arXiv subjects

Andrew McEvoy

Publications and source records attributed to Andrew McEvoy.

5 recordsLinked to original sources

arXiv2026

From Pre- to Intra-operative MRI: Predicting Brain Shift in Temporal Lobe Resection for Epilepsy Surgery

Introduction: In neurosurgery, image-guided Neurosurgery Systems (IGNS) highly rely on preoperative brain magnetic resonance images (MRI) to assist surgeons in locating surgical targets and determining surgical paths. However, brain shift invalidates the preoperative MRI after dural opening. Updated intraoperative brain MRI with brain shift compensation is crucial for enhancing the precision of neuronavigation systems and ensuring the optimal outcome of surgical interventions. Methodology: We propose NeuralShift, a U-Net-based model that predicts brain shift entirely from pre-operative MRI for patients undergoing temporal lobe resection. We evaluated our results using Target Registration Errors (TREs) computed on anatomical landmarks located on the resection side and along the midline, and DICE scores comparing predicted intraoperative masks with masks derived from intraoperative MRI. Results: Our experimental results show that our model can predict the global deformation of the brain (DICE of 0.97) with accurate local displacements (achieve landmark TRE as low as 1.12 mm), compensating for large brain shifts during temporal lobe removal neurosurgery. Conclusion: Our proposed model is capable of predicting the global deformation of the brain during temporal lobe resection using only preoperative images, providing potential opportunities to the surgical team to increase safety and efficiency of neurosurgery and better outcomes to patients. Our contributions will be publicly available after acceptance in https://github.com/SurgicalDataScienceKCL/NeuralShift.

cs.CV↗

arXiv2024

Seizure freedom after surgical resection of diffusion-weighted MRI abnormalities

Importance: Many individuals with drug-resistant epilepsy continue to have seizures after resective surgery. Accurate identification of focal brain abnormalities is essential for successful neurosurgical intervention. Current clinical approaches to identify structural abnormalities for surgical targeting in epilepsy do not use diffusion-weighted MRI (dMRI), despite evidence that dMRI abnormalities are present in epilepsy and may relate to the epileptogenic zone. Objective: To investigate whether surgical resection of diffusion abnormalities relates to post-operative seizure freedom. Design: This retrospective case-control study was conducted between 2009 and 2022. Data were acquired at the National Hospital for Neurology and Neurosurgery, UK. Study participants included 200 individuals with drug-resistant focal epilepsy, who underwent resective surgery, and 97 healthy controls used as a normative baseline. Main Outcomes: Spatial overlap between diffusion abnormality clusters and surgical resection masks, and relation to post-surgical outcome. Results: Surgical resections overlapping with the largest abnormal cluster significantly correlated with sustained seizure freedom at 12 months (83% vs 55%; p<0.0001) and over five years (p<0.0001). Notably, resecting only a small proportion of the largest cluster was associated with better seizure outcomes than cases with no resection of this cluster (p=0.008). Furthermore, sparing the largest cluster but resecting other large clusters still improved seizure freedom rates compared to no overlap (p=0.03). Conclusions: Our results suggest that abnormal clusters, identified using dMRI, are integral to the epileptogenic network, and even a partial removal of such an abnormal cluster is sufficient to achieve seizure freedom. The study highlights the potential of incorporating dMRI into pre-surgical planning to improve outcomes in focal epilepsy.

q-bio.NC↗

arXiv2023

Identifying epileptogenic abnormalities through spatial clustering of MEG interictal band power

Successful epilepsy surgery depends on localising and resecting cerebral abnormalities and networks that generate seizures. Abnormalities, however, may be widely distributed across multiple discontiguous areas. We propose spatially constrained clusters as candidate areas for further investigation, and potential resection. We quantified the spatial overlap between the abnormality cluster and subsequent resection, hypothesising a greater overlap in seizure-free patients. Thirty-four individuals with refractory focal epilepsy underwent pre-surgical resting-state interictal MEG recording. Fourteen individuals were totally seizure free (ILAE 1) after surgery and 20 continued to have some seizures post-operatively (ILAE 2+). Band power abnormality maps were derived using controls as a baseline. Patient abnormalities were spatially clustered using the k-means algorithm. The tissue within the cluster containing the most abnormal region was compared with the resection volume using the dice score. The proposed abnormality cluster overlapped with the resection in 71% of ILAE 1 patients. Conversely, an overlap only occurred in 15% of ILAE 2+ patients. This effect discriminated outcome groups well (AUC=0.82). Our novel approach identifies clusters of spatially similar tissue with high abnormality. This is clinically valuable, providing (i) a data-driven framework to validate current hypotheses of the epileptogenic zone localisation or (ii) to guide further investigation.

