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Patients with Parkinson’s Disease (PD) often suffer from cognitive decline. Accurate prediction of cognitive decline is essential for early treatment of at-risk patients. The aim of this study was to develop and evaluate a multimodal machine learning model for the prediction of continuous cognitive decline in patients with early PD. We included 213 PD patients from the Parkinson’s Progression Markers Initiative (PPMI) database. Machine learning was used to predict change in Montreal Cognitive Assessment (MoCA) score using the difference between baseline and 4-years follow-up data as outcome. Input features were categorized into four sets: clinical test scores, cerebrospinal fluid (CSF) biomarkers, brain volumes, and genetic variants. All combinations of input feature sets were added to a basic model, which consisted of demographics and baseline cognition. An iterative scheme using RReliefF-based feature ranking and support vector regression in combination with tenfold cross validation was used to determine the optimal number of predictive features and to evaluate model performance for each combination of input feature sets. Our best performing model consisted of a combination of the basic model, clinical test scores and CSF-based biomarkers. This model had 12 features, which included baseline cognition, CSF phosphorylated tau, CSF total tau, CSF amyloid-beta 1-42 , geriatric depression scale (GDS) scores, and anxiety scores. Interestingly, many of the predictive features in our model have previously been associated with Alzheimer’s disease, showing the importance of assessing Alzheimer’s disease pathology in patients with Parkinson’s disease.

Cerebral microbleeds (CMBs) are very frequent diagnoses with MRI imaging in the elderly or in patients with cerebral infarction, intracranial hemorrhage (ICH), and dementia. The mechanisms for CMBs are not fully understood but may be secondary to injury to the vascular wall from long-standing hypertension or amyloid deposition in the tissue. The presence of CMB increases the risk for stroke, dementia, and death. The increasing number of CMBs is also associated with a higher risk of hemorrhagic complications with the long-term use of anticoagulants in atrial fibrillation and in patients requiring thrombolysis for acute stroke. The presence of CMBs is however not a contraindication for anticoagulation or thrombolysis as was recently shown from the results of the CROMIS-2 study. This review will summarize our current understanding of the natural history of CMBs and offer suggestions on the best management in common clinical settings.

Introduction Cognitive decline in Alzheimer's disease is associated with amyloid beta (Aβ) accumulation, neurodegeneration, and cerebral small vessel disease, but the temporal relationships among these factors is not well established. Methods Data included white matter hyperintensity (WMH) load, gray matter (GM) atrophy and Alzheimer's Disease Assessment Scale‐Cognitive‐Plus (ADAS13) scores for 720 participants and cerebrospinal fluid amyloid (Aβ1–42) for 461 participants from the Alzheimer's Disease Neuroimaging Initiative. Linear regressions were used to assess the relationships among baseline WMH, GM, and Aβ1–42 to changes in WMH, GM, Aβ1–42, and cognition at 1‐year follow‐up. Results Baseline WMHs and Aβ1–42 predicted WMH increase and GM atrophy. Baseline WMHs and Aβ1–42 predicted worsening cognition. Only baseline Aβ1–42 predicted change in Aβ1–42. Discussion Baseline WMHs lead to greater future GM atrophy and cognitive decline, suggesting that WM damage precedes neurodegeneration and cognitive decline. Baseline Aβ1–42 predicted WMH increase, suggesting a potential role of amyloid in WM damage.

Introduction Despite the urgent need for remote neurobehavioral assessment of individuals with cognitive impairment, guidance is lacking. Our goal is to provide a multi‐dimensional framework for remotely assessing cognitive, functional, behavioral, and physical aspects of people with cognitive impairment, along with ethical and technical considerations. Methods Literature review on remote cognitive assessment and multidisciplinary expert opinion from behavioral neurologists, neuropsychiatrists, neuropsychologists, and geriatricians was integrated under the auspices of the Alzheimer Society of Canada Task Force on Dementia Care Best Practices for COVID‐19. Telephone and video approaches to assessments were considered. Results Remote assessment is shown to be acceptable to patients and caregivers. Informed consent, informant history, and attention to privacy and autonomy are paramount. A range of screening and domain‐specific instruments are available for telephone or video assessment of cognition, function, and behavior. Some neuropsychological tests administered by videoconferencing show good agreement with in‐person assessment but still lack validation and norms. Aspects of the remote dementia‐focused neurological examination can be performed reliably. Discussion Despite challenges, current literature and practice support implementation of telemedicine assessments for patients with cognitive impairment. Convergence of data across the clinical interview, reliable and brief remote cognitive tests, and remote neurological exam increase confidence in clinical interpretation and diagnosis.

