Publications by authors named "Brian S Caffo"

80 Publications

Identification of the Somatomotor Network from Language Task-based fMRI Compared with Resting-State fMRI in Patients with Brain Lesions.

Radiology 2021 Jul 20:204594. Epub 2021 Jul 20.

From the Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Science (E.B., R.J., A.M.S., S.A., J.J.P., S.K.G., H.I.S.) and the Department of Neurosurgery (J.J.P.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21287; Division of Neuroradiology, Department of Radiology, Temple University Hospital, Philadelphia, Pa (V.S.); Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md (G.W., M.A.L., B.S.C.); and the Malone Center for Engineering in Healthcare, the Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (H.I.S.).

Background Resting-state functional MRI (rs-fMRI) is a potential alternative to task-based functional MRI (tb-fMRI) for somatomotor network (SMN) identification. Brain networks can also be generated from tb-fMRI by using independent component analysis (ICA). Purpose To investigate whether the SMN can be identified by using ICA from a language task without a motor component, the sentence completion functional MRI (sc-fMRI) task, compared with rs-fMRI. Materials and Methods The sc-fMRI and rs-fMRI scans in patients who underwent presurgical brain mapping between 2012 and 2016 were analyzed, using the same imaging parameters (other than scanning time) on a 3.0-T MRI scanner. ICA was performed on rs-fMRI and sc-fMRI scans with use of a tool to separate data sets into their spatial and temporal components. Two neuroradiologists independently determined the presence of the dorsal SMN (dSMN) and ventral SMN (vSMN) on each study. Groups were compared by using tests, and logistic regression was performed to identify predictors of the presence of SMNs. Results One hundred patients (mean age, 40.9 years ± 14.8 [standard deviation]; 61 men) were evaluated. The dSMN and vSMN were identified in 86% (86 of 100) and 76% (76 of 100) of rs-fMRI scans and 85% (85 of 100) and 69% (69 of 100) of sc-fMRI scans, respectively. The concordance between rs-fMRI and sc-fMRI for presence of dSMN and vSMN was 75% (75 of 100 patients) and 53% (53 of 100 patients), respectively. In 10 of 14 patients (71%) where rs-fMRI did not show the dSMN, sc-fMRI demonstrated it. This rate was 67% for the vSMN (16 of 24 patients). Conclusion In the majority of patients, independent component analysis of sentence completion task functional MRI scans reliably demonstrated the somatomotor network compared with resting-state functional MRI scans. Identifying target networks with a single sentence completion scan could reduce overall functional MRI scanning times by eliminating the need for separate motor tasks. © RSNA, 2021 . See also the editorial by Field and Birn in this issue.
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http://dx.doi.org/10.1148/radiol.2021204594DOI Listing
July 2021

Long-term kidney function and survival in recipients of allografts from living kidney donors with hypertension: a national cohort study.

Transpl Int 2021 Jun 15. Epub 2021 Jun 15.

Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Allografts from living kidney donors with hypertension may carry subclinical kidney disease from the donor to the recipient and, thus, lead to adverse recipient outcomes. We examined eGFR trajectories and all-cause allograft failure in recipients from donors with versus without hypertension, using mixed-linear and Cox regression models stratified by donor age. We studied a US cohort from 1/1/2005 to 6/30/2017; 49 990 recipients of allografts from younger (<50 years old) donors including 597 with donor hypertension and 21 130 recipients of allografts from older (≥50 years old) donors including 1441 with donor hypertension. Donor hypertension was defined as documented predonation use of antihypertensive therapy. Among recipients from younger donors with versus without hypertension, the annual eGFR decline was -1.03 versus -0.53 ml/min/m (P = 0.002); 13-year allograft survival was 49.7% vs. 59.0% (adjusted allograft failure hazard ratio [aHR] 1.23; 95% CI 1.05-1.43; P = 0.009). Among recipients from older donors with versus without hypertension, the annual eGFR decline was -0.67 versus -0.66 ml/min/m (P = 0.9); 13-year allograft survival was 48.6% versus 52.6% (aHR 1.05; 95% CI 0.94-1.17; P = 0.4). In secondary analyses, our inferences remained similar for risk of death-censored allograft failure and mortality. Hypertension in younger, but not older, living kidney donors is associated with worse recipient outcomes.
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http://dx.doi.org/10.1111/tri.13947DOI Listing
June 2021

Default mode network connectivity and cognition in the aging brain: the effects of age, sex, and APOE genotype.

Neurobiol Aging 2021 Aug 2;104:10-23. Epub 2021 Apr 2.

Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD. Electronic address:

The default mode network (DMN) overlaps with regions showing early Alzheimer's Disease (AD) pathology. Age, sex, and apolipoprotein E ɛ4 are the predominant risk factors for developing AD. How these risk factors interact to influence DMN connectivity and connectivity-cognition relationships before the onset of impairment remains unknown. Here, we examined these issues in 475 cognitively normal adults, targeting total DMN connectivity, its anticorrelated network (acDMN), and the DMN-hippocampal component. There were four main findings. First, in the ɛ3 homozygous group, lower DMN and acDMN connectivity was observed with age. Second, sex and ɛ4 modified the relationship between age and connectivity for the DMN and hippocampus with ɛ4 vs. ɛ3 males showing sustained or higher connectivity with age. Third, in the ɛ3 group, age and sex modified connectivity-cognition relationships with the oldest participants having the most differential patterns due to sex. Fourth, ɛ4 carriers with lower connectivity had poorer cognitive performance. Taken together, our results show the three predominant risk factors for AD interact to influence brain function and function-cognition relationships.
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http://dx.doi.org/10.1016/j.neurobiolaging.2021.03.013DOI Listing
August 2021

Phase-locking of resting-state brain networks with the gastric basal electrical rhythm.

PLoS One 2021 5;16(1):e0244756. Epub 2021 Jan 5.

F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United Stated of America.

A network of myenteric interstitial cells of Cajal in the corpus of the stomach serves as its "pacemaker", continuously generating a ca 0.05 Hz electrical slow wave, which is transmitted to the brain chiefly by vagal afferents. A recent study combining resting-state functional MRI (rsfMRI) with concurrent surface electrogastrography (EGG), with cutaneous electrodes placed on the epigastrium, found 12 brain regions with activity that was significantly phase-locked with this gastric basal electrical rhythm. Therefore, we asked whether fluctuations in brain resting state networks (RSNs), estimated using a spatial independent component analysis (ICA) approach, might be synchronized with the stomach. In the present study, in order to determine whether any RSNs are phase-locked with the gastric rhythm, an individual participant underwent 22 scanning sessions; in each, two 15-minute runs of concurrent EGG and rsfMRI data were acquired. EGG data from three sessions had weak gastric signals and were excluded; the other 19 sessions yielded a total of 9.5 hours of data. The rsfMRI data were analyzed using group ICA; RSN time courses were estimated; for each run, the phase-locking value (PLV) was computed between each RSN and the gastric signal. To assess statistical significance, PLVs from all pairs of "mismatched" data (EGG and rsfMRI data acquired on different days) were used as surrogate data to generate a null distribution for each RSN. Of a total of 18 RSNs, three were found to be significantly phase-locked with the basal gastric rhythm, namely, a cerebellar network, a dorsal somatosensory-motor network, and a default mode network. Disruptions to the gut-brain axis, which sustains interoceptive feedback between the central nervous system and the viscera, are thought to be involved in various disorders; manifestation of the infra-slow rhythm of the stomach in brain rsfMRI data could be useful for studies in clinical populations.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244756PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785240PMC
May 2021

Examining the Safety, Pharmacokinetics, and Pharmacodynamics of a Rectally Administered IQP-0528 Gel for HIV Pre-Exposure Prophylaxis: A First-In-Human Study.

AIDS Res Hum Retroviruses 2021 06 25;37(6):444-452. Epub 2021 Jan 25.

Division of Clinical Pharmacology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

A lubricating microbicide gel designed for rectal and vaginal use would provide a behaviorally congruent strategy to enhance pre-exposure prophylaxis adherence and reduce HIV infection risk. In this study, we report the first-in-human evaluation of such a gel containing 1% IQP-0528, an investigational antiretroviral. Seven HIV-1-negative participants received one 10 mL rectal dose of radiolabeled 1% IQP-0528 gel. We assessed safety; IQP-0528 pharmacokinetics in plasma, and rectal and vaginal tissue; local pharmacodynamics (PD); and colorectal distribution. The 1% gel was determined to be safe with one mild event attributed to study product and no effects on rectal tissue histology. All concentrations measured in plasma and vaginal tissue were below the limit of quantitation. Median IQP-0528 concentrations in rectal tissue exceeded the EC against HIV-1 (0.07 ng/mg) by 3-5 h of dosing and remained above this concentration for at least 24 h, despite a 3-log reduction in concentration over this duration of time. Rectal tissue PD-assessed by HIV challenge-demonstrated significant p24 antigen reduction 3-5 h postdose compared with baseline ( = .05), but not 24-26 h postdose ( = .75). Single-photon emission computed tomography/computed tomography imaging revealed that product distribution was localized to the rectosigmoid. The IQP-0528 gel possesses desirable features for a topical microbicide including: local safety with no systemic absorption, delivery of locally high IQP-0528 concentrations, and significant reductions in HIV infectivity. However, the gel is limited by its rapid clearance and inability to penetrate vaginal tissues following rectal dosing. Clinical Trial Registration number: NCT03082690.
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http://dx.doi.org/10.1089/AID.2020.0188DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8213010PMC
June 2021

A whole-brain modeling approach to identify individual and group variations in functional connectivity.

Brain Behav 2021 01 18;11(1):e01942. Epub 2020 Nov 18.

Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, TX, USA.

