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Angular Gyrus

The Angular Gyrus is a region of the brain located in the parietal lobe, responsible for integrating visual, auditory, and somatosensory information.
It plays a key role in language processing, mathematical cognition, and spatial awareness.
Researchers studying the Angular Gyrus can leverage PubCompare.ai's AI-driven analysis to enhance the reproducibility and accuracy of their findings.
The platform helps locate the best protocols from literature, preprints, and patents using advanced AI comparisons, improving experimental design and identifying optimal solutions for Angular Gyrus studies.
With PubCompare.ai, scientists can quickly discover the most relavant information to advance their Angular Gyrus research.

Most cited protocols related to «Angular Gyrus»

1
Multimodal Imaging Protocol for Alzheimer's Disease

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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
2
Multi-modal Neuroimaging for Alzheimer's Assessment
Amyloid PET imaging was performed with Pittsburgh Compound B (Klunk et al., 2004 (link)) and FDG PET was obtained on the same day. CT was obtained for attenuation correction. Amyloid PET images were acquired from 40–60 min and FDG from 30–40 min after injection. Amyloid PET and FDG PET were analysed with our in-house fully automated image processing pipeline (Senjem et al., 2005 (link)) where image voxel values are extracted from automatically labelled regions of interest propagated from an MRI template. An amyloid PET standardized uptake value ratio (SUVR) was formed from the prefrontal, orbitofrontal, parietal, temporal, anterior cingulate, and posterior cingulate/precuneus regions of interest normalized to the cerebellum (grey plus white matter). The data were partial volume corrected for voxel CSF content using segmented co-registered MRI. An Alzheimer’s disease-characteristic FDG PET SUVR was formed from the angular gyrus, posterior cingulate, and inferior temporal cortical regions of interest normalized to pons and vermis (Landau et al., 2011 (link)). FDG PET data were not partial volume corrected. We and others have reported previously that diagnostic performance is slightly better if amyloid PET is partial volume corrected (Lowe et al., 2009 (link); Su et al., 2015 (link)), and is much better if FDG PET is not partial volume corrected (Lowe et al., 2009 (link); Curiati et al., 2011 (link)). Consequently these are the methods we used in the present analysis.
MRI was performed on one of three 3 T systems from the same vendor. Two MRI measures were used. We summed right and left hippocampal volumes from Freesurfer (v 5.3), and adjusted them for total intracranial volume (TIV) by calculating the residual from a linear regression of hippocampal volume (y) versus intracranial volume (x) among 133 cognitively normal subjects aged 30 to 59 (described in Jack et al., 2014a (link)). Adjusted hippocampal volume can be interpreted as the deviation in cm3 in a subject’s hippocampal volume from what is expected given their TIV. The second MRI measure was an Alzheimer’s disease signature cortical thickness measure composed of the following individual cortical thickness regions of interest: entorhinal, inferior temporal, middle temporal, and fusiform. In-house evaluation indicated that the Alzheimer’s disease signature composite cortical thickness measure was not correlated with TIV (Spearman rank correlation rs = −0.09, P = 0.16) in cognitively non-impaired individuals aged 50–60 (a subgroup chosen in which we are reasonably certain subjects do not harbour latent age-related pathology), whereas hippocampal volume and TIV were strongly correlated (rs = 0.62, P < 0.001). We therefore used TIV-adjusted hippocampal volume, but did not adjust the Alzheimer’s disease signature cortical thickness measure for TIV an approach adopted by other groups (Dickerson et al., 2009 (link)).
Jack CR J.r., Wiste H.J., Weigand S.D., Knopman D.S., Mielke M.M., Vemuri P., Lowe V., Senjem M.L., Gunter J.L., Reyes D., Machulda M.M., Roberts R, & Petersen R.C. (2015). Different definitions of neurodegeneration produce similar amyloid/neurodegeneration biomarker group findings. Brain, 138(12), 3747-3759.
Publication 2015
Adrenal Cortex Diseases Amyloid Proteins Angular Gyrus Cerebellum Cortex, Cerebral Diagnosis Gyrus, Anterior Cingulate Pittsburgh compound B Pons Posterior Cingulate Cortex Precuneus Temporal Lobe Vermis, Cerebellar Volume, Residual White Matter
3
Multimodal Neuroimaging Analysis Protocol

