Constructing the KOR152 Korean Young Adult Brain Atlas Utilizing the State-of-the-Art Method for the Age-Specific Population.

OBJECTIVE: Spatial normalization is an essential process for comparative analyses that heavily depends on the standard brain template used. Brain morphological differences are observed in different populations due to genetic and environmental factors, causing mismatches in regions when the data are normalized to different population templates. Recent studies have indicated differences between Caucasian and East Asian populations as well as within East Asian populations, suggesting the necessity of population-specific brain templates. Thus, this study aimed to construct a Korean young adult age-specific brain template utilizing an advanced method of template construction to update the currently available Korean template. METHODS: The KOR152 template was constructed via affine and nonlinear iterative procedures based on prior studies. We compared the morphological features of different population templates (MNI152, Indian_157, and CN200). The distance and volumetric changes before and after registering the data to these templates were calculated for registration accuracy. RESULTS: The KOR152 global brain features revealed a shorter overall length than the other population templates. The registration accuracy by distance and volumetric change was significantly lower than that of the other population templates, implying that the KOR152 was more accurate than other templates for the young adult Korean population. CONCLUSION: This study provided evidence for the need for a population-specific template that may be more appropriate for structural and functional studies in Korean populations.

Perspectives on Optimized Transcranial Electrical Stimulation Based on Spatial Electric Field Modeling in Humans.

Background: Transcranial electrical stimulation (tES) generates an electric field (or current density) in the brain through surface electrodes attached to the scalp. Clinical significance has been demonstrated, although with moderate and heterogeneous results partly due to a lack of control of the delivered electric currents. In the last decade, computational electric field analysis has allowed the estimation and optimization of the electric field using accurate anatomical head models. This review examines recent tES computational studies, providing a comprehensive background on the technical aspects of adopting computational electric field analysis as a standardized procedure in medical applications. Methods: Specific search strategies were designed to retrieve papers from the Web of Science database. The papers were initially screened based on the soundness of the title and abstract and then on their full contents, resulting in a total of 57 studies. Results: Recent trends were identified in individual- and population-level analysis of the electric field, including head models from non-neurotypical individuals. Advanced optimization techniques that allow a high degree of control with the required focality and direction of the electric field were also summarized. There is also growing evidence of a correlation between the computationally estimated electric field and the observed responses in real experiments. Conclusions: Computational pipelines and optimization algorithms have reached a degree of maturity that provides a rationale to improve tES experimental design and a posteriori analysis of the responses for supporting clinical studies.

CT-guided spatial normalization of nuclear hybrid imaging adapted to enlarged ventricles: Impact on striatal uptake quantification.

INTRODUCTION: Spatial normalization is a prerequisite step for the quantitative analysis of SPECT or PET brain images using volume-of-interest (VOI) template or voxel-based analysis. MRI-guided spatial normalization is the gold standard, but the wide use of PET/CT or SPECT/CT in routine clinical practice makes CT-guided spatial normalization a necessary alternative. Ventricular enlargement is observed with aging, and it hampers the spatial normalization of the lateral ventricles and striatal regions, limiting their analysis. The aim of the present study was to propose a robust spatial normalization method based on CT scans that takes into account features of the aging brain to reduce bias in the CT-guided striatal analysis of SPECT images. METHODS: We propose an enhanced CT-guided spatial normalization pipeline based on SPM12. Performance of the proposed pipeline was assessed on visually normal [123I]-FP-CIT SPECT/CT images. SPM12 default CT-guided spatial normalization was used as reference method. The metrics assessed were the overlap between the spatially normalized lateral ventricles and caudate/putamen VOIs, and the computation of caudate and putamen specific binding ratios (SBR). RESULTS: In total 231 subjects (mean age ± SD = 61.9 ± 15.5 years) were included in the statistical analysis. The mean overlap between the spatially normalized lateral ventricles of subjects and the caudate VOI and the mean SBR of caudate were respectively 38.40 % (± SD = 19.48 %) of the VOI and 1.77 (± 0.79) when performing SPM12 default spatial normalization. The mean overlap decreased to 9.13 % (± SD = 1.41 %, P < 0.001) of the VOI and the SBR of caudate increased to 2.38 (± 0.51, P < 0.0001) when performing the proposed pipeline. Spatially normalized lateral ventricles did not overlap with putamen VOI using either method. The mean putamen SBR value derived from the proposed spatial normalization (2.75 ± 0.54) was not significantly different from that derived from the default SPM12 spatial normalization (2.83 ± 0.52, P > 0.05). CONCLUSION: The automatic CT-guided spatial normalization used herein led to a less biased spatial normalization of SPECT images, hence an improved semi-quantitative analysis. The proposed pipeline could be implemented in clinical routine to perform a more robust SBR computation using hybrid imaging.

