Publications by authors named "Julia P Owen"

36 Publications

Myelin development in cerebral gray and white matter during adolescence and late childhood.

Neuroimage 2021 02 29;227:117678. Epub 2020 Dec 29.

Institute for Learning & Brain Sciences, University of Washington, Box 357988, Portage Bay Building, Seattle WA 98195, United States.

Myelin development during adolescence is becoming an area of growing interest in view of its potential relationship to cognition, behavior, and learning. While recent investigations suggest that both white matter (WM) and gray matter (GM) undergo protracted myelination during adolescence, quantitative relations between myelin development in WM and GM have not been previously studied. We quantitatively characterized the dependence of cortical GM, WM, and subcortical myelin density across the brain on age, gender, and puberty status during adolescence with the use of a novel macromolecular proton fraction (MPF) mapping method. Whole-brain MPF maps from a cross-sectional sample of 146 adolescents (age range 9-17 years) were collected. Myelin density was calculated from MPF values in GM and WM of all brain lobes, as well as in subcortical structures. In general, myelination of cortical GM was widespread and more significantly correlated with age than that of WM. Myelination of GM in the parietal lobe was found to have a significantly stronger age dependence than that of GM in the frontal, occipital, temporal and insular lobes. Myelination of WM in the temporal lobe had the strongest association with age as compared to WM in other lobes. Myelin density was found to be higher in males as compared to females when averaged across all cortical lobes, as well as in a bilateral subcortical region. Puberty stage was significantly correlated with myelin density in several cortical areas and in the subcortical GM. These findings point to significant differences in the trajectories of myelination of GM and WM across brain regions and suggest that cortical GM myelination plays a dominant role during adolescent development.
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http://dx.doi.org/10.1016/j.neuroimage.2020.117678DOI Listing
February 2021

Exploring a Structural Basis for Delayed Rod-Mediated Dark Adaptation in Age-Related Macular Degeneration Via Deep Learning.

Transl Vis Sci Technol 2020 12 15;9(2):62. Epub 2020 Dec 15.

Department of Ophthalmology and Visual Sciences, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.

Purpose: Delayed rod-mediated dark adaptation (RMDA) is a functional biomarker for incipient age-related macular degeneration (AMD). We used anatomically restricted spectral domain optical coherence tomography (SD-OCT) imaging data to localize de novo imaging features associated with and to test hypotheses about delayed RMDA.

Methods: Rod intercept time (RIT) was measured in participants with and without AMD at 5 degrees from the fovea, and macular SD-OCT images were obtained. A deep learning model was trained with anatomically restricted information using a single representative B-scan through the fovea of each eye. Mean-occlusion masking was utilized to isolate the relevant imaging features.

Results: The model identified hyporeflective outer retinal bands on macular SD-OCT associated with delayed RMDA. The validation mean standard error (MSE) registered to the foveal B-scan localized the lowest error to 0.5 mm temporal to the fovea center, within an overall low-error region across the rod-free zone and adjoining parafovea. Mean absolute error (MAE) on the test set was 4.71 minutes (8.8% of the dynamic range).

Conclusions: We report a novel framework for imaging biomarker discovery using deep learning and demonstrate its ability to identify and localize a previously undescribed biomarker in retinal imaging. The hyporeflective outer retinal bands in central macula on SD-OCT demonstrate a structural basis for dysfunctional rod vision that correlates to published histopathologic findings.

Translational Relevance: This agnostic approach to anatomic biomarker discovery strengthens the rationale for RMDA as an outcome measure in early AMD clinical trials, and also expands the utility of deep learning beyond automated diagnosis to fundamental discovery.
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http://dx.doi.org/10.1167/tvst.9.2.62DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745629PMC
December 2020

Smoking Is Associated with Higher Intraocular Pressure Regardless of Glaucoma: A Retrospective Study of 12.5 Million Patients Using the Intelligent Research in Sight (IRIS®) Registry.

Ophthalmol Glaucoma 2020 Jul - Aug;3(4):253-261. Epub 2020 Mar 31.

Department of Ophthalmology, University of Washington, Seattle, Washington; eScience Institute, University of Washington, Seattle, Washington. Electronic address:

Purpose: To compare the average intraocular pressure (IOP) among smokers, past smokers, and never smokers using the American Academy of Ophthalmology Intelligent Research in Sight (IRIS®) Registry.

Design: Retrospective database study of the IRIS® Registry data.

Participants: Intelligent Research in Sight Registry patients who were seen by an eye care provider during 2017.

Methods: Patients were divided into current smoker, past smoker, and never smoker categories. The IOP was based on an average measurement, and separate analyses were performed in patients with and without a glaucoma diagnosis based on International Classification of Diseases (Ninth Edition and Tenth Edition) codes. Stratified, descriptive statistics by glaucoma status were determined, and the relationship between smoking and IOP was assessed with a multivariate linear regression model.

Main Outcome Measures: Mean IOP.

Results: A total of 12 535 013 patients were included. Compared with never smokers, current and past smokers showed a statistically significantly higher IOP by 0.92 mmHg (95% confidence interval [CI], 0.88-0.95 mmHg) and 0.77 mmHg (95% CI, 0.75-0.79 mmHg), respectively, after adjusting for age, gender, glaucoma, age-related macular degeneration, diabetic retinopathy, cataract, glaucoma surgery, cataract surgery, and first-order interactions. In addition, the difference in IOP between current and never smokers was the highest in the fourth decade, regardless of the glaucoma status (glaucoma group, 1.14 mmHg [95% CI, 1.00-1.29 mmHg]; without glaucoma group, 0.68 mmHg [95% CI, 0.65-0.71 mmHg]).

Conclusions: Current smokers and past smokers have higher IOP than patients who never smoked. This difference is higher in patients with an underlying glaucoma diagnosis.
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http://dx.doi.org/10.1016/j.ogla.2020.03.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7532983PMC
March 2020

Connectome mapping with edge density imaging differentiates pediatric mild traumatic brain injury from typically developing controls: proof of concept.

Pediatr Radiol 2020 10 30;50(11):1594-1601. Epub 2020 Jun 30.

Neural Connectivity Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., Suite 350, San Francisco, CA, 94158, USA.

Background: Although acute neurologic impairment might be transient, other long-term effects can be observed with mild traumatic brain injury. However, when pediatric patients with mild traumatic brain injury present for medical care, conventional imaging with CT and MR imaging often does not reveal abnormalities.

Objective: To determine whether edge density imaging can separate pediatric mild traumatic brain injury from typically developing controls.

