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Uncovering cerebral blood flow patterns corresponding to Amyloid-beta accumulations in patients across the Alzheimer's disease continuum using the arterial spin labeling.
Neurol Sci
January 2025
School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder ranging from mild cognitive impairment (MCI) to AD dementia. Abnormal cerebral perfusion alterations, influenced by amyloid-beta (Aβ) accumulations, have been implicated in cognitive decline along this spectrum.
Objective: This study investigates the relationship between cerebrospinal fluid (CSF) Aβ1-42 levels and regional cerebral blood flow (CBF) changes across the AD continuum using the Arterial Spin Labeling (ASL) technique.
Distinct neural computations scale the violation of expected reward and emotion in social transgressions.
Commun Biol
January 2025
Department of Psychology, The University of Hong Kong, Hong Kong, China.
Traditional decision-making models conceptualize humans as adaptive learners utilizing the differences between expected and actual rewards (prediction errors, PEs) to maximize outcomes, but rarely consider the influence of violations of emotional expectations (emotional PEs) and how it differs from reward PEs. Here, we conducted a fMRI experiment (n = 43) using a modified Ultimatum Game to examine how reward and emotional PEs affect punishment decisions in terms of rejecting unfair offers. Our results revealed that reward relative to emotional PEs exerted a stronger prediction to punishment decisions.
View Article and Find Full Text PDFDeep self-representation learning with hyper-laplacian regularization for brain imaging genetics association analysis.
Methods
January 2025
School of Computer Science, Qufu Normal University, Rizhao 276826, China.
Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quantitative traits (QTs). However, most existing methods do not consider the nonlinear correlations between genotypic and phenotypic data, as well as potential higher-order relationships among subjects when identifying bi-multivariate associations. In this paper, a novel method called deep hyper-Laplacian regularized self-representation learning based structured association analysis (DHRSAA) is proposed which can learn genotype-phenotype associations and obtain relevant biomarkers.
View Article and Find Full Text PDFProbing Autism and ADHD subtypes using cortical signatures of the T1w/T2w-ratio and morphometry.
Neuroimage Clin
January 2025
The Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada; Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, Canada; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are neurodevelopmental conditions that share genetic etiology and frequently co-occur. Given this comorbidity and well-established clinical heterogeneity, identifying individuals with similar brain signatures may be valuable for predicting clinical outcomes and tailoring treatment strategies. Cortical myelination is a prominent developmental process, and its disruption is a candidate mechanism for both disorders.
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