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Alteration of functional connectivity in patients with Alzheimer's disease revealed by resting-state functional magnetic resonance imaging.

ABSTRACT: The main symptom of patients with Alzheimer's disease is cognitive dysfunction. Alzheimer's disease is mainly diagnosed based on changes in brain structure. Functional connectivity reflects the synchrony of functional activities between non-adjacent brain regions, and changes in functional connectivity appear earlier than those in brain structure. In this study, we detected resting-state functional connectivity changes in patients with Alzheimer's disease to provide reference evidence for disease prediction. Functional magnetic resonance imaging data from patients with Alzheimer's disease were used to show whether particular white and gray matter areas had certain functional connectivity patterns and if these patterns changed with disease severity. In nine white and corresponding gray matter regions, correlations of normal cognition, early mild cognitive impairment, and late mild cognitive impairment with blood oxygen level-dependent signal time series were detected. Average correlation coefficient analysis indicated functional connectivity patterns between white and gray matter in the resting state of patients with Alzheimer's disease. Functional connectivity pattern variation correlated with disease severity, with some regions having relatively strong or weak correlations. We found that the correlation coefficients of five regions were 0.3-0.5 in patients with normal cognition and 0-0.2 in those developing Alzheimer's disease. Moreover, in the other four regions, the range increased to 0.45-0.7 with increasing cognitive impairment. In some white and gray matter areas, there were specific connectivity patterns. Changes in regional white and gray matter connectivity patterns may be used to predict Alzheimer's disease; however, detailed information on specific connectivity patterns is needed. All study data were obtained from the Alzheimer's Disease Neuroimaging Initiative Library of the Image and Data Archive Database.

SUBMITTER: Zhao J

PROVIDER: S-EPMC6905343 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Alteration of functional connectivity in patients with Alzheimer's disease revealed by resting-state functional magnetic resonance imaging.

Zhao Jie J Du Yu-Hang YH Ding Xue-Tong XT Wang Xue-Hu XH Men Guo-Zun GZ

Neural regeneration research 20200201 2

The main symptom of patients with Alzheimer's disease is cognitive dysfunction. Alzheimer's disease is mainly diagnosed based on changes in brain structure. Functional connectivity reflects the synchrony of functional activities between non-adjacent brain regions, and changes in functional connectivity appear earlier than those in brain structure. In this study, we detected resting-state functional connectivity changes in patients with Alzheimer's disease to provide reference evidence for diseas ...[more]

PMID: 31552901

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Project description:Previous studies on brain functional connectivity networks in children have mainly focused on changes in function in specific brain regions, as opposed to whole brain connectivity in healthy children. By analyzing the independent components of activation and network connectivity between brain regions, we examined brain activity status and development trends in children aged 3 and 5 years. These data could provide a reference for brain function rehabilitation in children with illness or abnormal function. We acquired functional magnetic resonance images from 15 3-year-old children and 15 5-year-old children under natural sleep conditions. The participants were recruited from five kindergartens in the Nanshan District of Shenzhen City, China. The parents of the participants signed an informed consent form with the premise that they had been fully informed regarding the experimental protocol. We used masked independent component analysis and BrainNet Viewer software to explore the independent components of the brain and correlation connections between brain regions. We identified seven independent components in the two groups of children, including the executive control network, the dorsal attention network, the default mode network, the left frontoparietal network, the right frontoparietal network, the salience network, and the motor network. In the default mode network, the posterior cingulate cortex, medial frontal gyrus, and inferior parietal lobule were activated in both 3- and 5-year-old children, supporting the "three-brain region theory" of the default mode network. In the frontoparietal network, the frontal and parietal gyri were activated in the two groups of children, and functional connectivity was strengthened in 5-year-olds compared with 3-year-olds, although the nodes and network connections were not yet mature. The high-correlation network connections in the default mode networks and dorsal attention networks had been significantly strengthened in 5-year-olds vs. 3-year-olds. Further, the salience network in the 3-year-old children included an activated insula/inferior frontal gyrus-anterior cingulate cortex network circuit and an activated thalamus-parahippocampal-posterior cingulate cortex-subcortical regions network circuit. By the age of 5 years, nodes and high-correlation network connections (edges) were reduced in the salience network. Overall, activation of the dorsal attention network, default mode network, left frontoparietal network, and right frontoparietal network increased (the volume of activation increased, the signals strengthened, and the high-correlation connections increased and strengthened) in 5-year-olds compared with 3-year-olds, but activation in some brain nodes weakened or disappeared in the salience network, and the network connections (edges) were reduced. Between the ages of 3 and 5 years, we observed a tendency for function in some brain regions to be strengthened and for the generalization of activation to be reduced, indicating that specialization begins to develop at this time. The study protocol was approved by the local ethics committee of the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences in China with approval No. SIAT-IRB-131115-H0075 on November 15, 2013.

