The micro-architecture of the cerebral cortex: Functional neuroimaging models and metabolism (original) (raw)
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Journal of Cerebral Blood Flow & Metabolism, 2009
Unraveling the energy metabolism and the hemodynamic outcomes of excitatory and inhibitory neuronal activity is critical not only for our basic understanding of overall brain function, but also for the understanding of many brain disorders. Methodologies of magnetic resonance spectroscopy (MRS) and magnetic resonance imaging (MRI) are powerful tools for the non-invasive investigation of brain metabolism and physiology. However, the temporal and spatial resolution of in vivo MRS and MRI is not suitable to provide direct evidence for hypotheses that involve metabolic compartmentalization between different cell types, or to untangle the complex neuronal microcircuitry which results in changes of electrical activity. This review aims at describing how the current models of brain metabolism, mainly built on the basis of in vitro evidence, relate to experimental findings recently obtained in vivo by 1 H MRS, 13 C MRS and MRI. The hypotheses related to the role of different metabolic substrates, the metabolic neuron-glia interactions, along with the available theoretical predictions of the energy budget of neurotransmission, will be discussed. In addition, the cellular and network mechanisms that characterize different types of increased and suppressed neuronal activity will be considered within the sensitivity-constraints of MRS and MRI.
Mechanistic model for human brain metabolism and the neurovascular coupling
2022
The neurovascular coupling (NVC) connects cerebral activity, blood flow, and metabolism. This interconnection is used in for instance functional imaging, which analyses the blood-oxygen-dependent (BOLD) signal. The mechanisms underlying the NVC are complex, which warrants a model-based analysis of data. We have previously developed a mechanistically detailed model for the NVC, and others have proposed detailed models for cerebral metabolism. However, existing metabolic models are still not fully utilizing available magnetic resonance spectroscopy (MRS) data and are not connected to detailed models for NVC. Therefore, we herein present a new model that integrates mechanistic modelling of both MRS and BOLD data. The metabolic model covers central metabolism, and can describe time-series data for glucose, lactate, aspartate, and glutamine, measured after visual stimuli. Statistical tests confirm that the model can describe both estimation data and predict independent validation data, n...
Neural coupling mechanism in fMRI hemodynamics
Nonlinear Dynamics, 2021
Neural activity alters with the changes in cerebral blood flow (CBF) and blood oxygen saturation. Despite that these changes can be detected with functional magnetic resonance imaging (fMRI), the underlying physiological mechanism remains obscure. Upon activation of the specific brain region, CBF increases substantially, albeit with 6-8 s delay. Neuroscience has no scientific explanation for this experimental discovery yet. This study proposed a physiological mechanism for generating hemodynamic phenomena from the perspective of energy metabolism. The ratio of reduction (NADH) and oxidation states (NAD ?) of nicotinamide adenine dinucleotide in cell was considered as the variable for CBF regulation. After the specific brain region was activated, brain glycogen was rapidly consumed as reserve energy, resulting in no significant change in the ratio of NADH and NAD ? concentrations. However, when the stored energy in the cell is exhausted, the dynamic equilibrium state of the transition between NADH and NAD ? is changed, and the ratio of NADH and NAD ? concentrations is significantly increased, which regulates the blood flow to be greatly increased. Based on this physiological mechanism, this paper builds a large-scale visual nervous system network based on the Wang-Zhang neuron model, and quantitatively reproduced the hemodynamics observed in fMRI by computer numerical simulation. The results demonstrated that the negative energy mechanism, which was previously reported by our group using Wang-Zhang neuronal model, played a vital role in governing brain hemodynamics. Also, it precisely predicted the neural coupling mechanism between the energy metabolism and blood flow changes in the brain under stimulation. In nature, this mechanism is determined by imbalance and mismatch between the positive and negative energy during the spike of neuronal action potentials. A quantitative analysis was adopted to elucidate the physiological mechanism underlying this phenomenon, which would provide an insight into the principle of brain operation and the neural model of the overall brain function. Keywords fMRI Á Negative energy Á Energetics coding Á Hemodynamics Á Neural network Á Brain glycogen
Is functional neuroimaging a royal way to understand brain function or is it a new phrenology without an exact understanding what we measure? After two decades of imaging revolution, more and more authors ask this question. Brain functions are multidimensional, which can be approached from the point of (1) behavioural measures, (2) brain activation as re£ected by blood £ow and metabolic changes, (3) electrical activity of cells and cell-populations, and (4) neurotransmitter dynamics (release, receptor binding and reuptake). Using imaging techniques, we must take into consideration that even during the simplest task all of these processes operate in a closely interacting manner. Therefore, before drawing ¢nal conclusions about brain functions on the basis of a single aspect of these mechanisms, we must clarify the exact relationship among them. In this paper, we address this issue in order to draw attention to a number of uncertainties and controversies in the relationship of the four facets of brain functions.
Energetics of neuronal signaling and fMRI activity
Proceedings of the National Academy of Sciences, 2007
Energetics of resting and evoked fMRI signals were related to localized ensemble firing rates () measured by electrophysiology in rats. Two different unstimulated, or baseline, states were established by anesthesia. Halothane and ␣-chloralose established baseline states of high and low energy, respectively, in which forepaw stimulation excited the contralateral primary somatosensory cortex (S1). With ␣-chloralose, forepaw stimulation induced strong and reproducible fMRI activations in the contralateral S1, where the ensemble firing was dominated by slow signaling neurons (SSN; range of 1-13 Hz). Under halothane, weaker and less reproducible fMRI activations were observed in the contralateral S1 and elsewhere in the cortex, but ensemble activity in S1 was dominated by rapid signaling neurons (RSN; range of 13-40 Hz). For both baseline states, the RSN activity (i.e., higher frequencies, including the ␥ band) did not vary upon stimulation, whereas the SSN activity (i.e., ␣ band and lower frequencies) did change. In the high energy baseline state, a large majority of total oxidative energy [cerebral metabolic rate of oxygen consumption (CMRO2)] was devoted to RSN activity, whereas in the low energy baseline state, it was roughly divided between SSN and RSN activities. We hypothesize that in the high energy baseline state, the evoked changes in fMRI activation in areas beyond S1 are supported by rich intracortical interactions represented by RSN. We discuss implications for interpreting fMRI data where stimulus-specific ⌬CMRO2 is generally small compared with baseline CMRO2.
NeuroReport, 2003
Is functional neuroimaging a royal way to understand brain function or is it a new phrenology without an exact understanding what we measure? After two decades of imaging revolution, more and more authors ask this question. Brain functions are multidimensional, which can be approached from the point of (1) behavioural measures, (2) brain activation as re£ected by blood £ow and metabolic changes, (3) electrical activity of cells and cell-populations, and (4) neurotransmitter dynamics (release, receptor binding and reuptake). Using imaging techniques, we must take into consideration that even during the simplest task all of these processes operate in a closely interacting manner. Therefore, before drawing ¢nal conclusions about brain functions on the basis of a single aspect of these mechanisms, we must clarify the exact relationship among them. In this paper, we address this issue in order to draw attention to a number of uncertainties and controversies in the relationship of the four facets of brain functions. NeuroReport14 :1097^1106