Cerebral oscillatory activity during simulated driving using MEG

We aimed to examine cerebral oscillatory differences associated with psychological processes during simulated car driving. We recorded neuromagnetic signals in 14 healthy volunteers using magnetoencephalography (MEG) during simulated driving. MEG data were analyzed using synthetic aperture magnetometry to detect the spatial distribution of cerebral oscillations. Group effects between subjects were analyzed statistically using a nonparametric permutation test. Oscillatory differences were calculated by comparison between passive viewing and active driving. Passive viewing was the baseline, and oscillatory differences during active driving showed an increase or decrease in comparison with a baseline. Power increase in the theta band was detected in the superior frontal gyrus (SFG) during active driving. Power decreases in the alpha, beta, and low gamma bands were detected in the right inferior parietal lobe (IPL), left postcentral gyrus (PoCG), middle temporal gyrus (MTG), and posterior cingulate gyrus (PCiG) during active driving. Power increase in the theta band in the SFG may play a role in attention. Power decrease in the right IPL may reflect selectively divided attention and visuospatial processing, whereas that in the left PoCG reflects sensorimotor activation related to driving manipulation. Power decreases in the MTG and PCiG may be associated with object recognition

Multi-Scale Mathematical Modelling of Brain Networks in Alzheimer's Disease

Perturbations to brain network dynamics on a range of spatial and temporal scales are believed to underpin neurological disorders such as Alzheimer’s disease (AD). This thesis combines quantitative data analysis with tools such as dynamical systems and graph theory to understand how the network dynamics of the brain are altered in AD and experimental models of related pathologies. Firstly, we use a biophysical neuron model to elucidate ionic mechanisms underpinning alterations to the dynamics of principal neurons in the brain’s spatial navigation systems in an animal model of tauopathy. To uncover how synaptic deficits result in alterations to brain dynamics, we subsequently study an animal model featuring local and long-range synaptic degeneration. Synchronous activity (functional connectivity; FC) between neurons within a region of the cortex is analysed using two-photon calcium imaging data. Long-range FC between regions of the brain is analysed using EEG data. Furthermore, a computational model is used to study relationships between networks on these different spatial scales. The latter half of this thesis studies EEG to characterize alterations to macro-scale brain dynamics in clinical AD. Spectral and FC measures are correlated with cognitive test scores to study the hypothesis that impaired integration of the brain’s processing systems underpin cognitive impairment in AD. Whole brain computational modelling is used to gain insight into the role of spectral slowing on FC, and elucidate potential synaptic mechanisms of FC differences in AD. On a finer temporal scale, microstate analyses are used to identify changes to the rapid transitioning behaviour of the brain’s resting state in AD. Finally, the electrophysiological signatures of AD identified throughout the thesis are combined into a predictive model which can accurately separate people with AD and healthy controls based on their EEG, results which are validated on an independent patient cohort. Furthermore, we demonstrate in a small preliminary cohort that this model is a promising tool for predicting future conversion to AD in patients with mild cognitive impairment

Attentional refocusing between time and space in older adults:investigation of neural mechanisms and relation to driving

Older adults have a disproportionately high risk of causing collisions at intersections and causing collisions by failing to notice surrounding road signs or signals. Collisions caused by older drivers seem to result from attentional failures. There is limited research exploring the ability to refocus from orienting attention to events changing in time (i.e. temporal attention) to distributing attention spatially (i.e. spatial attention), a process that is particularly important while driving and, if impaired,could cause collisions. The aims of the project were firstly to assess whether the ability to refocus attention from time to space changes throughout the adult lifespan when assessed with a computer based task and in an ecologically valid scenario during simulated driving, secondly, to use magnetoencephalography (MEG) to identify changes to neural mechanism that might explain difficulties in attentional refocusing, and finally, use mobile electroencephalography to explore the neural mechanisms involved in attentional refocusing while driving. Results demonstrated age related declines in the ability to refocus attention from time to space both in a computer-based task and during simulated driving. MEG recorded in a computer-based attention refocusing task revealed that, compared to younger adults, older and middle-aged adults displayed task-related theta deficits in lower level visual processing areas, and instead, displayed compensatory increases in theta power and phase-related connectivity across frontal regions. Increased frontal lobe recruitment likely reflects enhanced top-down attention to cope with impaired lower level attention mechanisms,supporting compensatory recruitment models of ageing. During simulated driving, older participants displayed slower driving speeds and weaker beta desynchronization in preparation to read a road sign, instead displaying a stronger theta power increase in response to the road sign, further demonstrating neural and behavioural compensatory strategies that are only partially successful.Findings warrant the development of a training programme to improve attentional refocusing between time and space while driving

