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Inferior Frontal Gyrus

The Inferior Frontal Gyrus is a crucial brain region involved in a variety of cognitive functions, including language processing, inhibitory control, and decision-making.
Located in the frontal lobe, this gyrus plays a key role in the brain's executive control network.
Dysfunction in the Inferior Frontal Gyrus has been implicated in a range of neurological and psychiatric disorders, making it an important target for research and clinical investigation.
Understainding the role of this brain region can provide valuable insights into human cognition and behavior, and may lead to improved diagnostic and therapeutic interventions.

Most cited protocols related to «Inferior Frontal Gyrus»

1
Validating Modified ALE Meta-Analysis Approach
In order to assess the face validity of our modification to the ALE approach we also performed ALE meta-analyses on two simulated datasets. It should be noted that the “studies” included into these datasets do not correspond to any real data as published in the literature. Rather each study solely refers to a set of individual foci, i.e., MNI coordinates, which were generated in order to simulate situations occurring in meta-analyses.
The first simulated dataset consists of 25 studies. Each of these studies is supposed to having investigated 12 subjects in order to avoid confounding effects of different sample sizes. For every study, we set one focus on the inferior frontal gyrus corresponding to BA 44. Hence, this region is the location of the “true” activation, which is to be revealed by the meta-analyses. Furthermore, a single one out of the 25 studies also features an activation in the inferior parietal lobe (IPL). For this activation, however, 10 individual foci are given, corresponding to a situation where individual local maxima are listed in a very detailed fashion. This analysis aims at revealing the distinction between fixed- and random-effects analyses. Fixed-effects analyses as implemented in classical ALE assess the convergence between individual foci. It should therefore reveal a significant effect in the IPL because 10 foci cluster closely within this area. In contradistinction, this location should not become significant in a random-effects analysis, as all of these foci were reported in the same study and the object of inference is to reveal a convergence across studies. Both methods, however, should identify the clustering of activations in the inferior frontal gyrus.
The second dataset also consists of 25 studies, and again features a true convergence of the reported activations in BA 44. Out of these 25 studies, four are assumed to have investigated 30 subjects each. Due to the higher reliability resulting from such larger samples, these four foci all cluster very tightly around a presumed true location of the effect. The remaining 21 studies, however, only examined four subjects, i.e., had very small sample sizes. Consequently, the locations of the reported foci are simulated to be more variable (due to the larger influence of sampling effects). This analysis aims at testing the explicit variance model employed in the revised ALE approach. As outlined above, the between-subject variance enters the variance model scaled by the sample size resulting in smaller FWHMs for studies investigating larger samples. Consequently the latter studies should have increased localising power in the ALE meta-analysis. In the present simulation, we would hence expect that the results obtained from the revised ALE algorithm would be less influenced by the foci obtained from the smaller studies and, therefore, more confined to the location of the foci reported in the four larger studies.
To each of the individual studies in both simulated meta-analyses, 10 further foci are added, which were randomly (and independently across studies) allocated to grey matter voxels. In real datasets, these foci would correspond to activations evoked by other components of the respective tasks. In the context of these meta-analyses, however, they represent noise, as there is no convergence between them. Both datasets are then analysed using the original ALE algorithm and its revised version in the same manner as the experimental data described above.
Eickhoff S.B., Laird A.R., Grefkes C., Wang L.E., Zilles K, & Fox P.T. (2009). Coordinate‐based activation likelihood estimation meta‐analysis of neuroimaging data: A random‐effects approach based on empirical estimates of spatial uncertainty. Human Brain Mapping, 30(9), 2907-2926.
Publication 2009
Gray Matter Inferior Frontal Gyrus Parietal Lobe
2
Comprehensive Cortical Labeling Protocol
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.
Klein A, & Tourville J. (2012). 101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol. Frontiers in Neuroscience, 6, 171.
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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
3
Neural Correlates of Painting Recognition and Memory
Accuracies and reaction times were computed for each subject, category of paintings (portraits, landscapes, abstract paintings) and response type (Yes/No during the flower detection task; Remember/Know/New during the memory retrieval test). ANOVA was used to compare the various conditions.
