Bio

Bio


The research of my lab focuses on computational neuroscience aimed at identifying biomedical phenotypes to improve the mechanistic understanding, diagnosis, and treatment of neuropsychiatric disorders.

Academic Appointments


Administrative Appointments


  • Editorial Board, Medical Image Analysis (2017 - Present)
  • Associate Editor, IEEE Transactions on Medical Imaging (2016 - Present)
  • Review Editor, Frontiers in Brain Imaging Methods (2013 - Present)

Honors & Awards


  • Creative and Novel Ideas in HIV Research Award, The 20th International AIDS Conference (2014)
  • Two Top 10 most accessed papers, IEEE Transactions on Medical Image Analysis (2012)
  • Top 10 Paper (of 736 submissions), 8th International Symposium on Biomedical Imaging (2011)
  • IBM Research Accomplishment, IBM (2009)
  • Best Paper Prize (of 575 submissions), Medical Image Analysis-MICCAI 06 (2007)

Boards, Advisory Committees, Professional Organizations


  • Program Committee Member, Workshop on Biomedical Image Registration (2012 - 2018)
  • Program Committee Member, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (2011 - 2016)
  • Session Chair, National Alliance for Medical Image Computing Registration Retreat (2011 - 2011)
  • Program Committee Member, Biennial International Conference on Information Processing in Medical Imaging (2009 - 2017)
  • Program Committee Member, 9th Workshop on Mathematical Methods in Biomedical Image Analysis (2008 - 2008)

Professional Education


  • Ph.D., Massachusetts Institute of Technology, Computer Science (2005)
  • M.S., University of Karlsruhe, Karlsruhe, Germany, Mathematics (1999)
  • B.S., University of Karlsruhe, Karlsruhe, Germany, Mathematics (1995)

Research & Scholarship

Current Research and Scholarly Interests


The foundation of the laboratory of Associate Professor Kilian M. Pohl, PhD, is computational science aimed at identifying biomedical phenotypes improving the mechanistic understanding, diagnosis, and treatment of neuropsychiatric disorders. The biomedical phenotypes are discovered by unbiased, machine learning-based searches across biological, neuroimaging, and neuropsychological data. This data-driven discovery currently supports the adolescent brain research of the NIH-funded National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) and the Adolescent Brain Cognitive Development (ABCD), the largest long-term study of brain development and child health in the US. The laboratory also investigates brain patterns specific to alcohol use disorder and the human immunodeficiency virus (HIV) across the adult age range, and have advanced the understanding of a variety of brain diseases including schizophrenia, Alzheimer’s disease, glioma, and aging.

Publications

All Publications


  • Dissociable Contributions of Precuneus and Cerebellum to Subjective and Objective Neuropathy in HIV JOURNAL OF NEUROIMMUNE PHARMACOLOGY Zahr, N. M., Pohl, K. M., Pfefferbaum, A., Sullivan, E. 2019; 14 (3): 436–47
  • CNS Correlates of "Objective" Neuropathy in Alcohol Use Disorder. Alcoholism, clinical and experimental research Zahr, N. M., Pohl, K. M., Pfefferbaum, A., Sullivan, E. V. 2019

    Abstract

    BACKGROUND: Among the neurological consequences of alcoholism is peripheral neuropathy. Relative to HIV or diabetes-related neuropathies, neuropathy associated with Alcohol Use Disorders (AUD) is understudied. In both the diabetes and HIV literature, emerging evidence supports a CNS component to peripheral neuropathy.METHODS: In seeking a central substrate for AUD-related neuropathy, the current study was conducted in 154 individuals with AUD (43 women, ages 21-74) and 99 healthy controls (41 women, ages 21-77) and explored subjective symptoms (self-report) and objective signs (perception of vibration, deep tendon ankle reflex, position sense, 2-point discrimination) of neuropathy separately. In addition to regional brain volumes, risk factors for AUD-related neuropathy, including age, sex, total lifetime ethanol consumed, nutritional indices (i.e., thiamine, folate), and measures of liver integrity (i.e., gamma-glutamyl-transferase) were evaluated.RESULTS: The AUD group described more subjective symptoms of neuropathy and were more frequently impaired on bilateral perception of vibration. From 5 correlates, the number of AUD-related seizures was most significantly associated with subjective symptoms of neuropathy. There were 15 correlates of impaired perception of vibration among the AUD participants: of these, age and volume of frontal precentral cortex were the most robust predictors.CONCLUSIONS: This study supports CNS involvement in objective signs of neuropathy in AUD. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1111/acer.14162

    View details for PubMedID 31386216

  • Accelerated aging and motor control deficits are related to regional deformation of central cerebellar white matter in alcohol use disorder. Addiction biology Zhao, Q., Pfefferbaum, A., Podhajsky, S., Pohl, K. M., Sullivan, E. V. 2019

    Abstract

    The World Health Organization estimates a 12-month prevalence rate of 8+% for an alcohol use disorder (AUD) diagnosis in people age 15years and older in the United States and Europe, presenting significant health risks that have the potential of accelerating age-related functional decline. According to neuropathological studies, white matter systems of the cerebellum are vulnerable to chronic alcohol dependence. To pursue the effect of AUD on white matter structure and functions in vivo, this study used T1-weighted, magnetic resonance imaging (MRI) to quantify the total corpus medullare of the cerebellum and a finely grained analysis of its surface in 135 men and women with AUD (mean duration of abstinence, 248d) and 128 age- and sex-matched control participants; subsets of these participants completed motor testing. We identified an AUD-related volume deficit and accelerated aging in the total corpus medullare. Novel deformation-based surface morphometry revealed regional shrinkage of surfaces adjacent to lobules I-V, lobule IX, and vermian lobule X. In addition, accelerated aging was detected in the regional surface areas adjacent to lobules I-V, lobule VI, lobule VIIB, and lobules VIII, IX, and X. Sex differences were not identified for any measure. For both volume-based and surface-based analyses, poorer performance in gait and balance, manual dexterity, and grip strength were linked to greater regional white matter structural deficits. Our results suggest that local deformation of the corpus medullare has the potential of identifying structurally and functionally segregated networks affected in AUD.

