Psychiatry Research: Neuroimaging 181 (2010) 155–164

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Psychiatry Research: Neuroimaging

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EEG source analysis of chronic fatigue syndrome

Pierre Flor-Henrya,⁎, John C. Linda, Zoltan J. Kolesb

aAlberta Hospital Edmonton, Box 307, Edmonton, Edmonton, Alberta, Canada T5J 2J7bDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

⁎ Corresponding author. Department of Psychiatry,Hospital Edmonton, Box 307, Edmonton, Alberta, Canadafax: +1 780 472 5346.

E-mail addresses: Pierre.Flor-Henry@capitalhealth.caPierre.Flor-Henry@albertahealthservices.ca (P. Flor-Hen

0925-4927/$ – see front matter. Crown Copyright © 20doi:10.1016/j.pscychresns.2009.10.007

a b s t r a c t

a r t i c l e i n f o

Article history:Received 27 October 2008Received in revised form 27 August 2009Accepted 16 October 2009

Keywords:Chronic fatigue syndromeSpectral analysisEEGDiscriminant analysisLORETABEAMFORMER

Sixty-one dextral, unmedicated women with chronic fatigue syndrome (CFS) diagnosed according to theFukuda criteria (1994) and referred for investigation by rheumatologists and internists were studied withquantitative EEG (43 channels) at rest with eyes open and during verbal and spatial cognitive activation. TheEEGs from the patients were compared with recordings from 80 dextral healthy female controls. Only thosesubjects who could provide 20 1-s artefact-free segments of EEG were admitted into the study. The analysisconsisted of the identification of the spatial patterns in the EEGs that maximally differentiated the twogroups and the estimation of the cortical source distributions underlying these patterns. Spatial patternswere analyzed in the alpha (8–13 Hz) and beta (14–20 Hz) bands and the source distributions wereestimated using the Borgiotti–Kaplan BEAMFORMER algorithm. The results indicate that the spatial patternsidentified were effective in separating the two groups, providing a minimum correct retrospectiveclassification rate of 72% in both frequency bands while the subjects were at rest to a maximum of 83% in thealpha band during the verbal cognitive condition. Underlying cortical source distributions showed significantdifferences between the two groups in both frequency bands and in all cognitive conditions. Lateralizedcortical differences were evident between the two groups in the both frequency bands during both theverbal and spatial cognitive conditions. During these active cognitive conditions, the CFS group showedsignificantly greater source-current activity than the controls in the left frontal–temporal–parietal regions ofthe cortex.

Crown Copyright © 2009 Published by Elsevier Ireland Ltd. All rights reserved.

1. Introduction

Fukuda et al. (1994) have provided a modern operationaldefinition of chronic fatigue syndrome (CFS): 6 months or more ofincreased fatigue, severe enough to reduce daily activity below 50% ofpremorbid level with at least four of the following symptoms presentfor at least 6 months or more: mild fever, painful lymph nodes,myalgia, nonexudative pharyngitis, sleep disturbance (insomniaor hypersomnia), migratory arthralgia, memory dysfunction, post-exertional fatigue for more than 24h. The above, of course, requiresthat no primary medical or psychiatric illness be present that couldaccount for the symptomatology. Fibromyalgia (FM) differs from CFSin that pain at particular trigger points is found, whereas fatiguedominates the clinical picture in CFS, but the symptomatic overlap inthe two conditions is striking (Goldenberg, 1989). It has now beenwell established that no single virus is etiological in CFS, but in 70% ofcases it is triggered by a viral “flu-like” illness. A preexisting immunevulnerability is probable, given the overwhelming female suscepti-

University of Alberta, AlbertaT5J 2J7. Tel.: +1 780 472 5395;

,ry).

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bility, women being more prone to autoimmune diseases than men,for example, Hashimoto's thyroiditis, lupus, rheumatoid arthritis,Sjogren's syndrome, antiphospholipid syndrome, Graves' disease/hyperthyroiditis, scleroderma, myasthenia gravis, and multiplesclerosis. That the immunologic system is involved is shown byincrease in activated T lymphocytes, increase in cytotoxic T cells,increase in circulating cytokines and poor cellular function: lownatural killer cell cytotoxicity, poor response to mitogens in cultureand frequent immunoglobulin deficiencies: IgG1 and IgG3 (Landayet al., 1991; Lloyd et al., 1988). In recent years it has becomeincreasingly recognized that the brain and the immune systeminteract in a bidirectional chemical communication through neuro-transmitters and cytokines modulating corticotropin releasing factor,in turn regulating the hypothalamic–pituitary–adrenal axis (Black,1995). One of the most important conveyors of immunologicalinformation to the central nervous system (CNS) is the polypeptidecytokine interleukin-1 (Ur et al., 1992). At the subjective level,cognitive impairment is emphasized in patients with CFS, “mentalfog”, impaired concentration, poor memory and frequent wordfinding difficulties. Numerous neuropsychological studies haveconfirmed the presence of CNS dysfunction, essentially deficits ininformation processing, memory impairment, and poor learning ofinformation (Marcel et al., 1996; Altay et al., 1990; Deluca andSchmaling, 1995; Moss-Morris et al., 1996). Michiels and Cluydts

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156 P. Flor-Henry et al. / Psychiatry Research: Neuroimaging 181 (2010) 155–164

(2001) and Tiersky et al. (1997) have reviewed these findings. Joyceet al. (1996) further found that patients with CFS were also impairedon verbal tests of unrelated word association learning and letterfluency. Interestingly, Lange et al. (2005) found in a functionalmagnetic resonance imaging (fMRI) study of verbal working memorythat although individuals with CFS could process auditory informationas accurately as controls, they utilized more extensive regions of thenetwork associated with the verbal working memory system and thatmental fatigue accounted largely for the increased blood-oxygen-level-dependent (BOLD) signal change in the left superior parietalregion as well as increased activation bilaterally in the supplementaryand premotor regions. Numerous publications document a down-regulation of the hypothalamic–pituitary–adrenal axis in chronicfatigue (Parker et al., 2001; Demitrack et al., 1991; Demitrack, 1994;Bearn et al., 1995; Poteliakhoff, 1981). Some 30–40% of CFS patientshave hypocortisolism. Abnormalities in serotonin neurotransmissionhave been found in CFS: serotonin agonists lead to an increase inserumprolactin in CFS that is not seen in depressed or healthy controls,i.e. in CFS there is an up-regulation of serotoninergic neurotransmis-sion, while in depression the opposite is found, hypercortisolism andsuppressed serotonin-mediated prolactin response (Demitrack andCrofford, 1995). Excellent reviews of the general characteristics of CFShave been provided by Wessely (1995), Afari and Buchwald (2003),and Prins et al. (2006).

There are three diseases characterized by symptomatology that isremarkably similar to that of chronic fatigue syndrome: Addison'sdisease, Rickettsial diseases (Scientific American Medicine, 1987. 7:XVII: 1–10), and glucocorticoid deficiency. For example, Addison'sdisease is of sudden onset with an overrepresentation of middle-agedwomen and presentswith the following general symptoms: persistentfatigue, debilitation after exercise, weakness, fever, enlarged lymphnodes, myalgia, arthralgia, flu-like symptoms, sore throat, headaches,and dizziness upon standing (Baschetti, 2000). Hypotension andreduced aldosterone are an integral part of Addison's disease, and it isprobably not coincidental that postural hypotension (Bou-Holaigahet al., 1995) and symptomatic relief with fludrocortisone (Florinef)have been described in CFS. Remarkably, Scott et al. (1999)administered the adrenocorticotropin (ACTH) stimulation test in 30patients with CFS, in each case triggered by a viral influenza-typeillness, and found eight who had a blunted response to 1μmintravenously, i.e. failure to achieve a peak cortisol level greater than600 nmol/l 30min later. In all these subjects both adrenal glandswere 50% smaller than in controls on examination with computedtomography (CT).

