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Supramarginal Gyrus

The supramarginal gyrus is a key cortical region involved in a variety of cognitive and sensory functions.
Located in the parietal lobe, it plays a crucial role in language processing, attention, and body awareness.
Researchers can explore the latest protocols and techniques for studying the supramarginal gyrus, including the latest findings from literature, preprints, and patents.
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Most cited protocols related to «Supramarginal Gyrus»

1
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
2
Meta-Analytic Mapping of Functional Connectivity
Functional connectivity analysis was performed by a meta-analysis of published functional imaging results. The concept behind mapping functional connectivity via meta-analysis originates from the notion that functional connectivity should represent the correlation of spatially removed neurophysiologic events, which implies that functionally connected regions should coactivate above chance in functional imaging studies.
This concept of meta-analytic connectivity modeling (MACM) was first used to investigate functional connectivity based on the frequency distributions of concurrent activation foci (Koski and Paus, 2000 (link)). Following the emergence of databases on functional neuroimaging results (Fox and Lancaster, 2002 (link); Laird et al., 2009a ), this approach was extended to provide voxelwise co-occurrence maps across the whole brain (Toro et al., 2008 (link)). The concept of MACM has then been integrated with the activation likelihood estimation (ALE) approach for quantitative meta-analysis (Turkeltaub et al., 2002 (link)) to yield functional connectivity maps of the human amygdala (Robinson et al., 2009 (link)). More recently, finally, the mapping of functional connectivity via coordinate-based meta-analysis has been validated by comparison to resting-state connectivity (Smith et al., 2009 (link)), showing very good concordance between both approaches.
Here, MACM was performed using the BrainMap database (www.brainmap.org), which contains a summary of the results for (at the time of analysis) ~6500 individual functional neuroimaging experiments. Given the high standardization of neuroimaging data reports and in particular the ubiquitous adherence to standard coordinate systems, the results reported in these studies can readily be compared to each other with respect to the location of significant activation. Using this broad pool of neuroimaging results, MACM can then be used to test for associations between activation probabilities of different areas. Importantly, this inference is performed independently of the applied paradigms or other experimental factors, but rather is solely based on the likelihood of observing activation in a target region [e.g., the premotor cortex (PMC)], given that activation is present within the seed area (e.g., OP 1 or OP 4). Results from such an analysis are therefore robust across many different experimental designs. Database-aided MACM that assesses the coactivation pattern of OP 1 and OP 4 as defined by their MPM representation across a large number of imaging studies should hence allow the delineation and comparison of their functional connectivity. However, functional connectivity per se only allows the delineation of interacting networks but not the causal influences therein. In practice, MACM was performed using the following approach. Studies causing activation within OP 1 or OP 4 were obtained through the BrainMap database. Criteria for retrieval were as follows: only fMRI and positron emission tomography studies in healthy subjects that reported functional mapping experiments containing a somatosensory or motor component were considered. Those investigating age, gender, disease, or drug effects were excluded. No further constraints (e.g., on acquisition and analysis details, experimental design, or stimulation procedures) were enforced. Hereby we tried to avoid any bias in the data, but rather pool across as many different studies as possible.
Experiments that activate OP 1 or OP 4 were identified by comparing the foci reported for each of the ~1500 eligible experiments (functional mapping experiments available at the time of analysis that contained a somatosensory or motor component) in the BrainMap database to the cytoarchitectonic location of these cortical fields in the same reference space. The experiments used for the analysis of the functional connectivity of OP 1 (S2) were defined by the fact that (following correction for coordinates reported according to the Talairach reference space) they featured at least one focus of activation within the volume of cortex histologically delineated as OP 1, but no activation within the histologically delineated volume of OP 4. Hereby, the experiments that activated OP 1 or OP 4 were objectively identified. That is, activation within our seed areas was assessed observer independently by comparing the coordinates reported for all studies within the BrainMap database to the anatomical location of cytoarchitectonically defined OP 1 and OP 4 within the same reference space, independent of how this activation was termed in the original publication. Hereby, we avoided any influence of the fact that various labels have been used for activation in the region, e.g., SII, parietal operculum, Brodmann’s area (BA) 43, BA 40, parietal cortex, or subcentral gyrus. Studies activating exclusively one of these two areas (either OP 1 or OP 4) were defined by at least one reported focus in the MPM representation of this area and the absence of any reported activation focus in the respective other area or, to increase specificity, a four voxel border zone between OP 1 and OP 4.
