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Superior Temporal Gyrus

The Superior Temporal Gyrus is a key anatomical structure in the brain, located in the temporal lobe.
It plays a crucial role in auditory processing, language comprehension, and social cognition.
This gyrus is involved in a wide range of neurological and psychiatric disorders, making it an important target for research and clinical investigations.
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Most cited protocols related to «Superior Temporal Gyrus»

1
Automated Cortical Morphometry Using FreeSurfer
The FreeSurfer analysis pipeline comprises two main processing streams, a volume-based stream and a surface-based stream. The volume-based stream is designed to assign a neuroanatomical label to each (sub)cortical voxel, whereas the surface-based stream is developed to derive the white and pial surfaces from which, among others, cortical volumes and cortical thickness (CT) are derived. More details can be found in Document S1 and references [2] (link), [17] (link)–[28] (link).
The volumes are presented by FreeSurfer in the form of tables and labeled voxels. The tabulated volumes are more accurate than the voxel volumes because they are corrected for partial volume effects. Both types of volumes were used in our analysis (see Document S1 for more details).
In order to compare the results with accuracy results previously reported by Lehmann and colleagues [5] (link), a few white matter and grey matter regions were merged to produce a whole gyrus or lobe (left and right), such as medial-inferior temporal gyrus (MITG), superior temporal gyrus (STG), and temporal lobe (TempL). Simarly, total ventricle volume (Ventr) was constructed (left+right added together). In this manner, a total of 7 composite volumes were assembled. (The respective segmentation labels were kindly provided to us by Dr. Manja Lehmann, University College London, UK, see Document S1 for more details).
In total, we computed 190 (sub)cortical volumes (185 for v5.0.0) and 68 CT values. It should be noted that no manual corrections were made to any of the FreeSurfer results in order to ensure a valid analysis. However, a visual inspection was performed to check the segmentations.
Three versions of FreeSurfer were used: v4.3.1, released on 19 May 2009, version v4.5.0, released on 11 August 2009, and version v5.0.0, released on 16 August 2010. For the Mac workstations these are 32 bits versions (due to problems to build some third party libraries in 64 bits mode on the Mac), whereas for the HP workstation these are 64 bits versions.
Gronenschild E.H., Habets P., Jacobs H.I., Mengelers R., Rozendaal N., van Os J, & Marcelis M. (2012). The Effects of FreeSurfer Version, Workstation Type, and Macintosh Operating System Version on Anatomical Volume and Cortical Thickness Measurements. PLoS ONE, 7(6), e38234.
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Publication 2012
Cortex, Cerebral Gray Matter Heart Ventricle Inferior Temporal Gyrus Superior Temporal Gyrus Temporal Lobe White Matter
2
Automated Brain Morphometry from ADNI Data
VBM: Analysis was performed using previously described methods [8 (link)-10 ], as implemented in SPM5 (http://www. fil.ion.ucl.ac.uk/spm/). Briefly, scans were converted from DICOM to NIfTI format, co-registered to a standard T1 template image, bias corrected, and segmented into GM, WM, and CSF compartments using standard SPM5 templates. GM maps were then normalized to MNI atlas space as 1x1x1 mm voxels and smoothed using a 10 mm FWHM Gaussian kernel. In cases where the first MP-RAGE scan could not be successfully segmented we attempted to use the second MP-RAGE. This was successful for only 1 of 8 cases.
Region of Interest: A hippocampal ROI template was created by manual tracing of the left and right hippocampi in an independent sample of 40 HC participants enrolled in our study of brain aging and MCI at Dartmouth Medical School [25 (link), 58 (link)]. These ROIs were used to extract GM density values from smoothed, unmodulated normalized and modulated normalized GM maps for the ADNI cohort.
Automated Parcellation: VOIs, including bilateral hippocampi and amygdalar nuclei, were extracted using FreeSurfer V4 [56 (link), 59 (link)-62 ]. FreeSurfer was also used to extract cortical thickness values from the left and right entorhinal cortex, inferior, middle, and superior temporal gyri, inferior parietal gyrus, and precuneus.
