EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease

This is the final version. Available from Nature Research via the DOI in this record. The dynamics of the resting brain exhibit transitions between a small number of discrete networks, each remaining stable for tens to hundreds of milliseconds. These functional microstates are thought to be the building blocks of spontaneous consciousness. The electroencephalogram (EEG) is a useful tool for imaging microstates, and EEG microstate analysis can potentially give insight into altered brain dynamics underpinning cognitive impairment in disorders such as Alzheimer’s disease (AD). Since EEG is non-invasive and relatively inexpensive, EEG microstates have the potential to be useful clinical tools for aiding early diagnosis of AD. In this study, EEG was collected from two independent cohorts of probable AD and cognitively healthy control participants, and a cohort of mild cognitive impairment (MCI) patients with four-year clinical follow-up. The microstate associated with the frontoparietal working-memory/attention network was altered in AD due to parietal inactivation. Using a novel measure of complexity, we found microstate transitioning was slower and less complex in AD. When combined with a spectral EEG measure, microstate complexity could classify AD with sensitivity and specificity > 80%, which was tested on an independent cohort, and could predict progression from MCI to AD in a small preliminary test cohort of 11 participants. EEG microstates therefore have potential to be a non-invasive functional biomarker of AD.Engineering and Physical Sciences Research Council (EPSRC)Wellcome TrustAlzheimer’s SocietyGarfield Weston FoundationUniversity of BristolUniversity of San Marino and IS

Density Contrast Sedimentation Velocity for the Determination of Protein Partial-Specific Volumes

The partial-specific volume of proteins is an important thermodynamic parameter required for the interpretation of data in several biophysical disciplines. Building on recent advances in the use of density variation sedimentation velocity analytical ultracentrifugation for the determination of macromolecular partial-specific volumes, we have explored a direct global modeling approach describing the sedimentation boundaries in different solvents with a joint differential sedimentation coefficient distribution. This takes full advantage of the influence of different macromolecular buoyancy on both the spread and the velocity of the sedimentation boundary. It should lend itself well to the study of interacting macromolecules and/or heterogeneous samples in microgram quantities. Model applications to three protein samples studied in either H2O, or isotopically enriched H218O mixtures, indicate that partial-specific volumes can be determined with a statistical precision of better than 0.5%, provided signal/noise ratios of 50–100 can be achieved in the measurement of the macromolecular sedimentation velocity profiles. The approach is implemented in the global modeling software SEDPHAT

Double peaked P1 visual evoked potentials in healthy ageing

Objectives To robustly examine the prevalence of the double peaked P1 visual evoked potential in healthy younger and older adult populations. Methods The evoked potentials and spectral power changes to simple visual stimuli of 26 healthy younger (M = 20.0 y) and 26 healthy older adults (M = 76.0 y) were examined. Results Group and individual analyses showed a clear effect of age on P1 morphology and amplitude. Older adults showed significantly lower P1 amplitude and 44% of older adults showed a double peaked P1 compared to 12% of younger adults. Double peaked P1 responses were associated with an increase in spectral power in the gamma range. Conclusions The double peaked P1 may be more prevalent in older adults than previously demonstrated and may represent a de-synchronisation of the cortical sources of the visual P1 in healthy ageing. Increased power in post stimulus gamma in the double peak group may be indicative of compensatory neural processing. Significance Clinically the prevalence of the double peaked P1 may have been underestimated, and its reflectance of demyelinating disease overestimated. Experimentally the results suggest that any investigation of visual processing in older adults must control for early changes in P1 morphology

The effects of traffic calming on child pedestrian skills development

Engineering measures, such as traffic calming, are effective in reducing accidents for vulnerable road users such as child pedestrians and cyclists. However, their effect on the development of child pedestrian skills is unknown. This project reviewed relevant literature and re-examined existing data on child pedestrian exposure in calmed and uncalmed areas. This was followed by an empirical study which compared the pedestrian skills and exposure of children growing up in a traffic calmed area to those in a nearby 'untreated' control area. Pupils in schools local to each area were tested, and their parents/guardians interviewed. The study found little difference in the total exposure of children to traffic on local roads in calmed and control areas, although the patterns of exposure changed to some extent. Samples of 7-9 year old children from schools within the calmed area and the un-calmed control area were given both PC based and roadside tests of visual timing and gap selection, and a safe crossing location test. Although small differences were detected, none of the results showed any major difference in scores between children from the two areas, although scores did improve with age. It is possible that individual differences in pupils' road safety skills is due more to factors such as the attitudes of parents towards road safety, and differences between schools (overall academic ability) than to whether they live in a calmed or un-calmed environment

Dataset for article entitled "An empirical evaluation of methodologies used for emotion recognition via EEG signals"

The data is split into two parts according to the two experiments described within the article. The dataset includes movies and python codes for classifying emotions from experiment 1, and EEG and ERP measurements from experiment 2 along with associated code for analyzing those data. Experiment 1 tests the validity of the SEED dataset collated by Zheng, Dong, & Lu (2014) and, subsequently, our own stimuli. The objective was to test whether previous literature using such datasets as the aformentioned dataset by Zheng et al. is purportedly classifying between emotions based on emotion-related signals of interest and/or non-emotional ‘noise’. Experiment 2 used stimuli that have been well-validated within the psychological literature as reliably evoking specific embodiments of emotions within the viewer, namely the NimStim face and ADFES-BIV datasets with the objective of classifying a person's emotional status using EEG. All data was processed and analyses run in MATLAB or Python. All datasets used are included within the folders accompanied by the MATLAB or Python scripts for collating separable matrices and running the action.Running the scripts requires MATLAB and Python 3 (with packages imageio, matplotlib, myknn, NumPy, pylab, SciPy, sklearn)