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The renaissance of functional 18F-FDG PET brain activation imaging

  • Antoine Verger
  • Eric Guedj
Editorial
  • 506 Downloads

For a few years now, positron emission tomography (PET) has benefited from a renaissance of functional imaging through 18F-FDG PET brain activation studies. The first PET studies involving activation brain imaging nevertheless emerged in the 1980s, using intravenously administered oxygen-15-labelled water (15O-water) [1, 2]. This approach was used to measure regional cerebral blood flow (rCBF), and has been shown to be a sensitive method for quantifying regional brain activation during specific tasks [3, 4]. Such functional imaging studies proved extremely useful for mapping brain activation patterns involved in cognitive tasks such as word reading, mental imagery, timing or memory [5, 6]. The first clinical validations of quantitative analyses using Statistical Parametric Mapping (SPM, Wellcome Trust Centre for Neuroimaging, London, United Kingdom) were thus performed with PET imaging [7]. These PET imaging studies preceded the emergence of functional magnetic resonance imaging (fMRI) in the 1990s, which depicts the engagement of different brain regions within a distributed system through fluctuations of the blood-oxygen-level dependent (BOLD) signal [8]. fMRI applications have since been extended to reveal additional information about the degree to which components of large-scale neural systems are functionally coupled together to achieve specific tasks. This phenomenon underpins the study of functional connectivity, which is mathematically defined as the statistical association between two distinct time-series, i.e. the connectivity between brain regions that share functional properties. The fMRI approach enables the study of functional connectivity within single subjects or groups through serial imaging of the brain in the so-called resting state (i.e. without any specific stimulus or task) or in a condition of task-dependent activation [9].

In the past two decades MRI has largely supplanted PET for studies of brain activation, due to the lack of exposure to ionizing radiation and because of the superior spatial and especially temporal resolution, which allows for dynamic measurement of multiple responses within a single imaging session [8]. As such, fMRI has substantially advanced our understanding of normal and pathological brain functions, whether in resting-state or activation functional imaging studies [10, 11]. Nevertheless, fMRI does have some disadvantages. At the single-subject level, fMRI exhibits low signal-to-noise ratios and low variance concentrations in comparison with PET [12]. Indeed, to represent a given aggregate percentage of variance, many more components are needed for fMRI than for PET. Therefore, multiple runs of the same fMRI protocol are required to increase the sensitivity of measurements. Furthermore, recent work has shown that the signal-to-noise ratio may vary dramatically between fMRI runs, especially when performing a brief task, which degrades the robustness and reproducibility of this method at a single-subject level [13]. All these disadvantages generate poor out-of-sample replications in fMRI studies. In addition, fMRI is vulnerable to ferromagnetic artefacts, which are especially prominent in anterior and ventral brain areas [14]. These limitations could also cause others difficulties in performing such imaging due to the high prevalence of treatment with implantable devices for neurostimulation, most of which are not MR compatible [15]. Noise generated by the gradient coils, which could adversely affect listening to auditory inputs and can make recording of spoken outputs difficult, or movement artefacts that arise from speaking or other orofacial movements, could also cause others technical limitations [14]. Furthermore, the BOLD signal is a composite of effects attributable to the cerebral metabolic rates of oxygen, the cerebral blood flow, and the cerebral blood volume changes in response to excitatory neurotransmission [16]. In light of the above and given recent technological advances, functional PET imaging may be experiencing a renaissance.

Fluor-18-labelled fluorodeoxyglucose (18F-FDG) is the most widely used PET radiotracer due to its ability to provide an indication of the rate of glucose utilization by neuronal tissue, namely, the cerebral metabolic rate of glucose (CMRGlc), which directly reflects neuronal and glial activity [17]. 18F-FDG is currently the most accurate in vivo method for investigating regional human brain metabolism, and its clinical use can be regarded as being established for a number of diagnostic questions in neurology (dementia disorders, movements disorders, neuro-oncology) and psychiatry [18]. On the other hand, 18F-FDG PET imaging keeps an important clinical interest in epilepsy, particularly at inter-ictal state [19]. Beyond 18F-FDG-PET, brain activation studies are performed in the clinical setting with perfusion SPECT (single photon emission computed tomography) in ictal epilepsy [20]. An “ictal” scan, performed during an epileptic discharge, shows increased perfusion in the brain regions involved in seizure generation and early propagation [21]. Even though some activation studies have explored ictal 18F-FDG PET [22], the temporal resolution of 18F-FDG PET remains weak, having a longer uptake period (30 min) that leads to a mixture of interictal-, ictal-, and postictal-phase images [23], which renders the analysis of 18F-FDG PET difficult. Until now, and with some rare exceptions (previously reported ictal epilepsy, neuro-oncology, autoimmune encephalitis [24]), it should nevertheless be noted that visual or quantitative analyses of 18F-FDG imaging in a clinical routine typically rely on the identification of regions/voxels with relatively reduced metabolism. By contrast, functional 18F-FDG PET brain activation imaging is based on the identification of relative increased metabolism.

Functional 18F-FDG PET brain activation imaging may provide advantages to fMRI. Indeed, CMRGlc measured with 18F-FDG PET is known to precede the relative transient decrease in BOLD signals without any magnetic limitations [25]. Thus, CMRGlc and BOLD signals provide distinct information due to the neurometabolic and neurovascular uncoupling [26]. Indeed, while the 18F-FDG PET signal arises primarily from excitatory synaptic activity, which is localised to the grey matter tissue compartment, the BOLD-fMRI signal is more heavily weighted towards the draining veins and macrovessels [27]. In addition, better signal-to-noise ratios, variance concentrations and out-of-sample replications at the single-subject level are expected with PET imaging rather than fMRI [12], with unrivalled sensitivity in the ability to measure sub-picomolar concentrations [28]. Furthermore, activation 18F-FDG PET imaging provides the unique opportunity to study functional imaging during interactions with different environments by timely dissociating radiopharmaceutical injection to acquisitions records. In these lines, activation PET studies have been performed to explore cerebral plasticity after walking [29] and cochlear implants benefits, which could bring about magnetic interference in fMRI during deafness rehabilitation [30]. In addition, a recent original study has been proposed to use PET imaging sensitized by virtual reality exposure to objectify a virtual reality exposure therapy (VRET) response in patients with acrophobia [31]. The principle is to immerse the patient in a computer-generated virtual environment [32], and in this case, VRET allows the patient to be virtually confronted with intense phobia cues. VRET is thus easily coupled with PET imaging in the activation state. This approach can be extended to other psychiatric diseases or other virtual environments. Interestingly, recent technologic and methodologic advances in PET have increased the development of functional 18F-FDG PET brain activation imaging. The recent introduction of a solid-state digital photon counting PET detector, which enables faster time-of-flight timing resolution, allows for the realization of dynamic acquisitions with a high level of temporal resolution [33]. In parallel, original methodologic approaches have been developed, consisting of an analysis pipeline similar to that of fMRI, with a constant infusion of 18F-FDG defining within-session differential metabolic responses [34, 35, 36]. This novel method of dynamic PET imaging, described as “functional PET” (fPET) imaging, offers high sensitivity and regional specificity for multimodal brain imaging for groups or at single-subject levels, with nevertheless the cost of an increase in ionization doses required to perform PET experiments. The slowly infusing 18F-FDG over the course of the scan enables dynamic tracking of 18F-FDG uptake. In a proof-of-concept study, this slow continuous infusion technique yields sufficient 18F-FDG -PET signal in the occipital cortex during a checkerboard stimulation to be able to identify activation in the primary visual cortex [34]. Comparable results were recently obtained in the motor cortex [35, 36]. Interestingly, by using this novel method of dynamic PET imaging, the necessary task duration for reliable quantification of task-specific CMRGlu could be reduced to 2 minutes, therefore, approaching that of fMRI [36]. Looking further ahead, PET imaging with multiple 18F-FDG bolus administrations may also be performed to further increase the temporal resolution of PET imaging.

