Aperture neuro最新文献

{"title":"Brain network dynamics associated with intentional weight loss in older adults.","authors":"Heather M Shappell, W Jack Rejeski, Mohammadreza Khodaei, Robert G Lyday, Mohsen Bahrami, Barbara J Nicklas, Jason Fanning, Jonathan H Burdette, Paul J Laurienti","doi":"10.52294/001c.128461","DOIUrl":"10.52294/001c.128461","url":null,"abstract":"<p><p>Current obesity research suggests a link between obesity and challenges in executive functions such as decision-making, inhibitory control, and reward valuation, but what separates older individuals who are vulnerable to these deficiencies from those who are resilient remains unknown. We have mounting evidence that suggests disruptions in brain networks are a key factor. However, most of the brain connectivity analyses performed in the obesity literature estimate a static, unchanging brain network for each experimental scanning session. In this paper, we conduct a state-based dynamic functional connectivity analysis on functional magnetic resonance imaging (fMRI) data collected during a food-cue task from 58 older adults with obesity. Our intent was to build upon an original investigation of two functional networks (FN1 and FN2) found in an independent study that were associated with 18-month weight loss in older adults. We used a novel Hidden semi-Markov modeling (HSMM) approach to estimate five network states traversed by the individuals in this study, as well as the most probable sequence of states for each participant. We quantified the occupancy time, dwell time, and transition probabilities across states and compared these between older adults in a weight loss intervention who successfully lost 5% of their body weight at 6 months and those who did not. As in our prior study, adults with obesity spent much of their time in a network state where regions in FN1 and FN2 were largely segregated regardless of weight-loss success. However, we found that older adults who successfully lost weight after 6 months spent more total time in additional network states where insula and limbic structures were well integrated. On the contrary, those who were unsuccessful at losing weight after 6 months, spent more time in a state with strong connections between insula and motor structures, with limbic structures mostly segregated from the other regions. This recognition that weight management involves complex temporal shifts in brain states could lead to the development of new treatment options that complement current behavioral methods for weight management.</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/PMC12366634/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144980776","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":"QRATER: a collaborative and centralized imaging quality control web-based application.","authors":"Sofia Fernandez-Lozano, Mahsa Dadar, Cassandra Morrison, Ana Manera, Daniel Andrews, Reza Rajabli, Victoria Madge, Etienne St-Onge, Neda Shafiee, Alexandra Livadas, Vladimir Fonov, D Louis Collins","doi":"10.52294/001c.118616","DOIUrl":"https://doi.org/10.52294/001c.118616","url":null,"abstract":"<p><p>Quality control (QC) is an important part of all scientific analyses, including neuroscience. With manual curation considered the gold standard, there remains a lack of available tools that make manual neuroimaging QC accessible, fast, and easy. In this article we present Qrater, a containerized web-based Python application that enables viewing and rating of any type of image for QC purposes. Qrater functionalities allow collaboration between various raters on the same dataset which can facilitate completing large QC tasks. Qrater was used to evaluate QC rater performance on three different magnetic resonance (MR) image QC tasks by a group of raters having different amounts of experience. The tasks included QC of raw MR images (10,196 images), QC of linear registration to a standard template (10,196 images), and QC of skull segmentation (6,968 images). We measured the proportion of failed images, average rating time per image, intra- and inter-rater agreement, as well as the comparison against QC using a conventional method. The median time spent rating per image differed significantly between raters (depending on rater experience) in each of the three QC tasks. Evaluating raw MR images was slightly faster using Qrater than an image viewer (expert: 99 vs. 90 images in 63 min; trainee 99 vs 79 images in 98 min). Reviewing the linear registration using Qrater was twice faster for the expert (99 vs. 43 images in 36 min) and three times faster for the trainee (99 vs. 30 images in 37 min). The greatest difference in rating speed resulted from the skull segmentation task where the expert took a full minute to inspect the volume on a slice-by-slice basis compared to just 3 s using Qrater. Rating agreement also depended on the experience of the raters and the task at hand: trained raters' inter-rater agreements with the expert's gold standard were moderate for both raw images (Fleiss' Kappa = 0.44) and linear registration (Fleiss' Kappa = 0.56); the experts' inter-rater agreement of the skull segmentation task was excellent (Cohen's Kappa = 0.83). These results demonstrate that Qrater is a useful asset for QC tasks that rely on manual evaluation of QC images.