The computational properties of the human brain arise from an intricate interplay between billions of neurons connected in complex networks. However, our ability to study these networks in healthy human brain is limited by the necessity to use non-invasive technologies. This is in contrast to animal models where a rich, detailed view of cellular-level brain function with cell-type-specific molecular identity 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 non-invasive 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 non-invasive imaging signals. The general idea is that specific neuronal cell types have identifiable signatures in the way they drive changes in cerebral blood flow, cerebral metabolic rate of O2 (measurable with quantitative functional Magnetic Resonance Imaging), and electrical currents/potentials (measurable with magneto/electroencephalography). This forward model would then provide the 'ground truth' for the development of new tools for tackling the inverse problem-estimation of neuronal activity from multimodal non-invasive imaging data.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.

• Klíčová slova
• BOLD fMRI, CMRO2, cerebral blood flow, magnetoencephalography, neurometabolic, neurovascular,
• MeSH
• krysa rodu Rattus MeSH
• kyslík metabolismus   MeSH
• lidé MeSH
• magnetická rezonanční tomografie přístrojové vybavení   metody   MeSH
• mapování mozku přístrojové vybavení   metody   MeSH
• modely neurologické MeSH
• mozkový krevní oběh MeSH
• myši MeSH
• neurony fyziologie   MeSH
• somatosenzorické korové centrum fyziologie   MeSH
• zvířata MeSH
• Check Tag
• krysa rodu Rattus MeSH
• lidé MeSH
• myši MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH
• Research Support, N.I.H., Extramural MeSH
• Názvy látek
• kyslík MeSH

This paper presents a new approach to inverting (fitting) models of coupled dynamical systems based on state-of-the-art (cubature) Kalman filtering. Crucially, this inversion furnishes posterior estimates of both the hidden states and parameters of a system, including any unknown exogenous input. Because the underlying generative model is formulated in continuous time (with a discrete observation process) it can be applied to a wide variety of models specified with either ordinary or stochastic differential equations. These are an important class of models that are particularly appropriate for biological time-series, where the underlying system is specified in terms of kinetics or dynamics (i.e., dynamic causal models). We provide comparative evaluations with generalized Bayesian filtering (dynamic expectation maximization) and demonstrate marked improvements in accuracy and computational efficiency. We compare the schemes using a series of difficult (nonlinear) toy examples and conclude with a special focus on hemodynamic models of evoked brain responses in fMRI. Our scheme promises to provide a significant advance in characterizing the functional architectures of distributed neuronal systems, even in the absence of known exogenous (experimental) input; e.g., resting state fMRI studies and spontaneous fluctuations in electrophysiological studies. Importantly, unlike current Bayesian filters (e.g. DEM), our scheme provides estimates of time-varying parameters, which we will exploit in future work on the adaptation and enabling of connections in the brain.

• MeSH
• algoritmy * MeSH
• hemodynamika fyziologie   MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• metoda Monte Carlo MeSH
• modely neurologické * MeSH
• mozek krevní zásobení   fyziologie   MeSH
• nervové dráhy fyziologie   MeSH
• neurony fyziologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH