Imaging neuroscience (Cambridge, Mass.)最新文献

{"title":"Systematic fMRI signal differences across cohorts alter lifespan trajectories of functional brain networks.","authors":"Micaela Y Chan, Liang Han, Gagan S Wig","doi":"10.1162/IMAG.a.1338","DOIUrl":"10.1162/IMAG.a.1338","url":null,"abstract":"<p><p>Large-scale lifespan neuroimaging studies increasingly integrate data across distinct cohorts to characterize trajectories of brain development and aging. However, systematic differences in acquisition protocols and hardware across cohorts can alter signal characteristics in ways that bias downstream analyses. Here, we examine three cohorts from the Human Connectome Project (HCP), spanning development (HCP-D), young adulthood (HCP-YA) and aging (HCP-A), to illustrate this issue and evaluate existing strategies to mitigate it. HCP has set standards for open, deeply phenotyped, high-resolution human neuroimaging, which are frequently used as high-quality reference datasets in tool validation, replication studies, and cross-cohort meta-analyses. However, neuroimaging acquisitions have differed across HCP cohorts because of changes in scanner hardware and acquisition sequences across study phases. Because of HCP's widespread usage, even modest protocol differences between cohorts-and their downstream effects-can have outsized impacts on the field of neuroscience research. Our analysis reveals that the HCP-YA cohort exhibits systematically weaker temporal signal-to-noise ratio (tSNR) relative to HCP-D/A. These signal quality discrepancies propagate to downstream analyses, leading to differences in overall resting-state functional correlations and whole-brain and node-level measures of resting-state network organization (e.g., system segregation, modularity, participation coefficient). Consistent with protocol-driven signal differences, resting-state network measures derived from HCP-YA depart from expected lifespan trajectories, as confirmed by examination of two other lifespan datasets. Harmonization approaches accounting for protocol and scanner-model differences substantially lessen these artifactual differences in brain network measures. Our findings underscore that signal differences do not merely introduce noise, but can qualitatively alter estimated lifespan trajectories of functional network organization, including partially inverting expected lifespan patterns. Without appropriate harmonization, analyses that combine HCP cohorts can therefore result in biologically misleading inferences about brain development and aging. We demonstrate how small acquisition differences bias resting-state-derived network metrics, and how these effects can be mitigated. This work advances best practices for valid inference in multi-cohort lifespan neuroscience research.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13504956/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148820569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometric properties of caudate and putamen mark the progression of Huntington's disease.","authors":"Dorian Pustina, Haikel Bogale, Sandhitsu Das, Brian Avants, Swati Sathe, Cristina Sampaio, Andrew Wood","doi":"10.1162/IMAG.a.1351","DOIUrl":"10.1162/IMAG.a.1351","url":null,"abstract":"<p><p>Volumetric MRI of the caudate and putamen is a robust biomarker of striatal degeneration in Huntington's disease (HD). Striatal degeneration may be reflected beyond volume alone, in additional shape and intensity alterations. We evaluated five novel morphometric biomarkers of the caudate and putamen-curvature, flatness, eccentricity, elongation, and focal assessment of divergence estimation (FADE; T1-weighted signal shift)-using cross-sectional and longitudinal MRI data from healthy controls (N = 100), individuals with premanifest HD (preHD; N = 100), and individuals with early manifest HD (earlyHD; N = 100). Geometric measures showed significant cross-sectional group differences with large effect sizes, although volume remained the most sensitive individual biomarker. Longitudinally, most geometric measures detected abnormal rates of change, particularly in the caudate, with medium-to-large effect sizes. The direct comparison between preHD and earlyHD proved challenging for all biomarkers; however, elongation yielded the largest effect size and outperformed volume in this comparison. FADE detected abnormal signal increases in HD groups at baseline but not longitudinally. Power analyses confirmed that volume retains the highest sensitivity, while geometric measures follow closely. Combining volume and geometric measures improved the cross-validated classification accuracy starting at low sample sizes. Correlations between scores and results from atrophy simulations confirmed that HD leaves two signatures on the striatum: loss of volume and loss of shape. While volume remains the strongest standalone biomarker, geometric measures provide complementary information that sharpens disease detection and may enhance biomarker frameworks in future HD clinical trials.