This thesis describes how to build a solution for transmitting data from an           Electroencephalography (EEG) device to a server in real-time while guiding the user through a number of predefined exercises. This solution will be used by Spinal Cord Injury (SCI) patients suffering from neuropathic pain, in order to understand if it is possible to predict such pain from EEG. The collected data will help clinicians analyze the brain activity data from patients who can submit the data from their home. To accomplish this development task, an application was built that connects to a portable EEG device, gather brain activity data from patients, guides patients through a set of imaginary tasks and sends the data to a server. This project made use of a Software Development Kit (SDK) for the Python programming language and a web sockets server written in JavaScript. The application was tested both in terms of usability and end-to-end latency, showing high usability and low latency. The proposed solution will support a clinical trial in Spain with 40 SCI patients.

Comprendre comment différentes régions du cerveau interagissent afin d’exécuter une tâche, est un défi très complexe. La magnéto- et l’électroencéphalographie (MEEG) sont deux techniques non-invasive d’imagerie fonctionnelle utilisées pour mesurer avec une bonne résolution temporelle l’activité cérébrale. Estimer cette activité à partir des mesures MEEG est un problème mal posé. Il est donc crucial de le régulariser pour obtenir une solution unique. Il a été montré que l’homogénéité structurelle des régions corticales reflète leur homogénéité fonctionnelle. Un des buts principaux de ce travail est d’utiliser cette information structurelle pour définir des a priori permettant de contraindre de manière plus anatomique ce problème inverse de reconstruction de sources. L’imagerie par résonance magnétique de diffusion (IRMd) est, à ce jour, la seule technique non-invasive qui fournisse des informations sur l’organisation structurelle de la matière blanche. Cela justifie son utilisation pour contraindre notre problème inverse. Nous utilisons l’information fournie par l’IRMd de deux manière différentes pour reconstruire les activations du cerveau : (1) via une méthode spatiale qui utilise une parcellisation du cerveau pour contraindre l’activité des sources. Ces parcelles sont obtenues par un algorithme qui permet d’obtenir un ensemble optimal de régions structurellement homogènes pour une mesure de similarité donnée sur tout le cerveau. (2) dans une approche spatio-temporelle qui utilise les connexions anatomiques, calculées à partir des données d’IRMd, pour contraindre la dynamique des sources. Ces méthodes sont appliquée à des données synthétiques et réelles. / Understanding how brain regions interact to perform a given task is a very challenging task. Electroencephalography (EEG) and Magnetoencephalography (MEG) are two non-invasive functional imaging modalities used to record brain activity with high temporal resolution. As estimating brain activity from these measurements is an ill-posed problem. We thus must set a prior on the sources to obtain a unique solution. It has been shown in previous studies that structural homogeneity of brain regions reflect their functional homogeneity. One of the main goals of this work is to use this structural information to define priors to constrain more anatomically the MEG/EEG source reconstruction problem. This structural information is obtained using diffusion magnetic resonance imaging (dMRI), which is, as of today, the unique non-invasive structural imaging modality that provides an insight on the structural organization of white matter. This makes its use to constrain the EEG/MEG inverse problem justified. In our work, dMRI information is used to reconstruct brain activation in two ways: (1) In a spatial method which uses brain parcels to constrain the sources activity. These parcels are obtained by our whole brain parcellation algorithm which computes cortical regions with the most structural homogeneity with respect to a similarity measure. (2) In a spatio-temporal method that makes use of the anatomical connections computed from dMRI to constrain the sources’ dynamics. These different methods are validated using synthetic and real data.

The opponent process theory of emotion [Solomon, R.L., and Corbit, J.D. (1974). An opponent-process theory of motivation: I. Temporal dynamics of affect. Psychological Review, 81, 119-143.] predicts a temporary reversal of emotional valence during the recovery from emotional stimulation. We hypothesized that this affective contrast would be apparent in asymmetrical activity patterns in the frontal lobes, and would be more apparent for left frontally active individuals. The present study tested this prediction by examining EEG asymmetries during and after blocked presentations of aversive pictures selected from the International Affective Picture System (IAPS). 12 neutral images, 12 aversive images, and 24 neutral images were presented in blocks. Participants who were right frontally active at baseline did not show changes in EEG asymmetry while viewing aversive slides or after cessation. Participants left frontally active at baseline, however, exhibited greater relative left frontal activity after aversive stimulation than before stimulation. Asymmetrical activity patterns in the frontal lobes may relate to affect regulatory processes, including contrasting opponent after-reactions to aversive stimuli.