Given that OP 1 (S2) and OP 4 (PV) share a common border at which the face, hands, and feet are represented in either area, and acknowledging the fact that these two cortical fields are difficult to differentiate from each other functionally in nonhuman primates, the question evidently arises as to whether isolated activation in only one of these areas may be conceptually meaningful or most likely artificial. However, while S2 and PV tend to show concurrent activation in many experiments, there is already good evidence for differences in response properties between the various cortical fields on the parietal operculum of nonhuman primates (Robinson and Burton, 1980 (link); Hsiao et al., 1993 (link); Fitzgerald et al., 2004 (link), 2006a (link), 2006b (link)). Compared with electrophysiological experiments in monkeys, however, the range of tasks that may be assessed is considerably larger in human functional imaging experiments, including, in particular, experimental paradigms that investigate cognitive or affective influences on sensory-motor processing. It thus seems plausible that differences in response properties of opercular fields that have not yet been reported in monkeys may be unraveled in humans simply because the necessary paradigms are difficult to perform in animals. Moreover, differential response properties may manifest themselves as apparent shifts in somatotopic location in functional imaging data, in particular if differential contrasts between two conditions are considered. In this case, homogenous activation of both cortical fields by one condition may offset, leaving only an isolated peak of activation well within the cortical field that was more responsive to the other condition. This phenomenon, to which neurophysiologic mechanisms at the neuronal level may also contribute, has been discussed in great detail in a recent study by Burton et al. (2008b) (link). It is therefore very well conceivable that isolated activations within OP 1 or OP 4 are observed in human neuroimaging data despite their close proximity and the similarities in response characteristics.
It should be noted that the seeds representing OP 1 and OP 4, respectively, in the functional connectivity analysis were defined bilaterally. This approach was based on the observation that activation of the secondary somatosensory cortex is frequently bilateral, resulting in a much reduced and ultimately insufficient sample of studies reporting unilateral activation. These, however, would be required for a separate analysis of ipsilateral and contralateral connections.
Eickhoff S.B., Jbabdi S., Caspers S., Laird A.R., Fox P.T., Zilles K, & Behrens T.E. (2010). Anatomical and Functional Connectivity of Cytoarchitectonic Areas within the Human Parietal Operculum. The Journal of Neuroscience, 30(18), 6409-6421.
Publication 2010
Amygdaloid Body Animals Brain Mapping Brodmann Area 43 Cognition Contrast Media Cortex, Cerebral Face fMRI Foot Gender Healthy Volunteers Homo sapiens Microtubule-Associated Proteins Monkeys Neocortex Neurons Opercular Cortex Parietal Lobe Plant Embryos Positron-Emission Tomography Premotor Cortex Primates Somatosensory Cortex, Secondary Supramarginal Gyrus
3
Multimodal MRI Characterization of Alzheimer's

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Publication 2014
Angular Gyrus Brodmann Area 45 Cortex, Cerebral Cranium Entorhinal Area Inversion, Chromosome Lobe, Frontal MRI Scans Nerve Degeneration Occipital Lobe Parietal Lobe Pars Opercularis Poly(ADP-ribose) Polymerases Precuneus Seahorses Supramarginal Gyrus 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
Neuropsychological Correlates of Lesion-Induced Behavior
The RBT and the Beads Game were analyzed with mixed-model ANOVA, with group as a between-subjects factor and task measures (e.g. probability of winning) as within-subject factors. Greenhouse-Geisser correction was applied when homogeneity of variance was violated. Significant main effects of group were followed up by pair-wise comparisons using Fisher's least significant difference, which is suitable for post hoc testing in cases with 3 experimental groups (Cardinal and Aitken 2006 ). Effect sizes for pair-wise comparisons were computed using Cohen's d. As the data on the Probability Adjustment Task were noncontinuous, these were analyzed with nonparametric tests (Kruskal–Wallis tests). All statistical tests are reported 2-tailed and α was set at 0.05.
