<?xml version="1.0" encoding="utf-8"?>
    <rss version="2.0">
      <channel xmlns:content="http://purl.org/rss/1.0/modules/content/">
        <title>Frontiers in Neuroscience | Brain Imaging Methods section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/neuroscience/sections/brain-imaging-methods</link>
        <description>RSS Feed for Brain Imaging Methods section in the Frontiers in Neuroscience journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-05-12T23:52:58.229+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1838675</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1838675</link>
        <title><![CDATA[Multimodal CT radiomics-clinical ensemble machine learning model effectively predicts futile recanalization after endovascular treatment of acute ischemic stroke]]></title>
        <pubdate>2026-05-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhenxiong Wang</author><author>Yidong Gao</author><author>Pan Xu</author><author>Di Wu</author><author>Wuying Li</author><author>Huameng Huang</author><author>Weihua Deng</author><author>Honggang Xu</author><author>Xinhua Wei</author><author>Xing Li</author>
        <description><![CDATA[BackgroundsFutile recanalization (FR) poses a significant challenge in endovascular treatment and there is a lack of reliable predictive models for assessing treatment outcomes in stroke. The aim of this study is to develop a robust CT radiomics-clinical ensemble model that predicts FR in patients with acute ischemic stroke (AIS) following endovascular treatment (EVT) utilizing machine learning techniques.MethodsThis study enrolled 101 patients diagnosed with AIS who underwent successful EVT. A total of 946 radiomics features were, respectively, extracted from non-contrast CT (NCCT), contrast-enhanced CT (CECT), and various CT perfusion maps (CBF, CBV, MTT, and TTP) using PyRadiomics prior to the endovascular intervention. Demographic characteristics, along with baseline clinical, laboratory, and angiographic variables, were incorporated as clinical features in the model analysis. Feature engineering was performed using SelectKBest. Five traditional machine learning algorithms were employed for modeling. The dataset was randomly split into a training cohort (n = 71, 70%) and an internal validation cohort (n = 30, 30%). Receiver operating characteristic (ROC) curves were utilized to evaluate the performance of each model.ResultsAmong the 101 patients, FR occurred in 66 individuals (65%), as determined by the modified Rankin Scale (mRS) at 90 days. The ensemble model integrating clinical data, NCCT, and CBV achieved the highest performance, with an area under the curve (AUC) of 0.918 using the CatBoost algorithm.ConclusionThe multimodal CT radiomics-clinical ensemble machine learning model demonstrated excellent predictive capability for identifying FR in AIS patients with large vessel occlusion prior to EVT.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1817743</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1817743</link>
        <title><![CDATA[Estimation of head motion in structural MRI and its impact on cortical morphometry]]></title>
        <pubdate>2026-05-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Charles Bricout</author><author>Samira Ebrahimi Kahou</author><author>Sylvain Bouix</author>
        <description><![CDATA[Motion-related artifacts are inevitable in Magnetic Resonance Imaging (MRI) and can bias automated neuroanatomical metrics such as cortical thickness. These biases can interfere with statistical analysis which is a major concern as motion has been shown to be more prominent in certain populations such as children or individuals with ADHD. Manual review cannot objectively quantify motion in anatomical scans, and existing quantitative automated approaches often require specialized hardware or custom acquisition protocols. Here, we train a 3D convolutional neural network to estimate a summary motion metric in retrospective routine research scans by leveraging a large training dataset of synthetically motion-corrupted volumes. We validate our method with one held-out site from our training cohort and with 14 fully independent datasets, including one with manual ratings, achieving a Spearman Rank correlation of 0.71 vs. manual labels. We also tested the correlation of our predicted motion score with morphometric measurements known to be impacted by motion, achieving significant correlation on most datasets. Furthermore, our predicted motion correlates with subject age in line with prior studies. Our approach shows good generalization across scanner brands and protocols, enabling objective, scalable motion assessment in structural MRI studies without prospective motion correction. Finally, we provide empirical evidence that our motion estimator significantly improve model fitness when studying cortical thickness and volume. Our final model is made openly and freely available through “Agitation," a tool usable as a CLI, python package and integrated in Nipoppy and Boutiques. By providing reliable motion estimates, our method offers researchers a tool to assess and account for potential biases in cortical morphometric analyses.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1775687</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1775687</link>
        <title><![CDATA[CCPD under sparsity and low-rank constraints: multi-frequency dynamic functional network connectivity analysis in schizophrenia]]></title>
        <pubdate>2026-05-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Li-Dan Kuang</author><author>Yi-Wen Liu</author><author>Ting Tang</author><author>Wenjun Li</author><author>Jin Zhang</author><author>Weijun Liang</author>
