Asymmetry of radiomics features in the white matter of patients with primary progressive aphasia

Abstract

IntroductionPrimary Progressive Aphasia (PPA) is a neurological disease characterized by linguistic deficits. Semantic (svPPA) and non-fluent/agrammatic (nfvPPA) variants are the two main clinical subtypes. We applied a novel analytical framework, based on radiomic analysis, to investigate White Matter (WM) asymmetry and to examine whether asymmetry is associated with verbal fluency performance.MethodsAnalyses were performed on T1-weighted images including 56 patients with PPA (31 svPPA and 25 nfvPPA) and 53 age- and sex-matched controls. Asymmetry Index (AI) was computed for 86 radiomics features in 34 white matter regions. The relationships between AI, verbal fluency performance (semantic and phonemic) and Boston Naming Test score (BNT) were explored through Spearman correlation analysis.ResultsRelative to controls, WM asymmetry in svPPA patients involved regions adjacent to middle temporal cortex as part of the inferior longitudinal (ILF), fronto-occipital (IFOF) and superior longitudinal fasciculi. Conversely, nfvPPA patients showed an asymmetry of WM in lateral occipital regions (ILF/IFOF). A higher lateralization involving IFOF, cingulum and forceps minor was found in nfvPPA compared to svPPA patients. In nfvPPA patients, semantic fluency was positively correlated to asymmetry in ILF/IFOF tracts. Performances at BNT were associated with AI values of the middle temporal (ILF/SLF) and parahippocampal (ILF/IFOF) gyri in svPPA patients.DiscussionRadiomics features depicted distinct pathways of asymmetry in svPPA and nfvPPA involving damage of principal fiber tracts associated with speech and language. Assessing asymmetry of radiomics in PPA allows achieving a deeper insight into the neuroanatomical damage and may represent a candidate severity marker for language impairments in PPA patients.

Explainable machine learning radiomics model for Primary Progressive Aphasia classification

“…Diagnosis and classification of PPA patients present a complex challenge that requires the integration of multimodal information, encompassing clinical, biological, and radiological features ( Roytman et al, 2022 ). Concerning brain imaging alterations, several investigations have reported associations between language deficits and brain alterations in gray matter regions and white matter fiber bundles linking cortical areas associated with language ( Agosta et al, 2015 ; Nigro et al, 2021 ; Tafuri et al, 2023 ). Moreover, svPPA showed a disruption of the ventral stream, impacting the occipito-temporal lobes ( Galantucci et al, 2011 ; Agosta et al, 2013 ; Marcotte et al, 2017 ).…”

“…In the present study, we developed a radiomics-based classification approach to classify patients with PPA, conducting a secondary analysis from our previous work over the same population that evaluated the lateralized damage of structural white matter ( Tafuri et al, 2023 ). In particular, 1st-order and 2nd-order statistic measures extracted from white matter regions and combined with clinical information were used as inputs to a tree-based algorithm to distinguish svPPA and nfvPPA from healthy controls, and to differentiate between PPA phenotypes.…”