Materials And Methods: Subjects were recruited as part of the "Therapeutic Resources for Attention Improvement using Neuroimaging in Traumatic Brain Injury" (TRAIN-TBI) study. We included 24 adolescents (χ=14.1 years of age, σ=1.6 years, range 10-16 years), 14 with mild traumatic brain injury (TBI) and 10 typically developing controls. Neurocognitive assessments included the pediatric version of the California Verbal Learning Test (CVLT) and the Attention Network Task (ANT). Diffusion MR imaging was acquired on a 3-tesla (T) scanner. Edge density images were computed utilizing fiber tractography. Principal component analysis (PCA) and support vector machines (SVM) were used in an exploratory analysis to separate mild TBI and control groups. The diagnostic accuracy of edge density imaging, neurocognitive tests, and fractional anisotropy (FA) from diffusion tensor imaging (DTI) was computed with two-sample t-tests and receiver operating characteristic (ROC) metrics.

Results: Support vector machine-principal component analysis of edge density imaging maps identified three white matter regions distinguishing pediatric mild TBI from controls. The bilateral tapetum, sagittal stratum, and callosal splenium identified mild TBI subjects with sensitivity of 79% and specificity of 100%. Accuracy from the area under the ROC curve (AUC) was 94%. Neurocognitive testing provided an AUC of 61% (CVLT) and 71% (ANT). Fractional anisotropy yielded an AUC of 48%.

Conclusion: In this proof-of-concept study, we show that edge density imaging is a new form of connectome mapping that provides better diagnostic delineation between pediatric mild TBI and healthy controls than DTI or neurocognitive assessments of memory or attention.
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http://dx.doi.org/10.1007/s00247-020-04743-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7501221PMC
October 2020

Longitudinal increases in structural connectome segregation and functional connectome integration are associated with better recovery after mild TBI.

Hum Brain Mapp 2019 10 11;40(15):4441-4456. Epub 2019 Jul 11.

Department of Radiology and Biomedical Imaging, University of California, San Francisco, California.

Traumatic brain injury damages white matter pathways that connect brain regions, disrupting transmission of electrochemical signals and causing cognitive and emotional dysfunction. Connectome-level mechanisms for how the brain compensates for injury have not been fully characterized. Here, we collected serial MRI-based structural and functional connectome metrics and neuropsychological scores in 26 mild traumatic brain injury subjects (29.4 ± 8.0 years, 20 males) at 1 and 6 months postinjury. We quantified the relationship between functional and structural connectomes using network diffusion (ND) model propagation time, a measure that can be interpreted as how much of the structural connectome is being utilized for the spread of functional activation, as captured via the functional connectome. Overall cognition showed significant improvement from 1 to 6 months (t = -2.15, p = .04). None of the structural or functional global connectome metrics was significantly different between 1 and 6 months, or when compared to 34 age- and gender-matched controls (28.6 ± 8.8 years, 25 males). We predicted longitudinal changes in overall cognition from changes in global connectome measures using a partial least squares regression model (cross-validated R = .27). We observe that increased ND model propagation time, increased structural connectome segregation, and increased functional connectome integration were related to better cognitive recovery. We interpret these findings as suggesting two connectome-based postinjury recovery mechanisms: one of neuroplasticity that increases functional connectome integration and one of remote white matter degeneration that increases structural connectome segregation. We hypothesize that our inherently multimodal measure of ND model propagation time captures the interplay between these two mechanisms.
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http://dx.doi.org/10.1002/hbm.24713DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6865536PMC
October 2019

Diffusion tensor tractography in children with sensory processing disorder: Potentials for devising machine learning classifiers.

Neuroimage Clin 2019 24;23:101831. Epub 2019 Apr 24.

Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States of America; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, United States of America. Electronic address:

The "sensory processing disorder" (SPD) refers to brain's inability to organize sensory input for appropriate use. In this study, we determined the diffusion tensor imaging (DTI) microstructural and connectivity correlates of SPD, and apply machine learning algorithms for identification of children with SPD based on DTI/tractography metrics. A total of 44 children with SPD and 41 typically developing children (TDC) were prospectively recruited and scanned. In addition to fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD), we applied probabilistic tractography to generate edge density (ED) and track density (TD) from DTI maps. For identification of children with SPD, accurate classification rates from a combination of DTI microstructural (FA, MD, AD, and RD), connectivity (TD) and connectomic (ED) metrics with different machine learning algorithms - including naïve Bayes, random forest, support vector machine, and neural networks - were determined. In voxel-wise analysis, children with SPD had lower FA, ED, and TD but higher MD and RD compared to TDC - predominantly in posterior white matter tracts including posterior corona radiata, posterior thalamic radiation, and posterior body and splenium of corpus callosum. In stepwise penalized logistic regression, the only independent variable distinguishing children with SPD from TDC was the average TD in the splenium (p < 0.001). Among different combinations of machine learning algorithms and DTI/connectivity metrics, random forest models using tract-based TD yielded the highest accuracy in classification of SPD - 77.5% accuracy, 73.8% sensitivity, and 81.6% specificity. Our findings demonstrate impaired microstructural and connectivity/connectomic integrity in children with SPD, predominantly in posterior white matter tracts, and with reduced TD of the splenium of corpus callosum as the most distinctive pattern. Applying machine learning algorithms, these connectivity metrics can be used to devise novel imaging biomarkers for neurodevelopmental disorders.
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http://dx.doi.org/10.1016/j.nicl.2019.101831DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6488562PMC
March 2020

White Matter Connectome Correlates of Auditory Over-Responsivity: Edge Density Imaging and Machine-Learning Classifiers.

Front Integr Neurosci 2019 29;13:10. Epub 2019 Mar 29.

Department of Neurology, University of California, San Francisco, San Francisco, CA, United States.

Sensory over-responsivity (SOR) commonly involves auditory and/or tactile domains, and can affect children with or without additional neurodevelopmental challenges. In this study, we examined white matter microstructural and connectome correlates of auditory over-responsivity (AOR), analyzing prospectively collected data from 39 boys, aged 8-12 years. In addition to conventional diffusion tensor imaging (DTI) maps - including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD); we used DTI and high-resolution T1 scans to develop connectome Edge Density (ED) maps. The tract-based spatial statistics was used for voxel-wise comparison of diffusion and ED maps. Then, stepwise penalized logistic regression was applied to identify independent variable (s) predicting AOR, as potential imaging biomarker (s) for AOR. Finally, we compared different combinations of machine learning algorithms (i.e., naïve Bayes, random forest, and support vector machine (SVM) and tract-based DTI/connectome metrics for classification of children with AOR. In direct sensory phenotype assessment, 15 (out of 39) boys exhibited AOR (with or without neurodevelopmental concerns). Voxel-wise analysis demonstrates extensive impairment of white matter microstructural integrity in children with AOR on DTI maps - evidenced by lower FA and higher MD and RD; moreover, there was lower connectome ED in anterior-superior corona radiata, genu and body of corpus callosum. In stepwise logistic regression, the average FA of left superior longitudinal fasciculus (SLF) was the single independent variable distinguishing children with AOR ( = 0.007). Subsequently, the left SLF average FA yielded an area under the curve of 0.756 in receiver operating characteristic analysis for prediction of AOR ( = 0.008) as a region-of-interest (ROI)-based imaging biomarker. In comparative study of different combinations of machine-learning models and DTI/ED metrics, random forest algorithms using ED had higher accuracy for AOR classification. Our results demonstrate extensive white matter microstructural impairment in children with AOR, with specifically lower connectomic ED in anterior-superior tracts and associated commissural pathways. Also, average FA of left SLF can be applied as ROI-based imaging biomarker for prediction of SOR. Finally, machine-learning models can provide accurate and objective image-based classifiers for identification of children with AOR based on white matter tracts connectome ED.
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http://dx.doi.org/10.3389/fnint.2019.00010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6450221PMC
March 2019

Applying microstructural models to understand the role of white matter in cognitive development.

Dev Cogn Neurosci 2019 04 1;36:100624. Epub 2019 Feb 1.

Institute for Learning & Brain Sciences and Department of Speech and Hearing Sciences, University of Washington, Seattle, WA, 98195, United States.

Diffusion MRI (dMRI) holds great promise for illuminating the biological changes that underpin cognitive development. The diffusion of water molecules probes the cellular structure of brain tissue, and biophysical modeling of the diffusion signal can be used to make inferences about specific tissue properties that vary over development or predict cognitive performance. However, applying these models to study development requires that the parameters can be reliably estimated given the constraints of data collection with children. Here we collect repeated scans using a typical multi-shell diffusion MRI protocol in a group of children (ages 7-12) and use two popular modeling techniques to examine individual differences in white matter structure. We first assess scan-rescan reliability of model parameters and show that axon water faction can be reliably estimated from a relatively fast acquisition, without applying spatial smoothing or de-noising. We then investigate developmental changes in the white matter, and individual differences that correlate with reading skill. Specifically, we test the hypothesis that previously reported correlations between reading skill and diffusion anisotropy in the corpus callosum reflect increased axon water fraction in poor readers. Both models support this interpretation, highlighting the utility of these approaches for testing specific hypotheses about cognitive development.
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http://dx.doi.org/10.1016/j.dcn.2019.100624DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6969248PMC
April 2019

White Matter Microstructure Associations of Cognitive and Visuomotor Control in Children: A Sensory Processing Perspective.

Front Integr Neurosci 2018 14;12:65. Epub 2019 Jan 14.

Neuroscape Center, Departments of Neurology, Pediatrics, Physiology, Radiology, and Psychiatry, University of California, San Francisco, San Francisco, CA, United States.

: Recent evidence suggests that co-occurring deficits in cognitive control and visuomotor control are common to many neurodevelopmental disorders. Specifically, children with sensory processing dysfunction (SPD), a condition characterized by sensory hyper/hypo-sensitivity, show varying degrees of overlapping attention and visuomotor challenges. In this study, we assess associations between cognitive and visuomotor control abilities among children with and without SPD. In this same context, we also examined the common and unique diffusion tensor imaging (DTI) tracts that may support the overlap of cognitive control and visuomotor control. : We collected cognitive control and visuomotor control behavioral measures as well as DTI data in 37 children with SPD and 25 typically developing controls (TDCs). We constructed regressions to assess for associations between behavioral performance and mean fractional anisotropy (FA) in selected regions of interest (ROIs). : We observed an association between behavioral performance on cognitive control and visuomotor control. Further, our findings indicated that FA in the anterior limb of the internal capsule (ALIC), the anterior thalamic radiation (ATR), and the superior longitudinal fasciculus (SLF) are associated with both cognitive control and visuomotor control, while FA in the superior corona radiata (SCR) uniquely correlate with cognitive control performance and FA in the posterior limb of the internal capsule (PLIC) and the cerebral peduncle (CP) tract uniquely correlate with visuomotor control performance. : These findings suggest that children who demonstrate lower cognitive control are also more likely to demonstrate lower visuomotor control, and vice-versa, regardless of clinical cohort assignment. The overlapping neural tracts, which correlate with both cognitive and visuomotor control suggest a possible common neural mechanism supporting both control-based processes.
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http://dx.doi.org/10.3389/fnint.2018.00065DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339953PMC
January 2019

White Matter Connectome Edge Density in Children with Autism Spectrum Disorders: Potential Imaging Biomarkers Using Machine-Learning Models.

Brain Connect 2019 03;9(2):209-220

1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California.

Prior neuroimaging studies have reported white matter network underconnectivity as a potential mechanism for autism spectrum disorder (ASD). In this study, we examined the structural connectome of children with ASD using edge density imaging (EDI), and then applied machine-learning algorithms to identify children with ASD based on tract-based connectivity metrics. Boys aged 8-12 years were included: 14 with ASD and 33 typically developing children. The edge density (ED) maps were computed from probabilistic streamline tractography applied to high angular resolution diffusion imaging. Tract-based spatial statistics was used for voxel-wise comparison and coregistration of ED maps in addition to conventional diffusion tensor imaging (DTI) metrics of fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD). Tract-based average DTI/connectome metrics were calculated and used as input for different machine-learning models: naïve Bayes, random forest, support vector machines (SVMs), and neural networks. For these models, cross-validation was performed with stratified random sampling ( × 1,000 permutations). The average accuracy among validation samples was calculated. In voxel-wise analysis, the body and splenium of corpus callosum, bilateral superior and posterior corona radiata, and left superior longitudinal fasciculus showed significantly lower ED in children with ASD; whereas, we could not find significant difference in FA, MD, and RD maps between the two study groups. Overall, machine-learning models using tract-based ED metrics had better performance in identification of children with ASD compared with those using FA, MD, and RD. The EDI-based random forest models had greater average accuracy (75.3%), specificity (97.0%), and positive predictive value (81.5%), whereas EDI-based polynomial SVM had greater sensitivity (51.4%) and negative predictive values (77.7%). In conclusion, we found reduced density of connectome edges in the posterior white matter tracts of children with ASD, and demonstrated the feasibility of connectome-based machine-learning algorithms in identification of children with ASD.
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http://dx.doi.org/10.1089/brain.2018.0658DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6444925PMC
March 2019

Functional and Structural Brain Plasticity in Adult Onset Single-Sided Deafness.

Front Hum Neurosci 2018 27;12:474. Epub 2018 Nov 27.

Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, San Francisco, CA, United States.

