Software Pipeline for Midsagittal Corpus Callosum Thickness Profile Processing: Automated Segmentation, Manual Editor, Thickness Profile Generator, Group-Wise Statistical Comparison and Results Display

Spectral-based thickness profiling of the corpus callosum enhances anomaly detection in fetal alcohol spectrum disorders

Introduction

Fetal alcohol spectrum disorders (FASD) range from fetal alcohol syndrome (FAS) to non-syndromic forms (NS-FASD). The neuroanatomical consequences of prenatal alcohol exposure are mainly the reduction in brain size, but also focal abnormalities such as those of the corpus callosum (CC). We previously showed a narrowing of the CC for brain size, using manual measurement and its usefulness to improve diagnostic certainty. Our aim was to automate these measurements of the CC and identify more recurrent abnormalities in FAS subjects, independently of brain size reduction.

Methods

We developed a fast, automated, and normalization-free method based on spectral analysis to generate thicknesses of the CC continuously and at singular points (genu, body, isthmus, and splenium), and its length (LCC). We applied it on midsagittal section of the CC extracted from T1-anatomical brain MRI of 89 subjects with FASD (52 FAS, 37 NS-FASD) and 126 with typically development (6-20 y-o). After adjusting for batch effect, we compared the mean profiles and thicknesses of the singular points across the 3 groups. For each parameter, we established variations with age (growth charts) and brain size in the control group (scaling charts), then identified participants with abnormal measurements (<10th percentile).

Results

We confirmed the slimming of the posterior half of the CC in both FASD groups, and of the genu section in the FAS group, compared to the control group. We found a significant group effect for the LCC, genu, median body, isthmus, and splenium thicknesses (p < 0.05). We described a body hump whose morphology did not differ between groups. According to the growth charts, there was an excess of FASD subjects with abnormal LCC and isthmus, and of FAS subjects with abnormal genu and splenium. According to the scaling charts, this excess remained only for LCC, isthmus and splenium, undersized for brain size.

Conclusion

We characterized size-independent anomalies of the posterior part of the CC in FASD, with an automated method, confirming and extending our previous study. Our new tool brings the use of a neuroanatomical criterion including CC damage closer to clinical practice. Our results suggest that an FAS signature identified in NS-FASD, could improve diagnosis specificity.

A Comprehensive Corpus Callosum Segmentation Tool for Detecting Callosal Abnormalities and Genetic Associations from Multi Contrast MRIs

Structural alterations of the midsagittal corpus callosum (midCC) have been associated with a wide range of brain disorders. The midCC is visible on most MRI contrasts and in many acquisitions with a limited field-of-view. Here, we present an automated tool for segmenting and assessing the shape of the midCC from T1w, T2w, and FLAIR images. We train a UNet on images from multiple public datasets to obtain midCC segmentations. A quality control algorithm is also built-in, trained on the midCC shape features. We calculate intraclass correlations (ICC) and average Dice scores in a test-retest dataset to assess segmentation reliability. We test our segmentation on poor quality and partial brain scans. We highlight the biological significance of our extracted features using data from over 40,000 individuals from the UK Biobank; we classify clinically defined shape abnormalities and perform genetic analyses.

Midsagittal corpus callosal thickness and cognitive impairment in Parkinson's disease

People diagnosed with Parkinson's disease (PD) can experience significant neuropsychiatric symptoms, including cognitive impairment and dementia, the neuroanatomical substrates of which are not fully characterised. Symptoms associated with cognitive impairment and dementia in PD may relate to direct structural changes to the corpus callosum via primary white matter pathology, or as a secondary outcome due to the degeneration of cortical regions. Using magnetic resonance imaging, the corpus callosum can be investigated at the midsagittal plane, where it converges to a contiguous mass and is not intertwined with other tracts. The objective of this project was thus twofold; first, we investigated possible changes in the thickness of the midsagittal callosum and cortex in patients with PD with varying levels of cognitive impairment; and secondly, we investigated the relationship between the thickness of the midsagittal corpus callosum and the thickness of the cortex. Study participants included cognitively unimpaired PD participants (n = 35), PD participants with mild cognitive impairment (n = 22), PD participants with dementia (n = 17) and healthy controls (n = 27). We found thinning of the callosum in PD-related dementia compared to PD-related mild cognitive impairment and cognitively unimpaired PD participants. Regression analyses found thickness of the left medial orbitofrontal cortex to be positively correlated with thickness of the anterior callosum in PD-related mild cognitive impairment. This study suggests that a midsagittal thickness model can uncover changes to the corpus callosum in PD-related dementia, which occur in line with changes to the cortex in this advanced disease stage.

