Indirect structural changes and reduced controllability after temporal lobe epilepsy resection.

OBJECTIVE: To understand the potential behavioral and cognitive effects of mesial temporal resection for temporal lobe epilepsy (TLE) a method is required to characterize network-wide functional alterations caused by a discrete structural disconnection. The objective of this study was to investigate network-wide alterations in brain dynamics of patients with TLE before and after surgical resection of the seizure focus using average regional controllability (ARC), a measure of the ability of a node to influence network dynamics. METHODS: Diffusion-weighted imaging (DWI) data were acquired in 27 patients with drug-resistant unilateral mesial TLE who underwent selective amygdalohippocampectomy. Imaging data were acquired before and after surgery and a presurgical and postsurgical structural connectome was generated from whole-brain tractography. Edge-wise strength, node strength, and node ARC were compared before and after surgery. Direct and indirect edge-wise strength changes were identified using patient-specific simulated resections. Direct edges were defined as primary edges disconnected by the resection zone itself. Indirect edges were secondary measured edge strength changes. Changes in node strength and ARC were then related to both direct and indirect edge changes. RESULTS: We found nodes with significant postsurgical changes in both node strength and ARC surrounding the resection zone (paired t tests, p < .05, Bonferroni corrected). ARC identified additional postsurgical changes in nodes outside of the resection zone within the ipsilateral occipital lobe, which were associated with indirect edge-wise strength changes of the postsurgical network (Fisher's exact test, p < .001). These indirect edge-wise changes were facilitated through the "hub" nodes including the thalamus, putamen, insula, and precuneus. SIGNIFICANCE: Discrete network disconnection from TLE resection results in widespread structural and functional changes not predicted by disconnection alone. These can be well characterized by dynamic controllability measures such as ARC and may be useful for investigating changes in brain function that may contribute to seizure recurrence and behavioral or cognitive changes after surgery.

SynBOLD-DisCo: Synthetic BOLD images for distortion correction of fMRI without additional calibration scans.

The blood oxygen level dependent (BOLD) signal from functional magnetic resonance imaging (fMRI) is a noninvasive technique that has been widely used in research to study brain function. However, fMRI suffers from susceptibility-induced off resonance fields which may cause geometric distortions and mismatches with anatomical images. State-of-the-art correction methods require acquiring reverse phase encoded images or additional field maps to enable distortion correction. However, not all imaging protocols include these additional scans and thus cannot take advantage of these susceptibility correction capabilities. As such, in this study we aim to enable state-of-the-art distortion correction with FSL's topup algorithm of historical and/or limited fMRI data that include only a structural image and single phase encoded fMRI. To do this, we use 3D U-net models to synthesize undistorted fMRI BOLD contrast images from the structural image and use this undistorted synthetic image as an anatomical target for distortion correction with topup. We evaluate the efficacy of this approach, named SynBOLD-DisCo (synthetic BOLD images for distortion correction), and show that BOLD images corrected using our approach are geometrically more similar to structural images than the distorted BOLD data and are practically equivalent to state-of-the-art correction methods which require reverse phase encoded data. Future directions include additional validation studies, integration with other preprocessing operations, retraining with broader pathologies, and investigating the effects of spin echo versus gradient echo images for training and distortion correction. In summary, we demonstrate SynBOLD-DisCo corrects distortion of fMRI when reverse phase encoding scans or field maps are not available.

Distortion correction of functional MRI without reverse phase encoding scans or field maps.

Functional magnetic resonance images (fMRI) acquired using echo planar sequences typically suffer from spatial distortions due to susceptibility induced off-resonance fields, which may cause geometric mismatch with structural images and affect subsequent quantification and localization of brain function. State-of-the art distortion correction methods (for example, using FSL's topup or AFNI's 3dQwarp algorithms) require the collection of additional scans - either field maps or images with reverse phase encoding directions (i.e., blip-up/blip-down acquisitions) - to estimate and correct distortions. However, not all imaging protocols acquire these additional data and thus cannot take advantage of these post-acquisition corrections. In this study, we aim to enable state-of-the art processing of historical or limited datasets that do not include specific sequences for distortion correction by using only the acquired functional data and a single commonly acquired structural image. To achieve this, we synthesize an undistorted image with contrast similar to the fMRI data and use the non-distorted synthetic image as an anatomical target for distortion correction. We evaluate the efficacy of this approach, named SynBOLD-DisCo (Synthetic BOLD contrast for Distortion Correction), and show that this distortion correction process yields fMRI data that are geometrically similar to non-distorted structural images, with distortion correction virtually equivalent to acquisitions that do contain both blip-up/blip-down images. Our method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation and integration into existing fMRI preprocessing pipelines.

