Functional magnetic resonance spectroscopy of the brain
Functional magnetic resonance spectroscopy of the brain (fMRS) uses magnetic resonance imaging (MRI) to study brain metabolism during brain activation. The data generated by fMRS usually shows spectra of resonances, instead of a brain image, as with MRI. The area under peaks in the spectrum represents relative concentrations of metabolites.
fMRS is based on the same principles as in vivo magnetic resonance spectroscopy (MRS). However, while conventional MRS records a single spectrum of metabolites from a region of interest, a key interest of fMRS is to detect multiple spectra and study metabolite concentration dynamics during brain function. Therefore it is sometimes referred to as dynamic MRS, event-related MRS or time-resolved MRS. A novel variant of fMRS is functional diffusion-weighted spectroscopy (fDWS) which measures diffusion properties of brain metabolites upon brain activation.
Unlike in vivo MRS which is intensively used in clinical settings, fMRS is used primarily as a research tool, both in a clinical context, for example, to study metabolite dynamics in patients suffering from epilepsy, migraine and dyslexia, and to study healthy brains. fMRS can be used to study metabolism dynamics also in other parts of the body, for example, in muscles and heart; however, brain studies have been far more popular.
The main goals of fMRS studies are to contribute to the understanding of energy metabolism in the brain, and to test and improve data acquisition and quantification techniques to ensure and enhance validity and reliability of fMRS studies.
- 1 Basic principles
- 2 Advantages and limitations
- 3 Applications
- 4 See also
- 5 References
- 6 External links
Like in vivo MRS, fMRS can probe different nuclei, such as hydrogen (1H) and carbon (13C). The 1H nucleus is the most sensitive and is most commonly used to measure metabolite concentrations and concentration dynamics, whereas 13C is best suited for characterizing fluxes and pathways of brain metabolism. The natural abundance of 13C in the brain is only about 1%; therefore, 13C fMRS studies usually involve the isotope enrichment via infusion or ingestion.
In the literature 13C fMRS is commonly referred to as functional 13C MRS or just 13C MRS.
Spectral and temporal resolution
Typically in MRS a single spectrum is acquired by averaging enough spectra over a long acquisition time. Averaging is necessary because of the complex spectral structures and relatively low concentrations of many brain metabolites, which result in a low signal-to-noise ratio (SNR) in MRS of a living brain.
fMRS differs from MRS by acquiring not one but multiple spectra at different time points while the participant is inside the MRI scanner. Thus, temporal resolution is very important and acquisition times need to be kept adequately short to provide a dynamic rate of metabolite concentration change.
To balance the need for temporal resolution and sufficient SNR, fMRS requires a high magnetic field strength (1.5 T and above). High field strengths have the advantage of increased SNR as well as improved spectral resolution allowing to detect more metabolites and more detailed metabolite dynamics.
fMRS is continuously advancing as stronger magnets become more available and better data acquisition techniques are developed providing increased spectral and temporal resolution. With 7-tesla magnet scanners it is possible to detect around 18 different metabolites of 1H spectrum which is a significant improvement over less powerful magnets. Temporal resolution has increased from 7 minutes in the first fMRS studies  to 5 seconds in more recent ones.
In fMRS, depending on the focus of the study, either single-voxel or multi-voxel spectroscopic technique can be used.
In single-voxel fMRS the selection of the volume of interest (VOI) is often done by running a functional magnetic resonance imaging (fMRI) study prior to fMRS to localize the brain region activated by the task. Single-voxel spectroscopy requires shorter acquisition times; therefore it is more suitable for fMRS studies where high temporal resolution is needed and where the volume of interest is known.
Multi-voxel spectroscopy provides information about group of voxels and data can be presented in 2D or 3D images, but it requires longer acquisition times and therefore temporal resolution is decreased. Multi-voxel spectroscopy usually is performed when the specific volume of interest is not known or it is important to study metabolite dynamics in a larger brain region.
