AIMC Topic: Neuroimaging

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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...

Alzheimer's disease risk prediction using machine learning for survival analysis with a comorbidity-based approach.

Scientific reports
Alzheimer's disease (AD) presents a pressing global health challenge, demanding improved strategies for early detection and understanding its progression. In this study, we address this need by employing survival analysis techniques to predict transi...

Statistical variability in comparing accuracy of neuroimaging based classification models via cross validation.

Scientific reports
Machine learning (ML) has significantly transformed biomedical research, leading to a growing interest in model development to advance classification accuracy in various clinical applications. However, this progress raises essential questions regardi...

TA-SSM net: tri-directional attention and structured state-space model for enhanced MRI-Based diagnosis of Alzheimer's disease and mild cognitive impairment.

BMC medical imaging
Early diagnosis of Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI), is critical for effective prevention and treatment. Computer-aided diagnosis using magnetic resonance imaging (MRI) provides a cost-effective and objectiv...

Machine learning approach to DNA methylation and neuroimaging signatures as biomarkers for psychological resilience in young adults.

Behavioural brain research
Psychological resilience is influenced by both psychological and biological factors. However, the potential of using DNA methylation (DNAm) probes and brain imaging variables to predict psychological resilience remains unclear. This study aimed to in...

Multimodal neuroimaging unveils basal forebrain-limbic system circuit dysregulation in cognitive impairment with depression: a pathway to early diagnosis and intervention.

The journal of prevention of Alzheimer's disease
BACKGROUND: Alzheimer's disease (AD) frequently co-occurs with depressive symptoms, exacerbating both cognitive decline and clinical complexity, yet the neural substrates linking this co-occurrence remain poorly understood. We aimed to investigate th...

Neuroimaging and biological markers of different paretic hand outcomes after stroke.

Journal of neuroengineering and rehabilitation
BACKGROUND: Hand dysfunction significantly affects independence after stroke, with outcomes varying across individuals. Exploring biomarkers associated with the paretic hand can improve the prognosis and guide personalized rehabilitation. However, wh...

Advancements in Neuroanesthesia Through Artificial Intelligence.

Anesthesiology clinics
Artificial intelligence (AI) is transforming neuroanesthesia by enhancing precision and efficiency in managing patients during neurosurgical procedures. AI uses advanced algorithms and machine learning techniques to predict complications, optimize an...

Scalable geometric learning with correlation-based functional brain networks.

Scientific reports
Correlation matrices serve as fundamental representations of functional brain networks in neuroimaging. Conventional analyses often treat pairwise interactions independently within Euclidean space, neglecting the underlying geometry of correlation st...

A novel neuroimaging based early detection framework for alzheimer disease using deep learning.

Scientific reports
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive function, posing a major global health challenge. Despite its rising prevalence, particularly in low and middle-income countries, early diagnosi...