Resting-state functional connectivity is an important and widely used measure of individual and group differences. Yet, extant statistical methods are limited to linking covariates with variations in functional connectivity across subjects, especially at the voxel-wise level of the whole brain. This paper introduces a modeling approach that regresses whole-brain functional connectivity on covariates. Our approach is a mesoscale approach that enables identification of brain subnetworks. These subnetworks are composite of spatially independent components discovered by a dimension reduction approach (such as whole-brain group ICA) and covariate-related projections determined by the covariate-assisted principal regression, a recently introduced covariance matrix regression method. We demonstrate the efficacy of this approach using a resting-state fMRI dataset of a medium-sized cohort of subjects obtained from the Human Connectome Project. The results suggest that the approach may improve statistical power in detecting interaction effects of gender and alcohol on whole-brain functional connectivity, and in identifying the brain areas contributing significantly to the covariate-related differences in functional connectivity.
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http://dx.doi.org/10.1002/brb3.1942DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7821576PMC
January 2021

Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks using Big Data Population Priors.

J Am Stat Assoc 2020 21;115(531):1151-1177. Epub 2019 Nov 21.

Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322.

Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical Bayesian framework. We focus on estimation of brain networks as source signals in independent component analysis (ICA). We formulate a hierarchical "template" ICA model where source signals-including known population brain networks and subject-specific signals-are represented as latent variables. For estimation, we derive an expectation maximization (EM) algorithm having an explicit solution. However, as this solution is computationally intractable, we also consider an approximate subspace algorithm and a faster two-stage approach. Through extensive simulation studies, we assess performance of both methods and compare with dual regression, a popular but ad-hoc method. The two proposed algorithms have similar performance, and both dramatically outperform dual regression. We also conduct a reliability study utilizing the Human Connectome Project and find that template ICA achieves substantially better performance than dual regression, achieving 75-250% higher intra-subject reliability.
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http://dx.doi.org/10.1080/01621459.2019.1679638DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7556739PMC
November 2019

Machine learning to predict transplant outcomes: helpful or hype? A national cohort study.

Transpl Int 2020 11 28;33(11):1472-1480. Epub 2020 Jul 28.

Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA.

An increasing number of studies claim machine learning (ML) predicts transplant outcomes more accurately. However, these claims were possibly confounded by other factors, namely, supplying new variables to ML models. To better understand the prospects of ML in transplantation, we compared ML to conventional regression in a "common" analytic task: predicting kidney transplant outcomes using national registry data. We studied 133 431 adult deceased-donor kidney transplant recipients between 2005 and 2017. Transplant centers were randomly divided into 70% training set (190 centers/97 787 recipients) and 30% validation set (82 centers/35 644 recipients). Using the training set, we performed regression and ML procedures [gradient boosting (GB) and random forests (RF)] to predict delayed graft function, one-year acute rejection, death-censored graft failure C, all-cause graft failure, and death. Their performances were compared on the validation set using -statistics. In predicting rejection, regression (C =  0.611 ) actually outperformed GB (C =  0.591 ) and RF (C =  0.579 ). For all other outcomes, the C-statistics were nearly identical across methods (delayed graft function, 0.717-0.723; death-censored graft failure, 0.637-0.642; all-cause graft failure, 0.633-0.635; and death, 0.705-0.708). Given its shortcomings in model interpretability and hypothesis testing, ML is advantageous only when it clearly outperforms conventional regression; in the case of transplant outcomes prediction, ML seems more hype than helpful.
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http://dx.doi.org/10.1111/tri.13695DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8269970PMC
November 2020

The Effect of Age and Competition Level on Subtle Motor Performance in Adolescents Medically Cleared Postconcussion: Preliminary Findings.

Am J Phys Med Rehabil 2021 06;100(6):563-569

From the Kennedy Krieger Institute, Baltimore, Maryland (JEC, SJS); Division of Occupational Therapy, The Ohio State University, Columbus, Ohio (JEC); Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, Maryland (AER, CMH, SJS); Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (BSC); and Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland (SJS).

Objective: The aim of this study was to examine the effect of age and level of competition on subtle motor performance in adolescents who have recently been medically cleared postconcussion and never-injured controls.

Design: Thirty adolescents who were recently medically cleared postconcussion (12-18 yrs) and 30 never-concussed, typically developing controls were examined using the Revised Physical and Neurological Examination of Subtle Signs (PANESS) and the Immediate Post-Concussion Assessment and Cognitive Testing.

Results: Older age was associated with better Immediate Post-Concussion Assessment and Cognitive Testing scores in both groups, whereas only the control group showed improved motor performance on the PANESS with increasing age. Adolescents across both groups participating at a higher level of competition (school or travel level) had better motor performance on the PANESS than those participating at a lower level of competition (recreational level or no sports participation). Adolescents medically cleared postconcussion had greater motor deficits on the PANESS than controls did.

Conclusion: After medical clearance, adolescents with a history of recent concussion demonstrate alterations in the relationship between motor function and age. The PANESS merits further exploration as a measure that is sensitive to factors affecting motor performance, such as age and level of athletic competition, as well as to persistent subtle motor deficits in adolescents medically cleared postconcussion.
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http://dx.doi.org/10.1097/PHM.0000000000001589DOI Listing
June 2021

Sparse Principal Component based High-Dimensional Mediation Analysis.