Advancing the Economic and Environmental Sustainability of the NEWgenerator Nonsewered Sanitation System.

Achieving safely managed sanitation and resource recovery in areas that are rural, geographically challenged, or experiencing rapidly increasing population density may not be feasible with centralized facilities due to space requirements, site-specific concerns, and high costs of sewer installation. Nonsewered sanitation (NSS) systems have the potential to provide safely managed sanitation and achieve strict wastewater treatment standards. One such NSS treatment technology is the NEWgenerator, which includes an anaerobic membrane bioreactor (AnMBR), nutrient recovery via ion exchange, and electrochlorination. The system has been shown to achieve robust treatment of real waste for over 100 users, but the technology's relative life cycle sustainability remains unclear. This study characterizes the financial viability and life cycle environmental impacts of the NEWgenerator and prioritizes opportunities to advance system sustainability through targeted improvements and deployment. The costs and greenhouse gas (GHG) emissions of the NEWgenerator (general case) leveraging grid electricity were 0.139 [0.113-0.168] USD cap-1 day-1 and 79.7 [55.0-112.3] kg CO2-equiv cap-1 year-1, respectively. A transition to photovoltaic-generated electricity would increase costs to 0.145 [0.118-0.181] USD cap-1 day-1 but decrease GHG emissions to 56.1 [33.8-86.2] kg CO2-equiv cap-1 year-1. The deployment location analysis demonstrated reduced median costs for deployment in China (-38%), India (-53%), Senegal (-31%), South Africa (-31%), and Uganda (-35%), but at comparable or increased GHG emissions (-2 to +16%). Targeted improvements revealed the relative change in median cost and GHG emissions to be -21 and -3% if loading is doubled (i.e., doubled users per unit), -30 and -12% with additional sludge drying, and +9 and -25% with the addition of a membrane contactor, respectively, with limited benefits (0-5% reductions) from an alternative photovoltaic battery, low-cost housing, or improved frontend operation. This research demonstrates that the NEWgenerator is a low-cost, low-emission NSS treatment technology with the potential for resource recovery to increase access to safe sanitation.

J-Difference editing (MEGA) of lactate in the human brain at 3T.

PURPOSE: The need to detect and quantify brain lactate accurately by MRS has stimulated the development of editing sequences based on J coupling effects. In J-difference editing of lactate, threonine can be co-edited and it contaminates lactate estimates due to the spectral proximity of the coupling partners of their methyl protons. We therefore implemented narrow-band editing 180° pulses (E180) in MEGA-PRESS acquisitions to resolve separately the 1.3-ppm resonances of lactate and threonine. METHODS: Two 45.3-ms rectangular E180 pulses, which had negligible effects 0.15-ppm away from the carrier frequency, were implemented in a MEGA-PRESS sequence with TE 139 ms. Three acquisitions were designed to selectively edit lactate and threonine, in which the E180 pulses were tuned to 4.1 ppm, 4.25 ppm, and a frequency far off resonance. Editing performance was validated with numerical analyses and acquisitions from phantoms. The narrow-band E180 MEGA and another MEGA-PRESS sequence with broad-band E180 pulses were evaluated in six healthy subjects. RESULTS: The 45.3-ms E180 MEGA offered a difference-edited lactate signal with lower intensity and reduced contamination from threonine compared to the broad-band E180 MEGA. The 45.3 ms E180 pulse had MEGA editing effects over a frequency range larger than seen in the singlet-resonance inversion profile. Lactate and threonine in healthy brain were both estimated to be 0.4 ± 0.1 mM, with reference to N-acetylaspartate at 12 mM. CONCLUSION: Narrow-band E180 MEGA editing minimizes threonine contamination of lactate spectra and may improve the ability to detect modest changes in lactate levels.