A Deformation-Based Shape Study of the Corpus Callosum in First Episode Schizophrenia

Background: Previous first-episode schizophrenia (FES) studies have reported abnormalities in the volume and mid-sagittal size of the corpus callosum (CC), but findings have been inconsistent. Besides, the CC shape has rarely been analyzed in FES. Therefore, in this study, we investigated FES-related CC shape abnormalities using 198 participants [92 FES patients and 106 healthy controls (HCs)]. Methods: We conducted statistical shape analysis of the mid-sagittal CC curve in a large deformation diffeomorphic metric mapping framework. The CC was divided into the genu, body, and splenium (gCC, bCC, and sCC) to target the key CC sub-regions affected by the FES pathology. Gender effects have been investigated. Results: There were significant area differences between FES and HC in the entire CC and gCC but not in bCC nor sCC. In terms of the localized shape morphometrics, significant region-specific shape inward-deformations were detected in the superior portion of gCC and the anterosuperior portion of bCC in FES. These global area and local shape morphometric abnormalities were restricted to female FES but not male FES. Conclusions: gCC was significantly affected in the neuropathology of FES and this finding was specific to female FES. This study suggests that gCC may be a key sub-region that is vulnerable to the neuropathology of FES, specifically in female patients. The morphometrics of gCC may serve as novel and efficient biomarkers for screening female FES patients.

Morphometric mapping of the macrostructural abnormalities of midsagittal corpus callosum in Wilson’s disease

Background and Purpose

The corpus callosum (CC) consists of topographically arranged white matter (WM) fibers. Previous studies have indicated the CC to be discretely involved in WD. In this study, we strived to characterize the macrostructural properties of the CC using midsagittal cross-sectional area and thickness profile measurements.

Materials and Methods

This study was performed using archived magnetic resonance imaging (MRI) scans of 14 patients with WD and 14 age- and gender-matched healthy controls. Using an automated software pipeline for morphometric profiling, the midsagittal CC was segmented into five sub-regions (CC_1–5) according to the Hofer–Frahm scheme. The mean thickness and area of different CC segments and their clinical and cognitive correlates were identified.

Results

The mean area was significantly different only in CC₂ segment (94.2 ± 25.5 vs. 118.6 ± 19.7 mm², corrected P < 0.05). The mean thickness was significantly different in CC₁ (5.06 ± 1.15 vs. 6.93 ± 0.89 mm, corrected P < 0.05), CC₂ (3.73 ± 0.96 vs. 4.87 ± 1.01 mm, corrected P < 0.05), and CC₃ segments (3.42 ± 0.84 vs. 3.94 ± 0.72 mm, corrected P < 0.05). The age at onset of neurological symptoms and MMSE score was significantly correlated with the morphometric changes of CC₁ and CC₂ segments.

Conclusion

Morphological changes of the CC are discrete in WD. Morphometric loss of CC was associated with an earlier onset of neurological symptoms and cognitive dysfunction in WD.

Callosal thickness profiles for prognosticating conversion from mild cognitive impairment to Alzheimer's disease: A classification approach

INTRODUCTION

Alzheimer's disease (AD) is the most common form of dementia. Finding biomarkers to prognosticate transition from mild cognitive impairment (MCI) to AD is important to clinical medicine. Promising imaging biomarkers of AD conversion identified so far include atrophy of the cerebral cortex and subcortical gray matter nuclei.

METHODS

This study introduces thickness and bending angle of the corpus callosum as a putative white matter marker of MCI to AD conversion. The corpus callosum is computationally less demanding to segment automatically compared to more complicated structures and a subject can be processed in a few minutes. We aimed to demonstrate that callosal shape and thickness measures provide a simple, effective, and accurate prognostication tool in ADNI dataset. Using longitudinal datasets, we classified MCI subjects based on conversion to AD assessed via cognitive testing. We evaluated the classification accuracy of callosal shape features in comparison with the existing "gold standard" cortical thickness and subcortical gray matter volume measures.

RESULTS

The callosal thickness measures were less accurate in classifying conversion status by cognitive scores compared to gray matter measures for AD.

CONCLUSIONS

While this paper presented a negative result, this method may be more suitable for a disease of the white matter.

