Search tips
Search criteria 


Logo of amjtrLink to Publisher's site
Am J Transl Res. 2013; 5(2): 212–223.
Published online 2013 March 28.
PMCID: PMC3612516

Prefrontal lactate predicts exercise-induced cognitive dysfunction in Gulf War Illness


Background: 25% to 30% of Veterans deployed to the 1990 to 1991 Persian Gulf War exhibit an idiopathic syndrome of chronic fatigue, exertional exhaustion, pain, hyperalgesia, cognitive and affective dysfunction known as Gulf War Illness (GWI). Methods: Gulf War veterans (n=15) and sedentary veteran and civilian controls (n=11) completed a 2-back working memory test in an fMRI before and after two bicycle exercise stress test. We performed single voxel 1H MRS to evaluate brain metabolic differences in the left anterior cingulate cortex and the changes associated with exercise. Results: Eight GWI subjects increased their 2-back scores after exercise (labelled increasers) and seven GWI subjects decreased their 2-back scores after exercise (labelled decreasers). These phenotypic responses were absent for controls. Decreasers had significantly elevated prefrontal lactate levels compared to Increasers prior to completion of the exercise stress tests. Evaluation of prefrontal lactate levels prior to exercise demonstrated predictability (ROC analysis) of the two diametrically opposed subgroups. Conclusion: Prefrontal lactate levels may be a potential biomarker for exercise-induced subgroups in GWI. The alterations in brain energetics may be in part responsible for a subgroup of GWI and underlie some of the symptoms present in the patient population.

Keywords: Gulf War Illness (GWI), pre-frontal lactate, exercise-induced cognitive dysfunction, biomarker


Gulf War Illness (GWI), also known as Chronic Multi-symptom Illness (CMI), affects approximately a quarter of the one million military personnel who served in the 1990 - 1991 Persian Gulf War [1,2]. Veterans with GWI present with symptoms of cognitive dysfunction, chronic fatigue, and widespread pain that overlap with a larger group of idiopathic illnesses that include Chronic Fatigue Syndrome (CFS) [3], Myalgic encephalomyelitis [4] and fibromyalgia [5-8].

A common complaint is exertional exhaustion [1]. Physiological stressors such as exercise at greater than usual levels leads to an exacerbation of symptoms that may develop immediately or be characteristically delayed as long as 24 hours [2,4]. Experimental exercise models demonstrate acute symptom exacerbations in these disorders [9-11]. In animal models, exercise-induced pain, hyperalgesia and fatigue were not attributed to lactate build-up in peripheral muscles [12]. Instead, up - regulation of expression for the immediate early gene c-fos in brainstem nuclei and dysfunctional brain energetics suggested that central mechanisms may be responsible for exercise-induced changes in muscular performance, decreased activity (“fatigue”), and hyperalgesia (pain and tenderness) [13]. These studies suggest the hypothesis that differences in brain energetics may be associated to the heterogeneity of exercise-induced symptom exacerbations in GWI.

Glucose has traditionally been considered the principal energy source for the brain [14]. However, recent findings in brain energetics suggest that lactate is an alternative fuel under conditions of high neural demand for energy metabolism during cognition and memory acquisition [15,16]. Cerebral lactate metabolism is maintained by an interaction between astrocytes and neurons known as the astrocyte - neuron lactate shuttle [14]. Astrocytes generate and release lactate into the brain interstitial fluid. Neurons import the lactate and convert it to pyruvate for use in mitochondrial oxidative phosphorylation [14]. Lactate is a more efficient starting point for oxidative respiration than glucose since the neuron has a net gain of one ATP molecule, and the glucose can be reserved for skeletal muscle ATP production [17,18].

Lactate derived from exercising muscles is another energy source for neurons [17]. Proton – coupled monocarboxylate transporters (MCT) shuttle lactate across the blood - brain barrier and through astrocytes to neurons and other cells. Under normal conditions, astrocytes supply lactate by glycolysis followed by lactate export, and during muscular exercise by absorption of lactate from across the blood – brain barrier into the neuropil [14,17]. The flux is efficient so importation matches neural energy needs and brain lactate levels do not rise [19]. In contrast, elevated brain lactate has been associated with disrupted metabolite homeostasis, mitochondrial failure, and neuronal dysfunction in conditions such as aging [20].

Eighty percent of the energy consumed by the brain is for neuronal activity, with glutamate as the predominant neurotransmitter which is known to play a vital role in memory function [19,21]. Astrocytes play a critical role in glutamatergic neuronal activity. They ensheath synapses and rapidly absorb the glutamate that is released [14]. The glutamate is converted to glutamine that is then exported to neurons for conversion back into glutamate. This pathway is the glutamate - glutamine cycle. The molecular spectroscopy ratio of glutamate to glutamine is a measure of astrocyte function [22]. High glutamate turnover in astrocytes triggers glycolysis and the production of lactate that is shuttled to neurons. These processes link the glutamate – glutamine cycle and lactate shuttle in astrocytes. Astrocyte dysfunction leads to impaired synaptic glutamate clearance with inappropriately high synaptic glutamate concentrations and dysregulated neuronal activity [23]. There is no information on either of these indices in GWI, or their responses to exercise.

