Human Brain Mapping最新文献

{"title":"Impact of Deprivation and Preferential Usage on Functional Connectivity Between Early Visual Cortex and Category-Selective Visual Regions","authors":"Leland L. Fleming,&nbsp;Matthew K. Defenderfer,&nbsp;Pinar Demirayak,&nbsp;Paul Stewart,&nbsp;Dawn K. Decarlo,&nbsp;Kristina M. Visscher","doi":"10.1002/hbm.70064","DOIUrl":"https://doi.org/10.1002/hbm.70064","url":null,"abstract":"<p>Human behavior can be remarkably shaped by experience, such as the removal of sensory input. Many studies of conditions such as stroke, limb amputation, and vision loss have examined how removal of input changes brain function. However, an important question yet to be answered is: when input is lost, does the brain change its connectivity to preferentially use some remaining inputs over others? In individuals with healthy vision, the central portion of the retina is preferentially used for everyday visual tasks, due to its ability to discriminate fine details. When central vision is lost in conditions like macular degeneration, peripheral vision must be relied upon for those everyday tasks, with some portions receiving “preferential” usage over others. Using resting-state fMRI collected during total darkness, we examined how deprivation and preferential usage influence the intrinsic functional connectivity of sensory cortex by studying individuals with selective vision loss due to late stages of macular degeneration. Specifically, we examined functional connectivity between category-selective visual areas and the cortical representation of three areas of the retina: the lesioned area, a preferentially used region of the intact retina, and a non-preferentially used region. We found that cortical regions representing spared portions of the peripheral retina, regardless of whether they are preferentially used, exhibit plasticity of intrinsic functional connectivity in macular degeneration. Cortical representations of spared peripheral retinal locations showed stronger connectivity to MT, a region involved in processing motion. These results suggest that the long-term loss of central vision can produce widespread effects throughout spared representations in early visual cortex, regardless of whether those representations are preferentially used. These findings support the idea that connections to visual cortex maintain the capacity for change well after critical periods of visual development.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"45 17","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142685318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tractography-Based Automated Identification of Retinogeniculate Visual Pathway With Novel Microstructure-Informed Supervised Contrastive Learning","authors":"Sipei Li,&nbsp;Wei Zhang,&nbsp;Shun Yao,&nbsp;Jianzhong He,&nbsp;Jingjing Gao,&nbsp;Tengfei Xue,&nbsp;Guoqiang Xie,&nbsp;Yuqian Chen,&nbsp;Erickson F. Torio,&nbsp;Yuanjing Feng,&nbsp;Dhiego C. A. Bastos,&nbsp;Yogesh Rathi,&nbsp;Nikos Makris,&nbsp;Ron Kikinis,&nbsp;Wenya Linda Bi,&nbsp;Alexandra J. Golby,&nbsp;Lauren J. O'Donnell,&nbsp;Fan Zhang","doi":"10.1002/hbm.70071","DOIUrl":"10.1002/hbm.70071","url":null,"abstract":"<p>The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and expert labor costs, and is affected by inter-observer variability. In this paper, we present a novel deep learning framework, DeepRGVP, to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure-informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a new streamline-level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non-RGVP streamlines. In the experiments, we perform comparisons with several state-of-the-art deep learning methods that were designed for tractography parcellation. Furthermore, to assess the generalizability of the proposed RGVP method, we apply our method to dMRI tractography data from neurosurgical patients with pituitary tumors. In comparison with the state-of-the-art methods, we show superior RGVP identification results using DeepRGVP with significantly higher accuracy and F1 scores. In the patient data experiment, we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"45 17","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Method for Multimodal IVA Fusion Within a MISA Unified Model Reveals Markers of Age, Sex, Cognition, and Schizophrenia in Large Neuroimaging Studies","authors":"Rogers F. Silva,&nbsp;Eswar Damaraju,&nbsp;Xinhui Li,&nbsp;Peter Kochunov,&nbsp;Judith M. Ford,&nbsp;Daniel H. Mathalon,&nbsp;Jessica A. Turner,&nbsp;Theo G. M. van Erp,&nbsp;Tulay Adali,&nbsp;Vince D. Calhoun","doi":"10.1002/hbm.70037","DOIUrl":"10.1002/hbm.70037","url":null,"abstract":"<p>With the increasing availability of large-scale multimodal neuroimaging