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

Sendi, Mohammad S.E.; Zendehrouh, Elaheh; Ellis, Charles A.; Fu, Zening; Chen, Jiayu; Miller, Robyn L.; Mormino, Elizabeth C.; Salat, David H.; Calhoun, Vince D.

doi:10.1016/j.nicl.2023.103363

Cited by 7 publications

(6 citation statements)

References 33 publications

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“…Specifically, we found that the sensory network FNC exhibited greater prominence in the CN group compared to the MCI group. This finding is consistent with previous studies that have highlighted the importance of sensory network connectivity in predicting the progression of AD (Sendi et al, 2023, 2021). Importantly, we were able to replicate these results in an independent dataset (i.e., ADNI), confirming the robustness of our findings.…”

Section: Discussionsupporting

confidence: 93%

“…It is important to note that recent research has indicated that AD genetic risk can impact both functional and structural neuroimaging data (Chandler et al, 2022; Cho et al, 2035; Mirza-Davies et al, 2022; Sendi et al, 2023). Building upon this understanding, we hypothesized that our newly developed BRS would be capable of accounting for both genetic and environmental factors.…”

Section: Discussionmentioning

confidence: 99%

“…Magnetic resonance imaging (MRI) technology allows us to collect functional (fMRI) and structural (sMRI) information about the brain noninvasively (Grover et al, 2015). MRI has frequently been used to study structural and functional brain alteration linked to AD (Cuingnet et al, 2011;Jack et al, 2008;Sendi et al, 2023Sendi et al, , 2021. Since these functional and structural alterations in the brain might manifest years before symptoms show up, MRI has been studied as a possible technique for the early detection and diagnosis of AD (Abrol et al, 2020;Sendi et al, 2021;Tondelli et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Towards a multimodal neuroimaging-based risk score for mild cognitive impairment by combining clinical studies with a large (N>37000) population-based study

Zendehrouh,

Sendi,

Abrol

et al. 2024

Preprint

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Alzheimer’s disease (AD) is the most common form of age-related dementia, leading to a decline in memory, reasoning, and social skills. While numerous studies have investigated the genetic risk factors associated with AD, less attention has been given to identifying a brain imaging-based measure of AD risk. This study introduces a novel approach to assess mild cognitive impairment MCI, as a stage before AD, risk using neuroimaging data, referred to as a brain-wide risk score (BRS), which incorporates multimodal brain imaging. To begin, we first categorized participants from the Open Access Series of Imaging Studies (OASIS)-3 cohort into two groups: controls (CN) and individuals with MCI. Next, we computed structure and functional imaging features from all the OASIS data as well as all the UK Biobank data. For resting functional magnetic resonance imaging (fMRI) data, we computed functional network connectivity (FNC) matrices using fully automated spatially constrained independent component analysis. For structural MRI data we computed gray matter (GM) segmentation maps. We then evaluated the similarity between each participant’s neuroimaging features from the UK Biobank and the difference in the average of those features between CN individuals and those with MCI, which we refer to as the brain-wide risk score (BRS). Both GM and FNC features were utilized in determining the BRS. We first evaluated the differences in the distribution of the BRS for CN vs MCI within the OASIS-3 (using OASIS-3 as the reference group). Next, we evaluated the BRS in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort (using OASIS-3 as the reference group), showing that the BRS can differentiate MCI from CN in an independent data set. Subsequently, using the sMRI BRS, we identified 10 distinct subgroups and similarly, we identified another set of 10 subgroups using the FNC BRS. For sMRI and FNC we observed results that mutually validate each other, with certain aspects being complementary. For the unimodal analysis, sMRI provides greater differentiation between MCI and CN individuals than the fMRI data, consistent with prior work. Additionally, by utilizing a multimodal BRS approach, which combines both GM and FNC assessments, we identified two groups of subjects using the multimodal BRS scores. One group exhibits high MCI risk with both negative GM and FNC BRS, while the other shows low MCI risk with both positive GM and FNC BRS. Moreover, in the UKBB we have 46 participants diagnosed with AD showed FNC and GM patterns similar to those in high-risk groups, defined in both unimodal and multimodal BRS. Finally, to ensure the reproducibility of our findings, we conducted a validation analysis using the ADNI as an additional reference dataset and repeated the above analysis. The results were consistently replicated across different reference groups, highlighting the potential of FNC and sMRI-based BRS in early Alzheimer’s detection.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards a multimodal neuroimaging-based risk score for mild cognitive impairment by combining clinical studies with a large (N>37000) population-based study

Zendehrouh,

Sendi,

Abrol

et al. 2024

Preprint

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“…Dynamic functional network connectivity (dFNC) extracted from resting state functional magnetic resonance imaging (rs-fMRI) is a key measure of neural activity that has provided insight into neuropsychiatric disorders [1]- [3] and cognitive function [4], [5]. A common dFNC analysis involves computing correlation within time windows followed by clustering to assign them to clusters (i.e., states) [1]- [3]. The passage of a study participant through those states can then be quantified and provide novel insights.…”

Section: Introductionmentioning

confidence: 99%

“…As it has grown in popularity, dFNC has helped characterize many neurological and neuropsychiatric disorders [1]- [3] and aspects of cognition [4], [5]. While many approaches have been developed to analyze dFNC [11], one of the most common approaches involves the use of clustering [1]- [3]. In these analyses, FNC is computed within time windows followed by assignment to clusters.…”

Section: Introductionmentioning

confidence: 99%

Uncovering Effects of Schizophrenia upon a Maximally Significant, Minimally Complex Subset of Default Mode Network Connectivity Features

Seraji,

Ellis,

Sendi

et al. 2024

Preprint

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A common analysis approach for resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data involves clustering windowed correlation time-series and assigning time windows to clusters (i.e., states) that can be quantified to summarize aspects of the dFNC dynamics. However, those methods can be dominated by a select few features and obscure key dynamics related to less dominant features. This study presents an iterative feature learning approach to identify a maximally significant and minimally complex subset of dFNC features within the default mode network (DMN) in schizophrenia (SZ). Utilizing dFNC data from individuals with SZ and healthy controls (HC), our approach uncovers a subset of features that has a greater number of dFNC states with disorder-related dynamics than is found when all features are present in the clustering. We find that anterior cingulate cortex/posterior cingulate cortex (ACC/PCC) interactions are consistently related to SZ across the most significant iterations of the feature learning analysis and that individuals with SZ tend to spend more time in states with greater intra-ACC anticorrelation and almost no time in a state of high intra-ACC correlation that HCs periodically enter. Our findings highlight the need for nuanced analyses to reveal disorder-related dynamics and advance our understanding of neuropsychiatric disorders.

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Dynamic functional network connectivity in children with profound bilateral congenital sensorineural hearing loss

Cui,

Chen,

et al. 2024

Pediatr Radiol

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

Cited by 7 publications

References 33 publications

Towards a multimodal neuroimaging-based risk score for mild cognitive impairment by combining clinical studies with a large (N>37000) population-based study

Towards a multimodal neuroimaging-based risk score for mild cognitive impairment by combining clinical studies with a large (N>37000) population-based study

Uncovering Effects of Schizophrenia upon a Maximally Significant, Minimally Complex Subset of Default Mode Network Connectivity Features

Dynamic functional network connectivity in children with profound bilateral congenital sensorineural hearing loss

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