In a second step, significant effects of group were followed up by ROI analysis. The extent of damage within predefined ROIs was computed. ROIs were defined using the AAL template. For each vmPFC lesion patient, the volume of damage in the vmPFC as a whole, in the mOFC (AAL regions: Gyrus rectus and orbital parts of the middle and superior frontal gyri), and mPFC (AAL regions: anterior cingulum and medial superior frontal gyrus) subregions was calculated. For each pPAR lesion patient, the volume of damage in the pPAR as a whole, in the IPC (AAL regions: Inferior parietal lobe, angular gyrus, and supramarginal gyrus), and SPC (AAL regions: Superior parietal lobe and precuneus) subregions was calculated. Spearman's correlations were then calculated between these lesion volumes and the 3 behavioral indices on the RBT (final score, risk adjustment, and overall betting).
Studer B., Manes F., Humphreys G., Robbins T.W, & Clark L. (2013). Risk-Sensitive Decision-Making in Patients with Posterior Parietal and Ventromedial Prefrontal Cortex Injury. Cerebral Cortex (New York, NY), 25(1), 1-9.
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Publication 2013
Angular Gyrus factor A Gyrus Rectus Medial Frontal Gyrus neuro-oncological ventral antigen 2, human Parietal Lobe Patients Peroxisome Proliferator-Activated Receptors Precuneus Superior Frontal Gyrus Supramarginal Gyrus

Most recents protocols related to «Supramarginal Gyrus»

1
Preprocessing Pipeline for Resting-State fMRI Analysis
Data preprocessing was performed using the Data Processing Assistant for Resting-State fMRI (DPARSF) V4.5 Advanced Edition (State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, China), which is based on the Data Processing and Analysis of Brain Imaging (DPABI) Toolbox version 4.11 (Yan et al., 2016 (link)), with statistical parametrical mapping 12 (SPM 12; Wellcome Trust Center for Neuroimaging, University College London, London, UK) in Matlab 2015b (MathWorks, Inc., Natick, MA, USA). Based on experience in previous studies, the preprocessing of functional images was performed as follows: (1) slice timing correction; (2) realignment of images to the mean volume for correction of head motion; (3) co-registration to map functional information of resting fMRI images into an anatomical space (T1-weighted images) via intra-subject spatial alignment; and (4) segmentation of gray matter, white matter (WM) and cerebrospinal fluid (CSF) from coregistered T1 images using the unified segmentation model (Wu et al., 2016 (link)). Subjects with any instances of head movement exceeding 2 mm or 2° were excluded from further processing. The following nuisance variables were regressed: (1) six parameters of head movement calculated based on head motion with the Friston 24-parameter model translation and rotation during realignment in SPM12 (Friston et al., 1996 (link)); (2) the mean signal within the lateral ventricles for cerebral spinal fluid; and (3) the mean signal within a deep white matter region (centrum ovale). The images were normalized to the custom template from T1 weighted images of all subjects developed by the Montreal Neurological Institute (MNI) with resampled voxels at 2 mm × 2 mm × 2 mm. The resulting time series in each voxel was then linearly detrended and bandpass filtered (0.01–0.1 Hz) to extract low-frequency oscillations. Global signal regression (GSR) was not performed as it has been shown to exaggerate negative correlations (Murphy et al., 2009 (link); Weissenbacher et al., 2009 (link)) and/or to distort group differences (Saad et al., 2012 (link)). We used WFU Pick Atlas toolbox2 to generate a visual system template based on the modified human visual pathway model by Choi et al. (2020) (link). The visual system template includes the visual area [V1, V2, V3, V4, and V5/MT (BA 17, BA 18, and BA19)], inferior temporal area (BA 20), angular gyrus (BA 39), supramarginal gyrus (BA 40), and superior parietal lobule (BA 5, BA 7).
Hong T.Y., Yang C.J., Shih C.H., Fan S.F., Yeh T.C., Yu H.Y., Chen L.F, & Hsieh J.C. (2023). Enhanced intrinsic functional connectivity in the visual system of visual artist: Implications for creativity. Frontiers in Neuroscience, 17, 1114771.