Comput Stat Data Anal 2020 Feb 3;142. Epub 2019 Sep 3.

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health.

Causal mediation analysis aims to quantify the intermediate effect of a mediator on the causal pathway from treatment to outcome. When dealing with multiple mediators, which are potentially causally dependent, the possible decomposition of pathway effects grows exponentially with the number of mediators. An existing approach incorporated the principal component analysis (PCA) to address this challenge based on the fact that the transformed mediators are conditionally independent given the orthogonality of the principal components (PCs). However, the transformed mediator PCs, which are linear combinations of original mediators, can be difficult to interpret. A sparse high-dimensional mediation analysis approach is proposed which adopts the sparse PCA method to the mediation setting. The proposed approach is applied to a task-based functional magnetic resonance imaging study, illustrating its ability to detect biologically meaningful results related to an identified mediator.
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http://dx.doi.org/10.1016/j.csda.2019.106835DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7449232PMC
February 2020

Developmental score of the infant brain: characterizing diffusion MRI in term- and preterm-born infants.

Brain Struct Funct 2020 Nov 17;225(8):2431-2445. Epub 2020 Aug 17.

Russell H. Morgan Department of Radiology, Johns Hopkins University School of Medicine, Traylor 217, 720 Rutland Ave, Baltimore, MD, 21215, USA.

Large-scale longitudinal neuroimaging studies of the infant brain allow us to map the spatiotemporal development of the brain in its early phase. While the postmenstrual age (PMA) is commonly used as a time index to analyze longitudinal MRI data, the nonlinear relationship between PMA and MRI data imposes challenges for downstream analyses. We propose a mathematical model that provides a Developmental Score (DevS) as a data-driven time index to characterize the brain development based on MRI features. 319 diffusion tensor imaging (DTI) datasets were collected from 87 term-born and 66 preterm-born infants at multiple visits, which were automatically segmented based on the JHU neonatal atlas. The mean diffusivity (MD) and fractional anisotropy (FA) in 126 brain parcels were used in the model to derive DevS. We demonstrate that transforming the time index from PMA to DevS improves the linearity of the longitudinal changes in MD and FA in both gray and white matter structures. More importantly, regional developmental differences in DTI metrics between preterm- and term-born infants were identified more clearly using DevS, e.g. 79 structures showed significantly different regression patterns in MD between preterm- and term-born infants, compared to only 27 structures that showed group differences using PMA as the index. Therefore, the DevS model facilitates linear analyses of DTI metrics in the infant brain, and provides a useful tool to characterize altered brain development due to preterm-birth.
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http://dx.doi.org/10.1007/s00429-020-02132-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7554148PMC
November 2020

Multimodal neuroimaging data integration and pathway analysis.

Biometrics 2020 Aug 13. Epub 2020 Aug 13.

Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland.

With advancements in technology, the collection of multiple types of measurements on a common set of subjects is becoming routine in science. Some notable examples include multimodal neuroimaging studies for the simultaneous investigation of brain structure and function and multi-omics studies for combining genetic and genomic information. Integrative analysis of multimodal data allows scientists to interrogate new mechanistic questions. However, the data collection and generation of integrative hypotheses is outpacing available methodology for joint analysis of multimodal measurements. In this article, we study high-dimensional multimodal data integration in the context of mediation analysis. We aim to understand the roles that different data modalities play as possible mediators in the pathway between an exposure variable and an outcome. We propose a mediation model framework with two data types serving as separate sets of mediators and develop a penalized optimization approach for parameter estimation. We study both the theoretical properties of the estimator through an asymptotic analysis and its finite-sample performance through simulations. We illustrate our method with a multimodal brain pathway analysis having both structural and functional connectivity as mediators in the association between sex and language processing.
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http://dx.doi.org/10.1111/biom.13351DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7881049PMC
August 2020

Corrigendum to: Neuroconductor: an R platform for medical imaging analysis.

Biostatistics 2021 Jul;22(3):685

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA.

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http://dx.doi.org/10.1093/biostatistics/kxaa006DOI Listing
July 2021

Learning of skilled movements via imitation in ASD.

Autism Res 2020 05 26;13(5):777-784. Epub 2019 Dec 26.

Department of Neurology and Developmental Medicine, Kennedy Krieger Institute, Baltimore, Maryland.

