Merging Deep Learning with Expert Knowledge for Seizure Onset Zone localization from rs-fMRI in Pediatric Pharmaco Resistant Epilepsy

Surgical disconnection of Seizure Onset Zones (SOZs) at an early age is an effective treatment for Pharmaco-Resistant Epilepsy (PRE). Pre-surgical localization of SOZs with intra-cranial EEG (iEEG) requires safe and effective depth electrode placement. Resting-state functional Magnetic Resonance Imaging (rs-fMRI) combined with signal decoupling using independent component (IC) analysis has shown promising SOZ localization capability that guides iEEG lead placement. However, SOZ ICs identification requires manual expert sorting of 100s of ICs per patient by the surgical team which limits the reproducibility and availability of this pre-surgical screening. Automated approaches for SOZ IC identification using rs-fMRI may use deep learning (DL) that encodes intricacies of brain networks from scarcely available pediatric data but has low precision, or shallow learning (SL) expert rule-based inference approaches that are incapable of encoding the full spectrum of spatial features. This paper proposes DeepXSOZ that exploits the synergy between DL based spatial feature and SL based expert knowledge encoding to overcome performance drawbacks of these strategies applied in isolation. DeepXSOZ is an expert-in-the-loop IC sorting technique that a) can be configured to either significantly reduce expert sorting workload or operate with high sensitivity based on expertise of the surgical team and b) can potentially enable the usage of rs-fMRI as a low cost outpatient pre-surgical screening tool. Comparison with state-of-art on 52 children with PRE shows that DeepXSOZ achieves sensitivity of 89.79%, precision of 93.6% and accuracy of 84.6%, and reduces sorting effort by 6.7-fold. Knowledge level ablation studies show a pathway towards maximizing patient outcomes while optimizing the machine-expert collaboration for various scenarios.Comment: This paper is currently under review in IEEE Journa

Functional network correlates of language and semiology in epilepsy

Epilepsy surgery is appropriate for 2-3% of all epilepsy diagnoses. The goal of the presurgical workup is to delineate the seizure network and to identify the risks associated with surgery. While interpretation of functional MRI and results in EEG-fMRI studies have largely focused on anatomical parameters, the focus of this thesis was to investigate canonical intrinsic connectivity networks in language function and seizure semiology. Epilepsy surgery aims to remove brain areas that generate seizures. Language dysfunction is frequently observed after anterior temporal lobe resection (ATLR), and the presurgical workup seeks to identify the risks associated with surgical outcome. The principal aim of experimental studies was to elaborate understanding of language function as expressed in the recruitment of relevant connectivity networks and to evaluate whether it has value in the prediction of language decline after anterior temporal lobe resection. Using cognitive fMRI, we assessed brain areas defined by parameters of anatomy and canonical intrinsic connectivity networks (ICN) that are involved in language function, specifically word retrieval as expressed in naming and fluency. fMRI data was quantified by lateralisation indices and by ICN_atlas metrics in a priori defined ICN and anatomical regions of interest. Reliability of language ICN recruitment was studied in 59 patients and 30 healthy controls who were included in our language experiments. New and established language fMRI paradigms were employed on a three Tesla scanner, while intellectual ability, language performance and emotional status were established for all subjects with standard psychometric assessment. Patients who had surgery were reinvestigated at an early postoperative stage of four months after anterior temporal lobe resection. A major part of the work sought to elucidate the association between fMRI patterns and disease characteristics including features of anxiety and depression, and prediction of postoperative language outcome. We studied the efficiency of reorganisation of language function associated with disease features prior to and following surgery. A further aim of experimental work was to use EEG-fMRI data to investigate the relationship between canonical intrinsic connectivity networks and seizure semiology, potentially providing an avenue for characterising the seizure network in the presurgical workup. The association of clinical signs with the EEG-fMRI informed activation patterns were studied using the data from eighteen patients’ whose seizures and simultaneous EEG-fMRI activations were reported in a previous study. The accuracy of ICN_atlas was validated and the ICN construct upheld in the language maps of TLE patients. The ICN construct was not evident in ictal fMRI maps and simulated ICN_atlas data. Intrinsic connectivity network recruitment was stable between sessions in controls. Amodal linguistic processing and the relevance of temporal intrinsic connectivity networks for naming and that of frontal intrinsic connectivity networks for word retrieval in the context of fluency was evident in intrinsic connectivity networks regions. The relevance of intrinsic connectivity networks in the study of language was further reiterated by significant association between some disease features and language performance, and disease features and activation in intrinsic connectivity networks. However, the anterior temporal lobe (ATL) showed significantly greater activation compared to intrinsic connectivity networks – a result which indicated that ATL functional language networks are better studied in the context of the anatomically demarked ATL, rather than its functionally connected intrinsic connectivity networks. Activation in temporal lobe networks served as a predictor for naming and fluency impairment after ATLR and an increasing likelihood of significant decline with greater magnitude of left lateralisation. Impairment of awareness served as a significant classifying feature of clinical expression and was significantly associated with the inhibition of normal brain functions. Canonical intrinsic connectivity networks including the default mode network were recruited along an anterior-posterior anatomical axis and were not significantly associated with clinical signs