        <description><![CDATA[This study aims to jointly extract group-shared connectivity patterns and group-specific temporal and frequency information from multi-frequency dynamic functional network connectivity (dFNC) tensors of healthy controls (HCs) and schizophrenia patients (SZs) using a coupled canonical polyadic decomposition (CCPD) approach. Based on 145 subjects (71 SZs and 74 HCs) from the COBRE dataset, multi-frequency dFNC tensors were constructed via group independent component analysis and a filter-banked connectivity framework. A novel sparse and low-rank constrained CCPD (SLRCCPD) model was proposed to decompose the dFNC tensors, incorporating L1-norm regularization to enhance significant connections and nuclear norm-based low-rank approximation to improve clustering quality. The results revealed significant connectivity differences between SZs and HCs within auditory, somatomotor, cognitive control, visual, and cerebellar networks across five shared dynamic modules. Clustering of group-specific time-frequency weights showed that SZs had significantly higher fractional and dwell time in State 3 at both low- and high-frequency bands, along with fewer state transitions across all bands compared to HCs. The proposed SLRCCPD framework effectively captures abnormal multi-band dynamic functional connectivity in schizophrenia, providing a new computational tool and empirical pathway for investigating brain network dynamics and mechanistic studies of the disorder.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1771092</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1771092</link>
        <title><![CDATA[Frontotemporal dementia: does structural MRI-based clustering match clinical syndromes?]]></title>
        <pubdate>2026-05-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Neha Singh-Reilly</author><author>Irene Sintini</author><author>Farwa Ali</author><author>Joseph R. Duffy</author><author>Heather M. Clark</author><author>Rene L. Utianski</author><author>Gabriela Meade</author><author>Mary M. Machulda</author><author>Ryota Satoh</author><author>Val J. Lowe</author><author>Keith A. Josephs</author><author>Jennifer L. Whitwell</author>
        <description><![CDATA[BackgroundFrontotemporal dementia is an umbrella term that encompasses several clinical syndromes with impaired behavioral, language, and motor functions. These syndromes show considerable overlap in clinical features and imaging patterns. Therefore, there is a need to investigate the syndromic heterogeneity in FTD using unbiased data-driven approaches.MethodsWe used data-driven clustering analysis of structural magnetic resonance imaging (MRI) data on 400 patients with clinical FTD diagnoses [behavioral variant of frontotemporal dementia (bvFTD), semantic variant of primary progressive aphasia (svPPA), right temporal variant of frontotemporal dementia (rtvFTD), apraxia of speech with agrammatic aphasia (AOS-PAA), primary progressive apraxia of speech (PPAOS), progressive supranuclear palsy (PSP), corticobasal syndrome (CBS) and primary progressive aphasia who did not fit into the other diagnostic categories (PPA-other)]. MR images were w-scored relative to cognitively unimpaired individuals, and principal component analysis was performed. A clustering ensemble approach, including hierarchical algorithms, was applied to the MR-based principal components, and imaging and clinical characteristics of the clusters were investigated. Various numbers of clusters (K = 2, 3, or 4) were evaluated.ResultsThe K = 3 solution offered the most clinically meaningful separation of FTD syndromes. The first cluster captured mostly frontal MRI abnormalities related to the speech, language and behavioral clinical dimensions, including patients with AOS-PAA, PPAOS, PPA-other, and bvFTD. The second cluster captured mostly temporal abnormalities and included mainly patients with svPPA and rtvFTD, but also bvFTD, AOS-PAA, and PPA-other. The third cluster captured cortical and subcortical atrophy, particularly in the midbrain, and included atypical Parkinsonian syndromes, with all PSP and CBS patients captured in this cluster, as well as most PPAOS patients. Considerable overlap of clinical syndromes was noted across these clusters, whereby patients with AOS-PAA, svPPA, PPA-other, and bvFTD were captured in more than one cluster.DiscussionOur findings highlight heterogeneity in FTD, which mainly exists along three axes: speech, language and behavioral deficits reflecting frontal atrophy, language deficits reflecting temporal atrophy, and motor and motor speech deficits reflecting mostly midbrain and subcortical atrophy, with cortical involvement.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1690724</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1690724</link>
        <title><![CDATA[Altered glymphatic function in nasopharyngeal carcinoma following radiotherapy: novel insights from choroid plexus volume and free-water fraction analyses]]></title>
        <pubdate>2026-05-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lingling Deng</author><author>Jianchun Peng</author><author>Keyang Zhou</author><author>Kun Fan</author><author>Yuting Xia</author><author>Shuping Zhang</author><author>Li Li</author><author>Jian-ming Gao</author><author>Na Jiang</author><author>Youming Zhang</author>
        <description><![CDATA[Background and purposeRadiotherapy (RT) often causes delayed radiation-induced brain injury (RBI) with unclear pathophysiology; emerging evidence links this to glymphatic dysfunction, but radiation effects on cerebrospinal fluid (CSF) dynamics and interstitial fluid-CSF exchange are unstudied. Thus, we used choroid plexus (CP) volume and free-water fraction (FWF) imaging to assess glymphatic changes in Nasopharyngeal carcinoma (NPC) patients after RT.Materials and methodsIn this cross-sectional cohort of 101 NPC patients (45 pre-RT and 56 post-RT) underwent 3 T MRI, including T1-weighted and diffusion tensor imaging. Automated CP segmentation and tract-specific FWF analysis are performed. Spearman correlation models assessed radiation-dose relationships with CP volume and Whiter matter (WM) FWF.ResultsWe observed that post-RT patients exhibited significant bilateral CP enlargement (total CP: 2560.56 ± 636.72 mm3, left: 1196.92 ± 334.53 mm3, right: 1363.64 ± 