3-Dimensional Diffusion Tensor Imaging (DTI) Atlas of the Rat Brain

Anatomical atlases play an important role in the analysis of neuroimaging data in rodent neuroimaging studies. Having a high resolution, detailed atlas not only can expand understanding of rodent brain anatomy, but also enables automatic segmentation of new images, thus greatly increasing the efficiency of future analysis when applied to new data. These atlases can be used to analyze new scans of individual cases using a variety of automated segmentation methods. This project seeks to develop a set of detailed 3D anatomical atlases of the brain at postnatal day 5 (P5), 14 (P14), and adults (P72) in Sprague-Dawley rats. Our methods consisted of first creating a template image based on fixed scans of control rats, then manually segmenting various individual brain regions on the template. Using itk-SNAP software, subcortical and cortical regions, including both white matter and gray matter structures, were manually segmented in the axial, sagittal, and coronal planes. The P5, P14, and P72 atlases had 39, 45, and 29 regions segmented, respectively. These atlases have been made available to the broader research community.     

Dynamic Data Visualization with Weave and Brain Choropleths

This article introduces the neuroimaging community to the dynamic visualization workbench, Weave (https://www.oicweave.org/), and a set of enhancements to allow the visualization of brain maps. The enhancements comprise a set of brain choropleths and the ability to display these as stacked slices, accessible with a slider. For the first time, this allows the neuroimaging community to take advantage of the advanced tools already available for exploring geographic data. Our brain choropleths are modeled after widely used geographic maps but this mashup of brain choropleths with extant visualization software fills an important neuroinformatic niche. To date, most neuroinformatic tools have provided online databases and atlases of the brain, but not good ways to display the related data (e.g., behavioral, genetic, medical, etc). The extension of the choropleth to brain maps allows us to leverage general-purpose visualization tools for concurrent exploration of brain images and related data. Related data can be represented as a variety of tables, charts and graphs that are dynamically linked to each other and to the brain choropleths. We demonstrate that the simplified region-based analyses that underlay choropleths can provide insights into neuroimaging data comparable to those achieved by using more conventional methods. In addition, the interactive interface facilitates additional insights by allowing the user to filter, compare, and drill down into the visual representations of the data. This enhanced data visualization capability is useful during the initial phases of data analysis and the resulting visualizations provide a compelling way to publish data as an online supplement to journal articles.     

A Magnetic Resonance Image Based Atlas of the Rabbit Brain for Automatic Parcellation

Rabbit brain has been used in several works for the analysis of neurodevelopment. However, there are not specific digital rabbit brain atlases that allow an automatic identification of brain regions, which is a crucial step for various neuroimage analyses, and, instead, manual delineation of areas of interest must be performed in order to evaluate a specific structure. For this reason, we propose an atlas of the rabbit brain based on magnetic resonance imaging, including both structural and diffusion weighted, that can be used for the automatic parcellation of the rabbit brain. Ten individual atlases, as well as an average template and probabilistic maps of the anatomical regions were built. In addition, an example of automatic segmentation based on this atlas is described.     

The NeuARt II system: a viewing tool for neuroanatomical data based on published neuroanatomical atlases

Background  

Anatomical studies of neural circuitry describing the basic wiring diagram of the brain produce intrinsically spatial, highly complex data of great value to the neuroscience community. Published neuroanatomical atlases provide a spatial framework for these studies. We have built an informatics framework based on these atlases for the representation of neuroanatomical knowledge. This framework not only captures current methods of anatomical data acquisition and analysis, it allows these studies to be collated, compared and synthesized within a single system.     

Large-scale automated synthesis of human functional neuroimaging data

The rapid growth of the literature on neuroimaging in humans has led to major advances in our understanding of human brain function but has also made it increasingly difficult to aggregate and synthesize neuroimaging findings. Here we describe and validate an automated brain-mapping framework that uses text-mining, meta-analysis and machine-learning techniques to generate a large database of mappings between neural and cognitive states. We show that our approach can be used to automatically conduct large-scale, high-quality neuroimaging meta-analyses, address long-standing inferential problems in the neuroimaging literature and support accurate 'decoding' of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results have validated a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale.     

