Neuroinformatics最新文献

{"title":"Reducing Inter-Individual Differences in Task fMRI Preprocessing with OGRE (One-Step General Registration and Extraction) Preprocessing.","authors":"Mark P McAvoy, Lei Liu, Ruiwen Zhou, Benjamin A Philip","doi":"10.1007/s12021-025-09741-6","DOIUrl":"10.1007/s12021-025-09741-6","url":null,"abstract":"<p><p>Volumetric analysis methods continue to enjoy great popularity in the analysis of task-related functional MRI (fMRI) data. Among these methods, omnipresent throughout the field is the software tool FEAT, part of the FSL package (FMRIB, Oxford, UK). However, FEAT preprocessing involves multi-step interpolation (multiple sequential transformations), which at typical fMRI field strengths (i.e. 3T) has not been systematically compared with with alternative methods, in part due to lack of software tools compatible with FSL FEAT analysis. Here we developed the One-step General Registration and Extraction (OGRE) pipeline to combine FreeSurfer brain extraction, FSL FNIRT registration, one-step interpolation of preprocessing transformations, and output for FSL FEAT analysis. We compared OGRE with two alternative preprocessing methods (fMRIPrep and FSL) in a dataset wherein adult human volunteers (N = 53) performed a precision drawing task during fMRI scanning. OGRE preprocessing led to lower inter-subject variability than FSL (p = 7.3 × 10− 9) or fMRIPrep (p = 0.036); OGRE also led to the strongest detection of task-related magnitude in primary motor cortex contralateral to movement (OGRE > FSL p = 4.2 × 10− 4, other method pairs not significant). This pattern suggests that one-step interpolation (used by OGRE and fMRIPrep) reduces inter-individual variability during moderate-resolution (2 mm3) volumetric analysis of task fMRI data.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 4","pages":"47"},"PeriodicalIF":2.9,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12484288/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sketching a Space of Brain States.","authors":"Maria Mannone, Patrizia Ribino, Peppino Fazio, Norbert Marwan","doi":"10.1007/s12021-025-09739-0","DOIUrl":"10.1007/s12021-025-09739-0","url":null,"abstract":"<p><p>Brain functional connectivity alterations, that is, pathological changes in the signal exchange between areas of the brain, are occurring in several neurological diseases, including neurodegenerative and neuropsychiatric ones. They consist in changes in how brain functional networks work. By conceptualising a brain space as a space whose points are connectome configurations representing brain functional states, changes in brain network functionality can be represented by paths between these points. Paths from a healthy state to a diseased one, or between diseased states as instances of disease progression, are modelled as the action of the Krankheit-Operator, that produces changes from a brain functional state to another one. This study proposes a formal representation of the space of brain states and presents its computational definition. Moreover, references to patients affected by Parkinson's disease, schizophrenia, and Alzheimer-Perusini's disease are included for discussing the proposed approach and possible developments of the research toward a generalisation.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 3","pages":"45"},"PeriodicalIF":2.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12405039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144976640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Overcoming Site Variability in Multisite fMRI Studies: an Autoencoder Framework for Enhanced Generalizability of Machine Learning Models.","authors":"Fahad Almuqhim, Fahad Saeed","doi":"10.1007/s12021-025-09746-1","DOIUrl":"10.1007/s12021-025-09746-1","url":null,"abstract":"<p><p>Harmonizing multisite functional magnetic resonance imaging (fMRI) data is crucial for eliminating site-specific variability that hinders the generalizability of machine learning models. Traditional harmonization techniques, such as ComBat, depend on additive and multiplicative factors, and may struggle to capture the non-linear interactions between scanner hardware, acquisition protocols, and signal variations between different imaging sites. In addition, these statistical techniques require data from all the sites during their model training which may have the unintended consequence of data leakage for ML models trained using this harmonized data. The ML models trained using this harmonized data may result in low reliability and reproducibility when tested on unseen data sets, limiting their applicability for general clinical usage. In this study, we propose Autoencoders (AEs) as an alternative for harmonizing multisite fMRI data. Our designed and developed framework leverages the non-linear representation learning capabilities of AEs to reduce site-specific effects while preserving biologically meaningful features. Our evaluation using Autism Brain Imaging Data Exchange I (ABIDE-I) dataset, containing 1,035 subjects collected from 17 centers demonstrates statistically significant improvements in leave-one-site-out (LOSO) cross-validation evaluations. All AE variants (AE, SAE, TAE, and DAE) significantly outperformed the baseline mode (p < 0.01), with mean accuracy improvements ranging from 3.41% to 5.04%. Our findings demonstrate the potential of AEs to harmonize multisite neuroimaging data effectively enabling robust downstream analyses across various neuroscience applications while reducing data-leakage, and preservation of neurobiological features. Our open-source code is made available at https://github.com/pcdslab/Autoencoder-fMRI-Harmonization .