Objectives: Early‐ and late‐onset Alzheimer's disease (EOAD and LOAD) share the same neuropathological traits but show distinct cognitive features. We aimed to explore baseline and longitudinal outcomes of global and domain‐specific cognitive function in a well characterized cohort of patients with a biomarker‐based diagnosis.Methods: In this retrospective cohort study, 195 participants were included and classified according to their age, clinical status, and CSF AD biomarker profile: 89 EOAD, 37 LOAD, 46 young healthy controls (age ≤ 65 years), and 23 old healthy controls (>65 years). All subjects underwent clinical and neuropsychological assessment, neuroimaging, APOE genotyping and lumbar puncture.Results: We found distinct neuropsychological profiles between EOAD and LOAD at the time of diagnosis. Both groups showed similar performances on memory and language domains, but the EOAD patients displayed worsened deficits in visual perception, praxis, and executive tasks (p < 0.05). Longitudinally, cognitive decline in EOAD was more pronounced than LOAD in the global outcomes at the expense of these non‐amnestic domains. We found that years of education significantly influenced the decline in most of the neuropsychological tests. Besides, the APOE ε4 status showed a significant effect on the decline of memory‐related tasks within the EOAD cohort (p < 0.05).Interpretation: Age of onset is a main factor shaping the cognitive trajectories in AD patients, with younger age driving to a steeper decline of the non‐memory domains. Years of education are related to a transversal decline in all cognitive domains and APOE ε4 status to a specific decline in memory performance in EOAD.

Background/Aims: We identified and studied 13 patients carrying the P301L mutation in the MAPT gene from the same area (Baix Llobregat County) in Barcelona, Spain. Methods: The demographic and clinical features were reviewed retrospectively. Detailed neuropathological characterization was obtained in 9 subjects. To investigate the origin of the P301L mutation in these families, 20 single nucleotide polymorphisms (SNPs) in the MAPT gene were analyzed. Results: The mean age at disease onset was 51 years and the mean disease duration was 7 years. The most common initial symptoms were behavioral changes (54%), followed by language disturbances (31%) and memory loss (15%). 46% developed parkinsonism. Neuropathology showed an extensive neuronal and glial 4-repeat (4R) tauopathy with “mini-Pick”-like bodies in the dentate gyrus as the characteristic underlying pathology in all cases. In 1 subject, additional 4R globular glial inclusions were observed. All the mutation carriers showed the same haplotype for the SNPs analyzed, suggesting a common ancestor. Conclusion: These findings suggest a relative homogeneous clinicopathological phenotype in P301L MAPT mutation carriers in our series. This phenotype might help in the differential diagnosis from other tauopathies and be a morphological hint for genetic testing. The haplotype analysis results suggest a founder effect of the P301L mutation in this area.

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T‐T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2‐year follow‐up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross‐sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross‐sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross‐sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross‐sectional and longitudinal data.

Prior studies have described distinct patterns of brain gray matter and white matter alterations in Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD), as well as differences in their cerebrospinal fluid (CSF) biomarkers profiles. We aim to investigate the relationship between early‐onset AD (EOAD) and FTLD structural alterations and CSF biomarker levels. We included 138 subjects (64 EOAD, 26 FTLD, and 48 controls), all of them with a 3T MRI brain scan and CSF biomarkers available (the 42 amino acid‐long form of the amyloid‐beta protein [Aβ42], total‐tau protein [T‐tau], neurofilament light chain [NfL], neurogranin [Ng], and 14‐3‐3 levels). We used FreeSurfer and FSL to obtain cortical thickness (CTh) and fraction anisotropy (FA) maps. We studied group differences in CTh and FA and described the “AD signature” and “FTLD signature.” We tested multiple regression models to find which CSF‐biomarkers better explained each disease neuroimaging signature. CTh and FA maps corresponding to the AD and FTLD signatures were in accordance with previous literature. Multiple regression analyses showed that the biomarkers that better explained CTh values within the AD signature were Aβ and 14‐3‐3; whereas NfL and 14‐3‐3 levels explained CTh values within the FTLD signature. Similarly, NfL levels explained FA values in the FTLD signature. Ng levels were not predictive in any of the models. Biochemical markers contribute differently to structural (CTh and FA) changes typical of AD and FTLD.

Cognitive impairment and behavioral changes in amyotrophic lateral sclerosis (ALS) are now recognized as part of the disease. Whether it is solely related to the extent of TDP‐43 pathology is currently unclear. We aim to evaluate the influence of age, genetics, neuropathological features, and concomitant pathologies on cognitive impairment in ALS patients. We analyzed a postmortem series of 104 ALS patients and retrospectively reviewed clinical and neuropathological data. We assessed the burden and extent of concomitant pathologies, the role of APOE ε4 and mutations, and correlated these findings with cognitive status. We performed a logistic regression model to identify which pathologies are related to cognitive impairment. Cognitive decline was recorded in 38.5% of the subjects. Neuropathological features of frontotemporal lobar degeneration (FTLD) were found in 32.7%, explaining most, but not all, cases with cognitive impairment. Extent of TDP‐43 pathology and the presence of hippocampal sclerosis were associated with cognitive impairment. Mutation carriers presented a higher burden of TDP‐43 pathology and FTLD more frequently than sporadic cases. Most cases (89.4%) presented some degree of concomitant pathologies. The presence of concomitant pathologies was associated with older age at death. FTLD, but also Alzheimer’s disease, were the predominant underlying pathologies explaining the cognitive impairment in ALS patients. In sum, FTLD explained the presence of cognitive decline in most but not all ALS cases, while other non‐FTLD related findings can influence the cognitive status, particularly in older age groups.