So far the scientists have discovered that there is a particular type of glial cell in foetal brains that plays a role in the development of the corpus callosum. Disruption to these cells is one source of the disorder.
Scientists at QBI have also been investigating the genetics of corpus callosum agenesis. The researchers found that mutations in a gene called DCC led to malformation of the corpus callosum.
Depending on where along the DCC gene the mutations occur, a person can have one or both of the disorders together. Some people with corpus callosum agenesis are high functioning, and although they face some challenges, they are able to live full and productive lives. Researchers are interested in how the brains of these people are re-wired to compensate for the loss of this massive brain connection.
This work will help researchers to understand the process of brain plasticity and how the brain could re-wire itself during foetal life.
Recent studies have demonstrated the presence of small yet reliable changes in specific white-matter regions responding to tasks Ji et al. A few studies have also demonstrated neural-driven white-matter signal fluctuations during rest Peer et al.
Given that white matter densely connects different regions of the gray matter and accounts for nearly half of the human brain Teo et al. Ding and colleagues have found that BOLD signals in certain white-matter tracts are functionally correlated with specific gray-matter regions during different tasks Ding et al.
Moreover, Peer and colleagues demonstrated the presence of distinct symmetric white-matter functional networks WM-FNs in resting-state fMRI signals, which were closely related to both gray-matter functional networks GM-FNs and the underlying structural white-matter tracts Peer et al.
They suggested that the interaction between the WM- and GM-FNs provide clues as to how these spatially networks were connected. On the other hand, the symmetric WM-FNs corresponding to the gray-matter perception-motor system were altered in patients with schizophrenia, which demonstrates that connections between the WM- and GM-FNs are necessary to maintain the normal functionality of the brain Jiang et al.
As the largest white-matter fiber bundle, the corpus callosum connects the two hemispheres of human brain and contains more than million axons Nolte Thus, the corpus callosum plays a crucial role in transmitting sensory, motor, and cognitive information between homotopic regions of the two cerebral hemispheres Gazzaniga It has been demonstrated that a larger callosal area has a performance advantage in cognitive tasks Berlucchi ; Yazgan et al.
Because the primary function of corpus callosum is to act as the primary cortical projection system Rosas et al. For example, patients with schizophrenia have been reported to have a statistically significant reduction in cortical area within the corpus callosum compared to healthy controls Woodruff et al.
Patients with attention-deficit hyperactivity disorder have been shown to have a smaller area in the rostrum and rostral body of corpus callosum compared to healthy controls Giedd et al. A range of neurodevelopmental disorders and dysplasias has been reported to lead to corpus callosum agenesis Sebire et al.
Moreover, the morphologic anomalies of corpus callosum have also been reported for a wide variety of childhood neuropsychiatric illness and sexual dimorphism Giedd et al. Several studies on the structure of the corpus callosum have divided the corpus callosum into different subregions that connects bilateral corresponding cortical areas in the brain Aboitiz et al. For instance, by using light microscopic examination in 10 regions of the corpus callosum, Aboitiz and colleagues found the regional differentiation of fiber types in the corpus callosum Aboitiz et al.
Huang and colleagues divided the corpus callosum into six major subdivisions based on trajectories to different cortical areas by using DTI tractography Huang et al. In addition, the corpus callosum was parcellated into different subregions based on its cortical trajectories to specific cytoarchitectural regions using HARDI-based tractography and tract-based transformation Chao et al.
It has been shown that distinct regions of the corpus callosum were activated during different tasks, such as motor, tactile, visual, auditory, gustatory, and memory task Mazerolle et al. Moreover, these activation foci in the corpus callosum were distributed according the anterior taste stimuli , middle motor task , middle and posterior tactile stimuli and splenium visual stimuli Fabri et al.
These cumulative findings support that the subregions of the corpus callosum are associated with the distinct functions of the human brain. Although the corpus callosum has been structurally parcellated into different subregions, the underlying BOLD fMRI signals in the corpus callosum and how functional information is transferred within the corpus callosum and other brain regions have not been investigated.