    View details for PubMedID 30932270

  • Hippocampal subfield CA2+3 exhibits accelerated aging in Alcohol Use Disorder: A preliminary study. NeuroImage. Clinical Zahr, N. M., Pohl, K. M., Saranathan, M., Sullivan, E. V., Pfefferbaum, A. 2019; 22: 101764

    Abstract

    The profile of brain structural dysmorphology of individuals with Alcohol Use Disorders (AUD) involves disruption of the limbic system. In vivo imaging studies report hippocampal volume loss in AUD relative to controls, but only recently has it been possible to articulate different regions of this complex structure. Volumetric analysis of hippocampal regions rather than total hippocampal volume may augment differentiation of disease processes. For example, damage to hippocampal subfield cornu ammonis 1 (CA1) is often reported in Alzheimer's disease (AD), whereas deficits in CA4/dentate gyrus are described in response to stress and trauma. Two previous studies explored the effects of chronic alcohol use on hippocampal subfields: one reported smaller volume of the CA2+3 in alcohol-dependent subjects relative to controls, associated with years of alcohol consumption; the other, smaller volumes of presubiculum, subiculum, and fimbria in alcohol-dependent relative to control men. The current study, conducted in 24 adults with DSM5-diagnosed AUD (7 women, 53.7 ± 8.8) and 20 controls (7 women, 54.1 ± 9.3), is the first to use FreeSurfer 6.0, which provides state-of-the art hippocampal parcellation, to explore the sensitivity of hippocampal sufields to alcoholism. T1- and T2- images were collected on a GE MR750 system with a 32-channel Nova head coil. FreeSurfer 6.0 hippocampal subfield analysis produced 12 subfields: parasubiculum; presubiculum; subiculum; CA1; CA2+3; CA4; GC-ML-DG (Granule Cell (GC) and Molecular Layer (ML) of the Dentate Gyrus (DG)); molecular layer; hippocampus-amygdala-transition-area (HATA); fimbria; hippocampal tail; hippocampal fissure; and whole volume for left and right hippocampi. A comprehensive battery of neuropsychological tests comprising attention, memory and learning, visuospatial abilities, and executive functions was administered. Multiple regression analyses of raw volumetric data for each subfields by group, age, sex, hemisphere, and supratentorial volume (svol) showed significant effects of svol (p < .04) on nearly all structures (excluding tail and fissure). Volumes corrected for svol showed effects of age (fimbria, fissure) and group (subiculum, CA1, CA4, GC-ML-DG, HATA, fimbria); CA2+3 showed a diagnosis-by-age interaction indicating older AUD individuals had a smaller volume than would be expected for their age. There were no selective relations between hippocampal subfields and performance on neuropsychological tests, likely due to lack of statistical power. The current results concur with the previous study identifying CA2+3 as sensitive to alcoholism, extend them by identifying an alcoholism-age interaction, and suggest an imaging phenotype distinguishing AUD from AD and stress/trauma.

    View details for PubMedID 30904825

  • Novel Machine Learning Identifies Brain Patterns Distinguishing Diagnostic Membership of Human Immunodeficiency Virus, Alcoholism, and Their Comorbidity of Individuals. Biological psychiatry. Cognitive neuroscience and neuroimaging Adeli, E., Zahr, N. M., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2019

    Abstract

    The incidence of alcohol use disorder (AUD) in human immunodeficiency virus (HIV) infection is twice that of the rest of the population. This study documents complex radiologically identified, neuroanatomical effects of AUD+HIV comorbidity by identifying structural brain systems that predicted diagnosis on an individual basis. Applying novel machine learning analysis to 549 participants (199 control subjects, 222 with AUD, 68 with HIV, 60 with AUD+HIV), 298 magnetic resonance imaging brain measurements were automatically reduced to small subsets per group. Significance of each diagnostic pattern was inferred from its accuracy in predicting diagnosis and performance on six cognitive measures. While all three diagnostic patterns predicted the learning and memory score, the AUD+HIV pattern was the largest and had the highest predication accuracy (78.1%). Providing a roadmap for analyzing large, multimodal datasets, the machine learning analysis revealed imaging phenotypes that predicted diagnostic membership of magnetic resonance imaging scans of individuals with AUD, HIV, and their comorbidity.

    View details for DOI 10.1016/j.bpsc.2019.02.003

    View details for PubMedID 30982583

  • Dissociable Contributions of Precuneus and Cerebellum to Subjective and Objective Neuropathy in HIV. Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology Zahr, N. M., Pohl, K. M., Pfefferbaum, A., Sullivan, E. V. 2019

    Abstract

    Neuropathy, typically diagnosed by the presence of either symptoms or signs of peripheral nerve dysfunction, remains a frequently reported complication in the antiretroviral (ART)-treated HIV population. This study was conducted in 109 healthy controls and 57 HIV-infected individuals to investigate CNS regions associated with neuropathy. An index of objective neuropathy was computed based on 4 measures: deep tendon ankle reflex, vibration sense (great toes), position sense (great toes), and 2-point discrimination (feet). Subjective neuropathy (self-report of pain, aching, or burning; pins and needles; or numbness in legs or feet) was also evaluated. Structural MRI data were available for 126/166 cases. The HIV relative to the healthy control group was impaired on all 4 signs of neuropathy. Within the HIV group, an objective neuropathy index of 1 (bilateral impairment on 1 measure) or 2 (bilateral impairment on at least 2/4 measures) was associated with older age and a smaller volume of the cerebellar vermis. Moderate to severe symptoms of neuropathy were associated with more depressive symptoms, reduced quality of life, and a smaller volume of the parietal precuneus. This study is consistent with the recent contention that ART-treated HIV-related neuropathy has a CNS component. Distinguishing subjective symptoms from objective signs of neuropathy allowed for a dissociation between the precuneus, a brain region involved in conscious information processing and the vermis, involved in fine tuning of limb movements. Graphical Abstract In HIV patients, objective signs of neuropathy correlated with smaller cerebellar vermis (red) volumes whereas subjective symptoms of neuropathy were associated with smaller precuneus (blue) volumes.

