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The estimation of incidentally encoded durations of time intervals (retrospective duration processing) is thought to rely on the retrieval of contextual information associated with a sequence of events, automatically encoded in medial temporal lobe regions. “Time cells” have been described in the hippocampus (HC), encoding the temporal progression of events and their duration. However, whether the HC supports explicit retrospective duration judgments in humans, and which neural dynamics are involved, is still poorly understood. Here we used resting-state fMRI to test the relation between variations in intrinsic connectivity patterns of the HC, and individual differences in retrospective duration processing, assessed using a novel task involving the presentation of ecological stimuli. Results showed that retrospective duration discrimination performance predicted variations in the intrinsic connectivity of the bilateral HC with the right precentral gyrus; follow-up exploratory analyses suggested a role of the CA1 and CA4/DG subfields in driving the observed pattern. Findings provide insights on neural networks associated with implicit processing of durations in the second range.

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.

Following spinal cord injury (SCI) the degree of functional (motor, autonomous, or sensory) loss correlates with the severity of nervous tissue damage. An imaging technique able to capture non-invasively and simultaneously the complex mechanisms of neuronal loss, vascular damage, and peri-lesional tissue reorganization is currently lacking in experimental SCI studies. Synchrotron X-ray phase-contrast tomography (SXPCT) has emerged as a non-destructive three-dimensional (3D) neuroimaging technique with high contrast and spatial resolution. In this framework, we developed a multi-modal approach combining SXPCT, histology and correlative methods to study neurovascular architecture in normal and spinal level C4-contused mouse spinal cords (C57BL/6J mice, age 2-3 months). The evolution of SCI lesion was imaged at the cell resolution level during the acute (30 min) and subacute (7 day) phases. Spared motor neurons (MNs) were segmented and quantified in different volumes localized at and away from the epicenter. SXPCT was able to capture neuronal loss and blood-brain barrier breakdown following SCI. Three-dimensional quantification based on SXPCT acquisitions showed no additional MN loss between 30 min and 7 days post-SCI. In addition, the analysis of hemorrhagic (at 30 min) and lesion (at 7 days) volumes revealed a high similarity in size, suggesting no extension of tissue degeneration between early and later time-points. Moreover, glial scar borders were unevenly distributed, with rostral edges being the most extended. In conclusion, SXPCT capability to image at high resolution cellular changes in 3D enables the understanding of the relationship between hemorrhagic events and nervous structure damage in SCI.

Processing of incoming sensory stimulation triggers an increase of cerebral perfusion and blood oxygenation (neurovascular response) as well as an alteration of the metabolic neurochemical profile (neurometabolic response). Here, we show in human primary visual cortex (V1) that perceived and unperceived isoluminant chromatic flickering stimuli designed to have similar neurovascular responses as measured by blood oxygenation level-dependent functional magnetic resonance imaging (BOLD-fMRI) have markedly different neurometabolic responses as measured by proton functional magnetic resonance spectroscopy (1H-fMRS). In particular, a significant regional buildup of lactate, an index of aerobic glycolysis, and glutamate, an index of malate–aspartate shuttle, occurred in V1 only when the flickering was perceived, without any relation with other behavioral or physiological variables. Whereas the BOLD-fMRI signal in V1, a proxy for input to V1, was insensitive to flickering perception by design, the BOLD-fMRI signal in secondary visual areas was larger during perceived than unperceived flickering, indicating increased output from V1. These results demonstrate that the upregulation of energy metabolism induced by visual stimulation depends on the type of information processing taking place in V1, and that 1H-fMRS provides unique information about local input/output balance that is not measured by BOLD-fMRI.

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.