Therefore, we hypothesize that the corpus callosum may be differentially connected with WM-FNs. To address this hypothesis, we evaluated the WM-FNs by performing a clustering analysis to the voxel-based white-matter functional connectivity matrix, and studied the relationships between WM- and GM-FNs.
Subsequently, we used partial correlation analysis to explore the connectivity between the WM-FNs and each voxel in the corpus callosum. Using a winner-take-all algorithm, each callosal voxel was assigned to a single WM-FN with the most similar profile of connectivity. In this way, we identified the subregions of corpus callosum related to specific WM-FNs.
In the end, to assess the reproducibility of our findings, we validated the WM-FNs and divided the corpus callosum in different datasets.
Rest1 and Rest2 were acquired on different days. All subjects signed an informed consent. Imaging data were collected on a customized Siemens 3-T Connectome-Skyra scanner.
Because details about the imaging parameters have been described elsewhere in detail Van Essen et al. The data preprocessing included following steps. First, we discarded the first 10 volumes, correction for head-motion-related signal changes.
Linear trends were removed. Signal from CSF and 24 rigid body motion parameters 6 head motion parameters, 6 head motion parameters one time point before, and the 12 corresponding squared items were regressed from the data. The head motion scrubbing regressors were used in this study as it has been shown to be effective in reducing the effect of head motion at the spike on the signal without changing the correlation values Power et al.
To retain as much of the signal of interest as possible, the white-matter and global mean signals were not regressed from the dataset. Temporal filtering was done in the low-frequency range of 0. To avoid mixing white-matter and gray-matter signals, the functional images were minimally spatially smoothed separately 4-mm full-width half-maximum [FWHM], isotropic within the white-matter and gray-matter templates for each subject.
The white-matter and gray-matter voxels were identified by using the segmentation results from each subject using a threshold of 0. Finally, we merged the white-matter and gray-matter images into full functional images using the smoothed data.
Specifically, for each subject, we identified each voxel in the whole brain as belonging to one of the following three classes: white matter, gray matter, or CSF based on the maximum probability from the three segmentation images.
This step generated a binarized white-matter mask for each subject. Then, these masks were averaged across all subjects. Finally, the Harvard-Oxford Atlas Desikan et al. To minimize the effect of the gray matter, we used the group white-matter mask to limit the size of corpus callosum Fig. The group level white-matter mask is used to limit the size of corpus callosum. Since our study aimed to explore the correlation between the WM-FNs and corpus callosum, we excluded these voxels of corpus callosum in the group white-matter mask using the corpus callosum mask.
Every second voxel along the image rows and columns were taken and shifted by 1 between slices to reduce missing columns of data. Then, we could obtain a subsampled mask including voxels. To obtain a group-level correlation matrix, the correlation matrices for each subject was calculated and then averaged across all subjects.
K-means clustering distance metric correlation, 10 replications was performed on the group-level voxelwise correlation matrix to obtain the WM-FNs Blumensath et al. The numbers of clusters were chosen from 2 to 22, and the stability of each cluster was analyzed to get the optimal number of WM-FNs Yeo et al.
Specifically, the group-level correlation matrix was randomly divided into fourfolds. For each number of clusters, we computed the clustering process on each fold separately and analyzed the similarity.
To analyze the similarity of clustering in different folds, we calculated an adjacency matrix for each fold and compared them using Dice coefficient. The average Dice coefficient computed by comparing each other in all four adjacency matrices was computed to detect the stability for each number clustering Yeo et al.
A number of stable clusters were found at 4, 7, 9, and 12 clusters, and then we chose the most detailed level 12 WM-FNs Fig. Finally, we kept 10 WM-FNs for further analysis and excluded two white-matter cerebellum networks, since we focused on the cortex within this study. To test the reproducibility between the two runs, we obtained the WM-FNs using the clustering method from run 1 and reanalyzed the correlation between the WM-FNs and the corpus callosum for each of the two runs.
