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

Vidal JP(1)(2), Forno G(3)(4), Hornberger M(5), Cuadra MB(6)(7), Danet L(8), Kumar VJ(9), Péran P(8), Tourdias T(10)(11), Barbeau EJ(12).

Author information:
(1)Centre de recherche Cerveau et Cognition (Cerco), UMR5549, CNRS – Université
de Toulouse, Toulouse, France. .
(2)Univ Toulouse, Inserm, ToNIC, Toulouse, France. .
(3)Geroscience Center for Brain Health and Metabolism (GERO), Santiago, Chile.
(4)Neuropsychology and Clinical Neuroscience Laboratory (LANNEC),
Physiopathology Department – ICBM, Neuroscience and East Neuroscience
Departments, Faculty of Medicine, University of Chile, Avenida Salvador 486,
Providencia, Santiago, Chile.
(5)Clinical Neurosciences, Clinical and Experimental Sciences, Faculty of
Medicine, University of Southampton, Southampton, UK.
(6)Center for Biomedical Imaging (CIBM), University of Lausanne, Lausanne,
Switzerland.
(7)Radiology Department (CHUV), Lausanne University Hospital, Lausanne,
Switzerland.
(8)Univ Toulouse, Inserm, ToNIC, Toulouse, France.
(9)Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.
(10)Neuroimagerie diagnostique et thérapeutique, CHU de Bordeaux, 33000,
Bordeaux, France.
(11)Univ. Bordeaux, INSERM, Neurocentre Magendie, U1215, 3300, Bordeaux, France.
(12)Centre de recherche Cerveau et Cognition (Cerco), UMR5549, CNRS – Université
de Toulouse, Toulouse, France.

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.
SUPPLEMENTARY INFORMATION: The online version contains supplementary material
available at 10.1007/s00429-026-03097-6.

DOI: 10.1007/s00429-026-03097-6
PMCID: PMC13009120
PMID: 41870612

Conflict of interest statement: Declarations. Conflict of interest: The authors
declare no competing interests.

Auteurs Bordeaux Neurocampus