To be able to work with SPLASh, one needs a structural MRI of the subject's head. This can either be the MRI that is provided with the atlas dataset (as in our example shown here) or an MRI of the actual animal that is being implanted to take the individual anatomy into account. When working with an individual MRI, the relevant part of the brain needs to be extracted, flattened, and registered with the atlas. This, however, can be done with the standard functionality of Caret and is therefore not covered here. Using the atlas MRI has the advantage of being able to use SPLASh even when no individual MRI is available. The provided MRI is of high quality and the segmentation and registration with the atlas data have been performed by experts. However, individual anatomical deviations from the template are obviously neglected. Using an individual MRI has the advantage of working with the actual anatomy of the individual animal, but the quality of the MRI and how well segmentation and registration are being performed determines the precision of the results. Surface information needs to be extracted from the chosen MRI for the outer skull surface (possible locations for placing a recording chamber) as well as the outer brain surface (reference points for measuring the depth of a penetration). We have performed this extraction with the freely available software BrainSuite3 (Shattuck and Leahy, 2002 (link)). The result of this extraction for the macaque MRI coming with the macaque cortex atlas (see above) is shown in Figure 1 . Currently, SPLASh can only read skull/brain surface data in the DFS format written by BrainSuite. In addition to the structural MRI and the extracted surface information SPLASh also needs access to the (Caret) atlas data.
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Phenomena
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Natural Phenomenon or Process
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Anatomic Variation
Anatomic Variation
Anatomic Variation refers to the normal differences in the structure or arrangement of body parts among individuals.
These variations can occur in the size, shape, position, or number of anatomical features, and are often genetically or developmentally determined.
Studing anatomic variaiton is important for understanding human diversity, diagnosing medical conditions, and improving the accuracy of medical interventions.
PubCompare.ai provides a powerful platform to explore and compare research protocols related to Anatomic Variation, helping researchers identify the best approaches and enhance the reproducibility and accuracy of their work.
These variations can occur in the size, shape, position, or number of anatomical features, and are often genetically or developmentally determined.
Studing anatomic variaiton is important for understanding human diversity, diagnosing medical conditions, and improving the accuracy of medical interventions.
PubCompare.ai provides a powerful platform to explore and compare research protocols related to Anatomic Variation, helping researchers identify the best approaches and enhance the reproducibility and accuracy of their work.
Most cited protocols related to «Anatomic Variation»
Anatomic Variation
Animals
Brain
Cortex, Cerebral
Cranium
Head
Macaca
One of the critical steps of the whole procedure was to partition the participants' cortex into ROIs located in an identical topographic position for each participant despite interindividual anatomical variation. We used Freesurfer to register a labeled mesh from an average brain onto the brain of each individual participant, where each label corresponded to one of 66 anatomical cortical regions [54 (link)]. This output provided for every participant a standardized partition of the cortex into 66 regional areas. In a second step, each of these regional areas were subdivided on the Freesurfer average brain into a set of small and compact regions of about 1.5 cm2, resulting in 998 ROIs covering the entire cortex. This subdivision was then registered on the individual brain using the same transformation as for the 66 regional areas thus maintaining the topological constraints of mapping. Consequently, the resulting partitions of the cortex into 66 and 998 ROIs were in anatomically closely matched positions for all participants (Cammoun L, Gigandet X, Thiran JP, Do KQ, Maeder P, et al., unpublished data).
