Imaging Neuroscience最新文献

{"title":"Likelihood-free posterior estimation and uncertainty quantification for diffusion MRI models","authors":"Hazhar Sufi Karimi, Arghya Pal, Lipeng Ning, Y. Rathi","doi":"10.1162/imag_a_00088","DOIUrl":"https://doi.org/10.1162/imag_a_00088","url":null,"abstract":"Abstract Diffusion magnetic resonance imaging (dMRI) allows to estimate brain tissue microstructure as well as the connectivity of the white matter (known as tractography). Accurate estimation of the model parameters (by solving the inverse problem) is thus very important to infer the underlying biophysical tissue properties and fiber orientations. Although there has been extensive research on this topic with a myriad of dMRI models, most models use standard nonlinear optimization techniques and only provide an estimate of the model parameters without any information (quantification) about uncertainty in their estimation. Further, the effect of this uncertainty on the estimation of the derived dMRI microstructural measures downstream (e.g., fractional anisotropy) is often unknown and is rarely estimated. To address this issue, we first design a new deep-learning algorithm to identify the number of crossing fibers in each voxel. Then, at each voxel, we propose a robust likelihood-free deep learning method to estimate not only the mean estimate of the parameters of a multi-fiber dMRI model (e.g., the biexponential model), but also its full posterior distribution. The posterior distribution is then used to estimate the uncertainty in the model parameters as well as the derived measures. We perform several synthetic and in-vivo quantitative experiments to demonstrate the robustness of our approach for different noise levels and out-of-distribution test samples. Besides, our approach is computationally fast and requires an order of magnitude less time than standard nonlinear fitting techniques. The proposed method demonstrates much lower error (compared to existing methods) in estimating several metrics, including number of fibers in a voxel, fiber orientation, and tensor eigenvalues. The proposed methodology is quite general and can be used for the estimation of the parameters from any other dMRI model.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"55 4","pages":"1-22"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139875202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerated diffusion-weighted magnetic resonance imaging at 7 T: Joint reconstruction for shift-encoded navigator-based interleaved echo planar imaging (JETS-NAViEPI)","authors":"Zhengguo Tan, Patrick Alexander Liebig, R. Heidemann, F. B. Laun, Florian Knoll","doi":"10.1162/imag_a_00085","DOIUrl":"https://doi.org/10.1162/imag_a_00085","url":null,"abstract":"Abstract The pursuit of high spatial-angular-temporal resolution for in vivo diffusion-weighted magnetic resonance imaging (DW-MRI) at ultra-high field strength (7 T and above) is important in understanding brain microstructure and function. Such pursuit, however, faces several technical challenges. First, increased off-resonance and shorter T2 relaxation require faster echo train readouts. Second, existing high-resolution DW-MRI techniques usually employ in-plane fully-sampled multi-shot EPI, which not only prolongs the scan time but also induces a high specific absorption rate (SAR) at 7 T. To address these challenges, we develop in this work navigator-based interleaved EPI (NAViEPI) which enforces the same effective echo spacing (ESP) between the imaging and the navigator echo. First, NAViEPI renders no distortion mismatch between the two echoes, and thus simplifies shot-to-shot phase variation correction. Second, NAViEPI allows for a large number of shots (e.g., >4) with undersampled iEPI acquisition, thereby rendering clinically-feasible high-resolution sub-milliemeter protocols. To retain signal-to-noise ratio (SNR) and to reduce undersampling artifacts, we developed a ky-shift encoding among diffusion encodings to explore complementary k- q-space sampling. Moreover, we developed a novel joint reconstruction with overlapping locally low-rank regularization generalized to the multi-band multi-shot acquisition at 7 T (dubbed JETS-NAViEPI). Our method was demonstrated, with experimental results covering 1 mm isotropic resolution multi b-value DWI and sub-millimeter in-plane resolution fast TRACE acquisition.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"14 2","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139885638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MEBRAINS 1.0: A new population-based macaque atlas","authors":"Puiu F. Balan, Qi Zhu, Xiaolian Li, Meiqi Niu, Lucija Rapan, Thomas Funck, Haiyan Wang, Rembrandt Bakker, N. Palomero-Gallagher, W. Vanduffel","doi":"10.1162/imag_a_00077","DOIUrl":"https://doi.org/10.1162/imag_a_00077","url":null,"abstract":"Abstract Due to their fundamental relevance, the number of anatomical macaque brain templates is constantly growing. Novel templates aim to alleviate limitations of previously published atlases and offer the foundation to integrate multiscale multimodal data. Typical limitations of existing templates include their reliance on one subject, their unimodality (usually only T1 or histological images), or lack of anatomical details. The MEBRAINS template overcomes these limitations by using a combination of T1 and T2 images, from the same 10 