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Publication 2014
Amyloid Proteins Angular Gyrus Cerebellum Cortex, Cerebral Gyrus, Anterior Cingulate Head Microtubule-Associated Proteins Pathological Dilatation Pittsburgh compound B Pons Posterior Cingulate Cortex Precuneus Reading Frames Temporal Lobe Tissues Vermis, Cerebellar Volume, Residual
4
Multimodal Neuroimaging Protocol for Alzheimer's
MRI was performed at 3T with a 3D-MPRAGE sequence [9 (link)] Images were corrected for distortion due to gradient non-linearity and for bias field [10 (link), 11 ]. Our primary MRI measure was hippocampal volume measured with FreeSurfer software (version 4.5.0) [12 (link)]. Each subject’s raw hippocampal volume was adjusted by his/her total intracranial volume [13 (link)] to form an adjusted hippocampal volume (HVa). We calculated HVa as the residual from a linear regression of hippocampal volume (y) versus total intracranial volume (x).
PET images [14 (link)] were acquired using a PET/CT scanner. The 11C PIB-PET scan consisting of four 5-minute dynamic frames was acquired from 40–60 minutes after injection [15 (link), 16 (link)]. 18 Fluorodeoxyglucose (18F-FDG ) PET images were obtained 1 hour after the PIB scan. Subjects were injected with 18F-FDG and imaged after 30–38 minutes, for an 8-minute image acquisition consisting of four 2-minute dynamic frames.
Quantitative image analysis for both PIB and FDG was done using our in-house fully automated image processing pipeline [17 (link)]. A global cortical PIB-PET retention ratio was formed by calculating the median uptake over voxels in the prefrontal, orbitofrontal, parietal, temporal, anterior cingulate, and posterior cingulate/precuneus regions of interest (ROIs) for each subject and dividing this by the median uptake over voxels in the cerebellar gray matter ROI of the atlas [18 (link)]. FDG-PET scans were analyzed in a similar manner. We used angular gyrus, posterior cingulate, and inferior temporal cortical ROIs, as described in Landau et al [19 (link)], normalized to pons uptake.
Jack CR J.r., Knopman D.S., Weigand S.D., Wiste H.J., Vemuri P., Lowe V., Kantarci K., Gunter J.L., Senjem M.L., Ivnik R.J., Roberts R.O., Rocca W.A., Boeve B.F, & Petersen R.C. (2012). An Operational Approach to NIA-AA Criteria for Preclinical Alzheimer’s Disease. Annals of neurology, 71(6), 765-775.
Publication 2012
Angular Gyrus CAT SCANNERS X RAY Cerebellar Gray Matter Cortex, Cerebral Gyrus, Anterior Cingulate Pittsburgh compound B Pons Positron-Emission Tomography Posterior Cingulate Cortex Precuneus Radionuclide Imaging Reading Frames Retention (Psychology) Temporal Lobe
5
Multimodal MRI Characterization of Alzheimer's

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Publication 2014
Angular Gyrus Brodmann Area 45 Cortex, Cerebral Cranium Entorhinal Area Inversion, Chromosome Lobe, Frontal MRI Scans Nerve Degeneration Occipital Lobe Parietal Lobe Pars Opercularis Poly(ADP-ribose) Polymerases Precuneus Seahorses Supramarginal Gyrus Temporal Lobe

Most recents protocols related to «Angular Gyrus»