Specific-CT brain template construction and retrospective dosimetric comparison study in brain for nasopharyngeal carcinoma patients treated with IMRT or VMAT.

The current Radiotherapy (RT) technology still inevitably irradiated normal brain tissue, causing implicit radiation-induced injury. This study investigates the precise localization and the corresponding radiation dosage of brain regions susceptible to damage in nasopharyngeal carcinoma (NPC) patients following RT. Utilizing the Advanced Normalization Tools (ANTs) package, a computed tomography (CT) brain template was created in the standard Montreal Neurological Institute (MNI) space, based on 803 Chinese NPC patients (T0~T4) who underwent RT. With this template, all patients' CT and RTdose data were registered to the MNI space, and the RTdose distribution characteristics in normal brain tissues were compared for NPC patients treated with Intensity-modulated radiotherapy (IMRT) or Volumetric Modulated Arc Therapy (VMAT), with patients' age and gender as covariates. Analysis of the average dosages indicated that certain areas within the Limbic, Temporal, and Posterior Lobes, the Brainstem, and the Cerebellum Posterior Lobe were exposed to doses exceeding 50 Gy. Inter-group analysis revealed that IMRT delivered higher doses than VMAT to brain regions anterior to the nasopharyngeal tumor, whereas VMAT affected the posterior regions more. Interestingly, VMAT showed a drawback in preserving the normal brain tissues for T4-stage patients. This revealed that the two treatment modalities have unique characteristics in preserving normal brain tissue, each with advantages. With better localization precision, the created CT brain template in MNI space may be beneficial for NPC patients' toxicity and dosimetric analyses.

An MR-based brain template and atlas for optical projection tomography and light sheet fluorescence microscopy in neuroscience.

Introduction: Optical Projection Tomography (OPT) and light sheet fluorescence microscopy (LSFM) are high resolution optical imaging techniques, ideally suited for ex vivo 3D whole mouse brain imaging. Although they exhibit high specificity for their targets, the anatomical detail provided by tissue autofluorescence remains limited. Methods: T1-weighted images were acquired from 19 BABB or DBE cleared brains to create an MR template using serial longitudinal registration. Afterwards, fluorescent OPT and LSFM images were coregistered/normalized to the MR template to create fusion images. Results: Volumetric calculations revealed a significant difference between BABB and DBE cleared brains, leading to develop two optimized templates, with associated tissue priors and brain atlas, for BABB (OCUM) and DBE (iOCUM). By creating fusion images, we identified virus infected brain regions, mapped dopamine transporter and translocator protein expression, and traced innervation from the eye along the optic tract to the thalamus and superior colliculus using cholera toxin B. Fusion images allowed for precise anatomical identification of fluorescent signal in the detailed anatomical context provided by MR. Discussion: The possibility to anatomically map fluorescent signals on magnetic resonance (MR) images, widely used in clinical and preclinical neuroscience, would greatly benefit applications of optical imaging of mouse brain. These specific MR templates for cleared brains enable a broad range of neuroscientific applications integrating 3D optical brain imaging.

