Standardized methodology for assessing the presence, variants and area of the interthalamic adhesion using anatomical MRI (SNAP-IA): multicentric validation on 565 healthy individuals and multiple neurological disorders

Résumé fourni par la source

The interthalamic adhesion (IA) connects both thalami. Emerging research suggests it may support thalamo-cortical connectivity and could be involved in neurodevelopmental and neuropsychiatric conditions. However, inconsistent MRI evaluation hinders progress on this subject. We developed SNAP-IA, a standardized anatomical imaging protocol for consistent IA identification and quantification. This work leveraged the expertise from seven research teams (Toulouse, Santiago, Southampton, Lausanne, Tübingen, and Bordeaux). SNAP-IA includes three steps: (1) determination of IA presence/absence on T1-weighted MRI; (2) classification of IA variants (simple, broad, double, bilobar, and filiform); (3) segmentation-based area assessment. It was tested on 500 controls (20–69 yo) and patients (stroke, schizophrenia, bipolar disorder, and ADHD) with 0.6–1 mm isotropic T1-weighted MRI (3T to 9.4T). SNAP-IA application achieved high inter-dataset agreement (mean Dice ≈ 0.92), with an average identification time of 35 s. The IA was absent in 22.8% of controls. Simple and broad variants constituted 95% of identified IA while some variants (double, filiform) were observed less frequently. At 3T, females had a higher presence rate (84.4%) than males (69.8%) and a larger IA area. ANCOVA indicated that both age and gender were highly predictive of IA area, decreasing by 0.25 mm²/year. At 9.4T, absence rates were significantly higher (34.6%) than at 3T (18.1%, p = 0.002). Mean IA area did not differ significantly between 3T and 9.4T. Patients with neurodevelopmental or neuropsychiatric disorders had two times less IA presence, with significantly smaller IA. SNAP-IA provides a reliable, reproducible framework for anatomical IA assessment across populations and MRI sequences, enabling future research into its structural and functional roles and supporting automated, large-scale AI studies.

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