    View details for PubMedID 30741374

  • Logistic Regression Confined by Cardinality-Constrained Sample and Feature Selection. IEEE transactions on pattern analysis and machine intelligence Adeli, E., Li, X., Kwon, D., Zhang, Y., Pohl, K. 2019

    Abstract

    Many vision-based applications rely on logistic regression for embedding classification within a probabilistic context, such as recognition in images and videos or identifying disease-specific image phenotypes from neuroimages. Logistic regression, however, often performs poorly when trained on data that is noisy, has irrelevant features, or when the samples are distributed across the classes in an imbalanced setting; a common occurrence in visual recognition tasks. To deal with those issues, researchers generally rely on ad-hoc regularization techniques or model a subset of these issues. We instead propose a mathematically sound logistic regression model that selects a subset of (relevant) features and (informative and balanced) set of samples during the training process. The model does so by applying cardinality constraints (via l0 -'norm' sparsity) on the features and samples. l0 defines sparsity in mathematical settings but in practice has mostly been approximated (e.g., via l1 or its variations) for computational simplicity. We prove that a local minimum to the non-convex optimization problems induced by cardinality constraints can be computed by combining block coordinate descent with penalty decomposition. On synthetic, image recognition, and neuroimaging datasets, we furthermore show that the accuracy of the method is higher than alternative methods and classifiers commonly used in the literature.

    View details for DOI 10.1109/TPAMI.2019.2901688

    View details for PubMedID 30835210

  • Convergence of three parcellation approaches demonstrating cerebellar lobule volume deficits in Alcohol Use Disorder. NeuroImage. Clinical Sullivan, E. V., Zahr, N. M., Saranathan, M., Pohl, K. M., Pfefferbaum, A. 2019; 24: 101974

    Abstract

    Recent advances in robust and reliable methods of MRI-derived cerebellar lobule parcellation volumetry present the opportunity to assess effects of Alcohol Use Disorder (AUD) on selective cerebellar lobules and relations with indices of nutrition and motor functions. In pursuit of this opportunity, we analyzed high-resolution MRI data acquired in 24 individuals with AUD and 20 age- and sex-matched controls with a 32-channel head coil using three different atlases: the online automated analysis pipeline volBrain Ceres, SUIT, and the Johns Hopkins atlas. Participants had also completed gait and balance examination and hematological analysis of nutritional and liver status, enabling testing of functional meaningfulness of each cerebellar parcellation scheme. Compared with controls, each quantification approach yielded similar patterns of group differences in regional volumes: All three approaches identified AUD-related deficits in total tissue and total gray matter, but only Ceres identified a total white matter volume deficit. Convergent volume differences occurred in lobules I-V, Crus I, VIIIB, and IX. Coefficients of variation (CVs) were <20% for 46 of 56 regions measured and in general were graded: Ceres

    View details for DOI 10.1016/j.nicl.2019.101974

    View details for PubMedID 31419768

  • Longitudinally consistent estimates of intrinsic functional networks Human Brain Mapping Zhao, Q., Kwon, D., Müller-Oehring, E. M., Le Berre, A., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2019

    View details for DOI 10.1002/hbm.24541

  • Longitudinally consistent estimates of intrinsic functional networks. Human brain mapping Zhao, Q., Kwon, D., Müller-Oehring, E. M., Le Berre, A. P., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2019

    Abstract

    Increasing numbers of neuroimaging studies are acquiring data to examine changes in brain architecture by investigating intrinsic functional networks (IFN) from longitudinal resting-state functional MRI (rs-fMRI). At the subject level, these IFNs are determined by cross-sectional procedures, which neglect intra-subject dependencies and result in suboptimal estimates of the networks. Here, a novel longitudinal approach simultaneously extracts subject-specific IFNs across multiple visits by explicitly modeling functional brain development as an essential context for seeking change. On data generated by an innovative simulation based on real rs-fMRI, the method was more accurate in estimating subject-specific IFNs than cross-sectional approaches. Furthermore, only group-analysis based on longitudinally consistent estimates identified significant developmental effects within IFNs of 246 adolescents from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study. The findings were confirmed by the cross-sectional estimates when the corresponding group analysis was confined to the developmental effects. Those effects also converged with current concepts of neurodevelopment.

    View details for PubMedID 30806009

  • Hippocampal subfield CA2+3 exhibits accelerated aging in Alcohol Use Disorder: A preliminary study NEUROIMAGE-CLINICAL Zahr, N. M., Pohl, K. M., Saranathan, M., Sullivan, E., Pfefferbaum, A. 2019; 22
  • Relations between cognitive and motor deficits and regional brain volumes in individuals with alcoholism. Brain structure & function Fama, R., Le Berre, A. P., Sassoon, S. A., Zahr, N. M., Pohl, K. M., Pfefferbaum, A., Sullivan, E. V. 2019

    Abstract

    Despite the common co-occurrence of cognitive impairment and brain structural deficits in alcoholism, demonstration of relations between regional gray matter volumes and cognitive and motor processes have been relatively elusive. In pursuit of identifying brain structural substrates of impairment in alcoholism, we assessed executive functions (EF), episodic memory (MEM), and static postural balance (BAL) and measured regional brain gray matter volumes of cortical, subcortical, and cerebellar structures commonly affected in individuals with alcohol dependence (ALC) compared with healthy controls (CTRL). ALC scored lower than CTRL on all composite scores (EF, MEM, and BAL) and had smaller frontal, cingulate, insular, parietal, and hippocampal volumes. Within the ALC group, poorer EF scores correlated with smaller frontal and temporal volumes; MEM scores correlated with frontal volume; and BAL scores correlated with frontal, caudate, and pontine volumes. Exploratory analyses investigating relations between subregional frontal volumes and composite scores in ALC yielded different patterns of associations, suggesting that different neural substrates underlie these functional deficits. Of note, orbitofrontal volume was a significant predictor of memory scores, accounting for almost 15% of the variance; however, this relation was evident only in ALC with a history of a non-alcohol substance diagnosis and not in ALC without a non-alcohol substance diagnosis. The brain-behavior relations observed provide evidence that the cognitive and motor deficits in alcoholism are likely a result of different neural systems and support the hypothesis that a number of identifiable neural systems rather than a common or diffuse neural pathway underlies cognitive and motor deficits observed in chronic alcoholism.