</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 3","pages":"46"},"PeriodicalIF":2.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13105160/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144976556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Mild Hepatic Encephalopathy Based on Multi-level Functional Connectivity Hypernetwork.","authors":"Chi Zhang, Fei Liu, Yue Cheng, Wen Shen, Gaoyan Zhang","doi":"10.1007/s12021-025-09734-5","DOIUrl":"10.1007/s12021-025-09734-5","url":null,"abstract":"<p><p>Early diagnosis of mild hepatic encephalopathy is important for the reversion of hepatic encephalopathy. Brain hyper-connectivity networks with hyperedges have showed good performance for diagnosis of neurological disorders. However, the previous hyper-connectivity networks is essentially low-level since the temporal synchronization of regional signal fluctuation is merely considered. Here, we propose a novel high-level hyper-connectivity network based on the resting state functional magnetic resonance imaging to capture the complex interactions among brain regions for better diagnosis of neurological disorders. Resting-state functional magnetic resonance imaging data from 36 mild hepatic encephalopathy patients and 36 cirrhotic patients with no mild hepatic encephalopathy are included in the study. Multi-level high-level hyper-connectivity networks are constructed firstly. Then, we define and extract node hyperdegree, hyperedge global importance and hyperedge dispersion from both low-level and high-level hyper-connectivity networks and combine them. Finally, gradient boosting decision tree is used for feature selection and classification. The leave-one-out cross-validation is used to evaluate the performance. The public ASD resting state functional magnetic resonance imaging datasets from 3 sites are also used as testing set to evaluate the generalization power of our method. Our method showed considerable performance in both experiments which confirms the effectiveness and generalization ability of the model. Besides, important regions and hyperedge features are identified for the interpretability.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 3","pages":"44"},"PeriodicalIF":2.9,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144976547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latent Growth Models of Longitudinal Changes in Functional Connectivity during Early Stage Psychosis.","authors":"Kristina M Holton, Shi Yu Chan, Austin J Brockmeier, Mei-Hua Hall","doi":"10.1007/s12021-025-09742-5","DOIUrl":"10.1007/s12021-025-09742-5","url":null,"abstract":"<p><p>Resting state functional magnetic resonance imaging (fMRI) is a useful technique to characterize functional connectivity patterns between regions of the brain, based on the Fisher-transformed Pearson correlations in the BOLD signal. Pinpointing how connectivity patterns change in neuropathies like early-stage psychosis (ESP) can help understand the disorders and track progression. Using study data from 21 ESP subjects with complete data for three consecutive scans, we examined connectivity changes throughout the whole brain with a region of interest (ROI) to ROI-based approach for ROI defined by the Harvard-Oxford cortical and subcortical atlases, supplemented by the AAL atlas for the cerebellum, and by networks defined by the CONN toolbox independent component analysis of the Human Connectome Project. We applied latent growth modelling, which is a type of structural equation modelling, to these connectivity measurements across baseline and follow-up visits. The models use age, community functioning, and negative symptoms at baselines as the covariates for subject-specific slope and intercept of the longitudinal measurements. After stringent thresholding cutoffs of root mean square error of approximation, standardized root mean square residual, comparative fit index, and Benjamini-Hochberg corrected p-value, we found a subset of connectivity measurements with significant longitudinal slopes (N = 18 atlas, N = 6 network), and used the subject's slope estimates to stratify these subjects into three clusters based on how the ROI-to-ROI correlations of functional connectivity change over time. The connections with significant slopes include atlas level regions like the temporal lobe, fronto-parietal lobe, and cerebellum, and network level patterns like the DMN, FPN, and Salience Networks. The structural equation modelling approach identifies ROIs whose functional connectivity changes over time, indicating the ROIs most dynamic during ESP. This highlights the utility of latent growth models for the analysis of longitudinal functional connectivity measures across the whole brain with relatively small sample sizes.