Anatomical Brain Structures Research Articles

High resolution MRI anatomy of the cat brain at 3Tesla

Feline models of neurologic diseases, such as lysosomal storage diseases, leukodystrophies, Parkinson's disease, stroke and NeuroAIDS, accurately recreate many aspects of human disease allowing for comparative study of neuropathology and the testing of novel therapeutics. Here we describe in vivo visualization of fine structures within the feline brain that were previously only visible post mortem. 3Tesla MR images were acquired using T1-weighted (T1w) 3D magnetization-prepared rapid gradient echo (MPRAGE) sequence (0.4mm isotropic resolution) and T2-weighted (T2w) turbo spin echo (TSE) images (0.3mm×0.3mm×1mm resolution). Anatomic structures were identified based on feline and canine histology. T2w high resolution MR images with detailed structural identification are provided in transverse, sagittal and dorsal planes. T1w MR images are provided electronically in three dimensions for unrestricted spatial evaluation. Many areas of the feline brain previously unresolvable on MRI are clearly visible in three orientations, including the dentate, interpositus and fastigial cerebellar nuclei, cranial nerves, lateral geniculate nucleus, optic radiation, cochlea, caudal colliculus, temporal lobe, precuneus, spinocerebellar tract, vestibular nuclei, reticular formation, pyramids and rostral and middle cerebral arteries. Additionally, the feline brain is represented in three dimensions for the first time. These data establish normal appearance of detailed anatomical structures of the feline brain, which provide reference when evaluating neurologic disease or testing efficacy of novel therapeutics in animal models.

Imaging of Rayleigh damping properties of the in vivo brain using parametric Magnetic Resonance Elastography.

The three parameter Rayleigh damping (RD) model applied to time-harmonic Magnetic Resonance Elastography (MRE) has potential to better characterise fluid-saturated tissue systems. However, it is not uniquely identifiable at a single frequency. One solution to this problem involves simultaneous inverse problem solution of multiple input frequencies over a broad range. As data is often limited, an alternative elegant solution is a parametric RD reconstruction, where one of the RD parameters (μI or ρI) is globally constrained allowing accurate identification of the remaining two RD parameters. This research examines this parametric inversion approach as applied to in vivo brain imaging.Overall, success was achieved in reconstruction of the real shear modulus (μR) that showed good correlation with brain anatomical structures. The mean and standard deviation shear stiffness values of the white and gray matter were found to be 3 ± 0.11 kPa and 2.2 ± 0.11 kPa, respectively. Parametric results with globally constrained μI indicate that selecting a reasonable value for the μI distribution has a major effect on the reconstructed ρI image and concomitant damping ratio (Ξd). More specifically, the reconstructed ρI image using a realistic μI=333 Pa value representative of a greater portion of the brain tissue showed more accurate differentiation of the ventricles within the intracranial matter compared to μI=1000 Pa, and Ξd reconstruction with μI=330 Pa accurately captured the higher damping levels expected within the vicinity of the ventricles.Parametric RD reconstruction shows potential for accurate recovery of the stiffness characteristics and overall damping profile of the in vivo living brain despite its underlying limitations.

Tri-linear interpolation-based cerebral white matter fiber imaging.

Diffusion tensor imaging is a unique method to visualize white matter fibers three-dimensionally, non-invasively and in vivo, and therefore it is an important tool for observing and researching neural regeneration. Different diffusion tensor imaging-based fiber tracking methods have been already investigated, but making the computing faster, fiber tracking longer and smoother and the details shown clearer are needed to be improved for clinical applications. This study proposed a new fiber tracking strategy based on tri-linear interpolation. We selected a patient with acute infarction of the right basal ganglia and designed experiments based on either the tri-linear interpolation algorithm or tensorline algorithm. Fiber tracking in the same regions of interest (genu of the corpus callosum) was performed separately. The validity of the tri-linear interpolation algorithm was verified by quantitative analysis, and its feasibility in clinical diagnosis was confirmed by the contrast between tracking results and the disease condition of the patient as well as the actual brain anatomy. Statistical results showed that the maximum length and average length of the white matter fibers tracked by the tri-linear interpolation algorithm were significantly longer. The tracking images of the fibers indicated that this method can obtain smoother tracked fibers, more obvious orientation and clearer details. Tracking fiber abnormalities are in good agreement with the actual condition of patients, and tracking displayed fibers that passed though the corpus callosum, which was consistent with the anatomical structures of the brain. Therefore, the tri-linear interpolation algorithm can achieve a clear, anatomically correct and reliable tracking result.

