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Retrosplenial Cortex

The Retrosplenial Cortex is a region of the cerebral cortex located in the posterior part of the brain.
It plays a key role in spatial navigation, memory, and other cognitive functions.
This cortical area recieves input from the hippocampus and is thought to be involved in integrating spatial and contextual information.
Researchers studying the Retrosplenial Cortex can utilize PubCompare.ai's AI-driven protocol optimization to identify the most reproducible and accurate research methods from scientific literature, preprints, and patents.
This can help locate the best protocols and products to improve research outcomes through data-driven decision making.

Most cited protocols related to «Retrosplenial Cortex»

1
Functional Connectivity Analysis of Optical Imaging
Since previous functional connectivity studies [4] (link) with diffuse optical tomography showed similar maps using either HbO2, or HbR contrast, here we used only ΔHbO2 data for the connectivity analyses. Data were filtered to the functional connectivity band (0.009–0.08 Hz) following previous human functional connectivity algorithms [35] (link). While one might expect the frequencies involved in functional connectivity to scale with the size of the animal (as does heart and respiratory rate), studies of fcMRI in rat have used the same frequencies as found in humans [13] (link), [16] (link), [64] (link). Our results also demonstrate that low frequency fluctuations in mice predominately exist below 0.1 Hz. A representative power spectrum for a pixel's time trace before and after processing is shown in Fig. S9. After filtering, each pixel's time series was resampled from 30 Hz to 1 Hz for further analysis. The time traces of all pixels defined as brain were averaged to create a global brain signal. This global signal was regressed from every pixel's time trace to remove global sources of variance.
Using the atlas as a reference, seed locations were chosen at coordinates expected to correspond to the left and right visual, motor, somatosensory, frontal, cingulate, and retrosplenial cortices as well as the right and left superior colliculi and olfactory bulbs. A 0.5 mm diameter circle at each seed location was averaged to create a seed time trace. These seed traces were correlated against every other brain pixel to create functional connectivity maps. Because seed-based methods are dependent on the seed location, we also used seed-independent methods for determining connectivity patterns. The time traces in every pixel were correlated against every other pixel to create an N×N connectivity matrix (where N is the number of pixels defined as brain). This matrix contains all the functional connectivity information that could be gained from seed-based analysis, but has too much data to examine all at once. Taking the SVD of this matrix will yield an ordered set of orthogonal singular vectors that represent the spatial connectivity patterns. The associated singular values indicate the extent to which a particular singular vector contributes to the total variance in the data. The first few singular vectors thus demonstrate the most dominant connectivity patterns.
White B.R., Bauer A.Q., Snyder A.Z., Schlaggar B.L., Lee J.M, & Culver J.P. (2011). Imaging of Functional Connectivity in the Mouse Brain. PLoS ONE, 6(1), e16322.
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Publication 2011
Animal Scales Brain Cloning Vectors Heart Homo sapiens Maritally Unattached Mice, Laboratory Microtubule-Associated Proteins Olfactory Bulb Oxyhemoglobin Respiratory Rate Retrosplenial Cortex Tectum, Optic Tomography, Optical
2
Analyzing Widefield and Two-Photon Imaging Data
To analyze widefield data, we used SVD to compute the 200 highest-variance dimensions. These dimensions accounted for at least 90% of the total variance in the data. Using 500 dimensions accounted for little additional variance (~0.15%), indicating that additional dimensions were mostly capturing recording noise. SVD returns ‘spatial components’ U (of size pixels x components), ‘temporal components’ VT (of size components x frames) and singular values S (of size components x components) to scale components to match the original data. To reduce computational cost, all subsequent analysis was performed on the product SVT. SVT was high-pass filtered above 0.1Hz using a zero-phase, second-order Butterworth filter. Results of analyses on SVT were later multiplied with U, to recover results for the original pixel space. All widefield data was rigidly aligned to the Allen Common Coordinate Framework v3, using four anatomical landmarks: the left, center, and right points where anterior cortex meets the olfactory bulbs and the medial point at the base of retrosplenial cortex.
