Cognitive impairment is the hallmark of dementia. Cognitive problems, such as difficulties with memory, language and reasoning, are the most obvious, frustrating and debilitating aspects of most neurodegenerative diseases. As a result, assessment of a person's cognition is a vital component
of both diagnostic services and research investigations, and is the most common outcome measure by which the effectiveness of potential pharmaceutical and non-pharmaceutical therapies is judged. However, many traditional paper-and-pencil cognitive assessments have a number of limitations,
including the lack of independence across tests, the qualitative nature of cognitive profiling, the influence of practice effects, a failure to capture some critical aspects of performance, a limited dynamic range, the complexity of some test instructions, and their inability to adequately
assess some domains of cognition. Whilst sophisticated computational techniques are now used routinely to analyze neuroimaging data about changes in the shape of the brain, there have been few attempts to use comparable techniques to understand complex cognitive datasets. Here we attempt to
redress that imbalance by harnessing engineering, computational statistics and mathematics to improve the cognitive assessment of people with or at risk from dementia. The current project aims to develop a computational platform to support substantial improvements in the analysis and
visualisation of complex cognitive datasets, and the automatization, optimization and innovation of techniques and devices used to acquire cognitive data. The specific aims of the study represent an interlinked series of engineering solutions to the longstanding cognitive assessment problems
highlighted by clinicians. The first set of computational goals are to generate multidimensional cognitive profiles for different dementias by using multivariate machine learning algorithms, and to predict the evolution of cognitive deficits through the implementation of event-based models.
The second set of goals relate to attempts to improve existing cognitive tests either by devising ways to measure voice reaction times automatically, implementing psychophysical principles, and utilizing eyetracking to capture additional sensitive metrics of task performance. The third set
of goals involve the development of novel testing paradigms including 'instruction-less' tests of cognition suitable for patients with different types and severities of dementia, and the construction of sensors and virtual reality scenarios to measure social cognition. A critical aspect
of the project is the availability of four exceptionally well-characterized, longitudinally studied cohorts of individuals with or at risk of dementia in whom to develop and evaluate the new models and algorithms and pilot the improved and novel testing paradigms. The clinical cohorts include
individuals with a Familial Alzheimer's disease gene mutation and their non-carrier siblings, people with typical and atypical variants of Alzheimer's disease including the progressive visual syndrome Posterior Cortical Atrophy, and patients with behavioural or linguistic phenotypes of Frontotemporal
Dementia. In addition, data from 500 members of the MRC 1946 Birth Cohort whose cognition has been tracked through life and who are now of an age whereby a proportion will be in the early stages of neurodegeneration will also be evaluated. The project involves a richly interdisciplinary
team with an exciting blend of established collaborations and new partnerships. The work brings together one of the world's leading dementia units (Dementia Research Centre) with three other high profile UCL departments, namely UCL Computer Science, the Centre for Medical Image Analysis, and
the UCL Interaction Centre. The experts from these centres will work together with collaborators and patient and carer support groups to improve the study and implement its findings. Planned Impact The Computational PLatform for Assessment of Cognition in Dementia (C-PLACID) project is
intended to stimulate a paradigm-shift in attitudes toward the neuropsychology of dementia. True impact in terms of this study would constitute a change in approach toward cognitive assessment in both clinical and research settings. Cognitive assessment would no longer be regarded as the poor,
low-tech cousin to glitzier, more sophisticated structural and molecular neuroimaging technologies. Instead improving our ability to measure cognitive function would be seen as a technological challenge and research goal just as appealing and just as deserving of cutting-edge technological
innovation as any of its physiological or anatomical counterparts. Significant impact upon the patients, carers, clinicians and researchers might be observable in a number of ways: (i) Availability of a family of multivariate and event-based models and algorithms for describing, categorizing
and measuring change in large sets of cognitive functions. (ii) Shorter, more personalized testing sessions for patients. (iii) Provision of better, more informative interpretation and description of neuropsychological test results to patients/carer post-assessment. (iv) Provision
to patients and caregivers of a quantitated estimate (and range) of time to next significant cognitive loss, to assist them in care planning and maintaining quality of life. (v) Discontinuation of use of blunt assessment tools in clinical trials. (vi) Adoption of improved and novel
cognitive tests into international trials and studies. (vii) Use of C-PLACID to develop improved cognitive assessments tailored to specific cognitive domains, clinical populations or fields of investigation (initially in but not limited to dementia). (viii) Growth in use of vocal reaction
time measures, eyetracking and virtual reality to assess a broader range of response modalities and cognitive domains. (ix) Increase in registration for multidisciplinary psychology-engineering MSc and PhD projects. (x) Improving awareness of the global nature of cognitive decline
in Alzheimer's disease and other causes of dementia amongst the general public.
Tracking motor activity in Virtual Reality to reveal cognitive functioning: A preliminary study