Structure-Function Relationships in the Brain: Applications in Neurosurgery

Multimodal brain imaging allows the study of structure-function relationships of the brain at the individual level, a key subject in basic neuroscience with important applications in neurosurgery. The current thesis aims to better understand these relationships by (1) examining how cortical morphology metrics influence measures of brain function, (2) their visualization in augmented reality (AR), and (3) their application in neurosurgical planning. To achieve these objectives, we made use of multimodal magnetic resonance imaging (MRI) data: diffusion weighted imaging, resting-state functional MRI (rs-fMRI), task-based fMRI, and T1-weighted images. Various metrics were calculated: cortical thickness (CT), blood oxygen level dependent signal variability (BOLDSD), structural connectivity (SC), functional connectivity (FC), etc.. We found that BOLDSD measures are confounded by CT, developed an application to visualize SC and FC in AR, and used rs-fMRI to map language for epilepsy surgery. Overall, these studies provided a better understanding of structure-function relationships in the brain

Revealing Distinct Neural Signatures in Magnetoencephalography with Hidden Markov Models

Magnetoencephalography (MEG) is a functional neuroimaging method which measures the magnetic fields produced by neural communication in the brain. Specifically, the fields induced by dendritic current flow in assemblies of pyramidal neurons. Because these magnetic fields are generated directly by brain electrophysiology, and are mostly unperturbed by the skull, MEG data are rich in spatial and temporal information. This thesis is chiefly concerned with interpreting these data in a way that produces useful results whilst minimising bias. Hidden Markov modelling (HMM) is a robust statistical method which has been applied to fields as diverse as speech recognition and financial market prediction. It parses data into a number of ‘hidden states’, each with their own unique characteristics, in an unsupervised way. Because it is data-driven, it can create a model unique to each participant’s brain activity and specific to each task. In addition, the HMM framework itself is flexible so it can be applied to both sensor and source-space data and can be applied to multiple channels (multivariate) or to a single time course (univariate). Choice of an observation model allows states to be characterised by amplitude, spatial, or spectral content depending on the research question. The aim of this thesis is to apply hidden Markov modelling (HMM) to whole head MEG data to identify repeated patterns of transient neural activity occurring throughout the brain. Once these patterns were identified, the interaction between these short ‘bursts’ of activity across the cortex was established which provided a unique measure of functional connectivity. Three studies were undertaken: The role of transient spectral bursts in MEG functional connectivity: In recent years, the smoothly varying neural oscillations often studied in MEG (such as those trial-averaged responses in the traditional neurophysiological (such as alpha/beta) frequency bands) have been shown to be made up of single-trial high-amplitude ‘bursts’ of activity. These bursts can be observed in the beta frequency band and are therefore often referred to as beta bursts. In this study, a novel time-delay embedded HMM was used to identif bursts in broadband data based on their spectral content for MEG data from 66 healthy adult participants. The burst amplitude, duration and frequency of occurrence were characterised across the cortex in resting state data, and in a motor task the classic movement-related beta desynchronisation and post movement beta rebound were shown to be made up of changes in burst occurrence. A novel functional connectivity metric was then introduced based on the coincidence of bursts from distal brain regions, allowing the known beta band functional connectome to