A new method for structural volume analysis of longitudinal brain MRI data and its application in studying the growth trajectories of anatomical brain structures in childhood

Cross-sectional analysis of longitudinal anatomical magnetic resonance imaging (MRI) data may be suboptimal as each dataset is analyzed independently. In this study, we evaluate how much variability can be reduced by analyzing structural volume changes in longitudinal data using longitudinal analysis. We propose a two-part pipeline that consists of longitudinal registration and longitudinal classification. The longitudinal registration step includes the creation of subject-specific linear and nonlinear templates that are then registered to a population template. The longitudinal classification step comprises a four-dimensional expectation-maximization algorithm, using a priori classes computed by averaging the tissue classes of all time points obtained cross-sectionally.To study the impact of these two steps, we apply the framework completely (“LL method”: Longitudinal registration and Longitudinal classification) and partially (“LC method”: Longitudinal registration and Cross-sectional classification) and compare these with a standard cross-sectional framework (“CC method”: Cross-sectional registration and Cross-sectional classification).The three methods are applied to (1) a scan–rescan database to analyze reliability and (2) the NIH pediatric population to compare gray matter growth trajectories evaluated with a linear mixed model.The LL method, and the LC method to a lesser extent, significantly reduced the variability in the measurements in the scan–rescan study and gave the best-fitted gray matter growth model with the NIH pediatric MRI database. The results confirm that both steps of the longitudinal framework reduce variability and improve accuracy in comparison with the cross-sectional framework, with longitudinal classification yielding the greatest impact.Using the improved method to analyze longitudinal data, we study the growth trajectories of anatomical brain structures in childhood using the NIH pediatric MRI database. We report age- and gender-related growth trajectories of specific regions of the brain during childhood that could be used as a reference in studying the impact of neurological disorders on brain development.

Decision-making in moral conflict: A brain electrical tomography analysis

Background: The study of moral behavior has expanded to the area of neuroscience, with an increasing interest in understanding the neuronal network and anatomical brain structures involved in this complex human behavior. We studied decision-making during moral judgments using a philosophical moral dilemma, namely the trolley and footbridge dilemmas. Recent studies have shown that the trolley dilemma activates rational brain areas whereas the footbridge dilemma activates emotional brain areas. Our aim was to compare activation of the emotional network during personal moral conflict (footbridge dilemma) with impersonal moral conflict (trolley dilemma) through Standardized Low Resolution Electromagnetic Tomography (sLORETA). We detected a high beta 3 oscillation source located bilaterally in the insular cortex as well as in the anterior and posterior cingulate gyri. Furthermore, we detected hypoactivation in alpha 1 oscillation source located in the anterior and posterior cingulate cortices. Our results confirmed the relationship between personal moral decision- making and the emotional network. We propose that brain electrical tomography analyses can make an important contribution towards the neuroscience of decision-making

Factor structure of regional cerebral blood flow and glucose metabolism rate as a tool to study the default mode of the brain

The functional connectivity of anatomical and functional brain structures in the state of operational rest was assessed on the basis of positron emission tomography (PET) data to study the so-called default mode of the brain, i.e., the brain’s spontaneous activity at rest. It is concluded that the possibility of identifying neuroanatomical systems of the default mode (default mode network) in routine clinical PET studies of the cerebral blood flow and glucose metabolism is important for studying the functional organization of the brain in the normal state and its rearrangements in pathologies.

