@article{oai:hirosaki.repo.nii.ac.jp:00003684,
 author = {Herman, Peter and Sanganahalli, Basavaraju G. and Coman, Daniel and Blumenfeld, Hal and Hyder, Fahmeed},
 issue = {Supplement},
 journal = {弘前医学},
 month = {Jul},
 note = {Neuronal activity mapping of cerebral functions using oxidative energetics has become an accepted functional magnetic resonance imaging （fMRI） technique, termed calibrated fMRI. It requires calculation of oxygen consumption （CMRO2） from blood oxygenation level dependent （BOLD） signal using multi-modal measurements of blood flow （CBF） and volume （CBV）. This approach is based on a biophysical model which describes tissue oxygen extraction at steady-state, therefore it is unclear if this conventional steady-state BOLD model can be applied transiently for calculating dynamic CMRO2 changes. In particular, it is unknown whether calculation of CMRO2 from calibrated fMRI differs between brief and long stimuli. In this study linearity was experimentally demonstrated between BOLD-related components and neural activity. We used multi-modal fMRI （at 11.7T） and neuronal signal measurements of local fi eld potential （LFP） and multi-unit activity （MUA） in α-chloralose anesthetized rats during forepaw stimulation to show that respective transfer functions （of BOLD, CBV, CBF） generated by deconvolution with LFP（ or MUA） are time invariant, for events in the millisecond to minute range. Since the transfer functions are time invariant for event-related and steady-state stimuli, it is possible to use calibrated fMRI in a dynamic manner. The multi-modal results allowed assignment of a significant component of the BOLD signal that can be ascribed to CMRO2 transients. Here we discuss the importance of minimizing residual signal, represented by the diff erence between modeled and raw signals, in convolution analysis using multi-modal fMRI and neural signals., 弘前医学. 61(Suppl.), 2010, p.S11-S22},
 pages = {S11--S22},
 title = {＜Symposium I＞Transient neural energetics by fMRI for brief and long stimuli},
 volume = {61},
 year = {2010}
}