IntroductionThe Advancing Research and Treatment in Frontotemporal Lobar Degeneration and Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects longitudinal studies were designed to describe the natural history of familial‐frontotemporal lobar degeneration due to autosomal dominant mutations.MethodsWe examined cognitive performance, behavioral ratings, and brain volumes from the first time point in 320 MAPT, GRN, and C9orf72 family members, including 102 non–mutation carriers, 103 asymptomatic carriers, 43 mildly/questionably symptomatic carriers, and 72 carriers with dementia.ResultsAsymptomatic carriers showed similar scores on all clinical measures compared with noncarriers but reduced frontal and temporal volumes. Those with mild/questionable impairment showed decreased verbal recall, fluency, and Trail Making Test performance and impaired mood and self‐monitoring. Dementia was associated with impairment in all measures. All MAPT carriers with dementia showed temporal atrophy, but otherwise, there was no single cognitive test or brain region that was abnormal in all subjects.DiscussionImaging changes appear to precede clinical changes in familial‐frontotemporal lobar degeneration, but specific early clinical and imaging changes vary across individuals.

Frontotemporal dementia (FTD), marked by impairments in behavior, language and sometimes motor function, is a common form of early-onset dementia 1 . Approximately 20-30% of FTD is caused by autosomal dominant mutations (familial, or f-FTD), usually in one of three genes: chromosome 9 open reading frame 72 (C9orf72), progranulin (GRN) or microtubule-associated protein tau (MAPT) 2 . FTD is uniformly fatal, and there are no approved therapies; however, a growing number of new treatments targeting C9orf72, GRN and MAPT are moving into clinical trials 3,4 . Experience from Alzheimer's disease (AD), spinal muscular Temporal order of clinical and biomarker changes in familial frontotemporal dementia

This observation study characterizes regions and rates of atrophy in the 3 primary familial frontotemporal lobar degeneration genes ( MAPT , GRN , and C9orf72 ) across all disease stages from asymptomatic to dementia.

Increased presynaptic dysfunction measured by cerebrospinal fluid (CSF) growth-associated protein-43 (GAP43) may be observed in Alzheimer's disease (AD), but how CSF GAP43 increases relate to AD-core pathologies, neurodegeneration, and cognitive decline in AD requires further investigation. Methods: We analyzed 731 older adults with baseline β-amyloid (Aβ) positron emission tomography (PET), CSF GAP43, CSF phosphorylated tau181 (p-Tau 181 ), and 18 F-fluorodeoxyglucose PET, and longitudinal residual hippocampal volume and cognitive assessments. Among them, 377 individuals had longitudinal 18 F-fluorodeoxyglucose PET, and 326 individuals had simultaneous longitudinal CSF GAP43, Aβ PET, and CSF p-Tau 181 data. We compared baseline and slopes of CSF GAP43 among different stages of AD, as well as their associations with Aβ PET, CSF p-Tau 181 , residual hippocampal volume, 18 F-fluorodeoxyglucose PET, and cognition cross-sectionally and longitudinally. Results: Regardless of Aβ positivity and clinical diagnosis, CSF p-Tau 181 -positive individuals showed higher CSF GAP43 concentrations (p < 0.001) and faster rates of CSF GAP43 increases (p < 0.001) compared with the CSF p-Tau 181negative individuals. Moreover, higher CSF GAP43 concentrations and faster rates of CSF GAP43 increases were strongly related to CSF p-Tau 181 independent of Aβ PET. They were related to more rapid hippocampal atrophy, hypometabolism, and cognitive decline (p < 0.001), and predicted the progression from MCI to dementia (area under the curve for baseline 0.704; area under the curve for slope 0.717) over a median 4 years of follow up. Interpretation: Tau aggregations rather than Aβ plaques primarily drive presynaptic dysfunction measured by CSF GAP43, which may lead to sequential neurodegeneration and cognitive impairment in AD or neurodegenerative diseases.

A biological research framework to define Alzheimer’ disease with dichotomized biomarker measurement was proposed by National Institute on Aging–Alzheimer’s Association (NIA–AA). However, it cannot characterize the hierarchy spreading pattern of tau pathology. To reflect in vivo tau progression using biomarker, we constructed a refined topographic 18F-AV-1451 tau PET staging scheme with longitudinal clinical validation. Seven hundred and thirty-four participants with baseline 18F-AV-1451 tau PET (baseline age 73.9 ± 7.7 years, 375 female) were stratified into five stages by a topographic PET staging scheme. Cognitive trajectories and clinical progression were compared across stages with or without further dichotomy of amyloid status, using linear mixed-effect models and Cox proportional hazard models. Significant cognitive decline was first observed in stage 1 when tau levels only increased in transentorhinal regions. Rates of cognitive decline and clinical progression accelerated from stage 2 to stage 3 and stage 4. Higher stages were also associated with greater CSF phosphorylated tau and total tau concentrations from stage 1. Abnormal tau accumulation did not appear with normal β-amyloid in neocortical regions but prompt cognitive decline by interacting with β-amyloid in temporal regions. Highly accumulated tau in temporal regions independently led to cognitive deterioration. Topographic PET staging scheme have potentials in early diagnosis, predicting disease progression, and studying disease mechanism. Characteristic tau spreading pattern in Alzheimer’s disease could be illustrated with biomarker measurement under NIA–AA framework. Clinical–neuroimaging–neuropathological studies in other cohorts are needed to validate these findings.

Background Alzheimer’s disease, the most common cause of dementia, causes a progressive and irreversible deterioration of cognition that can sometimes be difficult to diagnose, leading to suboptimal patient care. Methods We developed a predictive model that computes multi-regional statistical morpho-functional mesoscopic traits from T1-weighted MRI scans, with or without cognitive scores. For each patient, a biomarker called “Alzheimer’s Predictive Vector” (ApV) was derived using a two-stage least absolute shrinkage and selection operator (LASSO). Results The ApV reliably discriminates between people with (ADrp) and without (nADrp) Alzheimer’s related pathologies (98% and 81% accuracy between ADrp - including the early form, mild cognitive impairment - and nADrp in internal and external hold-out test sets, respectively), without any a priori assumptions or need for neuroradiology reads. The new test is superior to standard hippocampal atrophy (26% accuracy) and cerebrospinal fluid beta amyloid measure (62% accuracy). A multiparametric analysis compared DTI-MRI derived fractional anisotropy, whose readout of neuronal loss agrees with ADrp phenotype, and SNPrs2075650 is significantly altered in patients with ADrp-like phenotype. Conclusions This new data analytic method demonstrates potential for increasing accuracy of Alzheimer diagnosis.

We propose a new approach, called as functional deep neural network (FDNN), for classifying multidimensional functional data. Specifically, a deep neural network is trained based on the principal components of the training data which shall be used to predict the class label of a future data function. Unlike the popular functional discriminant analysis approaches which only work for one‐dimensional functional data, the proposed FDNN approach applies to general non‐Gaussian multidimensional functional data. Moreover, when the log density ratio possesses a locally connected functional modular structure, we show that FDNN achieves minimax optimality. The superiority of our approach is demonstrated through both simulated and real‐world datasets.

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.