Role of ongoing, intrinsic activity of neuronal populations for quantitative neuroimaging of functional magnetic resonance imaging-based networks

Ongoing Brain Activity and Its Role in Cognition: Dual versus Baseline Models

What is the role of the brain's ongoing activity for cognition? The predominant perspectives associate ongoing brain activity with resting state, the default-mode network (DMN), and internally oriented mentation. This triad is often contrasted with task states, non-DMN brain networks, and externally oriented mentation, together comprising a "dual model" of brain and cognition. In opposition to this duality, however, we propose that ongoing brain activity serves as a neuronal baseline; this builds upon Raichle's original search for the default mode of brain function that extended beyond the canonical default-mode brain regions. That entails what we refer to as the "baseline model." Akin to an internal biological clock for the rest of the organism, the ongoing brain activity may serve as an internal point of reference or standard by providing a shared neural code for the brain's rest as well as task states, including their associated cognition. Such shared neural code is manifest in the spatiotemporal organization of the brain's ongoing activity, including its global signal topography and dynamics like intrinsic neural timescales. We conclude that recent empirical evidence supports a baseline model over the dual model; the ongoing activity provides a global shared neural code that allows integrating the brain's rest and task states, its DMN and non-DMN, and internally and externally oriented cognition.

A Non-cognitive Behavioral Model for Interpreting Functional Neuroimaging Studies

The dominant model for interpreting brain imaging experiments, which we refer to as the Standard Cognitive Model (SCM), assumes that the brain is organized in support of mental processes that control behavior. However, functional neuroimaging experiments of cognitive tasks have not shown clear anatomic segregation between mental processes originally proposed by this model. This failing has been blamed on limitations in imaging technology and non-linearity in the brain’s implementation of these processes. However, the validity of the underlying cognitive models used to describe the brain has rarely been questioned or directly tested against imaging results. We propose an alternative model of brain function, that we term the Non-cognitive Behavioral Model (NBM), which correlates observed human behavior directly with measured brain activity without making assumptions about intervening cognitive processes. Our model derives from behavioral psychology but is extended to include brain activity, in addition to behavior, as observables. A further extension is the role of neuroplasticity, as opposed to innate cognitive processes, in developing the brain’s support of cognitive behavior. We present the theoretical basis with which the SCM maps cognitive processes onto functional magnetic resonance and positron emission tomography images and compare and contrast with the NBM. We also describe how the NBM can be used experimentally to study how the brain supports behavior. Two applications are presented that support the usefulness of the NBM. In one, the NBM use of the total functional imaging signal (not just the differences between states) provides a stronger correlation of neural activity with the behavioral state of consciousness than the SCM approach in both anesthesia and coma. The second example reviews studies of facial and object recognition that provide evidence for the NBM proposal that neuroplasticity and experience play key roles in the brain’s support of recognition and other behaviors. The conclusions regarding neuroplasticity are then generalized to explain the incomplete functional segregation observed in the application of the SCM to neuroimaging.

Advances in Imaging Brain Metabolism

Metabolism is central to neuroimaging because it can reveal pathways by which neuronal and glial cells use nutrients to fuel their growth and function. We focus on advanced magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) methods used in brain metabolic studies. ¹⁷O-MRS and ³¹P-MRS, respectively, provide rates of oxygen use and ATP synthesis inside mitochondria, whereas ¹⁹F-MRS enables measurement of cytosolic glucose metabolism. Calibrated functional MRI (fMRI), an advanced form of fMRI that uses contrast generated by deoxyhemoglobin, provides maps of oxygen use that track neuronal firing across brain regions. ¹³C-MRS is the only noninvasive method of measuring both glutamatergic neurotransmission and cell-specific energetics with signaling and nonsignaling purposes. Novel MRI contrasts, arising from endogenous diamagnetic agents and exogenous paramagnetic agents, permit pH imaging of glioma. Overall, these magnetic resonance methods for imaging brain metabolism demonstrate translational potential to better understand brain disorders and guide diagnosis and treatment.

