Pleural effusion guidelines from ICS and NCCP Section 1: Basic principles, laboratory tests and pleural procedures

Pleural effusion is a common problem in our country, and most of these patients need invasive tests as they can't be evaluated by blood tests alone. The simplest of them is diagnostic pleural aspiration, and diagnostic techniques such as medical thoracoscopy are being performed more frequently than ever before. However, most physicians in India treat pleural effusion empirically, leading to delays in diagnosis, misdiagnosis and complications from wrong treatments. This situation must change, and the adoption of evidence-based protocols is urgently needed. Furthermore, the spectrum of pleural disease in India is different from that in the West, and yet Western guidelines and algorithms are used by Indian physicians. Therefore, India-specific consensus guidelines are needed. To fulfil this need, the Indian Chest Society and the National College of Chest Physicians; the premier societies for pulmonary physicians came together to create this National guideline. This document aims to provide evidence based recommendations on basic principles, initial assessment, diagnostic modalities and management of pleural effusions.

Mapping hippocampal and thalamic atrophy in epilepsy: A 7-T magnetic resonance imaging study

OBJECTIVE

Epilepsy patients are often grouped together by clinical variables. Quantitative neuroimaging metrics can provide a data-driven alternative for grouping of patients. In this work, we leverage ultra-high-field 7-T structural magnetic resonance imaging (MRI) to characterize volumetric atrophy patterns across hippocampal subfields and thalamic nuclei in drug-resistant focal epilepsy.

METHODS

Forty-two drug-resistant epilepsy patients and 13 controls with 7-T structural neuroimaging were included in this study. We measured hippocampal subfield and thalamic nuclei volumetry, and applied an unsupervised machine learning algorithm, Latent Dirichlet Allocation (LDA), to estimate atrophy patterns across the hippocampal subfields and thalamic nuclei of patients. We studied the association between predefined clinical groups and the estimated atrophy patterns. Additionally, we used hierarchical clustering on the LDA factors to group patients in a data-driven approach.

RESULTS

In patients with mesial temporal sclerosis (MTS), we found a significant decrease in volume across all ipsilateral hippocampal subfields (false discovery rate-corrected p [pFDR] < .01) as well as in some ipsilateral (pFDR < .05) and contralateral (pFDR < .01) thalamic nuclei. In left temporal lobe epilepsy (L-TLE) we saw ipsilateral hippocampal and some bilateral thalamic atrophy (pFDR < .05), whereas in right temporal lobe epilepsy (R-TLE) extensive bilateral hippocampal and thalamic atrophy was observed (pFDR < .05). Atrophy factors demonstrated that our MTS cohort had two atrophy phenotypes: one that affected the ipsilateral hippocampus and one that affected the ipsilateral hippocampus and bilateral anterior thalamus. Atrophy factors demonstrated posterior thalamic atrophy in R-TLE, whereas an anterior thalamic atrophy pattern was more common in L-TLE. Finally, hierarchical clustering of atrophy patterns recapitulated clusters with homogeneous clinical properties.

SIGNIFICANCE

Leveraging 7-T MRI, we demonstrate widespread hippocampal and thalamic atrophy in epilepsy. Through unsupervised machine learning, we demonstrate patterns of volumetric atrophy that vary depending on disease subtype. Incorporating these atrophy patterns into clinical practice could help better stratify patients to surgical treatments and specific device implantation strategies.

iEEG-recon: A fast and scalable pipeline for accurate reconstruction of intracranial electrodes and implantable devices

OBJECTIVE

Clinicians use intracranial electroencephalography (iEEG) in conjunction with noninvasive brain imaging to identify epileptic networks and target therapy for drug-resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of "electrode reconstruction," which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms.

METHODS

We created iEEG-recon, a scalable electrode reconstruction pipeline for semiautomatic iEEG annotation, rapid image registration, and electrode assignment on brain magnetic resonance imaging (MRI). Its modular architecture includes a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG-recon in a containerized format that allows integration into clinical workflows. We propose a cloud-based implementation of iEEG-recon and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts.

RESULTS

We used iEEG-recon to accurately reconstruct electrodes in both electrocorticography and stereoelectroencephalography cases with a 30-min running time per case (including semiautomatic electrode labeling and reconstruction). iEEG-recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre- and postimplant T1-MRI visual inspections. We also found that our use of ANTsPyNet deep learning-based brain segmentation for electrode classification was consistent with the widely used FreeSurfer segmentations.

SIGNIFICANCE

iEEG-recon is a robust pipeline for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting fast data analysis and integration into clinical workflows. iEEG-recon's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide.

Intracranial EEG Structure-Function Coupling and Seizure Outcomes After Epilepsy Surgery

BACKGROUND AND OBJECTIVES

Surgery is an effective treatment for drug-resistant epilepsy, which modifies the brain's structure and networks to regulate seizure activity. Our objective was to examine the relationship between brain structure and function to determine the extent to which this relationship affects the success of the surgery in controlling seizures. We hypothesized that a stronger association between brain structure and function would lead to improved seizure control after surgery.

METHODS

We constructed functional and structural brain networks in patients with drug-resistant focal epilepsy by using presurgery functional data from intracranial EEG (iEEG) recordings, presurgery and postsurgery structural data from T1-weighted MRI, and presurgery diffusion-weighted MRI. We quantified the relationship (coupling) between structural and functional connectivity by using the Spearman rank correlation and analyzed this structure-function coupling at 2 spatial scales: (1) global iEEG network level and (2) individual iEEG electrode contacts using virtual surgeries. We retrospectively predicted postoperative seizure freedom by incorporating the structure-function connectivity coupling metrics and routine clinical variables into a cross-validated predictive model.

RESULTS

We conducted a retrospective analysis on data from 39 patients who met our inclusion criteria. Brain areas implanted with iEEG electrodes had stronger structure-function coupling in seizure-free patients compared with those with seizure recurrence (p = 0.002, d = 0.76, area under the receiver operating characteristic curve [AUC] = 0.78 [95% CI 0.62-0.93]). Virtual surgeries on brain areas that resulted in stronger structure-function coupling of the remaining network were associated with seizure-free outcomes (p = 0.007, d = 0.96, AUC = 0.73 [95% CI 0.58-0.89]). The combination of global and local structure-function coupling measures accurately predicted seizure outcomes with a cross-validated AUC of 0.81 (95% CI 0.67-0.94). These measures were complementary to other clinical variables and, when included for prediction, resulted in a cross-validated AUC of 0.91 (95% CI 0.82-1.0), accuracy of 92%, sensitivity of 93%, and specificity of 91%.

DISCUSSION

Our study showed that the strength of structure-function connectivity coupling may play a crucial role in determining the success of epilepsy surgery. By quantitatively incorporating structure-function coupling measures and standard-of-care clinical variables into presurgical evaluations, we may be able to better localize epileptogenic tissue and select patients for epilepsy surgery.

