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White Matter, Cerebellar

White matter refers to the myelinated nerve fibers that connect different regions of the brain and facilitate communication between them.
The cerebellar white matter, in particular, plays a crucial role in coordinating movement, balance, and motor learning.
Researchers studying white matter and cerebellar function can optimize their work with PubCompare.ai, an AI-driven platform that enhances reproducibility and accuracy.
The tool helps locate the best protocols from literature, preprints, and patents using intelligent comparison tools, allowing researchers to discover the most effective methods and products to advance their work efficietly.

Most cited protocols related to «White Matter, Cerebellar»

1
Cerebellar Tissue Segmentation Protocol
The flatmap was based on the anatomical data from 20 participants originally used for the generation of the spatially unbiased infratentorial template (SUIT) [12 (link)]. The scans were segmented into grey and white matter using unified segmentation [13 (link)] and the cerebellum was isolated from the neocortex using an automatic algorithm [12 (link)]. After the first affine alignment to the SUIT template, we utilized the fast diffeomorphic anatomical registration algorithm (Dartel) [14 (link)] to generate a new, slightly sharper average grey-matter and white-matter template.
The extent of the cerebellar white-matter body was then estimated applying a threshold of p>0.4 to the average map of white-matter probabilities. The remaining cerebellar voxels were labeled as grey matter if the grey matter probability exceeded 0.5. The maps were then hand-edited to ensure that the grey matter of the posterior vermis was separated from the abutting hemispheres of lobule HIX.
Diedrichsen J, & Zotow E. (2015). Surface-Based Display of Volume-Averaged Cerebellar Imaging Data. PLoS ONE, 10(7), e0133402.
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Publication 2015
Cerebellum Gray Matter Human Body Microtubule-Associated Proteins Neocortex Radionuclide Imaging Vermis, Cerebellar White Matter White Matter, Cerebellar
2
Evaluating Multi-Atlas Segmentation for Structural MRI
We evaluated the proposed multi-atlas segmentation within AutoSeg with a dataset of 35 defaced T1-weighted structural MRI scans. Fifteen scans (5 males and 10 females with an age range of 19–34) were used as the multi-atlas population and the remaining 20 scans (8 males and 12 females with an age range of 18–90) were used for testing. Thus, the 20 testing MRI scans were segmented one-by-one using the 15 atlases. These MRI scans were selected from the Open Access Series of Imaging Studies (OASIS) database (http://www.oasis-brains.org) (Asman and Landman, 2013 (link)). This dataset has been used in the MICCAI 2012 Multi-Atlas Labeling challenge, URL: https://masi.vuse.vanderbilt.edu/workshop2012/. This dataset was expertly labeled courtesy of Neuromorphometrics, Inc. (Somerville, MA) and provided under a non-disclosure agreement of the Creative Commons Attribution-NonCommercial (CC BY-NC). For each atlas, a collection of 28 labels of subcortical structures were used (Asman and Landman, 2013 (link)): 3rd ventricle, 4th ventricle, brain stem, left/right hemispheric accumbens, cerebral White Matter (WM), cerebellar WM, caudate, amygdala, hippocampus, lateral ventricle, pallidum, putamen, thalamus, and ventral diencephalon (DC), as well as cerebellar vermal lobules I-V, VI-VII, and VIII-X. All images are 1 mm isotropic resolution.
Wang J., Vachet C., Rumple A., Gouttard S., Ouziel C., Perrot E., Du G., Huang X., Gerig G, & Styner M. (2014). Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline. Frontiers in Neuroinformatics, 8, 7.
