Dynamic training of a novelty classifier algorithm for real-time detection of early seizure onset.

OBJECTIVE: To develop an adaptive framework for seizure detection in real-time that is practical to use in the Epilepsy Monitoring Unit (EMU) as a warning signal, and whose output helps characterize epileptiform activity. METHODS: Our algorithm was tested on intracranial EEG from epilepsy patients admitted to the EMU for presurgical evaluation. Our framework uses a one-class Support Vector Machine (SVM) that is being trained dynamically according to past activity in all available channels to classify the novelty of the current activity. In this study we compared multiple configurations using a one-class SVM to assess if there is significance over specific neural features or electrode locations. RESULTS: Our results show that the algorithm reaches a sensitivity of 87% for early-onset seizure detection and of 97.7% as a generic seizure detection. CONCLUSIONS: Our algorithm is capable of running in real-time and achieving a high performance for early seizure-onset detection with a low false positive rate and robustness in detection of different type of seizure-onset patterns. SIGNIFICANCE: This algorithm offers a solution to warning systems in the EMU as well as a tool for seizure characterization during post-hoc analysis of intracranial EEG data for surgical resection of the epileptogenic network.

Dissociating electrophysiological correlates of contextual and perceptual learning in a visual search task.

Perceptual learning and contextual learning are two types of implicit visual learning that can co-occur in the same tasks. For example, to find an animal in the woods, you need to know where to look in the environment (contextual learning) and you must be able to discriminate its features (perceptual learning). However, contextual and perceptual learning are typically studied using distinct experimental paradigms, and little is known regarding their comparative neural mechanisms. In this study, we investigated contextual and perceptual learning in 12 healthy adult humans as they performed the same visual search task, and we examined psychophysical and electrophysiological (event-related potentials) measures of learning. Participants were trained to look for a visual stimulus, a small line with a specific orientation, presented among distractors. We found better performance for the trained target orientation as compared to an untrained control orientation, reflecting specificity of perceptual learning for the orientation of trained elements. This orientation specificity effect was associated with changes in the C1 component. We also found better performance for repeated spatial configurations as compared to novel ones, reflecting contextual learning. This context-specific effect was associated with the N2pc component. Taken together, these results suggest that contextual and perceptual learning are distinct visual learning phenomena that have different behavioral and electrophysiological characteristics.

Differences in the temporal processing between identification and categorization of durations: A behavioral and ERP study.

This study examined how different forms of decision-making modulate time perception. Participants performed temporal bisection and generalization tasks, requiring them to either categorize a stimulus duration as more similar to short or long standards (bisection), or identify whether or not a duration was the same as a previously-presented standard (generalization). They responded faster in the bisection task than in the generalization one for long durations. This behavioral effect was accompanied by modulation of event-related potentials (ERPs). More specifically, between 500 ms and 600 ms after stimulus offset, a late positive component (LPC), appearing in the centro-parietal region, showed lower amplitude in the bisection task than in the generalization one, for long durations, mirroring the behavioral result. Before (200-500 ms) and after (600-800 ms) this window, the amplitude of the LPC was globally larger in the generalization paradigm, independently of the presented duration. Finally, the LPC amplitude was higher for long durations than for shorter ones at the beginning of the component (between 200 and 300 ms after stimulus extinction) and was then higher for short durations than for longer ones (between 300 and 600 ms after offset), indicating that the decision about the former stimuli was made earlier than for the latter ones. Taken together, these results indicate that the categorization of durations engages fewer cognitive resources than their identification.

Early disruption of parvalbumin expression and perineuronal nets in the hippocampus of the Tg2576 mouse model of Alzheimer's disease can be rescued by enriched environment.

