Aperture neuro最新文献

{"title":"Feasibility of Integrating Real-Time fMRI Neurofeedback with rTMS: Study Design, Preliminary Results, and Technical Challenges.","authors":"Lysianne Beynel, Vinai Roopchansingh, Paul Taylor, Richard Reynolds, Linqing Li, Neil Baker, Daryl Bandy, Katherine Cameron, Zhi-De Deng, Ekaete Ekpo, Hannah Gura, Sahit Menon, Eva Wiener, Zeynab Rezaee, Justin Rajendra, Bruce Luber, Sarah H Lisanby","doi":"10.52294/001c.165511","DOIUrl":"10.52294/001c.165511","url":null,"abstract":"<p><p>Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain stimulation technique used in the treatment of several psychiatric disorders. However, its clinical efficacy remains limited in some cases, in part due to the lack of control over ongoing brain state during stimulation. Although some studies have combined rTMS with psychotherapy to engage the brain state during stimulation, brain state is still not objectively measured in such studies. Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-nf) offers a promising approach to objectively monitor and modulate brain state during rTMS. Here, we report the first study to investigate the feasibility of combining connectivity-based rTMS with rt-fMRI-nf to modulate amygdala activity. First, participants completed an fMRI emotion-matching task to identify an individualized medial prefrontal cortex (mPFC) target showing the strongest negative functional connectivity with the right amygdala. Across four subsequent visits, participants received active rTMS to either a mPFC target or to the vertex (control site) while engaged in rt-fMRI-nf. During rt-fMRI-nf, participants were presented with aversive images and were instructed either to passively look at them or to downregulate the height of a visual gauge representing their amygdala activity. We expected that rTMS during this downregulation would decrease amygdala activation even more than during downregulation alone. Feasibility, signal quality, rTMS-induced artifacts, and participant experience were systematically evaluated. We demonstrate that while concurrent connectivity-based rTMS and rt-fMRI-nf is feasible, the integration presents substantial technical and methodological challenges. These include limitations related to RF coil selection, signal-to-noise ratio in deep brain structures, prolonged rTMS-induced artifacts, and inter-individual variability in amygdala engagement. Despite these challenges, this work establishes a methodological foundation for future studies integrating real-time neuroimaging and neuromodulation. Continued advances in hardware, acquisition strategies, and individualized targeting may enable more precise state-dependent stimulation of emotion-related circuits, with potential implications for precision interventions in psychiatric disorders.</p>","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":"6 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13599442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of multi-echo ICA modeling decisions on motor-task fMRI analysis.","authors":"Neha A Reddy, Michelle C Medina, Jordan N Northrop, Chendong Zou, Rebecca G Clements, Aravind Nehrujee, Milap Sandhu, Molly G Bright","doi":"10.52294/001c.162891","DOIUrl":"10.52294/001c.162891","url":null,"abstract":"<p><p>Multi-echo independent component analysis (ME-ICA) has been demonstrated to improve sensitivity and reliability of task functional magnetic resonance imaging (fMRI) data and, in particular, motor-task data with inherent task-correlated head motion. However, previous work has shown that an overly aggressive ME-ICA denoising approach may unintentionally remove task-related signal, while a more conservative approach may not effectively mitigate noise. While the effects of varied implementations of ME-ICA on signal and noise characteristics have been tested thoroughly in breath-hold data, the effects of similar modeling decisions have not been studied in motor-task data, which present with a more localized neural response. Here, we tested and compared the impacts of three analysis methods using rejected ME-ICA components as regressors in subject-level modeling: Aggressive (simple inclusion of ME-ICA regressors), Moderate (excluding task-correlated ME-ICA regressors from the model), and Conservative (orthogonalization of ME-ICA regressors to the core model and accepted ME-ICA components). We applied these methods to data from healthy and multiple sclerosis populations that included performance of hand-grasp, shoulder-abduction, and ankle-flexion tasks. We found that when the amount of head motion and its correlation with the task was high and the expected task-evoked signal was relatively low, the Conservative method led to significantly higher activation, t-statistics, and test-retest similarity in motor regions compared to the Aggressive method. Future motor-task studies may wish to implement similar models to prevent loss of motor signal, while still mitigating the effects of task-correlated head motion.