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Parietal Lobule

The parietal lobule is a region of the cerebral cortex located in the superior portion of the parietal lobe.
It plays a crucial role in various cognitive and sensorimotor functions, including spatial awareness, attention, and visuomotor integration.
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With PubCompare.ai's advanced features, scientists can effeciently explore the power of this tool and gain valuable insights to advance their parietal lobule research.

Most cited protocols related to «Parietal Lobule»

1
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
2
Histologic Assessment of α-Synuclein Pathology
Diagnostic histologic methods were performed on standard blocks of tissue that were fixed in 4% buffered formaldehyde and then either dehydrated and embedded in paraffin or cryoprotected and cut on a freezing, sliding microtome. Paraffin sections from the olfactory bulb and tract, anterior medulla (two levels anterior to the obex), anterior and mid-pons, mid-amygdala with adjacent transentorhinal area, anterior cingulate gyrus (1–3 cm posterior to the coronal slice containing the genu of the corpus callosum), middle temporal gyrus (at the level of the lateral geniculate nucleus), middle frontal gyrus (4–5 cm posterior to the frontal pole), and inferior parietal lobule were stained immunohistochemically for α-synuclein using a polyclonal antibody raised against an α-synuclein peptide fragment phosphorylated at serine 129, after epitope exposure with proteinase K. The process leading to the choice of immunohistochemical method, as well as details of the method, have been described in a previous publication (7 (link)). The density of α-synuclein-immunoreactive Lewy bodies and neurites in each of the above-mentioned brain regions was scored, for more than 90% of slides, by a single observer (TGB), without knowledge of diagnosis, as none, sparse, moderate, frequent and very frequent, using the templates provided by the Dementia with Lewy Bodies Consortium (66 (link)). The remaining slides were scored by trainees under the instruction of the primary observer. For the substantia nigra (SN), LTS was estimated using the same scoring method but on thioflavine-S-stained thick (40 micron) sections due to the standard laboratory practice of sectioning the SN in this manner for unbiased morphometric analysis.
Beach T.G., Adler C.H., Lue L., Sue L.I., Bachalakuri J., Henry-Watson J., Sasse J., Boyer S., Shirohi S., Brooks R., Eschbacher J., White CL I.I.I., Akiyama H., Caviness J., Shill H.A., Connor D.J., Sabbagh M.N, & Walker D.G. (2009). Unified Staging System for Lewy Body Disorders: Correlation with Nigrostriatal Degeneration, Cognitive Impairment and Motor Dysfunction. Acta neuropathologica, 117(6), 613-634.
Publication 2009
Amygdaloid Body Brain Corpus Callosum Dementia Diagnosis Endopeptidase K Epitopes Formaldehyde Gyrus, Anterior Cingulate Histological Techniques Immunoglobulins Knee Lateral Geniculate Body Lewy Bodies Medial Frontal Gyrus Medulla Oblongata Microtomy Middle Temporal Gyrus Neurites Olfactory Bulb Paraffin Paraffin Embedding Parietal Lobule Peptide Fragments Pons Serine SNCA protein, human Substantia Nigra thioflavine Tissues
3
Functional Connectivity Metrics in Healthy Subjects

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Publication 2011
Anterior Prefrontal Cortex Brain Brain Mapping Healthy Volunteers Microtubule-Associated Proteins Motor Cortex Parietal Lobule Posterior Cingulate Cortex Prefrontal Cortex Radius Temporal Lobe
4
Comprehensive sCJD Neuropathology Evaluation
This study was based on 225 cases with an established diagnosis of sCJD obtained after clinical, neuropathological, and molecular examination. Cases of genetic and acquired forms of CJD were excluded. The study was restricted to cases from which large amounts of both frozen and fixed brain tissue were available. 200 patients were obtained from a group of ~240 consecutive cases referred for diagnosis (~40 cases lacked sufficient frozen tissue), whereas the remaining 25 were specifically chosen to increase the number of cases with mixed phenotypes. More precisely, the 25 additional cases were selected based on the demonstration of a mixed synaptic and perivacuolar pattern of PrP deposition by PrP immunohistochemistry (see “Results”). 61 patients died in the USA between 1990 and 2001, and 164 in Europe (124 in Italy, 34 in Germany, and 6 in Belgium) between 1993 and 2007.
