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Sleep is a universal and evolutionarily conserved behavior among many animal species, yet we do not have a fundamental understanding of why animals need to sleep. What we do know, however, is that sleep is critical for behavioral performance during the waking period and for long-term brain health. Here we provide an overview of some putative mechanisms that mediate the restorative effects of sleep, namely metabolic biosynthesis, fluid perfusion, and synaptic homeostasis. We then review recent experimental findings that advance the possibility of inducing sleep-like slow-wave activity (SWA) during wakefulness or enhance SWA during sleep in a top-down manner using noninvasive brain stimulation. SWA induction and SWA enhancement are believed to recapitulate the beneficial effects of sleep independent of the actual state of the subjects. If confirmed, these observations will change the way in which we investigate the neural correlates of sleep, thus paving the way for comprehending and actively controlling its restorative function.

Over the last two decades, it has been established that glucose metabolic fluxes in neurons and astrocytes are proportional to the rates of the glutamate/GABA-glutamine neurotransmitter cycles in close to 1:1 stoichiometries across a wide range of functional energy demands. However, there is presently no mechanistic explanation for these relationships. We present here a theoretical meta-analysis that tests whether the brain’s unique compartmentation of glycogen metabolism in the astrocyte and the requirement for neuronal glucose homeostasis lead to the observed stoichiometries. We found that blood-brain barrier glucose transport can be limiting during activation and that the energy demand could only be met if glycogenolysis supports neuronal glucose metabolism by replacing the glucose consumed by astrocytes, a mechanism we call Glucose Sparing by Glycogenolysis (GSG). The predictions of the GSG model are in excellent agreement with a wide range of experimental results from rats, mice, tree shrews, and humans, which were previously unexplained. Glycogenolysis and glucose sparing dictate the energy available to support neuronal activity, thus playing a fundamental role in brain function in health and disease.

Subtle semantic deficits can be observed in Alzheimer's disease (AD) patients even in the early stages of the illness. In this work, we tested the hypothesis that the semantic control network is deregulated in mild AD patients. We assessed the integrity of the semantic control system using resting-state functional magnetic resonance imaging in a cohort of patients with mild AD (n = 38; mean mini-mental state examination = 20.5) and in a group of age-matched healthy controls (n = 19). Voxel-wise analysis spatially constrained in the left fronto-temporal semantic control network identified two regions with altered functional connectivity (FC) in AD patients, specifically in the pars opercularis (POp, BA44) and in the posterior middle temporal gyrus (pMTG, BA21). Using whole-brain seed-based analysis, we demonstrated that these two regions have altered FC even beyond the semantic control network. In particular, the pMTG displayed a wide-distributed pattern of lower connectivity to several brain regions involved in language-semantic processing, along with a possibly compensatory higher connectivity to the Wernicke's area. We conclude that in mild AD brain regions belonging to the semantic control network are abnormally connected not only within the network, but also to other areas known to be critical for language processing.

One of the most common tasks in small rodents MRI pipelines is the voxel-wise segmentation of the volume in multiple classes. While many segmentation schemes have been developed for the human brain, fewer are available for rodent MRI, often by adaptation from human neuroimaging. Common methods include atlas-based and clustering schemes. The former labels the target volume by registering one or more pre-labeled atlases using a deformable registration method, in which case the result depends on the quality of the reference volumes, the registration algorithm and the label fusion approach, if more than one atlas is employed. The latter is based on an expectation maximization procedure to maximize the variance between voxel categories, and is often combined with Markov Random Fields and the atlas based approach to include spatial information, priors, and improve the classification accuracy. Our primary goal is to critically review the state of the art of rat and mouse segmentation of neuro MRI volumes and compare the available literature on popular, readily and freely available MRI toolsets, including SPM, FSL and ANTs, when applied to this task in the context of common pre-processing steps. Furthermore, we will briefly address the emerging Deep Learning methods for the segmentation of medical imaging, and the perspectives for applications to small rodents.

