MRI estimates of brain iron concentration in normal aging: Comparison of field-dependent (FDRI) and phase (SWI) methods

Different brain structures accumulate iron at different rates throughout the adult life span. Typically, striatal and brain stem structures are higher in iron concentrations in older than younger adults, whereas cortical white matter and thalamus have lower concentrations in the elderly than young adults. Brain iron can be measured in vivo with MRI by estimating the relaxivity increase across magnetic field strengths, which yields the Field-Dependent Relaxation Rate Increase (FDRI) metric. The influence of local iron deposition on susceptibility, manifests as MR phase effects, forms the basis for another approach for iron measurement, Susceptibility-Weighted Imaging (SWI), for which imaging at only one field strength is sufficient. Here, we compared the ability of these two methods to detect and quantify brain iron in 11 young (5 men, 6 women; 21 to 29 years) and 12 elderly (6 men, 6 women; 64 to 86 years) healthy adults. FDRI was acquired at 1.5 T and 3.0 T, and SWI was acquired at 1.5 T. The results showed that both methods detected high globus pallidus iron concentration regardless of age and significantly greater iron in putamen with advancing age. The SWI measures were more sensitive when the phase signal intensities themselves were used to define regions of interest, whereas FDRI measures were robust to the method of region of interest selection. Further, FDRI measures were more highly correlated than SWI iron estimates with published postmortem values and were more sensitive than SWI to iron concentration differences across basal ganglia structures. Whereas FDRI requires more imaging time than SWI, two field strengths, and across-study image registration for iron concentration calculation, FDRI appears more specific to age-dependent accumulation of non-heme brain iron than SWI, which is affected by heme iron and non-iron source effects on phase.National Institutes of Health (U.S.) (Grant AG017919)National Institutes of Health (U.S.) (Grant AA005965)National Institutes of Health (U.S.) (Grant AA017168

MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping

Quantifying tissue iron concentration in vivo is instrumental for understanding the role of iron in physiology and in neurological diseases associated with abnormal iron distribution. Herein, we use recently-developed Quantitative Susceptibility Mapping (QSM) methodology to estimate the tissue magnetic susceptibility based on MRI signal phase. To investigate the effect of different regularization choices, we implement and compare ℓ[subscript 1] and ℓ[subscript 2] norm regularized QSM algorithms. These regularized approaches solve for the underlying magnetic susceptibility distribution, a sensitive measure of the tissue iron concentration, that gives rise to the observed signal phase. Regularized QSM methodology also involves a pre-processing step that removes, by dipole fitting, unwanted background phase effects due to bulk susceptibility variations between air and tissue and requires data acquisition only at a single field strength. For validation, performances of the two QSM methods were measured against published estimates of regional brain iron from postmortem and in vivo data. The in vivo comparison was based on data previously acquired using Field-Dependent Relaxation Rate Increase (FDRI), an estimate of MRI relaxivity enhancement due to increased main magnetic field strength, requiring data acquired at two different field strengths. The QSM analysis was based on susceptibility-weighted images acquired at 1.5 T, whereas FDRI analysis used Multi-Shot Echo-Planar Spin Echo images collected at 1.5 T and 3.0 T. Both datasets were collected in the same healthy young and elderly adults. The in vivo estimates of regional iron concentration comported well with published postmortem measurements; both QSM approaches yielded the same rank ordering of iron concentration by brain structure, with the lowest in white matter and the highest in globus pallidus. Further validation was provided by comparison of the in vivo measurements, ℓ[subscript 1]-regularized QSM versus FDRI and ℓ[subscript 2]-regularized QSM versus FDRI, which again yielded perfect rank ordering of iron by brain structure. The final means of validation was to assess how well each in vivo method detected known age-related differences in regional iron concentrations measured in the same young and elderly healthy adults. Both QSM methods and FDRI were consistent in identifying higher iron concentrations in striatal and brain stem ROIs (i.e., caudate nucleus, putamen, globus pallidus, red nucleus, and substantia nigra) in the older than in the young group. The two QSM methods appeared more sensitive in detecting age differences in brain stem structures as they revealed differences of much higher statistical significance between the young and elderly groups than did FDRI. However, QSM values are influenced by factors such as the myelin content, whereas FDRI is a more specific indicator of iron content. Hence, FDRI demonstrated higher specificity to iron yet yielded noisier data despite longer scan times and lower spatial resolution than QSM. The robustness, practicality, and demonstrated ability of predicting the change in iron deposition in adult aging suggest that regularized QSM algorithms using single-field-strength data are possible alternatives to tissue iron estimation requiring two field strengths.National Institutes of Health (U.S.) (Grant NIH R01 EB007942)National Institutes of Health (U.S.) (Grant AG019717)National Institutes of Health (U.S.) (Grant AA005965)National Institutes of Health (U.S.) (Grant AA017168)National Institutes of Health (U.S.) (Grant EB008381)National Science Foundation (U.S.) (Grant 0643836)Siemens CorporationSiemens-MIT AllianceMIT-Center for Integration of Medicine and Innovative Technology (Medical Engineering Fellowship