Some neurological illnesses may provide insight into the patho-genesis of CFS: measles encephalomyelitis is a neurological disorder ofabrupt onset that starts, on average, 5 days after the rash withheadaches, generalized seizures, confusion, and motor deficits. Thevirus is not detected in the brain where the neuropathologicalchanges are similar to those seen in experimental allergic encepha-lomyelitis, suggesting an autoimmune pathogenesis (Johnson et al.,1984). There was in the small locality of Akureyri in Iceland anepidemic CNS illness in the winter of 1948–49 characterized by lowgrade fever (not higher than 37.8 °C) with painful muscles, weakness,nervousness and extreme fatigue. Seven years later, 75% had persistentsymptoms, 52% had muscle weakness and 65% had clear CNS signs(Goodnick and Sandoval, 1993).

A number of investigations have reported reduced cerebralcirculation and hypometabolic changes in CFS, principally in thefrontal and temporal regions with single-photon emission computedtomography (SPECT) and positron emission tomography (PET)techniques. Sarkar and Seastrunk (1998) studied 150 patients, halfwith CFS and half with idiopathic chronic fatigue, with SPECT andmagnetic resonance spectroscopy (MRS). With SPECT they found that“themost reproducible area of perfusion abnormality is the left frontal(or orbitofrontal) hypoperfusion in the cortical gray matter both in

early and delayed images on almost all patients”. MRS findings wereof decreased choline and increased lactate, again in the left frontallobe, correlating with SPECT hypoperfusion in that region. No suchchanges were found in the right frontal lobe. In the temporal lobesthe metabolites were normal except for a mild lactate increase onthe left. Tomoda et al. (2000) reported on three children, ages 11, 12and 13, with CFS that was triggered by a low grade flu-like illness.The children were investigated with SPECT, which showed that theleft temporal and occipital blood flow was strikingly reduced in twoof the patients whereas in the third the blood flow in the left basalganglia and thalamus was increased. MRS revealed “a remarkableelevation of the choline/creatine ratio”. Chaudhuri et al. (2003)studied eight patients with CFS without psychiatric complicationswith 1H-MRS and found a very significant increase in cholinecompounds in the left basal ganglia. They did not, however, studythe right basal ganglia. Schwartz et al. (1993) find on SPECT imagingof 45 patients with CFS that they had a similar number of regionaldefects when compared with 14 patients with unipolar depression.Tirelli et al. (1998), in a PET study (flurorodeoxyglucose, FDG) of 18patients with CFS, reported significant hypometabolism in the rightmedial frontal cortex and brainstem in these patients whencompared with healthy controls. Six psychiatric patients withdepression showed more severe hypometabolism bilaterally in themedial and upper frontal regions, with normal brainstem. Machaleet al. (2000) compared 30 patients with CFS without depression, 12psychiatric patients with major depression and 15 healthy controls,measuring cerebral perfusion with SPECT. In patients with CFS and inthose with depression, there was increased circulation in the rightthalamus, pallidum and putamen, the CFS patients in additionhaving increased perfusion in the left thalamus. Schmaling et al.(2003) studied 15 patients with CFS and 15 healthy controls withSPECT at rest and during the Paced Auditory Serial Addition test(PASAT). Contrary to their hypothesis, there was greater brainactivation in the CFS subjects during the PASAT task, which wasmore diffuse than in the controls, both groups performing equallywell on the test. Further, there was greater activation of the leftanterior cingulate in the CFS subjects. Siessmeier et al. (2003)evaluated cerebral glucose metabolism with FDG-PET in 26 patientswith CFS (13 female; 13 male) and found that 12 of 26 patients weresimilar to the controls, while 12 had bilateral hypometabolism of thecingulate and mesial cortical regions. In addition, five of these haddecreased metabolism in the orbital frontal cortex. Anxiety anddepression, but not fatigue, were correlated with reduced glucosemetabolism. Interestingly, Ichise et al. (1992), given the overlap insome of the symptoms found in depression and CFS, found nodifferences in cerebral circulation in depressed and non-depressedpatients with CFS. Schwartz et al. (1994) compared MRI and SPECTin 16 patients with CFS, finding that the magnetic resonance signalswere not statistically different from signals in 14 controls, althoughthe SPECT scanning revealed significantly more defects throughoutthe cortex in the patients (present in 81% of patients and 21% ofcontrols). Abu-Judeh et al. (1998) came to similar conclusions in 18patients with CFS, finding 13 abnormal SPECT results and 15 normalFDG-PET results in the CFS patients. Abnormalities of cerebralcirculation may occur with normal glucose metabolism. The threeabnormal PET scans all had hypometabolism of the left parietalregion. Brooks et al. (2000), investigating 7 patients with CFS and 10controls with 1H-MRS, found that the patients had a significantreduction of N-acetylaspartate in the right hippocampus, evenalthough the volume of both hippocampi was similar to that in thenormal control subjects. Only the right hippocampus was studied.Strikingly, the left hemisphere was implicated in many of theimaging investigations reviewed above. Thus, we thought it wouldbe of interest to carry out a quantitative EEG analysis (current sourcedensity) to see whether a similar lateralization might be foundelectrophysiologically.

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There have been few quantitative EEG investigations of CFSpublished. Sherlin et al. (2007) reported on a LORETA study ofmonozygotic twins discordant for CFS. They found that the CFS twinshad higher delta values in the left uncus and the left parahippo-campal gyrus and higher theta values in the cingulate gyrus and theright frontal gyrus. Their sample consisted of 17 pair of twins,studied with 19 electrodes. Although their subjects were recorded inthe Eyes Open, Eyes Closed condition and during a mental arithmetictask, they only reported the Eyes Closed data. Their twins wereunmedicated and they were administered the Diagnostic InterviewSchedule. Twenty-four percent of the CFS twins were depressed atthe time of study vs. none of the unaffected twins. Forty-sevenpercent had an acute onset with a flu-like illness with an averagefatigue duration of 7.4 years and, characteristically, 88% werefemale. The right hemisphere lateralization found here may reflectthe depressive aspect since there is evidence that the electrophys-iological correlates of depression are of right fronto-temporalexcitation (Flor-Henry et al., 2004). Lewis et al. (2001), however,in the study of 22 monozygotic twins, one of whom had CFS, foundthat cerebral blood flow (SPECT) was the same in the affected andunaffected twins. Guilleminault et al. (2006), in a sleep study of 14patients with CFS, found in the CFS group a significant increase inslow delta power relative to findings in normal controls. Siemionowet al. (2004), in only eight CFS patients, observed at significantincrease in theta relative power during voluntary motor actions.Loganovsky (2000) investigated the EEG characteristics of 100randomly selected clean-up workers of the Chernobyl Accident, whohad been irradiated, and who presented with the symptomatologyof CFS as defined by the CDC criteria, which were met by 26. Thespectral analysis of the EEG showed lateralized (left-sided) increasein theta power, decrease in alpha power and increase in beta power.The somatosensory evoked potentials showed topographic abnor-malities in the left temporo-parietal area. Scheffers et al. (1992)found that the visual ERPs in a short-term memory task were similarto those of normal controls in 13 cases, although mean reactiontimes in the cases were slower. In the study of a single case, a 21 yearold woman, treated with neurofeedback and self-hypnosis, thequantitative EEG showed increased left frontal theta activity(Hammond, 2001). Tomoda et al. (2007) in the study of 414children with CFS (ages 9–18) found prolonged P300 latency totarget stimuli in a subgroup increased P300 amplitude, and in a thirdsubgroup reduced P300 latency and amplitude.

Thus, these different approaches— neuropsychological, MRI, PET,SPECT, immunological and endocrine — all show that in CFS centralnervous system functions are altered and that when the CNSdysfunction is asymmetric, the left hemisphere is implicated. In anattempt to further study these issues, we carried out a quantitativeEEG study of CFS. The alpha (8–13 Hz) and beta (14–20 Hz) bands inthe EEG were chosen for this study since these bands are thought toreflect different aspects of cognitive activity: inhibitory and excitatorybrain functions, respectively (Pascual-Marqui et al., 1994).

