Use of perampanel oral suspension for the treatment of refractory and super-refractory status epilepticus.

INTRODUCTION: Status epilepticus (SE) is a medical emergency associated with a significant risk of disability and death. The treatment of SE follows a step-wise approach, with limited data on ideal antiseizure medications (ASMs) for refractory and super refractory SE (RSE/SRSE). Perampanel (PER), an AMPA receptor antagonist, has shown promise in animal models but still has limited data in humans. This study tried to evaluate optimal dosage and safety of PER in RSE and SRSE patients. MATERIALS AND METHODS: We retrospectively analysed 17 adult patients with RSE (1) or SRSE (16) treated with PER. Demographic and clinical data, including EEG patterns, ASMs administered, PER dosages, and PER plasma concentrations, were collected. For patients receiving a 24 mg PER loading dose (full dose group), the following treatment regimen was applied: 24 mg per day for 48 h following by 16 mg per day. The response to PER was assessed based on electroencephalographic (EEG) improvement from high to low epileptiform activity or from low to the absence of epileptiform activities. Safety was evaluated monitoring hepatic and renal function. RESULTS: A response rate of 58.82 % was observed, with significantly higher responses in the full dose group (81.82 %) compared to those receiving PER doses below 24 mg (low dose group) (16.67 %) (p-value = 0.004; OR 0.044, 95 % CI 0.003 to 0.621, p = 0.021). No other clinical factors significantly influenced treatment response. Hepatic enzymes become elevated in most patients (70.59 %) but spontaneously decreased. DISCUSSION: Our findings suggest that a 24 mg PER dose administered for 48 h may be more effective in managing RSE and SRSE compared to doses below 24 mg, potentially due to pharmacokinetic factors. CONCLUSION: More robust data on PER in RSE and SRSE, including standardized dosing procedures and plasma level monitoring are needed. PER's potential benefits should be explored further, particularly in patients with RSE and SRSE.

Temporal modes of hub synchronization at rest.

The brain is a dynamic system that generates a broad repertoire of perceptual, motor, and cognitive states by the integration and segregation of different functional domains represented in large-scale brain networks. However, the fundamental mechanisms underlying brain network integration remain elusive. Here, for the first time to our knowledge, we found that in the resting state the brain visits few synchronization modes defined as clusters of temporally aligned functional hubs. These modes alternate over time and their probability of switching leads to specific temporal loops among them. Notably, although each mode involves a small set of nodes, the brain integration seems highly vulnerable to a simulated attack on this temporal synchronization mechanism. In line with the hypothesis that the resting state represents a prior sculpted by the task activity, the observed synchronization modes might be interpreted as a temporal brain template needed to respond to task/environmental demands .

Cortical cores in network dynamics.

Spontaneous brain activity at rest is spatially and temporally organized in networks of cortical and subcortical regions specialized for different functional domains. Even though brain networks were first studied individually through functional Magnetic Resonance Imaging, more recent studies focused on their dynamic 'integration'. Integration depends on two fundamental properties: the structural topology of brain networks and the dynamics of functional connectivity. In this scenario, cortical hub regions, that are central regions highly connected with other areas of the brain, play a fundamental role in serving as way stations for network traffic. In this review, we focus on the functional organization of a set of hub areas that we define as the 'dynamic core'. In the resting state, these regions dynamically interact with other regions of the brain linking multiple networks. First, we introduce and compare the statistical measures used for detecting hubs. Second, we discuss their identification based on different methods (functional Magnetic Resonance Imaging, Diffusion Weighted Imaging, Electro/Magneto Encephalography). Third, we show that the degree of interaction between these core regions and the rest of the brain varies over time, indicating that their centrality is not stationary. Moreover, alternating periods of strong and weak centrality of the core relate to periods of strong and weak global efficiency in the brain. These results indicate that information processing in the brain is not stable, but fluctuates and its temporal and spectral properties are discussed. In particular, the hypothesis of 'pulsed' information processing, discovered in the slow temporal scale, is explored for signals at higher temporal resolution.

