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Contents:
  1. New brain atlases rewrite the textbooks on brain anatomy - A*STAR Research
  2. Brief Research Report ARTICLE
  3. Construction of brain atlases based on a multi-center MRI dataset of 2020 Chinese adults

We reported intermediate stages of this atlas over the years 19 — Herein we present a clinical MRI Deep Brain Atlas MDBA built from a unique anatomic specimen offering for the first time the most advanced version with detailed volumetric representation. Though mainly developed to identify structures of the deep brain on MRI for neurosurgical practices, it also offers to neuroscientists another representation of the topographic organization of the deep brain.

The brain specimen was obtained from a 65 year-old male subject who died of non-neurological cause. It was studied following our institutional rules and guidelines. The image data was initially manually contoured and labeled using a neurosurgical software Iplan, BrainLab, Munich, Germany. MRI cartography and labeling relied on the analysis of different signals and patterns of the deep brain structures The signal intensity of a voxel reflects the microarchitecture, i.

In addition, at the resolution available in our data set, the common separation of brain tissue into white and gray matters is not binary in the deep brain. For instance, at large scale centimetric the thalamus is made of gray matter; at small scale millimetric the thalamus is made of gray matter nuclei such as the ventromedial posterior nucleus, of white fascicles such as the mammillothalamic fascicle, and of mixed structures such as intralaminar nuclei or the reticular nucleus crossed by numerous white matter fibers. The cartography was performed structure by structure, starting from the most readily identifiable ones, such as the subthalamic nucleus.

In parallel to the progressive mapping of the 4. The different nuclei of the hypothalamus were parcellated into different structures according to proportional topography and structural connectivity 21 , The objects, i. The raw image data set were realigned along AC-PC line, slices being resampled accordingly; leading to a new image data set of 0.

Contours of unnamed structures were identified and labeled during this process Figure 1B. A unique color HSV color model was attributed to each object. Figure 1. Principle of contouring and voxel objects frontal view; coronal slices. A ventrocaudal medial nucleus of thalamus pink. The structures were labeled according to clinically known classical names 1 , 2 , 26 — 37 and ontologies 38 — These structures were essentially nuclear, i.

Complementary information, such as homonyms and French names were also added. Acronyms were created to reduce the text size of labels on plates.

New brain atlases rewrite the textbooks on brain anatomy - A*STAR Research

Structures not precisely identified or still unnamed were detailed and labeled according to the location and the aspect on MRI. For instance: i the retrolenticular reticularoid zone was observed laterally to the area or zone of Wernicke, hence in a retrolenticular position, Because of its reticular appearance low signal intensity it was named reticularoid Figure 2 ; ii the subthalamic tegmental field covered the historical Forel's H field, and was segregated into anterior, dorsal, medial, lateral and central zones; iii the area of reticular appearance, i.

The information is available as Supplementary Table. Figure 2. The structures were also specified according to four subregions of the deep brain, although these subdivisions are still not formally set 33 , 40 , namely the hypothalamus, the thalamus, the subthalamus or prethalamus and the telencephalon Supplementary Table. The labeling was not fully extensive, as we focused on structures identifiable on MRI for the thalamus, subthalamus, and telencephalon, or inferred from diagrams for the hypothalamus e.

For each MRI slice and related maps, structural MRI slice and topographic maps data were distributed on a double page or plate. Furthermore, the aspect of signal of structures is very similar to that observed in images routinely used in clinical practice, even if the latter have a lower spatial resolution, which is supramillimetric. The contours of structures were overlaid white line on each MRI slice, facilitating the identification of structures on the patients' individual imagery.

Summary of Elsevier's MRI Atlas of Human White Matter tool

The contrast of each MRI slice was enhanced by automatic adjustment of tones Photoshop CC, Adobe, San Jose, CA, USA , in a slice by slice fashion, minimizing heterogeneity of signals due to the presence of extremely high white and low black values within the volume of acquisition a legacy of the original MRI acquisition. Figure 3. B Same plate, colors are specified according to subregions proportional and millimeter scales ; thalamus blue gradation , subthalamus brown gradation and telencephalon green gradation.

C Same plate, MRI slice and white contours of structures no scale. The height of the thalamus was 18 mm. The proportional grid system numbers were used to name the slices and related maps. Hence, for one unique location in a plane, both absolute overlay of absolute millimeter distance grid and relative overlay of proportional distance grid positions were available. Three particular sections served as reference positions. Figure 4. On each plate whatever the orientation, one MRI slice and related position graphs and maps, were arranged for localization and comparison purposes.

Two related maps of the MRI slice were displayed: the first is the map of structures at the specific location with the contours and labels acronyms , on which is overlaid a millimetric grid absolute location ; the second map is made of the same structures but colored according to the four subregions, including the overlay of a proportional grid as well. This second map was colored using luminance gradients of the specific color of the subregion as follows.

The hypothalamus was colored in yellow, the thalamus in blue, the subthalamus in brown, and the telencephalon in green. The acronyms were classified in alphabetic order by subregion. The MDBA with structures and 52 plates provides an extensive 3D MRI structural analysis of the human deep brain mainly for clinical applications, but also researchers interested in direct visual identification of neuroanatomical structures.