Semi–Automatic Corpus Callosum Segmentation and 3D Visualization Using Active Contour Methods

Accurate 3D computer models of the brain, and also of parts of its structure such as the corpus callosum (CC) are increasingly used in routine clinical diagnostics. This study presents comparative research to assess the utility and performance of three active contour methods (ACMs) for segmenting the CC from magnetic resonance (MR) images of the brain, namely: an edge-based active contour model using an inflation/deflation force with a damping coefficient (EM), the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) method and the Distance Regularized Level Set Evolution (DRLSE) method. The pre-processing methods applied during research work were to improve the contrast, reduce noise and thus help segment the CC better. In this project, 3D CC models reconstructed based on the segmentations of cross-sections of MR images were also visualised. The results, as measured by quantitative tests of the similarity indice (SI) and overlap value (OV) are the best for the EM model (SI = 92%, OV = 82%) and are comparable to or better than those for other methods taken from a literature review. Furthermore, the properties of the EM model consisting in its ability to both expand and shrink at the same time allow segmentations to be better fitted in subsequent CC slices then in state-of-the art ACMs such as DRLSE or SBGFRLS. The CC contours from previous and subsequent iterations produced by the EM model can be used for initiation in subsequent or previous frames of MR images, which makes the segmentation process easier, particularly as the CC area can increase or decrease in subsequent MR image frames.

Caffeine for apnea of prematurity and brain development at 11 years of age

Objective

Caffeine therapy for apnea of prematurity has been reported to improve brain white matter microstructure at term‐equivalent age, but its long‐term effects are unknown. This study aimed to investigate whether caffeine affects (1) brain structure at 11 years of age, and (2) brain development from term‐equivalent age to 11 years of age, compared with placebo.

Methods

Preterm infants born ≤1250 g were randomly allocated to caffeine or placebo. Magnetic resonance imaging (MRI) was performed on 70 participants (33 caffeine, 37 placebo) at term‐equivalent age and 117 participants (63 caffeine, 54 placebo) at 11 years of age. Global and regional brain volumes and white matter microstructure were measured at both time points.

Results

In general, there was little evidence for differences between treatment groups in brain volumes or white matter microstructure at age 11 years. There was, however, evidence that the caffeine group had a smaller corpus callosum than the placebo group. Volumetric brain development from term‐equivalent to 11 years of age was generally similar between treatment groups. However, there was evidence that caffeine was associated with slower growth of the corpus callosum, and slower decreases in axial, radial, and mean diffusivities in the white matter, particularly at the level of the centrum semiovale, over time than placebo.

Interpretation

This study suggests any benefits of neonatal caffeine therapy on brain structure in preterm infants weaken over time and are not clearly detectable by MRI at age 11 years, although caffeine may have long‐term effects on corpus callosum development.

Automatic Extraction of the Centerline of Corpus Callosum from Segmented Mid-Sagittal MR Images

The centerline, as a simple and compact representation of object shape, has been used to analyze variations of the human callosal shape. However, automatic extraction of the callosal centerline remains a sophisticated problem. In this paper, we propose a method of automatic extraction of the callosal centerline from segmented mid-sagittal magnetic resonance (MR) images. A model-based point matching method is introduced to localize the anterior and posterior endpoints of the centerline. The model of the endpoint is constructed with a statistical descriptor of the shape context. Active contour modeling is adopted to drive the curve with the fixed endpoints to approximate the centerline using the gradient of the distance map of the segmented corpus callosum. Experiments with 80 segmented mid-sagittal MR images were performed. The proposed method is compared with a skeletonization method and an interactive method in terms of recovery error and reproducibility. Results indicate that the proposed method outperforms skeletonization and is comparable with and sometimes better than the interactive method.

Callosal circularity as an early marker for Alzheimer's disease

Background

Although brain atrophy is considered to be a downstream marker of Alzheimer's disease (AD), subtle changes may allow to identify healthy subjects at risk of developing AD. As the ability to select at-risk persons is considered to be important to assess the efficacy of drugs and as MRI is a widely available imaging technique we have recently developed a reliable segmentation algorithm for the corpus callosum (CC). Callosal atrophy within AD has been hypothesized to reflect both myelin breakdown and Wallerian degeneration.

Methods

We applied our fully automated segmentation and feature extraction algorithm to two datasets: the OASIS database consisting of 316 healthy controls (HC) and 100 patients affected by either mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD) and a second database that was collected at the Memory Clinic of Hospital Network Antwerp and consists of 181 subjects, including healthy controls, subjects with subjective cognitive decline (SCD), MCI, and ADD. All subjects underwent (among others) neuropsychological testing including the Mini-Mental State Examination (MMSE). The extracted features were the callosal area (CCA), the circularity (CIR), the corpus callosum index (CCI) and the thickness profile.

Results

CIR and CCI differed significantly between most groups. Furthermore, CIR allowed us to discriminate between SCD and HC with an accuracy of 77%. The more detailed callosal thickness profile provided little added value towards the discrimination of the different AD stages. The largest effect of normal ageing on callosal thickness was found in the frontal callosal midbody.

