Multivariate analysis of SEEG signals during seizure

Generalisability of epileptiform patterns across time and patients

The complexity of localising the epileptogenic zone (EZ) contributes to surgical resection failures in achieving seizure freedom. The distinct patterns of epileptiform activity during interictal and ictal phases, varying across patients, often lead to suboptimal localisation using electroencephalography (EEG) features. We posed two key questions: whether neural signals reflecting epileptogenicity generalise from interictal to ictal time windows within each patient, and whether epileptiform patterns generalise across patients. Utilising an intracranial EEG dataset from 55 patients, we extracted a large battery of simple to complex features from stereo-EEG (SEEG) and electrocorticographic (ECoG) neural signals during interictal and ictal windows. Our features (n = 34) quantified many aspects of the signals including statistical moments, complexities, frequency-domain and cross-channel network attributes. Decision tree classifiers were then trained and tested on distinct time windows and patients to evaluate the generalisability of epileptogenic patterns across time and patients, respectively. Evidence strongly supported generalisability from interictal to ictal time windows across patients, particularly in signal power and high-frequency network-based features. Consistent patterns of epileptogenicity were observed across time windows within most patients, and signal features of epileptogenic regions generalised across patients, with higher generalisability in the ictal window. Signal complexity features were particularly contributory in cross-patient generalisation across patients. These findings offer insights into generalisable features of epileptic neural activity across time and patients, with implications for future automated approaches to supplement other EZ localisation methods.

Semi-automatic Extraction of Functional Dynamic Networks Describing Patient's Epileptic Seizures

Intracranial electroencephalography (EEG) studies using stereotactic EEG (SEEG) have shown that during seizures, epileptic activity spreads across several anatomical regions from the seizure onset zone toward remote brain areas. A full and objective characterization of this patient-specific time-varying network is crucial for optimal surgical treatment. Functional connectivity (FC) analysis of SEEG signals recorded during seizures enables to describe the statistical relations between all pairs of recorded signals. However, extracting meaningful information from those large datasets is time consuming and requires high expertise. In the present study, we first propose a novel method named Brain-wide Time-varying Network Decomposition (BTND) to characterize the dynamic epileptogenic networks activated during seizures in individual patients recorded with SEEG electrodes. The method provides a number of pathological FC subgraphs with their temporal course of activation. The method can be applied to several seizures of the patient to extract reproducible subgraphs. Second, we compare the activated subgraphs obtained by the BTND method with visual interpretation of SEEG signals recorded in 27 seizures from nine different patients. As a whole, we found that activated subgraphs corresponded to brain regions involved during the course of the seizures and their time course was highly consistent with classical visual interpretation. We believe that the proposed method can complement the visual analysis of SEEG signals recorded during seizures by highlighting and characterizing the most significant parts of epileptic networks with their activation dynamics.

Redox Systemic Signaling and Induced Tolerance Responses During Soybean-Bradyrhizobium japonicum Interaction: Involvement of Nod Factor Receptor and Autoregulation of Nodulation

The symbiotic relationship between legumes and nitrogen-fixing rhizobia induces local and systemic responses, which ultimately lead to nodule formation. The autoregulation of nodulation (AON) is a systemic mechanism related to innate immunity that controls nodule development and involves different components ranging from hormones, peptides, receptors to small RNAs. Here, we characterized a rapid systemic redox changes induced during soybean-Bradyrhizobium japonicum symbiotic interaction. A transient peak of reactive oxygen species (ROS) generation was found in soybean leaves after 30 min of root inoculation with B. japonicum. The ROS response was accompanied by changes in the redox state of glutathione and by activation of antioxidant enzymes. Moreover, the ROS peak and antioxidant enzyme activation were abolished in leaves by the addition, in either root or leaf, of DPI, an NADPH oxidase inhibitor. Likewise, these systemic redox changes primed the plant increasing its tolerance to photooxidative stress. With the use of non-nodulating nfr5-mutant and hyper-nodulating nark-mutant soybean plants, we subsequently studied the systemic redox changes. The nfr5-mutant lacked the systemic redox changes after inoculation, whereas the nark-mutant showed a similar redox systemic signaling than the wild type plants. However, neither nfr5- nor nark-mutant exhibited tolerance to photooxidative stress condition. Altogether, these results demonstrated that (i) the early redox systemic signaling during symbiotic interaction depends on a Nod factor receptor, and that (ii) the induced tolerance response depends on the AON mechanisms.