Single-sided deafness (SSD) or profound unilateral hearing loss obligates the only serviceable ear to capture all acoustic information. This loss of binaural function taxes cognitive resources for accurate listening performance, especially under adverse environments or challenging tasks. We hypothesized that adults with SSD would manifest both functional and structural brain plasticity compared to controls with normal binaural hearing. We evaluated functional alterations using magnetoencephalographic imaging (MEGI) of brain activation during performance of a moderately difficult auditory syllable sequence reproduction task and assessed structural integrity using diffusion tensor imaging (DTI). MEGI showed the SSD cohort to have increased induced oscillations in the theta band over the left superior temporal cortex and decreased induced gamma band oscillations over the frontal and parietal cortices between 175 and 475 ms following stimulus onset. DTI showed the SSD cohort to have extensive fractional anisotropy (FA) reduction in both auditory and non-auditory tracts and regions. Overlaying functional and structural changes revealed by the two imaging techniques demonstrated close registration of cortical areas and white matter tracts that expressed brain plasticity. Hence, complete loss of input from one ear in adulthood triggers both functional and structural alterations to dorsal temporal and frontal-parietal areas.
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http://dx.doi.org/10.3389/fnhum.2018.00474DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6277679PMC
November 2018

Brain MR Imaging Findings and Associated Outcomes in Carriers of the Reciprocal Copy Number Variation at 16p11.2.

Radiology 2018 01 8;286(1):217-226. Epub 2017 Aug 8.

From the Departments of Radiology (J.P.O., O.A.G., S.S.N., P.M.) and Neurology (P.B., N.P., T.T., E.H.S.), University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158; Department of Biostatistics, Columbia University, New York, NY (Q.C., J.L., D.D.); Department of Medicine and Pediatrics, Baylor School of Medicine, Houston, Tex (J.V.H.); Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pa (J.I.B., T.P.R.); and Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Mass (R.B.).

Purpose To identify developmental neuroradiologic findings in a large cohort of carriers who have deletion and duplication at 16p11.2 (one of the most common genetic causes of autism spectrum disorder [ASD]) and assess how these features are associated with behavioral and cognitive outcomes. Materials and Methods Seventy-nine carriers of a deletion at 16p11.2 (referred to as deletion carriers; age range, 1-48 years; mean age, 12.3 years; 42 male patients), 79 carriers of a duplication at 16p11.2 (referred to as duplication carriers; age range, 1-63 years; mean age, 24.8 years; 43 male patients), 64 unaffected family members (referred to as familial noncarriers; age range, 1-46 years; mean age, 11.7 years; 31 male participants), and 109 population control participants (age range, 6-64 years; mean age, 25.5 years; 64 male participants) were enrolled in this cross-sectional study. Participants underwent structural magnetic resonance (MR) imaging and completed cognitive and behavioral tests. MR images were reviewed for development-related abnormalities by neuroradiologists. Differences in frequency were assessed with a Fisher exact test corrected for multiple comparisons. Unsupervised machine learning was used to cluster radiologic features and an association between clusters and cognitive and behavioral scores from IQ testing, and parental measures of development were tested by using analysis of covariance. Volumetric analysis with automated segmentation was used to confirm radiologic interpretation. Results For deletion carriers, the most prominent features were dysmorphic and thicker corpora callosa compared with familial noncarriers and population control participants (16%; P < .001 and P < .001, respectively) and a greater likelihood of cerebellar tonsillar ectopia (30.7%; P < .002 and P < .001, respectively) and Chiari I malformations (9.3%; P < .299 and P < .002, respectively). For duplication carriers, the most salient findings compared with familial noncarriers and population control participants were reciprocally thinner corpora callosa (18.6%; P < .003 and P < .001, respectively), decreased white matter volume (22.9%; P < .001, and P < .001, respectively), and increased ventricular volume (24.3%; P < .001 and P < .001, respectively). By comparing cognitive assessments to imaging findings, the presence of any imaging feature associated with deletion carriers indicated worse daily living, communication, and social skills compared with deletion carriers without any radiologic abnormalities (P < .005, P < .002, and P < .004, respectively). For the duplication carriers, presence of decreased white matter, callosal volume, and/or increased ventricle size was associated with decreased full-scale and verbal IQ scores compared with duplication carriers without these findings (P < .007 and P < .004, respectively). Conclusion In two genetically related cohorts at high risk for ASD, reciprocal neuroanatomic abnormalities were found and determined to be associated with cognitive and behavioral impairments. RSNA, 2017 Online supplemental material is available for this article.
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http://dx.doi.org/10.1148/radiol.2017162934DOI Listing
January 2018

Brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease.

PLoS Comput Biol 2017 Jun 22;13(6):e1005550. Epub 2017 Jun 22.

Department of Radiology, Weill Cornell Medical College, New York, New York, United States of America.

Recent research has demonstrated the use of the structural connectome as a powerful tool to characterize the network architecture of the brain and potentially generate biomarkers for neurologic and psychiatric disorders. In particular, the anatomic embedding of the edges of the cerebral graph have been postulated to elucidate the relative importance of white matter tracts to the overall network connectivity, explaining the varying effects of localized white matter pathology on cognition and behavior. Here, we demonstrate the use of a linear diffusion model to quantify the impact of these perturbations on brain connectivity. We show that the eigenmodes governing the dynamics of this model are strongly conserved between healthy subjects regardless of cortical and sub-cortical parcellations, but show significant, interpretable deviations in improperly developed brains. More specifically, we investigated the effect of agenesis of the corpus callosum (AgCC), one of the most common brain malformations to identify differences in the effect of virtual corpus callosotomies and the neurodevelopmental disorder itself. These findings, including the strong correspondence between regions of highest importance from graph eigenmodes of network diffusion and nexus regions of white matter from edge density imaging, show converging evidence toward understanding the relationship between white matter anatomy and the structural connectome.
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http://dx.doi.org/10.1371/journal.pcbi.1005550DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5480812PMC
June 2017

Periventricular White Matter Is a Nexus for Network Connectivity in the Human Brain.

Brain Connect 2016 09;6(7):548-57

Department of Radiology, University of California , San Francisco, San Francisco, California.

The edges of the structural connectome traverse the white matter to connect cortical and subcortical nodes, although the anatomic embedding of these edges is generally overlooked in the literature. Characterization of the geometry of the structural connectome could provide an improved understanding of the relative importance of various white matter regions to the network architecture of the human brain in normal development and aging, as well as in white matter diseases with regionally specific patterns of vulnerability. Edge density imaging (EDI) has previously been used to show that the posterior periventricular white matter contains a disproportionately large number of connectome edges. In this study, the regional distribution of connectome edges within cerebral white matter, including the importance of posterior periventricular white matter, is further investigated and demonstrated to be invariant to different gray matter parcellations and different diffusion MRI acquisition and postprocessing/tractography methods. An examination of the highest k-core edges and a virtual lesion analysis illuminate hemispheric asymmetries (left>right) in the embedding of connectome edges. Therefore, EDI reveals specific areas of vulnerability within the white matter connectivity of the human brain, especially in the periventricular white matter. The idea of a periventricular nexus fits with the known neurobiology of brain development and may result from simple geometrical considerations in minimizing wiring cost in structural brain connectivity.
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http://dx.doi.org/10.1089/brain.2016.0431DOI Listing
September 2016

Reciprocal white matter alterations due to 16p11.2 chromosomal deletions versus duplications.