</p>","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":"6 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13662799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An open, fully-processed data resource for studying mood and sleep variability in the developing brain.","authors":"Juliette B H Brook, Taylor Salo, Audrey C Luo, Joëlle Bagautdinova, Sage Rush, Aaron F Alexander-Bloch, Erica B Baller, Monica E Calkins, Matt Cieslak, Elena C Cooper, John A Detre, Mark A Elliott, Damien A Fair, Phoebe Freedman, Philip R Gehrman, Ruben C Gur, Raquel E Gur, Arno Klein, Nina Laney, Timothy O Laumann, Kahini Mehta, Kathleen Merikangas, Michael P Milham, Jonathan A Mitchell, Tyler M Moore, Steven M Nelson, Kosha Ruparel, Brooke L Sevchik, Sheila Shanmugan, Haochang Shou, Manuel Taso, Lauren K White, Daniel H Wolf, M Dylan Tisdall, David R Roalf, Theodore D Satterthwaite","doi":"10.52294/001c.151820","DOIUrl":"10.52294/001c.151820","url":null,"abstract":"<p><p>Brain development during adolescence and early adulthood coincides with shifts in emotion regulation and sleep. Despite this, few existing datasets simultaneously characterize affective dynamics, sleep variation, and multimodal measures of brain development. Here, we describe the study protocol and initial release (n = 10) of an open data resource of neuroimaging paired with densely sampled behavioral measures in adolescents and young adults. All participants complete multi-echo functional MRI, compressed-sensing diffusion MRI, and advanced arterial spin-labeled MRI. Behavioral measures include ecological momentary assessment, actigraphy, extensive cognitive assessments, and detailed clinical phenotyping focused on emotion regulation. Raw and processed data are openly available without a data use agreement and will be regularly updated as accrual continues. Together, this resource will accelerate research on the links between mood, sleep, and brain development.</p>","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":"6 SI 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13268368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148267189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A vendor-neutral functional MRI acquisition protocol for multi-site studies.","authors":"Jon-Fredrik Nielsen, Maximillian K Egan, Qingping Chen, Mojtaba Shafiekhani, James H Wang, Samuel A Hurley, Jeffrey S Soldate, Jonathan Lisinksi, Stephen M LaConte, Bradley P Sutton, R Allen Waggoner, Rüdiger Stirnberg, Tony Stöcker, Jeffrey A Fessler, Douglas C Noll, Maxim Zaitsev, Scott J Peltier","doi":"10.52294/001c.155279","DOIUrl":"10.52294/001c.155279","url":null,"abstract":"<p><p>We present an open, vendor-neutral BOLD SMS-EPI protocol tailored for multi-site fMRI studies, intended as a drop-in replacement for conventional vendor-specific acquisition and reconstruction pipelines. Built on Pulseq-an emerging standard for cross-platform MRI pulse sequence development-our protocol ensures identical SMS-EPI pulse sequences and image reconstruction across scanner vendors. This provides, for the first time, known and consistent experimental conditions across sites and scanner software versions. We begin by reviewing the current capabilities of the Pulseq framework, including vendor support and safety considerations. We then detail our SMS-EPI implementation and demonstrate its performance using resting-state fMRI pilot data from healthy volunteers, showing reduced site variance compared to corresponding vendor protocols on Siemens and GE scanners. To support adoption, we provide practical resources to help researchers integrate Pulseq fMRI into their studies, including example text for grant proposals and IRB submissions. These resources are freely available at https://github.com/HarmonizedMRI/Functional. Our vision is for Pulseq fMRI to become the standard for multi-site research, enabling more reproducible science and serving as a reference for the development of novel acquisition and reconstruction methods.</p>","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":"6 SI 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13268408/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148267162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain bases of real-time social interaction: A meta-analytic investigation of human neuroimaging studies.","authors":"Junaid S Merchant, Sophia Glaros, Esha Edakoth, Riley Harris, Nedelina Tchangalova, Elizabeth Redcay","doi":"10.52294/001c.138339","DOIUrl":"10.52294/001c.138339","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;Social interactions play a central role in shaping brain function, but neuroscientific research on interactive social behavior has been limited by the restrictions of brain imaging environments. Although a growing body of neuroimaging research situates participants in real-time social interactive contexts, questions remain about the brain systems critical for understanding social interaction. This study addresses three primary questions: 1) Is there a common network of brain regions that underlies diverse forms of social interaction? 