Dosimetric comparison of IMRT versus VMAT for advanced nasopharyngeal carcinoma using voxel-based method / 中国医学物理学杂志

Objective To identify the exact locations of the brain being irradiated in advanced nasopharyngeal carcinoma(NPC)patients during radiotherapy,and to analyze the differences in brain dose distribution between advanced NPC patients treated with intensity-modulated radiotherapy(IMRT)and volumetric modulated arc therapy(VMAT).Methods Based on the CT brain template provided by the Montreal Neurological Institute,the brain dose distribution was analyzed with voxel-based method.Results For advanced NPC patients,VMAT plans did not demonstrate superiority in normal brain tissue sparing,while IMRT performed better,with advantages observed in regions such as the brainstem,the posterior lobe of the cerebellum,the anterior lobe of the cerebellum,temporal lobes,occipital lobes,limbic lobes,and certain areas of the subcortical regions.Conclusion IMRT is advantageous over VMAT in protecting the normal brain tissues in advanced NPC patients.

A detailed dosimetric comparative study of IMRT and VMAT in normal brain tissues for nasopharyngeal carcinoma patients treated with radiotherapy.

Background: Radiotherapy (RT) is the primary treatment for nasopharyngeal carcinoma (NPC). However, it can cause implicit RT-induced injury by irradiating normal brain tissue. To date, there have been no detailed reports on the radiated exact location in the brain, the corresponding radiation dose, and their relationship. Methods: We analyzed 803 Chinese NPC patients treated with RT and used a CT brain template in a Montreal Neurological Institute (MNI) space to compare the group differences in RT dose distribution for different RT technologies (IMRT or VMAT). Results: Brain regions that received high doses (>50 Gy) of radiation were mainly located in parts of the temporal and limbic lobes, where radioactive damage often occurs. Brain regions that accepted higher doses with IMRT were mainly located near the anterior region of the nasopharyngeal tumor, while brain regions that accepted higher doses with VMAT were mainly located near the posterior region of the tumor. No significant difference was detected between IMRT and VMAT for T1 stage patients. For T2 stage patients, differences were widely distributed, with VMAT showing a significant dose advantage in protecting the normal brain tissue. For T3 stage patients, VMAT showed an advantage in the superior temporal gyrus and limbic lobe, while IMRT showed an advantage in the posterior cerebellum. For T4 stage patients, VMAT showed a disadvantage in protecting the normal brain tissue. These results indicate that IMRT and VMAT have their own advantages in sparing different organs at risk (OARs) in the brain for different T stages of NPC patients treated with RT. Conclusion: Our approach for analyzing dosimetric characteristics in a standard MNI space for Chinese NPC patients provides greater convenience in toxicity and dosimetry analysis with superior localization accuracy. Using this method, we found interesting differences from previous reports: VMAT showed a disadvantage in protecting the normal brain tissue for T4 stage NPC patients.

Volumetric registration framework for multimodal functional magnetic resonance and optoacoustic tomography of the rodent brain.

Optoacoustic tomography (OAT) provides a non-invasive means to characterize cerebral hemodynamics across an entire murine brain while attaining multi-parametric readouts not available with other modalities. This unique capability can massively impact our understanding of brain function. However, OAT largely lacks the soft tissue contrast required for unambiguous identification of brain regions. Hence, its accurate registration to a reference brain atlas is paramount for attaining meaningful functional readings. Herein, we capitalized on the simultaneously acquired bi-modal data from the recently-developed hybrid magnetic resonance optoacoustic tomography (MROT) scanner in order to devise an image coregistration paradigm that facilitates brain parcellation and anatomical referencing. We evaluated the performance of the proposed methodology by coregistering OAT data acquired with a standalone system using different registration methods. The enhanced performance is further demonstrated for functional OAT data analysis and characterization of stimulus-evoked brain responses. The suggested approach enables better consolidation of the research findings thus facilitating wider acceptance of OAT as a powerful neuroimaging tool to study brain functions and diseases.

Spatial normalization discrepancies between native and MNI152 brain template scans in gamma ventral capsulotomy patients.