Bayesian estimation of probabilistic atlas for tissue segmentation

Automatic anatomical brain image segmentation is still a challenge. In particular, algorithms have to address the partial volume effect (PVE) as well as the variability of the gray level of internal brain structures which may appear closer to gray matter (GM) than white matter (WM). Atlas based segmentation is one solution as it brings prior information. For such tasks, probabilistic atlases are very useful as they take account of the PVE information. In this paper, we provide a detailed analysis of a generative statistical model based on dense deformable templates that represents several tissue types observed in medical images. The inputs are gray level data whereas our atlas is composed of both an estimation of the deformation metric and probability maps of each tissue (called class). This atlas is used to guide the tissue segmentation of new images. Experiments are shown on brain T1 MRI datasets. This method only requires approximate pre-registration, as the latter is done jointly with the segmentation. Note however that an approximate registration is a reasonable pre-requisite given the application.     

Pathophysiology and functional consequences of human partial epilepsy: lessons from positron emission tomography studies

Positron emission tomography (PET) is a powerful clinical and research tool that, in the past two decades, has provided a great amount of novel data on the pathophysiology and functional consequences of human epilepsy. PET studies revealed cortical and subcortical brain dysfunction of a widespread brain circuitry, providing an unprecedented insight in the complex functional abnormalities of the epileptic brain. Correlation of metabolic and neuroreceptor PET abnormalities with electroclinical variables helped identify parts of this circuitry, some of which are directly related to primary epileptogenesis, while others, adjacent to or remote from the primary epileptic focus, may be secondary to longstanding epilepsy. PET studies have also provided detailed data on the functional anatomy of cognitive and behavioral abnormalities associated with epilepsy. PET, along with other neuroimaging modalities, can measure longitudinal changes in brain function attributed to chronic seizures as well as therapeutic interventions. This review demonstrates how development of more specific PET tracers and application of multimodality imaging by combining structural and functional neuroimaging with electrophysiological data can further improve our understanding of human partial epilepsy, and helps more effective application of PET in presurgical evaluation of patients with intractable seizures.     

Using Manifold Learning for Atlas Selection in Multi-Atlas Segmentation

Multi-atlas segmentation has been widely used to segment various anatomical structures. The success of this technique partly relies on the selection of atlases that are best mapped to a new target image after registration. Recently, manifold learning has been proposed as a method for atlas selection. Each manifold learning technique seeks to optimize a unique objective function. Therefore, different techniques produce different embeddings even when applied to the same data set. Previous studies used a single technique in their method and gave no reason for the choice of the manifold learning technique employed nor the theoretical grounds for the choice of the manifold parameters. In this study, we compare side-by-side the results given by 3 manifold learning techniques (Isomap, Laplacian Eigenmaps and Locally Linear Embedding) on the same data set. We assess the ability of those 3 different techniques to select the best atlases to combine in the framework of multi-atlas segmentation. First, a leave-one-out experiment is used to optimize our method on a set of 110 manually segmented atlases of hippocampi and find the manifold learning technique and associated manifold parameters that give the best segmentation accuracy. Then, the optimal parameters are used to automatically segment 30 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). For our dataset, the selection of atlases with Locally Linear Embedding gives the best results. Our findings show that selection of atlases with manifold learning leads to segmentation accuracy close to or significantly higher than the state-of-the-art method and that accuracy can be increased by fine tuning the manifold learning process.     