DMsan: A Multi-Criteria Decision Analysis Framework and Package to Characterize Contextualized Sustainability of Sanitation and Resource Recovery Technologies.

In resource-limited settings, conventional sanitation systems often fail to meet their goals-with system failures stemming from a mismatch among community needs, constraints, and deployed technologies. Although decision-making tools exist to help assess the appropriateness of conventional sanitation systems in a specific context, there is a lack of a holistic decision-making framework to guide sanitation research, development, and deployment (RD&D) of technologies. In this study, we introduce DMsan-an open-source multi-criteria decision analysis Python package that enables users to transparently compare sanitation and resource recovery alternatives and characterize the opportunity space for early-stage technologies. Informed by the methodological choices frequently used in literature, the core structure of DMsan includes five criteria (technical, resource recovery, economic, environmental, and social), 28 indicators, criteria weight scenarios, and indicator weight scenarios tailored to 250 countries/territories, all of which can be adapted by end-users. DMsan integrates with the open-source Python package QSDsan (quantitative sustainable design for sanitation and resource recovery systems) for system design and simulation to calculate quantitative economic (via techno-economic analysis), environmental (via life cycle assessment), and resource recovery indicators under uncertainty. Here, we illustrate the core capabilities of DMsan using an existing, conventional sanitation system and two proposed alternative systems for Bwaise, an informal settlement in Kampala, Uganda. The two example use cases are (i) use by implementation decision makers to enhance decision-making transparency and understand the robustness of sanitation choices given uncertain and/or varying stakeholder input and technology ability and (ii) use by technology developers seeking to identify and expand the opportunity space for their technologies. Through these examples, we demonstrate the utility of DMsan to evaluate sanitation and resource recovery systems tailored to individual contexts and increase transparency in technology evaluations, RD&D prioritization, and context-specific decision making.

Structural disconnection relates to functional changes after temporal lobe epilepsy surgery.

Epilepsy surgery consists of surgical resection of the epileptic focus and is recommended for patients with drug-resistant focal epilepsy. However, focal brain lesions can lead to effects in distant brain regions. Similarly, the focal resection in temporal lobe epilepsy surgery has been shown to lead to functional changes distant from the resection. Here we hypothesize that there are changes in brain function caused by temporal lobe epilepsy surgery in regions distant from the resection that are due to their structural disconnection from the resected epileptic focus. Therefore, the goal of this study was to localize changes in brain function caused by temporal lobe epilepsy surgery and relate them to the disconnection from the resected epileptic focus. This study takes advantage of the unique opportunity that epilepsy surgery provides to investigate the effects of focal disconnections on brain function in humans, which has implications in epilepsy and broader neuroscience. Changes in brain function from pre- to post-epilepsy surgery were quantified in a group of temporal lobe epilepsy patients (n = 36) using a measure of resting state functional MRI activity fluctuations. We identified regions with significant functional MRI changes that had high structural connectivity to the resected region in healthy controls (n = 96) and patients based on diffusion MRI. The structural disconnection from the resected epileptic focus was then estimated using presurgical diffusion MRI and related to the functional MRI changes from pre- to post-surgery in these regions. Functional MRI activity fluctuations increased from pre- to post-surgery in temporal lobe epilepsy in the two regions most highly structurally connected to the resected epileptic focus in healthy controls and patients-the thalamus and the fusiform gyrus ipsilateral to the side of surgery (PFWE < 0.05). Broader surgeries led to larger functional MRI changes in the thalamus than more selective surgeries (P < 0.05), but no other clinical variables were related to functional MRI changes in either the thalamus or fusiform. The magnitude of the functional MRI changes in both the thalamus and fusiform increased with a higher estimated structural disconnection from the resected epileptic focus when controlling for the type of surgery (P < 0.05). These results suggest that the structural disconnection from the resected epileptic focus may contribute to the functional changes seen after epilepsy surgery. Broadly, this study provides a novel link between focal disconnections in the structural brain network and downstream effects on function in distant brain regions.