365.84 mm3; all p < 0.05) and elevated FWF in critical WM tracts, including the pontine crossing tract (PCT), bilateral corticospinal tracts (CST), middle cerebellar peduncle, right inferior cerebellar peduncle, and left medial lemniscus. Radiational dose exhibit strong dose-dependent correlations with CP volume and WM FWF. Maximum doses to the brainstem (MDRT_BS) and left temporal lobe (MDRT_LT) showed the strongest associations: MDRT_LT correlated with left CP volume (r = 0.599, p < 0.001), right CP volume (r = 0.585, p < 0.001), and bilateral CST FWF (left: r = 0.414, p = 0.005; right: r = 0.354, p = 0.017). CP volume positively correlated with FWF in the PCT and CST (left CST vs. total CP: r = 0.374, p = 0.011). These associations remained significant after adjusting for age, gender, and intracranial volume (r = 0.31–0.58, all p < 0.05).ConclusionThe observed association between choroid plexus enlargement and elevated white matter free-water fraction suggests RT-associated glymphatic dysfunction in NPC, offers a novel perspective on the pathogenesis of RBI.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1746678</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1746678</link>
        <title><![CDATA[Clinical application of 1H MRS in the human brain at 7T]]></title>
        <pubdate>2026-04-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Graeme A. Keith</author><author>Rosemary A. Woodward</author><author>Evonne McLennan</author><author>Tracey Hopkins</author><author>Jon Trinder</author><author>Sarah Allwood-Spiers</author><author>Likhith Alakandy</author><author>Roddy O’Kane</author><author>Athanasios Grivas</author><author>Emanuela Molinari</author><author>Colin O’Leary</author><author>George Gorrie</author><author>Saif Razvi</author><author>Aoife Williamson</author><author>Anthony J. Chalmers</author><author>William Stewart</author><author>Keith W. Muir</author><author>David A. Porter</author><author>Natasha E. Fullerton</author>
        <description><![CDATA[Proton magnetic resonance spectroscopy (1H MRS) enables non-invasive biochemical sampling of tissues, potentially aiding diagnosis, prognosis and monitoring of various pathologies, while providing novel imaging biomarkers. Ultra-high-field (UHF) imaging at 7 tesla (7T) benefits from improved spectral dispersion due to an increase in chemical shift differences between metabolites, and a higher signal-to-noise ratio (SNR), making 1H MRS at 7T a particularly promising diagnostic tool for identifying and separating metabolites not clearly resolved at lower field strengths. However, 1H MRS at UHF presents technical challenges related to the short RF wavelength at 7T, resulting in B1 transmit field inhomogeneity, and the increased magnetic susceptibility gradients leading to B0 field inhomogeneity. Appropriate MRS methods are required to address these issues. In this article, we describe the technical aspects and challenges of 1H MRS at 7T, based on the experience in our centre, where single voxel 1H MRS has featured prominently in clinical 7T research applications for several years. We present data from six patients with glial tumours, including three who were post-operative, in whom post-surgical metalware affects the specific absorption rate (SAR), along with two patients with neuroinflammatory conditions and two with neurodegenerative diseases. The potential clinical use of 1H MRS for these pathologies and its possible integration as a promising biomarker into advanced imaging pathways are discussed.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1806164</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1806164</link>
        <title><![CDATA[Bibliometric analysis of neurite orientation dispersion and density imaging: research patterns, evolution, and frontier]]></title>
        <pubdate>2026-04-29T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Shenghan Wang</author><author>Chao Wang</author><author>Wenwen Song</author><author>Jiangnan Lin</author>
        <description><![CDATA[BackgroundNeurite orientation dispersion and density imaging (NODDI), an emerging diffusion MRI technique for estimating the microstructural pathology of brain tissue in vivo, has attracted significant research interest. However, a systematic bibliometric analysis of this field remains unexamined. This study aims to perform a bibliometric analysis of the NODDI literature to explore the current research landscape, identify emerging trends, and provide insights for future investigations.MethodsNODDI-related publications were retrieved from the Web of Science (WOS) and Scopus databases during the period of 2012 to 2025. CiteSpace, VOSviewer, and Bibliometrix R package were used to generate visualization maps.ResultsA total of 679 publications related to NODDI were identified from WOS, including 653 research articles and 26 review papers. 844 relevant publications were retrieved from the Scopus database. After 2012, the number of publications on NODDI increased rapidly. Sweden demonstrated the highest average citation per paper, while the United States contributed the largest number of publications. University College London was the most productive institution. Hui Zhang was identified as the most prolific author, while Alexander DC achieved the highest average citation count. NeuroImage was recognized as the leading journal in terms of publication frequency. Common keywords included “diffusion magnetic resonance imaging,” “NODDI,” “brain,” and “multiple sclerosis.” Recent studies show the research focus is shifting from methodological development to clinical application, especially in the field of neuropsychiatric disorders, and is being integrated with emerging methodologies such as Mendelian randomization.ConclusionsThis bibliometric analysis highlights potential directions for future NODDI-related research. Future studies may focus on optimizing imaging techniques, investigating neuropsychiatric disorders, and integrating advanced methodologies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1783910</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1783910</link>