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Publication 2023
Angular Gyrus Brain Cerebrospinal Fluid fMRI Gray Matter Head Head Movements Homo sapiens Superior Parietal Lobule Supramarginal Gyrus Ventricle, Lateral Visual Pathways White Matter
2
Resting-State Functional Connectivity of Salience Network
At age 23/24, participants underwent MRI of the brain using 3 T Siemens Prisma MRI scanner, which included a structural MRI T1-weighted sequence (voxel size 1 × 1 × 1 mm, 240 slices per slab, TR 2300 ms, TE 2.34 ms, TI 900 ms, and flip angle 8 degrees) and a 7-min closed-eyes resting state functional MRI (fMRI) (voxel size 3 × 3 × 3 mm, TR 2080 ms, TE 30 ms, flip angles 90 degrees, 39 slices, matrix 64 × 64, 200 measurements). Functional connectivity analysis was performed using CONN Functional Connectivity Toolbox and its default pre-processing pipeline70 (link). First, functional images were realigned, un-warped, and slice-timing corrected (interleaved bottom-up). Next, the images were co-registered with structural data and spatially normalized to the Montreal Neurological Institute (MNI) space.
Several steps have been undertaken to ensure that the functional connectivity is not impacted by head motion. Potential outlier scans due to subjects´ head movement were assessed using the ARtifact Detection Tools (ART)—based scrubbing. The threshold for potential identification of outliers was set at the 95th percentile in normative samples, a conservative setting for ART. The first-level covariates were realignment, quality assurance time series, the framewise displacement and scrubbing. Finally, the images were smoothed using a Gaussian kernel of 8 mm full width at half maximum and de-noised. Anatomical CompCor (see details71 (link)) was implemented, extracting a representative noise signal from white matter regions and cerebrospinal fluid, effectively removing outlier scans, using linear regression. Distribution of connectivity values and blood oxygenation level dependent (BOLD) time series were visually checked after de-noising, all participants passed the requirements. The data were band-pass filtered to 0.008 Hz–0.09 Hz.
Seed-to-voxel analysis assessed functional connectivity between the seeds of interest and the rest of the brain, the seeds having been selected based on Harvard–Oxford Structures Atlas. The seeds of interest were seven nodes of the salience network: anterior cingulate cortex (MNI 0; 22; 35); left anterior insula (− 44; 13; 1); right anterior insula (47; 14; 0); left rostral PFC (− 32; 45; 27); right rostral PFC (32; 46; 27); left supramarginal gyrus (− 60; − 39; 31) and right supramarginal gyrus (62; − 35; 32). Pearson’s correlation coefficients were calculated between the seed time course and the time course of all other voxels in the brain. Seed-to-voxel results are reported when significant at a voxel-wise threshold of level of p < 0.001 uncorrected and a cluster-level threshold of p < 0.05 corrected for FDR. The correlation coefficients were converted to normally distributed scores using Fisher’s transformation.
Cermakova P., Chlapečka A., Csajbók Z., Andrýsková L., Brázdil M, & Marečková K. (2023). Parental education, cognition and functional connectivity of the salience network. Scientific Reports, 13, 2761.
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Publication 2023
BLOOD Brain Cell Respiration Cerebrospinal Fluid Eye fMRI Gyrus, Anterior Cingulate Head Head Movements Insula of Reil prisma Radionuclide Imaging Semen Analysis Supramarginal Gyrus White Matter
3
Functional Connectivity Analysis of Brain Networks
Functional data was preprocessed using fMRIPrep30 (link); RRID:SCR_016216). Specifically, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) were estimated before any spatiotemporal filtering using mcflirt (FSL 5.0.9)36 (link). BOLD runs were slice-time corrected using 3dTshift from AFNI 201 602 0737 (link) (RRID:SCR_005927). The BOLD reference was then co-registered to the T1w reference using bbregister (FreeSurfer) which implements boundary-based registration38 . Co-registration was configured with six degrees of freedom. The BOLD time-series were resampled into standard MNI152NLin2009cAsym space. Several confounding time-series were calculated based on the preprocessed BOLD: Frame-wise displacement (FWD) was calculated from the six motion parameters and root-mean-square difference (RMSD) of the BOLD percentage signal in the consecutive volumes. Contaminated volumes were then detected and classified as outliers by the criteria FWD > 0.5 mm or RMSD > 0.3% and replaced with new volumes generated by linear interpolation of adjacent volumes. The three global signals are extracted within the cerebrospinal fluid (CSF), the white matter masks. A bandpass filter with cut-off frequencies of 0.01 and 0.09 Hz was used. Finally, the covariates corresponding to head motion (6 realignment parameters), outliers, and the BOLD time series from the subject-specific white matter and CSF masks were used in the connectivity analysis as predictors of no interest, and were removed from the BOLD functional time series using linear regression.
