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The comprehensive effect of sleep deprivation on biological and behavioral functions largely remains unknown. There is evidence to support that human sleep must be of sufficient duration and physiological continuity to ensure neurocognitive performance while we are waking. Insufficient sleep would lead to high risk of human-error related to accidents, injuries or even fatal outcomes. However, in modern society, more and more people suffer from sleep deprivation because of the increasing social, academic or occupational demand. It is important to study the effect of sleep deprivation, not only on task performance, but also on neurocognitive functions. Recent research that has explored brain effective connectivity has demonstrated the directed inference interaction among pairs of brain areas, which may bring important insight to understand how brain works to support neurocognitive function. This research aimed to identify the brain effective connectivity pattern associated with changes of a task performance, response time, following sleep deprivation. Experiments were conducted by colleagues at Neuroergonomics Department at Jagiellonian University, Krakow, Poland. Ten healthy young women, with an average age of 23-year-old, performed visual spatial sustained-attention tasks under two conditions: (1) the rest-wakeful (RW) condition, where participants had their usual sleep and (2) the sleep-deprived (SD) condition, where participants had 3 hours less sleep than their usual sleep, for 7 nights (amounting to 21 h of sleep debt). Measures included eye tracking performance and functional magnetic resonance imaging (fMRI). In each condition, each subject*s eye-position was monitored through 13 sessions, each with 46 trials, while fMRI data was recorded. There were two task performance measures, accuracy and response time. Accuracy measured the proportion of correct responses of all trials in each session. Response time measured the average amount of milliseconds until participants gazed at the target stimuli in each session. An experimental session could be treated as a short window. By splitting long trials of fMRI data into consecutive windows, Granger causality was applied based on short trials of fMRI data. This procedure helped to calculate pairwise causal influences with respect to time-varying property in brain causal interaction. Causal influence results were then averaged across sessions to create one matrix for each participant. This matrix was averaged within each condition to formulate a model of brain effective connectivity, which also served as a basis of comparison. In conclusion, significant effect of sleep deprivation was found on response time and brain effective connectivity. In addition, the change of brain effective connectivity after sleep deprivation was linked to the change of response time. First, an analysis of variance (ANOVA) showed significant difference for response time between the RW condition and the SD condition. No significant changes for accuracy were found. A paired t-test showed that response time was significantly shorter in sleep deprivation for the visual spatial sustained-attention task. Second, Granger causality analysis demonstrated a reduction of bidirectional connectivity and an increase of directed influences from low-level brain areas to high-level brain areas after sleep deprivation. This observation suggested that sleep deprivation provoked the effective connectivity engaged in salient stimuli processing, but inhibited the effective connectivity in biasing selection of attention on task and in maintaining self-awareness in day time. Furthermore, in the SD condition, attention at the visual spatial task seemed to be driven by a bottom-up modulation mechanism. Third, a relationship was found between brain effective connectivity with response time. Decreases of Granger causal influences in two directions, from medial frontal lobe to sub cortical gray nuclei and from medial parietal lobe to sub cortical gray nuclei, were associated with shorter response time in the SD condition. Additionally, an increase of Granger causal influence from medial parietal lobe to cerebellum was associated with longer response time in the SD condition.

The development of functional connectivity is often described as changing from local to distributed connections which give rise to the functional brain networks observed in adulthood. In contrast to the well-explored pattern found in functional connectivity, no research has been published describing effective connectivity development. Also, there is a plethora of literature describing functional connectivity patterns in a variety of neurodevelopmental and internalizing disorders, but there is little consistency in the connectivity patterns discovered for each disorder. Hence, this study aimed to describe functional and effective resting-state connectivity during adolescent development in a typically developing adolescent (TDA) group (n = 128) and to determine how adolescents with comorbid neurodevelopmental disorders (CND) (n = 46) differed. This was accomplished through functional and effective connectivity analysis within and between four networks: the Default Mode Network (DMN), the Salience Network (SN), the Dorsal Attention Network (DAN), and the Frontal Parietal Control Network (FPCN). The results from this study indicate that within-network connectivity decreased across age in the TDA group, which is in opposition to previous work which suggests strengthening within-network connectivity. The CND group displayed hyper-connectivity compared to the TDA group in between-network connectivity with no effect of age. The effective connectivity in the TDA group displayed decreasing connectivity within networks with increasing age, a novel effect not previously reported in the literature. The CND group’s effective connectivity was overall hyper-connected (for within- and between-networks). The functional connectivity patterns in the TDA group suggest that functional connectivity has subtle developmental change during adolescence. Further, the CND group consistently displayed hyper-connectivity in functional and effective connectivity. The CND group, and perhaps similar comorbid groups, may have less efficient networks which could contribute to their disorder(s).

