38 research outputs found
Local Analysis of Human Cortex in MRI Brain Volume
This paper describes a method for subcortical identification and labeling of
3D medical MRI images. Indeed, the ability to identify similarities between the most characteristic subcortical structures such as sulci and gyri is helpful for human brain mapping studies in general and medical diagnosis in particular. However, these structures vary greatly from one individual to another because they have different geometric properties. For this purpose, we have developed an efficient tool that allows a user to start with brain imaging, to segment the border gray/white matter, to simplify the obtained cortex surface, and to describe this shape locally in order to identify homogeneous features. In this paper, a segmentation procedure using geometric curvature properties that provide an efficient discrimination for local shape is implemented on the brain cortical surface. Experimental results demonstrate the effectiveness and the validity of our approach
Optimizing contrastive learning for cortical folding pattern detection
The human cerebral cortex has many bumps and grooves called gyri and sulci.
Even though there is a high inter-individual consistency for the main cortical
folds, this is not the case when we examine the exact shapes and details of the
folding patterns. Because of this complexity, characterizing the cortical
folding variability and relating them to subjects' behavioral characteristics
or pathologies is still an open scientific problem. Classical approaches
include labeling a few specific patterns, either manually or
semi-automatically, based on geometric distances, but the recent availability
of MRI image datasets of tens of thousands of subjects makes modern
deep-learning techniques particularly attractive. Here, we build a
self-supervised deep-learning model to detect folding patterns in the cingulate
region. We train a contrastive self-supervised model (SimCLR) on both Human
Connectome Project (1101 subjects) and UKBioBank (21070 subjects) datasets with
topological-based augmentations on the cortical skeletons, which are
topological objects that capture the shape of the folds. We explore several
backbone architectures (convolutional network, DenseNet, and PointNet) for the
SimCLR. For evaluation and testing, we perform a linear classification task on
a database manually labeled for the presence of the "double-parallel" folding
pattern in the cingulate region, which is related to schizophrenia
characteristics. The best model, giving a test AUC of 0.76, is a convolutional
network with 6 layers, a 10-dimensional latent space, a linear projection head,
and using the branch-clipping augmentation. This is the first time that a
self-supervised deep learning model has been applied to cortical skeletons on
such a large dataset and quantitatively evaluated. We can now envisage the next
step: applying it to other brain regions to detect other biomarkers.Comment: 9 pages, 6 figures, 1 table, SPIE Imaging 202
Cortical Gyrification and Sulcal Spans in Early Stage Alzheimer's Disease
Alzheimer's disease (AD) is characterized by an insidious onset of progressive cerebral atrophy and cognitive decline. Previous research suggests that cortical folding and sulcal width are associated with cognitive function in elderly individuals, and the aim of the present study was to investigate these morphological measures in patients with AD. The sample contained 161 participants, comprising 80 normal controls, 57 patients with very mild AD, and 24 patients with mild AD. From 3D T1-weighted brain scans, automated methods were used to calculate an index of global cortex gyrification and the width of five individual sulci: superior frontal, intra-parietal, superior temporal, central, and Sylvian fissure. We found that global cortex gyrification decreased with increasing severity of AD, and that the width of all individual sulci investigated other than the intra-parietal sulcus was greater in patients with mild AD than in controls. We also found that cognitive functioning, as assessed by Mini-Mental State Examination (MMSE) scores, decreased as global cortex gyrification decreased. MMSE scores also decreased in association with a widening of all individual sulci investigated other than the intra-parietal sulcus. The results suggest that abnormalities of global cortex gyrification and regional sulcal span are characteristic of patients with even very mild AD, and could thus facilitate the early diagnosis of this condition
A 3D explainability framework to uncover learning patterns and crucial sub-regions in variable sulci recognition
Precisely identifying sulcal features in brain MRI is made challenging by the
variability of brain folding. This research introduces an innovative 3D
explainability frame-work that validates outputs from deep learning networks in
their ability to detect the paracingulate sulcus, an anatomical feature that
may or may not be present on the frontal medial surface of the human brain.