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Publication 2014
Amygdaloid Body Brain Brain Stem Cerebellum CREB3L1 protein, human Diencephalon Females Globus Pallidus Males MRI Scans Putamen Radionuclide Imaging Seahorses Thalamus Ventricle, Lateral Ventricles, Fourth Ventricles, Third White Matter White Matter, Cerebellar
3
Multimodal Neuroimaging Protocol for Alzheimer's
All participants had an anatomical 3D T1-weighted magnetic resonance imaging (MRI) scan (3 T Siemens). The image analyses were performed using the Medical Image NetCDF software toolbox (www.bic.mni.mcgill.ca/ServicesSoftware/MINC). In brief, the T1-weighted images were corrected for field distortions, segmented, nonuniformity corrected, and processed using the CIVET pipeline [16 (link)]. Subsequently, the T1-weighted images were linearly registered to the MNI reference template space [17 (link)], whereas the PET images were automatically coregistered to the individual’s MRI space. Then, the final PET linear registration was performed using the transformations obtained from the MRI to MNI linear template and the PET to T1-weighted native image. PET images were then spatially smoothed to achieve a final resolution of 8-mm full-width at half maximum. ROIs were obtained from the MNI nonlinear ICBM atlas and subsequently reoriented to the individual’s linear space [18 (link)]. The ROIs were tailored from the frontal, medial prefrontal, orbitofrontal, precuneus, anterior (ACC) and posterior cingulate (PCC), lateral and mediobasal temporal, inferior parietal, parahippocampus, hippocampus, insula, occipitotemporal, occipital pole, and cerebellar cortices as well as from the striatum, the pons, and the telencephalon white matter (cerebellar white matter not included). Subsequently, the ROIs were applied to the dynamic PET frames to obtain the time–activity curve data. The parametric images and the ROI standardized uptake value ratios (SUVRs) were measured for multiple different scan time frames and were generated using the cerebellar gray matter as the reference. Amyloid-PET positivity was determined visually by two raters blind to clinical diagnosis. Further information regarding the imaging methods pipeline may be found elsewhere [19 (link), 20 (link)].
Pascoal T.A., Shin M., Kang M.S., Chamoun M., Chartrand D., Mathotaarachchi S., Bennacef I., Therriault J., Ng K.P., Hopewell R., Bouhachi R., Hsiao H.H., Benedet A.L., Soucy J.P., Massarweh G., Gauthier S, & Rosa-Neto P. (2018). In vivo quantification of neurofibrillary tangles with [18F]MK-6240. Alzheimer's Research & Therapy, 10, 74.
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Publication 2018
Amyloid Proteins Cerebellar Gray Matter Cortex, Cerebellar Diagnosis Insula of Reil Nuclear Magnetic Resonance Pons Posterior Cingulate Cortex Precuneus Radionuclide Imaging Reading Frames Seahorses Striatum, Corpus Telencephalon Visually Impaired Persons Viverridae White Matter White Matter, Cerebellar
4
Developmental Trajectories of White Matter Microstructure
With all data spatially aligned in a similar analysis space, mean developmental MWF, T1 and T2 trajectories were obtained for the genu, splenium and body of the corpus callosum, right and left hemisphere cingulum, corona radiata, internal capsule and optic radiation, and right and left hemisphere frontal, occipital, temporal, parietal and cerebellar white matter regions.
Anatomical masks for each of these regions (derived as outlined below) were superimposed onto each infant's dataset, and the mean and standard deviation calculated for each region. Only voxels with MWF values greater than 0.001 were included in the regional means.
For the frontal, occipital, parietal, temporal and cerebellar white matter,

