journal
https://read.qxmd.com/read/38530566/an-automated-tool-to-classify-and-transform-unstructured-mri-data-into-bids-datasets
#1
JOURNAL ARTICLE
Alexander Bartnik, Sujal Singh, Conan Sum, Mackenzie Smith, Niels Bergsland, Robert Zivadinov, Michael G Dwyer
The increasing use of neuroimaging in clinical research has driven the creation of many large imaging datasets. However, these datasets often rely on inconsistent naming conventions in image file headers to describe acquisition, and time-consuming manual curation is necessary. Therefore, we sought to automate the process of classifying and organizing magnetic resonance imaging (MRI) data according to acquisition types common to the clinical routine, as well as automate the transformation of raw, unstructured images into Brain Imaging Data Structure (BIDS) datasets...
March 26, 2024: Neuroinformatics
https://read.qxmd.com/read/38526701/deepn4-learning-n4itk-bias-field-correction-for-t1-weighted-images
#2
JOURNAL ARTICLE
Praitayini Kanakaraj, Tianyuan Yao, Leon Y Cai, Ho Hin Lee, Nancy R Newlin, Michael E Kim, Chenyu Gao, Kimberly R Pechman, Derek Archer, Timothy Hohman, Angela Jefferson, Lori L Beason-Held, Susan M Resnick, Eleftherios Garyfallidis, Adam Anderson, Kurt G Schilling, Bennett A Landman, Daniel Moyer
T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step...
March 25, 2024: Neuroinformatics
https://read.qxmd.com/read/38492127/updates-to-the-melbourne-children-s-regional-infant-brain-software-package-m-crib-s
#3
JOURNAL ARTICLE
Chris L Adamson, Bonnie Alexander, Claire E Kelly, Gareth Ball, Richard Beare, Jeanie L Y Cheong, Alicia J Spittle, Lex W Doyle, Peter J Anderson, Marc L Seal, Deanne K Thompson
The delineation of cortical areas on magnetic resonance images (MRI) is important for understanding the complexities of the developing human brain. The previous version of the Melbourne Children's Regional Infant Brain (M-CRIB-S) (Adamson et al. Scientific Reports, 10(1), 10, 2020) is a software package that performs whole-brain segmentation, cortical surface extraction and parcellation of the neonatal brain. Available cortical parcellation schemes in the M-CRIB-S are the adult-compatible 34- and 31-region per hemisphere Desikan-Killiany (DK) and Desikan-Killiany-Tourville (DKT), respectively...
March 16, 2024: Neuroinformatics
https://read.qxmd.com/read/38446357/mappertrac-a-massively-parallel-portable-and-reproducible-tractography-pipeline
#4
JOURNAL ARTICLE
Lanya T Cai, Joseph Moon, Paul B Camacho, Aaron T Anderson, Won Jong Chwa, Bradley P Sutton, Amy J Markowitz, Eva M Palacios, Alexis Rodriguez, Geoffrey T Manley, Shivsundaram Shankar, Peer-Timo Bremer, Pratik Mukherjee, Ravi K Madduri
Large-scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that requires many software packages with complex dependencies and high computational costs. We developed MaPPeRTrac, an edge-centric tractography pipeline that simplifies and accelerates this process in a wide range of high-performance computing (HPC) environments. It fully automates either probabilistic or deterministic tractography, starting from a subject's magnetic resonance imaging (MRI) data, including structural and diffusion MRI images, to the edge density image (EDI) of their structural connectomes...
March 6, 2024: Neuroinformatics
https://read.qxmd.com/read/38424371/decentralized-mixed-effects-modeling-in-coinstac
#5
JOURNAL ARTICLE
Sunitha Basodi, Rajikha Raja, Harshvardhan Gazula, Javier Tomas Romero, Sandeep Panta, Thomas Maullin-Sapey, Thomas E Nichols, Vince D Calhoun
Performing group analysis on magnetic resonance imaging (MRI) data with linear mixed-effects (LME) models is challenging due to its large dimensionality and inherent multi-level covariance structure. In addition, as large-scale collaborative projects become commonplace in neuroimaging, data must increasingly be stored and analyzed from different locations. In such settings, substantial overhead can occur in terms of data transfer and coordination between participating research groups. In some cases, data cannot be pooled together due to privacy or regulatory concerns...
March 1, 2024: Neuroinformatics
https://read.qxmd.com/read/38396218/visual-prompting-based-incremental-learning-for-semantic-segmentation-of-multiplex-immuno-flourescence-microscopy-imagery
#6
JOURNAL ARTICLE
Ryan Faulkenberry, Saurabh Prasad, Dragan Maric, Badrinath Roysam
Deep learning approaches are state-of-the-art for semantic segmentation of medical images, but unlike many deep learning applications, medical segmentation is characterized by small amounts of annotated training data. Thus, while mainstream deep learning approaches focus on performance in domains with large training sets, researchers in the medical imaging field must apply new methods in creative ways to meet the more constrained requirements of medical datasets. We propose a framework for incrementally fine-tuning a multi-class segmentation of a high-resolution multiplex (multi-channel) immuno-flourescence image of a rat brain section, using a minimal amount of labelling from a human expert...
