Institut Guttmann

BackgroundWhile many individuals’ episodic memory declines after age 60, this decline is not universal. Some individuals, often called superagers, have exceptional memory in older age. Despite growing research interest in superagers, their long‐term memory trajectories remain underexplored.MethodWe used longitudinal data from 220 participants (127 female, agemean=67.3 years, follow‐upmean=2.2 years) from the Barcelona Brain Health Initiative and the University of Barcelona Aging cohorts. We calculated episodic memory composites using the Face‐Name Associative Memory Test and the Rey Auditory Verbal Learning Test (RAVLT), and memory change using ordinary least squares slope estimates. We classified participants as ‘superagers’ if they scored at/above the mean for 16–29‐year‐olds on the RAVLT delayed recall and above 1SD below the mean for their age/education on the Trail Making Test B. We classified participants as ‘maintainers’ if their memory change was >=0. We then created four groups: superager maintainers, superager decliners, non‐superager maintainers, and non‐superager decliners. We conducted ANOVAs to compare groups’ memory at baseline/follow‐up and linear mixed effects models with a group/age interaction on memory. We controlled analyses for relevant demographics.ResultFigure 1 summarizes participant distribution across groups. Superagers had significantly better memory than non‐superagers at baseline/follow‐up (ps<0.001). Initially, decliners exhibited better memory than maintainers (p < 0.001), but this trend reversed at follow‐up with maintainers showing better memory (p < 0.001). Superagers, compared to non‐superagers (β=0.041, p = 0.005), and maintainers, compared to decliners (β=0.136, p < 0.001) showed positive interactions with age on memory. Similarly, compared to non‐superager decliners, superager maintainers (β=0.147, p < 0.001) and non‐superager maintainers (β=0.108, p < 0.001) exhibited significant positive interactions with age on memory, unlike superager decliners (β=0.042, p = 0.079) (Figure 2).ConclusionSuperager status benefited memory level and memory change while maintainer status benefited memory change and memory level at follow‐up only. Superagers can be further categorized into superager maintainers, who exhibit a more positive relationship between memory and age than superager decliners. Similarly, non‐superager maintainers show a more positive relationship between memory and age than non‐superager decliners. These findings underscore the complexity of cognitive aging trajectories, indicating superager status may offer insights into longitudinal patterns, though additional subgroups further clarify these relationships.

BackgroundThe relationship between cognitive age‐related changes and cortical macrostructural properties (i.e., cortical thickness [CTh]) has been extensively studied. However, less is known about the relation with microstructural characteristics (i.e., cortical mean diffusivity [cMD]) even though these are sensitive to preclinical phases of Alzheimer’s disease (AD). Furthermore, the relationship between macro‐ and microstructural measures with cognition, neurodegeneration and inflammation plasma biomarkers among healthy individuals has not been reported.MethodA total of 964 adults (age: 40‐82 years; 52% females) with normal neuropsychological profile and available structural and diffusion MRI data were included. FreeSurfer was used to obtain CTh maps. For cMD processing we used a homemade surface‐based approach after preprocessing the diffusion images with FSL. Surface maps were smoothed (FWHM=15) and the group vertex‐wise statistical maps were considered significant at p <0.05. The Preclinical Alzheimer’s Cognitive Composite (PACC) was used as the cognition measure. Plasma concentrations of phosphorylated tau (p‐Tau181), neurofilament light (NFL) and C‐reactive protein (hsCRP) were analyzed, together with APOE4 status.ResultWe identified a significant negative association between both cortical measures and age (Figure 1). cMD associations were more extensive at earlier ages (under 50 years), while CTh associations were greater at older ages (above 50 years) (Figure 2). cMD was positively correlated with PACC scores and both pTau‐181 and NFL concentrations in prefrontal regions, while the association was negative and more widespread for hsCRP (Figure 3). CTh results revealed smaller positive and negative clusters only for PACC and NFL, respectively (Figure 3). Correlating cMD with CTh differentiated somatosensory and associative areas with negative and positive correlations, respectively (Figure 3). This pattern was conserved after covarying for sex, age and biomarker data, and across age and risk (APOE4 carrier) subsamples.ConclusionWe show that cMD, which might reflect neuronal density loss accompanying or even preceding atrophy, is able to capture microstructural cortical changes occurring during natural aging before CTh alterations. Indeed, it seems more sensitive to age‐related cognitive decline. Furthermore, our results suggest a pattern relating the two metrics conserved across different aging trajectories and opposite trends of the two cortical measures in relation to neurodegenerative biomarkers levels.