Remote effects of temporal lobe epilepsy surgery: Long-term morphological changes after surgical resection.

OBJECTIVE: Epilepsy surgery is an effective treatment for drug-resistant patients. However, how different surgical approaches affect long-term brain structure remains poorly characterized. Here, we present a semiautomated method for quantifying structural changes after epilepsy surgery and compare the remote structural effects of two approaches, anterior temporal lobectomy (ATL), and selective amygdalohippocampectomy (SAH). METHODS: We studied 36 temporal lobe epilepsy patients who underwent resective surgery (ATL = 22, SAH = 14). All patients received same-scanner MR imaging preoperatively and postoperatively (mean 2 years). To analyze postoperative structural changes, we segmented the resection zone and modified the Advanced Normalization Tools (ANTs) longitudinal cortical pipeline to account for resections. We compared global and regional annualized cortical thinning between surgical treatments. RESULTS: Across procedures, there was significant cortical thinning in the ipsilateral insula, fusiform, pericalcarine, and several temporal lobe regions outside the resection zone as well as the contralateral hippocampus. Additionally, increased postoperative cortical thickness was seen in the supramarginal gyrus. Patients treated with ATL exhibited greater annualized cortical thinning compared with SAH cases (ATL: -0.08 ± 0.11 mm per year, SAH: -0.01 ± 0.02 mm per year, t = 2.99, P = 0.006). There were focal postoperative differences between the two treatment groups in the ipsilateral insula (P = 0.039, corrected). Annualized cortical thinning rates correlated with preoperative cortical thickness (r = 0.60, P < 0.001) and had weaker associations with age at surgery (r = -0.33, P = 0.051) and disease duration (r = -0.42, P = 0.058). SIGNIFICANCE: Our evidence suggests that selective procedures are associated with less cortical thinning and that earlier surgical intervention may reduce long-term impacts on brain structure.

Coronary Flow Reserve, Inflammation, and Myocardial Strain: The CIRT-CFR Trial.

Inflammation is a key determinant of cardiovascular outcomes, but its role in heart failure is uncertain. In patients with cardiometabolic disease enrolled in the prospective, multicenter ancillary study of CIRT (Cardiovascular Inflammation Reduction Trial), CIRT-CFR (Coronary Flow Reserve to Assess Cardiovascular Inflammation), impaired coronary flow reserve was independently associated with increased inflammation and myocardial strain despite well-controlled lipid, glycemic, and hemodynamic profiles. Inflammation modified the relationship between CFR and myocardial strain, disrupting the association between cardiac blood flow and function. Future studies are needed to investigate whether an early inflammation-mediated reduction in CFR capturing microvascular ischemia may lead to heart failure in patients with cardiometabolic disease. (Cardiovascular Inflammation Reduction Trial [CIRT]; NCT01594333; Coronary Flow Reserve to Assess Cardiovascular Inflammation [CIRT-CFR]; NCT02786134).

Functional anomaly mapping lateralizes temporal lobe epilepsy with high accuracy in individual patients.

Mesial temporal lobe epilepsy (mTLE) is associated with variable dysfunction beyond the temporal lobe. We used functional anomaly mapping (FAM), a multivariate machine learning approach to resting state fMRI analysis to measure subcortical and cortical functional aberrations in patients with mTLE. We also examined the value of individual FAM in lateralizing the hemisphere of seizure onset in mTLE patients. Methods: Patients and controls were selected from an existing imaging and clinical database. After standard preprocessing of resting state fMRI, time-series were extracted from 400 cortical and 32 subcortical regions of interest (ROIs) defined by atlases derived from functional brain organization. Group-level aberrations were measured by contrasting right (RTLE) and left (LTLE) patient groups to controls in a support vector regression models, and tested for statistical reliability using permutation analysis. Individualized functional anomaly maps (FAMs) were generated by contrasting individual patients to the control group. Half of patients were used for training a classification model, and the other half for estimating the accuracy to lateralize mTLE based on individual FAMs. Results: Thirty-two right and 14 left mTLE patients (33 with evidence of hippocampal sclerosis on MRI) and 94 controls were included. At group levels, cortical regions affiliated with limbic and somatomotor networks were prominent in distinguishing RTLE and LTLE from controls. At individual levels, most TLE patients had high anomaly in bilateral mesial temporal and medial parietooccipital default mode regions. A linear support vector machine trained on 50% of patients could accurately lateralize mTLE in remaining patients (median AUC =1.0 [range 0.97-1.0], median accuracy = 96.87% [85.71-100Significance: Functional anomaly mapping confirms widespread aberrations in function, and accurately lateralizes mTLE from resting state fMRI. Future studies will evaluate FAM as a non-invasive localization method in larger datasets, and explore possible correlations with clinical characteristics and disease course.

