Epilepsy & behavior : E&B · 2025
Multimodal imaging biomarkers in intractable temporal lobe Epilepsy: A comparative study of structural-functional coupling and ALFF.
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Qu Chuanyong, Chen Zexiang, Luo Cuimi, Qin Lu, Huang Dongying, Zhang Xiao, Fan Ligen, Zheng Jinou
Аннотация
BACKGROUND: Intractable temporal lobe epilepsy (ITLE) remains a clinical challenge due to its resistance to standard treatments and its profound impact on cognitive function. Multimodal neuroimaging techniques offer potential biomarkers for diagnosis and treatment monitoring, yet comparative evaluations of their utility remain limited.
METHODS: We conducted a longitudinal study comparing structural-functional coupling (SFC) and amplitude of low-frequency fluctuations (ALFF) between ITLE patients (pre- and post-treatment) and healthy controls. Regional features were extracted from resting-state functional Magnetic Resonance Imaging and diffusion tensor imaging, followed by machine learning-based classification and clustering analyses. Cognitive function was assessed via Montreal Cognitive Assessment (MoCA).
RESULTS: Both SFC and ALFF were significantly altered in ITLE, with partial normalization after treatment. ALFF showed superior classification accuracy (92.45%) compared to SFC (81.13%), with abnormalities predominantly in the frontal lobe. In contrast, SFC alterations localized to the occipital lobe correlated more strongly with MoCA improvements. Subgroup clustering revealed that longitudinal changes in SFC better predicted cognitive recovery.
CONCLUSIONS: Our findings highlight the complementary value of ALFF and SFC in ITLE: ALFF excels in diagnostic differentiation, while SFC more effectively tracks cognitive outcomes. A multimodal framework incorporating both metrics may enhance personalized management strategies in ITLE.
METHODS: We conducted a longitudinal study comparing structural-functional coupling (SFC) and amplitude of low-frequency fluctuations (ALFF) between ITLE patients (pre- and post-treatment) and healthy controls. Regional features were extracted from resting-state functional Magnetic Resonance Imaging and diffusion tensor imaging, followed by machine learning-based classification and clustering analyses. Cognitive function was assessed via Montreal Cognitive Assessment (MoCA).
RESULTS: Both SFC and ALFF were significantly altered in ITLE, with partial normalization after treatment. ALFF showed superior classification accuracy (92.45%) compared to SFC (81.13%), with abnormalities predominantly in the frontal lobe. In contrast, SFC alterations localized to the occipital lobe correlated more strongly with MoCA improvements. Subgroup clustering revealed that longitudinal changes in SFC better predicted cognitive recovery.
CONCLUSIONS: Our findings highlight the complementary value of ALFF and SFC in ITLE: ALFF excels in diagnostic differentiation, while SFC more effectively tracks cognitive outcomes. A multimodal framework incorporating both metrics may enhance personalized management strategies in ITLE.