ROI-to-ROI connectivity analysis was performed in CONN toolbox using 11 CONN resting state network nodes composing 3 networks (Default Mode Network (DMN): medial pre-frontal cortex (MPFC), precuneus cortex (PCC), bilateral lateral parietal (LP); Salience Network (SN): anterior cingulate cortex (ACC), bilateral anterior insula (AI), rostral pre-frontal cortex (RPFC), and supramarginal gyrus (SMG); Fronto-parietal Network (FP): bilateral lateral pre-frontal cortex (LPFC) and posterior parietal cortex (PPC)39 (link). The mean BOLD time series was computed across all voxels within each ROI. Bivariate regression analyses were used to determine the linear association of the BOLD time series between each pair of regions for each subject. Both positive and negative correlations were examined. The resultant correlation coefficients were transformed into z-scores using Fisher’s transformation to satisfy normality assumptions. The within network-level FC was calculated as the average of the FCs within the networks of SN, DMN and FPN.
Kim H., Zhu X., Zhao Y., Bell S., Gehrman P., Cohen D., Devanand D., Goldberg T, & Lee S. (2023). Resting-State Functional Connectivity Changes in Older Adults with Sleep Disturbance and the Role of Amyloid Burden. Research Square.
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Publication Preprint 2023
Cerebrospinal Fluid Cranium Default Mode Network Gyrus, Anterior Cingulate Head Insula of Reil Lobe, Frontal Plant Roots Posterior Parietal Cortex Precuneus Reading Frames Supramarginal Gyrus White Matter
4
Lesion Mapping of Aphasia Types
We used three traditional mass univariate methods: voxel-, region-, and connectivity-based lesion symptom mapping (VLSM, RLSM, CSLM). Whole brain V- and RLSM was used to identify brain damage associated with aphasia type (anomic or Broca’s). VLSM shows the statistical likelihood that damage to a given voxel is associated with aphasia type group membership, where each voxel in each patient is binarily demarcated as either damaged or undamaged (Bates, Wilson, Saygin, & et al., 2003 (link)). RLSM differs from VLSM in that instead of using binary voxel-wise values, it uses the percent of voxels damaged within each ROI as the predictor of aphasia type. This sacrifices spatial specificity while providing the advantage of analyzing the effects of damage over an entire region without requiring overlapping damage at level of an individual voxel. We conducted RLSM using the AICHA ROIs. We then conducted CLSM (Gleichgerrcht, Fridriksson, Rorden, & Bonilha, 2017 (link)) using resting-state functional connectivity based on the AICHA atlas, including all left-to-left, left-to-right, and right-to-right connections in the analysis. Only voxels (or regions for RLSM) where at least 5 patients had damage were considered, based on the minimum overlap recommendation of 10% of the patient sample (Baldo, Ivanova, Herron, & et al., 2022 ). All tests were two-tailed, with α = 0.05, and significance was determined via permutation testing, where stability of p-value were tested in increments of 1000 permutations, ranging from 1,000 permutation to 10,000 permutations.