In neurosurgery, spatial normalization emerged as a tool to minimize inter-subject variability and study target point locations based on standard coordinates. The Montreal Neurological Institute's 152 brain template (MNI152) has become the most widely utilized in neuroimaging studies, but has been noted to introduce partial volume effects, distortions, and increase structure size in all directions (x/y/z axes). These discrepancies question the accuracy of the MNI template, as well as its utility for studies that examine and form conclusions from group-level data. Given that surgical precision in obsessive-compulsive disorder is essential to patient outcomes, we retrospectively investigated lesion size and location in patients (n = 21) who underwent capsulotomy for intractable OCD, comparing deviations in the native scans to those in standard space. MNI measurements were significantly larger than native measurements across several structures in both coronal and axial slices, and we found that MNI transformation increases the size of many subcortical structures in a significant and proportional way for both females and males. These findings urge caution when using MNI as a reference space, as well as a stronger consideration of population-specific brain templates when examining connectivity-based networks.

Comparative survey of multigraph integration methods for holistic brain connectivity mapping.

One of the greatest scientific challenges in network neuroscience is to create a representative map of a population of heterogeneous brain networks, which acts as a connectional fingerprint. The connectional brain template (CBT), also named network atlas, presents a powerful tool for capturing the most representative and discriminative traits of a given population while preserving its topological patterns. The idea of a CBT is to integrate a population of heterogeneous brain connectivity networks, derived from different neuroimaging modalities or brain views (e.g., structural and functional), into a unified holistic representation. Here we review current state-of-the-art methods designed to estimate well-centered and representative CBT for populations of single-view and multi-view brain networks. We start by reviewing each CBT learning method, then we introduce the evaluation measures to compare CBT representativeness of populations generated by single-view and multigraph integration methods, separately, based on the following criteria: Centeredness, biomarker-reproducibility, node-level similarity, global-level similarity, and distance-based similarity. We demonstrate that the deep graph normalizer (DGN) method significantly outperforms other multi-graph and all single-view integration methods for estimating CBTs using a variety of healthy and disordered datasets in terms of centeredness, reproducibility (i.e., graph-derived biomarkers reproducibility that disentangle the typical from the atypical connectivity variability), and preserving the topological traits at both local and global graph-levels.

Predicting the evolution trajectory of population-driven connectional brain templates using recurrent multigraph neural networks.

The mapping of the time-dependent evolution of the human brain connectivity using longitudinal and multimodal neuroimaging datasets provides insights into the development of neurological disorders and the way they alter the brain morphology, structure and function over time. Recently, the connectional brain template (CBT) was introduced as a compact representation integrating a population of brain multigraphs, where two brain regions can have multiple connections, into a single graph. Given a population of brain multigraphs observed at a baseline timepoint t1, we aim to learn how to predict the evolution of the population CBT at follow-up timepoints t>t1. Such model will allow us to foresee the evolution of the connectivity patterns of healthy and disordered individuals at the population level. Here we present recurrent multigraph integrator network (ReMI-Net⋆) to forecast population templates at consecutive timepoints from a given single timepoint. In particular, we unprecedentedly design a graph neural network architecture to model the changes in the brain multigraph and identify the biomarkers that differentiate between the typical and atypical populations. Addressing such issues is of paramount importance in diagnosing neurodegenerative disorders at early stages and promoting new clinical studies based on the pinned-down biomarker brain regions or connectivities. In this paper, we demonstrate the design and use of the ReMI-Net⋆ model, which learns both the multigraph node level and time level dependencies concurrently. Thanks to its novel graph convolutional design and normalization layers, ReMI-Net⋆ predicts well-centered, discriminative, and topologically sound connectional templates over time. Additionally, the results show that our model outperforms all benchmarks and state-of-the-art methods by comparing and discovering the atypical connectivity alterations over time. Our ReMI-Net⋆ code is available on GitHub at https://github.com/basiralab/ReMI-Net-Star.

Template-based graph registration network for boosting the diagnosis of brain connectivity disorders.

Brain graphs are powerful representations to explore the biological roadmaps of the human brain in its healthy and disordered states. Recently, a few graph neural networks (GNNs) have been designed for brain connectivity synthesis and diagnosis. However, such non-Euclidean deep learning architectures might fail to capture the neural interactions between different brain regions as they are trained without guidance from any prior biological template-i.e., template-free learning. Here we assume that using a population-driven brain connectional template (CBT) that captures well the connectivity patterns fingerprinting a given brain state (e.g., healthy) can better guide the GNN training in its downstream learning task such as classification or regression. To this aim we design a plug-in graph registration network (GRN) that can be coupled with any conventional graph neural network (GNN) so as to boost its learning accuracy and generalizability to unseen samples. Our GRN is a graph generative adversarial network (gGAN), which registers brain graphs to a prior CBT. Next, the registered brain graphs are used to train typical GNN models. Our GRN can be integrated into any GNN working in an end-to-end fashion to boost its prediction accuracy. Our experiments showed that GRN remarkably boosted the prediction accuracy of four conventional GNN models across four neurological datasets.

