Anisha Keshavan

Predicting treatment response in social anxiety disorder from functional magnetic resonance imaging.

CONTEXTnCurrent behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD.nnnOBJECTIVEnTo measure brain activation in patients with SAD as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT).nnnDESIGNnFunctional magnetic resonance imaging (fMRI) data were collected prior to CBT intervention. Changes in clinical status were regressed on brain responses and tested for selectivity for social stimuli.nnnSETTINGnPatients were treated with protocol-based CBT at anxiety disorder programs at Boston University or Massachusetts General Hospital and underwent neuroimaging data collection at Massachusetts Institute of Technology.nnnPATIENTSnThirty-nine medication-free patients meeting DSM-IV criteria for the generalized subtype of SAD.nnnINTERVENTIONSnBrain responses to angry vs neutral faces or emotional vs neutral scenes were examined with fMRI prior to initiation of CBT.nnnMAIN OUTCOME MEASURESnWhole-brain regression analyses with differential fMRI responses for angry vs neutral faces and changes in Liebowitz Social Anxiety Scale score as the treatment outcome measure.nnnRESULTSnPretreatment responses significantly predicted subsequent treatment outcome of patients selectively for social stimuli and particularly in regions of higher-order visual cortex. Combining the brain measures with information on clinical severity accounted for more than 40% of the variance in treatment response and substantially exceeded predictions based on clinical measures at baseline. Prediction success was unaffected by testing for potential confounding factors such as depression severity at baseline.nnnCONCLUSIONSnThe results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions and more generally suggest that such biomarkers may offer evidence-based, personalized medicine approaches for optimally selecting among treatment options for a patient.

Association between thoracic spinal cord gray matter atrophy and disability in multiple sclerosis

IMPORTANCEnIn multiple sclerosis (MS), upper cervical cord gray matter (GM) atrophy correlates more strongly with disability than does brain or cord white matter (WM) atrophy. The corresponding relationships in the thoracic cord are unknown owing to technical difficulties in assessing GM and WM compartments by conventional magnetic resonance imaging techniques.nnnOBJECTIVESnTo investigate the associations between MS disability and disease type with lower thoracic cord GM and WM areas using phase-sensitive inversion recovery magnetic resonance imaging at 3 T, as well as to compare these relationships with those obtained at upper cervical levels.nnnDESIGN, SETTING, AND PARTICIPANTSnBetween July 2013 and March 2014, a total of 142 patients with MS (aged 25-75 years; 86 women) and 20 healthy control individuals were included in this cross-sectional observational study conducted at an academic university hospital.nnnMAIN OUTCOMES AND MEASURESnTotal cord areas (TCAs), GM areas, and WM areas at the disc levels C2/C3, C3/C4, T8/9, and T9/10. Area differences between groups were assessed, with age and sex as covariates.nnnRESULTSnPatients with relapsing MS (RMS) had smaller thoracic cord GM areas than did age- and sex-matched control individuals (mean differences [coefficient of variation (COV)]: 0.98 mm2 [9.2%]; Pu2009=u2009.003 at T8/T9 and 0.93 mm2 [8.0%]; Pu2009=u2009.01 at T9/T10); however, there were no significant differences in either the WM area or TCA. Patients with progressive MS showed smaller GM areas (mean differences [COV]: 1.02 mm2 [10.6%]; Pu2009<u2009.001 at T8/T9 and 1.37 mm2 [13.2%]; Pu2009<u2009.001 at T9/T10) and TCAs (mean differences [COV]: 3.66 mm2 [9.0%]; Pu2009<u2009.001 at T8/T9 and 3.04 mm2 [7.2%]; Pu2009=u2009.004 at T9/T10) compared with patients with RMS. All measurements (GM, WM, and TCA) were inversely correlated with Expanded Disability Status Scale score. Thoracic cord GM areas were correlated with lower limb function. In multivariable models (which also included cord WM areas and T2 lesion number, brain WM volumes, brain T1 and fluid-attenuated inversion recovery lesion loads, age, sex, and disease duration), cervical cord GM areas had the strongest correlation with Expanded Disability Status Scale score followed by thoracic cord GM area and brain GM volume.nnnCONCLUSIONS AND RELEVANCEnThoracic cord GM atrophy can be detected in vivo in the absence of WM atrophy in RMS. This atrophy is more pronounced in progressive MS than RMS and correlates with disability and lower limb function. Our results indicate that remarkable cord GM atrophy is present at multiple cervical and lower thoracic levels and, therefore, may reflect widespread cord GM degeneration.

BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

Association of HLA genetic risk burden with disease phenotypes in multiple sclerosis

IMPORTANCEnAlthough multiple HLA alleles associated with multiple sclerosis (MS) risk have been identified, genotype-phenotype studies in the HLA region remain scarce and inconclusive.nnnOBJECTIVESnTo investigate whether MS risk-associated HLA alleles also affect disease phenotypes.nnnDESIGN, SETTING, AND PARTICIPANTSnA cross-sectional, case-control study comprising 652 patients with MS who had comprehensive phenotypic information and 455 individuals of European origin serving as controls was conducted at a single academic research site. Patients evaluated at the Multiple Sclerosis Center at University of California, San Francisco between July 2004 and September 2005 were invited to participate. Spinal cord imaging in the data set was acquired between July 2013 and March 2014; analysis was performed between December 2014 and December 2015.nnnMAIN OUTCOMES AND MEASURESnCumulative HLA genetic burden (HLAGB) calculated using the most updated MS-associated HLA alleles vs clinical and magnetic resonance imaging outcomes, including age at onset, disease severity, conversion time from clinically isolated syndrome to clinically definite MS, fractions of cortical and subcortical gray matter and cerebral white matter, brain lesion volume, spinal cord gray and white matter areas, upper cervical cord area, and the ratio of gray matter to the upper cervical cord area. Multivariate modeling was applied separately for each sex data set.nnnRESULTSnOf the 652 patients with MS, 586 had no missing genetic data and were included in the HLAGB analysis. In these 586 patients (404 women [68.9%]; mean [SD] age at disease onset, 33.6 [9.4] years), HLAGB was higher than in controls (median [IQR], 0.7 [0-1.4] and 0 [-0.3 to 0.5], respectively; Pu2009=u20091.8u2009×u200910-27). A total of 619 (95.8%) had relapsing-onset MS and 27 (4.2%) had progressive-onset MS. No significant difference was observed between relapsing-onset MS and primary progressive MS. A higher HLAGB was associated with younger age at onset and the atrophy of subcortical gray matter fraction in women with relapsing-onset MS (standard βu2009=u2009-1.20u2009×u200910-1; Pu2009=u20091.7u2009×u200910-2 and standard βu2009=u2009-1.67u2009×u200910-1; Pu2009=u20092.3u2009×u200910-4, respectively), which were driven mainly by the HLA-DRB1*15:01 haplotype. In addition, we observed the distinct role of the HLA-A*24:02-B*07:02-DRB1*15:01 haplotype among the other common DRB1*15:01 haplotypes and a nominally protective effect of HLA-B*44:02 to the subcortical gray atrophy (standard βu2009=u2009-1.28u2009×u200910-1; Pu2009=u20095.1u2009×u200910-3 and standard βu2009=u20099.52u2009×u200910-2; Pu2009=u20093.6u2009×u200910-2, respectively).nnnCONCLUSIONS AND RELEVANCEnWe confirm and extend previous observations linking HLA MS susceptibility alleles with disease progression and specific clinical and magnetic resonance imaging phenotypic traits.

Intra- and interscanner variability of magnetic resonance imaging based volumetry in multiple sclerosis

Brain volumetric measurements in multiple sclerosis (MS) reflect not only disease-specific processes but also other sources of variability. The latter has to be considered especially in multicenter and longitudinal studies. Here, we compare data generated by three different 3-Tesla magnetic resonance scanners (Philips Achieva; Siemens Verio; GE Signa MR750). We scanned two patients diagnosed with relapsing remitting MS six times per scanner within three weeks (T1w and FLAIR, 3D). We assessed T2-hyperintense lesions by an automated lesion segmentation tool and determined volumes of grey matter (GM), white matter (WM) and whole brain (GM+WM) from the lesion-filled T1-weighted images using voxel-based morphometry (SPM8/VBM8) and SIENAX (FSL). We measured cortical thickness using FreeSurfer from both, lesion-filled and original T1-weighted images. We quantified brain volume changes with SIENA. In both patients, we found significant differences in total lesion volume, global brain tissue volumes and cortical thickness measures between the scanners. Morphometric measures varied remarkably between repeated scans at each scanner, independent of the brain imaging software tool used. We conclude that for cross-sectional multicenter studies, the effect of different scanners has to be taken into account. For longitudinal monocentric studies, the expected effect size should exceed the size of false positive findings observed in this study. Assuming a physiological loss of brain volume of about 0.3% per year in healthy adult subjects (Good et al., 2001), which may double in MS (De Stefano et al., 2010; De Stefano et al., 2015), with current tools reliable estimation of brain atrophy in individual patients is only possible over periods of several years.

