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Cerebellum

The cerebellum is a vital structure at the base of the brain that plays a crucial role in motor control, coordination, and balance.
It is responsible for regulating muscle tone, maintaining posture, and facilitating smooth, coordinated movements.
The cerebellum also contributes to cognitive and emotional processes, making it an important area of study for researchers investigating neurological and psychiatric disorders.
This MeSH term provides a comprehensive overview of the cerebellum's anatomy, functions, and relevance to various areas of biomedical research.
Explore the latest advancements in cerebellum studies and optimize your research with the innovative PubCompare.ai platform, which can help you locate the most effective protocols and enhance the reproducibility and accuracy of your findings.

Most cited protocols related to «Cerebellum»

We constructed the functional brain networks for each individual using two methods, differentiated according to the node definition, as either a region- or voxel-based network. 1) The region-based networks were constructed following the following manner. First, the AAL atlas was used to parcellate the entire brain into 90 regions (regions in cerebellum were excluded), which were considered as nodes in the network. Then, the mean time courses were extracted from each region and used to obtain a 90×90 correlation matrix of Pearson’s correlation coefficients between all possible connections of node pairs. 2) The voxel-based networks were constructed by directly considering GM voxels as nodes. The Pearson’s correlation coefficients were then computed between the time courses of all pairs of voxels to generate a ∼50,000×50,000 correlation matrix. All correlation matrices were transferred into z-score matrices using Fisher’s r-to-z transformation to improve normality.
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Publication 2013
Brain Cerebellum

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Publication 2016
Amygdaloid Body Amyloid Proteins Angular Gyrus AV-1451 Cerebellum Cortex, Cerebral Gray Matter Leg Pittsburgh compound B Pons Posterior Cingulate Cortex Precuneus Temporal Lobe Vermis, Cerebellar White Matter

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Publication 2008
1,2-dihexadecyl-sn-glycero-3-phosphocholine Alabaster austin Brain Stem Buffers Cells Cerebellum Chloroform Cholinergic Agents Cold Temperature Cycloheximide Deoxyribonucleases Digestion Dithiothreitol Endoribonucleases Ethanol G-substrate Goat HEPES inhibitors Isopropyl Alcohol Lipids Magnesium Chloride Mice, Laboratory Mice, Transgenic Motor Neurons Nonidet P-40 Polyribosomes Protease Inhibitors Purkinje Cells Ribosomal RNA RNA, Messenger Sodium Acetate Sodium Chloride Striatum, Corpus Teflon Tissues trizol

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Publication 2017
Ankylosis Ants Brain Brain Stem Cerebellum Cerebrospinal Fluid Cortex, Cerebral Cranium CREB3L1 protein, human Dementia Embarc Genetic Heterogeneity Gray Matter Hybrids Reconstructive Surgical Procedures Tissues White Matter
PiB and florbetapir image data were analyzed using 2 processing streams. The PET-template analysis method was described in a separate study (10 (link)). This method was applied to the raw and unsmoothed datasets. Briefly, image data were spatially normalized to standard atlas coordinates in Talairach space using statistical parametric mapping software (11 ). Mean tracer retention was calculated for 6 predefined target cortical regions of interest (medial orbital frontal, temporal, parietal, anterior cingulate, posterior cingulate, and precuneus) that resulted from a statistical contrast of AD patients and cognitively normal subjects (1 (link)).
The Freesurfer method for quantifying cortical Aβ was applied to the unsmoothed and smoothed datasets. This method was described in detail elsewhere (2 (link),12 (link)) and online (13 ). Structural 1.5-T or 3-T MRI scans (T1-weighted images) were used to define cortical regions of interest and the cerebellar reference region. In general, 2 structural MRI scans were acquired at each visit across several years of follow-up, with the result that several MR images were available for each subject. For processing the PiB images, we chose the T1 scans acquired concurrently with (or closest in time to) the first PiB scan; and for the florbetapir processing, we chose the T1 scans acquired concurrently with (or closest in time to) the florbetapir scan. Structural MR images were segmented and parceled into individual cortical regions with Freesurfer (version 4.5.0; surfer.nmr.mgh.harvard.edu/) and subsequently used to extract mean PiB and florbetapir cortical retention ratios from gray matter within lateral and medial frontal, anterior and posterior cingulate, lateral parietal, and lateral temporal regions.
To examine several reference regions, the unscaled cortical means for each analysis method were divided by mean retention in the following 3 reference regions: brain stem–pons, whole cerebellum (white and gray matter), and cerebellar gray matter, yielding 3 cortical retention ratios for each preprocessing method. Because Freesurfer creates a brain stem, but not pons, region as part of its automated processing stream, the brain stem was used for the Free-surfer processing analysis method and the pons was used for the PET-template processing method.
To summarize, for each of 3 PET sessions (2 PiB scans and 1 florbetapir scan), every subject had cortical retention ratios for 2 levels of processing and 2 analysis methods (raw and unsmoothed for the PET-template method and unsmoothed and smoothed for the Freesurfer method), using 3 reference regions (brain stem–pons, whole cerebellum, cerebellar gray matter), resulting in 36 mean cortical retention ratios per subject that were compared in subsequent statistical analyses.
Publication 2012
Brain Stem Cerebellar Gray Matter Cerebellum Cortex, Cerebral florbetapir Gray Matter Gyrus, Anterior Cingulate MRI Scans Patients Pons Posterior Cingulate Cortex Precuneus Radionuclide Imaging Retention (Psychology) Temporal Lobe

