Decoding sequence learning from single-trial intracranial EEG in humans

We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG) after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM) that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and one after a night of sleep) in two patients with depth electrodes implanted in several brain areas. They performed a visuomotor sequence (serial reaction time task, SRTT) using the fingers of their non-dominant hand. Our results show that the decoding algorithm correctly classified single iEEG trials from the trained sequence as belonging to either the initial training phase (day 1, before sleep) or a later consolidated phase (day 2, after sleep), whereas it failed to do so for trials belonging to a control condition (pseudo-random sequence). Accurate single-trial classification was achieved by taking advantage of the distributed pattern of neural activity. However, across all the contacts the hippocampus contributed most significantly to the classification accuracy for both patients, and one fronto-striatal contact for one patient. Together, these human intracranial findings demonstrate that a multivariate decoding approach can detect learning-related changes at the level of single-trial iEEG. Because it allows an unbiased identification of brain sites contributing to a behavioral effect (or experimental condition) at the level of single subject, this approach could be usefully applied to assess the neural correlates of other complex cognitive functions in patients implanted with multiple electrodes.     

Mapping the human protein interactome

Interactions are the essence of all biomolecules because they cannot fulfill their roles without interacting with other molecules. Hence, mapping the interactions of biomolecules can be useful for understanding their roles and functions. Furthermore, the development of molecular based systems biology requires an understanding of the biomolecular interactions. In recent years, the mapping of protein-protein interactions in different species has been reported, but few reports have focused on the large-scale mapping of protein-protein interactions in human. Here, we review the developments in protein interaction mapping and we discuss issues and strategies for the mapping of the human protein interactome.     

Human connectomics

Recent advances in non-invasive neuroimaging have enabled the measurement of connections between distant regions in the living human brain, thus opening up a new field of research: Human connectomics. Different imaging modalities allow the mapping of structural connections (axonal fibre tracts) as well as functional connections (correlations in time series), and individual variations in these connections may be related to individual variations in behaviour and cognition. Connectivity analysis has already led to a number of new insights about brain organization. For example, segregated brain regions may be identified by their unique patterns of connectivity, structural and functional connectivity may be compared to elucidate how dynamic interactions arise from the anatomical substrate, and the architecture of large-scale networks connecting sets of brain regions may be analysed in detail. The combined analysis of structural and functional networks has begun to reveal components or modules with distinct patterns of connections that become engaged in different cognitive tasks. Collectively, advances in human connectomics open up the possibility of studying how brain connections mediate regional brain function and thence behaviour.     

Increase trend of correlation and phase synchrony of microwire iEEG before macroseizure onset

Micro/macrowire intracranial EEG (iEEG) signals recorded from implanted micro/macroelectrodes in epileptic patients have received great attention and are considered to include much information of neuron activities in seizure transition compared to scalp EEG from cortical electrodes. Microelectrode is contacted more close to neurons than macroelectrode and it is more sensitive to neuron activity changes than macroelectrode. Microwire iEEG recordings are inevitably advantageous over macrowire iEEG recordings to reveal neuronal mechanisms contributing to the generation of seizures. In this study, we investigate the seizure generation from microwire iEEG recordings and discuss synchronization of microwire iEEGs in four frequency bands: alpha (1−30 Hz), gamma (30−80 Hz), ripple (80–250 Hz), and fast ripple (>250 Hz) via two measures: correlation and phase synchrony. We find that an increase trend of correlation or phase synchrony exists before the macroseizure onset mostly in gamma and ripple bands where the duration of the preictal states varied in different seizures ranging up to a few seconds (minutes). This finding is contrast to the well-known result that a decrease of synchronization in macro domains exists before the macroseizure onset. The finding demonstrates that it is only when the seizure has recruited enough surrounding brain tissue does the signal become strong enough to be observed on the clinical macroelectrode and as a result support the hypothesis of progressive coalescence of microseizure domains. The potential ramifications of such an early detection of microscale seizure activity may open a new window on treatment by making possible disruption of seizure activity before it becomes fully established.     

