{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T14:45:43Z","timestamp":1790865943779,"version":"4.1.0"},"reference-count":76,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Cereb Blood Flow Metab"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>\n                    The brain\u2019s resting-state energy consumption is expected to be driven by spontaneous activity. We previously used 50 resting-state fMRI (rs-fMRI) features to predict [\n                    <jats:sup>18<\/jats:sup>\n                    F]FDG SUVR as a proxy of glucose metabolism. Here, we expanded on our effort by estimating [\n                    <jats:sup>18<\/jats:sup>\n                    F]FDG kinetic parameters\n                    <jats:italic toggle=\"yes\">K<\/jats:italic>\n                    <jats:sub>i<\/jats:sub>\n                    (irreversible uptake),\n                    <jats:italic toggle=\"yes\">K<\/jats:italic>\n                    <jats:sub>1<\/jats:sub>\n                    (delivery),\n                    <jats:italic toggle=\"yes\">k<\/jats:italic>\n                    <jats:sub>3<\/jats:sub>\n                    (phosphorylation) in a large healthy control group (n\u2009=\u200947). Describing the parameters\u2019 spatial distribution at high resolution (216 regions), we showed that\n                    <jats:italic toggle=\"yes\">K<\/jats:italic>\n                    <jats:sub>1<\/jats:sub>\n                    is the least redundant (strong posteromedial pattern), and\n                    <jats:italic toggle=\"yes\">K<\/jats:italic>\n                    <jats:sub>i<\/jats:sub>\n                    and\n                    <jats:italic toggle=\"yes\">k<\/jats:italic>\n                    <jats:sub>3<\/jats:sub>\n                    have relevant differences (occipital cortices, cerebellum, thalamus). Using multilevel modeling, we investigated how much spatial variance of [\n                    <jats:sup>18<\/jats:sup>\n                    F]FDG parameters could be explained by a combination of a) rs-fMRI variables, b) cerebral blood flow (CBF) and metabolic rate of oxygen (CMRO\n                    <jats:sub>2<\/jats:sub>\n                    ) from\n                    <jats:sup>\n                      <jats:xref ref-type=\"bibr\">15<\/jats:xref>\n                    <\/jats:sup>\n                    O PET. Rs-fMRI-only models explained part of the individual variance in\n                    <jats:italic toggle=\"yes\">K<\/jats:italic>\n                    <jats:sub>i<\/jats:sub>\n                    (35%),\n                    <jats:italic toggle=\"yes\">K<\/jats:italic>\n                    <jats:sub>1<\/jats:sub>\n                    (14%),\n                    <jats:italic toggle=\"yes\">k<\/jats:italic>\n                    <jats:sub>3<\/jats:sub>\n                    (21%), while combining rs-fMRI and CMRO\n                    <jats:sub>2<\/jats:sub>\n                    led to satisfactory description of\n                    <jats:italic toggle=\"yes\">K<\/jats:italic>\n                    <jats:sub>i<\/jats:sub>\n                    (46%) especially.\n                    <jats:italic toggle=\"yes\">K<\/jats:italic>\n                    <jats:sub>i<\/jats:sub>\n                    was sensitive to both local rs-fMRI variables (\n                    <jats:italic toggle=\"yes\">ReHo<\/jats:italic>\n                    ) and CMRO\n                    <jats:sub>2<\/jats:sub>\n                    ,\n                    <jats:italic toggle=\"yes\">k<\/jats:italic>\n                    <jats:sub>3<\/jats:sub>\n                    to\n                    <jats:italic toggle=\"yes\">ReHo<\/jats:italic>\n                    ,\n                    <jats:italic toggle=\"yes\">K<\/jats:italic>\n                    <jats:sub>1<\/jats:sub>\n                    to CMRO\n                    <jats:sub>2<\/jats:sub>\n                    . This work represents a comprehensive assessment of the complex underpinnings of brain glucose consumption, and highlights links between 1) glucose phosphorylation and local brain activity, 2) glucose delivery and oxygen consumption.\n                  <\/jats:p>","DOI":"10.1177\/0271678x251329707","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T04:37:58Z","timestamp":1747283878000},"page":"1799-1815","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["The brain\u2019s \u201cdark energy\u201d puzzle\n                    <i>upgraded<\/i>\n                    : [\n                    <sup>18<\/sup>\n                    F]FDG uptake, delivery and phosphorylation, and their coupling with resting-state brain activity"],"prefix":"10.1177","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5451-6710","authenticated-orcid":false,"given":"Tommaso","family":"Volpi","sequence":"first","affiliation":[{"name":"Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA"},{"name":"Padova Neuroscience Center, University of Padova, Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2269-6267","authenticated-orcid":false,"given":"John J","family":"Lee","sequence":"additional","affiliation":[{"name":"Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrei G","family":"Vlassenko","sequence":"additional","affiliation":[{"name":"Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1970-4270","authenticated-orcid":false,"given":"Manu S","family":"Goyal","sequence":"additional","affiliation":[{"name":"Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maurizio","family":"Corbetta","sequence":"additional","affiliation":[{"name":"Padova Neuroscience Center, University of Padova, Padova, Italy"},{"name":"Department of Neuroscience, University of Padova, Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandra","family":"Bertoldo","sequence":"additional","affiliation":[{"name":"Padova Neuroscience Center, University of Padova, Padova, Italy"},{"name":"Department of Information Engineering, University of Padova, Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,5,15]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1113\/jphysiol.1890.sp000321"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.95.3.765"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1172\/JCI103159"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1126\/science. 1134405"},{"key":"e_1_3_3_6_2","unstructured":"Clarke DD Sokoloff L et\u00a0al. 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