An artificial-intelligence-based age-specific template construction framework for brain structural analysis using magnetic resonance images.

It is an essential task to construct brain templates and analyze their anatomical structures in neurological and cognitive science. Generally, templates constructed from magnetic resonance imaging (MRI) of a group of subjects can provide a standard reference space for analyzing the structural and functional characteristics of the group. With recent development of artificial intelligence (AI) techniques, it is desirable to explore AI registration methods for quantifying age-specific brain variations and tendencies across different ages. In this article, we present an AI-based age-specific template construction (called ASTC) framework for longitudinal structural brain analysis using T1-weighted MRIs of 646 subjects from 18 to 82 years old collected from four medical centers. Altogether, 13 longitudinal templates were constructed at a 5-year age interval using ASTC, and tissue segmentation and substructure parcellation were performed for analysis across different age groups. The results indicated consistent changes in brain structures along with aging and demonstrated the capability of ASTC for longitudinal neuroimaging study.

Comparison of performance between an F 18 -FDG PET normal brain template and a commercial template using the MIMneuro software.

Purpose: The aim of this study was to create and validate a normal brain template of F 18 -fluorodeoxyglucose ( F 18 - FDG ) uptake using the MIMneuro software to improve clinical practice. Approach: One hundred and nine volunteers underwent an F 18 - FDG positron emission tomography/computed tomography scan. Sixty-three participants with normal Alzheimer's disease (AD) biomarkers were used to create a template. A group of 23 participants with abnormal AD biomarkers and an additional group of 23 participants with normal AD biomarkers were used to validate the performance of the generated template. The MIMneuro software was used for the analysis and template creation. The performance of our newly created template was compared with that of the MIMneuro software template in the validation groups. Results were confirmed by visual analysis by nuclear medicine physicians. Results: Our created template provided higher sensitivity, specificity, positive predictive value, and negative predictive value (NPV; 90%, 97.83%, 100%, and 100%, respectively) than did the MIMneuro template when using the positive validation group. Similarly, slightly higher performance was observed for our template than for the MIMneuro template in the negative validation group (the highest specificity and NPV were 100% and 100%, respectively). Conclusions: Our normal brain template for F 18 - FDG was shown to be clinically useful because it enabled more accurate discrimination between aging brain and patients with AD. Thus, the template may improve the accuracy of AD diagnoses.

Development of an automatic multiplanar reconstruction processing method for head computed tomography.

BACKGROUND: Head computed tomography (CT) is a commonly used imaging modality in radiology facilities. Since multiplanar reconstruction (MPR) processing can produce different results depending on the medical staff in charge, there is a possibility that the antemortem and postmortem images of the same person could be assessed and identified differently. OBJECTIVE: To propose and test a new automatic MPR method in order to address and overcome this limitation. METHODS: Head CT images of 108 cases are used. We employ the standardized transformation of statistical parametric mapping 8. The affine transformation parameters are obtained by standardizing the captured CT images. Automatic MPR processing is performed by using this parameter. The sphenoidal sinus of the orbitomeatal cross section of the automatic MPR processing of this study and the conventional manual MPR processing are cropped with a matrix size of 128×128, and the value of zero mean normalized correlation coefficient is calculated. RESULTS: The computed zero mean normalized cross-correlation coefficient (Rzncc) of≥0.9, 0.8≤Rzncc < 0.9 and 0.7≤Rzncc < 0.8 are achieved in 105 cases (97.2%), 2 cases (1.9%), and 1 case (0.9%), respectively. The average Rzncc was 0.96±0.03. CONCLUSION: Using the proposed new method in this study, MPR processing with guaranteed accuracy is efficiently achieved.