Functional MRI data were analyzed in BrainVoyager QX Version 1.8 (Brain Innovation, Maastricht, The Netherlands). All volumes were realigned to the first volume, corrected for motion artefacts and spatially smoothed using a 5-mm FWHM Gaussian filter. The main effects during the study and test were analyzed using multiple regression (Friston et al., 1995 (link)). Based on the contrast of paintings vs. fixation, a set of ROIs was anatomically defined for each subject with clusters that showed a significant effect (p < 0.0001, uncorrected). These regions included the inferior occipital gyrus (IOG), fusiform gyrus (FG), dorsal occipital cortex (DOC), intraparietal sulcus (IPS), inferior frontal gyrus (IFG), insula and the anterior cingulate cortex (ACC). The contrasts of Remember vs. Know and Remember vs. New further revealed significant activation in the precuneus and in two medial temporal lobe structures, the parahippocampal cortex (PHC) and the hippocampus. In each subject and each ROI, the mean parameter estimates were calculated separately for each response type (Yes/No during flower detection task; Remember/Know/New during memory test) and were used for between-subjects random-effects analyses.
Finally, we tested whether reaction times and fMRI activation during the study phase could predict subsequent behavioral and neural responses to the old paintings during the test phase. Thus, responses during the flower detection task were sorted based on subsequent Remember and Know judgments subjects made during the retrieval test.
Wiesmann M, & Ishai A. (2008). Recollection- and Familiarity-Based Decisions Reflect Memory Strength. Frontiers in Systems Neuroscience, 2, 1.
Publication 2008
Brain Contrast Media Cortex, Cerebral fMRI Gyrus, Anterior Cingulate Inferior Frontal Gyrus Insula of Reil Intraparietal Sulcus Memory Nervousness neuro-oncological ventral antigen 2, human Occipital Gyrus Occipital Lobe Occipitotemporal Gyrus, Lateral Precuneus Seahorses Temporal Lobe
4
Automated Cortical Thickness Estimation in Chimpanzees
The MRI scans were processed in a pipeline that required several steps including a) skull stripping using FSL (Smith et al., 2004 ), diffeomorphic registration to a symmetric population-specific template chimpanzee brain (Avants et al., 2011b (link)) and subsequent probabilistic segmentation of the T1-weighted images into grey matter, white matter and CSF following procedures and software that have been described in detail elsewhere (Avants et al., 2008 (link); Avants et al., 2010a (link)).
After brain extraction, we used the openly available Advanced Normalization Tools (ANTS) (http://www.picsl.upenn.edu/ANTS/) to perform multivariate normalization and structure-specific processing of our data (Avants et al., 2008 (link); Klein et al., 2009 ). ANTs encodes current best practices in image registration, optimal template construction and segmentation and is scalable to large-scale, distributed computing environments. ANTs cross-sectional studies deform each individual dataset into a standard local template space and/or a canonical stereotactic coordinate system. The core processing maps T1 structural MRI to an optimal template space, which is defined as the population-specific, unbiased average shape and appearance image derived from a representative population (Avants and Gee, 2004 ; Avants et al., 2010b (link)). The average template was constructed to optimally represent both the original and a flipped version of the dataset such that the final template is symmetric about the midsagittal plane. The coordinate deformations themselves are smooth and invertible, that is, diffeomorphic – neuroanatomical neighbors remain neighbors under the mapping. At the same time, the algorithms used to create these deformations are biased towards the reference space chosen to compute the mappings. Moreover, these topology-preserving maps capture the large deformation necessary to aggregate populations of images in a common space. Recent evaluation studies suggest that ANTs-based normalization is currently the most stable and reliable method available. After defining the template image to target image coordinate transformation, we employ template-based priors and N4 inhomogeneity filed correction to accurately segment cortical gray matter segmentation and perform cortical parcellation (Das et al., 2009 (link)). After these initial processing steps, the diffeomorphic registration-based cortical thickness (DiReCT) method is used to compute cortical thickness (Das et al., 2009 (link)).