2. Methodology

2.1. Subjects

In order to further study the central nervous system characteristicsin chronic fatigue syndrome(CFS), quantitativeEEGanalysiswascarriedout in a group of patients referred by rheumatologists, internists andgeneral practitioners. All satisfied Fukuda's criteria (1994), all weredextral and were unmedicated at the time of study. In 67% of thesepatients the onset was acute. Data were recorded from 61 womensuffering from CFS. The mean age of the group was 45.65 (S.D.±9.87).

Data were also recorded from a total of 80 female controls. Themean age of this group was 25.57 (S.D.±8.37). These subjects werecompletely right handed as determined by the handedness ques-

tionnaires (Annett, 1970; Chapman and Chapman, 1987). All potentialcontrol subjects were administered a pretest interview. Any whoresponded in the affirmative to having a history of neurologicaldisease, birth complications, head trauma, psychiatric illness orsubstance abuse were excluded from the study. In addition to thepretest interview, the Quick Diagnostic Interview Schedule (Marcuset al., 1989) was systematically administered to these subjects. Thosesubjects with positive symptom categories were excluded from thestudy. The Basic Personality Inventory (Jackson, 1996), whichcompares the subject on 12 personality dimensions to the NorthAmerican female norms, and the Multidimensional Aptitude Battery(MAB-II), which provides Verbal, Performance and Full Scale IQ, wereadministered to all patients and controls (Jackson, 1998).

EEGs were recorded from all subjects during a passive condition,eyes open (EO), and two active cognitive conditions, Word Finding(WF) and Dot Localization (DL). At least 3 min of recording wasobtained from each subject. During the passive condition, thesubjects were told to relax, to think of nothing in particular and tofixate on a point across the room. The tasks producing the cognitivechallenges were developed by (Miller et al., 1995) to be psychomet-rically matched, in terms of difficulty, item-scale correlations andinternal consistency, using a sample of 350 normal subjects. Thesecognitive challenges were the same as those used by Davidson et al.(1990) and Koles et al. (2001). The test questions were presented tothe subjects as slides projected from the rear of the recordingchamber. Once having formulated an answer to a question, thesubjectswere asked to press a button to indicate that theywere aboutto verbalize a response. By using the recording of the button signal,the EEGs were separated into cognitive and motor phases. Only thecognitive phases were analyzed. Generally, the subjects required atleast 5s to formulate their answers and neither the first nor the lastsecond of the EEG recorded during this period was selected foranalysis. Also, to ensure compliance, only those EEGs correspondingto correct responses to the test questions were utilized. No restperiod was allowed the subjects between the questions.

The DL task contained 25 items with each item consisting of twovertically arranged open rectangles, the top rectangle containing apair of dots and the bottom rectangle, slightly offset to either the rightor the left, containing an array of numbers. The offset of the bottomrectangle differed randomly among items such that half of the itemswere offset to either the right or the left side. Subjects were asked toindicate which two numbers in the lower rectangle corresponded tothe location of the dots in the upper rectangle. Item difficulty isdetermined by the size of the number arrays, which varied in fivelevels from 8 to 50 numbers. In the WF task, subjects were asked tocorrectly identify an object when presented with a written definitionof the object. For example, the correct answer for the item a very largepiece of floating ice is iceberg. The WF task consisted of 30 items thatdiffered in an ascending order of difficulty.

The research protocol outlined above was reviewed by the EthicsReview Committee of the Alberta Hospital Edmonton and found to beacceptable within the limitations of human experimentation.

2.2. Recordings

EEG recordings were obtained using a 43-lead stretchable electrodecap containing tin electrodes. The 43 electrode sites representedstandard electrode positions described in the American Electroenceph-alography Society Guidelines (Sharbrough et al., 1990). The lead siteswere AF3, AF4, FC5, FC6, FC1, FC2, C3, C4, C1, C2, TP7, TP8, CP5, CP6, CP1, CP2,P3, P4, P1, P2, PO3, PO4, AFz, Fz, FCz, Cz, CPz, Pz, POz, FP1, FP2, F7, F8, F3, F4, FT7,FT8, T7, T8, P7, P8, O1, and O2. All leadswere referenced to the left ear andthe impedance at each electrode was adjusted to be below 5 kΩ. Threeadditional channels were recorded as follows: the electrocardiogram(EKG), a bipolar recording over the external canthus to the supra-orbitof the right eye to monitor vertical and horizontal eye movements

158 P. Flor-Henry et al. / Psychiatry Research: Neuroimaging 181 (2010) 155–164

(EOG), and one bipolar electromyogram (EMG) channel recorded fromthe temporomandibular region on the right and left sides. All channelswere amplified using a Grass Neurodata system and digitizedwith a 12-bit RC Electronics analog-to-digital converter at a rate of 256 samples/s.The band pass was set at 3–80 Hz with the high pass controlled by aGrassModel 12A5 two-pole Butterworthfilterwith the 3-dB point set at3 Hz. The anti-aliasingfilterwas an eight-pole Bessel functionfilterwiththe 3-dB point set at 80 Hz.

2.3. Spectral analysis

The digitized EEG records were visually edited off-line in anattempt to select from each subject 20 non-overlapping, 1-s datasegments corresponding to correct responses to the tasks. These wereselected randomly from all of the 1-s segments available from thesubject. Segments that contained voltage spikes, large eye move-ments, or muscle activity were not selected for analysis. Rejection ofsegments was frequent and resulted in 58, 25 and 34 CFS patientsbeing able to contribute 20-segment EEGs during the EO, WF and DLconditions, respectively. Of the control subjects, 79, 44 and 63 wereable to contribute 20-segment EEGs during these same conditions.Only those patients and subjects who were able to contribute the 20segments were admitted into the study.

The Fast Fourier Transform (FFT) was applied to each row of thedata matrix formed by the 46 EEG, EKG, EMG and EOG channels (asrows) and 256 time samples (as columns) collected during eachsecond. Prior to the application of the FFT, each row of the data matrixwas preprocessed by applying a 50% Hamming taper (Brillinger 1981,p.55). With 256 samples in each 1-s segment, the frequencyresolution of the FFT along the columns of each transformed datamatrix, V(f), was 1 Hz spanning the range of frequencies from 0 to255 Hz. Since the alpha band (8–13 Hz) and low beta band (14–20 Hz) were investigated in this study, only the columns 9 to 14 and15 to 21 in V(f), corresponding to these frequencies, were selected.

The selected columns from the transformed data matrices werecharacterized in terms of their cross-spectra. The cross-spectralmatrices Σ(α) and Σ(β) were formed as:

ΣðαÞ = VðαÞVTðαÞ and ΣðβÞ = VðβÞVTðβÞ ð1Þ

where V(α) and Σ(β) are the alpha and beta columns from V(f) and Tis the complex conjugate transpose. An average 46×46 complex-valued cross-spectral matrix over the 20 segments available from eachsubject was then calculated for each frequency band and for each ofthe EO, WF and DL conditions.

The correlated effects of the EKG, EOG and EMGwith the EEGwereremoved using a matched filter (Brillinger, 1981 p.299) resulting in43×43 cross-spectral matrices for each subject/frequency band/condition. Each matrix was then normalized by dividing all of itselements by its trace (the sum of its diagonal elements). Thisprocedure was implemented in order to remove the confoundingeffects of skull thickness on EEG power (Wheeler et al., 1993).

2.4. Reduction of dimensionality

To reduce the effects of measurement noise and to minimize thedependence of the EEG features captured in the cross-spectralmatrices on individual subjects, the dimensionality of the measure-ment space was reduced from 43×43 to 30×30 by factoring the sumof the average cross-spectral matrices obtained from the CFS andcontrol groups in each frequency band/condition as:

ΣF + ΣC = EλET ð2Þ

where ΣF and ΣC are the average cross-spectral matrices for the CFSand control groups, respectively, E is the matrix of eigenvectors and λ

is the diagonal matrix of corresponding eigenvalues arranged indecreasing order. The reduction in dimensionality of the cross-spectral matrices was obtained by using only the first 30 columns of Eto form:

ΣFR = ETR ΣF ER and ΣCR = ET

R ΣC ER ð3Þ

where ΣFR and ΣCR are the dimensionally reduced average cross-spectral matrices of each group and ER contains the first 30 columns ofE. The number 30 was chosen arbitrarily but, in every case, theretained components accounted for more than 99% of the combinedtemporal variances in the cross-spectral matrices from the twogroups.

