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Precuneus

The Precuneus is a region of the parietal lobe located in the medial surface of the brain.
It plays a role in a variety of cognitive functions, including episodic memory retrieval, visuospatial processing, and self-referential thought.
Researchers can optimize their Precuneus studies using PubCompare.ai, an advanced tool that helps locate the best research protocols from literature, preprints, and patents.
This AI-driven platform enhances reproducibility by identifying the optimal protocols and products for Precuneus research.
With PubCompare.ai, researchers can seamlessly compare and select the most effective approaches for their Precuneus investigations.

Most cited protocols related to «Precuneus»

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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
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
Participants were imaged at 23 sites using clinical PET and PET/computed tomographic scanners. Each participant underwent a 10-minute PET scan, which began 50 minutes after receiving an intravenous bolus of 370 MBq (10 mCi) florbetapir F 18. Images were acquired with a 128 × 128 matrix (zoom × 2) and were reconstructed using iterative or row action maximization likelihood algorithms.
Florbetapir-PET images were assessed visually using a semiquantitative score ranging from 0 (no amyloid) to 4 (high levels of cortical amyloid) by 3 board-certified nuclear medicine physicians who were not involved in any other aspects of the study. The only experience these physicians had with florbetapir-PET imaging occurred during a half-day training session. The median rating of the readers served as a primary outcome variable. Readers were blinded to clinical, demographic, and neuropathological information and viewed and rated images under the supervision and at the facility of the imaging core laboratory (ImageMetrix, a division of the American College of Radiology, Philadelphia, Pennsylvania). The initial 6 postmortem evaluations were rated by 4 readers and the median rating of the 4 raters served as the primary outcome variable for these 6 participants.
For the younger control cohort, the PET images were mixed in random order with 40 images from the autopsy cohort that had a median visual read score between 2 and 4 (inclusive). To remove image recognition bias, these images were rated as amyloid positive or negative at ImageMetrix by a different group of 3 external readers. The majority rating was used as the primary outcome variable for this analysis.
A semiautomated quantitative analysis of the ratio of cortical to cerebellar signal (SUVr) also was performed for florbetapir-PET images from all study participants. The images were first normalized to a standard template in the Talairach space and then the SUVrs were calculated for the 6 predefined cortical regions of interest (frontal, temporal, parietal, anterior cingulate, posterior cingulate, and precuneus). The whole cerebellum was used as the reference region.
Publication 2011
Amyloid Proteins Autopsy CAT SCANNERS X RAY Cerebellum Cortex, Cerebral florbetapir florbetapir F 18 Gyrus, Anterior Cingulate Inclusion Bodies Physicians Posterior Cingulate Cortex Precuneus Radiography Radionuclide Imaging Supervision
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)).
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

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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

Most recents protocols related to «Precuneus»