CLASSIFICATION OF EVIDENCE

This is a Class IV retrospective case series showing that structure-function mapping may help determine the outcome from surgical resection for treatment-resistant focal epilepsy.

The seizure severity score: a quantitative tool for comparing seizures and their response to therapy

Objective.Epilepsy is a neurological disorder characterized by recurrent seizures which vary widely in severity, from clinically silent to prolonged convulsions. Measuring severity is crucial for guiding therapy, particularly when complete control is not possible. Seizure diaries, the current standard for guiding therapy, are insensitive to the duration of events or the propagation of seizure activity across the brain. We present a quantitative seizure severity score that incorporates electroencephalography (EEG) and clinical data and demonstrate how it can guide epilepsy therapies.Approach.We collected intracranial EEG and clinical semiology data from 54 epilepsy patients who had 256 seizures during invasive, in-hospital presurgical evaluation. We applied an absolute slope algorithm to EEG recordings to identify seizing channels. From this data, we developed a seizure severity score that combines seizure duration, spread, and semiology using non-negative matrix factorization. For validation, we assessed its correlation with independent measures of epilepsy burden: seizure types, epilepsy duration, a pharmacokinetic model of medication load, and response to epilepsy surgery. We investigated the association between the seizure severity score and preictal network features.Main results.The seizure severity score augmented clinical classification by objectively delineating seizure duration and spread from recordings in available electrodes. Lower preictal medication loads were associated with higher seizure severity scores (p= 0.018, 97.5% confidence interval = [-1.242, -0.116]) and lower pre-surgical severity was associated with better surgical outcome (p= 0.042). In 85% of patients with multiple seizure types, greater preictal change from baseline was associated with higher severity.Significance.We present a quantitative measure of seizure severity that includes EEG and clinical features, validated on gold standard in-patient recordings. We provide a framework for extending our tool's utility to ambulatory EEG devices, for linking it to seizure semiology measured by wearable sensors, and as a tool to advance data-driven epilepsy care.

Quantifying interictal intracranial EEG to predict focal epilepsy

Introduction

Intracranial EEG (IEEG) is used for 2 main purposes, to determine: (1) if epileptic networks are amenable to focal treatment and (2) where to intervene. Currently these questions are answered qualitatively and sometimes differently across centers. There is a need for objective, standardized methods to guide surgical decision making and to enable large scale data analysis across centers and prospective clinical trials.

Methods

We analyzed interictal data from 101 patients with drug resistant epilepsy who underwent presurgical evaluation with IEEG. We chose interictal data because of its potential to reduce the morbidity and cost associated with ictal recording. 65 patients had unifocal seizure onset on IEEG, and 36 were non-focal or multi-focal. We quantified the spatial dispersion of implanted electrodes and interictal IEEG abnormalities for each patient. We compared these measures against the "5 Sense Score (5SS)," a pre-implant estimate of the likelihood of focal seizure onset, and assessed their ability to predict the clinicians' choice of therapeutic intervention and the patient outcome.

Results

The spatial dispersion of IEEG electrodes predicted network focality with precision similar to the 5SS (AUC = 0.67), indicating that electrode placement accurately reflected pre-implant information. A cross-validated model combining the 5SS and the spatial dispersion of interictal IEEG abnormalities significantly improved this prediction (AUC = 0.79; p<0.05). The combined model predicted ultimate treatment strategy (surgery vs. device) with an AUC of 0.81 and post-surgical outcome at 2 years with an AUC of 0.70. The 5SS, interictal IEEG, and electrode placement were not correlated and provided complementary information.

Conclusions

Quantitative, interictal IEEG significantly improved upon pre-implant estimates of network focality and predicted treatment with precision approaching that of clinical experts. We present this study as an important step in building standardized, quantitative tools to guide epilepsy surgery.

iEEG-recon: A Fast and Scalable Pipeline for Accurate Reconstruction of Intracranial Electrodes and Implantable Devices

Background

Collaboration between epilepsy centers is essential to integrate multimodal data for epilepsy research. Scalable tools for rapid and reproducible data analysis facilitate multicenter data integration and harmonization. Clinicians use intracranial EEG (iEEG) in conjunction with non-invasive brain imaging to identify epileptic networks and target therapy for drug-resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of "electrode reconstruction," which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. These tasks are still performed manually in many epilepsy centers. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms.

Methods

We created iEEG-recon, a scalable electrode reconstruction pipeline for semi-automatic iEEG annotation, rapid image registration, and electrode assignment on brain MRIs. Its modular architecture includes three modules: a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG-recon in a containerized format that allows integration into clinical workflows. We propose a cloud-based implementation of iEEG-recon, and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts.

Results

We used iEEG-recon to accurately reconstruct electrodes in both electrocorticography (ECoG) and stereoelectroencephalography (SEEG) cases with a 10 minute running time per case, and ~20 min for semi-automatic electrode labeling. iEEG-recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre- and post-implant T1-MRI visual inspections. Our use of ANTsPyNet deep learning approach for brain segmentation and electrode classification was consistent with the widely used Freesurfer segmentation.

Discussion

iEEG-recon is a valuable tool for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting efficient data analysis, and integration into clinical workflows. The tool's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide. Comprehensive documentation is available at https://ieeg-recon.readthedocs.io/en/latest/.

Improved Seizure Onset-Zone Lateralization in Temporal Lobe Epilepsy using 7T Resting-State fMRI: A Direct Comparison with 3T

Objective

Resting-state functional magnetic resonance imaging (rs-fMRI) at ultra high-field strengths (≥7T) is known to provide superior signal-to-noise and statistical power than comparable acquisitions at lower field strengths. In this study, we aim to provide a direct comparison of the seizure onset-zone (SOZ) lateralizing ability of 7T rs-fMRI and 3T rs-fMRI.

Methods

We investigated a cohort of 70 temporal lobe epilepsy (TLE) patients. A paired cohort of 19 patients had 3T and 7T rs-fMRI acquisitions for direct comparison between the two field strengths. Forty-three patients had only 3T, and 8 patients had only 7T rs-fMRI acquisitions. We quantified the functional connectivity between the hippocampus and other nodes within the default mode network (DMN) using seed-to-voxel connectivity, and measured how hippocampo-DMN connectivity could inform SOZ lateralization at 7T and 3T field strengths.