Learning related interactions among neuronal systems involved in memory processes

Functional neuroimaging techniques using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have provided new insights in our understanding of brain function from the molecular to the systems level. While subtraction strategy based data analyses have revealed the involvement of distributed brain regions in memory processes, covariance analysis based data analysis strategies allow functional interactions between brain regions of a neuronal network to be assessed. The focus of this chapter is to (1) establish the functional topography of episodic and working memory processes in young and old normal volunteers, (2) to assess functional interactions between modules of networks of brain regions by means of covariance based analyses and systems level modelling and (3) to relate neuroimaging data to the underpinning neural networks. Male normal young and old volunteers without neurological or psychiatric illness participated in neuroimaging studies (PET, fMRI) on working and episodic memory. Distributed brain areas are involved in memory processes (episodic and working memory) in young volunteers and show much of an overlap with respect to the network components. Systems level modelling analyses support the hypothesis of bihemispheric, asymmetric networks subserving memory processes and revealed both similarities in general and differences in the interactions between brain regions during episodic encoding and retrieval as well as working memory. Changes in memory function with ageing are evident from studies in old volunteers activating more brain regions compared to young volunteers and revealing more and stronger influences of prefrontal regions. We finally discuss the way in which the systems level models based on PET and fMRI results have implications for the understanding of the underlying neural network functioning of the brain.     

Infant brain atlases from neonates to 1- and 2-year-olds

Background

Studies for infants are usually hindered by the insufficient image contrast, especially for neonates. Prior knowledge, in the form of atlas, can provide additional guidance for the data processing such as spatial normalization, label propagation, and tissue segmentation. Although it is highly desired, there is currently no such infant atlas which caters for all these applications. The reason may be largely due to the dramatic early brain development, image processing difficulties, and the need of a large sample size.

Methodology

To this end, after several years of subject recruitment and data acquisition, we have collected a unique longitudinal dataset, involving 95 normal infants (56 males and 39 females) with MRI scanned at 3 ages, i.e., neonate, 1-year-old, and 2-year-old. State-of-the-art MR image segmentation and registration techniques were employed, to construct which include the templates (grayscale average images), tissue probability maps (TPMs), and brain parcellation maps (i.e., meaningful anatomical regions of interest) for each age group. In addition, the longitudinal correspondences between age-specific atlases were also obtained. Experiments of typical infant applications validated that the proposed atlas outperformed other atlases and is hence very useful for infant-related studies.

Conclusions

We expect that the proposed infant 0–1–2 brain atlases would be significantly conducive to structural and functional studies of the infant brains. These atlases are publicly available in our website, http://bric.unc.edu/ideagroup/free-softwares/.     

The neural systems that mediate human perceptual decision making

Perceptual decision making is the act of choosing one option or course of action from a set of alternatives on the basis of available sensory evidence. Thus, when we make such decisions, sensory information must be interpreted and translated into behaviour. Neurophysiological work in monkeys performing sensory discriminations, combined with computational modelling, has paved the way for neuroimaging studies that are aimed at understanding decision-related processes in the human brain. Here we review findings from human neuroimaging studies in conjunction with data analysis methods that can directly link decisions and signals in the human brain on a trial-by-trial basis. This leads to a new view about the neural basis of human perceptual decision-making processes.     

Use of Brain MRI Atlases to Determine Boundaries of Age-Related Pathology: The Importance of Statistical Method