A global binary white matter mask was calculated by thresholding the MNI white matter probability image provided within FSL (www.fmrib.ox.ac.uk/fsl) at 180.

Masks of the frontal, occipital, parietal, temporal and cerebellar lobes were obtained from the MNI database (Mazziotta et al., 2001 (link)). These were multiplied by the binary global mask (1) to obtain the regional white matter masks.

The white matter masks for each region were divided by hemisphere.

The registration transformation between the MNI template and the study template was calculated, and each masked transformed to the study space.

For the white matter tract masks, including genu, body and splenium of the corpus callosum, cingulum, corona radiata, internal capsule, and optic radiation, this same process was applied to the John Hopkins University DT-MRI white matter atlas (Oishi et al., 2008 (link)). The corona radiata mask comprised the anterior, superior and posterior portions; the internal capsule mask comprised the anterior, posterior and retrolenticular portions. Each of these regions, superimposed onto the mean study template is shown in Fig. 4.
Pearson correlations between MWF and T1; and MWF and T2 were calculated for each white matter tract and region across the full age range; as well as across developmental periods between 1) 0 and 6 months of age; 2) 6–12 months; 3) 12–24 months; 4) 24–36 months; 5) 36–48 months; and 6) 48–60 months of age.
Deoni S.C., Dean DC I.I.I., O'Muircheartaigh J., Dirks H, & Jerskey B.A. (2012). Investigating white matter development in infancy and early childhood using myelin water faction and relaxation time mapping. Neuroimage, 63(3), 1038-1053.
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Publication 2012
Body Regions Cerebellum Corpus Callosum Eye Human Body Internal Capsule Knee Radiotherapy Splenius White Matter White Matter, Cerebellar
5
Comprehensive Neuropathological Evaluation of Tau Pathology
Formalin fixed, paraffin-embedded tissue blocks from the investigated cases were evaluated. Immunostaining for tau was performed with anti-tau PHF-1 (Ser396/Ser404, 1:2000; Gift of Peter Davies) and the AT8 antibody (Ser202/Thr205, 1:200, Invitrogen/Thermofischer, MN1020, Carlsbad, USA. For concomitant proteinopathies, we evaluated these cases for Aβ, TDP-43, and alpha-synuclein pathologies as well as for vascular lesions [33 (link), 44 (link)].
We evaluated neuronal (tangles and diffuse cytoplasmic immunoreactivity and threads), astrocytic (tufted astrocytes and other morphologies pooled together), and oligodendroglial (coiled bodies together with threads in the white matter) tau pathologies using a semiquantitative score (none, mild, moderate, severe). The following anatomical regions were examined: The middle frontal gyrus, anterior cingulate, inferior parietal gyrus, superior and middle temporal gyrus, precentral gyrus, and occipital cortex (including the striate, para- and peristriate regions), hippocampus (pyramidal layers and dentate gyrus together), amygdala, the caudate-putamen, globus pallidus, thalamus and subthalamic nucleus (these are in one block), the midbrain tegmentum, substantia nigra, locus coeruleus, pontine base, tegmentum, and inferior olives of the medulla oblongata (together represented here as medulla oblongata for the conditional probability analysis), cerebellar white matter (threads and coiled bodies), and dentate nucleus (neuronal and rarely astroglial tau pathology). For the block containing the subthalamic nucleus and thalamus, neuronal tau pathology scores are provided for the subthalamic nucleus and astroglial and oligodendroglial for the thalamus.
Kovacs G.G., Lukic M.J., Irwin D.J., Arzberger T., Respondek G., Lee E.B., Coughlin D., Giese A., Grossman M., Kurz C., McMillan C.T., Gelpi E., Compta Y., van Swieten J.C., Laat L.D., Troakes C., Al-Sarraj S., Robinson J.L., Roeber S., Xie S.X., Lee V.M., Trojanowski J.Q, & Höglinger G.U. (2020). Distribution patterns of tau pathology in progressive supranuclear palsy. Acta Neuropathologica, 140(2), 99-119.
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Publication 2020
Amygdaloid Body Astrocytes Blood Vessel Body Regions Cardiac Arrest Coiled Bodies Cytoplasm Formalin Globus Pallidus Gyrus, Anterior Cingulate Gyrus, Dentate Immunoglobulins Locus Coeruleus Medial Frontal Gyrus Medulla Oblongata Middle Temporal Gyrus Neostriatum Neurons Nucleus, Dentate Occipital Lobe Olea Oligodendroglia Paraffin Pons Precentral Gyrus protein TDP-43, human Proteostasis Deficiencies Seahorses Substantia Nigra Subthalamic Nucleus Synucleinopathies Tegmentum Mesencephali Thalamus Tissues White Matter White Matter, Cerebellar