    View details for DOI 10.1007/s00429-019-01894-w

    View details for PubMedID 31161472

  • Jacobian Maps Reveal Under-reported Brain Regions Sensitive to Extreme Binge Ethanol Intoxication in the Rat FRONTIERS IN NEUROANATOMY Zhao, Q., Fritz, M., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M., Zahr, N. M. 2018; 12
  • Chained regularization for identifying brain patterns specific to HIV infection NEUROIMAGE Adeli, E., Kwon, D., Zhao, Q., Pfefferbaum, A., Zahr, N. M., Sullivan, E. V., Pohl, K. M. 2018; 183: 425–37
  • Distribution of brain iron accrual in adolescence: Evidence from cross-sectional and longitudinal analysis. Human brain mapping Peterson, E. T., Kwon, D., Luna, B., Larsen, B., Prouty, D., De Bellis, M. D., Voyvodic, J., Liu, C., Li, W., Pohl, K. M., Sullivan, E. V., Pfefferbaum, A. 2018

    Abstract

    To track iron accumulation and location in the brain across adolescence, we repurposed diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data acquired in 513 adolescents and validated iron estimates with quantitative susceptibility mapping (QSM) in 104 of these subjects. DTI and fMRI data were acquired longitudinally over 1year in 245 male and 268 female, no-to-low alcohol-consuming adolescents (12-21 years at baseline) from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study. Brain region average signal values were calculated for susceptibility to nonheme iron deposition: pallidum, putamen, dentate nucleus, red nucleus, and substantia nigra. To estimate nonheme iron, the corpus callosum signal (robust to iron effects) was divided by regional signals to generate estimated R2 (edwR2 for DTI) and R2 * (eR2 * for fMRI). Longitudinal iron deposition was measured using the normalized signal change across time for each subject. Validation using baseline QSM, derived from susceptibility-weighted imaging, was performed on 46 male and 58 female participants. Normalized iron deposition estimates from DTI and fMRI correlated with age in most regions; both estimates indicated less iron in boys than girls. QSM results correlated highly with DTI and fMRI results (adjusted R2 = 0.643 for DTI, 0.578 for fMRI). Cross-sectional and longitudinal analyses indicated an initial rapid increase in iron, notably in the putamen and red nucleus, that slowed with age. DTI and fMRI data can be repurposed for identifying regional brain iron deposition in developing adolescents as validated with high correspondence with QSM.

    View details for PubMedID 30496644

  • Regional growth trajectories of cortical myelination in adolescents and young adults: longitudinal validation and functional correlates. Brain imaging and behavior Kwon, D., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2018

    Abstract

    Adolescence is a time of continued cognitive and emotional evolution occurring with continuing brain development involving synaptic pruning and cortical myelination. The hypothesis of this study is that heavy myelination occurs in cortical regions with relatively direct, predetermined circuitry supporting unimodal sensory or motor functions and shows a steep developmental slope during adolescence (12-21years) until young adulthood (22-35years) when further myelination decelerates. By contrast, light myelination occurs in regions with highly plastic circuitry supporting complex functions and follows a delayed developmental trajectory. In support of this hypothesis, cortical myelin content was estimated and harmonized across publicly available datasets provided by the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) and the Human Connectome Project (HCP). The cross-sectional analysis of 226 no-to-low alcohol drinking NCANDA adolescents revealed relatively steeper age-dependent trajectories of myelin growth in unimodal primary motor cortex and flatter age-dependent trajectories in multimodal mid/posterior cingulate cortices. This pattern of continued myelination showed smaller gains when the same analyses were performed on 686 young adults of the HCP cohort free of neuropsychiatric diagnoses. Critically, a predicted correlation between a motor task and myelin content in motor or cingulate cortices was found in the NCANDA adolescents, supporting the functional relevance of this imaging neurometric. Furthermore, the regional trajectory slopes were confirmed by performing longitudinally consistent analysis of cortical myelin. In conclusion, coordination of myelin content and circuit complexity continues to develop throughout adolescence, contributes to performance maturation, and may represent active cortical development climaxing in young adulthood.

    View details for PubMedID 30406353

  • Chained regularization for identifying brain patterns specific to HIV infection. NeuroImage Adeli, E., Kwon, D., Zhao, Q., Pfefferbaum, A., Zahr, N. M., Sullivan, E. V., Pohl, K. M. 2018

    Abstract

    Human Immunodeficiency Virus (HIV) infection continues to have major adverse public health and clinical consequences despite the effectiveness of combination Antiretroviral Therapy (cART) in reducing HIV viral load and improving immune function. As successfully treated individuals with HIV infection age, their cognition declines faster than reported for normal aging. This phenomenon underlines the importance of improving long-term care, which requires better understanding of the impact of HIV on the brain. In this paper, automated identification of patients and brain regions affected by HIV infection are modeled as a classification problem, whose solution is determined in two steps within our proposed Chained-Regularization framework. The first step focuses on selecting the HIV pattern (i.e., the most informative constellation of brain region measurements for distinguishing HIV infected subjects from healthy controls) by constraining the search for the optimal parameter setting of the classifier via group sparsity (ℓ2,1-norm). The second step improves classification accuracy by constraining the parameterization with respect to the selected measurements and the Euclidean regularization (ℓ2-norm). When applied to the cortical and subcortical structural Magnetic Resonance Images (MRI) measurements of 65 controls and 65 HIV infected individuals, this approach is more accurate in distinguishing the two cohorts than more common models. Finally, the brain regions of the identified HIV pattern concur with the HIV literature that uses traditional group analysis models.

    View details for PubMedID 30138676

  • Accelerated and Premature Aging Characterizing Regional Cortical Volume Loss in Human Immunodeficiency Virus Infection: Contributions From Alcohol, Substance Use, and Hepatitis C Coinfection. Biological psychiatry. Cognitive neuroscience and neuroimaging Pfefferbaum, A., Zahr, N. M., Sassoon, S. A., Kwon, D., Pohl, K. M., Sullivan, E. V. 2018

