Progressive structural and covariance connectivity abnormalities in patients with Alzheimer’s disease

Xiao, Yaqiong; Wang, Jiaojian; Huang, Kaiyu; Gao, Lei; Yao, Shun

doi:10.3389/fnagi.2022.1064667

Cited by 6 publications

(5 citation statements)

References 56 publications

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“…The subcortical regions act as pivotal sensory gates, facilitating the exchange of information with the cortical regions. The previous studies (Cai et al, 2015; Khalilullah et al, 2023; Kim et al, 2023; Lesh et al, 2011; Tentolouris-Piperas et al, 2017; Xiao et al, 2022) also found abnormalities in the psychiatric diseases, particularly for the schizophrenia and Alzheimer’s patients. Our analysis of FNC and mean GM spatial maps (Figure 9c-d) also suggests positive engagement of the caudate nucleus and thalamus with multiple domains.…”

Section: Discussionmentioning

confidence: 74%

“…In this approach, we observed structural and functional alterations between healthy controls (HCs) and Alzheimer’s disease (AD) patients. Particularly noteworthy patterns in both structural and functional aspects were identified, with a focus on the auditory, visual, subcortical, cognitive control, and cerebellum regions that are recognized as being notably impacted areas for psychiatric disease, particularly for the Alzheimer’s disease (Agosta et al, 2023; Azeem A, 2023; Cai et al, 2015; Cheng et al, 2023; Elvira-Hurtado et al, 2023; Kawabata et al, 2022; Khalilullah et al, 2023; Kim et al, 2023; Lesh et al, 2011; Lin et al, 2017; Liu et al, 2018; Mavroudis, 2019; McEvoy et al, 2023; Sendi et al, 2023; Tang et al, 2021; Tarawneh et al, 2022; Tentolouris-Piperas et al, 2017; Xiao et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

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Parallel Multilink Group Joint ICA: Fusion of 3D Structural and 4D Functional Data Across Multiple Resting fMRI Networks

Khalilullah,

Agcaoglu,

Sui

et al. 2024

Preprint

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Multimodal neuroimaging research plays a pivotal role in understanding the complexities of the human brain and its disorders. Independent component analysis (ICA) has emerged as a widely used and powerful tool for disentangling mixed independent sources, particularly in the analysis of functional magnetic resonance imaging (fMRI) data. This paper extends the use of ICA as a unifying framework for multimodal fusion, introducing a novel approach termed parallel multilink group joint ICA (pmg-jICA). The method allows for the fusion of gray matter maps from structural MRI (sMRI) data to multiple fMRI intrinsic networks, addressing the limitations of previous models. The effectiveness of pmg-jICA is demonstrated through its application to an Alzheimer's dataset, yielding linked structure-function outputs for 53 brain networks. Our approach leverages the complementary information from various imaging modalities, providing a unique perspective on brain alterations in Alzheimer's disease. The pmg-jICA identifies several components with significant differences between HC and AD groups including thalamus, caudate, putamen with in the subcortical (SC) domain, insula, parahippocampal gyrus within the cognitive control (CC) domain, and the lingual gyrus within the visual (VS) domain, providing localized insights into the links between AD and specific brain regions. In addition, because we link across multiple brain networks, we can also compute functional network connectivity (FNC) from spatial maps and subject loadings, providing a detailed exploration of the relationships between different brain regions and allowing us to visualize spatial patterns and loading parameters in sMRI along with intrinsic networks and FNC from the fMRI data. In essence, developed approach combines concepts from joint ICA and group ICA to provide a rich set of output characterizing data-driven links between covarying gray matter networks, and a (potentially large number of) resting fMRI networks allowing further study in the context of structure/function links. We demonstrate the utility of the approach by highlighting key structure/function disruptions in Alzheimer's individuals.

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Section: Discussionmentioning

confidence: 74%

Section: Discussionmentioning

confidence: 99%

Parallel Multilink Group Joint ICA: Fusion of 3D Structural and 4D Functional Data Across Multiple Resting fMRI Networks

Khalilullah,

Agcaoglu,

Sui

et al. 2024

Preprint

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“…The human superior temporal gyrus is critical for speech comprehension [ 71 ], while the speech comprehension ability is damaged in AD patients. Significant atrophy of the bilateral superior temporal gyrus in AD patients has been reported in a recent case-control survey [ 72 ]. Our result expands the findings of the observational study and suggests that atrophy of the superior temporal gyrus is a potential causal factor of AD.…”

Section: Discussionmentioning

confidence: 99%

Bidirectional two-sample Mendelian randomization analyses support causal relationships between structural and diffusion imaging-derived phenotypes and the risk of major neurodegenerative diseases

