Increase in wasteosomes (corpora amylacea) in frontotemporal lobar degeneration with specific detection of tau, TDP-43 and FUS pathology.

Wasteosomes (or corpora amylacea) are polyglucosan bodies that appear in the human brain with aging and in some neurodegenerative diseases, and have been suggested to have a potential role in a nervous system cleaning mechanism. Despite previous studies in several neurodegenerative disorders, their status in frontotemporal lobar degeneration (FTLD) remains unexplored. Our study aims to characterize wasteosomes in the three primary FTLD proteinopathies, assessing frequency, distribution, protein detection, and association with aging or disease duration. Wasteosome scores were obtained in various brain regions from 124 post-mortem diagnosed sporadic FTLD patients, including 75 participants with tau (FTLD-tau), 42 with TAR DNA-binding protein 43 (FTLD-TDP), and 7 with Fused in Sarcoma (FTLD-FUS) proteinopathies, along with 29 control subjects. The wasteosome amount in each brain region for the different FLTD patients was assessed with a permutation test with age at death and sex as covariables, and multiple regressions explored associations with age at death and disease duration. Double immunofluorescence studies examined altered proteins linked to FTLD in wasteosomes. FTLD patients showed a higher accumulation of wasteosomes than control subjects, especially those with FTLD-FUS. Unlike FTLD-TDP and control subjects, wasteosome accumulation did not increase with age in FTLD-tau and FTLD-FUS. Cases with shorter disease duration in FTLD-tau and FTLD-FUS seemed to exhibit higher wasteosome quantities, whereas FTLD-TDP appeared to show an increase with disease progression. Immunofluorescence studies revealed the presence of tau and phosphorylated-TDP-43 in the periphery of isolated wasteosomes in some patients with FTLD-tau and FTLD-TDP, respectively. Central inclusions of FUS were observed in a higher number of wasteosomes in FTLD-FUS patients. These findings suggest a role of wasteosomes in FTLD, especially in the more aggressive forms of FLTD-FUS. Detecting these proteins, particularly FUS, in wasteosomes from cerebrospinal fluid could be a potential biomarker for FTLD.

Cognitive profile, neuroimaging and fluid biomarkers in post-acute COVID-19 syndrome.

We aimed to characterize the cognitive profile of post-acute COVID-19 syndrome (PACS) patients with cognitive complaints, exploring the influence of biological and psychological factors. Participants with confirmed SARS-CoV-2 infection and cognitive complaints ≥ 8 weeks post-acute phase were included. A comprehensive neuropsychological battery (NPS) and health questionnaires were administered at inclusion and at 1, 3 and 6 months. Blood samples were collected at each visit, MRI scan at baseline and at 6 months, and, optionally, cerebrospinal fluid. Cognitive features were analyzed in relation to clinical, neuroimaging, and biochemical markers at inclusion and follow-up. Forty-nine participants, with a mean time from symptom onset of 10.4 months, showed attention-executive function (69%) and verbal memory (39%) impairment. Apathy (64%), moderate-severe anxiety (57%), and severe fatigue (35%) were prevalent. Visual memory (8%) correlated with total gray matter (GM) and subcortical GM volume. Neuronal damage and inflammation markers were within normal limits. Over time, cognitive test scores, depression, apathy, anxiety scores, MRI indexes, and fluid biomarkers remained stable, although fewer participants (50% vs. 75.5%; p = 0.012) exhibited abnormal cognitive evaluations at follow-up. Altered attention/executive and verbal memory, common in PACS, persisted in most subjects without association with structural abnormalities, elevated cytokines, or neuronal damage markers.

Cortical thickness modeling and variability in Alzheimer's disease and frontotemporal dementia.

BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) and frontotemporal dementia (FTD) show different patterns of cortical thickness (CTh) loss compared with healthy controls (HC), even though there is relevant heterogeneity between individuals suffering from each of these diseases. Thus, we developed CTh models to study individual variability in AD, FTD, and HC. METHODS: We used the baseline CTh measures of 379 participants obtained from the structural MRI processed with FreeSurfer. A total of 169 AD patients (63 ± 9 years, 65 men), 88 FTD patients (64 ± 9 years, 43 men), and 122 HC (62 ± 10 years, 47 men) were studied. We fitted region-wise temporal models of CTh using Support Vector Regression. Then, we studied associations of individual deviations from the model with cerebrospinal fluid levels of neurofilament light chain (NfL) and 14-3-3 protein and Mini-Mental State Examination (MMSE). Furthermore, we used real longitudinal data from 144 participants to test model predictivity. RESULTS: We defined CTh spatiotemporal models for each group with a reliable fit. Individual deviation correlated with MMSE for AD and with NfL for FTD. AD patients with higher deviations from the trend presented higher MMSE values. In FTD, lower NfL levels were associated with higher deviations from the CTh prediction. For AD and HC, we could predict longitudinal visits with the presented model trained with baseline data. For FTD, the longitudinal visits had more variability. CONCLUSION: We highlight the value of CTh models for studying AD and FTD longitudinal changes and variability and their relationships with cognitive features and biomarkers.

Galectin-3 is upregulated in frontotemporal dementia patients with subtype specificity.

INTRODUCTION: Neuroinflammation is a major contributor to the progression of frontotemporal dementia (FTD). Galectin-3 (Gal-3), a microglial activation regulator, holds promise as a therapeutic target and potential biomarker. Our study aimed to investigate Gal-3 levels in patients with FTD and assess its diagnostic potential. METHODS: We examined Gal-3 levels in brain, serum, and cerebrospinal fluid (CSF) samples of patients with FTD and controls. Multiple linear regressions between Gal-3 levels and other FTD markers were explored. RESULTS: Gal-3 levels were increased significantly in patients with FTD, mainly across brain tissue and CSF, compared to controls. Remarkably, Gal-3 levels were higher in cases with tau pathology than TAR-DNA Binding Protein 43 (TDP-43) pathology. Only MAPT mutation carriers displayed increased Gal-3 levels in CSF samples, which correlated with total tau and 14-3-3. DISCUSSION: Our findings underscore the potential of Gal-3 as a diagnostic marker for FTD, particularly in MAPT cases, and highlights the relation of Gal-3 with neuronal injury markers.

Impact of demographics and comorbid conditions on plasma biomarkers concentrations and their diagnostic accuracy in a memory clinic cohort.

Plasma biomarkers have emerged as promising tools for identifying amyloid beta (Aß) pathology. Before implementation in routine clinical practice, confounding factors modifying their concentration beyond neurodegenerative diseases should be identified. We studied the association of a comprehensive list of demographics, comorbidities, medication and laboratory parameters with plasma p-tau181, glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) on a prospective memory clinic cohort and studied their impact on diagnostic accuracy for discriminating CSF/amyloid PET-defined Aß status. Three hundred sixty patients (mean age 66.5 years, 55% females, 53% Aß positive) were included. Sex, age and Aß status-adjusted models showed that only estimated glomerular filtration rate (eGFR, standardized ß -0.115 [-0.192 to -0.035], p = 0.005) was associated with p-tau181 levels, although with a much smaller effect than Aß status (0.685 [0.607-0.763], p < 0.001). Age, sex, body mass index (BMI), Charlson comorbidity index (CCI) and eGFR significantly modified GFAP concentration. Age, blood volume (BV) and eGFR were associated with NfL levels. p-tau181 predicted Aß status with 87% sensitivity and specificity with no relevant increase in diagnostic performance by adding any of the confounding factors. Using two cut-offs, plasma p-tau181 could have spared 62% of amyloid-PET/CSF testing. Excluding patients with chronic kidney disease did not change the proposed cut-offs nor the diagnostic performance. In conclusion, in a memory clinic cohort, age, sex, eGFR, BMI, BV and CCI slightly modified plasma p-tau181, GFAP and NfL concentrations but their impact on the diagnostic accuracy of plasma biomarkers for Aß status discrimination was minimal.

Design and validation of the 1-week memory battery for assessing episodic memory and accelerated long-term forgetting in cognitively unimpaired subjects.

