The sensorimotor cortex is a brain region comprising the primary motor cortex (MI) and the primary somatosensory (SI) cortex. In humans, investigation into these regions suggests that MI and SI are involved in the modulation and control of motor and somatosensory processing, and are somatotopically organized according to a body plan (Penfield & Boldrey, 1937). Additional investigations into somatotopic mapping in relation to the limbs in the peripheral nervous system and SI in central nervous system have further born out the importance of this body-based organization (Wall & Dubner, 1972). Understanding the nature of the sensorimotor cortex‟s structure and function has broad implications not only for human development, but also motor learning (Taubert et al., 2011) and clinical applications in structural plasticity in Parkinson‟s disease (Sehm et al., 2014), among others. The aim of the present thesis is to identify functionally meaningful subregions within the sensorimotor cortex via parcellation analysis. Previously, cerebral subregions were identified in postmortem brains by invasive procedures based on histological features (Brodmann, 1909; Vogt. & Vogt., 1919; Economo, 1926; Sanides, 1970). One widely used atlas is based on Brodmann areas (BA). Brodmann divided human brains into several areas based on the visually inspected cytoarchitecture of the cortex as seen under a microscope (Brodmann, 1909). In this atlas, BA 4, BA 3, BA 1 and BA 2 together constitute the sensorimotor cortex (Vogt. & Vogt., 1919; Geyer et al., 1999; Geyer et al., 2000). However, BAs are incapable of delineating the somatotopic detail reflected in other research (Blankenburg et al., 2003). And, although invasive approaches have proven reliable in the discovery of functional parcellation in the past, such approaches are marked by their irreversibility which, according to ethical standards, makes them unsuitable for scientific inquiry. Therefore, it is necessary to develop non-invasive approaches to parcellate functional brain regions. In the present study, a non-invasive and task-free approach to parcellate the sensorimotor cortex with resting-state fMRI was developed. This approach used functional connectivity patterns of brain areas in order to delineate functional subregions as connectivity-based parcellations (Wig et al., 2014). We selected two adjacent BAs (BA 3 and BA 4) from a standard template to cover the area along the central sulcus (Eickhoff et al., 2005). Then subregions within this area were generated using resting-state fMRI data. These subregions were organized somatotopically from medial-dorsal to ventral-lateral (corresponding roughly to the face, hand and foot regions, respectively) by comparing them with the activity maps obtained by using independent motor tasks. Interestingly, resting-state parcellation map demonstrated higher correspondence to the task-based divisions after individuals had performed motor tasks. We also observed higher functional correlations between the hand area and the foot and tongue area, respectively, than between the foot and tongue regions. The functional relevance of those subregions indicates the feasibility of a wide range of potential applications to brain mapping (Nebel et al., 2014). In sum, the present thesis provides an investigation of functional network, functional structure, and properties of the sensorimotor cortex by state-of-art neuroimaging technology. The methodology and the results of the thesis hope to carry on the future research of the sensorimotor system.

The sensorimotor cortex is a brain region comprising the primary motor cortex (MI) and the primary somatosensory (SI) cortex. In humans, investigation into these regions suggests that MI and SI are involved in the modulation and control of motor and somatosensory processing, and are somatotopically organized according to a body plan (Penfield & Boldrey, 1937). Additional investigations into somatotopic mapping in relation to the limbs in the peripheral nervous system and SI in central nervous system have further born out the importance of this body-based organization (Wall & Dubner, 1972). Understanding the nature of the sensorimotor cortex‟s structure and function has broad implications not only for human development, but also motor learning (Taubert et al., 2011) and clinical applications in structural plasticity in Parkinson‟s disease (Sehm et al., 2014), among others. The aim of the present thesis is to identify functionally meaningful subregions within the sensorimotor cortex via parcellation analysis. Previously, cerebral subregions were identified in postmortem brains by invasive procedures based on histological features (Brodmann, 1909; Vogt. & Vogt., 1919; Economo, 1926; Sanides, 1970). One widely used atlas is based on Brodmann areas (BA). Brodmann divided human brains into several areas based on the visually inspected cytoarchitecture of the cortex as seen under a microscope (Brodmann, 1909). In this atlas, BA 4, BA 3, BA 1 and BA 2 together constitute the sensorimotor cortex (Vogt. & Vogt., 1919; Geyer et al., 1999; Geyer et al., 2000). However, BAs are incapable of delineating the somatotopic detail reflected in other research (Blankenburg et al., 2003). And, although invasive approaches have proven reliable in the discovery of functional parcellation in the past, such approaches are marked by their irreversibility which, according to ethical standards, makes them unsuitable for scientific inquiry. Therefore, it is necessary to develop non-invasive approaches to parcellate functional brain regions. In the present study, a non-invasive and task-free approach to parcellate the sensorimotor cortex with resting-state fMRI was developed. This approach used functional connectivity patterns of brain areas in order to delineate functional subregions as connectivity-based parcellations (Wig et al., 2014). We selected two adjacent BAs (BA 3 and BA 4) from a standard template to cover the area along the central sulcus (Eickhoff et al., 2005). Then subregions within this area were generated using resting-state fMRI data. These subregions were organized somatotopically from medial-dorsal to ventral-lateral (corresponding roughly to the face, hand and foot regions, respectively) by comparing them with the activity maps obtained by using independent motor tasks. Interestingly, resting-state parcellation map demonstrated higher correspondence to the task-based divisions after individuals had performed motor tasks. We also observed higher functional correlations between the hand area and the foot and tongue area, respectively, than between the foot and tongue regions. The functional relevance of those subregions indicates the feasibility of a wide range of potential applications to brain mapping (Nebel et al., 2014). In sum, the present thesis provides an investigation of functional network, functional structure, and properties of the sensorimotor cortex by state-of-art neuroimaging technology. The methodology and the results of the thesis hope to carry on the future research of the sensorimotor system.