Functional connectivity between the 10 WM-FNs, and each voxel in the corpus callosum was used to identify the different subregions of the corpus callosum. Specifically, partial correlation analysis was calculated between the averaged time course of each WM-FN and every voxel time course in the corpus callosum mask, while controlling for the effect of all other nine WM-FNs.
All the correlation coefficients were then Fisher-transformed to Z-scores. A one-sample t -test was calculated across all participants, resulting in a statistical t-map of connectivity pattern for each WM-FN as region of interest ROI. To explain the reproducibility of our results, these analyses were performed on run 1 and run 2 data separately. Finally, to analyze whether current method was suitable for studies with small number of subjects, we repeated the clustering for WM-FNs and parcellation procedures in different sample sizes 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, , and Figs S5—1 and S5—2.
The WM-FNs were identified by using K-means clustering on the averaged functional connectivity matrix. The clustering and the Dice coefficient results showed that the largest number of WM-FNs was 12, which was selected for identifying final clustering results Fig.
Except for the left prefrontal network, the resulting WM-FNs showed an interlaced and a relatively symmetric pattern. WM-FNs identified using K-means clustering method. Left prefrontal WM-FN; Tempofrontal WM-FN; Inferior corticospinal WM-FN; Deep WM-FN; Occipital WM-FN; Orbitofrontal WM-FN; Anterior corona radiata WM-FN; Sensorimotor middle WM-FN; Posterior corona radiata WM-FN; Sensorimotor superficial WM-FN; Cerebellar inferior WM-FN; Cerebellar superior WM-FN.
Our study found strong correlations between different WM-FNs, including the deep, occipital, orbitofrontal, anterior corona radiata, sensorimotor middle, posterior corona radiata, and sensorimotor superficial WM-FNs Fig.
Functional connectivity of the WM-FNs in resting state. A and C show averaged functional connectivity strength between the different WM-FNs in run 1 and run 2 data, respectively.
The colorbar shows the correlation coefficient. Since WM-FNs were reproducibly identified in the bilateral hemispheres, we further tested how WM-FNs were related to interhemispheric connections, by examining functional connectivity between WM-FNs and each voxel of corpus callosum. Based on the geometrical landmark defined by Witelson and colleagues, the corpus callosum can be divided into seven subregions based on the length of corpus callosum including the rostrum, genu, rostral body, anterior midbody, posterior midbody, isthmus, and splenium of the corpus callosum Witelson We found that the WM-FNs significantly connected to the different regions of corpus callosum after thresholding by winner-take-all Fig.
Specifically, the left prefrontal WM-FN correlated with the anterior midbody of corpus callosum. The callosal subregions corresponding to tempofrontal WM-FN concentrated mainly on the splenium and genu. The subregions relating with deep WM-FN had distributions in the anterior midbody, isthmus, posterior midbody, and rostral body of corpus callosum. Occipital WM-FN was only connected with the splenium of corpus callosum. The orbitofrontal WM-FN was mainly associated with the rostral and genu of corpus callosum.
The regions corresponding to anterior corona radiata WM-FN distributed in seven areas of corpus callosum. The voxel of corpus callosum associated with the sensorimotor middle WM-FN mainly distributed in the anterior midbody, isthmus, and posterior midbody of corpus callosum. Finally, the posterior corona radiata WM-FN was related with the isthmus and splenium of corpus callosum. No voxels in the corpus callosum corresponded to sensorimotor superficial WM-FN.
The callosal regions corresponding to inferior corticospinal WM-FN exhibited differences between two runs. The inferior corticospinal WM-FN was associated with isthmus and splenium of corpus callosum in the run 1 data. However, there were no voxels corresponding to the inferior corticospinal WM-FN in the run 2 data.
