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The gamma-band auditory steady-state response (ASSR), primarily generated from the auditory cortex, has received substantial attention as a potential brain marker indicating the pathophysiology of schizophrenia. Previous studies have shown reduced gamma-band ASSR in patients with schizophrenia and demonstrated correlations with impaired neurocognition and psychosocial functioning. Recent studies in clinical and healthy populations have suggested that the neural substrates of reduced gamma-band ASSR may be distributed throughout the cortices surrounding the auditory cortex, especially in the right hemisphere. This study aimed to investigate associations between the gamma-band ASSR and white matter alterations in the bundles broadly connecting the right frontal, parietal and occipital cortices to clarify the networks underlying reduced gamma-band ASSR in patients with schizophrenia. We measured the 40 Hz ASSR using electroencephalography and diffusion tensor imaging in 42 patients with schizophrenia and 22 healthy comparison subjects. The results showed that the gamma-band ASSR was positively correlated with fractional anisotropy (an index of white matter integrity) in the regions connecting the right frontal, parietal and occipital cortices in healthy subjects (β = 0.41, corrected p = 0.075, uncorrected p = 0.038) but not in patients with schizophrenia (β = 0.17, corrected p = 0.46, uncorrected p = 0.23). These findings support our hypothesis that the generation of gamma-band ASSR is supported by white matter bundles that broadly connect the cortices and that these relationships may be disrupted in schizophrenia. Our study may help characterize and interpret reduced gamma-band ASSR as a useful brain marker of schizophrenia.
Pubmed ID: 38472253
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Interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. First developed on Matlab 5.3 under Linux, EEGLAB runs on Matlab v5 and higher under Linux, Unix, Windows, and Mac OS X (Matlab 7+ recommended). EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG and other dynamic brain data using independent component analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods. EEGLAB also incorporates extensive tutorial and help windows, plus a command history function that eases users'' transition from GUI-based data exploration to building and running batch or custom data analysis scripts. EEGLAB offers a wealth of methods for visualizing and modeling event-related brain dynamics, both at the level of individual EEGLAB ''datasets'' and/or across a collection of datasets brought together in an EEGLAB ''studyset.'' For experienced Matlab users, EEGLAB offers a structured programming environment for storing, accessing, measuring, manipulating and visualizing event-related EEG data. For creative research programmers and methods developers, EEGLAB offers an extensible, open-source platform through which they can share new methods with the world research community by publishing EEGLAB ''plug-in'' functions that appear automatically in the EEGLAB menu of users who download them. For example, novel EEGLAB plug-ins might be built and released to ''pick peaks'' in ERP or time/frequency results, or to perform specialized import/export, data visualization, or inverse source modeling of EEG, MEG, and/or ECOG data. EEGLAB Features * Graphic user interface * Multiformat data importing * High-density data scrolling * Defined EEG data structure * Open source plug-in facility * Interactive plotting functions * Semi-automated artifact removal * ICA & time/frequency transforms * Many advanced plug-in toolboxes * Event & channel location handling * Forward/inverse head/source modeling
View all literature mentionsPipeline which provides tools to extract whole-brain average and regional measurements from DTI images including FA, AD, RD and MD. Protocols for preprocessing, ENIGMA-DTI processing (skeletonization and ROI extraction), and GWAS analysis are available. Software tools used for each process are listed within the protocols.
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