2) Are there dissociable brain systems that contribute to different aspects of social interactive behavior? 3) What are the brain networks and cognitive functions associated with the socially interactive brain?&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We implemented a systematic search of the human neuroimaging literature to find studies involving social interaction - participants were &lt;i&gt;socially engaged&lt;/i&gt; and &lt;i&gt;interacted&lt;/i&gt; with perceived social partners in real-time - that contrasted against non-socially interactive control conditions. We used coordinate-based meta-analysis (CBMA) of 108 studies to elucidate common social interaction brain regions. We further analyzed subsets of studies to probe &lt;i&gt;social engagement&lt;/i&gt; with a human (versus non-human) partner, &lt;i&gt;interactive&lt;/i&gt; (versus non-interactive) social contexts, and reciprocal &lt;i&gt;initiating&lt;/i&gt; (participant elicits a response from a partner) and &lt;i&gt;responding&lt;/i&gt; (to partners actions). Finally, we used the Brainmap and Neurosynth databases to conduct meta-analytic coactivation modeling (MACM) and functional decoding to better characterize the neurocognitive systems associated with social interaction.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The overarching CBMA uncovered significant convergence in ten brain areas that cut across different large-scale brain networks. Follow-up analyses suggest that regions of the reward system contribute to perceived &lt;i&gt;social engagement&lt;/i&gt;, regions of the ventral attention network are associated with reciprocal &lt;i&gt;interaction&lt;/i&gt;, and partially dissociable brain systems relate to &lt;i&gt;initiating&lt;/i&gt; and &lt;i&gt;responding&lt;/i&gt; behaviors. MACM and functional decoding results suggest that 3-4 overlapping neurocognitive systems underlie social interaction: default mode network (temporoparietal junction, medial prefrontal cortex, precuneus, and cerebellum); lateral frontoparietal regions associated with cognitive control processes; and intermediary midcingulo-insular areas that are associated with reward and emotion.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Discussion: &lt;/strong&gt;The current study used a data-driven investigation of the neuroimaging literature to advance our understanding of the neural and cognitive systems critical for human social interaction. Our findings suggest that the myriad forms of social interaction may be subserved by a common network of brain areas that traverse multiple neurocognitive systems and adds support to","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":"5 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12617318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145543476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Moderation between resting-state connectivity and brain amyloid levels on speed of cognitive and physical function in older adults: Evidence for network-based cognitive reserve.","authors":"Paul J Laurienti, Stephen B Kritchevsky, Robert G Lyday, Chal E Tomlinson, Michael E Miller, Samuel N Lockhart, Melissa M Rundle, Christina E Hugenschmidt, Jonathan H Burdette, Heather M Shappell, Haiying Chen, Laura D Baker, Blake R Neyland, Roee Holtzer","doi":"10.52294/001c.141046","DOIUrl":"10.52294/001c.141046","url":null,"abstract":"<p><p>Cognitive and physical function are interrelated in aging. Co-occurring impairments in both domains can be debilitating and lead to increased risk of developing dementia. Amyloid beta (Aβ) deposition in the brain is linked to cognitive decline and is also associated with poorer physical function in older adults. However, significant inter-individual variability exists with respect to the influence of increased brain Aβ concentrations on cognitive and physical outcomes. Identifying factors that explain inter-individual variability in associations between Aβ and clinical outcomes could inform interventions designed to delay declines in both cognitive and physical function. Cognitive reserve (CR) is considered a buffer that allows for cognitive performance that is better than expected for a given level of brain injury or pathology. Although the neural mechanisms underlying CR remain unknown, there is growing evidence that resting-state brain networks may serve as a neural surrogate for CR. The current study utilized a statistical interaction model to evaluate if functional brain networks moderated associations between brain Aβ and cognitive and physical function in community-dwelling older adults from the Brain Networks and Mobility (B-NET) study. Significant moderations were found between Aβ levels and both the central executive and subcortical networks with cognitive and physical function. The findings suggest that brain networks serve as a buffer against the influence of Aβ accumulation on cognitive and physical function indicating that the network integrity could one of the neural mechanisms supporting CR.