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        <title>Frontiers in Neuroimaging | Brain Imaging Methods section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/neuroimaging/sections/brain-imaging-methods</link>
        <description>RSS Feed for Brain Imaging Methods section in the Frontiers in Neuroimaging journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-12T17:09:05.925+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2026.1753534</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2026.1753534</link>
        <title><![CDATA[Anatomically constrained volumetric smoothing enhances fMRI reliability while avoiding smoothing artifacts]]></title>
        <pubdate>2026-05-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>David G. Ellis</author><author>Michele R. Aizenberg</author>
        <description><![CDATA[IntroductionSmoothing fMRI data prior to analysis is a fundamental and widely used technique to increase sensitivity. Unconstrained smoothing can also reduce the spatial specificity of the analysis by introducing artifacts in the data. This study tested the effects of smoothing on the reliability and accuracy of both task fMRI and resting state data. The effects of unconstrained smoothing were compared to those of an anatomically constrained smoothing method, which prevents smoothing across the white and gray matter surfaces of the cortex.MethodsUnconstrained Gaussian smoothing and anatomically constrained smoothing were applied to simulated data, a sensory task fMRI dataset, a precision fMRI motor task mapping dataset, and a resting state fMRI dataset. Smoothing-related artifacts were tested for and compared between the smoothing methods, and the effects of the smoothing methods on the reliability and accuracy were measured.ResultsIn the experiments with simulated data, unconstrained Gaussian smoothing demonstrated decreased accuracy and increased white matter activation compared to constrained smoothing. In the sensory task activation analysis, both Gaussian and constrained smoothing increased the reliability of the sensory task fMRI activations, but Gaussian smoothing increased the percentage of active voxels in the white matter relative to constrained smoothing (p < 0.001). Relative to constrained smoothing, Gaussian smoothing with FWHM > 3 mm also decreased the accuracy of motor mapping results from individual sessions to the precision maps (p < 0.001). With cluster significance thresholding, mean false positive voxel percentages remained below 5% for both methods across the tested kernel widths. Both Gaussian and constrained smoothing demonstrated a biasing effect on the resting state connectivity of nearby regions and on the graph theory metrics of the functional connectomes.ConclusionThis study showed that unconstrained Gaussian smoothing spreads activation across cortical boundaries, increases white matter activation, and biases graph theory connectivity metrics. Anatomically constrained smoothing reduced some of these smoothing artifacts while still increasing reliability and may be a reasonable alternative to unconstrained Gaussian smoothing.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2026.1776934</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2026.1776934</link>
        <title><![CDATA[Gadolinium-based contrast agent-free (GBCA-free) versus gadolinium-based contrast agent-enhanced (GBCA-enhanced) magnetic resonance imaging as a screening tool for intracranial tumors]]></title>
        <pubdate>2026-04-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Haider Faeq Hadhratee Al-Rubaiee</author><author>Samr Adnan Abdaljabbar Al-Salm</author><author>Weronika Jensen</author><author>Agnieszka Monika Delekta</author><author>Yousef Yavarian</author><author>Boris Modrau</author>
        <description><![CDATA[BackgroundGBCA-enhanced MRI is considered the gold standard for diagnosing intracranial tumors. However, concerns regarding gadolinium-based contrast agents (GBCAs) and workflow limitations have led to increasing interest in GBCA-free MRI techniques.ObjectiveTo compare the diagnostic performance of GBCA-free MRI with GBCA-enhanced MRI for detecting intracranial tumors within a neurological diagnostic pathway.MethodsIn this retrospective cohort study, 191 patients with 195 tumors (124 intra-axial and 71 extra-axial) and 410 controls were included from a regional brain tumor diagnostic pathway between 2013 and 2022. Three senior neuroradiologists independently assessed anonymized MRI scans. Each scan was first evaluated using GBCA-free sequences and subsequently reassessed using the full GBCA-enhanced protocol. Sensitivity, specificity, and inter-method agreement were calculated.ResultsGBCA-free MRI demonstrated a sensitivity of 79.6% and a specificity of 96.1%, whereas GBCA-enhanced MRI showed a sensitivity of 73.3% and a specificity of 92.9% (p = 0.15). Agreement between the two imaging approaches was substantial (κ = 0.699). Subgroup analyses showed comparable performance for intra-axial tumors, while both imaging approaches demonstrated modest sensitivity for extra-axial tumors.ConclusionGBCA-free MRI demonstrated diagnostic performance comparable to that of GBCA-enhanced MRI for detecting intracranial tumors. These findings suggest that GBCA-free MRI may serve as a reliable first-line screening tool within the neurological diagnostic pathway, reserving GBCA administration for cases requiring further lesion characterization.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2026.1736950</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2026.1736950</link>