Advantages and limitations
fMRS has several advantages over other functional neuroimaging and brain biochemistry detection techniques. Unlike push-pull cannula, microdialysis and in vivo voltammetry, fMRS is a non-invasive method for studying dynamics of biochemistry in an activated brain. It is done without exposing subjects to ionizing radiation like it is done in positron emission tomography (PET) or single-photon emission computed tomography (SPECT) studies. fMRS gives a more direct measurement of cellular events occurring during brain activation than BOLD fMRI or PET which rely on hemodynamic responses and show only global neuronal energy uptake during brain activation while fMRS gives also information about underlying metabolic processes that support the working brain.
However, fMRS requires very sophisticated data acquisition, quantification methods and interpretation of results. This is one of the main reasons why in the past it received less attention than other MR techniques, but the availability of stronger magnets and improvements in data acquisition and quantification methods are making fMRS more popular.
Main limitations of fMRS are related to signal sensitivity and the fact that many metabolites of potential interest can not be detected with current fMRS techniques.
Because of limited spatial and temporal resolution fMRS can not provide information about metabolites in different cell types, for example, whether lactate is used by neurons or by astrocytes during brain activation. The smallest volume that can currently be characterized with fMRS is 1 cm3, which is too big to measure metabolites in different cell types. To overcome this limitation, mathematical and kinetic modeling is used.
Many brain areas are not suitable for fMRS studies because they are too small (like small nuclei in brainstem) or too close to bone tissue, CSF or extracranial lipids, which could cause inhomogeneity in the voxel and contaminate the spectra. To avoid these difficulties, in most fMRS studies the volume of interest is chosen from the visual cortex – because it is easily stimulated, has high energy metabolisms, and yields good MRS signals.
Unlike in vivo MRS which is intensively used in clinical settings, fMRS is used primarily as a research tool, both in a clinical context, for example, to study metabolite dynamics in patients suffering from epilepsy, migraine  and dyslexia, and to study healthy brains.
The main goals of fMRS studies are to contribute to the understanding of energy metabolism in the brain, and to test and improve data acquisition and quantification techniques to ensure and enhance validity and reliability of fMRS studies.
Brain energy metabolism studies
fMRS was developed as an extension of MRS in the early 1990s. Its potential as a research technology became obvious when it was applied to an important research problem where PET studies had been inconclusive, namely the mismatch between oxygen and glucose consumption during sustained visual stimulation. The 1H fMRS studies highlighted the important role of lactate in this process and significantly contributed to the research in brain energy metabolism during brain activation. It confirmed the hypothesis that lactate increases during sustained visual stimulation  and allowed the generalization of findings based on visual stimulation to other types of stimulation, e.g., auditory stimulation, motor task  and cognitive tasks.
1H fMRS measurements were instrumental in achieving the current consensus among most researchers that lactate levels increase during the first minutes of intense brain activation. However, there are no consistent results about the magnitude of increase, and questions about the exact role of lactate in brain energy metabolism still remain unanswered and are the subject of continuing research.
13C MRS is a special type of fMRS particularly suited for measuring important neurophysiological fluxes in vivo and in real time to assess metabolic activity both in healthy and diseased brains (e.g., in human tumor tissue ). These fluxes include TCA cycle, glutamate-glutamine cycle, glucose and oxygen consumption. 13C MRS can provide detailed quantitative information about glucose dynamics that can not be obtained with 1H fMRS, because of the low concentration of glucose in the brain and the spread of its resonances in several multiplets in the 1H MRS spectrum.
13C MRSs have been crucial in recognizing that the awake nonstimulated (resting) human brain is highly active using 70%–80% of its energy for glucose oxidation to support signaling within cortical networks which is suggested to be necessary for consciousness. This finding has an important implication for the interpretation of BOLD fMRI data where this high baseline activity is generally ignored and response to the task is shown as independent of the baseline activity. 13C MRS studies indicate that this approach can misjudge and even completely miss the brain activity induced by the task.