Two-Photon Autofluorescence Spectroscopy of Mouse Brains using Serial Two-Photon Tomography

Two-photon microscopy of endogenous fluorescence has proven to be a powerful method for quantification of tissue structure and biochemistry in 3D and with micron resolution. In particular, autofluorescence of mouse brain tissue provides potentially rich signatures for identifying brain anatomical regions and structures without the need for traditional Nissl or other exogenous staining. We quantitatively investigate the identities and distributions of endogneous fluorophores in whole mouse brains using Serial Two-Photon (STP) tomography. We vary the two-photon excitation wavelength over the range of 720 to 980 nm while simultaneously performing spectrally resolved imaging from 400 nm to 650 nm and report on background autofluorescence for use in segmenting, constructing brain atlases, and identifying specific neuronal regions.View Large Image | View Hi-Res Image | Download PowerPoint Slide

Automated Analysis of the Preterm Neonatal Cortex at Term Equivalent Age and Correlation with Cognitive Outcome at 1 Year Corrected Age

Background: Preterm birth (< 32weeks) is associated with differences in brain anatomical structure and adverse neurodevelopmental outcome. White matter volume and IQ have been shown to be reduced in ex-preterm infants in adolescence. Subtle differences in cortical folding at term equivalent age may be useful in predicting long-term neurodevelopmental outcome.Methods: We segment grey matter (GM) and white matter (WM) using a maximum a posteriori expectation maximisation routine with adaptive priors. The GM/WM surface is extracted using a level set, and at every point we define the local shape-index, describing how cup-like or saddle-like the surface is (fig1A), and the curvedness, describing the distortion of the surface relative to that of a flat sheet. The distributions of each index may be summarised in a histogram (fig1B) and we use the histogram entropy as a potential biomarker.A: GM/WM interface coloured by shape-index B: Population average local surface histograms of curvedness and shape index overlaid on single subject GM parcellation C. Correlation between shape index & combined cognitive/langauge outcome.Results: 39 infants without obvious structural abnormalities on MRI had brain volumes and cortical folding analysed from T1-weighted images. WM volume correlates significantly with gestation at birth (p< 0.05). Shape index and curvedness did not correlate with gestation at birth. Shape index correlates with combined cognitive / language outcome at 1 year using the Bayley III scales of infant development (fig1C).Conclusion: Cortical folding analysis may be useful, possibly in combination with white matter volumes in the development of biomarkers of neurodevelopmental outcome.

Scale-Free Modulation of Resting-State Neuronal Oscillations Reflects Prolonged Brain Maturation in Humans

Human neuronal circuits undergo life-long functional reorganization with profound effects on cognition and behavior. Well documented prolonged development of anatomical brain structures includes white and gray matter changes that continue into the third decade of life. We investigated resting-state EEG oscillations in 1433 subjects from 5 to 71 years. Neuronal oscillations exhibit scale-free amplitude modulation as reflected in power-law decay of autocorrelations--also known as long-range temporal correlations (LRTC)--which was assessed by detrended fluctuation analysis. We observed pronounced increases in LRTC from childhood to adolescence, during adolescence, and even into early adulthood (∼25 years of age) after which the temporal structure stabilized. A principal component analysis of the spatial distribution of LRTC revealed increasingly uniform scores across the scalp. Together, these findings indicate that the scale-free modulation of resting-state oscillations reflects brain maturation, and suggests that scaling analysis may prove useful as a biomarker of pathophysiology in neurodevelopmental disorders such as attention deficit hyperactivity disorder and schizophrenia.