Reproducible subregions of corpus callosum in the two runs using winner-take-all method. The detailed specification of callosal subregion corresponding to each WM-FN in runs 1 and 2 data. To detect the reproducibility of the correlation between the WM-FNs and corpus callosum, the Dice coefficient was computed on the result regions of corpus callosum corresponding to each WM-FN between two runs.
The spatial overlap for callosal subregions corresponding to each WM-FN showed different levels that were classified as moderate, moderate-high, and high Fig. Specifically, the subregions of corpus callosum corresponding to these four WM-FNs had a high Dice coefficient between two runs The Dice coefficients for callosal subregions corresponding to the deep, occipital, orbitofrontal, and posterior corona radiata WM-FNs were found to be 0.
The subregions of corpus callosum associated with these three WM-FNs revealed a moderate-high Dice coefficient between two runs The Dice coefficients for callosal subregions corresponding to the tempofrontal, anterior corona radiata, and sensorimotor middle WM-FNs were found to be 0. Finally, we found that the subregions of corpus callosum linking the left prefrontal WM-FN had only a moderate level overlap between two runs The Dice coefficient for callosal subregion corresponding to the left prefrontal WM-FN was 0.
We did not show the results of Dice coefficient corresponding to the callosal subregions related to the inferior corticospinal and sensorimotor superficial WM-FNs in the Figure 5 , because there were no relevant voxels between these two WM-FNs and corpus callosum calculated using run 2 data. The overlap between callosal subregions corresponding to each WM-FN from two runs.
The x -axis represents the eight subregions of corpus callosum using run 1 data, and the y -axis represents the eight subregions of corpus callosum using run 2 data. The color bar shows the Dice coefficient between callosal subregions corresponding to each WM-FN from two runs.
The remaining maps of callosal subregions after correction corresponding to other seven WM-FNs were shown in the Figure S3—1. The subregions of corpus callosum associated with these two WM-FNs the left prefrontal and sensorimotor superficial WM-FNs revealed a moderate—high Dice coefficient between two runs.
The subregions of corpus callosum linking the inferior corticospinal WM-FN had a low-moderate level overlap between two runs. One-sample t -test maps and subregions of corpus callosum from the two different runs. The left side shows the results of callosal one-sample t -test maps. The color bar shows the t value from the one-sample t -test.
For example, they would present an image of a flower to the right eye, but cover the left eye. They found that split-brain patients, when presented with a visual image to only their left eye, could not name the object shown in the image. Sperry and colleagues hypothesized that this occurred because visual information for the majority of the visual field travels to the opposite side of the brain to be processed.
If the object is shown to the left eye, most of the information travels to the right side of the brain. Normally, this information would then be shared with the opposite hemisphere by way of the corpus callosum. The researchers suggested that split-brain individuals could not name the object if it was shown only to the left eye because the visual information was not reaching the left side of the brain, which is where our important language centers are located.
Much of what you've heard about one cerebral hemisphere being dominant in the management of a particular skill or capacity is probably exaggerated. For example, someone who is creative doesn't likely have an overall bias toward thinking with the right side of her brain. Instead, most skills are spread fairly evenly throughout both hemispheres. Language, however, appears to be an exception. In most people, speech is generated in the left hemisphere, and thus the left hemisphere is considered to be the dominant hemisphere for language.
Thus, according to Sperry and Gazzaniga, because language centers are located in the left side of the brain, when an image is presented to the left eye of a split-brain patient, the patient's language areas are not privy to the visual information.
The information travels to the right hemisphere but does not cross back over to the left due to the severed corpus callosum. So, the ability to place a name to the object is limited. These experiments helped to demonstrate the importance of the left hemisphere in language processing as well as the importance of the corpus callosum in bridging the two cerebral hemispheres. However, they also demonstrated the versatility and resiliency of the brain, as in most split-brain patients other tracts like the anterior commissure still carry enough information between the cerebral hemispheres to allow overall functionality to be somewhat normal.
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