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Medial Frontal Gyrus

The Medial Frontal Gyrus is a region of the frontal lobe of the brain, located on the medial surface of the frontal lobe.
It plays a key role in various cognitive and motor functions, including decision-making, problem-solving, and executive control.
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Most cited protocols related to «Medial Frontal Gyrus»

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.
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

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Publication 2009
Gyrus, Anterior Cingulate Inferior Temporal Gyrus Medial Frontal Gyrus Middle Temporal Gyrus Occipitotemporal Gyrus, Lateral Orbitofrontal Cortex Tissues
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.
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
The goal of this work was to create a large dataset of consistently and accurately labeled cortices. To do so we adopted a modification of the DK protocol (Desikan et al., 2006 (link)). We modified the protocol for two reasons: (i) to make the region definitions as consistent and as unambiguous as possible, and (ii) to rely on region boundaries that are well suited to FreeSurfer’s classifier algorithm, such as sulcal fundi that are approximated by surface depth and curvature. This would make it easier for experienced raters to assess and edit automatically generated labels, and to minimize errors introduced by the automatic labeling algorithm. We also sought to retain major region divisions that are of interest to the neuroimaging community. In some cases, this necessitated the inclusion of anatomically variable sulci as boundary markers (such as subdivisions of the inferior frontal gyrus) or use of gyral crowns (such as the pericalarine cortex). Alternatively, common subdivisions of gyri that were not based on cortical surface curvature features (such as subdivisions of the cingulate gyrus and the middle frontal gyrus) were retained if the subdivision was wholly within the surface curvature features that defined the gyrus.
The DKT protocol has 31 cortical regions per hemisphere, one less than the DK protocol. We have also created a variant of the DKT protocol with 25 cortical regions per hemisphere to combine regions that are subdivisions of a larger gyral formation and whose divisions are not based on sulcal landmarks or are formed by sulci that are highly variable. The regions we combined include subdivisions of the cingulate gyrus, the middle frontal gyrus, and the inferior frontal gyrus. Since fewer regions means larger regions that lead to higher overlap measures when registering images to each other, note that comparisons should be made using the same labeling protocol. We refer to these two variants as the DKT31 and DKT25 cortical labeling protocols.
Figure 1 shows cortical regions in the DKT labeling protocol. We retained the coloring scheme and naming conventions of Desikan et al. (2006 (link)) for ease of comparison. The Appendix contains detailed definitions of the regions but we summarize modifications to the original DK protocol in Table 2. Table 3 lists the names and abbreviations for the bounding sulci used by the DKT protocol; the locations of these sulci are demonstrated in Figure 2. Three regions were eliminated from the original DK protocol: the frontal and temporal poles and the banks of the superior temporal sulcus. The poles were eliminated because their boundaries were comprised primarily of segments that “jumped” across gyri rather than along sulci. By redistributing these regions to surrounding gyri we have increased the portion of region boundaries that along similar curvature values, that is, along sulci and gyri rather than across them, which improves automatic labeling and the reliability of manual edits. The banks of the superior temporal sulcus region was eliminated because its anterior and posterior definitions were unclear and it spanned a major sulcus.
Additional, more minor, modifications took the form of establishing distinct sulcal boundaries when they approximated a boundary in the original protocol that was not clearly defined. For instance, the lateral boundary of the middle temporal gyrus anterior to the inferior frontal sulcus was defined explicitly as the lateral H-shaped orbital sulcus and the frontomarginal sulcus more anteriorly. Similarly, the boundary between the superior parietal and the lateral occipital regions was assigned to the medial segment of the transverse occipital sulcus. Other examples include establishing the rhinal sulcus and the temporal incisure as the lateral and anterior borders of the entorhinal cortex, and adding the first segment of the caudal superior temporal sulcus (Petrides, 2011 ) as part of the posterior border of the supramarginal gyrus. Several popular atlases informed these modifications, including Ono et al. (1990 ), Damasio (2005 ), Duvernoy (1999 ), and Mai et al. (2008 ). The recent sulcus and gyrus atlas from Petrides (2011 ) proved particularly useful because of its exhaustive catalog of small but common sulci.
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Publication 2012
Conferences Cortex, Cerebral Crowns Entorhinal Area Frontal Sulcus Gyrus Cinguli Inferior Frontal Gyrus Medial Frontal Gyrus Middle Temporal Gyrus Occipital Lobe Occipital Sulcus Supramarginal Gyrus Temporal Lobe Temporal Sulcus