Hum Brain Mapp 2016 08 24;37(8):2833-48. Epub 2016 May 24.

Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry Street, San Francisco, California, 94107.

Copy number variants at the 16p11.2 chromosomal locus are associated with several neuropsychiatric disorders, including autism, schizophrenia, bipolar disorder, attention-deficit hyperactivity disorder, and speech and language disorders. A gene dosage dependence has been suggested, with 16p11.2 deletion carriers demonstrating higher body mass index and head circumference, and 16p11.2 duplication carriers demonstrating lower body mass index and head circumference. Here, we use diffusion tensor imaging to elucidate this reciprocal relationship in white matter organization, showing widespread increases of fractional anisotropy throughout the supratentorial white matter in pediatric deletion carriers and, in contrast, extensive decreases of white matter fractional anisotropy in pediatric and adult duplication carriers. We find associations of these white matter alterations with cognitive and behavioral impairments. We further demonstrate the value of imaging metrics for characterizing the copy number variant phenotype by employing linear discriminant analysis to predict the gene dosage status of the study subjects. These results show an effect of 16p11.2 gene dosage on white matter microstructure, and further suggest that opposite changes in diffusion tensor imaging metrics can lead to similar cognitive and behavioral deficits. Given the large effect sizes found in this study, our results support the view that specific genetic variations are more strongly associated with specific brain alterations than are shared neuropsychiatric diagnoses. Hum Brain Mapp 37:2833-2848, 2016. © 2016 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/hbm.23211DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6867519PMC
August 2016

White Matter Microstructure is Associated with Auditory and Tactile Processing in Children with and without Sensory Processing Disorder.

Front Neuroanat 2015 26;9:169. Epub 2016 Jan 26.

Department of Radiology and Biomedical Imaging, University of California, San Francisco San Francisco, CA, USA.

Sensory processing disorders (SPDs) affect up to 16% of school-aged children, and contribute to cognitive and behavioral deficits impacting affected individuals and their families. While sensory processing differences are now widely recognized in children with autism, children with sensory-based dysfunction who do not meet autism criteria based on social communication deficits remain virtually unstudied. In a previous pilot diffusion tensor imaging (DTI) study, we demonstrated that boys with SPD have altered white matter microstructure primarily affecting the posterior cerebral tracts, which subserve sensory processing and integration. This disrupted microstructural integrity, measured as reduced white matter fractional anisotropy (FA), correlated with parent report measures of atypical sensory behavior. In this present study, we investigate white matter microstructure as it relates to tactile and auditory function in depth with a larger, mixed-gender cohort of children 8-12 years of age. We continue to find robust alterations of posterior white matter microstructure in children with SPD relative to typically developing children (TDC), along with more spatially distributed alterations. We find strong correlations of FA with both parent report and direct measures of tactile and auditory processing across children, with the direct assessment measures of tactile and auditory processing showing a stronger and more continuous mapping to the underlying white matter integrity than the corresponding parent report measures. Based on these findings of microstructure as a neural correlate of sensory processing ability, diffusion MRI merits further investigation as a tool to find biomarkers for diagnosis, prognosis and treatment response in children with SPD. To our knowledge, this work is the first to demonstrate associations of directly measured tactile and non-linguistic auditory function with white matter microstructural integrity - not just in children with SPD, but also in TDC.
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http://dx.doi.org/10.3389/fnana.2015.00169DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4726807PMC
February 2016

White Matter Changes of Neurite Density and Fiber Orientation Dispersion during Human Brain Maturation.

PLoS One 2015 26;10(6):e0123656. Epub 2015 Jun 26.

Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States of America; Program in Bioengineering, University of California San Francisco, San Francisco, California, United States of America.

Diffusion tensor imaging (DTI) studies of human brain development have consistently shown widespread, but nonlinear increases in white matter anisotropy through childhood, adolescence, and into adulthood. However, despite its sensitivity to changes in tissue microstructure, DTI lacks the specificity to disentangle distinct microstructural features of white and gray matter. Neurite orientation dispersion and density imaging (NODDI) is a recently proposed multi-compartment biophysical model of brain microstructure that can estimate non-collinear properties of white matter, such as neurite orientation dispersion index (ODI) and neurite density index (NDI). In this study, we apply NODDI to 66 healthy controls aged 7-63 years to investigate changes of ODI and NDI with brain maturation, with comparison to standard DTI metrics. Using both region-of-interest and voxel-wise analyses, we find that NDI exhibits striking increases over the studied age range following a logarithmic growth pattern, while ODI rises following an exponential growth pattern. This novel finding is consistent with well-established age-related changes of FA over the lifespan that show growth during childhood and adolescence, plateau during early adulthood, and accelerating decay after the fourth decade of life. Our results suggest that the rise of FA during the first two decades of life is dominated by increasing NDI, while the fall in FA after the fourth decade is driven by the exponential rise of ODI that overcomes the slower increases of NDI. Using partial least squares regression, we further demonstrate that NODDI better predicts chronological age than DTI. Finally, we show excellent test-retest reliability of NODDI metrics, with coefficients of variation below 5% in all measured regions of interest. Our results support the conclusion that NODDI reveals biologically specific characteristics of brain development that are more closely linked to the microstructural features of white matter than are the empirical metrics provided by DTI.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0123656PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4482659PMC
April 2016

Edge density imaging: mapping the anatomic embedding of the structural connectome within the white matter of the human brain.

Neuroimage 2015 Apr 12;109:402-17. Epub 2015 Jan 12.

Department of Radiology & Biomedical Imaging, University of California, San Francisco, USA; Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, USA. Electronic address:

The structural connectome has emerged as a powerful tool to characterize the network architecture of the human brain and shows great potential for generating important new biomarkers for neurologic and psychiatric disorders. The edges of the cerebral graph traverse white matter to interconnect cortical and subcortical nodes, although the anatomic embedding of these edges is generally overlooked in the literature. Mapping the paths of the connectome edges could elucidate the relative importance of individual white matter tracts to the overall network topology of the brain and also lead to a better understanding of the effect of regionally-specific white matter pathology on cognition and behavior. In this work, we introduce edge density imaging (EDI), which maps the number of network edges that pass through every white matter voxel. Test-retest analysis shows good to excellent reliability for edge density (ED) measurements, with consistent results using different cortical and subcortical parcellation schemes and different diffusion MR imaging acquisition parameters. We also demonstrate that ED yields complementary information to both traditional and emerging voxel-wise metrics of white matter microstructure and connectivity, including fractional anisotropy, track density, fiber orientation dispersion and neurite density. Our results demonstrate spatially ordered variations of ED throughout the white matter, notably including greater ED in posterior than anterior cerebral white matter. The EDI framework is employed to map the white matter regions that are enriched with pathways connecting rich club nodes and also those with high densities of intra-modular and inter-modular edges. We show that periventricular white matter has particularly high ED and high densities of rich club edges, which is significant for diseases in which these areas are selectively affected, ranging from white matter injury of prematurity in infants to leukoaraiosis in the elderly. Using edge betweenness centrality, we identify specific white matter regions involved in a large number of shortest paths, some containing highly connected rich club edges while others are relatively isolated within individual modules. Overall, these findings reveal an intricate relationship between white matter anatomy and the structural connectome, motivating further exploration of EDI for biomarkers of cognition and behavior.
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http://dx.doi.org/10.1016/j.neuroimage.2015.01.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4340739PMC
April 2015

Autism and sensory processing disorders: shared white matter disruption in sensory pathways but divergent connectivity in social-emotional pathways.

