Wanted: A Behavioral Neurology Of White Matter
The study of higher functions in humans requires consideration of all the brains neural tissues. Long neglected as a contributor to the organization of cognitive and emotional operations, white matter is the object of intense, intriguing, and increasingly fruitful efforts to improve our understanding. Studying people with white matter disorders to correlate their brain lesions with specic behavior changes promises a wealth of insights. Increasingly, this method will be complemented by sophisticated neuroimaging techniques that yield detailed visualization of white matter tracts as they participate in the cognitive and emotional operations of distributed neural networks.
In practical terms, an appreciation of the brain-behavioral importance of white matter disorders can greatly benet patients, especially as early recognition and treatment often determine an outcome. In theoretical terms, further study of white matter and its disorders expands our knowledge of the brain as an extraordinarily complex structure in which the connectivity provided by white matter is central to cognition, emotion, and consciousness itself.
Significance Of Magnetic Resonance Imaging Of The Brain
One imaging technique used in studying the brain is magnetic resonance imaging . This procedure employs radio waves and powerful magnets to produce three-dimensional anatomical images.
MRI is a non-invasive way of diagnosing and detecting diseases and monitoring treatment. According to an article published in the National Institutes of Health, MRI provides more explicit and detailed images of the brain, the spinal cord, nerves, tendons, and ligaments compared to computed tomography scans and X-rays.
A study suggested that tissue clustering in MRI analysis might pose possible advantages due to its partial volume effects, accountability, and simplicity.
Moreover, this MRI analysis method provides radiologists the flexibility to monitor and analyze specific brain regions.
A functional MRI can determine specific parts of the brain and the location where certain bodily functions, like memory, ensue. This imaging technique can also evaluate the effects of stroke and examine each part of the brain and its functional anatomy.
An fMRI procedures abilityto determine the precise location of the brains functional center can aid physicians in planning treatments and surgeries for a specific brain condition or disorder.
Ui Registry Is A Unique Resource For Neuroscientists
Importantly, the new findings were based on data from over 500 individual patients, which is a large number compared to previous studies and suggests the findings are robust. The data came from two registries one from Washington University in St. Louis, which provided data from 102 patients, and the Iowa Neurological Registry based at the UI, which provided data from 402 patients. The Iowa registry is over 40 years old and is one of the best characterized patient registries in the world, with close to 1000 subjects with well characterized cognition derived from hours of paper and pencil neuropsychological tests, and detailed brain imaging to map brain lesions. The registry is directed by Daniel Tranel, PhD, UI professor of neurology, and one of the study authors.
Reber notes that the study also illustrates the value of working with clinical patients as well as healthy individuals in terms of understanding relationships between brain structure and function.
I cannot stress enough how grateful we are that these patients have volunteered their time to help us without them, a lot of important research would be impossible, he adds.
In addition to Reber, Boes, and Tranel, the research team included UI researchers Kai Hwang, PhD, Mark Bowren, and Joel Bruss, as well as Pratik Mukherjee, MD, PhD, at the University of California, San Francisco.
University of IowaRoy J. and Lucille A. Carver College of Medicine451 Newton Road
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Phase Diagram Of Optimal Designs
In previous sections we derived conditions under which various designs are optimal in terms of minimizing conduction delays. Specifically, HD is optimal if ND2 2 and PD is optimal if ND2 2 and 1. Next, we illustrate these results on a phase diagram in terms of basic network parameters such as the local wire diameter d, the number of local connections per neuron n, global axon diameter D, and the total number of neurons in the brain N. To obtain the phase diagram, in the first-order perturbation theory, we substitute the expression for into ND2 2, and find that PD is optimal when 1/2D/n1/6d 1. In the linear-log space of Figure 6, this expression corresponds to the regime above the thick green line.
In this phase diagram, we show parameter regimes in which HD or PD are optimal in terms of the global axon diameter D, local wire diameter d, total neuron number N, and the number of local connections per neuron n. We assume n = 104 and d = 1 m for all empirical data points. Values of D in mammalian brains are from S. S. H. Wang and , and values of N in the neocortex are from . Value of N in rat neostriatum is from . For birds, we assume N = 107.
Scaling Estimate Of The Cortical Thickness
As fasciculated fibers are usually not observed in neocortical gray matter , we identify cortical thickness with gray matter module size, R. Our prediction for the optimal module size R0 can be rewritten by using Equations 4 and 5
Using n ~ 104 , d ~ 1 m , and D ~ 1 m , we predict cortical thickness R0 ~ 1 mm. This estimate agrees well with the existing anatomical data , despite being derived using scaling. By substituting these values into Equation 26, we find that is smaller than one, justifying our perturbation theory approach.
Next, we apply our results to the allometric scaling relationship between cortical thickness, R0, and brain volume, V. We assume that n and D both increase with brain size according to the following power laws: n ~ V1/3 and D ~ V1/6 . Then, by using Equation 27 and the constancy of the optimal local wire diameter d across different species , we predict that R0 ~ V4/27. This prediction agrees well with the empirically obtained power law relationship between cortical thickness and brain volume . Thus, our theory explains why the cortical thickness changes little while brain volume varies by several orders of magnitude between different species.
