S-S Poil1, S Bollmann2, C Ghisleni3, R L O'Gorman4, P Klaver5, J Ball6, D Eich-Höchli7, D Brandeis8, L Michels9. 1. Center for MR-Research, University Children's Hospital, Zürich, Switzerland; Center for Integrative Human Physiology (ZIHP), University of Zürich, Switzerland. 2. Center for MR-Research, University Children's Hospital, Zürich, Switzerland; Center for Integrative Human Physiology (ZIHP), University of Zürich, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zürich, Switzerland; Institute for Biomedical Engineering, University of Zürich and ETH Zürich, Zürich, Switzerland. 3. Center for MR-Research, University Children's Hospital, Zürich, Switzerland; Center for Integrative Human Physiology (ZIHP), University of Zürich, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zürich, Switzerland. 4. Center for MR-Research, University Children's Hospital, Zürich, Switzerland; Center for Integrative Human Physiology (ZIHP), University of Zürich, Switzerland; Pediatric Research Center, University Children's Hospital, Zürich, Switzerland. 5. Center for MR-Research, University Children's Hospital, Zürich, Switzerland; Center for Integrative Human Physiology (ZIHP), University of Zürich, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zürich, Switzerland; Institute of Psychology, University of Zürich, Switzerland. 6. Department of Child & Adolescent Psychiatry, University of Zürich, Switzerland. 7. Psychiatric University Hospital Zürich, University of Zürich, Switzerland. 8. Center for Integrative Human Physiology (ZIHP), University of Zürich, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zürich, Switzerland; Department of Child & Adolescent Psychiatry, University of Zürich, Switzerland; Department of Child & Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany. Electronic address: daniel.brandeis@kjpd.uzh.ch. 9. Institute of Neuroradiology, University of Zürich, Switzerland. Electronic address: lars.michels@kispi.uzh.ch.
Abstract
OBJECTIVE: Objective biomarkers for attention-deficit/hyperactivity disorder (ADHD) could improve diagnostics or treatment monitoring of this psychiatric disorder. The resting electroencephalogram (EEG) provides non-invasive spectral markers of brain function and development. Their accuracy as ADHD markers is increasingly questioned but may improve with pattern classification. METHODS: This study provides an integrated analysis of ADHD and developmental effects in children and adults using regression analysis and support vector machine classification of spectral resting (eyes-closed) EEG biomarkers in order to clarify their diagnostic value. RESULTS: ADHD effects on EEG strongly depend on age and frequency. We observed typical non-linear developmental decreases in delta and theta power for both ADHD and control groups. However, for ADHD adults we found a slowing in alpha frequency combined with a higher power in alpha-1 (8-10Hz) and beta (13-30Hz). Support vector machine classification of ADHD adults versus controls yielded a notable cross validated sensitivity of 67% and specificity of 83% using power and central frequency from all frequency bands. ADHD children were not classified convincingly with these markers. CONCLUSIONS: Resting state electrophysiology is altered in ADHD, and these electrophysiological impairments persist into adulthood. SIGNIFICANCE: Spectral biomarkers may have both diagnostic and prognostic value.
OBJECTIVE: Objective biomarkers for attention-deficit/hyperactivity disorder (ADHD) could improve diagnostics or treatment monitoring of this psychiatric disorder. The resting electroencephalogram (EEG) provides non-invasive spectral markers of brain function and development. Their accuracy as ADHD markers is increasingly questioned but may improve with pattern classification. METHODS: This study provides an integrated analysis of ADHD and developmental effects in children and adults using regression analysis and support vector machine classification of spectral resting (eyes-closed) EEG biomarkers in order to clarify their diagnostic value. RESULTS:ADHD effects on EEG strongly depend on age and frequency. We observed typical non-linear developmental decreases in delta and theta power for both ADHD and control groups. However, for ADHD adults we found a slowing in alpha frequency combined with a higher power in alpha-1 (8-10Hz) and beta (13-30Hz). Support vector machine classification of ADHD adults versus controls yielded a notable cross validated sensitivity of 67% and specificity of 83% using power and central frequency from all frequency bands. ADHDchildren were not classified convincingly with these markers. CONCLUSIONS: Resting state electrophysiology is altered in ADHD, and these electrophysiological impairments persist into adulthood. SIGNIFICANCE: Spectral biomarkers may have both diagnostic and prognostic value.
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