Lactate in the cerebral ventricles of CFS subjects is significantly higher than anxiety, major depression, and healthy volunteers [24-26]. Our own receiver operator analysis of the published data suggested an upper threshold of 0.5 institutional units for lactate for a combined anxiety and healthy volunteer group with sensitivity of 0.65 and specificity of 0.88 to discriminate from CFS values [26]. Frequency analysis of the CFS lactate distribution data demonstrated a wide range of levels suggesting a continuum or potential bimodal distribution. These authors did not find differences in lactate for brain parenchyma at rest between CFS and controls. There is no information on lactate after exercise.

Based on these data and the subjective overlap of GWI and CFS subjects, we predicted that GWI subjects would also show heterogeneous lactate levels. Molecular spectroscopy evidence of heterogeneous astrocyte - neuron lactate shuttle and glutamate – glutamine cycle dynamics were also anticipated to possibly show distinctions before and after exercise. These studies are of importance since the relationships between exercise - induced changes in brain metabolites, energetics and cognitive function have not been studied in GWI. Furthermore, biomarkers defined by molecular spectroscopy may be valuable for defining subsets, pathophysiological mechanisms, and responses to treatments. In order to address these issues, functional magnetic resonance imaging (fMRI) scans were performed at baseline prior to, and immediately following, two bicycle exercise stress tests. The second fMRI scan demonstrated post - exertional changes in the brain.

Cognition was tested during each fMRI scan using the letter variant 2-back working memory paradigm [27]. Working memory is a system that actively retains multiple fragments of information during goal directed behavior that is used at later moments in cognitive operations [28]. The cognitive testing revealed 2 GWI subsets that had diametrically opposite working memory responses to exercise and may represent functional phenotypes.

Metabolite concentrations were measured by single voxel 1H magnetic resonance spectroscopy (MRS) in the left anterior cingulate cortex. This region is dysfunctional in prolonged pain, fatigue, and affective disorders [29,30]. The level of lactate in this region before and after exercise was the primary outcome planned for this study.

Materials and methods

Subjects and recruitment

The protocol was approved by Georgetown University Institutional Review Board and USAMRMC Human Research Protection Office (HRPO #A-15547.0) ( identification number NCT01291758). The participant pool composed of 11 age and gender matched controls and 15 CMI subjects who also met CFS criteria (Georgetown University IRB #2009-229). All subjects signed informed consent, completed questionnaires, and physical examinations to validate CMI and CFS diagnosis. On – line questionnaires assessed an extensive set of psychometric qualities to investigate the distinctions between CMI, CFS and control subjects [31]. The current analysis is focused on pre – and post – exercise working memory accuracy scores and metabolite concentrations for subjects who met both CMI and CFS criteria.

Pre-arrival instructions and questionnaires

Fatigue was assessed with the ordinal fatigue rating and verified with the Chalder fatigue scale [32,33]. The ordinal fatigue assessment was anchored with 0 = no complaint, 1 = trivial, 2 = mild, 3 = moderate or 4 = severe intensity [32,34]. Inclusion of “trivial” allowed participants to verify complaints that were present but not bothersome enough to warrant treatment and/or other lifestyle changes [35]. Subjective pain perceptions were quantified with the McGill short form with its sensory, affective and total scores [36]. Relative disability and quality of life were compared using the Medical Outcomes Survey Short Form 36 (SF-36) [37].


Upon arrival to the Georgetown University Clinical Research Unit, subjects reviewed and signed their informed consent forms to participate in this four day protocol. On the 1st day, all subjects had history and physical examinations to assess CMI [2] and CFS [3] criteria, blood tests, and had a tour of the facilities. On the 2nd day subjects completed a pre-exercise (baseline) fMRI scan prior to the 1st bicycle exercise stress test. On the 3rd day, subjects completed their second, identical bicycle exercise stress test followed by a post-exercise fMRI and MRS scan, which was on average within 1 hour of the stress test. The 4th day was their discharge day. The neuroimaging analysis included 1H MRS, functional MRI (fMRI) during the working memory paradigm, structural/anatomic acquisition, and white matter diffusion tensor imaging. This study focuses on 1H MRS spectroscopy data and other fMRI data will be reported separately.

To assess changes in cognition, we used the working memory N-back paradigm during both fMRI scans. Prior to both fMRI scans, subjects completed N-back practice sessions on a computer. Blocks of 9 randomized letters (A, B, C, D) were presented. Subjects were given instructions to respond by pressing a button for the same letter (“0-Back”) or the one seen 2 letters previously (“2-Back”). Blocks for 0-back then 2-back tasks were presented for 5 cycles. A total score of 35 letters correct were possible during the 2-back working memory cognitive paradigm.