Another important benefit of 13C MRS is that it provides unique means for determining the time course of metabolite pools and measuring turnover rates of TCA and glutamate-glutamine cycles. As such, it has been proved to be important in aging research by revealing that mitochondrial metabolism is reduced with aging which may explain the decline in cognitive and sensory processes.
Water resonance studies
Usually, in 1H fMRS the water signal is suppressed to detect metabolites with much lower concentration than water. Though, an unsuppressed water signal can be used to estimate functional changes in the relaxation time T2* during cortical activation.
This approach has been proposed as an alternative to the BOLD fMRI technique and used to detect visual response to photic stimulation, motor activation by finger tapping and activations in language areas during speech processing. Recently functional real-time single-voxel proton spectroscopy (fSVPS) has been proposed as a technique for real-time neurofeedback studies in magnetic fields of 7 tesla (7 T) and above. This approach could have potential advantages over BOLD fMRI and is the subject of current research.
Migraine and pain studies
fMRS has been used in migraine and pain research. It has supported the important hypothesis of mitochondria dysfunction in migraine with aura (MwA) patients. Here the ability of fMRS to measure chemical processes in the brain over time proved crucial for confirming that repetitive photic stimulation causes higher increase of the lactate level and higher decrease of the N-acetylaspartate (NAA) level in the visual cortex of MwA patients compared to migraine without aura (MwoA) patients and healthy individuals.
In pain research fMRS complements fMRI and PET techniques. Although fMRI and PET are continuously used to localize pain processing areas in the brain, they can not provide direct information about changes in metabolites during pain processing that could help to understand physiological processes behind pain perception and potentially lead to novel treatments for pain. fMRS overcomes this limitation and has been used to study pain-induced (cold-pressure, heat, dental pain) neurotransmitter level changes in the anterior cingulate cortex, anterior insular cortex  and left insular cortex. These fMRS studies are valuable because they show that some or all Glx compounds (glutamate, GABA and glutamine) increase during painful stimuli in the studied brain regions.
Cognitive studies frequently rely on the detection of neuronal activity during cognition. The use of fMRS for this purpose is at present mainly at an experimental level but is rapidly increasing. Cognitive tasks where fMRS has been used and the major findings of the research are summarized below.
|Cognitive task||Brain region||Major findings|
|Silent word generation task||Left inferior frontal gyrus||Increased lactate level during the task in young alert participants, but not in young participants with prolonged wakefulness and aged participants implying that aging and prolonged wakefulness may result in a dysfunction of the brain energy metabolism and cause impairment of the frontal cortex.|
|Motor sequence learning task||Contralateral primary sensorimotor cortex||Decreased GABA level during the task suggesting that GABA modulation occurs with encoding of the task.|
|Prolonged match-to-sample working memory task||Left dorsolateral prefrontal cortex||Increased GABA level during the first working memory run and continuously decreased during subsequent three runs. Decrease of GABA over time correlated with decreases in reaction time and higher task accuracy.|
|Presentation of abstract and real world objects||Lateral occipital cortex||Higher increase in glutamate level with the presentation of abstract versus real world objects. In this study fMRS was used simultaneously with EEG and positive correlation between gamma-band activity and glutamate level changes was observed.|
|Stroop task||Anterior cingulate cortex (ACC)||Demonstration of phosphocreatine dynamics with 12s temporal resolution. Stroop task for this study was chosen because it has been previously shown that left ACC is significantly activated during the performance of stroop task. The main implication of this study was that reliable fMRS measures are possible in the ACC during cognitive tasks.|
- Brain metabolism
- Magnetic resonance imaging
- Nuclear magnetic resonance spectroscopy
- Fourier transform spectroscopy
- Adjusting the homogeneity of a magnetic field
- Frahm, J; Krüger, G; Merboldt, KD; Kleinschmidt, A (Feb 1996). "Dynamic uncoupling and recoupling of perfusion and oxidative metabolism during focal brain activation in man". Magnetic resonance in medicine 35 (2): 143–8. doi:10.1002/mrm.1910350202. PMID 8622575.
