Alzheimer's genetic risk effects on cerebral blood flow across the lifespan are proximal to gene expression

Chandler, Harold L.; Wise, Richard G.; Linden, David; Williams, Julie; Murphy, Kevin; Lancaster, Thomas

doi:10.1016/j.neurobiolaging.2022.08.001

Cited by 6 publications

(6 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

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

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Moreover, regions of high expression of AD risk genes overlapped with regions of worse cerebral hypoperfusion in areas affected early in the disease progression. 240 Lower baseline entorhinal, but not hippocampal, CBF predicted entorhinal cortical thinning, faster memory decline, and increasing WMH levels in non‐demented participants. 241 CBF alterations at the site of early disease may precede downstream events including the development of WMH.…”

Section: Adni's Contributions To Understanding Ad Disease Progressionmentioning

confidence: 91%

“…Impaired CBF was associated with AD risk alleles 240 . An AD genetic risk score was consistently associated with worse regional CBF over the lifespan.…”

Section: Adni's Contributions To Understanding Ad Disease Progressionmentioning

confidence: 94%

“…An AD genetic risk score was consistently associated with worse regional CBF over the lifespan. Moreover, regions of high expression of AD risk genes overlapped with regions of worse cerebral hypoperfusion in areas affected early in the disease progression 240 . Lower baseline entorhinal, but not hippocampal, CBF predicted entorhinal cortical thinning, faster memory decline, and increasing WMH levels in non‐demented participants 241 .…”

Section: Adni's Contributions To Understanding Ad Disease Progressionmentioning

confidence: 96%

“…Impaired CBF was associated with AD risk alleles. 240 An AD genetic risk score was consistently associated with worse regional CBF over the lifespan. Moreover, regions of high expression of AD risk genes overlapped with regions of worse cerebral hypoperfusion in areas affected early in the disease progression.…”

Section: Adni's Contributions To Understanding Ad Disease Progressionmentioning

confidence: 99%

See 2 more Smart Citations

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

Veitch,

Weiner,

Miller

et al. 2023

Alzheimer's & Dementia

View full text Add to dashboard Cite

The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co‐pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI‐4 aims to address by enrolling a diverse cohort.

show abstract

Arterial spin labeling perfusion MRI in the Alzheimer's Disease Neuroimaging Initiative: Past, present, and future

Thropp,

Phillips,

Jung

et al. 2024

Alzheimer's & Dementia

View full text Add to dashboard Cite

On the 20th anniversary of the Alzheimer's Disease Neuroimaging Initiative (ADNI), this paper provides a comprehensive overview of the role of arterial spin labeling (ASL) magnetic resonance imaging (MRI) in understanding perfusion changes in the aging brain and the relationship with Alzheimer's disease (AD) pathophysiology and its comorbid conditions. We summarize previously used acquisition protocols, available data, and the motivation for adopting a multi‐post‐labeling delay (PLD) acquisition scheme in the latest ADNI MRI protocol (ADNI 4). We also detail the process of setting up this scheme on different scanners, emphasizing the potential of ASL imaging in future AD research.Highlights The Alzheimer's Disease Neuroimaging Initiative (ADNI) adopted multimodal arterial spin labeling magnetic resonance imaging (ASL MRI) to meet evolving biomarker requirements. The ADNI provides one of the largest multisite, multi‐vendor ASL data collections. The ADNI 4 incorporates multi‐post‐labeling delay ASL techniques to jointly quantify cerebral blood flow and arterial transit time. ADNI 4 ASL MRI protocol is apt for detecting early Alzheimer's disease with cerebrovascular pathology.

show abstract

Alzheimer's genetic risk effects on cerebral blood flow across the lifespan are proximal to gene expression

Cited by 6 publications

References 46 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

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

Arterial spin labeling perfusion MRI in the Alzheimer's Disease Neuroimaging Initiative: Past, present, and future

Contact Info

Product

Resources

About