Source: NMR in Biomedicine Preprint Date: October 21, 2008 URL: http://www3.interscience.wiley.com/journal/121471355/abstract?CRETRY=1&SRETRY=0 Ventricular cerebrospinal fluid lactate is increased in chronic fatigue syndrome compared with generalized anxiety disorder: an in vivo 3.0 T ^1H MRS imaging study^** ---------------------------------------------------------------------------- Sanjay J. Mathew(a), Xiangling Mao(b), Kathryn A. Keegan(a), Susan M. Levine(c), Eric L.P. Smith(d), Linda A. Heier(b), Viktor Otcheretko(a), Jeremy D. Coplan(d) and Dikoma C. Shungu(b,*) a S. J. Mathew, K. A. Keegan, V. Otcheretko Department of Psychiatry, Mount Sinai School of Medicine, New York, NY, USA b X. Mao, L. A. Heier, D. C. Shungu Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA c S. M. Levine Private Practice, Infectious Disease/Internal Medicine, New York, NY, USA d E. L. P. Smith, J. D. Coplan Department of Psychiatry, Downstate Medical Center, Brooklyn, NY, USA * Correspondence to: D. C. Shungu, Department of Radiology, Citigroup Bio- medical Imaging Center, Weill Medical College of Cornell University, 516 E. 72nd Street, New York, NY 10021, USA. E-mail: dcs7001@med.cornell.edu **A preliminary version of this work was presented in an oral scientific session at the 14th Annual Meeting of the International Society for Magnetic Resonance in Medicine, 9 May 2006, Seattle, WA, USA. - Contract/grant sponsor: CFIDS Association of America, Inc. - Contract/grant sponsor: Weill Cornell Medical College New Faculty Develop- ment Funds. - Contract/grant sponsor: National Institutes of Health; contract/grant number: K23-MH-069656 and MO1-RR-00071. Received: 2 June 2008, Revised: 6 July 2008, Accepted: 7 July 2008, Published online in Wiley InterScience: 2008 Abstract Chronic fatigue syndrome (CFS) is a controversial diagnosis because of the lack of biomarkers for the illness and its symptom overlap with neuropsychiatric, infectious, and rheumatological disorders. We compared lateral ventricular volumes derived from tissue-segmented T_1-weighted volumetric MRI data and cerebrospinal fluid (CSF) lactate concentrations measured by proton MRS imaging (^1H MRSI) in 16 subjects with CFS (modified US Centers for Disease Control and Prevention criteria) with those in 14 patients with generalized anxiety disorder (GAD) and in 15 healthy volunteers, matched group-wise for age, sex, body mass index, handedness, and IQ. Mean lateral ventricular lactate concentrations measured by ^1H MRSI in CFS were increased by 297% compared with those in GAD (P<0.001) and by 348% compared with those in healthy volunteers (P<0.001), even after controlling for ventricular volume, which did not differ significantly between the groups. Regression analysis revealed that diagnosis accounted for 43% of the variance in ventricular lactate. CFS is associated with significantly raised concentrations of ventricular lactate, potentially consistent with recent evidence of decreased cortical blood flow, secondary mitochondrial dysfunction, and/or oxidative stress abnormalities in the disorder. Keywords: MRS; lactate; brain metabolism; chronic fatigue syndrome; anxiety disorder; cerebrospinal fluid Abbreviations used: BMI, body mass index; CFS, chronic fatigue syndrome; CSF, cerebrospinal fluid; FSS, Fatigue Severity Scale; GAD, generalized anxiety disorder; HAM-A, Hamilton Anxiety Rating Scale; MELAS, mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes; NAA, N-acetylaspartate; PSWQ, Penn State Worry Questionnaire 1 INTRODUCTION Chronic fatigue syndrome (CFS) is an illness marked by debilitating, medically unexplained fatigue, often accompanied by rheumatological, infectious, or neuropsychiatric symptoms. The 1994 US Centers for Disease Control and Prevention (CDC) guidelines (1) require at least 6 months of new-onset fatigue with four or more of the following symptoms: impaired memory or concentration, sore throat, tender cervical or axillary lymph nodes, muscle pain, multi-joint pain, new headaches, unrefreshing sleep, and post-exertional malaise. The pathophysiology of CFS is unknown; chronic immune activation, microbial infections, orthostatic intolerance, cholinergic and neuroendocrine abnormalities, and single-nucleotide polymorphisms in stress-related genes have been hypothesized to be important features of the illness (2-4). Recent reports have found decreased absolute cortical blood flow (5) and high concentrations of blood markers of oxidative stress, specifically isoprostanes (6), to be associated with the joint pain and post-exertional malaise characteristic