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> Anatomy > Body Part > Occipital Lobe

Occipital Lobe

The occipital lobe is the posterior part of the cerebral hemisphere responsible for visual processing.
It is the smallest of the four major lobes of the brain and contains the primary visual cortex, which receives and processes visual information from the retina.
The occipital lobe plays a crucial role in visual perception, object recognition, and spatial awareness.
It is also involved in various cognitive functions, such as visual attention, memory, and visual-motor coordination.
Researchers studying the occipital lobe can utilize PubCompare.ai's AI platform to optimize their research by easily locating and comparing protocols from literature, preprints, and patents, identifying the most accurate and reproducible methods.
Leveraging AI-driven analysis can help streamline research and accelerate discovery in this important area of neuroscience.
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Most cited protocols related to «Occipital Lobe»

1
Comprehensive Microarray Data Analysis Pipeline
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.
Miller J.A., Ding S.L., Sunkin S.M., Smith K.A., Ng L., Szafer A., Ebbert A., Riley Z.L., Aiona K., Arnold J.M., Bennet C., Bertagnolli D., Brouner K., Butler S., Caldejon S., Carey A., Cuhaciyan C., Dalley R.A., Dee N., Dolbeare T.A., Facer B.A., Feng D., Fliss T.P., Gee G., Goldy J., Gourley L., Gregor B.W., Gu G., Howard R.E., Jochim J.M., Kuan C.L., Lau C., Lee C.K., Lee F., Lemon T.A., Lesnar P., McMurray B., Mastan N., Mosqueda N.F., Naluai-Cecchini T., Ngo N.K., Nyhus J., Oldre A., Olson E., Parente J., Parker P.D., Parry S.E., Player A.S., Pletikos M., Reding M., Royall J.J., Roll K., Sandman D., Sarreal M., Shapouri S., Shapovalova N.V., Shen E.H., Sjoquist N., Slaughterbeck C.R., Smith M., Sodt A.J., Williams D., Zöllei L., Fischl B., Gerstein M.B., Geschwind D.H., Glass I.A., Hawrylycz M.J., Hevner R.F., Huang H., Jones A.R., Knowles J.A., Levitt P., Phillips J.W., Sestan N., Wohnoutka P., Dang C., Bernard A., Hohmann J.G, & Lein E.S. (2014). Transcriptional Landscape of the Prenatal Human Brain. Nature, 508(7495), 199-206.
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
2
Comprehensive Microarray Data Analysis Pipeline
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.
Miller J.A., Ding S.L., Sunkin S.M., Smith K.A., Ng L., Szafer A., Ebbert A., Riley Z.L., Aiona K., Arnold J.M., Bennet C., Bertagnolli D., Brouner K., Butler S., Caldejon S., Carey A., Cuhaciyan C., Dalley R.A., Dee N., Dolbeare T.A., Facer B.A., Feng D., Fliss T.P., Gee G., Goldy J., Gourley L., Gregor B.W., Gu G., Howard R.E., Jochim J.M., Kuan C.L., Lau C., Lee C.K., Lee F., Lemon T.A., Lesnar P., McMurray B., Mastan N., Mosqueda N.F., Naluai-Cecchini T., Ngo N.K., Nyhus J., Oldre A., Olson E., Parente J., Parker P.D., Parry S.E., Player A.S., Pletikos M., Reding M., Royall J.J., Roll K., Sandman D., Sarreal M., Shapouri S., Shapovalova N.V., Shen E.H., Sjoquist N., Slaughterbeck C.R., Smith M., Sodt A.J., Williams D., Zöllei L., Fischl B., Gerstein M.B., Geschwind D.H., Glass I.A., Hawrylycz M.J., Hevner R.F., Huang H., Jones A.R., Knowles J.A., Levitt P., Phillips J.W., Sestan N., Wohnoutka P., Dang C., Bernard A., Hohmann J.G, & Lein E.S. (2014). Transcriptional Landscape of the Prenatal Human Brain. Nature, 508(7495), 199-206.
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
3
Bimanual Finger Tapping in fMRI

