A three-dimensional MRI atlas of the zebra finch brain in stereotaxic coordinates

The neurobiology of birdsong, as a model for human speech, is a fast growing area of research in the neurosciences and involves electrophysiological, histological and more recently magnetic resonance imaging (MRI) approaches. Many of these studies require the identification and localization of different brain areas (nuclei) involved in the sensory and motor control of song. Until now, the only published atlases of songbird brains consisted in drawings based on histological slices of the canary and of the zebra finch brain. Taking advantage of high-magnetic field (7 Tesla) MRI technique, we present the first high-resolution (80 x 160 x 160 microm) 3-D digital atlas in stereotaxic coordinates of a male zebra finch brain, the most widely used species in the study of birdsong neurobiology. Image quality allowed us to discern most of the song control, auditory and visual nuclei. The atlas can be freely downloaded from our Web site and can be interactively explored with MRIcro. This zebra finch MRI atlas should become a very useful tool for neuroscientists working on birdsong, especially for co-registrating MRI data but also for determining accurately the optimal coordinates and angular approach for injections or electrophysiological recordings.     

Wavelet-based cluster analysis: data-driven grouping of voxel time courses with application to perfusion-weighted and pharmacological MRI of the rat brain

MRI time series experiments produce a wealth of information contained in two or three spatial dimensions that evolve over time. Such experiments can, for example, localize brain response to pharmacological stimuli, but frequently the spatiotemporal characteristics of the cerebral response are unknown a priori and variable, and thus difficult to evaluate using hypothesis-based methods alone. Here we used features in the temporal dimension to group voxels with similar time courses based on a nonparametric discrete wavelet transform (DWT) representation of each time course. Applying the DWT to each voxel decomposes its temporal information into coefficients associated with both time and scale. Discarding scales in the DWT that are associated with high-frequency oscillations (noise) provided a straight-forward data reduction step and decreased the computational burden. Optimization-based clustering was then applied to the remaining wavelet coefficients in order to produce a finite number of voxel clusters. This wavelet-based cluster analysis (WCA) was evaluated using two representative classes of MRI neuroimaging experiments. In perfusion-weighted MRI, following occlusion of the middle cerebral artery (MCAO), WCA differentiated healthy tissue and different regions within the ischemic hemisphere. Following an acute cocaine challenge, WCA localized subtle differences in the pharmacokinetic profile of the cerebral response. We conclude that WCA provides a robust method for blind analysis of time series image data.     

A scheme for automatically building three-dimensional morphometric anatomical atlases: application to a skull atlas

We present a general scheme for automatically building a morphometric anatomical atlas. We detail each stage of the method, including the non-rigid registration algorithm, three-dimensional line averaging and statistical processes. We apply the method to obtain a quantitative atlas of skull crest lines. Finally, we use the resulting atlas to study a craniofacial disease; we show how we can obtain qualitative and quantitative results by contrasting a skull affected by a mandible deformation with the atlas.     

Joint functional brain network atlas estimation and feature selection for neurological disorder diagnosis with application to autism

Image-based brain maps, generally coined as ‘intensity or image atlases’, have led the field of brain mapping in health and disease for decades, while investigating a wide spectrum of neurological disorders. Estimating representative brain atlases constitute a fundamental step in several MRI-based neurological disorder mapping, diagnosis, and prognosis. However, these are strikingly lacking in the field of brain connectomics, where connectional brain atlases derived from functional MRI (fRMI) or diffusion MRI (dMRI) are almost absent. On the other hand, conventional connectomic-based classification methods traditionally resort to feature selection methods to decrease the high-dimensionality of connectomic data for learning how to diagnose new patients. However, these are generally limited by high computational cost and a large variability in performance across different datasets, which might hinder the identification of reproducible biomarkers. To address both limitations, we unprecedentedly propose a brain network atlas-guided feature selection (NAG-FS) method to disentangle the healthy from the disordered connectome. To this aim, given a population of brain connectomes, we propose to learn how estimate a centered and representative functional brain network atlas (i.e., a population center) to reliably map the functional connectome and its variability across training individuals, thereby capturing their shared traits (i.e., connectional fingerprint of a population). Essentially, we first learn the pairwise similarities between connectomes in the population to map them into different subspaces. Next, we non-linearly diffuse and fuse connectomes living in each subspace, respectively. By integrating the produced subspace-specific network atlases we ultimately estimate the population network atlas. Last, we compute the difference between healthy and disordered network atlases to identify the most discriminative features, which are then used to train a predictive learner. Our method boosted the classification performance by 6% in comparison to state-of-the-art FS methods when classifying autistic and healthy subjects.     