Brains were removed at autopsy and either one half or selected coronal sections of tissue, including all major brain structures and nuclei, were immediately frozen and stored at −80°C. The remaining tissue was fixed in formalin and was used for neuropathological examination and PrP immunohistochemistry. Samples of frozen gray matter (between 50 and 100 mg) for protein analysis were obtained from the following regions: frontal (superior and middle frontal gyri), temporal (superior and middle temporal gyri), parietal (inferior parietal lobule), and occipital (calcarine cortex and lateral occipital gyrus) cortices, hippocampus (Ammon’s horn), limbic cortices (entorhinal cortex anterior, insular cortex, and cingulate gyrus), striatum (caudate and putamen nuclei), thalamus (medial and lateral nuclei), hypothalamus, brainstem (midbrain periaqueductal gray, pontine periaqueductal gray including locus coeruleus, medullar periventricular gray), cerebellum (hemisphere and vermis). The parietal cortex and the 3 samples from the brainstem were not available in 30 cases from Germany. Furthermore, 1 or 2 samples were occasionally lacking in some other cases [altogether 34 samples, mostly from the hypothalamus (n = 15) and amygdala (n = 11)]. Sampling of frozen tissues was performed by the same investigator (PP) according to a defined protocol across the whole series of cases. Blocks of fixed tissue were taken from the opposite half of the brain and were used for histopathologic examination. Sampling of fixed tissues was performed by the same investigator (PP) according to a defined protocol in 160 cases. The remaining 65 cases were sampled in Munich (Germany) and Indianapolis (USA). In these Centers, which are both involved in CJD National surveillance and Brain banking, CJD brains are sampled extensively according to protocols, which included all the areas that were of interest for this study. Sampling of both fixed and frozen tissue was performed twice in a subgroup of 10 codon 129 MM subjects.
Parchi P., Strammiello R., Notari S., Giese A., Langeveld J.P., Ladogana A., Zerr I., Roncaroli F., Cras P., Ghetti B., Pocchiari M., Kretzschmar H, & Capellari S. (2009). Incidence and spectrum of sporadic Creutzfeldt–Jakob disease variants with mixed phenotype and co-occurrence of PrPSc types: an updated classification. Acta Neuropathologica, 118(5), 659-671.
Publication 2009
Acquired CJD Amygdaloid Body Autopsy Birth Brain Brain Stem Cell Nucleus Central Gray Substance of Midbrain Cerebellum Codon Cortex, Cerebral Creutzfeldt-Jakob Disease, Sporadic Diagnosis Entorhinal Area Formalin Freezing Gray Matter Gyrus Cinguli Hippocampus Proper Hypothalamus Immunohistochemistry Insula of Reil Lobe, Limbic Locus Coeruleus Medial Frontal Gyrus Medulla Oblongata Mesencephalon Middle Temporal Gyrus Neostriatum Occipital Gyrus Parietal Lobe Parietal Lobule Patients Phenotype Pons Proteins Putamen Seahorses Striate Cortex Thalamus Tissues Vermis, Cerebellar
5
Neuropathological and Genetic Characterization of Alzheimer's Disease Subtypes
Gross and macroscopic neuropathologic assessment was performed by standardized procedures. Formalin-fixed, paraffin-embedded tissue samples from the primary motor and visual cortices, inferior parietal lobule, mid-frontal gyrus, superior temporal gyrus, amygdala, and posterior hippocampus were cut at 5 µm thickness, mounted on glass slides and stained with H&E. Thioflavin-S fluorescent microscopy was performed to evaluate SP (10× objective) and NFT densities (40× objective). Primitive, neuritic, and cored type plaques were included in the SP counts and were truncated at 50, which is twice the number required to meet Khachaturian’s criteria for AD diagnosis [20 (link)]. NFT distribution was assessed to determine Braak stage. Intracellular and extracellular NFT counts from two hippocampal regions (CA1 and subiculum) and three association cortices (inferior parietal, mid-frontal, and superior temporal) were used to determine AD subtypes. Additional sections previously underwent immunohistochemical staining and were processed using the DAKO Autostainer (DAKO Auto Machine Corporation, Carpinteria, CA, USA) with DAKO Envision+ HRP System. The posterior hippocampus was stained with an antibody that detects the 25 kDa C-terminal fragment of TDP-43 (a generous gift from Leonard Petrucelli, Mayo Clinic, FL, USA). Assessment of TDP-43 pathology was performed as previously described [2 (link)]. Cerebrovascular disease was assessed using a simple scheme proposed by Jellinger and Attems [18 ], as previously reported. Lewy body disease (LBD) pathology was assessed by use of immunohistochemistry [34 (link)], as previously described.