To analyze two-photon data we used Suite2P54 with model-based background subtraction. The algorithm was used to perform rigid motion correction on the image stack, identify neurons, extract their fluorescence, and correct for neuropil contamination. ΔF/F traces were produced using the method of Jia et al.55 , skipping the final filtering step. Using these traces, we produced a matrix of size neurons x time, and treated this similarly to SVT above. Finally, we confirmed imaging stability by examining the average firing rate of neurons over trials. If all neurons varied substantially at the beginning or end of a session, trials containing the unstable portion were discarded. Recording sessions yielded 188.63±19.28 neurons and contained 378.56±9 performed trials (mean±SEM).
As an image motion control, we also used the XY-translation values from the initial motion correction to ask whether motion of the imaging plane (due to imperfect registration or Z-motion) could contribute to our two-photon results. To account for this possibility, we removed all frames that were translated by more than 2 pixels in the X or Y direction and repeated the analysis (Extended Data Figure 10). In 4983 neurons, we observed negative unique contributions from the task-group, indicative of model overfitting when removing too many data frames. We therefore rejected these neurons from further analysis. For the remaining 8787 neurons, explained variance was highly similar as in our original findings, demonstrating that our results could not be explained by motion of the imaging plane.
To compute trial-averages, imaging data were double-aligned to the time when animals initiated a trial and to the stimulus onset. After alignment, single trials consisted of 1.8 s of baseline, 0.83 s of handle touch and 3.3 s following stimulus onset. The randomized additional interval between initiation and stimulus onset (0 - 0.25 s) was discarded in each trial and the resulting trials of equal length were averaged together.
Musall S., Kaufman M.T., Juavinett A.L., Gluf S, & Churchland A.K. (2019). Single-trial neural dynamics are dominated by richly varied movements. Nature neuroscience, 22(10), 1677-1686.
Publication 2019
Anatomic Landmarks Animals Cortex, Cerebral Fluorescence Muscle Rigidity Neurons Neuropil Olfactory Bulb Reading Frames Retrosplenial Cortex Strains Sulfur Touch
3
PiB PET Imaging Protocol for Amyloid Burden Quantification
Amyloid burden was measured with N-methyl-[11C]-2-(4-methylaminophenyl)-6-hydroxybenzothiazole (Pittsburgh Compound B; PiB), which binds to fibrillar amyloid, and was prepared at Massachusetts General Hospital as described previously (Klunk et al., 2004 (link); Mathis et al., 2003 (link)). Participants underwent PiB PET imaging as described previously (Gomperts et al., 2008 (link); Hedden et al., 2009 (link), 2012 (link); Sperling et al., 2009 (link)). Briefly, data were acquired using a Siemens/CTI ECAT HR+ scanner (3-D mode; 63 image planes; 15.2cm axial field of view; 5.6 mm transaxial resolution and 2.4mm slice interval; 39 frames: 8x15s, 4x60s, 27x120s). After a transmission scan, 8.5 to 15mCi [11C]-PiB was injected as a bolus and followed immediately by a 60-minute dynamic acquisition. PET data were reconstructed and attenuation corrected, and each frame was evaluated to verify adequate count statistics and absence of head motion (inter-frame head motion, if present, was corrected prior to further processing).
The dynamic PET data were reconstructed with scatter correction using commercially available routines for 3-D PET data. The average of the initial 0–8 minutes of dynamic PET data was spatially normalized to a PET MNI template using SPM8. PET data were parameterized by the distribution volume ratio (DVR) computed using the Logan graphical analysis technique (Logan et al., 1990 (link)) applied to the frame data acquired 40–60 minutes after injection; this method has been fully validated for PiB imaging (Price et al., 2005 (link)). Time-activity curves were measured in each brain region under analysis (region of interest or voxel) and in a reference region in cerebellar cortex known to contain low levels of fibrillar amyloid. This approach has been applied to numerous PiB studies (e.g., Fagan et al., 2006 (link); Johnson et al., 2007 (link); Lopresti et al., 2005 (link); Price et al., 2005 (link)) and yields data that are similar to arterial blood input methods (Lopresti et al., 2005 (link)).
For each subject an index of PiB binding in cortical regions was calculated using the dynamic data via Logan graphical modeling within a large aggregate cortical region of interest (ROI) consisting of frontal, lateral parietal and temporal, and retrosplenial cortices (the FLR region). PiB retention in the FLR region is substantial in patients with diagnosed AD and has been used as a summary measure of PiB retention in previous studies (Johnson et al., 2007 (link); Gomperts et al., 2008 (link); Hedden et al., 2009 (link), 2012 (link)). FLR DVR was log-transformed because of the non-normal distribution of PiB values and treated as a continuous variable in all analyses. Despite this transformation, non-normality of the distribution remains evident and individuals with very high values of PiB may have a disproportionate influence on the results.
