NeuroImage最新文献

{"title":"Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg","authors":"Ziyao Shang ,&nbsp;Misha Kaandorp ,&nbsp;Kelly Payette ,&nbsp;Marina Fernandez Garcia ,&nbsp;Roxane Licandro ,&nbsp;Georg Langs ,&nbsp;Jordina Aviles Verdera ,&nbsp;Jana Hutter ,&nbsp;Bjoern Menze ,&nbsp;Gregor Kasprian ,&nbsp;Meritxell Bach Cuadra ,&nbsp;Andras Jakab","doi":"10.1016/j.neuroimage.2026.121729","DOIUrl":"10.1016/j.neuroimage.2026.121729","url":null,"abstract":"<div><div>Magnetic resonance imaging (MRI) has played a crucial role in fetal neurodevelopmental research. Structural annotations of MR images are an important step for quantitative analysis of the developing human brain, with Deep Learning providing an automated alternative for this otherwise tedious manual process. However, segmentation performances of Convolutional Neural Networks often suffer from domain shift, where the network fails when applied to subjects that deviate from the distribution with which it is trained on. In this work, we aim to train networks capable of automatically segmenting fetal brain MRIs with a wide range of domain shifts pertaining to differences in subject physiology and acquisition environments, in particular shape-based differences commonly observed in pathological cases. We introduce a novel data-driven train-time sampling strategy that seeks to fully exploit the diversity of a given training dataset to enhance the domain generalizability of the trained networks. We adapted our sampler, together with other existing data augmentation techniques, to the SynthSeg framework, a generator that utilizes domain randomization to generate diverse training data. We ran thorough experimentations and ablation studies on a wide range of training/testing data to test the validity of the approaches. Our networks achieved notable improvements in the segmentation quality on testing subjects with intense anatomical abnormalities (<em>p</em> &lt; 1e-4), though at the cost of a slighter decrease in performance in cases with fewer abnormalities. Our work also lays the foundation for future works on creating and adapting data-driven sampling strategies for other training pipelines.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121729"},"PeriodicalIF":4.5,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145998489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Added value of quantitative [18F]FDG-PET analysis in MRI-negative epilepsy: A simulation-based study using realistic ground-truths","authors":"Andrés Perissinotti ,&nbsp;Arnau Farré-Melero ,&nbsp;Francisco J. López-González ,&nbsp;María del Carmen Mallón-Araujo ,&nbsp;Julia Cortés ,&nbsp;Xavier Setoain ,&nbsp;Andrea Fritsch ,&nbsp;Katherine Quintero ,&nbsp;Angela Esteban ,&nbsp;Silvia Morbelli ,&nbsp;Matteo Bauckneht ,&nbsp;Alberto Miceli ,&nbsp;Aida Niñerola-Baizán ,&nbsp;Pablo Aguiar ,&nbsp;Jesús Silva-Rodríguez","doi":"10.1016/j.neuroimage.2026.121740","DOIUrl":"10.1016/j.neuroimage.2026.121740","url":null,"abstract":"<div><h3>Purpose</h3><div>Quantitative analysis of [¹⁸F]FDG-PET images is expected to improve the localization of foci in non-lesional epilepsy. However, the lack of reliable gold standards has prevented a comprehensive evaluation of the potential improvements derived from this approach. Here, we aimed at evaluating these improvements using a novel dataset of realistic simulated studies.</div></div><div><h3>Methods</h3><div>125 realistic simulated [¹⁸F]FDG-PET studies were generated (100 with synthetic hypometabolic foci (HF) with different levels of identification complexity and 25 controls). Eight nuclear physicians performed visual rating (VR) and were given the chance to modify their assessment after reviewing quantitative results (QR). Physicians reported the presence/absence of HF, HF location, and diagnostic confidence (DC) before/after QR. Success Rate (SR) of physician’s assessments was analyzed, as well as inter-rater agreement and changes in DC.</div></div><div><h3>Results</h3><div>In 31.3% of the assessments, physicians changed their interpretation after QR, with SR increasing from 16.3% to 61.0% in these cases. Overall SR improved from 49.5% in VR to 63.5% in QR, mostly on pathologic cases (relative improvement: +34.0%). Improvement was found at each level of HF identification complexity and was higher for challenging cases (relative improvement: +71.8%). Inter-rater agreement also improved significantly (0.273 vs. 0.475, <em>p</em> &lt; 0.001). QR also significantly increased DC (\"High\" confidence of 8.1% on VR vs. 38.5% on QR, <em>p</em> &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>Quantitative analysis significantly improved diagnostic accuracy, confidence and inter-rater agreement, especially in challenging cases. Furthermore, this work introduces a novel methodological approach using simulated MRI-negative epilepsy [¹⁸F]FDG-PET images for realistic quantification research studies.