The final sample reported here passed site, ADNI MRI Core, and our internal quality control, and did not fail any step of the processing pipeline (Fig. 1).
Risacher S.L., Saykin A.J., West J.D., Shen L., Firpi H.A, & McDonald B.C. (2009). Baseline MRI Predictors of Conversion from MCI to Probable AD in the ADNI Cohort. Current Alzheimer Research, 6(4), 347-361.
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Publication 2009
Amygdaloid Body Brain Cell Nucleus Cortex, Cerebral Entorhinal Area Microtubule-Associated Proteins Precuneus Radionuclide Imaging Rage Seahorses Superior Temporal Gyrus
3
Epigenomic Profiling of Brain Tissues in Neurological Disorders

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Publication 2018
Alzheimer's Disease BLOOD Brain Cerebellum Chloroform Cortex, Cerebral Diagnosis DNA Methylation DNA Replication Donors Entorhinal Area Ethics Committees, Research Freezing Genome Homo sapiens Memory Methylation Neurodegenerative Disorders Phenols Prefrontal Cortex Schizophrenia Specialists Superior Temporal Gyrus Tissues
4
RNA-Seq Analysis of Autistic Brains

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Publication 2014
Autistic Disorder Brodmann Area 41 Brodmann Area 42 Cortex, Cerebral Gold Gray Matter Ribosomal RNA Superior Temporal Gyrus
5
Neuropathological and Genetic Characterization of Alzheimer's Disease Subtypes
Gross and macroscopic neuropathologic assessment was performed by standardized procedures. Formalin-fixed, paraffin-embedded tissue samples from the primary motor and visual cortices, inferior parietal lobule, mid-frontal gyrus, superior temporal gyrus, amygdala, and posterior hippocampus were cut at 5 µm thickness, mounted on glass slides and stained with H&E. Thioflavin-S fluorescent microscopy was performed to evaluate SP (10× objective) and NFT densities (40× objective). Primitive, neuritic, and cored type plaques were included in the SP counts and were truncated at 50, which is twice the number required to meet Khachaturian’s criteria for AD diagnosis [20 (link)]. NFT distribution was assessed to determine Braak stage. Intracellular and extracellular NFT counts from two hippocampal regions (CA1 and subiculum) and three association cortices (inferior parietal, mid-frontal, and superior temporal) were used to determine AD subtypes. Additional sections previously underwent immunohistochemical staining and were processed using the DAKO Autostainer (DAKO Auto Machine Corporation, Carpinteria, CA, USA) with DAKO Envision+ HRP System. The posterior hippocampus was stained with an antibody that detects the 25 kDa C-terminal fragment of TDP-43 (a generous gift from Leonard Petrucelli, Mayo Clinic, FL, USA). Assessment of TDP-43 pathology was performed as previously described [2 (link)]. Cerebrovascular disease was assessed using a simple scheme proposed by Jellinger and Attems [18 ], as previously reported. Lewy body disease (LBD) pathology was assessed by use of immunohistochemistry [34 (link)], as previously described.
For genotyping, genomic DNA was extracted from frozen brain by standard procedures. Genotyping for MAPT H1/H2 (SNP rs1052554 A/G, A = H1, G = H2), APOE alleles (SNP rs429358 C/T and rs7412 C/T), GRN (SNP rs5848 C/T), and TMEM106B (SNP rs1990622 C/T) was performed using a Taqman SNP genotyping assay (Applied Biosystems, Carlsbad, CA, USA). Genotype calls were obtained with SDS v2.2 software (Applied Biosystems). Although there is overlap in the HpScl-AD group between previous Genetic studies [9 (link), 32 (link)], Genetic information on the HpScl group has not been previously reported. Genotyping availability can be found in Supplementary methods.