Performing Behavioral Tasks in Subjects with Intracranial Electrodes

Patients having stereo-electroencephalography (SEEG) electrode, subdural grid or depth electrode implants have a multitude of electrodes implanted in different areas of their brain for the localization of their seizure focus and eloquent areas. After implantation, the patient must remain in the hospital until the pathological area of brain is found and possibly resected. During this time, these patients offer a unique opportunity to the research community because any number of behavioral paradigms can be performed to uncover the neural correlates that guide behavior. Here we present a method for recording brain activity from intracranial implants as subjects perform a behavioral task designed to assess decision-making and reward encoding. All electrophysiological data from the intracranial electrodes are recorded during the behavioral task, allowing for the examination of the many brain areas involved in a single function at time scales relevant to behavior. Moreover, and unlike animal studies, human patients can learn a wide variety of behavioral tasks quickly, allowing for the ability to perform more than one task in the same subject or for performing controls. Despite the many advantages of this technique for understanding human brain function, there are also methodological limitations that we discuss, including environmental factors, analgesic effects, time constraints and recordings from diseased tissue. This method may be easily implemented by any institution that performs intracranial assessments; providing the opportunity to directly examine human brain function during behavior.     

Identifying Neural Drivers with Functional MRI: An Electrophysiological Validation

Whether functional magnetic resonance imaging (fMRI) allows the identification of neural drivers remains an open question of particular importance to refine physiological and neuropsychological models of the brain, and/or to understand neurophysiopathology. Here, in a rat model of absence epilepsy showing spontaneous spike-and-wave discharges originating from the first somatosensory cortex (S1BF), we performed simultaneous electroencephalographic (EEG) and fMRI measurements, and subsequent intracerebral EEG (iEEG) recordings in regions strongly activated in fMRI (S1BF, thalamus, and striatum). fMRI connectivity was determined from fMRI time series directly and from hidden state variables using a measure of Granger causality and Dynamic Causal Modelling that relates synaptic activity to fMRI. fMRI connectivity was compared to directed functional coupling estimated from iEEG using asymmetry in generalised synchronisation metrics. The neural driver of spike-and-wave discharges was estimated in S1BF from iEEG, and from fMRI only when hemodynamic effects were explicitly removed. Functional connectivity analysis applied directly on fMRI signals failed because hemodynamics varied between regions, rendering temporal precedence irrelevant. This paper provides the first experimental substantiation of the theoretical possibility to improve interregional coupling estimation from hidden neural states of fMRI. As such, it has important implications for future studies on brain connectivity using functional neuroimaging.     

Forecasting Seizures Using Intracranial EEG Measures and SVM in Naturally Occurring Canine Epilepsy

Management of drug resistant focal epilepsy would be greatly assisted by a reliable warning system capable of alerting patients prior to seizures to allow the patient to adjust activities or medication. Such a system requires successful identification of a preictal, or seizure-prone state. Identification of preictal states in continuous long- duration intracranial electroencephalographic (iEEG) recordings of dogs with naturally occurring epilepsy was investigated using a support vector machine (SVM) algorithm. The dogs studied were implanted with a 16-channel ambulatory iEEG recording device with average channel reference for a mean (st. dev.) of 380.4 (+87.5) days producing 220.2 (+104.1) days of intracranial EEG recorded at 400 Hz for analysis. The iEEG records had 51.6 (+52.8) seizures identified, of which 35.8 (+30.4) seizures were preceded by more than 4 hours of seizure-free data. Recorded iEEG data were stratified into 11 contiguous, non-overlapping frequency bands and binned into one-minute synchrony features for analysis. Performance of the SVM classifier was assessed using a 5-fold cross validation approach, where preictal training data were taken from 90 minute windows with a 5 minute pre-seizure offset. Analysis of the optimal preictal training time was performed by repeating the cross validation over a range of preictal windows and comparing results. We show that the optimization of feature selection varies for each subject, i.e. algorithms are subject specific, but achieve prediction performance significantly better than a time-matched Poisson random predictor (p<0.05) in 5/5 dogs analyzed.     

Cerebral cartography and connectomics

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics.     