2.5. Separation of the groups

To assess the separation between the EEG features captured in thereduced cross-spectral matrices obtained from the CFS and controlgroups, the average cross-spectral matrix for each group were thenfactored as (Koles et al., 2001):

ΣFR = CΨF CT and ΣCR = CΨC C

T ð4Þ

where C is the matrix of spatial patterns common to the two groupsand ΨF and ΨC are diagonal matrices containing the eigenvaluescorresponding to each of the spatial patterns. The spatial patterns inthe columns of C are determined from C=(P−1)T where the matrix Pcontains the eigenvectors of either ΣFR

−1ΣCR or ΣCR−1ΣFR both being the

same. The columns of C could be scaled such that ΨF+ΨC= I, theidentity matrix. With this scaling, it is evident that the spatial patternin C that accounts for the largest variance in the CFS group (as given bythe corresponding eigenvalue in ΨF) will account for the leastvariance in the control group. Similarly, the spatial pattern in C thataccounts for the largest variance in the control group (as given by thecorresponding eigenvalue inΨC) will account for the least variance inthe CFS group. Using these decompositions, a measure of theMahalanobis distance between the CFS and control groups wascalculated as:

D2 = traceðΣ−1C ΣFÞ = traceðΨ−1

C ΨFÞ ð5Þ

D2 in Eq. (5) can be interpreted as the sum of the squared distances,measured in standard deviations, between the CFS and control groupsin the direction of each of the common spatial patterns. A D2 value of30 would indicate that the CFS and control groups did not differ interms of the features captured in their reduced cross-spectralmatrices.

Fig. 1 shows the square roots of the elements of the diagonaleigenvalue matrix ΨC

−1ΨF in Eq. (5) for the CFS and control groupscompared in the EO,WF andDL cognitive conditions and in both of thealpha and beta frequency bands. The elements of ΨF were orderedfrom largest to smallest (and hence ΨC from smallest to largest). Allsix curves have the same general shape tending to flatten in themiddle and to pitch up and down at the left and right ends,respectively. It is evident from the figure that the common spatialpatterns corresponding to the first and last eigenvalues account forthe greatest variance in one group compared with the other in allcognitive conditions and in both frequency bands.

2.6. Classification of individuals

To assess the level to which the features contained in the cross-spectral matrices obtained from the CFS and control groups wouldseparate the individual subjects in the groups, the classical complexdomain quadratic discriminant function was used to retrospectivelyclassify the subjects in each group. Under the assumption that the EEG

Fig. 1. The Mahalanobis distance D between the CFS and control groups contributed by

each of the elements of the diagonal eigenvalue matrixffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiΨ−1

C ΨF

q. The elements of ΨF

are ordered from largest to smallest.

159P. Flor-Henry et al. / Psychiatry Research: Neuroimaging 181 (2010) 155–164

is a zero mean Gaussian process, this classifier is given by (Shumway,1982):

QDF = traceððΣ−1F −Σ−1

C ÞΣi RÞ = traceðP ðΨ−1F −Ψ−1

C ÞPTΣi RÞ ð6Þ

where ΣiR is an unclassified subject's reduced cross-spectral matrix.Group centroids are obtained by setting ΣiR in Eq. (6) to ΣFR and ΣCR

respectively. This results in:

mF = traceðI−Ψ−1C ΨFÞ and mC = traceðΨ−1

F ΨC−IÞ ð7Þ

ΣiR is assigned to a group based on a decision line placed between thecentroids.

2.7. Estimation of cortical source-current density

The estimate of the cortical source-current density in each subject,si2, in each cognitive condition and frequency band was obtainedsimilarly to Koles et al. (2001):

s2i = diagðGER CPT∑i RPC

T ETRG

TÞ ð8Þ

where G is the Borgiotti–Kaplan (BK) BEAMFORMER solution to theEEG inverse problem (Sekihara et al., 2001). The diag operator in theequation indicates that only the diagonal elements of the matrixinside the brackets are used. G is a matrix with 43 rows and 51197columns and si2 is a column vector with 51197 elements containingthe estimates of the magnitude of the source-current density atlocations in the cerebral cortex of the head model used for this study.

The matrix product CPT in Eq. (8) was included prior to theBEAMFORMER transformation to eliminate the components in theEEGs that were common to the CFS and control groups. To do this,only those columns from C and P were retained that corresponded tothe largest and smallest values of ΨF (or the smallest and largestvalues of ΨC). These columns were chosen because they account formaximally different proportions of the temporal variances in the CFSand control groups. The full matrix product CPT yields the identitymatrix; however, with only two selected columns, this is not the caseand, while the product matrix in this case has dimensions 30×30, itsrank is only 2. The reduced eigenvector matrix ER is used to transformthe 30-dimensional space back to 43 dimensions.

2.8. The BEAMFORMER transformation

Transformation of the cross-spectral matrices, filtered with CPT andER, to source-current densities was implemented using the Borgiotti–Kaplan Beamformer (Sekihara et al., 2001). The BK Beamformer differsfrom the LORETA Transformation (LORETA-KEY®) in that it is not onlydependent on the lead-field matrix of the head model but also on thecross-spectral matrix of the EEG. It has been shown (Russell and Koles,2007) that the BK Beamformer is generally superior to LORETA in termsof both the accuracy and the dispersion of source-current estimates.

The BK Beamformer was implemented in a realistic head modelobtained as a digitized MRI from the Brain Imaging Centre, MontrealNeurological Institute (MNI). The model contains 4,079,350 cubicelements and was segmented into five tissue compartments, scalp,skull, CSF, graymatter, and other brain tissue. The elements contained ineach tissue compartment were assumed to be isotropic with conduc-tivities assigned according to Haueisen et al. (1997). The solution spaceconsisted of 51,197 current dipoles distributed as a sheet in the graymatter compartment of the head model. The lead-field matrix for thehead model was obtained using a variation of the preconditionedconjugate-gradientmethod developed byNeilson et al. (2005). TheMNIheadmodelwas used,without anymodification, to estimate the corticalsource-current density for of all of the subjects involved in this study.

To locate the positions of the electrodes on the head model, asphere that best approximated the scalp surface of theMNImodel wasused to locate a center point for the sphere. Radial lines from thecenter point were then projected toward the standard electrodelocations on the sphere and the electrodes placed on the model at thepoints where the radial lines intersected the scalp surface. The sameelectrode locations on the head model were used for all subjects.

Following the calculation of the estimates of the source-currentdensity vectors underlying the cross-spectral matrices for all of thesubjects, the elements of these vectors from the CFS and control groupswere statistically compared to identify brain regions that containedstatistically significant differences. The method used is based onrandomized t-tests of each element of the source-current densityvectors (Holmes et al., 1996). This method has the advantage of notrelying on underlying distributional assumptions while simultaneouslyreducing the probability of obtaining false-positive results. The Holmesmethod was used to determine the critical value of t, tc, such that if tN tcthe probability that there is no significant difference (two sided)between the CFS and control groups is less than the chosen value of 0.05(Pb0.05). The results of the statistical comparisonswere summarizedbydisplaying the statistically significant t scores on a three- dimensionalrendering of the cerebral cortex of the head model.

