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Temporal Lobe

The temporal lobe is a region of the cerebral cortex that plays a crucial role in various cognitive and sensory functions.
It is involved in processing and interpreting auditory information, as well as in memory formation, language processing, and emotional processing.
This lobe is responsible for the recognition of complex visual stimuli, such as faces and objects, and is also implicated in the perception of music and speech.
Researchers studying the temporal lobe often focus on understanding its anatomical structure, neurophysiological processes, and its involvement in neurological and psychiatric disorders, such as epilepsy, Alzheimer's disease, and schizophrenia.
By exploring the power of PubCompare.ai's AI-driven protocol optimization, researchers can unlock new insights and identify the most effective methodologies and products to advance their studies of the temporal lobe.

Most cited protocols related to «Temporal Lobe»

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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
All microarray data was subjected to QC and ERCC spike-in assessments, and any failing samples were omitted from the analysis. Biological outliers were identified by comparing samples from related structures using hierarchical clustering and inter-array correlation measures. Data for samples passing QC were normalized in three steps: 1) “within-batch” normalization to the 75th percentile expression values; 2) “cross-batch” bias reduction using ComBat57 ; and 3) "cross-brain" normalization as in step 1. Differential expression assessments were done using template vector correlation, where 1="in group" and 0="not in group", or by measuring the fold change, defined as mean expression in category divided by mean expression elsewhere. False discovery rates were estimated using permutation tests (Suppl. Methods). WGCNA was performed on all neocortical samples using the standard method36 ,58 , and on germinal layers by defining a consensus module in the 15 and 16pcw brains59 , only including genes differentially expressed across these layers (5494 genes; ANOVA p<0.01, Benjamini-Hochberg adjusted). Gene list characterizations were made using a combination of module eigengene / representative gene expression, gene ontology enrichment using DAVID60 , and enrichment for known brain-related categories (i.e.,61 ,62 ) using userListEnrichment63 . Module C31 is depicted using VisANT64 : the top 250 gene-gene connections based on topological overlap are shown, with histone genes removed for clarity. Rostral-caudal areal gradient genes were identified as follows: first, the center of each neocortical region was identified at 21pcw in Euclidean coordinates; second, the rostral/caudal region position was estimated as an angle along the lateral face of the brain centered at the temporal/frontal lobe juncture (ordering lobes roughly as frontal, parietal, occipital, temporal; Fig. 5c); third, for each brain gene expression in each layer was (Pearson) correlated with this region position; and finally, genes with R>0.5 in all four brains were identified. A similar strategy was used to identify unbiased areal gradient genes (Suppl. Methods). Enrichment of haCNSs was determined using hypergeometric tests. Samples in all plots are ordered in an anatomically relevant manner. Unless otherwise noted, all p-values are Bonferroni corrected for multiple comparisons.
Publication 2014
Biopharmaceuticals Brain Cloning Vectors Face Gene Expression Genes Histones Lobe, Frontal Microarray Analysis neuro-oncological ventral antigen 2, human Occipital Lobe Parietal Lobe Temporal Lobe
All microarray data was subjected to QC and ERCC spike-in assessments, and any failing samples were omitted from the analysis. Biological outliers were identified by comparing samples from related structures using hierarchical clustering and inter-array correlation measures. Data for samples passing QC were normalized in three steps: 1) “within-batch” normalization to the 75th percentile expression values; 2) “cross-batch” bias reduction using ComBat57 ; and 3) "cross-brain" normalization as in step 1. Differential expression assessments were done using template vector correlation, where 1="in group" and 0="not in group", or by measuring the fold change, defined as mean expression in category divided by mean expression elsewhere. False discovery rates were estimated using permutation tests (Suppl. Methods). WGCNA was performed on all neocortical samples using the standard method36 ,58 , and on germinal layers by defining a consensus module in the 15 and 16pcw brains59 , only including genes differentially expressed across these layers (5494 genes; ANOVA p<0.01, Benjamini-Hochberg adjusted). Gene list characterizations were made using a combination of module eigengene / representative gene expression, gene ontology enrichment using DAVID60 , and enrichment for known brain-related categories (i.e.,61 ,62 ) using userListEnrichment63 . Module C31 is depicted using VisANT64 : the top 250 gene-gene connections based on topological overlap are shown, with histone genes removed for clarity. Rostral-caudal areal gradient genes were identified as follows: first, the center of each neocortical region was identified at 21pcw in Euclidean coordinates; second, the rostral/caudal region position was estimated as an angle along the lateral face of the brain centered at the temporal/frontal lobe juncture (ordering lobes roughly as frontal, parietal, occipital, temporal; Fig. 5c); third, for each brain gene expression in each layer was (Pearson) correlated with this region position; and finally, genes with R>0.5 in all four brains were identified. A similar strategy was used to identify unbiased areal gradient genes (Suppl. Methods). Enrichment of haCNSs was determined using hypergeometric tests. Samples in all plots are ordered in an anatomically relevant manner. Unless otherwise noted, all p-values are Bonferroni corrected for multiple comparisons.
Publication 2014
Biopharmaceuticals Brain Cloning Vectors Face Gene Expression Genes Histones Lobe, Frontal Microarray Analysis neuro-oncological ventral antigen 2, human Occipital Lobe Parietal Lobe Temporal Lobe
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
ADNI PET image data were acquired as described online (adni.loni.ucla.edu/about-data-samples/image-data/), and processed by our laboratory as described online (adni.loni.ucla.edu/research/pet-post-processing/) and summarized briefly below.
Florbetapir image data were acquired 50 to 70 minutes postinjection, and images were averaged, spatially aligned, interpolated to a standard voxel size, and smoothed to a common resolution of 8mm full width at half maximum.
For quantification of florbetapir, we used 1 or, in most cases, 2 structural 1.5T or 3T MRI scans (magnetization-prepared rapid acquisition gradient echo [MPRAGE]) acquired concurrently with the florbetapir scan to define cortical regions of interest and the cerebellar reference region. MPRAGE scans within 1 year of the florbetapir scan were available for approximately 80% of subjects. For the remaining subjects, we used the MPRAGE that was closest in time to the florbetapir scan (average MRI–florbetapir interval = 1.9 ± 0.8 years). MPRAGE images were segmented and parcellated into individual cortical regions with Freesurfer version 4.5.0 (surfer.nmr.mgh.harvard.edu/), which was used to extract mean florbetapir uptake from gray matter within lateral and medial frontal, anterior, and posterior cingulate, lateral parietal, and lateral temporal regions relative to uptake in the whole cerebellum (white and gray matter). This summary measure was used as the florbetapir cortical mean for each subject.
Publication 2012
Cerebellum Cortex, Cerebral ECHO protocol florbetapir Gray Matter MRI Scans Posterior Cingulate Cortex Radionuclide Imaging Temporal Lobe