Bicycle stress tests

Two protocols were evaluated. First, standard VO2MAX cardiopulmonary stress tests were performed using Vmax equipment and software (SensorMedix) [38,39]. Second, subjects had submaximal stress tests at 70% predicted heart rate for 25 minutes which was followed by a brief climb to 85% heart rate to standardize exertion on a Schwinn AirDyne bicycle using the Vmax software and equipment (SensorMedix) [40]. Cardiac stress tests were negative and ruled out arrhythmias, ischemias, or other alterations. Outcomes related to the stress tests will be described in greater detail elsewhere.

Functional magnetic resonance imaging (fMRI) and MRI spectroscopy (MRS)

Data was acquired on a Siemens 3T Tim Trio scanner equipped using a transmit-receive body coil and commercial twelve-element head coil array. Structural 3D T1-weighted MPRAGE images parameters were: TE= 2.52 ms, TR= 1900 ms, TI= 900 ms, FOV= 250 mm, 176 slices, slice resolution= 1.0 mm, voxel size 1x1x1 mm. All MPRAGE images were processed using the Statistical Parametric Mapping software package (SPM8, Wellcome Department of Imaging Neuroscience, London, UK), which was run on MATLAB 10b (The MathWorks Inc., Natick, MA, USA).

Single voxel spectroscopy was used to assess metabolite concentrations. A voxel of 2 x 2 x 2 cm3 on edge (8.0 ml) was placed over the left anterior cingulate cortex (ACC) (Figure 1). Single voxel spectroscopy of 1H MRS uses the volume localized STEAM sequence with echo times (TE) of 20ms–144ms, repetition time (TR) 2000ms, modulation time TM 10ms, 300 transients, spectral width of 3 kHz, 1k complex data points. Automatic outer volume suppression (7 min scan) eliminated lipid and water signals.

Figure 1
Voxel placement and 2-back working memory scores. A. A single voxel was placed in the left anterior cingulate cortex to obtain metabolite concentrations. B. Before exercise, controls had significantly higher accuracies compared to CMI subjects (*P<0.001, ...

Metabolite concentrations of the in vivo spectrum were acquired using “LCModel” (Provencher Inc., Oakville, Canada) [41]. The method is fully automated excluding subjective inputs, referencing, and initial estimates. The in vivo spectrum is modelled as a linear combination of lined broadened individual metabolite spectra. A high-resolution anatomic scan was used for graphical positioning of the MRS voxels (Figure 1), which was also used for partial volume correction by determining the tissue composition and as previously described [42].

The voxels from the different locations were transformed onto the VBM (GM, WM, and CSF) maps to obtain the voxel tissue content. Reliability of the metabolite concentrations were based on Cramer-Rao lower bounds (CRB) from the Cramer-Rao inequality given as percent standard deviation of the brain parenchyma which was implemented for voxel CSF content correction [43]. The Cramer-Rao lower bounds for glutamate and glutamine were from 7% to 44% standard deviation for CMI subjects. The Cramer-Rao lower bounds for lactate were from 19% to 50% for all CMI subjects. One CMI subject had a lactate concentration level of 0 with a standard deviation of 999% and highlighted as a yellow triangle (Figure 2A). Even though this is undoubtedly a measurement error, this data point was included to minimize attrition given the small N for these analyses.

Figure 2
Exercise – induced changes in prefrontal lactate for GWI subgroups. A. Absolute values for lactate levels are shown for pre-exercise Day 1 (D1) and post-exercise Day 2 (D2) for Increasers (blue triangles) and Decreasers (green diamonds). Baseline ...

Statistical analysis

SPSS for Windows version 20 (Armonk, New York) and Microsoft Excel 2007 (Redmond, Washington) were used for database and statistical analysis. 2-back working memory scores, metabolite concentrations, pain and fatigue ratings were compiled and reported as means with [95% confidence intervals]. Significant differences between groups (P ≤ 0.05) were identified by two-tailed unpaired Student’s t-tests or Fisher’s exact tests. P values were not corrected for multiple comparisons due to the exploratory nature of this analysis. Metabolite concentrations were evaluated by receiver – operator curve (ROC) analysis as predictive measures with sensitivity, specificity, area under the curve (AUC), and asymptotic significance.

The differences in 2-back accuracy between pre – and post – exercise fMRI scans were determined for each subject. Visual inspection followed by receiver operator analysis identified two groups. (i) “Increasers” had higher accuracies with Day 2 minus Day 1 Δ ≥ 4 out of 35 letter responses after exercise. (ii) “Decreasers” had lower or essentially no change (“floor effect”) in accuracy with Δ ≤ 2 out of 35 responses. Hence, Increasers significantly improved their cognitive function following exercise, while Decreasers did significantly worse after exercise. These findings and correlations with blood oxygen dependent level (BOLD) findings are being reported in detail elsewhere.

Lactate, glutamine, and glutamate concentrations were correlated with 2-back working memory scores in univariate fashion using two - tailed Pearson’s function.