</p>","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12363495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144980842","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 Trifecta of Deep Learning Models: Assessing Brain Health by Integrating Assessment and Neuroimaging Data","authors":"M. Ajith, Dawn M. Aycock, Erin B. Tone, Jingyu Liu, Maria B. Misiura, Rebecca Ellis, Sergey M. Plis, Tricia Z. King, Vonetta M. Dotson, Vince D. Calhoun","doi":"10.52294/001c.118576","DOIUrl":"https://doi.org/10.52294/001c.118576","url":null,"abstract":"The investigation of brain health development is paramount, as a healthy brain underpins cognitive and physical well-being, and mitigates cognitive decline, neurodegenerative diseases, and mental health disorders. This study leverages the UK Biobank dataset containing static functional network connectivity (sFNC) data derived from resting-state functional magnetic resonance imaging (rs-fMRI) and assessment data. We introduce a novel approach to forecasting a brain health index (BHI) by deploying three distinct models, each capitalizing on different modalities for training and testing. The first model exclusively employs psychological assessment measures, while the second model harnesses both neuroimaging and assessment data for training but relies solely on assessment data during testing. The third model encompasses a holistic strategy, utilizing neuroimaging and assessment data for the training and testing phases. The proposed models employ a two-step approach for calculating the BHI. In the first step, the input data is subjected to dimensionality reduction using principal component analysis (PCA) to identify critical patterns and extract relevant features. The resultant concatenated feature vector is then utilized as input to variational autoencoders (VAE). This network generates a low-dimensional representation of the input data used for calculating BHI in new subjects without requiring imaging data. The results suggest that incorporating neuroimaging data into the BHI model, even when predicting from assessments alone, enhances its ability to accurately evaluate brain health. The VAE model exemplifies this improvement by reconstructing the sFNC matrix more accurately than the assessment data. Moreover, these BHI models also enable us to identify distinct behavioral and neural patterns. Hence, this approach lays the foundation for larger-scale efforts to monitor and enhance brain health, aiming to build resilient brain systems.","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":"37 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141348444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stable multivariate lesion symptom mapping","authors":"Alex Teghipco, R. Newman-Norlund, Makayla Gibson, L. Bonilha, John Absher, Julius Fridriksson, Christopher Rorden","doi":"10.52294/001c.117311","DOIUrl":"https://doi.org/10.52294/001c.117311","url":null,"abstract":"Multivariate lesion-symptom mapping (MLSM) considers lesion information across the entire brain to predict impairments. The strength of this approach is also its weakness—considering many brain features together synergistically can uncover complex brain-behavior relationships but exposes a high-dimensional feature space that a model is expected to learn. Successfully distinguishing between features in this landscape can be difficult for models, particularly in the presence of irrelevant or redundant features. Here, we propose stable multivariate lesion-symptom mapping (sMLSM), which integrates the identification of reliable features with stability selection into conventional MLSM and describe our open-source MATLAB implementation. Usage is showcased with our publicly available dataset of chronic stroke survivors (N=167) and further validated in our independent public acute stroke dataset (N = 1106). We demonstrate that sMLSM eliminates inconsistent features highlighted by MLSM, reduces variation in feature weights, enables the model to learn more complex patterns of brain damage, and improves model accuracy for predicting aphasia severity in a way that tends to be robust regarding the choice of parameters for identifying reliable features. Critically, sMLSM more consistently outperforms predictions based on lesion size alone. This advantage is evident starting at modest sample sizes (N>75). Spatial distribution of feature importance is different in sMLSM, which highlights the features identified by univariate lesion symptom mapping while also implicating select regions emphasized by MLSM. Beyond improved prediction accuracy, sMLSM can offer deeper insight into reliable biomarkers of impairment, informing our understanding of neurobiology.","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":" 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141371689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across measures","authors":"Stefanie A Tremblay, Zaki Alasmar, Amir Pirhadi, Felix Carbonell, Yasser Iturria-Medina, Claudine J Gauthier, Christopher J Steele","doi":"10.52294/001c.118427","DOIUrl":"https://doi.org/10.52294/001c.118427","url":null,"abstract":"Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging measures and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between measures. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox (https://github.com/neuralabc/mvcomp (https://github.com/neuralabc/mvcomp)). The toolbox allows different levels of analysis (i.e., group- or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI measures or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI measure (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":"7 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141265766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vestibular Function is Associated with Prefrontal and Sensorimotor Cortical Gray Matter Volumes in a Cross-Sectional Study of Healthy, Older Adults","authors":"Dominic Padova, Andreia Faria, J. Tilak Ratnanather, Raymond J. So, Stanley Zhu, Yuri Agrawal","doi":"10.52294/001c.116785","DOIUrl":"https://doi.org/10.52294/001c.116785","url":null,"abstract":"The vestibular system is associated with alterations in the structure and function of the central nervous system. Yet, whether age-related vestibular loss is related to volume loss in the cerebral cortical areas that have been reported to receive vestibular input remains unknown. In this cross-sectional study of 117 healthy, older adults from the Baltimore Longitudinal Study of Aging, we examine the relationships between age-related vestibular functions and the gray matter volumes of the prefrontal cortex and its subregions and of the sensorimotor cortex—regions known to process vestibular information. T1-weighted MRI scans were automatically segmented using MRICloud. Log-linear multiple regression was used to investigate the relationships between average regional volume and vestibular function, adjusting for age, sex, and intracranial volume. Permutation testing was used for hypothesis testing, and bootstrapping was used to estimate confidence intervals. We found that age-related changes in vestibular end-organ function are associated with differentially altered gray matter volumes in the prefrontal and sensorimotor cortices, with many findings persisting when considering left (or right) side only. Concomitant with age-related, global brain atrophy, lower canal and utricular function were associated with additional volume atrophy of the prefrontal cortex and middle frontal gyrus, respectively. Lower saccular and utricular function were associated with the preservation of the volumes of the sensorimotor cortex and the pole of the superior frontal gyrus, respectively, against age-related, global brain atrophy. Canal and utricular function were not associated with the volumes of the sensorimotor cortex, and saccular function was not associated with the relative volumes of the prefrontal cortex. Together, these findings of relative volume preservation or additional atrophy suggest that vestibular function may play a role in the resilience to or magnification of global age effects on cerebral cortical structure.","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":"10 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141105180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dr. Sean Marrett (1960 - 2023): A Life of Joyful Engagement","authors":"Peter Bandettini, Bruce Fischl, Richard Hoge, Albert Gjedde, Alan Evans","doi":"10.52294/001c.116897","DOIUrl":"https://doi.org/10.52294/001c.116897","url":null,"abstract":"Dr. Sean Marrett, a treasured neuroscientist and highly engaged member of the international brain mapping community for more than three decades, passed away on December 12, 2023 after a 16 month battle with Mesothelioma. His impact went beyond scientific productivity. He was a friend, colleage, and inspiration to perhaps thousands across disciplines. We briefly summarize his inspiring career and his approach to life in this editorial.","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":"54 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140965768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain, behavior, cognition, and physical health in first-onset adolescent anorexia nervosa: The BRAVE Study design and cohort profile","authors":"Cathelijne Steegers, K. Bracké, Tess van der Harst, Laura Monteiro Rente Dias, Stefan Ehrlich, Jeroen Legerstee, Bram Dierckx, Pieter de Nijs, Marieke Bax-van Berkel, Annemarie van Elburg, Eric van Furth, Lilian van Geelkerken, Marion Hekkelaan, Joke Hokke, Hetty de Jong-Zuidema, Lucas Korthals-Altes, Farida Lengton-van der Spil, Judith Luijkx, Margarita Slof-Op 't Landt, Femke Schuurmans, Carien Smeets, Elke Wezenberg, Lia van Wijk, Claire Woltering, M. Hillegers, Tonya White, Gwen Dieleman","doi":"10.52294/001c.116180","DOIUrl":"https://doi.org/10.52294/001c.116180","url":null,"abstract":"Anorexia nervosa is a severe psychiatric disorder with a heterogeneous course with one of the highest rates of morbidity and mortality of all psychiatric disorders. Little is known about factors that predict both course and treatment outcomes of this disorder. The BRAVE Study is a longitudinal first-onset anorexia nervosa cohort study focusing on four topics of interest in girls only: (1) behavior, (2) neurobiology, (3) cognitive functions, and (4) physical health. The