Limiting Factors for Mapping Corpus-Based Semantic Representations to Brain Activity

To help understand how semantic information is represented in the human brain, a number of previous studies have explored how a linear mapping from corpus derived semantic representations to corresponding patterns of fMRI brain activations can be learned. They have demonstrated that such a mapping for concrete nouns is able to predict brain activations with accuracy levels significantly above chance, but the more recent elaborations have achieved relatively little performance improvement over the original study. In fact, the absolute accuracies of all these models are still currently rather limited, and it is not clear which aspects of the approach need improving in order to achieve performance levels that might lead to better accounts of human capabilities. This paper presents a systematic series of computational experiments designed to identify the limiting factors of the approach. Two distinct series of artificial brain activation vectors with varying levels of noise are introduced to characterize how the brain activation data restricts performance, and improved corpus based semantic vectors are developed to determine how the word set and model inputs affect the results. These experiments lead to the conclusion that the current state-of-the-art input semantic representations are already operating nearly perfectly (at least for non-ambiguous concrete nouns), and that it is primarily the quality of the fMRI data that is limiting what can be achieved with this approach. The results allow the study to end with empirically informed suggestions about the best directions for future research in this area.     

Abnormal interictal gamma activity may manifest a seizure onset zone in temporal lobe epilepsy

Even though recent studies have suggested that seizures do not occur suddenly and that before a seizure there is a period with an increased probability of seizure occurrence, neurophysiological mechanisms of interictal and pre-seizure states are unknown. The ability of mathematical methods to provide much more sensitive tools for the detection of subtle changes in the electrical activity of the brain gives promise that electrophysiological markers of enhanced seizure susceptibility can be found even during interictal periods when EEG of epilepsy patients often looks 'normal'. Previously, we demonstrated in animals that hippocampal and neocortical gamma-band rhythms (30-100 Hz) intensify long before seizures caused by systemic infusion of kainic acid. Other studies in recent years have also drawn attention to the fast activity (>30 Hz) as a possible marker of epileptogenic tissue. The current study quantified gamma-band activity during interictal periods and seizures in intracranial EEG (iEEG) in 5 patients implanted with subdural grids/intracranial electrodes during their pre-surgical evaluation. In all our patients, we found distinctive (abnormal) bursts of gamma activity with a 3 to 100 fold increase in power at gamma frequencies with respect to selected by clinicians, quiescent, artifact-free, 7-20 min "normal" background (interictal) iEEG epochs 1 to 14 hours prior to seizures. Increases in gamma activity were largest in those channels which later displayed the most intensive electrographic seizure discharges. Moreover, location of gamma-band bursts correlated (with high specificity, 96.4% and sensitivity, 83.8%) with seizure onset zone (SOZ) determined by clinicians. Spatial localization of interictal gamma rhythms within SOZ suggests that the persistent presence of abnormally intensified gamma rhythms in the EEG may be an important tool for focus localization and possibly a determinant of epileptogenesis.     

Large-scale cortical dynamics of sleep slow waves

Slow waves constitute the main signature of sleep in the electroencephalogram (EEG). They reflect alternating periods of neuronal hyperpolarization and depolarization in cortical networks. While recent findings have demonstrated their functional role in shaping and strengthening neuronal networks, a large-scale characterization of these two processes remains elusive in the human brain. In this study, by using simultaneous scalp EEG and intracranial recordings in 10 epileptic subjects, we examined the dynamics of hyperpolarization and depolarization waves over a large extent of the human cortex. We report that both hyperpolarization and depolarization processes can occur with two different characteristic time durations which are consistent across all subjects. For both hyperpolarization and depolarization waves, their average speed over the cortex was estimated to be approximately 1 m/s. Finally, we characterized their propagation pathways by studying the preferential trajectories between most involved intracranial contacts. For both waves, although single events could begin in almost all investigated sites across the entire cortex, we found that the majority of the preferential starting locations were located in frontal regions of the brain while they had a tendency to end in posterior and temporal regions.     

Targeting and non-targeting effects of nanomaterials on DNA: challenges and perspectives

Due to their large-scale manufacture and widespread application, there have been a number of studies related to toxicological assessment of nanomaterials (NMs) over the past decade. Although there has been extensive research on the cytotoxicity of NMs, concerns have been raised about their possible genotoxicity. The genome is constantly exposed to genotoxic insults that can lead to DNA damage, which in turn can have consequences for health, such as the induction of carcinogenesis. This comprehensive review focuses on the direct and indirect interactions of NMs with DNA. Factors influencing the genotoxicity of NMs, such as their physicochemical characteristics, are also discussed. The mechanisms involved in the direct and indirect interactions of NMs with DNA are also reviewed. Many studies have shown that ENMs have genotoxic effects, such as chromosomal fragmentation, DNA strand breaks, point mutations, oxidative DNA adducts, apoptosis, hypoxic responses, mitochondrial dysfunction, and epigenetic modifications. As the data reported to date are inconsistent, it is difficult to draw definitive conclusions regarding the features of NMs that promote genotoxicity. Therefore, challenges and future research perspectives are discussed. This review provides insights into the genotoxic effects of NMs and their consequences for human health.

