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Background: Atrophy of thalamic subregions has been observed across the spectrum of frontotemporal dementia (FTD). To gain better insight into underlying tissue changes, we investigated how thalamic subregional fractional anisotropy (FA) and mean diffusivity (MD) derived from diffusion tensor imaging (DTI) are altered in genetic FTD. Method: We used our newly developed thalamus segmentation tool, which jointly combines structural and diffusion input MRI data, to segment thalami and extract thalamic subregional FA and MD values for 163 genetic mutation carriers and 126 non‐carriers with suitable 3T MRI data from the GENetic FTD Initiative (GENFI). Mutation carriers were divided according to their genetic diagnosis and CDR®+NACC FTLD global scores into presymptomatic/prodromal (≤0.5: 41 C9orf72, 59 GRN, 34 MAPT) and symptomatic (≥1: 8 C9orf72, 11 GRN, 10 MAPT) groups. Mean FA and MD values for thalamic subregions were obtained using diffusion tensors interpolated in the log domain and weighted by segmentation posterior probabilities. Thalamic subregional FA and MD values for presymptomatic and symptomatic mutation carriers within each genetic group were compared with non‐carriers using analysis of covariance with bootstrapping, where age, scanner type, and sex were covariates. We corrected for multiple comparisons and calculated percentage changes in adjusted FA and MD mean values for mutation carriers relative to non‐carriers. Result: The only significant change at the presymptomatic stage was found for C9orf72 expansion carriers, who showed FA reduction in the intralaminar subregion (5%) (Figure 1, Table 1). In symptomatic C9orf72 expansion carriers, FA was reduced in the laterodorsal (21%), lateral posterior (13%), anteroventral (13%) and intralaminar (11%) subregions. Symptomatic MAPT mutation carriers also showed FA reduction in the laterodorsal (15%) and anteroventral (11%) subregions. No significant FA reductions were found for GRN mutation carriers and no significant MD changes were observed for any group after correction for multiple comparisons. Conclusion: We detected FA reductions of thalamic subregions only for C9orf72 expansion carriers at the presymptomatic stage, and for C9orf72 and MAPT mutation carriers at the symptomatic stage. Combined with the lack of robust MD changes, our findings may warrant further assessment of thalamic microstructure with more advanced diffusion models. [ABSTRACT FROM AUTHOR]

Background: Blood‐based biomarkers have recently emerged as minimally‐invasive, accessible and relatively inexpensive diagnostic and prognostic tools for people with cognitive impairment. Before being routinely implemented in clinical practice, the diagnostic performance of distinct commercially available assays should be studied in real‐world cohorts. We aimed to study and compare the diagnostic accuracy of different plasma biomarkers measured using two different assay platforms in a memory clinic cohort. Method: Participants were selected from a prospective memory clinic cohort; all had Alzheimer's disease (AD) CSF biomarkers performed. Plasma p‐tau181, GFAP and NfL were measured using Simoa (Quanterix), while plasma p‐tau181, Aβ1‐40 and Aβ1‐42 were quantified using Lumipulse G (Fujirebio). Clinical diagnoses were made according to published criteria, blinded to plasma biomarkers. Aβ status (‐/+) was defined by CSF Aβ concentration using local cutoffs. Result: One hundred and ten participants were included (mean age [standard deviation] 66 [7.8] years, 56% women). Diagnostic categories included 10 cognitively unimpaired controls, 24 with suspected non‐neurodegenerative cause of cognitive impairment (SND), 53 AD, 9 Lewy body disease (LBD, 4 Aβ+) and 14 frontotemporal dementia (FTD, 1 Aβ+). Plasma p‐tau181Quanterix and Aβ1‐42/Aβ1‐40 had the highest diagnostic accuracy (Figure 1) to discriminate between SND and AD (AUC [CI] 0.94 [0.89‐0.99] and 0.94 [0.85‐1]), followed by GFAP (0.93 [0.87‐0.99]), p‐tau181Fujirebio (0.90 [0.82‐0.98]) and Aβ1‐42 (0.71 [0.58‐0.85]). Plasma NfL performed the best to differentiate FTD from SND and AD (AUC 0.95 [0.88‐1] and 0.85 [0.71‐0.99], respectively). For Aβ status discrimination (Figure 2), p‐tau181Quanterix had an AUC [CI] of 0.91 [0.85‐0.96], followed by p‐tau181Fujirebio (0.86 [0.79‐0.93]), Aβ1‐42/Aβ1‐40 (0.85 [0.76‐0.93]) and GFAP (0.84 [0.77‐0.92]) with no statistically significant differences in AUCs. Balanced (Youden index) cut‐offs were calculated to study diagnostic performance, resulting in sensitivities of 79‐83%, specificities of 74‐83% and accuracies of 76‐83%. No combination of plasma biomarkers resulted in a significantly increased discriminative accuracy for Aβ status. All plasma biomarkers were moderately correlated with p‐tau181Quanterix (ρ = 0.40‐0.75, Figure 3). Conclusion: In our cohort, p‐tau181Quanterix, p‐tau181Fujirebio, Aβ1‐42/Aβ1‐40 and GFAP had a high diagnostic performance to discriminate CSF‐defined Aβ status. Plasma NfL identified individuals with FTD. Further studies comparing different plasma biomarkers are needed before implementation in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Little is known about the influence of age at onset (AAO) on plasma biomarkers and their use as prognostic biomarkers in Alzheimer's disease (AD). Method: We selected patients with AD diagnosis with available neuropsychological testing (NPS) at time of diagnosis and two years later, and plasma biomarkers at baseline. NPS battery included Free and Cued Selective Reminding Test (FCSRT), Landscape test (visual memory), Boston Naming Test, Semantic Fluency, BDAE auditory comprehension, Constructional and Ideomotor Praxis, Visual Object and Space Perception (VOSP) Incomplete Letters and Number Location subtests, Trail Making Test (TMT) A and B, Phonemic Fluency, and Digit Span Forward and Backward. NPS scores were compared by AAO: early‐onset AD (EOAD; <65 years) vs. late onset AD (LOAD; >65y). We analyzed plasma biomarkers phosphorilated‐tau181 (p‐tau181), total tau (t‐tau), neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP) and ubiquitin C‐terminal hydrolase L1 (UCHL‐1) using the Quanterix Simoa p‐tau181 Advantage V2 and Neurology 4‐Plex A assays. Group comparisons and linear regressions adjusted by years of education (YOE) were performed in Stata/IC 16.1. Result: Forty‐two participants were included, 23 LOAD and 19 EOAD. Plasma p‐tau181 and GFAP levels were higher in LOAD (Table 1). We did not find differences between LOAD and EOAD in NPS tests at baseline or +2 years (Table 2). Plasma ptau‐181 was associated with progression in MMSE globally, VOSP‐Incomplete letters globally and in EOAD. Plasma NfL were associated to Boston Naming test globally and in EOAD, Semantic fluency test globally, VOSP‐incomplete letters in EOAD, and Free and Total Learning of FCSRT in LOAD. Plasma GFAP was associated to MMSE globally and in EOAD, Free learning of FCSRT in LOAD and VOSP‐Incomplete letters and number location globally. Plasma UCHL‐1 was associated to Semantic fluency test in LOAD (Table 3). Praxis and attention and executive function tests loss were not associated to plasma biomarkers. Conclusion: Plasma p‐tau18, NfL, GFAP and UCHL‐1 were associated to pogression in memory, language and visual tests. They were predominantly associated to memory loss in LOAD and language and visual function loss in EOAD. Results need to be interpreted cautiously due to small sample size. [ABSTRACT FROM AUTHOR]