q-bio.NC↗

arXiv2023

Interictal MEG abnormalities to guide intracranial electrode implantation and predict surgical outcome

Intracranial EEG (iEEG) is the gold standard technique for epileptogenic zone (EZ) localisation, but requires a hypothesis of which tissue is epileptogenic, guided by qualitative analysis of seizure semiology and other imaging modalities such as magnetoencephalography (MEG). We hypothesised that if quantifiable MEG band power abnormalities were sampled by iEEG, then patients' post-resection seizure outcome were better. Thirty-two individuals with neocortical epilepsy underwent MEG and iEEG recordings as part of pre-surgical evaluation. Interictal MEG band power abnormalities were derived using 70 healthy controls as a normative baseline. MEG abnormality maps were compared to electrode implantation, with the spatial overlap of iEEG electrodes and MEG abnormalities recorded. Finally, we assessed if the implantation of electrodes in abnormal tissue, and resection of the strongest abnormalities determined by MEG and iEEG explained surgical outcome. Intracranial electrodes were implanted in brain tissue with the most abnormal MEG findings in individuals that were seizure-free post-resection (T=3.9, p=0.003). The overlap between MEG abnormalities and iEEG electrodes distinguished outcome groups moderately well (AUC=0.68). In isolation, the resection of the strongest MEG and iEEG abnormalities separated surgical outcome groups well (AUC=0.71, AUC=0.74 respectively). A model incorporating all three features separated outcome groups best (AUC=0.80). Intracranial EEG is a key tool to delineate the EZ and help render patients seizure-free after resection. We showed that data-driven abnormalities derived from interictal MEG recordings have clinical value and may help guide electrode placement in individuals with neocortical epilepsy. Finally, our predictive model of post-operative seizure-freedom, which leverages both MEG and iEEG recordings, may aid patient counselling of expected outcome.

q-bio.NC↗

arXiv2023

MEG abnormalities and mechanisms of surgical failure in neocortical epilepsy

Neocortical epilepsy surgery fails to achieve post-operative seizure freedom in 30-40% of cases. It is not fully understood why surgery in some patients is unsuccessful. Comparing interictal MEG bandpower from patients to normative maps, which describe healthy spatial and population variability, we identify patient specific abnormalities relating to surgical failure. We propose three mechanisms contributing to poor surgical outcome; 1) failure to resect abnormalities, 2) failing to remove all epileptogenic abnormalities, and 3) insufficiently impacting the overall cortical abnormality. We develop markers of these mechanisms, validating them against patient outcomes. Resting-state MEG data were acquired for 70 healthy controls and 32 patients with refractory neocortical epilepsy. Relative bandpower maps were computed using source localised recordings from healthy controls. Patient and region-specific bandpower abnormalities were estimated as the maximum absolute z-score, using healthy data as a baseline. Resected regions were identified from post-operative MRI. We hypothesised our mechanism markers would discriminate patient's post-surgery seizure outcomes. Mechanisms of surgical failure discriminate surgical outcome groups (Abnormalities not targeted: AUC=0.80, Partial resection of the epileptogenic zone: AUC=0.68, Insufficient cortical abnormality impact: AUC=0.64). Leveraging all markers together found that 95% of those who were not seizure free had markers of surgical failure in at least one of the three proposed mechanisms. In contrast, of those patients markers for any mechanism, 80% were seizure-free. Abnormality mapping across the brain is important for a wide range of neurological conditions. Here we demonstrated that interictal MEG bandpower mapping has merit for localising pathology and improving our mechanistic understanding of epilepsy.

q-bio.NC↗