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Multimodal neuroimaging in humans at 9.4 T: a technological breakthrough towards an advanced metabolic imaging scanner

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Abstract

The aim of this paper is twofold: firstly, to explore the potential of simultaneously acquiring multimodal MR–PET–EEG data in a human 9.4 T scanner to provide a platform for metabolic brain imaging. Secondly, to demonstrate that the three modalities are complementary, with MRI providing excellent structural and functional imaging, PET providing quantitative molecular imaging, and EEG providing superior temporal resolution. A 9.4 T MRI scanner equipped with a PET insert and a commercially available EEG device was used to acquire in vivo proton-based images, spectra, and sodium- and oxygen-based images with MRI, EEG signals from a human subject in a static 9.4 T magnetic field, and demonstrate hybrid MR–PET capability in a rat model. High-resolution images of the in vivo human brain with an isotropic resolution of 0.5 mm and post-mortem brain images of the cerebellum with an isotropic resolution of 320 µm are presented. A 1H spectrum was also acquired from 2 × 2 × 2 mm voxel in the brain allowing 12 metabolites to be identified. Imaging based on sodium and oxygen is demonstrated with isotropic resolutions of 2 and 5 mm, respectively. Auditory evoked potentials measured in a static field of 9.4 T are shown. Finally, hybrid MR–PET capability at 9.4 T in the human scanner is demonstrated in a rat model. Initial progress on the road to 9.4 T multimodal MR–PET–EEG is illustrated. Ultra-high resolution structural imaging, high-resolution images of the sodium distribution and proof-of-principle 17O data are clearly demonstrated. Further, simultaneous MR–PET data are presented without artefacts and EEG data successfully corrected for the cardioballistic artefact at 9.4 T are presented.

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References

  • Allen PJ, Josephs O, Turner R (2000) A method for removing imaging artifact from continuous EEG recorded during functional MRI. Neuroimage 12:230–239

    Article  CAS  PubMed  Google Scholar 

  • Arrubla J, Neuner I, Hahn D et al (2013) Recording visual evoked potentials and auditory evoked P300 at 9.4T static magnetic field. PLoS One 8(5):e62915

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Arrubla J, Neuner I, Dammers J et al (2014) Methods for pulse artefact reduction: experiences with EEG data recorded at 9.4 T static magnetic field. J Neurosci Methods (in press)

  • Ashburner J, Friston KJ (1999) Nonlinear spatial normalization using basis functions. Hum Brain Mapp 7:254–266

    Article  CAS  PubMed  Google Scholar 

  • Atkinson IC, Thulborn KR (2010) Feasibility of mapping the tissue mass corrected bioscale of cerebral metabolic rate of oxygen consumption using 17-oxygen and 23-sodium MR imaging in a human brain at 9.4 T. Neuroimage 51(2):723–733

    Article  CAS  PubMed  Google Scholar 

  • Bassett DS, Bullmore ET (2009) Human brain networks in health and disease. Curr Opin Neurol 22:340–347

    Article  PubMed Central  PubMed  Google Scholar 

  • Beckmann CF, Smith SM (2005) Tensorial extensions of independent component analysis for multisubject FMRI analysis. Neuroimage 25:294–311

    Article  CAS  PubMed  Google Scholar 

  • Bélanger M, Allaman I, Magistretti PJ (2011) Brain energy metabolism: focus on astrocyte-neuron metabolic cooperation. Cell Metab 14(6):724–738

    Article  PubMed  Google Scholar 

  • Berker Y, Franke J, Salomon A et al (2012) MRI-based attenuation correction for hybrid PET/MRI systems: a 4-class tissue segmentation technique using a combined ultrashort-echo-time/dixon MRI sequence. J Nucl Med 53:796–804

    Article  PubMed  Google Scholar 

  • Biswal B, Yetkin FZ, Haughton VM et al (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537–541

    Article  CAS  PubMed  Google Scholar 

  • Boada FE, LaVerde G, Jungreis C et al (2005) Loss of cell ion homeostasis and cell viability in the brain: what sodium MRI can tell us. Curr Top Dev Biol 70:77–101

    Article  CAS  PubMed  Google Scholar 

  • Bokkers RPH, Bremmer JP, van Berckel BNM et al (2010) Arterial spin labeling perfusion mri at multiple delay times: a correlative study with H(2)(15)O positron emission tomography in patients with symptomatic carotid artery occlusion. J Cereb Blood Flow Metab 30:222–229

    Article  PubMed Central  PubMed  Google Scholar 

  • Budde J, Shajan G, Zaitsev M et al (2013) Functional MRI in human subjects with gradient-echo and spin-echo EPI at 9.4 T. Magn Reson Med. doi:10.1002/mrm.24656 [Epub ahead of print]

    Google Scholar 

  • Catana C, van der Kouwe A, Benner T et al (2010) Toward implementing an MRI-based PET attenuation-correction method for neurologic studies on the MR–PET brain prototype. J Nucl Med 51:1431–1438

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Catana C, Benner T, van der Kouwe A et al (2011) MRI-assisted PET motion correction for neurologic studies in an integrated MR–PET scanner. J Nucl Med 52:154–161

    Article  PubMed Central  PubMed  Google Scholar 

  • Chen K, Bandy D, Reiman E et al (1998) Noninvasive quantification of the cerebral metabolic rate for glucose using positron emission tomography, 18F-fluoro-2- deoxyglucose, the Patlak method, and an image-derived input function. J Cereb Blood Flow Metab 18:716–723

    Article  CAS  PubMed  Google Scholar 

  • Debener S, Ullsperger M, Siegel M et al (2006) Single-trial EEG-fMRI reveals the dynamics of cognitive function. Trends Cogn Sci 10:558–563

    Article  PubMed  Google Scholar 

  • Debener S, Mullinger KJ, Niazy RK et al (2008) Properties of the ballistocardiogram artefact as revealed by EEG recordings at 1.5, 3 and 7T static magnetic field strength. Int J Psychophysiol 67:189–199

    Article  PubMed  Google Scholar 

  • Duarte JM, Lei H, Mlynárik V et al (2012) The neurochemical profile quantified by in vivo 1H NMR spectroscopy. Neuroimage 61(2):342–362

    Article  CAS  PubMed  Google Scholar 

  • Duyn JH, van Gelderen P, Li TQ et al (2007) High-field MRI of brain cortical substructure based on signal phase. Proc Natl Acad Sci USA 104(28):11796–11801

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Eichele T, Calhoun VD, Debener S (2009) Mining EEG-fMRI using independent component analysis. Int J Psychophysiol 73:53–61

    Article  PubMed Central  PubMed  Google Scholar 

  • Feinberg DA, Oshio K (1994) Phase errors in multi-shot echo planar imaging. Magn Reson Med 32(4):535–539

    Article  CAS  PubMed  Google Scholar 

  • Feinberg DA, Reese TG, Wedeen VJ (2002) Simultaneous echo refocusing in EPI. Magn Reson Med 48(1):1–5

    Article  PubMed  Google Scholar 

  • Feinberg DA, Moeller S, Smith SM et al (2010) Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PLoS One 5(12):e15710

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Fiege DP, Romanzetti S, Mirkes CC et al (2013a) Simultaneous single-quantum and triple-quantum-filtered MRI of 23Na (SISTINA). Magn Reson Med 69(6):1691–1696

    Article  CAS  PubMed  Google Scholar 

  • Fiege DP, Romanzetti S, Tse DH et al (2013b) B0 insensitive multiple-quantum resolved sodium imaging using a phase-rotation scheme. J Magn Reson 228:32–36

    Article  CAS  PubMed  Google Scholar 

  • Frahm J, Merboldt KD, Hänicke W et al (1987) Flow suppression in rapid FLASH NMR images. Magn Reson Med 4(4):372–377

    Article  CAS  PubMed  Google Scholar 

  • Fukuyama H, Ogawa M, Yamauchi H et al (1994) Altered cerebral energy metabolism in Alzheimer’s disease: a PET study. J Nucl Med 35:1–6

    CAS  PubMed  Google Scholar 

  • Govindaraju V, Young K, Maudsley AA (2000) Proton NMR chemical shifts and coupling constants for brain metabolites. NMR Biomed 13:129–153

    Article  CAS  PubMed  Google Scholar 

  • He BJ, Snyder AZ, Zempel JM et al (2008) Electrophysiological correlates of the brain’s intrinsic large-scale functional architecture. Proc Natl Acad Sci USA 105:16039–16044

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Henriksen OM, Larsson HBW, Hansen AE et al (2012) Estimation of intersubject variability of cerebral blood flow measurements using mri and positron emission tomography. J Magn Reson Imaging 35:1290–1299

    Article  PubMed  Google Scholar 

  • Herzog H, Qaim SM, Tellmann L et al (2006) Assessment of the short-lived non-pure positron-emitting nuclide (120)I for PET imaging. Eur J Nucl Med Mol Imaging 33(11):1249–1257

    Article  CAS  PubMed  Google Scholar 

  • Herzog H, Pietrzyk U, Shah NJ et al (2010a) The current state, challenges and perspectives of MR–PET. Neuroimage 49:2072–2082

    Article  CAS  PubMed  Google Scholar 

  • Herzog H, Langen KJ, Kaffanke J et al (2010b) MR–PET opens new horizons in neuroimaging. Future Neurol 5:807–815

    Article  Google Scholar 

  • Herzog H, Langen KJ, Weirich C et al (2011) High resolution BrainPET combined with simultaneous MRI. Nuklearmedizin 50:74–82

    Article  CAS  PubMed  Google Scholar 

  • Hofmann M, Steinke F, Scheel V et al (2008) MRI-based attenuation correction for PET/MRI: a novel approach combining pattern recognition and atlas registration. J Nucl Med 49:1875–1883

    Article  PubMed  Google Scholar 

  • Horn M, Weidensteiner C, Scheffer H et al (2001) Detection of myocardial viability based on measurement of sodium content: a (23)Na-NMR study. Magn Reson Med 45:756–764

    Article  CAS  PubMed  Google Scholar 

  • Iida H, Hori Y, Ishida K et al (2013) Three-dimensional brain phantom containing bone and grey matter structures with a realistic head contour. Ann Nucl Med 27(1):25–36

    Article  PubMed Central  PubMed  Google Scholar 

  • Ishii H, Kitagaki M, Mori E (1996) Decreased medial temporal oxygen metabolism in Alzheimer’s disease shown by PET. J Nucl Med 37:1159–1165

    CAS  PubMed  Google Scholar 

  • Kann O, Huchzermeyer C, Kovacs R et al (2011) Gamma oscillations in the hippocampus require high complex I gene expression and strong functional performance of mitochondria. Brain 134:345–358

    Article  PubMed  Google Scholar 

  • Keereman V, Fierens Y, Broux T et al (2010) MRI-based attenuation correction for PET/MRI using ultrashort echo time sequences. J Nucl Med 51:812–818

    Article  PubMed  Google Scholar 

  • Koch KM, Papademetris X, Rothman DL et al (2006) Rapid calculations of susceptibility-induced magnetostatic field perturbations for in vivo magnetic resonance. Phys Med Biol 51(24):6381–6402

    Article  PubMed  Google Scholar 

  • Larkman DJ, Hajnal JV, Herlihy AH et al (2001) Use of multi-coil arrays for separation of signal from multiple slices simultaneously excited. J Magn Reson Imaging 13(2):313–317

    Article  CAS  PubMed  Google Scholar 

  • Lopresti BJ, Klunk WE, Mathis CA et al (2005) Simplified quantification of Pittsburgh compound B amyloid imaging PET studies: a comparative analysis. J Nucl Med 46:1959–1972

    CAS  PubMed  Google Scholar 

  • Lord LD, Expert P, Huckins JF et al (2013) Cerebral energy metabolism and the brain’s functional network architecture: an integrative review. J Cereb Blood Flow Metab 33(9):1347–1354

    Article  PubMed Central  PubMed  Google Scholar 

  • Lu A, Atkinson IC, Zhou XJ et al (2013) PCr/ATP ratio mapping of the human head by simultaneously imaging of multiple spectral peaks with interleaved excitations and flexible twisted projection imaging readout trajectories at 9.4 T. Magn Reson Med 69:538–544