datasets, it is necessary to develop data fusion methods which can extract cross-modal features. A general framework, multidataset independent subspace analysis (MISA), has been developed to encompass multiple blind source separation approaches and identify linked cross-modal sources in multiple datasets. In this work, we utilized the multimodal independent vector analysis (MMIVA) model in MISA to directly identify meaningful linked features across three neuroimaging modalities—structural magnetic resonance imaging (MRI), resting state functional MRI and diffusion MRI—in two large independent datasets, one comprising of control subjects and the other including patients with schizophrenia. Results show several linked subject profiles (sources) that capture age-associated decline, schizophrenia-related biomarkers, sex effects, and cognitive performance. For sources associated with age, both shared and modality-specific brain-age deltas were evaluated for association with non-imaging variables. In addition, each set of linked sources reveals a corresponding set of cross-modal spatial patterns that can be studied jointly. We demonstrate that the MMIVA fusion model can identify linked sources across multiple modalities, and that at least one set of linked, age-related sources replicates across two independent and separately analyzed datasets. The same set also presented age-adjusted group differences, with schizophrenia patients indicating lower multimodal source levels. Linked sets associated with sex and cognition are also reported for the UK Biobank dataset.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"45 17","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11574741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Connectivity-Based Real-Time Functional Magnetic Resonance Imaging Neurofeedback in Nicotine Users: Mechanistic and Clinical Effects of Regulating a Meta-Analytically Defined Target Network in a Double-Blind Controlled Trial","authors":"Arezoo Taebi,&nbsp;Klaus Mathiak,&nbsp;Benjamin Becker,&nbsp;Greta Kristin Klug,&nbsp;Jana Zweerings","doi":"10.1002/hbm.70077","DOIUrl":"10.1002/hbm.70077","url":null,"abstract":"<p>One of the fundamental questions in real-time functional magnetic resonance imaging neurofeedback (rt-fMRI NF) investigations is the definition of a suitable neural target for training. Previously, we applied a meta-analytical approach to define a network-level target for connectivity-based rt-fMRI NF in substance use disorders. The analysis yielded consistent connectivity alterations between the insula and anterior cingulate cortex (ACC) as well as the dorsal striatum and the ACC. In the current investigation, we addressed the feasibility of regulating this network and its functional relevance using connectivity-based neurofeedback. In a double-blind, sham-controlled design, 60 nicotine users were randomly assigned to the experimental or sham control group for one NF training session. The preregistered primary outcome was defined as improved inhibitory control performance after regulation of the target network compared to sham control. Secondary outcomes were (1) neurofeedback-specific changes in functional connectivity of the target network; (2) changes in smoking behavior and impulsivity measures; and (3) changes in resting-state connectivity profiles. Our results indicated no differences in behavioral measures after receiving feedback from the target network compared to the sham feedback. Target network connectivity was increased during regulation blocks compared to rest blocks, however, the experimental and sham groups could regulate to a similar degree. Accordingly, the observed activation patterns may be related to the mental strategies used during regulation attempts irrespective of the group assignment. We discuss several crucial factors regarding the efficacy of a single-session connectivity-based neurofeedback for the target network. This includes high fluctuation in the connectivity values of the target network that may impact controllability of the signal. To our knowledge, this investigation is the first randomized, double-blind controlled real-time fMRI study in nicotine users. This raises the question of whether previously observed effects in nicotine users are specific to the neurofeedback signal or reflect more general self-regulation attempts.