Hedden T., Mormino E.C., Amariglio R.E., Younger A.P., Schultz A.P., Becker J.A., Buckner R.L., Johnson K.A., Sperling R.A, & Rentz D.M. (2012). Cognitive Profile of Amyloid Burden and White Matter Hyperintensities in Cognitively Normal Older Adults. The Journal of Neuroscience, 32(46), 16233-16242.
Publication 2012
6-hydroxybenzothiazole Amyloid Fibrils Amyloid Proteins Arteries BLOOD Brain Cortex, Cerebellar Cortex, Cerebral Head Patients Pittsburgh compound B Radionuclide Imaging Reading Frames Retention (Psychology) Retrosplenial Cortex Transmission, Communicable Disease
4
Wide-field Calcium Imaging in Mice

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Publication 2018
C & B Metabond Calcium, Dietary Cerebellum Craniotomy Cranium Dental Anesthesia Dental Cements Dental Health Services Electron Microscopy Fluorescence Isoflurane Lens, Crystalline Light Males Mice, House Operative Surgical Procedures Periosteum Resins, Plant Retrosplenial Cortex Somatosensory Cortex Titanium Vision Visual Cortex Woman
5
Reduced DMN Connectivity in Mild Alzheimer's
Regions of interest (ROIs) within the DMN were identified in a separate group of mild AD and control participants (none used in the main analyses). fMRI data were analyzed from eight mild AD participants (CDR 1) (3 male, ages 64 to 88) with a family history of late-onset AD and eight randomly selected cognitively normal participants (CDR 0) without a family history of late-onset AD (2 male, ages 46 to 82). A 6-mm-radius sphere centered on the PCC (MNI coordinates: -2, -54, 16) was used as the seed. Functional connectivity maps were conventionally computed as the correlation between the time course extracted from the PCC seed and all other brain voxels. Correlation maps were converted to z-maps using Fisher’s transformation. In the following, we denote Fisher z-transformed Pearson correlations as z(r). Averaged PCC correlation maps were obtained for the mild AD and cognitively normal groups. A group difference map was produced by subtracting the averaged map of mild AD participants from cognitively normal individuals. This map was thresholded at z(r) ≥ 0.25 and cluster size ≥ 10 voxels. Mild AD participants showed reduced functional connectivity between the PCC and retrosplenial cortex extending to the precuneus, the left and right inferior parietal lobules (IPL), the left and right medial temporal lobe (MTL), and medial prefrontal cortex (MPFC) (Figure 1). Peak foci were identified in the difference map by automated peak search (Supplementary Table S1) and 6mm radius spherical ROIs were centered on these peaks for use in the main analysis that compared cognitively normal individuals (CDR 0) with a family history of late-onset AD to those with no family history of late-onset AD.
Wang L., Roe C.M., Snyder A.Z., Brier M.R., Thomas J.B., Xiong C., Benzinger T.L., Morris J.C, & Ances B.M. (2012). Alzheimer’s disease family history impacts resting state functional connectivity. Annals of neurology, 72(4), 571-577.
Publication 2012
Brain fMRI Males Microtubule-Associated Proteins Parietal Lobule Precuneus Prefrontal Cortex Radius Retrosplenial Cortex Temporal Lobe

Most recents protocols related to «Retrosplenial Cortex»

1
Analyzing Aquaporin-4 Expression Patterns
For each brain hemisphere in each section, all ROIs were manually drawn according to the Allen Brain Atlas and using auto fluorescence from the green channel following visible anatomical landmarks and confirming AQP4 expression visibility. Each subregion measured from 0.03 to 1.69 mm2. Therefore, a universal threshold was applied manually to all images as a preferred method for reduction of the background signal influence. Subsequently, a mean area fraction covered by AQP4 in both hemispheres/sections was measured for each ROI. In total, mean AQP4 expression was calculated for 11 ROIs (retrosplenial cortex - RSP, visual - VIS; somatosensory - SS; auditory - AUD; hippocampus - HIP; perirhinal - PERI; thalamus - TH; habenula - HAB; hypothalamus - HY; pericisternal - PCS; white matter - WM) in 4 WT mice. A nonparametric Kruskal-Wallis one-way ANOVA with Dunn’s post-hoc was employed to compare the mean AQP4 channel expressions between ROIs.
