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Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia.

ABSTRACT: 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.

SUBMITTER: Fu Z

PROVIDER: S-EPMC5860934 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia.

Fu Zening Z Tu Yiheng Y Di Xin X Du Yuhui Y Pearlson G D GD Turner J A JA Biswal Bharat B BB Zhang Zhiguo Z Calhoun V D VD

NeuroImage 20170920 Pt B

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-connectivi ...[more]

PMID: 28939432

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Project description:Objective: A limited number of studies have previously reported on the regional activity [amplitude of low-frequency fluctuation (ALFF)] and functional integration [functional connectivity (FC)] of the whole brain in deficit schizophrenia (DS). The present study investigates the resting-state characteristics of the fractional ALFF (fALFF) and the FC in both DS and non-deficit schizophrenia (NDS) patients, and further explores their correlations with neurocognitive features. Methods: Demographic, resting-state functional magnetic resonance imaging (MRI), and neurocognitive data were collected from 33 DS and 41 NDS male patients, as well as in 40 male healthy controls (HCs). The voxel-wise fALFF was measured to evaluate regional cerebral function. Regions with differences in fALFF between DS and NDS patients were used as seed points in whole-brain FC analysis. Partial correlation analysis was conducted to examine associations between the fALFF or the FC of altered regions and neurocognitive assessments. Results: Both patient groups showed decreased fALFF in the sensorimotor area, visual cortex, and frontoparietal pathway, but increased fALFF in the precuneus and middle cingulate gyrus when compared with the HCs. Moreover, the NDS group demonstrated higher fALFF than HCs in the left thalamus, caudate, and hippocampus. Compared with the NDS group, the fALFF of the visual cortex was specifically increased, but that of the bilateral insula, the anterior cingulate gyrus (ACG), and the regions extended to the frontotemporal cortex was decreased in the DS group. Numerous abnormal FCs of nerve pathways were found between the two patient groups, mainly concentrated in the frontooccipital, frontotemporal, insula-visual cortex, as well as the temporooccipital pathway. Correlation analysis indicated that, in the DS group, the FC value between the left insula and the visual cortex was positively correlated with cognitive flexibility. In the NDS group, the fALFF of the right insula was negatively correlated with speech fluency, and the FC value between the ACG and the visual cortex was positively correlated with visual spatial memory. Conclusion: The present study demonstrates different altered patterns of fALFF and FC between male patients with DS and NDS. The specific altered regions of the salience network (SN) associated with impaired neurocognition in male DS patients suggest novel insights into the pathogenesis of cognitive impairment in schizophrenia.

Project description:BackgroundStatic 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.MethodsA 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.ResultsIn 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.ConclusionsDecreased 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.

Dataset Information

Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia.

Publications

Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia.

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