Author name cluster

Kathryn A. Davis

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9 papers

1 author row

YNICL Journal 2023 Journal Article

Structural brain network deviations predict recovery after traumatic brain injury

James J. Gugger
Nishant Sinha
Yiming Huang
Alexa E. Walter
Cillian Lynch
Priyanka Kalyani
Nathan Smyk
Danielle Sandsmark

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.

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YNICL Journal 2023 Journal Article

Subcortical functional connectivity gradients in temporal lobe epilepsy

Alfredo Lucas
Sofia Mouchtaris
Eli J. Cornblath
Nishant Sinha
Lorenzo Caciagli
Peter Hadar
James J. Gugger
Sandhitsu Das

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.

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YNIMG Journal 2022 Journal Article

A framework For brain atlases: Lessons from seizure dynamics

Andrew Y. Revell
Alexander B. Silva
T. Campbell Arnold
Joel M. Stein
Sandhitsu R. Das
Russell T. Shinohara
Dani S. Bassett
Brian Litt

Brain maps, or atlases, are essential tools for studying brain function and organization. The abundance of available atlases used across the neuroscience literature, however, creates an implicit challenge that may alter the hypotheses and predictions we make about neurological function and pathophysiology. Here, we demonstrate how parcellation scale, shape, anatomical coverage, and other atlas features may impact our prediction of the brain's function from its underlying structure. We show how network topology, structure-function correlation (SFC), and the power to test specific hypotheses about epilepsy pathophysiology may change as a result of atlas choice and atlas features. Through the lens of our disease system, we propose a general framework and algorithm for atlas selection. This framework aims to maximize the descriptive, explanatory, and predictive validity of an atlas. Broadly, our framework strives to provide empirical guidance to neuroscience research utilizing the various atlases published over the last century.

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YNICL Journal 2022 Journal Article

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

T. Campbell Arnold
Ramya Muthukrishnan
Akash R. Pattnaik
Nishant Sinha
Adam Gibson
Hannah Gonzalez
Sandhitsu R. Das
Brian Litt

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.

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YNICL Journal 2019 Journal Article

Glutamate weighted imaging contrast in gliomas with 7 Tesla magnetic resonance imaging

Andrew Neal
Bradford A. Moffat
Joel M. Stein
Ravi Prakash Reddy Nanga
Patricia Desmond
Russell T. Shinohara
Hari Hariharan
Rebecca Glarin

INTRODUCTION: Diffuse gliomas are incurable malignancies, which undergo inevitable progression and are associated with seizure in 50-90% of cases. Glutamate has the potential to be an important glioma biomarker of survival and local epileptogenicity if it can be accurately quantified noninvasively. METHODS: We applied the glutamate-weighted imaging method GluCEST (glutamate chemical exchange saturation transfer) and single voxel MRS (magnetic resonance spectroscopy) at 7 Telsa (7 T) to patients with gliomas. GluCEST contrast and MRS metabolite concentrations were quantified within the tumour region and peritumoural rim. Clinical variables of tumour aggressiveness (prior adjuvant therapy and previous radiological progression) and epilepsy (any prior seizures, seizure in last month and drug refractory epilepsy) were correlated with respective glutamate concentrations. Images were separated into post-hoc determined patterns and clinical variables were compared across patterns. RESULTS: Ten adult patients with a histo-molecular (n = 9) or radiological (n = 1) diagnosis of grade II-III diffuse glioma were recruited, 40.3 +/- 12.3 years. Increased tumour GluCEST contrast was associated with prior adjuvant therapy (p = .001), and increased peritumoural GluCEST contrast was associated with both recent seizures (p = .038) and drug refractory epilepsy (p = .029). We distinguished two unique GluCEST contrast patterns with distinct clinical and radiological features. MRS glutamate correlated with GluCEST contrast within the peritumoural voxel (R = 0.89, p = .003) and a positive trend existed in the tumour voxel (R = 0.65, p = .113). CONCLUSION: This study supports the role of glutamate in diffuse glioma biology. It further implicates elevated peritumoural glutamate in epileptogenesis and altered tumour glutamate homeostasis in glioma aggressiveness. Given the ability to non-invasively visualise and quantify glutamate, our findings raise the prospect of 7 T GluCEST selecting patients for individualised therapies directed at the glutamate pathway. Larger studies with prospective follow-up are required.

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YNICL Journal 2019 Journal Article

High interictal connectivity within the resection zone is associated with favorable post-surgical outcomes in focal epilepsy patients

Preya Shah
John M. Bernabei
Lohith G. Kini
Arian Ashourvan
Jacqueline Boccanfuso
Ryan Archer
Kelly Oechsel
Sandhitsu R. Das

Patients with drug-resistant focal epilepsy are often candidates for invasive surgical therapies. In these patients, it is necessary to accurately localize seizure generators to ensure seizure freedom following intervention. While intracranial electroencephalography (iEEG) is the gold standard for mapping networks for surgery, this approach requires inducing and recording seizures, which may cause patient morbidity. The goal of this study is to evaluate the utility of mapping interictal (non-seizure) iEEG networks to identify targets for surgical treatment. We analyze interictal iEEG recordings and neuroimaging from 27 focal epilepsy patients treated via surgical resection. We generate interictal functional networks by calculating pairwise correlation of iEEG signals across different frequency bands. Using image coregistration and segmentation, we identify electrodes falling within surgically resected tissue (i.e. the resection zone), and compute node-level and edge-level synchrony in relation to the resection zone. We further associate these metrics with post-surgical outcomes. Greater overlap between resected electrodes and highly synchronous electrodes is associated with favorable post-surgical outcomes. Additionally, good-outcome patients have significantly higher connectivity localized within the resection zone compared to those with poorer postoperative seizure control. This finding persists following normalization by a spatially-constrained null model. This study suggests that spatially-informed interictal network synchrony measures can distinguish between good and poor post-surgical outcomes. By capturing clinically-relevant information during interictal periods, our method may ultimately reduce the need for prolonged invasive implants and provide insights into the pathophysiology of an epileptic brain. We discuss next steps for translating these findings into a prospectively useful clinical tool.