Research in olfaction is been quite diverse, for example with studies on semantics, brain activations, or distorted smells. Olfactory dysfunction can lead to reduced quality of life, poor dietary habits, sexual and/or mental dysfunctions. Especially in terms of the investigation of olfactory loss it is not only important to assess olfactory function with ratings subjectively assess but more objective measures should be considered. Use of EEG and fMRI has been quite well studied. I have focused my thesis on the use of newer or updated use of existing processing pipelines in order to understand olfaction in a better way.

Stroke is the main cause of adult motor disability. Nevertheless, recent meta-analyses show that the theoretical models conceived to explain the post-stroke brain reorganization are inaccurate and therefore misleading in laying the theoretical foundation for rehabilitation protocols. Mixed results are reported especially in works investigating the excitability properties of the stroke injured brain. Shedding light on the reasons that brought to such mixed results is the central topic of this doctoral thesis. In particular, this confounding evidence is here discussed and tackled in the light of recent works employing brain-state dependent stimulation protocols. These works have been of paramount importance, as they showed that the effects of non-invasive stimulation (TMS and/or rTMS) on the hand knob of the motor cortex depend on the instantaneous sensorimotor state. This local state is largely determined by the phase of the mu-alpha oscillations, with the negative peak representing a high excitability condition. Brain-state dependent results show that controlling for the local state at the moment of stimulation is crucial in order to reduce variability in studies investigating cortical excitability: an approach that has never been employed in stroke literature, so far. In this doctoral thesis, new evidence is provided on affected and unaffected hemispheres’ excitability properties depending on the local state at the moment of stimulation. This previously uncontrolled state-dependent variability is here proposed as one of the factors at the basis of the mixed results in stroke literature. Furthermore, the current models aimed at explaining post-stroke brain reorganization do not take into account factors that recent works suggest might contribute to stroke recovery. In fact, it is here suggested that: interhemispheric inhibition should not be interpreted as competition, structural reserve should be assessed also at the level of the corpus callosum, diaschisis processes should be taken into account and structural and functional connectivity patterns should be included in patients’ assessment. Finally, the excitability properties of the stroke brain have been often inferred comparing stroke patients’ with young healthy controls’. In this regard, it is here proposed that only healthy peers should be included in the control groups, as brain structural changes due to healthy aging have an impact on corticospinal excitability. The aforementioned functional and structural issues are addressed in the following chapters by means of different techniques (i.e. TMS-EEG, MRI, MEG). In particular, in Chapter 1, a new framework of post-stroke brain reorganization is proposed, in which previously over-looked factors are suggested to be essential in the understanding of the potential plastic changes following stroke. Specifically, a new account where interhemispheric inhibition is interpreted in terms of integration and not competition, is supported. Moreover, the proposed framework includes recent pieces of evidence suggesting that structural reserve should be evaluated in the individual patient not only at the level of the cortex, but also in the different sections of the callosum. Finally, it is proposed that structural damage is not static, but rather dynamic as it continues also after the stroke episode through dischiasis processes. In Chapter 2, new knowledge is provided on the different excitability properties of the two hemispheres of stroke patients. In this chapter, TMS-EEG data of stimulation on both the affected and unaffected motor cortex in severe chronic strokes are analysed with a brain-state dependent approach. For the first time, it is shown that the excitability properties of the affected and unaffected hemispheres differ as the local state at the moment of stimulation influences the two hemispheres’ response differently. In particular, the strong and simplified TMS-evoked response in the affected hemisphere, previously reported in severe patients, is shown to depend on a disruption of the differentiation between the high and low excitability states of the motor cortex, determined by the instantaneous phase of alpha oscillations. This low differentiation between excitability states in the affected hemisphere should be systematically investigated, as it could be a potential feature of patients who experience poor recovery. Furthermore, in Chapter 3, connectivity at the individual alpha peak is investigated in a big cohort of healthy participants, in a resting state MEG dataset. This work was implemented because alpha connectivity networks have been shown to predict stroke recovery. For this reason, there is a necessity to reliably assess connectivity at alpha before and after rehabilitation, as this could be informative on the efficacy of rehabilitation. Specifically, it is shown that using complementary phase-coherence metrics is more effective to estimate connectivity patterns at source level. This compound approach is proposed as a tool to better control the modulatory effects of rehabilitation stimulation protocols, in order to identify which are the changes in activity patterns that are potentially responsible for recovery. Finally, in Chapter 4 brain structural changes associated with healthy aging are investigated in a big cohort of participants aged between 18 and 90 years old, both in terms of cortical thinning and cortical myelin concentration loss. In particular, given recent evidence on the relationship between cortical myelin content and corticospinal excitability, it is shown that age-dependent myelin loss occurs mostly at the level of the premotor, motor and sensory cortices. These structural changes need to be taken into account when stroke patients are compared with controls. In fact, since stroke patients are often in their elderly, these age-related structural changes need to be controlled by including only age-matched healthy participants in control groups, as this is not often a fulfilled criterion in stroke studies. To conclude, this doctoral thesis proposes that the current models’ inaccuracy depends on 1) patients’ individual structural and functional factors that have not been taken into account in previous models of brain reorganization post-stroke (Chapter 1), 2) brain-state dependent variability in stimulation effects that have not been controlled for in stroke literature (Chapter 2), 3) a lack of a systematic method to assess the effects of stimulation rehabilitation protocols (Chapter 3) and 4) structural brain changes due to healthy aging, that affect also the stroke brain, and that are not taken into account when patients are compared with young controls in corticospinal excitability studies (Chapter 4). To the author’s knowledge, this is the first work aimed at explaining mixed results in stroke literature from different perspectives and using different neuroimaging techniques for functional and structural anomalies, exploiting recent brain-state dependent approaches for the analysis of stroke patients’ data.

