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.

This thesis provides a novel dynamic large-scale network perspective on brain activity of human sleep based on the analysis of unique human neuroimaging data. Specifically, I provide new information based on integrating spatial and temporal aspects of brain activity both in the transitions between and during wakefulness and various stages of non-rapid-eye movement (NREM) sleep. This is achieved through investigations of inter-regional interactions, functional connectivity (FC), between activity timecourses throughout the brain. Overall, the presented findings provide new important whole-brain insights for our current understanding of sleep, and potentially also of sleep disorders and consciousness in general. In Chapter 2 I present a robust global increase in similarity between the structural connectivity (SC) and the FC in slow-wave sleep (SWS) in almost all of the participants of two independent fMRI datasets. This could point to a decreased state repertoire and more rigid brain dynamics during SWS. Chapter 2 further identifies the changes in FC strengths between wakefulness and individual stages of NREM sleep across the whole-brain fMRI network. I report connectivity in posterior parts of the brain as particularly strong during wakefulness, while connections between temporal and frontal cortices are increased in strength during N1 and N2 sleep. SWS is characterised by a global drop in FC. In Chapter 3 I take advantage of rare MEG recordings of NREM sleep to show, for the first time, the feasibility of constructing source-space FC networks of sleep using power envelope correlations. The increased temporal information of MEG signals allows me to identify the specific frequencies underlying the FC differences identified in Chapter 2 with fMRI. The beta band (16 – 30 Hz) thus stands out as important for the strong posterior connectivity of wakefulness, while a range of frequency bands from delta (0.25 – 4 Hz) to sigma (13 – 16 Hz) all appear to contribute to N2-specific FC increases. Consistent with the fMRI results, slow-wave sleep shows the lowest level of FC. Interestingly, however, the MEG signals suggest a fronto-temporal network of high connectivity in the alpha band, possibly reflecting memory processes. In Chapter 4 I expand the within-frequency FC analysis of Chapter 3 to explore potential cross-frequency interactions in the MEG FC networks. It is shown that N2 sleep involves an abundance of frequency cross-talk, while SWS includes very little. A multi-layer network approach shows that the gamma band (30 – 48 Hz) is particularly integrated in wakefulness. Chapter 5 addresses the identified MEG FC findings from the perspective of traditional spectral sleep staging. By correlating temporal changes in spectral power at the sensor level to fluctuations in average FC, a specific type of transient events is found to underlie the strong N2-specific coupling in static FC values. Lastly, in Chapter 6 I make the leap out of the constraints of traditional low-resolution sleep staging, and extract dynamic states of FC from fMRI timecourses in a completely unsupervised fashion. This provides a novel representation of whole-brain states of sleep and the dynamics governing them. I argue that data-driven approaches like this are necessary to fully characterise the spatiotemporal principles underlying wakefulness and sleep in the human brain.

La même entrée sensorielle ne provoque pas toujours la même réaction. Dans les expériences en laboratoire, un stimulus donné peut engendrer une réponse différente à chaque nouvel essai, en particulier à proximité du seuil sensoriel. Ce phénomène est généralement attribué à une source de bruit non spécifique qui affecte la représentation sensorielle du stimulus ou le processus décisionnel. Dans cette thèse, nous examinons l'hypothèse selon laquelle cette variabilité des réponses peut être attribuée en partie à des fluctuations mesurables et spontanées de l'état cérébral. Dans ce but, nous développons et évaluons deux ensembles d'outils. L’un est un ensemble de modèles et de méthodes psychophysiques permettant de suivre les variations de la performance perceptive avec une bonne résolution temporelle et avec précision, sur différentes échelles de temps. Ces méthodes s’appuient sur des procédures adaptatives initialement développées pour mesurer efficacement les seuils de perception statiques et sont étendues ici dans le but de suivre des seuils qui varient au cours du temps. Le deuxième ensemble d'outils que nous développons comprend des méthodes d'analyse de données pour extraire de signaux d’électroencéphalographie (EEG) une quantité prédictive de la performance comportementale à diverses échelles de temps. Nous avons appliqué ces outils à des enregistrements conjoints d’EEG et de données comportementales collectées pendant que des auditeurs normo-entendants réalisaient une tâche de discrimination de fréquence sur des stimuli auditifs proche du seuil de discrimination. Contrairement à ce qui a été rapporté dans la littérature concernant des stimuli visuels, nous n'avons pas trouvé de preuve d’un quelconque effet des oscillations EEG spontanées de basse fréquence sur la performance auditive. En revanche, nous avons trouvé qu'une part importante de la variabilité des jugements peut s’expliquer par des effets de l'historique récent des stimuli et des réponses sur la décision prise à un moment donné. / The same sensory input does not always trigger the same reaction. In laboratory experiments, a given stimulus may elicit a different response on each trial, particularly near the sensory threshold. This is usually attributed to an unspecific source of noise that affects the sensory representation of the stimulus or the decision process. In this thesis we explore the hypothesis that response variability can in part be attributed to measurable, spontaneous fluctuations of ongoing brain state. For this purpose, we develop and test two sets of tools. One is a set of models and psychophysical methods to follow variations of perceptual performance with good temporal resolution and accuracy on different time scales. These methods rely on the adaptive procedures that were developed for the efficient measurements of static sensory thresholds and are extended here for the purpose of tracking time-varying thresholds. The second set of tools we develop encompass data analysis methods to extract from electroencephalography (EEG) signals a quantity that is predictive of behavioral performance on various time scales. We applied these tools to joint recordings of EEG and behavioral data acquired while normal listeners performed a frequency-discrimination task on near-threshold auditory stimuli. Unlike what was reported in the literature for visual stimuli, we did not find evidence for any effects of ongoing low-frequency EEG oscillations on auditory performance. However, we found that a substantial part of judgment variability can be accounted for by effects of recent stimulus-response history on an ongoing decision.

Transitions between normal and pathological brain states are manifested differently in the electroencephalogram (EEG). Traditional discrimination of these states is often subject to bias and strict definitions. A fuzzy logic-based analysis can permit the classification and tracking of brain states in a non-subjective and unsupervised manner. In this thesis, the combination of fuzzy c-means (FCM) clustering, wavelet, and information theory has revealed notable frequency features in epilepsy and anaesthetic-induced unconsciousness. It was shown that entropy changes in membership functions correlate to specific epileptiform activity and changes in anaesthetic dosages. Seizure episodes appeared in the 31-39 Hz band, suggesting changes in cortical functional organization. The induction of anaesthetics appeared in the 64-72 Hz band, while the return to consciousness appeared in the 32-40 Hz band. Changes in FCM activity were associated with the concentration of anaesthetics. These results can help with the treatment of epilepsy and the safe administration of anaesthesia.

Transitions between normal and pathological brain states are manifested differently in the electroencephalogram (EEG). Traditional discrimination of these states is often subject to bias and strict definitions. A fuzzy logic-based analysis can permit the classification and tracking of brain states in a non-subjective and unsupervised manner. In this thesis, the combination of fuzzy c-means (FCM) clustering, wavelet, and information theory has revealed notable frequency features in epilepsy and anaesthetic-induced unconsciousness. It was shown that entropy changes in membership functions correlate to specific epileptiform activity and changes in anaesthetic dosages. Seizure episodes appeared in the 31-39 Hz band, suggesting changes in cortical functional organization. The induction of anaesthetics appeared in the 64-72 Hz band, while the return to consciousness appeared in the 32-40 Hz band. Changes in FCM activity were associated with the concentration of anaesthetics. These results can help with the treatment of epilepsy and the safe administration of anaesthesia.