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@ARTICLE{Ke:1052300,
      author       = {Ke, Hongjie and Adhikari, Bhim M. and Pan, Yezhi and
                      Keator, David B. and Amen, Daniel and Gao, Si and Ma, Yizhou
                      and Thompson, Paul M. and Jahanshad, Neda and Turner,
                      Jessica A. and van Erp, Theo G. M. and Milad, Mohammed R.
                      and Soares, Jair C. and Calhoun, Vince D. and Dukart,
                      Juergen and Hong, L. Elliot and Ma, Tianzhou and Kochunov,
                      Peter},
      title        = {{P}redicting {R}egional {C}erebral {B}lood {F}low {U}sing
                      {V}oxel-{W}ise {R}esting-{S}tate {F}unctional {MRI}},
      journal      = {Brain Sciences},
      volume       = {15},
      number       = {9},
      issn         = {2076-3425},
      address      = {Basel},
      publisher    = {MDPI AG},
      reportid     = {FZJ-2026-00915},
      pages        = {908 -},
      year         = {2025},
      abstract     = {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},
      cin          = {INM-7},
      ddc          = {570},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.3390/brainsci15090908},
      url          = {https://juser.fz-juelich.de/record/1052300},
}