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Publication 2011
A-192 Basal Ganglia Epistropheus Fingers Foot Head Human Body Motor Cortex Movement Nervousness Occipital Lobe Precentral Gyrus Precipitating Factors Supplementary Motor Area TRIO protein, human
4
Multimodal MRI Spectroscopy Protocol
All studies were performed with a 4T magnet (Oxford Magnet Technology, Oxford, UK) with an INOVA console (Varian, Palo Alto, CA) as described before (3 (link),12 (link)). The scanner was equipped with a gradient system (40 mT/m, 400 μs rise-time), which included second-order shim coils with maximum shim strengths of XZ = YZ = 1.1 mT/m, Z2 = 2.8 mT/m, and XY = X2Y2 = 0.5 mT/m. Spectra from the occipital lobe were acquired with a quadrature 14 cm half volume coil (19 (link)) and spectra from the cerebellum and brainstem with a TEM volume coil (20 (link)). Sagittal, coronal and transverse multi-slice images were obtained with a fast spin echo sequence (repetition time TR = 4.5 s, echo train length = 8, echo spacing = 15 ms, 9 slices, 2 averages) for volume selection. All first- and second-order shims were adjusted using FASTMAP with EPI readout (21 (link)). Proton spectra from the occipital cortex (2.5 × 2.5 × 2.5 cm3), vermis (1.0 × 2.5 × 2.5 cm3), cerebellar hemisphere (1.7 × 1.7 × 1.7 cm3) and pons (1.6 × 1.6 × 1.6 cm3) were acquired both with the redesigned semi-LASER sequence (TR/TE = 4500/24 ms, spectral width = 6000 Hz, number of points = 4096, Fig. 1) and a STEAM sequence (TR/TM/TE = 4500/42/5 ms, spectral width = 6000 Hz, number of points = 4096) (11 (link)). Unsuppressed water spectra acquired from the same VOI were used to correct for eddy current effects and as an internal reference for metabolite quantification. The internal reference water spectrum was acquired without the OVS pulses to avoid magnetization transfer (MT) effects. Acquiring the water spectrum without the OVS resulted in 2–4% increase of signal in phantom measurements (without MT effects), while a ~37% higher water signal intensity was obtained without OVS relative to with OVS in vivo, indicating that ~34% of this increase was due to the removal of the MT affect on water.
Data were acquired in single scan mode, i.e. each single FID was saved separately. When the SNR was sufficient, these individual FIDs were frequency and phase corrected. When the SNR of single scan data was not sufficient (cerebellar and brainstem STEAM data), data were averaged over 4 – 8 scans and then corrected for frequency variations. Single scan or averaged data blocks that showed evidence for substantial motion (out-of-phase and/or lower intensity signal as determined by the operator) were excluded from final summation. Motion artifacts were rarely observed in this cohort of healthy volunteers.
Öz G, & Tkáč I. (2010). SHORT-ECHO, SINGLE-SHOT, FULL-INTENSITY 1H MRS FOR NEUROCHEMICAL PROFILING AT 4T: VALIDATION IN THE CEREBELLUM AND BRAINSTEM. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 65(4), 10.1002/mrm.22708.
Publication 2010
Brain Stem Cerebellum ECHO protocol Healthy Volunteers Occipital Lobe Pons Protons Pulse Rate Radionuclide Imaging SHIMS Steam Vermis, Cerebellar
5
Sampling of Brain Regions for AD Neuropathology
Thirty cases were included that were collected in two centers and the sampling of the blocks was carried out by two experienced neuropathologists (IA, TA). The cases were selected based on the Braak stage to which they were assigned by IA and TA after application of silver stain. The goal was to include all severities of the disease, that is, all the various stages of AD‐related neurofibrillary pathology. The samples were taken for the routine diagnostics and obtained within a 10‐year time span. The demographics of the subjects are given in Table 1. The selection of anatomical regions to be sampled was based on the requirements listed in current consensus criteria (NIA‐RI), and was also influenced by known general practice among neuropathologists. The specimens included were the samples from middle frontal gyrus, inferior parietal lobule, superior and middle temporal gyrus, occipital cortex including calcarine fissure, posterior hippocampus at the level of lateral geniculate nucleus and anterior hippocampus at the level of uncus. A total of eight sets of 7‐µm thick sections were produced from all six brain areas of the 30 cases.
Alafuzoff I., Arzberger T., Al‐Sarraj S., Bodi I., Bogdanovic N., Braak H., Bugiani O., Del‐Tredici K., Ferrer I., Gelpi E., Giaccone G., Graeber M.B., Ince P., Kamphorst W., King A., Korkolopoulou P., Kovács G.G., Larionov S., Meyronet D., Monoranu C., Parchi P., Patsouris E., Roggendorf W., Seilhean D., Tagliavini F., Stadelmann C., Streichenberger N., Thal D.R., Wharton S.B, & Kretzschmar H. (2008). Staging of Neurofibrillary Pathology in Alzheimer's Disease: A Study of the BrainNet Europe Consortium. Brain Pathology, 18(4), 484-496.
Publication 2008
Body Regions Brain Calcarine Sulcus Diagnosis Lateral Geniculate Body Medial Frontal Gyrus Middle Temporal Gyrus Neuropathologist Occipital Lobe Parietal Lobule Seahorses Silver Stains