A Dynamic Core Network and Global Efficiency in the Resting Human Brain.

Spontaneous brain activity is spatially and temporally organized in the absence of any stimulation or task in networks of cortical and subcortical regions that appear largely segregated when imaged at slow temporal resolution with functional magnetic resonance imaging (fMRI). When imaged at high temporal resolution with magneto-encephalography (MEG), these resting-state networks (RSNs) show correlated fluctuations of band-limited power in the beta frequency band (14-25 Hz) that alternate between epochs of strong and weak internal coupling. This study presents 2 novel findings on the fundamental issue of how different brain regions or networks interact in the resting state. First, we demonstrate the existence of multiple dynamic hubs that allow for across-network coupling. Second, dynamic network coupling and related variations in hub centrality correspond to increased global efficiency. These findings suggest that the dynamic organization of across-network interactions represents a property of the brain aimed at optimizing the efficiency of communication between distinct functional domains (memory, sensory-attention, motor). They also support the hypothesis of a dynamic core network model in which a set of network hubs alternating over time ensure efficient global communication in the whole brain.

Frequency specific interactions of MEG resting state activity within and across brain networks as revealed by the multivariate interaction measure.

Resting state networks (RSNs) are sets of brain regions exhibiting temporally coherent activity fluctuations in the absence of imposed task structure. RSNs have been extensively studied with fMRI in the infra-slow frequency range (nominally <10(-1)Hz). The topography of fMRI RSNs reflects stationary temporal correlation over minutes. However, neuronal communication occurs on a much faster time scale, at frequencies nominally in the range of 10(0)-10(2)Hz. We examined phase-shifted interactions in the delta (2-3.5 Hz), theta (4-7 Hz), alpha (8-12 Hz) and beta (13-30 Hz) frequency bands of resting-state source space MEG signals. These analyses were conducted between nodes of the dorsal attention network (DAN), one of the most robust RSNs, and between the DAN and other networks. Phase shifted interactions were mapped by the multivariate interaction measure (MIM), a measure of true interaction constructed from the maximization of imaginary coherency in the virtual channels comprised of voxel signals in source space. Non-zero-phase interactions occurred between homologous left and right hemisphere regions of the DAN in the delta and alpha frequency bands. Even stronger non-zero-phase interactions were detected between networks. Visual regions bilaterally showed phase-shifted interactions in the alpha band with regions of the DAN. Bilateral somatomotor regions interacted with DAN nodes in the beta band. These results demonstrate the existence of consistent, frequency specific phase-shifted interactions on a millisecond time scale between cortical regions within RSN as well as across RSNs.

Adding dynamics to the Human Connectome Project with MEG.

The Human Connectome Project (HCP) seeks to map the structural and functional connections between network elements in the human brain. Magnetoencephalography (MEG) provides a temporally rich source of information on brain network dynamics and represents one source of functional connectivity data to be provided by the HCP. High quality MEG data will be collected from 50 twin pairs both in the resting state and during performance of motor, working memory and language tasks. These data will be available to the general community. Additionally, using the cortical parcellation scheme common to all imaging modalities, the HCP will provide processing pipelines for calculating connection matrices as a function of time and frequency. Together with structural and functional data generated using magnetic resonance imaging methods, these data represent a unique opportunity to investigate brain network connectivity in a large cohort of normal adult human subjects. The analysis pipeline software and the dynamic connectivity matrices that it generates will all be made freely available to the research community.

Intrinsic functional architecture in the anaesthetized monkey brain.