    Article  CAS  PubMed  Google Scholar 

  • Maclaren J, Herbst M, Speck O et al (2013) Prospective motion correction in brain imaging: a review. Magn Reson Med 69(3):621–636

    Article  PubMed  Google Scholar 

  • Magistretti PJ, Pellerin L (1999) Cellular mechanisms of brain energy metabolism and their relevance to functional brain imaging. Philos Trans R Soc Lond B Biol Sci 354:1155–1163

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Martino FD, Esposito F, van de Moortele PF et al (2011) Whole brain high-resolution functional imaging at ultra high magnetic fields: an application to the analysis of resting state networks. Neuroimage 57:1031–1044

    Article  PubMed Central  PubMed  Google Scholar 

  • Metzger GJ, Snyder C, Akgun C et al (2008) Local B1+ shimming for prostate imaging with transceiver arrays at 7T based on subject-dependent transmit phase measurements. Magn Reson Med 59:396–409

    Article  PubMed Central  PubMed  Google Scholar 

  • Montandon ML, Zaidi H (2005) Atlas-guided non-uniform attenuation correction in cerebral 3D PET imaging. Neuroimage 25:278–286

    Article  PubMed  Google Scholar 

  • Mullinger K, Bowtell R (2011) Combining EEG and fMRI. Methods Mol Biol 711:303–326

    Article  CAS  PubMed  Google Scholar 

  • Nehrke K (2009) On the steady-state properties of actual flip angle imaging (AFI). Magn Reson Med 61:84–92

    Article  PubMed  Google Scholar 

  • Neuner I, Stöcker T, Kellermann T et al (2010) Electrophysiology meets fMRI: neural correlates of the startle reflex assessed by simultaneous EMG-fMRI data acquisition. Hum Brain Mapp 31:1675–1685

    PubMed  Google Scholar 

  • Neuner I, Kaffanke JB, Langen KJ et al (2012) Multimodal imaging utilising integrated MR–PET for human brain tumour assessment. Eur Radiol 22(12):2568–2580

    Article  PubMed  Google Scholar 

  • Neuner I, Arrubla J, Felder J et al (2013a) Simultaneous EEG-fMRI acquisition at low, high and ultra-high magnetic fields up to 9.4T: Perspectives and challenges. Neuroimage. doi:10.1016/j.neuroimage.2013.06.048 [Epub ahead of print]

    Google Scholar 

  • Neuner I, Warbrick T, Arrubla J et al (2013b) EEG acquisition in ultra-high static magnetic fields up to 9.4 T. Neuroimage 68:214–220

    Article  PubMed  Google Scholar 

  • Neuner I, Arrubla J, Felder J, Shah NJ (2013c) Simultaneous EEG-fMRI acquisition at low, high and ultra-high magnetic fields up to 9.4T: Perspectives and challenges. Neuroimage. doi:10.1016/j.neuroimage.2013.06.048 [Epub ahead of print]

    Google Scholar 

  • Niazy RK, Beckmann CF, Iannetti GD et al (2005) Removal of FMRI environment artifacts from EEG data using optimal basis sets. Neuroimage 28:720–737

    Article  CAS  PubMed  Google Scholar 

  • Nishida M, Juhasz C, Sood S et al (2008) Cortical glucose metabolism positively correlates with gamma-oscillations in nonlesional focal epilepsy. Neuroimage 42:1275–1284

    Article  PubMed Central  PubMed  Google Scholar 

  • Olman CA, Harel N, Feinberg DA et al (2012) Layer-specific fMRI reflects different neuronal computations at different depths in human V1. PLoS One 7:e32536

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Ouwerkerk R, Bleich K, Gillen J et al (2003) Tissue sodium concentration in human brain tumors as measured with 23Na MR imaging. Radiology 227:529–537

    Article  PubMed  Google Scholar 

  • Pauleit D, Stoffels G, Bachofner A et al (2009) Comparison of (18)F-FET and (18)F-FDG PET in brain tumors. Nucl Med Biol 36:779–787

    Article  CAS  PubMed  Google Scholar 

  • Provencher SW (2001) Automatic quantitation of localized in vivo 1H spectra with LCModel. NMR Biomed 14:260–264