PLoS One 2014 30;9(7):e103038. Epub 2014 Jul 30.

Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States of America.

Over 90% of children with Autism Spectrum Disorders (ASD) demonstrate atypical sensory behaviors. In fact, hyper- or hyporeactivity to sensory input or unusual interest in sensory aspects of the environment is now included in the DSM-5 diagnostic criteria. However, there are children with sensory processing differences who do not meet an ASD diagnosis but do show atypical sensory behaviors to the same or greater degree as ASD children. We previously demonstrated that children with Sensory Processing Disorders (SPD) have impaired white matter microstructure, and that this white matter microstructural pathology correlates with atypical sensory behavior. In this study, we use diffusion tensor imaging (DTI) fiber tractography to evaluate the structural connectivity of specific white matter tracts in boys with ASD (n = 15) and boys with SPD (n = 16), relative to typically developing children (n = 23). We define white matter tracts using probabilistic streamline tractography and assess the strength of tract connectivity using mean fractional anisotropy. Both the SPD and ASD cohorts demonstrate decreased connectivity relative to controls in parieto-occipital tracts involved in sensory perception and multisensory integration. However, the ASD group alone shows impaired connectivity, relative to controls, in temporal tracts thought to subserve social-emotional processing. In addition to these group difference analyses, we take a dimensional approach to assessing the relationship between white matter connectivity and participant function. These correlational analyses reveal significant associations of white matter connectivity with auditory processing, working memory, social skills, and inattention across our three study groups. These findings help elucidate the roles of specific neural circuits in neurodevelopmental disorders, and begin to explore the dimensional relationship between critical cognitive functions and structural connectivity across affected and unaffected children.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0103038PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4116166PMC
November 2015

Stochastic geometric network models for groups of functional and structural connectomes.

Neuroimage 2014 Nov 25;101:473-84. Epub 2014 Jul 25.

Department of Radiology & Biomedical Imaging, University of California, San Francisco, USA; Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, USA.

Structural and functional connectomes are emerging as important instruments in the study of normal brain function and in the development of new biomarkers for a variety of brain disorders. In contrast to single-network studies that presently dominate the (non-connectome) network literature, connectome analyses typically examine groups of empirical networks and then compare these against standard (stochastic) network models. The current practice in connectome studies is to employ stochastic network models derived from social science and engineering contexts as the basis for the comparison. However, these are not necessarily best suited for the analysis of connectomes, which often contain groups of very closely related networks, such as occurs with a set of controls or a set of patients with a specific disorder. This paper studies important extensions of standard stochastic models that make them better adapted for analysis of connectomes, and develops new statistical fitting methodologies that account for inter-subject variations. The extensions explicitly incorporate geometric information about a network based on distances and inter/intra hemispherical asymmetries (to supplement ordinary degree-distribution information), and utilize a stochastic choice of network density levels (for fixed threshold networks) to better capture the variance in average connectivity among subjects. The new statistical tools introduced here allow one to compare groups of networks by matching both their average characteristics and the variations among them. A notable finding is that connectomes have high "smallworldness" beyond that arising from geometric and degree considerations alone.
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http://dx.doi.org/10.1016/j.neuroimage.2014.07.039DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4165788PMC
November 2014

Aberrant white matter microstructure in children with 16p11.2 deletions.

J Neurosci 2014 Apr;34(18):6214-23

Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California 94107, Program in Bioengineering, Department of Neurology, University of California, San Francisco, San Francisco, California 94158, Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, Simons Foundation, New York, New York 10010, Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, New York 10032, and Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138.

Copy number variants (CNVs) of the chromosomal locus 16p11.2, consisting of either deletions or duplications, have been implicated in autism, schizophrenia, epilepsy, and other neuropsychiatric disorders. Since abnormal white matter microstructure can be seen in these more broadly defined clinical disorders, we used diffusion magnetic resonance imaging and tract-based spatial statistics to investigate white matter microstructural integrity in human children with 16p11.2 deletions. We show that deletion carriers, compared with typically developing matched controls, have increased axial diffusivity (AD) in many major central white matter tracts, including the anterior corpus callosum as well as bilateral internal and external capsules. Higher AD correlated with lower nonverbal IQ in the deletion carriers, but not controls. Increases in fractional anisotropy and mean diffusivity were also found in some of the same tracts with elevated AD. Closer examination with neurite orientation dispersion and density imaging revealed that fiber orientation dispersion was decreased in some central white matter tracts. Notably, these alterations of white matter are unlike microstructural differences reported for any other neurodevelopmental disorders, including autism spectrum disorders that have phenotypic overlap with the deletion carriers. These findings suggest that deletion of the 16p11.2 locus is associated with a unique widespread pattern of aberrant white matter microstructure that may underlie the impaired cognition characteristic of this CNV.
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http://dx.doi.org/10.1523/JNEUROSCI.4495-13.2014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6608115PMC
April 2014

Abnormal white matter microstructure in children with sensory processing disorders.

Neuroimage Clin 2013 23;2:844-53. Epub 2013 Jun 23.

Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry Street, San Francisco, CA 94107, USA ; Program in Bioengineering, University of California, San Francisco, 1700 4th St., San Francisco, CA 94158, USA.

