Publications

In this paper, we propose an automated white matter connectivity analysis method for machine learning classification and characterization of white matter abnormality via identification of discriminative fiber tracts. The proposed method uses diffusion MRI tractography and a data-driven approach to find fiber clusters corresponding to subdivisions of the white matter anatomy. Features extracted from each fiber cluster describe its diffusion properties and are used for machine learning. The method is demonstrated by application to a pediatric neuroimaging dataset from 149 individuals, including 70 children with autism spectrum disorder (ASD) and 79 typically developing controls (TDC). A classification accuracy of 78.33% is achieved in this cross-validation study. We investigate the discriminative diffusion features based on a two-tensor fiber tracking model. We observe that the mean fractional anisotropy from the second tensor (associated with crossing fibers) is most affected in ASD. We also find that local along-tract (central cores and endpoint regions) differences between ASD and TDC are helpful in differentiating the two groups. These altered diffusion properties in ASD are associated with multiple robustly discriminative fiber clusters, which belong to several major white matter tracts including the corpus callosum, arcuate fasciculus, uncinate fasciculus and aslant tract; and the white matter structures related to the cerebellum, brain stem, and ventral diencephalon. These discriminative fiber clusters, a small part of the whole brain tractography, represent the white matter connections that could be most affected in ASD. Our results indicate the potential of a machine learning pipeline based on white matter fiber clustering.

The regional distribution of white matter (WM) abnormalities in schizophrenia remains poorly understood, and reported disease effects on the brain vary widely between studies. In an effort to identify commonalities across studies, we perform what we believe is the first ever large-scale coordinated study of WM microstructural differences in schizophrenia. Our analysis consisted of 2359 healthy controls and 1963 schizophrenia patients from 29 independent international studies; we harmonized the processing and statistical analyses of diffusion tensor imaging (DTI) data across sites and meta-analyzed effects across studies. Significant reductions in fractional anisotropy (FA) in schizophrenia patients were widespread, and detected in 20 of 25 regions of interest within a WM skeleton representing all major WM fasciculi. Effect sizes varied by region, peaking at (d=0.42) for the entire WM skeleton, driven more by peripheral areas as opposed to the core WM where regions of interest were defined. The anterior corona radiata (d=0.40) and corpus callosum (d=0.39), specifically its body (d=0.39) and genu (d=0.37), showed greatest effects. Significant decreases, to lesser degrees, were observed in almost all regions analyzed. Larger effect sizes were observed for FA than diffusivity measures; significantly higher mean and radial diffusivity was observed for schizophrenia patients compared with controls. No significant effects of age at onset of schizophrenia or medication dosage were detected. As the largest coordinated analysis of WM differences in a psychiatric disorder to date, the present study provides a robust profile of widespread WM abnormalities in schizophrenia patients worldwide. Interactive three-dimensional visualization of the results is available at www.enigma-viewer.org.Molecular Psychiatry advance online publication, 17 October 2017; doi:10.1038/mp.2017.170.

BACKGROUND: Emphysema has considerable variability in its regional distribution. Craniocaudal emphysema distribution is an important predictor of the response to lung volume reduction. However, there is little consensus regarding how to define upper lobe-predominant and lower lobe-predominant emphysema subtypes. Consequently, the clinical and genetic associations with these subtypes are poorly characterized. METHODS: We sought to identify subgroups characterized by upper-lobe or lower-lobe emphysema predominance and comparable amounts of total emphysema by analyzing data from 9,210 smokers without alpha-1-antitrypsin deficiency in the Genetic Epidemiology of COPD (COPDGene) cohort. CT densitometric emphysema was measured in each lung lobe. Random forest clustering was applied to lobar emphysema variables after regressing out the effects of total emphysema. Clusters were tested for association with clinical and imaging outcomes at baseline and at 5-year follow-up. Their associations with genetic variants were also compared.

Diffusion MRI (dMRI) data acquired on different scanners varies significantly in its content throughout the brain even if the acquisition parameters are nearly identical. Thus, proper harmonization of such data sets is necessary to increase the sample size and thereby the statistical power of neuroimaging studies. In this paper, we present a novel approach to harmonize dMRI data (the raw signal, instead of dMRI derived measures such as fractional anisotropy) using rotation invariant spherical harmonic (RISH) features embedded within a multi-modal image registration framework. All dMRI data sets from all sites are registered to a common template and voxel-wise differences in RISH features between sites at a group level are used to harmonize the signal in a subject-specific manner. We validate our method on diffusion data acquired from seven different sites (two GE, three Philips, and two Siemens scanners) on a group of age-matched healthy subjects. We demonstrate the efficacy of our method by statistically comparing diffusion measures such as fractional anisotropy, mean diffusivity and generalized fractional anisotropy across these sites before and after data harmonization. Validation was also done on a group oftest subjects, which were not used to "learn" the harmonization parameters. We also show results using TBSS before and after harmonization for independent validation of the proposed methodology. Using synthetic data, we show that any abnormality in diffusion measures due to disease is preserved during the harmonization process. Our experimental results demonstrate that, for nearly identical acquisition protocol across sites, scanner-specific differences in the signal can be removed using the proposed method in a model independent manner.