        <title><![CDATA[Comparison of commercial 1Tx32Rx vs. 8Tx32Rx head coils for routine 7T neuroimaging]]></title>
        <pubdate>2026-03-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Carina Graf</author><author>Belinda Ding</author><author>Catarina Rua</author><author>Krzysztof Klodowski</author><author>Christopher T. Rodgers</author>
        <description><![CDATA[IntroductionHuman 7T MRI systems are manufactured by three vendors (Siemens, Philips, and GE) who all provide equivalent head coils from the same 3rd party manufacturer. Furthermore, many 7T MRI sites have two head coils available for neuroimaging: a 1Tx32Rx head coil for conventional single-channel transmit imaging and an 8Tx32Rx head coil for parallel-transmit (pTx) imaging.MethodsWe compared the performance of these coils in six healthy volunteers. All scans were done on a 7T MRI (MAGNETOM Terra, Siemens, Germany). We tested seven sequences in wide use at our centre: B0 and B1+ mapping, anatomical T1-weighted MP2RAGE, R2*-mapping, single-voxel spectroscopy (MRS), echo-planar imaging time series, and diffusion tensor imaging (DTI). Sequences were run unmodified and without any pTx pulses.ResultsData quality is comparable for both coils. The 8Tx32Rx coil had improved B1+ in inferior brain regions, enhanced spinal cord visibility in the cervical spine on anatomical MP2RAGE, higher SNR in MRS of the brainstem, and more defined fitted white matter tracts in the DTI images. All sequences showed acceptable data quality with the 8Tx32Rx coil.ConclusionIt is reasonable to substitute the 8Tx32Rx coil for the 1Tx32Rx coil for standard neuroimaging protocols. This will enable advanced parallel transmit sequences to be added to protocols with minimal disruption.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2026.1670604</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2026.1670604</link>
        <title><![CDATA[Diffusion MRI sampling schemes bias diffusion metrics and tractography]]></title>
        <pubdate>2026-02-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ivanei Bramati</author><author>Diego Szczupak</author><author>Marina Carneiro Monteiro</author><author>Fernanda Meireles</author><author>Daniel Menezes Guimarães</author><author>Ryan J. Dean</author><author>Lynn K. Paul</author><author>Fernanda Tovar-Moll</author>
        <description><![CDATA[IntroductionDiffusion MRI is increasingly used to study white-matter architecture, but tractography and diffusion metrics can be biased by different sampling schemes. We assessed systematic differences across four common protocols—single-shell high-angular resolution diffusion imaging (HARDI), Siemens clinical multi-shell (Sms), diffusion spectrum imaging (DSI), and Human Connectome Project multi-shell (HCPms)—in healthy adults and individuals with corpus callosum dysgenesis (CCD).MethodsAll data were acquired on a single 3 T scanner and processed uniformly to extract fractional anisotropy (FA), mean diffusivity (MD), effective contrast-to-noise ratio (eCNR), and orientation dispersion within the corpus callosum (CC), corona radiata (CR), and centrum semiovale (CSO). In controls, we measured tract volumes for CC, bilateral CR, anterior commissure (AC) and posterior commissure (PC), and streamline counts for AC and PC; in CCD, we quantified volumes of the Probst and sigmoid bundles.ResultsAcross participants, FA and MD showed moderate cross-scheme correlations for most ROIs, but matched means were rare (only Sms–HARDI in CC). eCNR and dispersion exhibited few cross-scheme correlations; however, means were similar for eCNR between Sms and HCPms and for dispersion among HARDI, DSI, and HCPms. Tract-based volumes correlated across Sms, DSI, and HCPms for CC in controls and for the right sigmoid and both Probst bundles in CCD. DSI and HCPms yielded similar volumes in all ROIs (controls and CCD). In controls, Sms volumes agreed with DSI/HCPms in CR, but were lower in CC and in all CCD ROIs. HARDI produced higher volumes in CC and bilateral CR in controls and in all CCD ROIs. For AC and PC in controls, tract-based means (volumes, streamlines, streamlines/volume) were consistent across schemes; nonetheless, correlations were limited—streamlines and streamlines/volume correlated for Sms, DSI, and HARDI in AC, and for DSI and HCPms in PC.DiscussionThese findings demonstrate systematic differences in voxel-wise metrics and tractography outcomes from four diffusion-sampling schemes. In addition to qualitatively informing attempts to consolidate or contrast data across schemes, future work could explore regression-based harmonization—and other methods—to reduce residual bias and enable pooled analyses across diverse protocols.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2026.1746464</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2026.1746464</link>
        <title><![CDATA[A systematic study on the integration of MRI connectivity metrics for Alzheimer's diagnosis, staging, and cognitive decline prediction]]></title>
        <pubdate>2026-02-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shahzad Ali</author><author>Wendy Kreshpa</author><author>Nicola Rosso</author><author>Michele Piana</author><author>Luca Roccatagliata</author><author>Alessio Cirone</author><author>Lorenzini Luigi</author><author>Cristina Campi</author><author>Matteo Pardini</author><author>Sara Garbarino</author>