Recent findings show that parvalbumin (PV) interneuron function is impaired in Alzheimer's disease (AD), and that this impairment in PV function can be linked to network dysfunction and memory deficits. PV cells are often associated with a specific extracellular matrix, the perineuronal net (PNN). PNNs are believed to protect PV cell integrity, and whether the amyloidopathy affects PNNs remains unclear. Here, we evaluated the number of PV cells with and without PNNs in the hippocampus of the Tg2576 mouse model of AD at different stages of the disease. We show a deficit of PV+ and/or PV+/PNN+ cells in the areas CA1, CA2, and CA3 in Tg2576 as young as 3 months of age. Importantly, transient exposure to an enriched environment, which has proven long-lasting beneficial effects on memory in AD subjects, rescues the PV/PNN cell number deficits. We conclude that cognitive improvements induced by enriched environment in AD mouse models could be supported by a remodeling of hippocampal PV cell network and their PNNs.

Microbial eukaryote communities exhibit robust biogeographical patterns along a gradient of Patagonian and Antarctic lakes.

Microbial eukaryotes play important roles in aquatic ecosystem functioning. Unravelling their distribution patterns and biogeography provides important baseline information to infer the underlying mechanisms that regulate the biodiversity and complexity of ecosystems. We studied the distribution patterns and factors driving diversity gradients in microeukaryote communities (total, abundant, uncommon and rare community composition) along a latitudinal gradient of lakes distributed from Argentinean Patagonia to Maritime Antarctica using both denaturing gradient gel electrophoresis (DGGE) and high-throughput sequencing (Illumina HiSeq). DGGE and abundant Illumina operational taxonomic units (OTUs) showed both decreasing richness with latitude and significant differences between Patagonian and Antarctic lakes communities. In contrast, total richness did not change significantly across the latitudinal gradient, although evenness and diversity indices were significantly higher in Patagonian lakes. Beta-diversity was characterized by a high species turnover, influenced by both environmental and geographical descriptors, although this pattern faded in the rare community. Our results suggest the co-existence of a 'core biosphere' containing reduced number of abundant/dominant OTUs on which classical ecological rules apply, together with a much larger seedbank of rare OTUs driven by stochastic and reduced dispersal processes. These findings shed new light on the biogeographical patterns and forces structuring inland microeukaryote composition across broad spatial scales.

Cytotoxicity of seven naturally occurring phenolic compounds towards multi-factorial drug-resistant cancer cells.

INTRODUCTION: In medical oncology, multi-drug resistance (MDR) of cancer cells continues to be a major impediment. We are in quest of novel anti-proliferative agents to overcome drug-resistant tumor cells. METHODS: In the present study, we investigated the cytotoxicity of 7 naturally occurring phenolic compounds including two isoflavonoids alpinumisoflavone (1) and laburnetin (2), one biflavonoid amentoflavone (3), three lignans pycnanthulignene A (4), pycnanthulignene B (5), and syringaresinol (7) and one xanthone, euxanthone (6) against 9 drug-sensitive and MDR cancer cell lines. The resazurin reduction assay was used to evaluate the cytotoxicity of these compounds, whilst caspase-Glo assay was used to detect caspase activation. Cell cycle, mitochondrial membrane potential (MMP) and levels of reactive oxygen species (ROS) were all analyzed via flow cytometry. RESULTS: The IC50 values for the investigational phenolics ranged from 5.91 µM (towards leukemia CEM/ADR5000 cells) to 65.65 µM (towards drug-resistant breast adenocarcinoma MDA-MB-231-BCRP cells) for 1, 27.63 µM (towards leukemia CCRF-CEM cells) to 107.57 µM (towards MDA-MB-231-pcDNA cells) for 2, from 5.84 µM (towards CEM/ADR5000 cells) to 65.32 µM (towards colon carcinoma HCT116 (p53(-/-)) cells) for 4 and 0.20 µM (towards CCRF-CEM cells) to 195.12 µM (towards leukemia CEM/ADR5000) for doxorubicin. Phenolics 3, 5, 6 and 7 displayed selectivity cytotoxic effects on cancer cells lines. Compounds 1 and 4 induced apoptosis in CCRF-CEM cells, mediated by loss of MMP and increase ROS production. CONCLUSIONS: The studied phenolics and mostly isoflavonoid 1 and lignan 4 are potential cytotoxic natural products that deserve more investigations to develop novel antineoplastic drugs against multifactorial drug-resistant cancers.