Using network analysis to localize the epileptogenic zone from invasive EEG recordings in intractable focal epilepsy

Treatment of medically intractable focal epilepsy (MIFE) by surgical resection of the epileptogenic zone (EZ) is often effective provided the EZ can be reliably identified. Even with the use of invasive recordings, the clinical differentiation between the EZ and normal brain areas can be quite challenging, mainly in patients without MRI detectable lesions. Consequently, despite relatively large brain regions being removed, surgical success rates barely reach 60–65%. Such variable and unfavorable outcomes associated with high morbidity rates are often caused by imprecise and/or inaccurate EZ localization. We developed a localization algorithm that uses network-based data analytics to process invasive EEG recordings. This network algorithm analyzes the centrality signatures of every contact electrode within the recording network and characterizes contacts into susceptible EZ based on the centrality trends over time. The algorithm was tested in a retrospective study that included 42 patients from four epilepsy centers. Our algorithm had higher agreement with EZ regions identified by clinicians for patients with successful surgical outcomes and less agreement for patients with failed outcomes. These findings suggest that network analytics and a network systems perspective of epilepsy may be useful in assisting clinicians in more accurately localizing the EZ.

Epilepsy is a disease that results in abnormal firing patterns in parts of the brain that comprise the epileptogenic network, known as the epileptogenic zone (EZ). Current methods to localize the EZ for surgical treatment often require observations of hundreds of thousands of EEG data points measured from many electrodes implanted in a patient’s brain. In this paper, we used network science to show that EZ regions may exhibit specific network signatures before, during, and after seizure events. Our algorithm computes the likelihood of each electrode being in the EZ and tends to agree more with clinicians during successful resections and less during failed surgeries. These results suggest that a networked analysis approach to EZ localization may be valuable in a clinical setting.

The Stereo-Electroencephalography Methodology

The stereo-electroencephalography (SEEG) methodology and technique was developed almost 60 years ago in Europe. The efficacy and safety of SEEG has been proven. The main advantage is the possibility to study the epileptogenic neuronal network in its dynamic and 3-dimensional aspect, with optimal time and space correlation, with the clinical semiology of the patient's seizures. The main clinical challenge for the near future remains in the further refinement of specific selection criteria for the different methods of invasive monitoring, with the ultimate goal of comparing and validating the results (long-term seizure-free outcome) obtained from different methods of invasive monitoring.

Computing network-based features from physiological time series: application to sepsis detection

Sepsis is a systemic deleterious host response to infection. It is a major healthcare problem that affects millions of patients every year in the intensive care units (ICUs) worldwide. Despite the fact that ICU patients are heavily instrumented with physiological sensors, early sepsis detection remains challenging, perhaps because clinicians identify sepsis by using static scores derived from bed-side measurements individually, i.e., without systematically accounting for potential interactions between these signals and their dynamics. In this study, we apply network-based data analysis to take into account interactions between bed-side physiological time series (PTS) data collected in ICU patients, and we investigate features to distinguish between sepsis and non-sepsis conditions. We treated each PTS source as a node on a graph and we retrieved the graph connectivity matrix over time by tracking the correlation between each pair of sources' signals over consecutive time windows. Then, for each connectivity matrix, we computed the eigenvalue decomposition. We found that, even though raw PTS measurements may have indistinguishable distributions in non-sepsis and early sepsis states, the median /I of the eigenvalues computed from the same data is statistically different (p <; 0.001) in the two states and the evolution of /I may reflect the disease progression. Although preliminary, these findings suggest that network-based features computed from continuous PTS data may be useful for early sepsis detection.