Most recents protocols related to «Occipital Lobe»

1
Histological analysis of traumatic brain injury
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.
Michaud J., Plu I., Parai J., Bourgault A., Tanguay C., Seilhean D, & Woulfe J. (2023). Ballooned neurons in semi-recent severe traumatic brain injury. Acta Neuropathologica Communications, 11, 37.
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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
2
Spatial Navigation Abilities in TBI Survivors
Opportunistic sampling was utilised for the recruitment of participants whereby an invitation to participate in the study was advertised by the “Brain Injury Support (BIS) Services” which is a private organisation which provides specialist cognitive rehabilitation therapy to individuals with brain injury, and “High Beyond C” which is an organisation that facilitates an interactive virtual program for brain injury survivors.
Thirty–eight (n = 38) participants were recruited to the study—fifteen (n = 15) with a history of TBI (mean age = 31.67 ± 12.34 yrs.) and twenty–three participants (n = 23) from the general population to serve as healthy controls (mean age = 32.61 ± 12.59 yrs.).
From the sample of thirty–eight participants that completed the SBSOD scale, a subsample of ten participants from the TBI group (n = 10) and a subsample of thirteen participants from the control group (n = 13) completed the SHQ navigation tasks. Thus, while the participant group remains small, this number is inline previous empirical studies; 14 TBI and 12 controls [23 (link)], TBI and 12 control [2 (link)], and eight TBI and 40 control [24 (link)]. See Table 1 for the demographic characteristics of the participants in the overall sample, as well as the subsamples that completed the SHQ game.
The research conducted in this study was undertaken in concordance with the University of Hertfordshire Health, Science, Engineering and Technology Ethics Committee with Delegated Authority. The ethics protocol number for this study was LMS/PGT/UH/04139.
Participants with a history of TBI (n = 15) disclosed the year in which they acquired the brain injury and the type of TBI; nine participants acquired a closed head injury, one participant acquired an open head injury, two participants acquired a skull fracture and lastly, three participants reported acquiring the TBI as a result of an ‘other’ mechanism of injury. Participants with a history of TBI also provided a self-report disclosing the location to which the injury was acquired (i.e., frontal lobe, temporal lobe, parietal lobe, or occipital lobe) and whether they experience persistent difficulties due to the TBI (i.e., headaches, dizziness, excessive physical or cognitive fatigue, concentration, memory, irritability, sleep, balance, vision or other). See Fig 1 for a summary of the reported frequencies of lasting difficulties according to self-reported location of damage.
Seton C., Coutrot A., Hornberger M., Spiers H.J., Knight R, & Whyatt C. (2023). Wayfinding and path integration deficits detected using a virtual reality mobile app in patients with traumatic brain injury. PLOS ONE, 18(3), e0282255.
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Publication 2023
Brain Injuries Cognition Cognitive Therapy Ethics Committees Fatigue Headache Head Injury, Open Injuries Injuries, Closed Head Lobe, Frontal Memory Occipital Lobe Parietal Lobe Physical Examination Rehabilitation Skull Fractures Sleep Survivors Temporal Lobe
3
Probing Excitation-Inhibition Balance in Bistable Perception
Neurochemical concentrations in the mid-occipital lobe were collected as part of a 7 T magnetic resonance spectroscopy (MRS) scan on the same day as behavioral SFM data. For full scanning details, see (Schallmo et al., 2023 (link)). Data were acquired on a Siemens MAGNETOM 7 T scanner with a custom surface radio frequency head coil using a STEAM sequence (Marjańska et al., 2017 (link)) with the following parameters: TR = 5000 ms, TE = 8 ms, volume size = 30 mm (left-right) × 18 mm (anterior-posterior) × 18 mm (inferior-superior), 3D outer volume suppression interleaved with VAPOR water suppression (Tkáč et al., 2001 (link)), 2048 complex data points with a 6000 Hz spectral bandwidth, chemical shift displacement error = 4% per ppm. B0 shimming was performed using FAST(EST) MAP to ensure a linewidth of water within the occipital voxel ≤ 15 Hz (Gruetter, 1992 ).
