FDG-PET Image Classification in Alzheimer's Disease: from Traditional Visual Analysis to Advanced Transfer Learning.

Purpose: Alzheimer's disease (AD) often coexists with other brain pathologies, and we aimed to classify people with AD using 18 F- Flouro-Deoxy-Glucose-Positron Emission Tomography (FDG-PET).

Method: Baseline FDG-PET data were collected as part of two large scale Phase III clinical trials of a novel tau aggregation inhibitor drug, Leuco-Methylthioninium (LMTX®). A total of 794, well-characterised probable AD subjects were included in the study and the images were classified into "typical AD"(temporoparietal hypometabolism) and "mixed" (patchy hypo-metabolism in other vascular territories of brain such as frontal and cerebellar regions along with temporo-parietal hypo-metabolism) patterns based on visual interpretation.

Obtaining personalized predictions from a randomized controlled trial on Alzheimer's disease.

The purpose of this article is to infer patient level outcomes from population level randomized control trials (RCTs). In this pursuit, we utilize the recently proposed synthetic nearest neighbors (SNN) estimator. At its core, SNN leverages information across patients to impute missing data associated with each patient of interest.

Inferring the underlying multivariate structure from bivariate networks with highly correlated nodes.

Complex systems are often described mathematically as networks. Inferring the actual interactions from observed dynamics of the nodes of the networks is a challenging inverse task. It is crucial to distinguish direct and indirect interactions to allow for a robust identification of the underlying network.

Crossed cerebellar diaschisis in Alzheimer's disease.

Background: Crossed cerebellar diaschisis (CCD) is characterized by hypometabolism and hypoperfusion on molecular imaging in the cerebellum due to a supratentorial lesion on the contralateral side. CCD is a well-established phenomenon in acute or subacute conditions such as infarction but it has been less well described in chronic conditions such as neurodegenerative dementias. Here, we investigate CCD in a large sample of 830 people meeting research criteria for Alzheimer's disease (AD) using [18F]fluorodeoxyglucose-positron emission tomography (FDG-PET).

Long-Term Hydromethylthionine Treatment Is Associated with Delayed Clinical Onset and Slowing of Cerebral Atrophy in a Pre-Symptomatic P301S MAPT Mutation Carrier.

One of the mutations in the microtubule-associated protein tau, P301S, is causative for dominantly inherited frontotemporal dementia characterized by extensive tau pathology for which no licensed treatment is available. Hydromethylthionine is a potent tau aggregation inhibitor. We report treatment of an asymptomatic carrier of the P301S mutation using hydromethylthionine over a 5-year period beginning at the mean age of onset of clinical decline in the family.

Concentration-Dependent Activity of Hydromethylthionine on Clinical Decline and Brain Atrophy in a Randomized Controlled Trial in Behavioral Variant Frontotemporal Dementia.

Background: Hydromethylthionine is a potent inhibitor of pathological aggregation of tau and TDP-43 proteins.

Objective: To compare hydromethylthionine treatment effects at two doses and to determine how drug exposure is related to treatment response in bvFTD.

Methods: We undertook a 52-week Phase III study in 220 bvFTD patients randomized to compare hydromethylthionine at 200 mg/day and 8 mg/day (intended as a control).

Concentration-Dependent Activity of Hydromethylthionine on Cognitive Decline and Brain Atrophy in Mild to Moderate Alzheimer's Disease.

Background: Although hydromethylthionine is a potent tau aggregation inhibitor, no difference was found in either of two Phase III trials in mild to moderate Alzheimer's disease (AD) comparing doses in the range 150-250 mg/day with 8 mg/day intended as a control.

Objective: To determine how drug exposure is related to treatment response.

Methods: A sensitive plasma assay for the drug was used in a population pharmacokinetic analysis of samples from 1,162 of the 1,686 patients who participated in either of the Phase III trials with available samples and efficacy outcome data.