Demographics and behavioral data

Fifteen veterans who met the 1998 CDC criteria for CMI [2] and eleven controls (age and gender matched) were recruited. There were no significant differences in demographics between groups (Table 1). Similar to previous reports of increased prevalence, all veterans (CMI subjects) also met CFS criteria [4,7]. CMI subjects had significantly higher pain and fatigue ratings and lower SF-36 quality of life scores than controls (P<0.0001; Table 1).

Table 1
Demographic and behavioral parameters

Working memory accuracies before and after exercise

Before exercise, CMI subjects had significantly lower accuracy than controls (P<0.001, 2-tailed unpaired t-test) (Table 1, Figure 1B). Exercise – induced changes in accuracy defined the CMI subgroups. Increasers raised their accuracy on average by 29.6% (n=8, P = 0.000018, 2-tailed paired t-test) whereas Decreasers on average had a drop of 24.5% (n=8, P= 0.087) (Figure 1B).

Lactate concentrations and accuracies

At baseline, prefrontal lactate was significantly higher in Decreasers (0.98 [0.71 to 1.25]) than Increasers (0.54 [0.44 to 0.643]; P<0.008, 2-tailed unpaired t-test) (Figure 2A). Receiver operator analysis demonstrated that pre – exercise lactate levels could predict Increaser and Decreaser status prior to the completion of the exercise stress tests (AUC=0.920, sensitivity= 71.4%, specificity=87.5%; P=0.007, asymptotic significance; Figure 2B).

The presence of distinct CMI subgroups was confirmed by the exercise – induced increase in prefrontal lactate in the Increaser subgroup (P=0.021, paired t-test; Figure 2A). In contrast, Decreasers had no significant change (P=0.19, paired t-test; Figure 2A). Prior to exercise, lactate levels were significantly correlated with working memory scores in Increasers (R2=0.50; Figure 3A) but not Decreasers (R2=0.04; Figure 3B). The same relationship was found after exercise for Increasers (R2=0.28; Figure 3C) but not Decreasers (R2=0.05; Figure 3D).

Figure 3
Pre-frontal lactate levels and correlation with working memory scores. A. Prior to exercise, prefrontal lactate concentration correlated with increasers but not (B) decreasers 2-back WM scores. C. After exercise, prefrontal lactate levels correlated with ...

Prefrontal glutamine and glutamate

Prefrontal glutamine, glutamate, and glutamine/glutamate ratio were not significantly different between Increasers and Decreasers prior to exercise (Figure 4A), and did not correlate with working memory scores, pain, or fatigue indices. The glutamine/glutamate ratio was marginally higher in Increasers compared to Decreasers after exercise (P =0.062, unpaired t-test; Figure 4B) driven primarily by changes in glutamine concentration post-exercise relative to pre-exercise such that there was an rise in the Increasers and a slight drop in the Decreasers.

Figure 4
Glutamine, Glutamate, Gln/Glu, and Receiver Operator Curve for Prefrontal Lactate Levels at baseline. A. Prior to exercise there was no significant difference between increasers (blue bars) and decreasers (green bars) for Glutamine (Gln), Glutamate (Glu) ...


The primary observation of this study was that baseline prefrontal lactate levels predicted future exercise - induced cognitive responses in CMI subjects. These concentration differences were observed before the two exercise stress tests when behavioral measures and 2-back task accuracies were equivalent between CMI subgroups. Prefrontal lactate levels and accuracies were significantly correlated for the Increaser subgroup both before and after exercise. No such relationship between brain lactate and cognition was found for Decreasers.

This represents an important advance by using objective findings to delineate functional subtypes. Exercise proved to be a critical stressor that perturbed the baseline state of CMI compensatory adaptations to more clearly reveal distinctions from controls as well as by Increaser and Decreaser cognitive – prefrontal lactate status. These CMI subjects also met CFS criteria, so future studies will be needed to confirm and expand our conclusions for the entire population of Persian Gulf War veterans, and to identify pathophysiological similarities with CFS.

The comparison of pre – exercise to post – exercise scans provided insights into brain energetics in CMI subgroups. At baseline, normal physiological activity in the adult brain oxidizes glucose [13,44]. However, working memory constitutes a cognitively demanding state where glutamatergic afferents to the prefrontal cortex underlie efficient activity [45-48]. During increased neural synaptic activity, lactate is the preferred energy source even in the presence of glucose [49]. Mono-carboxylate transporters (MCT) are responsible for shuttling lactate in the brain. MCT-2 is the predominate neuronal lactate transporter. MCT-1 and MCT-4 are the primary astrocytic lactate transporters [50]. The relationship between lactate and working memory scores in Increasers was consistent with preferential lactate metabolism by neurons during highly demanding cognitive situations [14,15].

