Join us, Friday, March 31st, 2023, at 1 PM ET for an exciting virtual talk by Dr. Dean Salisbury entitled: “From searching for lesions to exploring distributed circuitopathy in psychosis” as part of the activities of the Brain Space Initiative, co-sponsored by the Center for Translational Research in Neuroimaging and Data Science (TReNDS) and the Data Science Initiative, IEEE Signal Processing Society.
From searching for lesions to exploring distributed circuitopathy in psychosis
Researchers have sought for more than 100 years for a lesion (or lesions) that might lead to schizophrenia, the major psychotic disorder. Early imaging modalities revealed enlarged ventricles, which imply reduced brain volumes, but no characteristic cortical pathology was identified. High-resolution structural magnetic resonance imaging (MRI), finally provided a tool to identify cortical volume loss in vivo. Volumetric studies revealed not only initial gray matter loss in temporal and frontal cortices, but progressive loss after the emergence of psychosis. Advances in 3D microscopy in post-mortem studies indicated increased packing density of cortical pyramidal cells and reduced dendritic arborization in schizophrenia, leading to the idea that the disease reflected dendrotoxicity. Although specific areas (such as frontal and temporal cortex) showed more robust volume loss, the idea that schizophrenia reflected a dysconnectivity among brain regions emerged. Volumetric studies suggested that the areas with greatest loss were areas with the most extensive connectivity – heteromodal cortices or areas that served as hubs. Recently, thought has focused on structural and functional dysconnectivity in schizophrenia, with the emerging concept of the disorder being one of circuitopathy. Decomposing source-activity from high-temporal resolution methods such as EEG and MEG into spectral components allows for functional and effective connectivity measures between areas. Methods for spectral effective connectivity still need development and validation. Issues include reliable and valid source localization, methods for data reduction (particularly parcellation), identification of critical frequency bands underlying inter-areal communication, and derivation of networks from whole brain data. Data and approaches from my laboratory will be used to illustrate our approaches, and how spectral connectivity indicates alpha-band and theta-band dysconnectivity between cortical areas are central deficits in psychosis, even early in disease course at the emergence of psychosis.
Biosketch: After graduating from the Scholar's Program at Whittier College in 1985, Dr Salisbury began studying Biological Psychology and human auditory neurophysiology with Prof. Nancy K Squires at Stony Brook. He began a post-doctoral fellowship in 1990 in Biological Psychiatry with Prof. Robert W McCarley at Harvard Medical School to examine auditory neurophysiology in schizophrenia. The 2 year post-doc turned into a 22 year career at Harvard, where Dr Salisbury worked with Dr McCarley, Prof. Martha E Shenton, and many others examining neurophysiological and MRI structural and functional measures of impaired sensation, perception, and basic memory function in first episode psychosis. The work conducted in his laboratory at McLean Hospital helped to change the conceptualization of schizophrenia as a static, perinatal encephalopathy. It pioneered the combined use of structural brain imaging and electroencephalographic (EEG) measurement of auditory cortex responses to demonstrate that progressive gray matter loss during the early disease course of schizophrenia was linked to progressive auditory impairment. In 2012, he left Harvard to join the faculty at Western Psychiatric Hospital at the University of Pittsburgh School of Medicine. The continuing multimodal imaging work in first episode psychosis individuals aims to identify local and distributed circuit abnormalities in early disease course and to develop biomarkers to facilitate early identification of the disorder.
Meeting number: 2622 291 7067
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