IntroductionNeurodegenerative disease diagnoses may be supported by the comparison of an individual patient’s brain magnetic resonance image (MRI) with a voxel-based atlas of normal brain MRI. Most current brain MRI atlases are of young to middle-aged adults and parametric, e.g., mean ±standard deviation (SD); these atlases require data to be Gaussian. Brain MRI data, e.g., grey matter (GM) proportion images, from normal older subjects are apparently not Gaussian. We created a nonparametric and a parametric atlas of the normal limits of GM proportions in older subjects and compared their classifications of GM proportions in Alzheimer’s disease (AD) patients.MethodsUsing publicly available brain MRI from 138 normal subjects and 138 subjects diagnosed with AD (all 55–90 years), we created: a mean ±SD atlas to estimate parametrically the percentile ranks and limits of normal ageing GM; and, separately, a nonparametric, rank order-based GM atlas from the same normal ageing subjects. GM images from AD patients were then classified with respect to each atlas to determine the effect statistical distributions had on classifications of proportions of GM in AD patients.ResultsThe parametric atlas often defined the lower normal limit of the proportion of GM to be negative (which does not make sense physiologically as the lowest possible proportion is zero). Because of this, for approximately half of the AD subjects, 25–45% of voxels were classified as normal when compared to the parametric atlas; but were classified as abnormal when compared to the nonparametric atlas. These voxels were mainly concentrated in the frontal and occipital lobes.DiscussionTo our knowledge, we have presented the first nonparametric brain MRI atlas. In conditions where there is increasing variability in brain structure, such as in old age, nonparametric brain MRI atlases may represent the limits of normal brain structure more accurately than parametric approaches. Therefore, we conclude that the statistical method used for construction of brain MRI atlases should be selected taking into account the population and aim under study. Parametric methods are generally robust for defining central tendencies, e.g., means, of brain structure. Nonparametric methods are advisable when studying the limits of brain structure in ageing and neurodegenerative disease.     

More than a decade of developmental gene expression atlases: where are we now?

To unravel regulatory networks of genes functioning during embryonic development, information on in situ gene expression is required. Enormous amounts of such data are available in literature, where each paper reports on a limited number of genes and developmental stages. The best way to make these data accessible is via spatio-temporal gene expression atlases. Eleven atlases, describing developing vertebrates and covering at least 100 genes, were reviewed. This review focuses on: (i) the used anatomical framework, (ii) the handling of input data and (iii) the retrieval of information. Our aim is to provide insights into both the possibilities of the atlases, as well as to describe what more than a decade of developmental gene expression atlases can teach us about the requirements of the design of the ‘ideal atlas’. This review shows that most ingredients needed to develop the ideal atlas are already applied to some extent in at least one of the discussed atlases. A review of these atlases shows that the ideal atlas should be based on a spatial framework, i.e. a series of 3D reference models, which is anatomically annotated using an ontology with sufficient resolution, both for relations as well as for anatomical terms.     

Neural overlap in processing music and speech

Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing.     

Neuroimaging studies of autobiographical event memory

Commonalities and differences in findings across neuroimaging studies of autobiographical event memory are reviewed. In general terms, the overall pattern across studies is of medial and left-lateralized activations associated with retrieval of autobiographical event memories. It seems that the medial frontal cortex and left hippocampus in particular are responsive to such memories. However, there are also inconsistencies across studies, for example in the activation of the hippocampus and dorsolateral prefrontal cortex. It is likely that methodological differences between studies contribute to the disparate findings. Quantifying and assessing autobiographical event memories presents a challenge in many domains, including neuroimaging. Methodological factors that may be pertinent to the interpretation of the neuroimaging data and the design of future experiments are discussed. Consideration is also given to aspects of memory that functional neuroimaging might be uniquely disposed to examine. These include assessing the functionality of damaged tissue in patients and the estimation of inter-regional communication (effective connectivity) between relevant brain regions.     

Functional imaging of memory processes in humans: positron emission tomography and functional magnetic resonance imaging

Human memory is not a unitary function; it consists of multiple memory systems, with different characteristics and specialisations that are implemented in the brain. The cognitive neuroscience of human memory tries to comprehend how we encode, store, and retrieve memory items within and across those systems. The emergence of functional neuroimaging techniques offered the unprecedented opportunity to directly observe the brain regions engaged in memory functions. Brain imaging techniques can roughly be divided into those measuring the electric or magnetic fields generated by neuronal activity (EEG, magnetencephalography [MEG]) and those measuring the haemodynamic or metabolic sequelae of neuronal activity (positron emission tomography [PET], functional magnetic resonance imaging [fMRI]). Out of these techniques, the following two will be discussed in detail: fMRI and PET. Although functional neuroimaging is able to acquire images of the brain engaged in consolidating or retrieving memories, these processes are not clearly visible in the data. Statistical techniques are needed to reduce the complexity of the data and to extract the processes of interest. This article outlines the experimental and analytical procedures of neuroimaging studies with PET and fMRI. We will use a PET-study on episodic memory in human volunteers to illustrate design, analysis, and interpretation of functional imaging studies on memory.     

