Biomarkers.

Background: Remote diagnostic assessment of cognitively impaired individuals offers numerous potential benefits including increased access to care. However, remote cognitive and behavioral assessment also has limitations, and may not be appropriate for certain patients. Currently, evidence-based guidance on virtual assessment readiness is lacking.

Impact of a national dementia research consortium: The Canadian Consortium on Neurodegeneration in Aging (CCNA).

The Canadian Consortium on Neurodegeneration in Aging (CCNA) was created by the Canadian federal government through its health research funding agency, the Canadian Institutes for Health Research (CIHR), in 2014, as a response to the G7 initiative to fight dementia. Two five-year funding cycles (2014-2019; 2019-2024) have occurred following peer review, and a third cycle (Phase 3) has just begun. A unique construct was mandated, consisting of 20 national teams in Phase I and 19 teams in Phase II (with research topics spanning from basic to clinical science to health resource systems) along with cross-cutting programs to support them.

Multi-stage semi-supervised learning enhances white matter hyperintensity segmentation.

Introduction: White matter hyperintensities (WMHs) are frequently observed on magnetic resonance (MR) images in older adults, commonly appearing as areas of high signal intensity on fluid-attenuated inversion recovery (FLAIR) MR scans. Elevated WMH volumes are associated with a greater risk of dementia and stroke, even after accounting for vascular risk factors. Manual segmentation, while considered the ground truth, is both labor-intensive and time-consuming, limiting the generation of annotated WMH datasets.

Smaller cingulate grey matter mediates the association between dual-task gait and incident dementia.

Individuals with mild cognitive impairment that have high dual-task gait cost (≥20% slowing in gait speed while performing a cognitive brain demanding task), are three-fold more likely to progress to dementia. However, the cortical regions that may explain this association are unknown, which may identify potentially treatable areas. The aim of the current study is to investigate whether brain grey matter volume loss and motor cortex metabolite levels explain the association between dual-task cost and incident dementia in individuals with mild cognitive impairment.

HarmonyTM: multi-center data harmonization applied to distributed learning for Parkinson's disease classification.

Purpose: Distributed learning is widely used to comply with data-sharing regulations and access diverse datasets for training machine learning (ML) models. The traveling model (TM) is a distributed learning approach that sequentially trains with data from one center at a time, which is especially advantageous when dealing with limited local datasets. However, a critical concern emerges when centers utilize different scanners for data acquisition, which could potentially lead models to exploit these differences as shortcuts.

Kinematic and Kinetic Gait Features Associated With Mild Cognitive Impairment in Parkinson's Disease.

Mild cognitive impairment (MCI) and gait deficits are commonly associated with Parkinson's disease (PD). Early detection of MCI associated with Parkinson's disease (PD-MCI) and its biomarkers is critical to managing disability in PD patients, reducing caregiver burden and healthcare costs. Gait is considered a surrogate marker for cognitive decline in PD.

Cognitive Speed in Neurodegenerative Disease: Comparing Mean Rate and Inconsistency Within and Across the Alzheimer's and Lewy Body Spectra in the COMPASS-ND Study.

Background: Alzheimer's disease (AD) and Lewy body disease (LBD) are characterized by early and gradual worsening perturbations in speeded cognitive responses.

Objective: Using simple and choice reaction time tasks, we compared two indicators of cognitive speed within and across the AD and LBD spectra: mean rate (average reaction time across trials) and inconsistency (within person variability).

Methods: The AD spectrum cohorts included subjective cognitive impairment (SCI, n = 28), mild cognitive impairment (MCI, n = 121), and AD (n = 45) participants.

Quantifying cerebral microbleeds using quantitative susceptibility mapping from magnetization-prepared rapid gradient-echo.

T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) is commonly included in brain studies for structural imaging using magnitude images; however, its phase images can provide an opportunity to assess microbleed burden using quantitative susceptibility mapping (QSM). This potential application for MPRAGE-based QSM was evaluated using in vivo and simulated measurements. Possible factors affecting image quality were also explored.

PET Imaging in Dementia: Mini-Review and Canadian Perspective for Clinical Use.

PET imaging is increasingly recognized as an important diagnostic tool to investigate patients with cognitive disturbances of possible neurodegenerative origin. PET with 2-[F]fluoro-2-deoxy-D-glucose ([F]FDG), assessing glucose metabolism, provides a measure of neurodegeneration and allows a precise differential diagnosis among the most common neurodegenerative diseases, such as Alzheimer's disease, frontotemporal dementia or dementia with Lewy bodies. PET tracers specific for the pathological deposits characteristic of different neurodegenerative processes, namely amyloid and tau deposits typical of Alzheimer's Disease, allow the visualization of these aggregates .

