Test-retest reliability of structural brain networks from diffusion MRI

Structural brain networks constructed from diffusion MRI (dMRI) and tractography have been demonstrated in healthy volunteers and more recently in various disorders affecting brain connectivity. However, few studies have addressed the reproducibility of the resulting networks. We measured the test–retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.5 T on two separate occasions. Each T1-weighted brain was parcellated into 84 regions-of-interest and network connections were identified using dMRI and two alternative tractography algorithms, two alternative seeding strategies, a white matter waypoint constraint and three alternative network weightings. In each case, four common graph-theoretic measures were obtained. Network properties were assessed both node-wise and per network in terms of the intraclass correlation coefficient (ICC) and by comparing within- and between-subject differences. Our findings suggest that test–retest performance was improved when: 1) seeding from white matter, rather than grey; and 2) using probabilistic tractography with a two-fibre model and sufficient streamlines, rather than deterministic tensor tractography. In terms of network weighting, a measure of streamline density produced better test–retest performance than tract-averaged diffusion anisotropy, although it remains unclear which is a more accurate representation of the underlying connectivity. For the best performing configuration, the global within-subject differences were between 3.2% and 11.9% with ICCs between 0.62 and 0.76. The mean nodal within-subject differences were between 5.2% and 24.2% with mean ICCs between 0.46 and 0.62. For 83.3% (70/84) of nodes, the within-subject differences were smaller than between-subject differences. Overall, these findings suggest that whilst current techniques produce networks capable of characterising the genuine between-subject differences in connectivity, future work must be undertaken to improve network reliability

Adaptive thresholding for reliable topological inference in single subject fMRI analysis

Single subject fMRI has proved to be a useful tool for mapping functional areas in clinical procedures such as tumour resection. Using fMRI data, clinicians assess the risk, plan and execute such procedures based on thresholded statistical maps. However, because current thresholding methods were developed mainly in the context of cognitive neuroscience group studies, most single subject fMRI maps are thresholded manually to satisfy specific criteria related to single subject analyses. Here, we propose a new adaptive thresholding method which combines Gamma-Gaussian mixture modelling with topological thresholding to improve cluster delineation. In a series of simulations we show that by adapting to the signal and noise properties, the new method performs well in terms of the trade-off between false negative and positive cluster error rates as well as in terms of over and underestimation of the true activation border. We also show through simulations and a motor test-retest study on ten volunteer subjects that adaptive thresholding improves reliability, mainly by accounting for the global signal variance. This in turn increases the likelihood that the true activation pattern can be determined

Neural Network Back-Propagation Algorithm for Sensing Hypergols

Fast, continuous detection of a wide range of hazardous substances simultaneously is needed to achieve improved safety for personnel working with hypergolic fuels and oxidizers, as well as other hazardous substances, with a requirement for such detection systems to warn personnel immediately upon the sudden advent of hazardous conditions, with a high probability of detection and a low false alarm rate. The primary purpose of this software is to read the voltage outputs from voltage dividers containing carbon nano - tube sensors as a variable resistance leg, and to recognize quickly when a leak has occurred through recognizing that a generalized pattern change in resistivity of a carbon nanotube sensor has occurred upon exposure to dangerous substances, and, further, to identify quickly just what substance is present through detailed pattern recognition of the shape of the response provided by the carbon nanotube sensor

Reference Tracts and Generative Models for Brain White Matter Tractography †

Background: Probabilistic neighborhood tractography aims to automatically segment brain white matter tracts from diffusion magnetic resonance imaging (dMRI) data in different individuals. It uses reference tracts as priors for the shape and length of the tract, and matching models that describe typical deviations from these. We evaluated new reference tracts and matching models derived from dMRI data acquired from 80 healthy volunteers, aged 25–64 years. Methods: The new reference tracts and models were tested in 50 healthy older people, aged 71.8 ± 0.4 years. The matching models were further assessed by sampling and visualizing synthetic tracts derived from them. Results: We found that data-generated reference tracts improved the success rate of automatic white matter tract segmentations. We observed an increased rate of visually acceptable tracts, and decreased variation in quantitative parameters when using this approach. Sampling from the matching models demonstrated their quality, independently of the testing data. Conclusions: We have improved the automatic segmentation of brain white matter tracts, and demonstrated that matching models can be successfully transferred to novel data. In many cases, this will bypass the need for training data and make the use of probabilistic neighborhood tractography in small testing datasets newly practicable

Efficient organisation of the contralateral hemisphere connectome is associated with improvement in intelligence quotient after paediatric epilepsy surgery

ObjectiveAims of epilepsy surgery in childhood include optimising seizure control and facilitating cognitive development. Predicting which children will improve cognitively is challenging. We investigated the association of the pre-operative structural connectome of the contralateral non-operated hemisphere with improvement in intelligence quotient (IQ) post-operatively.MethodsConsecutive children who had undergone unilateral resective procedures for epilepsy at a single centre were retrospectively identified. We included those with pre-operative volume T1-weighted non-contrast brain magnetic resonance imaging (MRI), no visible contralateral MRI abnormalities, and both pre-operative and two years post-operative IQ assessment. The MRI of the hemisphere contralateral to the side of resection was anatomically parcellated into 34 cortical regions and the covariance of cortical thickness between regions was used to create binary and weighted group connectomes.ResultsEleven patients with a post-operative IQ increase of at least 10 points at two years were compared with twenty-four patients with no change in IQ score. Children who gained at least 10 IQ points post-operatively had a more efficiently structured contralateral hemisphere connectome with higher global efficiency (0.74) compared to those whose IQ did not change at two years (0.58, p=0.014). This was consistent across thresholds and both binary and weighted networks. There were no statistically significant group differences in age, sex, age at onset of epilepsy, pre-operative IQ, mean cortical thickness, side or site of procedure, two year post-operative Engel scores or use of anti-seizure medications between the two groups. ConclusionsSurgical procedures to reduce or stop seizures may allow children with an efficiently structured contralateral hemisphere to achieve their cognitive potential. <br/

TractoR: Magnetic Resonance Imaging and Tractography with R

Statistical techniques play a major role in contemporary methods for analyzing magnetic resonance imaging (MRI) data. In addition to the central role that classical statistical methods play in research using MRI, statistical modeling and machine learning techniques are key to many modern data analysis pipelines. Applications for these techniques cover a broad spectrum of research, including many preclinical and clinical studies, and in some cases these methods are working their way into widespread routine use.In this manuscript we describe a software tool called TractoR (for “Tractography with R”), a collection of packages for the R language and environment, along with additional infrastructure for straightforwardly performing common image processing tasks. TractoR provides general purpose functions for reading, writing and manipulating MR images, as well as more specific code for fitting signal models to diffusion MRI data and performing tractography, a technique for visualizing neural connectivity