Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-5. doi: 10.1109/EMBC58623.2025.11253918.
ABSTRACT
Anomic aphasia is a subtype of aphasia, characterized by impaired naming functions while other language abilities remain relatively intact. However, due to the relatively mild symptoms, patients with anomic aphasia are often prone to misdiagnosis or underdiagnosis, which may delay treatment and intervention. This study employed resting-state functional magnetic resonance imaging (rs-fMRI) and machine learning techniques to classify anomic aphasia and differentiate it from post-stroke non-aphasic subjects, while also investigating the neural mechanisms underlying its manifestation. Brain imaging analysis techniques, including fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and the Laterality Index (LI), were used to analyze data from 95 subjects to reveal significant differences in brain activity between anomic aphasia subjects and post-stroke non-aphasic subjects. Subsequently, these imaging-derived features were used to train and validate machine learning classifiers. Among the classifiers tested, the Multilayer Perceptron (MLP) achieved an accuracy of 94.74% in distinguishing between the two groups. Collectively, our findings highlight the potential of automated methods based on neuroimaging and machine learning in assisting clinicians to enhance diagnostic efficiency for anomic aphasia, enabling the early detection of symptoms and timely intervention.Clinical Relevance- Subjects with anomic aphasia exhibited increased rightward activation in regions such as the superior frontal gyrus and inferior frontal gyrus, coupled with reduced activation in the inferior parietal lobule and superior temporal gyrus.
PMID:41337239 | DOI:10.1109/EMBC58623.2025.11253918