        <description><![CDATA[Alzheimer's disease (AD) is a degenerative neurological disorder marked by cognitive decline and functional disability. Despite the extensive use of magnetic resonance imaging (MRI) in machine learning (ML)-based AD studies, the relative and combined contributions of MRI-derived morphometric (MO), microstructural (MS), and graph-theoretical (GT) features are still not well explored in a unified, comparative framework. It remains unclear whether adding multimodal MRI-derived features consistently improves the predictive performance of ML-based approaches for AD diagnosis and cognitive decline. Addressing this gap, this study systematically analyzed the individual (MO, MS, GT) and combined (MO+MS, MO+GT, MS+GT, MO+MS+GT) utility of MRI-based feature sets. We developed an ensemble-based ML framework with a nested cross-validation module for two key tasks: (i) Alzheimer's disease cognitive stage classification (DSC) and (ii) longitudinal cognitive decline prediction (LCDP) in terms of mini-mental state examination (MMSE) score. In this study, we conducted feature ablation and statistical analysis to evaluate performance improvements resulting from the incremental addition of feature sets. The results of the study indicated that the proposed ensemble-based ML approach achieved the best predictive performance (balanced accuracy [BACC]: 0.898 ± 0.051) using a combination of MO and MS feature sets for cognitively normal (CN) vs. AD dementia (CN–ADD). In contrast, the best results for mild cognitive impairment (MCI) vs. ADD (MCI–ADD) and CN–MCI were achieved using the MO feature set alone, with BACC of 0.769 ± 0.116 and 0.652 ± 0.044, respectively. Likewise, for the LCDP task, the MO-based ensemble learner achieved an R2 of 0.212 ± 0.177. These results demonstrate that MO features capture the most robust disease-related information, while multimodal integration offers task-specific and limited benefits. In addition, these findings demonstrate the potential of integrated MRI-derived features in ML frameworks for enhancing ADD diagnosis and cognitive decline prediction and underscore the importance of feature selection based on task complexity.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2025.1718444</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2025.1718444</link>
        <title><![CDATA[Optimizing ultra-rapid compressed-sensing MPRAGE acquisitions for brain morphometry]]></title>
        <pubdate>2026-01-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lindsay C. Hanford</author><author>Tom Hilbert</author><author>Tobias Kober</author><author>Randy L. Buckner</author><author>Ross W. Mair</author>
        <description><![CDATA[PurposeCompressed-sensing (CS) methods can decrease the acquisition time for T1-weighted (T1w) structural MRI images to 1–2 min. Rapid acquisitions reduce participant burden, reduce the risk of motion artifacts, and allow for repeat scans to be acquired within a session. This study investigated the tradeoffs of sparse sampling and CS image reconstruction for brain morphometric applications.MethodsMagnetization-Prepared Rapid Gradient Echo (MPRAGE) images were acquired at 1.0 mm spatial resolution. The effects of the acceleration factor (x2 to x8) and regularization factor were examined. Subcortical volumes and regional cortical thickness estimates of brain structure were obtained for all T1w images. Within-sequence agreement was evaluated by comparing estimates obtained using the same protocol in the same imaging session. Between-sequence agreement was evaluated by comparing estimates from a fully sampled MPRAGE protocol to the novel CS-accelerated MPRAGE protocols within the same session.ResultsHigher acceleration lowered the SNR in white matter but not in gray matter. SNR could be further manipulated by the regularization parameter. Within-sequence agreement was comparable across all protocols. In fact, the spread in estimates from the 58-s CSx8 protocol was similar to those from the fully sampled protocol. Similarly, high agreement was found between estimates from the fully sampled and under-sampled protocols for all acceleration levels up to eight. Modifying the regularization factor had a quantifiable effect on image smoothness, however it had minimal impact on the agreement of morphometric estimates.ConclusionAccelerated CS imaging protocols show comparable performance to traditional longer protocols for morphometric brain estimates.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2025.1653206</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2025.1653206</link>
        <title><![CDATA[Global signal regression reduces connectivity patterns related to physiological signals and does not alter EEG-derived connectivity]]></title>
        <pubdate>2025-12-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alba Xifra-Porxas</author><author>Michalis Kassinopoulos</author><author>Prokopis Prokopiou</author><author>Marie-Hélène Boudrias</author><author>Georgios D. Mitsis</author>