Sub‐millimeter isotropic MRI for segmentation of subcortical brain regions and brain visualization

To evaluate a rapid sub-millimeter isotropic spoiled gradient-echo (nonselective SPGR) to facilitate the brain subcortical segmentation and the visualization of brain volume compared with the commonly accepted inversion recovery-prepared SPGR (SPGR-IR) technique. The feasibility of the nonselective SPGR was evaluated for two segmentation algorithms. FAST was used to segment the brain into constituent tissue classes (white matter, gray matter, cerebrospinal fluid) and FreeSurfer was used to segment specific subcortical structures (hippocampus, caudate, putamen, and thalamus). Localized apparent signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) values for nonselective SPGR and the SPGR-IR were compared for the studied subcortical regions. The three-dimensional volume rendering was generated to evaluate the nonselective SPGR and the SPGR-IR for brain visualization. In basal ganglia regions, nonselective SPGR allows for consistent segmentation results for both FAST and FreeSurfer. This sequence also better differentiated gray/white matter compared with SPGR-IR. An approximate two-fold improvement of image quality in apparent SNR and CNR was indicated for subcortical brain anatomical structures with nonselective SPGR versus SPGR-IR. The nonselective SPGR improved clarity and yielded a more realistic depiction of the brain surface for visualization compared with SPGR-IR. Compared with SPGR-IR, nonselective SPGR allows for consistent segmentation results for basal ganglia regions and improved clarity for visualization of the brain.

A Hybrid System Using Symbolic and Numeric Knowledge for the Semantic Annotation of Sulco-Gyral Anatomy in Brain MRI Images

This paper describes an interactive system for the semantic annotation of brain magnetic resonance images. The system uses both a numerical atlas and symbolic knowledge of brain anatomical structures depicted using the Semantic Web standards. This knowledge is combined with graphical data, automatically extracted from the images by imaging tools. The annotations of parts of gyri and sulci, in a region of interest, rely on constraint satisfaction problem solving and description logics inferences. The system is run on a client-server architecture, using Web services and including a sophisticated visualization tool. An evaluation of the system was done using normal (healthy) and pathological cases. The results obtained so far demonstrate that the system produces annotations with high precision and quality.

Optimization of in vivo high-resolution DTI of non-human primates on a 3T human scanner

Magnetic resonance (MR) diffusion tensor imaging (DTI) has emerged as a unique technique to reveal small anatomical structures of brain by characterizing the diffusion process of water molecules within an image voxel. Combined with fiber tractography techniques, DTI can be further used to reveal white matter fibers and connectivity in the brain non-invasively. The non-human primate brain study provides important supplemental means for human brain exploration since the two species share close anatomical and functional similarities. There is therefore increasing interest in in vivo non-human primate DTI studies. However, several technical challenges need to be addressed to perform non-human primate brain DTI and fiber tractography. We have established an imaging protocol together with a post-acquisition procedure for high-resolution in vivo non-human primate DTI studies using a 3T human clinical scanner. Data acquired with this procedure is appropriate for accurate diffusion tensor quantification and fiber tractography, and is accessible within an acceptable scan time. We investigated in detail the effects of spatial resolution and SNR on diffusion tensor-derived quantities and fiber tractography. Our results should be of general utility for implementation of in vivo non-human primate DTI studies.

Morphometric analysis of brain images with reduced number of statistical tests: a study on the gender-related differentiation of the corpus callosum.