Wang,

Yang,

et al. 2024

Transl Psychiatry

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Previous observational investigations suggest that structural and diffusion imaging-derived phenotypes (IDPs) are associated with major neurodegenerative diseases; however, whether these associations are causal remains largely uncertain. Herein we conducted bidirectional two-sample Mendelian randomization analyses to infer the causal relationships between structural and diffusion IDPs and major neurodegenerative diseases using common genetic variants-single nucleotide polymorphism (SNPs) as instrumental variables. Summary statistics of genome-wide association study (GWAS) for structural and diffusion IDPs were obtained from 33,224 individuals in the UK Biobank cohort. Summary statistics of GWAS for seven major neurodegenerative diseases were obtained from the largest GWAS for each disease to date. The forward MR analyses identified significant or suggestively statistical causal effects of genetically predicted three structural IDPs on Alzheimer’s disease (AD), frontotemporal dementia (FTD), and multiple sclerosis. For example, the reduction in the surface area of the left superior temporal gyrus was associated with a higher risk of AD. The reverse MR analyses identified significantly or suggestively statistical causal effects of genetically predicted AD, Lewy body dementia (LBD), and FTD on nine structural and diffusion IDPs. For example, LBD was associated with increased mean diffusivity in the right superior longitudinal fasciculus and AD was associated with decreased gray matter volume in the right ventral striatum. Our findings might contribute to shedding light on the prediction and therapeutic intervention for the major neurodegenerative diseases at the neuroimaging level.

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“…There are automated analysis tools to quantify these structural changes, including commercial software (Pemberton et al, 2021), but standard radiology practice continues to rely on qualitative or semi-quantitative methods using visual rating scales (Fazekas et al, 1987;Harper et al, 2015;Scheltens et al, 1995). Advanced MR imaging and analysis have also revealed differences in cerebral blood flow and structural and functional connectivity in patients with cognitive impairment (Liu et al, 2023;Penalba-Sánchez et al, 2023;Teipel & Grothe, 2023;Xiao et al, 2023), but these methods are currently not recommended for memory clinic use since their diagnostic and prognostic value is less established (Egle et al, 2022;Haidar et al, 2023;Vemuri et al, 2012). Thus, our understanding of the links between brain and phenotypic changes is constrained by the data currently available (i.e., cohort studies rather than real-world clinical populations).…”

Section: Introductionmentioning

confidence: 99%

From Big Data to the clinic: methodological and statistical enhancements to implement the UK Biobank imaging framework in a memory clinic

Gillis,

Bhalerao,

Blane

et al. 2024

Preprint

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IntroductionThe analysis tools and statistical methods used in large neuroimaging research studies differ from those applied in clinical contexts, making it unclear whether these techniques can be translated to a memory clinic setting. The Oxford Brain Health Clinic (OBHC) was established in 2020 to bridge this gap between research studies and memory clinics.MethodsWe optimised the UK Biobank imaging framework for the memory clinic setting by integrating enhanced quality control (QC) processes (MRIQC, QUAD, and DSE decomposition) and supplementary dementia-informed analyses (lobar volumes, NBM volumes, WMH classification, PSMD, cortical diffusion MRI metrics, and tract volumes) into the analysis pipeline. We explored associations between resultant imaging-derived phenotypes (IDPs) and clinical phenotypes in the OBHC patient population (N=213), applying hierarchical FDR correction to account for multiple testing.Results14-24% of scans were flagged by automated QC tools, but upon visual inspection, only 0-2.4% of outputs were excluded. The pipeline successfully generated 5683 IDPs aligned with UK Biobank and 110 IDPs targeted towards dementia-related changes. We replicated established associations and found novel associations between brain metrics and age, cognition, and dementia-related diagnoses.ConclusionThe imaging protocol is feasible, acceptable, and yields high-quality data that is usable for both clinical and research purposes. We validated the use of this methodology in a real-world memory clinic population, which demonstrates the potential of this enhanced pipeline to bridge the gap between big data studies and clinical settings.Key PointsThe imaging methods, analysis techniques, and population characteristics in research studies often differ to those in traditional clinical settings.To bridge this gap, we optimised the UK Biobank imaging framework for memory clinic use by integrating enhanced quality control (QC) and supplementary analyses targeted towards dementia-related changes.We generated 5683 imaging-derived phenotypes (IDPs) aligned with UK Biobank and 110 supplementary dementia-informed IDPs that captured both established and novel associations between brain metrics and dementia-related clinical phenotypes, highlighting the value of integrating UK Biobank-aligned imaging and analyses in a real-world memory clinic population.

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Progressive structural and covariance connectivity abnormalities in patients with Alzheimer’s disease

Cited by 6 publications

References 56 publications

Parallel Multilink Group Joint ICA: Fusion of 3D Structural and 4D Functional Data Across Multiple Resting fMRI Networks

Parallel Multilink Group Joint ICA: Fusion of 3D Structural and 4D Functional Data Across Multiple Resting fMRI Networks

Bidirectional two-sample Mendelian randomization analyses support causal relationships between structural and diffusion imaging-derived phenotypes and the risk of major neurodegenerative diseases

From Big Data to the clinic: methodological and statistical enhancements to implement the UK Biobank imaging framework in a memory clinic

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