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Introduction: The established link between DNA methylation and pathophysiology of dementia, along with its potential role as a molecular mediator of lifestyle and environmental influences, positions blood-derived DNA methylation as a promising tool for early dementia risk detection., Methods: In conjunction with an extensive array of machine learning techniques, we employed whole blood genome-wide DNA methylation data as a surrogate for 14 modifiable and non-modifiable factors in the assessment of dementia risk in independent dementia cohorts., Results: We established a multivariate methylation risk score (MMRS) for identifying mild cognitive impairment cross-sectionally, independent of age and sex (P = 2.0 × 10 -3 ). This score significantly predicted the prospective development of cognitive impairments in independent studies of Alzheimer's disease (hazard ratio for Rey's Auditory Verbal Learning Test (RAVLT)-Learning = 2.47) and Parkinson's disease (hazard ratio for MCI/dementia   = 2.59)., Discussion: Our work shows the potential of employing blood-derived DNA methylation data in the assessment of dementia risk., Highlights: We used whole blood DNA methylation as a surrogate for 14 dementia risk factors. Created a multivariate methylation risk score for predicting cognitive impairment. Emphasized the role of machine learning and omics data in predicting dementia. The score predicts cognitive impairment development at the population level., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Introduction: We investigated blood DNA methylation patterns associated with 15 well-established cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) pathophysiology, neuroinflammation, and neurodegeneration., Methods: We assessed DNA methylation in 885 blood samples from the European Medical Information Framework for Alzheimer's Disease (EMIF-AD) study using the EPIC array., Results: We identified Bonferroni-significant differential methylation associated with CSF YKL-40 (five loci) and neurofilament light chain (NfL; seven loci) levels, with two of the loci associated with CSF YKL-40 levels correlating with plasma YKL-40 levels. A co-localization analysis showed shared genetic variants underlying YKL-40 DNA methylation and CSF protein levels, with evidence that DNA methylation mediates the association between genotype and protein levels. Weighted gene correlation network analysis identified two modules of co-methylated loci correlated with several amyloid measures and enriched in pathways associated with lipoproteins and development., Discussion: We conducted the most comprehensive epigenome-wide association study (EWAS) of AD-relevant CSF biomarkers to date. Future work should explore the relationship between YKL-40 genotype, DNA methylation, and protein levels in the brain., Highlights: Blood DNA methylation was assessed in the EMIF-AD MBD study. Epigenome-wide association studies (EWASs) were performed for 15 Alzheimer's disease (AD)-relevant cerebrospinal fluid (CSF) biomarker measures. Five Bonferroni-significant loci were associated with YKL-40 levels and seven with neurofilament light chain (NfL). DNA methylation in YKL-40 co-localized with previously reported genetic variation. DNA methylation potentially mediates the effect of single-nucleotide polymorphisms (SNPs) in YKL-40 on CSF protein levels., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Introduction: Assessing the potential sources of bias and variability of the Centiloid (CL) scale is fundamental for its appropriate clinical application., Methods: We included 533 participants from AMYloid imaging to Prevent Alzheimer's Disease (AMYPAD DPMS) and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts. Thirty-two CL pipelines were created using different combinations of reference region (RR), RR and target types, and quantification spaces. Generalized estimating equations stratified by amyloid positivity were used to assess the impact of the quantification pipeline, radiotracer, age, brain atrophy, and harmonization status on CL., Results: RR selection and RR type impact CL the most, particularly in amyloid-negative individuals. The standard CL pipeline with the whole cerebellum as RR is robust against brain atrophy and differences in image resolution, with 95% confidence intervals below ± 3.95 CL for amyloid beta positivity cutoffs (CL < 24)., Discussion: The standard CL pipeline is recommended for most scenarios. Confidence intervals should be considered when operationalizing CL cutoffs in clinical and research settings., Highlights: We developed a framework for evaluating Centiloid (CL) variability to different factors. Reference region selection and delineation had the highest impact on CL values. Whole cerebellum (WCB) and whole cerebellum plus brainstem (WCB+BSTM) as reference regions yielded consistent results across tracers. The standard CL pipeline is robust against atrophy and image resolution variation. Estimated within- and between-pipeline variability (95% confidence interval) in absolute CL units., (© None The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Objective: Alzheimer's disease (AD) and cerebral small vessel disease (cSVD), the two most common causes of dementia, are characterized by white matter (WM) alterations diverging from the physiological changes occurring in healthy aging. Diffusion tensor imaging (DTI) is a valuable tool to quantify WM integrity non-invasively and identify the determinants of such alterations. Here, we investigated main effects and interactions of AD pathology, APOE-ε4, cSVD, and cardiovascular risk on spatial patterns of WM alterations in non-demented older adults., Methods: Within the prospective European Prevention of Alzheimer's Dementia study, we selected 606 participants (64.9 ± 7.2 years, 376 females) with baseline