of CFS (7). Although the CDC case definition has enhanced diagnostic reliability, the construct and discriminative validity of the disorder remains controversial, because of the lack of bona fide illness biomarkers. With the lack of confirmation of earlier reports of discrete muscle pathology (8) and with substantial evidence of neuropsychological impairments (9) and non-specific neuroimaging abnormalities in global gray matter volumes, metabolism, neurochemistry, and blood flow (10-13), brain mechanisms underlying the illness have been increasingly scrutinized. However, it remains unknown if brain function differs between CFS and neuropsychiatric disorders associated with prominent fatigue, such as generalized anxiety disorder (GAD), which has symptom overlap with CFS. In contrast with CFS, which is a relatively rare condition, with a US prevalence of <0.50% (14,15), GAD has a lifetime prevalence of 4-5% (16), and, although the diagnostic criteria for GAD include fatigue as a cardinal symptom, the pathophysiology of fatigue in the disorder is unknown. Besides fatigue, GAD shares other phenomenological and demographic features with CFS, including long-term duration (6-month minimum), female preponderance, mean age of onset in the 30s, and varied somatic and cognitive symptoms, including muscle tension, difficulty sleeping, and impaired memory and concentration (17). In addition, CFS and GAD patients both have substantial lifetime mood disorder comorbidities (18). Thus, GAD would be a particularly appropriate control group for biological investigations of CFS in accordance with the CDC guidelines for CFS research (1). In this study, we focused specifically on measuring lactate concentrations in lateral ventricular cerebrospinal fluid (CSF) of patients with CFS by proton MRS imaging (^1H MRSI) to test the hypothesis ­ suggested by our earlier observations in an uncontrolled study (19) ­ that ventricular lactate is significantly raised in CFS compared with GAD and healthy volunteers. ^1H MRSI provides a safe, non-invasive method for in vivo ascertainment of brain chemistry using a clinical MR scanner by quantifying the concentrations of a number of ^1H-containing chemical compounds, including lactate. We postulate that differences in ventricular lactate concentrations between CFS and a comparison group, GAD, that has symptomatic overlap with CFS may enable assessment of the specificity of this metabolic abnormality in CFS in serving as a potential diagnostic marker of the illness. METHOD All participants provided written informed consent before all procedures and were compensated for their participation. Diagnostic and laboratory assessments were conducted at Mount Sinai School of Medicine, and neuroimaging scans were performed at Weill Cornell Medical College, with approval of the institutional review boards of both institutions. Subjects For all participants, general eligibility requirements included age between 18 and 55 years, negative urine toxicology at screening and day of scan, and, for females of reproductive age, use of an effective birth control method, including abstinence. Exclusion criteria included a history of psychotic disorder, neurological illness, substance abuse or dependence in the past year, or any persistent medical condition that required long-term care. In addition, subjects who were pregnant or who had any condition precluding clinical MR examination (e.g. pacemaker, metallic prosthesis) were excluded. Patients with CFS (n=16) were recruited from a private practice in New York City and were diagnosed by a board-certified internist and infectious disease specialist (S.M.L.), using modified US CDC guidelines (1). All patients had been in the care of the referring practitioner for at least 6 months at the time of the initial research evaluation. Those with a comorbid diagnosis of fibromyalgia were eligible for participation. Patients with CFS were excluded if they also met criteria for GAD according to the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (SCID) (20). Patients with GAD (n=18), recruited through media advertisement or clinician referral, were diagnosed by SCID by trained clinicians. None had been taking psychotropic drugs for at least 2 weeks before the MRSI scan. Medically healthy volunteers (n=18) were recruited from media advertisement. Healthy volunteers did not have any current medical conditions or axis I psychiatric disorders (per SCID-NP interview). Healthy volunteers, patients with GAD, and patients with CFS were frequency-matched to provide similar distributions of