9,874 research outputs found
A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments
In this study we propose a deformation-based framework to jointly model the
influence of aging and Alzheimer's disease (AD) on the brain morphological
evolution. Our approach combines a spatio-temporal description of both
processes into a generative model. A reference morphology is deformed along
specific trajectories to match subject specific morphologies. It is used to
define two imaging progression markers: 1) a morphological age and 2) a disease
score. These markers can be computed locally in any brain region. The approach
is evaluated on brain structural magnetic resonance images (MRI) from the ADNI
database. The generative model is first estimated on a control population,
then, for each subject, the markers are computed for each acquisition. The
longitudinal evolution of these markers is then studied in relation with the
clinical diagnosis of the subjects and used to generate possible morphological
evolution. In the model, the morphological changes associated with normal aging
are mainly found around the ventricles, while the Alzheimer's disease specific
changes are more located in the temporal lobe and the hippocampal area. The
statistical analysis of these markers highlights differences between clinical
conditions even though the inter-subject variability is quiet high. In this
context, the model can be used to generate plausible morphological trajectories
associated with the disease. Our method gives two interpretable scalar imaging
biomarkers assessing the effects of aging and disease on brain morphology at
the individual and population level. These markers confirm an acceleration of
apparent aging for Alzheimer's subjects and can help discriminate clinical
conditions even in prodromal stages. More generally, the joint modeling of
normal and pathological evolutions shows promising results to describe
age-related brain diseases over long time scales.Comment: NeuroImage, Elsevier, In pres
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The role of HG in the analysis of temporal iteration and interaural correlation
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Gait variability: methods, modeling and meaning
The study of gait variability, the stride-to-stride fluctuations in walking, offers a complementary way of quantifying locomotion and its changes with aging and disease as well as a means of monitoring the effects of therapeutic interventions and rehabilitation. Previous work has suggested that measures of gait variability may be more closely related to falls, a serious consequence of many gait disorders, than are measures based on the mean values of other walking parameters. The Current JNER series presents nine reports on the results of recent investigations into gait variability. One novel method for collecting unconstrained, ambulatory data is reviewed, and a primer on analysis methods is presented along with a heuristic approach to summarizing variability measures. In addition, the first studies of gait variability in animal models of neurodegenerative disease are described, as is a mathematical model of human walking that characterizes certain complex (multifractal) features of the motor control's pattern generator. Another investigation demonstrates that, whereas both healthy older controls and patients with a higher-level gait disorder walk more slowly in reduced lighting, only the latter's stride variability increases. Studies of the effects of dual tasks suggest that the regulation of the stride-to-stride fluctuations in stride width and stride time may be influenced by attention loading and may require cognitive input. Finally, a report of gait variability in over 500 subjects, probably the largest study of this kind, suggests how step width variability may relate to fall risk. Together, these studies provide new insights into the factors that regulate the stride-to-stride fluctuations in walking and pave the way for expanded research into the control of gait and the practical application of measures of gait variability in the clinical setting
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data
International audienceWe introduce a probabilistic generative model for disentangling spatio-temporal disease trajectories from series of high-dimensional brain images. The model is based on spatio-temporal matrix factorization, where inference on the sources is constrained by anatomically plausible statistical priors. To model realistic trajectories, the temporal sources are defined as monotonic and time-reparametrized Gaussian Processes. To account for the non-stationarity of brain images, we model the spatial sources as sparse codes convolved at multiple scales. The method was tested on synthetic data favourably comparing with standard blind source separation approaches. The application on large-scale imaging data from a clinical study allows to disentangle differential temporal progression patterns mapping brain regions key to neurodegeneration, while revealing a disease-specific time scale associated to the clinical diagnosis
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data
We introduce a probabilistic generative model for disentangling
spatio-temporal disease trajectories from series of high-dimensional brain
images. The model is based on spatio-temporal matrix factorization, where
inference on the sources is constrained by anatomically plausible statistical
priors. To model realistic trajectories, the temporal sources are defined as
monotonic and time-reparametrized Gaussian Processes. To account for the
non-stationarity of brain images, we model the spatial sources as sparse codes
convolved at multiple scales. The method was tested on synthetic data
favourably comparing with standard blind source separation approaches. The
application on large-scale imaging data from a clinical study allows to
disentangle differential temporal progression patterns mapping brain regions
key to neurodegeneration, while revealing a disease-specific time scale
associated to the clinical diagnosis
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Bayesian latent time joint mixed-effects model of progression in the Alzheimer's Disease Neuroimaging Initiative.
IntroductionWe characterize long-term disease dynamics from cognitively healthy to dementia using data from the Alzheimer's Disease Neuroimaging Initiative.MethodsWe apply a latent time joint mixed-effects model to 16 cognitive, functional, biomarker, and imaging outcomes in Alzheimer's Disease Neuroimaging Initiative. Markov chain Monte Carlo methods are used for estimation and inference.ResultsWe find good concordance between latent time and diagnosis. Change in amyloid positron emission tomography shows a moderate correlation with change in cerebrospinal fluid tau (ρ = 0.310) and phosphorylated tau (ρ = 0.294) and weaker correlation with amyloid-β 42 (ρ = 0.176). In comparison to amyloid positron emission tomography, change in volumetric magnetic resonance imaging summaries is more strongly correlated with cognitive measures (e.g., ρ = 0.731 for ventricles and Alzheimer's Disease Assessment Scale). The average disease trends are consistent with the amyloid cascade hypothesis.DiscussionThe latent time joint mixed-effects model can (1) uncover long-term disease trends; (2) estimate the sequence of pathological abnormalities; and (3) provide subject-specific prognostic estimates of the time until onset of symptoms
Electrophysiological evidence for domain-general processes in task-switching
open5noopenCapizzi, Mariagrazia; Ambrosini, Ettore; Arbula, Sandra; Mazzonetto, Ilaria; Vallesi, AntoninoCapizzi, Mariagrazia; Ambrosini, Ettore; Arbula, Sandra; Mazzonetto, Ilaria; Vallesi, Antonin
Spatio-Temporal Neural Changes After Task-Switching Training in Old Age
In the present study, we aimed at examining selective neural changes after taskswitching training in old age by not only considering the spatial location but also the
timescale of brain activation changes (i.e., sustained/block-related or transient/trialrelated timescales). We assigned a sample of 50 older adults to a task-switching
training or an active single-task control group. We administered two task paradigms,
either sensitive to transient (i.e., a context-updating task) or sustained (i.e., a delayedrecognition working-memory task) dynamics of cognitive control. These dynamics
were captured by utilizing an appropriate event-related or block-related functional
magnetic resonance imaging design. We captured selective changes in task activation
during the untrained tasks after task-switching training compared to an active control
group. Results revealed changes at the neural level that were not evident from only
behavioral data. Importantly, neural changes in the transient-sensitive context updating
task were found on the same timescale but in a different region (i.e., in the left
inferior parietal lobule) than in the task-switching training task (i.e., ventrolateral PFC,
inferior frontal junction, superior parietal lobule), only pointing to temporal overlap, while
neural changes in the sustained-sensitive delayed-recognition task overlapped in both
timescale and region with the task-switching training task (i.e., in the basal ganglia),
pointing to spatio-temporal overlap. These results suggest that neural changes after
task-switching training seem to be critically supported by the temporal organization of
neural processing.Deutsche Forschungsgemeinschaft (DFG
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