期刊
PROGRESS IN NEUROBIOLOGY
卷 213, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.pneurobio.2022.102263
关键词
Resting state; Cortical connectivity; Intrinsic signal optical imaging; FMRI; Sensorimotor cortex; Non-human primates
资金
- NIH [R01 NS105697]
- University of Pittsburgh Brain Institute
Resting state fMRI with high contrast and resolution can reveal cortical connectivity with columnar granularity. Comparisons with microstimulation-evoked activation and anatomical connections demonstrate a high correspondence in connectivity patterns. The fidelity of functional connectivity maps to cortical connections indicates that granular details of network organization are embedded in resting state signals. Intrinsic signal optical imaging provides a bridge between fMRI and invasive approaches, allowing high-resolution mapping of cortical networks in living animals.
Resting state (RS) fMRI is now widely used for gaining insight into the organization of brain networks. Functional connectivity (FC) inferred from RS-fMRI is typically at macroscale, which is too coarse for much of the detail in cortical architecture. Here, we examined whether imaging RS at higher contrast and resolution could reveal cortical connectivity with columnar granularity. In longitudinal experiments (~1.5 years) in squirrel monkeys, we partitioned sensorimotor cortex using dense microelectrode mapping and then recorded RS with intrinsic signal optical imaging (RS-ISOI, 20 mu m/pixel). FC maps were benchmarked against microstimulation-evoked activation and traced anatomical connections. These direct comparisons showed high correspondence in connectivity patterns across methods. The fidelity of FC maps to cortical connections indicates that granular details of network organization are embedded in RS. Thus, for recording RS, the field-of-view and effective resolution achieved with ISOI fills a wide gap between fMRI and invasive approaches (2-photon imaging, electrophysiology). RS-ISOI opens exciting opportunities for high resolution mapping of cortical networks in living animals.
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