        <title><![CDATA[Functional connectivity changes in the thalamocortical network due to neck pain and the multiscale regulatory effects of acupuncture: a cross-scale multi-omics neuroimaging study]]></title>
        <pubdate>2026-04-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhen Gao</author><author>Jing Zhang</author><author>Jia-Hui Chang</author><author>Hai-Jun Wang</author><author>Cheng Xu</author><author>Lai-Xi Ji</author>
        <description><![CDATA[BackgroundNeck pain correlates with multiscale brain abnormalities, but cross-scale mechanisms of acupuncture analgesia are unclear. This study aimed to: (1) Explore differential modulation of thalamic functional networks by verum vs. sham acupuncture; (2) Examine associations between functional connectivity changes and micro gene expression to unravel its multiscale mechanisms.MethodsA total of 130 participants were initially enrolled, and 100 eligible neck pain patients were randomized 1:1 to the verum (n = 50) or sham (n = 50) acupuncture groups. Finally, 49 patients in each group were included for the final analysis due to one case of exclusion in each group, with treatment administered twice a week for 2 weeks. Visual Analog Scale (VAS), resting-state functional magnetic resonance imaging (fMRI), and Allen Human Brain Atlas (AHBA) transcriptome data were analyzed via Partial Least Squares (PLS) regression.ResultsBoth groups showed reduced post-treatment VAS (p < 0.001), with the verum group exhibiting a superior effect (Z = −6.877, p < 0.001). Neuroimaging revealed that verum acupuncture (VA) specifically induced significant decreases in functional connectivity (FC) between the right thalamus and left anterior cingulate cortex (T = −4.498) as well as between the right thalamus and right Rolandic operculum (T = −4.532, voxel-level p < 0.01, cluster-level p < 0.05), an effect absent in the sham acupuncture group (SA). Gene- FC association analysis indicated that PLS2 component explained 39.83% of FC variance (Pspin: permutation test p < 0.05), with weight genes showing significant spatial correlation to connectivity changes (r = 0.445, Pspin = 0.0011). A total of 809 genes were enriched in the innate immune response and phosphorylation regulation pathways, whereas 1,222 genes were enriched in the GABA-ergic synapse and synaptic membrane-related pathways.ConclusionVA relieves pain via modulating thalamus-anterior cingulate cortex networks, involving immune-inflammation and neural inhibition, providing first multi-scale validation integrating neuroimaging and transcriptomics.Clinical trial registrationThis trial was registered with the International Traditional Medicine Clinical Trial Registry (registration number: ITMCTR2023000001) prior to participant enrollment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1754415</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1754415</link>
        <title><![CDATA[Oxygen extraction fraction is differentially associated with pathological biomarkers in Alzheimer’s disease and non-Alzheimer’s dementias]]></title>
        <pubdate>2026-04-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Arpita Misra</author><author>Yi Wang</author><author>M. Reza Taheri</author><author>Gloria C. Chiang</author><author>Junghun Cho</author>
        <description><![CDATA[IntroductionWe aimed to understand the pathophysiological differences between 16 Alzheimer’s disease (AD) and 15 non-AD dementia patients by quantifying oxygen extraction fraction (OEF) in cortical (CGM) and deep gray matter (DGM) regions.MethodsTo achieve this, we used a novel MRI-based OEF mapping technique, QQ, which estimates OEF from routine multi-echo gradient echo data. Multiple linear regression analyses were performed to compare the associations between OEF and white matter hyperintensities (WMH) or cognitive impairment (measured by Montreal Cognitive Assessment (MoCA) between the two groups.ResultsIn the AD and non-AD group, OEF showed negative associations with WMH in DGM and positive associations with MoCA in DGM and CGM.DiscussionOur study suggests that QQ is a promising tool for differentiating between AD and non-AD dementias, by revealing abnormalities in tissue oxygen usage and their relationships to microvascular changes and cognitive impairment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1798718</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1798718</link>
        <title><![CDATA[Altered static and dynamic regional homogeneity in basal ganglia–thalamocortical circuits and their association with neuropsychiatric manifestations in Wilson’s disease]]></title>
        <pubdate>2026-04-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhihua Zhou</author><author>Wenqing Xiao</author><author>Ning Yang</author><author>Weizhao Lin</author><author>Zichao Chen</author><author>Man Liang</author><author>Jiejing Li</author><author>Yunfan Wu</author>
        <description><![CDATA[PurposeWilson’s disease (WD) is an autosomal recessive disorder caused by ATP7B mutations, resulting in impaired copper metabolism and progressive neuropsychiatric manifestations. This study investigated spatiotemporal alterations in regional brain activity using static and dynamic resting-state fMRI with regional homogeneity (ReHo), and their relationships with clinical features.MethodsResting-state fMRI data were acquired from WD patients and healthy controls (HCs). Static and dynamic ReHo analyses were performed to characterize local synchronization strength and temporal variability of spontaneous neural activity. Group differences were assessed across the basal ganglia, thalamus, cerebellum, and cortical regions. Associations between altered ReHo metrics and clinical measures were evaluated with FDR correction for multiple comparisons.ResultsCompared with HCs, WD patients exhibited widespread ReHo abnormalities involving the basal ganglia (putamen and globus pallidus), thalamus, cerebellum, and cortical regions. Static ReHo in the left putamen and globus pallidus was positively associated with anxiety severity, while right putaminal ReHo was negatively associated with neurological severity and positively associated with disease duration. Dynamic ReHo in the left middle frontal gyrus showed negative associations with depression severity and disease duration. All brain–behavior correlations survived FDR correction, indicating robust effects.ConclusionWD is characterized by disrupted spatiotemporal organization of local functional synchronization within cerebellar and basal ganglia–thalamo–cortical circuits. These findings support a network-level dysfunction model involving subcortical synchronization deficits and cortical temporal instability, which together underpin neuropsychiatric manifestations and disease progression.