The Locust Standard Brain: A 3D Standard of the Central Complex as a Platform for Neural Network Analysis

Many insects use the pattern of polarized light in the sky for spatial orientation and navigation. We have investigated the polarization vision system in the desert locust. To create a common platform for anatomical studies on polarization vision pathways, Kurylas et al. (2008) have generated a three-dimensional (3D) standard brain from confocal microscopy image stacks of 10 male brains, using two different standardization methods, the Iterative Shape Averaging (ISA) procedure and the Virtual Insect Brain (VIB) protocol. Comparison of both standardization methods showed that the VIB standard is ideal for comparative volume analysis of neuropils, whereas the ISA standard is the method of choice to analyze the morphology and connectivity of neurons. The central complex is a key processing stage for polarization information in the locust brain. To investigate neuronal connections between diverse central-complex neurons, we generated a higher-resolution standard atlas of the central complex and surrounding areas, using the ISA method based on brain sections from 20 individual central complexes. To explore the usefulness of this atlas, two central-complex neurons, a polarization-sensitive columnar neuron (type CPU1a) and a tangential neuron that is activated during flight, the giant fan-shaped (GFS) neuron, were reconstructed 3D from brain sections. To examine whether the GFS neuron is a candidate to contribute to synaptic input to the CPU1a neuron, we registered both neurons into the standardized central complex. Visualization of both neurons revealed a potential connection of the CPU1a and GFS neurons in layer II of the upper division of the central body

The natverse, a versatile toolbox for combining and analysing neuroanatomical data.

To analyse neuron data at scale, neuroscientists expend substantial effort reading documentation, installing dependencies and moving between analysis and visualisation environments. To facilitate this, we have developed a suite of interoperable open-source R packages called the natverse. The natverse allows users to read local and remote data, perform popular analyses including visualisation and clustering and graph-theoretic analysis of neuronal branching. Unlike most tools, the natverse enables comparison across many neurons of morphology and connectivity after imaging or co-registration within a common template space. The natverse also enables transformations between different template spaces and imaging modalities. We demonstrate tools that integrate the vast majority of Drosophila neuroanatomical light microscopy and electron microscopy connectomic datasets. The natverse is an easy-to-use environment for neuroscientists to solve complex, large-scale analysis challenges as well as an open platform to create new code and packages to share with the community

The natverse, a versatile toolbox for combining and analysing neuroanatomical data.

