Editorial : Improving Neuroprosthetics Through Novel Techniques for Processing Electrophysiological Human Brain Signals

Non peer reviewe

Multi-task multiple kernel learning reveals relevant frequency bands for critical areas localization in focal epilepsy

The localization of epileptic zone in pharmacoresistant focal epileptic patients is a daunting task, typically performed by medical experts through visual inspection over highly sampled neural recordings. For a finer localization of the epileptogenic areas and a deeper understanding of the pathology both the identification of pathogenical biomarkers and the automatic characterization of epileptic signals are desirable. In this work we present a data integration learning method based on multi-level representation of stereo-electroencephalography recordings and multiple kernel learning. To the best of our knowledge, this is the first attempt to tackle both aspects simultaneously, as our approach is devised to classify critical vs. non-critical recordings while detecting the most discriminative frequency bands. The learning pipeline is applied to a data set of 18 patients for a total of 2347 neural recordings analyzed by medical experts. Without any prior knowledge assumption, the data-driven method reveals the most discriminative frequency bands for the localization of epileptic areas in the high-frequency spectrum (>=80 Hz) while showing high performance metric scores (mean balanced accuracy of 0.89 +- 0.03). The promising results may represent a starting point for the automatic search of clinical biomarkers of epileptogenicity

Comparison of methods to identify modules in noisy or incomplete brain networks

open6siCommunity structure, or "modularity," is a fundamentally important aspect in the organization of structural and functional brain networks, but their identification with community detection methods is confounded by noisy or missing connections. Although several methods have been used to account for missing data, the performance of these methods has not been compared quantitatively so far. In this study, we compared four different approaches to account for missing connections when identifying modules in binary and weighted networks using both Louvain and Infomap community detection algorithms. The four methods are "zeros," "row-column mean," "common neighbors," and "consensus clustering." Using Lancichinetti-Fortunato-Radicchi benchmark-simulated binary and weighted networks, we find that "zeros," "row-column mean," and "common neighbors" approaches perform well with both Louvain and Infomap, whereas "consensus clustering" performs well with Louvain but not Infomap. A similar pattern of results was observed with empirical networks from stereotactical electroencephalography data, except that "consensus clustering" outperforms other approaches on weighted networks with Louvain. Based on these results, we recommend any of the four methods when using Louvain on binary networks, whereas "consensus clustering" is superior with Louvain clustering of weighted networks. When using Infomap, "zeros" or "common neighbors" should be used for both binary and weighted networks. These findings provide a basis to accounting for noisy or missing connections when identifying modules in brain networks.openWilliams N.; Arnulfo G.; Wang S.H.; Nobili L.; Palva S.; Palva J.M.Williams, N.; Arnulfo, G.; Wang, S. H.; Nobili, L.; Palva, S.; Palva, J. M

Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings

cited By 0Phase synchronization of neuronal oscillations in specific frequency bands coordinates anatomically distributed neuronal processing and communication. Typically, oscillations and synchronization take place concurrently in many distinct frequencies, which serve separate computational roles in cognitive functions. While within-frequency phase synchronization has been studied extensively, less is known about the mechanisms that govern neuronal processing distributed across frequencies and brain regions. Such integration of processing between frequencies could be achieved via cross-frequency coupling (CFC), either by phase-amplitude coupling (PAC) or by n:m-cross-frequency phase synchrony (CFS). So far, studies have mostly focused on local CFC in individual brain regions, whereas the presence and functional organization of CFC between brain areas have remained largely unknown. We posit that interareal CFC may be essential for large-scale coordination of neuronal activity and investigate here whether genuine CFC networks are present in human resting-state (RS) brain activity. To assess the functional organization of CFC networks, we identified brain-wide CFC networks at mesoscale resolution from stereoelectroencephalography (SEEG) and at macroscale resolution from source-reconstructed magnetoencephalography (MEG) data. We developed a novel, to our knowledge, graph-theoretical method to distinguish genuine CFC from spurious CFC that may arise from nonsinusoidal signals ubiquitous in neuronal activity. We show that genuine interareal CFC is present in human RS activity in both SEEG and MEG data. Both CFS and PAC networks coupled theta and alpha oscillations with higher frequencies in large-scale networks connecting anterior and posterior brain regions. CFS and PAC networks had distinct spectral patterns and opposing distribution of low- and high-frequency network hubs, implying that they constitute distinct CFC mechanisms. The strength of CFS networks was also predictive of cognitive performance in a separate neuropsychological assessment. In conclusion, these results provide evidence for interareal CFS and PAC being 2 distinct mechanisms for coupling oscillations across frequencies in large-scale brain networks.Peer reviewe