- Duarte, JM; Lei, H; Mlynárik, V; Gruetter, R (Jun 2012). "The neurochemical profile quantified by in vivo 1H NMR spectroscopy". NeuroImage 61 (2): 342–62. doi:10.1016/j.neuroimage.2011.12.038. PMID 22227137.
- Mullins, PG; Haenschel, C; Lally, N; Roberts, M; Price, D; Gruber, T (2012). "The relationship between Glutamate and oscillatory activity in a repetition suppression paradigm – a combined MR-spectroscopy and EEG study". Front. Hum. Neurosci. Conference Abstract: XI International Conference on Cognitive Neuroscience (ICON XI) 5. doi:10.3389/conf.fnhum.2011.207.00094.
- Gussew, A; Rzanny, R; Erdtel, M; Scholle, HC; Kaiser, WA; Mentzel, HJ; Reichenbach, JR (Jan 15, 2010). "Time-resolved functional 1H MR spectroscopic detection of glutamate concentration changes in the brain during acute heat pain stimulation". NeuroImage 49 (2): 1895–902. doi:10.1016/j.neuroimage.2009.09.007. PMID 19761852.
- Branzoli, F; Techawiboonwong, A; Kan, H; Webb, A; Ronen, I (Nov 19, 2012). "Functional diffusion-weighted magnetic resonance spectroscopy of the human primary visual cortex at 7 T". Magnetic resonance in medicine. ahead of print, online version: n/a. doi:10.1002/mrm.24542. PMID 23165888.
- Shulman, RG; Hyder, F; Rothman, DL (Aug 2002). "Biophysical basis of brain activity: implications for neuroimaging". Quarterly reviews of biophysics 35 (3): 287–325. doi:10.1017/s0033583502003803. PMID 12599751.
- Morris, PG (Dec 2002). "Synaptic and cellular events: the last frontier?". European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology 12 (6): 601–7. doi:10.1016/S0924-977X(02)00109-8. PMID 12468023.
- Taylor, R; Williamson, PC; Théberge, J (2012). "Functional MRS in the Anterior Cingulate". International Society for Magnetic Resonance Imaging Meeting, Melbourne, Victoria, Australia.
- Mangia, S; Tkác, I; Gruetter, R; Van de Moortele, PF; Maraviglia, B; Uğurbil, K (May 2007). "Sustained neuronal activation raises oxidative metabolism to a new steady-state level: evidence from 1H NMR spectroscopy in the human visual cortex". Journal of cerebral blood flow and metabolism 27 (5): 1055–63. doi:10.1038/sj.jcbfm.9600401. PMID 17033694.
- Schaller, BM; Mekle, R; Xin, L; Gruetter, R (2011). "Metabolite concentration changes during visual stimulation using functional Magnetic Resonance Spectroscopy (fMRS) on a clinical 7T scanner". Proc. Intl. Soc. Mag. Reson. Med. 19: 309.
- Prichard, J; Rothman, D; Novotny, E; Petroff, O; Kuwabara, T; Avison, M; Howseman, A; Hanstock, C; Shulman, R (Jul 1, 1991). "Lactate rise detected by 1H NMR in human visual cortex during physiologic stimulation". Proceedings of the National Academy of Sciences of the United States of America 88 (13): 5829–31. doi:10.1073/pnas.88.13.5829. PMC 51971. PMID 2062861.
- Dager, SR; Layton, ME; Strauss, W; Richards, TL; Heide, A; Friedman, SD; Artru, AA; Hayes, CE; Posse, S (Feb 1999). "Human brain metabolic response to caffeine and the effects of tolerance". The American journal of psychiatry 156 (2): 229–37. PMID 9989559.
- Alger, JR (Apr 2010). "Quantitative proton magnetic resonance spectroscopy and spectroscopic imaging of the brain: a didactic review". Topics in magnetic resonance imaging : TMRI 21 (2): 115–28. doi:10.1097/RMR.0b013e31821e568f. PMC 3103086. PMID 21613876.