3. Results

At the time of testing, the CFS patients were normal on allpersonality dimensions except hypochondriasis, this reflecting thesomatic symptoms characteristic of CFS; notably, they did not indicatedepression (see Fig. 2). From the Quick Diagnostic Interview Scheduleit was found that over a lifetime 21 (or 34.4%) of the patients hadexperienced depressive episodes, 7 (or 11.5%) social phobias, 10 (or16.4%) agoraphobia, 9 (or 14.7%) generalized anxiety, 7 (or 11.5%)panic attacks, 8 (or 13.1%) somatoform, 15 (or 24.6%) obsessive–compulsive features, 2 (or 3.2%) bulimia, and 1 patient in the past hadexperimented with stimulants, sedatives, cocaine, and heroin. Hickieet al. (1990) evaluated the prevalence of psychiatric disorder in 48patients with CFS. The premorbid prevalence of major depression was12.5% and of total psychiatric disorder was 24.5%, which was nothigher than general community estimates. They further found that thepattern of psychiatric symptoms in the CFS patients was similar towhat is seen in other medical disorders and different from what wasfound in psychiatric patients.

Fig. 2. Basic Personality Inventory: women suffering from chronic fatigue syndrome versus women suffering from fibromyalgia.

160 P. Flor-Henry et al. / Psychiatry Research: Neuroimaging 181 (2010) 155–164

In the female controls the mean verbal IQ was 115.5, performanceIQ 113.5, and full scale IQ 115.6. In the CFS patients, the verbal IQ was111.2, performance IQ 111.9, and full scale IQ 112.1. There was nostatistically significant difference between the twogroups. Thewomenwith chronic fatigue are significantly older than the female controls(Table 1). However, we expect that the normalization of the cross-spectral matrix from each subject by its trace (total EEG power in theband) will help to eliminate any dependence of EEG power on age.Klimesch (1999), discussing alpha and theta oscillations that reflectcognitive and memory functions in a review of the relevant literature,concludes that with increasing age absolute power in the alpha andtheta band decreases whilst upper alpha power increases. Alphafrequency increases from early childhood to adulthood and thenprogressively decreases with age. Consequently, we analyzed the peakalpha frequency of the patients and controls. Across all leads onaverage the patientswere 0.3 Hz slower than the controls—whichwasnon-significant (Hotelling T-squared value: 14.919; F=1.14, d.f.=(12 128), sig.=0.33).

Fig. 2 shows the BPI profile of 63womenwith chronic fatigue and 39with fibromyalgia. (There are 2 extra women with CFS who were notincluded in the study as theywere tested after the analysis.) It is notablethat outside of the hypochondriasis scale (because of somatic

Table 1Demographics of unmedicated, dextral patients suffering from chronic fatiguesyndrome versus dextral controls.

Age Female controls Female chronic Fatigue

Eyes open⁎ N=79 N=58Mean 26.01 45.60S.D. 8.69 9.84

Eyes closed⁎ N=80 N=61Mean 25.57 45.65S.D. 8.37 9.87

Dot localization⁎ N=63 N=34Mean 25.48 45.62S.D. 8.41 10.68

Word Finding N=44 N=25Mean 25.82 45.44S.D. 8.02 9.59

⁎Pb0.01.

symptoms) both women with CFS and fibromyalgia are similar tonormal controls on 11 of the 12 dimensions. In particular neither thefibromyalgia nor the CFS subjects were depressed at the time of testing.

The rate of correct classification of the CFS and control groupsbased on only the two common spatial patterns that accounted formaximally different proportions of the temporal variances betweenthe groups is shown in Table 2. To do this, only the largest andsmallest values of ΨF and the smallest and largest values of ΨC,respectively, were inverted in Eqs. (4) and (5). The remaining valuesin the inverse were set to zero. The decision line between thecentroids of the groups was placed so that the groups would beclassified at the same rate.

Table 2 shows the results of the classification. Surprisingly, basedon only two spatial patterns in a subject's EEG, the rate of correctretrospective classification of the two groups is very high rangingfrom a low of 72% of 130 subjects correctly classified in the EOcondition to a high of 83% of 64 subjects in the alpha band during WF.Therefore, in the combined EO groups, 93 were correctly classified asCFS or control. In the combined WF groups, 53 were correctlyclassified as CFS or control. It would appear from the table that theWFcognitive condition in the alpha band is slightly better at separatingthe two groups than the beta band and that the active cognitiveconditions are better than the resting condition.

Figs. 3–8 show the statistical t (tN tc) maps of the cortical regionswere the source current density is significantly different (Pb0.05)between the CFS and control groups in the alpha and beta bands andin the three cognitive conditions. Shades of red indicate values of the t

Table 2The percentage of the subjects in the CFS and control groups correctly classified usingonly the two common spatial patterns that account for maximally different proportionsof the variance in the EEGs from the two groups in each of the alpha (8–13 Hz) and beta(14–20 Hz) bands.

Condition Alpha band Beta band

Eyes open 72% 71%Word finding 83% 80%Dot localization 74% 74%

The decision line between the two group centroids was placed so that the subjects ineach of the two groups were correctly classified at the same rate.

Fig. 3. Statistical t maps (tN tc at Pb0.05) of the cortical regions where the source current density is significantly greater in the CFS group than in the control group (red hues) andsignificantly greater in control group than in the CFS group (blue hues) in the alpha (8–13 Hz) band during the eyes open (EO) cognitive condition.

Fig. 4. Statistical t maps (tN tc at Pb0.05) of the cortical regions where the source current density is significantly greater in the CFS group than in the control group (red hues) andsignificantly greater in control group than in the CFS group (blue hues) in the alpha (8–13 Hz) band during the word finding (WF) cognitive condition.

Fig. 5. Statistical t maps (tN tc at Pb0.05) of the cortical regions where the source current density is significantly greater in the CFS group than in the control group (red hues) andsignificantly greater in control group than in the CFS group (blue hues) in the alpha (8–13 Hz) band during the dot localization (DL) cognitive condition.

Fig. 6. Statistical t maps (tN tc at Pb0.05) of the cortical regions where the source current density is significantly greater in the CFS group than in the control group (red hues) andsignificantly greater in control group than in the CFS group (blue hues) in the beta (14–20 Hz) band during the eyes open (EO) cognitive condition.

161P. Flor-Henry et al. / Psychiatry Research: Neuroimaging 181 (2010) 155–164

Fig. 7. Statistical t maps (tN tc at Pb0.05) of the cortical regions where the source current density is significantly greater in the CFS group than in the control group (red hues) andsignificantly greater in control group than in the CFS group (blue hues) in the beta (14–20 Hz) band during the word finding (WF) cognitive condition.

Fig. 8. Statistical t maps (tN tc at Pb0.05) of the cortical regions where the source current density is significantly greater in the CFS group than in the control group (red hues) andsignificantly greater in control group than in the CFS group (blue hues) in the beta (14–20 Hz) band during the dot localization (DL) cognitive condition.

162 P. Flor-Henry et al. / Psychiatry Research: Neuroimaging 181 (2010) 155–164

statistic in regions where the source-current density is greater in theCFS group than in the control group. Shades of blue indicate values ofthe t statistic in regions where the source-current density is greater inthe control group than in the CFS group. A perusal of all the figuresshows a lateralization of the differences in source-current activitybetween the groups in the WF and DL cognitive conditions. Elevatedsource-current activity in the CFS group in the left frontal temporalregions is particularly striking. This elevated activity is evident in thealpha band during theWF cognitive condition andmore so in the betaband during the DL cognitive condition.

Table 2 indicates that the strongest separation between the CFSand control groups in terms of the two common spatial patterns wasin the WF condition in both the alpha and beta bands. Examination ofthe corresponding Figs. 4 and 6 reveals a strong left–right asymmetryin the source-current density in both frequency bands with only thealpha band showing significantly greater source-current activity inthe CFS group. However, a large region of elevated source-currentdensity in the left frontal–temporal–parietal regions of the CFS groupis very evident in the beta band (Fig. 8).