    Abstract

    BACKGROUND: Life expectancy of successfully treated human immunodeficiency virus (HIV)-infected individuals is approaching normal longevity. The growing HIV population ≥50 years of age is now at risk of developing HIV-associated neurocognitive disorder, acquiring coinfection with the hepatitis C virus (HCV), and engaging in hazardous drinking or drug consumption that can adversely affect trajectories of the healthy aging of brain structures.METHODS: This cross-sectional/longitudinal study quantified regional brain volumes from 1101 magnetic resonance imaging scans collected over 14 years in 549 participants (25 to 75 years of age): 68 HIV-infected individuals without alcohol dependence, 60 HIV-infected individuals with alcohol dependence, 222 alcohol-dependent individuals, and 199 control subjects. We tested 1) whether localized brain regions in HIV-infected individuals exhibited accelerated aging, or alternatively, nonaccelerated premature aging deficits; and 2) the extent to which alcohol or substance dependence or HCV coinfection altered brain aging trajectories.RESULTS: The HIV-infected cohort exhibited steeper declining volume trajectories than control subjects, consistently in the frontal cortex. Nonaccelerated volume deficits occurred in the temporal, parietal, insular, and cingulate regions of all three diagnostic groups. Alcohol and drug dependence comorbidities and HCV coinfection exacerbated HIV-related volume deficits. Accelerated age interactions in frontal and posterior parietal volumes endured in HIV-infected individuals free of alcohol or substance dependence and HCV infection comorbidities. Functionally, poorer HIV-associated neurocognitive disorder scores and Veterans Aging Cohort Study indices correlated with smaller regional brain volumes in the HIV-infected individuals without alcohol dependence and alcohol-dependent groups.CONCLUSIONS: HIV infection itself may confer a heightened risk of accelerated brain aging, potentially exacerbated by HCV coinfection and substance dependency. Confirmation would require a prospective study with a preinfection baseline.

    View details for PubMedID 30093343

  • Alcohol use effects on adolescent brain development revealed by simultaneously removing confounding factors, identifying morphometric patterns, and classifying individuals SCIENTIFIC REPORTS Park, S., Zhang, Y., Kwon, D., Zhao, Q., Zahr, N. M., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M. 2018; 8: 8297

    Abstract

    Group analysis of brain magnetic resonance imaging (MRI) metrics frequently employs generalized additive models (GAM) to remove contributions of confounding factors before identifying cohort specific characteristics. For example, the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) used such an approach to identify effects of alcohol misuse on the developing brain. Here, we hypothesized that considering confounding factors before group analysis removes information relevant for distinguishing adolescents with drinking history from those without. To test this hypothesis, we introduce a machine-learning model that identifies cohort-specific, neuromorphometric patterns by simultaneously training a GAM and generic classifier on macrostructural MRI and microstructural diffusion tensor imaging (DTI) metrics and compare it to more traditional group analysis and machine-learning approaches. Using a baseline NCANDA MR dataset (N = 705), the proposed machine learning approach identified a pattern of eight brain regions unique to adolescents who misuse alcohol. Classifying high-drinking adolescents was more accurate with that pattern than using regions identified with alternative approaches. The findings of the joint model approach thus were (1) impartial to confounding factors; (2) relevant to drinking behaviors; and (3) in concurrence with the alcohol literature.

    View details for PubMedID 29844507

  • The Role of Aging, Drug Dependence, and Hepatitis C Comorbidity in Alcoholism Cortical Compromise JAMA PSYCHIATRY Sullivan, E. V., Zahr, N. M., Sassoon, S. A., Thompson, W. K., Kwon, D., Pohl, K. M., Pfefferbaum, A. 2018; 75 (5): 474–83

    Abstract

    The prevalence of alcohol misuse increased substantially over a decade in adults, particularly in those aged 65 years or older. Ramifications for brain structural integrity are significant, especially in older adults.To combine cross-sectional, longitudinal data to test age-alcoholism interactions and examine the association between prevalent comorbidities (drug dependence and hepatitis C virus [HCV] infection) and cortical volume deficits in alcohol dependence.During 14 years, 826 structural magnetic resonance images were acquired in 222 individuals with alcohol dependence and 199 age-matched control participants (aged 25-75 years at initial study), parcellated with a common atlas, and adjusted for brain volume. Longitudinal data were available on 116 participants with alcoholism and 96 control participants. DSM-IV criteria determined alcohol and drug diagnoses; serology testing determined HCV status. The study was conducted at SRI International and Stanford University School of Medicine from April 11, 2003, to March 3, 2017.Magnetic resonance imaging-derived regional cortical volumes corrected for supratentorial volume and sex.Of the 222 participants with alcoholism, 156 (70.3%) were men; mean (SD) age was 48.0 (10.0) years; the mean age for the 199 control participants was 47.6 (14.0) years. Participants with alcohol dependence had volume deficits in frontal (t = -5.732, P < .001), temporal (t = -3.151, P = .002), parietal (t = -5.063, P < .001), cingulate (t = -3.170, P = .002), and insular (t = -4.920, P < .001) cortices; deficits were prominent in frontal subregions and were not sex dependent. Accelerated aging occurred in frontal cortex (t = -3.019, P < .02) and precentral (t = -2.691, P < .05) and superior gyri (t = -2.763, P < .05) and could not be attributed to the amount of alcohol consumed, which was greater in younger-onset than older-onset participants with alcoholism (t = 6.1191, P < .001). Given the high drug-dependence incidence (54.5%) in the alcoholism group, analysis examined drug subgroups (cocaine, cannabis, amphetamines, opiates) compared with drug-dependence-free alcoholism and control groups. Although the alcohol plus cocaine (t = -2.310, P = .04) and alcohol plus opiate (t = -2.424, P = .04) groups had smaller frontal volumes than the drug-dependence-free alcoholism group, deficits in precentral (t = -2.575, P = .01), supplementary motor (t = -2.532, P = .01), and medial (t = -2.800, P = .01) volumes endured in drug-dependence-free participants with alcoholism compared with control participants. Those with HCV infection had greater deficits than those without HCV infection in frontal (t = 3.468, P = .01), precentral (t = 2.513, P = .03), superior (t = 2.533, P = .03), and orbital (t = 2.506, P = .03) volumes, yet total frontal (t = 2.660, P = .02), insular (t = 3.526, P = .003), parietal (t = 2.414, P = .03), temporal (t = 3.221, P = .005), and precentral (t = 3.180, P = .01) volume deficits persisted in the uninfected participants with alcoholism compared with control participants with known HCV status.Drug dependence and HCV infection compounded deleterious effects of alcohol dependence on frontal cortical volumes but could not account for the frontally distributed volume deficits in the drug-free participants with alcoholism. We speculate that age-alcohol interactions notable in frontal cortex put older adults at heightened risk for age-associated neurocompromise even if alcohol misuse is initiated later in life.

