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. 2024 Sep 29;14(10):992.
doi: 10.3390/brainsci14100992.

Estimated Disease Progression Trajectory of White Matter Disruption in Unilateral Temporal Lobe Epilepsy: A Data-Driven Machine Learning Approach

Daichi Sone  1   2 , Noriko Sato  1 , Yoko Shigemoto  1 , Iman Beheshti  3 , Yukio Kimura  1 , Hiroshi Matsuda  1
Affiliations

Estimated Disease Progression Trajectory of White Matter Disruption in Unilateral Temporal Lobe Epilepsy: A Data-Driven Machine Learning Approach

Daichi Sone et al. Brain Sci. .

Abstract

Background/objectives: Although the involvement of progressive brain alterations in epilepsy was recently suggested, individual patients' trajectories of white matter (WM) disruption are not known.

Methods: We investigated the disease progression patterns of WM damage and its associations with clinical metrics. We examined the cross-sectional diffusion tensor imaging (DTI) data of 155 patients with unilateral temporal lobe epilepsy (TLE) and 270 age/gender-matched healthy controls, and we then calculated the average fractional anisotropy (FA) values within 20 WM tracts of the whole brain. We used the Subtype and Stage Inference (SuStaIn) program to detect the progression trajectory of FA changes and investigated its association with clinical parameters including onset age, disease duration, drug-responsiveness, and the number of anti-seizure medications (ASMs).

Results: The SuStaIn algorithm identified a single subtype model in which the initial damage occurs in the ipsilateral uncinate fasciculus (UF), followed by damage in the forceps, superior longitudinal fasciculus (SLF), and anterior thalamic radiation (ATR). This pattern was replicated when analyzing TLE with hippocampal sclerosis (n = 50) and TLE with no lesions (n = 105) separately. Further-progressed stages were associated with longer disease duration (p < 0.001) and a greater number of ASMs (p = 0.001).

Conclusions: the disease progression model based on WM tracts may be useful as a novel individual-level biomarker.

Keywords: diffusion tensor imaging; machine learning; temporal lobe epilepsy; white matter.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The flow of analysis in this study. The DTI data were processed by tract-based spatial statistics (TBSS) and atlas-based calculation of fractional anisotropy (FA) within each WM tract. The Z-scores were analyzed by SuStaIn algorithm to estimate disease progression patterns.
Figure 2
Figure 2
The progression pattern of white matter (WM) tract disruption in temporal lobe epilepsy (TLE) (left) and the number of patients at each progression stage. Results of (A) all 155 patients with TLE, (B) the 50 patients with TLE with hippocampal sclerosis (HS), and (C) the 105 patients with TLE with no visible lesions. ATR: anterior thalamic radiation, CST: corticospinal tract, Cing (C): cingulum (cingulate gyrus), Cing (H): cingulum hippocampus, IFOF: inferior fronto-occipital fasciculus, ILF, inferior longitudinal fasciculus, SLF: superior longitudinal fasciculus, UF: uncinate fasciculus, SLF (T): superior longitudinal fasciculus (temporal projection).
Figure 3
Figure 3
Significant correlations of stage progression with disease duration (left) and the number of antiseizure medications (ASMs) used (right).
See this image and copyright information in PMC

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