animals (Macaca mulatta), which are averaged by the multi-brain toolbox for diffeomorphic registration and segmentation. The resulting volumetric T1 and T2 templates are supplemented with high-quality white and gray matter surfaces built with FreeSurfer. Human-curated segmentations of pial surface, the white/gray matter interface, and major subcortical nuclei were used to analyze the relative quality of the MEBRAINS template. Additionally, 9 computed tomography (CT) scans of the same monkeys were registered to the T1 modality and co-registered to the template. Through its main features (multi-subject, multimodal, volume-and-surface, traditional, and deep learning-based segmentations), MEBRAINS aims to improve integration of multimodal multi-scale macaque data and is quantitatively equal to, or better than, currently widely used macaque templates. We provide a detailed description of the algorithms/methods used to create the template aiming to furnish future researchers with a map-like perspective which should facilitate identification of an optimal pipeline for the task they have at hand. Finally, recently published 3D maps of the macaque inferior parietal lobe, (pre)motor and prefrontal cortex were warped to the MEBRAINS surface template, thus populating it with a parcellation scheme based on cyto- and receptor architectonic analyses. The template is integrated in the EBRAINS and Scalable Brain Atlas web-based infrastructures, each of which comes with its own suite of spatial registration tools.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"116 23","pages":"1-26"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139684800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precise and rapid whole-head segmentation from magnetic resonance images of older adults using deep learning","authors":"Skylar E. Stolte, A. Indahlastari, Jason Chen, Alejandro Albizu, Ayden L. Dunn, Samantha Pedersen, Kyle B. See, Adam J. Woods, Ruogu Fang","doi":"10.1162/imag_a_00090","DOIUrl":"https://doi.org/10.1162/imag_a_00090","url":null,"abstract":"Abstract Whole-head segmentation from Magnetic Resonance Images (MRI) establishes the foundation for individualized computational models using finite element method (FEM). This foundation paves the path for computer-aided solutions in fields such as non-invasive brain stimulation. Most current automatic head segmentation tools are developed using healthy young adults. Thus, they may neglect the older population that is more prone to age-related structural decline such as brain atrophy. In this work, we present a new deep learning method called GRACE, which stands for General, Rapid, And Comprehensive whole-hEad tissue segmentation. GRACE is trained and validated on a novel dataset that consists of 177 manually corrected MR-derived reference segmentations that have undergone meticulous manual review. Each T1-weighted MRI volume is segmented into 11 tissue types, including white matter, grey matter, eyes, cerebrospinal fluid, air, blood vessel, cancellous bone, cortical bone, skin, fat, and muscle. To the best of our knowledge, this work contains the largest manually corrected dataset to date in terms of number of MRIs and segmented tissues. GRACE outperforms five freely available software tools and a traditional 3D U-Net on a five-tissue segmentation task. On this task, GRACE achieves an average Hausdorff Distance of 0.21, which exceeds the runner-up at an average Hausdorff Distance of 0.36. GRACE can segment a whole-head MRI in about 3 seconds, while the fastest software tool takes about 3 minutes. In summary, GRACE segments a spectrum of tissue types from older adults’ T1-MRI scans at favorable accuracy and speed. The trained GRACE model is optimized on older adult heads to enable high-precision modeling in age-related brain disorders. To support open science, the GRACE code and trained weights are made available online and open to the research community upon publication at https://github.com/lab-smile/GRACE.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"80 10","pages":"1-21"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139824662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A database of the healthy human spinal cord morphometry in the PAM50 template space","authors":"J. Valošek, Sandrine Bédard, M. Keřkovský, Tomáš Rohan, Julien Cohen-Adad","doi":"10.1162/imag_a_00075","DOIUrl":"https://doi.org/10.1162/imag_a_00075","url":null,"abstract":"Abstract Measures of spinal cord morphometry computed from magnetic resonance images serve as relevant prognostic biomarkers for a range of spinal cord pathologies, including traumatic and non-traumatic spinal cord injury and neurodegenerative diseases. However, interpreting these imaging biomarkers is difficult due to considerable intra- and inter-subject variability. Yet, there is no clear consensus on a normalization method that would help reduce this variability and more insights into the distribution of these morphometrics are needed. In this study, we computed a database of normative values for six commonly used measures of spinal cord morphometry: cross-sectional area, anteroposterior diameter, transverse diameter, compression ratio, eccentricity, and solidity. Normative values were computed from a large open-access dataset of healthy adult volunteers (N = 203) and were brought to the common space of the PAM50 spinal cord template using a newly proposed normalization method based on linear interpolation. Compared to traditional image-based registration, the proposed normalization approach does not involve image transformations and, therefore, does not introduce distortions of spinal cord anatomy. This is a crucial consideration in preserving the integrity of the spinal cord anatomy in conditions such as spinal cord injury. This new morphometric database allows researchers to normalize based on sex and age, thereby minimizing inter-subject variability associated with demographic and biological factors. The proposed methodology is open-source and accessible through the Spinal Cord Toolbox (SCT) v6.0 and higher.