Background: Plasma Neurofilament light chain (pNfL) is a promising biomarker to discriminate Frontotemporal dementia (FTD) from other diagnoses such as Alzheimer's disease (AD) or psychiatric disorders. Currently, the diagnostic criteria for FTD syndromes are structured hierarchically into "Possible", "Probable" and "Definite" levels depending on the degree of confidence set by biomarkers. We aim to study the diagnostic and prognostic values of pNfL in the different confidence levels of FTD diagnosis. Method: Patients fulfilling criteria for FTD were classified as "Possible", "Probable" or "Definite" FTD based on the current diagnostic criteria. pNfL levels were determined with SiMoA, and then compared to Control and AD groups, as well as within the different levels of confidence in the FTD diagnosis. Result: 187 subjects were included: 37 controls, 79 AD and 71 FTD (11 Possible, 49 Probable and 11 Definite). The FTD group showed higher concentrations of pNfL than the Control (p < 0.001, AUC: 0.817) and the AD groups (p < 0.001, AUC: 0.677) (Figure 1A and 1B). Within the FTD group, pNfL levels differed across the different diagnostic confidence levels (Figure 1C). Subjects with Probable and Definite FTD showed higher levels of pNfL than subjects with Possible FTD (p < 0.001 and p < 0.01 respectively). By contrast, the Possible FTD group did not show differences in pNfL concentrations with the control group. In that sense, the diagnostic value of pNfL varied according to the different grades of diagnostic confidence (Figure 1D), with cases with probable or definite FTD exhibiting an excellent AUC to distinguish from controls (AUC 0.860 and 0.908 respectively), but with no discrimination between possible FTD and controls (AUC 0.583). pNfL presented an excellent performance in the differentiation of structural or functional impairment in the neuroimage (AUC 0.94, and AUC 0.86 respectively) (Figure 2). Finally, higher baseline pNfL levels were associated with worse clinical progression in FTD cases (Figure 3). Conclusion: pNfL were higher in FTD patients compared to controls or AD patients. However, we found significant variation across the different confidence levels of FTD diagnosis. Finally, pNfL predicted neuroimage alterations and a worse clinical progression in FTD patients. [ABSTRACT FROM AUTHOR]

Background: The diagnosis of early‐onset neurodegenerative dementias (<65 years) can represent a challenge due to their lower frequency respect to late‐onset dementias and atypical forms of presentation. Cognitive impairment has emerged as a frequent complaint after COVID‐19 infection. Method: We retrospectively reviewed (2016‐2021) the demographic and clinical data of the new referrals at our early onset dementia clinic (Hospital Clínic Barcelona). We used Fisher's Exact test and ANOVA in Stata/IC 16.1 to analyze differences between patients visited in 2021, 2020 and the period 2016‐2019. Result: We evaluated 296 patients in 2021, 104 in 2020 and 98 patients/year in 2016‐2019. In 2021, patients had an age at onset (AAO) of 50.1 years, lower than 2020 (53.4) and the period 2016‐2019 (53.0) (p<0.05). The percentage of women in 2021 (69.6%) was higher than 2020 (57.7) and 2016‐2019 (56.0) (p<0.05). Diagnostic delay was lower in 2021 (2.1 years) than 2020 (3.3) and 2016‐2019 (3.0) (p<0.05). No differences were found in Minimental (MMSE) scores (Table 1). In the period 2016‐2021, the number of neurodegenerative diseases (ND) remained steady, the number of subjective cognitive decline (SCD) decreased and the number of non neurodegenerative causes (NNC) experienced a large increase (Table 1), representing 77.7% of visits in 2021. We did not find differences in the type of ND diagnosis in each period (Figure 1A). ND subgroups did not show differences in AAO, sex or MMSE. In 2021, NND presented lower AAO, higher percentage of women, lower diagnostic delay (Figure 1B) and higher MMSE scores than previous years. No differences were found in the SCD group. Cognitive impairment after Covid‐19 accounted for 16.7% of NND in 2020 (n = 8, AAO 50.6 (11.8), 62.5% female, MMSE 26.8(2.3)) and 66.6% of NND in 2021 (n = 153, AAO 49,0 (10.0), 80.1% female, MMSE 27.8 (2.6)). Conclusion: In 2021 we visited approximately three times more patients than in 2016‐2020, among which we observed an increase in NND, mainly patients with cognitive impairment after Covid‐19. On contrast, we found a similar number of ND diagnosis and reduction in SCD. [ABSTRACT FROM AUTHOR]

Background: The CDR®+NACC FTLD Sum of Boxes (SB) score is a well‐established measure of disease severity in frontotemporal dementia (FTD), however, few studies have assessed longitudinal changes in score within familial forms of FTD. Method: 343 participants from the Genetic FTD Initiative (77 mutation‐negative controls, 109 C9orf72 expansion carriers, 105 GRN mutation carriers, and 52 MAPT mutation carriers), with available scores on the CDR®+NACC FTLD‐SB and the FTD Rating Scale (FRS) at baseline and follow‐up, were examined. Using the baseline FRS percentage scores, mutation carriers were stratified into four groups: asymptomatic/very mild (100‐97%), mild (96‐80%), moderate (79‐41%), and severe (40‐0%). Linear regression models with bootstrapping were used to assess annualised change in CDR®+NACC FTLD‐SB scores across genetic groups between participants' first and follow‐up visit. Result: C9orf72 and GRN mutation carriers in the moderate and severe groups, as well as mild GRN carriers, showed significantly greater change than controls (p < 0.050). No significant differences in change scores were observed between MAPT mutation carriers and controls. Comparisons within genetic groups revealed that both the moderate and severe GRN and C9orf72 expansion carriers showed larger annualised change relative to their asymptomatic/very mild counterparts (p < 0.001 and p = 0.037; p = 0.028 and p < 0.001, respectively). The moderate GRN and severe C9orf72 groups also demonstrated greater annualised score changes than their respective mild counterparts (p <.001; p = 0.001). For MAPT mutation carriers, no significant changes were observed. Between genetic group comparisons revealed significant change score differences between the moderate individuals, with GRN mutation carriers showing a greater change than the C9orf72 and MAPT groups (both p < 0.001), and the C9orf72 group showing a larger annualised change than the MAPT mutation carriers (p = 0.041). Conclusion: The CDR®+NACC FTLD‐SB detects significant changes in disease severity from baseline to a first follow‐up visit in familial forms of FTD, with GRN mutation carriers showing the greatest annualised change. [ABSTRACT FROM AUTHOR]

Background: Previous research in genetic frontotemporal dementia (FTD) has suggested that the FTD Rating Scale (FRS) may be a more sensitive measure of disease severity than the Clinical Dementia Rating scale plus National Alzheimer's Coordinating Centre Frontotemporal Lobar Degeneration score (CDR+NACC FTLD). This study aims to assess the potential of longitudinal measurement of the FRS to track disease trajectory, using data from the Genetic FTD Initiative (GENFI). Method: 119 mutation negative controls and 270 mutation carriers (52 MAPT, 107 GRN, 111 C9orf72) from the GENFI cohort completed the FRS at their baseline and follow‐up visits. Participants were grouped by disease severity according to their CDR+NACC FTLD global score at the baseline visit, which generated five mutation groups: asymptomatic (0), prodromal (0.5), mild (1), moderate (2), and severe (3), plus the control group. Annualised FRS change scores were generated for each participant (mean interval between visits = 1.3 years, standard deviation = 0.6). For each of the genetic groups, correlations with annualised change score for the MMSE and the CDR+NACC FTLD SOB were performed. Result: As disease becomes more severe, the annualised change in FRS was larger, peaking at the moderate stage: asymptomatic 0.6 (8.0), prodromal ‐5.1, (23.2), mild ‐7.2 (24.8), moderate ‐7.8 (11.6), severe ‐1.8 (8.2). The moderate group was significantly different from controls (p = 0.018) and the asymptomatic group (p = 0.030). The annualised change in FRS negatively correlated with the annualised change in CDR+NACC FTLD Sum of Boxes (Rho = ‐0.4, p<0.001) and positively correlated with the annualised change in MMSE (Rho = 0.3, p = 0.001) in GRN MCs but not in MAPT or C9orf72. Conclusion: The FRS shows promise as a sensitive clinical outcome measure, but only at certain stages of the disease. More sophisticated modelling utilising the wider GENFI cohort will help to establish the real potential for use in clinical settings. [ABSTRACT FROM AUTHOR]