age, sex, minority status, body mass index (BMI), handedness, and education. In all three groups, drugs and supplements were discontinued for at least 48 h before scanning. Use of over-the-counter nutritional supplements was reported by 4/16 (25%) patients with CFS. Four patients with GAD reported taking daily multivitamins, and one reported taking omega-3 fatty acids. Of the healthy volunteers, six reported taking multivitamins. All participants were requested to abstain from alcohol for at least 48 h before the scan. Clinical assessments Fatigue was assessed within 1 week of the scan by the Fatigue Severity Scale (FSS) (21), a self-report instrument with sound psychometric properties. A FSS total score of =<3 is considered appropriate for normal subjects. Ratings performed on day of scan included the 17-item Hamilton Rating Scale for Depression (22) and the Hamilton Anxiety Rating Scale (HAM-A) (23), and self-report instruments included the Penn State Worry Questionnaire (PSWQ) (24) and Pittsburgh Sleep Quality Index (25). IQ was measured with the Wechsler Abbreviated Scale of Intelligence (26). ^1H MRSI data acquisition and analysis All neuroimaging studies were conducted on a 3.0 T GE MRI system using a standard quadrature head coil. After sagittal T_1-weighted localizer imaging, each subject underwent a four-section (15 mm thick, 3.5 mm gap) T_1-weighted axial-oblique MRSI localizer imaging series, with the second most inferior slice traversing the bodies of the lateral ventricle, along a plane defined anteriorly by the genu of the corpus callosum and posteriorly by the splenium of the corpus callosum. A multislice ^1H MRSI scan was then performed in the same locations and with the same angulation, using a slice-interleaved spin-echo acquisition technique (27) that incorporated octagonally tailored outer volume presaturation pulses for pericranial fat and tissue suppression, and a single water-selective radiofrequency pulse followed by strong spoiler gradients for water suppression. The ^1H MRSI data were recorded in 29 min using the following acquisition parameters: TE/TR=280/2300 ms; field of view=240 mm; 32x32 phase-encoding steps with circularly sampled k-space; 512 time-domain points; a 2.5 kHz spectral width. The recorded raw MRSI data were transferred to an off-line Sun Microsystems (Mountain View, CA, USA) workstation for analysis by two study investigators (X.M., D.C.S.) blinded to the diagnosis, who used MRSI data analysis software of their own design. The raw data were sorted by slice, zero-filled twice along the acquisition domain (to 2048 sample points), filtered with Gauss­Lorentz and Hamming+Fermi windows along the time and spatial domains, respectively, and then processed by standard three-dimensional fast Fourier transformation. The resulting spectral data were automatically corrected for susceptibility shifts caused by slight variations in the magnetic field strength across the brain. From the integral of the point-spread function following spatial filtering and Fourier transformation, we estimated the actual size of each MRSI voxel to be 1.13 cm^3 or ~40% larger than the nominal voxel size (0.75x0.75x1.5 or 0.83 cm^3) that would be derived on the basis of spatial data parameters. For a more accurate tracing of the ventricular region of interest and voxel selection, each raw MRSI dataset was further zero-filled along the spatial domain to a 64x64 matrix, which effectively doubled spatial resolution without improving or degrading the information context. Using a registered grid overlay on the matching T_1-weighted MRSI localizer images, voxels of interest were selected within the entire lateral ventricle (see Fig. 2), and the spectral area around the ventricular lactate peak chemical shift (1.33 p/m 0.2 ppm) in each voxel was obtained using a frequency-domain non-linear least-squares fitting routine (28), and then a mean lactate value was computed for the whole ventricular region of interest by summing values for the individual voxels and dividing the result by the total number of voxels. The derived mean ventricular lactate concentrations thus derived were expressed in institutional units (i.u.) as ratios of peak areas to the root-mean-square (rms) of the background noise in each spectrum, as previously described (29,30). Determination of ventricular volume To adjust the ventricular lactate concentrations for the potential confounding effect of differences in ventricular size and 'partial-volume averaging', the volume of the ventricular space that contributed signal to the recorded MRSI data was determined