In search of biomarkers in psychiatry: EEG-based measures of brain function
Article first published online: 25 NOV 2013
© 2013 Wiley Periodicals, Inc.
American Journal of Medical Genetics Part B: Neuropsychiatric Genetics
Volume 165, Issue 2, pages 111–121, March 2014
How to Cite
2013. In Search of Biomarkers in Psychiatry: EEG-Based Measures of Brain Function. Am J Med Genet Part B 165B:111–121., , .
- Issue published online: 6 FEB 2014
- Article first published online: 25 NOV 2013
- Manuscript Accepted: 12 SEP 2013
- Manuscript Received: 8 MAY 2013
- Biomarkers for Marine PTSD Risk and Resilience. Grant Number: 1 R21 MH085240
- Expanding Rapid Ascertainment Networks of Schizophrenia Families in Taiwan. Grant Number: 1 R01 MH085560
- Genetic Predictors of Neuropsychological and Functional Outcomes in Schizophrenia. Grant Number: 1 R01 MH081861
- EEGLAB: Software for Analysis of Human Brain Dynamics. Grant Number: 2 R01 NS047293-09A1
- Swartz Foundation, Old Field, NY
Current clinical parameters used for diagnosis and phenotypic definitions of psychopathology are both highly variable and subjective. Intensive research efforts for specific and sensitive biological markers, or biomarkers, for psychopathology as objective alternatives to the current paradigm are ongoing. While biomarker research in psychiatry has focused largely on functional neuroimaging methods for identifying the neural functions that associate with psychopathology, scalp electroencephalography (EEG) has been viewed, historically, as offering little specific brain source information, as scalp appearance is only loosely correlated to its brain source dynamics. However, ongoing advances in signal processing of EEG data can now deliver functional EEG brain-imaging with distinctly improved spatial, as well as fine temporal, resolution. One computational approach proving particularly useful for EEG cortical brain imaging is independent component analysis (ICA). ICA decomposition can be used to identify distinct cortical source activities that are sensitive and specific to the pathophysiology of psychiatric disorders. Given its practical research advantages, relatively low cost, and ease of use, EEG-imaging is now both feasible and attractive, in particular for studies involving the large samples required by genetically informative designs to characterize causal pathways to psychopathology. The completely non-invasive nature of EEG data acquisition, coupled with ongoing advances in dry, wireless, and wearable EEG technology, makes EEG-imaging increasingly attractive and appropriate for psychiatric research, including the study of developmentally young samples. Applied to large genetically and developmentally informative samples, EEG imaging can advance the search for robust diagnostic biomarkers and phenotypes in psychiatry. © 2013 Wiley Periodicals, Inc.