Synopsis
Our ability to study human brain is limited by the necessity to use
noninvasive technologies. This is in contrast to animal models where a detailed
view of cellular-level brain function has become available due to recent
advances in microscopic optical imaging and genetics. Thus, a central challenge
facing neuroscience today is leveraging these mechanistic insights from animal
studies to accurately draw physiological inferences from noninvasive signals in
humans. On the essential path towards this goal is the development of a
detailed “bottom-up” forward model bridging neuronal activity at the level of
cell-type-specific populations to noninvasive imaging signals.
HIGHLIGHTS
- Blood
flow and energy metabolism are driven in parallel by neuronal activity
- The
“overshoot” prevents tissue oxygenation drop in between capillaries
- GABAergic
neurons play a key role in neurovascular coupling
- Astrocytes
may not drive CBF but contribute to BOLD by consuming O2
- Macroscopic
BOLD signal can be predicted by “bottom-up” modeling
- Free parameters in the Davis model have no
physiological meaning and should be treated simply as fitting parameters
TARGET AUDIENCE
MR
physicists developing models of fMRI signals and MDs/neuroscientists using fMRI
as a toolOUTCOME/OBJECTIVES
- Learn about recent developments in
microscopic imaging that have revealed the behavior of concrete physiological
parameters underlying BOLD fMRI
- Learn how these data can be integrated to
predict macroscopic BOLD signal
PURPOSE
Understanding
how neuronal activity drives changes in cerebral blood flow (CBF) and cerebral
metabolic rate of O2 (CMRO2) is critical for laying a
solid physiological foundation for interpreting the BOLD signal. In this talk,
we will review recent data on neurovascular and neurometabolic coupling that
have become available due to advances in microscopic imaging technology.
Further, we will introduce a theoretical framework to bridge between micro- and
macroscopic level of description and will discuss our working hypotheses on CBF
regulation and neurophysiological correlates of BOLD fMRI signals. METHODS
We will
review novel optical imaging methods with microscopic resolution that provide
definitive and quantitative measures of concrete physiological parameters. These
measures are used to predict macroscopic BOLD signal. The prediction is then
tested against BOLD-fMRI data to ensure validity of the model. RESULTS
We will provide examples of (1) how in vivo microscopic imaging technology can be used to test specific hypotheses on regulation of blood flow and identify cellular players and vasoactive messengers in neurovascular and neurometabolic coupling, and (2) how these microscopic data are integrated in a mechanistic framework to predict the BOLD signal.DISCUSSION/CONCLUSION
Recent
developments in optical microscopy now offer a versatile suite of tools for
high-resolution, high-sensitivity measurements of vascular, metabolic, and
neuronal parameters in deep tissue and local, cell-type specific manipulations
of neuronal activity. These technological advances have challenged the “too
hard to do” status quo for mechanistic studies in vivo and have defined a new standard for theoretical efforts
that now can be rooted in microscopic reality of concrete physiological
parameters’ behavior.Acknowledgements
- NIH BRAIN
Initiative grants U01 NS094232 and R01MH111359
- NIH Grants NS057198 and EB00790
References
- The roadmap for estimation of cell-type-specific neuronal activity from non-invasive measurements. Uhlirova H, Kılıç K, Tian P, Sakadžić S, Gagnon L, Thunemann M, Desjardins M, Saisan PA, Nizar K, Yaseen MA, Hagler DJ Jr, Vandenberghe M, Djurovic S, Andreassen OA, Silva GA, Masliah E, Kleinfeld D, Vinogradov S, Buxton RB, Einevoll GT, Boas DA, Dale AM, Devor A. Philos Trans R Soc Lond B Biol Sci. 2016 Oct 5;371(1705). PMID: 27574309
- Validation and optimization of hypercapnic-calibrated fMRI from oxygen-sensitive two-photon microscopy. Gagnon L, Sakadžić S, Lesage F, Pouliot P, Dale AM, Devor A, Buxton RB, Boas DA. Philos Trans R Soc Lond B Biol Sci. 2016 Oct 5;371(1705). PMID: 27574311