Exploiting macro- and micro-structural brain changes for improved Parkinson's disease classification from MRI data.

Parkinson's disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data.

A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm.

Distributed learning is a promising alternative to central learning for machine learning (ML) model training, overcoming data-sharing problems in healthcare. Previous studies exploring federated learning (FL) or the traveling model (TM) setup for medical image-based disease classification often relied on large databases with a limited number of centers or simulated artificial centers, raising doubts about real-world applicability. This study develops and evaluates a convolution neural network (CNN) for Parkinson's disease classification using data acquired by 83 diverse real centers around the world, mostly contributing small training samples.

Identifying Biases in a Multicenter MRI Database for Parkinson's Disease Classification: Is the Disease Classifier a Secret Site Classifier?

Sharing multicenter imaging datasets can be advantageous to increase data diversity and size but may lead to spurious correlations between site-related biological and non-biological image features and target labels, which machine learning (ML) models may exploit as shortcuts. To date, studies analyzing how and if deep learning models may use such effects as a shortcut are scarce. Thus, the aim of this work was to investigate if site-related effects are encoded in the feature space of an established deep learning model designed for Parkinson's disease (PD) classification based on T1-weighted MRI datasets.

Gray matter loss relates to dual task gait in Lewy body disorders and aging.

Background: Within the spectrum of Lewy body disorders (LBD), both Parkinson's disease (PD) and dementia with Lewy bodies (DLB) are characterized by gait and balance disturbances, which become more prominent under dual-task (DT) conditions. The brain substrates underlying DT gait variations, however, remain poorly understood in LBD.

Objective: To investigate the relationship between gray matter volume loss and DT gait variations in LBD.

Prevention of Falls in Parkinson's Disease: Guidelines and Gaps.

Background: People living with Parkinson's disease (PD) have a high risk for falls.

Objective: To examine gaps in falls prevention targeting people with PD as part of the Task Force on Global Guidelines for Falls in Older Adults.

Methods: A Delphi consensus process was used to identify specific recommendations for falls in PD.

A Metabolomics Analysis of a Novel Phenotype of Older Adults at Higher Risk of Dementia.

Background: Older adults presenting with dual-decline in cognition and walking speed face a 6-fold higher risk for dementia compared with those showing no decline. We hypothesized that the metabolomics profile of dual-decliners would be unique even before they show signs of decline in cognition and gait speed.

Objective: The objective of this study was to determine if plasma metabolomics signatures can discriminate dual-decliners from no decliners, purely cognitive decliners, and purely motor decliners prior to decline.

Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data.

Objective: This work investigates if deep learning (DL) models can classify originating site locations directly from magnetic resonance imaging (MRI) scans with and without correction for intensity differences.

Material And Methods: A large database of 1880 T1-weighted MRI scans collected across 41 sites originally for Parkinson's disease (PD) classification was used to classify sites in this study. Forty-six percent of the datasets are from PD patients, while 54% are from healthy participants.

Machine learning-based prediction of longitudinal cognitive decline in early Parkinson's disease using multimodal features.

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.

Using transfer learning for automated microbleed segmentation.

Introduction: Cerebral microbleeds are small perivascular hemorrhages that can occur in both gray and white matter brain regions. Microbleeds are a marker of cerebrovascular pathology and are associated with an increased risk of cognitive decline and dementia. Microbleeds can be identified and manually segmented by expert radiologists and neurologists, usually from susceptibility-contrast MRI.

Effects of Exercise Alone or Combined With Cognitive Training and Vitamin D Supplementation to Improve Cognition in Adults With Mild Cognitive Impairment: A Randomized Clinical Trial.

Importance: Exercise, cognitive training, and vitamin D may enhance cognition in older adults with mild cognitive impairment (MCI).

Objective: To determine whether aerobic-resistance exercises would improve cognition relative to an active control and if a multidomain intervention including exercises, computerized cognitive training, and vitamin D supplementation would show greater improvements than exercise alone.

Design, Setting, And Participants: This randomized clinical trial (the SYNERGIC Study) was a multisite, double-masked, fractional factorial trial that evaluated the effects of aerobic-resistance exercise, computerized cognitive training, and vitamin D on cognition.