This study trained and tested two networks, amalgamating local explainability
techniques GradCam and SHAP with a dimensionality reduction method. The
explainability framework provided both localized and global explanations, along
with accuracy of classification results, revealing pertinent sub-regions
contributing to the decision process through a post-fusion transformation of
explanatory and statistical features. Leveraging the TOP-OSLO dataset of MRI
acquired from patients with schizophrenia, greater accuracies of paracingulate
sulcus detection (presence or absence) were found in the left compared to right
hemispheres with distinct, but extensive sub-regions contributing to each
classification outcome. The study also inadvertently highlighted the critical
role of an unbiased annotation protocol in maintaining network performance
fairness. Our proposed method not only offers automated, impartial annotations
of a variable sulcus but also provides insights into the broader anatomical
variations associated with its presence throughout the brain. The adoption of
this methodology holds promise for instigating further explorations and
inquiries in the field of neuroscience
Mindboggle: Automated brain labeling with multiple atlases
BACKGROUND: To make inferences about brain structures or activity across multiple individuals, one first needs to determine the structural correspondences across their image data. We have recently developed Mindboggle as a fully automated, feature-matching approach to assign anatomical labels to cortical structures and activity in human brain MRI data. Label assignment is based on structural correspondences between labeled atlases and unlabeled image data, where an atlas consists of a set of labels manually assigned to a single brain image. In the present work, we study the influence of using variable numbers of individual atlases to nonlinearly label human brain image data. METHODS: Each brain image voxel of each of 20 human subjects is assigned a label by each of the remaining 19 atlases using Mindboggle. The most common label is selected and is given a confidence rating based on the number of atlases that assigned that label. The automatically assigned labels for each subject brain are compared with the manual labels for that subject (its atlas). Unlike recent approaches that transform subject data to a labeled, probabilistic atlas space (constructed from a database of atlases), Mindboggle labels a subject by each atlas in a database independently. RESULTS: When Mindboggle labels a human subject's brain image with at least four atlases, the resulting label agreement with coregistered manual labels is significantly higher than when only a single atlas is used. Different numbers of atlases provide significantly higher label agreements for individual brain regions. CONCLUSION: Increasing the number of reference brains used to automatically label a human subject brain improves labeling accuracy with respect to manually assigned labels. Mindboggle software can provide confidence measures for labels based on probabilistic assignment of labels and could be applied to large databases of brain images
The reliability and heritability of cortical folds and their genetic correlations across hemispheres
Cortical folds help drive the parcellation of the human cortex into functionally specific regions. Variations in the length, depth, width, and surface area of these sulcal landmarks have been associated with disease, and may be genetically mediated. Before estimating the heritability of sulcal variation, the extent to which these metrics can be reliably extracted from in-vivo MRI must be established. Using four independent test-retest datasets, we found high reliability across the brain (intraclass correlation interquartile range: 0.65-0.85). Heritability estimates were derived for three family-based cohorts using variance components analysis and pooled (total N > 3000); the overall sulcal heritability pattern was correlated to that derived for a large population cohort (N > 9000) calculated using genomic complex trait analysis. Overall, sulcal width was the most heritable metric, and earlier forming sulci showed higher heritability. The inter-hemispheric genetic correlations were high, yet select sulci showed incomplete pleiotropy, suggesting hemisphere-specific genetic influences
The reliability and heritability of cortical folds and their genetic correlations across hemispheres
Cortical folds help drive the parcellation of the human cortex into functionally specific regions. Variations in the length, depth, width, and surface area of these sulcal landmarks have been associated with disease, and may be genetically mediated. Before estimating the heritability of sulcal variation, the extent to which these metrics can be reliably extracted from in-vivo MRI must be established. Using four independent test-retest datasets, we found high reliability across the brain (intraclass correlation interquartile range: 0.65–0.85). Heritability estimates were derived for three family-based cohorts using variance components analysis and pooled (total N \u3e 3000); the overall sulcal heritability pattern was correlated to that derived for a large population cohort (N \u3e 9000) calculated using genomic complex trait analysis. Overall, sulcal width was the most heritable metric, and earlier forming sulci showed higher heritability. The inter-hemispheric genetic correlations were high, yet select sulci showed incomplete pleiotropy, suggesting hemisphere-specific genetic influences