Diffusion Kurtosis Imaging of neonatal Spinal Cord in clinical routine

Diffusion kurtosis imaging (DKI) has undisputed advantages over the more classical diffusion magnetic resonance imaging (dMRI) as witnessed by the fast-increasing number of clinical applications and software packages widely adopted in brain imaging. However, in the neonatal setting, DKI is still largely underutilized, in particular in spinal cord (SC) imaging, because of its inherently demanding technological requirements. Due to its extreme sensitivity to non-Gaussian diffusion, DKI proves particularly suitable for detecting complex, subtle, fast microstructural changes occurring in this area at this early and critical stage of development, which are not identifiable with only DTI. Given the multiplicity of congenital anomalies of the spinal canal, their crucial effect on later developmental outcome, and the close interconnection between the SC region and the brain above, managing to apply such a method to the neonatal cohort becomes of utmost importance. This study will (i) mention current methodological challenges associated with the application of advanced dMRI methods, like DKI, in early infancy, (ii) illustrate the first semi-automated pipeline built on Spinal Cord Toolbox for handling the DKI data of neonatal SC, from acquisition setting to estimation of diffusion measures, through accurate adjustment of processing algorithms customized for adult SC, and (iii) present results of its application in a pilot clinical case study. With the proposed pipeline, we preliminarily show that DKI is more sensitive than DTI-related measures to alterations caused by brain white matter injuries in the underlying cervical SC

Retrospective evaluation and SEEG trajectory analysis for interactive multi-trajectory planner assistant

Purpose: Focal epilepsy is a neurological disease that can be surgically treated by removing area of the brain generating the seizures. The stereotactic electroencephalography (SEEG) procedure allows patient brain activity to be recorded in order to localize the onset of seizures through the placement of intracranial electrodes. The planning phase can be cumbersome and very time consuming, and no quantitative information is provided to neurosurgeons regarding the safety and efficacy of their trajectories. In this work, we present a novel architecture specifically designed to ease the SEEG trajectory planning using the 3D Slicer platform as a basis. Methods: Trajectories are automatically optimized following criteria like vessel distance and insertion angle. Multi-trajectory optimization and conflict resolution are optimized through a selective brute force approach based on a conflict graph construction. Additionally, electrode-specific optimization constraints can be defined, and an advanced verification module allows neurosurgeons to evaluate the feasibility of the trajectory. Results: A retrospective evaluation was performed using manually planned trajectories on 20 patients: the planning algorithm optimized and improved trajectories in 98% of cases. We were able to resolve and optimize the remaining 2% by applying electrode-specific constraints based on manual planning values. In addition, we found that the global parameters used discards 68% of the manual planned trajectories, even when they represent a safe clinical choice. Conclusions: Our approach improved manual planned trajectories in 98% of cases in terms of quantitative indexes, even when applying more conservative criteria with respect to actual clinical practice. The improved multi-trajectory strategy overcomes the previous work limitations and allows electrode optimization within a tolerable time span

A digital repository with an extensible data model for biobanking and genomic analysis management