Abnormal functional connectivity of the posterior hypothalamus and other arousal regions in surgical temporal lobe epilepsy.

OBJECTIVE: This study sought to characterize resting-state functional MRI (fMRI) connectivity patterns of the posterior hypothalamus (pHTH) and the nucleus basalis of Meynert (NBM) in surgical patients with mesial temporal lobe epilepsy (mTLE), and to investigate potential correlations between functional connectivity of these arousal regions and neurocognitive performance. METHODS: The study evaluated resting-state fMRI in 60 patients with preoperative mTLE and in 95 healthy controls. The authors first conducted voxel-wise connectivity analyses seeded from the pHTH, combined anterior and tuberal hypothalamus (atHTH; i.e., the rest of the hypothalamus), and the NBM ipsilateral (ipsiNBM) and contralateral (contraNBM) to the epileptogenic zone. Based on these results, the authors included the pHTH, ipsiNBM, and frontoparietal neocortex in a network-based statistic (NBS) analysis to elucidate a network that best distinguishes patients from controls. The connections involving the pHTH and ipsiNBM from this network were included in age-corrected pairwise region of interest (ROI) analysis, along with connections between arousal structures, including the pHTH, ipsiNBM, and brainstem arousal regions. Finally, patient functional connectivity was correlated with clinical neurocognitive testing scores for IQ as well as attention and concentration tests. RESULTS: The voxel-wise analysis demonstrated that the pHTH, when compared with the atHTH, showed more widespread functional connectivity decreases in surgical mTLE patients when compared with controls. It was also observed that the ipsiNBM, but not the contraNBM, showed decreased functional connectivity in mTLE. The NBS analysis uncovered a perturbed network of frontoparietal regions, the pHTH, and ipsiNBM that distinguishes patients from controls. Age-corrected ROI analysis revealed functional connectivity decreases between the pHTH and bilateral superior frontal gyri, medial orbitofrontal cortices, rostral anterior cingulate cortices, and inferior parietal cortices in mTLE when compared with controls. For the ipsiNBM, there was reduced connectivity with bilateral medial orbitofrontal and rostral anterior cingulate cortices. Age-corrected ROI analysis also demonstrated upstream connectivity decreases from controls between the pHTH and the brainstem arousal regions, cuneiform/subcuneiform (CSC) nuclei, and ventral tegmental area, as well as the ipsiNBM and CSC nuclei. Reduced functional connectivity was also detected between the pHTH and ipsiNBM. Lastly, neurocognitive test scores for attention and concentration were found to be positively correlated with the functional connectivity between the pHTH and ipsiNBM, suggesting worse performance associated with connectivity perturbations. CONCLUSIONS: This study demonstrated perturbed resting-state functional connectivity of arousal regions in surgical mTLE and is one of the first investigations to demonstrate decreased functional connectivity of the pHTH with frontoparietal regions and other arousal regions. Connectivity disturbances in arousal regions may contribute to neurocognitive deficits in surgical mTLE patients.

The Interictal Suppression Hypothesis in focal epilepsy: network-level supporting evidence.

Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure-function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10-13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10-3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10-12). Structure-function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10-21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy.

Epilepsy with anterior temporal encephaloceles: Baseline characteristics, post-surgical outcomes, and comparison to mesial temporal sclerosis.