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1781534</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1781534</link>
        <title><![CDATA[Short-term intrinsic connectivity changes induced by cognitive exertion in healthy participants]]></title>
        <pubdate>2026-04-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Leighton Barnden</author><author>James Baraniuk</author><author>Maira Inderyas</author><author>Sonya Marshall-Gradisnik</author><author>Kiran Thapaliya</author>
        <description><![CDATA[IntroductionChanges in brain intrinsic connectivity on the timescale of minutes, as provoked by a cognitive task, have not been well documented.MethodsA total of two 7.5-min 7 Tesla functional MRI (fMRI) scans (Run 1 and Run 2), separated by 90 s, were acquired for 23 healthy participants during cognitive exertion induced by the Stroop color–word interference task. Independent component analysis (ICA) of the paired Run 1 and Run 2 fMRI acquisitions identified components with distinct spatial and temporal signatures.ResultsThe spatial extent of the ICA components coincided with hubs of the brain’s intrinsic networks. In addition, these components correlated with brain regions from other networks, thereby defining inter-network connectivity. Run 1 and Run 2 showed significantly different patterns of connections (p-FWE < 0.01) across 10 ICA-identified intrinsic networks and 20 inter-networks. Connectivity in Run 2 was higher in 12 nodes and lower in eight nodes, indicating dynamic changes during the task response. Overall, the right angular gyrus/supramarginal gyrus and the right frontal pole regions of the ventral attention network showed greater activity in Run 1, but activity shifted to the fusiform gyrus, supplementary motor area (SMA), and precentral and postcentral gyrus nodes in Run 2. Response times (RTs) and Stroop test accuracy did not change between runs in these healthy participants.ConclusionInter-network connectivity indicated that surveillance and task oversight nodes were required early in learning how to complete the Stroop task (Run 1), but these were replaced by object recognition and more automatic responses in Run 2. These findings define inter-networks that are sensitive to cognitive exertion and provide a framework for understanding cognitive dysfunction.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1791960</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1791960</link>
        <title><![CDATA[EEG-based stroke severity classification using higher-order topological features and graph convolutional networks]]></title>
        <pubdate>2026-04-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lu Zhang</author><author>Hanwen Zhang</author><author>Xiaomeng Fan</author><author>Qiang Li</author>
        <description><![CDATA[IntroductionElectroencephalography (EEG)-based stroke analysis has mainly relied on conventional signal and network descriptors, while higher-order brain network structures remain insufficiently characterized.MethodsWe used persistent homology to extract cycle-based topological features from EEG functional networks, capturing higher-order organization with reduced sensitivity to threshold selection. These features were integrated with conventional EEG representations and embedded into a graph convolutional network for stroke severity classification.ResultsThe proposed framework achieved 86% accuracy in discriminating mild from moderate stroke. Cycle ratio analysis further revealed that the prefrontal cortex exhibited the most prominent higher-order structures, indicating its prominent involvement in post-stroke brain network organization.DiscussionOur results suggest that higher-order topological features can enhance EEG-based stroke severity classification and offer additional insight into post-stroke brain network alterations.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1805907</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1805907</link>
        <title><![CDATA[Cerebral blood flow and functional connectivity immediate changes following intradermal acupuncture in major depressive disorder]]></title>
        <pubdate>2026-04-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zelin Yu</author><author>Xiaoting Wu</author><author>Jiajia Yang</author><author>Shaungyi Pei</author><author>Jia Wang</author><author>Zhijian Cao</author><author>Jianqiao Fang</author><author>Xiaomei Shao</author>
