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001052300 0247_ $$2doi$$a10.3390/brainsci15090908
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001052300 1001_ $$0P:(DE-HGF)0$$aKe, Hongjie$$b0$$eCorresponding author
001052300 245__ $$aPredicting Regional Cerebral Blood Flow Using Voxel-Wise Resting-State Functional MRI
001052300 260__ $$aBasel$$bMDPI AG$$c2025
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001052300 520__ $$aBackground: Regional cerebral blood flow (rCBF) is a putative biomarker for neuropsychiatric disorders, including major depressive disorder (MDD). Methods: Here, we show that rCBF can be predicted from resting-state functional MRI (rsfMRI) at the voxel level while correcting for partial volume averaging (PVA) artifacts. Cortical patterns of MDD-related CBF differences decoded from rsfMRI using a PVA-corrected approach showed excellent agreement with CBF measured using single-photon emission computed tomography (SPECT) and arterial spin labeling (ASL). A support vector machine algorithm was trained to decode cortical voxel-wise CBF from temporal and power-spectral features of voxel-level rsfMRI time series while accounting for PVA. Three datasets, Amish Connectome Project (N = 300; 179 M/121 F, both rsfMRI and ASL data), UK Biobank (N = 8396; 3097 M/5319 F, rsfMRI data), and Amen Clinics Inc. datasets (N = 372: N = 183 M/189 F, SPECT data), were used. Results: PVA-corrected CBF values predicted from rsfMRI showed significant correlation with the whole-brain (r = 0.54, p = 2 × 10−5) and 31 out of 34 regional (r = 0.33 to 0.59, p < 1.1 × 10−3) rCBF measures from 3D ASL. PVA-corrected rCBF values showed significant regional deficits in the UKBB MDD group (Cohen’s d = −0.30 to −0.56, p < 10−28), with the strongest effect sizes observed in the frontal and cingulate areas. The regional deficit pattern of MDD-related hypoperfusion showed excellent agreement with CBF deficits observed in the SPECT data (r = 0.74, p = 4.9 × 10−7). Consistent with previous findings, this new method suggests that perfusion signals can be predicted using voxel-wise rsfMRI signals. Conclusions: CBF values computed from widely available rsfMRI can be used to study the impact of neuropsychiatric disorders such as MDD on cerebral neurophysiology.Keywords:cerebral blood flow; partial volume correction; prediction; support vector machine; rsfMRI
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001052300 7001_ $$00009-0008-1047-4206$$aAdhikari, Bhim M.$$b1
001052300 7001_ $$aPan, Yezhi$$b2
001052300 7001_ $$aKeator, David B.$$b3
001052300 7001_ $$aAmen, Daniel$$b4
001052300 7001_ $$00000-0002-4473-1142$$aGao, Si$$b5
001052300 7001_ $$aMa, Yizhou$$b6
001052300 7001_ $$00000-0002-4720-8867$$aThompson, Paul M.$$b7
001052300 7001_ $$aJahanshad, Neda$$b8
001052300 7001_ $$aTurner, Jessica A.$$b9
001052300 7001_ $$avan Erp, Theo G. M.$$b10
001052300 7001_ $$aMilad, Mohammed R.$$b11
001052300 7001_ $$aSoares, Jair C.$$b12
001052300 7001_ $$aCalhoun, Vince D.$$b13
001052300 7001_ $$0P:(DE-Juel1)177727$$aDukart, Juergen$$b14
001052300 7001_ $$0P:(DE-HGF)0$$aHong, L. Elliot$$b15
001052300 7001_ $$00000-0003-3605-0811$$aMa, Tianzhou$$b16
001052300 7001_ $$0P:(DE-HGF)0$$aKochunov, Peter$$b17
001052300 773__ $$0PERI:(DE-600)2651993-8$$a10.3390/brainsci15090908$$gVol. 15, no. 9, p. 908 -$$n9$$p908 -$$tBrain Sciences$$v15$$x2076-3425$$y2025
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