Spatial normalization and quantification approaches of PET imaging for neurological disorders.

Quantification approaches of positron emission tomography (PET) imaging provide user-independent evaluation of pathophysiological processes in living brains, which have been strongly recommended in clinical diagnosis of neurological disorders. Most PET quantification approaches depend on spatial normalization of PET images to brain template; however, the spatial normalization and quantification approaches have not been comprehensively reviewed. In this review, we introduced and compared PET template-based and magnetic resonance imaging (MRI)-aided spatial normalization approaches. Tracer-specific and age-specific PET brain templates were surveyed between 1999 and 2021 for 18F-FDG, 11C-PIB, 18F-Florbetapir, 18F-THK5317, and etc., as well as adaptive PET template methods. Spatial normalization-based PET quantification approaches were reviewed, including region-of-interest (ROI)-based and voxel-wise quantitative methods. Spatial normalization-based ROI segmentation approaches were introduced, including manual delineation on template, atlas-based segmentation, and multi-atlas approach. Voxel-wise quantification approaches were reviewed, including voxel-wise statistics and principal component analysis. Certain concerns and representative examples of clinical applications were provided for both ROI-based and voxel-wise quantification approaches. At last, a recipe for PET spatial normalization and quantification approaches was concluded to improve diagnosis accuracy of neurological disorders in clinical practice.

Automatic detection of neuromelanin and iron in the midbrain nuclei using a magnetic resonance imaging-based brain template.

Parkinson disease (PD) is a chronic progressive neurodegenerative disorder characterized pathologically by early loss of neuromelanin (NM) in the substantia nigra pars compacta (SNpc) and increased iron deposition in the substantia nigra (SN). Degeneration of the SN presents as a 50 to 70% loss of pigmented neurons in the ventral lateral tier of the SNpc at the onset of symptoms. Also, using magnetic resonance imaging (MRI), iron deposition and volume changes of the red nucleus (RN), and subthalamic nucleus (STN) have been reported to be associated with disease status and rate of progression. Further, the STN serves as an important target for deep brain stimulation treatment in advanced PD patients. Therefore, an accurate in-vivo delineation of the SN, its subregions and other midbrain structures such as the RN and STN could be useful to better study iron and NM changes in PD. Our goal was to use an MRI template to create an automatic midbrain deep gray matter nuclei segmentation approach based on iron and NM contrast derived from a single, multiecho magnetization transfer contrast gradient echo (MTC-GRE) imaging sequence. The short echo TE = 7.5 ms data from a 3D MTC-GRE sequence was used to find the NM-rich region, while the second echo TE = 15 ms was used to calculate the quantitative susceptibility map for 87 healthy subjects (mean age ± SD: 63.4 ± 6.2 years old, range: 45-81 years). From these data, we created both NM and iron templates and calculated the boundaries of each midbrain nucleus in template space, mapped these boundaries back to the original space and then fine-tuned the boundaries in the original space using a dynamic programming algorithm to match the details of each individual's NM and iron features. A dual mapping approach was used to improve the performance of the morphological mapping of the midbrain of any given individual to the template space. A threshold approach was used in the NM-rich region and susceptibility maps to optimize the DICE similarity coefficients and the volume ratios. The results for the NM of the SN as well as the iron containing SN, STN, and RN all indicate a strong agreement with manually drawn structures. The DICE similarity coefficients and volume ratios for these structures were 0.85, 0.87, 0.75, and 0.92 and 0.93, 0.95, 0.89, 1.05, respectively, before applying any threshold on the data. Using this fully automatic template-based deep gray matter mapping approach, it is possible to accurately measure the tissue properties such as volumes, iron content, and NM content of the midbrain nuclei.

Automatic segmentation of deep grey nuclei using a high-resolution 7T magnetic resonance imaging atlas-Quantification of T1 values in healthy volunteers.