Quantifying the changes in oxygen extraction fraction and cerebral activity caused by caffeine and acetazolamide

A quantitative estimate of cerebral blood oxygen saturation is of critical importance in the investigation of cerebrovascular disease. We aimed to measure the change in venous oxygen saturation (Y_v) before and after the intake of the vaso-dynamic agents caffeine and acetazolamide with high spatial resolution using susceptibility mapping. Caffeine and acetazolamide were administered on separate days to five healthy volunteers to measure the change in oxygen extraction fraction. The internal streaking artifacts in the susceptibility maps were reduced by giving an initial susceptibility value uniformly to the structure-of-interest, based on a priori information. Using this technique, Y_v for normal physiological conditions, post-caffeine and post-acetazolamide was measured inside the internal cerebral veins as Y_Normal = 69.1 ± 3.3%, Y_Caffeine = 60.5 ± 2.8%, and Y_Acet = 79.1 ± 4.0%. This suggests that susceptibility mapping can serve as a sensitive biomarker for measuring reductions in cerebro-vascular reserve through abnormal vascular response. The percentage change in oxygen extraction fraction for caffeine and acetazolamide were found to be +27.0 ± 3.8% and -32.6 ± 2.1%, respectively. Similarly, the relative changes in cerebral blood flow in the presence of caffeine and acetazolamide were found to be -30.3% and + 31.5%, suggesting that the cerebral metabolic rate of oxygen remains stable between normal and challenged brain states for healthy subjects.

Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks

Overview: We model energy constraints in a network of spiking neurons, while exploring general questions of resource limitation on network function abstractly.

Background: Metabolic states like dietary ketosis or hypoglycemia have a large impact on brain function and disease outcomes. Glia provide metabolic support for neurons, among other functions. Yet, in computational models of glia-neuron cooperation, there have been no previous attempts to explore the effects of direct realistic energy costs on network activity in spiking neurons. Currently, biologically realistic spiking neural networks assume that membrane potential is the main driving factor for neural spiking, and do not take into consideration energetic costs.

Methods: We define local energy pools to constrain a neuron model, termed Spiking Neuron Energy Pool (SNEP), which explicitly incorporates energy limitations. Each neuron requires energy to spike, and resources in the pool regenerate over time. Our simulation displays an easy-to-use GUI, which can be run locally in a web browser, and is freely available.

Results: Energy dependence drastically changes behavior of these neural networks, causing emergent oscillations similar to those in networks of biological neurons. We analyze the system via Lotka-Volterra equations, producing several observations: (1) energy can drive self-sustained oscillations, (2) the energetic cost of spiking modulates the degree and type of oscillations, (3) harmonics emerge with frequencies determined by energy parameters, and (4) varying energetic costs have non-linear effects on energy consumption and firing rates.

Conclusions: Models of neuron function which attempt biological realism may benefit from including energy constraints. Further, we assert that observed oscillatory effects of energy limitations exist in networks of many kinds, and that these findings generalize to abstract graphs and technological applications.

Maturation of metabolic connectivity of the adolescent rat brain

Neuroimaging has been used to examine developmental changes of the brain. While PET studies revealed maturation-related changes, maturation of metabolic connectivity of the brain is not yet understood. Here, we show that rat brain metabolism is reconfigured to achieve long-distance connections with higher energy efficiency during maturation. Metabolism increased in anterior cerebrum and decreased in thalamus and cerebellum during maturation. When functional covariance patterns of PET images were examined, metabolic networks including default mode network (DMN) were extracted. Connectivity increased between the anterior and posterior parts of DMN and sensory-motor cortices during maturation. Energy efficiency, a ratio of connectivity strength to metabolism of a region, increased in medial prefrontal and retrosplenial cortices. Our data revealed that metabolic networks mature to increase metabolic connections and establish its efficiency between large-scale spatial components from childhood to early adulthood. Neurodevelopmental diseases might be understood by abnormal reconfiguration of metabolic connectivity and efficiency.