Autism spectrum disorder (ASD) consists of altered performance of a range of skills, including social/communicative and motor skills. It is unclear whether this altered performance results from atypical acquisition or learning of the skills or from atypical "online" performance of the skills. Atypicalities of skilled actions that require both motor and cognitive resources, such as abnormal gesturing, are highly prevalent in ASD and are easier to study in a laboratory context than are social/communicative skills. Imitation has long been known to be impaired in ASD; because learning via imitation is a prime method by which humans acquire skills, we tested the hypothesis that children with ASD show alterations in learning novel gestures via imitation. Eighteen participants with ASD and IQ > 80, ages 8-12.9 years, and 19 typically developing peers performed a task in which they watched a video of a model performing a novel, meaningless arm/hand gesture and copied the gesture. Each gesture video/copy sequence was repeated 4-6 times. Eight gestures were analyzed. Examination of learning trajectories revealed that while children with ASD made nearly as much progress in learning from repetition 1 to repetition 4, the shape of the learning curves differed. Causal modeling demonstrated the shape of the learning curve influenced both the performance of overlearned gestures and autism severity, suggesting that it is in the index of learning mechanisms relevant both to motor skills and to autism core features. Autism Res 2020, 13: 777-784.. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Imitation is a route by which humans learn a wide range of skills, naturally and in therapies. Imitation is known to be altered in autism spectrum disorder (ASD), but learning via imitation has not been rigorously examined. We found that the shape of the learning curve is altered in ASD, in a way that has a significant impact both on measures of autism severity and of other motor skills.
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http://dx.doi.org/10.1002/aur.2253DOI Listing
May 2020

Covariate Assisted Principal regression for covariance matrix outcomes.

Biostatistics 2021 07;22(3):629-645

Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, 1200 Pressler St, Houston, TX 77030, USA.

In this study, we consider the problem of regressing covariance matrices on associated covariates. Our goal is to use covariates to explain variation in covariance matrices across units. As such, we introduce Covariate Assisted Principal (CAP) regression, an optimization-based method for identifying components associated with the covariates using a generalized linear model approach. We develop computationally efficient algorithms to jointly search for common linear projections of the covariance matrices, as well as the regression coefficients. Under the assumption that all the covariance matrices share identical eigencomponents, we establish the asymptotic properties. In simulation studies, our CAP method shows higher accuracy and robustness in coefficient estimation over competing methods. In an example resting-state functional magnetic resonance imaging study of healthy adults, CAP identifies human brain network changes associated with subject demographics.
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http://dx.doi.org/10.1093/biostatistics/kxz057DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8286560PMC
July 2021

Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models.

Neuroimage 2019 05 10;191:243-257. Epub 2019 Feb 10.

Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.

The study of functional brain networks has grown rapidly over the past decade. While most functional connectivity (FC) analyses estimate one static network structure for the entire length of the functional magnetic resonance imaging (fMRI) time series, recently there has been increased interest in studying time-varying changes in FC. Hidden Markov models (HMMs) have proven to be a useful modeling approach for discovering repeating graphs of interacting brain regions (brain states). However, a limitation lies in HMMs assuming that the sojourn time, the number of consecutive time points in a state, is geometrically distributed. This may encourage inaccurate estimation of the time spent in a state before switching to another state. We propose a hidden semi-Markov model (HSMM) approach for inferring time-varying brain networks from fMRI data, which explicitly models the sojourn distribution. Specifically, we propose using HSMMs to find each subject's most probable series of network states and the graphs associated with each state, while properly estimating and modeling the sojourn distribution for each state. We perform a simulation study, as well as an analysis on both task-based fMRI data from an anxiety-inducing experiment and resting-state fMRI data from the Human Connectome Project. Our results demonstrate the importance of model choice when estimating sojourn times and reveal their potential for understanding healthy and diseased brain mechanisms.
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http://dx.doi.org/10.1016/j.neuroimage.2019.02.013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6504179PMC
May 2019

Modular preprocessing pipelines can reintroduce artifacts into fMRI data.

Hum Brain Mapp 2019 06 21;40(8):2358-2376. Epub 2019 Jan 21.

Biostatistics, Johns Hopkins School of Public Health, Baltimore, Maryland.

The preprocessing pipelines typically used in both task and resting-state functional magnetic resonance imaging (rs-fMRI) analysis are modular in nature: They are composed of a number of separate filtering/regression steps, including removal of head motion covariates and band-pass filtering, performed sequentially and in a flexible order. In this article, we illustrate the shortcomings of this approach, as we show how later preprocessing steps can reintroduce artifacts previously removed from the data in prior preprocessing steps. We show that each regression step is a geometric projection of data onto a subspace, and that performing a sequence of projections can move the data into subspaces no longer orthogonal to those previously removed, reintroducing signal related to nuisance covariates. Thus, linear filtering operations are not commutative, and the order in which the preprocessing steps are performed is critical. These issues can arise in practice when any combination of standard preprocessing steps including motion regression, scrubbing, component-based correction, physiological correction, global signal regression, and temporal filtering are performed sequentially. In this work, we focus primarily on rs-fMRI. We illustrate the problem both theoretically and empirically through application to a test-retest rs-fMRI data set, and suggest remedies. These include (a) combining all steps into a single linear filter, or (b) sequential orthogonalization of covariates/linear filters performed in series.
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http://dx.doi.org/10.1002/hbm.24528DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6865661PMC
June 2019

Mixed effect machine learning: A framework for predicting longitudinal change in hemoglobin A1c.

J Biomed Inform 2019 01 4;89:56-67. Epub 2018 Sep 4.

Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.