In contrast, the lack of correlation between working memory scores and the persistently elevated prefrontal lactate levels of Decreasers implied an inability to import or utilize lactate by neurons. MCT-2 transporter dysfunction is one potential explanation. Murine models show that blocking neuronal MCT-2 leads to an increase in glucose metabolism and dysfunction of the proposed astrocyte – neuron lactate shuttle [51]. Extrapolation of our data would suggest that cerebral glucose may have played a compensatory metabolic role in Decreasers so that their baseline 2-back accuracies were comparable to Increasers.

Mitochondrial dysfunction provides an alternative hypothesis to explain the significantly elevated prefrontal lactate levels before and after exercise in Decreasers. Loss of oxidative capabilities in neurons and astrocytes may cause a shift to glycolysis for ATP generation [20]. Lactate dehydrogenase (LDH) is responsible for the catalytic conversions between pyruvate and lactate. The isoform LDH-A is responsible for converting pyruvate to lactate whereas isoform LDH-B is responsible for converting lactate to pyruvate [13]. Mitochondrial dysfunction leads to increased transcriptional activity with higher relative levels of LDH-A [20]. This facilitates the regeneration of NAD+ substrates that can be reused in glycolysis to maintain ATP generation. This compensatory dependence on glycolysis may explain the increased lactate levels in Decreasers.

Exercise causes a multitude of changes in brain, muscle, liver and other organs [52]. During vigorous exercise, skeletal muscles dramatically increase glucose uptake in order to generate ATP for sustained contractions [52]. This depletes plasma glucose availability leading to a 32% decrease in brain glucose uptake [53]. In the normal brain, the lower glucose availability as a fuel is met by compensatory replacement with lactate [54].

Our results suggest Increasers were able to metabolically shift to the use of lactate as an energy source leading to an increase in the availability of glutamate, a key transmitter for memory function[19] leading to an increase in 2-back accuracy after exercise. However, Decreasers were not able to utilize lactate in the post-exercise setting when glucose would be preferentially sequestered to muscle. Inability to use lactate may have required decreasers to rely more heavily on glycolytic metabolism. This compensatory reliance on glucose combined with the fact that exercise is known to cause depleted plasma glucose levels [53] may have accounted for their neuronal dysfunction, inefficient working memory, and observed drop in their 2-back scores after exercise.

Vigorous exercise can increase brain lactate, glutamate and glutamine in healthy humans [55]. Glutamate is converted to glutamine in astrocytes. The glutamine/glutamate ratio may indirectly measure astrocyte function as the astrocyte is the only cell capable of transforming glutamate to glutamine [22]. Increases in glutamine and/or glutamate concentration are associated with active memory formation and subsequent consolidation to long term memory [56]. Although speculative, the trend towards elevated glutamine/glutamate ratios in Increasers but not Decreasers after exercise is suggestive of astrocyte dysfunction and inefficient glutamate-glutamine cycling in Decreasers [57]. An inability of astrocytes to uptake and recycle synaptically - released glutamate may have hindered synaptic activity and contributed to the lower 2-back scores of Decreasers after exercise.

Myo-inositol, choline, γ-aminobutyric acid, N-acetylaspartate, creatine and other analytes were not different between Increasers, Decreasers and controls either before or after exercise. This could indicate that their metabolism is not altered in CMI, or that molecular spectroscopy is not satisfactory to detect dynamic changes in their turnover during cognition.

The main limitation of this study was the small sample sizes in CMI and controls. Only 3 control subjects had post – exercise molecular spectroscopy over the prefrontal region because of a change in voxel placement (results to be reported elsewhere). Prefrontal metabolite concentrations should be cautiously interpretive as the spectroscopic voxels contains varying amounts of gray matter, white matter and cerebrospinal fluid. Statistics were not corrected for multiple comparisons due to the pilot nature of the study. No comparisons were made to civilians with CFS alone, or to anxiety, major affective disorder, or other conditions. Future studies should incorporate larger CMI sample sizes and multiple voxels in regions associated with pain and fatigue to assess the global changes in metabolite concentrations.


In this pilot study, we compared working memory scores and prefrontal metabolite concentrations via H1 MRS spectroscopy to assess underlying pathophysiological processes of CMI criteria and exertional exhaustion. To our knowledge, this is the first controlled study to compare the causal relationship between exercise induced changes in prefrontal metabolite concentrations in subgroups with Gulf War Illness.

Our primary finding is a proportion of subjects had elevated prefrontal lactate that was predictive of exercise induced cognitive dysfunction. Lactate levels significantly correlated with scores in the Increaser but not Decreaser subgroup both before and after exercise suggesting dysfunctional neuronal-astrocyte relationships in decreasers. We suggest that alterations in brain energetics may be in part responsible for a subgroup of GWI and underlie some of the symptoms present in the patient population.

Results may also provide broader support for pathophysiological processes underlying overlapping syndromes such as CFS and fibromyalgia. Future studies should validate our protocol with larger samples to confirm the exploratory data. Our findings may indicate ways to identify subgroups and metabolites that could serve as targets for therapeutic strategies for the treatment of GWI and other idiopathic “functional” syndromes.