Sensory processing disorders (SPD) affect 5-16% of school-aged children and can cause long-term deficits in intellectual and social development. Current theories of SPD implicate primary sensory cortical areas and higher-order multisensory integration (MSI) cortical regions. We investigate the role of white matter microstructural abnormalities in SPD using diffusion tensor imaging (DTI). DTI was acquired in 16 boys, 8-11 years old, with SPD and 24 age-, gender-, handedness- and IQ-matched neurotypical controls. Behavior was characterized using a parent report sensory behavior measure, the Sensory Profile. Fractional anisotropy (FA), mean diffusivity (MD) and radial diffusivity (RD) were calculated. Tract-based spatial statistics were used to detect significant group differences in white matter integrity and to determine if microstructural parameters were significantly correlated with behavioral measures. Significant decreases in FA and increases in MD and RD were found in the SPD cohort compared to controls, primarily involving posterior white matter including the posterior corpus callosum, posterior corona radiata and posterior thalamic radiations. Strong positive correlations were observed between FA of these posterior tracts and auditory, multisensory, and inattention scores (r = 0.51-0.78; p < 0.001) with strong negative correlations between RD and multisensory and inattention scores (r = - 0.61-0.71; p < 0.001). To our knowledge, this is the first study to demonstrate reduced white matter microstructural integrity in children with SPD. We find that the disrupted white matter microstructure predominantly involves posterior cerebral tracts and correlates strongly with atypical unimodal and multisensory integration behavior. These findings suggest abnormal white matter as a biological basis for SPD and may also distinguish SPD from overlapping clinical conditions such as autism and attention deficit hyperactivity disorder.
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http://dx.doi.org/10.1016/j.nicl.2013.06.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3778265PMC
November 2013

Complex-value coherence mapping reveals novel abnormal resting-state functional connectivity networks in task-specific focal hand dystonia.

Front Neurol 2013 10;4:149. Epub 2013 Oct 10.

Department of Radiology and Biomedical Imaging, University of California San Francisco , San Francisco, CA , USA.

Resting-state imaging designs are powerful in modeling functional networks in movement disorders because they eliminate task performance related confounds. However, the most common metric for quantifying functional connectivity, i.e., bivariate magnitude coherence (Coh), can sometimes be contaminated by spurious correlations in blood-oxygen level dependent (BOLD) signal due to smoothing and seed blur, thereby limiting the identification of true interactions between neighboring neural populations. Here, we apply a novel functional connectivity metric., i.e., imaginary coherence (ICoh), to BOLD fMRI data in healthy individuals and patients with task-specific focal hand dystonia (tspFHD), in addition to the traditional magnitude Coh metric. We reconstructed resting-state sensorimotor, basal ganglia, and default-mode networks using both Coh and ICoh. We demonstrate that indeed the ICoh metric eliminates spatial blur around seed placement and reflects slightly different networks from Coh. We then identified significant reductions in resting-state connectivity within both the sensorimotor and basal ganglia networks in patients with tspFHD, primarily in the hemisphere contralateral to the affected hand. Collectively, these findings direct our attention to the fact that multiple networks are decoupled in tspFHD that can be unraveled by different functional connectivity metrics, and that this aberrant communication contributes to clinical deficits in the disorder.
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http://dx.doi.org/10.3389/fneur.2013.00149DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3794296PMC
October 2013

Resting-state networks and the functional connectome of the human brain in agenesis of the corpus callosum.

Brain Connect 2013 16;3(6):547-62. Epub 2013 Nov 16.

1 Department of Radiology and Biomedical Imaging, University of California , San Francisco, California.

The corpus callosum is the largest white matter fiber bundle connecting the two cerebral hemispheres. In this work, we investigate the effect of callosal dysgenesis on functional magnetic resonance imaging (fMRI) resting-state networks and the functional connectome. Since alternate commissural routes between the cerebral hemispheres exist, we hypothesize that bilateral cortical networks can still be maintained in partial or even complete agenesis of the corpus callosum (AgCC). However, since these commissural routes are frequently indirect, requiring polysynaptic pathways, we hypothesize that quantitative measurements of interhemispheric functional connectivity in bilateral networks will be reduced in AgCC compared with matched controls, especially in the most highly interconnected cortical regions that are the hubs of the connectome. Seventeen resting-state networks were extracted from fMRI of 11 subjects with partial or complete AgCC and 11 matched controls. The results show that the qualitative organization of resting-state networks is very similar between controls and AgCC. However, interhemispheric functional connectivity of precuneus, posterior cingulate cortex, and insular-opercular regions was significantly reduced in AgCC. The preserved network organization was confirmed with a connectomic analysis of the resting-state fMRI data, showing five functional modules that are largely consistent across the control and AgCC groups. Hence, the reduction or even complete absence of callosal connectivity does not affect the qualitative organization of bilateral resting-state networks or the modular organization of the functional connectome, although quantitatively reduced functional connectivity can be demonstrated by measurements within bilateral cortical hubs, supporting the hypothesis that indirect polysynaptic pathways are utilized to preserve interhemispheric temporal synchrony.
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http://dx.doi.org/10.1089/brain.2013.0175DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3868398PMC
August 2014

Test-retest reliability of computational network measurements derived from the structural connectome of the human brain.

Brain Connect 2013 ;3(2):160-76

Department of Radiology & Biomedical Imaging, University of California, San Francisco, California 94107, USA.

Structural magnetic resonance (MR) connectomics holds promise for the diagnosis, outcome prediction, and treatment monitoring of many common neurodevelopmental, psychiatric, and neurodegenerative disorders for which there is currently no clinical utility for MR imaging (MRI). Before computational network metrics from the human connectome can be applied in a clinical setting, their precision and their normative intersubject variation must be understood to guide the study design and the interpretation of longitudinal data. In this work, the reproducibility of commonly used graph theoretic measures is investigated, as applied to the structural connectome of healthy adult volunteers. Two datasets are examined, one consisting of 10 subjects scanned twice at one MRI facility and one consisting of five subjects scanned once each at two different facilities using the same imaging platform. Global graph metrics are calculated for unweighed and weighed connectomes, and two levels of granularity of the connectome are evaluated: one based on the 82-node cortical and subcortical parcellation from FreeSurfer and one based on an atlas-free parcellation of the gray-white matter boundary consisting of 1000 cortical nodes. The consistency of the unweighed and weighed edges and the module assignments are also computed for the 82-node connectomes. Overall, the results demonstrate good-to-excellent test-retest reliability for the entire connectome-processing pipeline, including the graph analytics, in both the intrasite and intersite datasets. These findings indicate that measurements of computational network metrics derived from the structural connectome have sufficient precision to be tested as potential biomarkers for diagnosis, prognosis, and monitoring of interventions in neurological and psychiatric diseases.
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http://dx.doi.org/10.1089/brain.2012.0121DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3634151PMC
November 2013

Non-parametric statistical thresholding for sparse magnetoencephalography source reconstructions.

Front Neurosci 2012 26;6:186. Epub 2012 Dec 26.

Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California San Francisco San Francisco, CA, USA ; Joint Graduate Group in Bioengineering, University of California San Francisco/University of California Berkeley San Francisco, CA, USA.