Visuospatial attention is strongly lateralized, with the right hemisphere commonly exhibiting stronger activation and connectivity patterns than the left hemisphere during attentive processes. However, whether such asymmetry influences inter-hemispheric information transfer and behavioral performance is not known. Here we used a region of interest (ROI) and network-based approach to determine steady-state fMRI functional connectivity (FC) in the whole cerebral cortex during a leftward/rightward covert visuospatial attention task. We found that the global FC topology between either ROIs or networks was independent on the attended side. The side of attention significantly modulated FC strength between brain networks, with leftward attention primarily involving the connections of the right visual network with dorsal and ventral attention networks in both the left and right hemisphere. High hemispheric functional segregation significantly correlated with faster target detection response times (i.e., better performance). Our findings suggest that the dominance of the right hemisphere in visuospatial attention is associated with an hemispheric functional segregation that is beneficial for behavioral performance.

Processing of incoming sensory stimulation triggers an increase of cerebral perfusion and blood oxygenation (neurovascular response) as well as an alteration of the metabolic neurochemical profile (neurometabolic response). Here we show that perceived and unperceived isoluminant chromatic flickering stimuli designed to have similar neurovascular responses as measured by blood oxygenation level dependent functional MRI (BOLD-fMRI) in primary visual cortex (V1) have markedly different neurometabolic responses as measured by functional MRS. In particular, a significant regional buildup of lactate, an index of aerobic glycolysis, and glutamate, an index of malate-aspartate shuttle, occurred in V1 only when the flickering is perceived, without any relation with behavioral or physiological variables. Wheras the BOLD-fMRI signal in V1, a proxy for input to V1, was insensitive to flickering perception by design, the BOLD-fMRI signal in secondary visual areas was larger during perceived than unperceived flickering indicating increased output from V1. These results indicate that the upregulation of energy metabolism induced by visual stimulation depends on the type of information processing taking place in V1, and that 1H-fMRS provides unique information about local input/output balance that is not measured by BOLD-fMRI.Significance statementVisual perception has a measurable metabolic effect in the primary visual cortex (V1). Here we show that the upregulation of energy metabolism induced by isoluminant chromatic flickering depends on subjective visual perception. Within V1, perceived and unperceived stimuli that are contrast-matched to elicit similar blood-oxygenation level-dependent fMRI response are associated with clearly distinct neurochemical profiles. Specifically, regional accumulations of lactate (an index of aerobic glycolysis) and glutamate (an index of malate-aspartate shuttle) only develop during perceived stimuli, together with a larger activation of secondary visual areas. Our results imply a dissociation between metabolic and functional response, and indicate that that the upregulation of energy metabolism induced by visual stimulation depends on the type of information processing taking place in V1.

In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/. The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord., Measurement(s)spinal cordTechnology Type(s)magnetic resonance imagingFactor Type(s)manufacturer • siteSample Characteristic - OrganismHomo sapiensSample Characteristic - LocationCanada • Switzerland • Australia • United States of America • United Kingdom • Germany • French Republic • Czech Republic • Italy • Japan • Kingdom of Spain • China Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14052269

In‐scanner head motion represents a major confounding factor in functional connectivity studies and it raises particular concerns when motion correlates with the effect of interest. One such instance regards research focused on functional connectivity modulations induced by sustained cognitively demanding tasks. Indeed, cognitive engagement is generally associated with substantially lower in‐scanner movement compared with unconstrained, or minimally constrained, conditions. Consequently, the reliability of condition‐dependent changes in functional connectivity relies on effective denoising strategies. In this study, we evaluated the ability of common denoising pipelines to minimize and balance residual motion‐related artifacts between resting‐state and task conditions. Denoising pipelines—including realignment/tissue‐based regression, PCA/ICA‐based methods (aCompCor and ICA‐AROMA, respectively), global signal regression, and censoring of motion‐contaminated volumes—were evaluated according to a set of benchmarks designed to assess either residual artifacts or network identifiability. We found a marked heterogeneity in pipeline performance, with many approaches showing a differential efficacy between rest and task conditions. The most effective approaches included aCompCor, optimized to increase the noise prediction power of the extracted confounding signals, and global signal regression, although both strategies performed poorly in mitigating the spurious distance‐dependent association between motion and connectivity. Censoring was the only approach that substantially reduced distance‐dependent artifacts, yet this came at the great cost of reduced network identifiability. The implications of these findings for best practice in denoising task‐based functional connectivity data, and more generally for resting‐state data, are discussed., We showed the inefficacy of many denoising strategies in balancing motion‐related artifacts between functional conditions that are differently prone to motion, such as rest and cognitively demanding conditions. While no pipeline was able to completely suppress motion artifacts and simultaneously maximize network identifiability, we found that strategies exploiting an optimized aCompCor yielded the best results. Importantly, we advocate the use of censoring with caution, since we found the method to be cost ineffective, prone to introduce biases and to reduce network identifiability.