from tissue-segmented and co-registered volumetric MRI data, which were acquired using a T_1-weighted spoiled gradient-recalled echo (SPGR) imaging sequence with TR/TE=30/8 ms, flip angle 458, field of view 24 cm, 256x256 matrix, 150 coronal slices, and a slice thickness of 1.5 mm. Using a combination of public-domain and commercial volumetric imaging data analysis software packages (31-33), ventricular volumes for all subjects were derived by first reslicing the volumetric MRI data to match the angulation in the MRSI data, and then automatically segmenting the resulting volume into images that represented the gray matter, white matter, cerebrospinal fluid (CSF), and 'other' tissues (Fig. 1). Next, the segmented CSF images, which permitted optimal visualization of the lateral ventricular bodies, were digitally 'fused' (Fig. 1) with the four MRSI localizer images and automatically co-registered to correct for potential spatial mismatch due to subject motion between the scans (Fig. 1A). As the slice thickness of each SPGR image was 1.5 mm and that for each of the four MRSI localizer images was 15 mm, the 10 slices in the volumetric data that contained only the lateral ventricular volume fraction sampled by MRSI were identified and extracted for ventricular volume determination by manual tracing (Fig. 1B). To ensure reliable manual tracing of the ventricular contours, several trial runs were conducted in the presence of a trained neuroradiologist, who rated each run for tracing accuracy, with the derived volumes providing a measure of reproducibility. All investigators involved in data analysis were blinded to the diagnosis. Statistical analysis The comparability of the three study groups in baseline characteristics was tested for continuous variables with analysis of variance, and for dichotomous variables with Fisher exact tests. Post hoc analyses were performed using the Tukey HSD test and non-parametric Mann-Whitney tests. We examined the associations of MRSI lactate with clinical characteristics using Spearman correlation coefficients. Multivariate linear regression analysis was used to assess the relationship between lactate and relevant clinical predictor variables. The three groups were compared with respect to ventricular volumes to test whether any differences in concentrations of ventricular lactate could be accounted for by partial-volume effects due to differences in the proportion of ventricular space and CSF contributing to the MRSI signal. All analyses were performed in SPSS version 11.0 (SPSS Inc, Chicago, IL, USA), with a level of significance of P=<0.05 (two-tailed) for the primary metabolite of interest (lactate). Results are expressed as mean p/m SD. RESULTS Clinical characteristics Of the initial eligible sample (16 patients with CFS, 18 with GAD, and 18 healthy volunteers), viable MRSI scans were obtained for 16 patients with CFS, 14 patients with GAD, and 15 healthy volunteers. The scans of four patients with GAD and three healthy volunteers were excluded because of motion-degraded spectral quality. Demographic and clinical characteristics for subjects with viable MRS data are shown in Table 1. The three groups did not differ in demographic characteristics, and the two patient groups did not significantly differ in mood and other anxiety disorder comorbidity (Table 1). The three groups differed in fatigue severity (F2,67=78.62, P<0.001); patients with CFS had higher fatigue scores on the FSS (6.20 p/m 0.82) than did patients with GAD (4.46 p/m 1.15, P<0.001) or healthy controls (2.17 p/m 0.74; P<0.001), and patients with GAD scored higher than controls (P<0.001). Age did not correlate with FSS (r=0.125, P=0.401, n =47), and FSS scores did not differ by sex. Patients with GAD had more severe anxiety symptomatology than patients with CFS, as reflected by higher HAM-A (P<0.05) and PSWQ scores (P<0.001), but the two patient groups did not differ in depression severity (P=0.42) (Table 1). Primary hypothesis testing: ventricular lactate Figure 2 shows ^1H MRSI spectra from a voxel in the brain and in the lateral ventricles of a patient with CFS, a patient with GAD, and a healthy volunteer. Note the presence of a clear lactate doublet peak at 1.33 ppm in the ventricular spectrum for the patient with CFS, which was not present in either the brain or CSF spectrum of the patient with GAD or the healthy volunteer. However, as no significant concentrations of N-acetylaspartate (NAA), total creatine/phosphocreatine and total choline-containing compounds are expected in CSF, detection of these metabolites in the