Clinical reports were reviewed blind to pathologic diagnosis to collect education, family history, age of onset, disease duration, and Mini Mental State Examination (MMSE) scores. Family history was noted as positive if at least one first-degree relative had dementia. Age of onset was recorded as the age of initial cognitive abnormalities, as opposed to age of diagnosis. Disease duration was ascertained as the number of years elapsed between age of death and age of onset. Longitudinal decline was calculated as a slope of three or more MMSE scores, where the MMSE score was the dependent variable and elapsed years between testing and death were the independent variable. Antemortem clinical diagnosis of probable AD, possible AD and mild cognitive impairment was considered an amnestic diagnosis. Availability of clinical information can be found in Supplementary methods.
Murray M.E., Cannon A., Graff-Radford N.R., Liesinger A.M., Rutherford N.J., Ross O.A., Duara R., Carrasquillo M.M., Rademakers R, & Dickson D.W. (2014). Differential clinicopathologic and Genetic features of late-onset amnestic dementias. Acta neuropathologica, 128(3), 411-421.
Publication 2014
Alleles Amygdaloid Body Antemortem Diagnosis Apolipoproteins E Biological Assay Blindness Brain Cerebrovascular Disorders Cognition Cognitive Impairments, Mild Congenital Abnormality Cortex, Cerebral Dementia Diagnosis Formalin Freezing Genome Immunoglobulins Immunohistochemistry Lewy Body Disease MAPT protein, human Microscopy Mini Mental State Examination Neuritis Paraffin Embedding Parietal Lobule protein TDP-43, human Protoplasm Seahorses Senile Plaques Subiculum Superior Temporal Gyrus thioflavin S Visual Cortex

Most recents protocols related to «Superior Temporal Gyrus»

1
Brain Regions Correlated with Social Functions
Based on the results of previous studies, human social functions are correlated with specific brain regions, including the bilateral posterior inferior temporal sulcus, bilateral prefrontal cortex, bilateral anterior cingulum cortex, bilateral FFG, bilateral THA, bilateral AMYG, bilateral HIP, bilateral superior marginal gyrus, bilateral superior temporal gyrus caudal, bilateral occipital gyrus dorsal mouth, etc. Therefore, the above social-related brain regions were selected as the regions of interest (ROIs) for subsequent analysis. The ROI template was generated using the WFU_PickAtlas tool (https://www.nitrc.org/projects/wfu_pickatlas/ (accessed on 17 February 2022.)).
Wang Y., Xu L., Fang H., Wang F., Gao T., Zhu Q., Jiao G, & Ke X. (2023). Social Brain Network of Children with Autism Spectrum Disorder: Characterization of Functional Connectivity and Potential Association with Stereotyped Behavior. Brain Sciences, 13(2), 280.
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Publication 2023
Brain Cortex, Cerebral Homo sapiens Occipital Gyrus Oral Cavity Prefrontal Cortex Superior Frontal Gyrus Superior Temporal Gyrus Temporal Sulcus
2
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
3
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
4
Speech Production in Movement Disorders
Movement disorder patients undergoing intracranial electrode implantation for deep brain stimulation therapy participated in a speech production task (Bush et al., 2021 ), for which the baseline periods were analyzed in this study. One or two high-density subdural electrocorticography (ECoG) strips were temporary placed through the standard burr hole, targeting the left superior temporal gyrus (covering also the ventral sensorimotor cortex) and left inferior frontal gyrus. ECoG electrodes were removed at the end of the surgery. Dopaminergic medication was withdrawn the night before surgery. All procedures were approved by the University of Pittsburgh Institutional Review Board (IRB Protocol #PRO13110420) and all patients provided informed consent to participate in the study. The following cohorts of movement disorder patients participated in the study: 29 Parkinson’s disease patients (21M/8F, 65.6±7.1 years) undergoing awake subthalamic (STN) DBS surgery, all of which had ECoG recordings and 14 of which had simultaneous ECoG and DBS lead recordings; 5 Parkinson’s disease patients (5M/0F, 69.1±5.7 years) undergoing awake pallidal (GPi) DBS surgery, of which 4 had ECoG recordings and 3 had simultaneous ECoG and DBS lead recordings; 22 essential tremor patients (11M/11F, 65.3±9.7 years) undergoing awake thalamic (Vim) DBS surgery, of which 20 had ECoG recordings and 11 had simultaneous ECoG and DBS lead recordings.
