Self-consciousness has mostly been approached by philosophical enquiry and not by empirical neuroscientific study, leading to an overabundance of diverging theories and an absence of data-driven theories. Using robotic technology, we achieved specific bodily conflicts and induced predictable changes in a fundamental aspect of self-consciousness by altering where healthy subjects experienced themselves to be (self-location). Functional magnetic resonance imaging revealed that temporo-parietal junction (TPJ) activity reflected experimental changes in self-location that also depended on the first-person perspective due to visuo-tactile and visuo-vestibular conflicts. Moreover, in a large lesion analysis study of neurological patients with a well-defined state of abnormal self-location, brain damage was also localized at TPJ, providing causal evidence that TPJ encodes self-location. Our findings reveal that multisensory integration at the TPJ reflects one of the most fundamental subjective feelings of humans: the feeling of being an entity localized at a position in space and perceiving the world from this position and perspective.

Although inherently linked, body form and body action may be represented in separate neural substrates. Using repetitive transcranial magnetic stimulation in healthy individuals, we show that interference with the extrastriate body area impairs the discrimination of bodily forms, and interference with the ventral premotor cortex impairs the discrimination of bodily actions. This double dissociation suggests that whereas extrastriate body area mainly processes actors' body identity, premotor cortex is crucial for visual discriminations of actions

Emerging evidence indicates impairments in somatosensory function may be a major contributor to motor dysfunction associated with neurologic injury or disorders. However, the neuroanatomical substrates underlying the connection between aberrant sensory input and ineffective motor output are still under investigation. The primary somatosensory cortex (S1) plays a critical role in processing afferent somatosensory input and contributes to the integration of sensory and motor signals necessary for skilled movement. Neuroimaging and neurostimulation approaches provide unique opportunities to non-invasively study S1 structure and function including connectivity with other cortical regions. These research techniques have begun to illuminate casual contributions of abnormal S1 activity and connectivity to motor dysfunction and poorer recovery of motor function in neurologic patient populations. This review synthesizes recent evidence illustrating the role of S1 in motor control, motor learning and functional recovery with an emphasis on how information from these investigations may be exploited to inform stroke rehabilitation to reduce motor dysfunction and improve therapeutic outcomes.

Rhythmic brain activity plays an important role in neural processing and behavior. Features of these oscillations, including amplitude, phase, and spectrum, can be influenced by internal states (e.g., shifts in arousal, attention or cognitive ability) or external stimulation. Electromagnetic stimulation techniques such as transcranial magnetic stimulation, transcranial direct current stimulation, and transcranial alternating current stimulation are used increasingly in both research and clinical settings. Currently, the mechanisms whereby time-dependent external stimuli influence population-scale oscillations remain poorly understood. Here, we provide computational insights regarding the mapping between periodic pulsatile stimulation parameters such as amplitude and frequency and the response dynamics of recurrent, nonlinear spiking neural networks. Using a cortical model built of excitatory and inhibitory neurons, we explored a wide range of stimulation intensities and frequencies systematically. Our results suggest that rhythmic stimulation can form the basis of a control paradigm in which one can manipulate the intrinsic oscillatory properties of driven networks via a plurality of input-driven mechanisms. Our results show that, in addition to resonance and entrainment, nonlinear acceleration is involved in shaping the rhythmic response of our modeled network. Such nonlinear acceleration of spontaneous and synchronous oscillatory activity in a neural network occurs in regimes of intense, high-frequency rhythmic stimulation. These results open new perspectives on the manipulation of synchronous neural activity for basic and clinical research.