    Article  CAS  PubMed  Google Scholar 

  • Raichle ME, MacLeod AM, Snyder AZ et al (2001) A default mode of brain function. Proc Natl Acad Sci USA 98:676–682

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Reichenbach JR (2012) The future of susceptibility contrast for assessment of anatomy and function. Neuroimage 62(2):1311–1315

    Article  PubMed  Google Scholar 

  • Romanzetti S, Mirkes CC, Fiege DP et al (2011) A comparison of imaging sequences for sodium MR imaging on a 9.4T whole body machine. Proc Int Soc Mag Reson Med 19:1493

    Google Scholar 

  • Romanzetti S, Mirkes CC, Fiege DP et al (2014) Mapping tissue sodium concentration in the human brain: a comparison of MR sequences at 9.4 Tesla. Neuroimage 96C:44–53

    Article  Google Scholar 

  • Rota Kops E, Herzog H (2008) Template based attenuation correction for PET in MR–PET scanners. In: IEEE NSS conference record, pp 3786–3789

  • Sanabria-Bohorquez SM, Maes A, Dupont P et al (2003) Image-derived input function for [11C]flumazenil kinetic analysis in human brain. Mol Imaging Biol 5:72–78

    Article  PubMed  Google Scholar 

  • Schlemmer HP, Pichler BJ, Schmand M et al (2008) Simultaneous MR/PET imaging of the human brain: feasibility study. Radiology 248:1028–1035

    Article  PubMed  Google Scholar 

  • Schmahmann JD (2004) Disorders of the cerebellum: ataxia, dysmetria of thought, and the cerebellar cognitive affective syndrome. J Neuropsychiatry Clin Neurosci 16:367–378

    Article  PubMed  Google Scholar 

  • Schulz J, Siegert T, Reimer E et al (2012) An embedded optical tracking system for motion-corrected magnetic resonance imaging at 7T. MAGMA 25(6):443–453

    Article  PubMed  Google Scholar 

  • Shah NJ, Oros-Peusquens AM, Arrubla J et al (2013) Advances in multimodal neuroimaging: hybrid MR–PET and MR–PET–EEG at 3 T and 9.4 T. J Magn Reson 229:101–115

    Article  CAS  PubMed  Google Scholar 

  • Shah NJ, Herzog H, Weirich C et al (2014) Effects of magnetic fields of up to 9.4 T on resolution and contrast of PET images as measured with an MR-BrainPET. PLoS One 9(4):e95250

    Article  PubMed Central  PubMed  Google Scholar 

  • Sheline YI, Morris JC, Snyder AZ et al (2010) APOE4 allele disrupts resting state fMRI connectivity in the absence of amyloid plaques or decreased CSF Abeta42. J Neurosci 30:17035–17040

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Shulman RG, Rothman DL, Behar KL et al (2004) Energetic basis of brain activity: implications for neuroimaging. Trends Neurosci 27(8):489–495

    Article  CAS  PubMed  Google Scholar 

  • Smith SA, Levante TO, Meier BH et al (1994) Computer simulations in magnetic resonance. an object-oriented programming approach. J Magn Reson Series A 106:75–105

    Article  CAS  Google Scholar 

  • Stam CJ, Jones BF, Nolte G et al (2007) Small-world networks and functional connectivity in Alzheimer’s disease. Cereb Cortex 17:92–99

    Article  CAS  PubMed  Google Scholar 

  • Steinlin M (2008) Cerebellar disorders in childhood: cognitive problems. Cerebellum 7:607–610

    Article  PubMed  Google Scholar 

  • Thulborn KR, Lu A, Atkinson IC et al (2009) Quantitative sodium MR imaging and sodium bioscales for the management of brain tumors. Neuroimaging Clin N Am 19:615–624

    Article  PubMed Central  PubMed  Google Scholar 

  • Tkac I, Starcuk Z, Choi IY et al (1999) In vivo 1H NMR spectroscopy of rat brain at 1 ms echo time. Magn Reson Med 41:649–656

    Article  CAS  PubMed  Google Scholar 

  • Ullisch M, Weirich C, Scheins J et al (2012) MR-based PET Motion correction procedure for simultaneous MR–PET neuroimaging of human brain. PLoS One 7(11):e48149

