Network anatomy in logopenic variant of primary progressive aphasia

Mandelli, Maria Luisa; Lorca-Puls, Diego L; Lukic, Sladjana; Montembeault, Maxime; Gajardo-Vidal, Andrea; Licata, Abigail; Scheffler, Aaron; Battistella, Giovanni; Grasso, Stephanie; Bogley, Rian; Ratnasiri, Buddhika M; Joie, Renaud La; Mundada, Nidhi S.; Europa, Eduardo; Rabinovici, Gil D.; Miller, Bruce L.; Leon, Jessica De; Henry, Maya L.; Miller, Zachary A.; Gorno‐Tempini, Maria Luisa

doi:10.1002/hbm.26388

Cited by 4 publications

(1 citation statement)

References 119 publications

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“…The selection of regions of interest (ROIs) followed the framework outlined by Scotton et al. 33 After consultation with neurologists and PPA and neuroimaging experts (MB, JDW and CJDH) we decided to include regions which a) had previously been implicated in PPA [34][35][36] ; and b) were large enough for reliable segmentation. To streamline the model input and reduce feature complexity, some contiguous sub-regions were combined into composite regions where this was appropriate anatomically.…”

Section: Subtype and Stage Inference Algorithmmentioning

confidence: 99%

Data-driven neuroanatomical subtypes of primary progressive aphasia

Taylor,

Bocchetta,

Shand

et al. 2024

Preprint

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The primary progressive aphasias are rare, language-led dementias, with three main variants: semantic, non-fluent/agrammatic, and logopenic. Whilst semantic variant has a clear neuroanatomical profile, the non-fluent/agrammatic and logopenic variants are difficult to discriminate from neuroimaging. Previous phenotype-driven studies have characterised neuroanatomical profiles of each variant on MRI. In this work we used a machine learning algorithm known as SuStaIn to discover data-driven neuroanatomical 'subtype' progression profiles and performed an in-depth subtype -phenotype analysis to characterise the heterogeneity of primary progressive aphasia. Our study included 270 participants with primary progressive aphasia seen for research in the UCL Queen Square Institute of Neurology Dementia Research Centre, with follow-up scans available for 137 participants. This dataset included individuals diagnosed with all three main variants (semantic: n=94, non-fluent/agrammatic: n=109, logopenic: n=51) as well as individuals with un-specified primary progressive aphasia (n=16). A data set of 66 patients (semantic n=37, non-fluent/agrammatic: n=29) from the ALLFTD North American cohort study, was used to validate our results. MRI scans were segmented and SuStaIn was employed on 19 regions of interest to identify neuroanatomical profiles independent of the diagnosis. We assessed the assignment of subtypes and stages, as well as their longitudinal consistency. We discovered four neuroanatomical subtypes of primary progressive aphasia, labelled S1, S2, S3, S4, exhibiting robustness to statistical scrutiny. S1 correlated strongly with semantic variant, while S2, S3, and S4 showed mixed associations with the logopenic and non-fluent/agrammatic variants. Notably, S3 displayed a neuroanatomical signature akin to a logopenic only signature, yet a significant proportion of logopenic cases were allocated to S2. The non-fluent/agrammatic variant demonstrated diverse associations with S2, S3, and S4. No clear relationship emerged between any of the neuroanatomical subtypes and the unspecified cases. At first follow up 84% of patients subtype assignment was stable, and 91.9% of patients stage assignment was stable. The ALLFTD dataset validated the findings. Our study, leveraging machine learning on a large primary progressive aphasia dataset, delineated four distinct neuroanatomical patterns. The identification of multiple profiles within the logopenic and non-fluent/agrammatic variants, alongside intra-phenotypic overlap, supports recent conceptualisations of primary progressive aphasia as a spectrum of disorders rather than discrete entities. Understanding the multifaceted profiles of the disease, encompassing neuroanatomical, molecular, clinical, and cognitive dimensions, holds potential implications for clinical decision support.

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Section: Subtype and Stage Inference Algorithmmentioning

confidence: 99%

Data-driven neuroanatomical subtypes of primary progressive aphasia

Taylor,

Bocchetta,

Shand

et al. 2024

Preprint

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Network anatomy in logopenic variant of primary progressive aphasia

Mandelli

Lorca-Puls

Lukic

et al. 2023

Human Brain Mapping

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The logopenic variant of primary progressive aphasia (lvPPA) is a neurodegenerative syndrome characterized linguistically by gradual loss of repetition and naming skills resulting from left posterior temporal and inferior parietal atrophy. Here, we sought to identify which specific cortical loci are initially targeted by the disease (epicenters) and investigate whether atrophy spreads through predetermined networks. First, we used cross‐sectional structural MRI data from individuals with lvPPA to define putative disease epicenters using a surface‐based approach paired with an anatomically fine‐grained parcellation of the cortical surface (i.e., HCP‐MMP1.0 atlas). Second, we combined cross‐sectional functional MRI data from healthy controls and longitudinal structural MRI data from individuals with lvPPA to derive the epicenter‐seeded resting‐state networks most relevant to lvPPA symptomatology and ascertain whether functional connectivity in these networks predicts longitudinal atrophy spread in lvPPA. Our results show that two partially distinct brain networks anchored to the left anterior angular and posterior superior temporal gyri epicenters were preferentially associated with sentence repetition and naming skills in lvPPA. Critically, the strength of connectivity within these two networks in the neurologically‐intact brain significantly predicted longitudinal atrophy progression in lvPPA. Taken together, our findings indicate that atrophy progression in lvPPA, starting from inferior parietal and temporoparietal junction regions, predominantly follows at least two partially nonoverlapping pathways, which may influence the heterogeneity in clinical presentation and prognosis.

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Primary progressive aphasia

Mendez,

Henry

2025

Encyclopedia of the Human Brain

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Network anatomy in logopenic variant of primary progressive aphasia

Cited by 4 publications

References 119 publications

Data-driven neuroanatomical subtypes of primary progressive aphasia

Data-driven neuroanatomical subtypes of primary progressive aphasia

Network anatomy in logopenic variant of primary progressive aphasia

Primary progressive aphasia

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