Additionally, we analyzed awake restfulness data from 8 epilepsy patients (5M/3F, age: 18±11 years) undergoing stereo-EEG (sEEG) intracranial monitoring for epilepsy with additional electrodes implanted in the thalamus. This study was approved by the Massachusetts General Hospital (Boston, MA) Institutional Review Board (IRB Protocol #2020P000281).
Bush A., Zou J., Lipski W.J., Kokkinos V, & Richardson R.M. (2023). Broadband aperiodic components of local field potentials reflect inherent differences between cortical and subcortical activity. bioRxiv.
Publication Preprint 2023
Deep Brain Stimulation Dopaminergic Agents Electrocorticography Epilepsy Essential Tremor Globus Pallidus Inferior Frontal Gyrus Movement Disorders Operative Surgical Procedures Ovum Implantation Patients Sensorimotor Cortex Speech Subdural Space Superior Temporal Gyrus Thalamus Therapeutics Trephining
5
Multimodal Neuroimaging of Metabolic Aging and Alzheimer's
MRS data was processed using XSOS (Dikoma Shungu/Xiangling Mao; Weill Cornell Medicine) written in IDL [45 (link), 46 (link)] (Excelis Visual, Boulder, CO). Raw data was loaded into the program and processed using Hamming and Fermi k-space filters, a 7.5 mm center voxel shift, 20 Hz exponential filtering and zero-filling in time, x and y-domains prior to 3D Fast Fourier Transformation. A fixed first order phase of 4200° was applied to all spectra and data was automatically phased in zero order. Visualization of the PCr linewidth was done for the center voxel in each slice. The PCr peak was set at 0.0 ppm and the central spectrum set as a reference, and susceptibility corrections performed throughout the data set. Zero and first order phasing along with baseline correction was applied prior to peak area integration to all other voxels in the CSI data set by an experienced analyst (JPD). All spectra within a slice were analyzed contributing (8*8) 64 spectra in-plane * 4 slices for a total of 256 spectra per subject.
Peak area integration was performed around each of the seven well-resolved resonance peaks identified in S1 Fig: inorganic phosphate (Pi), phospho-creatine (PCr), total ATP (sum of α-ATP, β-ATP and γ-ATP moieties), phosphodiesters (PDE), and phosphomonoesters (PME). Peak areas of all reproducible resonances were found. The MRS processing software creates a 16x16 voxel image for each slice with the signal intensity equal to the peak area of the metabolite of interest in each voxel. The central 4 slices were co-registered in SPM to the 3D BRAVO sequence by using the 8-slice concordant image set acquired at the time of MRS. The integral of each metabolite resonance was calculated and expressed as a percent area of the total phosphorous signal in the corresponding spectrum. The ratios PCr/ATP, PCr/Pi, Pi/ATP, and PME/PDE were then computed, as this process allows for correction of many factors which can make absolute quantitation of 31P concentration difficult.