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121740"},"PeriodicalIF":4.5,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146030048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frontotemporal bursting supports human working memory","authors":"Vladimir Omelyusik ,&nbsp;Tyler S. Davis ,&nbsp;Satish S. Nair ,&nbsp;Behrad Noudoost ,&nbsp;Patrick D. Hackett ,&nbsp;Elliot H. Smith ,&nbsp;Shervin Rahimpour ,&nbsp;John D. Rolston ,&nbsp;Bornali Kundu","doi":"10.1016/j.neuroimage.2026.121718","DOIUrl":"10.1016/j.neuroimage.2026.121718","url":null,"abstract":"<div><div>Cortical neural activity varies dynamically during memory periods, when relevant information is not present in the environment. But how those dynamics are related to a code defining working memory (WM) performance is not known. Recent data shows brief bursts of activity in the high gamma (70-140 Hz) and beta (12-30 Hz) band within non-human primate lateral prefrontal cortex (PFC) is associated with WM processing. However, WM may be related to activity within a network of frontal executive and posterior sensory areas involved in stimulus perception. Here we tested whether gamma and beta bursting exist in lateral PFC and multisensory lateral temporal areas in humans during visual WM, and whether these areas are coupled via a phase-burst code. We used intracranial macroelectrode recordings from the middle frontal gyrus (MFG), which includes dorsolateral PFC, and from the middle temporal gyrus (MTG), an area important for visual processing. High gamma bursting increased in human left PFC during encoding and delay periods while beta bursting decreased. Interestingly, beta bursting increased in multisensory areas during encoding and remained high during the delay period, more so on the right. These effects varied with WM performance. Finally, we quantify the degree to which delay-period gamma bursting is locked to beta phase within and between regions of this network using a proposed metric termed ‘phase-burst coupling’ (PBC). We find evidence that delay-period gamma bursting in temporal areas is locked to beta phase in PFC. Our findings suggest that WM may use bursting to support memory maintenance until readout.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121718"},"PeriodicalIF":4.5,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145990042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT-free attenuation and scatter correction of [11C]CFT brain PET using a Bi-directional matching network","authors":"Wenxiang Ding ,&nbsp;Xiaolin Sun ,&nbsp;Qiaoqiao Ding ,&nbsp;Xiaoyue Tan ,&nbsp;Qing Zhang ,&nbsp;Shanzhen He ,&nbsp;Peiyong Li ,&nbsp;Qiu Huang ,&nbsp;Xiaoqun Zhang ,&nbsp;Lei Jiang","doi":"10.1016/j.neuroimage.2026.121721","DOIUrl":"10.1016/j.neuroimage.2026.121721","url":null,"abstract":"<div><div>Quantitative PET imaging requires accurate attenuation and scatter correction (ASC), but the standard CT-based method introduces additional radiation exposure—a significant concern for neurological studies involving repeated scans. Here we applied and extended a CT-free deep learning framework for [¹¹C]CFT brain PET that achieves diagnostic-comparable dopamine transporter (DAT) quantification while avoiding CT-associated radiation. A Bi-directional Discrete Process Matching (Bi-DPM) network was adapted to establish reversible transformations between non-corrected (NASC-PET) and fully corrected (ASC-PET) images through discrete consistency constraints, eliminating the need for pseudo-CT generation or anatomical priors. Evaluated on 90 Parkinsonian syndrome patients, Bi-DPM demonstrated superior performance to Cycle-Consistent Generative Adversarial Networks (CycleGAN), Pix2Pix, and Rectified Flow (RF) across quantitative metrics (lower MAE, higher PSNR/SSIM). For standardized uptake value mean (SUVmean) measurements, Bi-DPM showed excellent agreement with CT-ASC reference (CCC &gt; 0.98, PCC &gt; 0.98). Voxel-wise analysis of DAT-positive/-negative (DAT+/DAT−) groups confirmed Bi-DPM's clinical validity, with statistical significance maps closely aligned to CT-ASC (Dice = 0.953 vs. 0.938 for RF, 0.948 for Pix2Pix and 0.618 for CycleGAN). This approach reduces unnecessary radiation exposure by omitting CT scans while maintaining PET quantification accuracy.