Results

Differences between hippocampo-DMN connectivity ipsilateral and contralateral to the SOZ were significantly higher at 7T (pFDR=0.008) than at 3T (pFDR=0.80) when measured in the same subjects. We found that our ability to lateralize the SOZ, by distinguishing subjects with left TLE from subjects with right TLE, was superior at 7T (AUC = 0.97) than 3T (AUC = 0.68). Our findings were reproduced in extended cohorts of subjects scanned at either 3T or 7T. Our rs-fMRI findings at 7T, but not 3T, are consistent and highly correlated (Spearman Rho=0.65) with clinical FDG-PET lateralizing hypometabolism.

Significance

We show superior SOZ lateralization in TLE patients when using 7T relative to 3T rs-fMRI, supporting the adoption of high-field strength functional imaging in the epilepsy presurgical evaluation.

Quantitative approaches to guide epilepsy surgery from intracranial EEG

Over the past 10 years, the drive to improve outcomes from epilepsy surgery has stimulated widespread interest in methods to quantitatively guide epilepsy surgery from intracranial EEG (iEEG). Many patients fail to achieve seizure freedom, in part due to the challenges in subjective iEEG interpretation. To address this clinical need, quantitative iEEG analytics have been developed using a variety of approaches, spanning studies of seizures, interictal periods, and their transitions, and encompass a range of techniques including electrographic signal analysis, dynamical systems modeling, machine learning and graph theory. Unfortunately, many methods fail to generalize to new data and are sensitive to differences in pathology and electrode placement. Here, we critically review selected literature on computational methods of identifying the epileptogenic zone from iEEG. We highlight shared methodological challenges common to many studies in this field and propose ways that they can be addressed. One fundamental common pitfall is a lack of open-source, high-quality data, which we specifically address by sharing a centralized high-quality, well-annotated, multicentre dataset consisting of >100 patients to support larger and more rigorous studies. Ultimately, we provide a road map to help these tools reach clinical trials and hope to improve the lives of future patients.

Subcortical functional connectivity gradients in temporal lobe epilepsy

BACKGROUND AND MOTIVATION

Functional gradients have been used to study differences in connectivity between healthy and diseased brain states, however this work has largely focused on the cortex. Because the subcortex plays a key role in seizure initiation in temporal lobe epilepsy (TLE), subcortical functional-connectivity gradients may help further elucidate differences between healthy brains and TLE, as well as differences between left (L)-TLE and right (R)-TLE.

METHODS

In this work, we calculated subcortical functional-connectivity gradients (SFGs) from resting-state functional MRI (rs-fMRI) by measuring the similarity in connectivity profiles of subcortical voxels to cortical gray matter voxels. We performed this analysis in 24 R-TLE patients and 31 L-TLE patients (who were otherwise matched for age, gender, disease specific characteristics, and other clinical variables), and 16 controls. To measure differences in SFGs between L-TLE and R-TLE, we quantified deviations in the average functional gradient distributions, as well as their variance, across subcortical structures.

RESULTS

We found an expansion, measured by increased variance, in the principal SFG of TLE relative to controls. When comparing the gradient across subcortical structures between L-TLE and R-TLE, we found that abnormalities in the ipsilateral hippocampal gradient distributions were significantly different between L-TLE and R-TLE.

CONCLUSION

Our results suggest that expansion of the SFG is characteristic of TLE. Subcortical functional gradient differences exist between left and right TLE and are driven by connectivity changes in the hippocampus ipsilateral to the seizure onset zone.

Resting state functional connectivity demonstrates increased segregation in bilateral temporal lobe epilepsy

OBJECTIVE

Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy. An increasingly identified subset of patients with TLE consists of those who show bilaterally independent temporal lobe seizures. The purpose of this study was to leverage network neuroscience to better understand the interictal whole brain network of bilateral TLE (BiTLE).

METHODS

In this study, using a multicenter resting state functional magnetic resonance imaging (rs-fMRI) data set, we constructed whole-brain functional networks of 19 patients with BiTLE, and compared them to those of 75 patients with unilateral TLE (UTLE). We quantified resting-state, whole-brain topological properties using metrics derived from network theory, including clustering coefficient, global efficiency, participation coefficient, and modularity. For each metric, we computed an average across all brain regions, and iterated this process across network densities. Curves of network density vs each network metric were compared between groups. Finally, we derived a combined metric, which we term the "integration-segregation axis," by combining whole-brain average clustering coefficient and global efficiency curves, and applying principal component analysis (PCA)-based dimensionality reduction.

RESULTS

Compared to UTLE, BiTLE had decreased global efficiency (p = .031), and decreased whole brain average participation coefficient across a range of network densities (p = .019). Modularity maximization yielded a larger number of smaller communities in BiTLE than in UTLE (p = .020). Differences in network properties separate BiTLE and UTLE along the integration-segregation axis, with regions within the axis having a specificity of up to 0.87 for BiTLE. Along the integration-segregation axis, UTLE patients with poor surgical outcomes were distributed in the same regions as BiTLE, and network metrics confirmed similar patterns of increased segregation in both BiTLE and poor outcome UTLE.

SIGNIFICANCE

Increased interictal whole-brain network segregation, as measured by rs-fMRI, is specific to BiTLE, as well as poor surgical outcome UTLE, and may assist in non-invasively identifying this patient population prior to intracranial electroencephalography or device implantation.

Longitudinal Abnormalities in White Matter Extracellular Free Water Volume Fraction and Neuropsychological Functioning in Patients with Traumatic Brain Injury

Traumatic brain injury is a global public health problem associated with chronic neurological complications and long-term disability. Biomarkers that map onto the underlying brain pathology driving these complications are urgently needed to identify individuals at risk for poor recovery and to inform design of clinical trials of neuroprotective therapies. Neuroinflammation and neurodegeneration are two endophenotypes potentially associated with increases in brain extracellular water content, but the nature of extracellular free water abnormalities after neurotrauma and its relationship to measures typically thought to reflect traumatic axonal injury are not well characterized. The objective of this study was to describe the relationship between a neuroimaging biomarker of extracellular free water content and the clinical features of a cohort with primarily complicated mild traumatic brain injury. We analyzed a cohort of 59 adult patients requiring hospitalization for non-penetrating traumatic brain injury of all severities as well as 36 healthy controls. Patients underwent brain magnetic resonance imaging (MRI) at 2 weeks (n = 59) and 6 months (n = 29) post-injury, and controls underwent a single MRI. Of the participants with TBI, 50 underwent clinical neuropsychological assessment at 2 weeks and 28 at 6 months. For each subject, we derived a summary score representing deviations in whole brain white matter extracellular free water volume fraction (VF) and free water-corrected fractional anisotropy (fw-FA). The summary specific anomaly score (SAS) for VF was significantly higher in TBI patients at 2 weeks and 6 months post-injury relative to controls. SAS for VF exhibited moderate correlation with neuropsychological functioning, particularly on measures of executive function. These findings indicate abnormalities in whole brain white matter extracellular water fraction in patients with TBI and are an important step toward identifying and validating noninvasive biomarkers that map onto the pathology driving disability after TBI.

