Oral Tau Aggregation Inhibitor for Alzheimer's Disease: Design, Progress and Basis for Selection of the 16 mg/day Dose in a Phase 3, Randomized, Placebo-Controlled Trial of Hydromethylthionine Mesylate.

BACKGROUND: Hydromethylthionine mesylate is a tau aggregation inhibitor shown to have exposure-dependent pharmacological activity on cognitive decline and brain atrophy in two completed Phase 3 trials in mild/moderate Alzheimer's disease (AD). OBJECTIVES: The present report summarises the basis for selection of 16 mg/day as monotherapy as the optimal treatment regime and the design rationale of a confirmatory Phase 3 trial (LUCIDITY). DESIGN: The trial comprises a 12-month double-blind, placebo-controlled phase followed by a 12-month modified delayed-start open-label treatment phase. SETTING: 76 clinical research sites in North America and Europe. PARTICIPANTS: 545 patients with probable AD or MCI-AD in the final version of the protocol. INTERVENTION: Participants were assigned randomly to receive hydromethylthione mesylate at doses of 16 mg/day, 8 mg/day or placebo at a 4:1:4 ratio during the double-blind phase. All participants in the open-label phase receive the 16 mg/day dose. MEASUREMENTS: Co-primary clinical outcomes are the 11-item Alzheimer's Disease Assessment Scale (ADAS-cog11) and the 23-item Alzheimer's Disease Cooperative Study - Activities of Daily Living (ADCS-ADL23). Secondary biomarker measures include whole-brain atrophy and temporal lobe 18F-fluorodeoxyglucose positron emission tomography. RESULTS: 446 participants are expected to complete the 12-month placebo-controlled phase in March 2022. CONCLUSIONS: If the primary end points are met, the data will provide confirmatory evidence of the clinical and biomarker benefits of hydromethylthionine mesylate in minimal to moderate AD. As low-dose oral hydromethylthionine mesylate is simple to use clinically, does not cause amyloid-related imaging abnormalities and has a benign safety profile, it would likely improve AD management.

Functionally dissociating ventro-dorsal components within the rostro-caudal hierarchical organization of the human prefrontal cortex.

Cognitive control is proposed to rely on a rostral-to-caudal hierarchy of neural processing within the prefrontal cortex (PFC), with more rostral parts exerting control over more caudal parts. Anatomical and functional data suggest that this hierarchical organization of the PFC may be separated into a ventral and a dorsal component. Furthermore, recent studies indicate that the apex of the hierarchy resides within the mid-lateral rather the rostral PFC. However, investigating the hierarchical aspect of rostro-to-caudal processing requires quantification of the directed interactions between PFC regions. Using functional near-infrared spectroscopy (fNIRS) in a sample of healthy young adults we analyzed directed interactions between rostral and caudal PFC during passive watching of nature documentaries. Directed coherence (DC) as a measure of directed interaction was computed pairwise between 38 channels evenly distributed over the lateral prefrontal convexity. Results revealed an overall predominance of rostral-to-caudal directed interactions in the PFC that further dissociated along a ventro-dorsal axis: Dorsal regions exerted stronger rostro-caudally directed interactions on dorsal than on ventral regions and vice versa. Interactions between ventral and dorsal PFC were stronger from ventral to dorsal areas than vice versa. Results further support the notion that the mid-dorsolateral PFC constitutes the apex of the prefrontal hierarchy. Taken together these data provide novel evidence for parallel dorsal and ventral streams within the rostro-caudal hierarchical organization of the PFC. FNIRS-based analyses of directed interactions put forward a new perspective on the functional architecture of the prefrontal hierarchy and complement previous insights from functional magnetic resonance imaging.

EPILAB: a software package for studies on the prediction of epileptic seizures.

A Matlab®-based software package, EPILAB, was developed for supporting researchers in performing studies on the prediction of epileptic seizures. It provides an intuitive and convenient graphical user interface. Fundamental concepts that are crucial for epileptic seizure prediction studies were implemented. This includes, for example, the development and statistical validation of prediction methodologies in long-term continuous recordings. Seizure prediction is usually based on electroencephalography (EEG) and electrocardiography (ECG) signals. EPILAB is able to process both EEG and ECG data stored in different formats. More than 35 time and frequency domain measures (features) can be extracted based on univariate and multivariate data analysis. These features can be post-processed and used for prediction purposes. The predictions may be conducted based on optimized thresholds or by applying classifications methods such as artificial neural networks, cellular neuronal networks, and support vector machines. EPILAB proved to be an efficient tool for seizure prediction, and aims to be a way to communicate, evaluate, and compare results and data among the seizure prediction community.

A computational environment for long-term multi-feature and multi-algorithm seizure prediction.

The daily life of epilepsy patients is constrained by the possibility of occurrence of seizures. Until now, seizures cannot be predicted with sufficient sensitivity and specificity. Most of the seizure prediction studies have been focused on a small number of patients, and frequently assuming unrealistic hypothesis. This paper adopts the view that for an appropriate development of reliable predictors one should consider long-term recordings and several features and algorithms integrated in one software tool. A computational environment, based on Matlab (®), is presented, aiming to be an innovative tool for seizure prediction. It results from the need of a powerful and flexible tool for long-term EEG/ECG analysis by multiple features and algorithms. After being extracted, features can be subjected to several reduction and selection methods, and then used for prediction. The predictions can be conducted based on optimized thresholds or by applying computational intelligence methods. One important aspect is the integrated evaluation of the seizure prediction characteristic of the developed predictors.