On top of whole-brain analysis, we also restricted the analysis to the ‘dorsal stream’ areas, i.e. frontoparietal and superior temporal areas that are involved in form-to-NBS articulation during speech (Fridriksson et al., 2016 ). These areas would be hypothesized to be especially disrupted in individuals with Broca’s aphasia who struggle with many aspects of speech production compared to the relatively mild anomic cases where the individuals just have occasional word-finding difficulties. We included the AICHA ROIs corresponding to supramarginal gyrus, primary sensory and motor cortices, inferior frontal gyrus (Broca’s area), superior temporal gyrus, and rolandic operculum. This allowed us to restrict the # of connections while also allowing us to use a one-tailed analysis since we specifically hypothesized these connections would be associated with Broca’s aphasia. It is worth noting that we also tried the alternate analysis, using a different set of language regions that might be implicated in anomic aphasia more than Broca’s, but this did not reveal any significant results. This is likely because anomic aphasia as a behavioral syndrome may be caused by deficits at various functional levels within the language production system (conceptual, lexical, semantic, phonological, for example), so that similar surface behavior may result from different patterns of neural damage. In addition, in our own sample anomic aphasia was ‘less severe’ than Broca’s aphasia, on average, which would also make the detection of areas specifically related to the anomic group more difficult.
Zhao X., Riccardi N., den Ouden D.B., Desai R.H., Fridriksson J, & Wang Y. (2023). Network-based statistics distinguish anomic and Broca aphasia. ArXiv.
Publication Preprint 2023
Anomia Aphasia Brain Brain Injuries Broca Aphasia Broca Area Inferior Frontal Gyrus Joints Motor Cortex Nervousness Opercular Cortex Patients Speech Superior Temporal Gyrus Supramarginal Gyrus Syndrome
5
Structural and Functional Brain Covariance in Healthy Controls
We were interested in how covariance within and between for major networks differed between V and NV. For that aim, the functional connectivity toolbox CONN was used (61 (link)). CONN’s standard network atlas was based on an independent component analysis of the functional resting state data of a large sample of healthy adults (61 (link), 62 (link)). Although variances in the brain structure are expected in healthy controls and patient groups, applying the atlas information based on healthy adults for the investigation of patient groups is considered valid because previous studies have shown differences in the DNM, SN, and FPN based on different whole brain nodes and seeds [for a meta-analysis see Koch et al. (63 (link))] suggesting robust group differences in these networks despite of potential structural differences. The atlas provides an established brain parcellation that divided the DMN, SN, DAN, and FPN into 19 spatially distinct network nodes, which were parts of the brain networks (Figure 1). The DMN covered the medial prefrontal cortex (MPFC), the bilateral lateral parietal cortex (LPCs), and the precuneus (PCUN). The SN included the anterior cingulate cortex (ACC) as well as the bilateral anterior insula (AIs), the rostral prefrontal cortex (RPFCs), and the supramarginal gyrus (SMGs). The DAN consisted bilaterally of frontal eye fields (FEFs) and the intraparietal sulci (IPSs). The FPN comprised both the right and left lateral prefrontal cortex (LPFCs) and the posterior parietal cortex (PPCs). The 19 investigated network nodes served as ROIs and were used to extract structural and functional brain information from individuals in the V and NV groups. For each participant, brain data was averaged across all voxels belonging to a particular ROI. This yielded individual average GM volumes, average GM density and average functional resting state time series for each ROI. The extracted GM volumes, densities and time series were z-standardized individually. This z-standardization mainly served two purposes in the following analyses: (i) to ensure the comparability of ROIs, and (ii) to enable the interpretation of covariance measures as correlation (= normalized covariance). To avoid potential confounding effects in the brain data, we accounted for sex, age, MINI diagnosis, antidepressants, and the number of other psychotropic drugs. In the structural analyses, we also accounted for total intracranial volume. Numerical confounds were z-standardized, while scale confounds were dummy encoded. Deconfounding on the group level was performed for time series in CONN, while for GM volumes and densities it was done with NiftiMapsMasker from nilearn package (64 ). The extracted network information served as input for the estimation of structural covariance and functional connectivity matrices in each group.
Shymanskaya A., Kohn N., Habel U, & Wagels L. (2023). Brain network changes in adult victims of violence. Frontiers in Psychiatry, 14, 1040861.
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Publication 2023
Adult Antidepressive Agents Brain Diagnosis Frontal Eye Fields Gyrus, Anterior Cingulate Insula of Reil Intraparietal Sulcus Parietal Lobe Patients Plant Embryos Posterior Parietal Cortex Precuneus Prefrontal Cortex Psychotropic Drugs Supramarginal Gyrus

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