We processed our MRS data using the matspec toolbox (github.com/romainVala/matspec) in MATLAB, including frequency and phase correction. Concentrations for 18 different metabolites including glutamate, glutamine, and GABA were quantified in each scanning session using LCModel. We scaled metabolite concentrations relative to an unsuppressed water signal reference, after correcting for differences in gray matter, white matter, and CSF fractions within each subject’s MRS voxel, the proportion of water in these different tissue types, and the different T1 and T2 relaxation times of the different tissue types. Tissue fractions within the voxel were quantified in each subject using individual gray matter and white matter surface models from FreeSurfer (Fischl, 2012 (link)). MRS data sets were excluded based on the following data quality criteria: H2O line width > 15, LCModel spectrum line width > 5 Hz or LCModel SNR < 40. Out of a total of 193 MRS datasets (54 controls, 44 relatives, and 95 PwPP), 10 sets (1 control, 4 relatives, 5 PwPP) were excluded in this way, leaving 183 total MRS datasets. In addition to subjects whose SFM data we excluded for having poor real switch task performance, and excluding re-test sessions, this left a total of 114 participants with usable SFM and MRS data (37 controls, 33 relatives, and 44 PwPP).
In order to probe the possible role of excitatory and inhibitory markers during bi-stable perception in PwPP, we examined relationships between metabolite concentrations from MRS and our bi-stable SFM behavioral measures. Specifically, we used Spearman rank correlation to test for correlations between metabolite levels from MRS (i.e., GABA, glutamate, and glutamine) and average switch rates across participants from all three groups. As in our other correlational analyses, data from retest sessions were excluded, as Spearman correlations assume independence across data points.
Killebrew K.W., Moser H.R., Grant A.N., Marjańska M., Sponheim S.R, & Schallmo M.P. (2023). Faster bi-stable visual switching in psychosis. medRxiv.
Publication Preprint 2023
gamma Aminobutyric Acid Glutamate Glutamine Gray Matter Head Histocompatibility Testing Magnetic Resonance Spectroscopy Occipital Lobe Psychological Inhibition Steam Task Performance Tissues Water Vapor White Matter
4
Structural MRI Analysis with FreeSurfer
Structural T1 images were analyzed using FreeSurfer (version 6.0.03) to perform cortical modeling and volumetric segmentation. The standard processing procedures were performed separately on each cerebral hemisphere and included (1) motion correction and conform; (2) correction of signal strength non-uniformities caused by magnetic field inhomogeneities; (3) removal of no-brain issue (skull stripping); (4) affine registration to the Talairach atlas and segmentation of the subcortical white matter and deep gray matter structures. (5) tessellation of the gray-to-white and gray-to-cerebrospinal fluid surface boundaries; (6) automatic correction of topology defects; (7) surface deformation for optional placement of the gray-to-white and gray-to-CSF boundaries, two researchers blinded to the participants performed the initial visual inspection of the segmentation and minor manual corrections to the segmentation as needed; (8) smoothing with a 10 mm FWHM Gaussian smoothing kernel; (9) surface inflation and registration to a spherical atlas for intersubject matching of cortical folding patterns; (10) cortical parcellations were based on the PALS-B12 atlas (Van Essen, 2005 (link)). For each subject, per hemisphere, FreeSurfer parcellated five cortical regions (frontal, parietal, limbic, temporal, and occipital lobe) based on the PALS-B12 atlas and seven subcortical regions (nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus) (Fischl et al., 2002 (link)). The regional cortical thicknesses and volumes, subcortical GM volumes, and total intracranial volume (TIV) were extracted from the reconstructed brain images in the standard brain space.
Wang H., Xu J., Yu M., Zhou G., Ren J., Wang Y., Zheng H., Sun Y., Wu J, & Liu W. (2023). Functional and structural alterations as diagnostic imaging markers for depression in de novo Parkinson’s disease. Frontiers in Neuroscience, 17, 1101623.
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Publication 2023
Amygdaloid Body Brain Cerebral Hemispheres Cerebrospinal Fluid Cortex, Cerebral Cranium Globus Pallidus Gray Matter Magnetic Fields Nucleus Accumbens Occipital Lobe Papillon-Lefevre Disease Putamen Seahorses Thalamus White Matter
5
Multimodal Amyloid and Tau Imaging
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.
Millar P.R., Gordon B.A., Luckett P.H., Benzinger T.L., Cruchaga C., Fagan A.M., Hassenstab J.J., Perrin R.J., Schindler S.E., Allegri R.F., Day G.S., Farlow M.R., Mori H., Nübling G., Bateman R.J., Morris J.C, & Ances B.M. (2023). Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study. eLife, 12, e81869.
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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