Most recents protocols related to «White Matter, Cerebellar»

1
PET Quantification of 5-HT1A Receptor Binding
Following correction for tissue attenuation, PET scan of each patient was corrected for head motion, co-registered to the structural T1-weighted image. The latter was afterwards normalized to the Montreal Neurological Institute (MNI) space producing a transformation matrix that was further applied to normalize co-registered PET data to MNI space. All preprocessing steps were done using SPM (Wellcome Trust Centre for Neuroimaging, London, United Kingdom; http://www.fil.ion.ucl. ac.uk/spm/) and Matlab 2018a (The Mathworks Inc., Natick, MA, USA). Subsequently, time activity curves (TACs) were extracted for selected regions of interest (ROIs) - left and right DLPFC and cerebellar white matter (CWM). DLPFC ROIs were defined as a sphere with diameter of 10 mm around the MNI coordinate representing the individual application point of TBS treatment. The CWM ROI was extracted using an in-house created atlas [60 (link)]. To reduce the noise induced by short frames in the beginning of the scan, the first 2 min (frames 12 × 5 s and 6 × 10 s) of the measurement were resampled to 20-s frames.
The arterial input functions representing non-metabolized radioligand in plasma were obtained as product of the whole blood activity, plasma-to-whole blood ratio (average) and fraction of intact radioligand in the plasma (fitted with the Hill-type function). Afterwards, the specific volume of distribution (VS), representing the amount of radioligand bound solely to the target 5-HT1A receptor in the investigated target tissue, i.e. in DLPFC. Here, distribution volume VS is equal to the binding potential (BPP) of 5-HT1A receptor as defined by Innis et al. 2007 [61 (link)]. Quantification of 5-HT1A receptor VS was carried out utilizing a constrained two-tissue compartment model. Here, CWM was fitted and the ratio of K1/k2 (K1 - rate constant for transfer from arterial plasma to tissue, k2 - rate constant for transfer from tissue to arterial plasma) was fixed for the DLPFC regions [62 ]. Model fitting and quantification of [carbonyl-11C]WAY-100635 was carried out in PMOD 4.201 (PMOD Technologies Ltd., Zurich, Switzerland; www.pmod.com).
Murgaš M., Unterholzner J., Stöhrmann P., Philippe C., Godbersen G.M., Nics L., Reed M.B., Vraka C., Vanicek T., Wadsak W., Kranz G.S., Hahn A., Mitterhauser M., Hacker M., Kasper S., Lanzenberger R, & Baldinger-Melich P. (2023). Effects of bilateral sequential theta-burst stimulation on 5-HT1A receptors in the dorsolateral prefrontal cortex in treatment-resistant depression: a proof-of-concept trial. Translational Psychiatry, 13, 33.
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Publication 2023
Arteries BLOOD Dorsolateral Prefrontal Cortex Head Patients Plasma Positron-Emission Tomography Radionuclide Imaging Reading Frames Receptor, Serotonin, 5-HT1A Tissues WAY 100635 White Matter, Cerebellar
2
Quantifying Brain Microbleeds and Hemorrhages
Ratings of microbleeds and intracranial hemorrhages and estimation of R2* were performed for specific brain regions. Microbleeds were classified as well-defined, circular hyperintensities with diameters ranging from 2–10 mm on R2* map28 (link). Only definite microbleeds in seven anatomic regions (i.e., frontal, parietal, temporal, occipital, insula, deep white matter, and cerebellar regions) were counted and converted to the scale of 0–4, using cut-points (< 1, 1–4, 5–9, 10–19, ≥ 20)28 (link),49 . Microbleeds rating of the cerebral cortex was then computed as the average rating of the frontal, parietal, temporal, occipital, and insular regions. We also counted total brain regions showing intracranial hemorrhages50 (link),51 (link) according to the following anatomical division: frontal, parietal, temporal, occipital, and cerebellar regions. Rating of hyperintensities on T2-weighted scans followed Fazekas method52 (link) in the following regions: anterior, posterior, and inferior periventricular white matter; frontal, parietal, temporal, and occipital deep white