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Transcranial Magnetic Stimulation (TMS) is rapidly gaining acceptance as a non-invasive probe into brain functionality. We utilize TMS to study the connectivity of a simple motor network in patients of schizophrenia (N=19), and in healthy control subjects (N=9). TMS was used in an externally paced finger tapping task, perturbing the internal network oscillations invoked by the finger motion as it keeps pace with a metronome. TMS perturbations were synchronized to the metronome and applied to the network at the level of the primary motor cortex (M1). Contrary to initial expectations, TMS did not affect the sensorimotor synchronization of subjects with schizophrenia or their tapping accuracy. TMS did cause extreme deviations in the finger's trajectory, and altered the timing perceptions of subjects with schizophrenia. Additionally, it invoked high-level deficiencies related to attention and volition in the form of lapses, implying that the connectivity between modules in the brain that underlie motor control, sensorimotor synchronization, timing perception and awareness of action, can be disrupted by TMS in subjects with schizophrenia, but not in healthy subjects. The ability to disrupt high level network functions with perturbations to the lower level of M1 supports models describing deficits in connectivity of distributed networks in the brains of schizophrenia patients. It also demonstrates the use of TMS to probe connectivity between components of such networks.

There is growing dissatisfaction regarding the available diagnostic systems for psychiatric disorders (DSM, ICD). Psychiatrists acknowledge that though mental disease reflects brain disorders, the descriptive and symptom based nature of psychiatric diagnosis bears no relation to brain functions. According to Helmut's article published in the October 2003 issue of Science, in the coming decade researchers and psychiatrists will be called upon to propose a basis for the psychiatric diagnostic system of the future. I propose a new etiology-oriented diagnostic system for psychiatry by integrating two recently emerging bodies of knowledge, one regarding plasticity and the other involving complex systems. Plasticity refers to all brain processes involved in dynamic alterations within communicating neuronal ensembles or networks, in the brain. Complexity refers to certain formulations from system theories relevant to brain dynamics and plasticity. It is proposed to divide plasticity processes into three types based on time domains: (1) "developmental plasticity", (2) "tuning plasticity" and (3) "fast stabilizing plasticity". Each type of plasticity is related to different complexity models achieved by the brain, developmental plasticity is life-long brain organization, it is related to state-space configurations molded into brain representations internalized via processes such as Hebbian learning. Tuning plasticity is related to "matching complexity" a measure of adaptability between internal configurations in the brain-system and externally originating event stimuli. Fast stabilizing plasticity is related to "neural complexity" a measure of neural network integration in the brain. Neural complexity meets the mental requirement to extract important features from different sensory inputs and to simultaneously generate coherent perceptual and cognitive states, thus balancing specialized segregated brain processes with coherent globally integrated whole brain activity. Mental disorders can be reconceptualized as disorders of plasticity resulting in disturbances of state-space brain configurations, matching and neural complexities. Personality disorders result from altered internal representations of the psychosocial environment. Depression and anxiety have been recently linked to alterations of adaptive neuronal plasticity thus reconceptualized as disorders of matching complexity. Finally, psychoses, including schizophrenia spectrum disorders, are reconceptualized as disturbances of neural complexity resulting in altered fast stabilizing plasticity. The new diagnostic system generates testable predictions regarding diagnosis and treatments of mental disorders which may be the future of psychiatry.

Objective: The authors' goal was to improve the diagnosis of schizophrenia by using virtual reality technology to build a complex, multimodal environment in which cognitive functions can be studied (and measured) in parallel. Method: The authors studied sensory integration within working memory by means of computer navigation through a virtual maze. The simulated journey consisted of a series of rooms, each of which included three doors. Each door was characterized by three features (color, shape, and sound), and a single combination of features—the door-opening rule—was correct. Subjects had to learn the rule and use it. The participants were 39 schizophrenic patients and 21 healthy comparison subjects. Results: Upon completion, each subject was assigned a performance profile, including various error scores, response time, navigation ability, and strategy. A classification procedure based on the subjects' performance profile correctly predicted 85% of the schizophrenic patients (and all of the comparison subjects). Several performance variables showed significant correlations with scores on a standard diagnostic measure (Positive and Negative Syndrome Scale), suggesting potential use of these measurements for the diagnosis of schizophrenia. On the other hand, the patients did not show unusual repetition of response despite stimulus cessation (called ‘perseveration’ in classical studies of schizophrenia), which is a common symptom of the disease. This deficit appeared only when the subjects did not receive proper explanation of the task. Conclusions: The ability to study multimodal performance simultaneously by using virtual reality technology opens new possibilities for the diagnosis of schizophrenia with objective procedures. [ABSTRACT FROM AUTHOR]