DOI:http://dx.doi.org/10.7554/eLife.11571.001

The brain consumes a great deal of a sugar called glucose, which is delivered to the brain through blood vessels. Active regions of the brain need more glucose, and so the brain has a metabolic network that controls when and where glucose is metabolized. Yet precisely how this metabolic network changes during brain development is not yet understood.

Choi et al. have now monitored the patterns of glucose metabolism in the brains of awake rats as they matured from 'childhood' to early adulthood. The experiments involved injecting the rats with radioactive glucose, and then using a technique called positron emission tomography (commonly known as 'PET scan') to monitor the metabolism of these radioactive sugar molecules in the animals’ brains.

Choi et al. showed that the patterns of glucose consumption in the brain shift drastically as the rats mature. Importantly, the findings showed that these shifts in glucose metabolism seem to support the activity of long distance connections that develop as the brain matures. The findings also showed that the increased long distance connections were energy efficient. The results suggest that these metabolic changes are likely a way of maintaining high-energy efficiency that is crucial for the brain to perform normally.

Finally, in addition to revealing the changes involved in normal brain development, these findings may have implications in neurological and psychiatric disorders in which the brain fails to achieve efficient metabolic networks as it matures.

DOI:http://dx.doi.org/10.7554/eLife.11571.002

Insights from neuroenergetics into the interpretation of functional neuroimaging: an alternative empirical model for studying the brain's support of behavior

Functional neuroimaging measures quantitative changes in neurophysiological parameters coupled to neuronal activity during observable behavior. These results have usually been interpreted by assuming that mental causation of behavior arises from the simultaneous actions of distinct psychological mechanisms or modules. However, reproducible localization of these modules in the brain using functional magnetic resonance imaging (MRI) and positron emission tomography (PET) imaging has been elusive other than for sensory systems. In this paper, we show that neuroenergetic studies using PET, calibrated functional magnetic resonance imaging (fMRI), (13)C magnetic resonance spectroscopy, and electrical recordings do not support the standard approach, which identifies the location of mental modules from changes in brain activity. Of importance in reaching this conclusion is that changes in neuronal activities underlying the fMRI signal are many times smaller than the high ubiquitous, baseline neuronal activity, or energy in resting, awake humans. Furthermore, the incremental signal depends on the baseline activity contradicting theoretical assumptions about linearity and insertion of mental modules. To avoid these problems, while making use of these valuable results, we propose that neuroimaging should be used to identify observable brain activities that are necessary for a person's observable behavior rather than being used to seek hypothesized mental processes.

The coupling of cerebral blood flow and oxygen metabolism with brain activation is similar for simple and complex stimuli in human primary visual cortex

Quantitative functional MRI (fMRI) experiments to measure blood flow and oxygen metabolism coupling in the brain typically rely on simple repetitive stimuli. Here we compared such stimuli with a more naturalistic stimulus. Previous work on the primary visual cortex showed that direct attentional modulation evokes a blood flow (CBF) response with a relatively large oxygen metabolism (CMRO2) response in comparison to an unattended stimulus, which evokes a much smaller metabolic response relative to the flow response. We hypothesized that a similar effect would be associated with a more engaging stimulus, and tested this by measuring the primary human visual cortex response to two contrast levels of a radial flickering checkerboard in comparison to the response to free viewing of brief movie clips. We did not find a significant difference in the blood flow-metabolism coupling (n=%ΔCBF/%ΔCMRO2) between the movie stimulus and the flickering checkerboards employing two different analysis methods: a standard analysis using the Davis model and a new analysis using a heuristic model dependent only on measured quantities. This finding suggests that in the primary visual cortex a naturalistic stimulus (in comparison to a simple repetitive stimulus) is either not sufficient to provoke a change in flow-metabolism coupling by attentional modulation as hypothesized, that the experimental design disrupted the cognitive processes underlying the response to a more natural stimulus, or that the technique used is not sensitive enough to detect a small difference.