Different Features of a Metabolic Connectivity Map and the Granger Causality Method in Revealing Directed Dopamine Pathways: A Study Based on Integrated PET/MR Imaging

References

1. 1.↵
   1. Wang X,
   2. Wang R,
   3. Li F, et al

   . Large-scale Granger causal brain network based on resting-state fMRI data. Neuroscience 2020;425:169–80 doi:10.1016/j.neuroscience.2019.11.006 pmid:31794821

   CrossRefPubMed
2. 2.↵
   1. Cekic S,
   2. Grandjean D,
   3. Renaud O

   . Time, frequency, and time-varying Granger-causality measures in neuroscience. Stat Med 2018;37:1910–31 doi:10.1002/sim.7621 pmid:29542141

   CrossRefPubMed
3. 3.↵
   1. Scherr M,
   2. Utz L,
   3. Tahmasian M, et al

   . Effective connectivity in the default mode network is distinctively disrupted in Alzheimer’s disease: a simultaneous resting-state FDG-PET/fMRI study. Hum Brain Mapp 2021;42:4134–43 doi:10.1002/hbm.24517 pmid:30697878

   CrossRefPubMed
4. 4.↵
   1. Gao J,
   2. Zhang D,
   3. Wang L, et al

   . Altered effective connectivity in schizophrenic patients with auditory verbal hallucinations: a resting-state fMRI study with Granger causality analysis. Front Psychiatry 2020;11:575 doi:10.3389/fpsyt.2020.00575 pmid:32670108

   CrossRefPubMed
5. 5.↵
   1. Zhang Y,
   2. Li Q,
   3. Wen X, et al

   . Granger causality reveals a dominant role of memory circuit in chronic opioid dependence. Addict Biol 2017;22:1068–80 doi:10.1111/adb.12390 pmid:26987308

   CrossRefPubMed
6. 6.↵
   1. Seth AK,
   2. Barrett AB,
   3. Barnett L

   . Granger causality analysis in neuroscience and neuroimaging. J Neurosci 2015;35:3293–97 doi:10.1523/JNEUROSCI.4399-14.2015 pmid:25716830

   FREE Full Text
7. 7.↵
   1. Huang X,
   2. Zhang D,
   3. Wang P, et al

   . Altered amygdala effective connectivity in migraine without aura: evidence from resting-state fMRI with Granger causality analysis. J Headache Pain 2021;22:25 doi:10.1186/s10194-021-01240-8 pmid:33858323

   CrossRefPubMed
8. 8.↵
   1. Li H,
   2. Hu X,
   3. Gao Y, et al

   . Neural primacy of the dorsolateral prefrontal cortex in patients with obsessive-compulsive disorder. Neuroimage Clin 2020;28:102432 doi:10.1016/j.nicl.2020.102432 pmid:32987298

   CrossRefPubMed
9. 9.↵
   1. Riedl V,
   2. Utz L,
   3. Castrillón G, et al

   . Metabolic connectivity mapping reveals effective connectivity in the resting human brain. Proc Natl Acad Sci U S A 2016;113:428–33 doi:10.1073/pnas.1513752113 pmid:26712010

   Abstract/FREE Full Text
10. 10.↵
    1. Attwell D,
    2. Laughlin SB

    . An energy budget for signaling in the grey matter of the brain. J Cereb Blood Flow Metab 2001;21:1133–45 doi:10.1097/00004647-200110000-00001 pmid:11598490

    CrossRefPubMedWeb of Science
11. 11.↵
    1. Attwell D,
    2. Iadecola C

    . The neural basis of functional brain imaging signals. Trends Neurosci 2002;25:621–25 doi:10.1016/S0166-2236(02)02264-6 pmid:12446129

    CrossRefPubMedWeb of Science
12. 12.↵
    1. Harris JJ,
    2. Jolivet R,
    3. Attwell D

    . Synaptic energy use and supply. Neuron 2012;75:762–77 doi:10.1016/j.neuron.2012.08.019 pmid:22958818