Motivation: Molecular biology laboratories require extensive metadata to improve data collection and analysis. The heterogeneity of the collected metadata grows as research is evolving in to international multi-disciplinary collaborations and increasing data sharing among institutions. Single standardization is not feasible and it becomes crucial to develop digital repositories with flexible and extensible data models, as in the case of modern integrated biobanks management. Results: We developed a novel data model in JSON format to describe heterogeneous data in a generic biomedical science scenario. The model is built on two hierarchical entities: processes and events, roughly corresponding to research studies and analysis steps within a single study. A number of sequential events can be grouped in a process building up a hierarchical structure to track patient and sample history. Each event can produce new data. Data is described by a set of user-defined metadata, and may have one or more associated files. We integrated the model in a web based digital repository with a data grid storage to manage large data sets located in geographically distinct areas. We built a graphical interface that allows authorized users to define new data types dynamically, according to their requirements. Operators compose queries on metadata fields using a flexible search interface and run them on the database and on the grid. We applied the digital repository to the integrated management of samples, patients and medical history in the BIT-Gaslini biobank. The platform currently manages 1800 samples of over 900 patients. Microarray data from 150 analyses are stored on the grid storage and replicated on two physical resources for preservation. The system is equipped with data integration capabilities with other biobanks for worldwide information sharing. Conclusions: Our data model enables users to continuously define flexible, ad hoc, and loosely structured metadata, for information sharing in specific research projects and purposes. This approach can improve sensitively interdisciplinary research collaboration and allows to track patients' clinical records, sample management information, and genomic data. The web interface allows the operators to easily manage, query, and annotate the files, without dealing with the technicalities of the data grid.Peer reviewe

Studying functional networks in human brain through intracerebral spontaneous EEG

none6G.Arnulfo; A.Pigorini; M.Massimini; L.Nobili; A.Schenone; M.M. FatoArnulfo, Gabriele; Pigorini, A.; Massimini, M.; Nobili, L.; Schenone, Andrea; Fato, MARCO MASSIM

Automatic segmentation of deep intracerebral electrodes in computed tomography scans

Background: Invasive monitoring of brain activity by means of intracerebral electrodes is widely practiced to improve pre-surgical seizure onset zone localization in patients with medically refractory seizures. Stereo-Electroencephalography (SEEG) is mainly used to localize the epileptogenic zone and a precise knowledge of the location of the electrodes is expected to facilitate the recordings interpretation and the planning of resective surgery. However, the localization of intracerebral electrodes on post-implant acquisitions is usually time-consuming (i.e., manual segmentation), it requires advanced 3D visualization tools, and it needs the supervision of trained medical doctors in order to minimize the errors. In this paper we propose an automated segmentation algorithm specifically designed to segment SEEG contacts from a thresholded post-implant Cone-Beam CT volume (0.4 mm, 0.4 mm, 0.8 mm). The algorithm relies on the planned position of target and entry points for each electrode as a first estimation of electrode axis. We implemented the proposed algorithm into DEETO, an open source C++ prototype based on ITK library. Results: We tested our implementation on a cohort of 28 subjects in total. The experimental analysis, carried out over a subset of 12 subjects (35 multilead electrodes; 200 contacts) manually segmented by experts, show that the algorithm: (i) is faster than manual segmentation (i.e., less than 1s/subject versus a few hours) (ii) is reliable, with an error of 0.5 mm +/- 0.06 mm, and (iii) it accurately maps SEEG implants to their anatomical regions improving the interpretability of electrophysiological traces for both clinical and research studies. Moreover, using the 28-subject cohort we show here that the algorithm is also robust (error <0.005 mm) against deep-brain displacements (<12 mm) of the implanted electrode shaft from those planned before surgery. Conclusions: Our method represents, to the best of our knowledge, the first automatic algorithm for the segmentation of SEEG electrodes. The method can be used to accurately identify the neuroanatomical loci of SEEG electrode contacts by a non-expert in a fast and reliable manner.Peer reviewe

Global and local complexity of intracranial EEG decreases during NREM sleep

Key to understanding the neuronal basis of consciousness is the characterisation of the neural signatures of changes in level of consciousness during sleep. Here we analysed three measures of dynamical complexity on spontaneous depth electrode recordings from 10 epilepsy patients during wakeful rest and different stages of sleep: (i) Lempel-Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability over time of the set of channels active above a threshold; (iii) synchrony coalition entropy, which measures the variability over time of the set of synchronous channels. When computed across sets of channels that are broadly distributed across multiple brain regions, all 3 measures decreased substantially in all participants during early-night non-rapid eye movement (NREM) sleep. This decrease was partially reversed during late-night NREM sleep, while the measures scored similar to wakeful rest during rapid eye movement (REM) sleep. This global pattern was in almost all cases mirrored at the local level by groups of channels located in a single region. In testing for differences between regions, we found elevated signal complexity in the frontal lobe. These differences could not be attributed solely to changes in spectral power between conditions. Our results provide further evidence that the level of consciousness correlates with neural dynamical complexity