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13504955/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148820571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metabolic connectivity alterations in amyotrophic lateral sclerosis: Individual network analysis based on Wasserstein distances.","authors":"Chunmeng Tang, Joke De Vocht, Philip Van Damme, Koen Van Laere, Michel Koole","doi":"10.1162/IMAG.a.1343","DOIUrl":"10.1162/IMAG.a.1343","url":null,"abstract":"<p><p>Molecular connectivity analysis with positron emission tomography (PET) imaging offers a promising approach for characterising brain network alterations in neurodegenerative disorders. In this study, we introduce Wasserstein distance (WD) as an alternative to Kullback-Leibler divergence similarity estimation (KLSE) for constructing single-subject metabolic connectivity networks. Using ¹⁸F-FDG PET data from 167 individuals with amyotrophic lateral sclerosis (ALS), 36 healthy volunteers (HV), and 25 ALS mimics, we generated WD- and KLSE-based connectivity matrices across 77 atlas-defined brain regions and evaluated corresponding graph theory-based nodal metrics. WD- and KLSE-derived nodal measures were strongly correlated, indicating methodological consistency. Compared with HVs, age-matched subjects in the ALS group (ALS_amHV) showed significant alterations in frontal, temporal, cerebellar, and occipital network nodes, with WD-based metrics revealing differences across more brain regions than the KLSE-based approach. Support vector machine classification of ALS_amHV vs. HV demonstrated that both connectivity approaches matched voxel-wise PET performance with accuracy higher than 0.80 (yet significantly lower than voxel-wise data, <math><mrow><mi>p</mi> <mo><</mo> <mn>0.05</mn></mrow> </math> ), while significantly outperforming image-based classification for ALS vs. ALS mimics ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.01</mn></mrow> </math> ), with WD achieving the highest accuracy of 0.65 ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ). These findings support WD-based metabolic connectivity as a sensitive, data-efficient framework for detecting disease-related network alterations and motivate its application to a broader range of PET tracers and cohorts.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13494962/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Massively parallelized brain tractography using compute clusters, supercomputers, and graphics processing units.","authors":"John Kruper, Ariel Rokem","doi":"10.1162/IMAG.a.1341","DOIUrl":"10.1162/IMAG.a.1341","url":null,"abstract":"<p><p>Tractography based on diffusion-weighted MRI (dMRI) is the predominant <i>in vivo</i> method for mapping the brain's white matter. However, it is also one of the most computationally demanding steps in neuroimaging data analysis-requiring the generation and filtering of millions of streamlines per subject. Over the past decade, high-performance computing (HPC) and graphics processing units (GPUs) have reshaped the landscape of tractography. What once required hours or days per subject can now be performed in minutes or even seconds. This facilitates faster processing of large-scale studies and high-resolution datasets, supports real-time clinical applications, and enables more computationally demanding algorithms. This review surveys the recent wave of applications of HPC and GPUs to massively parallelize tractography. We discuss how these advances have accelerated tractography pipelines, identify common strategies for parallelization, and highlight opportunities where further parallelization could improve efficiency and accuracy.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13492401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functionally distinct alpha components are differentially modulated by attention and affect behavior.","authors":"Gamze Bilgen, Elie Rassi, Alma ElShafei, Julio Rodriguez-Larios, Saskia Haegens","doi":"10.1162/IMAG.a.1335","DOIUrl":"10.1162/IMAG.a.1335","url":null,"abstract":"<p><p>Alpha oscillations (~10 Hz) have been hypothesized to gate information flow in the brain by inhibiting task-irrelevant and disinhibiting task-relevant processing. Recent work using independent component analysis (ICA) showed that functionally distinct alpha components co-exist during a working-memory task. Here, we recorded magnetoencephalography (MEG) while participants performed a visual delayed match-to-sample task with pre- and retro-cueing to investigate the gating role of alpha and the impact of top-down attention on these dynamics. Consistent with prior findings, we observed that alpha power temporally tracked the presentation of the informative cue. Using ICA, we replicate previous results regarding the co-existence of two distinct alpha components (Alpha1 and Alpha2), and extend these findings by showing that these alpha components are differentially modulated during the decision delay with opposite relationship to behavioral outcomes. Furthermore, we report on preparatory mechanisms involving Alpha1 and Alpha2 and show that their subsequent impact on task performance varies with cue informativeness and the task relevance of upcoming input. When task relevance was unknown (i.e., in the retro-cue condition), increases in Alpha1 power before task-relevant input were associated with poorer performance. In contrast, when task relevance was known (i.e., in the pre-cue condition), pre-stimulus increases in Alpha2 power before task-irrelevant (i.e., to-be unattended) input was associated with better task performance. Combined, these findings are in line with our hypothesis that Alpha1 rhythms modulate visual processing while Alpha2 rhythms modulate the short-term storage of visual information. Crucially, these distinct (anticipatory) alpha dynamics only became evident using a decomposition approach, emphasizing the necessity of analysis methods that can disentangle overlapping rhythmic dynamics that stem from different sources.