     

Is the cardiac monitoring function related to the self in both the default network and right anterior insula?

The self has been proposed to be rooted in the neural monitoring of internal bodily signals and might thus involve interoceptive areas, notably the right anterior insula (rAI). However, studies on the self consistently showed the involvement of midline default network (DN) nodes, without referring to visceral monitoring. Here, we investigate this apparent discrepancy. We previously showed that neural responses to heartbeats in the DN encode two different self-dimensions, the agentive ‘I’ and the introspective ‘Me’, in a whole-brain analysis of magnetoencephalography (MEG) data. Here, we confirm and anatomically refine this result with intracranial recordings (intracranial electroencephalography, iEEG). In two patients, we show a parametric modulation of neural responses to heartbeats by the self-relatedness of thoughts, at the single trial level. A region-of-interest analysis of the insula reveals that MEG responses to heartbeats in the rAI encode the ‘I’ self-dimension. The effect in rAI was weaker than in the DN and was replicated in iEEG data in one patient out of two. We propose that a common mechanism, the neural monitoring of cardiac signals, underlies the self in both the DN and rAI. This might reconcile studies on the self highlighting the DN, with studies on interoception focusing on the insula.This article is part of the themed issue ‘Interoception beyond homeostasis: affect, cognition and mental health’.     

Modulation of aversive value coding in the vertebrate and invertebrate brain

Avoiding potentially dangerous situations is key for the survival of any organism. Throughout life, animals learn to avoid environments, stimuli or actions that can lead to bodily harm. While the neural bases for appetitive learning, evaluation and value-based decision-making have received much attention, recent studies have revealed more complex computations for aversive signals during learning and decision-making than previously thought. Furthermore, previous experience, internal state and systems level appetitive-aversive interactions seem crucial for learning specific aversive value signals and making appropriate choices. The emergence of novel methodologies (computation analysis coupled with large-scale neuronal recordings, neuronal manipulations at unprecedented resolution offered by genetics, viral strategies and connectomics) has helped to provide novel circuit-based models for aversive (and appetitive) valuation. In this review, we focus on recent vertebrate and invertebrate studies yielding strong evidence that aversive value information can be computed by a multitude of interacting brain regions, and that past experience can modulate future aversive learning and therefore influence value-based decisions.     

Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding

Brain-wide interactions generating complex neural dynamics are considered crucial for emergent cognitive functions. However, the irreducible nature of nonlinear and high-dimensional dynamical interactions challenges conventional reductionist approaches. We introduce a model-free method, based on embedding theorems in nonlinear state-space reconstruction, that permits a simultaneous characterization of complexity in local dynamics, directed interactions between brain areas, and how the complexity is produced by the interactions. We demonstrate this method in large-scale electrophysiological recordings from awake and anesthetized monkeys. The cross-embedding method captures structured interaction underlying cortex-wide dynamics that may be missed by conventional correlation-based analysis, demonstrating a critical role of time-series analysis in characterizing brain state. The method reveals a consciousness-related hierarchy of cortical areas, where dynamical complexity increases along with cross-area information flow. These findings demonstrate the advantages of the cross-embedding method in deciphering large-scale and heterogeneous neuronal systems, suggesting a crucial contribution by sensory-frontoparietal interactions to the emergence of complex brain dynamics during consciousness.     