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 3","pages":"43"},"PeriodicalIF":2.9,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NOWinBRAIN Public Repository: 3D Neuroimage Galleries.","authors":"Wieslaw L Nowinski","doi":"10.1007/s12021-025-09735-4","DOIUrl":"10.1007/s12021-025-09735-4","url":null,"abstract":"","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 3","pages":"42"},"PeriodicalIF":2.9,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144818073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated 3D Modeling and Functional Simulation of the Human Amygdala: A Novel Anatomical and Computational Analyses.","authors":"Eren Ogut","doi":"10.1007/s12021-025-09743-4","DOIUrl":"10.1007/s12021-025-09743-4","url":null,"abstract":"<p><p>The amygdala plays a central role in emotion, memory, and decision-making and comprises approximately 13 distinct nuclei with connectivity. Despite its functional importance, high-resolution subnuclear mapping is challenging. This study aimed to construct a 3D model of the anatomical location of the amygdala in the brain and a functional dynamic model of the amygdala, integrating deep learning and elastic shape metrics. We used multimodal datasets from the Julich-Brain Atlas, BigBrain Project, and FreeSurfer, which were aligned with the Montreal Neurological Institute (MNI) and Colin 27 spaces. Subnuclei segmentation was performed using a Bayesian Fully Convolutional Network (FCN), and geometric morphometrics were analyzed using elastic shape analysis on the unit sphere. Functional dynamics were simulated using a MATLAB-based model of the amygdala incorporating theta (4-8 Hz) and gamma (30-40 Hz) oscillations with spike-timing-dependent plasticity (STDP). The mean MNI coordinates of the left and right amygdalae were (-20, -4, -15) and (22, -2, -15), respectively, with an inter-amygdalar distance of 42.48 mm. The Dice Similarity Coefficients (DSCs) for FCN-based subnuclear segmentation were as follows: basolateral amygdala (BLA) nucleus = 0.89 ± 0.03, centromedial nucleus = 0.83 ± 0.04, and cortical nucleus = 0.81 ± 0.05. Principal component analysis of elastic shape metrics revealed post-traumatic stress disorder (PTSD)-related morphological deviations, with the first principal component (PC1) accounting for 38% of the variance (p < 0.01). Oscillatory simulations captured the BLA rhythm dynamics and STDP-induced synaptic changes. This study presents a comprehensive 3D model of the human amygdala that bridges anatomical accuracy with computational modeling. Unlike prior models that focus solely on structural or functional domains, our approach integrates subnuclear segmentation, morphometrics, and real-time functional simulation. This study introduces a fully integrated anatomical-functional 3D model of the human amygdala, providing a translational platform for neuromodulation targeting, psychiatric diagnostics, and computational neuroengineering applications.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 3","pages":"41"},"PeriodicalIF":2.9,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144795989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scikit-NeuroMSI: A Generalized Framework for Modeling Multisensory Integration.","authors":"Renato Paredes, Juan B Cabral, Peggy Seriès","doi":"10.1007/s12021-025-09738-1","DOIUrl":"10.1007/s12021-025-09738-1","url":null,"abstract":"<p><p>Multisensory integration is a fundamental neural mechanism crucial for understanding cognition. Multiple theoretical models exist to account for the computational processes underpinning this mechanism. However, there is an absence of a consolidated framework that facilitates the examination of multisensory integration across diverse experimental and computational contexts. We introduce Scikit-NeuroMSI, an accessible Python-based open-source framework designed to streamline the implementation and evaluation of computational models of multisensory integration. The capabilities of Scikit-NeuroMSI were demonstrated in enabling the implementation of multiple models of multisensory integration at different levels of analysis. Furthermore, we illustrate the utility of the software in systematically exploring the model's behavior in spatiotemporal causal inference tasks through parameter sweeps in simulations. Particularly, we conducted a comparative analysis of Bayesian and network models of multisensory integration to identify commonalities that may enable to bridge both levels of description, addressing a key research question within the field. We discuss the significance of this approach in generating computationally informed hypotheses in multisensory research. Recommendations for the improvement of this software and directions for future research using this framework are presented.