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"33 1","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139687429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gray matter correlates of attention-deficit hyperactivity disorder in boys versus girls with sensory processing dysfunction","authors":"Efstathios D. Gennatas, Jamie Wren-Jarvis, Rachel Powers, Maia C. Lazerwitz, Ioanna Bourla, Lanya T. Cai, Hannah L. Choi, Robyn Chu, Kaitlyn J. Trimarchi, R. Garcia, Elysa J. Marco, Pratik Mukherjee","doi":"10.1162/imag_a_00076","DOIUrl":"https://doi.org/10.1162/imag_a_00076","url":null,"abstract":"Abstract Neuroimaging shows volumetric alterations of gray matter in attention-deficit hyperactivity disorder (ADHD); however, results are conflicting. This may be due to heterogeneous phenotypic sampling and limited sensitivity of volumetric analysis. Creating more homogenous cohorts and investigating gray matter microstructure may yield meaningful biomarkers for scientific and clinical applications. Children with sensory processing dysfunction (SPD) have differences in white matter microstructure, but not gray matter volumetric differences. Approximately 40% of SPD children meet research criteria for ADHD. We apply deep learning segmentation of MRI to measure gray matter volume (GMV) and density (GMD) in SPD children with (SPD+ADHD) and without co-morbid ADHD (SPD-ADHD). We hypothesize GMV and GMD are linked to ADHD but with sex-specific regional patterns. We find boys with SPD+ADHD have widespread reduction of GMD in neocortex, limbic cortex, and cerebellum versus boys with SPD-ADHD. The greatest differences are in sensory cortex with less involvement of prefrontal regions associated with attention networks and impulse control. In contrast, changes of ADHD in girls with SPD are in brainstem nuclei responsible for dopaminergic, noradrenergic, and serotonergic neurotransmission. Hence, neural correlates of ADHD in SPD are sexually dimorphic. In boys, ADHD may result from downstream effects of abnormal sensory cortical development.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"60 1","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139687791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expectation Modifies the Representational Fidelity of Complex Visual Objects","authors":"Margaret Jane Moore, Amanda K. Robinson, J. Mattingley","doi":"10.1162/imag_a_00083","DOIUrl":"https://doi.org/10.1162/imag_a_00083","url":null,"abstract":"Abstract Prediction has been shown to play a fundamental role in facilitating efficient perception of simple visual features such as orientation and motion, but it remains unclear whether expectations modulate neural representations of more complex stimuli. Here, we addressed this issue by characterising patterns of brain activity evoked by two-dimensional images of familiar, real-world objects which were either expected or unexpected based on a preceding cue. Participants (n = 30) viewed stimuli in rapid serial visual presentation (RSVP) streams which contained both high-fidelity and degraded (diffeomorphically warped) object images. Multivariate pattern analyses of electroencephalography (EEG) data were used to quantify and compare the degree of information represented in neural activity when stimuli were random (unpredictable), expected, or unexpected. Degraded images elicited reduced representational fidelity relative to high-fidelity images. However, degraded images were represented with improved fidelity when they were presented in expected relative to random sequence positions; and stimuli in unexpected sequence positions yielded reduced representational fidelity relative to random presentations. Most notably, neural responses to unexpected stimuli contained information pertaining to the expected (but not presented) stimulus. Debriefing at the conclusion of the experiment revealed that participants were not aware of the relationship between cue and target stimuli within the RSVP streams, suggesting that the differences in stimulus decoding between conditions arose in the absence of explicit predictive knowledge. Our findings extend fundamental understanding of how the brain detects and employs predictive relationships to modulate high-level visual perception.