting signals from a large mask using principal component analysis.

“…Functional magnetic resonance imaging (fMRI) based on blood oxygen level‐dependent (BOLD) signals is an effective technique to uncover the neural activity and relevant functions of the gray matter in neuropsychology and clinical neurological diseases (Chen, Liu, et al, ; Dong et al, ; Duan et al, ; Jia et al, ; Zhong et al, ). Recently, greater attention focused on the detection of neural activation and functional organization in the white matter by using the fMRI (Ding et al, ; Fabri & Polonara, ; Fabri, Polonara, Mascioli, Salvolini, & Manzoni, ; Gawryluk, Mazerolle, Brewer, Beyea, & D'Arcy, ; Gawryluk, Mazerolle, & D'Arcy, ; Jiang, Luo, Li, Li, et al, ; Marussich, Lu, Wen, & Liu, ; Peer, Nitzan, Bick, Levin, & Arzyt, ). For example, the white‐matter functional activation has been observed in multiple tasks including perceptual, language, and motor tasks (Fabri et al, ; Fabri & Polonara, ; Gawryluk et al, ; Gawryluk et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…These studies provided accumulated evidence for the existence of functional brain activity in the white matter. Furthermore, a recent study found that the functional connectivity (FC) in the white matter is associated with the underlying pathological mechanisms in schizophrenia (Jiang, Luo, Li, Li, et al, ) and Parkinson's disease (Ji et al, ). These studies suggested that it is feasible to investigate the white‐matter dysfunctions in BECT by fMRI.…”

Section: Introductionmentioning

confidence: 99%

Dysfunctional white‐matter networks in medicated and unmedicated benign epilepsy with centrotemporal spikes

Jiang

et al. 2019

Self Cite

Benign epilepsy with centrotemporal spikes (BECT) is the most common childhood idiopathic focal epilepsy syndrome, which characterized with white‐matter abnormalities in the rolandic cortex. Although diffusion tensor imaging research could characterize white‐matter structural architecture, it cannot detect neural activity or white‐matter functions. Recent studies demonstrated the functional organization of white‐matter by using functional magnetic resonance imaging (fMRI), suggesting that it is feasible to investigate white‐matter dysfunctions in BECT. Resting‐state fMRI data were collected from 24 new‐onset drug‐naive (unmedicated [NMED]), 21 medicated (MED) BECT patients, and 27 healthy controls (HC). Several white‐matter functional networks were obtained using a clustering analysis on voxel‐by‐voxel correlation profiles. Subsequently, conventional functional connectivity (FC) was calculated in four frequency sub‐bands (Slow‐5:0.01–0.027, Slow‐4:0.027–0.073, Slow‐3:0.073–0.198, and Slow‐2:0.198–0.25 Hz). We also employed a functional covariance connectivity (FCC) to estimate the covariant relationship between two white‐matter networks based on their correlations with multiple gray‐matter regions. Compared with HC, the NMED showed increased FC and/or FCC in rolandic network (RN) and precentral/postcentral network, and decreased FC and/or FCC in dorsal frontal network, while these alterations were not observed in the MED group. Moreover, the changes exhibited frequency‐specific properties. Specifically, only two alterations were shared in at least two frequency bands. Most of these alterations were observed in the frequency bands of Slow‐3 and Slow‐4. This study provided further support on the existence of white‐matter functional networks which exhibited frequency‐specific properties, and extended abnormalities of rolandic area from the perspective of white‐matter dysfunction in BECT.