Imaging neuroscience (Cambridge, Mass.)最新文献

{"title":"Synthetic data in generalizable, learning-based neuroimaging.","authors":"Karthik Gopinath, Andrew Hoopes, Daniel C Alexander, Steven E Arnold, Yael Balbastre, Benjamin Billot, Adrià Casamitjana, You Cheng, Russ Yue Zhi Chua, Brian L Edlow, Bruce Fischl, Harshvardhan Gazula, Malte Hoffmann, C Dirk Keene, Seunghoi Kim, W Taylor Kimberly, Sonia Laguna, Kathleen E Larson, Koen Van Leemput, Oula Puonti, Livia M Rodrigues, Matthew S Rosen, Henry F J Tregidgo, Divya Varadarajan, Sean I Young, Adrian V Dalca, Juan Eugenio Iglesias","doi":"10.1162/imag_a_00337","DOIUrl":"10.1162/imag_a_00337","url":null,"abstract":"<p><p>Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)-a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties. This technique has enabled robust, adaptable models that are capable of handling diverse MRI contrasts, resolutions, and pathologies, while working out-of-the-box, without retraining. We have successfully applied this method to tasks such as whole-brain segmentation (SynthSeg), skull-stripping (SynthStrip), registration (SynthMorph, EasyReg), super-resolution, and MR contrast transfer (SynthSR). Beyond these applications, the paper discusses other possible use cases and future work in our methodology. Neural networks trained with synthetic data enable the analysis of clinical MRI, including large retrospective datasets, while greatly alleviating (and sometimes eliminating) the need for substantial labeled datasets, and offer enormous potential as robust tools to address various research goals.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"2 ","pages":"1-22"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11752692/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030118","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":"Processing, evaluating, and understanding FMRI data with afni_proc.py.","authors":"Richard C Reynolds, Daniel R Glen, Gang Chen, Ziad S Saad, Robert W Cox, Paul A Taylor","doi":"10.1162/imag_a_00347","DOIUrl":"10.1162/imag_a_00347","url":null,"abstract":"<p><p>FMRI data are noisy, complicated to acquire, and typically go through many steps of processing before they are used in a study or clinical practice. Being able to visualize and understand the data from the start through the completion of processing, while being confident that each intermediate step was successful, is challenging. AFNI's <i>afni_proc.py</i> is a tool to create and run a processing pipeline for FMRI data. With its flexible features, afni_proc.py allows users to both control and evaluate their processing at a detailed level. It has been designed to keep users informed about all processing steps: it does not just process the data, but also first outputs a fully commented processing script that the users can read, query, interpret, and refer back to. Having this full provenance is important for being able to understand each step of processing; it also promotes transparency and reproducibility by keeping the record of individual-level processing and modeling specifics in a single, shareable place. Additionally, afni_proc.py creates pipelines that contain several automatic self-checks for potential problems during runtime. The output directory contains a dictionary of relevant quantities that can be programmatically queried for potential issues and a systematic, interactive quality control (QC) HTML. All of these features help users evaluate and understand their data and processing in detail. We describe these and other aspects of <i>afni_proc.py</i> here using a set of task-based and resting-state FMRI example commands.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"2 ","pages":"1-52"},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11576932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689388","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":"NOise Reduction with DIstribution Corrected (NORDIC) principal component analysis improves brain activity detection across rodent and human functional MRI contexts.","authors":"Russell W Chan, Giles Hamilton-Fletcher, Bradley J Edelman, Muneeb A Faiq, Thajunnisa A Sajitha, Steen Moeller, Kevin C Chan","doi":"10.1162/imag_a_00325","DOIUrl":"10.1162/imag_a_00325","url":null,"abstract":"<p><p>NOise Reduction with DIstribution Corrected (NORDIC) principal component analysis (PCA) has been shown to selectively suppress thermal noise and improve the temporal signal-to-noise ratio (tSNR) in human functional magnetic resonance imaging (fMRI). However, the feasibility to improve data quality for rodent fMRI using NORDIC PCA remains uncertain. NORDIC PCA may also be particularly beneficial for improving topological brain mapping, as conventional mapping requires precise spatiotemporal signals from large datasets (ideally ~1 hour acquisition) for individual representations. In this study, we evaluated the effects of