4. Discussion

The results related to the differences in the distribution of source-current densities between the CFS and control groups were obtainedusing the Borgiotti–Kaplan (BM) BEAMFORMER. The algorithm wasmodified, however, by first projecting each subject's cross-spectralmatrix on two spatial patterns (eigenvectors) the first of whichaccounted for maximum variance in the EEGs of the CFS group andminimum variance in the EEGs of the control group while the secondaccounted for maximum variance in the EEGs of the control group andminimum variance in the EEGs of the CFS group. If it is assumed thateach of these two spatial patterns corresponds to a single source-current distribution that best characterizes the difference betweenone group and the other, it would seem then that the observed

differences between the groups can be interpreted in terms of theactivities of only two sources. Each source, however, consists of manyhundreds of individual dipole current sources that are temporallycorrelated and spatially distributed over large contiguous areas ofcortex. Because large areas of cortex would appear to be involved inCFS, single dipole localization methods would clearly not be suitablefor population studies of this kind. It has been shown (Russell andKoles, 2007) that the BK Beamformer is generally superior to otherdistributed source-localization algorithms in terms of both theaccuracy and the dispersion of estimated source-current distributions.

The essential findings in this investigation suggest that the featuresin the EEG which best separate chronic fatigue syndrome from normalbrain function in females are the result of cortical source-current activitythat is generally suppressed in the alpha band andbetabands in theEyesOpen condition in CFS. Further, during the Word Finding task there areincreased sources in the left frontal region in the alpha band and duringDot Localization in the beta band. In both cognitive tasks, and in bothbands the sources of the right hemisphere are reduced. It is noteworthythat the best classification, in both alpha and beta bands, is during theWord Finding task. It has been generally accepted in the past thatdecreased EEGpower in the alpha band and increased power in the betafrequency band correspond to increased functional activation in thecerebral cortex. However, study of theta (4–8 Hz) and gamma (28–64 Hz) oscillations showa significant increase in powerduringencodingpredicted subsequent recall (Sederberg et al., 2003). Hanslmayr et al.(2009) reports that successful encoding in a semantic task is related topower decrease in the alpha (8–12 Hz) as would be expected, but tobeta power decrease in the low beta (12–20 Hz) and increase in thegamma band (55–70 Hz). Similarly, Sederberg et al. (2007), whostudied in epileptics with implanted intracranial electrodes duringverbal memory recall tasks report an increase in gamma bandoscillations (44–64 Hz) with a decrease in other bands. However, withrespect to the alpha frequency band the physiological meaning of alphaoscillations ismore complex. As Basar et al. (1997) comments: “Alpha as

163P. Flor-Henry et al. / Psychiatry Research: Neuroimaging 181 (2010) 155–164

a universal code or universal operator…the major physiologicalmeaning of 10 Hz oscillations whichmay be comparable to the putativeuniversal role of gamma responses in brain signaling.” The fact thatalpha does not necessarily reflect passive states or idling of the brain isshown by the evoked 10 Hz oscillations that can be generated aftersensory stimulation and by event related alpha rhythms that can beinduced by cognitive processing. These findings are consistent with thefMRI study of verbal working memory in CFS which shows increasedcerebral circulation in the left superior parietal region (and bilateralactivation in the premotor regions) in CFS during the processing ofcomplex verbal information (Lange et al., 2005). In view of thesecomplexities the precise interpretation of our findings is complex: inline with the more generally accepted view that reduction of alphapower corresponds to increased cortical activation, then in the EyesOpen and during the Dot Localization condition the CFS group differsfrom controls in having increased cortical activation bilaterally. DuringWord Finding there is increased activation bilaterally, together withreduced activation in the left frontal region. If one accepts these generalpremises, in the beta band, the reduced bilateral sources in the EyesOpen condition imply increased bilateral activation and in the WordFinding condition the reduced sources in the right hemisphere similarlyimply increased activation of that hemisphere. In Dot Localization taskthe right hemisphere sources are decreased and the left hemispheresources are increased over the whole of the left hemisphere, i.e.activation of the right and extensive relative hypofunction of the lefthemisphere.

In a remarkable series of publications, Renoux and Biziere haveshown in right and left cortical lesioned rats, that in the mammalianbrain the T-cell immunological system is underlateralized in left braincortical control: with left hemisphere lesions there is a completecollapse of T-cell mediated events, but this is under feedback inhibitionfrom the right hemisphere. Animals with right brain lesions havenormal T-cell immunological reactivity and are similar to healthyanimals; however,with left brain lesions, there is atrophy of the thymus,whereas with right brain lesions there is hypertrophy of the thymus(Renoux et al., 1983a,b; Biziere et al., 1985; Renoux and Biziere, 1986).These findings were confirmed by Neveu (1988), who found that largelesions of the left cerebral cortex depressedwhereas lesions of the righthemicortex enhanced T-cell immunity. Interestingly, Davidson et al.(2003) studied 25 subjects before and after an8-week training course inmeditation with quantitative EEG. These subjects and a control groupwere then vaccinated with influenza vaccine. The meditators had asignificant increase in left brain activation (C3, C4) on an asymmetryindex in the 8–13 Hz band. Further, the meditators also had asignificantly greater rise in influenza antibody titers compared to thecompared with the controls that was significantly correlated to thedegree of left-sided anterior activation.

5. Conclusion

Thus, it would appear that dysregulation of the cortical hemisphericcontrols of immunological functional systems (left hemisphere) andhypothalamic–pituitary–adrenocortical axis is the underlying patho-physiology of CFS. The best discrimination from controls is duringcognitive activation of the left hemisphere during the Word Findingtask, over 80% correct (8–13 Hz). At the same time cognitive activationof the right hemisphere in the spatial task, presumably throughtranscallosal interaction, also induces increased sources in the lefthemisphere, (14–20 Hz). Quantitative EEG analysis would appear to bea promising diagnostic indicator for this disorder.

References

Abu-Judeh, H.H., Levine, S., Kumar, M., El-Zeftawy, H., Naddaf, S., Lou, J.Q., Abdel-Dayem, H.M., 1998. Comparison of SPET brain perfusion and 18F-FDG brain

metabolism in patients with chronic fatigue syndrome. Nuclear MedicineCommunications 19, 1065–1071.

Afari, N., Buchwald, D., 2003. Chronic fatigue syndrome: a review. American Journal ofPsychiatry 160, 221–236.

Altay, H.T., Abbey, S.E., Toner, B.B., Salit, I.E., Brooker, H., Garfinkel, P.E., 1990. Theneuropsychological dimensions of postinfectious neuromyasthenia (chronicfatigue syndrome): a preliminary report. International Journal of Psychiatry inMedicine 20 (2), 141–149.

Annett, M., 1970. A classification of hand preference by association analysis. BritishJournal of Psychology 61, 303–321.

Basar, E., Schurmann, M., Basar-Eroglu, C., Karakas, S., 1997. Alpha oscillations in brainfunctioning: an integrative theory. International Journal of Psychophysiology 26, 5–29.

Baschetti, R., 2000. Chronic fatigue syndrome: a form of Addison's disease. Journal ofInternal Medicine 247, 737–739.

Bearn, J., Allain, T., Coskeran, P., Munro, N., Butler, J., McGregor, A., Wessely, S., 1995.Neuroendocrine responses to d-fenfluramine and insulin-induced hypoglycemia inchronic fatigue syndrome. Biological Psychiatry 37, 245–252.

Biziere, K., Guillaumin, J.M., Degenne, D., Bardos, P., Renoux, M., Renoux, G., 1985.Lateralized neocortical modulation of the T-cell lineage. In: Guillemin, R., et al.(Ed.), Neuroal Modulation of Immunity. Raven Press, New York, pp. 81–94.

Black, P.H., 1995. Psychoneuroimmunology: brain and immunity. Scientific AmericanScience and Medicine 16–25 November/December.

Bou-Holaigah, I., Rowe, P.C., Kan, J., Calkins, H., 1995. The relationship between neurallymediated hypotension and the chronic fatigue syndrome. Journal of the AmericanMedical Association 274 (12), 961–967.

Brillinger, D.R., 1981. Time Series: Data Analysis and Theory. Holden-Day, San Francisco.Brooks, J.C.W., Roberts, N., Whitehouse, G., Majeed, T., 2000. Protonmagnetic resonance

spectroscopy and morphometry of the hippocampus in chronic fatigue syndrome.British Journal of Radiology 73, 1206–1208.

