Nicholas C. Firth

Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer’s Disease

Model-based investigations of transneuronal spreading mechanisms in neurodegenerative diseases relate the pattern of pathology severity to the brain’s connectivity matrix, which reveals information about how pathology propagates through the connectivity network. Such network models typically use networks based on functional or structural connectivity in young and healthy individuals, and only end-stage patterns of pathology, thereby ignoring/excluding the effects of normal aging and disease progression. Here, we examine the sequence of changes in the elderly brain’s anatomical connectivity over the course of a neurodegenerative disease. We do this in a data-driven manner that is not dependent upon clinical disease stage, by using event-based disease progression modeling. Using data from the Alzheimer’s Disease Neuroimaging Initiative dataset, we sequence the progressive decline of anatomical connectivity, as quantified by graph-theory metrics, in the Alzheimer’s disease brain. Ours is the first single model to contribute to understanding all three of the nature, the location, and the sequence of changes to anatomical connectivity in the human brain due to Alzheimer’s disease. Our experimental results reveal new insights into Alzheimer’s disease: that degeneration of anatomical connectivity in the brain may be a viable, even early, biomarker and should be considered when studying such neurodegenerative diseases.

Progression of regional grey matter atrophy in multiple sclerosis

See Stankoff and Louapre (doi:10.1093/brain/awy114) for a scientific commentary on this article. Grey matter atrophy in multiple sclerosis affects certain areas preferentially. Eshaghi et al. use a data-driven computational model to predict the order in which regions atrophy, and use this sequence to stage patients. Atrophy begins in deep grey matter nuclei and posterior cortical regions, before spreading to other cortical areas.

An image‐based model of brain volume biomarker changes in Huntington's disease

Determining the sequence in which Huntingtons disease biomarkers become abnormal can provide important insights into the disease progression and a quantitative tool for patient stratification. Here, we construct and present a uniquely fine‐grained model of temporal progression of Huntingtons disease from premanifest through to manifest stages.

Aging related cognitive changes associated with Alzheimer's disease in Down syndrome

Individuals with Down syndrome (DS) have an extremely high genetic risk for Alzheimers disease (AD), however, the course of cognitive decline associated with progression to dementia is ill‐defined. Data‐driven methods can estimate long‐term trends from cross‐sectional data while adjusting for variability in baseline ability, which complicates dementia assessment in those with DS.

Preparatory planning framework for Created Out of Mind: Shaping perceptions of dementia through art and science

Created Out of Mind is an interdisciplinary project, comprised of individuals from arts, social sciences, music, biomedical sciences, humanities and operational disciplines. Collaboratively we are working to shape perceptions of dementias through the arts and sciences, from a position within the Wellcome Collection. The Collection is a public building, above objects and archives, with a porous relationship between research, museum artefacts, and the public. This pre-planning framework will act as an introduction to Created Out of Mind. The framework explains the rationale and aims of the project, outlines our focus for the project, and explores a number of challenges we have encountered by virtue of working in this way.

Eyetracking Metrics in Young Onset Alzheimer’s Disease: A Window into Cognitive Visual Functions

Young onset Alzheimer’s disease (YOAD) is defined as symptom onset before the age of 65 years and is particularly associated with phenotypic heterogeneity. Atypical presentations, such as the clinic-radiological visual syndrome posterior cortical atrophy (PCA), often lead to delays in accurate diagnosis. Eyetracking has been used to demonstrate basic oculomotor impairments in individuals with dementia. In the present study, we aim to explore the relationship between eyetracking metrics and standard tests of visual cognition in individuals with YOAD. Fifty-seven participants were included: 36 individuals with YOAD (n = 26 typical AD; n = 10 PCA) and 21 age-matched healthy controls. Participants completed three eyetracking experiments: fixation, pro-saccade, and smooth pursuit tasks. Summary metrics were used as outcome measures and their predictive value explored looking at correlations with visuoperceptual and visuospatial metrics. Significant correlations between eyetracking metrics and standard visual cognitive estimates are reported. A machine-learning approach using a classification method based on the smooth pursuit raw eyetracking data discriminates with approximately 95% accuracy patients and controls in cross-validation tests. Results suggest that the eyetracking paradigms of a relatively simple and specific nature provide measures not only reflecting basic oculomotor characteristics but also predicting higher order visuospatial and visuoperceptual impairments. Eyetracking measures can represent extremely useful markers during the diagnostic phase and may be exploited as potential outcome measures for clinical trials.

