Mapping cortical disease-burden at individual-level in frontotemporal dementia: implications for clinical care and pharmacological trials

Abstract

Imaging studies of FTD typically present group-level statistics between large cohorts of genetically, molecularly or clinically stratified patients. Group-level statistics are indispensable to appraise unifying radiological traits and describe genotype-associated signatures in academic studies. However, in a clinical setting, the primary objective is the meaningful interpretation of imaging data from individual patients to assist diagnostic classification, inform prognosis, and enable the assessment of progressive changes compared to baseline scans. In an attempt to address the pragmatic demands of clinical imaging, a prospective computational neuroimaging study was undertaken in a cohort of patients across the spectrum of FTD phenotypes. Cortical changes were evaluated in a dual pipeline, using standard cortical thickness analyses and an individualised, z-score based approach to characterise subject-level disease burden. Phenotype-specific patterns of cortical atrophy were readily detected with both methodological approaches. Consistent with their clinical profiles, patients with bvFTD exhibited orbitofrontal, cingulate and dorsolateral prefrontal atrophy. Patients with ALS-FTD displayed precentral gyrus involvement, nfvPPA patients showed widespread cortical degeneration including insular and opercular regions and patients with svPPA exhibited relatively focal anterior temporal lobe atrophy. Cortical atrophy patterns were reliably detected in single individuals, and these maps were consistent with the clinical categorisation. Our preliminary data indicate that standard T1-weighted structural data from single patients may be utilised to generate maps of cortical atrophy. While the computational interpretation of single scans is challenging, it offers unrivalled insights compared to visual inspection. The quantitative evaluation of individual MRI data may aid diagnostic classification, clinical decision making, and assessing longitudinal changes.

Focal thalamus pathology in frontotemporal dementia: Phenotype-associated thalamic profiles

“…The six study groups (1) Volumetric analyses revealed anatomically widespread atrophy in bvFTD affecting all groups of nuclei in both hemispheres (Tables 1 & 2). C9orf72 negative ALS-FTD patients exhibited selective thalamic involvement with sparing of laterodorsal nuclei in both hemispheres and strikingly asymmetric, right-predominant pulvinar and leftpredominant lateroposterior, intralaminar and sensory nuclear involvement.…”

“…Frontotemporal dementia (FTD) is an umbrella term encompassing a clinically, radiologically, genetically, and pathologically diverse set of neurodegenerative conditions with distinct clinical phenotypes: behavioural variant FTD (bvFTD), non-fluent variant primary progressive aphasia (nfvPPA), semantic variant primary progressive aphasia (svPPA) and amyotrophic lateral sclerosis-FTD (ALS-FTD). In the clinical setting, FTD phenotypes are primarily linked to cortical atrophy patterns [1], but the contribution of subcortical pathology to cognitive and behavioural dysfunction is increasingly recognised [2][3][4][5][6][7].…”

Thalamic pathology in frontotemporal dementia: Predilection for specific nuclei, phenotype‐specific signatures, clinical correlates, and practical relevance

“…While the optimal combination of thalamic volumetric measurements is yet to be determined, a single study demonstrated that the volume of the pulvinar nuclei accurately differentiates C9orf72 from MAPT genotypes, and varying combinations of anterior, lateral, medial, and intralaminar nuclei volume reliably discriminate pathological subtypes (Bocchetta et al., 2020 ). The increasing availability of uniformly acquired normative datasets may help the radiological interpretation of single patients with FTD or suspected FTD (McKenna et al., 2022 ; McKenna et al., 2022 ; Tahedl et al., 2021 ). Machine learning applications are increasingly applied to large FTD and ALS–FTD datasets (McKenna et al., 2022 ).…”

Benefits of a mosaic approach for assessing cortical atrophy in individual multiple sclerosis patients

“…Alternatively, one can increase power by reducing the number of statistical comparisons, for example, by averaging across neighboring data points (Tahedl, 2020 ). This “parcellation method” has been suggested previously for CT data and has been demonstrated to yield clinically relevant information for various neurological conditions such as ALS (Tahedl et al., 2021 ) and FTD (McKenna et al., 2022 ). However, these studies did not interrogate effects of different CT parcellation schemes but have used a cortical parcellation of N = 1000 “patches” (in total for both hemisphere), using predefined and roughly equally sized atlas regions (Schaefer et al., 2018 ).…”

“…Moreover, we could show that MAP accurately reflects the histopathologic involvement of cortical disease burden: By contrasting (1) a purely upper motor neuron—that is, purely cortical—condition, namely, primary progressive lateral sclerosis (PLS), (2) a mixed upper‐/lower motor neuron condition (ALS), and (3) a primary lower motor neuron condition (poliomyelitis survivors), we could show that this degree of cortical involvement is captured by MAP—both cross‐sectionally and longitudinally (Tahedl et al., 2022 ). Furthermore, we have investigated the clinical utility of MAP outside of MND and applied it to the FTD spectrum, where we could also demonstrate its clinical utility for single patients (McKenna et al., 2022 ).…”