Chapman, L.J., Chapman, J.P., 1987. The measurement of handedness. Brain andCognition 6, 175–183.

Chaudhuri, A., Condon, B.R., Gow, J.W., Brennan, D., Hadley, D.M., 2003. Protonmagneticresonance spectroscopy of basal ganglia in chronic fatigue syndrome. NeuroReport14, 225–228.

Davidson, R.J., Chapman, J.P., Chapman, L.J., Henriques, J.B., 1990. Asymmetrical brainelectrical activity discriminates between psychometrically-matched verbal andspatial cognitive tasks. Psychophysiology 27 (5), 528–543.

Davidson, R.J., Kabat-Zinn, J., Schumacher, J., Rosenfranz, M., Muller, D., Santorelli, S.F.,Urbanowski, F., Harrington, A., Bonus, K., Sheridan, J.F., 2003. Alterations in brainand immune function produced by mindfulness meditation. PsychosomaticMedicine 65, 564–570.

DeLuca, J., Schmaling, K.B., 1995. Neurocognitive testing in chronic fatigue syndrome.Journal of Chronic Fatigue Syndrome 1 (3/4), 21–26.

Demitrack, M.A., 1994. Chronic fatigue syndrome: a disease of the hypothalamic–pituitary–adrenal axis? Annals of Medicine 26, 1–5.

Demitrack, M.A., Crofford, L.J., 1995. Hypothalamic–pituitary–adrenal axis dysregula-tion in fibromyalgia and chronic fatigue syndrome: an overview and hypothesis.Journal of Musculoskeletal Pain 3, 67–73.

Demitrack, M.A., Dale, J.K., Straus, S.E., Laue, L., Listwak, S.J., Kruesi, M.J.P., Chrousos, G.P.,Gold, P.W., 1991. Evidence for impaired activation of the hypothalamic–pituitary–adrenal axis in patients with chronic fatigue syndrome. Journal of ClinicalEndocrinology and Metabolism 73 (6), 1224–1234.

Flor-Henry, P., Lind, J.C., Koles, Z.J., 2004. A source-imaging (low-resolution electro-magnetic tomography) study of the EEGs from unmedicated males withdepression. Psychiatry Research: Neuroimaging 130, 191–207.

Fukuda, K., Straus, S.E., Hickie, I., Sharpe, M.C., Dobbins, J.G., Komaroff, A., 1994.International Chronic Fatigue Syndrome Study Group. The chronic fatiguesyndrome: a comprehensive approach to its definition and study. Annals ofInternal Medicine 121, 953–959.

Goldenberg, D.L., 1989. Fibromyalgia and its relation to chronic fatigue syndrome, viralillness and immune abnormalities. Journal of Rheumatology 16, S91–S93.

Goodnick, P.J., Sandoval, R., 1993. Treatment of chronic fatigue syndrome and relateddisorders: immunological approaches. In: Goodnick, P.J., Klimas, N.G. (Eds.),Chronic fatigue and related immune deficiency syndromes. American PsychiatricPress, Washington, D.C., pp. 109–129.

Guilleminault, C., Poyares, D., de Rosa, A., Kirisoglu, C., Almeida, T., Lopes, M.C., 2006.Chronic fatigue, unrefreshing sleep and nocturnal polysomnography. SleepMedicine 7, 513–520.

Hammond, D.C., 2001. Treatment of chronic fatigue with neurofeedback and self-hypnosis. NeuroRehabilitation 16, 295–300.

Hanslmayr, S., Spitzer, B., Bauml, K.-H., 2009. Brain oscillations dissociate betweensemantic and nonsemantic encoding of episodic memories. Cerebral Cortex 19,1631–1640.

Haueisen, J., Ramon, C., Eiselt, M., Brauer, H., Nowak, H., 1997. Influence of tissueresistivities on neuromagnetic fields and electric potentials studied with a finiteelementmodel of thehead. IEEETransactionsonBiomedical Engineering 44, 727–735.

Hickie, I., Lloyd, A., Wakefield, D., Parker, G., 1990. The psychiatric status of patientswith the chronic fatigue syndrome. British Journal of Psychiatry 156, 534–540.

Holmes, A.P., Blair, R.C., Watson, J.D., Ford, I., 1996. Nonparametric analysis of statisticimages from functional mapping experiments. Journal of Cerebral Blood Flow andMetabolism 16, 7–22.

Ichise, M., Salit, I.E., Abbey, S.E., Chung, D.-G., Gray, B., Kirsh, J.C., Freedman, M., 1992.Assessment of regional cerebral perfusion by 99Tcm-HMPAO SPECT in chronicfatigue syndrome. Nuclear Medicine Communications 13, 767–772.

Jackson, D.N., 1996. Basic Personality Inventory Manual, 2nd ed. Sigma AssessmentsSystems, Port Huron, MI.

164 P. Flor-Henry et al. / Psychiatry Research: Neuroimaging 181 (2010) 155–164

Jackson, D.N., 1998. Multidimensional Aptitude Battery II manual, 2nd ed. SigmaAssessment Systems, Port Huron, MI.

Johnson, R.T., Griffin, D.E., Hirsch, R.L., Wolinsky, J.S., Roedenbeck, S., Lindo de Soriano, I.,Vaisberg, A., 1984. Measles encephalomyelitis — clinical and immunologic studies.New England Journal of Medicine 310 (3), 137–141.

Joyce, E., Blumenthal, S., Wessely, S., 1996. Memory, attention, and executive functionin chronic fatigue syndrome. Journal of Neurology, Neurosurgery and Psychiatry 60,495–503.

Klimesch, W., 1999. EEG alpha and theta oscillations reflect cognitive and memoryperformance: a review and analysis. Brain Research Reviews 29, 169–195.

Koles, Z.J., Flor-Henry, P., Lind, J.C., 2001. Low-resolution electrical tomography of thebrain during psychometrically matched verbal and spatial cognitive tasks. HumanBrain Mapping 12, 144–156.

Landay, A.L., Jessop, C., Lennette, E.T., Levy, J.A., 1991. Chronic fatigue syndrome: clinicalcondition associated with immune activation. Lancet 338 (8769), 707–712.

Lange, G., Steffener, J., Cook, D.B., Bly, B.M., Christodoulou, C., Liu, W.-C., DeLuca, J.,Natelson, B.H., 2005. Objective evidence of cognitive complaints in chronic fatiguesyndrome: a BOLD fMRI study of verbal working memory. NeuroImage 26,513–524.

Lewis, D.H., Mayberg, H.S., Fischer, M.E., Goldberg, J., Ashton, S., Graham, M.M.,Buchwald, D., 2001. Monozygotic twins discordant for chronic fatigue syndrome:regional cerebral blood flow SPECT. Radiology 219, 766–773.

Lloyd, A.R., Wakefield, D., Boughton, C., Dwyer, J., 1988. What is myalgic encephalo-myelitis? Lancet 1 (8597), 1286–1287.

Loganovsky, K.N., 2000. Vegetative-vascular dystonia and osteoalgetic syndrome orchronic fatigue syndrome as a characteristic after-effect of radioecological disaster:the Chernobyl accident experience. Journal of Chronic Fatigue Syndrome 7 (3),3–16.

Machale, S.M., Lawrie, S.M., Cavanagh, J.T.O., Glabus, M.F., Murray, C.L., Goodwin, G.M.,Ebmeier, K.P., 2000. Cerebral perfusion in chronic fatigue syndrome anddepression. British Journal of Psychiatry 176, 550–554.

Marcel, B., Komaroff, A.L., Fagioli, L.R., Kornish II, R.J., Albert, M.S., 1996. Cognitivedeficits in patients with chronic fatigue syndrome. Biological Psychiatry 40,535–541.

Marcus, S., Robins, L.N., Bucholz, K., 1989. Quick Diagnostic Interview Schedule, III-R.Steven Marcus, St. Louis.

