Dataset Information

Magnetic resonance evidence of increased iron content in subcortical brain regions in asymptomatic Alzheimer's disease.

ABSTRACT: While iron over-accumulation has been reported in late stage Alzheimer's disease (AD), whether this occurs early in the asymptomatic stage of AD remains unknown. We aimed to assess brain iron levels in asymptomatic AD using quantitative MR relaxometry of effective transverse relaxation rate (R2*) and longitudinal relaxation rate (R1), and recruited 118 participants comprised of three groups including healthy young participants, and cognitively normal older individuals without or with positive AD biomarkers based on cerebrospinal fluid (CSF) proteomics analysis. Compared with the healthy young group, increased R2* was found in widespread cortical and subcortical regions in the older groups. Further, significantly higher levels of R2* were found in the cognitively normal older subjects with positive CSF AD biomarker (i.e., asymptomatic AD) compared with those with negative AD biomarker in subcortical regions including the left and right caudate, left and right putamen, and left and right globus pallidus (p < .05 for all regions), suggesting increased iron content in these regions. Subcortical R2* of some regions was found to significantly correlate with CSF AD biomarkers and neuropsychological assessments of visuospatial functions. In conclusion, R2* could be a valuable biomarker for studying early pathophysiological changes in AD.

SUBMITTER: Lin Q

PROVIDER: S-EPMC10171513 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Magnetic resonance evidence of increased iron content in subcortical brain regions in asymptomatic Alzheimer's disease.

Lin Qixiang Q Shahid Salman S Hone-Blanchet Antoine A Huang Shuai S Wu Junjie J Bisht Aditya A Loring David D Goldstein Felicia F Levey Allan A Crosson Bruce B Lah James J Qiu Deqiang D

Human brain mapping 20230316 8

While iron over-accumulation has been reported in late stage Alzheimer's disease (AD), whether this occurs early in the asymptomatic stage of AD remains unknown. We aimed to assess brain iron levels in asymptomatic AD using quantitative MR relaxometry of effective transverse relaxation rate (R2*) and longitudinal relaxation rate (R1), and recruited 118 participants comprised of three groups including healthy young participants, and cognitively normal older individuals without or with positive AD ...[more]

PMID: 36929676

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Project description:BackgroundStructural magnetic resonance imaging (sMRI) can reflect structural abnormalities of the brain. Due to its high tissue contrast and spatial resolution, it is considered as an MRI sequence in diagnostic tasks related to Alzheimer's disease (AD). Thus far, most studies based on sMRI have only focused on pathological changes in disease-related brain regions in Euclidean space, ignoring the association and interaction between brain regions represented in non-Euclidean space. This non-Euclidean spatial information can provide valuable information for brain disease research. However, few studies have combined Euclidean spatial information in images and graph spatial information in brain networks for the early diagnosis of AD. The purpose of this study is to explore how to effectively combine multispatial information for enhancing AD diagnostic performance.MethodsA multispatial information representation model (MSRNet) was constructed for the diagnosis of AD using sMRI. Specifically, the MSRNet included a Euclidean representation channel integrating a multiscale module and a feature enhancement module, in addition to a graph (non-Euclidean) representation channel integrating a node feature aggregation mechanism. This was accomplished through the adoption of a multilayer graph convolutional neural network and a node connectivity aggregation mechanism with fully connected layers. Each participants' gray-matter volume map and preconstructed radiomics-based morphology brain network (radMBN) were used as MSRNet inputs for the learning of multispatial information. Other than the multispatial information representation in MSRNet, an interactive mechanism was proposed to connect the Euclidean and graph representation channels by five disease-related brain regions which were identified based on a classifier operated on with two feature strategies of voxel intensities and radiomics features. MSRNet focused on disease-related brain regions while integrating multispatial information to effectively enhance disease discrimination.ResultsThe MSRNet was validated on four publicly available datasets, achieving accuracies 92.8% and 90.6% for AD in intra-database and inter-database cross-validation, respectively. The accuracy of MSRNet in distinguishing between late mild cognitive impairment (MCI) and early MCI, and between progressive MCI and stable MCI, reached 79.8% and 73.4%, respectively. The experiments demonstrated that the model's decision scores exhibited good detection capability for MCI progression. Furthermore, the potential of decision scores for improving diagnostic performance was exhibited by combining decision scores with other clinical indicators for AD identification.ConclusionsThe MSRNet model could conduct an effective multispatial information representation in the sMRI-based diagnosis of AD. The proposed interaction mechanism in the MSRNet could help the model focus on AD-related brain regions, thus further improving the diagnostic ability.

Project description:Despite observational studies linking brain iron levels to psychiatric disorders, the exact causal relationship remains poorly understood. This study aims to examine the relationship between iron levels in specific subcortical brain regions and the risk of psychiatric disorders. Utilizing two-sample Mendelian randomization (MR) analysis, this study investigates the causal associations between iron level changes in 16 subcortical nuclei and eight major psychiatric disorders, including schizophrenia (SCZ), major depressive disorder (MDD), autism spectrum disorders (ASD), attention-deficit/hyperactivity disorder, bipolar disorder, anxiety disorders, obsessive-compulsive disorder, and insomnia. The genetic instrumental variables linked to iron levels and psychiatric disorders were derived from the genome-wide association studies data of the UK Biobank Brain Imaging and Psychiatric Genomics Consortium. Bidirectional causal estimation was primarily obtained using the inverse variance weighting (IVW) method. Iron levels in the left substantia nigra showed a negative association with the risk of MDD (ORIVW = 0.94, 95% CI = 0.91-0.97, p < 0.001) and trends with risk of SCZ (ORIVW = 0.90, 95% CI = 0.82-0.98, p = 0.020). Conversely, iron levels in the left putamen were positively associated with the risk of ASD (ORIVW = 1.11, 95% CI = 1.04-1.19, p = 0.002). Additionally, several bidirectional trends were observed between subcortical iron levels and the risk for psychiatric disorders. Lower iron levels in the left substantia nigra may increase the risk of MDD, and potentially increase the risk of SCZ, indicating a potential shared pathogenic mechanism. Higher iron levels in the left putamen may lead to the development of ASD. The observed bidirectional trends between subcortical iron levels and psychiatric disorders, indicate the importance of the underlying biomechanical interactions between brain iron regulation and these disorders.

Dataset Information

Magnetic resonance evidence of increased iron content in subcortical brain regions in asymptomatic Alzheimer's disease.

Publications

Magnetic resonance evidence of increased iron content in subcortical brain regions in asymptomatic Alzheimer's disease.

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