Heutige Roboter sind nur begrenzt in der Lage etwas zu erlernen, sich unerwarteten Umständen anzupassen oder auf diese zu reagieren. Als Antwort auf diese Fragen, develomental robotics setzt sich den Aufbau eines künstlichen Systems zum Ziel, das motorische und kognitive Fähigkeiten analog zur menschlichen Entwicklung durch Interaktion mit der Umgebung entwickeln kann. In dieser Arbeit wird ein ähnlich Ansatz verwendet, mit Hilfe dessen grundlegende Verhaltenskomponenten identifiziert werden sollen, die eine autonome Aneignung motorischer und kognitive Fähigkeiten durch die Roboter ermöglichen könnten. Diese Arbeit untersucht die sensomotorische Interaktion als Mittel zur Schaffung von Erfahrungen. Es werden Experimente zu explorative Verhaltensweisen zur Aneigung von Arbewegungen, der Werkzeugnutzung und von interaktiven Fähigkeiten vorgestellt. In diesem Rahmen wird auch die Entwicklung sozialer Fähigkeiten, insbesondere durch joint attention, behandelt. Dabei werden zwei Vorraussetzugen zu joint attention untersucht: Zeigegesten und Erkennung von visueller Salienz. Dabei wurde das Framework der interen Modelle für die Darstellung von sensomotorischen Erfahrungen angewendet. Insbesondere wurden inverse und Vorwärtsmodelle mit unterschiedlichen Konfigurationen am sensorischen und motorischen Daten, die vom Roboter durch exploratives Verhalten, durch Beobachtung menschliche Vorführern, oder durch kinästhetisches Lehren erzeugt wurden geschult. Die Entscheidung zu Gunsten dieses Framework wurde getroffen, da es in der Lage ist, sensomotorische Zyklen zu simulieren. Diese Arbeit untersucht, wie grundlegende kognitive Fähigkeiten in einen humanoiden Roboter unter Berücksichtigung sensorischer und motorischer Erfahrungen implementiert werden können. Insbesondere wurden interne Simulationsprozesse für die Implementierung von Kognitivenfahigkeiten wie die Aktionsauswahl, die Werkzeugnutzung, die Verhaltenserkennung und die Self-Other distinction, eingesetzt. / State-of-the-art robots are still not properly able to learn from, adapt to, react to unexpected circumstances, and to autonomously and safely operate in uncertain environments. Researchers in developmental robotics address these issues by building artificial systems capable of acquiring motor and cognitive capabilities by interacting with their environment, inspired by human development. This thesis adopts a similar approach in finding some of those basic behavioural components that may allow for the autonomous development of sensorimotor and social skills in robots. Here, sensorimotor interactions are investigated as a mean for the acquisition of experience. Experiments on exploration behaviours for the acquisition of arm movements, tool-use and interactive capabilities are presented. The development of social skills is also addressed, in particular of joint attention, the capability to share the focus of attention between individuals. Two prerequisites of joint attention are investigated: imperative pointing gestures and visual saliency detection. The established framework of the internal models is adopted for coding sensorimotor experience in robots. In particular, inverse and forward models are trained with different configurations of low-level sensory and motor data generated by the robot through exploration behaviours, or observed by human demonstrator, or acquired through kinaesthetic teaching. The internal models framework allows the generation of simulations of sensorimotor cycles. This thesis investigates also how basic cognitive skills can be implemented in a humanoid robot by allowing it to recreate the perceptual and motor experience gathered in past interactions with the external world. In particular, simulation processes are used as a basis for implementing cognitive skills such as action selection, tool-use, behaviour recognition and self-other distinction.