In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/ . The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord.

Purpose Image processing plays a fundamental role in the study of central nervous system, for example in the analysis of the vascular network in neurodegenerative diseases. Synchrotron X-ray Phase-contrast micro-Tomography (SXPCT) is a very attractive method to study weakly absorbing samples and features, such as the vascular network in the spinal cord (SC). However, the identification and segmentation of vascular structures in SXPCT images is seriously hampered by the presence of image noise and strong contrast inhomogeneities, due to the sensitivity of the technique to small electronic density variations. In order to help with these tasks, we implemented a user-friendly ImageJ plugin based on a 3D Gaussian steerable filter, tuned up for the enhancement of tubular structures in SXPCT images. Methods The developed 3D Gaussian steerable filter plugin for ImageJ is based on the steerability properties of Gaussian derivatives. We applied it to SXPCT images of ex-vivo mouse SCs acquired at different experimental conditions. Results The filter response shows a strong amplification of the source image contrast-to-background ratio (CBR), independently of structures orientation. We found that after the filter application, the CBR ratio increases by a factor ranging from ~6 to ~60. In addition, we also observed an increase of 35% of the contrast to noise ratio in the case of injured mouse SC. Conclusion The developed tool can generally facilitate the detection/segmentation of capillaries, veins and arteries that were not clearly observable in non-filtered SXPCT images. Its systematic application could allow obtaining quantitative information from pre-clinical and clinical images.

Skull-stripping and region segmentation are fundamental steps in preclinical magnetic resonance imaging (MRI) studies, and these common procedures are usually performed manually. We present Multi-task U-Net (MU-Net), a convolutional neural network designed to accomplish both tasks simultaneously. MU-Net achieved higher segmentation accuracy than state-of-the-art multi-atlas segmentation methods with an inference time of 0.35 seconds and no pre-processing requirements. We evaluated the performance of our network in the presence of skip connections and recently proposed framing connections, finding the simplest network to be the most effective. We tested MU-Net with an unusually large dataset combining several independent studies consisting of 1,782 mouse brain MRI volumes of both healthy and Huntington animals, and measured average Dice scores of 0.906 (striati), 0.937 (cortex), and 0.978 (brain mask). These high evaluation scores demonstrate that MU-Net is a powerful tool for segmentation and skull-stripping, decreasing inter and intra-rater variability of manual segmentation. The MU-Net code and the trained model are publicly available at https://github.com/Hierakonpolis/MU-Net.

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.

In the present study, we aimed at determining the metabolic responses of the human visual cortex during the presentation of chromatic and achromatic stimuli, known to preferentially activate two separate clusters of neuronal populations (called “blobs” and “interblobs”) with distinct sensitivity to color or luminance features. Since blobs and interblobs have different cytochrome-oxidase (COX) content and micro-vascularization level (i.e., different capacities for glucose oxidation), different functional metabolic responses during chromatic vs. achromatic stimuli may be expected. The stimuli were optimized to evoke a similar load of neuronal activation as measured by the bold oxygenation level dependent (BOLD) contrast. Metabolic responses were assessed using functional 1H MRS at 7 T in 12 subjects. During both chromatic and achromatic stimuli, we observed the typical increases in glutamate and lactate concentration, and decreases in aspartate and glucose concentration, that are indicative of increased glucose oxidation. However, within the detection sensitivity limits, we did not observe any difference between metabolic responses elicited by chromatic and achromatic stimuli. We conclude that the higher energy demands of activated blobs and interblobs are supported by similar increases in oxidative metabolism despite the different capacities of these neuronal populations.

