Biology subjects

Zendehrouh, E.

Publications and source records attributed to Zendehrouh, E..

4 recordsLinked to original sources

bioRxiv2021

The link between static and dynamic brain functional network connectivity and genetic risk of Alzheimer's disease

Apolipoprotein E (APOE) polymorphic alleles are genetic factors associated with Alzheimers disease (AD) risk. Although previous studies have explored the link between AD genetic risk and static functional network connectivity (sFNC), to the best of our knowledge, no previous studies have evaluated the association between dynamic FNC (dFNC) and AD genetic risk. Here, we examined the link between sFNC, dFNC, and AD genetic risk with a reproducible, data-driven approach. We used rs-fMRI, demographic, and APOE data from cognitively normal individuals (N=894) between 42 to 95 years of age (mean = 70 years). We divided individuals into low, moderate, and high-risk groups. Using Pearson correlation, we calculated sFNC across seven brain networks. We also calculated dFNC with a sliding window and Pearson correlation. The dFNC windows were partitioned into three distinct states with k-means clustering. Next, we calculated the amount of time each subject spent in each state, called occupancy rate or OCR. We compared both sFNC and OCR, estimated from dFNC, across individuals with different genetic risk and found that both sFNC and dFNC are related to AD genetic risk. We found that higher AD risk reduces within-visual sensory network (VSN) sFNC and that individuals with higher AD risk spend more time in a state with lower within-VSN dFNC. Additionally, we found that AD genetic risk affects whole-brain sFNC and dFNC in women but not in men. In conclusion, we presented novel insights into the links between sFNC, dFNC, and AD genetic risk.

neuroscience↗

bioRxiv2021

Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity

BackgroundSchizophrenia affects around 1% of the global population. Functional connectivity extracted from resting-state functional magnetic resonance imaging (rs-fMRI) has previously been used to study schizophrenia and has great potential to provide novel insights into the disorder. Some studies have shown abnormal functional connectivity in the default mode network of individuals with schizophrenia, and more recent studies have shown abnormal dynamic functional connectivity (dFC) in individuals with schizophrenia. However, DMN dFC and the link between abnormal DMN dFC and symptom severity have not been well characterized. MethodResting-state fMRI data from subjects with schizophrenia (SZ) and healthy controls (HC) across two datasets were analyzed independently. We captured seven maximally independent subnodes in the DMN by applying group independent component analysis and estimated dFC between subnode time courses using a sliding window approach. A clustering method separated the dFCs into five reoccurring brain states. A feature selection method modeled the difference between SZs and HCs using the state-specific FC features. Finally, we used the transition probability of a hidden Markov model to characterize the link between symptom severity and dFC in SZ subjects. ResultsWe found decreases in the connectivity of the anterior cingulate cortex (ACC) and increases in the connectivity between the precuneus (PCu) and the posterior cingulate cortex (PCC) (i.e., PCu/PCC) of SZ subjects. In SZ, the transition probability from a state with weaker PCu/PCC and stronger ACC connectivity to a state with stronger PCu/PCC and weaker ACC connectivity increased with symptom severity. ConclusionsTo our knowledge, this was the first study to investigate DMN dFC and its link to schizophrenia symptom severity. We identified reproducible neural states in a data-driven manner and demonstrated that the strength of connectivity within those states differed between SZs and HCs. Additionally, we identified a relationship between SZ symptom severity and the dynamics of DMN functional connectivity. We validated our results across two datasets. These results support the potential of dFC for use as a biomarker of schizophrenia and shed new light upon the relationship between schizophrenia and DMN dynamics.

neuroscience↗

bioRxiv2021

Disrupted dynamic functional network connectivity among cognitive control networks in the progression of Alzheimer's disease

BackgroundAlzheimers disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from rs-fMRI. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations. MethodsGroup independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states and then estimated a hidden Markov model and the occupancy rate for each subject. Finally, we investigated the link between CCN dFC connectivity features with AD progression. ResultsProgression of AD symptoms were associated with increases in connectivity within the middle frontal gyrus. Also, the AD with mild and severer symptoms showed less connectivity within the inferior parietal lobule and between this region with the rest of CCN. Finally, comparing with mild dementia, we found that the normal brain spends significantly more time in a state with lower within middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease. ConclusionOur results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD. Impact StatementBy assuming that functional connectivity is static over time, many of previous studies have ignored the brain dynamic in Alzheimers disease progression. Here, a longitudinal resting-state functional magnetic resonance imaging data are used to explore the temporal changes of functional connectivity in the cognitive control network in Alzheimers disease progression. The result of this study would increase our understanding about the underlying mechanisms of Alzheimers Disease and help in finding future treatment of this neurological disorder.

neuroscience↗

bioRxiv2020

Impact of Model Order Choice on the Results of Parallel Independent Component Analysis

Parallel independent component analysis (pICA) is a data-driven method that identifies the maximally independent components of multiple imaging modalities while simultaneously investigating the strength of their correlations. Researchers using pICA are given the option to use the suggested model order calculated by the minimum descriptive length (MDL) algorithm, or they can choose their own model order. To date, there are no suggested guidelines for this choice. To test the sensitivity of pICA to the selection of model order, we applied it to a well-researched brain disorder, schizophrenia, looking at the correlations between patterns of grey matter volume (GM) volume and white matter integrity, measured using fractional anisotropy (FA). We varied model orders from low to high, and tested the sensitivity to disorder effects (cases vs controls), similarity of spatial maps identified across model orders, consolidation or distribution effects related to model order selection, and the performance of the minimum descriptive length (MDL) algorithm. The pICA results (multimodal analysis) were also compared to the ICA (unimodal analysis) for each imaging modality. Across model orders, there was consistent sensitivity to disorder effects, and clustered patterns of spatial maps for both the GM and FA reflecting those differences. The MDL-estimated model order captured the majority, but not all, of the spatial patterns present in the GM and FA. There was not the expected consolidation of spatial maps at lower model orders, nor the distribution of spatial maps at higher model orders. The spatial patterns identified in the ICA closely resemble those found in the pICA, although lacking the benefit of the optimization algorithm, were not as highly correlated. This offers some insight and guidance for researchers interested in using pICA with regard to selecting model order for their particular analysis of multiple imaging modalities.

neuroscience↗