The 3D T1-Weighted BRAVO MRI scan was automatically processed using FreeSurfer 6.0 running under the Centos 7 Linux environment. Images were then processed in Statistical Parametric Mapping (SPM8) (http://www.fil.ion.ucl.ac.uk/spm/) implemented in Matlab 2021 (MathWorks; Natick,MA) [10 (link), 28 (link)–32 (link)]. For each participant, we used the Normalized Mutual Information routine of SPM8 [47 (link)] to first align the T1 BRAVO sequence to the reference T2-FLAIR acquired at exactly the same location as the 31P-MRS CSI slices. The parametric metabolite MRS maps were then subsequently aligned with the skull stripped 3D T1-Weighted FreeSurfer scan. Volumetric MRI scans were resampled to a 256x256x256 matrix array whereas the parametric metabolite MRS maps were resized to 256x256 images but not interpolated beyond the original 16x16x8 matrix given partial volume errors would occur. The co-registered MRI and MRS maps were quantified using the subcortical gray and white matter segmentation tools implemented in FreeSurfer 6.0 and Desikan-Killiany Atlas-based regions of interest (ROI) [48 (link), 49 (link)] applied to the aligned MRI for regional sampling.
We focused on brain regions with known metabolic vulnerability to metabolic aging and AD, including: frontal cortex (middle and superior frontal gyrus); PCC (posterior cingulate gyrus and precuneus); temporal cortex (inferior, middle and superior temporal gyrus); and medial temporal lobe (hippocampus, amygdala, entorhinal and parahippocampal gyrus) [26 (link), 27 (link)]. The mean metabolite signal in each ROI was then computed using FreeSurfer. We also obtained total intracranial volume for normalization purposes.
Jett S., Dyke J.P., Boneu Yepez C., Zarate C., Carlton C., Schelbaum E., Jang G., Pahlajani S., Williams S., Diaz Brinton R, & Mosconi L. (2023). Effects of sex and APOE ε4 genotype on brain mitochondrial high-energy phosphates in midlife individuals at risk for Alzheimer’s disease: A 31Phosphorus MR spectroscopy study. PLOS ONE, 18(2), e0281302.
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Publication 2023
Amygdaloid Body Brain Cranium Creatine Lobe, Frontal Microtubule-Associated Proteins MRI Scans Parahippocampal Gyrus periodate-oxidized adenosine 5'-triphosphate Pharmaceutical Preparations Phosphates Phosphorus Posterior Cingulate Cortex Precuneus Radionuclide Imaging Seahorses Superior Frontal Gyrus Superior Temporal Gyrus Susceptibility, Disease Temporal Lobe Vibration White Matter

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More about "Superior Temporal Gyrus"

The Superior Temporal Gyrus (STG) is a key anatomical structure in the brain, located within the temporal lobe.
This critical region plays a vital role in various neurological processes, including auditory processing, language comprehension, and social cognition.
The STG's involvement spans a wide range of neurological and psychiatric disorders, making it a crucial target for research and clinical investigations.
Researchers leveraging platforms like PubCompare.ai can optimize their STG-related studies, locating relevant protocols from scientific literature, preprints, and patents.
This innovative AI-driven platform enables researchers to identify the best procedures and products, ensuring reproducibility and accuracy in their experiments.
When studying the STG, researchers may employ a variety of advanced techniques and technologies.
For instance, the HiSeq 2500 sequencing platform can be utilized for high-throughput genomic analyses, while Collagenase type I enzyme can facilitate tissue dissociation.
The MiRNeasy kit and DNase I treatment can be used for efficient extraction and purification of RNA, and the Genomic DNA-Tissue MiniPrep kit can be employed for DNA isolation.
Protease inhibitor cocktails help protect proteins from degradation, and the Ribo-Zero rRNA Removal Kit enables targeted depletion of ribosomal RNA for enhanced transcriptomic profiling.
The TruSeq RNA Sample Preparation Kit v2 provides a streamlined solution for RNA library preparation.
Imaging techniques, such as the use of Dual Quasar gradients, can provide valuable spatial information about the STG and its functional connectivity within the brain.
Additionally, the RLT buffer from the MiRNeasy kit can facilitate efficient lysis and homogenization of tissue samples for downstream molecular analyses.
By leveraging these cutting-edge tools and techniques, researchers can gain deeper insights into the structure, function, and pathological implications of the Superior Temporal Gyrus, ultimately advancing our understanding of the brain and contributing to the development of improved diagnostic and therapeutic strategies.