We evaluate the feasibility of applying dynamic recursive partitioning (DRP), an image analysis technique, to perform morphometric analysis. We apply DRP to detect and characterize discriminative morphometric characteristics between anatomical brain structures from different groups of subjects. Our method reduces the number of statistical tests, commonly required by pixel-wise statistics, alleviating the effect of the multiple comparison problem. The main idea of DRP is to partition the two-dimensional (2D) image adaptively into progressively smaller subregions until statistically significant discriminative regions are detected. The partitioning process is guided by statistical tests applied on groups of pixels. By performing statistical tests on groups of pixels rather than on individual pixels, the number of statistical tests is effectively reduced. This reduction of statistical tests restricts the effect of the multiple comparison problem (i.e., type-I error). We demonstrate an application of DRP for detecting gender-related morphometric differentiation of the corpus callosum. DRP was applied to template deformation fields computed from registered magnetic resonance images of the corpus callosum to detect regions of significant expansion or contraction between female and male subjects. DRP was able to detect regions comparable to those of pixel-wise analysis, while reducing the number of required statistical tests up to almost 50%. The detected regions were in agreement with findings previously reported in the literature. Statistically significant discriminative morphological variability was detected in the posterior corpus callosum region, the isthmus and the anterior corpus callosum. In addition, by operating on groups of pixels, DRP appears to be less prone to detecting spatially diffused and isolated outlier pixels as significant. DRP can be a viable approach for detecting discriminative morphometric characteristics among groups of subjects, having the potential to alleviate the multiple comparisons' effect by significantly reducing the number of required statistical tests.

Knife‐edge scanning microscopy for imaging and reconstruction of three‐dimensional anatomical structures of the mouse brain

Anatomical information at the cellular level is important in many fields, including organ systems development, computational biology and informatics. Creating data sets at resolutions that provide enough detail to reconstruct cellular structures across tissue volumes from 1 to 100 mm(3) has proven to be difficult and time-consuming. In this paper, we describe a new method for staining and imaging large volumes of tissue at sub-micron resolutions. Serial sections are cut using an automated ultra-microtome, whereas concurrently each section is imaged through a light microscope with a high-speed line-scan camera. This technique, knife-edge scanning microscopy, allows us to view and record large volumes of tissue in a relatively small amount of time (approximately 7 mm(2) s(-1)). The resolution and scanning speed of knife-edge scanning microscopy provides a new method for imaging tissue at sufficient resolution to reconstruct maps of cellular distribution and morphology. We show that these techniques preserve the alignment of serial sections accurately enough to allow for reconstruction of neuronal processes and microvasculature. Expanding these techniques to other tissues opens up the possibility of creating fully reconstructed cellular maps of entire organs.

Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models

In this paper, a hybrid discriminative/generative model for brain anatomical structure segmentation is proposed. The learning aspect of the approach is emphasized. In the discriminative appearance models, various cues such as intensity and curvatures are combined to locally capture the complex appearances of different anatomical structures. A probabilistic boosting tree (PBT) framework is adopted to learn multiclass discriminative models that combine hundreds of features across different scales. On the generative model side, both global and local shape models are used to capture the shape information about each anatomical structure. The parameters to combine the discriminative appearance and generative shape models are also automatically learned. Thus, low-level and high-level information is learned and integrated in a hybrid model. Segmentations are obtained by minimizing an energy function associated with the proposed hybrid model. Finally, a grid-face structure is designed to explicitly represent the 3-D region topology. This representation handles an arbitrary number of regions and facilitates fast surface evolution. Our system was trained and tested on a set of 3-D magnetic resonance imaging (MRI) volumes and the results obtained are encouraging.

Hierarchical microimaging for multiscale analysis of large vascular networks

There is a wide range of diseases and normal physiological processes that are associated with alterations of the vascular system in organs. Ex vivo imaging of large vascular networks became feasible with recent developments in microcomputed tomography (μCT). Current methods permit to visualize only limited numbers of physically excised regions of interests (ROIs) from larger samples. We developed a method based on modified vascular corrosion casting (VCC), scanning electron microscopy (SEM), and desktop and synchrotron radiation μCT (SRμCT) technologies to image vasculature at increasing levels of resolution, also referred to as hierarchical imaging. This novel approach allows nondestructive 3D visualization and quantification of large microvascular networks, while retaining a precise anatomical context for ROIs scanned at very high resolution. Scans of entire mouse brain VCCs were performed at 16-μm resolution with a desktop μCT system. Custom-made navigation software with a ROI selection tool enabled the identification of anatomical brain structures and precise placement of multiple ROIs. These were then scanned at 1.4-μm voxel size using SRμCT and a local tomography setup. A framework was developed for fast sample positioning, precise selection of ROIs, and sequential high-throughput scanning of a large numbers of brain VCCs. Despite the use of local tomography, exceptional image quality was achieved with SRμCT. This method enables qualitative and quantitative assessment of vasculature at unprecedented resolution and volume with relatively high throughput, opening new possibilities to study vessel architecture and vascular alterations in models of disease.