For genotyping, genomic DNA was extracted from frozen brain by standard procedures. Genotyping for MAPT H1/H2 (SNP rs1052554 A/G, A = H1, G = H2), APOE alleles (SNP rs429358 C/T and rs7412 C/T), GRN (SNP rs5848 C/T), and TMEM106B (SNP rs1990622 C/T) was performed using a Taqman SNP genotyping assay (Applied Biosystems, Carlsbad, CA, USA). Genotype calls were obtained with SDS v2.2 software (Applied Biosystems). Although there is overlap in the HpScl-AD group between previous Genetic studies [9 (link), 32 (link)], Genetic information on the HpScl group has not been previously reported. Genotyping availability can be found in Supplementary methods.
Clinical reports were reviewed blind to pathologic diagnosis to collect education, family history, age of onset, disease duration, and Mini Mental State Examination (MMSE) scores. Family history was noted as positive if at least one first-degree relative had dementia. Age of onset was recorded as the age of initial cognitive abnormalities, as opposed to age of diagnosis. Disease duration was ascertained as the number of years elapsed between age of death and age of onset. Longitudinal decline was calculated as a slope of three or more MMSE scores, where the MMSE score was the dependent variable and elapsed years between testing and death were the independent variable. Antemortem clinical diagnosis of probable AD, possible AD and mild cognitive impairment was considered an amnestic diagnosis. Availability of clinical information can be found in Supplementary methods.
Murray M.E., Cannon A., Graff-Radford N.R., Liesinger A.M., Rutherford N.J., Ross O.A., Duara R., Carrasquillo M.M., Rademakers R, & Dickson D.W. (2014). Differential clinicopathologic and Genetic features of late-onset amnestic dementias. Acta neuropathologica, 128(3), 411-421.