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YNICL Journal 2018 Journal Article

Clinical validation of automated hippocampal segmentation in temporal lobe epilepsy

Peter N. Hadar
Lohith G. Kini
Carlos Coto
Virginie Piskin
Lauren E. Callans
Stephanie H. Chen
Joel M. Stein
Sandhitsu R. Das

OBJECTIVE: To provide a multi-atlas framework for automated hippocampus segmentation in temporal lobe epilepsy (TLE) and clinically validate the results with respect to surgical lateralization and post-surgical outcome. METHODS: We retrospectively identified 47 TLE patients who underwent surgical resection and 12 healthy controls. T1-weighted 3 T MRI scans were acquired for all subjects, and patients were identified by a neuroradiologist with regards to lateralization and degree of hippocampal sclerosis (HS). Automated segmentation was implemented through the Joint Label Fusion/Corrective Learning (JLF/CL) method. Gold standard lateralization was determined from the surgically resected side in Engel I (seizure-free) patients at the two-year timepoint. ROC curves were used to identify appropriate thresholds for hippocampal asymmetry ratios, which were then used to analyze JLF/CL lateralization. RESULTS: The optimal template atlas based on subject images with varying appearances, from normal-appearing to severe HS, was demonstrated to be composed entirely of normal-appearing subjects, with good agreement between automated and manual segmentations. In applying this atlas to 26 surgically resected seizure-free patients at a two-year timepoint, JLF/CL lateralized seizure onset 92% of the time. In comparison, neuroradiology reads lateralized 65% of patients, but correctly lateralized seizure onset in these patients 100% of the time. When compared to lateralized neuroradiology reads, JLF/CL was in agreement and correctly lateralized all 17 patients. When compared to nonlateralized radiology reads, JLF/CL correctly lateralized 78% of the nine patients. SIGNIFICANCE: While a neuroradiologist's interpretation of MR imaging is a key, albeit imperfect, diagnostic tool for seizure localization in medically-refractory TLE patients, automated hippocampal segmentation may provide more efficient and accurate epileptic foci localization. These promising findings demonstrate the clinical utility of automated segmentation in the TLE MR imaging pipeline prior to surgical resection, and suggest that further investigation into JLF/CL-assisted MRI reading could improve clinical outcomes. Our JLF/CL software is publicly available at https://www.nitrc.org/projects/ashs/.

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YNIMG Journal 2017 Journal Article

Similar patterns of neural activity predict memory function during encoding and retrieval

James E. Kragel
Youssef Ezzyat
Michael R. Sperling
Richard Gorniak
Gregory A. Worrell
Brent M. Berry
Cory Inman
Jui-Jui Lin

Neural networks that span the medial temporal lobe (MTL), prefrontal cortex, and posterior cortical regions are essential to episodic memory function in humans. Encoding and retrieval are supported by the engagement of both distinct neural pathways across the cortex and common structures within the medial temporal lobes. However, the degree to which memory performance can be determined by neural processing that is common to encoding and retrieval remains to be determined. To identify neural signatures of successful memory function, we administered a delayed free-recall task to 187 neurosurgical patients implanted with subdural or intraparenchymal depth electrodes. We developed multivariate classifiers to identify patterns of spectral power across the brain that independently predicted successful episodic encoding and retrieval. During encoding and retrieval, patterns of increased high frequency activity in prefrontal, MTL, and inferior parietal cortices, accompanied by widespread decreases in low frequency power across the brain predicted successful memory function. Using a cross-decoding approach, we demonstrate the ability to predict memory function across distinct phases of the free-recall task. Furthermore, we demonstrate that classifiers that combine information from both encoding and retrieval states can outperform task-independent models. These findings suggest that the engagement of a core memory network during either encoding or retrieval shapes the ability to remember the past, despite distinct neural interactions that facilitate encoding and retrieval.

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YNIMG Journal 2016 Journal Article

Data integration: Combined imaging and electrophysiology data in the cloud

Lohith G. Kini
Kathryn A. Davis
Joost B. Wagenaar

There has been an increasing effort to correlate electrophysiology data with imaging in patients with refractory epilepsy over recent years. IEEG. org provides a free-access, rapidly growing archive of imaging data combined with electrophysiology data and patient metadata. It currently contains over 1200 human and animal datasets, with multiple data modalities associated with each dataset (neuroimaging, EEG, EKG, de-identified clinical and experimental data, etc.). The platform is developed around the concept that scientific data sharing requires a flexible platform that allows sharing of data from multiple file formats. IEEG. org provides high- and low-level access to the data in addition to providing an environment in which domain experts can find, visualize, and analyze data in an intuitive manner. Here, we present a summary of the current infrastructure of the platform, available datasets and goals for the near future.

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