        <description><![CDATA[BackgroundAcupuncture has been increasingly applied as an adjunctive treatment for major depressive disorder (MDD), yet its central neurobiological mechanisms remain insufficiently understood. Cerebral blood flow (CBF) and functional connectivity strength (FCS) provide complementary perspectives on regional metabolic activity and large-scale functional integration, and their coupling may reflect neurovascular coordination relevant to depression.MethodsTwenty patients with MDD and twenty matched healthy controls (HC) underwent resting-state MRI. Patients received intradermal acupuncture (IA) and were scanned before and immediately after stimulation, while healthy controls were scanned once. Voxel-wise analyses of CBF, FCS, and their ratio (CBF/FCS) were performed to characterize acupuncture-related changes in neurovascular coupling. Group comparisons and pre–post acupuncture effects were assessed at the whole gray matter level.ResultsAcupuncture induced significant alterations in CBF/FCS coupling across widespread brain regions, including the bilateral precuneus, postcentral gyrus, superior temporal pole, superior frontal gyrus, occipital cortex, and cerebellum. These regions are primarily involved in sensorimotor processing, cognitive control, and emotional regulation. Overall, IA was associated with an immediate increase in CBF/FCS coupling, suggesting enhanced coordination between cerebral perfusion and functional network integration.ConclusionThis study provides evidence that intradermal acupuncture modulates neurovascular coupling in patients with MDD, offering neuroimaging-based insights into its antidepressant mechanisms. The findings support the notion that acupuncture may facilitate more efficient brain function by optimizing the balance between neural activity and metabolic supply.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1741923</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1741923</link>
        <title><![CDATA[Spatial evolution in temporal dynamics of hemodynamic response function in human superior colliculi with ultra-high-resolution MRI at 9.4T]]></title>
        <pubdate>2026-04-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nooshin J. Fesharaki</author><author>Artemy Vinogradov</author><author>David Ress</author><author>Jung Hwan Kim</author>
        <description><![CDATA[The superior colliculus (SC) plays a crucial role in multisensory integration, visual information processing, saccadic target selection, visual selective attention, and decision making. In particular, the SC has a key role in oculomotor coordination, following a rostro-caudal organization. The rostral SC, which corresponds to foveal representation, is linked to fixation, microsaccades, smooth pursuit, and vergence adjustments. In contrast, the caudal SC, representing more peripheral visual field, is associated with the large gaze shifts (saccades). However, evidence regarding whether this functional gradient is preserved in the human SC remains limited. In this study, we employed a sequence-following visual-motor task to specifically engage SC activity. We measured blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) responses to brief neural activity, known as hemodynamic response function (HRF). We showed a spatial gradient of the BOLD positive HRFs (pHRF) along the rostro-caudal axis of the SC. The pHRF was primarily located in the rostral SC, and it gradually weakened toward the caudal SC, where negative HRF (nHRF) was often observed. The systematic rostro-caudal evolution of HRFs were consistent both within and across subjects, consistent with results from previous electrophysiological studies. Our work showed the feasibility of using ultra-high-field fMRI to non-invasively examine neurovascular dynamics in a small and deeply located subcortical structures of the human brain.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1793111</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1793111</link>
        <title><![CDATA[Diffusion tensor imaging-functional MRI fusion reveals disrupted white matter structure–function coupling in HIV-associated asymptomatic neurocognitive impairment]]></title>
        <pubdate>2026-04-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Wei Wang</author><author>Zhongkai Zhou</author><author>Ruichen Ren</author><author>Budong Chen</author><author>Wenru Gong</author><author>Chao Yuan</author><author>Tiantian Tian</author><author>Siyu Feng</author><author>Yanbin Shi</author><author>Hongjun Li</author><author>Lingling Zhao</author>
        <description><![CDATA[ObjectiveConventionally, blood oxygen level-dependent (BOLD) signals derived from resting-state functional magnetic resonance imaging (rs-fMRI) are attributed to gray matter, but recent evidence confirms stable low-frequency oscillations within white matter. While structure–function coupling is pivotal in neuropsychiatry, it remains underexplored in HIV-associated neurocognitive disorders (HAND). Focusing on Asymptomatic Neurocognitive Impairment (ANI), the earliest stage of HAND, this study establishes a white matter skeleton-based fusion framework integrating diffusion tensor imaging (DTI) and rs-fMRI to investigate underlying mechanisms.MethodsWe enrolled 47 patients with ANI and 48 matched healthy controls. Fractional anisotropy (FA) images from DTI and BOLD signals derived from rs-fMRI were projected onto a unified white matter skeleton to achieve structure–function spatial alignment. FA, skeleton-based white matter amplitude of low-frequency fluctuations (SWALFF), and its dynamic variability (dSWALFF) were calculated. Group differences in white matter structure and function were assessed, with structure–function coupling examined in regions showing overlapping FA-SWALFF and FA-dSWALFF alterations. Additionally, a novel White Matter Dys-coupling Index (WDI) was proposed to quantify the deviation between structural integrity and functional activity and evaluate its clinical relevance.ResultsCompared to controls, ANI patients exhibited widespread FA reductions and increased mean diffusivity (MD) and radial diffusivity (RD), indicating diffuse demyelination. Functionally, a spatial dissociation emerged: SWALFF was reduced in posterior occipital pathways (left vertical occipital fasciculus, forceps major), whereas SWALFF and dSWALFF were elevated in prefrontal pathways (forceps minor). Overlapping regions revealed complex coupling patterns, ranging from concordant decline to compensatory upregulation and decoupling. The interaction between FA and dSWALFF further highlighted instability in dynamic regulation. The WDI was significantly correlated with infection duration, immune status, and cognitive domain scores.ConclusionThis study identifies a characteristic “coupling imbalance” in the white matter of ANI patients, defined by the coexistence of structural degeneration and functional reorganization. We propose the WDI as a quantitative metric for this deviation. Its significant associations with clinical and cognitive metrics suggest its potential as a neuroimaging biomarker for the early identification and mechanistic understanding of HAND.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1772632</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1772632</link>
