PubMed · 14992993
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Robert Paul Liberman. 2004. Learning and brain function in schizophrenia.. https://doi.org/10.1176/appi.ajp.161.3.580
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Using high-resolution MRI, it is now possible to examine the living human cortex down to a resolution of less than 300 mum. Thus, in vivo imaging is now approaching the resolution that has been successfully used in histological analysis of the cerebral cortex for many years, e.g., low-magnification light microscopy. This allows unprecedented views of cortical microstructure that reflect defined histological features, specifically, individual cortical layers. As in histological brain mapping, it is possible to use the changes in the cortical lamination patterns to define individual cortical areas. This allows in vivo neuroanatomical maps to be generated for individual subjects and precise correlation of the results from functional imaging studies in these subjects with their own microanatomical information. To this end, we adapted the well-established observer-independent cytoarchitectonic mapping techniques for defining cortical borders based on changes in cortical lamination for in vivo parcellation of high-resolution structural MR images.
UNLABELLED: Speech production involves the integration of auditory, somatosensory, and motor information in the brain. This article describes a model of speech motor control in which a feedforward control system, involving premotor and primary motor cortex and the cerebellum, works in concert with auditory and somatosensory feedback control systems that involve both sensory and motor cortical areas. New speech sounds are learned by first storing an auditory target for the sound, then using the auditory feedback control system to control production of the sound in early repetitions. Repeated production of the sound leads to tuning of feedforward commands which eventually supplant the feedback-based control signals. Although parts of the model remain speculative, it accounts for a wide range of kinematic, acoustic, and neuroimaging data collected during speech production and provides a framework for investigating communication disorders that involve malfunction of the cerebral cortex and interconnected subcortical structures. LEARNING OUTCOMES: Readers will be able to: (1) describe several types of learning that occur in the sensory-motor system during babbling and early speech, (2) identify three neural control subsystems involved in speech production, (3) identify regions of the brain involved in monitoring auditory and somatosensory feedback during speech production, and (4) identify regions of the brain involved in feedforward control of speech.
BACKGROUND: Information on the inheritance of neurophysiological abnormalities might help elucidate the molecular genetic basis of schizophrenia. We used magnetoencephalography (MEG) and electroencephalography (EEG) to investigate the inheritance of auditory-cortical deficiencies in twin pairs discordant for schizophrenia. METHODS: Auditory EEG/MEG responses to frequent standard and occasional deviant tones were measured in mono- and dizygotic (MZ and DZ) twin pairs discordant for schizophrenia and demographically matched healthy twin pairs, recruited from a total population cohort. The MEG/EEG results were regressed against the genetic resemblance to patients with schizophrenia across the patients' unaffected MZ/DZ co-twins and control subjects (with genetic correlations of 1, .5, and 0 to schizophrenia patients, respectively). RESULTS: The EEG responses P50, N100, and mismatch negativity (MMN), as well as the MEG response P50m, were reduced in the schizophrenic patients. P50 and N100 were significantly decreased also in their unaffected co-twins, as compared with the control subjects. Importantly, the P50 and N100 decrease correlated with the unaffected subjects' genetic resemblance to schizophrenia patients. CONCLUSIONS: Our results suggest inherited abnormalities in cortical auditory processing in schizophrenia, reflected by the decreased P50/P50m and N100 amplitudes, whereas the MMN abnormalities might reflect predominantly state-dependent neurodegeneration.