Brief Research Report ARTICLE

The simple principle of cartography from reconstructed slices of one anatomic specimen without destruction of tissue greatly facilitates the 3D structural analysis, which is also dramatically improved by high spatial resolution with infra millimetric voxels. Although the result of parcellation according to T1-weighted contrast harvested a lot of data, further approaches using others MRI contrast, such as inversion-recovery sequences, or multimodal imaging with DTI, should refine the information.

Indeed the MDBA gives high level of structural details of white and gray matter structures substantially enhancing the current structural knowledge within this region. Although it can be used both at the individual level and in series, it is intrinsically a detailed data set of a unique specimen which must interpreted in this strict context as a topological descriptor of the deep brain architecture. Anyway this topological descriptor, could be the support of advanced probabilistic atlases enabling to integrate the variability, still not mastered, of the deep brain, through large cohorts of subjects.

Our approach has shown that it is feasible to identify the details of individual MRI anatomy. Whereas, the atlas-with proportional scales is still largely used for stereotactic targeting, nevertheless there must be kept in mind such unsolved issues as inhomogeneity of ontologies, weak cross-correspondences between atlases 45 and between set of slices within atlases questions On the other hand it can be assumed that machine learning approaches 46 could significantly enhance these anatomical uncertainties, therefore dramatically change paradigms to solve these challenges.

This is expected as the learning databases are rapidly becoming stronger. For instance the MDBA could be used in the work flow of learning methods including decision-based approaches whether supervised or not 47 — 49 to interpret the results. In the interim, the MDBA can assist significantly those who are willing to better master the deep brain architecture, which is particularly important for clinicians implanting devices in the deep brain.

In this latter condition, practitioners can use the atlas like classical histological atlases from the proportional grid plates, and at the same time they can adjust or specify directly the targets from the MRI plates. Furthermore, the MDBA is of considerable value to study injured and deformed brains as indirect methods are unreliable due to the hampered landmarks in the injured brain In this sense, it fuels the panoply of MRI-based brain atlases used for research and clinical purposes, notably in computer science see e.

Furthermore, the MDBA creates a link to pioneering data see Supplementary Table , which otherwise would remain into oblivion. Moreover, the MDBA serves as a new tool in the continuous effort of mastering the structural and functional anatomy of the human brain using either direct or indirect methods of cartography. AD contributed substantially to the interpretation of anatomical data of the MDBA and wrote sections of the manuscript. NM and RK contributed to the conception the work and wrote sections of the manuscript. All authors contributed to manuscript revision, read and approved the submitted version.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Construction of brain atlases based on a multi-center MRI dataset of 2020 Chinese adults

Schaltenbrand G, Wahren W. Atlas for Stereotaxy of the Human Brain.


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Stuttgart, NY: Thieme Google Scholar. Paris: Masson et Cie Atlas of the Human Brain. London: Academic Press A three-dimensional, histological and deformable atlas of the human basal ganglia. Intention tremor: the best indication for stereotaxic surgery. La Presse Med. PubMed Abstract Google Scholar.

Imaging of subthalamic nucleus and ventralis intermedius of the thalamus.


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Mov Disord. A three-dimensional histological atlas of the human basal ganglia. J Neurosurg. Ventrolateral motor thalamus abnormal connectivity in essential tremor before and after thalamotomy: a resting-state functional magnetic resonance imaging study. World Neurosurg. Structural and resting state functional connectivity of the subthalamic nucleus: identification of motor STN parts and the hyperdirect pathway. Direct visualization of deep brain stimulation targets in Parkinson Disease with the use of 7-tesla magnetic resonance imaging. Evaluating indirect subthalamic nucleus targeting with validated 3-tesla magnetic resonance imaging.

Stereotact Funct Neurosurg. Optimal MRI methods for direct stereotactic targeting of the subthalamic nucleus and globus pallidus. Localization of the subthalamic nucleus: optimization with susceptibility-weighted phase MR imaging. Am J Neuroradiol. Investigation of morphometric variability of subthalamic nucleus, red nucleus, and substantia nigra in advanced Parkinson's disease patients using automatic segmentation and PCA-based analysis.

Anyway this topological descriptor, could be the support of advanced probabilistic atlases enabling to integrate the variability, still not mastered, of the deep brain, through large cohorts of subjects. Our approach has shown that it is feasible to identify the details of individual MRI anatomy. Whereas, the atlas-with proportional scales is still largely used for stereotactic targeting, nevertheless there must be kept in mind such unsolved issues as inhomogeneity of ontologies, weak cross-correspondences between atlases 45 and between set of slices within atlases questions On the other hand it can be assumed that machine learning approaches 46 could significantly enhance these anatomical uncertainties, therefore dramatically change paradigms to solve these challenges.

This is expected as the learning databases are rapidly becoming stronger. For instance the MDBA could be used in the work flow of learning methods including decision-based approaches whether supervised or not 47 — 49 to interpret the results. In the interim, the MDBA can assist significantly those who are willing to better master the deep brain architecture, which is particularly important for clinicians implanting devices in the deep brain.