- Shestov, AA; Emir, UE; Kumar, A; Henry, PG; Seaquist, ER; Öz, G (Nov 2011). "Simultaneous measurement of glucose transport and utilization in the human brain". American journal of physiology. Endocrinology and metabolism 301 (5): E1040–9. doi:10.1152/ajpendo.00110.2011. PMC 3213999. PMID 21791622.
- Mangia, S; Simpson, IA; Vannucci, SJ; Carruthers, A (May 2009). "The in vivo neuron-to-astrocyte lactate shuttle in human brain: evidence from modeling of measured lactate levels during visual stimulation". Journal of neurochemistry. 109 Suppl 1 (Suppl 1): 55–62. doi:10.1111/j.1471-4159.2009.06003.x. PMC 2679179. PMID 19393009.
- Richards, Todd L. (2001). "Functional Magnetic Resonance Imaging and Spectroscopic Imaging of the Brain: Application of fMRI and fMRS to Reading Disabilities and Education". Learning Disability Quarterly 24 (3): 189. doi:10.2307/1511243.
- Reyngoudt, H; Paemeleire, K; Dierickx, A; Descamps, B; Vandemaele, P; De Deene, Y; Achten, E (Jun 2011). "Does visual cortex lactate increase following photic stimulation in migraine without aura patients? A functional (1)H-MRS study". The journal of headache and pain 12 (3): 295–302. doi:10.1007/s10194-011-0295-7. PMC 3094653. PMID 21301922.
- Teresi, Louis (2007). A Practicing Radiologists Guide to MR Spectroscopy. Xlibris. ISBN 1425746284.
- Chiappa, KH; Hill, RA; Huang-Hellinger, F; Jenkins, BG (1999). "Photosensitive epilepsy studied by functional magnetic resonance imaging and magnetic resonance spectroscopy". Epilepsia. 40 Suppl 4: 3–7. doi:10.1111/j.1528-1157.1999.tb00899.x. PMID 10487166.
- Sándor, PS; Dydak, U; Schoenen, J; Kollias, SS; Hess, K; Boesiger, P; Agosti, RM (Jul 2005). "MR-spectroscopic imaging during visual stimulation in subgroups of migraine with aura". Cephalalgia : an international journal of headache 25 (7): 507–18. doi:10.1111/j.1468-2982.2005.00900.x. PMID 15955037.
- Sarchielli, P; Tarducci, R; Presciutti, O; Gobbi, G; Pelliccioli, GP; Stipa, G; Alberti, A; Capocchi, G (Feb 15, 2005). "Functional 1H-MRS findings in migraine patients with and without aura assessed interictally". NeuroImage 24 (4): 1025–31. doi:10.1016/j.neuroimage.2004.11.005. PMID 15670679.
- Richards, TL; Dager, SR; Corina, D; Serafini, S; Heide, AC; Steury, K; Strauss, W; Hayes, CE; Abbott, RD; Craft, S; Shaw, D; Posse, S; Berninger, VW (Sep 1999). "Dyslexic children have abnormal brain lactate response to reading-related language tasks". AJNR. American journal of neuroradiology 20 (8): 1393–8. PMID 10512218.
- Meyerspeer, Martin; Robinson, Simon; Nabuurs, Christine I.; Scheenen, Tom; Schoisengeier, Adrian; Unger, Ewald; Kemp, Graham J.; Moser, Ewald (1 December 2012). "Comparing localized and nonlocalized dynamic 31P magnetic resonance spectroscopy in exercising muscle at 7 T". Magnetic Resonance in Medicine 68 (6): 1713–1723. doi:10.1002/mrm.24205. PMC 3378633. PMID 22334374.
