Imaging neuroscience (Cambridge, Mass.)最新文献

{"title":"Disarming emotional memories using targeted memory reactivation during rapid eye movement sleep.","authors":"Viviana Greco, Tamas A Foldes, Mahmoud E A Abdellahi, Marta Wawrzuta, Neil A Harrison, Kevin Murphy, Penelope A Lewis","doi":"10.1162/IMAG.a.924","DOIUrl":"https://doi.org/10.1162/IMAG.a.924","url":null,"abstract":"<p><p>Rapid Eye Movement Sleep (REM) is thought to process emotions via memory reactivation. Such REM reactivation can be triggered by presenting a tone associated with the target memory. This reduces subjective arousal ratings for negative stimuli. Here, we measure arousal objectively in brain and autonomic system. Participants rated negative image-sound pairs, half of which were then re-presented during subsequent REM. All images were re-rated in a Magnetic Resonance Imaging (MRI) scanner with pulse oximetry 48 h after encoding. Reactivation in REM reduced responses in the brain's Salience Network (SN), including Anterior Insula and dorsal Anterior Cingulate Cortex (dACC), and associated emotion-processing regions: orbitofrontal cortex, subgenual cingulate, and left amygdala. Memory reactivation in REM reduced heart rate deceleration (HRD). Subjective arousal ratings were reduced for more upsetting images and increased for less upsetting images. Our findings have implications for the use of memory reactivation to treat depression and anxiety disorders.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12516159/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145294672","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":"Automated segmentation of multiple sclerosis lesions, paramagnetic rims, and central vein sign on MRI provides reliable diagnostic biomarkers.","authors":"Fengling Hu, Zheng Ren, Luyun Chen, Alessandra M Valcarcel, Jordan Dworkin, Brian Renner, Lynn Daboul, Carly M O'Donnell, Elizabeth D Verter, Abigail R Manning, Kelly A Clark, Eunchan Bae, Christina Chen, Carolyn Lou, Theodore D Satterthwaite, Haochang Shou, Michel Bilello, Kunio Nakamura, Amit Bar-Or, Peter A Calabresi, Leorah Freeman, Roland G Henry, Erin E Longbrake, Jiwon Oh, Matthew K Schindler, Martina Absinta, Andrew J Solomon, Nancy L Sicotte, Daniel Ontaneda, Daniel S Reich, Pascal Sati, Russell T Shinohara","doi":"10.1162/IMAG.a.932","DOIUrl":"https://doi.org/10.1162/IMAG.a.932","url":null,"abstract":"<p><p>Multiple sclerosis (MS) is characterized by central nervous system lesions detectable via MRI. Existing diagnostic criteria incorporate presence of white matter lesions, but specificity can be improved using MS-specific imaging biomarkers, including paramagnetic rim lesions (PRLs) and central vein sign (CVS). However, manual segmentation of lesions, PRLs, and CVS is time-consuming and subjective. We propose a fully-automated joint segmentation method called Automated Lesion, PRL, and CVS Analysis (ALPaCA). We trained ALPaCA using subject-level cross-validation on 47 adults with MS and 50 adults with radiological MS mimics. ALPaCA uses a voxel-wise lesion segmentation method to propose a large set of lesion candidates. Lesion candidates are input into a multi-contrast, multi-label 3D convolutional neural network as 3D patches to produce lesion, PRL, and CVS predictions. When multiple lesions exist within a patch, an attention mechanism identifies which lesion candidate to classify. At the lesion level, ALPaCA achieves cross-validation areas under the receiver operating characteristic curve (AUROCs) of 0.95, 0.91, and 0.87 for lesion, PRL, and CVS classification, outperforming previous methods (all p < 0.001). Correlations between subject-level ALPaCA lesion and PRL scores with manual counts are higher than those of previous methods (p < 0.001; p = 0.03). Subject-level ALPaCA PRL and CVS scores are highly associated with MS in logistic regressions, when controlling for age and sex (p < 0.001). ALPaCA allows for fully-automated simultaneous segmentation of MS lesions, PRLs, and CVS using clinically-feasible scans. These segmentations outperform existing methods at the lesion and subject level.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12516162/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145294643","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 connectivity correlates of breathing and cardiac patterns in epilepsy: A study including SUDEP cases.","authors":"Michalis Kassinopoulos, Nicolo Rolandi, Laren Alphan, Ronald M Harper, Joana Oliveira, Catherine Scott, Lajos R Kozák, Maxime Guye, Louis Lemieux, Beate Diehl","doi":"10.1162/IMAG.a.918","DOIUrl":"10.1162/IMAG.a.918","url":null,"abstract":"<p><p>Sudden unexpected death in epilepsy (SUDEP) is the leading cause of premature mortality among people with epilepsy. Evidence from witnessed and monitored SUDEP cases indicates seizure-induced cardiovascular and respiratory failures; yet, the underlying mechanisms remain obscure. SUDEP occurs often during the night and early morning hours, suggesting that sleep or circadian rhythm-induced changes in physiology contribute to the fatal event. Resting-state functional MRI (fMRI) studies have found altered functional connectivity between brain structures involved in cardiorespiratory regulation in later SUDEP cases and in individuals at high risk of SUDEP. However, those connectivity findings have not been related to changes in cardiovascular or respiratory patterns. Here, we compared fMRI patterns of brain connectivity associated with regular and irregular cardiorespiratory rhythms in SUDEP cases with those of living epilepsy patients of varying SUDEP risk and healthy controls. We analysed resting-state fMRI data from 98 patients with epilepsy (9 who subsequently succumbed to SUDEP, 43 categorized as low SUDEP risk (no tonic-clonic seizures (TCS) in the year preceding the fMRI scan), and 46 as high SUDEP risk (>3 TCS in the year preceding the scan)), and 25 healthy controls. The global signal amplitude (GSA), defined as the moving standard deviation of the fMRI global signal, was used to identify periods with regular (\"low state\") and irregular (\"high state\") cardiorespiratory rhythms. Correlation maps were derived from seeds in 12 regions with a key role in autonomic or respiratory regulation for the low and high states. Following principal component analysis, component weights were compared between the groups. We found widespread alterations in connectivity of precuneus/posterior cingulate cortex in epilepsy compared with controls in the low state (regular cardiorespiratory activity). In the low state, and to a lesser degree in the high state, reduced anterior insula connectivity (mainly with anterior and posterior cingulate cortex) in epilepsy appeared, relative to healthy controls. For SUDEP cases, the insula connectivity differences were inversely related to the interval between the fMRI scan and death. The findings suggest that anterior insula connectivity measures may provide a biomarker of SUDEP risk. The neural correlates of autonomic brain structural activity associated with different cardiorespiratory rhythms may shed light on the mechanisms underlying the fatal event in SUDEP.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12511790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145282085","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 non-invasive approach to awake mouse fMRI compatible with multi-modal techniques.","authors":"Sam Laxer, Amr Eed, Miranda Bellyou, Peter Zeman, Kyle M Gilbert, Mohammad Naderi, Ravi S Menon","doi":"10.1162/IMAG.a.920","DOIUrl":"10.1162/IMAG.a.920","url":null,"abstract":"<p><p>Mouse functional magnetic resonance imaging (fMRI) studies contribute significantly to basic fundamental and translational neuroscience research. Performing fMRI in awake mice could facilitate complex tasks in the magnet and improve translational validity by avoiding anesthesia-related neural and neurovascular changes. Existing surgical approaches provide excellent motion control but are not desirable for all experiments aiming to scan awake mice. These include studies with transgenic mouse lines that are vulnerable to anesthesia or mice in longitudinal studies involving cognition. To address these needs, we propose a non-invasive restraint to scan mice in the awake state. The restraint was designed to be compatible with brain stimulation and recording approaches often combined with fMRI. It was evaluated on the basis of motion, fMRI data quality, and animal stress levels, and compared to a conventional headpost restraint. We found the proposed approach was effective at restraining mice across a broad range of weights without the need for any anesthesia for setup. The non-invasive restraint led to higher data attrition after censoring high motion volumes, but by acquiring roughly 25% more data we could obtain comparable network spatial specificity to the headpost approach. Our results demonstrate a simple open-source head restraint that can be used for awake mouse fMRI for certain cohorts, and we establish suitable acclimation and scanning protocols for use with this restraint.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12511802/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145282003","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":"Riemannian diffusion kernel-smoothed continuous structural connectivity on cortical surface.","authors":"Lu Wang, Didong Li, Zhengwu Zhang","doi":"10.1162/IMAG.a.912","DOIUrl":"10.1162/IMAG.a.912","url":null,"abstract":"<p><p>Atlas-free continuous structural connectivity has garnered increasing attention due to the limitations of atlas-based approaches, including the arbitrary selection of brain atlases and potential information loss. Typically, continuous structural connectivity is represented by a probability density function, with kernel density estimation as a common estimation method. However, constructing an appropriate kernel function on the cortical surface poses significant challenges. Current methods often inflate the cortical surface into a sphere and apply the spherical heat kernel, introducing distortions to density estimation. In this study, we propose a novel approach using the Riemannian diffusion kernel derived from the Laplace-Beltrami operator on the cortical surface to smooth streamline endpoints into a continuous density. Our method inherently accounts for the complex geometry of the cortical surface and exhibits computational efficiency, even with dense tractography datasets. Additionally, we investigate the number of streamlines or fiber tracts required to achieve a reliable continuous representation of structural connectivity. Through simulations and analyses of data from the Adolescent Brain Cognitive Development (ABCD) Study, we demonstrate the potential of the Riemannian diffusion kernel in enhancing the estimation and analysis of continuous structural connectivity.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12511801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145282054","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":"Intermediate endophenotypes and epigenetic mechanisms of psychiatric disorders.","authors":"Thomas Leon Kremer, David Antonio Grimaldi, Henri Fleischer, Emanuel Schwarz, Andreas Meyer-Lindenberg, Urs Braun, Heike Tost","doi":"10.1162/IMAG.a.914","DOIUrl":"10.1162/IMAG.a.914","url":null,"abstract":"<p><p>Human neuroimaging and epigenetic research stand ready to advance biological psychiatry. Drawing on the concept of <i>imaging genetics</i>, the analysis of associations between human neuroimaging and epigenetic data provides an attractive framework for investigating multi-scale biological mechanisms linking the environment to psychiatric risk and protection. A basic assumption is that environmental stress causes epigenetic changes that lead to alterations in cellular ensembles, which in turn can be measured as changes in brain structure and function using human neuroimaging. However, unlike genotypes, epigenetics varies within individuals, between cell types, or over time, and thus caution is required when inferring the immediacy and directionality of observed associations. In this review, we discuss recent advances and challenges to this methodological framework. Future studies should address causal hypotheses and explain within-individual variance in psychopathology through sophisticated contextualization of observed associations and rigorous analyses of longitudinal data. These advances will be critical for developing a comprehensive understanding of the biological contributions to mental health risk and protection.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12508773/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145282031","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":"Repeatability and reproducibility of rapid T₁ mapping of brain tissues at 64 mT: A multicentre study.","authors":"Beatrice Lena, Francesco Padormo, Rui Pedro A G Teixeira, Carly Bennallick, James Gholam, Ruben van den Broek, Samson Lecurieux Lafayette, Irene Vavasour, Mara Cercignani, Derek K Jones, Shannon Kolind, Jo Hajnal, Niall Bourke, Yiming Dong, William J Hollander, Todor Karaulanov, Sean C L Deoni, Steven C R Williams, Pia C Sundgren, Andrew G Webb, Emil Ljungberg","doi":"10.1162/IMAG.a.916","DOIUrl":"10.1162/IMAG.a.916","url":null,"abstract":"<p><p>Very-low-field MRI (<100 mT) holds promise for Point-of-Care brain imaging applications, including stroke and multiple sclerosis, with T₁ mapping emerging as a key biomarker for brain development and pathology. However, current low-field T₁ mapping protocols suffer from long acquisition times and limited multi-site repeatability. This study aimed to improve T₁ mapping at 64 mT using a clinically feasible 10-minute protocol and assess repeatability and reproducibility across sites. We present an analysis of the repeatability and reproducibility of rapid T₁ measurements in a commercially available phantom and in 60 volunteers, scanned with a portable 64 mT MRI systems at six sites. T₁ mapping was performed using an undersampled 3D inversion-recovery turbo spin-echo sequence with a 10.8-minute scan time, and reconstructed with a locally low-rank approach. Our results in phantom demonstrated high reproducibility in T₁ measurements (below 3% differences from the average), with non-significant differences between sites. Longitudinal measurements demonstrated high repeatability over time both in vivo and in phantom settings in one site, with minimal variability (average Coefficient of Variation of 0.6%). Average in vivo T₁ values for white matter and cortex were 290 ± 6 ms and 332 ± 8 ms, respectively and the values demonstrated high reproducibility, with differences of less than 4% from the average across sites. Our results demonstrate the feasibility of multi-site in vivo T₁ mapping at 64 mT, providing normative T₁ values at this field strength and supporting its use as a quantitative biomarker in clinical applications.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12508772/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145282021","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":"Differentiating BOLD and non-BOLD signals in fMRI time series using cross-cortical depth delay patterns.","authors":"Jingyuan E Chen, Anna I Blazejewska, Jiawen Fan, Nina E Fultz, Bruce R Rosen, Laura D Lewis, Jonathan R Polimeni","doi":"10.1162/IMAG.a.910","DOIUrl":"10.1162/IMAG.a.910","url":null,"abstract":"<p><p>Over the past two decades, rapid advancements in magnetic resonance technology have significantly enhanced the imaging resolution of functional Magnetic Resonance Imaging (fMRI), far surpassing its initial capabilities. Beyond mapping brain functional architecture at unprecedented scales, high-spatial-resolution acquisitions have also inspired and enabled several novel analytical strategies that can potentially improve the sensitivity and neuronal specificity of fMRI. With small voxels, one can sample from different levels of the vascular hierarchy within the cerebral cortex and resolve the temporal progression of hemodynamic changes from parenchymal to pial vessels. We propose that this characteristic pattern of temporal progression across cortical depths can aid in distinguishing neurogenic blood-oxygenation-level-dependent (BOLD) signals from typical nuisance factors arising from non-BOLD origins, such as head motion and pulsatility. In this study, we examine the feasibility of applying cross-cortical depth temporal lag (CortiLag) patterns to automatically categorize BOLD and non-BOLD signal components in modern-resolution BOLD-fMRI data. We construct an independent component analysis (ICA)-based framework for fMRI de-noising, analogous to previously proposed multi-echo independent component analysis (ME-ICA), except that here we explore the across-depth instead of across-echo dependence to distinguish BOLD and non-BOLD components. The efficacy of this framework is demonstrated using visual task data at three graded spatiotemporal resolutions (voxel sizes = 1.1, 1.5, and 2.0 mm isotropic at temporal intervals = 1700, 1120, and 928 ms). The proposed CortiLag-ICA framework leverages prior knowledge of the spatiotemporal properties of BOLD-fMRI and serves as an alternative to ME-ICA for cleaning moderate- and high-spatial-resolution fMRI data when multi-echo acquisitions are not available.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12501242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253821","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":"Mapping hand function with simultaneous brain-spinal cord functional MRI.","authors":"Valeria Oliva, Sandrine Bédard, Merve Kaptan, Dario Pfyffer, Brett Chy, Susanna Aufrichtig, Nazrawit Berhe, Akshay S Chaudhari, Suzanne Tharin, Serena S Hu, John Ratliff, Zachary A Smith, Andrew C Smith, Gary H Glover, Sean Mackey, Christine S W Law, Kenneth A Weber Ii","doi":"10.1162/IMAG.a.159","DOIUrl":"10.1162/IMAG.a.159","url":null,"abstract":"<p><p>Hand motor control depends on intricate brain-spinal cord interactions that regulate muscle activity. Hand function can be disrupted by injury to the brain, spinal cord, and peripheral nerves leading to weakness and impaired coordination. Functional MRI (fMRI) can map motor-related neural activity and potentially characterize the mechanisms underlying hand weakness and diminished coordination. Although brain motor control has been extensively studied, spinal cord mechanisms remain less explored. Here we use simultaneous brain-spinal cord fMRI to map neural activity related to hand strength and dexterity across the central nervous system using force matching and finger tapping tasks. We performed simultaneous brain-spinal cord fMRI in 28 right-handed healthy volunteers (age: 40.0 ± 13.8 years, 14 females, 14 males) using a 3T GE scanner. Participants performed a force-matching task at 10%, 20%, and 30% of maximum voluntary contraction. For the finger tapping task, participants completed button presses for three task levels: single-digit response with the second digit only, single-digit response with all digits in a sequential order, and single-digit response with all digits in a random order. Brain and spinal cord images were processed separately and assessed both activations and deactivations. Region of interest (ROI) analyses were also conducted to explore localized changes in activation across the task levels. Both tasks elicited activation in motor and sensory regions of the brain and spinal cord, with graded responses in the left primary motor (M1), left primary sensory (S1) cortex, and right spinal cord gray matter across task levels. Deactivation of the right M1 and S1 was also present for both tasks. Deactivation of the left spinal cord gray matter that scaled with task level was seen in the force matching task. The ROI analysis findings complemented the group level activity maps. Our study provides a detailed map of brain-spinal cord interactions in hand function, revealing graded neural activation and deactivation patterns across motor and sensory regions. Right M1 deactivation is likely evidence of interhemispheric inhibition that restricts extraneous motor output during unilateral tasks. For force matching, the deactivation of the left ventral and dorsal horns of the spinal cord provides the first evidence that the inhibition of motor areas during a unilateral motor task extends to the spinal cord. Whether this inhibition results from direct descending modulation from the brain or interneuronal inhibition in the spinal cord remains to be interrogated. These findings expand our understanding of central motor control mechanisms and could inform rehabilitation strategies for individuals with motor impairments. This approach may offer a foundation for studying motor dysfunction in conditions such as stroke, spinal cord injury, and neurodegenerative diseases.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12501243/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253848","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":"Correction to: Predictive modeling of significance thresholding in activation likelihood estimation meta-analysis.","authors":"Lennart Frahm, Kaustubh R Patil, Theodore D Satterthwaite, Peter T Fox, Simon B Eickhoff, Robert Langner","doi":"10.1162/IMAG.x.934","DOIUrl":"https://doi.org/10.1162/IMAG.x.934","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1162/imag_a_00423.].</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12501236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253873","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}