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121721"},"PeriodicalIF":4.5,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intrinsic divergence, repeatability, and distributional fingerprints of VFA, ME-SE, MDME, and MRF: a comparative evaluation of quantitative T1/T2 relaxometry in phantom and human brain at 3 T","authors":"Gaoyang Zhao ,&nbsp;Jialiang Teng ,&nbsp;Lina Zhang ,&nbsp;Yueyue Zuo ,&nbsp;Yanglei Wu ,&nbsp;Mengzhu Wang ,&nbsp;Yuncai Ran ,&nbsp;Jingliang Cheng ,&nbsp;Hongwei Zheng ,&nbsp;Yong Zhang","doi":"10.1016/j.neuroimage.2026.121755","DOIUrl":"10.1016/j.neuroimage.2026.121755","url":null,"abstract":"<div><div>Quantitative MRI (qMRI) relaxometry provides non-invasive biomarkers of brain microstructure, yet cross-method inconsistencies continue to hinder reliable comparison across studies and sites. To facilitate the clinical standardization of brain qMRI, this work systematically evaluated the accuracy and repeatability of three clinical brain T₁/T₂ relaxometry implementations under harmonized 3 T conditions: conventional variable flip-angle and multi-echo spin-echo (VFA/ME-SE), multi-dynamic multi-echo (MDME), and magnetic resonance fingerprinting (MRF).</div><div>A standardized ISMRM/NIST system phantom and two healthy volunteer cohorts were examined. The phantom experiment quantified accuracy and bias; a multi-site “traveling brain” cohort (<em>n</em> = 12) assessed inter-scanner repeatability; and a single-site population cohort (<em>n</em> = 38) characterized distributional “fingerprints” and biological sensitivity using a high-precision AI-based segmentation pipeline.</div><div>In phantom validation, MRF achieved the highest accuracy and stability across the physiological range, whereas ME-SE exhibited precision loss at long T₂. In vivo, all methods demonstrated excellent inter-site repeatability with coefficients of variation (CVs) below 5%, where MRF achieved the highest stability for T₁ (CV = 0.61%) and MDME yielded the highest stability for T₂ (CV = 0.42%). However, substantial intrinsic discrepancies persisted: relative to MRF, VFA systematically overestimated T₁ (particularly in deep gray matter), while MDME underestimated T₁. For T₂, a fundamental baseline shift was observed, with ME-SE and MDME yielding values nearly double those of MRF in iron-rich regions. Supplementary investigations confirmed these offsets arise from proprietary reconstruction and signal encoding differences (transient-state vs. steady-state) rather than simple protocol constraints. Biologically, the optimized segmentation pipeline enhanced sensitivity to age-related trends, revealing significant T₁ shortening across all methods, while VFA alone exhibited significant sex-bias confounding not observed in MRF or MDME.</div><div>These findings provide quantitative benchmarks and practical guidance for standardizing brain qMRI relaxometry across acquisition methods, scanners, and research sites.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121755"},"PeriodicalIF":4.5,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146053157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal dynamics of letter processing revealed by multivariate pattern analysis of EEG data","authors":"Miguel Domingues ,&nbsp;Susana Araújo ,&nbsp;Tânia Fernandes ,&nbsp;Inês Bramão","doi":"10.1016/j.neuroimage.2026.121750","DOIUrl":"10.1016/j.neuroimage.2026.121750","url":null,"abstract":"<div><div>Letters are the primitives of reading expertise. Single letter recognition relies on a hierarchy of processing stages, in which early visual features gradually evolve into abstract letter representations, but the temporal organization of these stages remains poorly understood. To address it, we applied multivariate pattern analysis (MVPA) to electroencephalography (EEG) data recorded while adult readers (<em>n</em> = 35) performed a one-back repetition detection task on single letters and pseudoletters. Traditional event-related potential (ERP) analyses revealed differences between letters and pseudoletters in the N1 (140–170 ms), P2 (210–270 ms), and P3 (300–500 ms) components. Multivariate temporal generalization analyses showed that neural patterns distinguishing letters from pseudoletters were highly generalizable from approximately 140 to 600 ms after stimulus onset. A spatiotemporal searchlight analysis indicated that, despite this temporal generalization, the topographic configuration of EEG channels contributing to classification changed along this window, suggesting that neural representations in later processing stages were transformed from earlier perceptual stages. These findings indicate that letter recognition unfolds as a cascade of continuous and interacting processes rather than via discrete stages. Early perceptual letter-specific activity, indexed by the N1 component, remains engaged throughout later, increasingly abstract, orthographic processing stages to jointly support letter identification.