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"45 17","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11574450/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imaging-genomic spatial-modality attentive fusion for studying neuropsychiatric disorders","authors":"Md Abdur Rahaman,&nbsp;Yash Garg,&nbsp;Armin Iraji,&nbsp;Zening Fu,&nbsp;Peter Kochunov,&nbsp;L. Elliot Hong,&nbsp;Theo G. M. Van Erp,&nbsp;Adrian Preda,&nbsp;Jiayu Chen,&nbsp;Vince Calhoun","doi":"10.1002/hbm.26799","DOIUrl":"10.1002/hbm.26799","url":null,"abstract":"<p>Multimodal learning has emerged as a powerful technique that leverages diverse data sources to enhance learning and decision-making processes. Adapting this approach to analyzing data collected from different biological domains is intuitive, especially for studying neuropsychiatric disorders. A complex neuropsychiatric disorder like schizophrenia (SZ) can affect multiple aspects of the brain and biologies. These biological sources each present distinct yet correlated expressions of subjects' underlying physiological processes. Joint learning from these data sources can improve our understanding of the disorder. However, combining these biological sources is challenging for several reasons: (i) observations are domain specific, leading to data being represented in dissimilar subspaces, and (ii) fused data are often noisy and high-dimensional, making it challenging to identify relevant information. To address these challenges, we propose a multimodal artificial intelligence model with a novel fusion module inspired by a bottleneck attention module. We use deep neural networks to learn latent space representations of the input streams. Next, we introduce a two-dimensional (spatio-modality) attention module to regulate the intermediate fusion for SZ classification. We implement spatial attention via a dilated convolutional neural network that creates large receptive fields for extracting significant contextual patterns. The resulting joint learning framework maximizes complementarity allowing us to explore the correspondence among the modalities. We test our model on a multimodal imaging-genetic dataset and achieve an SZ prediction accuracy of 94.10% (<i>p</i> &lt; .0001), outperforming state-of-the-art unimodal and multimodal models for the task. Moreover, the model provides inherent interpretability that helps identify concepts significant for the neural network's decision and explains the underlying physiopathology of the disorder. Results also show that functional connectivity among subcortical, sensorimotor, and cognitive control domains plays an important role in characterizing SZ. Analysis of the spatio-modality attention scores suggests that structural components like the supplementary motor area, caudate, and insula play a significant role in SZ. Biclustering the attention scores discover a multimodal cluster that includes genes CSMD1, ATK3, MOB4, and HSPE1, all of which have been identified as relevant to SZ. In summary, feature attribution appears to be especially useful for probing the transient and confined but decisive patterns of complex disorders, and it shows promise for extensive applicability in future studies.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"45 17","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11576332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Post Traumatic Epilepsy Using MR-Based Imaging Markers","authors":"Haleh Akrami,&nbsp;Wenhui Cui,&nbsp;Paul E. Kim,&nbsp;Christianne N. Heck,&nbsp;Andrei Irimia,&nbsp;Karim Jerbi,&nbsp;Dileep Nair,&nbsp;Richard M. Leahy,&nbsp;Anand A. Joshi","doi":"10.1002/hbm.70075","DOIUrl":"10.1002/hbm.70075","url":null,"abstract":"<p>Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Despite the high prevalence of PTE, current methods for predicting its occurrence remain limited. In this study, we aimed to identify imaging-based markers for the prediction of PTE using machine learning. Specifically, we examined three imaging features: Lesion volumes, resting-state fMRI-based measures of functional connectivity, and amplitude of low-frequency fluctuation (ALFF). We employed three machine-learning methods, namely, kernel support vector machine (KSVM), random forest, and an artificial neural network (NN), to develop predictive models. Our results showed that the KSVM classifier, with all three feature types as input, achieved the best prediction accuracy of 0.78 AUC (area under the receiver operating characteristic (ROC) curve) using nested cross-validation. Furthermore, we performed voxel-wise and lobe-wise group difference analyses to investigate the specific brain regions and features that the model found to be most helpful in distinguishing PTE from non-PTE populations. Our statistical analysis uncovered significant differences in bilateral temporal lobes and cerebellum between PTE and non-PTE groups. Overall, our findings demonstrate the complementary prognostic value of MR-based markers in PTE prediction and provide new insights into the underlying structural and functional alterations associated with PTE.