DiReCT uses the segmentation probability images to compute a continuous voxel-wise estimate of cortical thickness (Das et al., 2009 (link)). DiReCT is unique in that it exploits tissue segmentation probability maps to identify a maximum likelihood correspondence between the white matter surface and the outer gray matter surface where the correspondence mapping is constrained to be spatially regular, differentiable and invertible, i.e. diffeomorphic. As a consequence, DiReCT thickness estimates incorporate both shape constraints and subtle probabilistic information about the likely position of sulci that may not be visible in a hard segmentation. Thus, DiReCT is a robust image-based technique for identifying voxel-wise and regional thickness information. An independent implementation and validation of this method showed that it is competitive with Freesurfer, a commonly used program for estimating cortical thickness (Clarkson et al., 2011 (link)). We note, however, that the implementation used by Clarkson et al. (2011) (link) was never itself directly compared to that available in the ANTs toolkit and we suspect that differences between the Clarkson et al implementation and our own gold standard implementation may exist.
As has been done in some studies of cortical thickness in humans and monkeys (Styner et al., 2007 ; Van Essen et al., in press ), we used a seed or region-of-interest approach to quantify GM thickness in 12 select areas of interest (see Table 1) including the a) dorsal, mesial, and orbital prefrontal cortex b) superior, middle and inferior temporal gyri c) pre- and post-central gyri d) inferior frontal gyrus e) posterior superior temporal gyrus f) supramarginal gyrus and g) superior parietal lobe. The landmarks used to define each region of interest are provided below and they were selected for theoretical and pragmatic reasons. Specifically, there has been considerable comparative interest in the evolution of the prefrontal, temporal and parietal cortex (Deacon, 1997 ; Schoenemann et al., 2005 (link); Rilling, 2006 ; Sherwood et al., 2012 ) and therefore we focused on these regions. Similarly, there has been significant interest in the evolution of cortical organization and lateralization in the homologs to Broca’s and Wernicke’s area in chimpanzees (Cantalupo and Hopkins, 2001 (link); Keller et al., 2009 (link); Hopkins and Nir, 2010 (link); Schenker et al., 2010 (link); Spocter et al., 2010 ) and this was our reasoning for quantifying the inferior frontal gyrus and posterior superior temporal gyri. Finally, we also included the pre- and post-central gyri as regions of interest because they are primary motor and sensory cortex and we hypothesized that cortical thickness would be lower in these regions compared to the others. The region of interest masks were drawn on the chimpanzee template brain using a mouse-controlled pointer and then transformed back to the individual GM thickness maps for each subject, using the inverse matrix of the original registration. The masks were then applied to the individual subjects’ GM thickness map to derive average thickness measures for each of the 12 regions within each hemisphere. We also computed an average cortical thickness measure for each hemisphere. This was done by drawing a mask that covered the entire left or right hemisphere, excluding the brain stem and cerebellum. We note that, because the template is symmetric, the masks only had to be drawn on one hemisphere of the template brain and could then be transformed to label both left and right sides of the individual subject’s brain.
Hopkins W.D, & Avants B.B. (2013). Regional and Hemispheric Variation in Cortical Thickness in Chimpanzees (Pan troglodytes). The Journal of Neuroscience, 33(12), 5241-5248.