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121750"},"PeriodicalIF":4.5,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural-linguistic analysis for Alzheimer’s detection: A deep learning approach informed by cognitive neuroscience","authors":"Jianhui Lv ,&nbsp;Shalli Rani ,&nbsp;Keqin Li ,&nbsp;Ning Liu","doi":"10.1016/j.neuroimage.2026.121739","DOIUrl":"10.1016/j.neuroimage.2026.121739","url":null,"abstract":"<div><div>Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that disrupts cognitive function across multiple domains, particularly affecting language networks and speech production pathways in the brain. Patients demonstrate symptoms including aphasia, reduced syntactic complexity, and diminished verbal fluency that reflects underlying neural pathology in language-related cortical areas. Current detection methods rely on resource-intensive neuroimaging, invasive biomarker sampling, and extensive neuropsychological testing, creating substantial barriers to early diagnosis. While researchers have explored using acoustic features, paralinguistic markers, and text-based features for AD detection, existing approaches face fundamental limitations: traditional acoustic methods fail to capture semantic-cognitive content, text transcription is labor-intensive, and automatic speech recognition quality suffers due to pronunciation variations and cognitive impairments in elderly populations. This paper introduces cognitive acoustic symbolic transformation for ALzheimer’s (COASTAL), a neurobiologically-inspired framework that models hierarchical speech processing pathways. COASTAL transforms acoustic patterns into discrete symbolic elements through a specialized transformation module before applying contextual analysis that mirrors prefrontal-temporal language networks. Evaluated on the ADReSSo corpus, COASTAL achieved 70.42% accuracy, outperforming established baselines by 5.63%. Integration with complementary self-supervised approaches through hierarchical fusion improved performance to 77.46%. Analysis revealed that preserving fine-grained temporal features through shallower transformation architecture significantly enhanced diagnostic accuracy, aligning with neuropsychological evidence that subtle timing patterns in speech provide sensitive markers of cognitive decline.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121739"},"PeriodicalIF":4.5,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing electrode placement and information capacity for local field potentials in cortex","authors":"Jace A. Willis ,&nbsp;Christopher E. Wright ,&nbsp;Ruoqian Zhu ,&nbsp;Yilan Ruan ,&nbsp;Joshua Stallings ,&nbsp;Amada M. Abrego ,&nbsp;Takfarinas Medani ,&nbsp;Promit Moitra ,&nbsp;Arjun Ramakrishnan ,&nbsp;Charles E. Schroeder ,&nbsp;Anand A. Joshi ,&nbsp;Nitin Tandon ,&nbsp;Richard M. Leahy ,&nbsp;John C. Mosher ,&nbsp;John P. Seymour","doi":"10.1016/j.neuroimage.2026.121747","DOIUrl":"10.1016/j.neuroimage.2026.121747","url":null,"abstract":"<div><div>Recent neurosurgery advancements include improved stereotactic targeting and increased density and specificity of electrophysiological evaluation. This study introduces a subject-specific, in silico modeling tool for optimizing electrode placement and maximizing coverage with a variety of devices. The basis for optimization is the Shannon-Hartley information capacity of field potentials derived from dipolar sources. The approach integrates subject-specific MRI data with finite element modeling (FEM) used to simulate the sensitivity of subdural and intracortical devices. Sensitivity maps, or lead fields, from these models enable the comparison of different electrode placements, contact sizes, contact configurations, and substrate properties, which are often overlooked factors. One key tool is a genetic algorithm that optimizes electrode placement by maximizing information capacity. Another is a sparse sensor method, Sparse Electrode Placement for Input Optimization (SEPIO), that selects the best sensor subsets for accurate source classification. We demonstrate several use cases for clinicians, engineers, and researchers. Overall, these open-source tools provide a quantitative framework to select devices from a neurosurgical armament and to optimize device and contact placement. Using these tools may help refine electrode coverage with low channel count devices while minimizing the burden of invasive surgery. The study demonstrates that optimized electrode placement significantly improves the information capacity and signal quality of local field potential (LFP) recordings. The tools developed offer a valuable approach for refining neurosurgical techniques and enhancing the design of neural implants.