The traditional approach to studying brain function is to measure physiological responses to controlled sensory, motor and cognitive paradigms. However, most of the brain's energy consumption is devoted to ongoing metabolic activity not clearly associated with any particular stimulus or behaviour. Functional magnetic resonance imaging studies in humans aimed at understanding this ongoing activity have shown that spontaneous fluctuations of the blood-oxygen-level-dependent signal occur continuously in the resting state. In humans, these fluctuations are temporally coherent within widely distributed cortical systems that recapitulate the functional architecture of responses evoked by experimentally administered tasks. Here, we show that the same phenomenon is present in anaesthetized monkeys even at anaesthetic levels known to induce profound loss of consciousness. We specifically demonstrate coherent spontaneous fluctuations within three well known systems (oculomotor, somatomotor and visual) and the 'default' system, a set of brain regions thought by some to support uniquely human capabilities. Our results indicate that coherent system fluctuations probably reflect an evolutionarily conserved aspect of brain functional organization that transcends levels of consciousness.

The Human Connectome Project: a data acquisition perspective.

The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.

The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks.

In recent years, there has been an increasing interest in the study of large-scale brain activity interaction structure from the perspective of complex networks, based on functional magnetic resonance imaging (fMRI) measurements. To assess the strength of interaction (functional connectivity, FC) between two brain regions, the linear (Pearson) correlation coefficient of the respective time series is most commonly used. Since a potential use of nonlinear FC measures has recently been discussed in this and other fields, the question arises whether particular nonlinear FC measures would be more informative for the graph analysis than linear ones. We present a comparison of network analysis results obtained from the brain connectivity graphs capturing either full (both linear and nonlinear) or only linear connectivity using 24 sessions of human resting-state fMRI. For each session, a matrix of full connectivity between 90 anatomical parcel time series is computed using mutual information. For comparison, connectivity matrices obtained for multivariate linear Gaussian surrogate data that preserve the correlations, but remove any nonlinearity are generated. Binarizing these matrices using multiple thresholds, we generate graphs corresponding to linear and full nonlinear interaction structures. The effect of neglecting nonlinearity is then assessed by comparing the values of a range of graph-theoretical measures evaluated for both types of graphs. Statistical comparisons suggest a potential effect of nonlinearity on the local measures-clustering coefficient and betweenness centrality. Nevertheless, subsequent quantitative comparison shows that the nonlinearity effect is practically negligible when compared to the intersubject variability of the graph measures. Further, on the group-average graph level, the nonlinearity effect is unnoticeable.

Multimodal integration of fMRI and EEG data for high spatial and temporal resolution analysis of brain networks.

Two major non-invasive brain mapping techniques, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporal resolution. We propose an approach based on the integration of EEG and fMRI, enabling the EEG temporal dynamics of information processing to be characterized within spatially well-defined fMRI large-scale networks. First, the fMRI data are decomposed into networks by means of spatial independent component analysis (sICA), and those associated with intrinsic activity and/or responding to task performance are selected using information from the related time-courses. Next, the EEG data over all sensors are averaged with respect to event timing, thus calculating event-related potentials (ERPs). The ERPs are subjected to temporal ICA (tICA), and the resulting components are localized with the weighted minimum norm (WMNLS) algorithm using the task-related fMRI networks as priors. Finally, the temporal contribution of each ERP component in the areas belonging to the fMRI large-scale networks is estimated. The proposed approach has been evaluated on visual target detection data. Our results confirm that two different components, commonly observed in EEG when presenting novel and salient stimuli, respectively, are related to the neuronal activation in large-scale networks, operating at different latencies and associated with different functional processes.

Superior parietal cortex activation during spatial attention shifts and visual feature conjunction.

Positron emission tomography was used to measure changes in the regional cerebral blood flow of normal people while they searched visual displays for targets defined by color, by motion, or by a conjunction of color and motion. A region in the superior parietal cortex was activated only during the conjunction task, at a location that had previously been shown to be engaged by successive shifts of spatial attention. Correspondingly, the time needed to detect a conjunction target increased with the number of items in the display, which is consistent with the use of a mechanism that successively analyzes each item in the visual field.

Attentional modulation of neural processing of shape, color, and velocity in humans.