As previously mentioned, fMRI has currently developed novel approaches for studying brain connectivity. Functional connectivity can be more specifically defined as the temporal correlation between spatially remote neurophysiological events, expressed as a deviation from the statistical independence across these events in distributed neuronal groups and areas. 18F-FDG PET imaging interpretation has also recently evolved with the introduction of metabolic connectivity analyses, which refer to the relationships between the CMRGlc of different brain areas, in the same way as what is performed with the BOLD signal in fMRI [12]. Thus, metabolic connectivity at the group level can be analysed in resting-state conditions with an inter-regional correlation analysis [37, 38] or with an independent component analysis or graph theory, which enables studying connectivity without any a priori hypotheses [39, 40]. Due to the increased temporal resolution of PET imaging systems, metabolic connectivity can also be studied in functional PET imaging at a single-subject level, using a constant radiotracer infusion as mentioned above, at rest or also using an activation task, enabling development of functional connectivity in PET. The increasing availability of the combined PET/MR system is a good opportunity to develop multimodal brain activation imaging [41, 42]. One study found a close association between local metabolic activity and functional connectivity [43], whereas another, using dynamic 18F-FDG PET imaging, reflected differences in the temporal characteristics of glucose metabolism and brain activation as measured with fMRI [44]. In this line, the combination of functional and metabolic connectivity, named effective connectivity through the metabolic connectivity mapping, could help to study signalling hierarchies in the brain and their defects in brain disorders, such as hippocampal-cortical circuits in Alzheimer’s disease or fronto-midbrain interactions in major depression [45]. In addition, it is well known that decreasing mean synaptic activity of a region, i.e., decreasing metabolic connectivity, reduces the region’s sensitivity to afferent input from other regions, i.e., functional connectivity. Thus, the decoupling between both modalities could improve our understanding of the progressive evolution of neurodegenerative disorders [46].

Overall, recent advances in technologic and methodologic tools in brain PET imaging are conducive to a renaissance of functional 18F-FDG PET brain activation imaging. Improved temporal resolutions in PET systems, development of metabolic connectivity at the group level and also more recently at the single-subject level, can allow 18F-FDG PET imaging to be remade in the foreground of brain activation imaging. In this line, dynamic PET imaging, with a constant infusion of 18F-FDG, defined as functional PET imaging (fPET), will enable the development of functional connectivity in PET. In addition, the multimodality approach, which integrates functional connectivity obtained with fMRI with the PET data, can also receive profound interest due to the potential complementarity offered by both modalities [47].

Notes

Funding

This work has been conducted in the framework of DHU-Imaging thanks to the support of the A*MIDEX project (n°ANR-11-IDEX-0001-02) (« Investissements d’Avenir » French Government programme, managed by the French National Research Agency (ANR)).