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121747"},"PeriodicalIF":4.5,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Longitudinal P2X7R and myelin PET reveal distinct neuroinflammatory and white matter signatures compared with TSPO PET and DTI-MRI in the TgF344-AD rat model","authors":"Oscar Moreno ,&nbsp;Izaro Fernández ,&nbsp;Sandra Plaza-García ,&nbsp;Daniel Padro ,&nbsp;Zuriñe Baz ,&nbsp;Pedro Ramos-Cabrer ,&nbsp;Abraham Martín ,&nbsp;Jordi Llop","doi":"10.1016/j.neuroimage.2026.121753","DOIUrl":"10.1016/j.neuroimage.2026.121753","url":null,"abstract":"<div><div>Emerging evidence links glial activation, particularly microglia, to Alzheimer’s disease (AD) progression. While TSPO PET (positron emission tomography) imaging detects neuroinflammation, its limitations drive interest in alternative targets like the P2X7 receptor. Myelin loss, potentially tied to chronic inflammation, is increasingly recognized as a key hallmark in AD, though the timing and relationship between neuroinflammation and demyelination remain poorly understood.</div><div>We conducted a longitudinal PET study from 4 to 22 months of age in TgF344-AD rats and wild-type controls to assess neuroinflammation with [¹⁸F]JNJ-64413739 (P2X7R) and [¹⁸F]DPA-714 (TSPO) only at 22 months, alongside myelin content using [¹⁸F]Florbetaben. Diffusion tensor imaging (DTI) was used to study variations on myelin structure in old AD and WT rats. <em>In vitro</em> studies, including autoradiography, immunofluorescence and staining were used to support the <em>in vivo</em> results.</div><div>[¹⁸F]JNJ-64413739 PET showed increased P2X7 receptor expression in AD and control animals over time, while [¹⁸F]DPA-714 PET showed significant differences between groups at 22 months. [¹⁸F]Florbetaben PET showed different uptake in white matter rich areas between groups with observed demyelination in AD rats at 20 months in the brain stem, supported by diffusional MRI findings.</div><div>In our study, P2X7R overexpression was attributed to aging rather than genotype effects, and no link was found to the observed demyelination in AD rats. Conversely, increased TSPO neuroinflammation in TgF344-AD rats correlated with myelin loss and the reported cognitive decline in this model. Our results support the use of the TgF344-AD model to study early AD pathology, focusing on neuroinflammation and white matter integrity.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121753"},"PeriodicalIF":4.5,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146046383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A nap can recalibrate homeostatic and associative synaptic plasticity in the human cortex","authors":"Kristoffer D. Fehér ,&nbsp;Pauline Henckaerts ,&nbsp;Valentin Hirsch ,&nbsp;Ulrike Bucsenez ,&nbsp;Marion Kuhn ,&nbsp;Jonathan G. Maier ,&nbsp;Carlotta L. Schneider ,&nbsp;Elisabeth Hertenstein ,&nbsp;Christian Mikutta ,&nbsp;Dieter Riemann ,&nbsp;Bernd Feige ,&nbsp;Christoph Nissen","doi":"10.1016/j.neuroimage.2026.121723","DOIUrl":"10.1016/j.neuroimage.2026.121723","url":null,"abstract":"<div><h3>Background</h3><div>Nighttime sleep renormalizes net synaptic strength (homeostatic plasticity) and the inducibility of long-term potentiation (LTP)-like plasticity (associative plasticity) in the cortex. However, whether an afternoon nap is sufficient for this process remains to be characterized.</div></div><div><h3>Methods</h3><div>Twenty healthy adults participated in a repeated measures sleep laboratory study with an adaptation and two experimental sessions – nap and wake (1:15–2:15 <span>pm</span>). After the nap or wake session, non-invasive indices of net synaptic strength (indexed by transcranial magnetic stimulation, TMS-probed corticospinal excitability and wake EEG theta activity) and inducibility of LTP-like plasticity (indexed by TMS-induced motor evoked potentials, MEPs, following paired associative stimulation, PAS) were assessed.</div></div><div><h3>Results</h3><div>We observed indices of reduced net synaptic strength after sleep compared to wakefulness, evidenced by a higher TMS intensity needed to induce MEPs and reduced wake EEG theta activity. Additionally, we observed an increase in the inducibility of associative synaptic plasticity after sleep, as evidenced by a greater increase in TMS-induced MEPs in response to PAS.</div></div><div><h3>Conclusions</h3><div>The study reinforces the restorative effect of sleep for homeostatic and associative synaptic plasticity in the human cortex and demonstrates that even a short nap can promote this process.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"327 ","pages":"Article 121723"},"PeriodicalIF":4.5,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145990036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}