OBJECTIVE: To review clinical and neuropsychological characteristics and natural history of a series of patients with temporal lobe epilepsy (TLE) and anterior temporal encephaloceles (ATE) and compare them to a similar series of TLE patients with mesial temporal sclerosis (MTS) to identify characteristics suggestive of ATE-related epilepsy. METHODS: Patients with epilepsy and ATE were identified via clinic encounters and consensus epilepsy surgery conference at a Level 4 epilepsy center. The drug-resistant subset of these patients who underwent epilepsy surgery (twenty-two of thirty-five) were compared to age- and laterality-matched patients with MTS. Clinical, neuropsychological, electrophysiologic, and surgical data were abstracted through chart review. RESULTS: In comparison with MTS, ATE patients were more often female, had significantly later onset of epilepsy, and did not have prior febrile seizures. In addition, ATE patients were more likely to have chronic headaches and other historical features consistent with idiopathic intracranial hypertension (IIH). Failure to identify ATE on initial imaging was common. Most patients had limited temporal cortical resections sparing mesial structures. Of the twenty ATE patients who had a long-term postsurgical follow-up, seventeen (85%) had International League Against Epilepsy (ILAE) Class 1 or 2 outcomes. SIGNIFICANCE: A shorter duration of epilepsy, female gender, and lack of history of febrile seizures may suggest ATE as an etiology of refractory TLE in adults. Targeted encephalocele resections can result in seizure freedom, underscoring the importance of encephalocele identification.

Arousal and salience network connectivity alterations in surgical temporal lobe epilepsy.

OBJECTIVE: It is poorly understood why patients with mesial temporal lobe epilepsy (TLE) have cognitive deficits and brain network changes that extend beyond the temporal lobe, including altered extratemporal intrinsic connectivity networks (ICNs). However, subcortical arousal structures project broadly to the neocortex, are affected by TLE, and thus may contribute to these widespread network effects. The authors' objective was to examine functional connectivity (FC) patterns between subcortical arousal structures and neocortical ICNs, possible neurocognitive relationships, and FC changes after epilepsy surgery. METHODS: The authors obtained resting-state functional magnetic resonance imaging (fMRI) in 50 adults with TLE and 50 controls. They compared nondirected FC (correlation) and directed FC (Granger causality laterality index) within the salience network, default mode network, and central executive network, as well as between subcortical arousal structures; these 3 ICNs were also compared between patients and controls. They also used an fMRI-based vigilance index to relate alertness to arousal center FC. Finally, fMRI was repeated in 29 patients > 12 months after temporal lobe resection. RESULTS: Nondirected FC within the salience (p = 0.042) and default mode (p = 0.0008) networks, but not the central executive network (p = 0.79), was decreased in patients in comparison with controls (t-tests, corrected). Nondirected FC between the salience network and subcortical arousal structures (nucleus basalis of Meynert, thalamic centromedian nucleus, and brainstem pedunculopontine nucleus) was reduced in patients in comparison with controls (p = 0.0028-0.015, t-tests, corrected), and some of these connectivity abnormalities were associated with lower processing speed index, verbal comprehension, and full-scale IQ. Interestingly, directed connectivity measures suggested a loss of top-down influence from the salience network to the arousal nuclei in patients. After resection, certain FC patterns between the arousal nuclei and salience network moved toward control values in the patients, suggesting that some postoperative recovery may be possible. Although an fMRI-based vigilance measure suggested that patients exhibited reduced alertness over time, FC abnormalities between the salience network and arousal structures were not influenced by the alertness levels during the scans. CONCLUSIONS: FC abnormalities between subcortical arousal structures and ICNs, such as the salience network, may be related to certain neurocognitive deficits in TLE patients. Although TLE patients demonstrated vigilance abnormalities, baseline FC perturbations between the arousal and salience networks are unlikely to be driven solely by alertness level, and some may improve after surgery. Examination of the arousal network and ICN disturbances may improve our understanding of the downstream clinical effects of TLE.

Localizing seizure onset zones in surgical epilepsy with neurostimulation deep learning.