matter; MCP/cerebellar white matter; globus pallidus; brainstem; and the genu and splenium of the corpus callosum. Periventricular WMHs were rated as 0 (absence), 1 (“caps” or pencil-thin lining), 2 (smooth “halo”), and 3 (irregular hyperintensities extending into the deep white matter) whereas hyperintensities in the remaining brain regions were rated as 0 (absence), 1 (punctate foci), 2 (beginning confluence of foci), and 3 (large confluent areas). For the five whole brain specimens, number of microbleeds and anatomic volume were estimated in both hemispheres and then divided by two. For hyperintensities, the higher ratings among the two hemispheres were utilized. Missing/incomplete brain regions were excluded from the analysis (Table 1). All ratings were performed after intra-rater reliability assessed using Cohen’s kappa reached 0.80 or above (almost perfect).
Wang J.Y., Sonico G.J., Salcedo-Arellano M.J., Hagerman R.J, & Martínez-Cerdeño V. (2023). A postmortem MRI study of cerebrovascular disease and iron content at end-stage of fragile X-associated tremor/ataxia syndrome. Research Square.
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Publication Preprint 2023
Body Regions Brain Brain Stem Cerebellum Corpus Callosum Cortex, Cerebral Globus Pallidus Insula of Reil Intracranial Hemorrhage Knee Radionuclide Imaging Splenius White Matter White Matter, Cerebellar
3
Detailed Head Model Segmentation
The head models of the participants were represented by a grid of cubical voxels with a resolution of 0.40 mm using T1- and T2-weighted images. The models were segmented into 14 tissues/body fluids (i.e., the skin, fat, muscle, outer skull, inner skull, gray matter, white matter, cerebellar gray matter, cerebellar white matter, brainstem, nuclei, ventricles, cerebrospinal fluid, and eyes). To reconstruct the surfaces of the gray and white matter, brain tissues were segmented using FreeSurfer image analysis software from T1-weighted images [29 (link),30 (link)]. Non-brain tissues were segmented using T1- and T2-weighted images and a semi-automatic procedure which is described in more detail in [12 (link)] (Figure 1B). Note that T2 images were used to improve the quality of non-brain tissues.
Hikita K., Gomez-Tames J, & Hirata A. (2023). Mapping Brain Motor Functions Using Transcranial Magnetic Stimulation with a Volume Conductor Model and Electrophysiological Experiments. Brain Sciences, 13(1), 116.
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Publication 2023
Body Fluids Brain Brain Stem Cell Nucleus Cerebellar Gray Matter Cerebrospinal Fluid Cranium Eye Gray Matter Head Heart Ventricle Muscle Tissue Skin Tissues White Matter White Matter, Cerebellar
4
Multiparametric MRI Brain Volumetrics
The MAP MRI protocol is described in eMethods 1 (links.lww.com/WNL/D98). In this study, we obtained the volumes (in cubic centimeters) of the whole brain, total gray matter (cerebellar gray matter, cortical gray matter, and subcortical gray matter), total white matter (cerebellar white matter, cortical white matter), the hippocampus, and white matter hyperintensities (WMH).
Wang Z., Wang J., Guo J., Dove A., Arfanakis K., Qi X., Bennett D.A, & Xu W. (2023). Association of Motor Function With Cognitive Trajectories and Structural Brain Differences: A Community-Based Cohort Study. Neurology, 101(17), e1718-e1728.
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Publication 2023
Brain Cerebellar Gray Matter Cortex, Cerebral Cuboid Bone Gray Matter MAP protocol Seahorses White Matter White Matter, Cerebellar
5
Quantifying White Matter Hyperintensities from MRI
MRI scanning was performed at the MR Research Center of the University of Pittsburgh with a 3T Siemens Tim Trio MR scanner and a Siemens 64-channel head coil.23 (link) MRIs were magnetization-prepared rapid gradient echo (MPRAGE) T1-weighted sequence and T2-weighted (T2w) fluid-attenuated inversion recovery (FLAIR) sequence. MPRAGE images were acquired in the axial plane (parameters: repetition time, 2400 ms; echo time, 2.22 ms; T1, 1000 ms; flip angle, 8°; field of view, 256 × 240 mm; slice thickness, 0.8 mm; voxel size, 0.8 mm × 0.8mm; matrix size, 320 × 300; number of slices, 208). FLAIR images were acquired in the axial plane (parameters: repetition time, 9690 or 10 000 ms; echo time, 91 ms; T1, 2500 ms; flip angle, 135°; field of view, 256 × 256 mm; matrix, 320 × 320; slice thickness, 1.6 mm; voxel size, 0.8 mm × 0.8 mm; number of slices, 104).
