Uncovering Active Structural Subspaces Associated with Changes in Indicators for Alzheimer's Disease

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Biostatistics

Background:

• Alzheimer's disease (AD) diagnosis relies on clinical assessments and neuroimaging.
• Identifying specific brain region interactions related to disease progression is crucial.
• Current methods may not efficiently capture complex, multi-regional brain changes.

Purpose of the Study:

• To develop a framework for identifying brain subspaces associated with biological and cognitive indicators of disorders.
• To apply active subspace learning (ASL) to structural MRI data in Alzheimer's disease.
• To determine if these subspaces improve the interpretability of neuroimaging data without sacrificing predictive power.

Main Methods:

• Utilized active subspace learning (ASL) on structural MRI features from an Alzheimer's disease dataset.
• Identified co-varying subspaces of brain regions associated with biological age and Mini-Mental State Examination (MMSE) scores.
• Compared regression performance using projected MRI features against non-transformed and PCA-transformed features.

Main Results:

• Successfully identified sparse subspaces in the brain associated with Alzheimer's disease indicators.
• Projecting structural MRI components onto these subspaces yielded regression performance comparable to traditional methods.
• Demonstrated that ASL can extract meaningful, interpretable patterns from neuroimaging data.

Conclusions:

• The proposed framework effectively identifies key structural brain subspaces linked to disorder indicators.
• This approach offers a way to understand brain changes in Alzheimer's disease using neuroimaging and clinical data.
• The method extracts sparse, relevant subspaces without compromising predictive accuracy, enhancing interpretability.