    View details for PubMedID 29541774

    View details for PubMedCentralID PMC5875381

  • Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking. The American journal of psychiatry Pfefferbaum, A., Kwon, D., Brumback, T., Thompson, W. K., Cummins, K., Tapert, S. F., Brown, S. A., Colrain, I. M., Baker, F. C., Prouty, D., De Bellis, M. D., Clark, D. B., Nagel, B. J., Chu, W., Park, S. H., Pohl, K. M., Sullivan, E. V. 2018; 175 (4): 370–80

    Abstract

    OBJECTIVE: The authors sought evidence for altered adolescent brain growth trajectory associated with moderate and heavy alcohol use in a large national, multisite, prospective study of adolescents before and after initiation of appreciable alcohol use.METHOD: This study examined 483 adolescents (ages 12-21) before initiation of drinking and 1 and 2 years later. At the 2-year assessment, 356 participants continued to meet the study's no/low alcohol consumption entry criteria, 65 had initiated moderate drinking, and 62 had initiated heavy drinking. MRI was used to quantify regional cortical and white matter volumes. Percent change per year (slopes) in adolescents who continued to meet no/low criteria served as developmental control trajectories against which to compare those who initiated moderate or heavy drinking.RESULTS: In no/low drinkers, gray matter volume declined throughout adolescence and slowed in many regions in later adolescence. Complementing gray matter declines, white matter regions grew at faster rates at younger ages and slowed toward young adulthood. Youths who initiated heavy drinking exhibited an accelerated frontal cortical gray matter trajectory, divergent from the norm. Although significant effects on trajectories were not observed in moderate drinkers, their intermediate position between no/low and heavy drinkers suggests a dose effect. Neither marijuana co-use nor baseline volumes contributed significantly to the alcohol effect.CONCLUSIONS: Initiation of drinking during adolescence, with or without marijuana co-use, disordered normal brain growth trajectories. Factors possibly contributing to abnormal cortical volume trajectories include peak consumption in the past year and family history of alcoholism.

    View details for PubMedID 29084454

  • CNS CORRELATES OF HIV-ASSOCIATED PERIPHERAL NEUROPATHY AND POSTURAL INSTABILITY Zahr, N., Kwon, D., Pohl, K., Sullivan, E., Pfefferbaum, A. SPRINGER. 2018: S94
  • eCurves: A Temporal Shape Encoding IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Bernardis, E., Zhang, Y., Konukoglu, E., Ou, Y., Javitz, H. S., Axel, L., Metaxas, D., Desjardins, B., Pohl, K. M. 2018; 65 (4): 733–44

    Abstract

    This paper presents a framework for temporal shape analysis to capture the shape and changes of anatomical structures from three-dimensional+t(ime) medical scans.We first encode the shape of a structure at each time point with the spectral signature, i.e., the eigenvalues and eigenfunctions of the Laplace operator. We then expand it to capture morphing shapes by tracking the eigenmodes across time according to the similarity of their eigenfunctions. The similarity metric is motivated by the fact that small-shaped deformations lead to minor changes in the eigenfunctions. Following each eigenmode from the beginning to end results in a set of eigenmode curves representing the shape and its changes over time.We apply our encoding to a cardiac dataset consisting of series of segmentations outlining the right and left ventricles over time. We measure the accuracy of our encoding by training classifiers on discriminating healthy adults from patients that received reconstructive surgery for Tetralogy of Fallot (TOF). The classifiers based on our encoding significantly surpass deformation-based encodings of the right ventricle, the structure most impacted by TOF.The strength of our framework lies in its simplicity: It only assumes pose invariance within a time series but does not assume point-to-point correspondence across time series or a (statistical or physical) model. In addition, it is easy to implement and only depends on a single parameter, i.e., the number of curves.

    View details for DOI 10.1109/TBME.2017.2716365

    View details for Web of Science ID 000428526000003

    View details for PubMedID 28641243

    View details for PubMedCentralID PMC5732904

  • CNS CORRELATES OF HIV-ASSOCIATED PERIPHERAL NEUROPATHY AND POSTURAL INSTABILITY Zahr, N., Kwon, D., Pohl, K., Sullivan, E., Pfefferbaum, A. SPRINGER. 2018: S94
  • The mediating role of cortical thickness and gray matter volume on sleep slow-wave activity during adolescence BRAIN STRUCTURE & FUNCTION Goldstone, A., Willoughby, A. R., de Zambotti, M., Franzen, P. L., Kwon, D., Pohl, K. M., Pfefferbaum, A., Sullivan, E. V., Muller-Oehring, E. M., Prouty, D. E., Hasler, B. P., Clark, D. B., Colrain, I. M., Baker, F. C. 2018; 223 (2): 669–85

    Abstract

    During the course of adolescence, reductions occur in cortical thickness and gray matter (GM) volume, along with a 65% reduction in slow-wave (delta) activity during sleep (SWA) but empirical data linking these structural brain and functional sleep differences, is lacking. Here, we investigated specifically whether age-related differences in cortical thickness and GM volume and cortical thickness accounted for the typical age-related difference in slow-wave (delta) activity (SWA) during sleep. 132 healthy participants (age 12-21 years) from the National Consortium on Alcohol and NeuroDevelopment in Adolescence study were included in this cross-sectional analysis of baseline polysomnographic, electroencephalographic, and magnetic resonance imaging data. By applying mediation models, we identified a large, direct effect of age on SWA in adolescents, which explained 45% of the variance in ultra-SWA (0.3-1 Hz) and 52% of the variance in delta-SWA (1 to <4 Hz), where SWA was lower in older adolescents, as has been reported previously. In addition, we provide evidence that the structure of several, predominantly frontal, and parietal brain regions, partially mediated this direct age effect, models including measures of brain structure explained an additional 3-9% of the variance in ultra-SWA and 4-5% of the variance in delta-SWA, with no differences between sexes. Replacing age with pubertal status in models produced similar results. As reductions in GM volume and cortical thickness likely indicate synaptic pruning and myelination, these results suggest that diminished SWA in older, more mature adolescents may largely be driven by such processes within a number of frontal and parietal brain regions.