Accurate and reliable prediction of clinical progression over time has the potential to improve the outcomes of chronic disease. The classical approach to analyzing longitudinal data is to use (generalized) linear mixed-effect models (GLMM). However, linear parametric models are predicated on assumptions, which are often difficult to verify. In contrast, data-driven machine learning methods can be applied to derive insight from the raw data without a priori assumptions. However, the underlying theory of most machine learning algorithms assume that the data is independent and identically distributed, making them inefficient for longitudinal supervised learning. In this study, we formulate an analytic framework, which integrates the random-effects structure of GLMM into non-linear machine learning models capable of exploiting temporal heterogeneous effects, sparse and varying-length patient characteristics inherent in longitudinal data. We applied the derived mixed-effect machine learning (MEml) framework to predict longitudinal change in glycemic control measured by hemoglobin A1c (HbA1c) among well controlled adults with type 2 diabetes. Results show that MEml is competitive with traditional GLMM, but substantially outperformed standard machine learning models that do not account for random-effects. Specifically, the accuracy of MEml in predicting glycemic change at the 1st, 2nd, 3rd, and 4th clinical visits in advanced was 1.04, 1.08, 1.11, and 1.14 times that of the gradient boosted model respectively, with similar results for the other methods. To further demonstrate the general applicability of MEml, a series of experiments were performed using real publicly available and synthetic data sets for accuracy and robustness. These experiments reinforced the superiority of MEml over the other methods. Overall, results from this study highlight the importance of modeling random-effects in machine learning approaches based on longitudinal data. Our MEml method is highly resistant to correlated data, readily accounts for random-effects, and predicts change of a longitudinal clinical outcome in real-world clinical settings with high accuracy.
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http://dx.doi.org/10.1016/j.jbi.2018.09.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6495570PMC
January 2019

An M-estimator for reduced-rank system identification.

Pattern Recognit Lett 2017 Jan 19;86:76-81. Epub 2016 Dec 19.

Child Mind Institute, Baltimore 21205, USA.

High-dimensional time-series data from a wide variety of domains, such as neuroscience, are being generated every day. Fitting statistical models to such data, to enable parameter estimation and time-series prediction, is an important computational primitive. Existing methods, however, are unable to cope with the high-dimensional nature of these data, due to both computational and statistical reasons. We mitigate both kinds of issues by proposing an M-estimator for Reduced-rank System IDentification ( MR. SID). A combination of low-rank approximations, and penalties, and some numerical linear algebra tricks, yields an estimator that is computationally efficient and numerically stable. Simulations and real data examples demonstrate the usefulness of this approach in a variety of problems. In particular, we demonstrate that MR. SID can accurately estimate spatial filters, connectivity graphs, and time-courses from native resolution functional magnetic resonance imaging data. MR. SID therefore enables big time-series data to be analyzed using standard methods, readying the field for further generalizations including non-linear and non-Gaussian state-space models.
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http://dx.doi.org/10.1016/j.patrec.2016.12.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5790321PMC
January 2017

Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage.

Neuroimage 2018 05 14;172:478-491. Epub 2018 Feb 14.

Department of Biostatistics, Johns Hopkins University, USA.

Reliability of subject-level resting-state functional connectivity (FC) is determined in part by the statistical techniques employed in its estimation. Methods that pool information across subjects to inform estimation of subject-level effects (e.g., Bayesian approaches) have been shown to enhance reliability of subject-level FC. However, fully Bayesian approaches are computationally demanding, while empirical Bayesian approaches typically rely on using repeated measures to estimate the variance components in the model. Here, we avoid the need for repeated measures by proposing a novel measurement error model for FC describing the different sources of variance and error, which we use to perform empirical Bayes shrinkage of subject-level FC towards the group average. In addition, since the traditional intra-class correlation coefficient (ICC) is inappropriate for biased estimates, we propose a new reliability measure denoted the mean squared error intra-class correlation coefficient (ICC) to properly assess the reliability of the resulting (biased) estimates. We apply the proposed techniques to test-retest resting-state fMRI data on 461 subjects from the Human Connectome Project to estimate connectivity between 100 regions identified through independent components analysis (ICA). We consider both correlation and partial correlation as the measure of FC and assess the benefit of shrinkage for each measure, as well as the effects of scan duration. We find that shrinkage estimates of subject-level FC exhibit substantially greater reliability than traditional estimates across various scan durations, even for the most reliable connections and regardless of connectivity measure. Additionally, we find partial correlation reliability to be highly sensitive to the choice of penalty term, and to be generally worse than that of full correlations except for certain connections and a narrow range of penalty values. This suggests that the penalty needs to be chosen carefully when using partial correlations.
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http://dx.doi.org/10.1016/j.neuroimage.2018.01.029DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5957759PMC
May 2018

Neuroconductor: an R platform for medical imaging analysis.

Biostatistics 2019 04;20(2):218-239

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, USA.

Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience.
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http://dx.doi.org/10.1093/biostatistics/kxx068DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6409417PMC
April 2019

High-dimensional multivariate mediation with application to neuroimaging data.