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"45 17","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11574740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of ComBat Harmonization for Reducing Across-Tracer Differences in Regional Amyloid PET Analyses","authors":"Braden Yang,&nbsp;Tom Earnest,&nbsp;Sayantan Kumar,&nbsp;Deydeep Kothapalli,&nbsp;Tammie Benzinger,&nbsp;Brian Gordon,&nbsp;Aristeidis Sotiras","doi":"10.1002/hbm.70068","DOIUrl":"10.1002/hbm.70068","url":null,"abstract":"<p>Differences in amyloid positron emission tomography (PET) radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid-β uptake estimates. Harmonization of tracer-specific biases is crucial for optimal performance of downstream tasks. Here, we investigated the efficacy of ComBat, a data-driven harmonization model, for reducing tracer-specific biases in regional amyloid PET measurements from [¹⁸F]-florbetapir (FBP) and [¹¹C]-Pittsburgh compound-B (PiB). One hundred thirteen head-to-head FBP-PiB scan pairs, scanned from the same subject within 90 days, were selected from the Open Access Series of Imaging Studies 3 (OASIS-3) dataset. The Centiloid scale, ComBat with no covariates, ComBat with biological covariates, and GAM-ComBat with biological covariates were used to harmonize both global and regional amyloid standardized uptake value ratios (SUVR). Variants of ComBat, including longitudinal ComBat and PEACE, were also tested. Intraclass correlation coefficient (ICC) and mean absolute error (MAE) were computed to measure the absolute agreement between tracers. Additionally, longitudinal amyloid SUVRs from an anti-amyloid drug trial were simulated using linear mixed effects modeling. Differences in rates-of-change between simulated treatment and placebo groups were tested, and change in statistical power/Type-I error after harmonization was quantified. In the head-to-head tracer comparison, ComBat with no covariates was the best at increasing ICC and decreasing MAE of both global summary and regional amyloid PET SUVRs between scan pairs of the same group of subjects. In the clinical trial simulation, harmonization with both Centiloid and ComBat increased statistical power of detecting true rate-of-change differences between groups and decreased false discovery rate in the absence of a treatment effect. The greatest benefit of harmonization was observed when groups exhibited differing FBP-to-PiB proportions. ComBat outperformed the Centiloid scale in harmonizing both global and regional amyloid estimates. Additionally, ComBat improved the detection of rate-of-change differences between clinical trial groups. Our findings suggest that ComBat is a viable alternative to Centiloid for harmonizing regional amyloid PET analyses.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"45 16","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11561838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hearing Function Moderates Age-Related Differences in Brain Morphometry in the HCP Aging Cohort","authors":"Robert M. Kirschen,&nbsp;Amber M. Leaver","doi":"10.1002/hbm.70074","DOIUrl":"10.1002/hbm.70074","url":null,"abstract":"<p>There are well-established relationships between aging and neurodegenerative changes, and between aging and hearing loss. The goal of this study was to determine how structural brain aging is influenced by hearing loss. Human Connectome Project Aging data were analyzed, including T1-weighted Magnetic Resonance Imaging (MRI) and Words in noise (WIN) thresholds (<i>n</i> = 623). Freesurfer extracted gray and white matter volume, and cortical thickness, area, and curvature. Linear regression models targeted (1) interactions between age and WIN threshold and (2) correlations with WIN threshold adjusted for age, both corrected for false discovery rate (p_FDR &lt; 0.05). WIN threshold moderated age-related increase in volume in bilateral inferior lateral ventricles, with a higher threshold associated with increased age-related ventricle expansion. Age-related differences in the occipital cortex also increased with higher WIN thresholds. When controlling for age, high WIN threshold was correlated with reduced cortical thickness in Heschl's gyrus, calcarine sulcus, and other sensory regions, and reduced temporal lobe white matter. Older volunteers with poorer hearing and cognitive scores had the lowest volume in left parahippocampal white matter. These results suggest that better hearing is associated with reduced age-related differences in medial temporal lobe, while better hearing at any age is associated with greater cortical tissue in auditory and other sensory regions. Future longitudinal studies are needed to assess the causal nature of these relationships, but these results indicate interventions that preserve or protect hearing function may combat some neurodegenerative changes in aging.