Structural brain network deviations predict recovery after traumatic brain injury

OBJECTIVE

Traumatic brain injury results in diffuse axonal injury and the ensuing maladaptive alterations in network function are associated with incomplete recovery and persistent disability. Despite the importance of axonal injury as an endophenotype in TBI, there is no biomarker that can measure the aggregate and region-specific burden of axonal injury. Normative modeling is an emerging quantitative case-control technique that can capture region-specific and aggregate deviations in brain networks at the individual patient level. Our objective was to apply normative modeling in TBI to study deviations in brain networks after primarily complicated mild TBI and study its relationship with other validated measures of injury severity, burden of post-TBI symptoms, and functional impairment.

METHOD

We analyzed 70 T1-weighted and diffusion-weighted MRIs longitudinally collected from 35 individuals with primarily complicated mild TBI during the subacute and chronic post-injury periods. Each individual underwent longitudinal blood sampling to characterize blood protein biomarkers of axonal and glial injury and assessment of post-injury recovery in the subacute and chronic periods. By comparing the MRI data of individual TBI participants with 35 uninjured controls, we estimated the longitudinal change in structural brain network deviations. We compared network deviation with independent measures of acute intracranial injury estimated from head CT and blood protein biomarkers. Using elastic net regression models, we identified brain regions in which deviations present in the subacute period predict chronic post-TBI symptoms and functional status.

RESULTS

Post-injury structural network deviation was significantly higher than controls in both subacute and chronic periods, associated with an acute CT lesion and subacute blood levels of glial fibrillary acid protein (r = 0.5, p = 0.008) and neurofilament light (r = 0.41, p = 0.02). Longitudinal change in network deviation associated with change in functional outcome status (r = -0.51, p = 0.003) and post-concussive symptoms (BSI: r = 0.46, p = 0.03; RPQ: r = 0.46, p = 0.02). The brain regions where the node deviation index measured in the subacute period predicted chronic TBI symptoms and functional status corresponded to areas known to be susceptible to neurotrauma.

CONCLUSION

Normative modeling can capture structural network deviations, which may be useful in estimating the aggregate and region-specific burden of network changes induced by TAI. If validated in larger studies, structural network deviation scores could be useful for enrichment of clinical trials of targeted TAI-directed therapies.

Subcortical Functional Connectivity Gradients in Temporal Lobe Epilepsy

Background and Motivation

Functional gradients have been used to study differences in connectivity between healthy and diseased brain states, however this work has largely focused on the cortex. Because the subcortex plays a key role in seizure initiation in temporal lobe epilepsy (TLE), subcortical functional-connectivity gradients may help further elucidate differences between healthy brains and TLE, as well as differences between left (L)-TLE and right (R)-TLE.

Methods

In this work, we calculated subcortical functional-connectivity gradients (SFGs) from resting-state functional MRI (rs-fMRI) by measuring the similarity in connectivity profiles of subcortical voxels to cortical gray matter voxels. We performed this analysis in 23 R-TLE patients and 32 L-TLE patients (who were otherwise matched for age, gender, disease specific characteristics, and other clinical variables), and 16 controls. To measure differences in SFGs between L-TLE and R-TLE, we quantified deviations in the average functional gradient distributions, as well as their variance, across subcortical structures.

Results

We found an expansion, measured by increased variance, in the principal SFG of TLE relative to controls. When comparing the gradient across subcortical structures between L-TLE and R-TLE, we found that abnormalities in the ipsilateral hippocampal gradient distributions were significantly different between L-TLE and R-TLE.

Conclusion

Our results suggest that expansion of the SFG is characteristic of TLE. Subcortical functional gradient differences exist between left and right TLE and are driven by connectivity changes in the hippocampus ipsilateral to the seizure onset zone.

A knowledge integration strategy for the selection of a robust multi-stress biomarkers panel for Bacillus subtilis

One challenge in the engineering of biological systems is to be able to recognise the cellular stress states of bacterial hosts, as these stress states can lead to suboptimal growth and lower yields of target products. To enable the design of genetic circuits for reporting or mitigating the stress states, it is important to identify a relatively reduced set of gene biomarkers that can reliably indicate relevant cellular growth states in bacteria. Recent advances in high-throughput omics technologies have enhanced the identification of molecular biomarkers specific states in bacteria, motivating computational methods that can identify robust biomarkers for experimental characterisation and verification. Focused on identifying gene expression biomarkers to sense various stress states in Bacillus subtilis, this study aimed to design a knowledge integration strategy for the selection of a robust biomarker panel that generalises on external datasets and experiments. We developed a recommendation system that ranks the candidate biomarker panels based on complementary information from machine learning model, gene regulatory network and co-expression network. We identified a recommended biomarker panel showing high stress sensing power for a variety of conditions both in the dataset used for biomarker identification (mean f1-score achieved at 0.99), as well as in a range of independent datasets (mean f1-score achieved at 0.98). We discovered a significant correlation between stress sensing power and evaluation metrics such as the number of associated regulators in a B. subtilis gene regulatory network (GRN) and the number of associated modules in a B. subtilis co-expression network (CEN). GRNs and CENs provide information relevant to the diversity of biological processes encoded by biomarker genes. We demonstrate that quantitatively relating meaningful evaluation metrics with stress sensing power has the potential for recognising biomarkers that show better sensitivity and robustness to an extended set of stress conditions and enable a more reliable biomarker panel selection.

Improving automated diagnosis of epilepsy from EEGs beyond IEDs

Objective.Clinical diagnosis of epilepsy relies partially on identifying interictal epileptiform discharges (IEDs) in scalp electroencephalograms (EEGs). This process is expert-biased, tedious, and can delay the diagnosis procedure. Beyond automatically detecting IEDs, there are far fewer studies on automated methods to differentiate epileptic EEGs (potentially without IEDs) from normal EEGs. In addition, the diagnosis of epilepsy based on a single EEG tends to be low. Consequently, there is a strong need for automated systems for EEG interpretation. Traditionally, epilepsy diagnosis relies heavily on IEDs. However, since not all epileptic EEGs exhibit IEDs, it is essential to explore IED-independent EEG measures for epilepsy diagnosis. The main objective is to develop an automated system for detecting epileptic EEGs, both with or without IEDs. In order to detect epileptic EEGs without IEDs, it is crucial to include EEG features in the algorithm that are not directly related to IEDs.Approach.In this study, we explore the background characteristics of interictal EEG for automated and more reliable diagnosis of epilepsy. Specifically, we investigate features based on univariate temporal measures (UTMs), spectral, wavelet, Stockwell, connectivity, and graph metrics of EEGs, besides patient-related information (age and vigilance state). The evaluation is performed on a sizeable cohort of routine scalp EEGs (685 epileptic EEGs and 1229 normal EEGs) from five centers across Singapore, USA, and India.Main results.In comparison with the current literature, we obtained an improved Leave-One-Subject-Out (LOSO) cross-validation (CV) area under the curve (AUC) of 0.871 (Balanced Accuracy (BAC) of 80.9%) with a combination of three features (IED rate, and Daubechies and Morlet wavelets) for the classification of EEGs with IEDs vs. normal EEGs. The IED-independent feature UTM achieved a LOSO CV AUC of 0.809 (BAC of 74.4%). The inclusion of IED-independent features also helps to improve the EEG-level classification of epileptic EEGs with and without IEDs vs. normal EEGs, achieving an AUC of 0.822 (BAC of 77.6%) compared to 0.688 (BAC of 59.6%) for classification only based on the IED rate. Specifically, the addition of IED-independent features improved the BAC by 21% in detecting epileptic EEGs that do not contain IEDs.Significance.These results pave the way towards automated detection of epilepsy. We are one of the first to analyze epileptic EEGs without IEDs, thereby opening up an underexplored option in epilepsy diagnosis.