        <description><![CDATA[IntroductionFunctional brain connectivity measures extracted from resting-state functional magnetic resonance imaging (fMRI) scans have generated wide interest as potential noninvasive biomarkers. In this context, performing global signal regression (GSR) as a preprocessing step remains controversial. Specifically, while it has been shown that a considerable fraction of global signal variations is associated with physiological and motion sources, GSR may also result in removing neural activity.MethodsHere, we address this question by examining the fundamental sources of resting global signal fluctuations using simultaneous electroencephalography (EEG)-fMRI data combined with cardiac and breathing recordings.ResultsOur results suggest that systemic physiological fluctuations account for a significantly larger fraction of global signal variability compared to electrophysiological fluctuations. Furthermore, we show that GSR reduces artifactual connectivity due to heart rate and breathing fluctuations, but preserves connectivity patterns associated with electrophysiological activity within the alpha and beta frequency ranges.DiscussionOverall, these results provide evidence that the neural component of resting-state fMRI-based connectivity is preserved after the global signal is regressed out.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2025.1608390</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2025.1608390</link>
        <title><![CDATA[Strategies for automatic generation of information processing pathway maps]]></title>
        <pubdate>2025-11-25T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Anirudh Lakra</author><author>Cai Wingfield</author><author>Chao Zhang</author><author>Andrew Thwaites</author>
        <description><![CDATA[Information Processing Pathway Maps (IPPMs) are a concise way to represent the evidence for the transformation of information as it travels around the brain. However, their construction currently relies on hand-drawn maps from electrophysical recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). This is both inefficient and contains an element of subjectivity. A better approach would be to automatically generate IPPMs from the data and objectively evaluate their accuracy. In this work, we propose a range of possible strategies and compare them to select the best. To this end, we (a) provide a test dataset against which automatic IPPM creation procedures can be evaluated; (b) suggest two novel evaluation metrics—causality violation and transform recall—from which these proposed procedures can be evaluated; (c) conduct a simulation study to evaluate how well ground-truth IPPMs can be recovered using the automatic procedure; and (d) propose and evaluate a selection of different IPPM creation procedures. Our results suggest that the max pooling approach gives the best results on these metrics. We conclude with a discussion of the limitations of this framework, and possible future directions.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2025.1649749</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2025.1649749</link>
        <title><![CDATA[Nonlinear kernel-based fMRI activation detection]]></title>
        <pubdate>2025-09-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chendi Han</author><author>Zhengshi Yang</author><author>Xiaowei Zhuang</author><author>Dietmar Cordes</author>
        <description><![CDATA[Kernel Canonical Correlation Analysis (KCCA) is an effective method for globally detecting brain activation with reduced computational complexity. However, the current KCCA is limited to linear kernels, and the performance of more general types of kernels remains uncertain. This study aims to expand the current KCCA method to arbitrary nonlinear kernels. Our contributions are twofold: First, we propose an inverse mapping algorithm that works for general types of nonlinear kernels. Second, we demonstrate that nonlinear kernels yield improved performance, particularly when the true neural activation deviates from the hypothesized hemodynamic response function due to the complex nature of neural responses. Our results, based on a simulated fMRI dataset and two task-based fMRI datasets, indicate that nonlinear kernels outperform linear kernels and effectively reduce activation in undesired regions.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2025.1599966</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2025.1599966</link>
        <title><![CDATA[Leveling up: along-level diffusion tensor imaging in the spinal cord of multiple sclerosis patients]]></title>
        <pubdate>2025-08-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Atlee A. Witt</author><author>Anna J. E. Combes</author><author>Grace Sweeney</author><author>Logan E. Prock</author><author>Delaney Houston</author><author>Seth Stubblefield</author><author>Colin D. McKnight</author><author>Kristin P. O’Grady</author><author>Seth A. Smith</author><author>Kurt G. Schilling</author>