ventricular spectrum (Fig. 2) indicates that our voxel size was not sufficiently small to avoid partial-volume averaging of ventricular signals and surrounding tissues. Therefore, our analysis of ventricular lactate data specifically controlled for potential differences in the proportion of ventricular volume contributing signal to the recorded spectra (vide infra). In the aggregate, the mean ventricular lactate concentration was higher in patients with CFS (0.856 p/m 0.47 i.u.) than in patients with GAD (0.289 p/m 0.337 i.u.; P<0.001) and healthy control subjects (0.246 p/m 0.206 i.u.; P<0.001). Lactate concentrations for the patients with GAD and healthy volunteers did not differ (P<0.94) (Fig. 3). Ventricular lactate and ventricular volume The mean ventricular volumes did not differ between the CFS (14 296 p/m 4003 mm^3), GAD (15 881 p/m 5625 mm^3), and healthy control (14 283 p/m 4128 mm^3) groups, with one-way analysis of variance revealing no main effect of diagnosis (F(2,46)=0.32, P=0.727). Across all participants, ventricular lactate did not correlate with ventricular volume (r=-0.059, P=0.700). Likewise, within the CFS cohort, there was no correlation between ventricular lactate and volume (r=0.233, P<0.385). As an internal measure of the validity of our volumetric analysis, age correlated strongly with ventricular volume (r=0.52, P<0.001) across all participants, consistent with the well-known association between cortical atrophy and age. Associations with demographic and clinical variables Ventricular lactate did not correlate with any continuous demographic variable examined, including age, BMI, years of education, or IQ. Within each diagnostic group, there were no significant correlations between lactate and fatigue severity, and there were no significant correlations between ventricular lactate and rating scale measures of depression, anxiety, or sleep quality (P>0.38 for all). Stepwise multiple linear regression analysis of all three groups using four variables (diagnosis, fatigue, depression, anxiety) as covariates revealed that only diagnosis was a significant predictor of ventricular lactate concentration (beta=0.776, adjusted R^2=0.435, P<0.001). Thus, diagnosis accounted for over 43% of the variance in ventricular lactate concentrations. Exploratory analyses of ventricular lactate It can be seen in Fig. 3 that a subgroup of patients with CFS did not show abnormal ventricular lactate concentrations. In exploratory post hoc analyses, these 'low lactate' patients (n=6) were compared with 'high lactate' patients (n=10), defined as having a lactate concentration >2 SDs above the healthy volunteer mean lactate concentration. The two CFS subgroups did not differ significantly in any demographic variable (mean age, BMI, IQ, age of illness onset, duration of illness, or sex distribution), clinical rating scale (anxiety, depression, fatigue, sleep quality), or rates of comorbid fibromyalgia. DISCUSSION Using ^1H MRSI, we found that patients with CFS had significantly higher ventricular lactate concentrations than healthy volunteers and patients with GAD. Diagnostic group was the only significant predictor of lactate concentrations, and the result was not driven by mood or anxiety symptoms, ventricular volume, or demographic variables. Although we found a highly significant group effect for ventricular lactate, a subgroup of patients with CFS did not have increased ventricular lactate, and lactate did not correlate significantly with fatigue severity or other clinical variables. These findings support neurobiological distinctions between CFS and a phenomenologically similar disorder, GAD, and suggest significant heterogeneity within patients who fulfill the case definition for CFS. Although further studies are required to establish the cause of the significant cross-sectional increase in ventricular lactate in patients with CFS, this reproducible observation is potentially consistent with an emerging theory of CFS (34,35), which implicates oxidative stress and its effects on cortical blood flow and/or mitochondrial function as contributory factors. A recent well-designed and carefully executed study (6) reported finding in CFS, compared with matched controls, significantly raised concentrations of 8-iso-prostaglandin-F_2a-isoprostanes, which are not only considered to be among the most reliable blood markers of oxidative stress, but are also known to have potent vasoconstrictor effects on cerebral arterioles (36). The presence of significant increases in isoprostanes in patients with CFS thus may explain the results