be reproduced. Bursts coincident across spatially separate brain regions were also shown to correspond to periods of heightened coherence, lending evidence to the communication by coherence (Fries 2005, 2015) hypothesis. Post-stimulus responses across the cortex: During a motor task, both primary (during stimulation) and post stimulus responses (PSR) can be observed. These are well characterised in the literature, but little is known about their functional significance. The PSR in particular is modified in a range of seemingly unrelated neurological conditions with variable symptoms, such as schizophrenia (Robson et al. 2016), autism spectrum disorder (Gaetz et al. 2020) and multiple sclerosis (Barratt et al. 2017), indicating that the PSR is a fundamental neurophysiological process, the disturbance of which has implications on both healthy and pathological brain function. This work therefore tested the hypothesis that the PSR is present across the cortex. MEG data were acquired and analysed from two experiments with 15 healthy adult volunteers each – the first was a right-hand grip task with visual feedback, the second involved passive left visual field stimulation. Both experiments varied stimulus duration (2s, 5s and 10s) with a 30s rest-period between trials to allow characterisation of the full PSR. A univariate 3-state time-delay-embedded hidden Markov model (HMM) was used to characterise the spatial distributions of the primary and PSR across the cortex for both tasks. Results showed that for both tasks, the primary response state was more bilateral over the sensorimotor or visual areas (depending on task) where the PSR state was more unilateral and confined to the contralateral sensorimotor or visual areas (again, dependant on task). A state coincidence metric was then used to investigate the integration of the primary and PSR states across brain regions as a measure of task-related functional connectivity. Hidden Markov modelling of the interictal brain: Epilepsy is a highly heterogeneous disease with variations in the temporal morphology and localisation of epileptiform activity across patients. Unsupervised machine learning techniques like the HMM allow us to take into account this variability and ensure that every model is tailored to each individual. In this work, a multivariate time-delay embedded HMM was used to identify brain states based on their spatial and spectral properties in sensor-level MEG data acquired as part of standard clinical care for patients at the Children’s Hospital of Philadelphia. State allocations were used together with a linearly constrained minimum variance (LCMV) beamformer to produce a 3D map of state variance, hence localising probable epileptogenic foci. Clinical MEG epilepsy data are routinely analysed by excess kurtosis mapping (EKM) and so the performance of the HMM was assessed against this for three patient groups, each with increasingly complex epilepsy manifestation (10 patients in total). The difference in localization of epileptogenic foci for the two methods was 7 ± 2mm (mean ± SD over all 10 patients); and 94 ± 13% of EKM temporal markers were matched by an HMM state visit. It is therefore clear that this method localizes epileptogenic areas in agreement with EKM and in patients with more than one focus the HMM provides additional information about the relationship between them

29th Annual Computational Neuroscience Meeting: CNS*2020

Meeting abstracts This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests. Virtual | 18-22 July 202

DICOM for EIT

With EIT starting to be used in routine clinical practice [1], it important that the clinically relevant information is portable between hospital data management systems. DICOM formats are widely used clinically and cover many imaging modalities, though not specifically EIT. We describe how existing DICOM specifications, can be repurposed as an interim solution, and basis from which a consensus EIT DICOM ‘Supplement’ (an extension to the standard) can be writte