scholarly journals Robust Cortical Thickness Morphometry of Neonatal Brain and Systematic Evaluation Using Multi-Site MRI Datasets

2021 ◽  
Vol 15 ◽  
Author(s):  
Mengting Liu ◽  
Claude Lepage ◽  
Sharon Y. Kim ◽  
Seun Jeon ◽  
Sun Hyung Kim ◽  
...  
Keyword(s):  
Cortical Thickness ◽  
Age Groups ◽  
Temporal Cortex ◽  
Inferior Temporal Cortex ◽  
Qualitative Assessment ◽  
Systematic Evaluation ◽  
Imaging Data ◽  
The Mean ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Imaging Data

The human brain grows the most dramatically during the perinatal and early post-natal periods, during which pre-term birth or perinatal injury that may alter brain structure and lead to developmental anomalies. Thus, characterizing cortical thickness of developing brains remains an important goal. However, this task is often complicated by inaccurate cortical surface extraction due to small-size brains. Here, we propose a novel complex framework for the reconstruction of neonatal WM and pial surfaces, accounting for large partial volumes due to small-size brains. The proposed approach relies only on T1-weighted images unlike previous T2-weighted image-based approaches while only T1-weighted images are sometimes available under the different clinical/research setting. Deep neural networks are first introduced to the neonatal magnetic resonance imaging (MRI) pipeline to address the mis-segmentation of brain tissues. Furthermore, this pipeline enhances cortical boundary delineation using combined models of the cerebrospinal fluid (CSF)/GM boundary detection with edge gradient information and a new skeletonization of sulcal folding where no CSF voxels are seen due to the limited resolution. We also proposed a systematic evaluation using three independent datasets comprising 736 pre-term and 97 term neonates. Qualitative assessment for reconstructed cortical surfaces shows that 86.9% are rated as accurate across the three site datasets. In addition, our landmark-based evaluation shows that the mean displacement of the cortical surfaces from the true boundaries was less than a voxel size (0.532 ± 0.035 mm). Evaluating the proposed pipeline (namely NEOCIVET 2.0) shows the robustness and reproducibility across different sites and different age-groups. The mean cortical thickness measured positively correlated with post-menstrual age (PMA) at scan (p < 0.0001); Cingulate cortical areas grew the most rapidly whereas the inferior temporal cortex grew the least rapidly. The range of the cortical thickness measured was biologically congruent (1.3 mm at 28 weeks of PMA to 1.8 mm at term equivalent). Cortical thickness measured on T1 MRI using NEOCIVET 2.0 was compared with that on T2 using the established dHCP pipeline. It was difficult to conclude that either T1 or T2 imaging is more ideal to construct cortical surfaces. NEOCIVET 2.0 has been open to the public through CBRAIN (https://mcin-cnim.ca/technology/cbrain/), a web-based platform for processing brain imaging data.

scholarly journals Robust T1 MRI cortical surface pipeline for neonatal brain and systematic evaluation using multi-site MRI datasets

2021 ◽  
Author(s):  
Mengting Liu ◽  
Claude Lepage ◽  
Sharon Y. Kim ◽  
Seun Jeon ◽  
Sun Hyung Kim ◽  
...  
Keyword(s):  
Cortical Thickness ◽  
Age Groups ◽  
Temporal Cortex ◽  
Inferior Temporal Cortex ◽  
Qualitative Assessment ◽  
Systematic Evaluation ◽  
Cortical Surface ◽  
Imaging Data ◽  
Postmenstrual Age ◽  
The Mean