Positron emission tomography (PET) was used to measure changes in regional cerebral blood flow of normal subjects, while they were discriminating different attributes (shape, color, and velocity) of the same set of visual stimuli. Psychophysical evidence indicated that the sensitivity for discriminating subtle stimulus changes was higher when subjects focused attention on one attribute than when they divided attention among several attributes. Correspondingly, attention enhanced the activity of different regions of extrastriate visual cortex that appear to be specialized for processing information related to the selected attribute.

A common network of functional areas for attention and eye movements.

Functional magnetic resonance imaging (fMRI) and surface-based representations of brain activity were used to compare the functional anatomy of two tasks, one involving covert shifts of attention to peripheral visual stimuli, the other involving both attentional and saccadic shifts to the same stimuli. Overlapping regional networks in parietal, frontal, and temporal lobes were active in both tasks. This anatomical overlap is consistent with the hypothesis that attentional and oculomotor processes are tightly integrated at the neural level.

Voluntary orienting is dissociated from target detection in human posterior parietal cortex.

Human ability to attend to visual stimuli based on their spatial locations requires the parietal cortex. One hypothesis maintains that parietal cortex controls the voluntary orienting of attention toward a location of interest. Another hypothesis emphasizes its role in reorienting attention toward visual targets appearing at unattended locations. Here, using event-related functional magnetic resonance (ER-fMRI), we show that distinct parietal regions mediated these different attentional processes. Cortical activation occurred primarily in the intraparietal sulcus when a location was attended before visual-target presentation, but in the right temporoparietal junction when the target was detected, particularly at an unattended location.

The McCollough effect reveals orientation discrimination in a case of cortical blindness.

BACKGROUND: The McCollough effect is a colour after-effect that is contingent on the orientation of the patterns used to induce it. To produce the effect, two differently oriented grating patterns--such as a red-and-black vertical grating and a green-and-black horizontal grating--are viewed alternatively for a few minutes. After this period of adaptation, if the black-and-white test gratings are viewed in the same orientation as the adaptation patterns, the white sections of the vertical grating will appear pale green and the white sections of the horizontal grating will appear pink. The McCollough effect indicates that colour- and orientation-coding mechanisms interact at some point during visual processing; but the question remains as to whether this interaction occurs at an early or later stage in the cortical visual pathways. In an attempt to answer this question, we studied a patient who had suffered extensive damage to extrastriate visual areas of the brain, which had left him able to see colour but little else. RESULTS: Neuropsychological and perceptual tests demonstrated that the patient, P.B., has a profound impairment in form perception and is even unable to discriminate between 90 degrees differences in the orientation of grating stimuli. He is also unable to use orientation information to control his reaching or grasping. Nevertheless, P.B. can name and discriminate different colours reliably, including those used to induce the McCollough effect. After adaptation with red-and-green gratings, P.B. appropriately reported the orientation-contingent aftereffect colours, even though he continued to be unable to discriminate the orientations of the test patterns. CONCLUSIONS: These results indicate that at some level in P.B.'s visual system orientation is being coded, but it is at a level that he is unable to use in making orientation judgements or in visuomotor control. Given the massive insult to the extrastriate cortex in P.B., it is likely that the anatomical locus of the mechanisms underlying the McCollough effect is within primary visual cortex or even earlier in the visual pathway.

Behavioural clusters and predictors of performance during recovery from stroke.

We examined the patterns and variability of recovery post-stroke in multiple behavioral domains. A large cohort of first time stroke patients with heterogeneous lesions was studied prospectively and longitudinally at 1-2 weeks, 3 months and one year post-injury with structural MRI to measure lesion anatomy and in-depth neuropsychological assessment. Impairment was described at all timepoints by a few clusters of correlated deficits. The time course and magnitude of recovery was similar across domains, with change scores largely proportional to the initial deficit and most recovery occurring within the first three months. Damage to specific white matter tracts produced poorer recovery over several domains: attention and superior longitudinal fasciculus II/III, language and posterior arcuate fasciculus, motor and corticospinal tract. Finally, after accounting for the severity of the initial deficit, language and visual memory recovery/outcome was worse with lower education, while the occurrence of multiple deficits negatively impacted attention recovery.