This dissertation introduces a novel approach at gauging patterns of informa- tion flow using brain connectivity analysis and partial directed coherence (PDC) in epilepsy. The main objective of this dissertation is to assess the key characteristics that delineate neural activities obtained from patients with epilepsy, considering both focal and generalized seizures. The use of PDC analysis is noteworthy as it es- timates the intensity and direction of propagation from neural activities generated in the cerebral cortex, and it ascertains the coefficients as weighted measures in formulating the multivariate autoregressive model (MVAR). The PDC is used here as a feature extraction method for recorded scalp electroencephalograms (EEG) as means to examine the interictal epileptiform discharges (IEDs) and reflect the phys- iological changes of brain activity during interictal periods. Two experiments were set up to investigate the epileptic data by using the PDC concept. For the investigation of IEDs data (interictal spike (IS), spike and slow wave com- plex (SSC), and repetitive spikes and slow wave complex (RSS)), the PDC analysis estimates the intensity and direction of propagation from neural activities gener- ated in the cerebral cortex, and analyzes the coefficients obtained from employing MVAR. Features extracted by using PDC were transformed into adjacency matrices using surrogate data analysis and were classified by using the multilayer Perceptron (MLP) neural network. The classification results yielded a high accuracy and pre- cision number. The second experiment introduces the investigation of intensity (or strength) of information flow. The inflow activity deemed significant and flowing from other regions into a specific region together with the outflow activity emanating from one region and spreading into other regions were calculated based on the PDC results and were quantified by the defined regions of interest. Three groups were considered for this study, the control population, patients with focal epilepsy, and patients with generalized epilepsy. A significant difference in inflow and outflow validated by the nonparametric Kruskal-Wallis test was observed for these groups. By taking advantage of directionality of brain connectivity and by extracting the intensity of information flow, specific patterns in different brain regions of interest between each data group can be revealed. This is rather important as researchers could then associate such patterns in context to the 3D source localization where seizures are thought to emanate in focal epilepsy. This research endeavor, given its generalized construct, can extend for the study of other neurological and neurode- generative disorders such as Parkinson, depression, Alzheimers disease, and mental illness.

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Theory of Mind (ToM) is the ability to infer mental states of others and this skill relies on a distributed network of brain regions. A brain region that has been traditionally disregarded in relation to non-motor functions is the cerebellum. Here, we leveraged large-scale multimodal neuroimaging data to elucidate the structural and functional role of the cerebellum in ToM. We used functional activations to determine whether the cerebellum has a domain-general or domain-specific functional role. We found that the cerebellum is organized in a domain-specific way. We used effective connectivity and probabilistic tractography to map the cerebello-cerebral ToM network. We found a left cerebellar effective and structural lateralization, with more and stronger effective connections from the left cerebellar hemisphere to the contralateral cerebral ToM areas and greater cerebello-thalamo-cortical (CTC) and cortico-ponto-cerebellar (CPC) streamline counts from and to the left cerebellum. Lastly, we examined the relationship between CTC and CPC white matter and ToM speed and accuracy but found no correlation. Our study provides novel insights to the network organization of the cerebellum, an overlooked brain structure, and ToM, one of humans’ most essential abilities to navigate the social world. / Psychology

Balance of motor network activity between the two brain hemispheres after stroke is crucial for functional recovery. Several studies have extensively studied the role of the affected brain hemisphere to better understand changes in motor network activity following stroke. Very few studies have examined the role of the unaffected brain hemisphere and confirmed the testretest reliability of connectivity measures on unaffected hemisphere. We recorded blood oxygenation level dependent functional magnetic resonance imaging (fMRI) signals from nine stroke survivors with hemiparesis of the left or right hand. Participants performed a motor execution task with affected hand, unaffected hand, and both hands simultaneously. Participants returned for a repeat fMRI scan 1 week later. Using dynamic causal modeling (DCM), we evaluated effective connectivity among three motor areas: the primary motor area (M1), the premotor cortex (PMC) and the supplementary motor area for the affected and unaffected hemispheres separately. Five participants manual motor ability was assessed by Fugl-Meyer Motor Assessment scores and root-mean square error of participants tracking ability during a robot-assisted game. We found (i) that the task performance with the affected hand resulted in strengthening of the connectivity pattern for unaffected hemisphere, (ii) an identical network of the unaffected hemisphere when participants performed the task with their unaffected hand, and (iii) the pattern of directional connectivity observed in the affected hemisphere was identical for tasks using the affected hand only or both hands. Furthermore, paired t-test comparison found no significant differences in connectivity strength for any path when compared with one-week follow-up. Brain-behavior linear correlation analysis showed that the connectivity patterns in the unaffected hemisphere more accurately reflected the behavioral conditions than the connectivity patterns in the affected hemisphere. Above findings enrich our knowledge of unaffected brain hemisphere following stroke, which further strengthens our neurobiological understanding of stroke-affected brain and can help to effectively identify and apply stroke-treatments.