Brain activity at rest is characterized by widely distributed and spatially specific patterns of synchronized low-frequency blood-oxygenation level-dependent (BOLD) fluctuations, which correspond to physiologically relevant brain networks. This network behaviour is known to persist also during task execution, yet the details underlying task-associated modulations of within- and between-network connectivity are largely unknown. In this study we exploited a multi-parametric and multi-scale approach to investigate how low-frequency fluctuations adapt to a sustained n-back working memory task. We found that the transition from the resting state to the task state involves a behaviourally relevant and scale-invariant modulation of synchronization patterns within both task-positive and default mode networks. Specifically, decreases of connectivity within networks are accompanied by increases of connectivity between networks. In spite of large and widespread changes of connectivity strength, the overall topology of brain networks is remarkably preserved. We show that these findings are strongly influenced by connectivity at rest, suggesting that the absolute change of connectivity (i.e., disregarding the baseline) may be not the most suitable metric to study dynamic modulations of functional connectivity. Our results indicate that a task can evoke scale-invariant, distributed changes of BOLD fluctuations, further confirming that low frequency BOLD oscillations show a specialized response and are tightly bound to task-evoked activation. The human brain is organized in functional networks, characterized by long range functional connections between brain areas. This network behavior is modulated by the execution of tasks. In our work, we show that modulations associated to a task are massive and widespread, but changes are scale invariant and the overall topology of the networks is well preserved under stimulation, confirming that the functional networks are intrinsic features of the human brain function. We found also that the amplitude of the massive change we observed is heavily influenced by the degree of connectivity at rest, indicating that the magnitude of connectivity change is not an independent metric for the assessment of functional network dynamics.

Epilepsy is a heterogeneous family of neurological disorders that manifest as seizures, i.e. the hypersynchronous activity of large population of neurons. About 30% of epileptic patients do not respond to currently available antiepileptic drugs. Decades of intense research have elucidated the involvement of a number of possible signaling pathways, however, at present we do not have a fundamental understanding of epileptogenesis. In this paper, we review the literature on epilepsy under a wide-angle perspective, a mandatory choice that responds to the recurrent and unanswered question about what is epiphenomenal and what is causal to the disease. While focusing on the involvement of K+ and glutamate/GABA in determining neuronal hyperexcitability, emphasis is given to astrocytic contribution to epileptogenesis, and especially to loss-of-function of astrocytic glutamine synthetase following reactive astrogliosis, a hallmark of epileptic syndromes. We finally introduce the potential involvement of abnormal glycogen synthesis induced by excess glutamate in increasing susceptibility to seizures.

For many years, a tenet of cerebral metabolism held that glucose was the obligate energy substrate of the mammalian brain and that neuronal oxidative metabolism represented the majority of this glucose utilization. In 1994, Pellerin and Magistretti formulated the astrocyte-neuron lactate shuttle (ANLS) hypothesis, in which astrocytes, not neurons, metabolized glucose, with subsequent transport of the glycolytically derived lactate to fuel the energy needs of the neuron during neurotransmission. By considering the concentrations and kinetic characteristics of the nutrient transporter proteins, Simpson et al later supported the opposite view, in which lactate flows from neurons to astrocytes, thus leading to the neuron-astrocyte lactate shuttle (NALS). Most recently, a commentary was published in this journal attempting to discredit the NALS. This challenge has stimulated the present response in which we detail the inaccuracies of the commentary and further model several different possibilities. Although our simulations continue to support the predominance of neuronal glucose utilization during activation and neuronal to astrocytic lactate flow, the most important result is that, regardless of the direction of the flow, the overall contribution of lactate to cerebral glucose metabolism is found to be so small as to make this ongoing debate ‘much ado about nothing'.

In this article, a review is made of the current knowledge concerning the physiology and metabolism of neuronal activity, as provided by the application of NMR approaches in vivo. The evidence furnished by other functional spectroscopic and imaging techniques, such as PET and optical methods, are also discussed. In spite of considerable amounts of studies presented in the literature, several controversies concerning the mechanisms underlying brain function still remain, mainly due to the difficult assessment of the single vascular and metabolic dynamics which generally influence the functional signals. In this framework, methodological and technical improvements are required to provide new and reliable experimental elements, which can support or eventually modify the current models of activation.

