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/

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/

Multimodal image analysis of the human brain

Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI

Causal connectivity of evolved neural networks during behavior

To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics

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

Single Dose of a Dopamine Agonist Impairs Reinforcement Learning in Humans: Evidence from Event-related Potentials and Computational Modeling of Striatal-Cortical Function

Animal findings have highlighted the modulatory role of phasic dopamine (DA) signaling in incentive learning, particularly in the acquisition of reward-related behavior. In humans, these processes remain largely unknown. In a recent study, we demonstrated that a single low dose of a D2/D3 agonist (pramipexole) - assumed to activate DA autoreceptors and thus reduce phasic DA bursts - impaired reward learning in healthy subjects performing a probabilistic reward task. The purpose of this study was to extend these behavioral findings using event-related potentials and computational modeling. Compared with the placebo group, participants receiving pramipexole showed increased feedback-related negativity to probabilistic rewards and decreased activation in dorsal anterior cingulate regions previously implicated in integrating reinforcement history over time. Additionally, findings of blunted reward learning in participants receiving pramipexole were simulated by reduced presynaptic DA signaling in response to reward in a neural network model of striatal-cortical function. These preliminary findings offer important insights on the role of phasic DA signals on reinforcement learning in humans and provide initial evidence regarding the spatiotemporal dynamics of brain mechanisms underlying these processes.Psycholog

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Magnetoencephalography

This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician