An Open-Source Tool for Anisotropic Radiation Therapy Planning in Neuro-oncology Using DW-MRI Tractography.

There is evidence from histopathological studies that glioma tumor cells migrate preferentially along large white matter bundles. If the peritumoral white matter structures can be used to predict the likely trajectory of migrating tumor cells outside of the surgical margin, then this information could be used to inform the delineation of radiation therapy (RT) targets. In theory, an anisotropic expansion that takes large white matter bundle anatomy into account may maximize the chances of treating migrating cancer cells and minimize the amount of brain tissue exposed to high doses of ionizing radiation. Diffusion-weighted MRI (DW-MRI) can be used in combination with fiber tracking algorithms to model the trajectory of large white matter pathways using the direction and magnitude of water movement in tissue. The method presented here is a tool for translating a DW-MRI fiber tracking (tractography) dataset into a white matter path length (WMPL) map that assigns each voxel the shortest distance along a streamline back to a specified region of interest (ROI). We present an open-source WMPL tool, implemented in the package Diffusion Imaging in Python (DIPY), and code to convert the resulting WMPL map to anisotropic contours for RT in a commercial treatment planning system. This proof-of-concept lays the groundwork for future studies to evaluate the clinical value of incorporating tractography modeling into treatment planning

Neurocognitive Basis of Repetition Deficits in Primary Progressive Aphasia

Previous studies indicate that repetition is affected in primary progressive aphasia (PPA), particularly in the logopenic variant, due to limited auditory-verbal short-term memory (avSTM). We tested repetition of phrases varied by length (short, long) and meaning (meaningful, non-meaningful) in 58 participants (22 logopenic, 19 nonfluent, and 17 semantic variants) and 21 healthy controls using a modified Bayles repetition test. We evaluated the relation between cortical thickness and repetition performance and whether sub-scores could discriminate PPA variants. Logopenic participants showed impaired repetition across all phrases, specifically in repeating long phrases and any phrases that were non-meaningful. Nonfluent, semantic, and healthy control participants only had difficulty repeating long, non-meaningful phrases. Poor repetition of long phrases was associated with cortical thinning in left temporo-parietal areas across all variants, highlighting the importance of these areas in avSTM. Finally, Bayles repetition phrases can assist classification in PPA, discriminating logopenic from nonfluent/semantic participants with 89% accuracy

Mindcontrol: a web application for brain segmentation quality control

Tissue classification plays a crucial role in the investigation of normal neural development, brain-behavior relationships, and the disease mechanisms of many psychiatric and neurological illnesses. Ensuring the accuracy of tissue classification is important for quality research and, in particular, the translation of imaging biomarkers to clinical practice. Assessment with the human eye is vital to correct various errors inherent to all currently available segmentation algorithms. Manual quality assurance becomes methodologically difficult at a large scale - a problem of increasing importance as the number of data sets is on the rise. To make this process more efficient, we have developed Mindcontrol, an open-source web application for the collaborative quality control of neuroimaging processing outputs. The Mindcontrol platform consists of a dashboard to organize data, descriptive visualizations to explore the data, an imaging viewer, and an in-browser annotation and editing toolbox for data curation and quality control. Mindcontrol is flexible and can be configured for the outputs of any software package in any data organization structure. Example configurations for three large, open-source datasets are presented: the 1000 Functional Connectomes Project (FCP), the Consortium for Reliability and Reproducibility (CoRR), and the Autism Brain Imaging Data Exchange (ABIDE) Collection. These demo applications link descriptive quality control metrics, regional brain volumes, and thickness scalars to a 3D imaging viewer and editing module, resulting in an easy-to-implement quality control protocol that can be scaled for any size and complexity of study

Assessing the viability of studying motion indicators of autism spectrum disorders in infants at high and low risk for ASD using a passive motion capture system

Gemstone Team AMIRAAutism Spectrum Disorders (ASD) are a group of socially debilitating disorders that affect 1 in 110 children. Researchers have long understood that early diagnosis and intervention lead to the best possible outcome for children with ASD, compelling researchers to develop early diagnostic methods. Researchers believe that a better understanding of the effect of ASD on movement will aid in developing these early diagnostic techniques. To assist in understanding the effect of ASD on movement, our team performed a proof of concept study to determine if a passive motion capture system can be used to characterize motion indicators of ASD. To accomplish this goal, our team analyzed three distinct movements in infants, six to twelve months, at high and low risk for ASD. We determined that passive motion capture systems can characterize movement indicators of infants at high and low risk for ASD

Power estimation for non-standardized multisite studies

AbstractA concern for researchers planning multisite studies is that scanner and T1-weighted sequence-related biases on regional volumes could overshadow true effects, especially for studies with a heterogeneous set of scanners and sequences. Current approaches attempt to harmonize data by standardizing hardware, pulse sequences, and protocols, or by calibrating across sites using phantom-based corrections to ensure the same raw image intensities. We propose to avoid harmonization and phantom-based correction entirely. We hypothesized that the bias of estimated regional volumes is scaled between sites due to the contrast and gradient distortion differences between scanners and sequences. Given this assumption, we provide a new statistical framework and derive a power equation to define inclusion criteria for a set of sites based on the variability of their scaling factors. We estimated the scaling factors of 20 scanners with heterogeneous hardware and sequence parameters by scanning a single set of 12 subjects at sites across the United States and Europe. Regional volumes and their scaling factors were estimated for each site using Freesurfer's segmentation algorithm and ordinary least squares, respectively. The scaling factors were validated by comparing the theoretical and simulated power curves, performing a leave-one-out calibration of regional volumes, and evaluating the absolute agreement of all regional volumes between sites before and after calibration. Using our derived power equation, we were able to define the conditions under which harmonization is not necessary to achieve 80% power. This approach can inform choice of processing pipelines and outcome metrics for multisite studies based on scaling factor variability across sites, enabling collaboration between clinical and research institutions

kesshijordan/Publication_Repository: Publication of CCI

The cluster confidence index demo (cci.py) is referenced in a publication

Diffusion MR Image Processing Tools for Reliable Fiber Tracking Analyses: Neurosurgery and Radiation Oncology Applications

Neurosurgery and Disconnection Syndrome research have a symbiotic relationship. The human brain is a staggeringly complex system, unique to each individual. Even at birth there is already incredible diversity to this network, upon which we add a lifetime of experiences, influencing our brain structure and function by the way we use it. One of the best ways to study such a variable and complex system is to see what happens when it is perturbed. Neurosurgical intervention presents a rare opportunity to interact with the human brain in a controlled environment and see what happens when transient or permanent interference occurs. In return, the lessons learned about the relationship between brain structure and function can guide surgical intervention to minimize the risk of surgical injury causing permanent functional deficits. The risk a person is willing to take on to a functional system is a very personal decision; to some people, motor or language function may be what makes life worth living and others are willing to risk deficits to treat a pathology more aggressively. Understanding what damage patterns result in deficits is key to empowering the patient to make these decisions. The brain's white matter connections can be modeled with Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) Fiber Tracking (also called tractography), a process by which water diffusion is used to deduce pathways of axon bundles. Neurosurgical applications present particular engineering challenges due to a variety of factors influenced by both the pathology and intervention. This thesis details several tools developed to address these challenges including methods to quality-control tractography streamline datasets, a processing pipeline to model disconnections caused by surgical intervention, a method to translate tractography information to a format tractable for integration with radiation therapy planning, and a pipeline relating electrode stimulation to white matter connectivity. All of the code is open-source so that researchers can use these tools to conduct their own studies

Cluster-viz: A Tractography QC Tool

Cluster-viz is a web application that provides a platform for cluster-based interactive quality-control of tractography algorithm outputs. This tool facilitates the creation of white matter fascicle models by employing a cluster-based approach to allow the user to select streamline bundles for inclusion/exclusion in the final fascicle model. This project was started at the 2016 Neurohackweek and BrainHack events and is still under development. We welcome contributions to the Cluster-viz github repository (https://github.com/kesshijordan/Cluster-viz)