ADVANCES IN FUNCTIONAL MAGNETIC RESONANCE IMAGING FOR THE EVALUATION OF NEURODEGENERATIVE DISEASES

Abstract

Functional magnetic resonance imaging (fMRI) has become an indispensable tool in the research and clinical assessment of neurodegenerative diseases such as Alzheimer’s, Parkinson’s and amyotrophic lateral sclerosis (ALS). Its ability to map changes in cerebral blood flow associated with neuronal activity provides insights into the mechanisms underlying these diseases, allowing for advances in diagnosis and monitoring. This paper aims to analyze recent advances in the application of fMRI in the assessment of neurodegenerative diseases, highlighting its usefulness in early detection, monitoring disease progression and evaluating therapeutic responses. The literature review explores the use of functional magnetic resonance imaging (fMRI) in the study of neurodegenerative diseases, addressing technical advances, biomarkers based on functional connectivity, and deep learning applications. The analysis includes studies on Alzheimer’s, Parkinson’s, multiple sclerosis and frontotemporal dementia, with a focus on diagnosis, disease progression and potential therapies. Advances in fMRI, such as resting-state functional connectivity and machine learning-based analysis, have made it possible to identify specific patterns of altered brain connectivity associated with neurodegenerative diseases. Recent studies show that fMRI can detect subtle changes in the early stages of these conditions, enabling early interventions. In addition, the use of standardized protocols and the integration of fMRI with other modalities, such as PET and EEG, have improved diagnostic accuracy and provided a comprehensive view of brain alterations. Therefore, fMRI represents one of the most promising technologies for understanding and managing neurodegenerative diseases. Its technical and methodological advances are expanding its application, from early diagnosis to the evaluation of personalized therapies. However, challenges such as inter-individual variability and high costs still need to be overcome for this technology to be widely incorporated into clinical practice.