NORDIC PCA compared with \"Standard\" processing in various rodent fMRI contexts that range from task-evoked optogenetic fMRI to resting-state fMRI. We also evaluated the effects of NORDIC PCA on human resting-state and retinotopic mapping fMRI via population receptive field (pRF) modeling. In rodent optogenetic fMRI, apart from doubling the tSNR, NORDIC PCA resulted in a larger number of activated voxels and a significant decrease in the variance of evoked brain responses without altering brain morphology. In rodent resting-state fMRI, we found that NORDIC PCA induced a nearly threefold increase in tSNR and preserved task-free relative cerebrovascular reactivity (rCVR) across cortical depth. NORDIC PCA further improved the detection of TGN020-induced aquaporin-4 inhibition on rCVR compared with Standard processing without NORDIC PCA. NORDIC PCA also increased the tSNR for both human resting-state and pRF fMRI, and for the latter also increased activation cluster sizes while retaining retinotopic organization. This suggests that NORDIC PCA preserves the spatiotemporal precision of fMRI signals needed for pRF analysis, and effectively captures small activity changes with high sensitivity. Taken together, these results broadly demonstrate the value of NORDIC PCA for the enhanced detection of neural dynamics across various rodent and human fMRI contexts. This can in turn play an important role in improving fMRI image quality and sensitivity for translational and preclinical neuroimaging research.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"2 ","pages":"1-18"},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11506209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514071","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":"Measurement variability of blood-brain barrier permeability using dynamic contrast-enhanced magnetic resonance imaging.","authors":"Aravinthan Varatharaj, Carmen Jacob, Angela Darekar, Brian Yuen, Stig Cramer, Henrik Larsson, Ian Galea","doi":"10.1162/imag_a_00324","DOIUrl":"https://doi.org/10.1162/imag_a_00324","url":null,"abstract":"<p><p>Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to quantify the blood-brain barrier (BBB) permeability-surface area product. Serial measurements can indicate changes in BBB health, of interest to the study of normal physiology, neurological disease, and the effect of therapeutics. We performed a scan-rescan study to inform both sample size calculation for future studies and an appropriate reference change value for patient care. The final dataset included 28 healthy individuals (mean age 53.0 years, 82% female) scanned twice with mean interval 9.9 weeks. DCE-MRI was performed at 3T using a 3D gradient echo sequence with whole brain coverage, T1 mapping using variable flip angles, and a 16-min dynamic sequence with a 3.2-s time resolution. Segmentation of white and grey matter (WM/GM) was performed using a 3D magnetization-prepared gradient echo image. The influx constant K_i was calculated using the Patlak method. The primary outcome was the within-subject coefficient of variation (CV) of K_i in both WM and GM. K_i values followed biological expectations in relation to known GM/WM differences in cerebral blood volume (CBV) and consequently vascular surface area. Subject-derived arterial input functions showed marked within-subject variability which were significantly reduced by using a venous input function (CV of area under the curve 46 vs. 12%, p < 0.001). Use of the venous input function significantly improved the CV of K_i in both WM (30 vs. 59%, p < 0.001) and GM (21 vs. 53%, p < 0.001). Further improvement was obtained using motion correction, scaling the venous input function by the artery, and using the median rather than the mean of individual voxel data. The final method gave CV of 27% and 17% in WM and GM, respectively. No further improvement was obtained by replacing the subject-derived input function by one standard population input function. CV of K_i was shown to be highly sensitive to dynamic sequence duration, with shorter measurement periods giving marked deterioration especially in WM. In conclusion, measurement variability of 3D brain DCE-MRI is sensitive to analysis method and a large precision improvement is obtained using a venous input function.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"2 ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11497077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514070","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":"ECCENTRIC: A fast and unrestrained approach for high-resolution in vivo metabolic imaging at ultra-high field MR.","authors":"Antoine Klauser, Bernhard Strasser, Wolfgang Bogner, Lukas Hingerl, Sebastien Courvoisier, Claudiu Schirda, Bruce R Rosen, Francois Lazeyras, Ovidiu C Andronesi","doi":"10.1162/imag_a_00313","DOIUrl":"10.1162/imag_a_00313","url":null,"abstract":"<p><p>A novel method for fast and high-resolution metabolic imaging, called ECcentric Circle ENcoding TRajectorIes for Compressed sensing (ECCENTRIC), has been developed at 7 Tesla MRI. ECCENTRIC is a non-Cartesian spatial-spectral encoding method designed to accelerate magnetic resonance spectroscopic imaging (MRSI) with high signal-to-noise at ultra-high field. The approach provides flexible and random sampling of the Fourier space without temporal interleaving to improve spatial response function and spectral quality. ECCENTRIC enables the implementation of spatial-spectral MRSI with reduced gradient amplitudes and slew-rates, thereby mitigating electrical, mechanical, and thermal stress of the scanner hardware. Moreover, it exhibits robustness against timing imperfections and eddy-current delay. Combined with a model-based low-rank reconstruction, this approach enables simultaneous imaging of up to 14 metabolites over the whole brain at 2-3 mm isotropic resolution in 4-10 min. MRSI ECCENTRIC was performed on four healthy volunteers, yielding high-resolution spatial mappings of neurochemical profiles within the human brain. This innovative tool introduces a novel approach to neuroscience, providing new insights into the exploration of brain activity and physiology.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"2 ","pages":"1-20"},"PeriodicalIF":0.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142831009","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 novel, robust, and portable platform for magnetoencephalography using optically-pumped magnetometers.","authors":"Holly Schofield, Ryan M Hill, Odile Feys, Niall Holmes, James Osborne, Cody Doyle, David Bobela, Pierre Corvilain, Vincent Wens, Lukas Rier, Richard Bowtell, Maxime Ferez, Karen J Mullinger, Sebastian Coleman, Natalie Rhodes, Molly Rea, Zoe Tanner, Elena Boto, Xavier de Tiège, Vishal Shah, Matthew J Brookes","doi":"10.1162/imag_a_00283","DOIUrl":"10.1162/imag_a_00283","url":null,"abstract":"<p><p>Magnetoencephalography (MEG) measures brain function via assessment of magnetic fields generated by neural currents. Conventional MEG uses superconducting sensors, which place significant limitations on performance, practicality, and deployment; however, the field has been revolutionised in recent years by the introduction of optically-pumped magnetometers (OPMs). OPMs enable measurement of the MEG signal without cryogenics, and consequently the conception of \"OPM-MEG\" systems which ostensibly allow increased sensitivity and resolution, lifespan compliance, free subject movement, and lower cost. However, OPM-MEG is in its infancy with existing limitations on both sensor and system design. Here, we report a new OPM-MEG design with miniaturised and integrated electronic control, a high level of portability, and improved sensor dynamic range. We show that this system produces equivalent measures compared with an established OPM-MEG instrument; specifically, when measuring task-induced beta-band, gamma-band, and evoked neuro-electrical responses, source localisations from the two systems were comparable and temporal correlation of measured brain responses was >0.7 at the individual level and >0.9 for groups. Using an electromagnetic phantom, we demonstrate improved dynamic range by running the system in background fields up to 8 nT. We show that the system is effective in gathering data during free movement (including a sitting-to-standing paradigm) and that it is compatible with simultaneous electroencephalography (EEG). Finally, we demonstrate portability by moving the system between two laboratories. Overall, our new system is shown to be a significant step forward for OPM-MEG and offers an attractive platform for next generation functional medical imaging.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"2 ","pages":"1-22"},"PeriodicalIF":0.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11533384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142585165","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":"Free-water: A promising structural biomarker for cognitive decline in aging and mild cognitive impairment.","authors":"Aditi Sathe, Yisu Yang, Kurt G Schilling, Niranjana Shashikumar, Elizabeth Moore, Logan Dumitrescu, Kimberly R Pechman, Bennett A Landman, Katherine A Gifford, Timothy J Hohman, Angela L Jefferson, Derek B Archer","doi":"10.1162/imag_a_00293","DOIUrl":"https://doi.org/10.1162/imag_a_00293","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Diffusion MRI derived free-water (FW) metrics show promise in predicting cognitive impairment and decline in aging and Alzheimer's disease (AD). FW is sensitive to subtle changes in brain microstructure, so it is possible these measures may be more sensitive than traditional structural neuroimaging biomarkers. In this study, we examined the associations among FW metrics (measured in the hippocampus and two AD signature meta-ROIs) with cognitive performance, and compared FW findings to those from more traditional neuroimaging biomarkers of AD. We leveraged data from a longitudinal cohort (n&lt;sub&gt;participants&lt;/sub&gt; = 296, n&lt;sub&gt;observations&lt;/sub&gt; = 870, age at baseline: 73 ± 7 years, 40% mild cognitive impairment [MCI]) of older adults who underwent serial neuropsychological assessment (episodic memory, information processing speed, executive function, language, and visuospatial skills) and brain MRI over a maximum of four time points, including baseline (n = 284), 18-month (n = 246), 3-year (n = 215), and 5-year (n = 125) visits. The mean follow-up period was 2.8 ± 1.3 years. Structural MRI was used to quantify hippocampal volume, in addition to Schwarz and McEvoy AD Signatures. FW and FW-corrected fractional anisotropy (FAFWcorr) were quantified in the hippocampus (hippocampal FW) and the AD signature areas (Schwarz&lt;sub&gt;FW&lt;/sub&gt;, McEvoy&lt;sub&gt;FW&lt;/sub&gt;) from diffusion-weighted (dMRI) images using bi-tensor modeling (FW elimination and mapping method). Linear regression assessed the association of each biomarker with baseline cognitive performance. Additionally, linear mixed-effects regression assessed the association between baseline biomarker values and longitudinal cognitive performance. A subsequent competitive model analysis was conducted on both baseline and longitudinal data to determine how much additional variance in cognitive performance was explained by each biomarker compared to the covariate only model, which included age, sex, race/ethnicity, apolipoprotein-ε4 status, cognitive status, and modified Framingham Stroke Risk Profile scores. All analyses were corrected for multiple comparisons using an FDR procedure. Cross-sectional results indicate that hippocampal volume, hippocampal FW, Schwarz and McEvoy AD Signatures, and the Schwarz&lt;sub&gt;FW&lt;/sub&gt; and McEvoy&lt;sub&gt;FW&lt;/sub&gt; metrics are all significantly associated with memory performance. Baseline competitive model analyses showed that the McEvoy AD Signature and Schwarz&lt;sub&gt;FW&lt;/sub&gt; explain the most unique variance beyond covariates for memory (ΔR&lt;sub&gt;adj&lt;/sub&gt; &lt;sup&gt;2&lt;/sup&gt; = 3.47 ± 1.65%) and executive function (ΔR&lt;sub&gt;adj&lt;/sub&gt; &lt;sup&gt;2&lt;/sup&gt; = 2.43 ± 1.63%), respectively. Longitudinal models revealed that hippocampal FW explained substantial unique variance for memory performance (ΔR&lt;sub&gt;adj&lt;/sub&gt; &lt;sup&gt;2&lt;/sup&gt; = 8.13 ± 1.25%), and outperformed all other biomarkers examined in predicting memory decline (p&lt;sub&gt;FDR&lt;/sub&gt; = 1.95 x 10&lt;sup&gt;-11&lt;/sup&gt;). This study shows that hippocampal FW i","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"2 ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607737","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 diffusion tensor imaging white matter atlas of the domestic canine brain.","authors":"Fiona M Inglis, Paul A Taylor, Erica F Andrews, Raluca Pascalau, Henning U Voss, Daniel R Glen, Philippa J Johnson","doi":"10.1162/imag_a_00276","DOIUrl":"https://doi.org/10.1162/imag_a_00276","url":null,"abstract":"<p><p>There is increasing reliance on magnetic resonance imaging (MRI) techniques in both research and clinical settings. However, few standardized methods exist to permit comparative studies of brain pathology and function. To help facilitate these studies, we have created a detailed, MRI-based white matter atlas of the canine brain using diffusion tensor imaging. This technique, which relies on the movement properties of water, permits the creation of a three-dimensional diffusivity map of white matter brain regions that can be used to predict major axonal tracts. To generate an atlas of white matter tracts, thirty neurologically and clinically normal dogs underwent MRI imaging under anesthesia. High-resolution, three-dimensional T1-weighted sequences were collected and averaged to create a population average template. Diffusion-weighted imaging sequences were collected and used to generate diffusivity maps, which were then registered to the T1-weighted template. Using these diffusivity maps, individual white matter tracts-including association, projection, commissural, brainstem, olfactory, and cerebellar tracts-were identified with reference to previous canine brain atlas sources. To enable the use of this atlas, we created downloadable overlay files for each white matter tract identified using manual segmentation software. In addition, using diffusion tensor imaging tractography, we created tract files to delineate major projection pathways. This comprehensive white matter atlas serves as a standard reference to aid in the interpretation of quantitative changes in brain structure and function in clinical and research settings.