EEG spectral coherence data distinguish chronic fatigue syndrome patients from healthy controls and depressed patients-A case control study

摘要: 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 determine if spectral coherence, a computational derivative of analysis the electroencephalogram (EEG), could distinguish patients with healthy control subjects and not erroneously classify depressed as having CFS. This is study, conducted an academic medical center electroencephalography laboratory, 632 subjects: 390 normal controls, 70 carefully defined CFS, 24 major depression, 148 general fatigue. Aside fatigue, all were medically by history examination. EEGs obtained coherences calculated after extensive artifact removal. Principal Components Analysis identified coherence factors corresponding factor loading patterns. Discriminant determined whether reliably discriminate without misclassifying depression EEG data large sample (n = 632) controls 40 explaining 55.6% total variance. Factors showed highly significant group differentiation (p < .0004) identifying 89.5% unmedicated female 92.4% controls. Recursive jackknifing predictions stable. A conservative 10-factor discriminant function model subsequently applied, also discrimination .001), accurately classifying 88.9% males 82.4% male No patient classified less accurate (73.9%) taking psychoactive medications. involving temporal lobes primary importance. providing evidence that demonstrate brain physiology observed normals or Studies new comparison groups required possible utility this test. results concur other finding neurological abnormalities implicate lobe pathophysiology.