Localizing epileptogenic tissues in epilepsy: are we losing (the) focus?

This scientific commentary refers to ‘Source-sink connectivity: a novel interictal EEG marker for seizure localization’ by Gunnarsdottir et al. (https://doi.org/10.1093/awac300).

Addressing spatial bias in intracranial EEG functional connectivity analyses for epilepsy surgical planning

Objective.To determine the effect of epilepsy on intracranial electroencephalography (EEG) functional connectivity, and the ability of functional connectivity to localize the seizure onset zone (SOZ), controlling for spatial biases.Approach.We analyzed intracranial EEG data from patients with drug-resistant epilepsy admitted for pre-surgical planning. We calculated intracranial EEG functional networks and determined whether changes in functional connectivity lateralized the SOZ using a spatial subsampling method to control for spatial bias. We developed a 'spatial null model' to localize the SOZ electrode using only spatial sampling information, ignoring EEG data. We compared the performance of this spatial null model against models incorporating EEG functional connectivity and interictal spike rates.Main results.About 110 patients were included in the study, although the number of patients differed across analyses. Controlling for spatial sampling, the average connectivity was lower in the SOZ region relative to the same anatomic region in the contralateral hemisphere. A model using intra-hemispheric connectivity accurately lateralized the SOZ (average accuracy 75.5%). A spatial null model incorporating spatial sampling information alone achieved moderate accuracy in classifying SOZ electrodes (mean AUC = 0.70, 95% CI 0.63-0.77). A model incorporating intracranial EEG functional connectivity and spike rate data further outperformed this spatial null model (AUC 0.78,p= 0.002 compared to spatial null model). However, a model incorporating functional connectivity without spike rate data did not significantly outperform the null model (AUC 0.72,p= 0.38).Significance.Intracranial EEG functional connectivity is reduced in the SOZ region, and interictal data predict SOZ electrode localization and laterality, however a predictive model incorporating functional connectivity without interictal spike rates did not significantly outperform a spatial null model. We propose constructing a spatial null model to provide an estimate of the pre-implant hypothesis of the SOZ, and to serve as a benchmark for further machine learning algorithms in order to avoid overestimating model performance because of electrode sampling alone.

Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI

Accurate segmentation of surgical resection sites is critical for clinical assessments and neuroimaging research applications, including resection extent determination, predictive modeling of surgery outcome, and masking image processing near resection sites. In this study, an automated resection cavity segmentation algorithm is developed for analyzing postoperative MRI of epilepsy patients and deployed in an easy-to-use graphical user interface (GUI) that estimates remnant brain volumes, including postsurgical hippocampal remnant tissue. This retrospective study included postoperative T1-weighted MRI from 62 temporal lobe epilepsy (TLE) patients who underwent resective surgery. The resection site was manually segmented and reviewed by a neuroradiologist (JMS). A majority vote ensemble algorithm was used to segment surgical resections, using 3 U-Net convolutional neural networks trained on axial, coronal, and sagittal slices, respectively. The algorithm was trained using 5-fold cross validation, with data partitioned into training (N = 27) testing (N = 9), and validation (N = 9) sets, and evaluated on a separate held-out test set (N = 17). Algorithm performance was assessed using Dice-Sørensen coefficient (DSC), Hausdorff distance, and volume estimates. Additionally, we deploy a fully-automated, GUI-based pipeline that compares resection segmentations with preoperative imaging and reports estimates of resected brain structures. The cross-validation and held-out test median DSCs were 0.84 ± 0.08 and 0.74 ± 0.22 (median ± interquartile range) respectively, which approach inter-rater reliability between radiologists (0.84-0.86) as reported in the literature. Median 95 % Hausdorff distances were 3.6 mm and 4.0 mm respectively, indicating high segmentation boundary confidence. Automated and manual resection volume estimates were highly correlated for both cross-validation (r = 0.94, p < 0.0001) and held-out test subjects (r = 0.87, p < 0.0001). Automated and manual segmentations overlapped in all 62 subjects, indicating a low false negative rate. In control subjects (N = 40), the classifier segmented no voxels (N = 33), <50 voxels (N = 5), or a small volumes<0.5 cm3 (N = 2), indicating a low false positive rate that can be controlled via thresholding. There was strong agreement between postoperative hippocampal remnant volumes determined using automated and manual resection segmentations (r = 0.90, p < 0.0001, mean absolute error = 6.3 %), indicating that automated resection segmentations can permit quantification of postoperative brain volumes after epilepsy surgery. Applications include quantification of postoperative remnant brain volumes, correction of deformable registration, and localization of removed brain regions for network modeling.

Normative intracranial EEG maps epileptogenic tissues in focal epilepsy

Planning surgery for patients with medically refractory epilepsy often requires recording seizures using intracranial EEG. Quantitative measures derived from interictal intracranial EEG yield potentially appealing biomarkers to guide these surgical procedures; however, their utility is limited by the sparsity of electrode implantation as well as the normal confounds of spatiotemporally varying neural activity and connectivity. We propose that comparing intracranial EEG recordings to a normative atlas of intracranial EEG activity and connectivity can reliably map abnormal regions, identify targets for invasive treatment and increase our understanding of human epilepsy. Merging data from the Penn Epilepsy Center and a public database from the Montreal Neurological Institute, we aggregated interictal intracranial EEG retrospectively across 166 subjects comprising >5000 channels. For each channel, we calculated the normalized spectral power and coherence in each canonical frequency band. We constructed an intracranial EEG atlas by mapping the distribution of each feature across the brain and tested the atlas against data from novel patients by generating a z-score for each channel. We demonstrate that for seizure onset zones within the mesial temporal lobe, measures of connectivity abnormality provide greater distinguishing value than univariate measures of abnormal neural activity. We also find that patients with a longer diagnosis of epilepsy have greater abnormalities in connectivity. By integrating measures of both single-channel activity and inter-regional functional connectivity, we find a better accuracy in predicting the seizure onset zones versus normal brain (area under the curve = 0.77) compared with either group of features alone. We propose that aggregating normative intracranial EEG data across epilepsy centres into a normative atlas provides a rigorous, quantitative method to map epileptic networks and guide invasive therapy. We publicly share our data, infrastructure and methods, and propose an international framework for leveraging big data in surgical planning for refractory epilepsy.

