Uit een grootschalige studie van Komaroff en kollega's blijkt dat
de spektrale samenhang (samenhang tussen verschillende
hersengolven tijdens een EEG)
duidelijk afwijken van gezonde mensen, maar belangrijke nog, ook van depressieve mensen.
De afwijkingen duiden met name op afwijkingen in de temporale kwabben (figuur, groen).
Uiteraard blijft ME/CVS in de ogen van sommigen medisch onverklaarbaar en
uiteraard is ook deze afwijking met gedrags-en-oefentherapie te verhelpen...
EEG spectral coherence data distinguish chronic fatigue syndrome patients from healthy controls and depressed patients - A case control study.
BMC Neurology 2011, 11:82. doi:10.1186/1471-2377-11-82.
Duffy FH, McAnulty GB, McCreary MC, Cuchural GJ, Komaroff AL.
Published: 1 July 2011
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.
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.
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.
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.