Publication 2013
Ants Biological Evolution Brain Brain Stem Cerebellum Cerebral Hemispheres Cortex, Cerebral Cranium Deacons Gold Gray Matter Homo sapiens Inferior Frontal Gyrus Inferior Temporal Gyrus Left Posterior Superior Temporal Gyrus Mice, House Microtubule-Associated Proteins Monkeys MRI Scans Orbitofrontal Cortex Pan troglodytes Parietal Lobe Population Group Postcentral Gyrus Supramarginal Gyrus Tissues Wernicke Area White Matter
5
Dynamic Causal Modeling of Mismatch Negativity Responses

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Publication 2008
Auditory Perception Brain Cerebrospinal Fluid Cortex, Cerebral Cranium Electric Conductivity Electromagnetics fMRI Head Inferior Frontal Gyrus Mental Orientation Primary Auditory Cortex Scalp Superior Temporal Gyrus

Most recents protocols related to «Inferior Frontal Gyrus»

1
Dual-Brain Hyperscanning Using fNIRS
Optical imaging was performed using a continuous-wave CW6 NIRS system (TechEn, Milford, MA) at a sampling rate of 20 Hz. The data were measured simultaneously at two wavelengths, 690  and 830 nm. Light intensity was automatically adjusted by the system to provide optimal gain. Fiber optics were split between two caps worn by both members of the dyad, which allowed for simultaneous measurement of mother and child brain activities (i.e. hyperscanning). A total of eight channels were measured from four sources and seven detectors for the mother. A total of 12 channels were measured from six sources and nine detectors for the child. The child probe featured more channels due to the larger project’s focus on longitudinal changes in child neural response to reward-related stimuli. The distance between each source and detector was 3 cm. Sensors were mounted on a neoprene cap sized based on head circumference. The probe extended over the inferior frontal gyrus, including the anterior medial PFC (Brodmann Area 10) to the parietal regions for both mother and child (Whiteman et al., 2017 ). For each participant, the NIRS cap was positioned according to the international 10–20 coordinate system at the center of the lower edge of the probe (detector 5 for child, detector 13 for mother) aligned with FpZ.
Morgan J.K., Santosa H., Conner K.K., Fridley R.M., Forbes E.E., Iyengar S., Joseph H.M, & Huppert T.J. (2023). Mother–child neural synchronization is time linked to mother–child positive affective state matching. Social Cognitive and Affective Neuroscience, 18(1), nsad001.
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Publication 2023
Anterior Prefrontal Cortex Brain Child Head Inferior Frontal Gyrus Light Mothers Neoprene Nervousness Parietal Lobe Spectroscopy, Near-Infrared
2
Preprocessing and Analysis of Resting-State fMRI
Preprocessing and analysis of resting-state functional MRI data was carried out using the CONN toolbox [44 (link)] and SPM12 (https://www.fil.ion.ucl.ac.uk/spm/). Preprocessing was performed according to CONN’s default preprocessing pipeline. Following coregistration, T1-weighted structural images were segmented and normalized to a template in Montreal Neurological Institute (MNI) space with a 1 x 1 x 1 mm resolution. T2*-weighted functional images were spatially realigned after coregistration and normalized to a MNI template with a 2 x 2 x 2 mm resolution using direct spatial segmentation and normalization. Functional scans were spatially smoothed using a 6 mm full-width half-maximum (FWHM) Gaussian filter. Outliers were defined using the artifact detection tool implemented in CONN. Here, outliers are identified using the observed BOLD signal and subject-motion in the scanner. Outliers were defined as a framewise displacement of at least 0.9 mm or a change of the global BOLD signal above five standard deviations. The framewise displacement is calculated based on 140 x 180x 115mm bounding box around the brain and estimating the largest displacement among six control points. The global BOLD signal change is defined as a change in the average BOLD signal within SPM’s global-mean mask scaled to standard deviation units. Framewise displacement as well as the global BOLD signal are computed at each timepoint. To deal with outliers, a variable number of noise components (one for each of the identified outlier) is used as potential confounding effect to account for any influence of the previously identified outlier scans on the BOLD signal [45 ]. Further denoising steps were applied to functional scans, including high-pass temporal filtering and linear detrending, removal of physiological confounds using the aCompCor method [46 (link)]. We also performed mainly two quality assurance checks to judge reliability of the imaging data. First, we checked, if the normalization process of structural and functional data was successful by generating figures of the normalized and realigned images (for each subject separately, but also averaged across all subjects). Secondly, we used quality assurance reports generated in CONN to examine the following parameters: the number of valid and invalid scans, the maximum, and mean extent of motion, but also the maximum and mean change of the global BOLD signal for every subject and the total sample.