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"2 ","pages":"1-21"},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11409835/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302497","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":"Evaluating the effect of denoising submillimeter auditory fMRI data with NORDIC.","authors":"Lonike K Faes, Agustin Lage-Castellanos, Giancarlo Valente, Zidan Yu, Martijn A Cloos, Luca Vizioli, Steen Moeller, Essa Yacoub, Federico De Martino","doi":"10.1162/imag_a_00270","DOIUrl":"10.1162/imag_a_00270","url":null,"abstract":"<p><p>Functional magnetic resonance imaging (fMRI) has emerged as an essential tool for exploring human brain function. Submillimeter fMRI, in particular, has emerged as a tool to study mesoscopic computations. The inherently low signal-to-noise ratio (SNR) at submillimeter resolutions warrants the use of denoising approaches tailored at reducing thermal noise-the dominant contributing noise component in high-resolution fMRI. NOise Reduction with DIstribution Corrected Principal Component Analysis (NORDIC PCA) is one of such approaches, and has been benchmarked against other approaches in several applications. Here, we investigate the effects that two versions of NORDIC denoising have on auditory submillimeter data. While investigating auditory functional responses poses unique challenges, we anticipated NORDIC to have a positive impact on the data on the basis of previous applications. Our results show that NORDIC denoising improves the detection sensitivity and the reliability of estimates in submillimeter auditory fMRI data. These effects can be explained by the reduction of the noise-induced signal variability. However, we did observe a reduction in the average response amplitude (percent signal change) within regions of interest, which may suggest that a portion of the signal of interest, which could not be distinguished from general i.i.d. noise, was also removed. We conclude that, while evaluating the effects of the signal reduction induced by NORDIC may be necessary for each application, using NORDIC in high-resolution auditory fMRI studies may be advantageous because of the large reduction in variability of the estimated responses.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"2 ","pages":"1-18"},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11726685/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985621","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 Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth, and interactive QC with afni_proc.py and more.","authors":"Paul A Taylor, Daniel R Glen, Gang Chen, Robert W Cox, Taylor Hanayik, Chris Rorden, Dylan M Nielson, Justin K Rajendra, Richard C Reynolds","doi":"10.1162/imag_a_00246","DOIUrl":"10.1162/imag_a_00246","url":null,"abstract":"<p><p>Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically underdiscussed aspect of reproducibility. This includes checking datasets at their very earliest stages (acquisition and conversion) through their processing steps (e.g., alignment and motion correction) to regression modeling (correct stimuli, no collinearity, valid fits, enough degrees of freedom, etc.) for each subject. There are a wide variety of features to verify throughout any single-subject processing pipeline, both quantitatively and qualitatively. We present several FMRI preprocessing QC features available in the AFNI toolbox, many of which are automatically generated by the pipeline-creation tool, <i>afni_proc.py</i>. These items include a modular HTML document that covers full single-subject processing from the raw data through statistical modeling, several review scripts in the results directory of processed data, and command line tools for identifying subjects with one or more quantitative properties across a group (such as triaging warnings, making exclusion criteria, or creating informational tables). The HTML itself contains several buttons that efficiently facilitate interactive investigations into the data, when deeper checks are needed beyond the systematic images. The pages are linkable, so that users can evaluate individual items across a group, for increased sensitivity to differences (e.g., in alignment or regression modeling images). Finally, the QC document contains rating buttons for each \"QC block,\" as well as comment fields for each, to facilitate both saving and sharing the evaluations. This increases the specificity of QC, as well as its shareability, as these files can be shared with others and potentially uploaded into repositories, promoting transparency and open science. We describe the features and applications of these QC tools for FMRI.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"2 ","pages":"1-39"},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11382598/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302498","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}