Quantitative assessment of cerebral connectivity deficiency and cognitive impairment in children with prenatal alcohol exposure.

It is common knowledge that alcohol consumption during pregnancy would cause cognitive impairment in children. However, recent works suggested that the risk of drinking during pregnancy may have been exaggerated. It is critical to determine whether and up to which amount the consumption of alcohol will affect the cognitive development of children.

Potential of Low Dose Leuco-Methylthioninium Bis(Hydromethanesulphonate) (LMTM) Monotherapy for Treatment of Mild Alzheimer's Disease: Cohort Analysis as Modified Primary Outcome in a Phase III Clinical Trial.

Background: LMTM is being developed as a treatment for AD based on inhibition of tau aggregation.

Objectives: To examine the efficacy of LMTM as monotherapy in non-randomized cohort analyses as modified primary outcomes in an 18-month Phase III trial in mild AD.

Methods: Mild AD patients (n = 800) were randomly assigned to 100 mg twice a day or 4 mg twice a day.

Efficacy and safety of tau-aggregation inhibitor therapy in patients with mild or moderate Alzheimer's disease: a randomised, controlled, double-blind, parallel-arm, phase 3 trial.

Background: Leuco-methylthioninium bis(hydromethanesulfonate; LMTM), a stable reduced form of the methylthioninium moiety, acts as a selective inhibitor of tau protein aggregation both in vitro and in transgenic mouse models. Methylthioninium chloride has previously shown potential efficacy as monotherapy in patients with Alzheimer's disease. We aimed to determine whether LMTM was safe and effective in modifying disease progression in patients with mild to moderate Alzheimer's disease.

Granger causal time-dependent source connectivity in the somatosensory network.

Exploration of transient Granger causal interactions in neural sources of electrophysiological activities provides deeper insights into brain information processing mechanisms. However, the underlying neural patterns are confounded by time-dependent dynamics, non-stationarity and observational noise contamination. Here we investigate transient Granger causal interactions using source time-series of somatosensory evoked magnetoencephalographic (MEG) elicited by air puff stimulation of right index finger and recorded using 306-channel MEG from 21 healthy subjects.

Epileptic seizure predictors based on computational intelligence techniques: a comparative study with 278 patients.

The ability of computational intelligence methods to predict epileptic seizures is evaluated in long-term EEG recordings of 278 patients suffering from pharmaco-resistant partial epilepsy, also known as refractory epilepsy. This extensive study in seizure prediction considers the 278 patients from the European Epilepsy Database, collected in three epilepsy centres: Hôpital Pitié-là-Salpêtrière, Paris, France; Universitätsklinikum Freiburg, Germany; Centro Hospitalar e Universitário de Coimbra, Portugal. For a considerable number of patients it was possible to find a patient specific predictor with an acceptable performance, as for example predictors that anticipate at least half of the seizures with a rate of false alarms of no more than 1 in 6 h (0.

The EPILEPSIAE database: an extensive electroencephalography database of epilepsy patients.

From the very beginning the seizure prediction community faced problems concerning evaluation, standardization, and reproducibility of its studies. One of the main reasons for these shortcomings was the lack of access to high-quality long-term electroencephalography (EEG) data. In this article we present the EPILEPSIAE database, which was made publicly available in 2012.

A common strategy and database to compare the performance of seizure prediction algorithms.

A reliable algorithm for the timely prediction of epileptic seizures would be a milestone in epilepsy research. Prediction performances have so far been determined using retrospective data assessment, leaving open the question as to whether they prove statistically significant and clinically useful under prospective conditions. To this aim, a Seizure Prediction Competition has been set up.

Revealing direction of coupling between neuronal oscillators from time series: phase dynamics modeling versus partial directed coherence.

The problem of determining directional coupling between neuronal oscillators from their time series is addressed. We compare performance of the two well-established approaches: partial directed coherence and phase dynamics modeling. They represent linear and nonlinear time series analysis techniques, respectively.