cerebrospinal fluid samples of amyloid β 1-42 and p-Tau 181 and MRI scans, including DTI scans. Longitudinal scans (mean follow-up time = 1.3 ± 0.5 years) were obtained in a subset (n = 223). WM integrity was assessed by extracting fractional anisotropy and mean diffusivity in relevant tracts. To identify the determinants of WM disruption, we performed a multimodel inference to identify the best linear mixed-effects model for each tract., Results: AD pathology, APOE-ε4, cSVD burden, and cardiovascular risk were all associated with WM integrity within several tracts. While limbic tracts were mainly impacted by AD pathology and APOE-ε4, commissural, associative, and projection tract integrity was more related to cSVD burden and cardiovascular risk. AD pathology and cSVD did not show any significant interaction effect., Interpretation: Our results suggest that AD pathology and cSVD exert independent and spatially different effects on WM microstructure, supporting the role of DTI in disease monitoring and suggesting independent targets for preventive medicine approaches., (© 2024 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

The increasing prevalence of Alzheimer's disease in the general population presents a number of medical, economic and social challenges for the years to come. After 20 years of research with no new treatment option, a new class of drugs is set to be introduced in Europe. Anti-amyloid drugs, which are already available in the United-States, slow the disease progression by targeting the biological processes causing the disease, unlike the symptomatic treatments that are currently available. However, their precise indications and the monitoring of their adverse events are still to be defined. Several other drugs are in advanced stages of development, such as those targeting the tau protein or neuroinflammation, suggesting that the management of the disease will be quite different in the years to come., Competing Interests: Le Dr G. Frisoni a été investigateur dans l’étude EMERGE avec l’aducanumab. Il a reçu des compensations ou des honoraires pour des enseignements magistraux, des présentations, des écrits ou des événements éducatifs de la part de Biogen, Roche, Novo Nordisk et GE HealthCare. Il a également reçu des honoraires de consultation de la part de Biogen, Diadem et Roche. Tous les honoraires ont été perçus à travers son employeur, les HUG. Il a été l’investigateur principal pour des essais cliniques de Roche, OM Pharma, Eisai, Biogen et Novo Nordisk. Les Drs D. Donayre et A. Lathuilière sont des sous-investigateurs dans des essais cliniques menés par Biogen et Novo Nordisk. Les autres auteurs n’ont déclaré aucun conflit d’intérêts en lien avec cet article.

Introduction: Despite increasing evidence of a role of rare genetic variation in the risk of Alzheimer's disease (AD), limited attention has been paid to its contribution to AD-related biomarker traits indicative of AD-relevant pathophysiological processes., Methods: We performed whole-exome gene-based rare-variant association studies (RVASs) of 17 AD-related traits on whole-exome sequencing (WES) data generated in the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study (n = 450) and whole-genome sequencing (WGS) data from ADNI (n = 808)., Results: Mutation screening revealed a novel probably pathogenic mutation (PSEN1 p.Leu232Phe). Gene-based RVAS revealed the exome-wide significant contribution of rare coding variation in RBKS and OR7A10 to cognitive performance and protection against left hippocampal atrophy, respectively., Discussion: The identification of these novel gene-trait associations offers new perspectives into the role of rare coding variation in the distinct pathophysiological processes culminating in AD, which may lead to identification of novel therapeutic and diagnostic targets., (© 2022 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Patients with breast cancer have experienced advancements both in oncological treatment and in aesthetics as a result of developments in reconstructive techniques. We aimed to present our experience with the reverse expansion technique, summarizing the results of our first 100 cases of reconstruction after skin-sparing mastectomy and nipple-sparing mastectomy., Methods: From January 2010 to September 2018, 253 breast reconstruction procedures were performed on 100 patients. The reverse expansion technique consists of autologous fat tissue transplantation requiring the combined use of a skin expander and of multiple lipofilling sessions. At the beginning of every session the breast expander was deflated by removing a saline volume similar to that of the fat to be injected., Results: Overall, 56 breast reconstructions after skin-sparing mastectomy and 44 after nipple-sparing mastectomy were performed. An average of 661.5 cm 3 of fat per session was harvested and an average of 305.3 cm 3 per breast was injected. The average number of sessions to achieve breast reconstruction was 2.53. Only four complications after 253 procedures (1.5%) were reported: one donor site hemorrhage due to genetic lack of coagulation factors, and three surgical site infections., Conclusions: Considering the large number of positive factors such as a fast postoperative recovery, an easy learning curve, a lack of need of a specialized surgical team, a natural look of the breast shape, and the soft consistency of the grafted tissue, we believe this technique could be the first choice for autologous reconstruction after skin-sparing mastectomy and nipple-sparing mastectomy., Competing Interests: The authors have no financial interest to declare in relation to the content of this article.Disclosure statements are at the end of this article, following the correspondence information., (Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The American Society of Plastic Surgeons.)