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High capacity cdna reverse transcription kit by Thermo Fisher Scientific
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The High-Capacity cDNA Reverse Transcription Kit is a laboratory tool used to convert RNA into complementary DNA (cDNA) molecules. It provides a reliable and efficient method for performing reverse transcription, a fundamental step in various molecular biology applications.
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Rpmi 1640 medium by Merck Group
Sourced in United States, Germany, United Kingdom, Japan, Italy, China, France, Sao Tome and Principe, Switzerland, Austria, Australia, Spain, Macao, Belgium, Canada, Hungary, Brazil, Sweden, Ireland, India, Poland, Israel, Mexico, Denmark, Hong Kong, Czechia, Argentina, Portugal, Holy See (Vatican City State)
RPMI-1640 is a widely used cell culture medium formulation, developed at Roswell Park Memorial Institute. It is a complete and balanced medium designed to support the growth and maintenance of a variety of cell types, including human and animal cell lines. The medium contains essential nutrients, vitamins, amino acids, and other components necessary for cell proliferation and survival in in vitro cell culture applications.
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24 well tissue culture plate by Sarstedt
Sourced in Germany
Sarstedt's 24-well tissue culture plates are a laboratory equipment designed for cell culture applications. These plates provide a standardized and controlled environment for the growth and maintenance of cells. Each plate contains 24 individual wells, allowing for multiple experiments or samples to be cultivated simultaneously.

More about "Occipital Lobe"

The Occipital Lobe, the posterior region of the cerebral hemisphere, is a crucial component of the visual processing system.
This small lobe, one of the four major lobes of the brain, houses the primary visual cortex, responsible for receiving and interpreting visual information from the retina.
Researchers studying the Occipital Lobe can leverage a range of advanced tools and techniques to optimize their investigations.
PubCompare.ai's AI platform is a powerful resource for locating and comparing protocols from literature, preprints, and patents, enabling researchers to identify the most accurate and reproducible methods for their Occipital Lobe studies.
By harnessing the power of AI-driven analysis, researchers can streamline their workflow and accelerate their discoveries in this vital area of neuroscience.
In addition to PubCompare.ai, researchers may also utilize software such as MATLAB and PMOD, which offer sophisticated data analysis and visualization capabilities.
The Tim Trio MRI system, equipped with a 32-channel head coil, can provide high-resolution imaging of the Occipital Lobe, while a stereotaxic frame can assist in precise targeting and manipulation of specific regions.
Furtheremore, techniques like flow cytometry (FCS) and high-capacity cDNA reverse transcription can be employed to study the cellular and molecular mechanisms underlying Occipital Lobe function.
Cell culture models, such as those using RPMI-1640 medium and 24-well tissue culture plates, can also provide valuable insights into the complex interplay of factors that contribute to visual perception, object recognition, and spatial awareness.
By leveraging these advanced tools and techniques, researchers can delve deeper into the Occipital Lobe, unraveling its role in various cognitive processes and accelerating the pace of discovery in this captivating field of neuroscience.
Explore the wealth of resources available and take your Occipital Lobe studies to new heights.