        <description><![CDATA[IntroductionMultiple sclerosis (MS) is a chronic neuroinflammatory disease marked by demyelination and axonal degeneration, processes that can be probed using diffusion tensor imaging (DTI). In the brain, white matter (WM) tractography enables anatomically specific analysis of microstructural changes. However, in the spinal cord (SC), anatomical localization is inherently defined by cervical levels, offering an alternative framework for regional analysis.MethodsThis study employed an along-level approach to assess both microstructural (e.g., fractional anisotropy) and macrostructural (e.g., cross-sectional area) features of the SC in persons with relapsing-remitting MS (pwRRMS) relative to healthy controls (HCs).ResultsCompared to conventional whole-cord averaging, along-level analyses provided enhanced sensitivity to group differences. Detailed segmentation of WM tracts and gray matter (GM) subregions revealed spatially discrete alterations along the cord and within axial cross-sections. Notably, while GM atrophy was associated with clinical disability, microstructural changes did not exhibit significant correlations with disability measures.DiscussionThese findings underscore the utility of level-specific analysis in detecting localized pathology and suggest a refined framework for characterizing SC alterations in MS.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2025.1537440</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2025.1537440</link>
        <title><![CDATA[Imaging joy with generalized slice dithered enhanced resolution and SWAT reconstruction: 3T high spatial–temporal resolution fMRI]]></title>
        <pubdate>2025-06-03T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Jennifer D. Townsend</author><author>Angela Martina Muller</author><author>Zanib Naeem</author><author>Alexander Beckett</author><author>Bhavesh Kalisetti</author><author>Reza Abbasi-Asl</author><author>Congyu Liao</author><author>An Thanh Vu</author>
        <description><![CDATA[To facilitate high spatial–temporal resolution fMRI (≦1mm3) at more broadly available field strengths (3T) and to better understand the neural underpinnings of joy, we used SE-based generalized Slice Dithered Enhanced Resolution (gSLIDER). This sequence increases SNR efficiency utilizing sub-voxel shifts along the slice direction. To improve the effective temporal resolution of gSLIDER, we utilized the temporal information within individual gSLIDER RF encodings to develop gSLIDER with Sliding Window Accelerated Temporal resolution (gSLIDER-SWAT). We first validated gSLIDER-SWAT using a classic hemifield checkerboard paradigm, demonstrating robust activation in primary visual cortex even with stimulus frequency increased to the Nyquist frequency of gSLIDER (i.e., TR = block duration). gSLIDER provided ~2× gain in tSNR over traditional SE-EPI. GLM and ICA results suggest improved signal detection with gSLIDER-SWAT’s nominal 5-fold higher temporal resolution that was not seen with simple temporal interpolation. Next, we applied gSLIDER-SWAT to investigate the neural networks underlying joy using naturalistic video stimuli. Regions significantly activated during joy included the left amygdala, specifically the basolateral subnuclei, and rostral anterior cingulate, both part of the salience network; the hippocampus, involved in memory; the striatum, part of the reward circuit; prefrontal cortex, part of the executive network and involved in emotion processing and regulation [bilateral mPFC/BA10/11, left MFG (BA46)]; and throughout visual cortex. This proof of concept study demonstrates the feasibility of measuring the networks underlying joy at high resolutions at 3T with gSLIDER-SWAT, and highlights the importance of continued innovation of imaging techniques beyond the limits of standard GE fMRI.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2025.1573816</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2025.1573816</link>
        <title><![CDATA[Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors]]></title>
        <pubdate>2025-05-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Deepa Tilwani</author><author>Christian O'Reilly</author><author>Nicholas Riccardi</author><author>Valerie L. Shalin</author><author>Dirk-Bart den Ouden</author><author>Julius Fridriksson</author><author>Svetlana V. Shinkareva</author><author>Amit P. Sheth</author><author>Rutvik H. Desai</author>
        <description><![CDATA[Several decades of research have investigated the neural connections between stroke-induced brain damage and language difficulties. Typically, lesion-symptom mapping (LSM) studies that address this connection have relied on mass univariate statistics, which do not account for multidimensional relationships between variables. Machine learning (ML) techniques, which can capture these intricate connections, offer a promising complement to LSM methods. To test this promise, we benchmarked ML models on structural and functional MRI to predict aphasia severity (N = 238) and naming impairment (N = 191) for a cohort of chronic-stage stroke survivors. We used nested cross-validation to examine performance along three dimensions: (1) parcellation schemes (JHU, AAL, BRO, and AICHA atlases), (2) neuroimaging modalities (resting-state functional connectivity, structural connectivity, mean diffusivity, fractional anisotropy, and lesion location) and (3) ML methods (Random Forest, Support Vector Regression, Decision Tree, K Nearest Neighbors, and Gradient Boosting). The best results were obtained by combining the JHU atlas, lesion location, and the Random Forest model. This combination yielded moderate to high correlations with the two different behavioral scores. Key regions identified included several perisylvian areas and pathways within the language network. This work complements existing LSM methods with new tools for improving the prediction of language outcomes in stroke survivors.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2025.1549727</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2025.1549727</link>