Automated brain tissue assessment in the elderly and demented population: construction and validation of a sub-volume probabilistic brain atlas

OBJECTIVES: To develop an automated imaging assessment tool that accommodates the anatomic variability of the elderly and demented population as well as the registration errors occurring during spatial normalization. METHODS: 20 subjects with Alzheimer's disease (AD), mild cognitive impairment, or normal cognition underwent MRI brain imaging and had their 3D volumetric datasets manually partitioned into 68 regions of interest (ROI) termed sub-volumes. Gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) voxel counts were then made in the subject's native space for comparison against automated volumetric measures within three sub-volume probabilistic atlas (SVPA) models. The three SVPAs were constructed using 12 parameter affine (12 p), 2nd order (2nd), and 6th order (6th) transforms derived from registering the manually partitioned scans into a Talairach compatible AD population-based target. The three SVPA automated measures were compared to the manually derived measures in the 20 subjects' native space with a "jack-knife" procedure in which each subject was assessed by an SVPA they did not contribute toward constructing. RESULTS: The mean left and right GM ratio (GM ratio = [GM + CSF] / CSF) "r values" for the 3 SVPAs compared to the manually derived ratios across the 68 ROIs were 0.85 for the 12p SVPA, 0.88 for the 2nd SVPA, and 0.89 for the 6th SVPA. The mean left and right WM ratio (WM ratio = [WM + CSF] / CSF) "r values" for the 3 SVPAs being 0.84 for the 12p SVPA, 0.86 for the 2nd SVPA, and 0.88 for the 6th SVPA. CONCLUSION: We have constructed, from an elderly and demented cohort, an automated brain volumetric tool that has excellent accuracy compared to a manual gold standard and is capable of regional hypothesis testing and individual patient assessment compared to a population.     

A principal components-based method for the detection of neuronal activity maps: application to optical imaging

We present a novel analysis technique for the extraction of neuronal activity patterns from functional imaging data. We illustrate this technique on data from optical imaging. Optical imaging of the mammalian visual cortex probe the patterns in which the neuronal responses to various aspects of the visual world, such as orientation and color, are spatially organized within the cortex. Recovering these patterns from the image data is a challenging problem as the neuronal response signal is extremely weak in comparison to the background vegetative processes (e.g., circulation and respiration). The proposed technique obtains the neuronal activity pattern using a combination of principal component analysis and statistical significance testing. The performance of this method is compared with the results of existing analysis techniques. The comparison shows the new method to be more sensitive than previous methods.     

Diffusion tensor imaging-based tissue segmentation: validation and application to the developing child and adolescent brain

We present and validate a novel diffusion tensor imaging (DTI) approach for segmenting the human whole-brain into partitions representing grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF). The approach utilizes the contrast among tissue types in the DTI anisotropy vs. diffusivity rotational invariant space. The DTI-based whole-brain GM and WM fractions (GMf and WMf) are contrasted with the fractions obtained from conventional magnetic resonance imaging (cMRI) tissue segmentation (or clustering) methods that utilized dual echo (proton density-weighted (PDw)), and spin-spin relaxation-weighted (T2w) contrast, in addition to spin-lattice relaxation weighted (T1w) contrasts acquired in the same imaging session and covering the same volume. In addition to good correspondence with cMRI estimates of brain volume, the DTI-based segmentation approach accurately depicts expected age vs. WM and GM volume-to-total intracranial brain volume percentage trends on the rapidly developing brains of a cohort of 29 children (6-18 years). This approach promises to extend DTI utility to both micro and macrostructural aspects of tissue organization.     

A gene required for class II-restricted antigen presentation maps to the major histocompatibility complex.

We have previously described a set of mutants (16.23-selected mutants) of a B lymphoblastoid cell line that are defective in the presentation of intact proteins to class II-restricted T cells, but effectively present immunogenic peptides. The mutations in these mutants are recessive in somatic cell hybrids and are not in Class II structural genes. Here, we report on a unique mutant, 5.2.4, in which a similar defect in class II-restricted antigen presentation has occurred in association with a one-megabase homozygous deletion in the class II region of the major histocompatibility complex (MHC). The defects in class II presentation among three of the 16.23-selected mutants, and between these mutants and 5.2.4, are noncomplementary in somatic cell hybrids. This suggests that the class II presentation-defective phenotype in all four mutants results from lesions in a single MHC-linked gene, a conclusion strengthened by the finding that in a hybrid made with a second, unrelated MHC deletion mutant, T2, the class II presentation defect in a 16.23-selected mutant is also not complemented. Mutant 5.2.4, in addition to its class II presentation defect, is also defective in surface expression of MHC class I molecules, most likely because its deletion encompasses the peptide supply factor 1 gene, whose function is known to be required for normal abundance of cell surface class I molecules. However, the surface abundance of class I molecules is normal in the 16.23-selected mutants, suggesting that the lesions affecting class I surface abundance and class II presentation result from mutations in different genes.     