Conclusions

To the best of our knowledge, this is the first study investigating changes in corpus callosum morphometry in normal ageing and AD by exploring both summarizing features (CCA, CIR and CCI) and the complete CC thickness profile in two independent cohorts using a completely automated algorithm. We showed that callosal circularity allows to discriminate between an important subgroup of the early AD spectrum (SCD) and age and sex matched healthy controls.

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Callosal circularity allows to discriminate between subjects with subjective cognitive decline and matched healthy controls

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Callosal circularity is smaller in subjects with AD dementia as compared to matched subjects with mild cognitive impairment

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The callosal thickness profile differs between AD and HC, but not between the different clinical AD stages

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The AD thickness profile strongly correlates with age in HCs

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Callosal circularity correlates with CSF biomarkers (T-tau and P-tau) in MCI.

Computational methods for corpus callosum segmentation on MRI: A systematic literature review

BACKGROUND AND OBJECTIVE

The corpus callosum (CC) is the largest white matter structure in the brain and has a significant role in central nervous system diseases. Its volume correlates with the severity and/or extent of neurodegenerative disease. Even though the CC's role has been extensively studied over the last decades, and different algorithms and methods have been published regarding CC segmentation and parcellation, no reviews or surveys covering such developments have been reported so far. To bridge this gap, this paper presents a systematic literature review of computational methods focusing on CC segmentation and parcellation acquired on magnetic resonance imaging.

METHODS

IEEExplore, PubMed, EBSCO Host, and Scopus database were searched with the following search terms: ((Segmentation OR Parcellation) AND (Corpus Callosum) AND (DTI OR MRI OR Diffusion Tensor Imag* OR Diffusion Tractography OR Magnetic Resonance Imag*)), resulting in 802 publications. Two reviewers independently evaluated all articles and 36 studies were selected through the systematic literature review process.

RESULTS

This work reviewed four main segmentation methods groups: model-based, region-based, thresholding, and machine learning; 32 different validity metrics were reported. Even though model-based techniques are the most recurrently used for the segmentation task (13 articles), machine learning approaches achieved better outcomes of 95% when analyzing mean values for segmentation and classification metrics results. Moreover, CC segmentation is better established in T₁-weighted images, having more methods implemented and also being tested in larger datasets, compared with diffusion tensor images.

CONCLUSIONS

The analyzed computational methods used to perform CC segmentation on magnetic resonance imaging have not yet overcome all presented challenges owing to metrics variability and lack of traceable materials.

Corpus callosum macro and microstructure in late-life depression

BACKGROUND

Differences in corpus callosum (CC) morphology and microstructure have been implicated in late-life depression and may distinguish between late and early-onset forms of the illness. However, a multimodal approach using complementary imaging techniques is required to disentangle microstructural alterations from macrostructural partial volume effects.

METHODS

107 older adults were assessed: 55 currently-depressed patients without dementia and 52 controls without cognitive impairment. We investigated group differences and clinical associations in 7 sub-regions of the mid-sagittal corpus callosum using T1 anatomical data, white matter hyperintensity (WMH) quantification and two different diffusion MRI (dMRI) models (multi-tissue constrained spherical deconvolution, yielding apparent fibre density, AFD; and diffusion tensor imaging, yielding fractional anisotropy, FA and radial diffusivity, RD).

RESULTS

Callosal AFD was lower in patients compared to controls. There were no group differences in CC thickness, surface area, FA, RD, nor whole brain or WMH volume. Late-onset of depression was associated with lower FA, higher RD and lower AFD. There were no associations between any imaging measures and psychotic features or depression severity as assessed by the geriatric depression scale. WMH volume was associated with lower FA and AFD, and higher RD in patients.

LIMITATIONS

Patients were predominantly treatment-resistant. Measurements were limited to the mid-sagittal CC. dMRI analysis was performed on a smaller cohort, n=77. AFD was derived from low b-value data.

CONCLUSIONS

Callosal structure is largely preserved in LLD. WMH burden may impact on CC microstructure in late-onset depression suggesting vascular pathology has additional deleterious effects in these patients.

Novel Findings in Obstetric Brachial Plexus Palsy: A Study of Corpus Callosum Volumetry and Resting-State Functional Magnetic Resonance Imaging of Sensorimotor Network

BACKGROUND

The response of the brain to obstetric brachial plexus palsy (OBPP) is not clearly understood. We propose that even a peripheral insult at the developmental stage may result in changes in the volume of white matter of the brain, which we studied using corpus callosum volumetry and resting-state functional magnetic resonance imaging (rsfMRI) of sensorimotor network.