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"45 16","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11561423/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-invasive Assessment of Cerebral Hemodynamics Using Resting-State Functional Magnetic Resonance Imaging in Multiple Sclerosis and Age-Related White Matter Lesions","authors":"Ahmed Khalil,&nbsp;Susanna Asseyer,&nbsp;Rebekka Rust,&nbsp;Tanja Schmitz-Hübsch,&nbsp;Jochen B. Fiebach,&nbsp;Friedemann Paul,&nbsp;Claudia Chien","doi":"10.1002/hbm.70076","DOIUrl":"10.1002/hbm.70076","url":null,"abstract":"<p>Perfusion changes in white matter (WM) lesions and normal-appearing brain regions play an important pathophysiological role in multiple sclerosis (MS). However, most perfusion imaging methods require exogenous contrast agents, the repeated use of which is discouraged. Using resting-state functional MRI (rs-fMRI), we aimed to investigate differences in perfusion between white matter lesions and normal-appearing brain regions in MS and healthy participants. A total of 41 MS patients and 41 age- and sex-matched healthy participants received rs-fMRI, from which measures of cerebral hemodynamics and oxygenation were extracted and compared across brain regions and study groups using within- and between-group nonparametric tests, linear mixed models, and robust multiple linear regression. We found longer blood arrival times and lower blood volumes in lesions than in normal-appearing WM. Higher blood volumes were found in MS patients' deep WM lesions compared to healthy participants, and blood arrival time was more delayed in MS patients' deep WM lesions than in healthy participants. Delayed blood arrival time in the cortical grey matter was associated with greater cognitive impairment in MS patients. Perfusion imaging using rs-fMRI is useful for WM lesion characterization. rs-fMRI-based blood arrival times and volumes are associated with cognitive function.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"45 16","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11558553/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Site-Wise Reliability Analysis of the ABCD Diffusion Fractional Anisotropy and Cortical Thickness: Impact of Scanner Platforms","authors":"Yezhi Pan,&nbsp;L. Elliot Hong,&nbsp;Ashley Acheson,&nbsp;Paul M. Thompson,&nbsp;Neda Jahanshad,&nbsp;Alyssa H. Zhu,&nbsp;Jiaao Yu,&nbsp;Chixiang Chen,&nbsp;Tianzhou Ma,&nbsp;Ho-Ling Liu,&nbsp;Jelle Veraart,&nbsp;Els Fieremans,&nbsp;Nicole R. Karcher,&nbsp;Peter Kochunov,&nbsp;Shuo Chen","doi":"10.1002/hbm.70070","DOIUrl":"10.1002/hbm.70070","url":null,"abstract":"<p>The Adolescent Brain and Cognitive Development (ABCD) project is the largest study of adolescent brain development. ABCD longitudinally tracks 11,868 participants aged 9–10 years from 21 sites using standardized protocols for multi-site MRI data collection and analysis. While the multi-site and multi-scanner study design enhances the robustness and generalizability of analysis results, it may also introduce nonbiological variances including scanner-related variations, subject motion, and deviations from protocols. ABCD imaging data were collected biennially within a period of ongoing maturation in cortical thickness and integrity of cerebral white matter. These changes can bias the classical test–retest methodologies, such as intraclass correlation coefficients (ICC). We developed a site-wise adaptive ICC (AICC) to evaluate the reliability of imaging-derived phenotypes while accounting for ongoing brain development. AICC iteratively estimates the population-level age-related brain development trajectory using a weighted mixed model and updates age-corrected site-wise reliability until convergence. We evaluated the test–retest reliability of regional fractional anisotropy (FA) measures from diffusion tensor imaging and cortical thickness (CT) from structural MRI data for each site. The mean AICC for 20 FA tracts across sites was 0.61 ± 0.19, lower than the mean AICC for CT in 34 regions across sites, 0.76 ± 0.12. Remarkably, sites using Siemens scanners consistently showed significantly higher AICC values compared with those using GE/Philips scanners for both FA (AICC = 0.71 ± 0.12 vs. 0.46 ± 0.17, <i>p</i> &lt; 0.001) and CT (AICC = 0.80 ± 0.10 vs. 0.69 ± 0.11, <i>p</i> &lt; 0.001). These findings demonstrate site-and-scanner related variations in data quality and underscore the necessity for meticulous data curation in subsequent association analyses.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"45 16","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11551787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}