Disease Progression Modeling and Prediction through Random Effect Gaussian Processes and Time Transformation

The development of statistical approaches for the joint modelling of the temporal changes of imaging, biochemical, and clinical biomarkers is of paramount importance for improving the understanding of neurodegenerative disorders, and for providing a reference for the prediction and quantification of the pathology in unseen individuals. Nonetheless, the use of disease progression models for probabilistic predictions still requires investigation, for example for accounting for missing observations in clinical data, and for accurate uncertainty quantification. We tackle this problem by proposing a novel Gaussian process-based method for the joint modeling of imaging and clinical biomarker progressions from time series of individual observations. The model is formulated to account for individual random effects and time reparameterization, allowing non-parametric estimates of the biomarker evolution, as well as high flexibility in specifying correlation structure, and time transformation models. Thanks to the Bayesian formulation, the model naturally accounts for missing data, and allows for uncertainty quantification in the estimate of evolutions, as well as for probabilistic prediction of disease staging in unseen patients. The experimental results show that the proposed model provides a biologically plausible description of the evolution of Alzheimer's pathology across the whole disease time-span as well as remarkable predictive performance when tested on a large clinical cohort with missing observations.Comment: 13 pages, 2 figure

Behavioral and Neurophysiological Effects of Transdermal Rotigotine in Atypical Parkinsonism

Effective therapies for the so-called atypical parkinsonian syndrome (APS) such as multiple system atrophy (MSA), progressive supranuclear palsy (PSP), or corticobasal syndrome (CBS) are not available. Dopamine agonists (DA) are not often used in APS because of inefficacy and in a minority of case, their side effects, like dyskinesias, impairment of extrapyramidal symptoms or the appearance of psychosis, and REM sleep behavioral disorders (RBD). Transdermal rotigotine (RTG) is a non-ergot dopamine agonist indicated for use in early and advanced Parkinson’s disease with a good tolerability and safety. Moreover, its action on a wide range of dopamine receptors, D1, D2, D3, unlike other DA, could make it a good option in APS, where a massive dopamine cell loss is documented. In this pilot, observational open-label study we evaluate the efficacy and tolerability of RTG in patients affected by APS. Thirty-two subjects with diagnosis of APS were treated with transdermal RTG. APS diagnosis was: MSA parkinsonian type (MSA-P), MSA cerebellar type (MSA-C), PSP, and CBS. Patients were evaluated by UPDRS-III, neuropsychiatric inventory, mini mental state examination at baseline, and after 6, 12, and 18 months. The titration schedule was maintained very flexible, searching the major clinical effect and the minor possible adverse events (AEs) at each visit. AEs were recorded. APS patients treated with RTG show an overall decrease of UPDRS-III scores without increasing behavioral disturbances. Only three patients were dropped out of the study. Main AEs were hypotension, nausea, vomiting, drowsiness, and tachycardia. The electroencephalographic recording power spectra analysis shows a decrease of theta and an increase of low alpha power. In conclusion, transdermal RTG seems to be effective and well tolerated in APS patients

Comparison of the effects of transdermal and oral rivastigmine on cognitive function and EEG markers in patients with Alzheimer’s disease

Background: Alzheimer's disease (AD) is the most common cause of dementia in older patients. Rivastigmine (RV, Exelon®, Novartis), a reversible cholinesterase inhibitor, improves clinical manifestations of AD and may enhance ACh-modulated electroencephalogram (EEG) alpha frequency. This pilot study aimed to determine the effects of two formulations of RV (transdermal patch [TV-RDP] and oral capsules [TV-CP]) on alpha frequency, in particular the posterior dominant rhythm, and cognitive function (assessed by the Mini-Mental State Examination [MMSE]) in patients with AD.Methods: Subjects with AD were assigned to receive either RV-TDP 10 cm2 or RV-CP 12 mg/day. All patients underwent EEG recordings at the beginning and end of the 18-month study period using P3, P4, O1 and O2 electrodes, each at high (10.5–13.0 Hz) and low (8.0–10.5 Hz) frequency. MMSE scores were determined at the start of the study (T0) and at three successive 6-month intervals (T1, T2 and T3).Results: RV-TDP administration (n=10) maintained cognitive function as evidenced by stable MMSE scores from baseline to 18 months (21.07 ± 2.4 to 21.2 ± 3.1) compared with a decrease in MMSE score with RV-CP (n=10) over 18 months (18.3 ± 3.6 to 13.6 ± 5.06 [adjusted for covariates p=0.006]). MMSE scores were significantly different between treatment groups from 6 months (p=0.04). RV-TDP also increased the spectral power of alpha waves in the posterior region measured with electrode P3 in a significantly great percentage of patients than TV-CP from baseline to 18 months; 80% versus 30%, respectively (p=0.025 [χ2 test]).Conclusion: RV-TDP was associated with a greater proportion of patients with increased posterior region alpha wave spectral power and significantly higher cognitive function at 18 months, compared with RV-CP treatment. Our findings suggest that RV-TDP provides an effective long-term management option in patients with AD compared with oral RV-CP. This study is a pilot, open-label study with