To analyse neuron data at scale, neuroscientists expend substantial effort reading documentation, installing dependencies and moving between analysis and visualisation environments. To facilitate this, we have developed a suite of interoperable open-source R packages called the natverse. The natverse allows users to read local and remote data, perform popular analyses including visualisation and clustering and graph-theoretic analysis of neuronal branching. Unlike most tools, the natverse enables comparison across many neurons of morphology and connectivity after imaging or co-registration within a common template space. The natverse also enables transformations between different template spaces and imaging modalities. We demonstrate tools that integrate the vast majority of Drosophila neuroanatomical light microscopy and electron microscopy connectomic datasets. The natverse is an easy-to-use environment for neuroscientists to solve complex, large-scale analysis challenges as well as an open platform to create new code and packages to share with the community

Rat strain differences in brain structure and neurochemistry in response to binge alcohol

RATIONALE: Ventricular enlargement is a robust phenotype of the chronically dependent alcoholic human brain, yet the mechanism of ventriculomegaly is unestablished. Heterogeneous stock Wistar rats administered binge EtOH (3 g/kg intragastrically every 8 h for 4 days to average blood alcohol levels (BALs) of 250 mg/dL) demonstrate profound but reversible ventricular enlargement and changes in brain metabolites (e.g., N-acetylaspartate (NAA) and choline-containing compounds (Cho)). OBJECTIVES: Here, alcohol-preferring (P) and alcohol-nonpreferring (NP) rats systematically bred from heterogeneous stock Wistar rats for differential alcohol drinking behavior were compared with Wistar rats to determine whether genetic divergence and consequent morphological and neurochemical variation affect the brain's response to binge EtOH treatment. METHODS: The three rat lines were dosed equivalently and approached similar BALs. Magnetic resonance imaging and spectroscopy evaluated the effects of binge EtOH on brain. RESULTS: As observed in Wistar rats, P and NP rats showed decreases in NAA. Neither P nor NP rats, however, responded to EtOH intoxication with ventricular expansion or increases in Cho levels as previously noted in Wistar rats. Increases in ventricular volume correlated with increases in Cho in Wistar rats. CONCLUSIONS: The latter finding suggests that ventricular volume expansion is related to adaptive changes in brain cell membranes in response to binge EtOH. That P and NP rats responded differently to EtOH argues for intrinsic differences in their brain cell membrane composition. Further, differential metabolite responses to EtOH administration by rat strain implicate selective genetic variation as underlying heterogeneous effects of chronic alcoholism in the human condition

Evaluating the Effect of Tissue Anisotropy on Brain Tumor Growth using a Mechanically-coupled Reaction-Diffusion Model

Glioblastoma (GBM), the most frequent malignant brain tumor in adults, is char- acterized by rapid growth and healthy tissue invasion. Long-term prognosis for GBM remains poor with median overall survival between 1 y to 2 y [15]. GBM presents with different growth phenotypes, ranging from invasive tumors without notable mass-effect to strongly displacing lesions. Biomechanical forces, such as those resulting from displacive tumor growth, shape the tumor environment and contribute to tumor progression [9]. We present an extended version of a mechanically–coupled reaction-diffusion model of brain tu- mor growth [1] that simulates tumor evolution over time and across different brain regions using literature-based parameter estimates for tumor cell proliferation, as well as isotropic motility, and mechanical tissue properties. This model yielded realistic estimates of the mechanical impact of a growing tumor on intra-cranial pressure. However, comparison to imaging data showed that asymmetric shapes could not be reproduced by isotropic growth assumptions. We modified this model to account for structural tissue anisotropy which is known to affect the directionality of tumor cell migration and may influence the mechanical behavior of brain tissue. Tumors were seeded at multiple locations in a human MR-DTI brain atlas and their spatio-temporal evolution was simulated using the Finite-Element Method. We evaluated the impact of tissue anisotropy on the model’s ability to reproduce the aspherical shapes of real pathologies by comparing predicted lesions to publicly available GBM imaging data. We found the impact on tumor shape to be strongly location dependent and highest for tumors located in brain regions that are characterized by a single dominant white matter direction, such as the corpus callosum. However, despite strongly anisotropic growth assumptions, all simulated tumors remained more spherical than real lesions at the corresponding location and similar volume. This finding is in agreement with previous studies [17, 6] suggesting that anisotropic cell migration along white matter fiber tracks is not a major determinant of tumor shape in the setting of reaction-diffusion based tumor growth models and for most locations across the brain