OBJECTIVE: Subtle decline in memory is thought to arise in the preclinical phase of Alzheimer's disease (AD). However, detecting these initial cognitive difficulties cross-sectionally has been challenging, and the exact nature of the decline is still debated. Accelerated long-term forgetting (ALF) has been recently suggested as one of the earliest and most sensitive indicators of memory dysfunction in subjects at risk of developing AD. The objective of this study was to design and validate the 1-week memory battery (1WMB) for assessing episodic memory and ALF in cognitively unimpaired individuals. METHOD: The 1WMB is unique in that it assesses multimodal memory and measures recall at both short delay (20 min) and at long term (1 week). Forty-five cognitively unimpaired subjects were assessed with 1WMB and standardized neuropsychological tests. Subjective cognitive decline (SCD), levels of anxiety and depression, and cognitive reserve were also measured. RESULTS: The tests of 1WMB showed a high internal consistency, and concurrent validity was observed with standard tests of episodic memory and executive functions. The analysis revealed a greater loss of information at 1 week compared to short-term forgetting (20 min). Performance in the 1WMB was affected by age and educational level, but was not associated with levels of anxiety and depression. Unlike standard tests, performance in the 1WMB correlated with measures of SCD. CONCLUSION: Our findings indicate that the 1WMB has good psychometric properties, and future studies are needed to explore its potential usefulness to assess cognitively unimpaired subjects at increased risk of developing AD. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data.

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.

The APOE4 effect: structural brain differences in Alzheimer's disease according to the age at symptom onset.

BACKGROUND AND PURPOSE: How the APOE genotype can differentially affect cortical and subcortical memory structures in biomarker-confirmed early-onset (EOAD) and late-onset (LOAD) Alzheimer's disease (AD) was assessed. METHOD: Eighty-seven cerebrospinal fluid (CSF) biomarker-confirmed AD patients were classified according to their APOE genotype and age at onset. 28 were EOAD APOE4 carriers (+EOAD), 21 EOAD APOE4 non-carriers (-EOAD), 23 LOAD APOE4 carriers (+LOAD) and 15 LOAD APOE4 non-carriers (-LOAD). Grey matter (GM) volume differences were analyzed using voxel-based morphometry in Papez circuit regions. Multiple regression analyses were performed to determine the relation between GM volume loss and cognition. RESULTS: Significantly more mammillary body atrophy in +EOAD compared to -EOAD is reported. The medial temporal and posterior cingulate cortex showed less GM in +LOAD compared to -LOAD. Medial temporal GM volume loss was also found in +EOAD compared to -LOAD. With an exception for +EOAD, medial temporal GM was strongly associated with episodic memory in the three groups, whilst posterior cingulate cortex GM volume was more related with visuospatial abilities. Visuospatial abilities and episodic memory were also associated with the anterior thalamic nucleus in -LOAD. CONCLUSIONS: Our results show that the APOE genotype has a significant effect on GM integrity as a function of age of disease onset. Specifically, whilst LOAD APOE4 genotype is mostly associated with increased medial temporal and parietal atrophy compared to -LOAD, for EOAD APOE4 might have a more specific effect on subcortical (mammillary body) structures. The findings suggest that APOE genotype needs to be taken into account when classifying patients by age at onset.

Loss of brainstem white matter predicts onset and motor neuron symptoms in C9orf72 expansion carriers: a GENFI study.

