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Identifying individuals with Alzheimer's disease-like brains based on structural imaging in the Human Connectome Project Aging cohort.

ABSTRACT: Given the difficulty in factoring out typical age effects from subtle Alzheimer's disease (AD) effects on brain structure, identification of very early, as well as younger preclinical "at-risk" individuals has unique challenges. We examined whether age-correction procedures could be used to better identify individuals at very early potential risk from adults who did not have any existing cognitive diagnosis. First, we obtained cross-sectional age effects for each structural feature using data from a selected portion of the Human Connectome Project Aging (HCP-A) cohort. After age detrending, we weighted AD structural deterioration with patterns quantified from data of the Alzheimer's Disease Neuroimaging Initiative. Support vector machine was then used to classify individuals with brains that most resembled atrophy in AD across the entire HCP-A sample. Additionally, we iteratively adjusted the pipeline by removing individuals classified as AD-like from the HCP-A cohort to minimize atypical brain structural contributions to the age detrending. The classifier had a mean cross-validation accuracy of 94.0% for AD recognition. It also could identify mild cognitive impairment with more severe AD-specific biomarkers and worse cognition. In an independent HCP-A cohort, 8.8% were identified as AD-like, and they trended toward worse cognition. An "AD risk" score derived from the machine learning models also significantly correlated with cognition. This work provides a proof of concept for the potential to use structural brain imaging to identify asymptomatic individuals at young ages who show structural brain patterns similar to AD and are potentially at risk for a future clinical disorder.

SUBMITTER: Li B

PROVIDER: S-EPMC8559490 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The original Human Connectome Project yielded a rich data set on structural and functional connectivity in a large sample of healthy young adults using improved methods of data acquisition, analysis, and sharing. More recent efforts are extending this approach to include infants, children, older adults, and brain disorders. This paper introduces and describes the Human Connectome Project in Aging (HCP-A), which is currently recruiting 1200 + healthy adults aged 36 to 100+, with a subset of 600 + participants returning for longitudinal assessment. Four acquisition sites using matched Siemens Prisma 3T MRI scanners with centralized quality control and data analysis are enrolling participants. Data are acquired across multimodal imaging and behavioral domains with a focus on factors known to be altered in advanced aging. MRI acquisitions include structural (whole brain and high resolution hippocampal) plus multiband resting state functional (rfMRI), task fMRI (tfMRI), diffusion MRI (dMRI), and arterial spin labeling (ASL). Behavioral characterization includes cognitive (such as processing speed and episodic memory), psychiatric, metabolic, and socioeconomic measures as well as assessment of systemic health (with a focus on menopause via hormonal assays). This dataset will provide a unique resource for examining how brain organization and connectivity changes across typical aging, and how these differences relate to key characteristics of aging including alterations in hormonal status and declining memory and general cognition. A primary goal of the HCP-A is to make these data freely available to the scientific community, supported by the Connectome Coordination Facility (CCF) platform for data quality assurance, preprocessing and basic analysis, and shared via the NIMH Data Archive (NDA). Here we provide the rationale for our study design and sufficient details of the resource for scientists to plan future analyses of these data. A companion paper describes the related Human Connectome Project in Development (HCP-D, Somerville et al., 2018), and the image acquisition protocol common to both studies (Harms et al., 2018).

Project description:IntroductionA hallmark of Alzheimer's disease (AD) is the presence of senile plaques composed of aggregated amyloid β (Aβ) peptides. Pathological aging (PA) is a postmortem classification that has been used to describe brains with plaque pathology similar in extent to AD, minimal cortical tau pathology, and no accompanying history of cognitive decline in the brain donor prior to death. PA may represent either a prodromal phase of AD, a benign form of Aβ accumulation, or inherent individual resistance to the toxic effects of Aβ accumulation. To attempt to distinguish between these possibilities we have systematically characterized Aβ peptides in a postmortem series of PA, AD and non-demented control (NDC) brains.MethodsAβ was sequentially extracted with tris buffered saline (TBS), radioimmunoprecipitation buffer (RIPA), 2% sodium dodecyl sulfate (SDS) and 70% formic acid (FA) from the pre-frontal cortex of 16 AD, eight PA, and six NDC patients. These extracts were analyzed by 1) a panel of Aβ sandwich ELISAs, 2) immunoprecipitation followed by mass spectrometry (IP/MS) and 3) western blotting. These studies enabled us to asses Aβ levels and solubility, peptide profiles and oligomeric assemblies.ResultsIn almost all extracts (TBS, RIPA, 2% SDS and 70% FA) the average levels of Aβ1-40, Aβ1-42, Aβ total, and Aβx-42 were greatest in AD. On average, levels were slightly lower in PA, and there was extensive overlap between Aβ levels in individual PA and AD cases. The profiles of Aβ peptides detected using IP/MS techniques also showed extensive similarity between the PA and AD brain extracts. In select AD brain extracts, we detected more amino-terminally truncated Aβ peptides compared to PA patients, but these peptides represented a minor portion of the Aβ observed. No consistent differences in the Aβ assemblies were observed by western blotting in the PA and AD groups.ConclusionsWe found extensive overlap with only subtle quantitative differences between Aβ levels, peptide profiles, solubility, and SDS-stable oligomeric assemblies in the PA and AD brains. These cross-sectional data indicate that Aβ accumulation in PA and AD is remarkably similar. Such data would be consistent with PA representing a prodromal stage of AD or a resistance to the toxic effects of Aβ.

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Identifying individuals with Alzheimer's disease-like brains based on structural imaging in the Human Connectome Project Aging cohort.

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