- Pluim, BM; Lamb, HJ; Kayser, HW; Leujes, F; Beyerbacht, HP; Zwinderman, AH; van der Laarse, A; Vliegen, HW; de Roos, A; van der Wall, EE (Feb 24, 1998). "Functional and metabolic evaluation of the athlete's heart by magnetic resonance imaging and dobutamine stress magnetic resonance spectroscopy". Circulation 97 (7): 666–72. doi:10.1161/01.CIR.97.7.666. PMID 9495302.
- Rothman, DL; Behar, KL; Hyder, F; Shulman, RG (2003). "In vivo NMR studies of the glutamate neurotransmitter flux and neuroenergetics: implications for brain function". Annual review of physiology 65: 401–27. doi:10.1146/annurev.physiol.65.092101.142131. PMID 12524459.
- Fox, PT; Raichle, ME; Mintun, MA; Dence, C (Jul 22, 1988). "Nonoxidative glucose consumption during focal physiologic neural activity". Science 241 (4864): 462–4. doi:10.1126/science.3260686. PMID 3260686.
- Mangia, S; Tkác, I; Gruetter, R; Van De Moortele, PF; Giove, F; Maraviglia, B; Uğurbil, K (May 2006). "Sensitivity of single-voxel 1H-MRS in investigating the metabolism of the activated human visual cortex at 7 T". Magnetic resonance imaging 24 (4): 343–8. doi:10.1016/j.mri.2005.12.023. PMID 16677939.
- Richards, TL; Gates, GA; Gardner, JC; Merrill, T; Hayes, CE; Panagiotides, H; Serafini, S; Rubel, EW (Apr 1997). "Functional MR spectroscopy of the auditory cortex in healthy subjects and patients with sudden hearing loss". AJNR. American journal of neuroradiology 18 (4): 611–20. PMID 9127020.
- Kuwabara, T; Watanabe, H; Tsuji, S; Yuasa, T (Jan 30, 1995). "Lactate rise in the basal ganglia accompanying finger movements: a localized 1H-MRS study". Brain research 670 (2): 326–8. doi:10.1016/0006-8993(94)01353-J. PMID 7743199.
- Urrila, AS; Hakkarainen, A; Heikkinen, S; Vuori, K; Stenberg, D; Häkkinen, AM; Lundbom, N; Porkka-Heiskanen, T (Aug 2003). "Metabolic imaging of human cognition: an fMRI/1H-MRS study of brain lactate response to silent word generation". Journal of cerebral blood flow and metabolism 23 (8): 942–8. doi:10.1097/01.WCB.0000080652.64357.1D. PMID 12902838.
- Figley, CR (Mar 30, 2011). "Lactate transport and metabolism in the human brain: implications for the astrocyte-neuron lactate shuttle hypothesis". The Journal of neuroscience 31 (13): 4768–70. doi:10.1523/JNEUROSCI.6612-10.2011. PMID 21451014.
- Lin, Y; Stephenson, MC; Xin, L; Napolitano, A; Morris, PG (Aug 2012). "Investigating the metabolic changes due to visual stimulation using functional proton magnetic resonance spectroscopy at 7 T". Journal of cerebral blood flow and metabolism 32 (8): 1484–95. doi:10.1038/jcbfm.2012.33. PMC 3421086. PMID 22434070.
- Wijnen, JP; Van der Graaf, M; Scheenen, TW; Klomp, DW; de Galan, BE; Idema, AJ; Heerschap, A (Jun 2010). "In vivo 13C magnetic resonance spectroscopy of a human brain tumor after application of 13C-1-enriched glucose". Magnetic resonance imaging 28 (5): 690–7. doi:10.1016/j.mri.2010.03.006. PMID 20399584.
- Mangia, S; Giove, F; Tkác, I; Logothetis, NK; Henry, PG; Olman, CA; Maraviglia, B; Di Salle, F; Uğurbil, K (Mar 2009). "Metabolic and hemodynamic events after changes in neuronal activity: current hypotheses, theoretical predictions and in vivo NMR experimental findings". Journal of cerebral blood flow and metabolism 29 (3): 441–63. doi:10.1038/jcbfm.2008.134. PMC 2743443. PMID 19002199.