Most recents protocols related to «Cerebellum»

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Publication 2023
Basal Ganglia Brain Stem Care, Prenatal Cell Nucleus Cerebellum Cerebral Hemispheres Cortex, Cerebral Gray Matter Heart Ventricle Neurologists Ventricle, Lateral Ventricles, Fourth Ventricles, Third Vermis, Cerebellar White Matter

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Publication 2023
Brain Cerebellum Cerebral Hemispheres Cortex, Cerebral Fetal Development Fetal Growth Fetus Gestational Age Heart Ventricle Seizures Ventricles, Fourth Vermis, Cerebellar

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Publication 2023
Auditory Perception Brain Cerebellum Cognition CREB3L1 protein, human Cuboid Bone Default Mode Network derivatives fMRI Microtubule-Associated Proteins Young Adult
The SPM Marsbar toolbox (version 0.44, http://marsbar.sourceforge.net/) was used to obtain the parcellation of the subjects’ fMRI volumes in N = 90 regions of interest (ROIs) of the Automated Anatomical Labeling (AAL) atlas33 (link), representing the nodes for the functional connectivity (FC) analysis. We selected all AAL ROIs except from the ones located in the cerebellum and vermis, which were not covered by the fMRI volumes in some or all subjects.
For each subject, we extracted the mean BOLD time series of the voxels within each ROI (node). Using Matlab in-house scripts, instantaneous statistical dependencies among ROIs were assessed by computing the Pearson correlation coefficients between the BOLD time series of each pair of ROIs, resulting in a N×N FC adjacency matrix for each participant, whose elements represent the pairwise cross-correlation between the BOLD time series of the corresponding ROIs. Only the functional connections corresponding to significant Pearson correlation values (p < 0.05) were considered, by setting to zero the non-significant ones. The resulting subject-level FC matrices (either weighted or binarized using an arbitrary positive 0.5 threshold) were further analysed to extract FC features of interest.
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Publication 2023
Body Regions Cerebellum fMRI Temporal Lobe Vermis, Cerebellar
For LFP recordings, two custom made silver electrodes (250 μm dimeter) were implanted in the brain and glued to the skull using dental cement. One electrode was positioned in the S1Fl. The other electrode was used as reference and implanted in the cerebellum. LFP signals were recorded as difference between electric currents detected by the electrode in the S1Fl and the reference electrode using a differential amplifier (DPA-2FX, NPI Electronics, Tamm, Germany). Signals were recorded at 2 kHz using a multifunction data acquisition device (PCIe-6363, National Instruments, Austin, Texas, United States) and a custom-written LabView script (National Instruments). LFP recordings were performed for 3 min during stimulation which evoked neuronal activation in S1Fl. Only LFP recordings with visible response to stimulation were evaluated. LFP data were normalized to their baseline and the FFT was calculated using MATLAB. Time courses and spectra were manually analyzed with respect to a neuronal correlate of HDO, as no HDO detection algorithm was available for LFP data.
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Publication 2023
austin Brain Cerebellum Cranium Dental Cements Electricity Medical Devices Neurons Silver

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