Anatomical Substrate and Scalp EEG Markers are Correlated in Subjects with Cognitive Impairment and Alzheimer's Disease

Dementia is a syndromic diagnosis, encompassing various stage of severity and different anatomo-physiological substrates. The hippocampus is one of the first and most affected brain regions affected by both Alzheimer's disease (AD) and mild cognitive impairment (MCI). Moreover, chronic cerebrovascular disease (CVD) is one of the major risk factor for developing dementia. Recent studies have demonstrated different relationship between the anatomical substrate and scalp electroencephalography (EEG) markers. Indeed, modifications of EEG rhythmicity is not proportional to the hippocampal atrophy, whereas changes in EEG activity are directly proportional to the load of subcortical CVD. The computation of the EEG spectral power and the analysis of the functional coupling of brain areas, through linear coherence, are two of the most known processing methods in EEG research. Two specific EEG markers, theta/gamma and alpha3/alpha2 frequency ratio have been reliable associated to the atrophy of amygdalo–hippocampal complex. Moreover, theta/gamma ratio has been related to MCI conversion in dementia and alpha3/alpha2 ratio has been specifically related to MCI conversion in AD. The functional coupling of brain areas is also modulated by hippocampal atrophy. In the MCI subjects, hippocampal atrophy is linked to an increase of interhemispheric coherence seen on frontal and temporal regions whereas subcortical CVD is linked to a decrease of coherence in fronto-parietal regions. In the present study the most significant results of recent studies on correlation between scalp EEG, cognitive decline, and anatomical substrate have been reviewed, with particular attention to the relationships between EEG changes and hippocampal atrophy. The following review is not intended to provide a comprehensive summary of the literature. Rather it identifies and discusses selected studies that are designed to find the specific correlation between scalp EEG markers and anatomo-pathological substrate. The principal aim is to propose a plausible neurophysiological theoretical model of the cognitive decline as mirrored by both structural and functional tools of research

Electroencephalographic Rhythms in Alzheimer's Disease

Physiological brain aging is characterized by synapses loss and neurodegeneration that slowly lead to an age-related decline of cognition. Neural/synaptic redundancy and plastic remodelling of brain networking, also due to mental and physical training, promotes maintenance of brain activity in healthy elderly subjects for everyday life and good social behaviour and intellectual capabilities. However, age is the major risk factor for most common neurodegenerative disorders that impact on cognition, like Alzheimer's disease (AD). Brain electromagnetic activity is a feature of neuronal network function in various brain regions. Modern neurophysiological techniques, such as electroencephalography (EEG) and event-related potentials (ERPs), are useful tools in the investigation of brain cognitive function in normal and pathological aging with an excellent time resolution. These techniques can index normal and abnormal brain aging analysis of corticocortical connectivity and neuronal synchronization of rhythmic oscillations at various frequencies. The present review suggests that discrimination between physiological and pathological brain aging clearly emerges at the group level, with suggested applications also at the level of single individual. The possibility of combining the use of EEG together with biological/neuropsychological markers and structural/functional imaging is promising for a low-cost, non-invasive, and widely available assessment of groups of individuals at-risk

LCC-Demons: a robust and accurate symmetric diffeomorphic registration algorithm

International audienceNon-linear registration is a key instrument for computational anatomy to study the morphology of organs and tissues. However, in order to be an effective instrument for the clinical practice, registration algorithms must be computationally efficient, accurate and most importantly robust to the multiple biases affecting medical images. In this work we propose a fast and robust registration framework based on the log-Demons diffeomorphic registration algorithm. The transformation is parameterized by stationary velocity fields (SVFs), and the similarity metric implements a symmetric local correlation coefficient (LCC). Moreover, we show how the SVF setting provides a stable and consistent numerical scheme for the computation of the Jacobian determinant and the flux of the deformation across the boundaries of a given region. Thus, it provides a robust evaluation of spatial changes. We tested the LCC-Demons in the inter-subject registration setting, by comparing with state-of-the-art registration algorithms on public available datasets, and in the intra-subject longitudinal registration problem, for the statistically powered measurements of the longitudinal atrophy in Alzheimer's disease. Experimental results show that LCC-Demons is a generic, flexible, efficient and robust algorithm for the accurate non-linear registration of images, which can find several applications in the field of medical imaging. Without any additional optimization, it solves equally well intra & inter-subject registration problems, and compares favorably to state-of-the-art methods