Integrating Network Neuroscience Into Epilepsy Care: Progress, Barriers, and Next Steps

Drug resistant epilepsy is a disorder involving widespread brain network alterations. Recently, many groups have reported neuroimaging and electrophysiology network analysis techniques to aid medical management, support presurgical planning, and understand postsurgical seizure persistence. While these approaches may supplement standard tests to improve care, they are not yet used clinically or influencing medical or surgical decisions. When will this change? Which approaches have shown the most promise? What are the barriers to translating them into clinical use? How do we facilitate this transition? In this review, we will discuss progress, barriers, and next steps regarding the integration of brain network analysis into the medical and presurgical pipeline.

Perspectives on Understanding Aberrant Brain Networks in Epilepsy

Epilepsy is a neurological disorder affecting approximately 70 million people worldwide. It is characterized by seizures that are complex aberrant dynamical events typically treated with drugs and surgery. Unfortunately, not all patients become seizure-free, and there is an opportunity for novel approaches to treat epilepsy using a network view of the brain. The traditional seizure focus theory presumed that seizures originated within a discrete cortical area with subsequent recruitment of adjacent cortices with seizure progression. However, a more recent view challenges this concept, suggesting that epilepsy is a network disease, and both focal and generalized seizures arise from aberrant activity in a distributed network. Changes in the anatomical configuration or widespread neural activities spanning lobes and hemispheres could make the brain more susceptible to seizures. In this perspective paper, we summarize the current state of knowledge, address several important challenges that could further improve our understanding of the human brain in epilepsy, and invite novel studies addressing these challenges.

Clinical risk assessment of chronic kidney disease patients using genetic programming

Chronic kidney disease (CKD) is one of the serious health concerns in the twenty-first century. CKD impacts over 37 million Americans. By applying machine learning (ML) techniques to clinical data, CKD can be diagnosed early. This early detection of CKD can prevent numerous loss of life. In this work, clinical data set of 400 patients, available on the UCI repository, are taken. Unfortunately, this data set doesn't have an equal distribution of CKD and Non-CKD samples. This imbalanced nature of data highly influences the learning capabilities of classifiers. Genetic Programming (GP) is an ML technique based on the evolution of species. GP with standard fitness function, also impacted by this imbalanced nature of data. A new Euclidean distance-based fitness function in GP is proposed to handle this imbalanced nature of the data set. To compare the robustness of the proposed work, other classification techniques, K-nearest neighborhood (KNN), KNN with particle swarm optimization (PSO), and GP with the standard fitness function, is also applied. For ten-fold cross-validation, the KNN shows an accuracy of 83.54% with an AUC value of 0.69, the PSO-KNN shows an accuracy of 96.79% with an AUC value of 0.94, and the GP, with the newly proposed fitness function, supersedes KNN and PSO-KNN and shows the accuracy of 99.33% with an AUC value of 0.99.

Quantification of brain proton longitudinal relaxation (T1 ) in lithium-treated and lithium-naïve patients with bipolar disorder in comparison to healthy controls

BACKGROUND

Proton longitudinal relaxation (T₁ ) is a quantitative MRI-derived tissue parameter sensitive to myelin, macromolecular, iron and water content. There is some evidence to suggest that cortical T₁ is elevated in bipolar disorder and that lithium administration reduces cortical T₁ . However, T₁ has not yet been quantified in separate groups containing lithium-treated patients, lithium-naïve patients, and matched healthy controls.

METHODS

Euthymic patients with bipolar disorder receiving lithium (n = 18, BDL) and those on other medications but naïve to lithium (n = 20, BDC) underwent quantitative T₁ mapping alongside healthy controls (n = 18, HC). T₁ was compared between groups within the cortex, white matter and subcortical structures using regions of interest (ROI) derived from the Desikan-Killiany atlas. Effect sizes for each ROI were computed for BDC vs BDL groups and Bipolar Disorder vs HC groups.

RESULTS

No significant differences in T₁ were identified between BDL and BDC groups when corrected for multiple comparisons. Patients with bipolar disorder had significantly higher mean T₁ in a range of ROIs compared to healthy controls, including bilateral motor, somatosensory and superior temporal regions, subcortical structures and white matter.

CONCLUSIONS

The higher T₁ values observed in the patients with bipolar disorder may reflect abnormal tissue microstructure. Whilst the precise mechanism remains unknown, these findings may have a basis in differences in myelination, macromolecular content, iron and water content between patients and controls.

Focal to bilateral tonic-clonic seizures are associated with widespread network abnormality in temporal lobe epilepsy

OBJECTIVE

Our objective was to identify whether the whole-brain structural network alterations in patients with temporal lobe epilepsy (TLE) and focal to bilateral tonic-clonic seizures (FBTCS) differ from alterations in patients without FBTCS.

METHODS

We dichotomized a cohort of 83 drug-resistant patients with TLE into those with and without FBTCS and compared each group to 29 healthy controls. For each subject, we used diffusion-weighted magnetic resonance imaging to construct whole-brain structural networks. First, we measured the extent of alterations by performing FBTCS-negative (FBTCS-) versus control and FBTCS-positive (FBTCS+) versus control comparisons, thereby delineating altered subnetworks of the whole-brain structural network. Second, by standardizing each patient's networks using control networks, we measured the subject-specific abnormality at every brain region in the network, thereby quantifying the spatial localization and the amount of abnormality in every patient.

RESULTS

Both FBTCS+ and FBTCS- patient groups had altered subnetworks with reduced fractional anisotropy and increased mean diffusivity compared to controls. The altered subnetwork in FBTCS+ patients was more widespread than in FBTCS- patients (441 connections altered at t > 3, p < .001 in FBTCS+ compared to 21 connections altered at t > 3, p = .01 in FBTCS-). Significantly greater abnormalities-aggregated over the entire brain network as well as assessed at the resolution of individual brain areas-were present in FBTCS+ patients (p < .001, d = .82, 95% confidence interval = .32-1.3). In contrast, the fewer abnormalities present in FBTCS- patients were mainly localized to the temporal and frontal areas.