BACKGROUND: Given the central role of the thalamus in motor, sensory, and cognitive development, methods to study emerging thalamocortical connectivity in early infancy are of great interest. PURPOSE: To determine the feasibility of performing probabilistic tractography-based thalamic parcellation (PTbTP) in typically developing (TD) neonates and to compare the results with a pilot sample of neonates with congenital heart disease (CHD). STUDY TYPE: Institutional Review Board (IRB)-approved cross-sectional study. MODEL: We prospectively recruited 20 TD neonates and five CHD neonates (imaged preoperatively). FIELD STRENGTH/SEQUENCE: MRI was performed at 3.0T including diffusion-weighted imaging (DWI) and 3D magnetization prepared rapid gradient-echo (MPRAGE). ASSESSMENT: A radiologist and trained research assistants segmented the thalamus and seven cortical targets for each hemisphere. Using the thalami as seeds and the cortical labels as targets, FSL library tools were used to generate probabilistic tracts. A Hierarchical Dirichlet Process algorithm was then used for clustering analysis. A radiologist qualitatively assessed the results of clustering. Quantitative analyses were also performed. STATISTICAL TESTS: We summarized the demographic data and results of clustering with descriptive statistics. Linear regressions covarying for gestational age were used to compare groups. RESULTS: In 17 of 20 TD neonates, we identified five connectivity-determined clusters, which correlate with known thalamic nuclei and subnuclei. In four neonates with CHD we observed a spectrum of abnormalities including fewer and disorganized clusters or small supernumerary clusters (up to seven per thalamus). After covarying for differences in corrected gestational age (cGA), the fractional anisotropy (FA), volume, and normalized thalamic volume were significantly lower in CHD neonates (P 0.01). DATA CONCLUSIONS: Using PTbTP clusters, correlating well with the location and connectivity of known thalamic nuclei, were identified in TD neonates. Differences in thalamic clustering outputs were identified in four neonates with CHD, raising concern for disordered thalamic connectivity. PTbTP is feasible in TD and CHD neonates. Preliminary findings suggest the prenatal origins of altered connectivity in CHD. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2018;47:1626-1637.

In schizophrenia, abnormalities in structural connectivity between brain regions known to contain mirror neurons and their relationship to negative symptoms related to a domain of social cognition are not well understood. Diffusion tensor imaging (DTI) scans were acquired in 16 patients with first episode schizophrenia and 16 matched healthy controls. FA and Trace of the tracts interconnecting regions known to be rich in mirror neurons, i.e., anterior cingulate cortex (ACC), inferior parietal lobe (IPL) and premotor cortex (PMC) were evaluated. A significant group effect for Trace was observed in IPL-PMC white matter fiber tract (F (1, 28) = 7.13, p = .012), as well as in the PMC-ACC white matter fiber tract (F (1, 28) = 4.64, p = .040). There were no group differences in FA. In addition, patients with schizophrenia showed a significant positive correlation between the Trace of the left IPL-PMC white matter fiber tract, and the Ability to Feel Intimacy and Closeness score (rho = .57, p = 0.034), and a negative correlation between the Trace of the left PMC-ACC and the Relationships with Friends and Peers score (rho = remove -.54, p = 0.049). We have demonstrated disrupted white mater microstructure within the white matter tracts subserving brain regions containing mirror neurons. We further showed that such structural disruptions might impact negative symptoms and, more specifically, contribute to the inability to feel intimacy (a measure conceptually related to theory of mind) in first episode schizophrenia. Further studies are needed to understand the potential of our results for diagnosis, prognosis and therapeutic interventions.