Psychophysiological correlates of aggression and violence: an integrative review

This paper reviews existing psychophysiological studies of aggression and violent behaviour including research employing autonomic, electrocortical and neuroimaging measures. Robust physiological correlates of persistent aggressive behaviour evident in this literature include low baseline heart rate, enhanced autonomic reactivity to stressful or aversive stimuli, enhanced EEG slow wave activity, reduced P300 brain potential response and indications from structural and functional neuroimaging studies of dysfunction in frontocortical and limbic brain regions that mediate emotional processing and regulation. The findings are interpreted within a conceptual framework that draws on two integrative models in the literature. The first is a recently developed hierarchical model of impulse control (externalizing) problems, in which various disinhibitory syndromes including aggressive and addictive behaviours of different kinds are seen as arising from common as well as distinctive aetiologic factors. This model represents an approach to organizing these various interrelated phenotypes and investigating their common and distinctive aetiologic substrates. The other is a neurobiological model that posits impairments in affective regulatory circuits in the brain as a key mechanism for impulsive aggressive behaviour. This model provides a perspective for integrating findings from studies employing different measures that have implicated varying brain structures and physiological systems in violent and aggressive behaviour.     

Multi-Contrast Multi-Atlas Parcellation of Diffusion Tensor Imaging of the Human Brain

In this paper, we propose a novel method for parcellating the human brain into 193 anatomical structures based on diffusion tensor images (DTIs). This was accomplished in the setting of multi-contrast diffeomorphic likelihood fusion using multiple DTI atlases. DTI images are modeled as high dimensional fields, with each voxel exhibiting a vector valued feature comprising of mean diffusivity (MD), fractional anisotropy (FA), and fiber angle. For each structure, the probability distribution of each element in the feature vector is modeled as a mixture of Gaussians, the parameters of which are estimated from the labeled atlases. The structure-specific feature vector is then used to parcellate the test image. For each atlas, a likelihood is iteratively computed based on the structure-specific vector feature. The likelihoods from multiple atlases are then fused. The updating and fusing of the likelihoods is achieved based on the expectation-maximization (EM) algorithm for maximum a posteriori (MAP) estimation problems. We first demonstrate the performance of the algorithm by examining the parcellation accuracy of 18 structures from 25 subjects with a varying degree of structural abnormality. Dice values ranging 0.8–0.9 were obtained. In addition, strong correlation was found between the volume size of the automated and the manual parcellation. Then, we present scan-rescan reproducibility based on another dataset of 16 DTI images – an average of 3.73%, 1.91%, and 1.79% for volume, mean FA, and mean MD respectively. Finally, the range of anatomical variability in the normal population was quantified for each structure.     

Effort d’échantillonnage et atlas floristiques – exhaustivité des mailles et caractérisation des lacunes dans la connaissance

Floristic atlases have an important input to flora conservation planning even though their data quality varied greatly across countries. This study aimed to assess survey completeness of cells of floristic atlases. Then, a surveying guide is designed to overcome as efficiently as possible sampling biases. A review and analyses on a wide dataset were carried out to select an estimator of the true species richness of surveyed cells. The Jackknife 1, a non-parametric estimator, appeared as the best compromise for regional floristic atlases. The number of records in each cell was used as an estimator of sampling effort. The ratio between the observed species richness and the estimated species richness measures the completeness of inventories in each surveyed cell. Eighteen variables were selected to describe current inventories and design new surveys. These variables highlight locations, periods and species to be given priority in future studies.