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Article ; Online: Engineers drive new directions in translational epilepsy research.

Litt, Brian

2022 Volume 145, Issue 11, Page(s) 3725–3726

MeSH term(s)	Humans ; Epilepsy ; Engineering ; Translational Research, Biomedical
Language	English
Publishing date	2022-11-22
Publishing country	England
Document type	Journal Article ; Comment
ZDB-ID	80072-7
ISSN	1460-2156 ; 0006-8950
ISSN (online)	1460-2156
ISSN	0006-8950
DOI	10.1093/brain/awac375
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Article ; Online: Thalamic stereo-EEG in epilepsy surgery: where do we stand?

Bernabei, John M / Litt, Brian / Cajigas, Iahn

Brain : a journal of neurology

2023 Volume 146, Issue 7, Page(s) 2663–2665

MeSH term(s)	Humans ; Epilepsy/diagnosis ; Epilepsy/surgery ; Seizures ; Brain ; Electroencephalography
Language	English
Publishing date	2023-06-29
Publishing country	England
Document type	Editorial ; Research Support, N.I.H., Extramural ; Comment
ZDB-ID	80072-7
ISSN	1460-2156 ; 0006-8950
ISSN (online)	1460-2156
ISSN	0006-8950
DOI	10.1093/brain/awad178
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Article ; Online: Engineering the next generation of brain scientists.

Litt, Brian

Neuron

2015 Volume 86, Issue 1, Page(s) 16–20

Abstract: New technologies to probe the nervous system are propelling innovation and discovery at blinding speed, but are our trainees prepared to maximize this power? The growing role of engineering in research, such as materials, computing, electronics, and ... ...

Abstract	New technologies to probe the nervous system are propelling innovation and discovery at blinding speed, but are our trainees prepared to maximize this power? The growing role of engineering in research, such as materials, computing, electronics, and devices, compels us to rethink neuroscience education. Core technology requirements, cross-disciplinary education, open-source resources, and experiential learning are new ways we can efficiently equip future leaders to make the next disruptive discoveries.
MeSH term(s)	Animals ; Brain/physiology ; Engineering/trends ; Humans ; Research Personnel/trends
Language	English
Publishing date	2015-04-08
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	808167-0
ISSN	1097-4199 ; 0896-6273
ISSN (online)	1097-4199
ISSN	0896-6273
DOI	10.1016/j.neuron.2015.03.029
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Zs.A 2496: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MG 92: Show issues

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Article ; Online: Low-field MRI: Clinical promise and challenges.

Arnold, Thomas Campbell / Freeman, Colbey W / Litt, Brian / Stein, Joel M

Journal of magnetic resonance imaging : JMRI

2022 Volume 57, Issue 1, Page(s) 25–44

Abstract: Modern MRI scanners have trended toward higher field strengths to maximize signal and resolution while minimizing scan time. However, high-field devices remain expensive to install and operate, making them scarce outside of high-income countries and ... ...

Abstract	Modern MRI scanners have trended toward higher field strengths to maximize signal and resolution while minimizing scan time. However, high-field devices remain expensive to install and operate, making them scarce outside of high-income countries and major population centers. Low-field strength scanners have drawn renewed academic, industry, and philanthropic interest due to advantages that could dramatically increase imaging access, including lower cost and portability. Nevertheless, low-field MRI still faces inherent limitations in image quality that come with decreased signal. In this article, we review advantages and disadvantages of low-field MRI scanners, describe hardware and software innovations that accentuate advantages and mitigate disadvantages, and consider clinical applications for a new generation of low-field devices. In our review, we explore how these devices are being or could be used for high acuity brain imaging, outpatient neuroimaging, MRI-guided procedures, pediatric imaging, and musculoskeletal imaging. Challenges for their successful clinical translation include selecting and validating appropriate use cases, integrating with standards of care in high resource settings, expanding options with actionable information in low resource settings, and facilitating health care providers and clinical practice in new ways. By embracing both the promise and challenges of low-field MRI, clinicians and researchers have an opportunity to transform medical care for patients around the world. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.
MeSH term(s)	Humans ; Child ; Magnetic Resonance Imaging/methods ; Brain/diagnostic imaging ; Software
Language	English
Publishing date	2022-09-19
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1146614-5
ISSN	1522-2586 ; 1053-1807
ISSN (online)	1522-2586
ISSN	1053-1807
DOI	10.1002/jmri.28408
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Zs.A 3648: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MO 236: Show issues

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

Lucas, Alfredo / Scheid, Brittany H / Pattnaik, Akash R / Gallagher, Ryan / Mojena, Marissa / Tranquille, Ashley / Prager, Brian / Gleichgerrcht, Ezequiel / Gong, Ruxue / Litt, Brian / Davis, Kathryn A / Das, Sandhitsu / Stein, Joel M / Sinha, Nishant