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Groups 2c2e Multifocal Or Focal Cortical And Subcortical Dysgenesis
2c. Curvilinear heterotopia with CSF-like spaces
Each row depicts images from the same patient. Midline sagittal. Axial. Axial. Coronal. Axial images. Curvilinear subcortical band heterotopia with CSF-like spaces. Curvilinear SUBH with agenesis of the corpus callosum âinterhemispheric cysts. Transmantle columnar HET. Images from individual LR05-282 show extensive giant HET with curvilinear CSF-like spaces in the right hemisphere . The HET connects to both the overlying cortex and the ependyma in several instances. The left hemisphere crosses the midline. The right frontal horn and right caudate nucleus cannot be identified instead, a large heterotopic mass is seen . LR01-079 shows HET with curvilinear and nodular SUBH with extensive involvement of the right hemisphere. Also here the right frontal horn and right caudate nucleus cannot be identified . The cerebellum is hypoplastic with a Dandy-Walker configuration . LR13-408 has extensive involvement of both hemispheres with both curvilinear HET connecting to the cortex and multiple nodular SUBH . LR03-395: bilateral asymmetric HET with IHEM. The cysts are denoted with asterisks . Note also ACC and hypoplastic pons and cerebellar vermis . CSF-like spaces are identifiable . LR08-415: transmantle columnar HET in the left frontal lobe . The overlying cortex is dysplastic , and the lateral ventricles are mildly enlarged.
2d. Curvilinear heterotopia with ACCâinterhemispheric cysts
2e. Transmantle columnar heterotopia
Group 4 Malformations Due To Abnormal Postmigrational Development With Subcortical Or Transmantle Component
4a. Transmantle columnar and fan-like heterotopia Â± clefts
We observed 2 patients with solid transmantle columnar and fan-like HET in addition to clefts typical of schizencephaly . In both, 2 or more distinct transmantle HET or clefts were seen in each hemisphere. One boy had a brother with bilateral severe open-lip schizencephaly and was previously published as a rare form of familial schizencephaly, although the EMX2 sequence variant reported is highly unlikely to be pathogenic.
Each row depicts images from the same patient. Midline sagittal. Axial at the level of the basal ganglia. Axial. Coronal. Axial. Coronal. Axial. Partial agenesis of the corpus callosum, transmantle columnar subcortical band heterotopia and fan-like SUBH and bilateral clefts . Deeply infolded HETâparieto-occipital subtype was identified in LP97-007 and LR12-324, originating posteriorly in the perisylvian areas with an oblique orientation relative to the sagittal plane . Deeply infoldedâparasagittal subtype. Images show a highly similar pattern of bilateral parasagittal deep infolded HET touching the ependyma . Both individuals are microcephalic . Other brain structures are relatively preserved.
4câ4e. Deeply infolded HET
In the remaining 3 patients with deep infolding , HET occurred in a different location, but all involved the frontal lobes.
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The Role Deep Grey Matter In The Brain Plays In Ms
Deep grey matter is profoundly involved in the disease progression of MS patients
Our brains consist of grey matter and white matter. Grey matter contains the nerve cells, while white matter is composed of nerve fibres, which connect the nerve cells and are protected by myelin. Myelin is needed for our nerves to work properly.
When the myelin sheath is damaged, the flow of impulses along nerve fibres slows down or fails completely. As a result, brain functions become hampered or are lost.
MS has long been considered a disease of white matter, but more recent studies have highlighted the importance of grey matter demyelination.
Optimality Condition For Segregated Designs
In the previous section, we showed that in the regime ND2 2, there is at least one segregated design with local conduction delay shorter than that in HD. However, we did not specify which design is the optimal one. In this section, we give a necessary condition for a segregated design to be optimal in the regime ND2 2 and ND2 G/.
As the advantage of segregation becomes apparent when the total cross-section of global axons ND2 ~ 2, it is natural to expect that a similar condition defines the optimal gray matter module size R0, which minimizes local conduction delays. In other words, the number of neurons in the gray matter module is such that the total cross-sectional area of their global axons is given by 2. As the number of neurons in the sphere of radius R0 is 2/D2 and the number of neurons in the sphere of radius is n, we have
Thus, we can formulate the following theorem:
In the regime ND2 2 and ND2 G/, the minimum local conduction delay is achieved by the segregated design with the gray matter module containing 2/D2 neurons.
To prove this theorem, we consider designs with gray matter module size smaller and greater than R0, and show that they have a local conduction delay greater than that in the design with module size R0.
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Gray Matter: How We Process Information
We normal, everyday people have all probably heard the term gray matter when talking about the brain. Weve also probably heard about white matter. However this article is going to focus on what is gray matter, why is it called gray, and what is the difference between the two colored matters? What is it made of and what does its structure look like? What are some neat facts about the matter?
Grey Is The New White Not In The Brain
Figure 1: Reconstructed white matter fiber tracts of the brain, brainstem, and spinal cord. View through the mid-sagittal plane.Rendering is work of Thomas Schultz, using a modified version of the BioTensor application developed at the University of Utah. The dataset is courtesy of Gordon Kindlmann at the Scientific Computing and Imaging Institute, University of Utah, and Andrew Alexander, W.M. Keck Laboratory for Functional Brain Imaging and Behaviour, University of Wisconsin, Madison.