Gomolka R.S., Hablitz L.M., Mestre H., Giannetto M., Du T., Hauglund N.L., Xie L., Peng W., Martinez P.M., Nedergaard M, & Mori Y. (2023). Loss of aquaporin-4 results in glymphatic system dysfunction via brain-wide interstitial fluid stagnation. eLife, 12, e82232.
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Publication 2023
Anatomic Landmarks Auditory Perception Brain Cerebral Hemispheres Fluorescence Habenula Hypothalamus Mice, Laboratory neuro-oncological ventral antigen 2, human Retrosplenial Cortex Seahorses Thalamus White Matter
2
Quantifying Brain Vascular Density
ROIs were manually outlined around each anatomical subregion according to the Allen Brain Atlas, and using visible anatomical landmarks. Due to differences in fluorescent labeling intensity, each region was thresholded individually to isolate labeled blood vessels from the background, so each region measured from 0.023 to 4.67 mm2 within each hemisphere/section. Area fraction of blood vessels above the threshold was measured for each ROI. In total, mean vascular density was calculated from multiple subregions for 17 ROI (olfactory area - OLF; cingulate cortex - CA; retrosplenial cortex - RSP; primary visual - V1; primary somatosensory - S1; primary motor area - M1; auditory - AUD; hippocampus - HIP; perirhinal - PERI; insular - INS; thalamus - TH; habenula - HAB; hypothalamus - HY; caudate putamen - CP; white matter - WM; pericisternal - PCS; ependymal around lateral ventricles - EPD) in 6 KO and 6 WT animals. Further statistical comparison was performed assuming inhomogeneous signal distribution properties between different ROI (similarly as for DWI). Hence, considering independent measurements of vascular densities among ROI analyzed and due to small group size, nonparametric Mann-Whitney U-test was employed to compare the vessel densities from KO and WT animals ROI-wise.
For ROI-wise correlation analysis, a mean value of AQP4 expressions as well as vascular densities at ROI was calculated from all respective animals strain-wise.
Gomolka R.S., Hablitz L.M., Mestre H., Giannetto M., Du T., Hauglund N.L., Xie L., Peng W., Martinez P.M., Nedergaard M, & Mori Y. (2023). Loss of aquaporin-4 results in glymphatic system dysfunction via brain-wide interstitial fluid stagnation. eLife, 12, e82232.
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Publication 2023
Anatomic Landmarks Animals Auditory Perception Blood Vessel Brain Cingulate Cortex Ependyma Habenula Hypothalamus Motor Cortex, Primary Neostriatum Retrosplenial Cortex Seahorses Sense of Smell Strains Thalamus Ventricle, Lateral White Matter
3
Widefield Calcium Imaging of Mouse Brain
Widefield imaging was done as reported previously23 (link),32 (link),77 (link) using an inverted tandem-lens macroscope and an sCMOS camera (Edge 5.5, PCO) running at 30 frames per second (fps). The focal lengths of the top lens (DC-Nikkor, Nikon) and bottom lens (85M-S, Rokinon) were 105 mm and 85 mm, respectively. The field of view was 12.5 × 10.5 mm2 and the imaging resolution was 640 × 540 pixels after 4× spatial binning, resulting in a spatial resolution of ~20 μm per pixel. To capture GCaMP fluorescence, a 525-nm bandpass filter (86-963, Edmund optics) was placed in front of the camera. Using excitation light at two different wavelengths, we isolated Ca2+-dependent fluorescence and corrected for intrinsic signals (for example, hemodynamic responses)22 (link),25 (link). Excitation light was projected on the cortical surface using a 495 nm long-pass dichroic mirror (T495lpxr, Chroma) placed between the two macro lenses. The excitation light was generated by a collimated blue LED (470 nm, M470L3, Thorlabs) and a collimated violet LED (405 nm, M405L3, Thorlabs) that were coupled into the same excitation path using a dichroic mirror (87-063, Edmund optics). We alternated illumination between the two LEDs from frame to frame, resulting in one set of frames with blue and the other with violet excitation at 15 fps each. Excitation of GCaMP at 405 nm results in non-calcium-dependent fluorescence78 (link), allowing us to isolate the true calcium-dependent signal by rescaling and subtracting frames with violet illumination from the preceding frames with blue illumination. Subsequent analyses were based on this differential signal. Imaging data were then rigidly aligned to the Allen Mouse Brain Common Coordinate Framework (CCF), using four anatomical landmarks: the left, center and right points where anterior cortex meets the olfactory bulbs, and the medial point at the base of retrosplenial cortex. Retinotopic visual mapping experiments30 (link),79 (link) confirmed accurate CCF alignment and showed high correspondence between functionally identified visual areas and the CCF across PyN types (Fig. 1c).
