This article presents the revised consensus criteria for the diagnosis of frontotemporal dysfunction in amyotrophic lateral sclerosis (ALS) based on an international research workshop on frontotemporal dementia (FTD) and ALS held in London, Canada in June 2015. Since the publication of the Strong criteria, there have been considerable advances in the understanding of the neuropsychological profile of patients with ALS. Not only is the breadth and depth of neuropsychological findings broader than previously recognised - - including deficits in social cognition and language - but mixed deficits may also occur. Evidence now shows that the neuropsychological deficits in ALS are extremely heterogeneous, affecting over 50% of persons with ALS. When present, these deficits significantly and adversely impact patient survival. It is the recognition of this clinical heterogeneity in association with neuroimaging, genetic and neuropathological advances that has led to the current re-conceptualisation that neuropsychological deficits in ALS fall along a spectrum. These revised consensus criteria expand upon those of 2009 and embrace the concept of the frontotemporal spectrum disorder of ALS (ALS-FTSD).

Because individuals develop dementia as a manifestation of neurodegenerative or neurovascular disorder, there is a need to develop reliable approaches to their identification. We are undertaking an observational study (Ontario Neurodegenerative Disease Research Initiative [ONDRI]) that includes genomics, neuroimaging, and assessments of cognition as well as language, speech, gait, retinal imaging, and eye tracking. Disorders studied include Alzheimer's disease, amyotrophic lateral sclerosis, frontotemporal dementia, Parkinson's disease, and vascular cognitive impairment. Data from ONDRI will be collected into the Brain-CODE database to facilitate correlative analysis. ONDRI will provide a repertoire of endophenotyped individuals that will be a unique, publicly available resource.RÉSUMÉ: L'initiative de recherche sur les maladies neurodégénératives en Ontario. La démence constituant la manifestation d'un trouble neurodégénératif ou neurovasculaire, il importe de mettre au point des approches fiables permettant son identification. Nous somme ainsi en train de mener une étude observationnelle -Initiative de recherche sur les maladies neurodégénératives en Ontario ou « ONDRI » -qui inclut l'analyse du génome, la neuro-imagerie et diverses techniques d'évaluation en lien avec les aspects suivants : la cognition, le langage, la démarche, l'imagerie rétinienne et le suivi du regard. Parmi les affections à l'étude, on peut mentionner la maladie d'Alzheimer, la sclérose latérale amyotrophique, la démence fronto-temporale, la maladie de Parkinson et la déficience cognitive vasculaire. Les données de l'ONDRI seront recueillies à partir de la base de données du Brain-CODE afin de faciliter les analyses de corrélation. De plus, l'ONDRI entend fournir un répertoire des endophénotypes associés aux sujets de recherche, répertoire unique en son genre qui sera accessible au public.

IntroductionThe objective of this study was to assess the utility of novel verbal fluency scores for predicting conversion from mild cognitive impairment (MCI) to clinical Alzheimer's disease (AD).MethodVerbal fluency lists (animals, vegetables, F, A, and S) from 107 MCI patients and 51 cognitively normal controls were transcribed into electronic text files and automatically scored with traditional raw scores and five types of novel scores computed using methods from machine learning and natural language processing. Additional scores were derived from structural MRI scans: region of interest measures of hippocampal and ventricular volumes and gray matter scores derived from performing ICA on measures of cortical thickness. Over 4 years of follow-up, 24 MCI patients converted to AD. Using conversion as the outcome variable, ensemble classifiers were constructed by training classifiers on the individual groups of scores and then entering predictions from the primary classifiers into regularized logistic regression models. Receiver operating characteristic curves were plotted, and the area under the curve (AUC) was measured for classifiers trained with five groups of available variables.ResultsClassifiers trained with novel scores outperformed those trained with raw scores (AUC 0.872 vs 0.735; P < .05 by DeLong test). Addition of structural brain measurements did not improve performance based on novel scores alone.ConclusionThe brevity and cost profile of verbal fluency tasks recommends their use for clinical decision making. The word lists generated are a rich source of information for predicting outcomes in MCI. Further work is needed to assess the utility of verbal fluency for early AD.

Background:The association of cognitive and motor impairments in Alzheimer’s disease and other neurodegenerative diseases is thought to be related to damage in the common brain networks shared by cognitive and cortical motor control processes. These common brain networks play a pivotal role in selecting movements and postural synergies that meet an individual’s needs. Pathology in this “highest level” of motor control produces abnormalities of gait and posture referred to as highest-level gait disorders. Impairments in cognition and mobility, including falls, are present in almost all neurodegenerative diseases, suggesting common mechanisms that still need to be unraveled.Objective:To identify motor-cognitive profiles across neurodegenerative diseases in a large cohort of patients.Methods:Cohort study that includes up to 500 participants, followed every year for three years, across five neurodegenerative disease groups: Alzheimer’s disease/mild cognitive impairment, frontotemporal degeneration, vascular cognitive impairment, amyotrophic lateral sclerosis, and Parkinson’s disease. Gait and balance will be assessed using accelerometers and electronic walkways, evaluated at different levels of cognitive and sensory complexity, using the dual-task paradigm.Results:Comparison of cognitive and motor performances across neurodegenerative groups will allow the identification of motor-cognitive phenotypes through the standardized evaluation of gait and balance characteristics.Conclusions:As part of the Ontario Neurodegenerative Research Initiative (ONDRI), the gait and balance platform aims to identify motor-cognitive profiles across neurodegenerative diseases. Gait assessment, particularly while dual-tasking, will help dissect the cognitive and motor contribution in mobility and cognitive decline, progression to dementia syndromes, and future adverse outcomes including falls and mortality.