Compliance with ethical standards

Conflict of interest

None.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

References

  1. 1.
    Diksic M, Yamamoto YL, Feindel W. An on-line synthesis of [15O]N2O: new blood-flow tracer for PET imaging. J Nucl Med Off Publ Soc Nucl Med. 1983;24:603–7.Google Scholar
  2. 2.
    Raichle ME, Martin WR, Herscovitch P, Mintun MA, Markham J. Brain blood flow measured with intravenous H2(15)O. II. Implementation and validation. J Nucl Med Off Publ Soc Nucl Med. 1983;24:790–8.Google Scholar
  3. 3.
    Celesia GG, Polcyn RD, Holden JE, Nickles RJ, Gatley JS, Koeppe RA. Visual evoked potentials and positron emission tomographic mapping of regional cerebral blood flow and cerebral metabolism: can the neuronal potential generators be visualized? Electroencephalogr Clin Neurophysiol. 1982;54:243–56.CrossRefPubMedCentralGoogle Scholar
  4. 4.
    Fox PT, Raichle ME. Stimulus rate dependence of regional cerebral blood flow in human striate cortex, demonstrated by positron emission tomography. J Neurophysiol. 1984;51:1109–20.CrossRefPubMedCentralGoogle Scholar
  5. 5.
    Posner MI, Petersen SE, Fox PT, Raichle ME. Localization of cognitive operations in the human brain. Science. 1988;240:1627–31.CrossRefPubMedCentralGoogle Scholar
  6. 6.
    Petersen SE, Fox PT, Posner MI, Mintun M, Raichle ME. Positron emission tomographic studies of the cortical anatomy of single-word processing. Nature. 1988;331:585–9.CrossRefPubMedCentralGoogle Scholar
  7. 7.
    Signorini M, Paulesu E, Friston K, Perani D, Colleluori A, Lucignani G, et al. Rapid assessment of regional cerebral metabolic abnormalities in single subjects with quantitative and nonquantitative [18F]FDG PET: a clinical validation of statistical parametric mapping. NeuroImage. 1999;9:63–80.CrossRefPubMedCentralGoogle Scholar
  8. 8.
    Ogawa S, Lee TM, Kay AR, Tank DW. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci USA. 1990;87:9868–72.CrossRefPubMedCentralGoogle Scholar
  9. 9.
    Rogers BP, Morgan VL, Newton AT, Gore JC. Assessing functional connectivity in the human brain by fMRI. Magn Reson Imaging. 2007;25:1347–57.CrossRefPubMedCentralGoogle Scholar
  10. 10.
    Smitha KA, Akhil Raja K, Arun KM, Rajesh PG, Thomas B, Kapilamoorthy TR, et al. Resting state fMRI: a review on methods in resting state connectivity analysis and resting state networks. Neuroradiol J. 2017;30:305–17.CrossRefPubMedCentralGoogle Scholar
  11. 11.
    Havsteen I, Madsen KH, Christensen H, Christensen A, Siebner HR. Diagnostic approach to functional recovery: functional magnetic resonance imaging after stroke. Front Neurol Neurosci. 2013;32:9–25.CrossRefPubMedCentralGoogle Scholar
  12. 12.
    Yakushev I, Drzezga A, Habeck C. Metabolic connectivity: methods and applications. Curr Opin Neurol. 2017;30:677–85.CrossRefPubMedCentralGoogle Scholar
  13. 13.
    Cheng H. Variation of noise in multi-run functional MRI using generalized autocalibrating partially parallel acquisition (GRAPPA). J Magn Reson Imaging JMRI. 2012;35:462–70.CrossRefPubMedCentralGoogle Scholar
  14. 14.
    Horwitz B, Simonyan K. PET neuroimaging: plenty of studies still need to be performed: comment on Cumming: “PET neuroimaging: the white elephant packs his trunk?”. NeuroImage. 2014;84:1101–3.CrossRefPubMedCentralGoogle Scholar
  15. 15.
    Edwards CA, Kouzani A, Lee KH, Ross EK. Neurostimulation devices for the treatment of neurologic disorders. Mayo Clin Proc. 2017;92:1427–44.CrossRefGoogle Scholar
  16. 16.
    Buxton RB. Dynamic models of BOLD contrast. NeuroImage. 2012;62:953–61.CrossRefPubMedCentralGoogle Scholar
  17. 17.
    Phelps ME, Huang SC, Hoffman EJ, Selin C, Sokoloff L, Kuhl DE. Tomographic measurement of local cerebral glucose metabolic rate in humans with (F-18)2-fluoro-2-deoxy-D-glucose: validation of method. Ann Neurol. 1979;6:371–88.CrossRefGoogle Scholar