Publication 2014
Alleles Amygdaloid Body Antemortem Diagnosis Apolipoproteins E Biological Assay Blindness Brain Cerebrovascular Disorders Cognition Cognitive Impairments, Mild Congenital Abnormality Cortex, Cerebral Dementia Diagnosis Formalin Freezing Genome Immunoglobulins Immunohistochemistry Lewy Body Disease MAPT protein, human Microscopy Mini Mental State Examination Neuritis Paraffin Embedding Parietal Lobule protein TDP-43, human Protoplasm Seahorses Senile Plaques Subiculum Superior Temporal Gyrus thioflavin S Visual Cortex

Most recents protocols related to «Parietal Lobule»

1
Neuroimaging Analysis of Alzheimer's Disease
For both structural and functional imaging modalities analyses, a region of interest approach was adopted, allowing us to focus on a priori brain regions known to be affected in AD (Yetkin et al., 2006 (link)). For fMRI, six cortical regions of interest (ROIs) were extracted from the high working memory load contrast maps: inferior, middle, and superior frontal gyri, anterior cingulate, inferior parietal lobule, and medial temporal lobe. ROIs were identified with the Human Automated Anatomical Labeling (AAL) atlas (Tzourio-Mazoyer et al., 2002 (link)) within the WFU Pickatlas tool (V1.2), and signal intensity of the high working memory load contrast was extracted using the MarsBaR toolbox, implemented in SPM8 on MATLAB (The MathWorks Inc., MA, USA). For DTI, data from five white matter ROIs, known to be affected in AD (Alves et al., 2012 (link)) were extracted in MNI space, using an in-house matlab script, from the FA and MD maps: genu of the corpus callosum, fornix, the cingulum bundles adjacent to the cingulate cortex, superior longitudinal fasciculi and the uncinate fasciculi. White matter tract ROIs were derived from the JHU ICBM-DTI-81 WM Atlas (Mori et al., 2008 (link)), after examining registration between the FA ROIs and white matter tracts. Values at each voxel within an ROI were averaged to obtain a mean value for each ROI. fMRI raw signal was extracted from the left and right homologous regions and was averaged across both sides. DTI signal was thresholded for above 0.2. Finally, subsequent ROI data was transferred to SPSS for further statistical analysis.
Ben-Ami L., Ravona-Springer R., Tsarfaty G., Raizman R., Shumacher A., Sharvit-Ginon I., Greenbaum L., Bendlin B.B., Okun E., Heymann A., Schnaider Beeri M, & Livny A. (2023). Neural correlates of subjective cognitive decline in adults at high risk for Alzheimer’s disease. Frontiers in Aging Neuroscience, 15, 1067196.
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Publication 2023
Brain Cingulate Cortex Corpus Callosum Cortex, Cerebral fMRI Fornix, Brain Gyrus, Anterior Cingulate Homo sapiens Knee Memory Microtubule-Associated Proteins Parietal Lobule Superior Frontal Gyrus Temporal Lobe Uncinate Fasciculus White Matter
2
Functional NIRS Mapping of Working Memory
A NIRSport portable device with 8 sources and 8 detectors was used to measure brain activation during the WM task. Fibre optic cables transported infrared light from the fNIRS device to a tailored cap designed to collect HbO and HbR concentration. Four cap sizes (50 cm, 52 cm, 54 cm, and 56 cm) were used to accommodate varying head sizes. Source-detector separation was scaled according to cap size (50 cm: 2.5 cm; 52 cm: 2.6 cm; 54 cm: 2.7 cm; and 56 cm: 2.8 cm). Data was collected at 7.81 Hz using wavelengths of 860 nm and 750 nm. Potential positions for sources and detectors based on the 10–20 System of Electrode Placement were already indicated on the fNIRS caps (from the manufacturers). From these positions, a subset were chosen such that the channels would overlay the frontal and parietal cortices implicated in previous WM studies (Todd and Marois, 2004 (link), Pessoa and Ungerleider, 2004 (link)). The positions were chosen such that the final probe geometry consisted of 14 channels, with 8 channels covering the frontal cortex (2 channels each covering right and left middle frontal gyrus and inferior frontal gyrus) and 6 covering the parietal cortex (one channel each covering left and right inferior parietal lobule, superior occipital gyrus, and supramarginal gyrus) - see Fig. 1b.
Davidson C., Shing Y.L., McKay C., Rafetseder E, & Wijeakumar S. (2023). The first year in formal schooling improves working memory and academic abilities. Developmental Cognitive Neuroscience, 60, 101205.
