Vascular risk factors affect different brain regions in people with Alzheimer’s disease

Introduction

Vascular risk factors including hypertension, diabetes and dyslipidaemia promote diverse pathological mechanisms in the brain leading to cerebral hypoperfusion and ultimately cognitive decline in people. Medial temporal, medial frontal and anterior cingulate atrophy has been closely associated with diabetes and medial temporal lobe atrophy is associated with hypertension in people with Alzheimer’s disease (AD).

Objectives

To assess if hypertension, diabetes and dyslipidaemia have differential effects on different brain locations using brain imaging in people with AD.

Methods

The current study is based on [¹⁸F] fluorodeoxyglucose- positron emission tomography (FDG-PET) data of 970 participants from two large Phase III multi-centre clinical trials of a novel tau aggregation inhibitor drug Leuco-Methylthioninium (LMTX)meeting research criteria for mild to moderate AD. Vascular risk factor data including hypertension, diabetes and dyslipidaemia were collected and quantification of FDG PET hypo-metabolism was done by calculating Standardized Uptake Value Ratio(SUVR).

Results

Hypertension, diabetes and dyslipidaemia were found to have differential effects on brain locations in people with AD. When people with hypertension, diabetes and dyslipidaemia were compared to those without, mean SUVR was increased significantly in both left and right parietal and occipital lobes and decreased in left and right anterior cingulate gyri in hypertensives. SUVR was significantly higher in both left and right temporal lobes in diabetics andlower in both left and right anterior cingulate gyri in people with dyslipidaemia.

Conclusions

Vascular risk factors including hypertension, diabetes and dyslipidaemia have differential effects on different brain regions, measured using SUVR analysis of FDG-PET.

Disclosure

The FDG-PET data was taken from participants of two large phase III clinical trials sponsored by TauRx Therapeutics (Singapore). TauRx Therapeutics has contributed towards my studentship during my PhD but the data related to drug used in the clinical tria

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.