Author contributions: JNB and JWV developed the protocol. RUR, JNB, CRT, OA carried out the protocol, recruited subjects, did the statistical and clinical analysis. MPR carried out fMRI scans, processed and analyzed spectroscopy values. RUR and JNB wrote the manuscript. All authors were involved in manuscript preparation and approved the final manuscript. Funding: Support was provided by Department of Defense Congressionally Directed Medical Research Program (CDMRP) award W81-XWH-09-1-0526. This project has been funded in whole or in part with Federal funds (Grant # UL1TR000101, previously UL1RR031975) from the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), through the Clinical and Translational Science Awards Program (CTSA), a trademark of DHHS, part of the Roadmap Initiative, “Re-Engineering the Clinical Research Enterprise”.

Competing interests

The authors declare they have no competing interests.


1. RAC-GWVI (Research Advisory Committee on Gulf War Veterans’ Illnesses) Gulf War Illness and the Health of Gulf War Veterans (U.S.) Washington, DC: Government Printing Office; 2008.
2. Fukuda K, Nisenbaum R, Stewart G, Thompson WW, Robin L, Washko RM, Noah DL, Barrett DH, Randall B, Herwaldt BL, Mawle AC, Reeves WC. Chronic multisymptom illness affecting air force veterans of the gulf war. JAMA. 1998;280:981–988. [PubMed]
3. Fukuda K, Straus SE, Hickie I, Sharpe MC, Dobbins JG, Komaroff A. The chronic fatigue syndrome: A comprehensive approach to its definition and study. International chronic fatigue syndrome study group. Ann Intern Med. 1994;121:953–959. [PubMed]
4. Carruthers BM, van de Sande MI, De Meirleir KL, Klimas NG, Broderick G, Mitchell T, Staines D, Powles AC, Speight N, Vallings R, Bateman L, Baumgarten-Austrheim B, Bell DS, Carlo-Stella N, Chia J, Darragh A, Jo D, Lewis D, Light AR, Marshall-Gradisbik S, Mena I, Mikovits JA, Miwa K, Murovska M, Pall ML, Stevens S. Myalgic Encephalomyelitis: International Consensus Criteria. J Intern Med. 2011;4:327–338. [PMC free article] [PubMed]
5. Wolfe F, Smythe HA, Yunus MB, Bennett RM, Bombardier C, Goldenberg DL, Tugwell P, Campbell SM, Abeles M, Clark P, et al. The American College of Rheumatology 1990 Criteria for the Classification of Fibromyalgia. Report of the Multicenter Criteria Committee. Arthritis Rheum. 1990;2:160–172. [PubMed]
6. Eisen SA, Kang HK, Murphy FM, Blanchard MS, Reda DJ, Henderson WG, Toomey R, Jackson LW, Alpern R, Parks BJ, Klimas N, Hall C, Pak HS, Hunter J, Karlinsky J, Battistone MJ, Lyons MJ. Gulf War Study Participating Investigators. Gulf war veterans’ health: Medical evaluation of a U.S. cohort. Ann Intern Med. 2005;142:881–890. [PubMed]
7. Baraniuk JN, Zheng Y. Relationships among rhinitis, fibromyalgia, and chronic fatigue. Allergy Asthma Proc. 2010;31:169–178. [PubMed]
8. VanNess JM, Stevens SR, Bateman L, Stiles L, Snell CR. Postexertional malaise in women with chronic fatigue syndrome. J Womens Health (Larchmt) 2010;19:239–244. [PubMed]
9. Kosek E, Ekholm J, Hansson P. Modulation of pressure pain thresholds during and following isometric contraction in patients with fibromyalgia and in healthy controls. Pain. 1996;64:415–423. [PubMed]
10. Cook DB, Stegner AJ, Ellingson LD. Exercise alters pain sensitivity in gulf war veterans with chronic musculoskeletal pain. J Pain. 2010;11:764–772. [PubMed]
11. Yokoyama T, Lisi TL, Moore SA, Sluka KA. Muscle fatigue increses the probability of developing hyperalgesia in mice. J Pain. 2007;8:692–699. [PMC free article] [PubMed]
12. Sluka KA, Rasmussen LA. Fatiguing exercise enhances hyperalgesia to muscle inflammation. Pain. 2010;2:188–197. [PMC free article] [PubMed]
13. Pellerin L, Magistretti PJ. Sweet sixteen for ANLS. J Cereb Blood Flow Metab. 2012;7:1152–1166. [PMC free article] [PubMed]
14. Newman LA, Korol DL, Gold PE. Lactate produced by glycogenolysis in astrocytes regulates memory processing. PLoS One. 2011;12:e28427. [PMC free article] [PubMed]
15. Suzuki A, Stern SA, Bozdagi O, Huntley GW, Walker RH, Magistretti PJ, Alberini CM. Astrocyte-neuron lactate transport is required for long-term memory formation. Cell. 2011;5:810–823. [PMC free article] [PubMed]