Neuroimaging.

Recent advances in neuroimaging have led to an increase in the types of studies possible in the field of cognitive neuroscience. Researchers are now using neuroimaging to enhance classic approaches, such as lesion-behavior studies, as well as provide information about normal functions at levels that were previously difficult to assess.

Areas involved in encoding and applying directional expectations to moving objects.

Two experiments used functional magnetic resonance imaging (fMRI) to examine the cortical areas involved in establishing an expectation about the direction of motion of an upcoming object and applying that expectation to the analysis of the object. In Experiment 1, subjects saw a stationary cue that either indicated the direction of motion of a subsequent test stimulus (directional cue) or provided no directional information (neutral cue). Their task was to detect the presence of coherent motion in the test stimulus. The stationary directional cue produced larger modulations than the neutral cue, with respect to a passive viewing baseline, both in motion-sensitive areas such as left MT+ and the anterior intraparietal sulcus, as well as motion-insensitive areas such as the posterior intraparietal sulcus and the junction of the left medial precentral sulcus and superior frontal sulcus. Experiment 2 used an event-related fMRI technique to separate signals during the cue period, in which the expectation was encoded and maintained, from signals during the subsequent test period, in which the expectation was applied to the test object. Cue period activations from a stationary, directional cue included many of the same motion-sensitive and -insensitive areas from Experiment 1 that produced directionally specific modulations. Prefrontal activations were not observed during the cue period, even though the stationary cue information had to be translated into a format appropriate for influencing motion detection, and this format was maintained for the duration of the cue period (approximately 5 sec).

Separating processes within a trial in event-related functional MRI I. The Method.

Many behavioral paradigms involve temporally overlapping sensory, cognitive, and motor components within a single trial. The complex interplay among these factors makes it desirable to separate the components of the total response without assumptions about shape of the underlying hemodynamic response. We present a method that does this. Four conditions were studied in four subjects to validate the method. Two conditions involved rapid event-related studies, one with a low-contrast (5%) flickering checkerboard and another with a high-contrast (95%) checkerboard. In the third condition, the same high-contrast checkerboard was presented with widely spaced trials. Finally, multicomponent trials were formed from temporally adjacent low-contrast and high-contrast stimuli. These trials were presented as a rapid event-related study. Low-contrast stimuli presented in isolation (partial trials) made it possible to uniquely estimate both the low-contrast and high-contrast responses. These estimated responses matched those measured in the first three conditions, thereby validating the method. Nonlinear interactions between adjacent low-contrast and high-contrast responses were shown to be significant but weak in two of the four subjects.

Separating processes within a trial in event-related functional MRI II. Analysis.

Many cognitive processes occur on time scales that can significantly affect the shape of the blood oxygenation level-dependent (BOLD) response in event-related functional MRI. This shape can be estimated from event related designs, even if these processes occur in a fixed temporal sequence (J. M. Ollinger, G. L. Shulman, and M. Corbetta. 2001. NeuroImage 13: 210-217). Several important considerations come into play when interpreting these time courses. First, in single subjects, correlations among neighboring time points give the noise a smooth appearance that can be confused with changes in the BOLD response. Second, the variance and degree of correlation among estimated time courses are strongly influenced by the timing of the experimental design. Simulations show that optimal results are obtained if the intertrial intervals are as short as possible, if they follow an exponential distribution with at least three distinct values, and if 40% of the trials are partial trials. These results are not particularly sensitive to the fraction of partial trials, so accurate estimation of time courses can be obtained with lower percentages of partial trials (20-25%). Third, statistical maps can be formed from F statistics computed with the extra sum of square principle or by t statistics computed from the cross-correlation of the time courses with a model for the hemodynamic response. The latter method relies on an accurate model for the hemodynamic response. The most robust model among those tested was a single gamma function. Finally, the power spectrum of the measured BOLD signal in rapid event-related paradigms is similar to that of the noise. Nevertheless, high-pass filtering is desirable if the appropriate model for the hemodynamic response is used.