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Publication 2023
Brain Head Inferior Frontal Gyrus Infrared Rays Lobe, Frontal Medial Frontal Gyrus Medical Devices Occipital Gyrus Parietal Lobe Parietal Lobule Supramarginal Gyrus
3
Structural Brain Differences in ASD vs. TD Children
First, in order to estimate the structural brain differences between the ASD and TD groups of children, we performed the whole-brain analysis for each parameter (brain volume, cortical thickness, gyrification, sulcus depth, and fractal dimension) to reveal the ROIs that differed between groups of children; volumetric analysis was based on the neuromorphometric atlas (http://www.neuromorphometrics.com), whereas cortical statistics were based on the Desikan-Killiany anatomical atlas53 (link). Statistical design for each parameter was created in SPM toolbox and performed in CAT12 (see details further).
Second, for those ROIs that significantly differed between groups of children, we fitted linear models to analyze the relationships between ROI parameters and the severity of autistic symptoms (‘autism scores’, AQ questionnaire and calibrated ADOS severity score), according to the formula: lm(ROI ~ AQ + ADOS, data = data). The correction for multiple comparisons (Bonferroni) was applied in R (function p.adjust.methods = "bonferroni"), so that all reported p-values are Bonferroni-corrected.
Finally, linear models were used to examine the association between language-related ROI parameters and the language abilities of children with ASD (MLS). Following the Dual-Stream model of speech processing54 (link), we chose such ROIs in the left hemisphere as transverse temporal gyrus, superior temporal gyrus, middle temporal gyrus, and inferior temporal gyrus (they were merged into one ROI, referred to as temporal cortex); orbital, triangular, opercular parts of the inferior frontal gyrus and precentral gyrus (they were merged into one ROI, referred to as speech motor cortex); and inferior parietal lobule (referred to as inferior parietal cortex) (Fig. 1). The structure of the models was as follows: lm(ROI ~ MLS + AQ + ADOS + IQ + age, data = data). AQ, ADOS, IQ, and age were included in the models to control the possible account of other factors besides the language. All reported p-values were Bonferroni-corrected according to function p.adjust.methods = "bonferroni" in R.

Language-related regions of interest (ROIs) in the left hemisphere.

Numeric variables in all models were centered to avoid multicollinearity. All models were estimated in R55 with the lme456 (link) package and the data were plotted with ggpubr57 and ggplot258 .
Arutiunian V., Gomozova M., Minnigulova A., Davydova E., Pereverzeva D., Sorokin A., Tyushkevich S., Mamokhina U., Danilina K, & Dragoy O. (2023). Structural brain abnormalities and their association with language impairment in school-aged children with Autism Spectrum Disorder. Scientific Reports, 13, 1172.
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Publication 2023
Autistic Disorder beta-apocarotenoid-14',13'-dioxygenase Brain Brodmann Area 45 Child Cortex, Cerebral Inferior Temporal Gyrus Middle Temporal Gyrus Motor Cortex Opercular Cortex Parietal Cortex, Inferior Parietal Lobule Precentral Gyrus Speech Superior Temporal Gyrus Temporal Lobe Transverse Temporal Gyri
4
Functional Brain Parcellation using Brainnetome Atlas
For the extraction of signal time courses in each anatomical district, we used the Human Brainnetome Atlas (BNA) [94 (link)]. The BNA atlas divided the brain into 246 regions of interest (ROIs), with 123 for each hemisphere, comprising 210 cortical and 36 subcortical ROIs. For each subject, we extracted the time courses from each of the 246 ROIs using the Data Processing Assistant for Resting-State fMRI (DPARSF) (http://www.rfmri.org (accessed on 20 May 2022)). The extracted data were then normalized within each subject by T-score transformation in order to minimize the global signal differences between subjects.
Starting from the 246 ROIs that emerged from the analysis, we selected 17 ROIs for each brain hemisphere, using an average value of the resting-state BOLD signal of areas belonging to the same brain region. The ROIs were the dorsolateral prefrontal cortex (DLPFC), the ventrolateral prefrontal cortex (VLPFC), the orbitofrontal cortex (OFC), the precuneus (PCun), the inferior parietal lobule (IPL), the temporo-parietal junction (TPJ), the superior temporal gyrus (STG), the ventral anterior insula (vaIC), the dorsal anterior insula (daIC), the posterior insula (pIC), the lateral occipital cortex (LOC), the dorsal anterior cingulate cortex (dACC), the amygdala (Amy), the nucleus accumbens (NaC), the ventral caudate (vCa), the dorsal caudate (dCa), and the putamen (Pu). The coordinates and the dimension of the ROIs are summarized in Table S1.
