AIMC Topic: Brain

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The chronODE framework for modelling multi-omic time series with ordinary differential equations and machine learning.

Nature communications
Many genome-wide studies capture isolated moments in cell differentiation or organismal development. Conversely, longitudinal studies provide a more direct way to study these kinetic processes. Here, we present an approach for modeling gene-expressio...

FTIR spectroscopy imaging coupled with machine learning reveals biochemical changes in the brains of diabetic mice.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Diabetic encephalopathy is a progressive complication of type 2 diabetes, yet its region-specific biochemical changes remain unclear. In this study, we applied Fourier Transform Infrared Microspectroscopy (FTIRM) to assess metabolic alterations in th...

Neural encoding of temporal and spatial plausibility in naturalistic motion: an awake monkey fMRI study.

Neuropsychologia
Time flows in one direction, a physical constraint humans recognize, as reflected in proverbs such as "there is no crying over spilt milk." Recent work from our laboratory has demonstrated that human participants rely on specific cues to discriminate...

AI-driven fusion of multimodal data for Alzheimer's disease biomarker assessment.

Nature communications
Alzheimer's disease (AD) diagnosis hinges on detecting amyloid beta (Aβ) plaques and neurofibrillary tau (τ) tangles, typically assessed using PET imaging. While accurate, these modalities are expensive and not widely accessible, limiting their utili...

High-resolution mapping of alcohol-related brain connectivity in adults using 7T fMRI and multivoxel pattern classification.

Psychiatry research. Neuroimaging
BACKGROUND: Emerging evidence suggests that alcohol use disrupts large-scale brain network interactions, particularly within the triple network model-comprising the Salience Network (SN), Default Mode Network (DMN), and Frontoparietal Network (FPN). ...

Improving early detection of Alzheimer's disease through MRI slice selection and deep learning techniques.

Scientific reports
Alzheimer's disease is a progressive neurodegenerative disorder marked by cognitive decline, memory loss, and behavioral changes. Early diagnosis, particularly identifying Early Mild Cognitive Impairment (EMCI), is vital for managing the disease and ...

Advancements in the investigation of the mechanisms underlying cognitive aging.

Biogerontology
Cognitive aging, a pivotal domain at the intersection of neuroscience and psychology, exhibits a strong association with neurodegenerative disorders; however, its comprehensive underlying mechanisms remain incompletely elucidated. This review aims to...

Artificial intelligence with feature fusion empowered enhanced brain stroke detection and classification for disabled persons using biomedical images.

Scientific reports
Brain stroke is an illness which affects almost every age group, particularly people over 65. There are two significant kinds of strokes: ischemic and hemorrhagic strokes. Blockage of brain vessels causes an ischemic stroke, while cracks in blood ves...

Longitudinal structural MRI-based deep learning and radiomics features for predicting Alzheimer's disease progression.

Alzheimer's research & therapy
BACKGROUND: Alzheimer's disease (AD) is the principal cause of dementia and requires the early diagnosis of people with mild cognitive impairment (MCI) who are at high risk of progressing. Early diagnosis is imperative for optimizing clinical managem...

Circulating epigenetic signatures classifying brain insulin resistance in humans.

Science translational medicine
Brain insulin action plays an important role in metabolic and cognitive health, but there is no biomarker available to assess brain insulin resistance in humans. Here, we developed a machine learning framework based on blood DNA methylation profiles ...