Since the introduction of functional magnetic resonance imaging (fMRI), several computational approaches have been developed to examine the effect of the morphology and arrangement of blood vessels on the blood oxygenation-level dependent (BOLD) signal in the brain. In the present work, we implemented the original Ogawa's model using a numerical simulation based on the finite element method (FEM) instead of the analytical models. In literature, there are different works using analytical methods to analyse the transverse relaxation rate ( R 2 ∗ ), which BOLD signal is related to, modelling the vascular system with simple and canonical geometries such as an infinite cylinder model (ICM) or a set of cylinders. We applied the numerical simulation to the extravascular BOLD signal as a function of angular vessel distribution (perpendicular vs parallel to the static magnetic field) relevant for anatomical districts characterized by geometrical symmetries, such as spinal cord. Numerical simulations confirmed analytical results for the canonical ICM. Moreover, the perturbation to the magnetic field induced by blood deoxyhaemoglobin, as quantified assuming Brownian diffusion of water molecules around the vessel, revealed that vessels contribute the most to the variation of the R 2 ∗ when they are preferentially perpendicular to the external magnetic field, regardless of their size. Our results indicate that the numerical simulation method is sensitive to the effects of different vascular geometry. This work highlights the opportunity to extend R 2 ∗ simulations to realistic models of vasculature based on high-resolution anatomical images.

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.

Cognitive decline in Down syndrome generally shows neurodegenerative aspects similar to what is observed in Alzheimer’s disease. Few studies reported information on white matter integrity. The aim of this study was to evaluate white matter alterations in a cohort of young Down subjects, without dementia, by means of DTI technique, compared to a normal control group. The study group consisted of 17 right-handed subjects with DS and many control subjects. All individuals participating in this study were examined by MR exam including DTI acquisition (32 non-coplanar directions); image processing and analysis were performed using FMRIB Software Library (FSL version 4.1.9, http://www.fmrib.ox.ac.uk/fsl )) software package. Finally, the diffusion tensor was estimated voxel by voxel and the FA map derived from the tensor. A two-sample t test was performed to assess differences between DS and control subjects. The FA is decreased in DS subjects, compared to control subjects, in the region of the anterior thalamic radiation, the inferior fronto-occipital fasciculum, the inferior longitudinal fasciculum, and the cortico-spinal tract, bilaterally. The early white matter damage visible in our DS subjects could have great impact in the therapeutic management, in particular in better adapting the timing of therapies to counteract the toxic effect of the deposition of amyloid that leads to oxidative stress.

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.

Synchrotron X-ray Phase Contrast micro-Tomography (SXrPCμT) is a powerful tool in the investigation of biological tissues, including the central nervous system (CNS), and it allows to simultaneously detect the vascular and neuronal network avoiding contrast agents or destructive sample preparations. However, specific sample preparation procedures aimed to optimize the achievable contrast- and signal-to-noise ratio (CNR and SNR, respectively) are required. Here we report and discuss the effects of perfusion with two different fixative agents (ethanol and paraformaldehyde) and with a widely used contrast medium (MICROFIL®) on mouse spinal cord. As a main result, we found that ethanol enhances contrast at the grey/white matter interface and increases the contrast in correspondence of vascular features and fibres, thus providing an adequate spatial resolution to visualise the vascular network at the microscale. On the other hand, ethanol is known to induce tissue dehydration, likely reducing cell dimensions below the spatial resolution limit imposed by the experimental technique. Nonetheless, neurons remain well visible using either perfused paraformaldehyde or MICROFIL® compound, as these latter media do not affect tissues with dehydration effects. Paraformaldehyde appears as the best compromise: it is not a contrast agent, like MICROFIL®, but it is less invasive than ethanol and permits to visualise well both cells and blood vessels. However, a quantitative estimation of the relative grey matter volume of each sample has led us to conclude that no significant alterations in the grey matter extension compared to the white matter occur as a consequence of the perfusion procedures tested in this study.