February 23, 2024: Neuroinformatics
https://read.qxmd.com/read/38386228/inspectro-gadget-a-tool-for-estimating-neurotransmitter-and-neuromodulator-receptor-distributions-for-mrs-voxels
#7
JOURNAL ARTICLE
Elizabeth McManus, Nils Muhlert, Niall W Duncan
Magnetic resonance spectroscopy (MRS) is widely used to estimate concentrations of glutamate and [Formula: see text]-aminobutyric acid (GABA) in specific regions of the living human brain. As cytoarchitectural properties differ across the brain, interpreting these measurements can be assisted by having knowledge of such properties for the MRS region(s) studied. In particular, some knowledge of likely local neurotransmitter receptor patterns can potentially give insights into the mechanistic environment GABA- and glutamatergic neurons are functioning in...
February 22, 2024: Neuroinformatics
https://read.qxmd.com/read/38341830/age-prediction-using-resting-state-functional-mri
#8
JOURNAL ARTICLE
Jose Ramon Chang, Zai-Fu Yao, Shulan Hsieh, Torbjörn E M Nordling
The increasing lifespan and large individual differences in cognitive capability highlight the importance of comprehending the aging process of the brain. Contrary to visible signs of bodily ageing, like greying of hair and loss of muscle mass, the internal changes that occur within our brains remain less apparent until they impair function. Brain age, distinct from chronological age, reflects our brain's health status and may deviate from our actual chronological age. Notably, brain age has been associated with mortality and depression...
February 11, 2024: Neuroinformatics
https://read.qxmd.com/read/38332409/network-representation-of-fmri-data-using-visibility-graphs-the-impact-of-motion-and-test-retest-reliability
#9
JOURNAL ARTICLE
Govinda R Poudel, Prabin Sharma, Valentina Lorenzetti, Nicholas Parsons, Ester Cerin
Visibility graphs provide a novel approach for analysing time-series data. Graph theoretical analysis of visibility graphs can provide new features for data mining applications in fMRI. However, visibility graphs features have not been used widely in the field of neuroscience. This is likely due to a lack of understanding of their robustness in the presence of noise (e.g., motion) and their test-retest reliability. In this study, we investigated visibility graph properties of fMRI data in the human connectome project (N = 1010) and tested their sensitivity to motion and test-retest reliability...
February 9, 2024: Neuroinformatics
https://read.qxmd.com/read/38042764/a-deep-learning-based-ensemble-method-for-early-diagnosis-of-alzheimer-s-disease-using-mri-images
#10
JOURNAL ARTICLE
Sina Fathi, Ali Ahmadi, Afsaneh Dehnad, Mostafa Almasi-Dooghaee, Melika Sadegh
Recently, the early diagnosis of Alzheimer's disease has gained major attention due to the growing prevalence of the disease and the resulting costs imposed on individuals and society. The main objective of this study was to propose an ensemble method based on deep learning for the early diagnosis of AD using MRI images. The methodology of this study consisted of collecting the dataset, preprocessing, creating the individual and ensemble models, evaluating the models based on ADNI data, and validating the trained model based on the local dataset...
December 2, 2023: Neuroinformatics
https://read.qxmd.com/read/38036915/preserving-derivative-information-while-transforming-neuronal-curves
#11
JOURNAL ARTICLE
Thomas L Athey, Daniel J Tward, Ulrich Mueller, Laurent Younes, Joshua T Vogelstein, Michael I Miller
The international neuroscience community is building the first comprehensive atlases of brain cell types to understand how the brain functions from a higher resolution, and more integrated perspective than ever before. In order to build these atlases, subsets of neurons (e.g. serotonergic neurons, prefrontal cortical neurons etc.) are traced in individual brain samples by placing points along dendrites and axons. Then, the traces are mapped to common coordinate systems by transforming the positions of their points, which neglects how the transformation bends the line segments in between...
November 30, 2023: Neuroinformatics
https://read.qxmd.com/read/37981636/high-density-exploration-of-activity-states-in-a-multi-area-brain-model
#12
JOURNAL ARTICLE
David Aquilué-Llorens, Jennifer S Goldman, Alain Destexhe
To simulate whole brain dynamics with only a few equations, biophysical, mesoscopic models of local neuron populations can be connected using empirical tractography data. The development of mesoscopic mean-field models of neural populations, in particular, the Adaptive Exponential (AdEx mean-field model), has successfully summarized neuron-scale phenomena leading to the emergence of global brain dynamics associated with conscious (asynchronous and rapid dynamics) and unconscious (synchronized slow-waves, with Up-and-Down state dynamics) brain states, based on biophysical mechanisms operating at cellular scales (e...
November 20, 2023: Neuroinformatics
https://read.qxmd.com/read/37966621/editorial-on-the-economics-of-neuroscientific-data-sharing
#13
EDITORIAL
John Darrell Van Horn
No abstract text is available yet for this article.