</p>","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":"5 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12807530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145999860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consensus recommendations for clinical functional MRI applied to language mapping.","authors":"Natalie L Voets, Manzar Ashtari, Christian F Beckmann, Christopher F Benjamin, Tammie Benzinger, Jeffrey R Binder, Alberto Bizzi, Bruce Bjornson, Edward F Chang, Linda Douw, Jodie Gawryluk, Karsten Geletneky, Matthew F Glasser, Sven Haller, Mark Jenkinson, Jorge Jovicich, Eric Leuthardt, Asim Mian, Thomas E Nichols, Oiwi Parker Jones, Cyril Pernet, Puneet Plaha, Monika Połczyńska-Bletsos, Cathy J Price, Geert-Jan Rutten, Michael Scheel, Joshua S Shimony, Joanna Sierpowska, Lynne J Williams, Ghoufran Talib, Michael Zeineh, Andreas Bartsch, Susan Bookheimer","doi":"10.52294/001c.128149","DOIUrl":"10.52294/001c.128149","url":null,"abstract":"<p><p>Ample reports highlight fMRI's added value to guide neurosurgical interventions near brain regions supporting speech and language. However, fMRI's usefulness for clinical language mapping remains controversial, partly fueled by 1) differences from clinical standard tools it is often compared against, and 2) wide heterogeneity in how data are acquired, analyzed and interpreted. Both factors limit objective assessment of the benefits and efficacy of presurgical fMRI. This underscores the need for standardization of fMRI protocols to enable data pooling across centers and facilitate learning from patient outcomes. The OHBM Working Group on clinical fMRI language mapping was formed in 2017. Its scope was to review and propose best practice recommendations addressing specific challenges posed by applications in patient populations. Objectives were to: 1) consider language tasks and designs, optimized for specific clinical objectives, and incorporating modifications for patients with existing impairments; 2) offer practical guidance, based on high-quality research, for each step from fMRI acquisition and analysis to reporting individual patients' data. In considering these challenges we focus on implementations that have proven feasible based on approaches in active use today. When widely available practices deviate from optimal practices, we highlight emerging developments meriting further evaluation and incorporation into clinical use. This document was created in collaboration with the OHBM Committee on Best Practices, incorporating community feedback. It aims to provide a framework for improved standardization of fMRI to enable much-needed evaluations of its ultimate goals; namely, minimization of invasive intraoperative testing and, ultimately, of new post-operative language deficits. Accordingly, the single strongest recommendation is for greater transparency and reporting of longitudinal outcomes in patients undergoing clinical fMRI.</p>","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":"5 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13343883/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148414024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-frequency ICA-based approach for estimating voxelwise frequency difference patterns in fMRI data.","authors":"Neda Behzadfar, D H Mathalon, A Preda, A Iraji, Vince D Calhoun","doi":"10.52294/001c.129695","DOIUrl":"10.52294/001c.129695","url":null,"abstract":"<p><p>In resting-state functional magnetic resonance imaging (fMRI) studies, blood oxygenation level dependent (BOLD) signals exhibit temporal correlation across voxels. Prior research has established resting-state functional connectivity (RSFC) across multiple frequency ranges, indicating that functional integration between brain regions occurs at various frequencies. While a few studies have studied RSFC data filtered into different frequency ranges, prior work has focused on extracting the main effects of frequency and comparing these posthoc, rather than focusing on estimating multivariate spatial patterns explicitly capturing frequency differences. Here, we propose a novel multi-stage independent component analysis (ICA)-based approach for estimating frequency difference patterns (FDPs) in fMRI data. Our novel approach involves separating fMRI images into four frequency sub-bands, concatenating them, and then applying group ICA to extract informative components. After removing