Power estimation for non-standardized multisite studies

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

Mindcontrol: A web application for brain segmentation quality control

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

Predicting Treatment Response from Resting State fMRI Data: Comparison of Parcellation Approaches

Resting state fMRI reveals intrinsic network characteristics present in the brain. They are correlated with behavioral measures, and have made surprising insights in the brains connectivity structure possible. At the core of many of those studies is the correlation of behavioral measures, and the characteristics of networks among a set of brain regions. In this paper we evaluate methods that identify functional networks in resting state fMRI in light of predicting treatment response of patients suffering from social anxiety disorder. Results illustrate differences in prediction when obtaining network labelings by population-wide-clustering, subject-specific parcellation, transferring anatomical region labels, or mapping networks from a previous large scale resting state study.

Cluster Confidence Index: A Streamline-Wise Pathway Reproducibility Metric for Diffusion-Weighted MRI Tractography

Diffusion‐weighted magnetic resonance imaging tractography can be used to create models of white matter fascicles. Anatomical and pathological variability between subjects can drastically alter the tractography output, so standardizing results across a cohort is nontrivial. Furthermore, tractography methods have inherently low reproducibility due to stochasticity (for probabilistic methods) and subjective decisions, since the final fascicle model often requires a manual intervention step performed by an expert human operator to control both outliers and systematic false‐positive pathways, as defined by prior knowledge of anatomy.

Investigating The Functional Consequence Of White Matter Damage: An Automatic Pipeline To Create Longitudinal Disconnection Tractograms

Neurosurgical resection is one of the few opportunities researchers have to image the human brain both prior to and following focal damage. One of the challenges associated with studying brains undergoing surgical resection is that they often do not fit the brain templates most image-processing methodologies are based on, so manual intervention is required to reconcile the pathology and the most extreme cases must be excluded. Manual intervention requires significant time investment and introduces reproducibility concerns. We propose an automatic longitudinal pipeline based on High Angular Resolution Diffusion Imaging acquisitions to facilitate a Pathway Lesion Symptom Mapping analysis relating focal white matter injury to functional deficits. This two-part approach includes (i) automatic segmentation of focal white matter injury from anisotropic power differences, and (ii) modeling disconnection using tractography on the single-subject level, which specifically identifies the disconnections associated with focal white matter damage. The advantages of this approach stem from (1) objective and automatic lesion segmentation and tractogram generation, (2) objective and precise segmentation of affected tissue likely to be associated with damage to long-range white matter pathways (defined by anisotropic power), (3) good performance even in the cases of anatomical distortions by use of nonlinear tensor-based registration in the patient space, which aligns images using white matter contrast. Mapping a system as variable and complex as the human brain requires sample sizes much larger than the current technology can support. This pipeline can be used to execute large-scale, sufficiently powered analyses by meeting the need for an automatic approach to objectively quantify white matter disconnection. Abbreviations DTI Diffusion Tensor Imaging IOS Intra-Operative Stimulation VLSM Voxel-Based Lesion-Symptom Mapping MD mean diffusivity FA fractional anisotropy B0 minimally diffusion-weighted image AP anisotropic power ASAP automatic segmentation of anisotropic power changes HARDI High Angular Resolution Diffusion Imaging MRI Magnetic Resonance Imaging FSL FMRIB Software Library Dipy Diffusion Imaging in Python APM Anisotropic Power Map was calculated DTI-TK Diffusion Tensor Imaging ToolKit TFCE Threshold-Free-Cluster-Enhancement ROI Region of Interest CCI Cluster Confidence Index AF arcuate Fascicle SLF II and SLF III components 2 and 3 of the SLF SLF-tp temporo-parietal component of the SLF IFOF inferior fronto-occipital Fascicle UF uncinate Fascicle ILF inferior longitudinal Fascicle Md-LF middle longitudinal Fascicle CST corticospinal tract OR optic radiation QC quality-control Funding This work was supported by the National Institutes of Health [5R01NS066654-05]; KJ was supported by the Department of Defense (DoD) [National Defense Science & Engineering Graduate Fellowship (NDSEG) Program].