Seed-based whole-brain correlations (Fisher-transformed bivariate Pearson correlation coefficient) were calculated in CONN using seed regions of interest (ROIs) from the Harvard-Oxford atlas. Based on the results of Maier et al. (2016), the following seeds were included in the analysis: Inferior frontal gyrus left and right, medial frontal gyrus left and right, superior frontal gyrus left and right, precentral gyrus left and right, insular cortex left and right, anterior and posterior cingulate cortex [7 (link)]. Significant associations between each ISAm score (total, hypokinesia and levodopa-induced dyskinesia) and functional connectivity between a respective seed ROI and every other voxel were evaluated using a general linear model in CONN. Age, gender, LEDD, and UPDRS-III scores were included as covariates in each analysis. All results of these multiple regression analyses were corrected for multiple comparisons using family-wise error (FWE) correction at cluster level.
Buchwitz T.M., Ruppert-Junck M.C., Greuel A., Maier F., Thieken F., Jakobs V, & Eggers C. (2023). Exploring impaired self-awareness of motor symptoms in Parkinson’s disease: Resting-state fMRI correlates and the connection to mindfulness. PLOS ONE, 18(2), e0279722.
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Publication 2023
Brain Dyskinesias fMRI Gender Inferior Frontal Gyrus Insula of Reil Levodopa Medial Frontal Gyrus physiology Posterior Cingulate Cortex Precentral Gyrus Radionuclide Imaging Strains Superior Frontal Gyrus
3
Analysis of TREM2 Splice Transcripts in Alzheimer's Disease
The generation of RNA-Seq data in the Accelerating Medicines Partnership for Alzheimer’s Disease Consortium (AMP-AD) and demographic information has been previously described in detail [33 (link)]. Briefly, RNA-Seq data were downloaded from the (AMP-AD) through the Synapse database (https://www.synapse.org/): the Mayo Clinic Brain Bank (Mayo Clinic) [34 (link)], the Mount Sinai Medical Center Brain Bank (MSBB) [35 ], and the Religious Orders Study and Memory and Aging Project (ROS/MAP) cohorts [36 (link)].
In the Mayo Clinic, RNA-Seq data were generated from the temporal cortex and cerebellum. In the MSBB, RNA-Seq data were generated from the parahippocampal gyrus, inferior frontal gyrus, superior temporal gyrus, and frontal pole. In ROSMAP, RNA-Seq data were generated from the dorsolateral prefrontal cortices. The procedures for sample collection, post-mortem sample descriptions, tissue and RNA preparation methods, library preparation and sequencing methods, and sample quality controls were previously described in detail [34 (link)–39 (link)]. We converted each mapped BAM file into a FASTQ file using samtools v.1.9 and then re-mapped the converted FASTQ files onto the hg19 human reference genome using STAR aligner v.2.5, as previously described in detail [40 (link)]. Using the processed RNA-Seq data, we identified TREM2 splice transcripts and calculated their expression levels. We used the software tool RSEM to accurately estimate the TREM2 transcripts expressions from RNA-Seq [41 (link)]. RSEM generates three different TREM2 transcript sequence references, and RNA-Seq reads are mapped to them. After the alignment of reads, RSEM uses a statistical model to accurately calculate transcript abundances by estimating a maximum likelihood (ML) based on expectation-maximization (EM) algorithm. Additionally, by utilizing paired-end reads to classify the different isoforms, RSEM improves the estimation of the relative isoform levels within single genes. Based on RSEM’s statistical model and additional benefits, it accurately estimates transcript abundances from reads mapped to distinct and shared regions among the three isoforms. Differential expression analysis of the TREM2 splice transcripts between cognitively normal controls and AD patients was done using a generalized linear regression model [33 (link)]. The regression was performed with the “glm” function of the stats package in R (version 3.6.1). Age and sex were used as covariates. We used the false discovery rate (FDR) to correct for multiple testing.
Moutinho M., Coronel I., Tsai A.P., Di Prisco G.V., Pennington T., Atwood B.K., Puntambekar S.S., Smith D.C., Martinez P., Han S., Lee Y., Lasagna-Reeves C.A., Lamb B.T., Bissel S.J., Nho K, & Landreth G.E. (2023). TREM2 splice isoforms generate soluble TREM2 species that disrupt long-term potentiation. Genome Medicine, 15, 11.