ABSTRACTThe human brain grows the most dramatically during the perinatal and early postnatal periods, during which preterm birth or perinatal injury that may alter brain structure and lead to developmental anomalies. Thus, characterizing cortical thickness of developing brains remains an important goal. However, this task is often complicated by inaccurate cortical surface extraction due to small-size brains. Here, we propose a novel complex framework for the reconstruction of neonatal WM and pial surfaces, accounting for large partial volumes due to small-size brains. The proposed approach relies only on T1-weighted images unlike previous T2-weighted image-based approaches while only T1-weighted images are sometimes available under the different clinical/research setting. Deep neural networks are first introduced to the neonatal MRI pipeline to address the mis-segmentation of brain tissues. Furthermore, this pipeline enhances cortical boundary delineation using combined models of the CSF/GM boundary detection with edge gradient information and a new skeletonization of sulcal folding where no CSF voxels are seen due to the limited resolution. We also proposed a systematic evaluation using three independent datasets comprising 736 preterm and 97 term neonates. Qualitative assessment for reconstructed cortical surfaces shows that 86.9% are rated as accurate across the three site datasets. In addition, our landmark-based evaluation shows that the mean displacement of the cortical surfaces from the true boundaries was less than a voxel size (0.532±0.035mm). Evaluating the proposed pipeline (namely NEOCIVET 2.0) shows the robustness and reproducibility across different sites and different age-groups. The mean cortical thickness measured positively correlated with postmenstrual age (PMA) at scan (p<0.0001); Cingulate cortical areas grew the most rapidly whereas the inferior temporal cortex grew the least rapidly. The range of the cortical thickness measured was biologically congruent (1.3mm at 28 weeks of PMA to 1.8mm at term equivalent). Cortical thickness measured on T1 MRI using NEOCIVET 2.0 was compared with that on T2 using the established dHCP pipeline. It was difficult to conclude that either T1 or T2 imaging is more ideal to construct cortical surfaces. NEOCIVET 2.0 has been open to the public through CBRAIN (https://mcin-cnim.ca/technology/cbrain/), a web-based platform for processing brain imaging data.

scholarly journals Machine Learning Framework for Fully Automatic Quality Checking of Rigid and Affine Registrations in Big Data Brain MRI

2020 ◽  
Author(s):  
Sudhakar Tummala ◽  
Niels K. Focke
Keyword(s):  
Machine Learning ◽  
Data Exchange ◽  
Brain Mri ◽  
Structural Mri ◽  
Imaging Data ◽  
Normalized Mutual Information ◽  
Learning Framework ◽  
Fully Automatic ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Imaging Data

ABSTRACTRigid and affine registrations to a common template are the essential steps during pre-processing of brain structural magnetic resonance imaging (MRI) data. Manual quality check (QC) of these registrations is quite tedious if the data contains several thousands of images. Therefore, we propose a machine learning (ML) framework for fully automatic QC of these registrations via local computation of the similarity functions such as normalized cross-correlation, normalized mutual-information, and correlation ratio, and using these as features for training of different ML classifiers. To facilitate supervised learning, misaligned images are generated. A structural MRI dataset consisting of 215 subjects from autism brain imaging data exchange is used for 5-fold cross-validation and testing. Few classifiers such as kNN, AdaBoost, and random forest reached testing F1-scores of 0.98 for QC of both rigid and affine registrations. These tested ML models could be deployed for practical use.

scholarly journals Increased contrast of the grey-white matter boundary in the motor, visual and auditory areas in Autism Spectrum Disorders

2019 ◽  
Author(s):  
Marion Fouquet ◽  
Nicolas Traut ◽  
Anita Beggiato ◽  
Richard Delorme ◽  
Thomas Bourgeron ◽  
...  
Keyword(s):  
White Matter ◽  
Data Exchange ◽  
Primary Motor Cortex ◽  
Autism Spectrum ◽  
Imaging Data ◽  
Sensory Motor ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Imaging Data

AbstractThe contrast of the interface between the neocortical grey matter and the white matter is emerging as an important neuroimaging phenotype for several brain disorders. To date, a single in vivo study has analysed the cortical grey-to-white matter percent contrast (GWPC) on Magnetic Resonance Imaging (MRI), and has shown a significant decrease of this contrast in several areas in individuals with Autism Spectrum Disorder (ASD). Our goal was to replicate this study across a larger cohort, using the multicenter data from the Autism Brain Imaging Data Exchange 1 and 2 gathering data from 2,148 subjects. Multiple linear regression was used to study the effect of the diagnosis of ASD on the GWPC. Contrary to the first study, we found a statistically significant increase of GWPC among individuals with ASD in left auditory and bilateral visual sensory areas, as well as in the left primary motor cortex. These results were still statistically significant after inclusion of cortical thickness as covariate. There are numerous reports of sensory-motor atypicalities in patients with ASD, which may be the reason for the differences in GWPC that we observed. Further investigation could help us determine the potential role of a defect or a delay in intra-cortical myelination of sensory-motor regions in ASD. Code: https://github.com/neuroanatomy/GWPC.

scholarly journals The Brain Imaging Data Structure: a standard for organizing and describing outputs of neuroimaging experiments

2015 ◽  
Author(s):  
Krzysztof J. Gorgolewski ◽  
Tibor Auer ◽  
Vince D. Calhoun ◽  
R. Cameron Craddock ◽  
Samir Das ◽  
...  
Keyword(s):  
Data Structure ◽  
Brain Imaging ◽  
Imaging Data ◽  
Non Invasive ◽  
File Formats ◽  
Neuroscience Research ◽  
Imaging Experiment ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Imaging Data ◽  
The Brain