        <title><![CDATA[MambaSSM: efficient segmentation of brain structures in anisotropic 3D EM images via state-space models]]></title>
        <pubdate>2026-04-16T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Minjie Liu</author><author>Laifu Fang</author><author>Qinhu Zhang</author><author>Hongyu Yang</author>
        <description><![CDATA[Accurate segmentation of brain structures from anisotropic 3D electron microscopy (EM) images remains challenging due to the trade-off between global context modeling and computational efficiency. While state-space models (SSMs) like Mamba have shown promise in capturing long-range dependencies, their direct application to anisotropic EM data has been limited. We introduce MambaSSM, a novel network that adapts SSMs to anisotropic 3D EM images via a tailored scanning strategy. Our method features two core modules: an SSM-based anisotropic adaptation module for early-stage feature learning and an SSM-based isotropic adaptation module for later-stage refinement. These modules are interleaved with convolutional layers to enable multi-scale feature extraction. Evaluated on two public datasets (SNEMI3D and MitoEM-R), MambaSSM achieves superior segmentation accuracy with significantly lower memory usage compared to CNN, Transformer, and Mamba based baselines.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1696114</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1696114</link>
        <title><![CDATA[Aberrant local and global neural activation patterns in pediatric Prader–Willi syndrome]]></title>
        <pubdate>2026-04-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jie Liu</author><author>Zhongxin Huang</author><author>Jinhua Cai</author><author>Min Zhu</author><author>Song Peng</author><author>Shuang Ding</author><author>Longlun Wang</author><author>Wei Tang</author><author>Chunlan Sun</author><author>Jiaxin Su</author>
        <description><![CDATA[PurposeAlthough cognitive disorders in children with Prader–Willi syndrome (PWS) are linked to abnormalities in spontaneous neural activation and functional connectivity (FC), the specific neural activation patterns remain uncertain, especially in young children with PWS.MethodsThe current study set out to explore specific local and global neural activation in pediatric PWS using the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and seed-based whole brain FC. Information was gathered from 35 pediatric PWS patients and 33 healthy controls (HC). Both groups’ ALFF and ReHo values were computed, and FC were constructed on the basis of altered ALFF and ReHo regions. The relationships between altered ALFF, ReHo, and FC and the Griffiths Developmental Scales (GDS) of the PWS group were analyzed using partial correlation analysis.ResultsBoth ALFF and ReHo exhibited decreases in occipital lobe, temporal lobe, and cingulate gyrus, and altered ReHo was present in parietal lobe, frontal lobe, and basal ganglia areas. Moreover, ALFF and ReHo also exhibited increases in occipital and temporal lobes. Decreased FC was detected in the visual network (VN), sensorimotor network (SMN), salience network (SAN), and default mode network (DMN). The SMN-, cingulate-, and occipital lobe-related neural activation patterns were significantly positively correlated with the GDS score.ConclusionThe PWS group was characterized mainly by decreased neuronal physiological function and the ReHo was similar to ALFF but more extensive. The decreased local and global brain neural activation patterns may serve as early physiological indicators of cognitive abnormalities.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1782306</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1782306</link>
        <title><![CDATA[A calibration-aware hierarchical CNN-SWIN fusion framework for robust Cross-Dataset brain MRI analysis]]></title>
        <pubdate>2026-04-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ayesha Younis</author><author>Li Qiang</author><author>Abdur Rehman</author><author>Hamid Hussain</author><author>Mohammed Jajere Adamu</author><author>Halima Bello Kawuwa</author>
        <description><![CDATA[IntroductionDeep learning approaches have become central to brain MRI analysis; however, their reliability under dataset shift remains a critical barrier to safe and scalable deployment in neuroscience and clinical research. While convolutional neural networks (CNNs) provide strong locality-driven inductive biases for robust feature extraction, they lack global contextual awareness. Conversely, transformer-based architectures capture long-range dependencies but often exhibit reduced robustness and miscalibrated confidence when applied to heterogeneous medical imaging data, particularly in Cross-Dataset settings.MethodsIn this work, we propose a calibration-aware hierarchical CNN-Transformer fusion framework designed for robust brain MRI analysis under dataset shift. The architecture integrates a pretrained multi-scale CNN backbone with a hierarchical transformer branch and performs scale-aligned fusion through cross-attention mechanisms. By allowing local convolutional features to selectively query global contextual representations, the proposed design maintains stable feature contributions during fusion and mitigates overconfident reliance on transformer features when generalization degrades across datasets. The framework is evaluated using a strict Cross-Dataset protocol, where models are trained on one dataset and tested on a distinct