    View details for PubMedID 28913599

    View details for PubMedCentralID PMC5828920

  • End-To-End Alzheimer’s Disease Diagnosis and Biomarker Identification Stanford University / SRI International Esmaeilzadeh, S. 2018
  • Multi-label Transduction for Identifying Disease Comorbidity Patterns Adeli, E., Kwon, D., Pohl, K. M., Frangi, A. F., Schnabel, J. A., Davatzikos, C., AlberolaLopez, C., Fichtinger, G. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 575–83
  • A Riemannian Framework for Longitudinal Analysis of Resting-State Functional Connectivity Zhao, Q., Kwon, D., Pohl, K. M., Frangi, A. F., Schnabel, J. A., Davatzikos, C., AlberolaLopez, C., Fichtinger, G. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 145–53
  • End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification Esmaeilzadeh, S., Belivanis, D., Pohl, K. M., Adeli, E., Shi, Y., Suk, H. I., Liu, M. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 337–45
  • Jacobian Maps Reveal Under-reported Brain Regions Sensitive to Extreme Binge Ethanol Intoxication in the Rat. Frontiers in neuroanatomy Zhao, Q., Fritz, M., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M., Zahr, N. M. 2018; 12: 108

    Abstract

    Individuals aged 12-20 years drink 11% of all alcohol consumed in the United States with more than 90% consumed in the form of binge drinking. Early onset alcohol use is a strong predictor of future alcohol dependence. The study of the effects of excessive alcohol use on the human brain is hampered by limited information regarding the quantity and frequency of exposure to alcohol. Animal models can control for age at alcohol exposure onset and enable isolation of neural substrates of exposure to different patterns and quantities of ethanol (EtOH). As with humans, a frequently used binge exposure model is thought to produce dependence and affect predominantly corticolimbic brain regions. in vivo neuroimaging enables animals models to be examined longitudinally, allowing for each animal to serve as its own control. Accordingly, we conducted 3 magnetic resonance imaging (MRI) sessions (baseline, binge, recovery) to track structure throughout the brains of wild type Wistar rats to test the hypothesis that binge EtOH exposure affects specific brain regions in addition to corticolimbic circuitry. Voxel-based comparisons of 13 EtOH- vs. 12 water- exposed animals identified significant thalamic shrinkage and lateral ventricular enlargement as occurring with EtOH exposure, but recovering with a week of abstinence. By contrast, pretectal nuclei and superior and inferior colliculi shrank in response to binge EtOH treatment but did not recover with abstinence. These results identify brainstem structures that have been relatively underreported but are relevant for localizing neurocircuitry relevant to the dynamic course of alcoholism.

    View details for PubMedID 30618652

  • Adolescent Executive Dysfunction in Daily Life: Relationships to Risks, Brain Structure and Substance Use FRONTIERS IN BEHAVIORAL NEUROSCIENCE Clark, D. B., Chung, T., Martin, C. S., Hasler, B. P., Fitzgerald, D. H., Luna, B., Brown, S. A., Tapert, S. F., Brumback, T., Cummins, K., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M., Colrain, I. M., Baker, F. C., De Bellis, M. D., Nooner, K. B., Nagel, B. J. 2017; 11
  • Peripheral TNF alpha Levels Correlate With Hippocampal Volume in Alcoholism but not in HIV Infection Zahr, N., Kwon, D., Pohl, K., Sullivan, E., Pfefferbaum, A. NATURE PUBLISHING GROUP. 2017: S277–S278
  • Eveningness and Later Sleep Timing Are Associated with Greater Risk for Alcohol and Marijuana Use in Adolescence: Initial Findings from the National Consortium on Alcohol and Neurodevelopment in Adolescence Study. Alcoholism, clinical and experimental research Hasler, B. P., Franzen, P. L., de Zambotti, M., Prouty, D., Brown, S. A., Tapert, S. F., Pfefferbaum, A., Pohl, K. M., Sullivan, E. V., De Bellis, M. D., Nagel, B. J., Baker, F. C., Colrain, I. M., Clark, D. B. 2017; 41 (6): 1154-1165

    Abstract

    Abundant cross-sectional evidence links eveningness (a preference for later sleep-wake timing) and increased alcohol and drug use among adolescents and young adults. However, longitudinal studies are needed to examine whether eveningness is a risk factor for subsequent alcohol and drug use, particularly during adolescence, which is marked by parallel peaks in eveningness and risk for the onset of alcohol use disorders. This study examined whether eveningness and other sleep characteristics were associated with concurrent or subsequent substance involvement in a longitudinal study of adolescents.Participants were 729 adolescents (368 females; age 12 to 21 years) in the National Consortium on Alcohol and Neurodevelopment in Adolescence study. Associations between the sleep variables (circadian preference, sleep quality, daytime sleepiness, sleep timing, and sleep duration) and 3 categorical substance variables (at-risk alcohol use, alcohol bingeing, and past-year marijuana use [y/n]) were examined using ordinal and logistic regression with baseline age, sex, race, ethnicity, socioeconomic status, and psychiatric problems as covariates.At baseline, greater eveningness was associated with greater at-risk alcohol use, greater bingeing, and past-year use of marijuana. Later weekday and weekend bedtimes, but not weekday or weekend sleep duration, showed similar associations across the 3 substance outcomes at baseline. Greater baseline eveningness was also prospectively associated with greater bingeing and past-year use of marijuana at the 1-year follow-up, after covarying for baseline bingeing and marijuana use. Later baseline weekday and weekend bedtimes, and shorter baseline weekday sleep duration, were similarly associated with greater bingeing and past-year use of marijuana at the 1-year follow-up after covarying for baseline values.Findings suggest that eveningness and sleep timing may be under recognized risk factors and future areas of intervention for adolescent involvement in alcohol and marijuana that should be considered along with other previously identified sleep factors such as insomnia and insufficient sleep.