BACKGROUND AND OBJECTIVES: The C9orf72 expansion is the most common genetic cause of frontotemporal dementia (FTD) and/or motor neuron disease (MND). Corticospinal degeneration has been described in post-mortem neuropathological studies in these patients, especially in those with MND. We used MRI to analyze white matter (WM) volumes in presymptomatic and symptomatic C9orf72 expansion carriers and investigated whether its measure may be helpful in predicting the onset of symptoms. METHODS: We studied 102 presymptomatic C9orf72 mutation carriers, 52 symptomatic carriers: 42 suffering from FTD and 11 from MND, and 75 non-carriers from the Genetic Frontotemporal dementia Initiative (GENFI). All subjects underwent T1-MRI acquisition. We used FreeSurfer to estimate the volume proportion of WM in the brainstem regions (midbrain, pons, and medulla oblongata). We calculated group differences with ANOVA tests and performed linear and non-linear regressions to assess group-by-age interactions. RESULTS: A reduced WM ratio was found in all brainstem subregions in symptomatic carriers compared to both noncarriers and pre-symptomatic carriers. Within symptomatic carriers, MND patients presented a lower ratio in pons and medulla oblongata compared with FTD patients. No differences were found between presymptomatic carriers and non-carriers. Clinical severity was negatively associated with the WM ratio. C9orf72 carriers presented greater age-related WM loss than non-carriers, with MND patients showing significantly more atrophy in pons and medulla oblongata. DISCUSSION: We find consistent brainstem WM loss in C9orf72 symptomatic carriers with differences related to the clinical phenotype supporting the use of brainstem measures as neuroimaging biomarkers for disease tracking.

Sex differences in early-onset Alzheimer's disease.

BACKGROUND AND PURPOSE: Sex is believed to drive heterogeneity in Alzheimer's disease (AD), although evidence in early-onset AD (EOAD; <65 years) is scarce. METHODS: We included 62 EOAD patients and 44 healthy controls (HCs) with core AD cerebrospinal fluid (CSF) biomarkers, neurofilament light chain levels, neuropsychological assessment, and 3-T magnetic resonance imaging. We measured cortical thickness (CTh) and hippocampal subfield volumes (HpS) using FreeSurfer. Adjusted linear models were used to analyze sex-differences and the relationship between atrophy and cognition. RESULTS: Compared to same-sex HCs, female EOAD subjects showed greater cognitive impairment and broader atrophy burden than male EOAD subjects. In a direct female-EOAD versus male-EOAD comparison, there were slight differences in temporal CTh, with no differences in cognition or HpS. CSF tau levels were higher in female EOAD than in male EOAD subjects. Greater atrophy was associated with worse cognition in female EOAD subjects. CONCLUSIONS: At diagnosis, there are sex differences in the pattern of cognitive impairment, atrophy burden, and CSF tau in EOAD, suggesting there is an influence of sex on pathology spreading and susceptibility to the disease in EOAD.

Evaluating the performance of Bayesian and frequentist approaches for longitudinal modeling: application to Alzheimer's disease.

Linear mixed effects (LME) modelling under both frequentist and Bayesian frameworks can be used to study longitudinal trajectories. We studied the performance of both frameworks on different dataset configurations using hippocampal volumes from longitudinal MRI data across groups-healthy controls (HC), mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients, including subjects that converted from MCI to AD. We started from a big database of 1250 subjects from the Alzheimer's disease neuroimaging initiative (ADNI), and we created different reduced datasets simulating real-life situations using a random-removal permutation-based approach. The number of subjects needed to differentiate groups and to detect conversion to AD was 147 and 115 respectively. The Bayesian approach allowed estimating the LME model even with very sparse databases, with high number of missing points, which was not possible with the frequentist approach. Our results indicate that the frequentist approach is computationally simpler, but it fails in modelling data with high number of missing values.

Differential Gene Expression in Sporadic and Genetic Forms of Alzheimer's Disease and Frontotemporal Dementia in Brain Tissue and Lymphoblastoid Cell Lines.