The metabolic events of neuronal energetics during functional activity are still partially unexplained. In particular, lactate (and not glucose) was recently proposed as the main substrate for neurons during activity [Proc Natl Acad Sci USA 91 (1994) 10625] . By means of proton magnetic resonance spectroscopy, lactate was reported to increase during the first minutes of prolonged stimulation Prichard et al 1991 , Sappey-Marinier et al 1992 , Frahm et al 1996 , but the studies reported thus far suffered from low temporal resolution. In the present study we used a time-resolved proton magnetic resonance spectroscopy strategy in order to analyse the evolution of lactate during the early seconds following a brief visual stimulation (event-related design). A significant decrease in lactate concentration was observed 5 s after the stimulation, while a recovering of the baseline was observed at 12 s.

Recent advances in brain energy metabolism support the notion that glycogen in astrocytes is necessary for the clearance of neuronally-released K(+) from the extracellular space. However, how the multiple metabolic pathways involved in K(+)-induced increase in glycogen turnover are regulated is only partly understood. Here we summarize the current knowledge about the mechanisms that control glycogen metabolism during enhanced K(+) uptake. We also describe the action of the ubiquitous Na(+)/K(+) ATPase for both ion transport and intracellular signaling cascades, and emphasize its importance in understanding the complex relation between glycogenolysis and K(+) uptake.

The energetics of cerebral activity critically relies on the functional and metabolic interactions between neurons and astrocytes. Important open questions include the relation between neuronal versus astrocytic energy demand, glucose uptake and intercellular lactate transfer, as well as their dependence on the level of activity. We have developed a large-scale, constraint-based network model of the metabolic partnership between astrocytes and glutamatergic neurons that allows for a quantitative appraisal of the extent to which stoichiometry alone drives the energetics of the system. We find that the velocity of the glutamate-glutamine cycle ($V_{cyc}$) explains part of the uncoupling between glucose and oxygen utilization at increasing $V_{cyc}$ levels. Thus, we are able to characterize different activation states in terms of the tissue oxygen-glucose index (OGI). Calculations show that glucose is taken up and metabolized according to cellular energy requirements, and that partitioning of the sugar between different cell types is not significantly affected by $V_{cyc}$. Furthermore, both the direction and magnitude of the lactate shuttle between neurons and astrocytes turn out to depend on the relative cell glucose uptake while being roughly independent of $V_{cyc}$. These findings suggest that, in absence of ad hoc activity-related constraints on neuronal and astrocytic metabolism, the glutamate-glutamine cycle does not control the relative energy demand of neurons and astrocytes, and hence their glucose uptake and lactate exchange., Comment: 21 pages, incl. supporting text and tables

Several brain regions exhibit a sustained negative BOLD response (NBR) during specific tasks, as assessed with functional magnetic resonance imaging. The origin of the NBR and the relationships between the vascular/metabolic dynamics and the underlying neural activity are highly debated. Converging evidence indicates that NBR, in human and non-human primates, can be interpreted in terms of decrease in neuronal activity under its basal level, rather than a purely vascular phenomenon. However, the scarcity of direct experimental evidence suggests caution and encourages the ongoing utilization of multimodal approaches in the investigation of this effect.

Glutamate is one of the most versatile molecules present in the human brain, involved in protein synthesis, energy production, ammonia detoxification, and transport of reducing equivalents. Aside from these critical metabolic roles, glutamate plays a major part in brain function, being not only the most abundant excitatory neurotransmitter, but also the precursor for γ-aminobutyric acid (GABA), the predominant inhibitory neurotransmitter. Regulation of glutamate levels is pivotal for normal brain function, as abnormal extracellular concentration of glutamate can lead to impaired neurotransmission, neurodegeneration and even neuronal death. Understanding how the neuron-astrocyte functional and metabolic interactions modulate glutamate concentration during different activation status and under physiological and pathological conditions is a challenging task, and can only be tentatively estimated from current literature. In this paper, we focus on describing the various metabolic pathways which potentially affect glutamate concentration in the brain, and emphasize which ones are likely to produce the variations in glutamate concentration observed during enhanced neuronal activity in human studies.