SIGNIFICANCE

The whole-brain structural network is altered to a greater and more widespread extent in patients with TLE and FBTCS. We suggest that these abnormal networks may serve as an underlying structural basis or consequence of the greater seizure spread observed in FBTCS.

Structural Brain Network Abnormalities and the Probability of Seizure Recurrence After Epilepsy Surgery

OBJECTIVE

We assessed preoperative structural brain networks and clinical characteristics of patients with drug-resistant temporal lobe epilepsy (TLE) to identify correlates of postsurgical seizure recurrences.

METHODS

We examined data from 51 patients with TLE who underwent anterior temporal lobe resection (ATLR) and 29 healthy controls. For each patient, using the preoperative structural, diffusion, and postoperative structural MRI, we generated 2 networks: presurgery network and surgically spared network. Standardizing these networks with respect to controls, we determined the number of abnormal nodes before surgery and expected to be spared by surgery. We incorporated these 2 abnormality measures and 13 commonly acquired clinical data from each patient into a robust machine learning framework to estimate patient-specific chances of seizures persisting after surgery.

RESULTS

Patients with more abnormal nodes had a lower chance of complete seizure freedom at 1 year and, even if seizure-free at 1 year, were more likely to relapse within 5 years. The number of abnormal nodes was greater and their locations more widespread in the surgically spared networks of patients with poor outcome than in patients with good outcome. We achieved an area under the curve of 0.84 ± 0.06 and specificity of 0.89 ± 0.09 in predicting unsuccessful seizure outcomes (International League Against Epilepsy [ILAE] 3-5) as opposed to complete seizure freedom (ILAE 1) at 1 year. Moreover, the model-predicted likelihood of seizure relapse was significantly correlated with the grade of surgical outcome at year 1 and associated with relapses up to 5 years after surgery.

CONCLUSION

Node abnormality offers a personalized, noninvasive marker that can be combined with clinical data to better estimate the chances of seizure freedom at 1 year and subsequent relapse up to 5 years after ATLR.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that node abnormality predicts postsurgical seizure recurrence.

A novel fitness function in genetic programming for medical data classification

In the last decade, machine learning (ML) techniques have been widely applied to identify different diseases. This facilitates an early diagnosis and increases the chance of survival. The majority of medical data-sets are unbalanced. Due to this, ML classification techniques give biased classification over the majority class. In this paper, a novel fitness function in Genetic Programming, for medical data classification has been proposed that handles the problem of unbalanced data. Four benchmark medical data-sets named chronic kidney disease (CKD), fertility, BUPA liver disorder, and Wisconsin diagnostic breast cancer (WDBC) have been taken from the University of California (UCI) machine learning repository. Classification is done using the proposed technique. The proposed technique achieved the best accuracy for CKD, WDBC, Fertility, and BUPA dataset as 100%, 99.12%, 85.0%, and 75.36% respectively, and the best AUC as 1.0, 0.99, 0.92, and 0.75 respectively. The result outcomes show an improvement over other GP and SVM methods that confirm the efficiency of our proposed algorithm.

Removal of Interictal MEG-Derived Network Hubs Is Associated With Postoperative Seizure Freedom

Objective: To investigate whether MEG network connectivity was associated with epilepsy duration, to identify functional brain network hubs in patients with refractory focal epilepsy, and assess if their surgical removal was associated with post-operative seizure freedom. Methods: We studied 31 patients with drug refractory focal epilepsy who underwent resting state magnetoencephalography (MEG), and structural magnetic resonance imaging (MRI) as part of pre-surgical evaluation. Using the structural MRI, we generated 114 cortical regions of interest, performed surface reconstruction and MEG source localization. Representative source localized signals for each region were correlated with each other to generate a functional brain network. We repeated this procedure across three randomly chosen one-minute epochs. Network hubs were defined as those with the highest intra-hemispheric mean correlations. Post-operative MRI identified regions that were surgically removed. Results: Greater mean MEG network connectivity was associated with a longer duration of epilepsy. Patients who were seizure free after surgery had more hubs surgically removed than patients who were not seizure free (AUC = 0.76, p = 0.01) consistently across three randomly chosen time segments. Conclusion: Our results support a growing literature implicating network hub involvement in focal epilepsy, the removal of which by surgery is associated with greater chance of post-operative seizure freedom.

Consensus and development of document for management of stabilized acute decompensated heart failure with reduced ejection fraction in India

Aim

Ensuring adherence to guideline-directed medical therapy (GDMT) is an effective strategy to reduce mortality and readmission rates for heart failure (HF). Use of a checklist is one of the best tools to ensure GDMT. The aim was to develop a consensus document with a robust checklist for stabilized acute decompensated HF patients with reduced ejection fraction. While there are multiple checklists available, an India-specific checklist that is easy to fill and validated by regional and national subject matter experts (SMEs) is required.

Methodology

A total of 25 Cardiology SMEs who consented to participate from India discussed data from literature, current evidence, international guidelines and practical experiences in two national and four regional meetings.

Results

Recommendations included HF management, treatment optimization, and patient education. The checklist should be filled at four time points- (a) transition from intensive care unit to ward, (b) at discharge, (c) 1st follow-up and (d) subsequent follow-up. The checklist is the responsibility of the consultant or the treating physician which can be delegated to a junior resident or a trained HF nurse.

Conclusion

This checklist will ensure GDMT, simplify transition of care and can be used by all doctors across India. Institutions, associations, and societies should recommend this checklist for adaptability in public and private hospital. Hospital administrations should roll out policy for adoption of checklist by ensuring patient files have the checklist at the time of discharge and encourage practice of filling it diligently during follow-up visits.

Interictal intracranial electroencephalography for predicting surgical success: The importance of space and time

Objective

Predicting postoperative seizure freedom using functional correlation networks derived from interictal intracranial electroencephalography (EEG) has shown some success. However, there are important challenges to consider: (1) electrodes physically closer to each other naturally tend to be more correlated, causing a spatial bias; (2) implantation location and number of electrodes differ between patients, making cross-subject comparisons difficult; and (3) functional correlation networks can vary over time but are currently assumed to be static.

Methods

In this study, we address these three challenges using intracranial EEG data from 55 patients with intractable focal epilepsy. Patients additionally underwent preoperative magnetic resonance imaging (MRI), intraoperative computed tomography, and postoperative MRI, allowing accurate localization of electrodes and delineation of the removed tissue.

Results

We show that normalizing for spatial proximity between nearby electrodes improves prediction of postsurgery seizure outcomes. Moreover, patients with more extensive electrode coverage were more likely to have their outcome predicted correctly (area under the receiver operating characteristic curve > 0.9, P « 0.05) but not necessarily more likely to have a better outcome. Finally, our predictions are robust regardless of the time segment analyzed.