        <title><![CDATA[T1-relaxation times along the corticospinal tract as a diagnostic marker in patients with amyotrophic lateral sclerosis]]></title>
        <pubdate>2025-02-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fiona Dierksen</author><author>Johanna S. Geibel</author><author>Janika Albrecht</author><author>Sabine Hofer</author><author>Peter Dechent</author><author>Amelie C. Hesse</author><author>Jens Frahm</author><author>Mathias Bähr</author><author>Jan C. Koch</author><author>Jan Liman</author><author>Ilko L. Maier</author>
        <description><![CDATA[Background and purposeIn the differential diagnostic workup of amyotrophic lateral sclerosis (ALS), magnetic resonance imaging (MRI) is primarily used to rule out significant differential diagnoses. So far, whole-brain T1-mapping has not been assessed as a diagnostic tool in this patient population.MethodsWe investigated the diagnostic potential of a novel T1-mapping method based on real-time MRI with 0.5 mm in-plane resolution and 4s acquisition time per slice. The study included patients aged 18 to 90 years who met the revised El Escorial criteria for at least possible ALS. T1-relaxation times were measured along the corticospinal tract in predefined regions of interest.ResultsTwenty-nine ALS-patients and 43 control group patients (CG) were included in the study. Median ALS Functional Rating Scale revised (ALSFRS-R) was 37 (IQR, 35–44) points and the mean duration from symptom onset to MRI was 21 ± 17 (SD) months. ALS patients showed significantly higher T1-relaxation times in all ROIs compared to CG with mean differences in the hand knob of 50 ms (p < 0.001), corona radiata 24 ms (p = 0.034), internal capsule 27 ms (p = 0.002) and midbrain peduncles 48 ms (p < 0.001). There was a consistent negative correlation between the ALSFRS-R and T1-relaxation times in all ROIs.ConclusionsT1-relaxation times along the corticospinal tract are significantly elevated in ALS patients compared to CG and associated with lower ALSFRS-R. These results imply the analysis of T1-relaxation times as a promising diagnostic tool that can distinguish ALS patients from the control group. Ongoing longitudinal studies may provide deeper insights into disease progression and the effects of therapeutic interventions.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2025.1506126</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2025.1506126</link>
        <title><![CDATA[Awake brain MRSI reveals anesthetic sensitivity and regional aging effects on [13C]bicarbonate metabolism in mice]]></title>
        <pubdate>2025-02-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Maiko Ono</author><author>Rena Kono</author><author>Kosei Hirata</author><author>Keita Saito</author><author>Motonao Nakao</author><author>Yoichi Takakusagi</author><author>Rikita Araki</author><author>Akira Sumiyoshi</author><author>Yuhei Takado</author>
        <description><![CDATA[Abnormalities and alterations in the glycolytic pathway in the pathology of neurodegenerative diseases and brain aging have received much attention, as clinical applications of proton-based magnetic resonance spectroscopy (MRS) have recently illuminated the elevation of lactate concentrations in the brains of patients with neurodegenerative diseases, including Alzheimer’s disease. Hyperpolarized [1-13C]pyruvate MRS has shown promise for neurological applications because it enables the real-time in vivo detection of glycolysis and oxidative phosphorylation flux. In studies of the mouse brain using hyperpolarized [1-13C]pyruvate, there are few reports that the signal of [13C]bicarbonate, a product of oxidative phosphorylation metabolized from [1-13C]pyruvate, was detected using MR spectroscopic imaging (MRSI) that allows spatial mapping of metabolism, although there have been reports of [13C]bicarbonate signals being detected by pulse-acquire sequences in the entire brain. In the present study, we compared hyperpolarized [1-13C]pyruvate metabolism between the brains of awake and isoflurane-anesthetized mice using a custom-made awake mouse restraint device with MRSI. Although the signal for [1-13C]lactate, a product of glycolysis metabolized from [1-13C]pyruvate, was detectable in multiple brain regions that include the orbitofrontal cortex and hippocampus in both awake and anesthetized mice, the signal for [13C]bicarbonate metabolized from [1-13C]pyruvate was only detectable in the brains of awake mice. Moreover, a comparison of hyperpolarized [1-13C]pyruvate metabolism in young and aged mouse brains using awake MRSI detected age-related decreases in oxidative phosphorylation flux in brain regions that include the hippocampus with variations in the extent of these changes across different brain regions. These results demonstrate that hyperpolarized [1-13C]pyruvate MRSI under awake conditions is useful for the spatial detection of abnormalities and alterations in glycolysis and oxidative phosphorylation flux in the brains of mice. Thus, the use of hyperpolarized [1-13C]pyruvate MRSI has potential in pathological and mechanistic studies of brain diseases and brain aging.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2024.1524901</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2024.1524901</link>