dataset.ResultsExperimental results demonstrate that the proposed fusion model achieves competitive classification performance while substantially improving probabilistic calibration relative to both CNN-only and transformer-only baselines. Specifically, the model attains an average accuracy of 99.20% and achieves lower Expected Calibration Error (ECE = 0.0041), Brier score (0.0028), and Negative Log-Likelihood (NLL = 0.0277) compared to a standalone Swin Transformer and a strong ResNet50 baseline.DiscussionThese findings demonstrate that calibration-aware hierarchical CNN-Transformer fusion enhances both predictive reliability and robustness under Cross-Dataset evaluation. By improving the alignment between predictive confidence and empirical correctness, the proposed method supports safer large-scale analysis of heterogeneous brain MRI data, with important implications for multi-center neuroscience studies and trustworthy clinical decision support.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1709659</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1709659</link>
        <title><![CDATA[Causal network analysis-based assessment of gray matter alteration in post-radiotherapy nasopharyngeal carcinoma patients using 3D T1-weighted MRI]]></title>
        <pubdate>2026-04-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chunhong Qin</author><author>Huan Lin</author><author>Yujie Liu</author><author>Hongzhuo Wang</author><author>Jie An</author><author>Fuhong Duan</author><author>Donglin Wu</author><author>Qianli Wang</author><author>Shijun Qiu</author><author>Xi Leng</author>
        <description><![CDATA[ObjectivesTo explore the temporal and causal relationships underlying brain structural changes in post-radiotherapy (RT) nasopharyngeal carcinoma (NPC) patients.MethodsA total of 38 post-radiotherapy NPC patients (33 males, 5 females; median age: 50.0 years, range: 27–63 years; median time post-RT: 17.2 months, range: 0.5–108 months) and 23 healthy controls (16 males, 7 females; median age: 37 years, range: 24–61 years) underwent T1-weighted magnetic resonance (MR) images, and their images were evaluated. The causal structural covariance network (CaSCN) analysis approach was applied to assess the causal relationships underlying radiation-induced brain structural alterations in these patients. Granger causality (GC) analysis was employed to morphometric data derived from T1-weighted MR images, which were ordered by the time elapsed post-RT.ResultsThe source-like directed associations were observed in the bilateral parahippocampal gyrus (PHG), the right gyrus rectus (REC.R), and the right caudate nucleus (CAU.R). The directed network analysis revealed that the parahippocampal gyrus (PHG), REC.R and CAU.R exhibited typical source-like characteristics, and their structural changes exerted a key regulatory effect on GM volume alterations across multiple brain regions. While the left precuneus (PCUN.L), left temporal pole: middle temporal gyrus (TPOmid.L) and the left inferior temporal gyrus (ITG.L) were typical sink-like brain region that mainly received regulatory effects from source-like brain regions, acting as major target regions of structural damage.ConclusionOver time, post-radiotherapy NPC patients exhibited progressive changes in GM volume, where the PHG.L, PHG.R, REC.R and CAU.R were core source-like brain regions. The PCUN.L, TPOmid.L, and ITG.L show distinct sink-like features, which mainly receive regulatory effects from source-like brain regions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnins.2026.1749851</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnins.2026.1749851</link>
        <title><![CDATA[In silico exploration of electric field distribution in tDCS: integrating white matter anisotropy and subject-specific structural connectivity]]></title>
        <pubdate>2026-04-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Giulia Caiani</author><author>Eleonora Arrigoni</author><author>Alberto Pisoni</author><author>Serena Fiocchi</author>
        <description><![CDATA[Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique with promising application in the treatment of neurological and psychiatric disorders. However, its effectiveness is often limited by the high inter-subject variability of the induced effects, mainly attributable to individual anatomical differences, which are not considered in the design of the stimulation protocols. Among these, structural connectivity plays a crucial role but remains often overlooked in tDCS research. Objective—This study aims to evaluate how variations in structural connectivity influence the distribution of the electric field (EF) during tDCS session. In particular, we analyse how the inclusion of white matter anisotropy affects the EF distribution and spread compared to classical isotropic models, and how the strength of connection across cortical parcels affects the EF spread. Approach—The study proposes an advancement in the computational modelling of tDCS through the integration of white matter anisotropy into finite element method (FEM) simulations. By combining advanced computational approaches, we explore the relationship between EF strength and cortical connectivity. Main results—Neglecting white matter anisotropy in electromagnetic simulations lead to a relative error in EF magnitude greater than 10% and to an orientation error of the EF vector of almost 20 degrees. The DTI-informed simulations lead to a more focalized EF distribution, moreover it was found a positive and significant (p < 0.05) correlation between EF focality and the strength of connectivity between cortical areas below P2 and P1 electrodes. Significance—These findings highlight the importance of including white matter anisotropy into tDCS simulation to prevent distortions in EF distribution and suggest the need to integrate structural connectivity information into the definition of subject-specific dose in tDCS protocols.]]></description>
      </item>
      </channel>
    </rss>