Most recents protocols related to «Temporal Lobe»

Authorizations for reporting these three cases were granted by the Eastern Ontario Regional Forensic Unit and the Laboratoire de Sciences Judiciaires et de Médecine Légale du Québec.
The sampling followed a relatively standardized protocol for all TBI cases: samples were collected from the cortex and underlying white matter of the pre-frontal gyrus, superior and middle frontal gyri, temporal pole, parietal and occipital lobes, deep frontal white matter, hippocampus, anterior and posterior corpus callosum with the cingula, lenticular nucleus, thalamus with the posterior limb of the internal capsule, midbrain, pons, medulla, cerebellar cortex and dentate nucleus. In some cases, gross pathology (e.g. contusions) mandated further sampling along with the dura and spinal cord if available. The number of available sections for these three cases was 26 for case1, and 24 for cases 2 and 3.
For the detection of ballooned neurons, all HE or HPS sections, including contusions, were screened at 200×.
Representative sections were stained with either hematoxylin–eosin (HE) or hematoxylin-phloxin-saffron (HPS). The following histochemical stains were used: iron, Luxol-periodic acid Schiff (Luxol-PAS) and Bielschowsky. The following antibodies were used for immunohistochemistry: glial fibrillary acidic protein (GFAP) (Leica, PA0026,ready to use), CD-68 (Leica, PA0073, ready to use), neurofilament 200 (NF200) (Leica, PA371, ready to use), beta-amyloid precursor-protein (β-APP) (Chemicon/Millipore, MAB348, 1/5000), αB-crystallin (EMD Millipore, MABN2552 1/1000), ubiquitin (Vector, 1/400), β-amyloid (Dako/Agilent, 1/100), tau protein (Thermo/Fisher, MN1020 1/2500), synaptophysin (Dako/Agilent, ready to use), TAR DNA binding protein 43 (TDP-43) ((Protein Tech, 10,782-2AP, 1/50), fused in sarcoma binding protein (FUS) (Protein tech, 60,160–1-1 g, 1/100), and p62 (BD Transduc, 1/25). In our index cases, the following were used for the evaluation of TAI: β-APP, GFAP, CD68 and NF200; for the neurodegenerative changes: αB-crystallin, NF200, ubiquitin, tau protein, synaptophysin, TDP-43, FUS were used.
For the characterization of the ballooned neurons only, two cases of fronto-temporal lobar degeneration, FTLD-Tau, were used as controls. One was a female aged 72 who presented with speech difficulties followed by neurocognitive decline and eye movement abnormalities raising the possibility of Richardson’s disorder. The other was a male aged 67 who presented with a primary non-fluent aphasia progressing to fronto-temporal demαentia. In both cases, the morphological findings were characteristic of a corticobasal degeneration.
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Publication 2023
Amyloid beta-Protein Precursor Amyloid Proteins Antibodies Broca Aphasia Cloning Vectors Congenital Abnormality Contusions Corpus Callosum Cortex, Cerebellar Cortex, Cerebral Corticobasal Degeneration Crystallins Dura Mater Eosin Eye Abnormalities Eye Movements Frontotemporal Lobar Degeneration FUBP1 protein, human Glial Fibrillary Acidic Protein Hematoxylin Immunohistochemistry Internal Capsule Iron Males Medial Frontal Gyrus Medulla Oblongata Mesencephalon Movement Movement Disorders neurofilament protein H Neurons Nucleus, Dentate Nucleus, Lenticular Occipital Lobe Periodic Acid phloxine Pons Proteins protein TDP-43, human RNA-Binding Protein FUS Saffron Sarcoma Seahorses Speech Spinal Cord Staining Synaptophysin Temporal Lobe Thalamus Ubiquitin White Matter Woman
FRAP experiments were conducted with a FluoView 1200 CLSM (Olympus,
Tokyo, Japan) by recording the fluorescence intensity of SLBs doped
with ATTO 488-DPPE and bleached via a rapid laser pulse (λbleach = 488 nm, 20 mW). The fluorescence intensity was tracked
over time in a region of interest (Supporting Information, Figure S1A,B) and a frame time of 65 ms. The
diffusion coefficient and immobile fraction calculation were performed
according to Jönsson et al.67 (link)
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Publication 2023
bis(diphenylphosphine)ethane Fluorescence Reading Frames Temporal Lobe
The Chl a concentration maps are extracted from the Ocean and Land Colour Instrument (OLCI) Level-2 full resolution Near Real Time (NRT) product, obtained by the EUMETCast broadcast system. The OLCI sensors on board ESA Sentinel-3A and B satellites launched in February 2016 respectively April 2018, and have large swath widths (~1270 km) covering large regions with high temporal resolution (approximately 1.5-day global coverage with both sensors). The OC4ME algorithm which uses a polynomial approach of a maximum band ratio algorithm of 4 reflectances at 443, 490 and 510 nm over the 560 nm was extracted directly from OLCI Level 2 products and gives the Chl a pigment concentration in [mg/m3]. The cloud free chlorophyll orbit tiles are binned and averaged on a daily base to obtain one image per day. This leads to a very good resolution, coverage in reasonable timeframe and meets the requirements of monitoring water dynamics also on smaller spatial scales.