Brain- and heart-type fatty acid-binding proteins in the brain: tissue distribution and clinical utility

BACKGROUND: Detection of brain injury by serum markers is not a standard procedure in clinical practice, although several proteins, such as S100B, neuron-specific enolase (NSE), myelin basic protein, and glial fibrillary acidic protein, show promising results. We investigated the tissue distribution of brain- and heart-type fatty acid-binding proteins (B-FABP and H-FABP) in segments of the human brain and the potential of either protein to serve as plasma marker for diagnosis of brain injury. METHODS: B-FABP and H-FABP were measured immunochemically in autopsy samples of the brain (n = 6) and in serum samples from (a) patients with mild traumatic brain injury (MTBI; n = 130) and (b) depressed patients undergoing bilateral electroconvulsive therapy (ECT; n = 14). The protein markers S100B and NSE were measured for comparison. Reference values of B-FABP and H-FABP were established in healthy individuals (n = 92). RESULTS: The frontal, temporal, and occipital lobes, the striatum, the pons, and the cerebellum had different tissue concentrations of B-FABP and of H-FABP. B-FABP ranged from 0.8 microg/g wet weight in striatum tissue to 3.1 microg/g in frontal lobe. H-FABP was markedly higher, ranging from 16.2 microg/g wet weight in cerebellum tissue to 39.5 microg/g in pons. No B-FABP was detected in serum from healthy donors. H-FABP serum reference value was 6 microg/L. In the MTBI study, serum B-FABP was increased in 68% and H-FABP in 70% of patients compared with S100B (increased in 45%) and NSE (increased in 51% of patients). In ECT, serum B-FABP was increased in 6% of all samples (2 of 14 patients), whereas H-FABP was above its upper reference limit (6 microg/L) in 17% of all samples (8 of 14 patients), and S100B was above its upper reference limit (0.3 microg/L) in 0.4% of all samples. CONCLUSIONS: B-FABP and H-FABP patterns differ among brain tissues, with the highest concentrations in the frontal lobe and pons, respectively. However, in each part of the brain, the H-FABP concentration was at least 10 times higher than that of B-FABP. Patient studies indicate that B-FABP and H-FABP are more sensitive markers for minor brain injury than the currently used markers S100B and NSE.     

A similarity measure for nonrigid volume registration using known joint distribution of targeted tissue: application to dynamic CT data of the liver

A similarity measure for nonrigid volume registration with known joint distribution of a targeted tissue is developed to process tissue slide at the boundaries between the targeted and non-targeted tissues. Pre-segmentation of the targeted tissue is unnecessary. This measure is applied to registering volumes acquired at different time-phases in dynamic CT scans of the liver using contrast materials and can be derived for the case where only the joint distribution of the targeted tissue is known. The similarity measure is formulated as a likelihood by introducing a concept termed 'exclusivity condition' and embedded into a cost function for nonrigid registration to be combined with the smoothness term. In addition, a practical method for estimating the joint distribution of the liver from unregistered clinical CT data is described. We demonstrate experimentally that tissue slide is effectively processed by this proposed measure using simulated dynamic CT data generated from a software phantom and clinical CT data of eight patients.     

CT angiography with cardiac MRI: non-invasive functional and anatomical assessment for the etiology in newly diagnosed heart failure