Background: Semantic and socioemotional knowledge, including person recognition, can be altered in frontotemporal dementia (FTD), and is often associated with the right temporal lobe variant. Using data from the Genetic FTD Initiative, we investigated person recognition deficits in genetic FTD. Method: 901 GENFI participants (279 mutation negative controls, 280 C9orf72 mutation carriers (MCs), 101 MAPTMCs and 241 GRN MCs) were grouped using the Clinical Dementia Rating scale plus National Alzheimer's Coordinating Centre Frontotemporal Lobar Degeneration (CDR plus NACC FTLD) global score where 0 denotes asymptomatic, 0.5 as prodromal, and 1+ as mild to severe symptoms (C9orf72: 135 = 0, 48 = 0.5, 97 = 1+; GRN: 143 = 0, 35 = 0.5, 63 = 1+; MAPT: 50 = 0, 20 = 0.5, 31 = 1+). Person recognition (PR) was assessed using a single question within a structured clinical questionnaire, scoring the ability to recognise people who should be familiar by face or voice to them, with a value between 0 (absent) to 3 (severe), similar to the CDR scale. The percentage of participants with PR deficits was calculated for each group. Logistic regression with bootstrapping compared the PR score between groups with age, gender, and education as covariates. Result: 16.1% of C9orf72 MCs (0 = 0.7%, 0.5 = 2.1%, 1+ = 44.3%), 7.5% of GRN (0 = 0.0%, 0.5 = 8.6%, 1+ = 23.8%) and 17.8% of MAPT carriers (0 = 2%, 0.5 = 10%, 1+ = 48.4%) showed PR deficits. Mean (standard deviation) severity in each group was: C9orf72 0 = 0.0(0.0), 0.5 = 0.0(0.1), 1+ = 0.6(0.9); GRN 0 = 0.0(0.0), 0.5 = 0.0(0.1), 1+ = 0.2(0.6); MAPT 0 = 0.0(0.2), 0.5 = 0.1(0.3), 1+ = 0.6(0.8). Each of the symptomatic genetic groups had a significantly greater PR deficit than the control group (p<0.001), with the prodromal MAPT (p = 0.006) and GRN (p<0.001) groups also showing a greater impairment than controls. There was a trend to significance in the C9orf72asymptomatic and prodromal groups compared with controls (p = 0.058 and p = 0.059 respectively). Symptomatic C9orf72 and MAPT carriers showed greater impairment than the symptomatic GRN carriers (both p = 0.005). Conclusion: Person recognition is a key early marker of disease in some individuals with genetic FTD and further imaging analyses will help to reveal the underlying mechanism of this deficit. [ABSTRACT FROM AUTHOR]

Background: The application of blood‐based biomarkers for the identification of Alzheimer's disease (AD) and the development of novel digital technologies as cognitive screening tests are critical to moving toward a reliable, more accessible early diagnosis. Our aim was to evaluate the diagnostic performance of a machine learning‐based cognitive assessment known as Altoida's digital neuro‐signature (DNS) in patients with non‐degenerative mild cognitive impairment (ndMCI) and MCI due to AD (prodromal AD) and its association with CSF and plasma biomarkers. Method: Altoida's MCI‐DNS is a 10‐minute cognitive test battery evaluating activities of daily living via motoric and augmented reality tasks. The test consists of placing and finding virtual objects in a real environment and its final score is obtained by weighting multi‐modal digital data features, such as hands' micro‐movements, speed, reaction times, or navigation trajectories. We included 81 participants, classified according to their clinical status and CSF AD biomarker profile as: cognitively unimpaired controls, CTR (n = 10; age = 68.5±5.9; MMSE = 29.4±1.1), ndMCI (n = 25; age = 67.6±7.2; MMSE = 26.9±1.9) and prodromal AD (n = 46; age = 70.8±4.9; MMSE = 24.3±3.3). We further investigated a subsample of participants classified according to their plasma pTau181 levels as measured by SiMoA [cutoff = 1.37 pg/mL (Sarto et al., 2022)]: pTau181 negative (n = 27; age = 68.5±5.9; MMSE = 26.9±2.7) or pTau181 positive (n = 30; age = 70.3±5.5; MMSE = 24.1±3.7). Result: Significant differences were found in MCI‐DNS scores between CTR group and ndMCI (F = 23.5; p<0.001) and prodromal AD (F = 114.4; p<0.001) groups. Also, ndMCI showed higher MCI‐DNS scores than the prodromal AD group (F = 4.53; p<0.05, Fig. 1). ROC curves showed an excellent diagnostic accuracy of the MCI‐DNS in the discrimination between CTR vs. ndMCI (AUC = 0.879) and CTR vs. prodromal AD (AUC = 0.975) (Fig. 1). Further analyses showed differences in MCI‐DNS scores between CSF Aβ42 negative and CSF Aβ42 positive (F = 18.9; p<0.001; Fig. 2), as well as between plasma pTau181 negative and plasma pTau181 positive (F = 6.16; p<0.01; Fig. 2). Finally, MCI DNS scores significantly correlated with CSF Aβ42, CSF Aβ42/pTau ratio, CSF neurofilament‐light chain (NfL) and plasma pTau181 concentrations (Fig. 3). Conclusion: Altoida's MCI‐DNS test allows excellent discrimination between CTR and patients with MCI. MCI‐DNS scores significantly correlate with CSF AD core biomarkers, biomarkers of neurodegeneration and blood‐based biomarkers (i.e., plasma pTau181). [ABSTRACT FROM AUTHOR]

Background: One of the clinical problems for biomarkers' clinical use is the ability to differentiate between Alzheimer's disease (AD), frontotemporal dementia (FTD), and healthy subjects (CTR). This clearly challenges diagnosis and prognosis. We implemented a ML algorithm that provides individual probabilistic diagnoses for these dementias based on magnetic resonance imaging (MRI) and we correlated the results with biochemical markers. Method: We studied 3T‐T1w MRI of 432 subjects. A subset of these participants had cerebrospinal fluid (CSF) and plasma biomarkers (Table 1). We obtained regional subcortical gray matter volumes and cortical thickness measures using Freesurfer. We implemented a calibrated classifier with a Support Vector Machine with only the MRI data. We tested paired‐wise classification and classification across the 3 groups. We obtained individual probabilities associated with group correspondence. We studied the correlation between these probabilities and CSF and plasma biomarkers. For this, we subdivided the groups into true‐group (subjects with classification according to clinical diagnosis) and false‐group (subjects which did not coincide with clinical diagnosis). Finally, we implemented a permutation test to find the importance of each region in the classification. Result: We obtained accuracies of 90.7 ± 6.7% in the CTR vs AD classification, 88.6 ± 4.5% for CTR vs FTD, 79.3 ± 8.8% for AD vs FTD, and 79.9 ± 5.1% when discriminating the 3 groups. We obtained a significant positive correlation for plasma p‐tau181 for the false‐AD in the comparison AD vs CTR (Figure 1). The correlation of the false‐CTR was significantly positive for the CSF and plasma NfL. Finally, in the AD vs FTD, the true‐FTD had a significant negative correlation with CSF NfL. The other biochemical biomarkers did not provide additional information. The most important regions for classification are shown in Figure 2. Conclusion: The ML algorithm gave high accuracies. Within wrongly classified AD patients, probabilities correlated positively with plasma p‐tau181, suggesting hidden pathological processes associated with subjects clinically classified as CTR. Finally, the group probability within well‐classified FTD patients in comparison with AD negatively correlated with CSF NfL. We suggest that this approach can be used as a tool to try to develop personalized diagnoses. [ABSTRACT FROM AUTHOR]