The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology

Brain–computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies

Corticomuscular interactions during different movement periods in a multi-joint compound movement

While much is known about motor control during simple movements, corticomuscular communication profiles during compound movement control remain largely unexplored. Here, we aimed at examining frequency band related interactions between brain and muscles during different movement periods of a bipedal squat (BpS) task utilizing regression corticomuscular coherence (rCMC), as well as partial directed coherence (PDC) analyses. Participants performed 40 squats, divided into three successive movement periods (Eccentric (ECC), Isometric (ISO) and Concentric (CON)) in a standardized manner. EEG was recorded from 32 channels specifically-tailored to cover bilateral sensorimotor areas while bilateral EMG was recorded from four main muscles of BpS. We found both significant CMC and PDC (in beta and gamma bands) during BpS execution, where CMC was significantly elevated during ECC and CON when compared to ISO. Further, the dominant direction of information flow (DIF) was most prominent in EEG-EMG direction for CON and EMG-EEG direction for ECC. Collectively, we provide novel evidence that motor control during BpS is potentially achieved through central motor commands driven by a combination of directed inputs spanning across multiple frequency bands. These results serve as an important step toward a better understanding of brain-muscle relationships during multi joint compound movements.V.V.N was supported by the HSE Basic Research Program and the Russian Academic Excellence Project '5–100'. This study was supported by the Max-Planck Society

Statistical causality in the EEG for the study of cognitive functions in healthy and pathological brains

Understanding brain functions requires not only information about the spatial localization of neural activity, but also about the dynamic functional links between the involved groups of neurons, which do not work in an isolated way, but rather interact together through ingoing and outgoing connections. The work carried on during the three years of PhD course returns a methodological framework for the estimation of the causal brain connectivity and its validation on simulated and real datasets (EEG and pseudo-EEG) at scalp and source level. Important open issues like the selection of the best algorithms for the source reconstruction and for time-varying estimates were addressed. Moreover, after the application of such approaches on real datasets recorded from healthy subjects and post-stroke patients, we extracted neurophysiological indices describing in a stable and reliable way the properties of the brain circuits underlying different cognitive states in humans (attention, memory). More in detail: I defined and implemented a toolbox (SEED-G toolbox) able to provide a useful validation instrument addressed to researchers who conduct their activity in the field of brain connectivity estimation. It may have strong implication, especially in methodological advancements. It allows to test the ability of different estimators in increasingly less ideal conditions: low number of available samples and trials, high inter-trial variability (very realistic situations when patients are involved in protocols) or, again, time varying connectivity patterns to be estimate (where stationary hypothesis in wide sense failed). A first simulation study demonstrated the robustness and the accuracy of the PDC with respect to the inter-trials variability under a large range of conditions usually encountered in practice. The simulations carried on the time-varying algorithms allowed to highlight the performance of the existing methodologies in different conditions of signals amount and number of available trials. Moreover, the adaptation of the Kalman based algorithm (GLKF) I implemented, with the introduction of the preliminary estimation of the initial conditions for the algorithm, lead to significantly better performance. Another simulation study allowed to identify a tool combining source localization approaches and brain connectivity estimation able to provide accurate and reliable estimates as less as possible affected to the presence of spurious links due to the head volume conduction. The developed and tested methodologies were successfully applied on three real datasets. The first one was recorded from a group of healthy subjects performing an attention task that allowed to describe the brain circuit at scalp and source level related with three important attention functions: alerting, orienting and executive control. The second EEG dataset come from a group of healthy subjects performing a memory task. Also in this case, the approaches under investigation allowed to identify synthetic connectivity-based descriptors able to characterize the three main memory phases (encoding, storage and retrieval). For the last analysis I recorded EEG data from a group of stroke patients performing the same memory task before and after one month of cognitive rehabilitation. The promising results of this preliminary study showed the possibility to follow the changes observed at behavioural level by means of the introduced neurophysiological indices