Method for preparing permanent brain slices and serial slice images for education and MRI correlation

It is important to understand the anatomical structures of the human brain in horizontal planes. Serially sectioned brain slices can easily be made with a meat slicer. The objective of this research was to enhance the educational value of serial brain slices made with a meat slicer through various applications. Two brains were taken out of two cadavers and embedded with gelatin solution to make two brain blocks. The first brain block was serially sectioned at 5 mm thickness using a meat slicer to make 28 horizontal brain slices. Each brain slice was embedded with a synthetic resin mixture to make 28 permanent specimens. The second brain block was magnetic resonance-scanned at 1.4 mm thickness to make 130 horizontal magnetic resonance images, then serially sectioned at the same thickness using the meat slicer to make 130 horizontal brain slices. Each brain slice was scanned into a computer to make a series of slice images. Ten anatomical structures in the slice images were outlined to make segmented images. Corresponding magnetic resonance images, slice images, and segmented images were stacked and volume-reconstructed to make three-dimensional images, which were sectioned and rotated at free angles. We show that the serial brain slices made with a meat slicer can be permanently preserved and used in a variety of educational settings. Anat Rec (Part B: New Anat) 289B:64-71, 2006. (c) 2006 Wiley-Liss, Inc.

Content-based image database system for epilepsy

We have designed and implemented a human brain multi-modality database system with content-based image management, navigation and retrieval support for epilepsy. The system consists of several modules including a database backbone, brain structure identification and localization, segmentation, registration, visual feature extraction, clustering/classification and query modules. Our newly developed anatomical landmark localization and brain structure identification method facilitates navigation through an image data and extracts useful information for segmentation, registration and query modules. The database stores T1-, T2-weighted and FLAIR MRI and ictal/interictal SPECT modalities with associated clinical data. We confine the visual feature extractors within anatomical structures to support semantically rich content-based procedures. The proposed system serves as a research tool to evaluate a vast number of hypotheses regarding the condition such as resection of the hippocampus with a relatively small volume and high average signal intensity on FLAIR. Once the database is populated, using data mining tools, partially invisible correlations between different modalities of data, modeled in database schema, can be discovered. The design and implementation aspects of the proposed system are the main focus of this paper.

Study of the genesis of pathological EEG patterns in tumor and traumatic lesions of the human brain

Computer-assisted superimposition of the equivalent dipole sources (EDSs) of the main pathological EEG patterns with the results of computer tomography and magnetic resonance imaging of the brain was performed for patients with organic CNS lesions. The greatest degree of structural determination was revealed for local and lateralized (in a traumatic injury) slow waves, whose EDSs mapped to the perifocal zone of the injury focus. The EDSs of epileptiform patterns could be located in the zone of damaged brain substance or in the intact hemisphere (when the limbic structures were damaged), reflecting the formation of secondary functional foci in the latter case. Bilateral paroxysmal patterns were more frequently determined by the functional state of the medial brain structures. The EDSs of pathological (diencephalic, hippocampal) α activity were closely associated with the corresponding anatomical brain structures, although without a strict dependence on the degree of their morphological intactness.

An anatomical fetal brain structure and a normal variant mimicking anomalies on routine neurosonographic imaging: report of two cases

We present two cases in which an anatomical structure, the calcar avis, and a normal variant, a bifid choroid plexus, mimicked abnormalities on routine prenatal ultrasound examination. To the best of our knowledge these pitfalls have only been described in neonates. A familiarity with these false images is important to avoid erroneous diagnoses.