Background: At present, there are limited fluid biomarkers which measure the underlying pathophysiology of frontotemporal dementia (FTD). Approximately a third of people with FTD have a genetic cause where the pathological basis is well understood. Studying fluid biomarkers in these genetic forms therefore allows greater insight into the relationship between the measure and the underlying disease mechanism. Based on prior work identifying synaptic and lysosomal dysfunction as common mechanisms across the different forms of FTD, we performed a targeted search of proteins related with synapses and the lysosomal pathway on an unbiased proteomic dataset generated from the GENetic FTD Initiative (GENFI) study. Method: The dataset was obtained from a total of 248 cerebrospinal fluid samples (CSF) from the GENFI cohort including 109 presymptomatic (44 C9orf72, 39 GRN, 26 MAPT) and 63 symptomatic (34 C9orf72, 17 GRN, 12 MAPT) mutation carriers as well as 76 mutation‐negative controls where Tandem Mass Tag (TMT) proteomics had been performed. We selected the clusters obtained from a Gene Ontology analysis that corresponded to the terms "synapse" and "lysosome" and generated a list of proteins related to each category. We then analysed the differences in each specific protein between genetic groups. Result: A total of 42 of the synaptic proteins preselected were significantly changed in symptomatic MAPT mutation carriers, 70 in symptomatic GRN mutation carriers, and 92 in the symptomatic C9orf72 group. Among all of these, there were only 9 proteins that overlapped in the three genetic forms. This list included reelin, which was significantly decreased in each symptomatic group when compared to controls (MAPT p‐value = 0.0016, GRN = 0.0008, C9orf72 = 0.0064), and calretinin, which was significantly increased in symptomatic groups compared to controls (MAPT = 0.0260, GRN = 0.0000, C9orf72 = 0.0100). We then used the same approach to study lysosomal proteins, and found that 38 proteins were significantly changed in symptomatic MAPT, 31 in the symptomatic GRN group and 41 in symptomatic C9orf72 expansion carriers. Conclusion: This study provides new insights into candidate markers to assess synaptic and lysosomal dysfunction in FTD. [ABSTRACT FROM AUTHOR]

Background: In healthy ageing, there is evidence of sex differences in vulnerability of hippocampal subfields to volume loss. However, this has not been investigated in early‐onset Alzheimer's disease (<65 years; EOAD). Method: We included 106 subjects: 62 EOAD (A+T+N+, MMSE>15) and 44 healthy controls (HC; A‐T‐N‐) that underwent lumbar puncture for analysis of AD biomarkers, 3T‐MRI scan and neuropsychological assessment. Hippocampal subfield segmentation was performed using T1‐weighted images and Freesurfer 6.0. Volume was adjusted by intracranial volume. Adjusted linear models were used to analyze differences between EOAD and HC and differences between sexes. We calculated Cohen's d as a measure of the effect size of volume change by sex, restricted to volume differences between EOAD and HC. In EOAD, we used linear models adjusted by age and education to investigate the association of volume loss with 18 cognition z‐scores. Results were adjusted using Bonferroni correction for multiple comparisons. Result: There were no demographic differences across groups. APOEε4 carriers were higher in EOAD‐female/EOAD‐male than HC‐female. EOAD‐female showed higher T‐Tau and P‐Tau levels than EOAD‐male (all p<0.05; Table1). Comparing EOAD vs. HC, differences were found in volume of all subfields and hippocampus (p<0.05, Bonferroni corrected), except for bilateral parasubiculum and right cornu ammonis (CA) 2/3. No differences were found between EOAD‐female and EOAD‐male (p>0.05). When compared to HC of the same sex, EOAD‐female showed differences in the same regions as the whole sample, while EOAD‐male showed differences in bilateral hippocampal tail and left presubiculum and hippocampus (all p<0.05, Bonferroni corrected, Figure 1). We observed larger effect sizes than in women in these regions, except for left hippocampus (Figure 2). In EOAD, higher volume in left CA2/3 predicted higher memory z‐scores, including free and total learning and delayed total recall (p<0.05 Bonferroni corrected; Figure 3). No associations were found in EOAD‐female nor EOAD‐male. Conclusion: In EOAD, the pattern of volume loss in hippocampal subfields was similar between sexes. However, females showed more marked differences from HC, except for bilateral hippocampal tail and left presubiculum. Further studies are necessary to elucidate the existence of sex differences in EOAD and their implication for cognitive impairment. [ABSTRACT FROM AUTHOR]

Background: Sleep dysfunction is common in neurodegenerative disorders, however, its neural correlates, remain poorly characterized in genetic frontotemporal dementia (FTD). Atrophy in two hypothalamic nuclei, the suprachiasmatic nucleus and the lateral hypothalamic area, important for sleep regulation, may be related to this dysfunction. Thus, we examined changes in cerebral and hypothalamic structure across the lifespan in genetic FTD and their relations to measures of sleep dysfunction. Method: Data was retrieved from the Genetic Frontotemporal Dementia Initiative (GENFI). T1‐weighted structural MRI images and scores on the Cambridge Behavioural Inventory‐Revised (CBI‐R) sleep subscale were obtained from subjects with mutations causative of FTD (n = 491, scan number = 1029) and healthy controls (n = 321, scan number = 739). MRI images were processed for cortical thickness using CIVET 2.1 and hypothalamic volumes using a deep learning segmentation algorithm (Billot et al., NeuroImage 2020). Using linear mixed‐effects models, we examined changes in sleep dysfunction, vertex‐wise differences in cortical thickness, and volumetric changes in hypothalamic regions in mutation carriers compared to controls. Further, using linear mixed‐effects models, we examined associations between cortical and hypothalamic atrophy and changes in the CBI‐R sleep subscale while controlling for age, sex, scanning site, and disease severity based on the MMSE. Result: Mutation carriers showed greater sleep dysfunction across the lifespan, and this increased closer to the predicted onset of symptoms, compared to controls (p < 0.01), with MAPT carriers having greater dysfunction overall (figure 1). All mutation carriers showed patterns of cortical thinning (figure 2) commensurate with the literature (p < 0.05, FDR corrected). Further, cortical thinning in frontal and parietal regions were associated with greater sleep disturbance in C9orf72 and GRN mutation carriers (p < 0.05, FDR corrected) (figure 3). Lastly, MAPT mutation carriers showed consistently significant hypothalamic volume loss across the lifespan (figure 4) (p < 0.01) and reduced hypothalamic volumes were related to increased sleep dysfunction (p < 0.05) (Figure 5). Conclusion: These findings suggest that while cortical thinning in C9orf72 and GRN carriers non‐specifically correlate with increased sleep dysfunction, the increased sleep dysfunction observed in MAPT carriers may be attributable to increased hypothalamic atrophy. [ABSTRACT FROM AUTHOR]

Introduction: We tested whether changes in functional networks predict cognitive decline and conversion from the presymptomatic prodrome to symptomatic disease in familial frontotemporal dementia (FTD). Methods: For hypothesis generation, 36 participants with behavioral variant FTD (bvFTD) and 34 controls were recruited from one site. For hypothesis testing, we studied 198 symptomatic FTD mutation carriers, 341 presymptomatic mutation carriers, and 329 family members without mutations. We compared functional network dynamics between groups, with clinical severity and with longitudinal clinical progression. Results: We identified a characteristic pattern of dynamic network changes in FTD, which correlated with neuropsychological impairment. Among presymptomatic mutation carriers, this pattern of network dynamics was found to a greater extent in those who subsequently converted to the symptomatic phase. Baseline network dynamic changes predicted future cognitive decline in symptomatic participants and older presymptomatic participants. Discussion: Dynamic network abnormalities in FTD predict cognitive decline and symptomatic conversion. Highlights: We investigated brain network predictors of dementia symptom onsetFrontotemporal dementia results in characteristic dynamic network patternsAlterations in network dynamics are associated with neuropsychological impairmentNetwork dynamic changes predict symptomatic conversion in presymptomatic carriersNetwork dynamic changes are associated with longitudinal cognitive decline [ABSTRACT FROM AUTHOR]