PTFOS: Flexible and Absorbable Intracranial Electrodes for Magnetic Resonance Imaging

Intracranial electrocortical recording and stimulation can provide unique knowledge about functional brain anatomy in patients undergoing brain surgery. This approach is commonly used in the treatment of medically refractory epilepsy. However, it can be very difficult to integrate the results of cortical recordings with other brain mapping modalities, particularly functional magnetic resonance imaging (fMRI). The ability to integrate imaging and electrophysiological information with simultaneous subdural electrocortical recording/stimulation and fMRI could offer significant insight for cognitive and systems neuroscience as well as for clinical neurology, particularly for patients with epilepsy or functional disorders. However, standard subdural electrodes cause significant artifact in MRI images, and concerns about risks such as cortical heating have generally precluded obtaining MRI in patients with implanted electrodes. We propose an electrode set based on polymer thick film organic substrate (PTFOS), an organic absorbable, flexible and stretchable electrode grid for intracranial use. These new types of MRI transparent intracranial electrodes are based on nano-particle ink technology that builds on our earlier development of an EEG/fMRI electrode set for scalp recording. The development of MRI-compatible recording/stimulation electrodes with a very thin profile could allow functional mapping at the individual subject level of the underlying feedback and feed forward networks. The thin flexible substrate would allow the electrodes to optimally contact the convoluted brain surface. Performance properties of the PTFOS were assessed by MRI measurements, finite difference time domain (FDTD) simulations, micro-volt recording, and injecting currents using standard electrocortical stimulation in phantoms. In contrast to the large artifacts exhibited with standard electrode sets, the PTFOS exhibited no artifact due to the reduced amount of metal and conductivity of the electrode/trace ink and had similar electrical properties to a standard subdural electrode set. The enhanced image quality could enable routine MRI exams of patients with intracranial electrode implantation and could also lead to chronic implantation solutions.     

Comparison of human protein-protein interaction maps

MOTIVATION: Large-scale mappings of protein-protein interactions have started to give us new views of the complex molecular mechanisms inside a cell. After initial projects to systematically map protein interactions in model organisms such as yeast, worm and fly, researchers have begun to focus on the mapping of the human interactome. To tackle this enormous challenge, different approaches have been proposed and pursued. While several large-scale human protein interaction maps have recently been published, their quality remains to be critically assessed. RESULTS: We present here a first comparative analysis of eight currently available large-scale maps with a total of over 10,000 unique proteins and 57,000 interactions included. They are based either on literature search, orthology or by yeast-two-hybrid assays. Comparison reveals only a small, but statistically significant overlap. More importantly, our analysis gives clear indications that all interaction maps imply considerable selection and detection biases. These results have to be taken into account for future assembly of the human interactome. AVAILABILITY: An integrated human interaction network called Unified Human Interactome (UniHI) is made publicly accessible at http://www.mdc-berlin.de/unihi. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Pathogenesis of hepatic encephalopathy and brain edema in acute liver failure: role of glutamine redefined

Acute liver failure (ALF) is characterized neuropathologically by cytotoxic brain edema and biochemically by increased brain ammonia and its detoxification product, glutamine. The osmotic actions of increased glutamine synthesis in astrocytes are considered to be causally related to brain edema and its complications (intracranial hypertension, brain herniation) in ALF. However studies using multinuclear (1)H- and (13)C-NMR spectroscopy demonstrate that neither brain glutamine concentrations per se nor brain glutamine synthesis rates correlate with encephalopathy grade or the presence of brain edema in ALF. An alternative mechanism is now proposed whereby the newly synthesized glutamine is trapped within the astrocyte as a consequence of down-regulation of its high affinity glutamine transporter SNAT5 in ALF. Restricted transfer out of the cell rather than increased synthesis within the cell could potentially explain the cell swelling/brain edema in ALF. Moreover, the restricted transfer of glutamine from the astrocyte to the adjacent glutamatergic nerve terminal (where glutamine serves as immediate precursor for the releasable/transmitter pool of glutamate) could result in decreased excitatory transmission and excessive neuroinhibition that is characteristic of encephalopathy in ALF. Paradoxically, in spite of renewed interest in arterial ammonia as a predictor of raised intracranial pressure and brain herniation in ALF, ammonia-lowering agents aimed at reduction of ammonia production in the gut have so far been shown to be of limited value in the prevention of these cerebral consequences. Mild hypothermia, shown to prevent brain edema and intracranial hypertension in both experimental and human ALF, does so independent of effects on brain glutamine synthesis; whether or not hypothermia restores expression levels of SNAT5 in ALF awaits further studies. While inhibitors of brain glutamine synthesis such as methionine sulfoximine, have been proposed for the prevention of brain edema in ALF, potential adverse effects have so far limited their applicability.