Potentiation of Carboplatin-Mediated DNA Damage by the Mdm2 Modulator Nutlin-3a in a Humanized Orthotopic Breast-to-Lung Metastatic Model.

Triple-negative breast cancers (TNBC) are typically resistant to treatment, and strategies that build upon frontline therapy are needed. Targeting the murine double minute 2 (Mdm2) protein is an attractive approach, as Mdm2 levels are elevated in many therapy-refractive breast cancers. The Mdm2 protein-protein interaction inhibitor Nutlin-3a blocks the binding of Mdm2 to key signaling molecules such as p53 and p73α and can result in activation of cell death signaling pathways. In the present study, the therapeutic potential of carboplatin and Nutlin-3a to treat TNBC was investigated, as carboplatin is under evaluation in clinical trials for TNBC. In mutant p53 TMD231 TNBC cells, carboplatin and Nutlin-3a led to increased Mdm2 and was strongly synergistic in promoting cell death in vitro. Furthermore, sensitivity of TNBC cells to combination treatment was dependent on p73α. Following combination treatment, γH2AX increased and Mdm2 localized to a larger degree to chromatin compared with single-agent treatment, consistent with previous observations that Mdm2 binds to the Mre11/Rad50/Nbs1 complex associated with DNA and inhibits the DNA damage response. In vivo efficacy studies were conducted in the TMD231 orthotopic mammary fat pad model in NOD.Cg-Prkdc(scid)Il2rg(tm1Wjl)/SzJ (NSG) mice. Using an intermittent dosing schedule of combined carboplatin and Nutlin-3a, there was a significant reduction in primary tumor growth and lung metastases compared with vehicle and single-agent treatments. In addition, there was minimal toxicity to the bone marrow and normal tissues. These studies demonstrate that Mdm2 holds promise as a therapeutic target in combination with conventional therapy and may lead to new clinical therapies for TNBC.

Discovery and structural diversity of the hepatitis C virus NS3/4A serine protease inhibitor series leading to clinical candidate IDX320.

Exploration of the P2 region by mimicking the proline motif found in BILN2061 resulted in the discovery of two series of potent HCV NS3/4A protease inhibitors. X-ray crystal structure of the ligand in contact with the NS3/4A protein and modulation of the quinoline heterocyclic region by structure based design and modeling allowed for the optimization of enzyme potency and cellular activity. This research led to the selection of clinical candidate IDX320 having good genotype coverage and pharmacokinetic properties in various species.

Alpha-band EEG activity in perceptual learning.

In studies of perceptual learning (PL), subjects are typically highly trained across many sessions to achieve perceptual benefits on the stimuli in those tasks. There is currently significant debate regarding what sources of brain plasticity underlie these PL-based learning improvements. Here we investigate the hypothesis that PL, among other mechanisms, leads to task automaticity, especially in the presence of the trained stimuli. To investigate this hypothesis, we trained participants for eight sessions to find an oriented target in a field of near-oriented distractors and examined alpha-band activity, which modulates with attention to visual stimuli, as a possible measure of automaticity. Alpha-band activity was acquired via electroencephalogram (EEG), before and after training, as participants performed the task with trained and untrained stimuli. Results show that participants underwent significant learning in this task (as assessed by threshold, accuracy, and reaction time improvements) and that alpha power increased during the pre-stimulus period and then underwent greater desynchronization at the time of stimulus presentation following training. However, these changes in alpha-band activity were not specific to the trained stimuli, with similar patterns of posttraining alpha power for trained and untrained stimuli. These data are consistent with the view that participants were more efficient at focusing resources at the time of stimulus presentation and are consistent with a greater automaticity of task performance. These findings have implications for PL, as transfer effects from trained to untrained stimuli may partially depend on differential effort of the individual at the time of stimulus processing.