Sporadic early-onset Alzheimer's disease (EOAD) and autosomal dominant Alzheimer's disease (ADAD) provide the opportunity to investigate the physiopathological mechanisms in the absence of aging, present in late-onset forms. Frontotemporal dementia (FTD) causes early-onset dementia associated to tau or TDP43 protein deposits. A 15% of FTD cases are caused by mutations in C9orf72, GRN, or MAPT genes. Lymphoblastoid cell lines (LCLs) have been proposed as an alternative to brain tissue for studying earlier phases of neurodegenerative diseases. The aim of this study is to investigate the expression profile in EOAD, ADAD, and sporadic and genetic FTD (sFTD and gFTD, respectively), using brain tissue and LCLs. Sixty subjects of the following groups were included: EOAD, ADAD, sFTD, gFTD, and controls. Gene expression was analyzed with Clariom D microarray (Affymetrix). Brain tissue pairwise comparisons revealed six common differentially expressed genes (DEG) for all the patients' groups compared with controls: RGS20, WIF1, HSPB1, EMP3, S100A11 and GFAP. Common up-regulated biological pathways were identified both in brain and LCLs (including inflammation and glial cell differentiation), while down-regulated pathways were detected mainly in brain tissue (including synaptic signaling, metabolism and mitochondrial dysfunction). CD163, ADAMTS9 and LIN7A gene expression disruption was validated by qPCR in brain tissue and NrCAM in LCLs in their respective group comparisons. In conclusion, our study highlights neuroinflammation, metabolism and synaptic signaling disturbances as common altered pathways in different AD and FTD forms. The use of LCLs might be appropriate for studying early immune system and inflammation, and some neural features in neurodegenerative dementias.

Baseline MRI atrophy predicts 2-year cognitive outcomes in early-onset Alzheimer's disease.

BACKGROUND: MRI atrophy predicts cognitive status in AD. However, this relationship has not been investigated in early-onset AD (EOAD, < 65 years) patients with a biomarker-based diagnosis. METHODS: Forty eight EOAD (MMSE ≥ 15; A + T + N +) and forty two age-matched healthy controls (HC; A - T - N -) from a prospective cohort underwent full neuropsychological assessment, 3T-MRI scan and lumbar puncture at baseline. Participants repeated the cognitive assessment annually. We used linear mixed models to investigate whether baseline cortical thickness (CTh) or subcortical volume predicts two-year cognitive outcomes in the EOAD group. RESULTS: In EOAD, hemispheric CTh and ventricular volume at baseline were associated with global cognition, language and attentional/executive functioning 2 years later (p < 0.0028). Regional CTh was related to most cognitive outcomes (p < 0.0028), except verbal/visual memory subtests. Amygdalar volume was associated with letter fluency test (p < 0.0028). Hippocampal volume did not show significant associations. CONCLUSION: Baseline hemispheric/regional CTh, ventricular and amygdalar volume, but not the hippocampus, predict two-year cognitive outcomes in EOAD.

Longitudinal brain atrophy and CSF biomarkers in early-onset Alzheimer's disease.

There is evidence of longitudinal atrophy in posterior brain areas in early-onset Alzheimer's disease (EOAD; aged < 65 years), but no studies have been conducted in an EOAD cohort with fluid biomarkers characterization. We used 3T-MRI and Freesurfer 6.0 to investigate cortical and subcortical gray matter loss at two years in 12 EOAD patients (A + T + N + ) compared to 19 controls (A-T-N-) from the Hospital Clínic Barcelona cohort. We explored group differences in atrophy patterns and we correlated atrophy and baseline CSF-biomarkers levels in EOAD. We replicated the correlation analyses in 14 EOAD (A + T + N + ) and 55 late-onset AD (LOAD; aged ≥ 75 years; A + T + N + ) participants from the Alzheimer's disease Neuroimaging Initiative. We found that EOAD longitudinal atrophy spread with a posterior-to-anterior gradient and beyond hippocampus/amygdala. In EOAD, higher initial CSF NfL levels correlated with higher ventricular volumes at baseline. On the other hand, higher initial CSF Aß42 levels (within pathological range) predicted higher rates of cortical loss in EOAD. In EOAD and LOAD subjects, higher CSF t-tau values at baseline predicted higher rates of subcortical atrophy. CSF p-tau did not show any significant correlation. In conclusion, posterior cortices, hippocampus and amygdala capture EOAD atrophy from early stages. CSF Aß42 might predict cortical thinning and t-tau/NfL subcortical atrophy.

Contribution of CSF biomarkers to early-onset Alzheimer's disease and frontotemporal dementia neuroimaging signatures.

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 [Aß42], 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 Aß 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.