November 15, 2023: Neuroinformatics
https://read.qxmd.com/read/37924429/topological-data-analysis-captures-task-driven-fmri-profiles-in-individual-participants-a-classification-pipeline-based-on-persistence
#14
JOURNAL ARTICLE
Michael J Catanzaro, Sam Rizzo, John Kopchick, Asadur Chowdury, David R Rosenberg, Peter Bubenik, Vaibhav A Diwadkar
BOLD-based fMRI is the most widely used method for studying brain function. The BOLD signal while valuable, is beset with unique vulnerabilities. The most notable of these is the modest signal to noise ratio, and the relatively low temporal and spatial resolution. However, the high dimensional complexity of the BOLD signal also presents unique opportunities for functional discovery. Topological Data Analyses (TDA), a branch of mathematics optimized to search for specific classes of structure within high dimensional data may provide particularly valuable applications...
November 4, 2023: Neuroinformatics
https://read.qxmd.com/read/37924428/improving-the-eligibility-of-task-based-fmri-studies-for-meta-analysis-a-review-and-reporting-recommendations
#15
REVIEW
Freya Acar, Camille Maumet, Talia Heuten, Maya Vervoort, Han Bossier, Ruth Seurinck, Beatrijs Moerkerke
Decisions made during the analysis or reporting of an fMRI study influence the eligibility of that study to be entered into a meta-analysis. In a meta-analysis, results of different studies on the same topic are combined. To combine the results, it is necessary that all studies provide equivalent pieces of information. However, in task-based fMRI studies we see a large variety in reporting styles. Several specific meta-analysis methods have been developed to deal with the reporting practices occurring in task-based fMRI studies, therefore each requiring a specific type of input...
November 4, 2023: Neuroinformatics
https://read.qxmd.com/read/37864741/analyzing-thalamocortical-tract-tracing-experiments-in-a-common-reference-space
#16
JOURNAL ARTICLE
Nestor Timonidis, Mario Rubio-Teves, Carmen Alonso-Martínez, Rembrandt Bakker, María García-Amado, Paul Tiesinga, Francisco Clascá
Current mesoscale connectivity atlases provide limited information about the organization of thalamocortical projections in the mouse brain. Labeling the projections of spatially restricted neuron populations in thalamus can provide a functionally relevant level of connectomic analysis, but these need to be integrated within the same common reference space. Here, we present a pipeline for the segmentation, registration, integration and analysis of multiple tract-tracing experiments. The key difference with other workflows is that the data is transformed to fit the reference template...
October 21, 2023: Neuroinformatics
https://read.qxmd.com/read/37725217/correction-to-geometric-reliability-of-super-resolution-reconstructed-images-from-clinical-fetal-mri-in-the-second-trimester
#17
Tommaso Ciceri, Letizia Squarcina, Alessandro Pigoni, Adele Ferro, Florian Montano, Alessandra Bertoldo, Nicola Persico, Simona Boito, Fabio Maria Triulzi, Giorgio Conte, Paolo Brambilla, Denis Peruzzo
No abstract text is available yet for this article.
September 19, 2023: Neuroinformatics
https://read.qxmd.com/read/37581850/confounding-effects-on-the-performance-of-machine-learning-analysis-of-static-functional-connectivity-computed-from-rs-fmri-multi-site-data
#18
JOURNAL ARTICLE
Oswaldo Artiles, Zeina Al Masry, Fahad Saeed
Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive imaging technique widely used in neuroscience to understand the functional connectivity of the human brain. While rs-fMRI multi-site data can help to understand the inner working of the brain, the data acquisition and processing of this data has many challenges. One of the challenges is the variability of the data associated with different acquisitions sites, and different MRI machines vendors. Other factors such as population heterogeneity among different sites, with variables such as age and gender of the subjects, must also be considered...
August 15, 2023: Neuroinformatics
https://read.qxmd.com/read/37578650/editorial-is-now-the-time-for-foundational-theory-of-brain-connectivity
#19
EDITORIAL
John Darrell Van Horn, Zachary Jacokes, Benjamin Newman, Teague Henry
No abstract text is available yet for this article.
August 14, 2023: Neuroinformatics
https://read.qxmd.com/read/37458971/auto-segmentation-and-classification-of-glioma-tumors-with-the-goals-of-treatment-response-assessment-using-deep-learning-based-on-magnetic-resonance-imaging
#20
JOURNAL ARTICLE
Zahra Papi, Sina Fathi, Fatemeh Dalvand, Mahsa Vali, Ali Yousefi, Mohammad Hemmatyar Tabatabaei, Alireza Amouheidari, Iraj Abedi
Glioma is the most common primary intracranial neoplasm in adults. Radiotherapy is a treatment approach in glioma patients, and Magnetic Resonance Imaging (MRI) is a beneficial diagnostic tool in treatment planning. Treatment response assessment in glioma patients is usually based on the Response Assessment in Neuro Oncology (RANO) criteria. The limitation of assessment based on RANO is two-dimensional (2D) manual measurements. Deep learning (DL) has great potential in neuro-oncology to improve the accuracy of response assessment...
July 17, 2023: Neuroinformatics
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