Detecting frontotemporal dementia syndromes using MRI biomarkers

Abstract

BackgroundDiagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another.MethodsIn this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200).ResultsThe anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index.ConclusionThis study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia.

MRI-guided histology of TDP-43 knock-in mice implicates parvalbumin interneuron loss, impaired neurogenesis and aberrant neurodevelopment in ALS-FTD

“…Here, using structural MRI, we detected an extensive pattern of brain volume loss in TDP-43 Q331K knockin mice, a preclinical model for ALS-FTD, while still at an early stage of disease. Significantly, many of the brain areas affected in mutant mice are analogous to those involved in patients with FTD (Meeter et al, 2017;Bruun et al, 2019). Similar regions have also been implicated in recent studies of patients with ALS .…”

Sex is a defining feature of neuroimaging phenotypes in major brain disorders

“…However, a recent machine learning study of dementia patients in two European memory clinics (N=1,213) showed that only 59% of FTD patients demonstrated a distinct frontotemporal atrophy pattern; the rest showed a subcortical atrophy pattern similar to AD [Bruun et al, 2019]. Further, across all dementia subtypes (including AD and VaD), the highest predictive accuracy was achieved using asymmetric frontotemporal atrophy to distinguish PPA and bvFTD subtypes, and temporal pole volume to distinguish the fluent and nonfluent PPA subtypes (85% AUC for both analyses) [Bruun et al, 2019]. These findings may reflect sex differences that are inherent to male-dominant bvFTD subtype, and a female-dominant nonfluent PPA subtype, but further work is needed to interpret these results.…”

Diagnostic imaging of dementia with Lewy bodies, frontotemporal lobar degeneration, and normal pressure hydrocephalus

“…8, 9), while a voxelbased morphometry study [41] showed evidence of metabolic and atrophic changes in the bilateral frontal and temporal lobes in FTD patients, whereas the affected regions of metabolism are more severe and larger than those of atrophy in the frontal and temporal lobe. Further, detecting FTLD using MRI atrophy biomarkers, derived from normalized volumes by automated calculation of the anterior versus posterior index, the asymmetry index, and the temporal pole left index, was reported to provide additional diagnostic assessment and assist in diagnosing FTLD in clinical practice [42].…”