A longitudinal study of tremor frequencies in Parkinson's disease and essential tremor.

OBJECTIVE: There is evidence that the tremor frequency in essential tremor (ET) decreases with time. Longitudinal studies on the evolution of tremor frequencies in Parkinson's disease (PD) have so far not been published. Here, we present a longitudinal analysis of tremor frequencies in PD and ET. METHODS: We analyzed the standardized accelerometric and electromyographic tremor recordings of 53 patients with PD and 38 patients with ET who underwent repeated routine tremor recordings between 1991 and 2002. RESULTS: In an average follow-up period of 44.9 months in PD and 50.6 months in ET, the average number of tremor recordings was 3.3 in PD and 3.7 in ET. In both disorders, tremor frequencies tended to decrease with time. The average annual decrease of the tremor frequency was 0.09 Hz/year in Parkinsonian rest tremor, 0.08 Hz/year in Parkinsonian postural tremor and 0.12 Hz/year in ET. CONCLUSIONS: The tremor frequency decreases with time in both PD and ET. The similarity of this decrease in PD and ET may point to a common underlying pathophysiological mechanism. SIGNIFICANCE: Decreasing tremor frequencies with time may be functionally important by inducing larger tremor amplitudes due to the low-pass filtering properties of muscles and limbs.

Mixing properties of the Rössler system and consequences for coherence and synchronization analysis.

Cross-spectral and synchronization analysis of two independent, identical chaotic Rössler systems suggest a coupling although there is no interaction. This spuriously detected interaction can either be explained by the absence of mixing or by finite size effects. To decide which alternative holds the phase dynamics is studied by a model of the fluctuations derived from the system's equations. The basic assumption of the model is a diffusive character for the system which corresponds to mixing. Comparison of theoretical properties of the model with empirical properties of the Rössler system suggests that the system is mixing but the rate of mixing appears to be rather low.

On studentising and blocklength selection for the bootstrap on time series.

For independent data, non-parametric bootstrap is realised by resampling the data with replacement. This approach fails for dependent data such as time series. If the data generating process is at least stationary and mixing, the blockwise bootstrap by drawing subsamples or blocks of the data saves the concept. For the blockwise bootstrap a blocklength has to be selected. We propose a method for selecting the optimal blocklength. To improve the finite size properties of the blockwise bootstrap, studentised statistics is considered. If the statistic can be represented as a smooth function model this studentisation can be approximated efficiently. The studentised blockwise bootstrap method is applied for testing hypotheses on medical time series.

Dynamic synchronisation of central oscillators in essential tremor.

OBJECTIVE: Coherence analysis of electromyography (EMG) signals in essential tremor (ET) suggests that tremor in the right and left arm is induced by independent central oscillators. The sensorimotor cortex seems to be part of the tremor-generating neuronal network in ET. Here, we investigated using electroencephalography (EEG) whether the independence of central oscillators in ET is supported by the analysis of cortical activity. METHODS: In 8 patients with ET, bilateral hand tremor was activated by wrist extension. EMGs from the wrist flexors and extensors were recorded simultaneously with an EEG. EEG-EMG coherence was estimated for 74 epochs of 60 s duration. RESULTS: In 42.6% of the cases, EEG-EMG coherence at the tremor frequency existed only with the contralateral sensorimotor cortex. However, 21.6% of the tremor-EMGs were coherent with EEG activity over both the contralateral and ipsilateral sensorimotor cortex. Bilateral and exclusively contralateral EEG-EMG coherence could alternate within the same recording. Bilateral EEG-EMG coherence was associated with increased right-left EEG-EEG coherence, increased right-left EMG-EMG coherence as well as with increased tremor strength. CONCLUSIONS: In ET, central oscillators in the right and left brain are not entirely independent of each other. They may dynamically synchronise, presumably by interhemispheric coupling via the corpus callosum.

Tremor-correlated cortical activity in essential tremor.

BACKGROUND: In patients with parkinsonian resting tremor, tremor-correlated activity in the contralateral sensorimotor cortex has been studied by both magnetoencephalography (MEG) and electroencephalography (EEG). In essential tremor, MEG failed to detect cortical involvement. The objective of this study was to investigate whether EEG recording can reveal tremor-correlated cortical activity in patients with essential tremor or enhanced physiological tremor. METHODS: Seven patients with essential tremor and three patients with enhanced physiological tremor participated in the study. Unilateral postural tremor was activated by wrist extension on the right or on the left side. Electromyography (EMG) signals arising from the wrist extensor and flexor muscles, and a high-resolution EEG were recorded simultaneously. Coherences between the time series of the rectified tremor EMG and the EEG were estimated. FINDINGS: In five of nine arms with essential tremor, we found highly significant coherences at the tremor frequency between the tremor EMG and the EEG. Isocoherence maps illustrating the topography of significant coherences over the scalp showed that the maximum coherences were located over the contralateral sensorimotor cortex. In the patients with enhanced physiological tremor, we were unable to detect consistent significant corticomuscular coherences at the tremor frequency. INTERPRETATION: Using simultaneous EEG-EMG recordings, we showed that significant corticomuscular coherences at the tremor frequency can be found in essential tremor. This finding contrasts with a recent study based on MEG recordings. The results suggest that the sensorimotor cortex is involved in the generation of essential tremor, in a similar way to that previously shown in parkinsonian resting tremor.