Musall S., Sun X.R., Mohan H., An X., Gluf S., Li S.J., Drewes R., Cravo E., Lenzi I., Yin C., Kampa B.M, & Churchland A.K. (2023). Pyramidal cell types drive functionally distinct cortical activity patterns during decision-making. Nature Neuroscience, 26(3), 495-505.
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Publication 2023
Anatomic Landmarks Brain Calcium Cortex, Cerebral Eye Fluorescence Hemodynamics Lens, Crystalline Light Mus Olfactory Bulb Reading Frames Retrosplenial Cortex Viola
4
Cerebellar and Forebrain Immunostaining Quantification
Staining and analysis was carried out as detailed elsewhere [13 (link)]. In brief, mid-sagittal cerebellar vibratome sections were blocked and permeabilized with 10% normal goat serum in PBS containing 0.2% Triton X-100 and immunostained with a mouse anti-calbindin D28k antibody (Sigma-Aldrich, 1:1000) followed by incubation with Alexa Fluor 555-labeled goat anti-mouse secondary antibody (Thermo Fisher Scientific, 1:1000).
Coronal forebrain sections were blocked and permeabilized as above, stained with rat anti-MBP (Merck Millipore, Burlington, VT, USA, 1:300), mouse anti-PV (Millipore, 1:1000), or mouse anti-GAD67 (Millipore, 1:200), and incubated with Alexa Fluor 555-labeled secondary antibody produced in goat (Thermo Fisher Scientific, all 1:1000). To visualize myelin, sections were incubated with FluoroMyelin Green Stain according to the manufacturer’s instructions (Molecular Probes, Eugene, OR, USA, 1:300 dye dilution). Sections between Bregma 1.045 and −1.555 were employed for these analyses.
Pictures were taken with an Olympus AX70 microscope or Zeiss ApoTome2. For quantification of the Purkinje cell (PC) outgrowth, thickness of the molecular layer (ML) that reflects the dimension of the PC dendritic tree was determined at three different positions in lobules III, IV and V using ImageJ. PV positive neurons were counted in all layers of the somatosensory and retrosplenial cortex and normalized to the size of the analyzed area. For quantifying MBP, GAD67, and FluoroMyelin staining intensities, the respective integrated fluorescence signal intensities per area were measured using ImageJ software. Wt average values were set as 1.0. Blinding was achieved by attributing random numbers to the pictures. For each analysis, four brain sections per animal from 3–5 mice per experimental group were employed.
Chen J., Salveridou E., Liebmann L., Sundaram S.M., Doycheva D., Markova B., Hübner C.A., Boelen A., Visser W.E., Heuer H, & Mayerl S. (2023). Triac Treatment Prevents Neurodevelopmental and Locomotor Impairments in Thyroid Hormone Transporter Mct8/Oatp1c1 Deficient Mice. International Journal of Molecular Sciences, 24(4), 3452.
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Publication 2023
Alexa Fluor 555 Animals Antibodies, Anti-Idiotypic Antigen-Presenting Cells Brain Calbindin 1 Cerebellum Dye Dilution Technique Fluorescence glutamate decarboxylase 1 (brain, 67kDa), human Goat Immunoglobulins Microscopy Molecular Probes Mus Myelin Sheath Neurons Prosencephalon Purkinje Cells Retrosplenial Cortex Serum Stains Trees Triton X-100
5
Neuronal Recordings and Anatomical Mapping in the Mouse Brain
Approximately 2 weeks after recording was completed, animals were transcardially perfused and their brains fixed (24–48hrs in 4%PFA) and cryopreserved (30% Sucrose). Brains were coronally sectioned with a cryostat at 60uM thickness. DiI electrode tracks were imaged with a NanoZoomer (Hamamatsu Photonics). Probe trajectories were reconstructed, following previous work37 (link) (http://github.com/petersaj/AP_histology; with minor adaptations). Trajectories were cross referenced with the A/P and M/L positioning of insertion sites derived from widefield images, to determine their final positioning. Correlograms of spiking activity showed strong concordance between estimated anatomical boundaries and functional boundaries in spiking activity (Fig. 1d). In one animal, probe insertion location had to be shifted for two electrodes to avoid large blood vessels: resulting in two recordings missing retrosplenial cortex, and one recording missing whisker somatosensory cortex.