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.
Publication 2023
Angular Gyrus Biological Markers Cortex, Cerebral Glucose Metabolism Nerve Degeneration Pons Posterior Cingulate Cortex Precuneus Reading Frames Temporal Lobe Vermis, Cerebellar
Amyloid PET imaging was performed using the Pittsburgh Compound B (PiB) tracer. Details on PiB‐PET imaging in the MCSA have been published elsewhere (33 (link), 34 (link)). Briefly, PiB scans, consisting of four 5‐min dynamic frames, were acquired 40–60 min after intravenous injection with 292–728 MBq of 11C‐PiB. We used an in‐house, fully automated image processing pipeline to analyze images. Herein, image voxel values were extracted from automatically labeled regions of interest (ROI) propagated from regions defined on each participant's own magnetic resonance imaging (MRI). The prefrontal, orbitofrontal, parietal, temporal, anterior cingulate, and posterior cingulate/precuneus ROI were normalized to the cerebellar gray matter to form a global amyloid PET standardized uptake value ratio (SUVR). We defined abnormal PiB‐PET retention (PiB‐PET+) by an SUVR ≥1.48, which is the current cut‐off used in the MCSA (33 (link), 35 (link)). We ran the linear‐mixed effects models with continuous, z‐scored PiB‐PET SUVR.
Publication 2023
Amyloid Proteins Cerebellar Gray Matter Gyrus, Anterior Cingulate Pittsburgh compound B Posterior Cingulate Cortex Precuneus Radionuclide Imaging Reading Frames Retention (Psychology)
Amyloid burden was imaged with PET using (11 C)-Pittsburgh Compound B (PIB; Klunk et al., 2004 (link)) or (18 F)-Florbetapir (AV45; Wong et al., 2010 (link)). Regional standard uptake ratios (SUVRs) were modeled from 30 to 60 min after injection for PIB and from 50 to 70 min for AV45, using cerebellar gray as the reference region (Su et al., 2013 (link)). Regions of interest were segmented automatically using FreeSurfer 5.3 (Fischl, 2012 (link)). Global amyloid burden was defined as the mean of partial-volume-corrected (PVC) SUVRs from bilateral precuneus, superior and rostral middle frontal, lateral and medial orbitofrontal, and superior and middle temporal regions (Su et al., 2013 (link)). Amyloid summary SUVRs were harmonized across tracers using a centiloid conversion (Su et al., 2018 (link)).
Tau deposition was imaged with PET using (18 F)-Flortaucipir (AV-1451; Chien et al., 2013 (link)). Regional SUVRs were modeled from 80 to 100 min after injection, using cerebellar gray as the reference region. A tau summary measure was defined in the mean PVC SUVRs from bilateral amygdala, entorhinal, inferior temporal, and lateral occipital regions (Mishra et al., 2017 (link)).
CSF was collected via lumbar puncture using methods described previously (Fagan et al., 2006 (link)). After overnight fasting, 20–30 mL samples of CSF were collected, centrifuged, then aliquoted (500 µL) in polypropylene tubes, and stored at –80°C. CSF amyloid β peptide 42 (Aβ42), Aβ40, and phosphorylated tau-181 (pTau) were measured with automated Lumipulse immunoassays (Fujirebio, Malvern, PA, USA) using a single lot of assays for each analyte. Aβ42 and pTau estimates were each normalized for individual differences in CSF production rates by forming a ratio with Aβ40 as the denominator (Hansson et al., 2019 (link); Guo et al., 2020 (link)). As pTau/Aβ40 was highly skewed, we applied a log transformation to these estimates before statistical analysis.
Amyloid positivity was defined using previously published cutoffs for PIB (SUVR > 1.42; Vlassenko et al., 2016 (link)) or AV45 (SUVR > 1.19; Su et al., 2019 (link)). Additionally, the CSF Aβ42/Aβ40 ratio has been shown to be highly concordant with amyloid PET (positivity cutoff < 0.0673; Schindler et al., 2018 (link); Volluz et al., 2021 (link)). Thus, participants were defined as amyloid-positive (for CN/A+ and CI groups) if they had either a PIB, AV45, or CSF Aβ42/Aβ40 ratio measure in the positive range. Participants with discordant positivity between PET and CSF estimates were defined as amyloid-positive.
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Publication 2023
Amygdaloid Body Amyloid beta-Peptides Amyloid Proteins AV-1451 Biological Assay Cerebellum florbetapir flortaucipir Immunoassay Occipital Lobe Pittsburgh compound B Polypropylenes Precuneus Punctures, Lumbar Temporal Lobe
Structural, diffusion, and resting-state functional MRIs were acquired on a Siemens 3T Trio Tim scanner using a 24-channel phased-array head coil. Diffusion tensor imaging and resting-state functional MRI scans were obtained with the same scanner using an identical protocol for all participants during a single visit. The Tracts Constrained by Underlying Anatomy (TRACULA) tool within FreeSurfer 7.124 was used for diffusion tensor imaging data processing and tractography to estimate the posterior probability of the 18 major white matter tracts. Among the 18 tracts, there were 5 tracts with segments that showed significant differences in both fractional anisotropy (FA) and mean diffusivity (MD) among the ELBW group (with or without PGF): the forceps major of the corpus callosum (Fmajor), right anterior thalamic radiation (RATR), left inferior longitudinal fasciculus, left superior longitudinal fasciculus–parietal bundle (LSLFP), and left superior longitudinal fasciculus–temporal bundle (Figure 1). The CONN toolbox25 was used for the seed-based functional connectivity analysis.26 (link) To select the region of interest as a seed, the multivoxel–multivariate pattern analysis method was used, which showed group differences in 4 regions: the precuneus, the left and right superior lateral occipital cortex (sLOC), and the posterior cingulate cortex (PCC) (eFigure 2 in Supplement 1). A seed-based functional connectivity analysis was performed for whole-brain regions with the selected 4 regions of interest in the multivoxel–multivariate pattern analysis. Functional connectivity strength values were extracted from the brain regions of the preterm infants (uncorrected height threshold of P < .001 and cluster-level false discovery rate–corrected P < .05). The diffusion metrics of the selected tracts and the FCS values were used in the correlational analysis with clinical and neuropsychological measures. Details are provided in the eMethods in Supplement 1.
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Publication 2023
Anisotropy Anterior Nuclear Group Brain Corpus Callosum Dietary Supplements Diffusion fMRI Forceps Head Occipital Lobe Posterior Cingulate Cortex Precuneus Preterm Infant Radiotherapy Semen Analysis TRIO protein, human White Matter
Amyloid PET scans with 11C‐Pittsburgh Compound B (or PiB) were obtained via previously described methods, and images were processed using the PET unified pipeline (PUP, https://github.com/ysu001/PUP).34, 35 Briefly, dynamically acquired PET data were reconstructed into frames that underwent affine registration to correct for inter‐frame motion.36, 37 Standardized uptake value ratios (SUVRs) from the 30–60 min post‐injection window were calculated using the cerebellar gray matter as the reference region.34, 38 Images were smoothed using a gaussian kernel to achieve a spatial resolution of 8 mm. Data were then summarized in regions of interest defined by the Desikan–Killiany atlas derived from the MRI. Partial volume correction was implemented via a geometric transfer matrix approach.35, 39 An amyloid PET summary value was calculated from the arithmetic mean of SUVRs for the following bilateral regions (average of right‐ and left‐sided structures): precuneus, superior frontal and rostral middle frontal regions, lateral orbitofrontal and medial orbitofrontal regions, and superior temporal and middle temporal regions.34 Individuals were classified as amyloid positive if the mean cortical SUVR was greater than 1.42.34 Centiloid values were calculated using Equation 1.40 Centiloid=45.0meancorticalSUVR47.5
Publication 2023
Amyloid Proteins Cerebellar Gray Matter Cortex, Cerebral Lobe, Frontal Orbitofrontal Cortex Pittsburgh compound B Positron-Emission Tomography Precuneus Reading Frames Temporal Lobe