Background: Currently used biomarkers for the differential diagnosis of cognitive impairment are expensive and/or relatively invasive, limiting their availability to the general population. Blood protein biomarkers have showed promising results for screening, differential diagnosis, and prognosis. We aimed to study the diagnostic performance of plasma p‐tau181 in a daily clinical practice, prospective memory clinic cohort. Method: All patients referred for a first clinical evaluation with suspected cognitive impairment between January 1st, 2020 and March 30th, 2021, were invited to participate in the study. Plasma p‐tau181 was measured using SIMOA technology (Quanterix). Clinical diagnoses were made following current diagnostic criteria and blinded to plasma p‐tau181 results. Result: A total of 232 participants were recruited (mean age 69y, mean MMSE 24), including 25 cognitively unimpaired (CU) controls (mean age 67y, MMSE 28). Clinical diagnoses were AD (82 subjects, 43 of them with prodromal AD [CDR = 0.5]), 83 non‐neurodegenerative cognitive impairment (non‐ND, 75 of them CDR = 0.5), 22 frontotemporal dementia (FTD) and 20 Lewy body disease (LBD). Plasma p‐tau181 levels were statistically higher in AD (mean 2.39 pg/mL) compared with CU (mean 1.09 pg/mL), non‐ND (mean 1.43 pg/mL), FTD (mean 1.69 pg/mL) and LBD (mean 1.64 pg/mL) with a relatively good diagnostic performance for the differential diagnosis between AD and CU, non‐ND, LBD and FTD (AUC of 0.89, 0.80, 0.73 and 0.71, respectively). 128 subjects (55% of the cohort) had specific AD biomarkers (CSF or PET) available. In this subgroup, p‐tau181 differentiated AD from CU and non‐ND (AUC 0.91 and 0.92, respectively) and prodromal AD from CDR = 0.5 non‐ND participants (2.32 vs 1.02 pg/mL, p<0.001, AUC 0.90). Plasma p‐tau181 discriminated between a positive and negative amyloid beta status (defined by CSF/PET) with an AUC of 0.87 and AD subjects from those with a clinical diagnosis of LBD and FTD who had a negative amyloid beta status (AUC of 0.94 and 0.86, respectively). Plasma p‐tau181 correlated with CSF p‐tau181 (rs = 0.48, p<0.001). Conclusion: In our cohort of everyday clinical practice, plasma p‐tau181 is an accurate biomarker for predicting the AD pathophysiological process and discriminating AD from other neurodegenerative and non‐neurodegenerative causes of cognitive impairment. [ABSTRACT FROM AUTHOR]

The presymptomatic stages of frontotemporal dementia (FTD) are still poorly defined and encompass a long accrual of progressive biological (preclinical) and then clinical (prodromal) changes, antedating the onset of dementia. The heterogeneity of clinical presentations and the different neuropathological phenotypes have prevented a prior clear description of either preclinical or prodromal FTD. Recent advances in therapeutic approaches, at least in monogenic disease, demand a proper definition of these predementia stages. It has become clear that a consensus lexicon is needed to comprehensively describe the stages that anticipate dementia. The goal of the present work is to review existing literature on the preclinical and prodromal phases of FTD, providing recommendations to address the unmet questions, therefore laying out a strategy for operationalizing and better characterizing these presymptomatic disease stages. [ABSTRACT FROM AUTHOR]

Background: Development of endpoints for clinical trials in frontotemporal dementia (FTD) is increasingly urgent. In other neurodegenerative diseases composite scores are often used as outcome measures but are, as of yet, lacking in FTD. The aim of this study was to create gene‐specific cognitive composite scores for MAPT, GRN and C9orf72 mutation carriers and provide recommendations on recruitment and trial duration. Method: 69 C9orf72, 41 GRN, 28 MAPT mutation carriers with a CDR® plus NACC‐FTLD global score ≥0.5 and 275 controls completed a neuropsychological battery covering five cognitive domains. Logistic regression was used to identify the combination of tests that discriminated best between mutation carrier groups and controls. Weighted averages of the test scores in the models were calculated based on the regression coefficients (GENFI‐cog). Sample size estimates were calculated for individual tests and composite. The treatment effect was estimated as the mean difference between CDR® plus NACC‐FTLD 0.5 and 1 groups. Time‐to‐event analysis was used to determine the fraction of patients within GENFI that converted from CDR® plus NACC‐FTLD 0.5 to ≥1. Result: The most sensitive model in C9orf72 mutation carriers included a combination of executive, social cognitive and visuoconstructive tests (Table 1). A combination of executive, social cognitive, semantic and memory tests was most sensitive in GRN mutation carriers, and a combination of social cognitive, attention, semantic and memory tests in MAPT mutation carriers, resulted in the most sensitive model. The estimated sample size to detect a treatment effect was lower for the composite than for most individual tests (Table 2). A Kaplan‐Meier curve (Figure 1) showed that after three years 50% of individuals convert from CDR® plus NACC‐FTLD global score 0.5 to 1 or more. Conclusion: We created gene‐specific cognitive composite scores for C9orf72, GRN and MAPT mutation carriers (GENFI‐cog) which resulted in substantially lower estimated sample sizes to detect a treatment effect than most individual cognitive tests. Only 50% of patients with a CDR® plus NACC‐FTLD of 0.5 convert to ≥1 in a three‐year period. GENFI‐cog has potential as a cognitive endpoint for upcoming trials and the results from this study provide important information concerning trial duration and sample sizes. [ABSTRACT FROM AUTHOR]

Background: Connections among brain regions allow pathological perturbations to spread from a single source node to multiple nodes. Patterns of neurodegeneration in multiple diseases, including behavioral variant of frontotemporal dementia (bvFTD), resemble the network architecture (Seeley et al., 2009, Neuron), but how bvFTD‐related atrophy patterns relate to the network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture, such that connected regions display similar atrophy patterns. Method: Deformation‐based morphometry (DBM) was used to estimate regional changes in tissue volume density from T1‐weighted magnetic resonance images of 75 genetic bvFTD patients and 247 healthy controls (GENFI, http://genfi.org.uk/). We used linear mixed effects model to obtain a bvFTD‐related atrophy map, controlling for age, sex and aquision site. Structural and functional connectivity (SC and FC), derived from an independent sample of 70 healthy participants (Griffa et al., 2019, Zenodo), were used to estimate mean neighbor atrophy values of each region. Relationship between node and neighbor atrophy was examined by correlating neighbor atrophy with nodal atrophy. Statistical significance of the analyses was assessed using a spatial autocorrelation‐preserving null model. Analyses were replicated in an independent dataset (FTLDNI, AG032306) with 70 sporadic bvFTD patients and 123 healthy controls. Result: Distributed atrophy patterns were observed in bvFTD, mainly targeting areas associated with limbic intrinsic network and insular cytoarchitectonic class (Fig 1a). A node’s atrophy was significantly correlated with atrophy of its connected neighbors (e.g. high resolution: r=0.58, p=0.006 and r=0.54, p=0.0006, for SC‐ and FC‐ defined neighbors respectively) (Fig 2c). Relationship between node and neighbor atrophy was consistent across resolutions and greater in empirical networks compared to null networks. While a number of frontotemporal regions were identified as potential disease epicenters, anterior insula was the most likely one. Results were consistent in the sporadic cohort (Fig 1b&2d). Conclusion: Using connectivity models and rigorous statistical analyses that account for spatial autocorrelation, we demonstrate that bvFTD‐related neurodegeneration is conditioned by connectome architecture, accounting for 30‐40% of variance in atrophy. Atrophy is most profound in regions associated with insular cortex. [ABSTRACT FROM AUTHOR]

Background: Approximately a third of frontotemporal dementia (FTD) is genetic with mutations in three genes accounting for the majority of the inheritance: C9orf72, GRN and MAPT. Synaptic dysfunction is a common mechanism in all of them and the use of fluid biomarkers could be helpful to improve the diagnostic accuracy and useful as a readout of cellular dysfunction within therapeutic trials. Method: A total of 193 cerebrospinal fluid samples from the GENetic FTD Initiative including 77 presymptomatic (31 C9orf72, 23 GRN, 23 MAPT), 55 symptomatic mutation carriers (26 C9orf72, 17 GRN, 12 MAPT) and 61 mutation‐negative controls were measured using a microflow LC PRM‐MS set‐up targeting 15 synaptic proteins: 14‐3‐3 proteins (eta, zeta/delta and epsilon), AP‐2 complex subunit beta, beta‐synuclein, gamma‐synuclein, complexin‐2, neurogranin, neuronal pentraxin receptor (NPTXR), neuronal pentraxin 1 (NPTX1), neuronal pentraxin 2 (NPTX2), phosphatidylethanolamine‐binding protein 1 (PEBP‐1), rab GDP dissociation inhibitor α (rab GDIα), syntaxin‐1B and syntaxin‐7. Mutation carrier groups were compared to each other and to controls using a bootstrapped linear regression model, adjusting for age and sex. Result: Eight proteins were increased only in symptomatic MAPT mutation carriers (compared with controls) and not in symptomatic C9orf72 or GRN mutation carriers: 14‐3‐3‐eta, beta‐synuclein, gamma‐synuclein, neurogranin, PEBP‐1, rab GDIα, syntaxin‐1B and syntaxin‐7. In contrast, NPTX1 and NPTX2 were affected in all three genetic groups (decreased compared to controls), with NPTXR being affected in C9orf72 and GRN mutation carriers only (decreased compared to controls). No changes were seen in presymptomatic mutation carriers in these proteins. Figure 1 contains p‐values for all significant changes. Conclusion: Differential involvement of synaptic markers is seen in the genetic forms of FTD, with impairment particularly in those with MAPT mutations, with only the neuronal pentraxins affected in GRN and C9orf72 mutation carriers. Further work is needed to explore correlations with clinical and imaging biomarkers, whether there are changes in the late presymptomatic period, and how these markers change over time. [ABSTRACT FROM AUTHOR]