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Publication 2011
Cortex, Cerebral Medial Frontal Gyrus Neurofibrillary Tangle Parietal Cortex, Inferior Subiculum

Most recents protocols related to «Medial Frontal Gyrus»

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
T1 images were preprocessed using fslmaths and FreeSurfer software. DTI images were preprocessed using the FSL software. RsfMRI images were preprocessed using the SPM and BRANT software. Details of preprocessing could be found in our previously published study [37 (link)]. Briefly, T1 images for each subject were preprocessed using fslmaths command with a threshold of 80 for background noise reduction and using the FreeSurfer software package version 5.3.0 for brain extraction and segmentation [38 (link)]. DTI images were preprocessed using the FSL software FDT toolbox, including BET for brain mask generation, Eddy correct for correction of eddy current distortions, DTIFIT for head motion correction and reconstruction of diffusion tensors, and Bedpostx for local modeling of diffusion parameters [39 (link)]. Then, brain-extracted DTI images were registered with betted and non-betted T1 image of the same subject and Montreal Neurological Institute (MNI) standard space image using Registration module. The regions of interest (ROIs) were reoriented from FreeSurfer space to structural space and registered to the diffusion space using FLIRT, with nearest neighbor interpolation, and to the MNI standard space using FNIRT. The data of rsfMRI were preprocessed by SPM12 [40 (link)] and BRANT [41 (link)], following steps including slice timing correction, head motion correction, co-registration of segmented T1 image with the mean rsfMRI image, spatial normalization, spatial smoothing using Gaussian kernel with full-width at half maximum of 6 mm, regressing out linear trend, mean time series extracted from tissue masks and six head motion parameters, and temporal filtering using a 0.01–0.08 Hz band-pass filter.
After preprocessing, ten ROIs were obtained from T1 data for each subject, including bilateral caudate, putamen, hippocampus, PCC, and rostral middle frontal gyrus (rMFG), which likely represents DLPFC [42 (link)]. The volume (absolute volume) of each ROI was calculated from the FreeSurfer software automatically. Then, the relative volume was calculated as the percentage of absolute volume in intracranial volume, to correct the effect of difference in brain size among subjects. The DTI data were analyzed using the Probtrack (probabilistic tracking) module in the FSL software. Bilateral caudate, putamen, and hippocampus were set as seed ROIs separately, and bilateral PCC and rMFG were set as waypoints masks separately. At the end, six white matter tracts (fdt paths) were obtained, including bilateral caudate-rMFG, putamen-rMFG, and hippocampus-PCC tracts. Masks for each tract were generated with the threshold of 100. Diffusion parameters including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AxD), and radial diffusivity (RD) were measured using fslstats command. Fiber numbers were obtained from the waytotal output file. A seed-based approach was performed on rsfMRI data to calculate the functional connectivity (FC). Mean rsfMRI signals were extracted from each ROI separately by averaging the time courses signals of all voxels within the ROI. Pearson’s correlation coefficients (r values) were computed between caudate, putamen, and rMFG and between hippocampus and PCC and then transformed to z values to make it in accordance with Gaussian distribution. Z values of each pair of ROIs represent FC.
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Publication 2023
Anisotropy Brain Diffusion Dorsolateral Prefrontal Cortex Fibrosis Head Medial Frontal Gyrus Neostriatum Putamen Reconstructive Surgical Procedures Seahorses Tissues White Matter
Generalized psychophysiological interactions (gPPI; McLaren et al., 2012 (link)) were analyzed using the CONN toolbox (Version 20b; Whitfield-Gabrieli and Nieto-Castanon, 2012 (link)). Unlike correlational measures of functional connectivity, PPI models how the strength of coupling between a seed region and a voxel elsewhere in the brain differs across conditions. To this end, a GLM predicting the time series of the target voxel by the interaction regressor is constructed. The interaction regressor is created by multiplying the time series of the seed region with the task regressor for each condition (i.e., the condition onset times convolved with the HRF). The PPI analysis focused on effects of sustained control, as we had predicted greater age differences here.
The main effects of the two sustained control conditions (single and mixed blocks) and the effect of the nuisance regressors previously applied to the sustained GLM were regressed from the BOLD signal time series before estimating the interaction factor. Importantly, voxel-level time series were estimated using the smoothed data, while ROI level (i.e., seed) time series were estimated using unsmoothed data to prevent a “spillage” of the BOLD signal of voxels outside the ROI into the ROI time series. Interaction parameters were estimated separately for each condition, i.e., single and mixed blocks (McLaren et al., 2012 (link)).