Biostatistics 2018 04;19(2):121-136

Department of Biostatistics, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205, USA.

Mediation analysis is an important tool in the behavioral sciences for investigating the role of intermediate variables that lie in the path between a treatment and an outcome variable. The influence of the intermediate variable on the outcome is often explored using a linear structural equation model (LSEM), with model coefficients interpreted as possible effects. While there has been significant research on the topic, little work has been done when the intermediate variable (mediator) is a high-dimensional vector. In this work, we introduce a novel method for identifying potential mediators in this setting called the directions of mediation (DMs). DMs linearly combine potential mediators into a smaller number of orthogonal components, with components ranked based on the proportion of the LSEM likelihood each accounts for. This method is well suited for cases when many potential mediators are measured. Examples of high-dimensional potential mediators are brain images composed of hundreds of thousands of voxels, genetic variation measured at millions of single nucleotide polymorphisms (SNPs), or vectors of thousands of variables in large-scale epidemiological studies. We demonstrate the method using a functional magnetic resonance imaging study of thermal pain where we are interested in determining which brain locations mediate the relationship between the application of a thermal stimulus and self-reported pain.
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http://dx.doi.org/10.1093/biostatistics/kxx027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862274PMC
April 2018

Parallel group independent component analysis for massive fMRI data sets.

PLoS One 2017 9;12(3):e0173496. Epub 2017 Mar 9.

Department of Biostatistics, Johns Hopkins University, Baltimore, United States of America.

Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0173496PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5344430PMC
August 2017

Ten Simple Rules for Effective Statistical Practice.

PLoS Comput Biol 2016 06 9;12(6):e1004961. Epub 2016 Jun 9.

Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada.

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http://dx.doi.org/10.1371/journal.pcbi.1004961DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4900655PMC
June 2016

Decoupling of reaction time-related default mode network activity with cognitive demand.

Brain Imaging Behav 2017 Jun;11(3):666-676

Kennedy Krieger Institute, 707 N. Broadway, Baltimore, MD, 21205, USA.

Reaction Time (RT) is associated with increased amplitude of the Blood Oxygen-Level Dependent (BOLD) response in task positive regions. Few studies have focused on whether opposing RT-related suppression of task activity also occurs. The current study used two Go/No-go tasks with different cognitive demands to examine regions that showed greater BOLD suppression for longer RT trials. These RT-related suppression effects occurred within the DMN and were task-specific, localizing to separate regions for the two tasks. In the task requiring working memory, RT-related de-coupling of the DMN occurred. This was reflected by opposing RT-BOLD effects for different DMN regions, as well as by reduced positive RT-related Psycho-Physiological Interaction (PPI) connectivity within the DMN and a lack of negative RT-related PPI connectivity between DMN and task positive regions. The results suggest that RT-related DMN suppression is task-specific. RT-related de-coupling of the DMN with more complex task demands may contribute to lapses of attention and performance decrements that occur during cognitively-demanding tasks.
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http://dx.doi.org/10.1007/s11682-016-9543-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5967252PMC
June 2017

Reproducibility and Temporal Structure in Weekly Resting-State fMRI over a Period of 3.5 Years.

PLoS One 2015 30;10(10):e0140134. Epub 2015 Oct 30.

Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America.

Resting-state functional MRI (rs-fMRI) permits study of the brain's functional networks without requiring participants to perform tasks. Robust changes in such resting state networks (RSNs) have been observed in neurologic disorders, and rs-fMRI outcome measures are candidate biomarkers for monitoring clinical trials, including trials of extended therapeutic interventions for rehabilitation of patients with chronic conditions. In this study, we aim to present a unique longitudinal dataset reporting on a healthy adult subject scanned weekly over 3.5 years and identify rs-fMRI outcome measures appropriate for clinical trials. Accordingly, we assessed the reproducibility, and characterized the temporal structure of, rs-fMRI outcome measures derived using independent component analysis (ICA). Data was compared to a 21-person dataset acquired on the same scanner in order to confirm that the values of the single-subject RSN measures were within the expected range as assessed from the multi-participant dataset. Fourteen RSNs were identified, and the inter-session reproducibility of outcome measures-network spatial map, temporal signal fluctuation magnitude, and between-network connectivity (BNC)-was high, with executive RSNs showing the highest reproducibility. Analysis of the weekly outcome measures also showed that many rs-fMRI outcome measures had a significant linear trend, annual periodicity, and persistence. Such temporal structure was most prominent in spatial map similarity, and least prominent in BNC. High reproducibility supports the candidacy of rs-fMRI outcome measures as biomarkers, but the presence of significant temporal structure needs to be taken into account when such outcome measures are considered as biomarkers for rehabilitation-style therapeutic interventions in chronic conditions.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140134PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4627782PMC
June 2016

Neural Correlates of Visuomotor Learning in Autism.

J Child Neurol 2015 Dec 8;30(14):1877-86. Epub 2015 Sep 8.

Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD, USA Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA.