Background: A novel panel of 14 proteins measured in the CSF could separate individuals with genetic frontotemporal dementia (FTD) from controls, with most significant findings observed for neurofilament medium (NEFM), neuronal pentraxin 2 (NPTX2), neurosecretory protein VGF (VGF) and aquaporin 4 (AQP4) [1]. However, it is currently unknown whether these altered protein levels in the CSF reflect neurodegenerative changes in the brain. The aim of this study was to explore the cross‐sectional associations between the previously identified CSF biomarker candidates and cortical thickness in presymptomatic and symptomatic mutation carriers, and whether those associations differ by FTD mutation type. Method: We have analyzed T1 MRI scans alongside concurrent CSF samples from 202 individuals from the GENFI cohort, belonging to families that carry FTD mutations in either C9orf72, GRN or MAPT genes. The study sample included symptomatic mutation carriers, presymptomatic mutation carriers and non‐carrier controls. Cortical thickness was estimated with FreeSurfer and CSF protein levels were measured via a multiplexed antibody‐based suspension bead array. The correlations between regional cortical thickness and protein levels were calculated via linear regression. Result: Altered levels of NEFM, AQP4, APOA1, PTPRN2, CTSS, SERPINA3, C4, AMPH and CD14 were all correlated with increased atrophy of at least one cortical region. Some effects were mutation specific, but NEFM, AQP4 and APOA1 correlated with atrophy in all mutation groups. We also observed mutation specific effects for 10 of the proteins. CTSS levels were only correlated with cortical atrophy in C9orf72 mutation carriers while NPTX2, VGF and PTPRN2 correlated with atrophy in GRN mutation carriers. In MAPT mutation carriers, 6 different proteins correlated with atrophy in the right temporal pole. Conclusion: The proposed fluid biomarker candidates continue to show promise and further longitudinal studies will contribute to elucidate their relationship to cortical atrophy and their prognostic value in genetic FTD. [1] Bergström et al. Mol Neurodegener. 2021; 16(1):79 [ABSTRACT FROM AUTHOR]

Background: Despite previous studies establishing cognitive impairment as a major complaint in post‐acute COVID‐19 syndrome (PACS), a deeper understanding of the neuropsychological features and underlying causes is needed. We aimed to characterize the cognitive profile of patients affected with cognitive PACS and the influence of biological and psychological factors. Method: We performed a prospective single‐center study. We included participants with confirmed SARS‐CoV‐2 infection and long‐term symptoms ≥ 8 weeks after onset who were referred to our unit because of cognitive complaints. All participants completed a comprehensive neuropsychological battery (NPS) and questionnaires assessing depression (Beck Depression Inventory), anxiety (Beck Anxiety Inventory), apathy (Starkstein Apathy Scale) and fatigue (Multidimensional Fatigue Inventory) at baseline and +1, +3 and +6 months. We collected blood samples and cerebrospinal fluid (CSF) to obtain biochemical and immunological profiles. Group comparisons, correlations and Principal component analysis (PCA) were performed. Longitudinal analyses are ongoing. Result: Forty‐nine participants were included (79.6% female, mean age 50.1 (SD 7.9). At the time of assessment, they presented multiple symptoms other than cognitive complaints (88% fatigue, 61% headache, 63% dyspnea, and 10% fever). The NPS showed that executive functions were the most affected (up to 29% of the sample had at least one test altered), followed by memory (at least one test altered in 25%) (Figure 1). On the contrary, language and praxis were preserved. Participants presented with anxiety symptoms (minimal 8.7%, mild in 34.8%, moderate 26.1%, severe 30.4%), depressive symptoms (none 34.8%, mild 26.1%, borderline clinical depression 23.9%, moderate 8.7%, severe 6.5%), and clinically relevant apathy in 64.4%. The sample presented a mean score of total fatigue of 58 (min 48, max 68), (scores 20‐100). Fever and or moderate/severe anxiety were associated with lower scores in some memory and executive functions subtests (Figure 2). Most of the variability in the sample was explained by executive functions subtests (PCA, Figure 3). Patients presented increased levels of interleukins (IL) IL‐1b, IL‐17a and IL‐18 in CSF compared to controls. Conclusion: Cognitive PACS predominantly affected executive functions and memory. Fever and moderate/severe anxiety were associated with worse cognitive outcomes. Several inflammatory markers were altered in cognitive PACS. [ABSTRACT FROM AUTHOR]

Background: Post‐acute Covid‐19 syndrome (PACS) frequently refers to cognitive complaints. It is not yet clear whether there is an association between cognitive symptoms with brain changes or neuropsychiatric symptoms. Our aims are 1) to study cross‐sectional and longitudinal MRI brain measures in a cohort of PACS and 2) their association with cognitive performance and mood disturbances. Method: We performed a prospective single‐center study with 3T‐T1w MRI of 49 PACS patients at a cross‐sectional level. These participants had confirmed SARS‐CoV2 infection, ≥ 8 weeks after symptoms onset and cognitive complaints. All participants completed a comprehensive neuropsychological battery (NPS) and questionnaires assessing depression, anxiety, and subjective cognitive complaints (SCD). We obtained global MRI measures (e.g; gray and white matter volumes and mean cortical thickness) with FreeSurfer. We measured correlations of global MRI measures with SCD, memory and executive function outcomes, anxiety, and depression. All analyses were corrected for multiple comparisons. 44 PACS subjects had a 6‐month follow‐up MRI: in these, we performed longitudinal analyses with Generalized Linear Mixed‐Effects Models to study changes between visits in global MRI measures. Result: Demographics are shown in Table 1. We did not find any correlation between clinical outcomes (SCD, anxiety, depression) and MRI findings. Visual memory (Rey figure's recall) and cognitive interference inhibition and processing speed ((Stroop's color‐word condition) were positively correlated with global gray and white matter volume measures (Figure 1). We did not identify changes in global MRI measures at 6 months in PACS participants. Conclusion: In PACS, worse visual memory and executive function, but not other clinical outcomes, were associated with lower global structural MRI indexes. We did not observe global longitudinal changes in MRI. [ABSTRACT FROM AUTHOR]