Giordano G.M., Pezzella P., Giuliani L., Fazio L., Mucci A., Perrottelli A., Blasi G., Amore M., Rocca P., Rossi A., Bertolino A, & Galderisi S. (2023). Resting-State Brain Activity Dysfunctions in Schizophrenia and Their Associations with Negative Symptom Domains: An fMRI Study. Brain Sciences, 13(1), 83.
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Publication 2023
Amygdaloid Body Brain Cerebral Hemispheres Cortex, Cerebral Dorsolateral Prefrontal Cortex fMRI Gyrus, Anterior Cingulate Homo sapiens Insula of Reil Nucleus Accumbens Occipital Lobe Orbitofrontal Cortex Parietal Lobule Posterior Cingulate Cortex Precuneus Prefrontal Cortex Putamen Superior Temporal Gyrus Temporal Lobe
5
Multimodal Neuroimaging of Response Inhibition
Statistical analyses for behavioural data were conducted using IBM SPSS Statistics Version 28 [77 ]. Normality and other assumptions were tested using appropriate visual and statistical methods, as suggested by Field [78 ]. No transformations were required and all relevant assumptions were met. Group differences in demographic characteristics, personality traits, and behavioural performance were tested using ANOVA with bootstrapped confidence intervals (1000 samples) and post-hoc tests using Bonferroni corrections for multiple comparisons. We report ω2 effect sizes alongside ANOVA results, where a small effect is .01, a medium effect is .06 and a large effect is .14 [78 ].
Imaging data were preprocessed and subsequently analyzed using FEAT (FMRI Expert Analysis Tool) Version 5.98 and other packages from FSL (FMRIB software library; http://www.fmrib.ox.ac.uk/fsl) [79 (link)]. Participant data were first individually visually checked for any major distortions or movement noise. Data were then corrected for motion using MCFLIRT [80 (link)] and B0 unwarping was applied to correct for distortions resulting from magnetic field inhomogeneities. Functional data were also temporally filtered with a 90-s high pass filter and spatially smoothed using a Gaussian kernel of 5-mm full-width half-maxima. Individual functional data were co-registered to their respective high resolution 3D anatomical brain scan and normalized to Montreal Neurological Institute space (MNI-152) using a combination of linear and non-linear transformations with FLIRT and FNIRT [80 (link), 81 ]. Finally, an independent component analysis was conducted on the data acquired from each functional run using MELODIC [82 (link)] to isolate noise components that were identified by their spatial profile, time-course, and power spectrum. These noise components were removed prior to statistical analysis.
Each run of the GNG task for each participant was put into a general linear model (GLM) with the different events being specified as explanatory variables (EVs) or predictors in the model and then convolved with a double-gamma model of the hemodynamic response function. The first-level GLM included three EVs in total, with each including all images and both EPI runs within the sequence. These EVs included Go, No-Go, and Baseline. The first-level GLM included 12 contrasts composed of every possible comparison of the three EVs (e.g., No-Go>Go, No-Go>[Baseline+Go], etc.). The first-level GLM resulted in one parameter estimate image being created for each EV and contrast, for each run and each participant.