Uncovering brain activity from magnetoencephalography (MEG) data requires solving an ill-posed inverse problem, greatly confounded by noise, interference, and correlated sources. Sparse reconstruction algorithms, such as Champagne, show great promise in that they provide focal brain activations robust to these confounds. In this paper, we address the technical considerations of statistically thresholding brain images obtained from sparse reconstruction algorithms. The source power distribution of sparse algorithms makes this class of algorithms ill-suited to "conventional" techniques. We propose two non-parametric resampling methods hypothesized to be compatible with sparse algorithms. The first adapts the maximal statistic procedure to sparse reconstruction results and the second departs from the maximal statistic, putting forth a less stringent procedure that protects against spurious peaks. Simulated MEG data and three real data sets are utilized to demonstrate the efficacy of the proposed methods. Two sparse algorithms, Champagne and generalized minimum-current estimation (G-MCE), are compared to two non-sparse algorithms, a variant of minimum-norm estimation, sLORETA, and an adaptive beamformer. The results, in general, demonstrate that the already sparse images obtained from Champagne and G-MCE are further thresholded by both proposed statistical thresholding procedures. While non-sparse algorithms are thresholded by the maximal statistic procedure, they are not made sparse. The work presented here is one of the first attempts to address the problem of statistically thresholding sparse reconstructions, and aims to improve upon this already advantageous and powerful class of algorithm.
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http://dx.doi.org/10.3389/fnins.2012.00186DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3530032PMC
December 2012

The structural connectome of the human brain in agenesis of the corpus callosum.

Neuroimage 2013 Apr 23;70:340-55. Epub 2012 Dec 23.

Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA 94107, USA.

Adopting a network perspective, the structural connectome reveals the large-scale white matter connectivity of the human brain, yielding insights into cerebral organization otherwise inaccessible to researchers and clinicians. Connectomics has great potential for elucidating abnormal connectivity in congenital brain malformations, especially axonal pathfinding disorders. Agenesis of the corpus callosum (AgCC) is one of the most common brain malformations and can also be considered a prototypical genetic disorder of axonal guidance in humans. In this exploratory study, the structural connectome of AgCC is mapped and compared to that of the normal human brain. Multiple levels of granularity of the AgCC connectome are investigated, including summary network metrics, modularity analysis, and network consistency measures, with comparison to the normal structural connectome after simulated removal of all callosal connections ("virtual callostomy"). These investigations reveal four major findings. First, global connectivity is abnormally reduced in AgCC, but local connectivity is increased. Second, the network topology of AgCC is more variable than that of the normal human connectome, contradicting the predictions of the virtual callostomy model. Third, modularity analysis reveals that many of the tracts that comprise the structural core of the cerebral cortex have relatively weak connectivity in AgCC, especially the cingulate bundles bilaterally. Finally, virtual lesions of the Probst bundles in the AgCC connectome demonstrate that there is consistency across subjects in many of the connections generated by these ectopic white matter tracts, and that they are a mixture of cortical and subcortical fibers. These results go beyond prior diffusion tractography studies to provide a systems-level perspective on anomalous connectivity in AgCC. Furthermore, this work offers a proof of principle for the utility of the connectome framework in neurodevelopmental disorders.
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http://dx.doi.org/10.1016/j.neuroimage.2012.12.031DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4127170PMC
April 2013

Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data.

Neuroimage 2012 Mar 23;60(1):305-23. Epub 2011 Dec 23.

Biomagnetic Imaging Laboratory, Dept. Radiology and Biomedical Imaging, UCSF San Francisco, CA, USA.

In this paper, we present an extensive performance evaluation of a novel source localization algorithm, Champagne. It is derived in an empirical Bayesian framework that yields sparse solutions to the inverse problem. It is robust to correlated sources and learns the statistics of non-stimulus-evoked activity to suppress the effect of noise and interfering brain activity. We tested Champagne on both simulated and real M/EEG data. The source locations used for the simulated data were chosen to test the performance on challenging source configurations. In simulations, we found that Champagne outperforms the benchmark algorithms in terms of both the accuracy of the source localizations and the correct estimation of source time courses. We also demonstrate that Champagne is more robust to correlated brain activity present in real MEG data and is able to resolve many distinct and functionally relevant brain areas with real MEG and EEG data.
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http://dx.doi.org/10.1016/j.neuroimage.2011.12.027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4096349PMC
March 2012

Removal of spurious coherence in MEG source-space coherence analysis.

IEEE Trans Biomed Eng 2011 Nov 4;58(11):3121-9. Epub 2011 Aug 4.

Department of Systems Design and Engineering, Tokyo Metropolitan University, Tokyo, Japan.

Source-space coherence analysis has become a popular method to estimate functional connectivity based on MEG/EEG. Source-space analysis involves solving the inverse problem, estimating the time courses of specific brain regions, and then examining the coherence between activities at different brain regions. However, source-space coherence analysis can be confounded by spurious coherence caused due to the leakage properties of the inverse algorithm employed. Such spurious coherence is typically manifested as an artifactual large peak around the seed voxel, called seed blur, in the resulting coherence images. This seed blur often obscures important details of brain interactions. This paper proposes the use of the imaginary part of the coherence to remove the spurious coherence caused by the leakage of an imaging algorithm. We present a theoretical analysis that explains how the use of imaginary part can remove this spurious coherence. We then present results from both computer simulations and experiments using resting-state MEG data which demonstrate the validity of our analysis.
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http://dx.doi.org/10.1109/TBME.2011.2162514DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4096348PMC
November 2011

Resting functional connectivity in patients with brain tumors in eloquent areas.

Ann Neurol 2011 Mar 11;69(3):521-32. Epub 2011 Mar 11.

Department of Neurological Surgery, Hospital Universitario Marqués de Valdecilla, Instituto de Formación e Investigación Marqués de Valdecilla, Santander, Cantabria, Spain.

Objective: Resection of brain tumors adjacent to eloquent areas represents a challenge in neurosurgery. If maximal resection is desired without inducing postoperative neurological deficits, a detailed knowledge of the functional topography in and around the tumor is crucial. The aim of the present work is to evaluate the value of preoperative magnetoencephalography (MEG) imaging of functional connectivity to predict the results of intraoperative electrical stimulation (IES) mapping, the clinical gold standard for neurosurgical localization of functional areas.

Methods: Resting-state whole-cortex MEG recordings were obtained from 57 consecutive subjects with focal brain tumors near or within motor, sensory, or language areas. Neural activity was estimated using adaptive spatial filtering algorithms, and the mean imaginary coherence between the rest of the brain and voxels in and around brain tumors were compared to the mean imaginary coherence between the rest of the brain and contralesional voxels as an index of functional connectivity. IES mapping was performed in all subjects. The cortical connectivity pattern near the tumor was compared to the IES results.

Results: Maps with decreased resting-state functional connectivity in the entire tumor area had a negative predictive value of 100% for absence of eloquent cortex during IES. Maps showing increased resting-state functional connectivity within the tumor area had a positive predictive value of 64% for finding language, motor, or sensory cortical sites during IES mapping.

Interpretation: Preoperative resting state MEG connectivity analysis is a useful noninvasive tool to evaluate the functionality of the tissue surrounding tumors within eloquent areas, and could potentially contribute to surgical planning and patient counseling.
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http://dx.doi.org/10.1002/ana.22167DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4090361PMC
March 2011