  18. 18.
    Varrone A, Asenbaum S, Vander Borght T, Booij J, Nobili F, Någren K, et al. EANM procedure guidelines for PET brain imaging using [18F]FDG, version 2. Eur J Nucl Med Mol Imaging. 2009;36:2103–10.CrossRefGoogle Scholar
  19. 19.
    Verger A, Lagarde S, Maillard L, Bartolomei F, Guedj E. Brain molecular imaging in pharmacoresistant focal epilepsy: current practice and perspectives. Rev Neurol (Paris). 2018;174:16–27.CrossRefGoogle Scholar
  20. 20.
    Sasaki K, Ohsawa Y, Sasaki M, Kaga M, Takashima S, Matsuda H. Cerebral cortical dysplasia: assessment by MRI and SPECT. Pediatr Neurol. 2000;23:410–5.CrossRefPubMedCentralGoogle Scholar
  21. 21.
    Devous MD, Thisted RA, Morgan GF, Leroy RF, Rowe CC. SPECT brain imaging in epilepsy: a meta-analysis. J Nucl Med Off Publ Soc Nucl Med. 1998;39:285–93.Google Scholar
  22. 22.
    Siclari F, Prior JO, Rossetti AO. Ictal cerebral positron emission tomography (PET) in focal status epilepticus. Epilepsy Res. 2013;105:356–61.CrossRefPubMedCentralGoogle Scholar
  23. 23.
    Kim S, Mountz JM. SPECT imaging of epilepsy: an overview and comparison with F-18 FDG PET. Int J Mol Imaging. 2011;2011:813028.CrossRefPubMedCentralGoogle Scholar
  24. 24.
    Arbizu J, Giuliani A, Gállego Perez-Larraya J, Riverol M, Jonsson C, García-García B, et al. Emerging clinical issues and multivariate analyses in PET investigations. Q J Nucl Med Mol Imaging. 2017;61:386–404.Google Scholar
  25. 25.
    Magistretti PJ, Pellerin L. Cellular mechanisms of brain energy metabolism and their relevance to functional brain imaging. Philos Trans R Soc Lond Ser B Biol Sci. 1999;354:1155–63.CrossRefGoogle Scholar
  26. 26.
    Leiderman DB, Balish M, Sato S, Kufta C, Reeves P, Gaillard WD, et al. Comparison of PET measurements of cerebral blood flow and glucose metabolism for the localization of human epileptic foci. Epilepsy Res. 1992;13:153–7.CrossRefPubMedCentralGoogle Scholar
  27. 27.
    Wehrl HF, Hossain M, Lankes K, Liu C-C, Bezrukov I, Martirosian P, et al. Simultaneous PET-MRI reveals brain function in activated and resting state on metabolic, hemodynamic and multiple temporal scales. Nat Med. 2013;19:1184–9.CrossRefPubMedCentralGoogle Scholar
  28. 28.
    Gunn RN, Rabiner EA. PET neuroimaging: the elephant unpacks his trunk: comment on Cumming: “PET neuroimaging: the white elephant packs his trunk?”. NeuroImage. 2014;94:408–10.CrossRefPubMedCentralGoogle Scholar
  29. 29.
    Shimada H, Ishii K, Makizako H, Ishiwata K, Oda K, Suzukawa M. Effects of exercise on brain activity during walking in older adults: a randomized controlled trial. J Neuroengineering Rehabil. 2017;14:50.CrossRefGoogle Scholar
  30. 30.
    Coez A, Zilbovicius M, Ferrary E, Bouccara D, Mosnier I, Ambert-Dahan E, et al. Cochlear implant benefits in deafness rehabilitation: PET study of temporal voice activations. J Nucl Med. 2007;49:60–7.CrossRefPubMedCentralGoogle Scholar
  31. 31.
    Verger A, Malbos E, Reynaud E, Mallet P, Mestre D, Pergandi JM, et al. Brain metabolism and related connectivity in patients with acrophobia treated by virtual reality therapy: an 18F-FDG PET pilot study sensitized by virtual exposure. EJNMMI Res. In press.Google Scholar
  32. 32.
    Coelho CM, Santos JA, Silvério J, Silva CF. Virtual reality and acrophobia: one-year follow-up and case study. Cyberpsychology Behav Impact Internet Multimed Virtual Real Behav Soc. 2006;9:336–41.Google Scholar
  33. 33.
    Wright CL, Binzel K, Zhang J, Knopp MV. Advanced functional tumor imaging and precision nuclear medicine enabled by digital PET technologies. Contrast Media Mol Imaging. 2017;2017:5260305.CrossRefPubMedCentralGoogle Scholar
  34. 34.