non-gray matter components (edge effects, white matter, ventricles), we compute voxelwise differences between these sub-bands and perform a second ICA stage. This allows us to identify distinct covarying spatial patterns associated with FDPs. Understanding the frequency-dependent characteristics is crucial for uncovering the underlying spatial and temporal signatures of brain activity across different frequency bands. This method allows for a more comprehensive spatial analysis of frequency specific filtered fMRI data as it captures the frequency differences within maximally spatially independent spatial maps via a multivariate model. We applied our method to fMRI data from 90 subjects with schizophrenia (SZ) and 90 healthy controls. Our approach revealed structured spatial and temporal patterns which showed frequency-specific partial overlap with known resting-state networks (RSNs) but also exhibited unique spatial patterns. Our frequency-specific analysis unveils connectivity that might be overlooked by single frequency band methods, providing a new window into the brain's functional architecture. These findings suggest that RSFC is a spatially distributed multi-frequency band phenomenon and highlight the potential for further investigation of BOLD signals in relation to cognitive processes.</p>","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":"5 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145082460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BrainEffeX: A Web App for Exploring fMRI Effect Sizes.","authors":"Hallee Shearer, Matt Rosenblatt, Jean Ye, Rongtao Jiang, Link Tejavibulya, Maya Foster, Qinghao Liang, Javid Dadashkarimi, Margaret L Westwater, Catherine Cahill, Iris Cheng, Alexandra Fischbach, Ashley Humphries, Aneesh Kumar Baskaran, Max J Rolison, Hannah Peterson, Brendan D Adkinson, Saloni Mehta, Chris Camp, Thomas E Nichols, Joshua Curtiss, Dustin Scheinost, Stephanie Noble","doi":"10.52294/001c.146251","DOIUrl":"10.52294/001c.146251","url":null,"abstract":"<p><p>Effect size estimation is crucial for power analyses and experimental design, but poses unique challenges in fMRI research due to the complexity of the data and analysis techniques. Here, we introduce an interactive web application for exploring fMRI effect maps (neuroprismlab.shinyapps.io/BrainEffeX). We utilized large fMRI datasets to obtain precise voxel-wise and multivariate effect size estimates from \"typical\" fMRI study designs: brain-behavior correlation, task vs. rest, and between-group analyses of functional connectivity and task-based activation maps. The app is intentionally designed as a growing resource, and we welcome contributions of large (n > 500) datasets.</p>","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":"5 SI 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12889895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146168128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lead-Tutor: An Open-Access Educational Resource for Deep Brain Stimulation Electrode Localizations.","authors":"Savir Madan, Lauren A Hart, Ningfei Li, Till A Dembek, Konstantin Butenko, Garance M Meyer, Nanditha Rajamani, Lukas Goede, Bahne Hendrik Bahners, Julianna Pijar, Patricia Zvarova, Ilkem Aysu Sahin, Helen Friedrich, Ignacio Aracil-Bolanos, Melissa Chua, Clemens Neudorfer, Jordy Tasserie, Andreas Horn","doi":"10.52294/001c.129658","DOIUrl":"10.52294/001c.129658","url":null,"abstract":"<p><p>Deep Brain Stimulation (DBS) is a neurosurgical procedure that involves implanting electrodes into specific brain regions to treat brain disorders. Accurate reconstruction of electrode placement is crucial for treatment optimization. Several systems, such as Lead-DBS, have been developed to reconstruct DBS electrodes, and typically require expert user input. However, open DBS datasets with localized electrodes are not available, posing a challenge to train research personnel on accurate use of these methods. In this paper, we introduce Lead-Tutor, an open-access educational resource that combines an imaging dataset of anonymized DBS cases with a software tool for self-teaching. This resource includes a dataset of pre- and post-operative magnetic resonance imaging (MRI) and computed tomography (CT) scans from ten patients with DBS implants. Along with this dataset, we provide a means for users to practice and enhance their electrode localization skills through the Lead-DBS pipeline. Aimed at new scientists in the DBS field, Lead-Tutor is a comprehensive resource available within Lead-DBS that promotes open science and education for enhanced reproducibility and potential clinical applications.</p>","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":"5 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439812/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145082482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}