A consistent and prominent feature of brain functional magnetic resonance imaging (fMRI) data is the presence of low-frequency (2+ signaling. Specifically, neurotransmission induces pulses of Ca2+ concentration in astrocytes, resulting in increased cerebral perfusion and neuroactive transmitter release by these cells (i.e., gliotransmission), which in turn stimulates neuronal activity. Noticeably, the level of neuron-astrocyte cross talk regulates the periodic behavior of the Ca2+ wave-induced BOLD fluctuations. Our results suggest that the spontaneous ongoing activity of neuroglial networks is a potential source of the observed slow fMRI signal oscillations.

Purpose: To compare the effects of anisotropic and Gaussian smoothing on the outcomes of diffusion tensor imaging (DTI) voxel-based (VB) analyses in the clinic, in terms of signal-to-noise ratio (SNR) enhancement and directional information and boundary structures preservation. Materials and Methods: DTI data of 30 Alzheimer's disease (AD) patients and 30 matched control subjects were obtained at 3T. Fractional anisotropy (FA) maps with variable degrees and quality (Gaussian and anisotropic) of smoothing were created and compared with an unsmoothed dataset. The two smoothing approaches were evaluated in terms of SNR improvements, capability to separate differential effects between patients and controls by a standard VB analysis, and level of artifacts introduced by the preprocessing. Results: Gaussian smoothing regionally biased the FA values and introduced a high variability of results in clinical analysis, greatly dependent on the kernel size. On the contrary, anisotropic smoothing proved itself capable of enhancing the SNR of images and maintaining boundary structures, with only moderate dependence of results on smoothing parameters. Conclusion: Our study suggests that anisotropic smoothing is more suitable in DTI studies; however, regardless of technique, a moderate level of smoothing seems to be preferable considering the artifacts introduced by this manipulation.

Objective To assess the contribution of regional grey matter (GM) atrophy and functional disconnection in determining the level of cognitive decline in patients with Alzheimer9s disease (AD) at different clinical stages. Methods Ten patients with amnesic mild cognitive impairment (a-MCI), 11 patients with probable AD and 10 healthy controls were recruited. T1 volumes were obtained from each subject and postprocessed according to an optimised voxel based morphometry protocol. Resting state functional MRI data were also collected from the same individuals and analysed to produce connectivity maps after identification of the default mode network (DMN) by independent component analysis. Results Compared with healthy controls, both AD and a-MCI patients showed a similar regional pattern of brain disconnection between the posterior cingulate cortex (PCC) and the medial prefrontal cortex and the rest of the brain. Conversely, the distribution of GM atrophy was significantly more restricted in a-MCI than in AD patients. Interestingly, the PCC showed reduced connectivity in a-MCI patients in the absence of GM atrophy, which was, in contrast, detectable at the stage of fully developed AD. Conclusions This study indicates that disconnection precedes GM atrophy in the PCC, which is a critical area of the DMN, and supports the hypothesis that GM atrophy in specific regions of AD brains likely reflects a long term effect of brain disconnection. In this context, our study indicates that GM atrophy in PCC accompanies the conversion from MCI to AD.

Parkinson's disease is a neurological disorder associated with the disfunction of dopaminergic pathways of the basal ganglia, mainly resulting in a progressive alteration in the execution of voluntary movements. We present a functional magnetic resonance imaging (fMRI) study on cortical activations during simple motor task performance, in six early–stage hemiparkinsonian patients and seven healthy volunteers. We acquired data in three sessions, during which subjects performed the task with right or left hand, or bimanually. We observed consistent bilateral activations in cingulate cortex and dorsolateral prefrontal cortex of Parkinsonian subjects during the execution of the task with the affected hand. In addition, patients showed both larger and stronger activations in motor cortex of the affected hemisphere with respect to the healthy hemisphere. Compared with the control group, patients showed a hyperactivation of the dorsolateral prefrontal cortex of the affected hemisphere. We concluded that a presymptomatic reorganization of the motor system is likely to occur in Parkinson's disease at earlier stages than previously hypothesized. Moreover, our results support fMRI as a sensitive technique for revealing the initial involvement of motor cortex areas at the debut of this degenerative disorder.

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 noninvasive 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 1H MRS, 13C 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.