- Shulman, RG; Hyder, F; Rothman, DL (Jul 7, 2009). "Baseline brain energy supports the state of consciousness". Proceedings of the National Academy of Sciences of the United States of America 106 (27): 11096–101. doi:10.1073/pnas.0903941106. PMC 2708743. PMID 19549837.
- Hyder, F; Rothman, DL (Aug 15, 2012). "Quantitative fMRI and oxidative neuroenergetics". NeuroImage 62 (2): 985–94. doi:10.1016/j.neuroimage.2012.04.027. PMC 3389300. PMID 22542993.
- Gusnard, DA; Raichle, ME; Raichle, ME (Oct 2001). "Searching for a baseline: functional imaging and the resting human brain". Nature Reviews Neuroscience 2 (10): 685–94. doi:10.1038/35094500. PMID 11584306.
- Boumezbeur, F; Mason, GF; de Graaf, RA; Behar, KL; Cline, GW; Shulman, GI; Rothman, DL; Petersen, KF (Jan 2010). "Altered brain mitochondrial metabolism in healthy aging as assessed by in vivo magnetic resonance spectroscopy". Journal of cerebral blood flow and metabolism 30 (1): 211–21. doi:10.1038/jcbfm.2009.197. PMC 2949111. PMID 19794401.
- Hennig, J (Aug 15, 2012). "Functional spectroscopy to no-gradient fMRI". NeuroImage 62 (2): 693–8. doi:10.1016/j.neuroimage.2011.09.060. PMID 22001263.
- Koush, Yury; Elliott, Mark A.; Mathiak, Klaus (15 September 2011). "Single Voxel Proton Spectroscopy for Neurofeedback at 7 Tesla". Materials 4 (9): 1548–1563. doi:10.3390/ma4091548.
- Mullins, PG; Rowland, LM; Jung, RE; Sibbitt WL, Jr (Jun 2005). "A novel technique to study the brain's response to pain: proton magnetic resonance spectroscopy". NeuroImage 26 (2): 642–6. doi:10.1016/j.neuroimage.2005.02.001. PMID 15907322.
- Kupers, R; Danielsen, ER; Kehlet, H; Christensen, R; Thomsen, C (Mar 2009). "Painful tonic heat stimulation induces GABA accumulation in the prefrontal cortex in man". Pain 142 (1–2): 89–93. doi:10.1016/j.pain.2008.12.008. PMID 19167811.
- Gutzeit, A; Meier, D; Meier, ML; von Weymarn, C; Ettlin, DA; Graf, N; Froehlich, JM; Binkert, CA; Brügger, M (Apr 2011). "Insula-specific responses induced by dental pain. A proton magnetic resonance spectroscopy study". European radiology 21 (4): 807–15. doi:10.1007/s00330-010-1971-8. PMID 20890705.
- Urrila, AS; Hakkarainen, A; Heikkinen, S; Vuori, K; Stenberg, D; Häkkinen, AM; Lundbom, N; Porkka-Heiskanen, T (Jun 2004). "Stimulus-induced brain lactate: effects of aging and prolonged wakefulness". Journal of sleep research 13 (2): 111–9. doi:10.1111/j.1365-2869.2004.00401.x. PMID 15175090.
- Floyer-Lea, A; Wylezinska, M; Kincses, T; Matthews, PM (Mar 2006). "Rapid modulation of GABA concentration in human sensorimotor cortex during motor learning". Journal of neurophysiology 95 (3): 1639–44. doi:10.1152/jn.00346.2005. PMID 16221751.
- Michels, L; Martin, E; Klaver, P; Edden, R; Zelaya, F; Lythgoe, DJ; Lüchinger, R; Brandeis, D; O'Gorman, RL (2012). "Frontal GABA levels change during working memory". In Koenig, Thomas. PloS one 7 (4): e31933. doi:10.1371/journal.pone.0031933. PMC 3317667. PMID 22485128.