OBJECTIVE

To study the central neural effects in OBPP.

METHODS

We performed an MRI study on a cohort of 14 children who had OBPP and 14 healthy controls. The mean age of the test subjects was 10.07 ± 1.22 yr (95% confidence interval). Corpus callosum volumetry was compared with that of age-matched healthy subjects. Hofer and Frahm segmentation was used. Resting-state fMRI data were analyzed using the FSL software (FMRIB Software Library v5.0, Oxford, United Kingdom), and group analysis of the sensorimotor network was performed.

RESULTS

Statistical analysis of corpus callosum volume revealed significant differences between the OBPP cohort and healthy controls, especially in the motor association areas. Independent t-test revealed statistically significant volume loss in segments I (prefrontal), II (premotor), and IV (primary sensory area). rsfMRI of sensorimotor network showed decreased activation in the test hemisphere (the side contralateral to the injured brachial plexus) and also decreased activation in the ipsilateral hemisphere, when compared with healthy controls.

CONCLUSION

OBPP occurs in an immature brain and causes central cortical changes. There is secondary corpus callosum atrophy which may be due to retrograde transneuronal degeneration. This in turn may result in disruption of interhemispheric coactivation and consequent reduction in activation of sensorimotor network even in the ipsilateral hemisphere.

Reliability of measuring regional callosal atrophy in neurodegenerative diseases

The Corpus Callosum (CC) is an important structure connecting the two brain hemispheres. As several neurodegenerative diseases are known to alter its shape, it is an interesting structure to assess as biomarker. Yet, currently, the CC-segmentation is often performed manually and is consequently an error prone and time-demanding procedure. In this paper, we present an accurate and automated method for corpus callosum segmentation based on T1-weighted MRI images.

After the initial construction of a CC atlas based on healthy controls, a new image is subjected to a mid-sagittal plane (MSP) detection algorithm and a 3D affine registration in order to initialise the CC within the extracted MSP. Next, an active shape model is run to extract the CC. We calculated the reliability of most popular CC features (area, circularity, corpus callosum index and thickness profile) in healthy controls, Alzheimer's Disease patients and Multiple Sclerosis patients. Importantly, we also provide inter-scanner reliability estimates.

We obtained an intra-class correlation coefficient (ICC) of over 0.95 for most features and most datasets. The inter-scanner reliability assessed on the MS patients was remarkably well and ranged from 0.77 to 0.97.

In summary, we have constructed an algorithm that reliably detects the CC in 3D T1 images in a fully automated way in healthy controls and different neurodegenerative diseases. Although the CC area and the circularity are the most reliable features (ICC > 0.97); the reliability of the thickness profile (ICC > 0.90; excluding the tip) is sufficient to warrant its inclusion in future clinical studies.

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A completely automated segmentation of the Corpus Callosum

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Both traditional features and the thickness profile using Laplace's equation are calculated.

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Excellent reproducibility and accuracy in healthy controls

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Excellent reproducibility and accuracy in Alzheimer's Dementia and Multiple Sclerosis patients

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Excellent inter-scanner reliability enabling the pooling of multi-center data

The relationship between regional microstructural abnormalities of the corpus callosum and physical and cognitive disability in relapsing-remitting multiple sclerosis

Significant corpus callosum (CC) involvement has been found in relapsing–remitting multiple sclerosis (RRMS), even if conventional magnetic resonance imaging measures have shown poor correlation with clinical disability measures. In this work, we tested the potential of multimodal imaging of the entire CC to explain physical and cognitive disability in 47 patients with RRMS. Values of thickness, fractional anisotropy (FA) and mean diffusivity (MD) were extracted from 50 regions of interest (ROIs) sampled along the bundle. The relationships between clinical, neuropsychological and imaging variables were assessed by using Spearman's correlation. Multiple linear regression analysis was employed in order to identify the relative importance of imaging metrics in modeling different clinical variables. Regional fiber composition of the CC differentially explained the response variables (Expanded Disability Status Scale [EDSS], cognitive impairment). Increases in EDSS were explained by reductions in CC thickness and MD. Cognitive impairment was mainly explained by FA reductions in the genu and splenium. Regional CC imaging properties differentially explained disability within RRMS patients revealing strong, distinct patterns of correlation with clinical and cognitive status of patients affected by this specific clinical phenotype.

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We assess corpus callosum damage in relapsing–remitting multiple sclerosis.

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We used no a priori subdivisions to model the bundle in a continuous fashion.

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Imaging–clinical relationship was explored by correlation and regression analyses.

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Damage of large, heavily myelinated axons was mainly linked to physical disability.

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Damage of small-diameter axons was mainly linked to cognitive impairment.