ABSTRACT Background Digital Twins (DTs) have transitioned from theory to reality, with growing applications in healthcare. Data generated by technologies (e.g. rehabilitation robots), essential for DT implementation, though widely produced in clinical settings, remains untapped in DT stroke rehabilitation, highlighting a gap compared to broader healthcare use. Objectives We conducted a scoping review to i) define DT rehabilitation objectives, their input data, generation methods and user involvement; ii) analyze mechanisms underpinning DT models and outputs; iii) map key stakeholders driving innovation; iv) identify desirable properties for DT studies from broader healthcare literature and map them to stroke rehabilitation DT studies. Methods Following PRISMA-ScR guidelines, PubMed, Scopus, Web of Science and Google Scholar were searched for studies including only empirical data. Full-text reviews were conducted by three reviewers through repeated calibration. Results Sixteen studies were included, addressing five rehabilitation objectives: upper-limb (10), gait (3), and engagement, mental health, and general/planning (1 each). Patient sample sizes varied widely, with one retrospective study including 1,216 patients, while 15 studies involved 54 patients in total (median = 1). We identified 16 DTs mechanisms (e.g. variational autoencoders, Hill muscle models) and outcomes (e.g. exoskeleton control, upper-limb exercise delivery, gait torque estimation, impaired hand-mobility quantification). Academic institutions conducted 12 studies, Europe contributed 8 studies across 6 countries. Of 25 desirable properties identified, 8 (e.g. reproducible algorithms) showed high adoption, while 15 (e.g. cost-effectiveness, clinical integration) showed low/very low adoption by included studies. Conclusions DTs in stroke rehabilitation show promise, though challenges remain (e.g. patient involvement, scalability).

This observational, descriptive anatomical cadaveric study aimed to identify, characterize, and analyze the morphometric parameters of the filum terminale (FT) and macroscopically describe the distal insertion of the FTE. The FT is a complex, three-dimensional, fibro-cellular structure of connective tissue and glial cells, extending from the conus medullaris (CM) to the dural sac (DS) and coccyx. It is divided into two segments: an intradural filum terminale internum (FTI) and extradural filum terminale externum (FTE). Few studies have comprehensively addressed its morphometric characteristics in the last decades. Thirty-eight embalmed (M = 16, F = 22) human cadavers were examined to determine the CM-FTI and DS-FTE vertebral levels and FT, FTI, and FTE lengths and widths. FTI and FTE segmental diameters, correlations, gross characteristics, tension, and mobility in situ and ex vivo were assessed. FTE distal insertion is thoroughly described. FT, FTI, and FTE mean lengths were 254.32 mm (±26.46), 152.75 mm (±22.02), and 106.64 mm (±12.21), respectively. The CM-FTI junction was observed at the L1-L2 disk space (32.1%), DS-FTE fusion in the upper third of S2 (39.3%), and FTI-DS fusion in the DS midline (46.4%). FT length and FTI, FTE lengths were directly correlated, as were all FTI diameters. FT gross characteristics were an irregular surface (71.4%), bright hue (57.1%), macroscopic FTI-CM contrast (64.3%), filiform shape (60.7%), and movement-resistance (53.6%). The FTE exhibited a flattened shape (64.3%), immobility (60.7%), distal insertion at Cx1 (67.8%) and one distal strand (60.7%). FTI segments ≥ 2 mm were uncommon (21.4%). The FTE distal insertion is variable, inserting as strands, with vascular tissue surrounding it. A distal coccygeal venous plexus and single or multiple strand-like insertions of the distal FTE are for the first time described in detail. Discrepancies in the morphometric parameters of the FT between studies highlight the need for standardized protocols, especially given the FT's anatomic-clinical importance and potential role as a neural progenitor niche. We provide a comprehensive basis for future standardized morphometric analyses, acknowledging the limitations of embalmed cadaveric studies.