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Anatomic Variation
Body Regions
Brain
Cortex, Cerebral
One-Step dentin bonding system
Anatomic Variation
Aorta
Conferences
Hypersensitivity
Ilium
Malignant Neoplasms
Oncologists
Parametrium
Physicians
Radiation Oncologists
Radiologist
Rectum
Vagina
Anatomic Variation
Brain
Gray Matter
Healthy Volunteers
Population Health
Protein Subunits
African American
American Indian or Alaska Native
Anatomic Variation
Asian Persons
Cardiomyopathies
Cardiovascular Diseases
Cardiovascular System
Caucasoid Races
Children's Health
Congenital Heart Defects
Echocardiography
Electrocardiography
Ethnicity
Heart Diseases
Hispanics
Obesity
Pacific Islander Americans
Premature Birth
Respiratory Rate
Most recents protocols related to «Anatomic Variation»
All patients were offered investigation with EEG (available from N = 60), extended MRI sequences of the neurocranium (available from N = 57), and ECG (available from N = 60). EEGs were examined by the respective ward clinician and retrospectively assessed for regional or focal slowing, intermittent generalized delta/theta activity (IRTA/IRDA), and epileptic activity. Independent component analysis (ICA) of EEGs with automatic calculation of IRDA/IRTA density was additionally performed as previously described [31 (link)]. MRI included the following sequences on a 3 Tesla scanner (MAGNETOM Prisma, Siemens Healthcare GmbH, Erlangen, Germany) in most patients: T1-weighted sequences with magnetization-prepared rapid gradient echo (MPRAGE) with isotropic 1-mm3 voxels for atrophy diagnostics, diffusion-weighted imaging (DWI) with axial 5-mm slices for stroke detection, fluid-attenuated inversion recovery (FLAIR) sequences with isotropic 1-mm3 voxels for the detection of signal alterations, and further innovative analyses such as diffusion tensor imaging (DTI) and pseudo-continuous arterial spin labeling. All MRIs were assessed and evaluated by experienced senior neuroradiologists. MRI abnormalities were categorized as white-/gray-matter alterations, atrophy, vascular changes, cysts, tumors, and anatomical variants, among others. An automated volume- and region-based approach (https://www.VEObrain.com ) was used with the MPRAGE sequences for fully automated whole-brain volumetry for the detection of volume loss (VEOmorph, Freiburg, Germany). Selected cases (N = 7) with abnormalities in routine diagnostic work-up suggestive of an autoimmune cause were examined with cerebral [18F]fluorodeoxyglucose positron emission tomography (FDG-PET). Two patients received exome analysis due to suspected syndromal genesis.
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Anatomic Variation
Arteries
Atrophy
Blood Vessel
Brain
Cerebrovascular Accident
Congenital Abnormality
Cyst
Diagnosis
Diffusion
ECHO protocol
Epilepsy
Exome
F18, Fluorodeoxyglucose
Inversion, Chromosome
Leukoaraiosis
Neoplasms
Patients
Positron-Emission Tomography
prisma
Signal Detection (Psychology)
Syndrome
A short history and demographic information were collected from the patients, and they were given a 10-point visual analog scale (VAS) score to assess pain intensity and ability to perform daily tasks. All the patients were diagnosed clinically using the Finkelstein test. A US examination was used to confirm the diagnosis of DQT. In addition, the examination of the opposite hand was also performed for comparison. A B-mode US examination with a sufficient amount of gel was performed in both the transverse and sagittal planes to allow for the proper evaluation and visualization of anatomical structures, followed by a color Doppler US mode to detect peri-tendinous hyperemia. The B-mode gain was decreased and the color gain was increased at a threshold just below aliasing to optimize the visualization of low-velocity flow. Complete data on the baseline sonographic findings were collected, including the thickness of the extensor retinaculum, tendon sheath effusion, paratendinous hyperemia and anatomical variation.
Anatomic Variation
Diagnosis
Hyperemia
Patients
Severity, Pain
Tendons
Ultrasonography
Visual Analog Pain Scale
A multi-organ (or multi-object) shape complex is defined as a set of solid shapes, each representing a single and connected biological structure, assembled together within a common coordinate frame. This shape complex contains the shape, scale, and positional information for each organ structure, thereby containing the relative pose and orientation between different organ structures in the shape complex. Multi-organ shape structures have alignment variations between the organs that reflect subject-wise anatomical variations relevant to how the organs are relatively positioned and aligned with respect to each other. These alignment variations should not be factored out by the initial rigid alignment techniques that are usually performed prior to the shape modeling process. These geometric relationships between the organs are of significant importance, especially in biomechanics-based shape modeling Agrawal et al. (2020) , Zhang et al. (2016) (link), and Kainmueller et al. (2009) .
Here, we define the notations for the multi-organ shape modeling problem that will be used in the following sections. Given an ensemble of N subjects such that each subject has 3D surfaces defined for K organs. Thus, the ensemble is defined as . Each surface (or shape) is represented by a set of Mk correspondence particles, where each particle is d − dimensional1 such that is the total number of particles representing a multi-organ shape sample. x n,k is the realization of the configuration space random variable X n,k for the n − th subject and k − th organ and the corresponding shape space variable is Z n,k such that its realization is .
