Artikel ; Online: Cortical connectivity is embedded in resting state at columnar resolution.
2022 Band 213, Seite(n) 102263
Abstract: 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. ... ...
Abstract | 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 µm/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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Mesh-Begriff(e) | Animals ; Brain/physiology ; Brain Mapping/methods ; Humans ; Magnetic Resonance Imaging/methods ; Rest/physiology |
Sprache | Englisch |
Erscheinungsdatum | 2022-03-12 |
Erscheinungsland | England |
Dokumenttyp | Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't |
ZDB-ID | 185535-9 |
ISSN | 1873-5118 ; 0301-0082 |
ISSN (online) | 1873-5118 |
ISSN | 0301-0082 |
DOI | 10.1016/j.pneurobio.2022.102263 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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