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"20 4","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139688186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining multimodal connectivity information improves modelling of pathology spread in Alzheimer’s disease","authors":"Elinor Thompson, A. Schroder, Tiantian He, Cameron Shand, Sonja Soskic, N. Oxtoby, F. Barkhof, Daniel C. Alexander","doi":"10.1162/imag_a_00089","DOIUrl":"https://doi.org/10.1162/imag_a_00089","url":null,"abstract":"Abstract Cortical atrophy and aggregates of misfolded tau proteins are key hallmarks of Alzheimer’s disease. Computational models that simulate the propagation of pathogens between connected brain regions have been used to elucidate mechanistic information about the spread of these disease biomarkers, such as disease epicentres and spreading rates. However, the connectomes that are used as substrates for these models are known to contain modality-specific false positive and false negative connections, influenced by the biases inherent to the different methods for estimating connections in the brain. In this work, we compare five types of connectomes for modelling both tau and atrophy patterns with the network diffusion model, which are validated against tau PET and structural MRI data from individuals with either mild cognitive impairment or dementia. We then test the hypothesis that a joint connectome, with combined information from different modalities, provides an improved substrate for the model. We find that a combination of multimodal information helps the model to capture observed patterns of tau deposition and atrophy better than any single modality. This is validated with data from independent datasets. Overall, our findings suggest that combining connectivity measures into a single connectome can mitigate some of the biases inherent to each modality and facilitate more accurate models of pathology spread, thus aiding our ability to understand disease mechanisms, and providing insight into the complementary information contained in different measures of brain connectivity","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"297 1","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139824120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of respiration-induced field modulations in fMRI: A concurrent and navigator-free approach","authors":"Alexander Jaffray, C. Kames, Michelle Medina, Christina Graf, Adam Clansey, Alexander Rauscher","doi":"10.1162/imag_a_00091","DOIUrl":"https://doi.org/10.1162/imag_a_00091","url":null,"abstract":"Abstract Functional Magnetic Resonance Imaging (fMRI) is typically acquired using gradient-echo sequences with a long echo time at high temporal resolution. Gradient-echo sequences inherently encode information about the magnetic field in the often discarded image phase. We demonstrate a method for processing the phase of reconstructed fMRI data to isolate temporal fluctuations in the harmonic fields associated with respiration by solving a blind source separation problem. The fMRI-derived field fluctuations are shown to be in strong agreement with breathing belt data acquired during the same scan. This work presents a concurrent, hardware-free measurement of respiration-induced field fluctuations, providing a respiratory regressor for fMRI analysis which is independent of local contrast changes, and with potential applications in image reconstruction and fMRI analysis.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"78 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139822203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Violations of physical and psychological expectations in the human adult brain","authors":"Shari Liu, Kirsten Lydic, Lingjie Mei, Rebecca Saxe","doi":"10.1162/imag_a_00068","DOIUrl":"https://doi.org/10.1162/imag_a_00068","url":null,"abstract":"Abstract After seeing one solid object apparently passing through another, or a person taking the long route to a destination when a shortcut was available, human adults classify those events as surprising. When tested on these events in violation-of-expectation (VOE) experiments, infants look longer at the same outcomes, relative to similar but expected outcomes. What cognitive processes underlie these judgments from adults, and perhaps infants’ sustained attention to these events? As one approach to test this question, we used functional magnetic resonance imaging (fMRI) to scan the brains of human adults (total N = 49, 22 female, mean age of 26 years) while they viewed stimuli that were originally designed to test for physical and psychological expectations in infants. We examined non-mutually exclusive candidates for the processes underlying the VOE effect, including domain-general processes, like visual prediction error and curiosity, and domain-specific processes, like prediction error with respect to distinctively physical and psychological expectations (objects are solid; agents behave rationally). Early visual regions did not distinguish between expected and unexpected events from either domain. By contrast, multiple demand regions, involved in goal-directed attention, responded more to unexpected events in both domains, providing evidence for domain-general goal-directed attention as a mechanism for VOE. Left supramarginal gyrus (LSMG) was engaged during physical prediction and responded preferentially to unexpected events from the physical domain, providing evidence for domain-specific physical prediction error. Thus, in adult brains, violations of physical and psychological expectations involve domain-specific, and domain-general, though not purely visual, computations.","PeriodicalId":507939,"journal":{"name":"Imaging Neuroscience","volume":"57 1","pages":"1-25"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139686844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}