Exclusion of ischemia is important in patients with newly diagnosed systolic heart failure (HF). We prospectively compared standard-of-care invasive catheter angiography (iCA) and echocardiography to a novel non-invasive strategy of both Coronary Computed Tomographic Angiography (CCTA) and Cardiovascular MRI (CMR) to determine the etiology of myocardial dysfunction Prospective data were collected from consecutive patients referred for iCA to investigate echocardiographically-confirmed new onset HF. CMR (1.5T GE) and dual source CCTA were performed within 2-7 days of iCA. Results were blinded and separately analyzed by expert readers. 426 coronary segments from 28 prospectively enrolled patients were analyzed by CCTA and quantitative iCA. The per-patient sensitivity and specificity of CCTA was 100% and 90%, respectively, negative predictive value (NPV) 100%, positive predictive value (PPV) 78%. Mean ejection fraction by CMR was 24%. Presence of ischemic-type LGE on CMR conferred a 67% sensitivity, 100% specificity, 90% NPV and 100% PPV. Combining CCTA with CMR conferred 100% specificity, 100% sensitivity, 100% PPV and 100% NPV for detection or exclusion of coronary disease. In patients with negative CCTA all invasive angiograms could have been avoided. In addition, two patients with no ischemic LGE by CMR had severe coronary disease on both CCTA and iCA, indicating global hibernation. This is a noteworthy finding in contrast to previous reports which suggested that absence of LGE rules out significant CAD. CCTA with CMR in newly-diagnosed HF enables non-invasive assessment of coronary artery disease, the severity and etiology of myocardial dysfunction and defines suitability for revascularization. Absence of ischemic-type LGE at CMR does not exclude CAD as a cause of LV dysfunction. A first-line strategy of functional and anatomic imaging with CMR and CCTA appears appropriate in newly diagnosed HF.     

Simultaneous segmentation and grading of anatomical structures for patient's classification: application to Alzheimer's disease

In this paper, we propose an innovative approach to robustly and accurately detect Alzheimer's disease (AD) based on the distinction of specific atrophic patterns of anatomical structures such as hippocampus (HC) and entorhinal cortex (EC). The proposed method simultaneously performs segmentation and grading of structures to efficiently capture the anatomical alterations caused by AD. Known as SNIPE (Scoring by Non-local Image Patch Estimator), the novel proposed grading measure is based on a nonlocal patch-based frame-work and estimates the similarity of the patch surrounding the voxel under study with all the patches present in different training populations. In this study, the training library was composed of two populations: 50 cognitively normal subjects (CN) and 50 patients with AD, randomly selected from the ADNI database. During our experiments, the classification accuracy of patients (CN vs. AD) using several biomarkers was compared: HC and EC volumes, the grade of these structures and finally the combination of their volume and their grade. Tests were completed in a leave-one-out framework using discriminant analysis. First, we showed that biomarkers based on HC provide better classification accuracy than biomarkers based on EC. Second, we demonstrated that structure grading is a more powerful measure than structure volume to distinguish both populations with a classification accuracy of 90%. Finally, by adding the ages of subjects in order to better separate age-related structural changes from disease-related anatomical alterations, SNIPE obtained a classification accuracy of 93%.     

Recognizing and treating ischemic insults to the brain: the role of brain tissue oxygen monitoring

This article describes the potential application of brain tissue oxygen monitoring technology in the care of patients who have sustained traumatic brain injury (TBI) or subarachnoid hemorrhage (SAH). To accomplish this objective, a review of the intracranial dynamics that are created by primary and secondary brain injury, and the challenges of optimizing oxygen delivery to the injured brain are presented. Furthermore, interventions that facilitate cerebral oxygen supply and reduce oxygen consumption are identified. Finally, application of this technology is highlighted by using case vignettes of patients who have TBI or SAH.     

Magnetic resonance imaging of changes elicited by status epilepticus in the rat brain: diffusion-weighted and T2-weighted images,regional blood volume maps,and direct correlation with tissue and cell damage

The rat brain was investigated with structural and functional magnetic resonance imaging (MRI) 12 h after the arrest of pilocarpine-induced status epilepticus lasting 4 h. Histopathological data, obtained immediately after MRI analysis, were correlated with the images through careful evaluation of tissue shrinkage. Diffusion-weighted and T2-weighted imaging showed changes throughout the cerebral cortex, hippocampus, amygdala, and medial thalamus. However, only T2-weighted imaging, based on rapid acquisition relaxation-enhanced sequences, revealed in the cortex inhomogeneous hyperintensity that was highest in a band corresponding to layer V. Regional cerebral blood volume (rCBV) maps were generated using T2*-weighted gradient-echo images and an ultrasmall superparamagnetic iron oxide contrast agent. In the cortex, rCBV peaked in superficial and deep bands exhibiting a distribution complementary to the highest T2-weighted intensity. Selective rCBV increase was also documented in the hippocampus and subcortical structures. In tissue sections, alterations indicative of marked edema were found with Nissl staining in areas corresponding to the highest T2-weighted intensity. Degenerating neurons, revealed by FluoroJadeB histochemistry, were instead concentrated in tissue exhibiting hyperperfusion in rCBV maps, such as hippocampal subfields and dentate gyrus, cortical layers II/III and VI, and medial thalamus. The data indicate that:(i) T2-weighted imaging provides a sensitive tool to investigate edematous brain alterations that follow sustained seizures; (ii) rCBV maps reveal regional hyperperfusion; (iii) rCBV peaks in tissue exhibiting marked neurodegeneration, which may not be selectively revealed by structural MRI. The findings provide an interpretation of the brain response to sustained seizures revealed in vivo by different strategies of MRI analysis.     