Amyloid-β accumulation starts in highly connected brain regions and is associated with functional connectivity alterations in the early stages of Alzheimer's disease. This regional vulnerability is related to the high neuronal activity and strong fluctuations typical of these regions. Recently, dynamic functional connectivity was introduced to investigate changes in functional network organization over time. High dynamic functional connectivity variations indicate increased regional flexibility to participate in multiple subnetworks, promoting functional integration. Currently, only a limited number of studies have explored the temporal dynamics of functional connectivity in the pre-dementia stages of Alzheimer's disease. We study the associations between abnormal cerebrospinal fluid amyloid and both static and dynamic properties of functional hubs, using eigenvector centrality, and their relationship with cognitive performance, in 701 non-demented participants from the European Prevention of Alzheimer's Dementia cohort. Voxel-wise eigenvector centrality was computed for the whole functional magnetic resonance imaging time series (static), and within a sliding window (dynamic). Differences in static eigenvector centrality between amyloid positive (A+) and negative (A-) participants and amyloid-tau groups were found in a general linear model. Dynamic eigenvector centrality standard deviation and range were compared between groups within clusters of significant static eigenvector centrality differences, and within 10 canonical resting-state networks. The effect of the interaction between amyloid status and cognitive performance on dynamic eigenvector centrality variability was also evaluated with linear models. Models were corrected for age, sex, and education level. Lower static centrality was found in A+ participants in posterior brain areas including a parietal and an occipital cluster; higher static centrality was found in a medio-frontal cluster. Lower eigenvector centrality variability (standard deviation) occurred in A+ participants in the frontal cluster. The default mode network and the dorsal visual networks of A+ participants had lower dynamic eigenvector centrality variability. Centrality variability in the default mode network and dorsal visual networks were associated with cognitive performance in the A- and A+ groups, with lower variability being observed in A+ participants with good cognitive scores. Our results support the role and timing of eigenvector centrality alterations in very early stages of Alzheimer's disease and show that centrality variability over time adds relevant information on the dynamic patterns that cause static eigenvector centrality alterations. We propose that dynamic eigenvector centrality is an early biomarker of the interplay between early Alzheimer's disease pathology and cognitive decline., Competing Interests: K.B. has served as a consultant, at advisory boards, or at data monitoring committees for Abcam, Axon, Biogen, JOMDD/Shimadzu. Julius Clinical, Lilly, MagQu, Novartis, Prothena, Roche Diagnostics, and Siemens Healthineers, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (all outside the work presented in this paper). JDG has received speaker’s fees from Biogen and Philips., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Long document summarization poses obstacles to current generative transformer-based models because of the broad context to process and understand. Indeed, detecting long-range dependencies is still challenging for today's state-of-the-art solutions, usually requiring model expansion at the cost of an unsustainable demand for computing and memory capacities. This paper introduces Emma, a novel efficient memory-enhanced transformer-based architecture. By segmenting a lengthy input into multiple text fragments, our model stores and compares the current chunk with previous ones, gaining the capability to read and comprehend the entire context over the whole document with a fixed amount of GPU memory. This method enables the model to deal with theoretically infinitely long documents, using less than 18 and 13 GB of memory for training and inference, respectively. We conducted extensive performance analyses and demonstrate that Emma achieved competitive results on two datasets of different domains while consuming significantly less GPU memory than competitors do, even in low-resource settings.