Epilepsia

2024 Volume 65, Issue 3, Page(s) 817–829

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Humans ; Electrocorticography/methods ; Retrospective Studies ; Prospective Studies ; Epilepsy/diagnostic imaging ; Epilepsy/surgery ; Magnetic Resonance Imaging/methods ; Electrodes ; Electroencephalography/methods ; Electrodes, Implanted
Language	English
Publishing date	2024-01-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	216382-2
ISSN	1528-1167 ; 0013-9580
ISSN (online)	1528-1167
ISSN	0013-9580
DOI	10.1111/epi.17863
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Zs.A 517: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MO 475: Show issues

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Article ; Online: Simulated diagnostic performance of low-field MRI: Harnessing open-access datasets to evaluate novel devices.

Arnold, T Campbell / Baldassano, Steven N / Litt, Brian / Stein, Joel M

Magnetic resonance imaging

2021 Volume 87, Page(s) 67–76

Abstract: The purpose of this study is to demonstrate a method for virtually evaluating novel imaging devices using machine learning and open-access datasets, here applied to a new, low-field strength portable 64mT MRI device. Paired 3 T and 64mT brain images were ...

Abstract	The purpose of this study is to demonstrate a method for virtually evaluating novel imaging devices using machine learning and open-access datasets, here applied to a new, low-field strength portable 64mT MRI device. Paired 3 T and 64mT brain images were used to develop and validate a transformation converting standard clinical images to low-field quality images. Separately, 3 T images were aggregated from open-source databases spanning four neuropathologies: low-grade glioma (LGG, N = 76), high-grade glioma (HGG, N = 259), stroke (N = 28), and multiple sclerosis (MS, N = 20). The transformation method was then applied to the open-source data to generate simulated low-field images for each pathology. Convolutional neural networks (DenseNet-121) were trained to detect pathology in axial slices from either 3 T or simulated 64 mT images, and their relative performance was compared to characterize the potential diagnostic capabilities of low-field imaging. Algorithm performance was measured using area under the receiver operating characteristic curve. Across all cohorts, pathology detection was similar between 3 T and simulated 64mT images (LGG: 0.97 vs. 0.98; HGG: 0.96 vs. 0.95; stroke: 0.94 vs. 0.94; MS: 0.90 vs 0.87). Pathology detection was further characterized as a function of lesion size, intensity, and contrast. Simulated images showed decreasing sensitivity for lesions smaller than 4 cm
MeSH term(s)	Brain Neoplasms/pathology ; Datasets as Topic ; Glioma/diagnostic imaging ; Glioma/pathology ; Humans ; Magnetic Resonance Imaging/methods ; Neural Networks, Computer ; Prospective Studies
Language	English
Publishing date	2021-12-28
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	604885-7
ISSN	1873-5894 ; 0730-725X
ISSN (online)	1873-5894
ISSN	0730-725X
DOI	10.1016/j.mri.2021.12.007
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Article ; Online: Artificial intelligence in epilepsy phenotyping.

Knight, Andrew / Gschwind, Tilo / Galer, Peter / Worrell, Gregory A / Litt, Brian / Soltesz, Ivan / Beniczky, Sándor

Epilepsia

2023

Abstract: Artificial intelligence (AI) allows data analysis and integration at an unprecedented granularity and scale. Here we review the technological advances, challenges, and future perspectives of using AI for electro-clinical phenotyping of animal models and ... ...

Abstract	Artificial intelligence (AI) allows data analysis and integration at an unprecedented granularity and scale. Here we review the technological advances, challenges, and future perspectives of using AI for electro-clinical phenotyping of animal models and patients with epilepsy. In translational research, AI models accurately identify behavioral states in animal models of epilepsy, allowing identification of correlations between neural activity and interictal and ictal behavior. Clinical applications of AI-based automated and semi-automated analysis of audio and video recordings of people with epilepsy, allow significant data reduction and reliable detection and classification of major motor seizures. AI models can accurately identify electrographic biomarkers of epilepsy, such as spikes, high-frequency oscillations, and seizure patterns. Integrating AI analysis of electroencephalographic, clinical, and behavioral data will contribute to optimizing therapy for patients with epilepsy.
Language	English
Publishing date	2023-11-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	216382-2
ISSN	1528-1167 ; 0013-9580
ISSN (online)	1528-1167
ISSN	0013-9580
DOI	10.1111/epi.17833
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Zs.MO 475: Show issues

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Article ; Online: External drivers of BOLD signal's non-stationarity.