The AGES-Reykjavik study atlases: Non-linear multi-spectral template and atlases for studies of the ageing brain

Quantitative analyses of brain structures from Magnetic Resonance (MR) image data are often performed using automatic segmentation algorithms. Many of these algorithms rely on templates and atlases in a common coordinate space. Most freely available brain atlases are generated from relatively young individuals and not always derived from well-defined cohort studies. In this paper, we introduce a publicly available multi-spectral template with corresponding tissue probability atlases and regional atlases, optimised to use in studies of ageing cohorts (mean age 75 ± 5 years). Furthermore, we provide validation data from a regional segmentation pipeline to assure the integrity of the dataset.     

Reference biometry of foetal brain by prenatal MRI and the distribution of measurements in foetuses with ventricular septal defect

ObjectiveTo provide the reference biometric measurements of the normal foetal brain by prenatal MRI and describe the distribution of measurements in the foetuses with ventricular septal defect (VSD).MethodsThis retrospective study analysed the biometric measurements of 218 foetuses between 18 − 37 gestational weeks with normal MRI findings from July 2014 to August 2019, as well as 18 foetuses with VSD. The measurements included fronto-occipital diameter (FOD), biparietal diameter (BPD), and transverse cerebellar diameter (TCD). All the prenatal MRI examinations have been taken on the same 1.5 T MR unit with a standard protocol of the foetal brain. All the linear measurements of the foetal brain were obtained on the T2-weighted imaging. The distribution of measurements in 18 foetuses with VSD was plotted on centile curves.ResultsThe reference data were presented in mean, standard deviation, 95% predicted confidence intervals, and the 3rd, 10th, 25th, 50th, 75th, 90th, 97th centiles at each gestational age. The value of TCD in 56% (10/18 cases) foetuses with VSD was lower than the 3rd centile, and the rate for FOD and BPD was 33% (6/18 cases) and 22% (4/18 cases) separately. On the curves, most VSD cases with measurements lower than the 3rd centile were in relatively early gestational stage (≤28 weeks).ConclusionsWe have presented reference linear biometry of the foetal brain by prenatal MRI from 18 to 37 gestational weeks, which could help us to interpret and monitor the brain development for foetuses with VSD and other congenital heart diseases.

Key messages:

1. We have presented reference linear biometry of the foetal brain by prenatal MRI from 18 to 37 gestational weeks in multiple statistical methods: mean and standard deviation; 95% predicted confidence intervals and the 3rd, 10th, 25th, 50th, 75th, 90th, 97th centiles.
2. Our data showed that the involvement of the brain in VSD may be not globally, but regionally, and the cerebellum may be more possible to be involved.
3. We speculated that the earlier the VSD diagnosed the worse the brain involved, which might suggest a poor outcome and necessary follow-up.

     

Multiple motion encoding in phase-contrast MRI: A general theory and application to elastography imaging

While MRI allows to encode the motion of tissue in the magnetization’s phase, it remains yet a challenge to obtain high fidelity motion images due to wraps in the phase for high encoding efficiencies. Therefore, we propose an optimal multiple motion encoding method (OMME) and exemplify it in Magnetic Resonance Elastography (MRE) data. OMME is formulated as a non-convex least-squares problem for the motion using an arbitrary number of phase-contrast measurements with different motion encoding gradients (MEGs). The mathematical properties of OMME are proved in terms of standard deviation and dynamic range of the motion’s estimate for arbitrary MEGs combination which are confirmed using synthetically generated data. OMME’s performance is assessed on MRE data from in vivo human brain experiments and compared to dual encoding strategies. The unwrapped images are further used to reconstruct stiffness maps and compared to the ones obtained using conventional unwrapping methods. OMME allowed to successfully combine several MRE phase images with different MEGs, outperforming dual encoding strategies in either motion-to-noise ratio (MNR) or number of successfully reconstructed voxels with good noise stability. This lead to stiffness maps with greater resolution of details than obtained with conventional unwrapping methods. The proposed OMME method allows for a flexible and noise robust increase in the dynamic range and thus provides wrap-free phase images with high MNR. In MRE, the method may be especially suitable when high resolution images with high MNR are needed.