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Vaishnavi SN, Vlassenko AG, Rundle MM et al (2010) Regional aerobic glycolysis in the human brain. Proc Natl Acad Sci USA 107:17757–17762

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Xu G, Rowley HA, Wu G et al (2010) Reliability and precision of pseudo-continuous arterial spin labeling perfusion MRI on 3.0 T and comparison with 15O-water PET in elderly subjects at risk for Alzheimer’s disease. NMR Biomed 23:286–293

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Zwart NR, Pipe JG. http://www.ismrm.org/mri_unbound/sequence.htm

  • Yacoub E, Van De Moortele PF, Shmuel A et al (2005) Signal and noise characteristics of Hahn SE and GE BOLD fMRI at 7 T in humans. Neuroimage 24:738–750

    Article  PubMed  Google Scholar 

  • Yacoub E, Harel N, Ugurbil K (2008) High-field fMRI unveils orientation columns in humans. Proc Natl Acad Sci USA 105(30):10607–10612

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Yamaji S, Ishii K, Saski M et al (1997) Changes in cerebral blood flow and oxygen metabolism related to magnetic resonance imaging white matter hyperintensities in Alzheimer’s disease. J Nucl Med 38:1471–1474

    CAS  PubMed  Google Scholar 

  • Yun SD, Reske M, Vahedipour K et al (2013) Parallel imaging acceleration of EPIK for reduced image distortions in fMRI. Neuroimage 73:135–143

    Article  PubMed  Google Scholar 

  • Zaitsev M, Zilles K, Shah NJ (2001) Shared k-space echo planar imaging with keyhole. Magn Reson Med 45:109–117

    Article  CAS  PubMed  Google Scholar 

  • Zaitsev M, Arcy JD, Collins DJ et al (2005) Dual-contrast echo planar imaging with keyhole: application to dynamic contrast-enhanced perfusion studies. Phys Med Biol 50:4491–4505

    Article  CAS  PubMed  Google Scholar 

  • Zhang K, Herzog H, Filss C et al (2013) Simultaneous arterial spin labelling MRI and H2O15 position emission tomography. Proc Int Soc Mag Reson Med 21:0109

    CAS  Google Scholar 

  • Zhang K, Herzog H, Mauler J et al (2014) Comparison of cerebral blood flow acquired by simultaneous [15O]water positron emission tomography and arterial spin labeling magnetic resonance imaging. J Cereb Blood Flow Metab. (in press)

  • Zhu XH, Zhang N, Zhang Y et al (2005) In vivo 17O NMR approaches for brain study at high field. NMR Biomed 18:83–103

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

The author would like to thank Dr. Jorge Arrubla, Prof. Hans Herzog, Johannes Lindemeyer, Klaus Moellenhoff, PD Dr. Irene Neuner, Dr. Ana-Maria Oros-Peusquens, Dr. Sandro Romanzetti, Dr. Desmond Tse, Christoph Weirich, and Dr. Seong Dae Yun for performing aspects of the work reported herein. It is also a pleasure to thank Dr. Michael Poole for proofreading and Martina Bunn for valuable assistance with manuscript preparation. Funding from the Bundesministerium für Bildung and Forschung (Grant No.: 13N9121) and generous support from Siemens for construction of the 9.4 T MR–PET scanner are both gratefully acknowledged. This work was funded in part by the Helmholtz Alliance ICEMED—Imaging and Curing Environmental Metabolic Diseases, through the Initiative and Network Fund of the Helmholtz Association. The author is supported in part through the EU FP7 project TRIMAGE (Grant no. 602621).

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  1. Institute of Neuroscience and Medicine-4, Research Centre Jülich, 52425, Jülich, Germany

    N. Jon Shah

  2. Department of Neurology, Faculty of Medicine, RWTH Aachen University, Aachen, Germany

    N. Jon Shah

  3. Jülich Aachen Research Alliance (JARA), Translational Brain Medicine, Aachen and Jülich, Germany

    N. Jon Shah

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Shah, N.J. Multimodal neuroimaging in humans at 9.4 T: a technological breakthrough towards an advanced metabolic imaging scanner. Brain Struct Funct 220, 1867–1884 (2015). https://doi.org/10.1007/s00429-014-0843-4

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