Tuesday, Jun 27: 10:30 AM - 11:45 AM
Oral Sessions
Tuesday, June 27, 2017 10:30-11:45
Vancouver Convention Centre
Room: Ballroom AB
Chair
Catie Chang, NIH
Presentations
Higher Angular Resolution Diffusion Imaging (HARDI) methods, such as Diffusion Spectrum Imaging (DSI[1]) and multishell Q-ball imaging[2] are robust tools for studying in vivo white matter architecture. These methods capture the complex intravoxel crossings [1] in Orientation Distribution Functions (ODFs). To use these ODFs in tractography algorithms the fiber directions in each voxel must be identified. Limited angular resolution and intrinsic ODF peak width [3] make it however difficult to correctly estimate fiber directions when the relative angle between the bundles is small [4,5]. Most methods fail to detect crossing angles less than 40° [4,5]. Even after deconvolving the ODFs with a Fiber Response Function, it remains difficult to reliably detect crossing angles smaller than 30°[6].
Here we propose a new approach inspired by key concepts first introduced in MR Fingerprinting [7,8]. Instead of a dictionary with spin evolutions at different T1 and T2 relaxation times, we generate a library of ODF-fingerprints and identify the fiber directions of ODFs by assessing the similarity between the measured data and the elements in our library (Fig 1a). We demonstrate this method on both simulated and in vivo measured ODFs.
Presenter
Steven Baete, NYU School of Medicine
The targeted age group, 0-2 years, has been underserved with respect to specialized computational tools that can robustly and accurately analyze brain MRI images. Several segmentation solutions exist for specific ages within this range, for example, for newborns (Prastawa, Gilmore et al. 2005, Wang, Shi et al. 2011, Gui, Lisowski et al. 2012, Gousias, Hammers et al. 2013, Wang, Gao et al. 2015), for 1-year olds (Wang, Gao et al. 2015), and for 2-year olds(Gousias, Rueckert et al. 2008),), some relying on access to longitudinal time points from the same subject to handle segmentation of the more challenging ages (Wang, Shi et al. 2011), but none can handle this relatively wide age range exclusively on clinical T1-weighted images.
Presenter
Lilla Zöllei, Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital
In a typical fMRI experiment responses are recorded under a few conditions (e.g. abstract words and concrete words) and then contrasts are performed between conditions. Locations of significant differences are reported, usually in a table listing peak locations in standardized space. However, statistical thresholds are usually not directly comparable across experiments because of differences in design and analysis. Thus, it is difficult to replicate experiments or synthesize results across them.
Naturalistic experiments and voxel-wise modeling provide one alternative to the contrast-based approach. These studies sample the stimulus space broadly and characterize the relationship between linearized stimulus features and brain activity in single voxels. Here, we provide a means to bridge between contrast-based studies and naturalistic studies. Specifically, we present a web-based replication engine that uses data derived from naturalistic voxel-wise modeling experiments to simulate any simple language contrast that can be expressed in terms of a list of words reflecting each of two conditions.
Presenter
Leila Wehbe, UC Berkeley
Null hypothesis testing lies at the foundation of human brain mapping as the core method for fMRI inference. However, recent studies have shown that under optimal conditions the null hypothesis is never true [1]. As ambitious, large-sample studies have become available (e.g. Human Connectome Project, N=1,200; UK Biobank final N=100,000), this we have high-quality, high-power data for which the null hypothesis test essentially shows universal activation even with stringent correction.
To overcome this, we apply recent work [2] to develop confidence sets (CSs) on clusters found in thresholded maps. Whereas traditional inferences indicate where the null, i.e. an effect size of 0, is rejected, the CSs are statements about non-zero effect sizes analogous to confidence intervals. For a cluster constructed with cluster-forming threshold c, the CSs comprise two sets of voxels: The upper CS is smaller, giving the voxels we infer to be truly larger than c; the larger lower CS is best described by its complement -- all voxels outside this set we infer to be truly smaller than c.
Here we describe the method, evaluate it with simulations and apply it to HCP data. We focus on inference on the percentage BOLD change map.
Presenter
Alexander Bowring, University of Warwick
A growing body of literature has demonstrated the ability to delineate cortical areas in the human brain based upon the detection of spatial transitions in intrinsic functional connectivity (iFC) profiles (Cohen et al., 2008; Wig et al., 2014). In particular, gradient-based parcellation approaches have gained popularity due to their ability to recapitulate previously established cytoarchitectonic brain areas. Here, we demonstrate the feasibility of extending the application of parcellation approaches to non-human primates (NHP), demonstrating the reliability of these parcellations and comparing the cortical areas revealed to those obtained in humans.
Presenter
Ting Xu, Child Mind Institute
There is growing interest in the rich temporal and spectral properties of Electro- and Magnetoencephalography (E/MEG) signals in order to study the functional connectome of the brain [1, 2]. However, the spatial resolution of E/MEG data is limited, because several thousand sources of activation in the brain must be estimated from maximally a few hundred recording sites. This limited spatial resolution causes the so-called leakage problem: activity estimated in one region of interest (ROI) can be affected by leakage from locations outside this ROI [3, 4]. E/MEG studies typically adopt parcellations from structural or fMRI research for whole-brain connectivity analysis [5]. However, considering the spatial resolution of E/MEG, these parcellations are unlikely to be optimal [6]. Here, we utilise Cross-Talk Functions (CTFs) as a direct measure of spatial leakage [7] and utilise two CTF-informed image segmentation algorithms in order to parcellate the cortical surface into the maximum number of distinguishable ROIs.
Presenter
Seyedehrezvan Farahibozorg, University of Cambridge