Motor impairments are prevalent in children with autism spectrum disorder. The Serial Reaction Time Task, a well-established visuomotor sequence learning probe, has produced inconsistent behavioral findings in individuals with autism. Moreover, it remains unclear how underlying neural processes for visuomotor learning in children with autism compare to processes for typically developing children. Neural activity differences were assessed using functional magnetic resonance imaging during a modified version of the Serial Reaction Time Task in children with and without autism. Though there was no group difference in visuomotor sequence learning, underlying patterns of neural activation significantly differed when comparing sequence (i.e., learning) to random (i.e., nonlearning) blocks. Children with autism demonstrated decreased activity in brain regions implicated in visuomotor sequence learning: superior temporal sulcus and posterior cingulate cortex. The findings implicate differences in brain mechanisms that support initial sequence learning in autism and can help explain behavioral observations of autism-associated impairments in skill development (motor, social, communicative) reliant on visuomotor integration.
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http://dx.doi.org/10.1177/0883073815600869DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4941625PMC
December 2015

Neural Correlates of Visuomotor Learning in Autism.

J Child Neurol 2015 Dec 8;30(14):1877-86. Epub 2015 Sep 8.

Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD, USA Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA.

Motor impairments are prevalent in children with autism spectrum disorder. The Serial Reaction Time Task, a well-established visuomotor sequence learning probe, has produced inconsistent behavioral findings in individuals with autism. Moreover, it remains unclear how underlying neural processes for visuomotor learning in children with autism compare to processes for typically developing children. Neural activity differences were assessed using functional magnetic resonance imaging during a modified version of the Serial Reaction Time Task in children with and without autism. Though there was no group difference in visuomotor sequence learning, underlying patterns of neural activation significantly differed when comparing sequence (i.e., learning) to random (i.e., nonlearning) blocks. Children with autism demonstrated decreased activity in brain regions implicated in visuomotor sequence learning: superior temporal sulcus and posterior cingulate cortex. The findings implicate differences in brain mechanisms that support initial sequence learning in autism and can help explain behavioral observations of autism-associated impairments in skill development (motor, social, communicative) reliant on visuomotor integration.
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http://dx.doi.org/10.1177/0883073815600869DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4941625PMC
December 2015

Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models.

Comput Stat Data Anal 2015 Sep;89:126-133

Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA, 21205.

Data reproducibility is a critical issue in all scientific experiments. In this manuscript, the problem of quantifying the reproducibility of graphical measurements is considered. The image intra-class correlation coefficient (I2C2) is generalized and the graphical intra-class correlation coefficient (GICC) is proposed for such purpose. The concept for GICC is based on multivariate probit-linear mixed effect models. A Markov Chain Monte Carlo EM (mcm-cEM) algorithm is used for estimating the GICC. Simulation results with varied settings are demonstrated and our method is applied to the KIRBY21 test-retest dataset.
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http://dx.doi.org/10.1016/j.csda.2015.02.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4501040PMC
September 2015

Resting brain activity in disorders of consciousness: a systematic review and meta-analysis.

Neurology 2015 Mar 20;84(12):1272-80. Epub 2015 Feb 20.

From the Departments of Anesthesiology and Critical Care Medicine (Y.H., R.D.S.), Neurology (Y.H., R.D.S.), Radiology (H.I.S., R.D.S.), and Neurosurgery (R.D.S.), Johns Hopkins University School of Medicine, Baltimore; and Department of Biostatistics (M.A.L., B.S.C.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.

Objective: To quantitatively synthesize results from neuroimaging studies that evaluated patterns of resting functional activity in patients with disorders of consciousness (DOC).

Methods: We performed a systematic review and coordinate-based meta-analysis of studies published up to May 2014. Studies were included if they compared resting-state functional neuroimaging data acquired in patients with DOC (coma, minimally conscious state, emergence from minimally conscious state, or vegetative state) with a group of healthy controls. Coordinate-based meta-analysis was performed in studies that included voxel-based comparisons at the whole-brain level and if analysis was accomplished with data-driven approaches.

Results: A total of 36 studies (687 patients, 637 healthy controls) were included in the systematic review. Reported DOC were vegetative state (43.2%), coma (23.4%), minimally conscious state (22.8%), and emergence from minimally conscious state (1.6%); the most common etiologies of DOC were traumatic brain injury (37.7%) and anoxic brain injury (36.9%). Functional neuroimaging was accomplished using fMRI (16 studies), PET (15 studies), SPECT (4 studies), and both PET and SPECT in one study. Meta-analysis in 13 studies (272 patients, 259 healthy controls) revealed consistently reduced activity in patients with DOC in bilateral medial dorsal nucleus of the thalamus, left cingulate, posterior cingulate, precuneus, and middle frontal and medial temporal gyri.

Conclusions: In patients with DOC evaluated in the resting state, functional neuroimaging indicates markedly reduced activity within midline cortical and subcortical sites, anatomical structures that have been linked to the default-mode network. Studies are needed to determine the relation between activation (and coherence) within these structures and the emergence of conscious awareness.
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http://dx.doi.org/10.1212/WNL.0000000000001404DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4366089PMC
March 2015