Abstract BackgroundThe extended life expectancy in individuals with Down Syndrome (DS) has led to the emergence of age‐related diseases, with Alzheimer’s disease (AD) being particularly noteworthy due to its nearly full penetrance. The level of intellectual disability (ID), regarded as a proxy for cognitive reserve (CR), explains heterogeneity in cognitive and functional abilities. Despite this, there is a notable lack of exploration into the characterization of resilience factors and their potential influence on the progression along the AD continuum in this population. The main goal was to study whether premorbid ID levels (i.e., Mild, Moderate, Severe) could influence the AD clinical onset (prodromal [pDS] or dementia [dDS] diagnosis) and cognitive trajectories among asymptomatic DS (aDS).MethodWe analyzed longitudinal data (&gt; 6 months follow‐up) from a single‐center cohort study involving DS adults (aged &gt;18 years) recruited at the Alzheimer‐Down Unit from the Catalan Down Syndrome Foundation and Hospital of Sant Pau. Clinical progression was evaluated using Kaplan‐Meier curves, ANOVA was conducted to test differences in the age of onset across ID groups, and cognitive trajectories (assessed with Cambridge Cognitive Examination for Older Adults with Down Syndrome [CAMCOG‐DS]) were analyzed using linear mixed‐effects models to explore age‐related effects by ID.ResultA total of 778 adults with DS (mean [SD] age, 43.32 [11.07] years; 361 women [46.4%]; aDS = 527, pDS = 86, dDS = 165) were included. Kaplan‐Meier curves showed that aDS with ID‐Severe progressed faster (p = .0016; Figure 1A), whereas no differences were found in the prodromal stage (p = 0.270; Figure 1B). While no significant differences were identified regarding the age of onset for pDS (Figure 1C), the ID‐Severe slightly showed a later age of dDS diagnosis than ID‐Mild (p = .044; Figure 1D). CAMCOG‐DS scores were higher for ID‐Mild than those with ID‐Moderate (mean [SE] difference, −19.24 [1.68]); p&lt;.001). Nevertheless, there were no group differences among trajectories (Figure 2A), neither when estimated by different age ranges (Figure 2B).ConclusionDespite the lack of evidence, there is an increasing interest in understanding how CR proxies contribute to cognitive resilience in DS. Further analyses including AD‐related biomarkers are essential to uncover the neurobiological mechanisms involved, which could significantly contribute to targeted primary prevention strategies.

Abstract BackgroundMild cognitive impairment (MCI), is characterized by cognitive dysfunction not severe enough to affect one’s activities of daily living (ADLs)1. Annually, approximately 15‐20% adults 65 and older will present with MCI 1. MCI is considered a significant risk factor and a robust predictor for developing dementia. The time course for progression to dementia can vary substantially between individuals and is impacted by the specific pathology underlying the MCI, and the cognitive deficits associated with cognitive impairment (CI) subtypes 2,3. Despite the conversion risk of MCI to dementia and the effectiveness of early lifestyle interventions to mitigate the conversion risk, many investigations do not account for MCI in their CI prediction models. This research investigates binary and 3‐class ML‐enabled modeling to classify CI status leveraging multiple modalities of cognition extracted from the Digital Clock and Recall (DCR), a brief digital cognitive assessment.MethodData from 983 participants in the Bio‐Hermes‐001 multi‐site study (age mean±SD=72±6.7; 56% female; years of education mean±SD=15±2.7; primary language English), a priori classified as cognitively unimpaired (CU; n=417), mild cognitively impaired (n=309), or probable Alzheimer’s dementia (n=257) based on expert consensus clinical diagnosis and neuropsychological evaluation were analyzed. A random forest model was trained on DCTclock and word recall data to classify cognitive impairment using a binary (CI and CU) and 3‐tier (CI, Indeterminate, and CU) prediction thresholding schemes.ResultThe 3‐tier model predictions performed well (AUC=0.887; accuracy=0.834; NPV=0.801; PPV=0.859) outperforming the binary predictions (AUC=0.865; accuracy=0.79; NPV=0.737; PPV=0.837) when measured on Biohermes’ cohort diagnosis. The sensitivity and specificity of the 3‐tier predictions were 0.858 and 0.8, respectively.ConclusionThe DCR, a 3‐minute digital cognitive assessment can be used to classify MCI and probable Alzheimer’s dementia with high accuracy, NPV, and PPV.