Le lobe temporal est classiquement divisé en deux systèmes fonctionnels: la voie visuelle ventrale et le système mnésique temporal médial. Cependant, cette séparation fonctionnelle a été remise en question par plusieurs études suggérant que le lobe temporal médial serait mieux compris comme une extension de la voie visuelle ventrale hiérarchiquement organisée. Le but de cette thèse était d’étudier le rôle perceptivo-mnésique du lobe temporal médial. Nous avons ainsi voulu tester à l’aide d’enregistrement électro-encéphalographiques intracérébraux (SEEG) si : (i) les régions médiale, ventrale et latérale du lobe temporal pouvaient être regroupées dans des ensembles fonctionnellement distincts et, (ii) quelles régions étaient à l’interface de ces ensembles fonctionnels. Pour répondre à ces questions, nous avons étudié la connectivité effective dérivée de potentiels évoqués cortico-corticaux issus de stimulations électriques de faible intensité et de basse fréquence (0.5mA, 1Hz, 4ms) réalisées chez 16 patients atteints d’épilepsies pharmaco-résistantes temporale ou temporo-occipitale, lors de leur évaluation pré-chirurgicale par la surveillance à long terme des activités intracérebrales électro-encéphalographiques. Onze régions d'intérêt ont été délimitées au sein du lobe temporal en fonction de repères anatomiques tels que les gyri et sulci. Un modèle de connectivité effective au sein du lobe temporal a été extrait, basé sur les caractéristiques électrophysiologiques des potentiels évoqués cortico-corticaux, telles que les occurrences, amplitudes et latences des pics de la première composante du potentiel évoqué. Le modèle a été discuté du point de vue d’une organisation fonctionnelle globale du fait de la non-indépendance de ses caractéristiques électrophysiologiques. L’amplitude et la latence des potentiels évoqués ont montré des distributions de probabilité cohérente avec les observations de transfert d'informations cognitive. La théorie des graphes nous a fourni des algorithmes pouvant extraire les caractéristiques pertinentes du réseau, telles que la centralité des régions, leur ségrégation fonctionnelle ou les voies d’interaction entre les sous-ensembles fonctionnels des régions du lobe temporal. En particulier, le cortex rhinal est apparu comme la structure la plus centrale du lobe temporal alors que le gyrus temporal supérieur était la moins centrale. Le lobe temporal médial a pu être ségrégué en un ensemble fonctionnel à part entière. Le pôle temporal est apparu comme une zone de convergence et de transfert de l’information provenant des structures du lobe temporal latéral vers les structures médiales. Le gyrus fusiforme antérieur a montré des caractéristiques d’interface duale de convergence d’afférences néocorticales et de rétrocontrôle issu des structures médiales. Par ailleurs, l'hippocampe a montré un rôle d’amplificateur du signal des afférences néocorticales passant par l’amygdale et le cortex rhinal, pour être redistribuée vers les structures limbiques. L’hippocampe postérieur se comportait comme une voie de sortie des structures médiales modulant le gyrus parahippocampique postérieur de manière quasi-unidirectionnelle. Notre étude fournit un modèle régional ou macroscopique du transfert de l’information électro-physiologique au sein du lobe temporal humain. Nos résultats ont été discutés à la lumière des modèles cognitifs actuels de ‘complétion de l’information’ et du ‘binding temporel’ / The temporal lobe is conventionally divided into two functional systems: the ventral visual pathway and the medial temporal mnemonic system. However, this functional separation has been questioned by several studies suggesting that the medial temporal lobe would be better understood as an extension of the ventral visual pathway hierarchically organized. The aim of this thesis was to study the perceptive-mnestic role of the medial temporal lobe. We have tested using intracerebral EEG recordings (SEEG) if (i) the medial, lateral and ventral regions of the temporal lobe could be grouped into functionally distinct modules and, (ii) which areas were at the interface of these functional modules. To answer these questions, we studied the effective connectivity derived from cortico-cortical evoked potentials elicited by electrical stimulations from low intensity, low frequency (0.5mA, 1Hz, 4ms) conducted in 16 patients with drug-resistant temporal or temporo-occipital epilepsies at their pre-surgical long-term monitoring of intracerebral electroencephalographic activity. Eleven regions of interest were defined within the temporal lobe based on anatomical landmarks such as gyri and sulci. We built an effective connectivity model within the temporal lobe, based on the electrophysiological characteristics of cortico-cortical evoked potentials, such as the occurences, latencies and amplitudes of the peaks of the first component of the evoked potential. The model was discussed from the perspective of a global functional organization due to the non-independence of its electrophysiological characteristics. The amplitude and latency of the evoked potentials showed consistent probability distributions with the information transfer in cognitive observations. Graph theory has provided algorithms that can extract the relevant characteristics of the network, such as the centrality of the regions, their functional segregations or the ways of interaction between functional subsets of the regions within the temporal lobe. In particular, the rhinal cortex appeared as the most central structure of the temporal lobe whereas the superior temporal gyrus appeared as the less central. The medial temporal lobe could be segregated as a functional module by itself. The temporal pole appeared as a convergence area of the information transfer from the lateral temporal lobe structures to the medial structures. Anterior fusiform gyrus acted as a dual interface of convergence of neocortical afferences and feedback from medial structures. Furthermore, the hippocampus behaved as an amplifier of neocortical afferent signal passing through the amygdala and the rhinal cortex, and redistributed this amplification to the limbic structures. The posterior hippocampus acted as a way out of the medial structures modulating the posterior parahippocampal gyrus in a almost unidirectional manner. Our study provides a regional or macroscopic model of the transfer of electro-physiological information within the human temporal lobe. Our results were discussed in line with current cognitive models of 'pattern completion' and 'temporal binding'