OBJECTIVE: In drug-resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) localization that use brief interictal recordings could supplement presurgical evaluations and improve care. Thus, the authors sought to localize SOZs by training a multichannel convolutional neural network on stereoelectroencephalography (SEEG) cortico-cortical evoked potentials. METHODS: The authors performed single-pulse electrical stimulation in 10 drug-resistant temporal lobe epilepsy patients implanted with SEEG. Using 500,000 unique poststimulation SEEG epochs, the authors trained a multichannel 1-dimensional convolutional neural network to determine whether an SOZ had been stimulated. RESULTS: SOZs were classified with mean sensitivity of 78.1% and specificity of 74.6% according to leave-one-patient-out testing. To achieve maximum accuracy, the model required a 0- to 350-msec poststimulation time period. Post hoc analysis revealed that the model accurately classified unilateral versus bilateral mesial temporal lobe seizure onset, as well as neocortical SOZs. CONCLUSIONS: This was the first demonstration, to the authors' knowledge, that a deep learning framework can be used to accurately classify SOZs with single-pulse electrical stimulation-evoked responses. These findings suggest that accurate classification of SOZs relies on a complex temporal evolution of evoked responses within 350 msec of stimulation. Validation in a larger data set could provide a practical clinical tool for the presurgical evaluation of drug-resistant epilepsy.

Vigilance associates with the low-dimensional structure of fMRI data.

The human brain exhibits rich dynamics that reflect ongoing functional states. Patterns in fMRI data, detected in a data-driven manner, have uncovered recurring configurations that relate to individual and group differences in behavioral, cognitive, and clinical traits. However, resolving the neural and physiological processes that underlie such measurements is challenging, particularly without external measurements of brain state. A growing body of work points to underlying changes in vigilance as one driver of time-windowed fMRI connectivity states, calculated on the order of tens of seconds. Here we examine the degree to which the low-dimensional spatial structure of instantaneous fMRI activity is associated with vigilance levels, by testing whether vigilance-state detection can be carried out in an unsupervised manner based on individual BOLD time frames. To investigate this question, we first reduce the spatial dimensionality of fMRI data, and apply Gaussian Mixture Modeling to cluster the resulting low-dimensional data without any a priori vigilance information. Our analysis includes long-duration task and resting-state scans that are conducive to shifts in vigilance. We observe a close alignment between low-dimensional fMRI states (data-driven clusters) and measurements of vigilance derived from concurrent electroencephalography (EEG) and behavior. Whole-brain coactivation analysis revealed cortical anti-correlation patterns that resided primarily during higher behavioral- and EEG-defined levels of vigilance, while cortical activity was more often spatially uniform in states corresponding to lower vigilance. Overall, these findings indicate that vigilance states may be detected in the low-dimensional structure of fMRI data, even within individual time frames.

Hierarchical particle optimization for cortical shape correspondence in temporal lobe resection.

BACKGROUND: Anterior temporal lobe resection is an effective treatment for temporal lobe epilepsy. The post-surgical structural changes could influence the follow-up treatment. Capturing post-surgical changes necessitates a well-established cortical shape correspondence between pre- and post-surgical surfaces. Yet, most cortical surface registration methods are designed for normal neuroanatomy. Surgical changes can introduce wide ranging artifacts in correspondence, for which conventional surface registration methods may not work as intended. METHODS: In this paper, we propose a novel particle method for one-to-one dense shape correspondence between pre- and post-surgical surfaces with temporal lobe resection. The proposed method can handle partial structural abnormality involving non-rigid changes. Unlike existing particle methods using implicit particle adjacency, we consider explicit particle adjacency to establish a smooth correspondence. Moreover, we propose hierarchical optimization of particles rather than full optimization of all particles at once to avoid trappings of locally optimal particle update. RESULTS: We evaluate the proposed method on 25 pairs of T1-MRI with pre- and post-simulated resection on the anterior temporal lobe and 25 pairs of patients with actual resection. We show improved accuracy over several cortical regions in terms of ROI boundary Hausdorff distance with 4.29 mm and Dice similarity coefficients with average value 0.841, compared to existing surface registration methods on simulated data. In 25 patients with actual resection of the anterior temporal lobe, our method shows an improved shape correspondence in qualitative and quantitative evaluation on parcellation-off ratio with average value 0.061 and cortical thickness changes. We also show better smoothness of the correspondence without self-intersection, compared with point-wise matching methods which show various degrees of self-intersection. CONCLUSION: The proposed method establishes a promising one-to-one dense shape correspondence for temporal lobe resection. The resulting correspondence is smooth without self-intersection. The proposed hierarchical optimization strategy could accelerate optimization and improve the optimization accuracy. According to the results on the paired surfaces with temporal lobe resection, the proposed method outperforms the compared methods and is more reliable to capture cortical thickness changes.