The majority of surgeons choose an implant-based breast reconstruction after mastectomy. Nevertheless, lipofilling is a constantly growing technique allowing a complete breast reconstruction without prosthesis. We introduce our experience using reverse expansion for breast reconstruction following a nipple-sparing mastectomy (NSM) with a subpectoral skin expander. In the period January 2010-August 2021, 106 breast reconstruction procedures were performed on 50 patients after a NSM. We harvested an amount of fat tissue using a 2.5 mm liposuction cannula, we centrifuged it 3 min at 4000 rpm and injected in the recipient site using 3 ml syringes and Coleman cannulas. At the beginning of every session, the breast expander was deflated of a saline volume similar to the one of the fat to be injected. We harvested an average of 679.2 ccs of fat per session and injected an average of 319.3 ccs per breast. The mean number of sessions has been 2.4 per breast. The average number of sessions in a radiotreated patients' subgroup has been slightly higher than a control group. The mean follow-up time was 63.5 months and we observed no complications in 105 over 106 procedures. Lipofilling has proven to be a safe and effective technique for complete breast reconstruction. Our procedure considers the use of a breast expander as a device to prepare the recipient site. Reverse expansion after a NSM allows a like-with-like reconstruction and it might be the first reconstructive choice in a selected group of patients.Level of Evidence IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 ., (© 2021. Springer Science+Business Media, LLC, part of Springer Nature and International Society of Aesthetic Plastic Surgery.)

Frontotemporal dementia in genetic forms is highly heterogeneous and begins many years to prior symptom onset, complicating disease understanding and treatment development. Unifying methods to stage the disease during both the presymptomatic and symptomatic phases are needed for the development of clinical trials outcomes. Here we used the contrastive trajectory inference (cTI), an unsupervised machine learning algorithm that analyzes temporal patterns in high-dimensional large-scale population datasets to obtain individual scores of disease stage. We used cross-sectional MRI data (gray matter density, T1/T2 ratio as a proxy for myelin content, resting-state functional amplitude, gray matter fractional anisotropy, and mean diffusivity) from 383 gene carriers (269 presymptomatic and 115 symptomatic) and a control group of 253 noncarriers in the Genetic Frontotemporal Dementia Initiative. We compared the cTI-obtained disease scores to the estimated years to onset (age-mean age of onset in relatives), clinical, and neuropsychological test scores. The cTI based disease scores were correlated with all clinical and neuropsychological tests (measuring behavioral symptoms, attention, memory, language, and executive functions), with the highest contribution coming from mean diffusivity. Mean cTI scores were higher in the presymptomatic carriers than controls, indicating that the method may capture subtle pre-dementia cerebral changes, although this change was not replicated in a subset of subjects with complete data. This study provides a proof of concept that cTI can identify data-driven disease stages in a heterogeneous sample combining different mutations and disease stages of genetic FTD using only MRI metrics., (© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Alzheimer's disease (AD) biomarkers represent several neurodegenerative processes, such as synaptic dysfunction, neuronal inflammation and injury, as well as amyloid pathology. We performed an exome-wide rare variant analysis of six AD biomarkers (β-amyloid, total/phosphorylated tau, NfL, YKL-40, and Neurogranin) to discover genes associated with these markers. Genetic and biomarker information was available for 480 participants from two studies: EMIF-AD and ADNI. We applied a principal component (PC) analysis to derive biomarkers combinations, which represent statistically independent biological processes. We then tested whether rare variants in 9576 protein-coding genes associate with these PCs using a Meta-SKAT test. We also tested whether the PCs are intermediary to gene effects on AD symptoms with a SMUT test. One PC loaded on NfL and YKL-40, indicators of neuronal injury and inflammation. Four genes were associated with this PC: IFFO1, DTNB, NLRC3, and SLC22A10. Mediation tests suggest, that these genes also affect dementia symptoms via inflammation/injury. We also observed an association between a PC loading on Neurogranin, a marker for synaptic functioning, with GABBR2 and CASZ1, but no mediation effects. The results suggest that rare variants in IFFO1, DTNB, NLRC3, and SLC22A10 heighten susceptibility to neuronal injury and inflammation, potentially by altering cytoskeleton structure and immune activity disinhibition, resulting in an elevated dementia risk. GABBR2 and CASZ1 were associated with synaptic functioning, but mediation analyses suggest that the effect of these two genes on synaptic functioning is not consequential for AD development., (© 2022. The Author(s).)