The analysis of satellite-derived ocean color was complemented using SeaWiFs (1997–2002, https://oceandata.sci.gsfc.nasa.gov/SeaWiFS/) and MODIS (2003–2020, https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/) level 3 Chl a data processed with the default chlorophyll algorithm (chlor_a) which employs the standard OC3/OC4 (OCx) band ratio algorithm merged with the color index (CI) in 8-day and 9 km resolution.
First, the long-term analysis of the bloom presence (Fig. 2a) was performed from January 1997 to December 2020, covering the ocean-color satellite era. We defined the bloom presence when the 8-day mean satellite-derived Chl a, averaged over 4°–8° E and 67.8°–68.4° S, was larger than the 23-year long mean Chl a + 1 standard deviation over that area (i.e., 1.14 mg m−3, Fig. 2a).
In addition, the bloom duration in 2019 was calculated as the period between the first occurrence of the bloom during the Austral summer 2019 and its end, before the following Austral winter. The long-term median of the average Chl a concentration over the area 4°–8° E and 67.8°–68.4° S was used as a threshold to detect the bloom onset and end above which we considered a phytoplankton bloom present. Because of the presence of clouds and sea ice, satellite-derived ocean color did not detect any pixel with Chl-a data in the bloom area before January 9, 2019, and after March 14, 2019, which we, thus, defined as the bloom duration. It is possible, however, that the bloom started earlier and terminated later than our conservative estimate.
Daily sea ice concentration (1997–2020) were obtained from the NASA’s Nimbus‐7 Scanning Multichannel Microwave Radiometer (SMMR) and Defense Meteorological Satellite Program (DMSP)‐F13, ‐F17, and ‐F18 Special Sensor Microwave/Imager (SSM/I). Data with a spatial resolution of 25 km were provided by the National Snow and Ice Data Centre, University of Colorado in Boulder, CO (http://nsidc.org), with prior processing using the NASA team algorithms62 .
Daily east-west and north-south wind components at the sea surface were obtained for the study region from the ERA-Interim global atmospheric reanalysis63 .
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Publication 2023
Chlorophyll Microtubule-Associated Proteins Microwaves Orbit Phytoplankton Pigmentation Satellite Viruses Sea Ice Cover Snow Temporal Lobe Wind
All included patients and healthy subjects underwent spectral-domain OCT (SD-OCT; Spectralis, Heidelberg Engineering, Heidelberg, Germany) that provided 40,000 A-scans per second with 7-μm optical and 3.5-μm digital axial resolution. An internal fixation target was used, and the patient’s other eye was covered during scanning. OCT peripapillary RNFL circular scans centered on the optic disc of each patient were obtained. In addition, macular thickness measurements in the central 1-mm area and in each quadrant in the 3- and 6-mm areas were obtained. Swept-source OCT (DRI OCT Triton Plus; Topcon Corporation, Tokyo, Japan) coupled with non-invasive OCTA technology was also completed for all PD patients and healthy subjects. The details have been previously described43 (link),44 (link). The SRCP slab was automatically segmented from 3 µm under the internal limiting membrane (ILM) to 15 µm below the IPL, and the DRCP slab was automatically segmented from 15 to 70 µm under the IPL following a formerly corroborated method by Park et al.45 (link). The radial peripapillary capillary (RPC) segment ranged from the ILM to the posterior boundary of the RNFL. Vessel density was determined as the percentage of the total area occupied by vessels and microvasculature, quantitatively expressed as color-coded vessels in a localized region that was obtained by automatically applying an Early Treatment Diabetic Retinopathy Study (ETDRS) grid overlay containing the two inner rings of the ETDRS grid pattern to the fovea, which yielded a calculation of the density in each layer. The parafoveal ring divided the macular region into the temporal, nasal, inferior, and superior sections (Fig. 3). All participants completed both OCT and OCTA imaging within 1 day. The software generated TopQ image quality values for each OCTA scan and vessel density measurement. To assess scan quality, we included the scan images based on the quality assessment criteria suggested by Fenner et al.46 (link). Expert graders reviewed and verified all images (D.L. and K.-A.P.). In patients with PD and healthy controls, bilateral eyes were analyzed except OCTA images, which are difficult to analyze due to motion artifacts or incomplete acquisitions.Representative fundus photography and OCTA images of the macular area in a healthy control.