        <title><![CDATA[Neurological complications of left atrial myxoma: a case report on stroke with left atrial myxoma and postoperative brain metastasis and cerebral aneurysm]]></title>
        <pubdate>2024-12-19T00:00:00Z</pubdate>
        <category>Case Report</category>
        <author>Xudong Ai</author><author>Qingqing Shao</author><author>Xueyan Tian</author><author>Yicheng Zhou</author><author>Tiantian Zhou</author>
        <description><![CDATA[Atrial myxoma is a rare benign tumor that can cause a variety of complications, including cerebral infarction. We present a case of a 52-year-old female patient who developed cerebral infarction caused by an atrial myxoma. The patient underwent successful surgical resection of the tumor, and the infarction was managed accordingly. However, 15-months post-surgery, the patient developed new neurological symptoms. Imaging studies revealed multiple cerebral metastases, consistent with the possibility of seeding of tumor cells. This rare complication emphasizes the importance of long-term monitoring after the resection of atrial myxomas. The occurrence of metastasis in the brain, though rare, should be considered in follow-up care, particularly in patients who have had embolic events related to atrial myxomas. Our case highlights the potential for cerebral myxoma metastasis even after initial successful surgical intervention, underscoring the need for comprehensive follow-up and vigilant monitoring of such patients.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2024.1445952</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2024.1445952</link>
        <title><![CDATA[Pre- and post-therapy functional MRI connectivity in severe acute brain injury with suppression of consciousness: a comparative analysis to epilepsy features]]></title>
        <pubdate>2024-10-01T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Emilio G. Cediel</author><author>Erika A. Duran</author><author>Jeffrey Laux</author><author>William Reuther</author><author>Olivia Leggio</author><author>Belfin Robinson</author><author>Varina L. Boerwinkle</author>
        <description><![CDATA[Severe acute brain injury (SABI) with suppressed consciousness is a major societal burden, with early prognosis being crucial for life-and-death treatment decisions. Resting-state functional MRI (rs-fMRI) is promising for prognosis and identifying epileptogenic activity in SABI. While established for SABI prognosis and seizure networks (SzNET) identification in epilepsy, the rs-fMRI use for SzNET detection in SABI is limited. This study compared evolution of SzNET and resting-state networks (RSN) pre-to-post treatment in SABI and epilepsy, hypothesizing that changes would align with clinical evolution. Therapies included epilepsy surgery for the epilepsy group and antiseizure medication for the SABI group. Independent component analysis (ICA) was used to identify SzNET and RSNs in all rs-fMRI. High-frequency BOLD (HF-BOLD), an ICA power spectrum-based index, quantified RSN and SzNET changes by the patient. Confidence intervals measured HF-BOLD changes pre-to-post-therapy. Baseline HF-BOLD and HF-BOLD changes were compared using linear-mixed models and interaction tests. Five SABI and ten epilepsy patients were included. SzNET were identified in all SABI's pre-therapy rs-fMRI. The clinical changes in SABI and epilepsy were consistent with rs-fMRI findings across groups. HF-BOLD reduced in the epilepsy group RSN post-therapy (−0.78, 95% CI −3.42 to −0.33), but the evidence was insufficient to determine an HF-BOLD reduction in SABI patients or SzNET. The HF-BOLD change trend in pre-to-post epilepsy surgery scans paralleled the clinical improvement, suggesting that the power spectrum may quantify the degree of abnormality on ICA-derived networks. Despite limitations such as small sample sizes, this exploratory study provides valuable insights into network dysfunction in SABI and epilepsy.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2024.1423770</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2024.1423770</link>
        <title><![CDATA[Probing hippocampal stimulation in experimental temporal lobe epilepsy with functional MRI]]></title>
        <pubdate>2024-08-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Niels Schwaderlapp</author><author>Enya Paschen</author><author>Pierre LeVan</author><author>Dominik von Elverfeldt</author><author>Carola A. Haas</author>
        <description><![CDATA[Electrical neurostimulation is currently used to manage epilepsy, but the most effective approach for minimizing seizure occurrence is uncertain. While functional MRI (fMRI) can reveal which brain areas are affected by stimulation, simultaneous deep brain stimulation (DBS)-fMRI examinations in patients are rare and the possibility to investigate multiple stimulation protocols is limited. In this study, we utilized the intrahippocampal kainate mouse model of mesial temporal lobe epilepsy (mTLE) to systematically examine the brain-wide responses to electrical stimulation using fMRI. We compared fMRI responses of saline-injected controls and epileptic mice during stimulation in the septal hippocampus (HC) at 10 Hz and demonstrated the effects of different stimulation amplitudes (80–230 μA) and frequencies (1–100 Hz) in epileptic mice. Motivated by recent studies exploring 1 Hz stimulation to prevent epileptic seizures, we furthermore investigated the effect of prolonged 1 Hz stimulation with fMRI. Compared to sham controls, epileptic mice showed less propagation to the contralateral HC, but significantly stronger responses in the ipsilateral HC and a wider spread to the entorhinal cortex and septal region. Varying the stimulation amplitude had little effect on the resulting activation patterns, whereas the stimulation frequency represented the key parameter and determined whether the induced activation remained local or spread from the hippocampal formation into cortical areas. Prolonged stimulation of epileptic mice at 1 Hz caused a slight reduction in local excitability. In this way, our study contributes to a better understanding of these stimulation paradigms.