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Mri Of The White And Gray Matter In The Brain
Image 1. MRI of the brain, T1-weighted axial view.1, Cerebral hemisphere . 2, Grey matter. 3, White matter .
Image 2. MRI of the brain, T1-weighted axial view.1, Insula . 2, Putamen . 3, Corpus callosum .
Image 3. MRI of the brain, T1-weighted axial view.1, Right anterior limb, internal capsule . 2, Caudate nucleus . 3, Left posterior limb, internal capsule . 4, Thalamus .
Image 4 of 4. MRI of the brain, T1-weighted axial view.1, Substantia nigra . 2, White matter of midbrain. 3, Periaqueductal gray matter.
At the level of the cerebral hemisphere, gray matter is mainly distributed in the periphery while the white matter is deep. However, there is gray matter in depth of the brain called basal ganglia.
In the brainstem, gray matter is usually found in the depth while the white matter is superficial.
In the spinal cord, gray matter is located centrally, while the white matter forms the bulk of the superficial parts.
The distribution white matter – gray matter inside the brain is illustrated by MRI of the brain . On some axial cuts, the thalamus and some basal ganglia were given as an example of deep gray matter. On other cuts, the corpus callosum and internal capsule have been indicated as an example of white matter tracts.
Several studies explored different approaches of using magnetic resonance imaging in studying the white and gray matter of the brain.
Combining Local And Global Connections Increases Conduction Delays
After having considered conduction delays in local and global connections separately, now we are in a position to analyze how they are combined in the brain. Here we argue that the main difficulty in integration arises when introducing global connections into local networks.
We adopt a model combining both local and global connections proposed by Ruppin et al. and Murre and Sturdy . In this model, each neuron connects with n local neurons and sends a global axon to another arbitrarily chosen local network in the brain. For simplicity, we neglect specificity and assume that local connections are made with nearest n neurons located in a sphere of radius centered on a given neuron, where is given by Equation 4. Although local and global connections may be highly specific , this approximation is sufficient to understand brain segregation into white and gray matter.
where t is conduction delay in unperturbed local circuits given by Equation 6. As before, numerical factors are neglected in the spirit of the scaling estimate.
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Damage To White Matter Is Linked To Worse Cognitive Outcomes After Brain Injury
A new University of Iowa study challenges the idea that gray matter is more important than white matter when it comes to cognitive health and function. The findings may help neurologists better predict the long-term effects of strokes and other forms of traumatic brain injury.
The most unexpected aspect of our findings was that damage to gray matter hubs of the brain that are really interconnected with other regions didn’t really tell us much about how poorly people would do on cognitive tests after brain damage. On the other hand, people with damage to the densest white matter connections did much worse on those tests, explains Justin Reber, PhD, a UI postdoctoral research fellow in psychology and first author on the study. This is important because both scientists and clinicians often focus almost exclusively on the role of gray matter. This study is a reminder that connections between brain regions might matter just as much as those regions themselves, if not more so.
How Age Is Related To Gray Matters
Age is negatively associated with gray matter volume. Elderly people shows lower volumes of gray matters in the brain. Gray matters are directly linked to memory and reduction of gray matters causes memory problems for elderly people. Aging is associated with cognitive decline, diminished brain function. Brain function depends on large-scale distributed networks, and aging disrupted the structural and functional brain connectivity. However, scientists observed that with proper yoga, meditation, exercises and diet can increase the gray matter in the brain.
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What Is The Gray Matter And White Matter
Gray and white matter are two different regions of the central nervous system. In the brain, gray matter refers to the darker, outer portion, while white matter describes the lighter, inner section underneath. In the spinal cord, this order is reversed: The white matter is on the outside, and the gray matter sits within.
Gray matter is primarily composed of neuron somas , and white matter is mostly made of axons wrapped in myelin . The different composition of neuron parts is why the two appear as separate shades on certain scans.
Each region serves a different role. Gray matter is primarily responsible for processing and interpreting information, while white matter transmits that information to other parts of the nervous system.
Gray Matter And White Matter In The Brain
The brain contains both gray matter and white matter. The gray matter is made up up of the neurons that have non-myelinated axons. The white matter is made up of neurons that have myleniated axons. The myelin sheath is white. The primary thinking, perceiving and cognitive functions of the brain happen on in the gray matter of the brain. The neurons in this part of the brain are short enough that the electrical signal would not degrade from the start of the cell body to the axon terminal, so no insulation or ion channel signal boosting is required.
In contrast, the axons and thus axonal tracts that go from the cerebral cortex to the inner brain structures and the spinal cord, are longer and need the insulation and signal boost. Thus where these neurons begin to predominate, the brain matter actually becomes white. As the surface of the brain is filled of many hills and valleys, the inner face between the gray matter and the white matter is irregular as well. The gray matter and white matter interface can be seen below.
The gray matter and white matter interface can be seen on this photograph of an autopsied human brain.
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