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More about "Precuneus"

The Precuneus, a region within the parietal lobe, plays a crucial role in various cognitive functions, such as episodic memory retrieval, visuospatial processing, and self-referential thought.
Researchers can optimize their Precuneus studies by utilizing advanced tools like PubCompare.ai, which helps identify the best research protocols from literature, preprints, and patents.
PubCompare.ai is an AI-driven platform that enhances the reproducibility of Precuneus research by providing researchers with the optimal protocols and products.
By seamlessly comparing and selecting the most effective approaches, researchers can improve the quality and reliability of their Precuneus investigations.
In addition to PubCompare.ai, researchers may also leverage other advanced imaging technologies, such as the 3.0T Biograph mMR (PET-MR) scanner, Biograph 40 scanner, High Resolution Research Tomograph, Ingenia, PET/CT scanner, and Biograph mMR PET/MR scanner.
These cutting-edge imaging tools can provide valuable insights into the structure and function of the Precuneus, complementing the protocol optimization capabilities of PubCompare.ai.
Furthermore, researchers can utilize specialized software like CortexID Suite and PMOD image analysis software to facilitate the analysis and interpretation of Precuneus-related data.
NeuroMarQ, a comprehensive platform for neuroimaging research, can also be a valuable resource for Precuneus studies, offering a range of tools and functionalities.
By integrating these advanced technologies and software solutions, researchers can enhance their understanding of the Precuneus and optimize their research efforts, ultimately contributing to the advancement of our knowledge in this field.