前頭側頭葉変性症

INTRODUCTION: Frontotemporal lobar degeneration (FTLD) often affects younger patients, making time to diagnosis especially consequential. Most evidence comes from high-income countries, with limited data from Latin America. METHODS: We studied 415 individuals with FTLD-associated syndromes from 12 sites in six Latin American countries in the Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat) cohort. Time to diagnosis was calculated using clinician-reported symptom onset (T-Reported) and criteria-based onset (T-Criteria). Associations with phenotype, social determinants of health, education, age at onset, sex, symptom presentation, and genetic status were examined. RESULTS: Mean time to diagnosis was 3.33 years using T-Reported and 2.97 years using T-Criteria. Times were longest in behavioral variant frontotemporal dementia and primary progressive aphasia variants. Younger age at onset was associated with longer time to diagnosis. Genetic carriers showed earlier onset but similar time to diagnosis. DISCUSSION: Time to diagnosis remains substantial, particularly in younger patients and canonical frontotemporal dementia phenotypes. UNLABELLED: Mean time to diagnosis remains high across frontotemporal lobar degeneration (FTLD) syndromes.Diagnostic delay in FTLD in Latin America averages ≈3 years.Younger onset is linked to longer time to diagnosis.Canonical frontotemporal dementia phenotypes show the longest diagnostic delays.

Many neurodegenerative diseases are characterized by pathological protein aggregation in the brain. Alzheimer's disease displays amyloid-β and tau inclusions in the form of amyloid-β plaques and tau neurofibrillary tangles. Synucleinopathies comprise Parkinson's disease and Dementia with Lewy bodies, which are classified by α-synuclein depositions in the form of Lewy bodies, as well as multiple system atrophy, which displays glial cytoplasmic α-synuclein inclusions. Tar DNA binding protein 43 (TDP-43) inclusions are observed in amyotrophic lateral sclerosis and frontotemporal lobar dementia with TDP-43 inclusions. A separate subgroup of frontotemporal lobar dementias, including Pick's disease, progressive supranuclear palsy and corticobasal degeneration, are characterized by disease-specific patterns of tau pathology and are termed primary tauopathies. Despite these classifications, it is not often appreciated that neurodegenerative diseases commonly display amyloid-β, tau, α-synuclein, and/or TDP-43 co-pathologies not typically associated with that specific disease's pathophysiology. Additionally, in vitro and in vivo proteinopathy models show interactions between pathological forms of these proteins that increase protein aggregation and neurotoxicity, suggesting distinct mechanisms underlying co-pathologies that play a significant role in neurodegeneration. In this review, we describe the frequency of protein co-pathologies across neurodegenerative diseases and preclinical work demonstrating pathological protein synergies that exacerbate protein aggregation and toxicity. We also discuss granulovacuolar degeneration bodies, proteolytically active lysosomal structures that are induced by either pathological tau or α-synuclein accumulation, as an example of a shared cellular response to, and link between, distinct protein pathologies. Finally, we highlight interventional clinical trials which target multiple pathologies and/or specifically target co-pathologies in neurodegenerative diseases, noting that current preclinical and clinical research is limited and this line of investigation should be pursued more vigorously. In all, we find that protein co-pathologies are frequently observed in the brains of common neurodegenerative diseases and serve as important future therapeutic targets for combatting neurodegeneration across clinically distinct diseases.

The semi-quantitative assessment of brain [18F]FDG PET provides a more objective interpretation and improved accuracy in differentiating across neurodegenerative diseases. However, the correct identification of anatomical regions of interest without a structural MRI is challenging. Thus, this study aims to develop a deep-learning-based (DL-based) model for the automatic segmentation of 52 anatomical regions in brain [18F]FDG PET images and validate it across different metabolic profiles. 1628 brain [18F]FDG PET images of 1099 subjects were included in the internal dataset, comprising cognitively normal subjects (n=537), and patients with mild cognitive impairment (n=538), subjective memory concerns (n=70), Alzheimer's disease (n=330), frontotemporal lobar degeneration (n=95) and Lewy body dementia (n=58). Train-test split yielded 1109/519 images (631/468 subjects) for training and internal testing of a DL-based model, respectively. An additional dataset of 108 [18F]FDG PET images was included for external validation. Ground-truth segmentation was performed on the paired T1-weighted MRI image for each [18F]FDG PET image, for a total of 52 anatomical regions of interest. Atlas-based segmentation was used as a benchmark. The Dice similarity coefficient (DSC) was used to assess segmentation performance. Agreement in mean pons-based standardised uptake value ratio (SUVRmean) quantification was assessed through the intraclass correlation coefficient (ICC) and relative deviation in absolute value. Per-region mean DSC for the DL-based segmentations ranged from 0.729 to 0.923 in the internal test set. Global mean DSC was 0.84±0.06. SUVRmean quantification of the different regions using the DL-based segmentation masks showed strong agreement with that obtained using the ground-truth segmentation masks, with an average ICC of 0.96±0.02. The per-region average of relative SUVRmean deviation did not exceed 5%. DL-based segmentation significantly outperformed atlas-based segmentation (p<0.05). Similar segmentation performance was obtained in the external validation dataset. DL-based anatomical segmentation of brain [18F]FDG PET proved to be robust across a wide spectrum of neurodegenerative diseases. Semi-quantitative assessment was comparable with that obtained with MRI-based segmentation. DL-based segmentation, therefore, proved to be a reliable alternative when MRI isn't available, and is a better option than the commonly used atlas-based approach.