    CrossRefPubMedWeb of Science
13. 13.↵
    1. Vergara RC,
    2. Jaramillo-Riveri S,
    3. Luarte A, et al

    . The energy homeostasis principle: neuronal energy regulation drives local network dynamics generating behavior. Front Comput Neurosci 2019;13:49 doi:10.3389/fncom.2019.00049] pmid:31396067

    CrossRefPubMed
14. 14.↵
    1. Hahn A,
    2. Breakspear M,
    3. Rischka L, et al

    . Reconfiguration of functional brain networks and metabolic cost converge during task performance. ELife 2020;9:e52443 doi:10.7554/eLife.52443

    CrossRef
15. 15.↵
    1. Di X,
    2. Biswal BB

    , Alzheimer's Disease Neuroimaging Initiative. Metabolic brain covariant networks as revealed by FDG-PET with reference to resting-state fMRI networks. Brain Connect 2012;2:275–83 doi:10.1089/brain.2012.0086 pmid:23025619

    CrossRefPubMed
16. 16.↵
    1. Sala A,
    2. Caminiti SP,
    3. Presotto L, et al

    . Altered brain metabolic connectivity at multiscale level in early Parkinson’s disease. Sci Rep 2017;7:4256 doi:10.1038/s41598-017-04102-z pmid:28652595

    CrossRefPubMed
17. 17.↵
    1. Arias-Carrión O,
    2. Stamelou M,
    3. Murillo-Rodríguez E, et al

    . Dopaminergic reward system: a short integrative review. Int Arch Med 2010;3:24–26 doi:10.1186/1755-7682-3-24 pmid:20925949

    CrossRefPubMed
18. 18.↵
    1. Ikemoto S,
    2. Yang C,
    3. Tan A

    . Basal ganglia circuit loops, dopamine and motivation: a review and enquiry. Behav Brain Res 2015;290:17–31 doi:10.1016/j.bbr.2015.04.018 pmid:25907747

    CrossRefPubMed
19. 19.↵
    1. Calabresi P,
    2. Picconi B,
    3. Tozzi A, et al

    . Direct and indirect pathways of basal ganglia: a critical reappraisal. Nat Neurosci 2014;17:1022–30 doi:10.1038/nn.3743 pmid:25065439

    CrossRefPubMed
20. 20.↵
    1. Haber SN

    . Corticostriatal circuitry. Dialogues Clin Neurosci 2016;18:7–21 doi:10.31887/DCNS.2016.18.1/shaber pmid:27069376

    CrossRefPubMed
21. 21.↵
    1. Frank GK,
    2. DeGuzman MC,
    3. Shott ME, et al

    . Association of brain reward learning response with harm avoidance, weight gain, and hypothalamic effective connectivity in adolescent anorexia nervosa. JAMA Psychiatry 2018;75:1071–80 doi:10.1001/jamapsychiatry.2018.2151 pmid:30027213

    CrossRefPubMed
22. 22.↵
    1. Jamadar SD,
    2. Ward PG,
    3. Close TG, et al

    . Simultaneous BOLD-fMRI and constant infusion FDG-PET data of the resting human brain. Sci Data 2020;7:363 doi:10.1038/s41597-020-00699-5 pmid:33087725

    CrossRefPubMed
23. 23.↵
    1. Jamadar SD,
    2. Ward PG,
    3. Carey A, et al

    . Radiotracer administration for high temporal resolution positron emission tomography of the human brain: application to FDG-fPET. J Vis Exp 2019;22:(152) doi:10.3791/60259 pmid:31710045

    CrossRefPubMed
24. 24.↵
    1. Jamadar SD,
    2. Ward PG,
    3. Liang EX, et al

    . Metabolic and hemodynamic resting-state connectivity of the human brain: a high-temporal resolution simultaneous BOLD-fMRI and FDG-fPET multimodality study. Cerebral Cortex 2021;31:2855–67 doi:10.1093/cercor/bhaa393 pmid:33529320