Significance

Future studies should account for the spatial proximity of electrodes in functional network construction to improve prediction of postsurgical seizure outcomes. Greater coverage of both removed and spared tissue allows for predictions with higher accuracy.

Computer modelling of connectivity change suggests epileptogenesis mechanisms in idiopathic generalised epilepsy

Patients with idiopathic generalised epilepsy (IGE) typically have normal conventional magnetic resonance imaging (MRI), hence diagnosis based on MRI is challenging. Anatomical abnormalities underlying brain dysfunctions in IGE are unclear and their relation to the pathomechanisms of epileptogenesis is poorly understood.

In this study, we applied connectometry, an advanced quantitative neuroimaging technique for investigating localised changes in white-matter tissues in vivo. Analysing white matter structures of 32 subjects we incorporated our in vivo findings in a computational model of seizure dynamics to suggest a plausible mechanism of epileptogenesis.

Patients with IGE have significant bilateral alterations in major white-matter fascicles. In the cingulum, fornix, and superior longitudinal fasciculus, tract integrity is compromised, whereas in specific parts of tracts between thalamus and the precentral gyrus, tract integrity is enhanced in patients. Combining these alterations in a logistic regression model, we computed the decision boundary that discriminated patients and controls. The computational model, informed with the findings on the tract abnormalities, specifically highlighted the importance of enhanced cortico-reticular connections along with impaired cortico-cortical connections in inducing pathological seizure-like dynamics.

We emphasise taking directionality of brain connectivity into consideration towards understanding the pathological mechanisms; this is possible by combining neuroimaging and computational modelling. Our imaging evidence of structural alterations suggest the loss of cortico-cortical and enhancement of cortico-thalamic fibre integrity in IGE. We further suggest that impaired connectivity from cortical regions to the thalamic reticular nucleus offers a therapeutic target for selectively modifying the brain circuit for reversing the mechanisms leading to epileptogenesis.

•

Significant focal alterations along major white-matter fascicles in IGE patients are characterised.

•

Increased white matter integrity found in thalamo-cortical connections.

•

Decreased white matter integrity found in cortico-cortical connections.

•

Disease mechanism is investigated by combining the neuroimaging findings with a dynamical model of seizure activity.

•

Model implicates cortical projections to the thalamic reticular nucleus in IGE.

Computational fluid dynamics analysis of the hydraulic (filtration) efficiency of a residential swimming pool

Hydraulic or filtration efficiency of residential swimming pools, quantified in terms of residence time characteristics, is critical to disinfection and thus important to public health. In this study, a three-dimensional computational fluid dynamics model together with Eulerian and Lagrangian-based techniques are used for investigating the residence time characteristics of a passive tracer and particles in the water, representative of chemicals and pathogens, respectively. The flow pattern in the pool is found to be characterized by dead zone regions where water constituents may be retained for extended periods of times, thereby potentially decreasing the pool hydraulic efficiency. Two return-jet configurations are studied in order to understand the effect of return-jet location and intensity on the hydraulic efficiency of the pool. A two-jet configuration is found to perform on par with a three-jet configuration in removing dissolved constituents but the former is more efficient than the latter in removing or flushing particles. The latter result suggests that return-jet location and associated flow circulation pattern have an important impact on hydraulic efficiency. Thus return-jet configuration should be incorporated as a key parameter in the design of swimming pools complementing current design standards.

Isotopic study of intraseasonal variations of plant transpiration: an alternative means to characterise the dry phases of monsoon

The isotopic characteristics of plant transpired water are strongly controlled by soil evaporation process, primarily by relative humidity. The monsoon system is characterised by large variability of several atmospheric parameters; the primary one being the rainfall, which in turn, modulates the relative humidity. Due to the strong dependency of transpiration on relative humidity, it is expected that this process would vary in accordance with the active and break periods of the monsoon season, which are known to produce cycles of humid and relatively dry phases during a monsoon season. To study the transpiration process, an experiment was conducted wherein rainwater and transpired water were collected from a few plants and analyzed for their isotopic ratios during the summer monsoon seasons of 2016 and 2017. The difference between the isotopic characteristics of the transpired water and rain water is expected to be nominally positive, however, a large variability was observed. This difference is found to be high (low) during the reduced (enhanced) humidity conditions and varies in tandem with the break and active phases of the monsoon season. This characteristic feature may thus be used to delineate the dry and wet phases of monsoon on local to regional scale.

The impact of epilepsy surgery on the structural connectome and its relation to outcome

Background

Temporal lobe surgical resection brings seizure remission in up to 80% of patients, with long-term complete seizure freedom in 41%. However, it is unclear how surgery impacts on the structural white matter network, and how the network changes relate to seizure outcome.

Methods

We used white matter fibre tractography on preoperative diffusion MRI to generate a structural white matter network, and postoperative T1-weighted MRI to retrospectively infer the impact of surgical resection on this network. We then applied graph theory and machine learning to investigate the properties of change between the preoperative and predicted postoperative networks.

Results

Temporal lobe surgery had a modest impact on global network efficiency, despite the disruption caused. This was due to alternative shortest paths in the network leading to widespread increases in betweenness centrality post-surgery. Measurements of network change could retrospectively predict seizure outcomes with 79% accuracy and 65% specificity, which is twice as high as the empirical distribution. Fifteen connections which changed due to surgery were identified as useful for prediction of outcome, eight of which connected to the ipsilateral temporal pole.

Conclusion

Our results suggest that the use of network change metrics may have clinical value for predicting seizure outcome. This approach could be used to prospectively predict outcomes given a suggested resection mask using preoperative data only.

Transport of Liposome Encapsulated Drugs in Voxelized Computational Model of Human Brain Tumors

There are many obstacles in the transport of chemotherapeutic drugs to tumor cells that lead to irregular and non-uniform uptake of drugs inside tumors. The study of these transport problems will help with accurate prediction of drug transport and optimizing treatment strategy. To this end, liposome mediated drug delivery has emerged as an excellent anticancer therapy due to its ability to deliver drugs at site of action and reducing the chances of side effects to the healthy tissues. In this paper, a computational fluid dynamics (CFD) model based on realistic vasculature of human brain tumor is presented. This model utilizes dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data to account for heterogeneity in tumor vasculature. Porosity of the interstitial space inside the tumor and normal tissue is determined voxel-wise by processing the DCE-MRI images by general tracer kinetic model (GTKM). The CFD model is applied to predict transport of two different types of liposomes (stealth and conventional) in tumors. The amount of accumulated liposomes is compared with accumulated free drug (doxorubicin) in the interstitial space. Simulation results indicate that stealth liposomes accumulate more and remain for longer periods of time in tumors as compared with conventional liposomes and free drug. The present model provides us a qualitative and quantitative examination on the transport and deposition of liposomes as well as free drugs in actual human brain tumors.