Individualisation of transcranial electric stimulation to improve motor function after stroke:Current challenges and future perspective

Transcranial electric stimulation (tES) is a non-invasive brain stimulation technique that could potentially improve motor rehabilitation after stroke. However, the effects of tES are in general stronger in healthy individuals compared to people with stroke. Interindividual variability in brain structure and function due to stroke potentially explain this difference in effects. This thesis describes the development of methods to facilitate the individualisation of tES in people with stroke and identifies objective neurophysiological correlates of motor learning that could potentially help to monitor the response to tES.In chapter 2, EEG correlates of explicit motor task learning were derived in healthy, young participants. Chapter 3 investigated the effects of 3 different tDCS configurations (sham, targeting contralateral M1 and targeting the full resting motor network) on corticospinal excitability. Both conventional and motor network tDCS did not increase corticospinal excitability relative to sham stimulation. Chapter 4 describes methods to create head models of people with stroke and assesses the effects of stroke lesions on the electric fields within stimulation targets. Chapter 5 describes a method to experimentally determine the electric conductivity of the stroke lesion. Finally, Chapter 6 analyses the electric fields generated by conventional tDCS in people with stroke and age-matched controls. It is shown that the one-size-fits-all approach results in more variable electric fields in people with stroke compared to controls. Optimisation of the electrode positions to maximise the electric field in stimulation targets increases the electric fields in people with stroke to the same level as found in healthy controls.This thesis shows anatomical and motor function variability exists between people with stroke due to differences in lesion characteristics. While there are several opportunities to individualise tES, more research is needed to investigate if this improves the effects of tES. As such, clinical implementation of tES seems unrealistic in the foreseeable future.<br/

Transcranial Alternating Current Stimulation (tACS) Does Not Affect Sports People’s Explosive Power: A Pilot Study

Purpose: This study is aimed to preliminary investigate whether transcranial alternating current stimulation (tACS) could affect explosive power considering genetic background in sport subjects. Methods: Seventeen healthy sports volunteers with at least 3 years of sports activities participated in the experiment. After 2 weeks of familiarization performed without any stimulation, each participant received either 50 Hz-tACS or sham-tACS. Before and after stimulation, subjects performed the following tests: (1) the squat jump with the hands on the hips (SJ); (2) countermovement jump with the hands on the hips (CMJ); (3) countermovement jump with arm swing (CMJ-AS); (4) 15-s Bosco’s test; (5) seated backward overhead medicine ball throw (SBOMBT); (6) seated chest pass throw (SCPT) with a 3-kg rubber medicine ball; and (7) hand-grip test. Additionally, saliva samples were collected from each participant. Genotyping analysis was carried out by polymerase chain reaction (PCR). Results: No significant differences were found in sport performance of subjects after 50 Hz-tACS. Additionally, we did not find any influence of genetic background on tACS-related effect on physical performance. These results suggest that tACS at gamma frequency is not able to induce an after-effect modulating sport performance. Further investigations with larger sample size are needed in order to understand the potential role of non-invasive brain stimulation techniques (NIBS) in motor performances. Conclusions: Gamma-tACS applied before the physical performance fails to improve explosive power in sport subjects