    CrossRefPubMed
25. 25.↵
    1. Jamadar SD,
    2. Ward PG,
    3. Li S, et al

    . Simultaneous task-based BOLD-fMRI and [18-F] FDG functional PET for measurement of neuronal metabolism in the human visual cortex. Neuroimage 2019;189:258–66 doi:10.1016/j.neuroimage.2019.01.003 pmid:30615952

    CrossRefPubMed
26. 26.↵
    1. Whitfield-Gabrieli S,
    2. Nieto-Castanon A

    . Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect 2012;2:125–41 doi:10.1089/brain.2012.0073 pmid:22642651

    CrossRefPubMed
27. 27.↵
    1. Behzadi Y,
    2. Restom K,
    3. Liau J, et al

    . A component-based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 2007;37:90–101 doi:10.1016/j.neuroimage.2007.04.042 pmid:17560126

    CrossRefPubMedWeb of Science
28. 28.↵
    1. Power JD,
    2. Mitra A,
    3. Laumann TO, et al

    . Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 2014;84:320–41 doi:10.1016/j.neuroimage.2013.08.048 pmid:23994314

    CrossRefPubMed
29. 29.↵
    1. Hallquist MN,
    2. Hwang K,
    3. Luna B

    . The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. Neuroimage 2013;82:208–25 doi:10.1016/j.neuroimage.2013.05.116 pmid:23747457

    CrossRefPubMedWeb of Science
30. 30.↵
    1. Jenkinson M,
    2. Beckmann CF,
    3. Behrens TE, et al

    . FSL. Neuroimage 2012;62:782–90 doi:10.1016/j.neuroimage.2011.09.015 pmid:21979382

    CrossRefPubMedWeb of Science
31. 31.↵
    1. Ikemoto S

    . Brain reward circuitry beyond the mesolimbic dopamine system: a neurobiological theory. Neurosci Biobehav Rev 2010;35:129–50 doi:10.1016/j.neubiorev.2010.02.001 pmid:20149820

    CrossRefPubMed
32. 32.↵
    1. Volkow ND,
    2. Wise RA,
    3. Baler R

    . The dopamine motive system: implications for drug and food addiction. Nat Rev Neurosci 2017;18:741–52 doi:10.1038/nrn.2017.130 pmid:29142296

    CrossRefPubMed
33. 33.↵
    1. Shin J,
    2. Tsui W,
    3. Li Y, et al

    . Resting-state glucose metabolism level is associated with the regional pattern of amyloid pathology in Alzheimer’s disease. Int J Alzheimers Dis 2011;2011:759780 doi:10.4061/2011/759780 pmid:21461406

    CrossRefPubMed
34. 34.↵
    1. Wang J,
    2. Shan Y,
    3. Dai J, et al

    . Altered coupling between resting‐state glucose metabolism and functional activity in epilepsy. Ann Clin Transl Neurol 2020;7:1831–42 doi:10.1002/acn3.51168 pmid:32860354

    CrossRefPubMed
35. 35.↵
    1. Volkow ND,
    2. Michaelides M,
    3. Baler R

    . The neuroscience of drug reward and addiction. Physiol Rev 2019;99:2115–40 doi:10.1152/physrev.00014.2018 pmid:31507244

    CrossRefPubMed
36. 36.↵
    1. Chen H,
    2. Uddin LQ,
    3. Zhang Y, et al

    . Atypical effective connectivity of thalamo-cortical circuits in autism spectrum disorder: impaired thalamo-cortical connectivity in ASD. Autism Res 2016;9:1183–90 doi:10.1002/aur.1614 pmid:27868393

    CrossRefPubMed
37. 37.↵
    1. de la Cruz F,
    2. Wagner G,
    3. Schumann A, et al

    . Interrelations between dopamine and serotonin producing sites and regions of the default mode network. Hum Brain Mapp 2021;42:811–23 doi:10.1002/hbm.25264 pmid:33128416

    CrossRefPubMed
38. 38.↵
    1. Luo Q,
    2. Lu W,
    3. Cheng W, et al

    . Spatio-temporal Granger causality: a new framework. Neuroimage 2013;79:241–63 doi:10.1016/j.neuroimage.2013.04.091 pmid:23643924

    CrossRefPubMed