Background: Lewy Body Dementia (LBD) is the second most common neurodegenerative dementia. To date, no validated biochemical marker is available to support clinical diagnosis. The development of the Real‐Time Quaking‐Induced Conversion (RT‐QuIC) assay for detecting alpha‐synuclein (aSyn) seeds in biological samples can be a sensitive biomarker specific for the diagnosis easily applicable in a clinical setting. We aimed to describe the diagnostic performance of RT‐QuIC aSyn assay in cerebrospinal fluid (CSF) to diagnose LBD in a clinical cohort with cognitive impairment. Method: A cohort of subjects with cognitive impairment (neurodegenerative and non‐neurodegenerative) with available CSF sample at the moment of first evaluation was selected by convenience sample in our database. Subjects had clinical follow‐ups ranging from 6 months to 12 years and current clinical diagnosis according to established consensus criteria. The diagnostic performance of RT‐QuIC aSyn for the diagnosis of LBD were evaluated. Result: The test was evaluated in 155 subjects (age 65 years (SD 11), MMSE 24 (SD 4.5), 56% male) with the following clinical diagnoses: LBD (n = 40), Alzheimer's disease (AD) (n = 73)(all with compatible CSF profile), frontotemporal dementia (FTD) (n = 7), non‐neurodegenerative mild cognitive impairment (MCI) (n = 21), other neurodegenerative diagnoses (n = 12) and healthy controls (n = 2). aSyn was detected in 33/40 (83%) LBD patients, 7/73 (10%) AD, 0/7 FTD patients, 0/21 MCI patients, 0/12 patients with other neurodegenerative diseases and 0/2 healthy controls. Only 3/7 (42%) LBD subjects with a negative RT‐QuIC asyn fulfilled criteria for LBD (established or prodromal) at the moment of first evaluation as compared with 23/33 (69%) of LBD with positive RTQuIC suggesting a more initial disease in the negative group at the moment of CSF sampling. The sensitivity and specificity of the assay were 83% and 94% respectively for the diagnosis of LBD, with a PPV of 83% and a NPV of 94%. Conclusion: Detection of αSyn seeds by RT‐QuIC has a good performance in identifying patients with LBD in a clinical cohort of cognitively impaired subjects. The test also identifies aSyn co‐pathology in a subgroup of subjects diagnosed with AD. [ABSTRACT FROM AUTHOR]

Introduction: Apathy adversely affects prognosis and survival of patients with frontotemporal dementia (FTD). We test whether apathy develops in presymptomatic genetic FTD, and is associated with cognitive decline and brain atrophy. Methods: Presymptomatic carriers of MAPT, GRN or C9orf72 mutations (N = 304), and relatives without mutations (N = 296) underwent clinical assessments and MRI at baseline, and annually for 2 years. Longitudinal changes in apathy, cognition, gray matter volumes, and their relationships were analyzed with latent growth curve modeling. Results: Apathy severity increased over time in presymptomatic carriers, but not in non‐carriers. In presymptomatic carriers, baseline apathy predicted cognitive decline over two years, but not vice versa. Apathy progression was associated with baseline low gray matter volume in frontal and cingulate regions. Discussion: Apathy is an early marker of FTD‐related changes and predicts a subsequent subclinical deterioration of cognition before dementia onset. Apathy may be a modifiable factor in those at risk of FTD. [ABSTRACT FROM AUTHOR]

Introduction: The presymptomatic phase of neurodegenerative disease can last many years, with sustained cognitive function despite progressive atrophy. We investigate this phenomenon in familial frontotemporal dementia (FTD). Methods: We studied 121 presymptomatic FTD mutation carriers and 134 family members without mutations, using multivariate data‐driven approach to link cognitive performance with both structural and functional magnetic resonance imaging. Atrophy and brain network connectivity were compared between groups, in relation to the time from expected symptom onset. Results: There were group differences in brain structure and function, in the absence of differences in cognitive performance. Specifically, we identified behaviorally relevant structural and functional network differences. Structure‐function relationships were similar in both groups, but coupling between functional connectivity and cognition was stronger for carriers than for non‐carriers, and increased with proximity to the expected onset of disease. Discussion: Our findings suggest that the maintenance of functional network connectivity enables carriers to maintain cognitive performance. [ABSTRACT FROM AUTHOR]

Background: Mutations in the MAPT gene are known to cause frontotemporal dementia (FTD), but there is heterogeneity in FTD phenotype across individuals. Here we used an unsupervised learning algorithm – Subtype and Stage Inference (SuStaIn) – to relate phenotypic heterogeneity to specific mutations in the MAPT gene. Method: SuStaIn evaluates the optimal grouping of individuals into disease subtypes, where each subtype consists of a sequence (set of stages) in which biomarkers transition between different z‐scores. We applied SuStaIn to cross‐sectional regional brain volumes extracted from T1‐weighted MRI data from MAPT carriers in the GENFI study to find the best stratification of the data into subtypes, and the temporal progression of each subtype. We used data from 82 MAPT carriers (57 presymptomatic and 25 symptomatic) to identify subtypes and data from a control group of 300 non‐carriers to derive z‐scores. We subtyped and staged individuals at up to five annual follow‐up visits to assess the consistency of the subtypes longitudinally. We compared the specific mutations and clinical and neuropsychological test scores of individuals assigned to each subtype. Result: SuStaIn identified two groups of MAPT carriers with distinct atrophy patterns (Figure 1), which we termed a 'temporal' subtype and a 'frontotemporal' subtype. The subtype assignments were consistent at follow‐up visits (Table 1): there were no individuals that changed from the temporal to the frontotemporal subtype or vice‐versa. Subtype assignment was strongly associated with IVS10+16, R406W and P301L mutations (Table 2): there was a one‐to‐one mapping between IVS10+16 and R406W mutations and the temporal subtype, and a near one‐to‐one mapping between P301L mutations and the frontotemporal subtype. The temporal subtype was associated with memory problems, whereas the frontotemporal subtype was associated with worse performance on tests of attention and visuospatial skills (Table 3). Conclusion: Our results demonstrate the utility of SuStaIn for identifying disease subgroups and associating imaging patterns with genetics and cognition. We show that different MAPT mutations give rise to distinct atrophy patterns and clinical syndromes, providing insights into the underlying disease biology, and potential utility for patient stratification. [ABSTRACT FROM AUTHOR]

Background: The ongoing COVID‐19 pandemic and related care policies have affected dementia patients. The characteristics of early‐onset dementia (EOD, <65 years) patients in 2020 may provide insights on how to rearrange the provision of care. Method: We retrospectively reviewed, from 2016 to 2020, the demographic and clinical data of the new referrals at our EOD clinic (Hospital Clínic Barcelona). We used Fisher's Exact test and Mann–Whitney U test in R4.0.2 (http://www.R‐project.org/) to analyze differences between 2020 and the period 2016‐2019. Result: In 2020, we did not visit any new referral from 15th march to 31th may. We evaluated 104 patients in 2020 and 392 patients in 2016‐2019 (mean=98(SD=11.8) patients/year). No differences were found in age at onset (AAO), sex, diagnostic delay and MMSE score (Table1). Significant differences were found in the diagnoses obtained in each period (p<0.000005, Figure1A). In 2020, 19.2% of the patients were diagnosed with neurodegenerative diseases (ND), 48.1% with non‐neurodegenerative diseases (NND) and 32.7% with subjective cognitive decline (SCD). On contrast, in 2016‐2019, 26% of the patients were diagnosed with ND, 22.2% with NND and 51.8% with SCD. Compared to 2016‐2019, ND, but not SCD or NND, presented longer diagnostic delay in 2020 (p<0.0005, Figure1B). ND, NND and SCD did not show differences between periods in AAO, sex or MMSE. We did not find differences in the type of ND in each period (Figure1A). Compared to 2016‐2019, Frontotemporal Lobar Degeneration (FTLD) presented longer diagnostic delay in 2020 (p<0.005, Figure1B) while ND subgroups did not show differences in AAO, sex or MMSE. Cognitive disturbances in recovered COVID‐19 patients accounted for 16% of NND in 2020 [N=8, AAO 50.63(12), 63% female, MMSE 26.8(2.3)]. Conclusion: In 2020, albeit we were forced to stop our normal activity during 2.5 months, we visited a similar number of patients among which we observed an increase in NND, including cognitive disturbances in patients with recovered COVID‐19. On contrast, we found a reduction in SCD and, to a lesser extent, ND. ND showed a longer diagnostic delay in 2020 that mainly affected FTLD. Whether COVID‐19 pandemic entails a diagnostic delay in dementia patients must be confirmed in 2021. [ABSTRACT FROM AUTHOR]