Identification of seizure onset zone and preictal state based on characteristics of high frequency oscillations.

OBJECTIVE: We investigate the relevance of high frequency oscillations (HFO) for biomarkers of epileptogenic tissue and indicators of preictal state before complex partial seizures in humans. METHODS: We introduce a novel automated HFO detection method based on the amplitude and features of the HFO events. We examined intracranial recordings from 33 patients and compared HFO rates and characteristics between channels within and outside the seizure onset zone (SOZ). We analyzed changes of HFO activity from interictal to preictal and to ictal periods. RESULTS: The average HFO rate is higher for SOZ channels compared to non-SOZ channels during all periods. Amplitudes and durations of HFO are higher for events within the SOZ in all periods compared to non-SOZ events, while their frequency is lower. All analyzed HFO features increase for the ictal period. CONCLUSIONS: HFO may occur in all channels but their rate is significantly higher within SOZ and HFO characteristics differ from HFO outside the SOZ, but the effect size of difference is small. SIGNIFICANCE: The present results show that based on accumulated dataset it is possible to distinguish HFO features different for SOZ and non-SOZ channels, and to show changes in HFO characteristics during the transition from interictal to preictal and to ictal periods.

Network dynamics of the brain and influence of the epileptic seizure onset zone.

The human brain is a dynamic networked system. Patients with partial epileptic seizures have focal regions that periodically diverge from normal brain network dynamics during seizures. We studied the evolution of brain connectivity before, during, and after seizures with graph-theoretic techniques on continuous electrocorticographic (ECoG) recordings (5.4 ± 1.7 d per patient, mean ± SD) from 12 patients with temporal, occipital, or frontal lobe partial onset seizures. Each electrode was considered a node in a graph, and edges between pairs of nodes were weighted by their coherence within a frequency band. The leading eigenvector of the connectivity matrix, which captures network structure, was tracked over time and clustered to uncover a finite set of brain network states. Across patients, we found that (i) the network connectivity is structured and defines a finite set of brain states, (ii) seizures are characterized by a consistent sequence of states, (iii) a subset of nodes is isolated from the network at seizure onset and becomes more connected with the network toward seizure termination, and (iv) the isolated nodes may identify the seizure onset zone with high specificity and sensitivity. To localize a seizure, clinicians visually inspect seizures recorded from multiple intracranial electrode contacts, a time-consuming process that may not always result in definitive localization. We show that network metrics computed from all ECoG channels capture the dynamics of the seizure onset zone as it diverges from normal overall network structure. This suggests that a state space model can be used to help localize the seizure onset zone in ECoG recordings.

Immunohistochemical application of a highly sensitive and specific murine monoclonal antibody recognising the extracellular domain of the human hepatocyte growth factor receptor (MET).

AIMS: Development of novel targeted therapies directed against hepatocyte growth factor (HGF) or its receptor (MET) necessitates the availability of quality diagnostics to facilitate their safe and effective use. Limitations of some commercially available anti-MET antibodies have prompted development of the highly sensitive and specific clone A2H2-3. Here we report its analytical properties when applied by an automated immunohistochemistry method. METHODS AND RESULTS: Excellent antibody specificity was demonstrated by immunoblot, ELISA, and IHC evaluation of characterised cell lines including NIH3T3 overexpressing the related kinase MST1R (RON). Sensitivity was confirmed by measurements of MET in cell lines or characterised tissues. IHC correlated well with FISH and quantitative RT-PCR assessments of MET (P < 0.001). Good total agreement (89%) was observed with the anti-MET antibody clone SP44 using whole-tissue sections, but poor positive agreement (21-47%) was seen in tissue microarray cores. Multiple lots displayed appropriate reproducibility (R(2)  > 0.9). Prevalence of MET positivity by IHC was higher in non-squamous cell NSCLC, MET or EGFR amplified cases, and in tumours harbouring abnormalities in EGFR exon 19 or 21. CONCLUSIONS: The anti-MET antibody clone A2H2-3 displays excellent specificity and sensitivity. These properties make it suitable for clinical trial investigations and development as a potential companion diagnostic.