Project description:BackgroundPrevious studies suggested bipolar disorder caused an aberrant alteration in the insular, putamen, and left superior frontal gyrus, which are the main components of the hate circuit. However, the relationship between the hate circuit and the pathophysiologic substrate underlying different phases of bipolar disorder remain unclear. In this study, we aimed to identify group differences of resting-state functional connectivity within the hate circuit in healthy controls (HCs) and bipolar patients in different mood states.MethodsResting-state functional magnetic resonance imaging of the brain were acquired from 54 HCs and 81 patients with bipolar disorder including 20 with bipolar mania (BM), 35 with bipolar depression (BD), and 26 with bipolar euthymia (BE). We selected bilateral insula (L.INS and R.INS), bilateral putamen (L.PUT and R.PUT), and left superior frontal gyrus (L.SFGd) as seed regions, and conducted the seed-based functional connectivity analysis to identify group differences of connectivity strength within the hate circuit. Spearman correlations were performed to evaluate the relationship between the hate circuit and manic/depressive symptoms.ResultsSignificant group differences of connectivity strength within the hate circuit were found in links of the R.INS-L.SFGd, R.PUT-L.SFGd, and L.INS- R.PUT after false discovery rate was corrected. The BM group showed an opposite hate circuit pattern to BD, BE, and HCs. The BD group showed decreased hate circuit connectivity in the L.INS-R.PUT compared with the BE group. No significant difference was detected among BD, BE, and HCs. Furthermore, functional connectivity of the R.INS-L.SFGd and R.PUT-L.SFGd were positively correlated with manic symptoms, while the L.INS- R.PUT was negatively correlated with depressive symptoms.ConclusionsOur preliminary findings suggest that altered functional connectivity of the hate circuit in different mood phases may be related to state markers and underpin the neuropathological basis of bipolar disorder.

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Project description:Introduction: The selection of an appropriate window size, window function, and functional connectivity (FC) metric in the sliding window method is not straightforward due to the absence of ground truth. Methods: A previously proposed wavelet-based method was accordingly adjusted for estimating time-varying FC (TVFC) and was applied to a large high-quality, low-motion dataset of 400 resting-state functional magnetic resonance imaging data. Specifically, the wavelet coherence magnitude and relative phase were averaged across wavelet (frequency) scales to yield TVFC and synchronization patterns. To assess whether the observed fluctuations in TVFC were statistically significant (dynamic FC [dFC]; the distinction between TVFC and dFC is intentional), surrogate data were generated using the multivariate phase randomization (MVPR) and multivariate autoregressive randomization (MVAR) methods to define the null hypothesis of dFC absence. Results: By averaging across all frequencies, core regions of the default mode network (DMN; medial prefrontal and posterior cingulate cortices, inferior parietal lobes, hippocampal formation) were found to exhibit dFC (test-retest reproducibility of 90%) and were also synchronized in activity (-15° ≤ phase ≤15°). When averaging across distinct frequency bands, the same dynamic connections were identified, with the majority of them identified in the frequency range (0.01, 0.198) Hz, though with lower test-retest reproducibility (<66%). Additional analysis suggested that MVPR method better preserved properties (p < 10-10), including time-averaged coherence, of the original data compared with MVAR approach. Conclusions: The wavelet-based approach identified dynamic associations between the core DMN regions with fewer choices in parameters, compared with sliding window method. Impact statement We employed a wavelet-based method, previously used in the literature, and proposed modifications to assess time-varying functional connectivity in resting-state functional magnetic resonance imaging. With this approach, dynamic connections within the default mode network were identified, involving the medial prefrontal and posterior cingulate cortices, inferior parietal lobes, and hippocampal formation, which were also highly consistent in test-retest analysis (test-retest reproducibility of 90%), without the need to select window size, window function, and functional connectivity metric as with the sliding window method, whereby no consensus on the appropriate choices of hyperparameters currently exists in the literature.

Dataset Information

Alteration of functional connectivity in patients with Alzheimer's disease revealed by resting-state functional magnetic resonance imaging.

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Alteration of functional connectivity in patients with Alzheimer's disease revealed by resting-state functional magnetic resonance imaging.

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