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 3","pages":"40"},"PeriodicalIF":3.1,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12289733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of Gray Matter Structural Changes on Clinical Disability in Multiple Sclerosis: Voxel-and Surface-Based Analyses.","authors":"Hongping Chen, Weihua Zhang, Yuchao Ma, Jiayun Ren, Di Zhong","doi":"10.1007/s12021-025-09740-7","DOIUrl":"10.1007/s12021-025-09740-7","url":null,"abstract":"<p><p>This study used voxel- and surface-based morphometry to analyze the changes in gray matter structure in MS patients and their correlation with clinical scales. An analysis was conducted on the structural magnetic resonance imaging data of 30 patients with MS who met the inclusion criteria and 30 healthy controls (HCs). Clinical disability was evaluated using the Expanded Disability Status Scale (EDSS) and the timed 25-foot walk test (T25FW). Cognitive function was assessed with the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Psychiatric symptoms were measured via the Hamilton Anxiety Scale (HAMA) and Hamilton Depression Scale (HAMD). Imaging data were also collected from the MS and healthy control groups, and SPM12 and CAT12 analyzed the images. After controlling for age and gender, voxel- and surface-based morphometry were used to study inter-group differences. Finally, the discrepancy data were correlated with the clinical scales. Compared to the HC group, the gray matter volume reduction in the MS group was mainly concentrated in the deep gray matter, with a small portion located in the cortical gray matter (FWE-corrected p-value < 0.05). Cortical thickness was significantly reduced in multiple dispersed regions of the brain bilaterally in the MS group compared to hc (FWE-corrected p-value < 0.05), and there was no obvious anatomical connection between these regions. Correlation analyses revealed: A negative correlation between caudate nucleus volume and EDSS scores (R = -0.415, p = 0.031); a positive correlation between the right parahippocampal gyrus and HAMA scores (R = 0.392, p = 0.039); a positive correlations of the right postcentral gyrus with both MMSE (R = 0.433, p = 0.021) and MoCA scores (R = 0.431, p = 0.022); a positive correlation between the left paracentral lobule and MoCA scores (R = 0.389, p = 0.041). A pattern of multiple gray matter structural changes was identified in our study, and a clinical correlation between structural changes was found. Grey matter volume and cortical thickness hold substantial promise as markers of disease progression and have the potential to respond to neuroprotective treatments for MS neurodegeneration.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 3","pages":"39"},"PeriodicalIF":2.9,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Cerebrospinal Fluid (CSF) Pressure with Generative Adversarial Network Synthetic Plasma-CSF Biomarker Pairing.","authors":"Phani Paladugu, Rahul Kumar, Jahnavi Yelamanchi, Ethan Waisberg, Joshua Ong, Mouayad Masalkhi, Chirag Gowda, Ryung Lee, Dylan Amiri, Ram Jagadeesan, Nasif Zaman, Alireza Tavakkoli, Andrew G Lee","doi":"10.1007/s12021-025-09729-2","DOIUrl":"10.1007/s12021-025-09729-2","url":null,"abstract":"<p><p>Non-invasive intracranial pressure (ICP) monitoring can help clinicians safely and efficiently monitor spaceflight-associated neuro-ocular syndrome (SANS), idiopathic intracranial hypertension, and traumatic brain injury in astronauts. Current invasive ICP measurement techniques are unsuitable for austere environments like spaceflight. In this study, we explore the potential of plasma-derived cell-free RNA (cfRNA) biomarkers as non-invasive alternatives to cerebrospinal fluid (CSF) markers for ICP assessment. We conducted a secondary analysis of NASA's Open Science Data Repository datasets 363-364, focusing on plasma and CSF biomarkers related to ICP and neurovascular health. An ensemble model combining Support Vector Machine, Gradient Boosting Regressor, and Ridge Regression was developed to capture plasma-CSF biomarker relationships. To address limited sample size, we employed a Generative Adversarial Network (GAN) to generate synthetic plasma-CSF biomarker pairs, expanding the dataset from 29 to 279 samples. The model's performance was evaluated using Mean Squared Error (MSE) and validated against real biomarker data. The GAN-augmented ensemble model achieved high predictive accuracy with an MSE of 0.0044. Synthetic plasma-CSF pairs closely aligned with actual biomarker distributions, demonstrating their effectiveness in reducing overfitting and enhancing model robustness. Strong correlations between plasma-derived RNA biomarkers and corresponding CSF indicators support their potential as non-invasive proxies for ICP assessment. This study establishes a novel framework for non-invasive ICP monitoring using plasma cfRNA profiles enriched with GAN-generated synthetic data. The approach shows promise for both spaceflight and clinical applications, potentially broadening diagnostic capabilities for ICP-related conditions. However, further validation across diverse populations is necessary, along with careful consideration of bioethical and data security issues associated with synthetic data use in clinical diagnostics.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 3","pages":"38"},"PeriodicalIF":2.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}