    Villien M, Wey H-Y, Mandeville JB, Catana C, Polimeni JR, Sander CY, et al. Dynamic functional imaging of brain glucose utilization using fPET-FDG. NeuroImage. 2014;100:192–9.CrossRefPubMedCentralGoogle Scholar
  35. 35.
    Hahn A, Gryglewski G, Nics L, Hienert M, Rischka L, Vraka C, et al. Quantification of task-specific glucose metabolism with constant infusion of 18F-FDG. J Nucl Med. 2016;57:1933–40.CrossRefPubMedCentralGoogle Scholar
  36. 36.
    Rischka L, Gryglewski G, Pfaff S, Vanicek T, Hienert M, Klöbl M, et al. Reduced task durations in functional PET imaging with [ 18 F]FDG approaching that of functional MRI. NeuroImage. 2018;181:323–30.CrossRefGoogle Scholar
  37. 37.
    Verger A, Roman S, Chaudat R-M, Felician O, Ceccaldi M, Didic M, et al. Changes of metabolism and functional connectivity in late-onset deafness: evidence from cerebral 18F-FDG-PET. Hear Res. 2017;353:8–16.CrossRefGoogle Scholar
  38. 38.
    Trotta N, Baete K, Van Laere K, Goldman S, De Tiège X, Wens V. Letter to the Editor: Neurometabolic resting-state networks derived from seed-based functional connectivity analysis. J Nucl Med. 2018;jnumed.118.212878.Google Scholar
  39. 39.
    Savio A, Fünger S, Tahmasian M, Rachakonda S, Manoliu A, Sorg C, et al. Resting-state networks as simultaneously measured with functional MRI and PET. J Nucl Med. 2017;58:1314–7.CrossRefGoogle Scholar
  40. 40.
    Verger A, Klesse E, Chawki MB, Witjas T, Azulay J-P, Eusebio A, et al. Brain PET substrate of impulse control disorders in Parkinson’s disease: a metabolic connectivity study. Hum Brain Mapp. 2018;39:3178–86.CrossRefPubMedCentralGoogle Scholar
  41. 41.
    Siebner HR, Strafella AP, Rowe JB. The white elephant revived: a new marriage between PET and MRI: comment to Cumming: “PET neuroimaging: the white elephant packs his trunk?”. NeuroImage. 2014;84:1104–6.CrossRefPubMedCentralGoogle Scholar
  42. 42.
    Cumming P. Not shooting an elephant. NeuroImage. 2014;94:411–2.CrossRefPubMedCentralGoogle Scholar
  43. 43.
    Passow S, Specht K, Adamsen TC, Biermann M, Brekke N, Craven AR, et al. Default-mode network functional connectivity is closely related to metabolic activity: metabolic activity and DMN connectivity. Hum Brain Mapp. 2015;36:2027–38.CrossRefPubMedCentralGoogle Scholar
  44. 44.
    Tomasi DG, Shokri-Kojori E, Wiers CE, Kim SW, Demiral ŞB, Cabrera EA, et al. Dynamic brain glucose metabolism identifies anti-correlated cortical-cerebellar networks at rest. J Cereb Blood Flow Metab. 2017;37:3659–70.CrossRefPubMedCentralGoogle Scholar
  45. 45.
    Riedl V, Utz L, Castrillón G, Grimmer T, Rauschecker JP, Ploner M, et al. Metabolic connectivity mapping reveals effective connectivity in the resting human brain. Proc Natl Acad Sci. 2016;113:428–33.CrossRefPubMedCentralGoogle Scholar
  46. 46.
    The Alzheimer’s Disease Neuroimaging Initiative, Scherr M, Pasquini L, Benson G, Nuttall R, Gruber M, et al. Decoupling of local metabolic activity and functional connectivity links to amyloid in Alzheimer’s disease. J Alzheimers Dis. 2018;64:405–15.Google Scholar
  47. 47.
    Chen Z, Jamadar SD, Li S, Sforazzini F, Baran J, Ferris N, et al. From simultaneous to synergistic MR-PET brain imaging: A review of hybrid MR-PET imaging methodologies. Hum Brain Mapp [Internet]. 2018 [cited 2018 Aug 29]; Available from: http://doi.wiley.com/10.1002/hbm.24314.

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Nuclear Medicine & Nancyclotep Imaging PlatformCHRU Nancy, Lorraine UniversityNancyFrance
  2. 2.IADI, INSERM, UMR 1254, Lorraine UniversityNancyFrance
  3. 3.Aix-Marseille Université, CNRS, Ecole Centrale Marseille, UMR 7249, Institut FresnelMarseilleFrance
  4. 4.Department of Nuclear Medicine, Assistance Publique-Hôpitaux de MarseilleTimone University HospitalMarseilleFrance
  5. 5.CERIMED, Aix-Marseille UniversitéMarseilleFrance

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