16. Quistorff B, Secher NH, Van Lieshout JJ. Lactate fuels the human brain during exercise. FASEB J. 2008;10:3443–3449. [PubMed]
17. Magistretti PJ, Pellerin L. Metabolic coupling during activation A cellular view. Adv Exp Med Biol. 1997;413:161–166. [PubMed]
18. Dalsgaard MK, Quistorff B, Danielsen ER, Selmer C, Vogelsang T, Secher NH. A reduced cerebral metabolic ratio in exercise reflects metabolism and not accumulation of lactate within the human brain. J Physiol. 2004;554:571–578. [PubMed]
19. Newcomer JW, Farber NB, Olney JW. NMDA receptor function, memory, and brain aging. Dialogues Clin Neurosci. 2000;3:219–232. [PMC free article] [PubMed]
20. Ross JM, Öberg J, Brené S, Coppotelli G, Terzioglu M, Pernold K, Goiny M, Sitnikov R, Kehr J, Trifunovic A, Larsson NG, Hoffer BJ, Olson L. High brain lactate is a hallmark of aging and caused by a shift in the lactate dehydrogenase A/B ratio. Proc Natl Acad Sci. 2010;46:20087–20092. [PubMed]
21. Herculano-Houzel S. Scaling of brain metabolism with a fixed energy budget per neuron: implications for neuronal activity, plasticity and evolution. PLoS One. 2011;6:e17514. [PMC free article] [PubMed]
22. Sibson NR, Dhankar A, Mason GF, Behar KL, Rothman DL, Shulman RG. In vivo C NMD measurements of cerebral glutamine synthesis as evidence for glutamate-glutamine cycling. Proc Natl Acad Sci. 1997;94:2699–2704. [PubMed]
23. Okabe Y, Takahashi T, Mitsumasu C, Kosai K, Tanaka E, Matsuishi T. Alterations of gene expression and glutamate clearance in astrocytes derived from an MeCP2-null mouse model of Rett syndrome. PLoS One. 2012;7:e35354. [PMC free article] [PubMed]
24. Shungu DC, Weiduschat N, Murrough JW, Mao X, Pillemer S, Dyke JP, Medow MS, Natelson BH, Stewart JM, Mathew SJ. Increased ventricular lactate in chronic fatigue syndrome III Relationships to cortical glutathione and clinical symptoms implicate oxidative stress in disorder pathophysiology. NMR Biomed. 2012;9:1073–1087. [PubMed]
25. Murrough JW, Mao X, Collins KA, Kelly C, Andrade G, Nestadt P, Levine SM, Mathew SJ, Shungu DC. Increased ventricular lactate in chronic fatigue syndrome measured by 1H MRS imaging at 30 T II: comparison with major depressive disorder. NMR Biomed. 2010;6:643–650. [PubMed]
26. Mathew SJ, Mao X, Keegan KA, Levine SM, Smith EL, Heier LA, Otcheretko V, Coplan JD, Shungu DC. Ventricular cerebrospinal fluid lactate is increased in chronic fatigue syndrome compared with generalized anxiety disorder: an in vivo 30 T (1)H MRS imaging study. NMR Biomed. 2009;3:251–258. [PubMed]
27. Owen AM, McMillan KM, Laird AR, Bullmore E. N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum Brain Mapp. 2005;1:46–59. [PubMed]
28. Baddeley A. Working memory. Science. 1992;5044:556–559. [PubMed]
29. Leite-Almeida H, Cerqueira JJ, Wei H, Ribeiro-Costa N, Anjos-Martins H, Sousa N, Pertovaara A, Almeida A. Differential effects of left/right neuropathy on rats’ anxiety and cognitive behavior. Pain. 2012;11:2218–2225. [PubMed]
30. Hermens DF, Lagopoulos J, Naismith SL, Tobias-Webb J, Hickie IB. Distinct neurometabolic profiles are evident in the anterior cingulate of young people with major psychiatric disorders. Transl Psychiatry. 2012;2:e110. [PMC free article] [PubMed]
31. [Accessed Jan 10 2013].
32. Ravindran MK, Zheng Y, Timbol C, Merck SJ, Baraniuk JN. Migraine headaches in chronic fatigue syndrome (CFS): comparison of two prospective cross-sectional studies. BMC Neurol. 2011;11:30. [PMC free article] [PubMed]
33. Chalder T, Berelowitz G, Pawlikowska T, Watts L, Wessely S, Wright D, Wallace EP. Development of a fatigue scale. J Psychosom Res. 1993;2:147–153. [PubMed]
34. Baraniuk JN, Clauw JD, Gaumond E. Rhinitis symptoms in chronic fatigue syndrome. Ann Allergy Asthma Immunol. 1998;81:359–365. [PubMed]
35. Wasserfallen JB, Gold K, Schulman KA, Milzman D, Baraniuk JN. Development and validation of a rhinoconjunctivitis and asthma symptom score for use as an outcome measure in clinical trials. J Allergy Clin Immunol. 1997;100:16–22. [PubMed]