Aortopulmonary window: a rare congenital heart disease.

We report the case of a 3,5 month-old infant presented at cardiology consultation of our institution for investigation of a heart murmur associated with failure to thrive and respiratory distress. Echocardiogram showed dilatation of left heart cavities, patent foramen ovale, small muscular ventricular septal defect and pulmonary hypertension. Angio-CT was performed on a 64-slice CT to better characterize the congenital heart disease. Aortopulmonary window was diagnosed and surgically corrected. Patient evolution was good with discharge 10 days after surgery. The authors review the literature and stress the importance of angio-CT in pre-operative evaluation.

Computing network-based features from intracranial EEG time series data: Application to seizure focus localization.

The surgical resection of the epileptogenic zone (EZ) is the only effective treatment for many drug-resistant epilepsy (DRE) patients, but the pre-surgical identification of the EZ is challenging. This study investigates whether the EZ exhibits a computationally identifiable signature during seizures. In particular, we compute statistics of the brain network from intracranial EEG (iEEG) recordings and track the evolution of network connectivity before, during, and after seizures. We define each node in the network as an electrode and weight each edge connecting a pair of nodes by the gamma band cross power of the corresponding iEEG signals. The eigenvector centrality (EVC) of each node is tracked over two seizures per patient and the electrodes are ranked according to the corresponding EVC value. We hypothesize that electrodes covering the EZ have a signature EVC rank evolution during seizure that differs from electrodes outside the EZ. We tested this hypothesis on multi-channel iEEG recordings from 2 DRE patients who had successful surgery (i.e., seizures were under control with or without medications) and 1 patient who had unsuccessful surgery. In the successful cases, we assumed that the resected region contained the EZ and found that the EVC rank evolution of the electrodes within the resected region had a distinct "arc" signature, i.e., the EZ ranks first rose together shortly after seizure onset and then fell later during seizure.

Encoding of episodic information through fast task-irrelevant perceptual learning.

The mechanisms guiding our learning and memory processes are of key interest to human cognition. While much research shows that attention and reinforcement processes help guide the encoding process, there is still much to know regarding how our brains choose what to remember. Recent research of task-irrelevant perceptual learning (TIPL) has found that information presented coincident with important events is better encoded even if participants are not aware of its presence (see Seitz & Watanabe, 2009). However a limitation of existing studies of TIPL is that they provide little information regarding the depth of encoding supported by pairing a stimulus with a behaviorally relevant event. The objective of this research was to understand the depth of encoding of information that is learned through TIPL. To do so, we adopted a variant of the "remember/know" paradigm, recently reported by Ingram, Mickes, and Wixted (2012), in which multiple confidence levels of both familiar (know) and remember reports are reported (Experiment 1), and in which episodic information is tested (Experiment 2). TIPL was found in both experiments, with higher recognition performance for target-paired than for distractor-paired images. Furthermore, TIPL benefitted both "familiar" and "remember" reports. The results of Experiment 2 indicate that the most confident "remember" response was associated with episodic information, where participants were able to access the location of image presentation for these items. Together, these results indicate that TIPL results in a deep enhancement in the encoding of target-paired information.

High resolution, high capacity, spatial specificity in perceptual learning.

Research of perceptual learning has received significant interest due to findings that training on perceptual tasks can yield learning effects that are specific to the stimulus features of that task. However, recent studies have demonstrated that while training a single stimulus at a single location can yield a high-degree of stimulus specificity, training multiple features, or at multiple locations can reveal a broad transfer of learning to untrained features or stimulus locations. We devised a high resolution, high capacity, perceptual learning procedure with the goal of testing whether spatial specificity can be found in cases where observers are highly trained to discriminate stimuli in many different locations in the visual field. We found a surprising degree of location specific learning, where performance was significantly better when target stimuli were presented at 1 of the 24 trained locations compared to when they were placed in 1 of the 12 untrained locations. This result is particularly impressive given that untrained locations were within a couple degrees of visual angle of those that were trained. Given the large number of trained locations, the fact that the trained and untrained locations were interspersed, and the high-degree of spatial precision of the learning, we suggest that these results are difficult to account for using attention or decision strategies and instead suggest that learning may have taken place for each location separately in retinotopically organized visual cortex.