Macular OCTA measurement was obtained by automatically applying an Early Treatment Diabetic Retinopathy Study (ETDRS) grid overlay containing the two inner rings of the ETDRS grid pattern to the fovea, which yielded the vessel density in each layer. C center, T temporal quadrant, I inferior quadrant, N nasal quadrant, S superior quadrant.

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Publication 2023
Blood Vessel Capillaries Diabetic Retinopathy Eye Fingers Fracture Fixation, Internal Healthy Volunteers Macula Lutea microvasculature Nose Optic Disk Patients Radionuclide Imaging Temporal Lobe Tissue, Membrane Vision
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

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More about "Temporal Lobe"

The temporal lobe is a crucial region of the cerebral cortex, responsible for a variety of cognitive and sensory functions.
This lobe plays a vital role in processing and interpreting auditory information, as well as in memory formation, language processing, and emotional processing.
Researchers often focus on understanding the anatomical structure, neurophysiological processes, and the involvement of the temporal lobe in various neurological and psychiatric disorders, such as epilepsy, Alzheimer's disease, and schizophrenia.
To study the temporal lobe, researchers may utilize advanced neuroimaging techniques, such as MATLAB-based analysis, 32-channel head coils, and Trio scanners.
These technologies allow for detailed examination of the lobe's structure and function.
Additionally, molecular biology techniques, such as the use of TRIzol reagent and the RNeasy Mini Kit, can be employed to investigate gene expression and cellular processes within the temporal lobe.
The Magnetom Trio scanner and PMOD software are also valuable tools for researchers studying the temporal lobe.
These technologies enable high-resolution imaging and comprehensive data analysis, which can lead to a deeper understanding of the lobe's role in various cognitive and sensory processes.
Furthermore, researchers may explore the use of protease inhibitor cocktails to investigate the role of specific proteins and enzymes in the temporal lobe's function.
The Tim Trio system, a specialized MRI scanner, can also provide valuable insights into the temporal lobe's structure and connectivity.
By utilizing the power of PubCompare.ai's AI-driven protocol optimization, researchers can unlock new insights and identify the most effective methodologies and products to advance their studies of the temporal lobe.
This tool can help streamline the research process and lead to breakthroughs in our understanding of this crucial brain region.