Here, we define the notations for the multi-organ shape modeling problem that will be used in the following sections. Given an ensemble of N subjects such that each subject has 3D surfaces defined for K organs. Thus, the ensemble is defined as . Each surface (or shape) is represented by a set of Mk correspondence particles, where each particle is d − dimensional
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Anatomic Variation
Biomechanical Phenomena
Biopharmaceuticals
Muscle Rigidity
Reading Frames
All axons with the minimum axon diameter visible within the field of view of each image were analyzed. Image size was selected to permit accurate classification of myelin appearance (compact or decompacted laminae) and as a result, larger diameter axons may have been disproportionately excluded from analysis due to their higher likelihood of falling partially outside of the field of view. Axons were classified based on the profile of their myelin sheath; specifically, the extent to which the ensheathing myelin was held in compact layers, or whether separation of the myelin laminae was visible. Classifications were adapted from previous studies and defined as follows: axons with compact myelin were characterized by thick, dark myelin appearance with <10% axon circumference affected by decompaction (Figure 2 a); axons with moderately decompacted myelin had 10–35% of the axon circumference affected by decompaction (Figure 2 b); axons with severely decompacted myelin had 36–99% of the axon circumference with decompaction (Figure 2 c); axons with completely decompacted myelin were fully ensheathed in myelin with the appearance of multiple diffuse layers, encompassing 100% of the axon diameter (Figure 2 d); and unmyelinated axons with a regular round or oval appearance without surrounding myelin (Figure 2 e) but distinct from glial cell processes, which are elongated in shape. Additional categories were included to describe other observed anatomical variations: paranodal profiles defined as axons with a large axolemmal space; lightly myelinated axons surrounded by thick, compact, low electron density (light-appearing) myelin with <10% axon circumference with observed decompaction; and aberrant profiles with constricted axoplasm showing empty membrane folds and aberrant extracellular compartments [33 (link),34 (link),63 (link),67 (link)].
Axons characterized with ‘compact myelin’ were further analyzed to determine axon diameter, myelin thickness, fiber diameter, and G ratio (axon diameter/fiber diameter). The minimum axon diameter was used, and myelin thickness was measured at that same location (Figure 2 f). The reported myelin thickness was the average of the two measurements on each side of the minimum axon diameter. Fiber diameter included the minimum axon diameter plus the myelin sheath and axolemmal space (Figure 2 f). These measurements were not collected from axons with decompacted myelin because decompaction distorts the apparent thickness of myelin and fiber diameter and prevents consistent measurement. All image analyses were conducted using FIJI Image J (version 1.53) software with investigators blinded to animal identity and treatment status.
Axons characterized with ‘compact myelin’ were further analyzed to determine axon diameter, myelin thickness, fiber diameter, and G ratio (axon diameter/fiber diameter). The minimum axon diameter was used, and myelin thickness was measured at that same location (
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Anatomic Variation
Animals
ARID1A protein, human
Axon
Electrons
Fibrosis
Light
Myelin Sheath
Neuroglia
Tissue, Membrane
In a first step, centrally trained researchers assessed the quality of the original Digital Imaging and Communications in Medicine (DICOM) images at level T12 and L3. In a second step, the research assistants evaluated the CT scans and selected a single slice at level T12. For this purpose, we used SliceOmatic Software version 5.0 (TomoVision, Montreal, Quebec, QC, Canada). We excluded all images with incomplete depiction of T12 and in case that the muscle tissue was out of range and/or if contrast did not allow discrimination. There were no anatomical variations that led to exclusion.
Muscle and visceral tissue were distinguished from subcutaneous adipose tissue using tissue-specific Hounsfield Unit (HU) ranges and anatomical knowledge. We followed the Alberta protocol [23 (link)] and set Hounsfield ranges to −29 to 150 HU for skeletal muscle, −190 to −30 HU for subcutaneous and intramuscular adipose tissue, and −150 to −50 HU for visceral adipose tissue. Muscles included in the cross-sectional measurements at T12 with different muscle groups, including the erector spinae, latissimus dorsi, external and internal oblique, rectus abdominis and external and internal intercostal muscles. Every slice was evaluated twice to improve the interrater reliability with the aim to achieve >99%. Researchers improved the slicing and measuring criteria after the first round.