Alzheimer's disease (AD) is the most frequent neurodegenerative disease with an increasing prevalence in industrialized, aging populations. AD susceptibility has an established genetic basis which has been the focus of a large number of genome-wide association studies (GWAS) published over the last decade. Most of these GWAS used dichotomized clinical diagnostic status, i.e., case vs. control classification, as outcome phenotypes, without the use of biomarkers. An alternative and potentially more powerful study design is afforded by using quantitative AD-related phenotypes as GWAS outcome traits, an analysis paradigm that we followed in this work. Specifically, we utilized genotype and phenotype data from n = 931 individuals collected under the auspices of the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study to perform a total of 19 separate GWAS analyses. As outcomes we used five magnetic resonance imaging (MRI) traits and seven cognitive performance traits. For the latter, longitudinal data from at least two timepoints were available in addition to cross-sectional assessments at baseline. Our GWAS analyses revealed several genome-wide significant associations for the neuropsychological performance measures, in particular those assayed longitudinally. Among the most noteworthy signals were associations in or near EHBP1 (EH domain binding protein 1; on chromosome 2p15) and CEP112 (centrosomal protein 112; 17q24.1) with delayed recall as well as SMOC2 (SPARC related modular calcium binding 2; 6p27) with immediate recall in a memory performance test. On the X chromosome, which is often excluded in other GWAS, we identified a genome-wide significant signal near IL1RAPL1 (interleukin 1 receptor accessory protein like 1; Xp21.3). While polygenic score (PGS) analyses showed the expected strong associations with SNPs highlighted in relevant previous GWAS on hippocampal volume and cognitive function, they did not show noteworthy associations with recent AD risk GWAS findings. In summary, our study highlights the power of using quantitative endophenotypes as outcome traits in AD-related GWAS analyses and nominates several new loci not previously implicated in cognitive decline., Competing Interests: HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Alector, Annexon, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Pinteon Therapeutics, Red Abbey Labs, Passage Bio, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen, and Roche, and was a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which was a part of the GU Ventures Incubator Program. FB is supported by the NIHR biomedical research centre at UCLH. JP received consultation honoraria from Nestle Institute of Health Sciences, Ono Pharma, OM Pharma, and Fujirebio, unrelated to the submitted work. CT has a collaboration contract with ADx Neurosciences, Quanterix and Eli Lilly, performed contract research or received grants from AC-Immune, Axon Neurosciences, Biogen, Brainstorm Therapeutics, Celgene, EIP Pharma, Eisai, PeopleBio, Roche, Toyama, Vivoryon. She serves on editorial boards of Medidact Neurologie/Springer, Alzheimer Research and Therapy, Neurology: Neuroimmunology and Neuroinflammation, and was editor of a Neuromethods book Springer. CT also holds a speaker’s contract with Roche, Inc. KB has served as a consultant, at advisory boards, or at data monitoring committees for Abcam, Axon, BioArctic, Biogen, JOMDD/Shimadzu. Julius Clinical, Lilly, MagQu, Novartis, Pharmatrophix, Prothena, Roche Diagnostics, and Siemens Healthineers, and was a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which was a part of the GU Ventures Incubator Program, outside the work presented in this paper. SL is currently an employee at Janssen Medical UK. JS was an employee of Janssen R&D, LLC., and is currently an employee and chief medical officer of AC Immune SA. JR was an employee of Neurosciences Therapeutic Area, GlaxoSmithKline R&D, Stevenage, UK., (Copyright © 2022 Homann, Osburg, Ohlei, Dobricic, Deecke, Bos, Vandenberghe, Gabel, Scheltens, Teunissen, Engelborghs, Frisoni, Blin, Richardson, Bordet, Lleó, Alcolea, Popp, Clark, Peyratout, Martinez-Lage, Tainta, Dobson, Legido-Quigley, Sleegers, Van Broeckhoven, Wittig, Franke, Lill, Blennow, Zetterberg, Lovestone, Streffer, ten Kate, Vos, Barkhof, Visser and Bertram.)

This paper studies the problem of detecting human beings in non-line-of-sight (NLOS) conditions using an ultra-wideband radar. We perform an extensive measurement campaign in realistic environments, considering different body orientations, the obstacles' materials, and radar-obstacle distances. We examine two main scenarios according to the radar position: (i) placed on top of a mobile cart; (ii) handheld at different heights. We empirically analyze and compare several input representations and machine learning (ML) methods-supervised and unsupervised, symbolic and non-symbolic-according to both their accuracy in detecting NLOS human beings and their adaptability to unseen cases. Our study proves the effectiveness and flexibility of modern ML techniques, avoiding environment-specific configurations and benefiting from knowledge transference. Unlike traditional TLC approaches, ML allows for generalization, overcoming limits due to unknown or only partially known observation models and insufficient labeled data, which usually occur in emergencies or in the presence of time/cost constraints.