Background: Several fluid biomarkers for genetic frontotemporal dementia (FTD) have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain (NfL) and phosphorylated neurofilament heavy chain (pNfH)), synapse dysfunction (neuronal pentraxin 2 (NPTX2)), gliosis (glial fibrillary acidic protein (GFAP)) and complement activation (C3b, C1q). Determining the sequence in which biomarkers become abnormal over the course of disease could facilitate disease staging in FTD and enable us to identify mutation carriers with prodromal or early‐stage FTD, which is especially important as pharmaceutical interventions emerge. We aimed to model the sequence of biomarker abnormalities in presymptomatic and symptomatic genetic FTD using cross‐sectional data from the Genetic Frontotemporal dementia Initiative (GENFI). Method: 276 presymptomatic and 142 symptomatic carriers of mutations in GRN, C9orf72 or MAPT, as well as 247 non‐carriers, were selected from the GENFI cohort based on availability of one or more of the aforementioned biomarkers. Nine presymptomatic carriers developed symptoms within 18 months of data collection ('converters'). Sequences of biomarker abnormalities were modelled for the entire group using discriminative event‐based modelling (DEBM) and for each genetic subgroup using co‐initialized DEBM. These models estimate probabilistic biomarker abnormalities in a data‐driven way and do not rely on prior diagnostic information or biomarker cut‐off points. We estimated individual disease severity scores based on the position of subjects along the disease progression timeline through cross‐validation. Result: Cerebrospinal fluid (CSF) NPTX2 was the first detectable abnormal biomarker, followed by blood and CSF NfL, blood GFAP, blood pNfH and finally CSF C1q and C3b (Fig. 1). Biomarker orderings did not differ significantly between genetic subgroups. Estimated disease severity scores (Fig. 2) could distinguish symptomatic from presymptomatic carriers and non‐carriers with areas under the curve (AUC) of 0.84 and 0.90 respectively. The AUC to distinguish converters from non‐converting presymptomatic carriers was 0.85. Conclusion: In our data‐driven disease progression models of genetic FTD, NPTX2 and NfL were the first biomarkers to become abnormal. Further research should focus on their utility as candidate selection tools for pharmaceutical trials. Estimating disease stages using DEBM could enable us to identify presymptomatic carriers approaching symptom onset and track the efficacy of therapeutic interventions. [ABSTRACT FROM AUTHOR]

Background: Genetic frontotemporal dementia (FTD) is highly heterogeneous, with carriers of mutations in the same gene manifesting different phenotypes. Using in vivo MR images from the Genetic FTD Initiative (GENFI), we aimed to identify subgroups within the same genetic group whose brains were affected differently. Method: Cortical and subcortical volumes of interest were generated using automated parcellation methods on volumetric 3T T1‐weighted MRI scans for 479 carriers (198 GRN, 202 C9orf72, and 79 MAPT mutation carriers). W‐scores for 85 volumes of interest were computed from a linear regression model carried out on 298 non‐carrier cognitively normal controls adjusting for the effect of age, sex, total intracranial volume and scanner type. Cluster analyses with the Ward agglomerating method were performed on all w‐scores while considering the three genetic groups independently. The identified clusters were then compared for age, estimated years from onset, global and sum of boxes scores of the CDR® plus NACC FTLD (at baseline and after one year), neurofilament light chain (NfL) levels in the plasma and w‐scores in brain regions typically showing early atrophy (Kruskal‐Wallis test). Result: We identified three clusters among the GRN mutation carriers and four in the MAPT and C9orf72 groups, which were all significantly different for the variables reported in the Table (p‐value<0.003). For all three genetic groups, one cluster was formed by patients with a clinical diagnosis of FTD, with more extensive atrophy and increased disease severity. For the remaining clusters, there seemed to be an association with disease severity for MAPT and GRN mutation carriers but not so for C9orf72 expansion carriers where clinical scores were not clearly associated with a specific cluster. Conclusion: By only looking at regional brain volumes, we were able to detect different clusters within carriers of mutations in the same gene, with C9orf72 expansion carriers being the most heterogenous group. Further investigations with specific cognitive, clinical and biomarkers correlates, including further follow‐up visits, are needed. [ABSTRACT FROM AUTHOR]

Background: Mutations in MAPT are associated with frontotemporal dementia (FTD), but little is known about the progression in its early stages. We aimed at identifying the presence of early brain changes in MAPT mutation carriers. Method: We included 3T MRIs from 84 MAPT carriers [27 symptomatic: mean(SD) age 58(8) years; 57 presymptomatic: 40(11) years] from the Genetic FTD Initiative (GENFI) and from 77 age‐matched non‐carrier healthy controls (44(14) years). Based on their expected years to symptom onset (EYO), we divided the presymptomatic carriers into early (n=35, <‐10 years) and late (n=22, >‐10 years) groups. First, we performed voxel‐based morphometry (VBM) comparing 24 symptomatic carriers with 32 controls to identify the regions of interest (ROIs) which were atrophic in the symptomatic stage of MAPT. We then used automated and manual segmentations to extract these ROI volumes in all carriers. To remove the effect of age, gender, total intracranial volume and scanner type, we transformed the volumes into w‐scores, considering the controls as the reference group. A w‐score of <‐1.28 (corresponding to the 10th percentile) was considered abnormal. Result: From the VBM we identified seven structures significantly atrophic in symptomatic carriers: the nucleus accumbens, amygdala, hippocampus, orbitofrontal cortex, temporal pole, anterior insula and hypothalamus. The percentage of early presymptomatic carriers with abnormal w‐scores was 14% for nucleus accumbens and hippocampus, and 20% orbitofrontal cortex. In the late group, 36% showed abnormal amygdala and temporal pole, 27% abnormal hippocampus, 32% abnormal anterior insula and 9% abnormal hypothalamus. In the symptomatic group, the percentages were higher: nucleus accumbens (41%), amygdala, hippocampus and temporal pole (85%), orbitofrontal cortex (56%), anterior insula (93%) and hypothalamus (48%). Conclusion: Abnormal limbic regions are a frequent feature in presymptomatic MAPT carriers, showing early structural changes before symptom onset. Further investigations on the associated cognitive and white matter changes are ongoing. [ABSTRACT FROM AUTHOR]

Background: Genetic frontotemporal dementia is highly heterogeneous, with different progression patterns seen between individuals. Using in vivo MR images from the Genetic FTD Initiative (GENFI), we aimed to identify clinical progression in genetic mutation carriers from their brain volumes at baseline. Method: Cortical and subcortical volumes of interest (VOIs) were generated using automated parcellation methods on volumetric 3T T1‐weighted MRI scans for 480 carriers (198 GRN, 202 C9orf72, 80 MAPT). W‐scores for 79 VOIs were computed from a linear regression model carried out on 298 non‐carrier cognitively normal controls adjusting for the effect of age, sex, total intracranial volume and scanner type. Carriers were divided into three disease stages based on their global CDR® plus NACC FTLD score (CDR‐GS): asymptomatic (0), possibly or mildly symptomatic (0.5) and fully symptomatic (1 or more). Cut‐off points for each VOI were derived from Youden indices estimated with ROC curves to distinguish between CDR‐GS=0 and CDR‐GS≥1 within each gene. CDR‐GS=0.5 carriers (30 GRN, 32 C9orf72, 13 MAPT) were classified as 'normal' or 'abnormal' based on these cut‐off points. We compared the CDR® plus NACC FTLD sum‐of‐boxes scores (CDR‐SOB) at one year follow‐up in these two groups. Result: Compared to those with normal baseline volumes, C9orf72 expansion carriers at CDR‐GS=0.5 showed significantly higher CDR‐SOB scores at follow‐up if they had abnormal volumes in the total frontal (+5 points), orbitofrontal (+3), dorsolateral prefrontal (+6), or anterior cingulate (+4) cortices, the basal‐paralaminar amygdala region (+2), CA1 region of the hippocampus (+4), total hippocampus (+6), cerebellar lobule VIIIb (+4), or lateral ventricles (+8). GRN mutation carriers showed significantly higher CDR‐SOB scores if their volumes were abnormal in the frontal (+10), parietal (+14), insula (+8), orbitofrontal (+12), or medial parietal (+7) cortices, or the total hippocampus (+10). MAPT mutation carriers with abnormal volumes in the lobule VI and dentate nucleus had 1 point higher CDR‐SOB scores at follow‐up. Conclusion: Abnormal baseline volumes in specific VOI within each of the genetic groups were related to worse CDR‐SOB scores over time. Future studies including longer follow‐up intervals and other longitudinal biomarkers are needed to explore this further. [ABSTRACT FROM AUTHOR]