Muscle and visceral tissue were distinguished from subcutaneous adipose tissue using tissue-specific Hounsfield Unit (HU) ranges and anatomical knowledge. We followed the Alberta protocol [23 (link)] and set Hounsfield ranges to −29 to 150 HU for skeletal muscle, −190 to −30 HU for subcutaneous and intramuscular adipose tissue, and −150 to −50 HU for visceral adipose tissue. Muscles included in the cross-sectional measurements at T12 with different muscle groups, including the erector spinae, latissimus dorsi, external and internal oblique, rectus abdominis and external and internal intercostal muscles. Every slice was evaluated twice to improve the interrater reliability with the aim to achieve >99%. Researchers improved the slicing and measuring criteria after the first round.
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Anatomic Variation
Discrimination, Psychology
Intercostal Muscle
Internal Abdominal Oblique Muscle
Latissimus Dorsi
Muscle Tissue
Rectus Abdominis
Skeletal Muscles
Subcutaneous Fat
Tissue, Adipose
Tissues
Visceral Fat
X-Ray Computed Tomography
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More about "Anatomic Variation"
Anatomic Variation refers to the normal differences in the structure or arrangement of body parts among individuals.
These variations can occur in the size, shape, position, or number of anatomical features, and are often genetically or developmentally determined.
Studying anatomic variation is important for understanding human diversity, diagnosing medical conditions, and improving the accuracy of medical interventions.
PubCompare.ai provides a powerful platform to explore and compare research protocols related to Anatomic Variation, helping researchers identify the best approaches and enhance the reproducibility and accuracy of their work.
This AI-driven platform allows users to locate relevant protocols from literature, pre-prints, and patents, and utilize AI-powered comparisons to identify the best protocols and products.
Exploring anatomic variation is crucial for a wide range of applications, from medical diagnostics and treatments to forensic identification and anthropological research.
Understanding the normal range of human anatomical diversity is essential for accurate medical imaging interpretation, surgical planning, and the development of personalized medical interventions.
Researchers studying anatomic variation may utilize various software and tools, such as MATLAB for image processing and statistical analysis, SPSS version 20 for data analysis, and Progreat for 3D visualization and modeling.
Additionally, tools like SPSS Statistics for Windows, Version 20.0, Drill-Cut X handpiece, and Form Wash can be used in the study and analysis of anatomical structures.
The Advantage Workstation and JMP 14 software can also be employed to enhance the exploration and visualization of anatomic variation data, enabling researchers to identify patterns, trends, and relationships within the data.
By leveraging these advanced tools and technologies, researchers can gain deeper insights into the complexities of human anatomy and improve the accuracy and reproducibility of their work.
In summary, the study of Anatomic Variation is a critical field of research that can provide valuable insights into human diversity, medical diagnostics, and the optimization of medical interventions.
PubCompare.ai's AI-powered platform offers a robust solution for exploring and comparing research protocols related to this important topic, empowering researchers to enhance the quality and impact of their work.
These variations can occur in the size, shape, position, or number of anatomical features, and are often genetically or developmentally determined.
Studying anatomic variation is important for understanding human diversity, diagnosing medical conditions, and improving the accuracy of medical interventions.
PubCompare.ai provides a powerful platform to explore and compare research protocols related to Anatomic Variation, helping researchers identify the best approaches and enhance the reproducibility and accuracy of their work.
This AI-driven platform allows users to locate relevant protocols from literature, pre-prints, and patents, and utilize AI-powered comparisons to identify the best protocols and products.
Exploring anatomic variation is crucial for a wide range of applications, from medical diagnostics and treatments to forensic identification and anthropological research.
Understanding the normal range of human anatomical diversity is essential for accurate medical imaging interpretation, surgical planning, and the development of personalized medical interventions.
Researchers studying anatomic variation may utilize various software and tools, such as MATLAB for image processing and statistical analysis, SPSS version 20 for data analysis, and Progreat for 3D visualization and modeling.
Additionally, tools like SPSS Statistics for Windows, Version 20.0, Drill-Cut X handpiece, and Form Wash can be used in the study and analysis of anatomical structures.
The Advantage Workstation and JMP 14 software can also be employed to enhance the exploration and visualization of anatomic variation data, enabling researchers to identify patterns, trends, and relationships within the data.
By leveraging these advanced tools and technologies, researchers can gain deeper insights into the complexities of human anatomy and improve the accuracy and reproducibility of their work.
In summary, the study of Anatomic Variation is a critical field of research that can provide valuable insights into human diversity, medical diagnostics, and the optimization of medical interventions.
PubCompare.ai's AI-powered platform offers a robust solution for exploring and comparing research protocols related to this important topic, empowering researchers to enhance the quality and impact of their work.