Both individual runs for each participant were then entered into a second-level fixed effects analysis and the resulting statistical images (contrast of parameter estimate [COPE] maps) were subsequently entered into a higher-level group analysis for the examination of group mean effects and group differences. These second-level COPE maps were also entered into additional higher-level dimensional analyses for each of the PiCD pathological personality traits. Statistical inference was conducted using a non-parametric approach as implemented by the Randomise tool (http://www.fmrib.ox.ac.uk/fsl/randomise) in conjunction with threshold-free cluster-enhancement (TFCE) [83 (link)]. TFCE identifies clusters of activity without pre-determining an arbitrary cluster-defining threshold and using permutation testing, which can achieve a multi-threshold meta-analysis of the random field theory cluster p-values to determine statistical significance. We used 10000 permutations for statistical inference, using a corrected family-wise threshold of p < 0.05.
In addition to whole-brain analysis, ROI analyses were performed to examine response inhibition-related functional brain activity. Selection of ROIs for this study was informed by the GNG relevant clusters obtained from a seminal meta-analysis [21 (link)]. The top three clusters were selected, which created a ROI mask composed of the right insula, MFG, IFG, inferior parietal lobule, and MedFG, as selected from the Harvard-Oxford Structural Atlas. To complement the ROI mask based on Swick et al. [21 (link)] and to be exhaustive, an additional exploratory ROI mask was constructed. The additional mask was composed of the significant task-based activity found when collapsing response-inhibition-related activity across all participants.
A gPPI analysis was conducted using FSL to examine task-dependent changes in functional connectivity. Time series data were first extracted from a seed voxel selected based on the most significant clusters of activation identified by the No-Go>Go contrast in the whole-brain univariate analysis. This was then submitted to an independent gPPI analysis as follows. A first-level GLM was implemented with the following task conditions and variables specified as EVs: EV1: Go; EV2: No-Go; EV3: Baseline; EV4: Time series of seed voxel; EV5: Go x Time series of seed voxel; EV6: No-Go x Time series of seed voxel; and EV7: Baseline x Time series of seed voxel. All possible contrasts between EV5, EV6 and EV7 were also specified to identify any differential patterns of functional connectivity between task conditions. At the second level, a fixed effects analysis was then conducted by combining the runs for each participant, and finally, group level inference was completed using the randomise tool in conjunction with TFCE.
Cane C., Carcone D., Gardhouse K., Lee A.C, & Ruocco A.C. (2023). An exploratory study of functional brain activation underlying response inhibition in major depressive disorder and borderline personality disorder. PLOS ONE, 18(1), e0280215.
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Publication 2023
Brain cDNA Library Contrast Media fMRI Gamma Rays Hemodynamics Insula of Reil Magnetic Fields Microtubule-Associated Proteins Movement neuro-oncological ventral antigen 2, human Parietal Lobule Psychological Inhibition Radionuclide Imaging

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More about "Parietal Lobule"

The parietal lobe, a crucial region of the cerebral cortex, houses the parietal lobule, which plays a pivotal role in various cognitive and sensorimotor functions.
This area, located in the superior portion of the parietal lobe, is responsible for spatial awareness, attention, and visuomotor integration.
Researchers studying the parietal lobule can leverage advanced tools like PubCompare.ai, an innovative AI-driven platform, to optimize their research process.
PubCompare.ai helps scientists locate the best protocols from literature, preprints, and patents through smart comparisons, enhancing the reproducibility and accuracy of parietal lobule studies.
With features like Stereo Investigator software, MATLAB R2009a, and Signa Premier 3T scanner, researchers can delve deeper into the complexities of the parietal lobule and gain valuable insights to advance their work.
Additionally, tools like Clone AT8, SPSS Statistics software package, and Trio 3T scanner can be utilized to further explore the parietal lobule.
The DotSlide virtual microscopy system and 12-channel head coil can also aid in visualizing and analyzing the intricate structure and function of this crucial brain region.
By leveraging the power of PubCompare.ai and integrating these advanced tools and technologies, researchers can optimze their parietal lobule research, leading to more accurate and reporduciible findings.
This, in turn, can drive breakthroughs in our understanding of the parietal lobule and its role in cognitive and sensorimotor processes.
Ultimately, this knowledge can contribute to the advancement of neuroscience and the development of more effective treatments for related disorders.