Harvard Medical School: EEG spectral coherence data distinguish ME/CFS patients from healthy controls and depressed patients

Frank H. Duffy1§, Gloria B. McAnulty2, Michelle C. McCreary3, George J. Cuchural4,
Anthony L. Komaroff3:

Department of Neurology, Children’s Hospital Boston and Harvard Medical School, 300
Longwood Ave, Boston, Massachusetts 02115, USA
2 Department of Psychiatry, Children’s Hospital Boston and Harvard Medical School, 300
Longwood Ave, Boston, Massachusetts 02115, USA
3Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School,
75 Francis St, Boston, Massachusetts 02115, USA
4Department of Medicine, Tufts Medical Center, 800 Washington Street, Boston,
Massachusetts 02111, USA
§Corresponding author
Email addresses:
FHD: fhd@sover.net frank.duffy@childrens.harvard.edu
GBM: gloria.mcanulty@childrens.harvard.edu
MCM: mcmccreary@gmail.com
GJC: gcuchural@tufts-nemc.org
ALK: anthony_komaroff@hms.harvard.edu

Abstract
Background
Previous studies suggest central nervous system involvement in chronic fatigue
syndrome (CFS), yet there are no established diagnostic criteria. CFS may be difficult to
differentiate from clinical depression. The study’s objective was to determine if spectral
coherence, a computational derivative of spectral analysis of the electroencephalogram
(EEG), could distinguish patients with CFS from healthy control subjects and not
erroneously classify depressed patients as having CFS.

Methods
This is a study, conducted in an academic medical center electroencephalography
laboratory, of 632 subjects: 390 healthy normal controls, 70 patients with carefully
defined CFS, 24 with major depression, and 148 with general fatigue. Aside from fatigue,
all patients were medically healthy by history and examination. EEGs were obtained and
spectral coherences calculated after extensive artifact removal. Principal

Components
Analysis identified coherence factors and corresponding factor loading patterns.
Discriminant analysis determined whether spectral coherence factors could reliably
discriminate CFS patients from healthy control subjects without misclassifying
depression as CFS.

Results
Analysis of EEG coherence data from a large sample (n=632) of patients and
healthy controls identified 40 factors explaining 55.6% total variance. Factors showed
highly significant group differentiation (p<.0004) identifying 89.5% of unmedicated
female CFS patients and 92.4% of healthy female controls. Recursive jackknifing showed
predictions were stable. A conservative 10-factor discriminant function model was
subsequently applied, and also showed highly significant group discrimination (p<.001),
accurately classifying 88.9% unmedicated males with CFS, and 82.4% unmedicated male
healthy controls. No patient with depression was classified as having CFS. The model
was less accurate (73.9%) in identifying CFS patients taking psychoactive medications.
Factors involving the temporal lobes were of primary importance.

Conclusions
EEG spectral coherence analysis identified unmedicated patients with CFS and
healthy control subjects without misclassifying depressed patients as CFS, providing
evidence that CFS patients demonstrate brain physiology that is not observed in healthy
normals or patients with major depression. Studies of new CFS patients and comparison
groups are required to determine the possible clinical utility of this test. The results
concur with other studies finding neurological abnormalities in CFS, and implicate
temporal lobe involvement in CFS pathophysiology. Read more>>