Time-domain analysis of blood-oxygenation level-dependent (BOLD) signals allows the identification of clusters of voxels responding to photic stimulation in primary visual cortex (V1). However, the characterization of information encoding into temporal properties of the BOLD signals of an activated cluster is poorly investigated. Here, we used Shannon entropy to determine spatial and temporal information encoding in the BOLD signal within the most strongly activated area of the human visual cortex during a hemifield photic stimulation. We determined the distribution profile of BOLD signals during epochs at rest and under stimulation within small (19-121 voxels) clusters designed to include only voxels driven by the stimulus as highly and uniformly as possible. We found consistent and significant increases (2-4% on average) in temporal information entropy during activation in contralateral but not ipsilateral V1, which was mirrored by an expected loss of spatial information entropy. These opposite changes coexisted with increases in both spatial and temporal mutual information (i.e., dependence) in contralateral V1. Thus, we showed that the first cortical stage of visual processing is characterized by a specific spatiotemporal rearrangement of intracluster BOLD responses. Our results indicate that while in the space domain BOLD maps may be incapable of capturing the functional specialization of small neuronal populations due to relatively low spatial resolution, some information encoding may still be revealed in the temporal domain by an increase of temporal information entropy.

The investigation of the neuronal network in mouse spinal cord models represents the basis for the research on neurodegenerative diseases. In this framework, the quantitative analysis of the single elements in different districts is a crucial task. However, conventional 3D imaging techniques do not have enough spatial resolution and contrast to allow for a quantitative investigation of the neuronal network. Exploiting the high coherence and the high flux of synchrotron sources, X-ray Phase-Contrast multiscale-Tomography allows for the 3D investigation of the neuronal microanatomy without any aggressive sample preparation or sectioning. We investigated healthy-mouse neuronal architecture by imaging the 3D distribution of the neuronal-network with a spatial resolution of 640 nm. The high quality of the obtained images enables a quantitative study of the neuronal structure on a subject-by-subject basis. We developed and applied a spatial statistical analysis on the motor neurons to obtain quantitative information on their 3D arrangement in the healthy-mice spinal cord. Then, we compared the obtained results with a mouse model of multiple sclerosis. Our approach paves the way to the creation of a “database” for the characterization of the neuronal network main features for a comparative investigation of neurodegenerative diseases and therapies.

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.

Epilepsy is a family of brain disorders with a largely unknown etiology and high percentage of pharmacoresistance. The clinical manifestations of epilepsy are seizures, which originate from aberrant neuronal synchronization and hyperexcitability. Reactive astrocytosis, a hallmark of the epileptic tissue, develops into loss-of-function of glutamine synthetase, impairment of glutamate-glutamine cycle and increase in extracellular and astrocytic glutamate concentration. Here, we argue that chronically elevated intracellular glutamate level in astrocytes is instrumental to alterations in the metabolism of glycogen and leads to the synthesis of polyglucosans. Unaccessibility of glycogen-degrading enzymes to these insoluble molecules compromises the glycogenolysis-dependent reuptake of extracellular K(+) by astrocytes, thereby leading to increased extracellular K(+) and associated membrane depolarization. Based on current knowledge, we propose that the deterioration in structural homogeneity of glycogen particles is relevant to disruption of brain K(+) homeostasis and increased susceptibility to seizures in epilepsy.