36. Melzack R. The short-form McGill pain questionnaire. Pain. 1987;30:191–197. [PubMed]
37. Ware JE, Sherbourne CD. The MOS 36-item short-form health survey (SF-36) I. Conceptual framework and item selection. Medical Care. 1995;30:473–483. [PubMed]
39. American Thoracic Society; American College of Chest Physicians. ATS/ACCP Statement on cardiopulmonary exercise testing. Am J Respir Crit Care Med. 2003;167:211–277. [PubMed]
40. Light AR, White AT, Hughen RW, Light KC. Moderate exercise increases expression for sensory, adrenergic, and immune genes in chronic fatigue syndrome patients but not in normal subjects. J Pain. 2009;10:1099–1112. [PMC free article] [PubMed]
41. Provencher SW. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn Reson Med. 1993;30:672–679. [PubMed]
42. Gropman AL, Seltzer RR, Yudkoff M, Sawyer A, VanMeter J, Fricke ST. H MRS allows brain phenotype differentiation in sisters with late onset ornithine transcarbamylase deficiency (OTCD) and discordant clinical presentations. Mol Genet Metab. 2008;94:52–60. [PMC free article] [PubMed]
43. Cavassila S, Deval S, Huegen C, van Ormondt D, Graveron-Demilly D. Creamer-Rao bounds: an evaluation tool for quantitation. NMR Biomed. 2001;14:278–283. [PubMed]
44. Dalsgaard MK, Ide K, Cai Y, Quistorff B, Secher NH. The intent to exercise influences the cerebral 02/carbohydrate uptake ratio in humans. J Physiol. 2002;540:681–689. [PubMed]
45. Timofeeva OA, Levin ED. Glutamate and nicotinic receptor interactions in working memory: importance for the cognitive impairment of schizophrenia. Neuroscience. 2011;195:21–36. [PubMed]
46. Aultman JM, Moghaddam B. Distinct contributions of glutamate and dopamine receptors to temporal aspects of rodent working memory using a clinically relevant task. Psychopharmacology (Berl) 2001;153:353–364. [PubMed]
47. Romanides AJ, Duffy P, Kalivas PW. Glutamatergic and dopaminergic afferents to the prefrontal cortex regulate spatial working memory in rats. Neuroscience. 1999;92:97–106. [PubMed]
48. Yuen EY, Liu W, Karatsoreos IN, Feng J, McEwen BS, Yan Z. Acute stress enhances glutamatergic transmission in prefrontal cortex and facilitates working memory. Proc Natl Acad Sci. 2009;106:14075–14079. [PubMed]
49. Wyss MT, Jolivet R, Buck A, Magistretti PJ, Weber B. In vivo evidence for lactate as a neuronal energy source. J Neurosci. 2011;31:7477–7485. [PubMed]
50. Pellerin L, Bergersen LH, Halestrap AP, Pierre K. Cellular and subcellular distribution of monocarboxylate transporters in cultured brain cells and in the adult brain. J Neurosci Res. 2005;79:55–64. [PubMed]
51. Erlichman JS, Hewitt A, Damon TL, Hart M, Kurascz J, Li A, Leiter JC. Inhibition of monocarboxylate transporter 2 in the retrotrapezoid nucleus in rats: a test of the astrocyte-neuron lactate-shuttle hypothesis. J Neurosci. 2008;28:4888–4896. [PMC free article] [PubMed]
52. Pedersen BK, Febbraio M. Muscle-derived interleukin-6--a possible link between skeletal muscle, adipose tissue, liver, and brain. Brain Behav Immun. 2005;5:371–376. [PubMed]
53. Kemppainen J, Aalto S, Fujimoto T, Kalliokoski KK, Långsjö J, Oikonen V, Rinne J, Nuutila P, Knuuti J. High intensity exercise decreases global brain glucose uptake in humans. The Journal of Physiology. 2005;568:323–332. [PubMed]
54. Smith D, Pernet A, Hallett WA, Bingham E, Marsden PK, Amiel SA. Lactate: A preferred fuel for human brain metabolism in vivo. J Cereb Blood Flow Metab. 2003;6:658–664. [PubMed]
55. Maddock RJ, Casazza GA, Buonocore MH, Tanase C. Vigorous exercise increases brain lactate and Glx (glutamate+glutamine): a dynamic 1H-MRS study. Neuroimage. 2011;57:1324–1330. [PubMed]
56. Hertz L, O’Dowd BS, Ng KT, Gibbs ME. Reciprocal changes in forebrain contents of glycogen and of glutamate/glutamine during early memory consolidation in the day-old chick. Brain Res. 2003;994:226–233. [PubMed]
57. Seifert G, Schilling K, Steinhauser C. Astrocyte dysfunction in neurological disorders: a molecular perspective. Nat Rev Neurosci. 2006;7:194–206. [PubMed]

Articles from American Journal of Translational Research are provided here courtesy of e-Century Publishing Corporation