Magnetic resonance image (MRI) processing pipelines use average templates to enable standardization of individual MRIs in a common space. MNI-ICBM152 is currently used as the standard template by most MRI processing tools. However, MNI-ICBM152 represents an average of 152 healthy young adult brains and is vastly different from brains of patients with neurodegenerative diseases. In those populations, extensive atrophy might cause inevitable registration errors when using an average template of young healthy individuals for standardization. Disease-specific templates that represent the anatomical characteristics of the populations can reduce such errors and improve downstream driven estimates. We present multi-sequence average templates for Alzheimer’s Dementia (AD), Fronto-temporal Dementia (FTD), Lewy Body Dementia (LBD), Mild Cognitive Impairment (MCI), cognitively intact and impaired Parkinson’s Disease patients (PD-CIE and PD-CI, respectively), individuals with Subjective Cognitive Impairment (SCI), AD with vascular contribution (V-AD), Vascular Mild Cognitive Impairment (V-MCI), Cognitively Intact Elderly (CIE) individuals, and a human phantom. We also provide separate templates for males and females to allow better representation of the diseases in each sex group.Measurement(s)Human BrainTechnology Type(s)Magnetic resonance imagingSample Characteristic - OrganismHomo SapiensSample Characteristic - LocationCanada

We examined age-related changes in the cerebral white matter. Structural magnetic resonance images (MRIs) and diffusion tensor images (DTIs) were acquired from 69 healthy subjects aged 22–84years. Quantitative DTI tractography was performed for nine different white matter tracts to determine tract volume, fractional anisotropy (FA), mean diffusivity (MD), axial, and radial diffusivities. We used automated and manual segmentation to determine volumes of gray matter (GM), white mater (WM), cerebrospinal fluid (CSF), and intracranial space. The results showed significant effects of aging on WM, GM, CSF volumes, and selective effects of aging on structural integrity of different white matter tracts. WM of the prefrontal region was the most vulnerable to aging, while temporal lobe connections, cingulum, and parieto-occipital commissural connections showed relative preservation with age. This study was cross-sectional, and therefore, additional longitudinal studies are needed to confirm our findings.

Parkinson’s disease (PD) and other synucleinopathies, namely dementia with Lewy bodies (DLB) and multiple system atrophy (MSA), are common degenerative neurological disorders that share synuclein pathology. Although certain cardinal features of parkinsonism, including bradykinesia and rigidity, respond well to levodopa, axial features, such as gait and balance impairment, are less reliably responsive to dopaminergic therapy and surgical interventions. Consequently, falls are common in PD and other synucleinopathies and are a major contributor toward injury and loss of independence. This underscores the need for appropriate fall risk assessment and implementation of preventative measures in all patients with parkinsonism. The aim of this review is therefore to explore modifiable and non-modifiable risk factors for falls in synucleinopathies. We next review and evaluate the evidence for pharmacological, nonpharmacological, and surgical approaches for fall prevention, and emphasize individualized and multifaceted approaches. Les risques de chute dans le cas des synucléinopathies. La maladie de Parkinson (MP), de même que d’autres synucléinopathies comme la démence à corps de Lewy (DCL) et l’atrophie multi-systématisée (AMS), sont des troubles neurologiques dégénératifs courants qui ont en commun l’accumulation anormale de protéine synucléine. Bien que certains des principaux symptômes caractéristiques de la MP, par exemple la bradykinésie et la rigidité, répondent bien à la lévodopa, d’autres signes axiaux, par exemple une altération de l’équilibre et de la démarche, vont répondre de façon moins efficace à un traitement dopaminergique et à des interventions chirurgicales. Il s’ensuit que les chutes de patients atteints de la MP et d’autres synucléinopathies contribuent grandement à leur perte d’autonomie, et ce, en raison de blessures. Cette situation met en évidence la nécessité de procéder à une évaluation appropriée des risques de chute chez ces patients et de mettre en œuvre des mesures préventives destinées à tous les patients souffrant de parkinsonisme. L’objectif de cette étude consiste donc, dans le cas des synucléinopathies, à examiner les facteurs de risque modifiables et non-modifiables liés aux chutes. Nous passerons ainsi en revue et évaluerons les approches pharmacologiques, non-pharmacologiques et chirurgicales dans la prévention des chutes pour ensuite mettre en relief des approches individuelles et multidimensionnelles.