We present a new consensus atlas of deep grey nuclei obtained by shape-based averaging of manual segmentation of two experienced neuroradiologists and optimized from 7T MP2RAGE images acquired at (.6 mm)3 in 60 healthy subjects. A group-wise normalization method was used to build a high-contrast and high-resolution T1 -weighted brain template (.5 mm)3 using data from 30 out of the 60 controls. Delineation of 24 deep grey nuclei per hemisphere, including the claustrum and 12 thalamic nuclei, was then performed by two expert neuroradiologists and reviewed by a third neuroradiologist according to tissue contrast and external references based on the Morel atlas. Corresponding deep grey matter structures were also extracted from the Morel and CIT168 atlases. The data-derived, Morel and CIT168 atlases were all applied at the individual level using non-linear registration to fit the subject reference and to extract absolute mean quantitative T1 values derived from the 3D-MP2RAGE volumes, after correction for residual B1+ biases. Three metrics (the Dice and the volumetric similarity coefficients and a novel Hausdorff distance) were used to estimate the inter-rater agreement of manual MRI segmentation and inter-atlas variability, and these metrics were measured to quantify biases due to image registration, and their impact on the measurements of the quantitative T1 values was highlighted. This represents a fully automated segmentation process permitting the extraction of unbiased normative T1 values in a population of young healthy controls as a reference for characterizing subtle structural alterations of deep grey nuclei relevant to a range of neurological diseases.

Non-parametric MRI Brain Atlas for the Polish Population.

Introduction: The application of magnetic resonance imaging (MRI) to acquire detailed descriptions of the brain morphology in vivo is a driving force in brain mapping research. Most atlases are based on parametric statistics, however, the empirical results indicate that the population brain tissue distributions do not exhibit exactly a Gaussian shape. Our aim was to verify the population voxel-wise distribution of three main tissue classes: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), and to construct the brain templates for the Polish (Upper Silesian) healthy population with the associated non-parametric tissue probability maps (TPMs) taking into account the sex and age influence. Material and Methods: The voxel-wise distributions of these tissues were analyzed using the Shapiro-Wilk test. The non-parametric atlases were generated from 96 brains of the ethnically homogeneous, neurologically healthy, and radiologically verified group examined in a 3-Tesla MRI system. The standard parametric tissue proportion maps were also calculated for the sake of comparison. The maps were compared using the Wilcoxon signed-rank test and Kolmogorov-Smirnov test. The volumetric results segmented with the parametric and non-parametric templates were also analyzed. Results: The results confirmed that in each brain structure (regardless of the studied sub-population) the data distribution is skewed and apparently not Gaussian. The determined non-parametric and parametric templates were statistically compared, and significant differences were found between the maps obtained using both measures (the maps of GM, WM, and CSF). The impacts of applying the parametric and non-parametric TPMs on the segmentation process were also compared. The GM volumes are significantly greater when using the non-parametric atlas in the segmentation procedure, while the CSF volumes are smaller. Discussion and Conclusion: To determine the population atlases the parametric measures are uncritically and widely used. However, our findings suggest that the mean and parametric measures of such skewed distribution may not be the most appropriate summary statistic to find the best spatial representations of the structures in a standard space. The non-parametric methodology is more relevant and universal than the parametric approach in constructing the MRI brain atlases.

Ratlas-LH: An MRI template of the Lister hooded rat brain with stereotaxic coordinates for neurosurgical implantations.

There is currently no brain atlas available to specifically determine stereotaxic coordinates for neurosurgery in Lister hooded rats despite the popularity of this strain for behavioural neuroscience studies in the United Kingdom and elsewhere. We have created a dataset, which we refer to as 'Ratlas-LH' (for Lister hooded). Ratlas-LH combines in vivo magnetic resonance images of the brain of young adult male Lister hooded rats with ex vivo micro-computed tomography images of the ex vivo skull, as well as a set of delineations of brain regions, adapted from the Waxholm Space Atlas of the Sprague Dawley Rat Brain. Ratlas-LH was produced with an isotropic resolution of 0.15 mm. It has been labelled in such a way as to provide a stereotaxic coordinate system for the determination of distances relative to the skull landmark of bregma. We have demonstrated that the atlas can be used to determine stereotaxic coordinates to accurately target brain regions in the Lister hooded rat brain. Ratlas-LH is freely available to facilitate neurosurgical procedures in the Lister hooded rat.