AbstractThe development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.

scholarly journals Early brain biomarkers of post-traumatic seizures: initial report of the multicentre epilepsy bioinformatics study for antiepileptogenic therapy (EpiBioS4Rx) prospective study

2020 ◽  
Vol 91 (11) ◽  
pp. 1154-1157
Author(s):  
Evan S. Lutkenhoff ◽  
Vikesh Shrestha ◽  
Jesus Ruiz Tejeda ◽  
Courtney Real ◽  
David L. McArthur ◽  
...  
Keyword(s):  
Temporal Cortex ◽  
Clinical Score ◽  
Inferior Temporal Cortex ◽  
Imaging Biomarkers ◽  
Imaging Data ◽  
Post Injury ◽  
Initial Report ◽  
Post Traumatic ◽  
Volume Deficit ◽  
Early Seizures

BackgroundTraumatic brain injury (TBI) causes early seizures and is the leading cause of post-traumatic epilepsy. We prospectively assessed structural imaging biomarkers differentiating patients who develop seizures secondary to TBI from patients who do not.DesignMulticentre prospective cohort study starting in 2018. Imaging data are acquired around day 14 post-injury, detection of seizure events occurred early (within 1 week) and late (up to 90 days post-TBI).ResultsFrom a sample of 96 patients surviving moderate-to-severe TBI, we performed shape analysis of local volume deficits in subcortical areas (analysable sample: 57 patients; 35 no seizure, 14 early, 8 late) and cortical ribbon thinning (analysable sample: 46 patients; 29 no seizure, 10 early, 7 late). Right hippocampal volume deficit and inferior temporal cortex thinning demonstrated a significant effect across groups. Additionally, the degree of left frontal and temporal pole thinning, and clinical score at the time of the MRI, could differentiate patients experiencing early seizures from patients not experiencing them with 89% accuracy.Conclusions and relevanceAlthough this is an initial report, these data show that specific areas of localised volume deficit, as visible on routine imaging data, are associated with the emergence of seizures after TBI.

scholarly journals MEG-BIDS: an extension to the Brain Imaging Data Structure for magnetoencephalography

2017 ◽  
Author(s):  
Guiomar Niso ◽  
Krzysztof J. Gorgolewski ◽  
Elizabeth Bock ◽  
Teon L. Brooks ◽  
Guillaume Flandin ◽  
...  
Keyword(s):  
Data Structure ◽  
Brain Imaging ◽  
Brain Activity ◽  
Large Data ◽  
Imaging Data ◽  
Source Imaging ◽  
Standard Data ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Imaging Data ◽  
The Brain

AbstractWe present a significant extension of the Brain Imaging Data Structure (BIDS) to support the specific aspects of magnetoencephalography (MEG) data. MEG provides direct measurement of brain activity with millisecond temporal resolution and unique source imaging capabilities. So far, BIDS has provided a solution to structure the organization of magnetic resonance imaging (MRI) data, which nature and acquisition parameters are different. Despite the lack of standard data format for MEG, MEG-BIDS is a principled solution to store, organize and share the typically-large data volumes produced. It builds on BIDS for MRI, and therefore readily yields a multimodal data organization by construction. This is particularly valuable for the anatomical and functional registration of MEG source imaging with MRI. With MEG-BIDS and a growing range of software adopting the standard, the MEG community has a solution to minimize curation overheads, reduce data handling errors and optimize usage of computational resources for analytics. The standard also includes well-defined metadata, to facilitate future data harmonization and sharing efforts.

scholarly journals The Higher Parietal Cortical Thickness in Abstinent Methamphetamine Patients Is Correlated With Functional Connectivity and Age of First Usage

2021 ◽  
Vol 15 ◽  
Author(s):  
Ru Yang ◽  
Lei He ◽  
Zhixue Zhang ◽  
Wenming Zhou ◽  
Jun Liu
Keyword(s):  
Functional Connectivity ◽  
Parietal Cortex ◽  
Cortical Thickness ◽  
Gray Matter Volume ◽  
Correlation Coefficients ◽  
Brain Regions ◽  
Healthy Controls ◽  
Central Gyrus ◽  
The Mean ◽  
Magnetic Resonance Imaging Mri