Low frequency fluctuations (LFFs) of the BOLD signal are a major discovery in the study of the resting brain with functional magnetic resonance imaging (fMRI). Two fMRI-based measures, functional connectivity (FC), a measure of signal synchronicity, and the amplitude of LFFs (ALFF), a measure of signal periodicity, have been proved to be sensitive to changes induced by several neurological diseases, including degenerative dementia. In spite of the increasing use of these measures, whether and how they are related to each other remains to be elucidated. In this work we used voxel-wise FC and ALFF computed in different frequency bands (slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.073 Hz; and full-band: 0.01-0.073 Hz), in order to assess their relationship in healthy elderly as well as the relevant changes induced by Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI). We found that in healthy elderly subjects FC and ALFF are positively correlated in anterior and posterior cingulate cortex (full-band, slow-4 and slow-5), temporal cortex (full-band and slow-5), and in a set of subcortical regions (full-band and slow-4). These correlation patterns between FC and ALFF were absent in either AD or MCI patients. Notably, the loss of correlation between FC and ALFF in the AD group was primarily due to changes in FC rather than in ALFF. Our results indicate that degenerative dementia is characterized by a loss of global connection rather than by a decrease of fluctuation amplitude.

The aim of the present study was to analyze blood oxygenation level-dependent (BOLD) signal variation during an apnea-based task in order to assess the capability of a functional magnetic resonance imaging (MRI) procedure to estimate cerebral vascular dynamic effects. We measured BOLD contrast by hierarchical cluster analysis in healthy subjects undergoing an fMRI experiment, in which the task paradigm was one phase of inspirational apnea (IA). By processing the time courses of the fMRI experiment, analysis was performed only on a subclass of all the possible signal patterns; basically, root mean square and absolute variation differences have been calculated. Considering the baseline value obtained by computing the mean value of the initial rest period as reference, particular voxels showed relative important variations during the IA task and during the recovery phase following the IA. We focused our interest on the signal response of voxels that would correspond mainly to white and gray matter regions and that also may be affected by the proximity of large venous vessels. The results are presented as maps of space-temporal distribution of time series variations with two levels of hierarchical clustering among voxels with low to high initial response. Furthermore, we have presented a clustering of the signal response delay, conducting to a partition and identification of specified brain sites. (c) 2006 Elsevier Inc. All rights reserved.

Brain activity involves essential functional and metabolic interactions between neurons and astrocytes. The importance of astrocytic functions to neuronal signaling is supported by many experiments reporting high rates of energy consumption and oxidative metabolism in these glial cells. In the brain, almost all energy is consumed by the Na+/K+ ATPase, which hydrolyzes 1 ATP to move 3 Na+ outside and 2 K+ inside the cells. Astrocytes are commonly thought to be primarily involved in transmitter glutamate cycling, a mechanism that however only accounts for few % of brain energy utilization. In order to examine the participation of astrocytic energy metabolism in brain ion homeostasis, here we attempted to devise a simple stoichiometric relation linking glutamatergic neurotransmission to Na+ and K+ ionic currents. To this end, we took into account ion pumps and voltage/ligand-gated channels using the stoichiometry derived from available energy budget for neocortical signaling and incorporated this stoichiometric relation into a computational metabolic model of neuron-astrocyte interactions. We aimed at reproducing the experimental observations about rates of metabolic pathways obtained by 13C-NMR spectroscopy in rodent brain. When simulated data matched experiments as well as biophysical calculations, the stoichiometry for voltage/ligand-gated Na+ and K+ fluxes generated by neuronal activity was close to a 1:1 relationship, and specifically 63/58 Na+/K+ ions per glutamate released. We found that astrocytes are stimulated by the extracellular K+ exiting neurons in excess of the 3/2 Na+/K+ ratio underlying Na+/K+ ATPase-catalyzed reaction. Analysis of correlations between neuronal and astrocytic processes indicated that astrocytic K+ uptake, but not astrocytic Na+-coupled glutamate uptake, is instrumental for the establishment of neuron-astrocytic metabolic partnership. Our results emphasize the importance of K+ in stimulating the activation of astrocytes, which is relevant to the understanding of brain activity and energy metabolism at the cellular level. Electronic supplementary material The online version of this article (doi:10.1007/s11064-016-2048-0) contains supplementary material, which is available to authorized users.