1
FDG-PET Imaging Biomarker for Alzheimer's
FDG‐PET imaging which consisted of four 2‐min dynamic frames, was performed 30 min after injecting 366–399 MBq of 18fluorodeoxyglucose intravenously. Images were analyzed using our in‐house fully automated image processing pipeline (36 (link)) in which image voxel values were extracted from automatically labeled cortical ROI (37 (link)) After combining the left and right regions from the Atlas, there were 19 ROI and the meta‐region of interest consisted of bilateral angular gyrus, posterior cingulate/precuneus, and inferior temporal cortical regions from both hemispheres and was identified as AD signature ROI (38 (link), 39 (link)). SUVR was formed by the ratio of this AD signature ROI and two reference regions, namely the pons and the cerebellar vermis which have preserved glucose metabolism in AD (40 (link)). Participants were classified as having glucose hypometabolism, which is a measure of neurodegeneration as defined by NIA‐AA criteria (N+) (14 (link)) based on SUVR of ≤1.47 (33 (link)). We ran the linear‐mixed effects models with continuous, z‐scored FDG‐PET SUVR. We additionally flipped the sign of the z‐score so that higher values would correspond with a worsening of the biomarker, thereby allowing for a similar interpretation as for the PiB‐PET analysis.
Pink A., Krell‐Roesch J., Syrjanen J.A., Christenson L.R., Lowe V.J., Vemuri P., Fields J.A., Stokin G.B., Kremers W.K., Scharf E.L., Jack CR J.r., Knopman D.S., Petersen R.C., Vassilaki M, & Geda Y.E. (2023). Interactions Between Neuropsychiatric Symptoms and Alzheimer's Disease Neuroimaging Biomarkers in Predicting Longitudinal Cognitive Decline. Psychiatric Research and Clinical Practice, 5(1), 4-15.
Publication 2023
Angular Gyrus Biological Markers Cortex, Cerebral Glucose Metabolism Nerve Degeneration Pons Posterior Cingulate Cortex Precuneus Reading Frames Temporal Lobe Vermis, Cerebellar
2
Preprocessing Pipeline for Resting-State fMRI Analysis
Data preprocessing was performed using the Data Processing Assistant for Resting-State fMRI (DPARSF) V4.5 Advanced Edition (State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, China), which is based on the Data Processing and Analysis of Brain Imaging (DPABI) Toolbox version 4.11 (Yan et al., 2016 (link)), with statistical parametrical mapping 12 (SPM 12; Wellcome Trust Center for Neuroimaging, University College London, London, UK) in Matlab 2015b (MathWorks, Inc., Natick, MA, USA). Based on experience in previous studies, the preprocessing of functional images was performed as follows: (1) slice timing correction; (2) realignment of images to the mean volume for correction of head motion; (3) co-registration to map functional information of resting fMRI images into an anatomical space (T1-weighted images) via intra-subject spatial alignment; and (4) segmentation of gray matter, white matter (WM) and cerebrospinal fluid (CSF) from coregistered T1 images using the unified segmentation model (Wu et al., 2016 (link)). Subjects with any instances of head movement exceeding 2 mm or 2° were excluded from further processing. The following nuisance variables were regressed: (1) six parameters of head movement calculated based on head motion with the Friston 24-parameter model translation and rotation during realignment in SPM12 (Friston et al., 1996 (link)); (2) the mean signal within the lateral ventricles for cerebral spinal fluid; and (3) the mean signal within a deep white matter region (centrum ovale). The images were normalized to the custom template from T1 weighted images of all subjects developed by the Montreal Neurological Institute (MNI) with resampled voxels at 2 mm × 2 mm × 2 mm. The resulting time series in each voxel was then linearly detrended and bandpass filtered (0.01–0.1 Hz) to extract low-frequency oscillations. Global signal regression (GSR) was not performed as it has been shown to exaggerate negative correlations (Murphy et al., 2009 (link); Weissenbacher et al., 2009 (link)) and/or to distort group differences (Saad et al., 2012 (link)). We used WFU Pick Atlas toolbox2 to generate a visual system template based on the modified human visual pathway model by Choi et al. (2020) (link). The visual system template includes the visual area [V1, V2, V3, V4, and V5/MT (BA 17, BA 18, and BA19)], inferior temporal area (BA 20), angular gyrus (BA 39), supramarginal gyrus (BA 40), and superior parietal lobule (BA 5, BA 7).
Hong T.Y., Yang C.J., Shih C.H., Fan S.F., Yeh T.C., Yu H.Y., Chen L.F, & Hsieh J.C. (2023). Enhanced intrinsic functional connectivity in the visual system of visual artist: Implications for creativity. Frontiers in Neuroscience, 17, 1114771.