AimThis study aimed to explore the changes of cortical thickness in abstinent methamphetamine (MA) patients compared with healthy controls.Materials and MethodsThree-tesla structural and functional magnetic resonance imaging (MRI) was obtained from 38 abstinent methamphetamine-dependent (AMD) patients and 32 demographically equivalent healthy controls. The cortical thickness was assessed using FreeSurfer software. General linear model was used to get brain regions with significant different cortical thickness between groups (p &lt; 0.05, Monte Carlo simulation corrected). The mean cortical thickness value and functional connectivity with all other brain regions was extracted from those significant regions. Moreover, correlation coefficients were calculated in the AMD group to assess the relations between the mean cortical thickness, functional connectivity and age when they first took MA and the duration of both MA use and abstinence.ResultsThe AMD group showed significant cortical thickness increase in one cluster located in the parietal cortex, including right posterior central gyrus, supramarginal gyrus, and superior parietal lobule. In addition, cortical thickness values of those regions were all significant and negatively correlated with the age when patients first used MA. The cortical thickness of right posterior gyrus were positively correlated with its functional connectivities with left middle frontal gyrus and both left and right medial orbitofrontal gyrus.ConclusionThe higher cortical thickness in the parietal cortex of the AMD group is in agreement with findings in related studies of increased glucose metabolism and gray matter volume. Importantly, the negative correlation between parietal cortical thickness and age of first MA suggested that adolescent brains are more vulnerable to MA’s neurotoxic effect.

scholarly journals SUDMEX CONN: The Mexican MRI dataset of patients with cocaine use disorder

2021 ◽  
Author(s):  
Diego Angeles-Valdez ◽  
Jalil Rasgado-Toledo ◽  
Victor Issa-Garcia ◽  
Thania Balducci ◽  
Viviana Villicaña ◽  
...  
Keyword(s):  
Angular Resolution ◽  
Population Sample ◽  
Resting State Fmri ◽  
Economic Consequences ◽  
High Angular Resolution ◽  
Imaging Data ◽  
Cocaine Use ◽  
High Relapse Rate ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Imaging Data

AbstractCocaine use disorder (CUD) is a substance use disorder (SUD) characterized by compulsion to seek, use and abuse of cocaine, with severe health and economic consequences for the patients, their families and society. Due to the lack of successful treatments and high relapse rate, more research is needed to understand this and other SUD. Here, we present the SUDMEX CONN dataset, a Mexican open dataset of CUD patients and matched healthy controls that includes demographic, cognitive, clinical, and magnetic resonance imaging (MRI) data. MRI data includes: 1) structural (T1-weighted), 2) multishell high-angular resolution diffusion-weighted (DWI-HARDI) and 3) functional (resting state fMRI) sequences. The repository contains unprocessed MRI data available in brain imaging data structure (BIDS) format with corresponding metadata available at the OpenNeuro data sharing platform. Researchers can pursue brain variability between these groups or use a single group for a larger population sample.

scholarly journals Reduced parahippocampal cortical thickness in subjects at ultra-high risk for psychosis

2013 ◽  
Vol 44 (3) ◽  
pp. 489-498 ◽  
Author(s):  
S. Tognin ◽  
A. Riecher-Rössler ◽  
E. M. Meisenzahl ◽  
S. J. Wood ◽  
C. Hutton ◽  
...  
Keyword(s):  
High Risk ◽  
Cortical Thickness ◽  
Grey Matter ◽  
Temporal Cortex ◽  
Parahippocampal Gyrus ◽  
Grey Matter Volume ◽  
Imaging Data ◽  
Matter Volume ◽  
Ultra High Risk ◽  
The Right

BackgroundGrey matter volume and cortical thickness represent two complementary aspects of brain structure. Several studies have described reductions in grey matter volume in people at ultra-high risk (UHR) of psychosis; however, little is known about cortical thickness in this group. The aim of the present study was to investigate cortical thickness alterations in UHR subjects and compare individuals who subsequently did and did not develop psychosis.MethodWe examined magnetic resonance imaging data collected at four different scanning sites. The UHR subjects were followed up for at least 2 years. Subsequent to scanning, 50 UHR subjects developed psychosis and 117 did not. Cortical thickness was examined in regions previously identified as sites of neuroanatomical alterations in UHR subjects, using voxel-based cortical thickness.ResultsAt baseline UHR subjects, compared with controls, showed reduced cortical thickness in the right parahippocampal gyrus (p < 0.05, familywise error corrected). There were no significant differences in cortical thickness between the UHR subjects who later developed psychosis and those who did not.ConclusionsThese data suggest that UHR symptomatology is characterized by alterations in the thickness of the medial temporal cortex. We did not find evidence that the later progression to psychosis was linked to additional alterations in cortical thickness, although we cannot exclude the possibility that the study lacked sufficient power to detect such differences.

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