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13483082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data quality biases normative models derived from fetal brain MRI.","authors":"Thomas Sanchez, Angeline Mihailov, Gerard Martí-Juan, Nadine Girard, Aurélie Manchon, Mathieu Milh, Elisenda Eixarch, Vincent Dunet, Mériam Koob, Léo Pomar, Joanna Sichitiu, Miguel A González Ballester, Oscar Camara, Gemma Piella, Meritxell Bach Cuadra, Guillaume Auzias","doi":"10.1162/IMAG.a.1337","DOIUrl":"10.1162/IMAG.a.1337","url":null,"abstract":"<p><p>Normative modeling is increasingly used to characterize typical growth trajectories and identify atypical neurodevelopment, including early brain development using magnetic resonance imaging (MRI) acquired before birth. Recent work has emphasized the importance of large sample sizes for accurate and robust centile estimation. In this study, we investigate how image quality influences fetal brain normative models, a critical factor in this context where MRI is acquired on a moving fetus <i>in utero</i>. Using a multi-centric cohort of 635 fetal MRI scans, we applied a standardized visual quality control (QC) protocol with continuous quality ratings. We fit normative models for multiple brain structures under progressively relaxed QC stringency, and quantified the deviations in centile estimates relative to a high-quality reference subgroup. Our results showed that including lower-quality data systematically biased normative centiles, with the strongest effects observed in the outer centiles, particularly the lower tail (1st-10th). Bias increased progressively as QC stringency was relaxed and could not be attributed solely to the number of scans used to fit the models. Quality-induced bias was structure dependent, and often not visually apparent at the segmentation level. These findings highlight that image quality is an important source of bias in normative fetal brain modeling, and that increasing sample size at the expense of quality may systematically affect centile estimates, potentially jeopardizing the utility of the model.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13479347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decision processes underlying effort avoidance and their relationship with metacognition.","authors":"Kazuki Yoshida, Ryuji Saito, Ryota Hayashi, Seiichi Watanabe, Hiroki Okada","doi":"10.1162/IMAG.a.1332","DOIUrl":"10.1162/IMAG.a.1332","url":null,"abstract":"<p><p>Effort-based decision making relies on cost-benefit computations that require individuals to evaluate expected rewards against the effort needed to obtain them. However, the temporal dynamics underlying the neural processing of reward and effort, as well as the influence of metacognitive ability on this process, remain unclear. The present study investigated these dynamics by recording electroencephalogram (EEG) data while participants performed an effort-based decision-making task and by assessing their metacognitive ability. Decoding analyses of the EEG data revealed that neural representations of effort-avoidance decisions emerged early (approximately 260 ms after stimulus onset) and remained stable over time. Crucially, representational similarity analysis showed that the brain processes reward information before effort information. Although both types of information were primarily represented in parietal electrodes, effort processing engaged a broader network that also included frontal regions. Behaviorally, higher metacognitive ability was associated with better task performance and reduced sensitivity to immediate rewards. These findings suggest a sequential decision-making process in which individuals first evaluate potential rewards and subsequently integrate information about the required effort when deciding whether to avoid effort. Furthermore, higher metacognitive ability appears to buffer against immediate reward impulses, thereby promoting long-term goal-directed behavior. Overall, this study provides direct neural evidence for the temporal dynamics of cost-benefit computation and highlights the critical role of metacognition in effort-based decision making.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13479346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human cerebellar microstructure with 7T MRI: Spherical b-tensor encoding and super resolution reconstruction.","authors":"Emma J P Brouwer, Wietske van der Zwaag, René In 't Zandt, Nikos Priovoulos, Filip Szczepankiewicz, Markus Nilsson, Karin Markenroth Bloch","doi":"10.1162/IMAG.a.1304","DOIUrl":"10.1162/IMAG.a.1304","url":null,"abstract":"<p><p>Cerebellar function and structure remain less studied compared with the cerebrum, and while high-resolution 7T gives superior anatomical information, methods to visualise its microstructure in vivo are not widely available. However, improved understanding of cerebellar microstructure is highly clinically relevant: the granular layer of the cerebellum, located in the grey matter, contains the majority of the brain's neurons and has been shown in post mortem studies to be affected by various diseases. The limited spatial resolution of MR does not resolve individual cell layers, but advanced diffusion techniques can infer properties of the microstructure. The aim of this work was to implement a combination of spherical b-tensor encoding using free waveforms, <math> <mrow><msubsup><mi>B</mi> <mn>1</mn> <mo>+</mo></msubsup> </mrow> </math> field shimming and super-resolution reconstruction (SRR) at 7T and evaluate the method in healthy volunteers. Diffusion-weighted signals