The use of biomarkers in Alzheimer’s disease (AD) has been fundamental for early diagnosis. Currently, biomarkers in use for clinical purposes assess the presence or quantify molecular markers of the disease, i.e., ß-amyloid or Tau protein, or quantify the medial-temporal atrophy caused by the disease. Neuroimaging techniques such as structural, functional and diffusion magnetic resonance imaging and positron-emission tomography have been essential in showing how Alzheimer’s disease pathology spreads within resting-state networks, ultimately impairing their functioning. However, neuroimaging techniques provide indirect measures that do not capture the physiological status of the affected cerebral tissues. Neurophysiological techniques, such as transcranial magnetic stimulation (TMS) and electroencephalography (EEG), are established techniques that can be used in combination to capture both the status of a target cortex and its connections through the brain through TMS-evoked potentials (TEPs). Therefore, TEPs have gained momentum as a possible novel AD biomarker. In the last decade, a specific five-phase framework for the development of novel AD biomarkers has been developed, with the goal of standardizing the steps needed to bring a measure from research to clinical practice. Phase 1 for TEPs, concerning the rationale of using them as a biomarker in AD, could be considered completed, while most of the research is now focusing on phase 2. In this phase, the ability of a measure to distinguish between healthy elderly individuals and AD patients is assessed, together with the reliability and replicability of the measure. In this thesis, we address several aims of phase 2 by testing whether early TEP responses could be used to differentiate between healthy elderly, prodromal, late-onset, and early-onset AD. Then, we test the sensitivity of TEPs to different preprocessing pipelines to assess their robustness. Third, we review the current literature on TEP reliability and describe which tests are missing for this measure to enter clinical practice. Finally, we propose a tool to promote replicability in noninvasive brain stimulation paradigms, such as TMS–EEG. We conclude that despite a solid rationale for the employment of TEPs in clinical practice, several methodological issues need to be addressed before TEPs can gain clinical utility.

Infants prefer familiar adults (e.g. parents) to unfamiliar adults (e.g. strangers), but they also vary in which strangers they prefer. By 6-months, infants look longer at attractive than unattractive faces (e.g., Langlois et al., 1987); and by 12-months, infants show approach behaviors toward attractive strangers and withdrawal behaviors toward unattractive strangers (Langlois, Roggman, & Rieser-Danner, 1990). These preferences may be due to a mechanism referred to as cognitive averaging (e.g., Rubenstein, Kalakanis, & Langlois, 1999). Infants cognitively average face exemplars to form a face prototype. Infants likely perceive attractive faces as familiar because these faces are similar to the face prototype; and they likely perceive unattractive faces as especially novel because these face are dissimilar from the face prototype. Even young infants may be more motivated to approach attractive than unattractive faces but do not fully express this motivation due to limitations in locomotion and communication. I applied EEG asymmetry to study neural correlates of approach and withdrawal motivation in response to attractive and unattractive faces with 6- and 10-month-olds. More specifically, I measured EEG alpha power at mid-frontal regions while 39 infants viewed a series of attractive and unattractive faces. Left EEG asymmetry relates to approach motivation whereas right EEG asymmetry relates to withdrawal motivation. I predicted infants would show greater left EEG asymmetry (i.e., approach motivation) when viewing attractive faces than when viewing unattractive faces, and that 6-montholds would show even greater left asymmetry than 10-month-olds due to developmental differences in stranger wariness. Results supported the main hypothesis but not hypotheses regarding age. Infant EEG asymmetry was greater in response to attractive faces than unattractive faces suggesting that infants are more motivated to approach attractive people than unattractive people as early as 6-months. These results link visual preferences evident at 6-months to overt behaviors evident by 12-months providing additional information regarding rudiments of attractiveness stereotypes. Furthermore, this investigation supports the use of EEG asymmetry methodology to measure infant approach/withdrawal motivation, providing infant researchers one more tool to better understand how infants evaluate novel individuals in their social environment as they decide whom to approach and whom to avoid. / text