of a number of studies, which found reduced absolute (5) and relative (37,38) cortical and subcortical blood flow in CFS. Insofar as cerebral hypoperfusion is known to increase brain lactate (39), raised CSF lactate in CFS is potentially consistent with a pathophysiological model in which by-products of oxidative stress ­ isoprostanes ­ with a potent vasoconstrictor effect on peripheral vasculature lead to decreased regional cerebral blood flow, with consequent increases in anaerobic glycolysis and brain lactate, the end product of glycolysis. The observation of increased blood concentrations of oxidative stress markers in CFS also raises the possibility that increased lactate in the disorder may be the result of a secondary mitochondrial dysfunction. Mitochondria, a rich source of reactive oxygen species, which are involved in oxidative stress, can themselves be damaged by accumulation of reactive oxygen species (40), leading to mitochondrial dysfunction and sub-cellular conditions that could stimulate glycolytic activity and increase lactate production. The possibility of an oxidative stress-induced mitochondrial dysfunction in CFS is suggested by the similarity in magnitude between the ventricular lactate increases in our current CFS cohort to those observed in our previous investigation of oligosymptomatic carriers of the A3243G mitochondrial DNA point mutation for the syndrome of mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) (29). However, a mitochondrial dysfunction in CFS, if present, would appear to be relatively mild, as, in fully symptomatic MELAS patients, lactate increases are detected not only in the lateral ventricle, but also extensively throughout the brain parenchyma (41-44). Furthermore, we did not observe a significant reduction in the mitochondrial metabolite, NAA, in CFS, which also seems to argue against a significant primary mitochondrial dysfunction in the disorder. A question that might be raised about our postulated effects of oxidative stress on lactate increases in CFS is why such increases are observed in ventricular bodies and not in brain parenchyma. There are a number of possibilities. First, examination of voxels in the brain parenchyma of patients with CFS did not show a lactate peak, whereas a peak was clearly detectable in the ventricular voxels in these patients (Fig. 2). Although our multislice ^1H MRSI protocol did not yield voxel sizes that were sufficiently small to completely eliminate contamination from extraventricular tissues, we found no significant differences in ventricular volume across all subjects in this study, indicating that partial-volume effects caused by differences in the relative fractions of CFS being sampled do not account for the observed increases in lactate in CFS. Another possibility is that CSF lactate might be more readily detectable than parenchymal lactate because of a combination of narrower resonance linewidths, longer T_2 relaxation times in CSF than in brain (42), and, possibly, greater lactate concentration in CSF than in parenchyma. Moreover, previous ^1H MRSI investigations of MELAS by our group (29) and others (41­44) have suggested that lactate, the intracerebral concentration of which is ~0.5 mmol/g under normal physiological conditions, is not detectable within the limits of the ^1H MRSI acquisition parameters used in this study. Therefore, these possibilities strongly suggest that our derived lactate values in CFS reflect measures of lactate that accumulated and concentrated in the bodies of the lateral ventricles. We should, however, note that this does not preclude the possibility that the measured CSF lactate is, in fact, parenchymal in origin and is transported to the ventricular bodies as part of the circulating CSF (45), where it becomes MRSI-visible. Although the patient groups were well matched in demographic and mood symptoms, they were not matched by fatigue severity. Recruitment of a control sample with a comparable degree of fatigue severity would be a critical next step to determine the diagnostic specificity of increased lactate (although regression analyses failed to show that fatigue severity by itself contributed to the main effect of diagnosis for lactate). The sample size of this study was limited, preventing adequately powered subgroup analyses. For example, it would be of interest to know if high lactate is equally represented in patients with CFS with predominantly infectious, rheumatological, or neuropsychiatric symptoms. Besides sample size considerations, the lack of significant correlation of lactate