Évaluation complète d’une étude de cohorte canadienne portant sur la démence et la neuro-dégénérescence. Contexte : L’évaluation globale de la neuro-dégénérescence et de la démence (COMPASS-ND), étude de cohorte du Consortium canadien en neuro-dégénérescence associée au vieillissement (CCNV), représente une initiative nationale visant à promouvoir la recherche portant sur la démence et à soutenir les programmes de recherche des équipes du CCNV. Totalisant 2310 sujets recrutés partout au pays, cette cohorte longitudinale regroupe des individus fortement « phénotypés » qui présentent diverses formes de démence et de pertes de mémoire légères. En plus de sujets âgés dont les fonctions cognitives sont intactes, ces 2310 sujets ont permis de valider les hypothèses formulées par les équipes du CCNV. Méthodes : Nous avons utilisé de nombreux documents pour décrire cette étude : le protocole de la COMPASS-ND ; la demande initiale de subvention ; le cinquième rapport d’étape semi-annuel du CCNV soumis aux Instituts de recherche en santé du Canada (IRSC) en décembre 2017 ; ainsi que d’autres documents produits à la suite de modifications consécutives à la mise en œuvre de ce projet. Résultats : L’étude de cohorte COMPASS-ND du CCNV inclut des participants de partout au Canada dont les divers états cognitifs sont associés à des maladies neurodégénératives ou au risque d’en souffrir. Ils feront l’objet d’un large éventail d’examens expérimentaux, cliniques, génétiques et d’imagerie afin d’aborder de manière spécifique les causes, le diagnostic, le traitement et la prévention de ces états cognitifs chez les personnes âgées. Les données obtenues à la suite d’évaluations cliniques et cognitives, ainsi que celles issues d’échantillons biologiques, d’imagerie cérébrale, de tests génétiques et de dons de cerveaux, seront utilisées pour tester les hypothèses générées par les équipes de recherche du CCNV et d’autres chercheurs canadiens. Cette étude constitue donc à ce jour l’étude canadienne la plus complète et la plus ambitieuse au sujet de la démence. La présentation des données initiales ayant eu lieu en 2018, la cohorte devrait atteindre sa taille maximale d’ici à 2020.Conclusion : La disponibilité des données de l’étude COMPASS-ND stimulera considérablement la recherche sur la démence au Canada au cours des prochaines années.

Introduction Cognitive impairment is the hallmark of Alzheimer's disease (AD) and related dementias. However, motor decline has been recently described as a prodromal state that can help to detect at‐risk individuals. Similarly, sensory changes, sleep and behavior disturbances, and frailty have been associated with higher risk of developing dementia. These clinical findings, together with the recognition that AD pathology precedes the diagnosis by many years, raises the possibility that non‐cognitive changes may be early and non‐invasive markers for AD or, even more provocatively, that treating non‐cognitive aspects may help to prevent or treat AD and related dementias. Methods A subcommittee of the Canadian Consensus Conference on Diagnosis and Treatment of Dementia reviewed areas of emerging evidence for non‐cognitive markers of dementia. We examined the literature for five non‐cognitive domains associated with future dementia: motor, sensory (hearing, vision, olfaction), neuro‐behavioral, frailty, and sleep. The Grading of Recommendations Assessment, Development, and Evaluation system was used to assign the strength of the evidence and quality of the recommendations. We provide recommendations to primary care clinics and to specialized memory clinics, answering the following main questions: (1) What are the non‐cognitive and functional changes associated with risk of developing dementia? and (2) What is the evidence that sensory, motor, behavioral, sleep, and frailty markers can serve as potential predictors of dementia? Results Evidence supported that gait speed, dual‐task gait speed, grip strength, frailty, neuropsychiatric symptoms, sleep measures, and hearing loss are predictors of dementia. There was insufficient evidence for recommending assessing olfactory and vision impairments as a predictor of dementia. Conclusions Non‐cognitive markers can assist in identifying people at risk for cognitive decline or dementia. These non‐cognitive markers may represent prodromal symptoms and several of them are potentially amenable to treatment that might delay the onset of cognitive decline.

Persons with Parkinson's disease (PD) differentially progress to cognitive impairment and dementia. With a 3-year longitudinal sample of initially non-demented PD patients measured on multiple dementia risk factors, we demonstrate that machine learning classifier algorithms can be combined with explainable artificial intelligence methods to identify and interpret leading predictors that discriminate those who later converted to dementia from those who did not. Participants were 48 well-characterized PD patients ( = 71.6; = 4.8; 44% female). We tested 38 multi-modal predictors from 10 domains (e.g., motor, cognitive) in a computationally competitive context to identify those that best discriminated two unobserved baseline groups, PD No Dementia (PDND), and PD Incipient Dementia (PDID). We used Random Forest (RF) classifier models for the discrimination goal and Tree SHapley Additive exPlanation (Tree SHAP) values for deep interpretation. An excellent RF model discriminated baseline PDID from PDND ( = 0.84; normalized = 0.76). Tree SHAP showed that ten leading predictors of PDID accounted for 62.5% of the model, as well as their relative importance, direction, and magnitude (risk threshold). These predictors represented the motor (e.g., poorer gait), cognitive (e.g., slower Trail A), molecular (up-regulated metabolite panel), demographic (age), imaging (ventricular volume), and lifestyle (activities of daily living) domains. Our data-driven protocol integrated RF classifier models and Tree SHAP applications to selectively identify and interpret early dementia risk factors in a well-characterized sample of initially non-demented persons with PD. Results indicate that leading dementia predictors derive from multiple complementary risk domains.