A triple dissociation between learning of target, distractors, and spatial contexts.

When we perform any task, we engage a diverse set of processes. These processes can be optimized with learning. While there exists substantial research that probes specific aspects of learning, there is a scarcity of research regarding interactions between different types of learning. Here, we investigate possible interactions between Perceptual Learning (PL) and Contextual Learning (CL), two types of implicit learning that have garnered much attention in the psychological sciences and that often co-occur in natural settings. PL increases sensitivity to features of task targets and distractors and is thought to involve improvements in low-level perceptual processing. CL regards learning of regularities in the environment (such as spatial relations between objects) and is consistent with improvements in higher level perceptual processes. Surprisingly, we found CL, PL for target features, and PL for distractor features to be independent. This triple dissociation demonstrates how different learning processes may operate in parallel as tasks are mastered.

Characterization of early partial seizure onset: frequency, complexity and entropy.

OBJECTIVE: A clear classification of partial seizures onset features is not yet established. Complexity and entropy have been very widely used to describe dynamical systems, but a systematic evaluation of these measures to characterize partial seizures has never been performed. METHODS: Eighteen different measures including power in frequency bands up to 300 Hz, Gabor atom density (GAD), Higuchi fractal dimension (HFD), Lempel-Ziv complexity, Shannon entropy, sample entropy, and permutation entropy, were selected to test sensitivity to partial seizure onset. Intracranial recordings from 45 patients with mesial temporal, neocortical temporal and neocortical extratemporal seizure foci were included (331 partial seizures). RESULTS: GAD, Lempel-Ziv complexity, HFD, high frequency activity, and sample entropy were the most reliable measures to assess early seizure onset. CONCLUSIONS: Increases in complexity and occurrence of high-frequency components appear to be commonly associated with early stages of partial seizure evolution from all regions. The type of measure (frequency-based, complexity or entropy) does not predict the efficiency of the method to detect seizure onset. SIGNIFICANCE: Differences between measures such as GAD and HFD highlight the multimodal nature of partial seizure onsets. Improved methods for early seizure detection may be achieved from a better understanding of these underlying dynamics.

Tolosa-Hunt syndrome in children.

We report a case of Tolosa-Hunt syndrome in a 4-year-old girl. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) revealed a left cavernous sinus involvement with internal carotid artery occlusion. Clinical signs included left ophthalmoplegia, convulsions and right hemiplegia. Etiopathogenesis of this syndrome is unclear but as in our case, response to corticosteroid therapy is spectacular and avoids unnecessary invasive diagnostic procedures. We underline the importance of cross-sectional imaging in the diagnostic approach. Several other diseases including tumors, vascular lesions and infections can have a similar clinical and/or imaging presentation. However, closely monitored evolution on imaging proves to be decisive in the establishment of final diagnosis of this syndrome.

Improving early seizure detection.

Over the last decade, the search for a method able to reliably predict seizures hours in advance has been largely replaced by the more realistic goal of very early detection of seizure onset, which would allow therapeutic or warning devices to be triggered prior to the onset of disabling clinical symptoms. We explore in this article the steps along the pathway from data acquisition to closed-loop applications that can and should be considered to design the most efficient early seizure detection. Microelectrodes, high-frequency oscillations, high sampling rate, high-density arrays, and modern analysis techniques are all elements of the recording and detection process that in combination with modeling studies can provide new insights into the dynamics of seizure onsets. Each of these steps needs to be considered if detection devices that will favorably impact the quality of life of patients are to be implemented. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.