with FSS scores in the CFS group may be due to a ceiling effect for fatigue severity scales, which has probably hindered previous attempts to relate fatigue severity to brain imaging measures (12). Finally, the effects of previous treatment with antioxidants and nutritional supplements on ventricular lactate in CFS is unknown, and 48 h may not have been a sufficient medication-free interval for ruling out an effect on spectra. Importantly, no participant reported taking methylsulfonylmethane, an additive in many dietary supplements that has been demonstrated to be highly visible by MRS (46). The present study has shown an abnormal increase in ventricular lactate in CFS and suggests distinct neurochemical differences between CFS and a phenomenologically similar disorder, GAD. Increased oxidative stress is postulated as contributory, although large-scale longitudinal studies characterizing genetic, neuroimaging, and clinical features of CFS and appropriate comparison groups are necessary to adequately test this hypothesis Acknowledgements We thank Beena Alex, Lili Bernstein, Paul Nestadt, Ayesha Sattar, and Josefino Borja for their valuable contributions. We also thank Drs Steven Haker and Robert Mulkern of Harvard University for assistance in using the SLICER3D software package for co-registration of the volumetric MRI and the MRSI data. FIGURE CAPTIONS Figure 1. Procedure for determination of the ventricular volume fraction from which CSF lactate was measured by ^1H MRSI. (A) Co-registration of the four 15 mm MRSI slices with the segmented T_1-weighted volumetric MR images of the CSF. Note full co-registration of the two sets of images in the axial image (left panel). (B) Depiction of the ventricular volume of interest (colored structure) after manual tracing by an investigator blinded to diagnosis. Figure 2. T_1-weighted MR brain images showing (A) brain parenchyma and (B) the lateral ventricular voxels of interest, with corresponding ^1H spectra directly above the panels for a patient with CFS, a patient with GAD, and a medically healthy volunteer (HV). To achieve a smooth representation and drawing of the ventricular region of interest, the MRSI data matrix was zero-filled to 64x64 along the spatial domains, which effectively halved the voxel size without increasing or decreasing information content-current information is simply replicated in twice as many voxels. The ^1H spectra in (B) are from a single such voxel (see arrow) in the left horn of the lateral ventricular region of interest. Spectral resonances identified are for total choline-containing compounds (tCho at 3.24 ppm), total creatine/phosphocreatine (tCr at 3.03 ppm), NAA (at 2.02 ppm), and lactate (LAC at 1.33 ppm), which is clearly visible only in the spectrum for the patient with CFS. All the spectra are plotted using the same vertical-axis scale. Figure 3. Concentrations of lactate in ventricular cerebrospinal fluid as determined by ^1H MRSI and presented in institutional units (i.u.), as ratios of the lactate peak area to the root-mean square (rms) of background noise. TABLE Table 1. Clinical characteristics and demographics of the subjects ------------------------------------------------------------------------------------------- Characteristic GAD (n=14) CFS (n=16) Healthy volunteers (n=15) ------------------------------------------------------------------------------------------- Age (years) 37.9 p/m 14.2 37.6 p/m 9.9 35.3 p/m 10.3 Female 10 (71) 11 (69) 9 (60) Body mass index 25.3 p/m 6.8 24.3 p/m 5.3 25.9 p/m 4.3 Right-handed 11 (79) 13 (81)^a 14 (93) Full-scale IQ 109.3 p/m 6.7^b 112.2 p/m 7.4^b 114.7 p/m 8.2 Education (years) 16.6 p/m 3.1 17.6 p/m 3.3 15.8 p/m 1.7 Psychotropic drug-naive 9 (64) 3 (19) 15 (100) Age at onset of illness (years) 25.3 p/m 13.2 32.2 p/m 8.4 N/A Duration of illness (years) 12.3 p/m 14.7 5.7 p/m 5.0 N/A Mood disorders 4 (29) 1 (6) 0 Anxiety disorders 5 (36) 2 (12) 0 Fibromyalgia 0 8 (50) 0 Clinical rating scales FSS 4.4 p/m 1.2 6.2 p/m 0.9 2.0 p/m 0.6 Range (possible range 1-7) 1.8-6.6 4.3-7 1.3-3 HAM-A 20.1 p/m 5.5 15.4 p/m 6.3 2.0 p/m 1.9 Range (possible range 0-56) 12-29 7-28 0-6 PSWQ 61.9 p/m 11.4 40.1 p/m 14.4^a 30.9 p/m 9.7 Hamilton Rating Scale for 14.1 p/m 4.0 12.3 p/m 5.2 2.0 p/m 1.9 Depression - 17 item Range (possible range 0-50) 7-19 6-22 0-7 Pittsburgh Sleep Quality Index 8.3 p/m 2.3^b 8.63 p/m 4.1 3.3 p/m 1.8 ------------------------------------------------------------------------------------------- Values are mean p/m SD or number (%). ^a n=15. ^b n=13. 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