and normalised diffusion signals were compared between the grey and white matter of the cerebellum and the occipital lobe as well as across 30 smaller ROIs in the cerebellum. The normalised diffusion signal obtained with spherical tensor encoding relates to cell density, and was found to be increased in the cerebellar cortex relative to the occipital cortex, consistent with previous findings. In the cerebellar cortex, the normalised diffusion signal followed a pattern similar to the thickness of the granular layer reported post mortem. Specifically, increased normalised diffusion signal values were found in lobules VII-VIII and Crus I/II, while lower values were found in lobules I-V. We successfully implemented spherical tensor encoding and SRR at 7T to image the human cerebellum, showing consistent results with those obtained at 3T, with an increased normalised diffusion signal in the cerebellum relative to that found in the cerebral cortex.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13471089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148762179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What makes our brain unique: Insights from a two-night fMRI-EEG sleep study.","authors":"Fan Nils Yang, Dante Picchioni, Jacco A de Zwart, Peter van Gelderen, Jeff H Duyn","doi":"10.1162/IMAG.a.1333","DOIUrl":"10.1162/IMAG.a.1333","url":null,"abstract":"<p><p>Functional connectomes (FCs) derived from fMRI are unique to an individual and can be reliably distinguished from those of others. However, the neurobiological basis for this \"fingerprint\"-like uniqueness remains unclear. One hypothesis holds that FC fingerprinting reflects idiosyncratic conscious experiences, while an alternative suggests that stable, intrinsic brain functional network structure underpins individual specificity. To test these hypotheses, we analyzed FC fingerprints across sleep stages using a two-night concurrent EEG-fMRI dataset from healthy participants and exploited the natural fluctuations in consciousness that occur across the sleep-wake cycle: during sleep, consciousness gradually diminishes from N1 to N3 non-REM (NREM) sleep, then partially reemerges during REM sleep. Contrary to the consciousness-dependent hypothesis, we found that FC fingerprinting remained robust, and in fact improved during deep, unconscious sleep (N3) relative to wakefulness. Moreover, the brain networks contributing most to individual identification shifted across sleep stages, with hippocampal connectivity playing a larger role during N3. Finally, using a deep-learning classifier (multi-layer perceptron) on short (90 seconds) fMRI segments, we demonstrated high identification accuracy; notably, models trained on deep-sleep data generalized well across all stages, including wakefulness. These findings favor the view that individual-specific network architecture rather than moment-to-moment conscious experience underlies FC fingerprints, with important implications for their use as biomarkers in both healthy and clinical populations.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13459065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148714952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A robust and reproducible automated MRI pipeline for quantifying tissue outcomes after experimental stroke in multi-center preclinical networks.","authors":"Kirsten M Lynch, Ryan P Cabeen, Andreia Lopes de Morais, Xuyan Jin, Erendiz Tarakci, Jessica Lamb, Basavaraju G Sanganahalli, Jelena M Mihailovic, Yamileck Olivas-Garcia, David B Berry, Marcio A Diniz, Joseph Mandeville, Fahmeed Hyder, Daniel R Thedens, Ali Arbab, Shuning Huang, Adnan Bibic, Wyatt Austin, Bingren Hu, Mohammad B Khan, Pradip K Kamat, Arthur W Toga, Patrick Lyden, Cenk Ayata","doi":"10.1162/IMAG.a.1328","DOIUrl":"10.1162/IMAG.a.1328","url":null,"abstract":"<p><p>The failure to translate promising preclinical stroke therapies into clinical success is a multi-faceted problem; however, a critical contributing factor is the lack of rigorous, reproducible preclinical outcome measures. While magnetic resonance imaging (MRI) offers a translational alternative to traditional histology, its use in large, multi-site trials is challenged by data heterogeneity and the need for scalable analysis. To address this, we developed and validated a fully automated, open-source image analysis pipeline for the Stroke Preclinical Assessment Network (SPAN), a six-center preclinical trial network. The pipeline processed T2-weighted and apparent diffusion coefficient (ADC) maps from over 2,000 mice and rats, incorporating steps for cross-site data harmonization, deep learning-based brain extraction, and rule-based segmentation to quantify infarct volume, brain swelling, and atrophy. The pipeline demonstrated high accuracy, as automated lesion volumes strongly correlated with manual expert tracing on both MRI (R = 0.96) and 2,3,5-triphenyl-tetrazolium chloride (TTC)-stained tissue (R = 0.86). The U-net model for brain extraction achieved a Dice score of 0.96, and our harmonization method successfully reduced inter-site variability in quantitative MRI parameters. This robust and reproducible pipeline provides a scalable framework for standardizing tissue outcome assessment, enhancing the rigor of multi-site preclinical studies.</p>","PeriodicalId":73341,"journal":{"name":"Imaging neuroscience (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13455521/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148708680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}