Financial Viability and Environmental Sustainability of Fecal Sludge Treatment with Pyrolysis Omni Processors.

Omni Processors (OPs) are community-scale systems for non-sewered fecal sludge treatment. These systems have demonstrated their capacity to treat excreta from tens of thousands of people using thermal treatment processes (e.g., pyrolysis), but their relative sustainability is unclear. In this study, QSDsan (an open-source Python package) was used to characterize the financial viability and environmental implications of fecal sludge treatment via pyrolysis-based OP technology treating mixed and source-separated human excreta and to elucidate the key drivers of system sustainability. Overall, the daily per capita cost for the treatment of mixed excreta (pit latrines) via the OP was estimated to be 0.05 [0.03-0.08] USD·cap-1·d-1, while the treatment of source-separated excreta (from urine-diverting dry toilets) was estimated to have a per capita cost of 0.09 [0.08-0.14] USD·cap-1·d-1. Operation and maintenance of the OP is a critical driver of total per capita cost, whereas the contribution from capital cost of the OP is much lower because it is distributed over a relatively large number of users (i.e., 12,000 people) for the system lifetime (i.e., 20 yr). The total emissions from the source-separated scenario were estimated to be 11 [8.3-23] kg CO2 eq·cap-1·yr-1, compared to 49 [28-77] kg CO2 eq·cap-1·yr-1 for mixed excreta. Both scenarios fall below the estimates of greenhouse gas (GHG) emissions for anaerobic treatment of fecal sludge collected from pit latrines. Source-separation also creates opportunities for resource recovery to offset costs through nutrient recovery and carbon sequestration with biochar production. For example, when carbon is valued at 150 USD·Mg-1 of CO2, the per capita cost of sanitation can be further reduced by 44 and 40% for the source-separated and mixed excreta scenarios, respectively. Overall, our results demonstrate that pyrolysis-based OP technology can provide low-cost, low-GHG fecal sludge treatment while reducing global sanitation gaps.

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.

Presurgical temporal lobe epilepsy connectome fingerprint for seizure outcome prediction.

Temporal lobe epilepsy presents a unique situation where confident clinical localization of the seizure focus does not always result in a seizure-free or favourable outcome after mesial temporal surgery. In this work, magnetic resonance imaging derived functional and structural whole-brain connectivity was used to compute a network fingerprint that captures the connectivity profile characteristics that are common across a group of nine of these patients with seizure-free outcome. The connectivity profile was then computed for 38 left-out patients with the hypothesis that similarity to the fingerprint indicates seizure-free surgical outcome. Patient profile distance to the fingerprint was compared with 1-year seizure outcome and standard clinical parameters. Distance to the fingerprint was higher for patients with Engel III-IV 1-year outcome compared with those with Engel Ia, Ib-d, and II outcome (Kruskal-Wallis, P < 0.01; Wilcoxon rank-sum p corr <0.05 Bonferroni-corrected). Receiver operator characteristic analysis revealed 100% sensitivity and 90% specificity in identifying patients with Engel III-IV outcome based on distance to the fingerprint in the left-out patients. Furthermore, distance to the fingerprint was not related to any individual clinical parameter including age at scan, duration of disease, total seizure frequency, presence of mesial temporal sclerosis, lateralizing ictal, interictal scalp electroencephalography, invasive stereo-encephalography, or positron emission tomography. And two published algorithms utilizing multiple clinical measures for predicting seizure outcome were not related to distance to the fingerprint, nor predictive of seizure outcome in this cohort. The functional and structural connectome fingerprint provides quantitative, clinically interpretable and significant information not captured by standard clinical assessments alone or in combinations. This automated and simple method may improve patient-specific prediction of seizure outcome in patients with a clinically identified focus in the mesial temporal lobe.