Michiels, V., Cluydts, R., 2001. Neuropsychological functioning in chronic fatiguesyndrome. A review. Acta Psychiatrica Scandinavica 103, 84–93.

Miller, E.N., Fujioka, T.A.T., Chapman, L.J., Chapman, J.P., 1995. Psychometricallymatched tasks for assessment of hemispheric asymmetries of function. Brain andCognition 28, 1–13.

Moss-Morris, R., Petrie, K.J., Large, R.G., Kydd, R.R., 1996. Neuropsychological deficits inchronic fatigue syndrome: artifact or reality? Journal of Neurology, Neurosurgeryand Psychiatry 60, 474–477.

Neilson, L.A., Kovalyov, M., Koles, Z.J., 2005. A computationally efficient method foraccurately solving the EEG forward problem in a finely discretized head model.Clinical Neurophysiology 116, 2302–2314.

Neveu, P.J., 1988. Cerebral neocortex modulation of immune functions. Life Sciences 42,1917–1923.

Parker, A.J.R., Wessely, S., Cleare, A.J., 2001. The neuroendocrinology of chronic fatiguesyndrome and fibromyalgia. Psychological Medicine 31, 1331–1345.

Pascual-Marqui, R.D., Michel, C.M., Lehmann, D., 1994. Low resolution electromagnetictomography: a new method for localizing electrical activity in the brain.International Journal of Psychophysiology 18, 49–65.

Poteliakhoff, A., 1981. Adrenocortical activity and some clinical findings in acute andchronic fatigue. Journal of Psychosomatic Research 25 (2), 91–95.

Prins, J.B., van der Meer, J.W.M., Bleijenberg, G., 2006. Chronic fatigue syndrome. Lancet367, 346–355 Jan.28.

Renoux, G., Biziere, K., 1986. Brain neocortex lateralized control of immune recognition.Integrative Psychiatry 4, 32–40.

Renoux, G., Biziere, K., Renoux, M., Guillaumin, J.M., 1983a. The production of T-cell-inducing factors in mice is controlled by the brain neocortex. Scandinavian Journalof Immunology 17, 45–50.

Renoux, G., Biziere, K., Renoux, M., Guillaumin, J.-M., Degenne, D., 1983b. A balancedbrain asymmetry modulates I cell-mediated events. Journal of Neuroimmunology5, 227–238.

Russell, J.P., Koles, Z.J., 2007. A comparison of LORETA and the Borgiotti-KaplanBeamformer in simulated EEG source localization with a realistic head model.International Journal of Bioelectromagnetism 9, 112–113.

Sarkar, S.M., Seastrunk, J.W., 1998. Develpment of radiology markers to assess clinicallydiagnosed chronic fatigue syndrome. Nuclear brain SPECT and magnetic resonancespectroscopy results. Rivista di Neuroradiologia 11 (Supp. 2), 60–65.

Scheffers, M.K., Johnson Jr., R., Grafman, J., Kale, J.K., Straus, S.E., 1992. Attention qndshort-term memory in chronic fatigue syndrome patients: an event-relatedpotential analysis. Neurology 42, 1667–1675.

Schmaling, K.B., Lewis, D.H., Fiedelak, J.I., Mahurin, R., Buchwald, D.S., 2003. Single-photon emission computerized tomography and neurocognitive function inpatients with chronic fatigue syndrome. Psychosomatic Medicine 65, 129–136.

Schwartz, R.B., Komaroff, A.L., Garada, B.M., Gleit, M., Doolittle, T.H., Bates, D.W., Vasile,R.G., Holman, B.L., 1993. SPECT imaging of the brain: comparison of findings inpatients with chronic fatigue syndrome, AIDS dementia complex, and majorunipolar depression. American Journal of Roentgenology 162, 943–951.

Schwartz, R.B., Garada, B.M., Komaroff, A.L., Tice, H.M., Gleit, M., Jolesz, F.A., Holman, B.L.,1994. Detection of intracranial abnormalities in patients with chronic fatiguesyndrome: comparison of MR imaging and SPECT. American Journal of Roentgen-ology 162, 935–941.

Scott, L.V., Teh, J., Reznek, R., Martin, A., Sohaib, A., Dinan, T.G., 1999. Small adrenalglands in chronic fatigue syndrome: a preliminary computer tomography study.Psychoneuroendocrinology 24, 759–768.

Sederberg, P.B., Kahana, M.J., Howard, M.W., Donner, E.J., Madsen, J.R., 2003. Theta andgamma oscillations during encoding predict subsequent recall. Journal ofNeuroscience 23 (34), 10809–10814.

Sederberg, P.B., Schulze-Bonhage, A., Madsen, J.R., Bromfield, E.B., McCarthy, D.C., Brandt,A., Tully, T.S., Kahana, M.J., 2007. Hippocampal and neocortical gamma oscillationspredict memory formation in humans. Cerebral Cortex 17 (5), 1190–1196.

Sekihara, K., Nagarajan, S.S., Poeppel, D., Marantz, A., Miyashita, Y., 2001. Reconstruct-ing spatio-temporal activities of neural sources using an MEG vector beamformertechnique. IEEE Transactions on Biomedical Engineering 48, 760–771.

Sharbrough, F., Chatrian, G., Lesser, R.P., Luders, H., Nuwer, M., Picton, T.W., 1990.Guidelines for standard electrode position nomenclature. American Electroen-cephalographic Society, Bloomfield, CT.

Sherlin, L., Budzynski, T., Kudzynski, H.K., Congedo, M., Fischer, M.E., Buchwald, D.,2007. Low-resolution electromagnetic twins discordant for chronic fatiguesyndrome. NeuroImage 34, 1438–1442.

Shumway, R.H., 1982. Discriminant analysis for time series. In: Krishniah, P.R. (Ed.),Handbook of statistics (Vol. II). North-Holland, Amsterdam, pp. 1–43.

Siemionow, V., Fang, Y., Calabrese, L., Sahgal, V., Yue, G.H., 2004. Altered central nervoussystem signal during motor performance in chronic fatigue syndrome. ClinicalNeurophysiology 115 (10), 2372–2381.

Siessmeier, T., Nix, W.A., Hardt, J., Schreckenberger, M., Egle, U.T., Bartenstein, P., 2003.Observer independent analysis of cerebral glucose metabolism in patients withchronic fatigue syndrome. Journal of Neurology, Neurosurgery and Psychiatry 74,922–928.

Tiersky, L.A., Johnson, S.K., Lange, G., Natelson, B.H., DeLuca, J., 1997. Neuropsychologyof chronic fatigue syndrome: a critical review. Journal of Clinical and ExperimentalNeuropsychology 19 (4), 560–586.

Tirelli, U., Chierichetti, F., Tavio, M., Simonelli, C., Bianchin, G., Zanco, P., Ferlin, G., 1998.Brain Positron tomography (PET) in chronic fatigue syndrome : preliminary data.American Journal of Medicine 105 (3A), 54S–58S.

Tomoda, A., Miike, T., Yamada, E., Honda, H., Moroi, T., Ogawa, M., Ohtani, Y., Morishita,S., 2000. Chronic fatigue syndrome in childhood. Brain & Development 22, 60–64.

Tomoda, A., Mizuno, K., Murayama, N., Joudoi, T., Igasaki, T., Miyazaki, M., Miike, T.,2007. Event-related potentials in Japanese childhood chronic fatigue syndrome.Journal of Pediatric Neurology 5, 199–208.

Ur, E., White, P.D., Grossman, A., 1992. Hypothesis: cytokines may be activated to causedepressive illness and chronic fatigue syndrome. European Archives of Psychiatryand Clinical Neuroscience 241, 317–322.

Wessely, S., 1995. The epidemiology of chronic fatigue syndrome. EpidemiologicReviews 17 (1), 139–151.

Wheeler, R.E., Davidson, R.J., Tomarken, A.J., 1993. Frontal brain asymmetry andemotional reactivity: a biological substrate of affective style. Psychophysiology 30,82–89.