goal of this paper is to introduce the BRAVE Study. The primary aim of the BRAVE Study is to identify predictors of treatment response in a large sample of 12-to-22-year-old females with first-onset typical or atypical anorexia nervosa. The second aim is to longitudinally investigate the association between clinically significant changes in eating disorder symptoms with the underlying behavioral, neurobiological, cognitive and physical health changes. The results of this study will allow us to develop more precise treatment strategies in order to provide more optimal treatment. The BRAVE Study implements a longitudinal case-control design. Study recruitment was designed within a collaborative network of 16 Dutch mental health organizations, each with expertise in the diagnosis and treatment of patients with anorexia nervosa. After obtaining informed consent, assessments were performed at baseline and one-year follow-up. Patients with anorexia nervosa received treatment as usual. The primary outcome measures at one year are restoration of weight and a reduction of eating disorder symptomatology. Predictive measures include neurobiological, cognitive, behavioral and physical health measures. In the BRAVE Study 79 girls with anorexia nervosa and 75 typically developing girls were included between May 2017 and October 2021. This period of time partially overlapped with the COVID-19 pandemic. 72% of the girls with anorexia nervosa and 88% of the typically developing girls also completed measurements at follow-up. The mean time between data collections points was 13 months. The groups were comparable in education level of their mothers, neurodevelopmental disorders, and ethnical background. The girls with anorexia nervosa were slightly younger than the typically developing girls. The BRAVE Study aligns with one of the most important study priorities in the field of anorexia nervosa by examining (i) predictors of treatment response and (ii) investigating how symptoms with eating disorder symptoms track with changes in neurobiological, cognitive, behavorial and physical health functioning. Moreover, the study is innovative by its longitunal case control design, relatively large study sample and broad selection of measures.","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140999632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proceedings of the OHBM Brainhack 2022","authors":"Stefano Moia, Hao-Ting Wang, A. S. Heinsfeld, Dorota Jarecka, Yu Fang Yang, Stephan Heunis, Michele Svanera, Benjamin De Leener, Andrea Gondová, Sin Kim, Arshitha Basavaraj, Johanna Margarete Marianne Bayer, Roza G. Bayrak, P. Bazin, I. Bilgin, Steffen Bollmann, Daniel Borek, Valentina Borghesani, Trang Cao, Gang Chen, Alejandro de la Vega, Sebastian Dresbach, Philipp Ehses, J. Ernsting, Inês Esteves, Oscar Ferrante, Kelly G. Garner, R´emi Gau, Elodie Germani, Tara Ghafari, Satrajit S. Ghosh, Sarah E. Goodale, Cassandra D. Gould van Praag, Samuel Guay, O. Gulban, Y. Halchenko, Michael Hanke, P. Herholz, Katja Heuer, F. Hoffstaedter, Ruoqi Huang, R. Huber, Ole Jensen, Kan Keeratimahat, Julian Q. Kosciessa, Sladjana Lukic, Neville Magielse, C. Markiewicz, Caroline G. Martin, Camille Maumet, Anna Menacher, Jeffrey Mentch, Christian Mönch, Shammi More, Leonardo Muller-Rodriguez, Samuel A. Nastase, Eliana Nicolaisen-Sobesky, Dylan M. Nielson, Christopher R. Nolan, François Paugam, Pedro Pinheiro-Chagas, Ana Lu","doi":"10.52294/001c.92760","DOIUrl":"https://doi.org/10.52294/001c.92760","url":null,"abstract":"OHBM Brainhack 2022 took place in June 2022. The first hybrid OHBM hackathon, it had an in-person component taking place in Glasgow and three hubs around the globe to improve inclusivity and fit as many timezones as possible. In the buzzing setting of the Queen Margaret Union and of the virtual platform, 23 projects were presented after development. Following are the reports of 14 of those, as well as a recapitulation of the organisation of the event.","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":"57 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140232414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of 2D Line-scanning Method","authors":"David Hike, Sangcheon Choi, Bei Zhang, Yuanyuan Jiang, Xiaochen Liu, R. Pohmann, Sascha Koehler, Xin Yu","doi":"10.52294/001c.92792","DOIUrl":"https://doi.org/10.52294/001c.92792","url":null,"abstract":"Recently, there has been a growing interest in ultra-fast fMRI mapping. We are providing an optimized pulse sequence method for a 2D line-scanning technique, allowing for the detection of dynamic MRI signals with a high temporal resolution (6 ms). This work addresses an intriguing observation using MRI to directly detect neuronal activity in the brain; a topic that has been investigated by many scientists in the past few decades. This FLASH-based fMRI pulse sequence enables the ultrafast sampling of signals by reshuffling single k-space line acquisitions across multiple repetitions as a function of time for a given block design stimulation paradigm.","PeriodicalId":72250,"journal":{"name":"Aperture neuro","volume":"38 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139780077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}