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are neurodegenerative disorders with partially overlapping clinical manifestations, making early and differential diagnosis challenging. This study investigated whether electroencephalography (EEG)-derived spectral features and Granger-causality (GC)-based directed functional connectivity features can characterize and classify AD, FTD, and healthy control (HC) subjects. Resting-state eyes-closed EEG recordings from 88 participants were analyzed, including 36 AD, 23 FTD, and 29 HC subjects. Spectral features included absolute and relative band power and spectral ratios, while directed connectivity features were extracted from broadband and frequency-specific GC matrices. Statistical analyses identified theta/alpha ratio (TAR) as the dominant spectral marker, with the strongest three-group differences observed in frontal and global TAR features. GC analysis revealed group-related alterations mainly in alpha-band regional directed connectivity, although three-group GC features did not survive false discovery rate (FDR) correction at q < 0.05. In the main nested cross-validation analysis, the spectral-only model achieved the best three-class performance, with balanced accuracy of 0.572 and macro-F1 of 0.557. For dementia group (DEM) vs. HC classification, the combined GC + spectral feature (GC + SPEC) set achieved balanced accuracy of 0.710 and macro-F1 of 0.665. For AD vs. FTD classification, the combined GC + SPEC feature set achieved the highest numerical performance in the main nested cross-validation (CV) comparison, with balanced accuracy of 0.584 and macro-F1 of 0.559. In the separate long permutation-testing analysis, which used a reduced hyperparameter grid for computational feasibility, above-chance performance was confirmed for the three-class spectral model and the DEM vs. HC GC + SPEC model (p < 0.001), but not for AD vs. FTD (p = 0.270). An exploratory photobiomodulation (PBM) single-case analysis showed longitudinal EEG reorganization, including increased alpha power, reduced delta/alpha ratio (DAR) and beta/alpha ratio (BAR), mixed TAR changes, and HC-like GC/GC + SPEC centroid projections. Overall, the results support the value of spectral and directed connectivity EEG markers for dementia-related EEG characterization, while highlighting the persistent difficulty of AD vs. FTD differentiation.

Signalling between the ER and mitochondria regulates a number of key cellular functions that are damaged in frontotemporal dementia and related amyotrophic lateral sclerosis (FTD/ALS). This signalling involves close physical contacts between the two organelles that are mediated by the VAPB-PTPIP51 ER-mitochondria "tethering" proteins. A number of studies have shown that mutant genes which cause familial FTD/ALS disrupt the VAPB-PTPIP51 tethers and that this involves activation of GSK3β. TDP43 is one such mutant and altered TDP43 metabolism is central to FTD/ALS pathogenesis. Loss of Sigma-1 receptor function is also seen in FTD/ALS and there is evidence that Sigma-1 receptor agonists can repair damaged ER-mitochondria signalling. However, the underlying mechanisms are not properly understood. In this study, we show that the reference Sigma-1 receptor agonist PRE-084 stimulates VAPB-PTPIP51 binding and rescues FTD/ALS mutant TDP43-induced disruption to the VAPB-PTPIP51 interaction and linked ER-mitochondria Ca2+ delivery. We also show that these effects involve inhibition of the kinase GSK3β, a known negative regulator of VAPB-PTPIP51 binding. Finally, we show that ANAVEX2-73, a further Sigma-1 receptor agonist which is in clinical trials for Alzheimer's disease, also stimulates VAPB-PTPIP51 binding via GSK3β inhibition. Our findings provide novel insights into the mechanisms by which Sigma-1 receptor agonists influence defective ER-mitochondria signalling in FTD/ALS.