Ashourvan, Arian / Pequito, Sérgio / Bertolero, Maxwell / Kim, Jason Z / Bassett, Danielle S / Litt, Brian

PloS one

2022 Volume 17, Issue 9, Page(s) e0257580

Abstract: A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that ...

Abstract	A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. This dynamical systems model of the brain offers information on the structure and dimensionality of the BOLD signal's external drivers and shines a light on the likely external sources contributing to the BOLD signal's non-stationarity. Our findings show the role of exogenous inputs in the BOLD dynamics and highlight the importance of accounting for external inputs to unravel the brain's time-varying functional dynamics.
MeSH term(s)	Brain/diagnostic imaging ; Brain/physiology ; Connectome ; Humans ; Magnetic Resonance Imaging/methods
Language	English
Publishing date	2022-09-19
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0257580
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Article ; Online: Generalization of finetuned transformer language models to new clinical contexts.

Xie, Kevin / Terman, Samuel W / Gallagher, Ryan S / Hill, Chloe E / Davis, Kathryn A / Litt, Brian / Roth, Dan / Ellis, Colin A

JAMIA open

2023 Volume 6, Issue 3, Page(s) ooad070

Abstract: Objective: We have previously developed a natural language processing pipeline using clinical notes written by epilepsy specialists to extract seizure freedom, seizure frequency text, and date of last seizure text for patients with epilepsy. It is ... ...

Abstract	Objective: We have previously developed a natural language processing pipeline using clinical notes written by epilepsy specialists to extract seizure freedom, seizure frequency text, and date of last seizure text for patients with epilepsy. It is important to understand how our methods generalize to new care contexts. Materials and methods: We evaluated our pipeline on unseen notes from nonepilepsy-specialist neurologists and non-neurologists without any additional algorithm training. We tested the pipeline out-of-institution using epilepsy specialist notes from an outside medical center with only minor preprocessing adaptations. We examined reasons for discrepancies in performance in new contexts by measuring physical and semantic similarities between documents. Results: Our ability to classify patient seizure freedom decreased by at least 0.12 agreement when moving from epilepsy specialists to nonspecialists or other institutions. On notes from our institution, textual overlap between the extracted outcomes and the gold standard annotations attained from manual chart review decreased by at least 0.11 F Discussion and conclusion: Model generalization performance decreased on notes from nonspecialists; out-of-institution generalization on epilepsy specialist notes required small changes to preprocessing but was especially good for seizure frequency text and date of last seizure text, opening opportunities for multicenter collaborations using these outcomes.
Language	English
Publishing date	2023-08-16
Publishing country	United States
Document type	Journal Article
ISSN	2574-2531
ISSN (online)	2574-2531
DOI	10.1093/jamiaopen/ooad070
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Article ; Online: Sidewalks, trees and shade matter: A visual landscape assessment approach to understanding people’s preferences for walking

Tabatabaie, Sara / Litt, Jill S. / Muller, Brian H.F.

Urban Forestry & Urban Greening. 2023 June, v. 84 p.127931-

2023

Abstract: Public health professionals and urban designers consider neighborhood sidewalks as a vehicle for promoting outdoor physical activity. The design of streetscape and available greenness may also influence walking behaviors and physical activity more ... ...

Abstract	Public health professionals and urban designers consider neighborhood sidewalks as a vehicle for promoting outdoor physical activity. The design of streetscape and available greenness may also influence walking behaviors and physical activity more generally. Data collection included a visual landscape assessment (VLA) survey, followed by focus groups to evaluate streetscape features that influence participants’ preferences for and choice of walking routes. With the considerations of equity in access to the environmental amenities, the study was conducted in marginalized communities. Sixty-nine people from low-income neighborhoods in Denver, CO participated in this study. Data were analyzed both qualitatively and quantitatively, using multilevel statistical models. More shade and trees, higher levels of maintenance, and the presence of a buffer between the street and sidewalk increase the likelihood of intuitively choosing a street for walking. The availability of natural surveillance, the presence of an open view, and the presence of attractive buildings increase the likelihood of cognitively choosing a street for walking. People‘s preferences for and choice of walking routes, which include desired shade and walkability, should be considered in neighborhood planning to promote walkable environments.
Keywords	data collection ; landscapes ; monitoring ; people ; physical activity ; public health ; surveys ; urban forestry ; Walking ; Urban greenness ; Equity planning ; Streetscape design ; Visual landscape assessment ; Participatory evaluation method ; Health behaviors
Language	English
Dates of publication	2023-06
Publishing place	Elsevier GmbH
Document type	Article ; Online
ISSN	1618-8667
DOI	10.1016/j.ufug.2023.127931
Database	NAL-Catalogue (AGRICOLA)

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