Based on the prominent role of the left IFJ in task switching (Derrfuss et al., 2005 (link), Kim et al., 2012 (link), Richter and Yeung, 2014 ) and the fact that it showed activation in our univariate analysis, we selected it as a seed region for the PPI analysis. More specifically, a sphere of 6 mm radius was placed around the group peak coordinates of the mixed > single contrast of the sustained GLM.
We compared the difference in seed-to-whole brain connectivity for the IFJ for mixed vs. single blocks between children and adults. Analyses were performed on a gray-matter mask of the whole brain and corrected for multiple comparisons at the cluster level (p < .05 FDR-corrected, voxel threshold at p < .001 uncorrected). Beta estimates were extracted from clusters showing age differences in connectivity for the mixed > single contrast to visualize the results and to relate connectivity patterns to performance in the task. Additionally, the large cluster in the prefrontal cortex (PFC) was split into a medial and lateral part, as these have been associated with different aspects during task switching (Koechlin et al., 2000 (link)). More specifically, the medial PFC (mPFC) was defined by the superior frontal and paracingulate gyrus and the lateral PFC (lPFC) by the frontal pole and middle frontal gyrus of the Harvard-Oxford Atlas (Makris et al., 2006 (link)), thresholded at a probability of 30%.
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Publication 2023
Activation Analysis Adult Brain Child Gray Matter Medial Frontal Gyrus Prefrontal Cortex Radius Training Programs
Clinical and postmortem diagnoses, brain harvesting procedures, tissue processing, and assessment of AD pathology were performed and described in detail elsewhere (35 (link)). Deidentified brain tissues of the middle frontal gyrus (a region affected early by AD) (35 (link)) of clinically and neuropathologically characterized AD patients and controls were obtained from the Mount Sinai Brain Bank through the Neurobiobank (https://neurobiobank.nih.gov/). Three groups of decedents were included in this study: cognitively normal (n=11), mild cognitive impairment (MCI, n=10), and AD patients (n=11). SI Appendix, Tables S1 and S2 describe this cohort in detail. Cognitive status at death was ascertained using the clinical dementia rating (CDR) scale (61 (link)), which assesses cognitive and functional impairments associated with dementia and provides specific severity criteria for classifying subjects as nondemented (CDR = 0), questionably demented (CDR = 0.5), or with increasing levels of severity of dementia from CDR ≥ 1. Participants with an acute neurological condition such as stroke or traumatic brain injury were excluded.
Publication 2023
Brain Cerebrovascular Accident Cognition Medial Frontal Gyrus Nervous System Disorder Patients Presenile Dementia Tissues Traumatic Brain Injury
Paraffin-embedded brain tissue sections, from the middle frontal gyrus, were deparaffinized in xylene, rehydrated in graded ethanol, and washed with PBS containing 0.1% Triton X100 (PBS-T). Brain sections were boiled in 10 mM citric acid buffer (pH 6) for antigen retrieval and washed with PBS-T. The endogenous peroxidase activity of samples was quenched using 3% hydrogen peroxide solution for 60 min, followed by washing and incubation with Zyblack (Zytovision, BS-0002-8) for 30 min to reduce auto fluorescence. Endogenous biotin was blocked with the Biotin-Blocking Kit (Invitrogen, E21390). Staining was performed by multiplexing three Tyramide SuperBoost kits (Invitrogen, B40936, B40912, B40923). First, sections were blocked using blocking buffer for 60 min, then incubated with primary antibodies [biotinylated anti-OPN 1:50 (R&D systems, BAF1433), anti CD11c 1:150 (Novus, NBP2-44598) and anti Iba-1 1:500 (Wako, 019-19741)] at 4 °C overnight. After washing, sections were incubated with HRP-conjugated streptavidin for 60 min, washed and incubated with Alexa Fluor™ 647 Tyramide streptavidin reagent for 10 min, followed by reaction stop solution for 5 min. This was followed by superboost kits Alexa Fluor™ 488 tyramid anti-mouse and Alexa Fluor™ 555 tyramid anti-rabbit. Cell nuclei were marked with Hoechst, and slides were mounted with the Immu-Mount medium (Thermofisher Scientific).
Brain slices of control (n=5), MCI (n=9) and AD (n=8) were coded and 10 to 12 images were captured under a confocal microscope (Leica DMi8), under the same conditions of laser intensities, at three separate sequences; laser 408 nm at 5%, lasers 488 nm at 0.1%, laser 638 nm at 6.5%, and laser 552 nm at 3% exposures using magnification of X400. Triple-positive cells expressing Iba-1, CD11c, and OPN were counted, as well as total Iba-1-positive cells from each slice using ImageJ software (NIH). The percent of CD11c+OPN+ microglia (CD11c+OPN+Iba-1+) out of total Iba-1-positive cells was calculated for each slice, and an average was calculated for each brain sample. Blinded analysis was performed by an independent investigator.
Publication 2023
alexa fluor 488 Alexa Fluor 555 Alexa Fluor 647 Antibodies Antigens Biotin Brain Cardiac Arrest Cell Nucleus Cells Citric Acid Ethanol Fluorescence Medial Frontal Gyrus Microglia Microscopy, Confocal Mus Novus Paraffin Peroxidase Peroxide, Hydrogen Rabbits Streptavidin Tissues Triton X-100 Xylene