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2024.1336887</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2024.1336887</link>
        <title><![CDATA[Dynamic off-resonance correction improves functional image analysis in fMRI of awake behaving non-human primates]]></title>
        <pubdate>2024-06-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mo Shahdloo</author><author>Nima Khalighinejad</author><author>Luke Priestley</author><author>Matthew Rushworth</author><author>Mark Chiew</author>
        <description><![CDATA[IntroductionUse of functional MRI in awake non-human primate (NHPs) has recently increased. Scanning animals while awake makes data collection possible in the absence of anesthetic modulation and with an extended range of possible experimental designs. Robust awake NHP imaging however is challenging due to the strong artifacts caused by time-varying off-resonance changes introduced by the animal's body motion. In this study, we sought to thoroughly investigate the effect of a newly proposed dynamic off-resonance correction method on brain activation estimates using extended awake NHP data.MethodsWe correct for dynamic B0 changes in reconstruction of highly accelerated simultaneous multi-slice EPI acquisitions by estimating and correcting for dynamic field perturbations. Functional MRI data were collected in four male rhesus monkeys performing a decision-making task in the scanner, and analyses of improvements in sensitivity and reliability were performed compared to conventional image reconstruction.ResultsApplying the correction resulted in reduced bias and improved temporal stability in the reconstructed time-series data. We found increased sensitivity to functional activation at the individual and group levels, as well as improved reliability of statistical parameter estimates.ConclusionsOur results show significant improvements in image fidelity using our proposed correction strategy, as well as greatly enhanced and more reliable activation estimates in GLM analyses.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2024.1356713</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2024.1356713</link>
        <title><![CDATA[3D inversion recovery ultrashort echo time MRI can detect demyelination in cuprizone-treated mice]]></title>
        <pubdate>2024-05-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Adam C. Searleman</author><author>Yajun Ma</author><author>Srihari Sampath</author><author>Srinath Sampath</author><author>Robert Bussell</author><author>Eric Y. Chang</author><author>Lisa Deaton</author><author>Andrew M. Schumacher</author><author>Jiang Du</author>
        <description><![CDATA[PurposeTo test the ability of inversion-recovery ultrashort echo time (IR-UTE) MRI to directly detect demyelination in mice using a standard cuprizone mouse model.MethodsNon-aqueous myelin protons have ultrashort T2s and are “invisible” with conventional MRI sequences but can be detected with UTE sequences. The IR-UTE sequence uses an adiabatic inversion-recovery preparation to suppress the long T2 water signal so that the remaining signal is from the ultrashort T2 myelin component. In this study, eight 8-week-old C57BL/6 mice were fed cuprizone (n = 4) or control chow (n = 4) for 5 weeks and then imaged by 3D IR-UTE MRI. The differences in IR-UTE signal were compared in the major white matter tracts in the brain and correlated with the Luxol Fast Blue histochemical marker of myelin.ResultsIR-UTE signal decreased in cuprizone-treated mice in white matter known to be sensitive to demyelination in this model, such as the corpus callosum, but not in white matter known to be resistant to demyelination, such as the internal capsule. These findings correlated with histochemical staining of myelin content.Conclusions3D IR-UTE MRI was sensitive to cuprizone-induced demyelination in the mouse brain, and is a promising noninvasive method for measuring brain myelin content.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnimg.2024.1405806</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnimg.2024.1405806</link>
        <title><![CDATA[Corrigendum: A structural connectivity atlas of limbic brainstem nuclei]]></title>
        <pubdate>2024-04-30T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Simon Levinson</author><author>Michelle Miller</author><author>Ahmed Iftekhar</author><author>Monica Justo</author><author>Daniel Arriola</author><author>Wenxin Wei</author><author>Saman Hazany</author><author>Josue M. Avecillas-Chasin</author><author>Taylor P. Kuhn</author><author>Andreas Horn</author><author>Ausaf A. Bari</author>
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