Publication 2023
Angular Gyrus Brain Cerebrospinal Fluid fMRI Gray Matter Head Head Movements Homo sapiens Superior Parietal Lobule Supramarginal Gyrus Ventricle, Lateral Visual Pathways White Matter
3
Tissue-Specific Enhancer Mutability Analysis
The size of enhancer regions differs widely between tissues. Furthermore, the mutability of the tissue-specific enhancer region differs significantly. The mutability of FBSEs, human gain enhancers, and adult brain-specific enhancers was estimated using the previously defined framework for DNMs (34 (link)). The framework for the null mutation model is based on the tri-nucleotide context, where the second base is mutated. Using this framework, the probability of mutation for each enhancer was estimated based on the DNA sequence of the enhancer. The probability of mutation of all the enhancers within the enhancer set (FBSEs, human gain enhancers, and adult brain subsections) was summed to estimate the probability of mutation for the entire enhancer set. The sequence composition and overall size in base pair vary significantly between fetal brain enhancers, human gain enhancers, and enhancers from adult brain subsections; hence, they may have different background mutation rates. To perform a valid comparison between the observed number of DNMs between fetal brain enhancers and adult brain enhancers, we normalised to the background mutation rate of fetal brain enhancers.
For example, the background mutation rate for FBSEs is 0.970718 and we observed 53 DNMs. Similarly, the background mutation rate for the adult brain subsection angular gyrus is 0.680226 and we observed 22 DNMs. Because of the difference in background mutation rate, we cannot directly compare the number of DNMs between fetal brain enhancers and angular gyrus. Hence, we normalised the observed number of DNMs in angular gyrus enhancers to a background mutation rate of 0.970718 using the following formula. (Observed number of DNMs in angular gyrus enhancers×mutation rate of fetal brain enhancers)mutation rate of angular gyrus enhancers, 22×0.9707180.680226 =31.395
Similarly, we normalised the observed number of mutations from all adult brain subsections and human gain enhancers to a background mutation rate of 0.970718 (Table S6) so that we could perform a valid comparison between the observed number of mutations from various enhancer sets. The significance level between DNMs observed in FBSEs (n = 2) and adult brain enhancers (n = 8) was calculated using a two-tailed t test.
De Vas M.G., Boulet F., Joshi S.S., Garstang M.G., Khan T.N., Atla G., Parry D., Moore D., Cebola I., Zhang S., Cui W., Lampe A.K., Lam W.W., Ferrer J., Pradeepa M.M, & Atanur S.S. (2023). Regulatory de novo mutations underlying intellectual disability. Life Science Alliance, 6(5), e202201843.
Publication 2023
Adult Angular Gyrus Base Pairing Brain Care, Prenatal Enhancer Elements, Genetic Homo sapiens Mutation Nucleotides Null Mutation Tissues Tissue Specificity
4
Identifying Fetal Brain Enhancers
Roadmap Epigenomic Project (18 (link)) chromHMM segmentations across 127 tissues and cell types were used to define brain-specific enhancers. We selected all genic (intronic) and intergenic enhancers (6_EnhG and 7_Enh) from a male (E081) and a female fetal brain (E082). This was accomplished using genome-wide chromHMM chromatin state classification in rolling 200-bp windows. All consecutive 200-bp windows assigned as an enhancer in the fetal brain were merged to obtain enhancer boundaries. A score was assigned to each enhancer based on the total number of 200-bp windows covered by each enhancer. Next, for each fetal brain enhancer, we counted the number of 200-bp segments assigned as an enhancer in the remaining 125 tissues and cell types. This provided enhancer scores across 127 tissues and cell types for all fetal brain enhancers. To identify FBSEs, Z scores were calculated for each fetal brain enhancer using the enhancer scores. Z scores were calculated independently for male and female fetal brain enhancers. Independent Z scores cutoffs were used for both male and female fetal brain enhancers such that ∼35% of enhancers were selected. To define open accessible chromatin regions within brain-specific enhancers, we intersected enhancers with DNAse-seq data from the Roadmap Epigenomic Project (18 (link)) from a male (E081) and a female fetal brain (E082), respectively. Next, the male and female FBSEs were merged to get a final set of 27,420 FBSEs. We used a similar approach to identify enhancers that were specifically active in adult brain subsections, which include angular gyrus (E067), anterior caudate (E068), cingulate gyrus (E069), germinal matrix (E070), hippocampus middle (E071), inferior temporal lobe (E072), dorsolateral prefrontal cortex (E073), and substantia nigra (E074).