Sequence of cognitive changes associated with development of Alzheimer's disease in Down syndrome - data driven analysis

Objective Individuals with Down syndrome (DS) have an extremely high genetic risk for Alzheimer’s disease (AD) however the course of cognitive decline associated with progression to dementia is ill-defined. Data-driven methods can estimate long-term trends from cross-sectional data while adjusting for variability in baseline ability, which complicates dementia assessment in those with DS. Methods We applied an event-based model to cognitive test data and informant-rated questionnaire data from 283 adults with DS (the largest study of cognitive functioning in DS to date) to estimate the sequence of cognitive decline and individuals’ disease stage. Results Decline in tests of memory, sustained attention / motor coordination, and verbal fluency occurred early, demonstrating that AD in DS follows a similar pattern of change to other forms of AD. Later decline was found for informant measures. Using the resulting staging model, we showed that adults with a clinical diagnosis of dementia and those with APOE 3:4 or 4:4 genotype were significantly more likely to be staged later, suggesting the model is valid. Interpretation Our results identify tests of memory and sustained attention may be particularly useful measures to track decline in the preclinical/prodromal stages of AD in DS whereas informant-measures may be useful in later stages (i.e. during conversion to dementia, or post-diagnosis). These results have implications for the selection of outcome measures of treatment trials to delay or prevent cognitive decline due to AD in DS. As clinical diagnoses are generally made late into AD progression, early assessment is essential.

Non-Parametric Mixture Modelling and its Application to Disease Progression Modelling

Dementia is characterised by its progressive degeneration of cognitive abilities. In research cohorts, detailed neuropsychological test batteries are often administered to better understand how cognition changes over time. Understanding cognitive changes in dementia is of great importance, particularly in determining how structural changes in the brain may affect cognition and in facilitating earlier detection of symptomatic changes. Disease progression models are often applied to these data to understand how a disease changes over time from cross-sectional data or to disease trajectories from large numbers of individuals. Previous disease progression models used to build longitudinal models from cross-sectional data have focused on brain imaging data; however, these models are not directly applicable to cognitive data. Here we use the novel, non-parametric, Kernel Density Estimation Mixture Modelling (KDEMM) approach and demonstrate accurate modelling of the progression of cognitive test data. We found that using KDEMM resulted in more accurate models of disease progression in simulated data compared to Gaussian Mixture Models (GMMs) for the majority of parameters used to simulate the data. When comparing KDEMM and GMM to cognitive data collected in different Alzheimers Disease subtypes, we found the KDEMM resulted in a model much more in line with clinical phenotype. We anticipate that the KDEMM will be used to integrate cognitive test data, and other non-normally distributed datasets into complex disease progression models.

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique—Subtype and Stage Inference (SuStaIn)—able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer’s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 × 10−4) or temporal stage (p = 3.96 × 10−5). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine.Progressive diseases tend to be heterogeneous in their underlying aetiology mechanism, disease manifestation, and disease time course. Here, Young and colleagues devise a computational method to account for both phenotypic heterogeneity and temporal heterogeneity, and demonstrate it using two neurodegenerative disease cohorts.

AN EVENT BASED MODEL OF ALZHEIMER’S DISEASE IN APOE+ SUBJECTS USING ROBUST BIOMARKERS OF VOLUMETRIC CHANGE IN REGIONAL BRAIN STRUCTURE

thresholded and convolved with a Gaussian kernel to match the PET camera resolution. PVC was performed according to the M€ueller-G€artner and Meltzer approaches. Composite SUVR was computed using a grouping of four larger cortical regions, equally weighted, with subcortical white (SWM) or cerebellar grey (CGM) matter as reference regions. Results: Strong correlation was observed between PVC2 and no PVC SUVR values normalized to SWM uptake (r21⁄40.94). PVC2 SUVRs were significantly higher than no PVC SUVRs (P<0.001). A Bland-Altman plot revealed proportional bias between PVC2 and no PVC SUVRs: bias increased with higher SUVRs. Repeated measures ANOVA showed that PVC3 SUVRs were higher than PVC2 and no PVC, and PVC2 SUVRs were higher than no PVC (all tests P<0.0001). SUVRs were normalized to CGM uptake. Conclusions:Despite strong correlation between partial volume corrected and uncorrected SUVR values, they are not merely interchangeable. At higher SUVR values PVC2 tends to have a larger effect, plausibly related to the amount of atrophy. P3-420 AN EVENT BASED MODEL OF ALZHEIMER’S DISEASE IN APOE+ SUBJECTS USING ROBUST BIOMARKERS OF VOLUMETRIC CHANGE IN REGIONAL BRAIN STRUCTURE Leon M. Aksman, Nicholas Firth, Marzia Antonella Scelsi, Jonathan M. Schott, Sebastien Ourselin, Andre Altmann, Centre for Medical Image Computing, University College London, London, United Kingdom; Centre for Medical Image Computing, University College London, London, United Kingdom; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, United Kingdom; Dementia ResearchCentre, UCL Institute of Neurology, London, United Kingdom. Contact e-mail: l.aksman@ucl.ac.uk