<p>The introduction of high density EEG (hd-EEG) nets for easy application on subjects of all ages has improved the possibilities to investigate the development of the infant neurophysiology. This dissertation consists of three studies (I – III) that investigate the visual motion system and mirror neuron system of the infant, and methodological sections that outline the bioelectrical background and the characteristics of the methods used. </p><p>Study I covers the maturation of cortical areas involved in motion perception in adults and infants using an ERP paradigm. Over three age groups (2, 3 and 5 month olds) the cortical activation increased dramatically. All infant groups showed significant activation when moving displays was contrasted to static displays on a video screen. The study shows that 5-month-old infants and older can be expected to process motion in a similar fashion as adults.</p><p>Study II covers the infant mirror neuron system (MNS). In adults the mu rhythm perturbations is considered a reliable measure of activation of the MNS. This study presented both a mu rhythm analysis and a ERP analysis to detect MNS activity in 6-month-olds and in adults. This study concludes that the infant MNS can be measured using ERPs and that the development of mu rhythm perturbations requires further study.</p><p>Study III focused on exploring the mu rhythm suppressions. 8-month-olds observed a live actor that performed goal directed reaches and non-goal directed hand movements. The results show robust mu rhythm perturbations time-locked to the grasping moment. The study concluded that the MNS activity is possible to evaluate by analysis of mu rhythm perturbations and that the MNS show mature characteristics at the age of 8 months.</p><p>In summary, Study 2 and 3 present new methods to investigate the infant mirror neuron system and shows that the infant MNS is active at 6 months of age. At 8 months of age the infant MNS show mature EEG responses to simple actions such as reaching. How the MNS development relates to the infants’ motor development, and how the MNS interacts with the development of social skills requires further studies that could benefit from the methods presented here.</p>

To effect movement, the central nervous system must appropriately coordinate the activities of pools of motoneurons (MNs), the cells which control muscle fibers. Sources of neural drive are often distributed to many MNs of a pool, and thus can synchronize the activities of targeted MNs. In this thesis, synchronization among MNs is used to investigate the strength, temporal progression, and anatomical distribution of neural drive to the hypoglossal motor nucleus (HMN), which controls muscles of the tongue. The HMN is an ideal target for such an investigation because it processes a host of functionally diverse inputs, such as those related to breathing, speaking, and swallowing. Study 1 characterizes motor unit (MU) synchronization within and across bellies of the human genioglossus (GG) muscle when MUs are activated by cortical drive (during voluntary tongue protrusion) or by automatic, brainstem-mediated drive (during rest breathing). We show that voluntary tongue protrusion synchronizes MU spike timing and firing rates within but not across bellies of the GG, whereas during rest breathing, MU firing rates are moderately synchronized both within and across muscle bellies. Study 2 documents respiratory-related synchronization of MU activities in muscles of the tongue and respiratory pump using an anesthetized rat model. The results of this study indicate that upper airway and respiratory pump MN pools share a low frequency respiratory-related drive, but that higher frequency (>8 Hz) synchronization is strongest in MU pairs of the chest-wall. Finally, Study 3 examines the potential for GG multi-unit and single MU activities to be entrained by cortical input. We show that during voluntary tongue protrusion, cortical oscillations in the 15-40 Hz range weakly synchronize MU population activity, and that EEG oscillations in this range intermittently influence the spike timing of individual GG MUs. These studies are the first to characterize MU synchronization by different sources of neural input to the HMN and establish a broad foundation for further investigation of hypoglossal motor control.

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