Background Large and complex studies are now routine, and quality assurance and quality control (QC) procedures ensure reliable results and conclusions. Standard procedures may comprise manual verification and double entry, but these labour-intensive methods often leave errors undetected. Outlier detection uses a data-driven approach to identify patterns exhibited by the majority of the data and highlights data points that deviate from these patterns. Univariate methods consider each variable independently, so observations that appear odd only when two or more variables are considered simultaneously remain undetected. We propose a data quality evaluation process that emphasizes the use of multivariate outlier detection for identifying errors, and show that univariate approaches alone are insufficient. Further, we establish an iterative process that uses multiple multivariate approaches, communication between teams, and visualization for other large-scale projects to follow. Methods We illustrate this process with preliminary neuropsychology and gait data for the vascular cognitive impairment cohort from the Ontario Neurodegenerative Disease Research Initiative, a multi-cohort observational study that aims to characterize biomarkers within and between five neurodegenerative diseases. Each dataset was evaluated four times: with and without covariate adjustment using two validated multivariate methods – Minimum Covariance Determinant (MCD) and Candès’ Robust Principal Component Analysis (RPCA) – and results were assessed in relation to two univariate methods. Outlying participants identified by multiple multivariate analyses were compiled and communicated to the data teams for verification. Results Of 161 and 148 participants in the neuropsychology and gait data, 44 and 43 were flagged by one or both multivariate methods and errors were identified for 8 and 5 participants, respectively. MCD identified all participants with errors, while RPCA identified 6/8 and 3/5 for the neuropsychology and gait data, respectively. Both outperformed univariate approaches. Adjusting for covariates had a minor effect on the participants identified as outliers, though did affect error detection. Conclusions Manual QC procedures are insufficient for large studies as many errors remain undetected. In these data, the MCD outperforms the RPCA for identifying errors, and both are more successful than univariate approaches. Therefore, data-driven multivariate outlier techniques are essential tools for QC as data become more complex. Electronic supplementary material The online version of this article (10.1186/s12874-019-0737-5) contains supplementary material, which is available to authorized users.

As large research initiatives designed to generate big data on clinical cohorts become more common, there is an increasing need to establish standard quality assurance (QA; preventing errors) and quality control (QC; identifying and correcting errors) procedures for critical outcome measures. The present article describes the QA and QC approach developed and implemented for the neuropsychology data collected as part of the Ontario Neurodegenerative Disease Research Initiative study. We report on the efficacy of our approach and provide data quality metrics. Our findings demonstrate that even with a comprehensive QA protocol, the proportion of data errors still can be high. Additionally, we show that several widely used neuropsychological measures are particularly susceptible to error. These findings highlight the need for large research programs to put into place active, comprehensive, and separate QA and QC procedures before, during, and after protocol deployment. Detailed recommendations and considerations for future studies are provided.

Background Remote health monitoring with wearable sensor technology may positively impact patient self-management and clinical care. In individuals with complex health conditions, multi-sensor wear may yield meaningful information about health-related behaviors. Despite available technology, feasibility of device-wearing in daily life has received little attention in persons with physical or cognitive limitations. This mixed methods study assessed the feasibility of continuous, multi-sensor wear in persons with cerebrovascular (CVD) or neurodegenerative disease (NDD). Methods Thirty-nine participants with CVD, Alzheimer’s disease/amnestic mild cognitive impairment, frontotemporal dementia, Parkinson’s disease, or amyotrophic lateral sclerosis (median age 68 (45–83) years, 36% female) wore five devices (bilateral ankles and wrists, chest) continuously for a 7-day period. Adherence to device wearing was quantified by examining volume and pattern of device removal (non-wear). A thematic analysis of semi-structured de-brief interviews with participants and study partners was used to examine user acceptance. Results Adherence to multi-sensor wear, defined as a minimum of three devices worn concurrently, was high (median 98.2% of the study period). Non-wear rates were low across all sensor locations (median 17–22 min/day), with significant differences between some locations ( p = 0.006). Multi-sensor non-wear was higher for daytime versus nighttime wear ( p < 0.001) and there was a small but significant increase in non-wear over the collection period ( p = 0.04). Feedback from de-brief interviews suggested that multi-sensor wear was generally well accepted by both participants and study partners. Conclusion A continuous, multi-sensor remote health monitoring approach is feasible in a cohort of persons with CVD or NDD.

A visual search task was used to investigate how visual attention and intraindividual variability changes with mild cognitive impairment (MCI). Specifically, we examined the contribution of shifting efficacy, distribution of attention, and controlled processing to declines in visual attention in two groups with MCI (single-domain amnestic and multi-domain amnestic), and measured changes in intraindividual variability. Our results demonstrate that visual search performance is attenuated in multi-domain amnestic MCI, but not single-domain amnestic MCI. In addition, we found that the multi-domain amnestic MCI group was more variable than the older controls and single-domain amnestic MCI participants. These between-group differences in search efficacy and intraindividual variability increased as a function of task complexity. We attribute these decrements in performance to changes in the control of attention and shifting efficacy, but not the distribution of attention.