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Publication 2023
Alzheimer's Disease Autopsy Brain cDNA Library Cerebellum Dorsolateral Prefrontal Cortex Gene Expression Profiling Genes Genome, Human Inferior Frontal Gyrus Memory Parahippocampal Gyrus Patients Pharmaceutical Preparations Protein Isoforms RNA-Seq Specimen Collection Superior Temporal Gyrus Synapses Temporal Lobe Tissues Transcription, Genetic TREM2 protein, human
4
ABCD Neuroimaging Protocol for LPFC
The lateral prefrontal cortex (LPFC) variables were derived from the ABCD structural magnetic resonance imaging (sMRI) module.26 The ABCD study consortium conducted and preprocessed all neuroimaging data. Morphologic features (eg, volume and thickness) of the LPFC and its subregions (eg, lateral orbitofrontal cortex [LOFC], middle frontal gyrus [MFG], and inferior frontal gyrus [IFG]) were estimated using Freesurfer version 5.3.0 (Freesurfer).27 (link) Brain imaging was performed at wave 1 and wave 3 data collection. The methods of the brain imaging protocol are published elsewhere.28 (link)
Sakib M.N., Best J.R, & Hall P.A. (2023). Bidirectional Associations Between Adiposity and Cognitive Function and Mediation by Brain Morphology in the ABCD Study. JAMA Network Open, 6(2), e2255631.
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Publication 2023
Brain Inferior Frontal Gyrus Lateral Orbitofrontal Cortex Medial Frontal Gyrus Prefrontal Cortex
5
Glioma Surgery in Broca's Area
This study included sixteen patients, 6 men and 10 women aged 28–69 (mean 52.69 ± 12.7 years, all right-handed), with brain glioma located in areas surrounding classic Broca’s area, inferior frontal gyrus and ventral precentral gyrus. All surgeries were performed by the same surgeon, who commonly finishes about 150 glioma surgeries every year in his department. All of the subjects provided written informed consent. All of the procedures in this study were approved by the ethics committee of Xijing Hospital and conducted under the guidelines of the Declaration of Helsinki.
Li S., Mu Y., Rao Y., Sun C., Li X., Liu H., Yu X., Yan X., Ding Y., Wang Y, & Fei Z. (2023). Preoperative individual-target transcranial magnetic stimulation demonstrates an effect comparable to intraoperative direct electrical stimulation in language-eloquent glioma mapping and improves postsurgical outcome: A retrospective fiber-tracking and electromagnetic simulation study. Frontiers in Oncology, 13, 1089787.
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Publication 2023
Brain Broca Area Ethics Committees, Clinical Glioma Inferior Frontal Gyrus Operative Surgical Procedures Patients Precentral Gyrus Surgeons Woman

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More about "Inferior Frontal Gyrus"

The Inferior Frontal Gyrus (IFG) is a crucial brain region involved in a variety of cognitive functions, including language processing, inhibitory control, and decision-making.
Situated in the frontal lobe, this gyrus plays a key role in the brain's executive control network.
Dysfunction in the IFG has been implicated in a range of neurological and psychiatric disorders, making it an important target for research and clinical investigation.
Understanding the role of the IFG can provide valuable insights into human cognition and behavior, and may lead to improved diagnostic and therapeutic interventions.
Researchers often utilize advanced neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS) using the LABNIRS or NIRScout systems, or magnetic resonance imaging (MRI) with the MR750 3.0 Tesla scanner, to study the IFG and its involvement in various cognitive processes.
Additionally, genetic and molecular analyses, including RNA sequencing with tools like the HiSeq 2500 system and the TruSeq RNA Sample Preparation Kit v2 or the Ribo-Zero rRNA Removal Kit (Human/Mouse/Rat), can shed light on the underlying mechanisms and potential biomarkers associated with IFG dysfunction.
Computational modeling and analysis using software like MATLAB (e.g., MATLAB 2018b) can also contribute to our understanding of the IFG's role in complex cognitive functions.
By exploring the Inferior Frontal Gyurs and its connections to various neurological and psychiatric disorders, researchers can work towards developing more effective diagnostic tools and targeted therapeutic interventions, ultimately improving patient outcomes and advancing our knowledge of the human brain.