PURPOSE: To develop a phantom for validating MRI pulse sequences and data processing methods to quantify microscopic diffusion anisotropy in the human brain. METHODS: Using a liquid crystal consisting of water, detergent, and hydrocarbon, we designed a 0.5-L spherical phantom showing the theoretically highest possible degree of microscopic anisotropy. Data were acquired on the Connectome scanner using echo-planar imaging signal readout and diffusion encoding with axisymmetric b-tensors of varying magnitude, anisotropy, and orientation. The mean diffusivity, fractional anisotropy (FA), and microscopic FA (µFA) parameters were estimated. RESULTS: The phantom was observed to have values of mean diffusivity similar to brain tissue, and relaxation times compatible with echo-planar imaging echo times on the order of 100 ms. The estimated values of µFA were at the theoretical maximum of 1.0, whereas the values of FA spanned the interval from 0.0 to 0.8 as a result of varying orientational order of the anisotropic domains within each voxel. CONCLUSIONS: The proposed phantom can be manufactured by mixing three widely available chemicals in volumes comparable to a human head. The acquired data are in excellent agreement with theoretical predictions, showing that the phantom is ideal for validating methods for measuring microscopic diffusion anisotropy on clinical MRI systems. Magn Reson Med 79:1817-1828, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Free Water Imaging is a novel diffusion magnetic resonance (MR) imaging method that is able to separate changes affecting the extracellular space from those that reflect changes in neuronal cells and processes. A previous Free Water Imaging study in schizophrenia identified significantly greater extracellular water volume in the early stages of the disorder; however, its clinical and functional sequelae have not yet been investigated. Here, we applied Free Water Imaging to a larger cohort of 63 first-episode patients with psychosis and 70 healthy matched controls to better understand the functional significance of greater extracellular water. We used diffusion MR imaging data and the Tract-Based Spatial Statistics analytic pipeline to first analyze fractional anisotropy (FA), the most commonly employed metric for assessing white matter. This comparison was then followed by Free Water Imaging analysis, where two parameters, the fractional volume of extracellular free-water (FW) and cellular tissue FA (FA-t), were estimated and compared across the entire white matter skeleton between groups, and correlated with cognitive measures at baseline and following 12 weeks of antipsychotic treatment. Our results indicated lower FA across the whole brain in patients compared with healthy controls that overlap with significant increases in FW, with only limited decreases in FA-t. In addition, higher FW correlated with better neurocognitive functioning following 12 weeks of antipsychotic treatment. We believe this is the first study to suggest that an extracellular water increase during the first-episode of psychosis, which may be indicative of an acute neuroinflammatory process, and/or cerebral edema may predict better functional outcome.

PURPOSE: To develop an efficient acquisition for high-resolution diffusion imaging and allow in vivo whole-brain acquisitions at 600- to 700-μm isotropic resolution. METHODS: We combine blipped-controlled aliasing in parallel imaging simultaneous multislice (SMS) with a novel slab radiofrequency (RF) encoding gSlider (generalized slice-dithered enhanced resolution) to form a signal-to-noise ratio-efficient volumetric simultaneous multislab acquisition. Here, multiple thin slabs are acquired simultaneously with controlled aliasing, and unaliased with parallel imaging. To achieve high resolution in the slice direction, the slab is volumetrically encoded using RF encoding with a scheme similar to Hadamard encoding. However, with gSlider, the RF-encoding bases are specifically designed to be highly independent and provide high image signal-to-noise ratio in each slab acquisition to enable self-navigation of the diffusion’s phase corruption. Finally, the method is combined with zoomed imaging (while retaining whole-brain coverage) to facilitate low-distortion single-shot in-plane encoding with echo-planar imaging at high resolution. RESULTS: A 10-slices-per-shot gSlider-SMS acquisition was used to acquire whole-brain data at 660 and 760 μm isotropic resolution with b-values of 1500 and 1800 s/mm, respectively. Data were acquired on the Connectome 3 Tesla scanner with 64-channel head coil. High-quality data with excellent contrast were achieved at these resolutions, which enable the visualization of fine-scale structures. CONCLUSIONS: The gSlider-SMS approach provides a new, efficient way to acquire high-resolution diffusion data. Magn Reson Med 79:141-151, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

BACKGROUND: Schizophrenia has a large genetic component, and the pathways from genes to illness manifestation are beginning to be identified. The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) Consortium aims to clarify the role of genetic variation in brain abnormalities underlying schizophrenia. This article describes the GENUS Consortium sample collection. METHODS: We identified existing samples collected for schizophrenia studies consisting of patients, controls, and/or individuals at familial high-risk (FHR) for schizophrenia. Samples had single nucleotide polymorphism (SNP) array data or genomic DNA, clinical and demographic data, and neuropsychological and/or brain magnetic resonance imaging (MRI) data. Data were subjected to quality control procedures at a central site. RESULTS: Sixteen research groups contributed data from 5199 psychosis patients, 4877 controls, and 725 FHR individuals. All participants have relevant demographic data and all patients have relevant clinical data. The sex ratio is 56.5% male and 43.5% female. Significant differences exist between diagnostic groups for premorbid and current IQ (both p<1×10). Data from a diversity of neuropsychological tests are available for 92% of participants, and 30% have structural MRI scans (half also have diffusion-weighted MRI scans). SNP data are available for 76% of participants. The ancestry composition is 70% European, 20% East Asian, 7% African, and 3% other. CONCLUSIONS: The Consortium is investigating the genetic contribution to brain phenotypes in a schizophrenia sample collection of >10,000 participants. The breadth of data across clinical, genetic, neuropsychological, and MRI modalities provides an important opportunity for elucidating the genetic basis of neural processes underlying schizophrenia.