An automated pipeline was used to segment WMH on the T2w FLAIR images using previously validated methods.29 (link) Cerebral and cerebellar white matter were segmented on the T1w image and mapped into the T2w FLAIR image space using SPM mapping software version 12 (Functional Imaging Laboratory, UCL Queen Square Institute of Neurology) and FreeSurfer processing, analyzing, and visualizing software version 7.1.1 (Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School). Cerebellar white matter represented normal-appearing white matter; its intensity mean and SD were used for Z-transformation of the T2w FLAIR image. A threshold of 2 was applied on Z-transformed FLAIR images. Z-transformation also reduces intensity variations across individual FLAIR images.
In the processing, analyzing, and visualizing software, white matter was parcellated according to its nearest cortex with the Deskian-Killiany atlas, used to generate the cortical white matter masks for frontal, temporal, parietal, and occipital lobes for localization of WMHs. White matter parcellations corresponding to frontal cortex regions were combined to create a frontal cortical white matter mask to localize frontal WMHs. Cortical white matter masks were generated for temporal, parietal, and occipital lobes. These lobular cortical white matter masks did not overlap and were combined to create an overall cortical and deep white matter mask. White matter surrounding the ventricles that was not part of the cortical and deep white matter mask comprised the periventricular white matter mask. The total and regional WMHV (in centimeters cubed) were normalized as WMH divided by intracranial volume and log transformed.
Thurston R.C., Jakubowski K., Chang Y., Wu M., Barinas Mitchell E., Aizenstein H., Koenen K.C, & Maki P.M. (2023). Posttraumatic Stress Disorder Symptoms and Cardiovascular and Brain Health in Women. JAMA Network Open, 6(11), e2341388.
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Publication 2023
Cortex, Cerebral ECHO protocol Head Heart Ventricle Inversion, Chromosome Lobe, Frontal Magnetic Resonance Imaging Occipital Lobe TRIO protein, human White Matter White Matter, Cerebellar

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More about "White Matter, Cerebellar"

The brain's white matter is composed of myelinated nerve fibers that enable communication between different regions, playing a crucial role in coordinating movement, balance, and motor learning.
Researchers studying white matter and cerebellar function can leverage advanced tools like the DM500 microscope, RM2255 microtome, and MC120 HD camera to visualize and analyze these critical neural structures.
Integrating PET imaging from a 3.0T Biograph mMR system and MRI data from a 1.5-T GE scanner can provide comprehensive insights.
Histological techniques like the Vectastain ABC kit and Scion Image for Windows software further enable detailed examination of white matter and cerebellar tissue.
Stereo Investigator and MATLAB 8.1 are powerful tools for quantitative analysis and modeling of these neural networks.
By leveraging the latest research methods and technologies, scientists can optimize their work on white matter and cerebellar function, advancing our undestanding of the brain's complex communication pathways and motor control mechanisms.
PubCompare.ai, an AI-driven platform, can enhance the reproducibility and accuracy of this important research by helping locate the best protocols from literature, preprints, and patents using intelligent comparison tools.
Disovering the most effective methods and products will allow researchers to progress their work on white matter and cereeblllar function more efficietly.