Background: Magnetic resonance imaging (MRI) measures may be used as outcome markers in frontotemporal dementia (FTD)., Objectives: To predict MRI cortical thickness (CT) at follow-up at the single subject level, using brain MRI acquired at baseline in preclinical FTD., Methods: 84 presymptomatic subjects carrying Granulin mutations underwent MRI scans at baseline and at follow-up (31.2±16.5 months). Multivariate nonlinear mixed-effects model was used for estimating individualized CT at follow-up based on baseline MRI data. The automated user-friendly preGRN-MRI script was coded., Results: Prediction accuracy was high for each considered brain region (i.e., prefrontal region, real CT at follow-up versus predicted CT at follow-up, mean error ≤1.87%). The sample size required to detect a reduction in decline in a 1-year clinical trial was equal to 52 subjects (power = 0.80, alpha = 0.05)., Conclusion: The preGRN-MRI tool, using baseline MRI measures, was able to predict the expected MRI atrophy at follow-up in presymptomatic subjects carrying GRN mutations with good performances. This tool could be useful in clinical trials, where deviation of CT from the predicted model may be considered an effect of the intervention itself.

Objective: Although the presymptomatic stages of frontotemporal dementia (FTD) provide a unique chance to delay or even prevent neurodegeneration by early intervention, they remain poorly defined. Leveraging a large multicenter cohort of genetic FTD mutation carriers, we provide a biomarker-based stratification and biomarker cascade of the likely most treatment-relevant stage within the presymptomatic phase: the conversion stage., Methods: We longitudinally assessed serum levels of neurofilament light (NfL) and phosphorylated neurofilament heavy (pNfH) in the Genetic FTD Initiative (GENFI) cohort (n = 444), using single-molecule array technique. Subjects comprised 91 symptomatic and 179 presymptomatic subjects with mutations in the FTD genes C9orf72, GRN, or MAPT, and 174 mutation-negative within-family controls., Results: In a biomarker cascade, NfL increase preceded the hypothetical clinical onset by 15 years and concurred with brain atrophy onset, whereas pNfH increase started close to clinical onset. The conversion stage was marked by increased NfL, but still normal pNfH levels, while both were increased at the symptomatic stage. Intra-individual change rates were increased for NfL at the conversion stage and for pNfH at the symptomatic stage, highlighting their respective potential as stage-dependent dynamic biomarkers within the biomarker cascade. Increased NfL levels and NfL change rates allowed identification of presymptomatic subjects converting to symptomatic disease and capture of proximity-to-onset. We estimate stage-dependent sample sizes for trials aiming to decrease neurofilament levels or change rates., Interpretation: Blood NfL and pNfH provide dynamic stage-dependent stratification and, potentially, treatment response biomarkers in presymptomatic FTD, allowing demarcation of the conversion stage. The proposed biomarker cascade might pave the way towards a biomarker-based precision medicine approach to genetic FTD. ANN NEUROL 2022;91:33-47., (© 2021 The Authors. Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

The automatic extraction of biomedical events from the scientific literature has drawn keen interest in the last several years, recognizing complex and semantically rich graphical interactions otherwise buried in texts. However, very few works revolve around learning embeddings or similarity metrics for event graphs. This gap leaves biological relations unlinked and prevents the application of machine learning techniques to promote discoveries. Taking advantage of recent deep graph kernel solutions and pre-trained language models, we propose Deep Divergence Event Graph Kernels (DDEGK), an unsupervised inductive method to map events into low-dimensional vectors, preserving their structural and semantic similarities. Unlike most other systems, DDEGK operates at a graph level and does not require task-specific labels, feature engineering, or known correspondences between nodes. To this end, our solution compares events against a small set of anchor ones, trains cross-graph attention networks for drawing pairwise alignments (bolstering interpretability), and employs transformer-based models to encode continuous attributes. Extensive experiments have been done on nine biomedical datasets. We show that our learned event representations can be effectively employed in tasks such as graph classification, clustering, and visualization, also facilitating downstream semantic textual similarity. Empirical results demonstrate that DDEGK significantly outperforms other state-of-the-art methods.