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Static and dynamic alterations in the amplitude of low-frequency fluctuation in patients with amyotrophic lateral sclerosis.

ABSTRACT: Background:Static changes in local brain activity in patients suffering from amyotrophic lateral sclerosis (ALS) have been studied. However, the dynamic characteristics of local brain activity are poorly understood. Whether dynamic alterations could differentiate patients with ALS from healthy controls (HCs) remains unclear. Methods:A total of 54 patients with ALS (mean age = 48.71 years, male/female = 36/18) and 54 (mean age = 48.30 years, male/female = 36/18) HCs underwent magnetic resonance imaging scans. To depict static alterations in cortical activity, amplitude of low-frequency fluctuations (ALFF) which measures the total power of regional activity was computed. Dynamic ALFF (d-ALFF) from all subjects was calculated using a sliding-window approach. Statistical differences in ALFF and d-ALFF between both groups were used as features to explore whether they could differentiate ALS from HC through support vector machine method. Results:In contrast with HCs, patients with ALS displayed increased ALFF in the right inferior temporal gyrus and bilateral frontal gyrus and decreased ALFF in the left middle occipital gyrus and left precentral gyrus. Furthermore, patients with ALS demonstrated lower d-ALFF in widespread regions, including the right lingual gyrus, left superior temporal gyrus, bilateral precentral gyrus, and left paracentral lobule by comparison with HCs. In addition, the ALFF in the left superior orbitofrontal gyrus had a tendency of correlation with ALSFRS-R score and disease progression rate. The classification performance in distinguishing ALS was higher with both features of ALFF and d-ALFF than that with a single approach. Conclusions:Decreased dynamic brain activity in the precentral gyrus, paracentral gyrus, lingual gyrus, and temporal regions was found in the ALS group. The combined ALFF and d-ALFF could distinguish ALS from HCs with a higher accuracy than ALFF and d-ALFF alone. These findings may provide important evidence for understanding the neuropathology underlying ALS.

SUBMITTER: Ma X

PROVIDER: S-EPMC7643554 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Static and dynamic alterations in the amplitude of low-frequency fluctuation in patients with amyotrophic lateral sclerosis.

Ma Xujing X Lu Fengmei F Chen Heng H Hu Caihong C Wang Jiao J Zhang Sheng S Zhang Shuqin S Yang Guiran G Zhang Jiuquan J

PeerJ 20201102

<h4>Background</h4>Static changes in local brain activity in patients suffering from amyotrophic lateral sclerosis (ALS) have been studied. However, the dynamic characteristics of local brain activity are poorly understood. Whether dynamic alterations could differentiate patients with ALS from healthy controls (HCs) remains unclear.<h4>Methods</h4>A total of 54 patients with ALS (mean age = 48.71 years, male/female = 36/18) and 54 (mean age = 48.30 years, male/female = 36/18) HCs underwent magne ...[more]

PMID: 33194375

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Project description:The human brain is a highly dynamic system with non-stationary neural activity and rapidly-changing neural interaction. Resting-state dynamic functional connectivity (dFC) has been widely studied during recent years, and the emerging aberrant dFC patterns have been identified as important features of many mental disorders such as schizophrenia (SZ). However, only focusing on the time-varying patterns in FC is not enough, since the local neural activity itself (in contrast to the inter-connectivity) is also found to be highly fluctuating from research using high-temporal-resolution imaging techniques. Exploring the time-varying patterns in brain activity and their relationships with time-varying brain connectivity is important for advancing our understanding of the co-evolutionary property of brain network and the underlying mechanism of brain dynamics. In this study, we introduced a framework for characterizing time-varying brain activity and exploring its associations with time-varying brain connectivity, and applied this framework to a resting-state fMRI dataset including 151 SZ patients and 163 age- and gender matched healthy controls (HCs). In this framework, 48 brain regions were first identified as intrinsic connectivity networks (ICNs) using group independent component analysis (GICA). A sliding window approach was then adopted for the estimation of dynamic amplitude of low-frequency fluctuation (dALFF) and dFC, which were used to measure time-varying brain activity and time-varying brain connectivity respectively. The dALFF was further clustered into six reoccurring states by the k-means clustering method and the group difference in occurrences of dALFF states was explored. Lastly, correlation coefficients between dALFF and dFC were calculated and the group difference in these dALFF-dFC correlations was explored. Our results suggested that 1) ALFF of brain regions was highly fluctuating during the resting-state and such dynamic patterns are altered in SZ, 2) dALFF and dFC were correlated in time and their correlations are altered in SZ. The overall results support and expand prior work on abnormalities of brain activity, static FC (sFC) and dFC in SZ, and provide new evidence on aberrant time-varying brain activity and its associations with brain connectivity in SZ, which might underscore the disrupted brain cognitive functions in this mental disorder.

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Dataset Information

Static and dynamic alterations in the amplitude of low-frequency fluctuation in patients with amyotrophic lateral sclerosis.

Publications

Static and dynamic alterations in the amplitude of low-frequency fluctuation in patients with amyotrophic lateral sclerosis.

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