The Computational Neuropsychiatry Lab aims to bridge the gap between clinical practice and research, neurology, psychiatry, physics and psychology in order to re-formulate our understanding of the human self and its pathologies. We work with and for neurological and psychiatric patients in the department of neurology, neuropsychiatry clinic, invasive neurophysiological unit and in the operation room. We use state of the art computational methods applied directly on clinical data, particularly tailored to improve clinical management and scientific understanding of neuropsychiatric disorders. Our multidisciplinary team of medical doctors, physicists, computer scientists and psychologists perform electrophysiological and metabolic recordings in healthy subjects and neuropsychiatric patients. We perform sophisticated analyses and modeling in order to better understand the human "self" in health and disease.
Lesions of posterior parietal cortex result in a constellation of symptoms challenging the habitual perception of the human body and self, often referred to as the ‘parietal syndrome’. These include, for instance, hemispatial neglect, asomatognosia, anosognosia, somatoparaphrenia or out-of-body experiences. Using specific body- and self-related paradigms and fMRI together with analysis of the behaviour of parietally damaged patients, and anatomical localization of the lesions responsible for the different symptoms, we try to understand the physiological mechanisms underlying these symptoms.
We are studying the functional effects of psychothropic drugs in neurological and psychiatric patients.
rs-fMRI uses the fact that different parts of the brain are activated in the resting state, activations which are probably related to "intrinsic" activity of the "internal milieu" rather than to interaction with the external world. rs-fMRIwas found in recent years to encompass valuable data with respect to non-lesional neuropsychiatric disorders. We apply rs-fMRI on specific neuropsychiatric clinical disorders in which rs-fMRI may have a special advantage, combined with the development of new analysis strategies from the fields of graph theory and complex systems, to establish rs-fMRI as robust and useful clinical tool.
While classical neurology deals mostly with well-defined cerebral functions, such as motor, sensory, visual or linguistic systems, the majority of brain metabolism is directed to brain activity in other parts of the cerebral cortex, combining the so-called "intrinsic" system. This "intrinsic" system maintains essential functions related to one’s own self, such as agency of one’s actions, ownership of one’s body, autobiographical memory of one’s past, planning of one’s future, or social interaction with one’s environment. We use functional investigations (fMRI, electrical neuroimaging, PET) to reveal selective alterations of brain activity and rhythms in the intrinsic system during neuropsychiatric disorders in comparison to challenges in healthy human subjects. Computational modeling enables us to detect specific patterns underlying these functions and their modifications in health and disease.
Many neuropsychiatric disorders, such as migraine, psychogenic phenomena, dissociative disorders, or movements disorders, embrace an extensive range of motor, sensory, autonomic and mental manifestations, similar to that of epileptic disorders, yet without underlying epileptic activity. We record intracranial and extracranial electrophysiological data in order to better understand the neurology of these disorders.
While computational methods and technology develop in an unbelievable pace, Neuropsychiatry leans mainly on traditional clinical methods as well as structural neuroimaging. However, various Neuropsychiatric conditions do not involve structural lesions and therefore these methods fail to detect them effectively. To solve this problem we use advanced and sophisticated computational methods to decode hidden information, enabling patients and clinicians to better understand these conditions in the individual patient, and to “tailor” appropriate investigations and treatment. These are further fed into our algorithms in order to refine follow-up and treatment in real-time.