Atlas Toolkit: Fast registration of 3D morphological datasets in the absence of landmarks

Image registration is a gateway technology for Developmental Systems Biology, enabling computational analysis of related datasets within a shared coordinate system. Many registration tools rely on landmarks to ensure that datasets are correctly aligned; yet suitable landmarks are not present in many datasets. Atlas Toolkit is a Fiji/ImageJ plugin collection offering elastic group-wise registration of 3D morphological datasets, guided by segmentation of the interesting morphology. We demonstrate the method by combinatorial mapping of cell signalling events in the developing eyes of chick embryos, and use the integrated datasets to predictively enumerate Gene Regulatory Network states

Patient Specific Dosimetry Phantoms Using Multichannel LDDMM of the Whole Body

This paper describes an automated procedure for creating detailed patient-specific pediatric dosimetry phantoms from a small set of segmented organs in a child's CT scan. The algorithm involves full body mappings from adult template to pediatric images using multichannel large deformation diffeomorphic metric mapping (MC-LDDMM). The parallel implementation and performance of MC-LDDMM for this application is studied here for a sample of 4 pediatric patients, and from 1 to 24 processors. 93.84% of computation time is parallelized, and the efficiency of parallelization remains high until more than 8 processors are used. The performance of the algorithm was validated on a set of 24 male and 18 female pediatric patients. It was found to be accurate typically to within 1-2 voxels (2–4 mm) and robust across this large and variable data set

Frontally mediated inhibitory processing and white matter microstructure: age and alcoholism effects

RationaleThe NOGO P3 event-related potential is a sensitive marker of alcoholism, relates to EEG oscillation in the δ and θ frequency ranges, and reflects activation of an inhibitory processing network. Degradation of white matter tracts related to age or alcoholism should negatively affect the oscillatory activity within the network.ObjectiveThis study aims to evaluate the effect of alcoholism and age on δ and θ oscillations and the relationship between these oscillations and measures of white matter microstructural integrity.MethodsData from ten long-term alcoholics to 25 nonalcoholic controls were used to derive P3 from Fz, Cz, and Pz using a visual GO/NOGO protocol. Total power and across trial phase synchrony measures were calculated for δ and θ frequencies. DTI, 1.5 T, data formed the basis of quantitative fiber tracking in the left and right cingulate bundles and the genu and splenium of the corpus callosum. Fractional anisotropy and diffusivity (λL and λT) measures were calculated from each tract.ResultsNOGO P3 amplitude and δ power at Cz were smaller in alcoholics than controls. Lower δ total power was related to higher λT in the left and right cingulate bundles. GO P3 amplitude was lower and GO P3 latency was longer with advancing age, but none of the time-frequency analysis measures displayed significant age or diagnosis effects.ConclusionsThe relation of δ total power at CZ with λT in the cingulate bundles provides correlational evidence for a functional role of fronto-parietal white matter tracts in inhibitory processing

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

Learning from connectomics on the fly.

Parallels between invertebrates and vertebrates in nervous system development, organisation and circuits are powerful reasons to use insects to study the mechanistic basis of behaviour. The last few years have seen the generation in Drosophila melanogaster of very large light microscopy data sets, genetic driver lines and tools to report or manipulate neural activity. These resources in conjunction with computational tools are enabling large scale characterisation of neuronal types and their functional properties. These are complemented by 3D electron microscopy, providing synaptic resolution data. A whole brain connectome of the fly larva is approaching completion based on manual reconstruction of electron-microscopy data. An adult whole brain dataset is already publicly available and focussed reconstruction is under way, but its 40× greater volume would require ∼500-5000 person-years of manual labour. Nevertheless rapid technical improvements in imaging and especially automated segmentation will likely deliver a complete adult connectome in the next 5 years. To enhance our understanding of the circuit basis of behaviour, light and electron microscopy outputs must be integrated with functional and physiological information into comprehensive databases. We review presently available data, tools and opportunities in Drosophila. We then consider the limits and potential of future progress and how this may impact neuroscience in rich model systems provided by larger insects and vertebrates

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

NBLAST: Rapid, Sensitive Comparison of Neuronal Structure and Construction of Neuron Family Databases.

Neural circuit mapping is generating datasets of tens of thousands of labeled neurons. New computational tools are needed to search and organize these data. We present NBLAST, a sensitive and rapid algorithm, for measuring pairwise neuronal similarity. NBLAST considers both position and local geometry, decomposing neurons into short segments; matched segments are scored using a probabilistic scoring matrix defined by statistics of matches and non-matches. We validated NBLAST on a published dataset of 16,129 single Drosophila neurons. NBLAST can distinguish neuronal types down to the finest level (single identified neurons) without a priori information. Cluster analysis of extensively studied neuronal classes identified new types and unreported topographical features. Fully automated clustering organized the validation dataset into 1,052 clusters, many of which map onto previously described neuronal types. NBLAST supports additional query types, including searching neurons against transgene expression patterns. Finally, we show that NBLAST is effective with data from other invertebrates and zebrafish. VIDEO ABSTRACT.This work was supported by the Medical Research Council [MRC file reference U105188491] and European Research Council Starting and Consolidator Grants to G.S.X.E.J., who is an EMBO Young Investigator.This is the final version of the article. It first appeared from Cell Press via http://dx.doi.org/10.1016/j.neuron.2016.06.01