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More about "Medial Frontal Gyrus"

The Medial Frontal Gyrus (MFG) is a crucial region within the frontal lobe of the brain, located on the medial surface.
It plays a pivotal role in various cognitive and motor functions, such as decision-making, problem-solving, and executive control.
Researchers studying the MFG can leverage PubCompare.ai's AI-driven platform to optimize their research by locating the best protocols from literature, preprints, and patent sources.
This platform's comparison tools can enhance the reproducibility and accuracy of their studies, providing a seamless research experience.
The MFG, also known as the anterior cingulate cortex (ACC) or supplementary motor area (SMA), is responsible for a wide range of neurological processes.
These include conflict monitoring, error detection, reward-based learning, and the regulation of emotional responses.
Understanding the function of the MFG is crucial for researchers investigating conditions like attention-deficit/hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), and other neuropsychiatric disorders.
To facilitate their research on the MFG, scientists can leverage various experimental techniques and tools.
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Additionally, they may employ devices like the Tim Trio for magnetic resonance imaging (MRI) acquisition, the RLT buffer for RNA extraction, and the Infinium Global Screening Array for genetic analysis.
Other relevant tools include the Magneton Skyra MRI scanner, the MN1020 brain atlas, and the DC-Stimulator Plus for transcranial direct current stimulation (tDCS) studies.
By incorporating these techniques and resources, researchers can gain a deeper understanding of the MFG's structure, function, and its involvement in various neurological and cognitive processes.
PubCompare.ai's AI-driven platform can further enhance their research by providing access to the best protocols and facilitating the comparison of experimental findings, ultimately leading to more reproducible and accurate results.