De Vas M.G., Boulet F., Joshi S.S., Garstang M.G., Khan T.N., Atla G., Parry D., Moore D., Cebola I., Zhang S., Cui W., Lampe A.K., Lam W.W., Ferrer J., Pradeepa M.M, & Atanur S.S. (2023). Regulatory de novo mutations underlying intellectual disability. Life Science Alliance, 6(5), e202201843.
Publication 2023
Adult Angular Gyrus Brain Care, Prenatal Cells Chromatin Deoxyribonuclease I Dorsolateral Prefrontal Cortex Females Genes Genome Gyrus Cinguli Introns Males Seahorses Substantia Nigra Temporal Lobe Tissues
5
Structural Brain Imaging and TMS Effects
T1-weighted structural images were collected using a Philips Achieva 3.0T scanner, using an MPRAGE sequence (1x1 x 1 mm3 voxel resolution, 240x240 x 200 grid, TR = 7 \ms, TE = 3.2ms, flip angle = 8°). Voxel-based morphometry (VBM) was performed to explore associations between differences in local grey matter volume (GM) and the impact of TMS on the distractor effect. Structural MRI data were analysed using the FSL-VBM protocol (Douaud et al., 2007 (link); Good et al., 2001 (link); Smith et al., 2004 (link)). Each participant’s scan was brain-extracted, grey matter-segmented, and nonlinearly registered to the MNI 152 standard space (Andersson et al., 2007 (link)). The resulting images were averaged to create a study specific grey matter template. Native grey matter images were then nonlinearly registered to this template and modulated to correct for local expansion/contraction due to the non-linear component of the spatial transformation. The modulated images were then smoothed with an isotropic Gaussian kernel (sigma = 3 mm), and a voxelwise GLM was applied using permutation-based nonparametric testing (5000 permutations). Correction for multiple comparisons across space was performed using threshold free cluster enhancement (TFCE).
Since we set out to explore the effect of TMS, we restricted the analysis to the regions in which we applied TMS, that is the left MIP as well as the left MT as a control region. A region of interest (ROI) analysis was chosen because (1) we tested the effect of TMS and our hypotheses were therefore focused on the specific TMS sites used, and (2) all participants were sampled from a neurotypical population, suggesting that any structural differences associated with decision-making would be small. The region of interest covered large areas of the left parietal and occipital grey matter regions, and was defined as the left superior parietal lobule, the left angular gyrus, and the left inferior lateral occipital cortex, as defined by the Harvard-Oxford Cortical Structural Atlas (see also Figure 2—figure supplement 2).
Kohl C., Wong M.X., Wong J.J., Rushworth M.F, & Chau B.K. (2023). Intraparietal stimulation disrupts negative distractor effects in human multi-alternative decision-making. eLife, 12, e75007.
Publication 2023
Angular Gyrus Brain Cortex, Cerebral Dietary Supplements Gray Matter Occipital Lobe Radionuclide Imaging Superior Parietal Lobule

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More about "Angular Gyrus"

The angular gyrus is a region of the brain located in the parietal lobe, known for its pivotal role in integrating various sensory inputs, including visual, auditory, and somatosensory information.
This brain area plays a crucial part in language processing, mathematical cognition, and spatial awareness.
Researchers studying the angular gyrus can leverage advanced AI-driven analysis tools like PubCompare.ai to enhance the reproducibility and accuracy of their findings.
The platform helps scientists quickly locate the best protocols from literature, preprints, and patents using sophisticated AI comparisons, improving experimental design and identifying optimal solutions for their angular gyrus studies.
This can be particularly useful when leveraging specialized equipment like the Magstim Rapid2 system, PMOD v3.7 software, Multichannel Acquisition Processor systems, MATLAB R2020b, 3.0 Tesla MR750 MRI scanners, SPSS Statistics, Advance scanners, Diamond Pro 2070SB-BK displays, and EXACT/HR analysis tools.
By utilizing these resources, researchers can gain deeper insights into the complex functions of the angular gyrus and drive their research forward with greater confidence and efficiency.