Recorded neurons were grouped by anatomical location, as labeled in the Allen Brain Atlas Common Coordinates Framework43 (link) (CCF v3). Prelimbic (PL; n=1527) included neurons from CCF parent regions Prelimbic (PL), Infralimbic (ILA), Dorsal Anterior Cingulate Area (ACAd). Frontal Motor (FMR; n=1257) included neurons from Secondary Motor Area (Mos). Visual (VIS; n=716) included neurons from Posteromedial (VISpm), Anterior (VISa), and Anteromedial (VISam) visual areas. Somatosensory (SS; n=833) included neurons from nose (SSp-n) mouth (SSp-m) and unassigned (SSp-un) primary somatosensory areas. Whisker (WHS; n=805) included neurons from Primary Somatosensory Barrel Field area (SSp-bfd). Retrosplenial (RSP; 640) included neurons from Dorsal and Lateral Agranular Retrosplenial areas (RSPd and RSPagl, respectively). Hippocampus (HPC; n=353) included neurons from Dentate Gyrus (DG) and Ammon’s horn (CA). Thalamus (TH, n=389) included neurons from the Lateral Group (LAT), Medial Group (MED), and Intralaminar nuclei (ILM) of the dorsal Thalamus and Epithalamus (EPI).
MacDowell C.J., Libby A., Jahn C.I., Tafazoli S, & Buschman T.J. (2023). Multiplexed Subspaces Route Neural Activity Across Brain-wide Networks. bioRxiv.
Publication Preprint 2023
Acclimatization Animals Blood Vessel Brain Epithalamus Gyrus, Dentate Gyrus Cinguli Hippocampus Proper Intralaminar Nuclear Group MAVS protein, human Neurons Nose Oral Cavity Parent Premotor Cortex Retrosplenial Cortex Seahorses Somatosensory Cortex Somatosensory Cortex, Primary Sucrose Thalamus Vibrissae

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More about "Retrosplenial Cortex"

The Retrosplenial Cortex (RSC) is a crucial region of the cerebral cortex located in the posterior part of the brain.
This area plays a pivotal role in spatial navigation, memory formation, and various cognitive functions.
The RSC receives input from the hippocampus and is believed to be responsible for integrating spatial and contextual information.
Researchers studying the Retrosplenial Cortex can leverage the power of PubCompare.ai's AI-driven protocol optimization to identify the most reproducible and accurate research methods from scientific literature, preprints, and patents.
This data-driven approach can help locate the best protocols and products, ultimately improving research outcomes through informed decision-making.
The RSC is closely linked to the hippocampus, a region essential for spatial memory and navigation.
Techniques like FV1000MPE2 and XLPlanN25xW microscopy can be employed to visualize and study the anatomical structure and neuronal activity within the RSC.
Furthermore, the BX61WI and MaiTai-HP DeepSee systems enable high-resolution imaging of this cortical area.
Molecular analyses, such as those facilitated by the High-Capacity cDNA Reverse Transcription Kit and the HiSeq 2000 system, can provide insights into the gene expression profiles and signaling pathways within the Retrosplenial Cortex.
The Bcl2fastq (version 1.8.4) software can be used for the analysis of sequencing data, while the TruSeq RNA prep kit facilitates RNA extraction and library preparation.
Electrophysiological recordings using the PowerLab system can help researchers understand the neural activity and functional connectivity patterns within the RSC.
Additionally, the ECAT EXACT HR+ PET scanner can be utilized to investigate the metabolic and neurochemical changes associated with Retrosplenial Cortex function.
By incorporating these advanced techniques and leveraging the insights from PubCompare.ai, researchers can gain a more comprehensive understanding of the Retrosplenial Cortex and its role in various cognitive processes.
This knowledge can lead to breakthroughs in our understanding of spatial navigation, memory formation, and other higher-order brain functions.