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. 2023:39:103471.
doi: 10.1016/j.nicl.2023.103471. Epub 2023 Jul 11.

Structural and microstructural thalamocortical network disruption in sporadic behavioural variant frontotemporal dementia

David Jakabek  1 , Brian D Power  2 , Nicola Spotorno  3 , Matthew D Macfarlane  4 , Mark Walterfang  5 , Dennis Velakoulis  5 , Christer Nilsson  3 , Maria Landqvist Waldö  6 , Jimmy Lätt  7 , Markus Nilsson  7 , Danielle van Westen  8 , Olof Lindberg  3 , Jeffrey C L Looi  9 , Alexander F Santillo  10
Affiliations

Structural and microstructural thalamocortical network disruption in sporadic behavioural variant frontotemporal dementia

David Jakabek et al. Neuroimage Clin. 2023.

Abstract

Background: Using multi-block methods we combined multimodal neuroimaging metrics of thalamic morphology, thalamic white matter tract diffusion metrics, and cortical thickness to examine changes in behavioural variant frontotemporal dementia. (bvFTD).

Method: Twenty-three patients with sporadic bvFTD and 24 healthy controls underwent structural and diffusion MRI scans. Clinical severity was assessed using the Clinical Dementia Rating scale and behavioural severity using the Frontal Behaviour Inventory by patient caregivers. Thalamic volumes were manually segmented. Anterior and posterior thalamic radiation fractional anisotropy and mean diffusivity were extracted using Tract-Based Spatial Statistics. Finally, cortical thickness was assessed using Freesurfer. We used shape analyses, diffusion measures, and cortical thickness as features in sparse multi-block partial least squares (PLS) discriminatory analyses to classify participants within bvFTD or healthy control groups. Sparsity was tuned with five-fold cross-validation repeated 10 times. Final model fit was assessed using permutation testing. Additionally, sparse multi-block PLS was used to examine associations between imaging features and measures of dementia severity.

Results: Bilateral anterior-dorsal thalamic atrophy, reduction in mean diffusivity of thalamic projections, and frontotemporal cortical thinning, were the main features predicting bvFTD group membership. The model had a sensitivity of 96%, specificity of 68%, and was statistically significant using permutation testing (p = 0.012). For measures of dementia severity, we found similar involvement of regional thalamic and cortical areas as in discrimination analyses, although more extensive thalamo-cortical white matter metric changes.

Conclusions: Using multimodal neuroimaging, we demonstrate combined structural network dysfunction of anterior cortical regions, cortical-thalamic projections, and anterior thalamic regions in sporadic bvFTD.

Keywords: Behavioural variant frontotemporal dementia; Diffusion; MRI; Partial least squares; Shape analysis.

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Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Sparse PLS-DA group comparison. Multiblock group comparison. Panel A shows cortical loading values for the sparse model. Panel B shows displacement from an average thalamic shape after deformation by sparsely selected momenta. Panel C shows sparsely selected DTI tracts. ATR, anterior thalamic radiation; PTR, posterior thalamic radiation; FA, fractional anisotropy; MD, mean diffusivity. Scales show relative weighting in the discrimination selection model and are comparable in colour between Panels A and B.
Fig. 2
Fig. 2
Sparse PLS association with FTLD-CDR-SB. Multiblock group comparison. Panel A shows cortical loading values for the sparse model. Panel B shows displacement from an average thalamic shape after deformation by sparsely selected momenta. Panel C shows sparsely selected DTI tracts. ATR, anterior thalamic radiation; PTR, posterior thalamic radiation; FA, fractional anisotropy; MD, mean diffusivity. Scales show weighting in the discrimination selection model and are comparable in colour between Panels A and B.
Fig. 3
Fig. 3
Sparse PLS association with FBI total score. Multiblock group comparison. Panel A shows cortical loading values for the sparse model. Panel B shows displacement from an average thalamic shape after deformation by sparsely selected momenta. Panel C shows sparsely selected DTI tracts. ATR, anterior thalamic radiation; PTR, posterior thalamic radiation; FA, fractional anisotropy; MD, mean diffusivity. Scales show weighting in the discrimination selection model and are comparable in colour between Panels A and B.
Fig. 4
Fig. 4
Sparse PLS association with FBI score on items 1 to 10. Multiblock group comparison. Panel A shows cortical loading values for the sparse model. Panel B shows displacement from an average thalamic shape after deformation by sparsely selected momenta. Panel C shows sparsely selected DTI tracts. ATR, anterior thalamic radiation; PTR, posterior thalamic radiation; FA, fractional anisotropy; MD, mean diffusivity. Scales show weighting in the discrimination selection model and are comparable in colour between Panels A and B.
See this image and copyright information in PMC

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