TY  - JOUR
AU  - Ke, Hongjie
AU  - Adhikari, Bhim M.
AU  - Pan, Yezhi
AU  - Keator, David B.
AU  - Amen, Daniel
AU  - Gao, Si
AU  - Ma, Yizhou
AU  - Thompson, Paul M.
AU  - Jahanshad, Neda
AU  - Turner, Jessica A.
AU  - van Erp, Theo G. M.
AU  - Milad, Mohammed R.
AU  - Soares, Jair C.
AU  - Calhoun, Vince D.
AU  - Dukart, Juergen
AU  - Hong, L. Elliot
AU  - Ma, Tianzhou
AU  - Kochunov, Peter
TI  - Predicting Regional Cerebral Blood Flow Using Voxel-Wise Resting-State Functional MRI
JO  - Brain Sciences
VL  - 15
IS  - 9
SN  - 2076-3425
CY  - Basel
PB  - MDPI AG
M1  - FZJ-2026-00915
SP  - 908 -
PY  - 2025
AB  - Background: 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
LB  - PUB:(DE-HGF)16
DO  - DOI:10.3390/brainsci15090908
UR  - https://juser.fz-juelich.de/record/1052300
ER  -