In neuroscience and biomedical engineering fields, one of the most important issues nowadays is finding a relationship between different brain regions when it is stimulated. Connectivity is an important research area in neuroscience which tries to determine the relationship between different brain region when the brain is stimulated externally or internally. Three main type of connectivity are discussed in this field: Anatomical, Functional and Effective connectivity. Importance of effective connectivity is its ability to detect brain disorders in early stages. Some brain disorders are Schizophrenia, MS and Major Depression disease. Comparing the effective connectivity between a healthy and unhealthy brain will help to diagnose brain disorder. In this master study, two methods named Dynamic Causal Modeling (DCM) and Psychophysiological Interaction (PPI) are used to compare effective connectivity and neuronal activity between different regions of brain when there are three different stimulations. Since the neural activity is latent in fMRI data, there is a need to a model which is able to transfer data from neuronal level to a visible data like Blood-Oxygen level dependent (BOLD) signal. DCM uses a haemodynamic balloon model (HD) to represent this data transfer. The hemodynamic model must be so that the parameters of neural and BOLD signal be the same. It should be noted that what is looked for is not the BOLD signal but the neuronal activity. In this study, as the first step, we did preprocessing of MR images and after ROI`s are created using the program MARSBAR. Ten ROIs, which are thought to have connections between them are selected by considering the stimulations used in the experiments in obtaining the data used in this thesis. The data used contains fMRI images of 11 healthy subjects. Stimulations of experiment are applied to images got from group analysis of 11 healthy subjects. These Stimulations are then used in preparing the design matrix and the parameters related to DCM. These parameters are the values related to connection matrices defining bilinear dynamic model on ROI. Bayesian method is used to select best model between all these models. Another method of PPI is also applied to analyze effective connectivity between 10 ROIs. This method considers two issues of physiological and psychological effects. Like DCM, the preprocessing steps and ROI selection is done for PPI and hemodynamic model is designed for this method. Neural and hemodynamic responses of ROIs are compared using this method.

Episodic memory is a key feature in learning. One must remember past events to act upon a present situation. Episodic memory has been reported to be impaired in individuals with schizophrenia. In order to have an intact episodic memory the contextual features (context) must be bound to the content of the event; this mechanism is referred to as contextual binding. It is proposed that binding errors during the encoding process are responsible for episodic memory impairments in schizophrenia. Since the hippocampal formation is considered to be the central element for contextual binding, it is hypothesized that the synaptic disorganization described in this condition results in such a deficit. Moreover, the hippocampus mediates and influences other cognitive processes such as learning and executive functioning. Hence, a contextual binding deficit can have important consequences on cognition, behaviour and emotions. The object of this dissertation was to investigate the neurofunctioning, neuroanatomy and neurofunctional connectivity of the hippocampus while performing a task that utilized contextual binding mechanisms. Since spatial relational processing is part of contextual binding and is rooted in the hippocampal regions, visuospatial navigation, more precisely a wayfinding task, was used as a probe to activate the hippocampus and its associated regions in a group of patients with schizophrenia and matched healthy controls. The following dissertation presents three original research papers contributing to our understanding of the contextual binding and hippocampal deficits in schizophrenia. The first paper investigates the neurofunctioning of the hippocampus with a wayfinding task. The second paper investigates the hippocampal structural abnormality in schizophrenia and how it relates to performance during the wayfinding task. The third paper explores effective connectivity of the hippocampus with other brain regions involved in navigation in schizophrenia with a particular interest in the prefrontal cortex. These three studies demonstrate significant neurofunctional, neuroanatomical, and neurofunctional connectivity deficits in the hippocampus of the patients with schizophrenia compared to a healthy control population. Results of all three papers are further discussed in terms of research and clinical implications.