    View details for DOI 10.1111/acer.13401

    View details for PubMedID 28421617

  • Influences of Age, Sex, and Moderate Alcohol Drinking on the Intrinsic Functional Architecture of Adolescent Brains. Cerebral cortex Müller-Oehring, E. M., Kwon, D., Nagel, B. J., Sullivan, E. V., Chu, W., Rohlfing, T., Prouty, D., Nichols, B. N., Poline, J., Tapert, S. F., Brown, S. A., Cummins, K., Brumback, T., Colrain, I. M., Baker, F. C., De Bellis, M. D., Voyvodic, J. T., Clark, D. B., Pfefferbaum, A., Pohl, K. M. 2017: 1-15

    View details for DOI 10.1093/cercor/bhx014

    View details for PubMedID 28168274

  • Effects of prior testing lasting a full year in NCANDA adolescents: Contributions from age, sex, socioeconomic status, ethnicity, site, family history of alcohol or drug abuse, and baseline performance. Developmental cognitive neuroscience Sullivan, E. V., Brumback, T., Tapert, S. F., Prouty, D., Fama, R., Thompson, W. K., Brown, S. A., Cummins, K., Colrain, I. M., Baker, F. C., Clark, D. B., Chung, T., De Bellis, M. D., Hooper, S. R., Nagel, B. J., Nichols, B. N., Chu, W., Kwon, D., Pohl, K. M., Pfefferbaum, A. 2017; 24: 72-83

    Abstract

    Longitudinal study provides a robust method for tracking developmental trajectories. Yet inherent problems of retesting pose challenges in distinguishing biological developmental change from prior testing experience. We examined factors potentially influencing change scores on 16 neuropsychological test composites over 1year in 568 adolescents in the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) project. The twice-minus-once-tested method revealed that performance gain was mainly attributable to testing experience (practice) with little contribution from predicted developmental effects. Group mean practice slopes for 13 composites indicated that 60% to ∼100% variance was attributable to test experience; General Ability accuracy showed the least practice effect (29%). Lower baseline performance, especially in younger participants, was a strong predictor of greater gain. Contributions from age, sex, ethnicity, examination site, socioeconomic status, or family history of alcohol/substance abuse were nil to small, even where statistically significant. Recognizing that a substantial proportion of change in longitudinal testing, even over 1-year, is attributable to testing experience indicates caution against assuming that performance gain observed during periods of maturation necessarily reflects development. Estimates of testing experience, a form of learning, may be a relevant metric for detecting interim influences, such as alcohol use or traumatic episodes, on behavior.

    View details for DOI 10.1016/j.dcn.2017.01.003

    View details for PubMedID 28214667

  • Adolescent Executive Dysfunction in Daily Life: Relationships to Risks, Brain Structure and Substance Use. Frontiers in behavioral neuroscience Clark, D. B., Chung, T., Martin, C. S., Hasler, B. P., Fitzgerald, D. H., Luna, B., Brown, S. A., Tapert, S. F., Brumback, T., Cummins, K., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M., Colrain, I. M., Baker, F. C., De Bellis, M. D., Nooner, K. B., Nagel, B. J. 2017; 11: 223

    Abstract

    During adolescence, problems reflecting cognitive, behavioral and affective dysregulation, such as inattention and emotional dyscontrol, have been observed to be associated with substance use disorder (SUD) risks and outcomes. Prior studies have typically been with small samples, and have typically not included comprehensive measurement of executive dysfunction domains. The relationships of executive dysfunction in daily life with performance based testing of cognitive skills and structural brain characteristics, thought to be the basis for executive functioning, have not been definitively determined. The aims of this study were to determine the relationships between executive dysfunction in daily life, measured by the Behavior Rating Inventory of Executive Function (BRIEF), cognitive skills and structural brain characteristics, and SUD risks, including a global SUD risk indicator, sleep quality, and risky alcohol and cannabis use. In addition to bivariate relationships, multivariate models were tested. The subjects (n = 817; ages 12 through 21) were participants in the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study. The results indicated that executive dysfunction was significantly related to SUD risks, poor sleep quality, risky alcohol use and cannabis use, and was not significantly related to cognitive skills or structural brain characteristics. In multivariate models, the relationship between poor sleep quality and risky substance use was mediated by executive dysfunction. While these cross-sectional relationships need to be further examined in longitudinal analyses, the results suggest that poor sleep quality and executive dysfunction may be viable preventive intervention targets to reduce adolescent substance use.

    View details for PubMedID 29180956

  • Computing group cardinality constraint solutions for logistic regression problems MEDICAL IMAGE ANALYSIS Zhang, Y., Kwon, D., Pohl, K. M. 2017; 35: 58–69

    Abstract

    We derive an algorithm to directly solve logistic regression based on cardinality constraint, group sparsity and use it to classify intra-subject MRI sequences (e.g. cine MRIs) of healthy from diseased subjects. Group cardinality constraint models are often applied to medical images in order to avoid overfitting of the classifier to the training data. Solutions within these models are generally determined by relaxing the cardinality constraint to a weighted feature selection scheme. However, these solutions relate to the original sparse problem only under specific assumptions, which generally do not hold for medical image applications. In addition, inferring clinical meaning from features weighted by a classifier is an ongoing topic of discussion. Avoiding weighing features, we propose to directly solve the group cardinality constraint logistic regression problem by generalizing the Penalty Decomposition method. To do so, we assume that an intra-subject series of images represents repeated samples of the same disease patterns. We model this assumption by combining series of measurements created by a feature across time into a single group. Our algorithm then derives a solution within that model by decoupling the minimization of the logistic regression function from enforcing the group sparsity constraint. The minimum to the smooth and convex logistic regression problem is determined via gradient descent while we derive a closed form solution for finding a sparse approximation of that minimum. We apply our method to cine MRI of 38 healthy controls and 44 adult patients that received reconstructive surgery of Tetralogy of Fallot (TOF) during infancy. Our method correctly identifies regions impacted by TOF and generally obtains statistically significant higher classification accuracy than alternative solutions to this model, i.e., ones relaxing group cardinality constraints.

    View details for PubMedID 27318592

    View details for PubMedCentralID PMC5099121

  • Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models SIAM JOURNAL ON IMAGING SCIENCES Niethammer, M., Pohl, K. M., Janoos, F., Wells, W. M. 2017; 10 (3): 1069–1103

    Abstract

    Segmentation is a fundamental task for extracting semantically meaningful regions from an image. The goal of segmentation algorithms is to accurately assign object labels to each image location. However, image-noise, shortcomings of algorithms, and image ambiguities cause uncertainty in label assignment. Estimating the uncertainty in label assignment is important in multiple application domains, such as segmenting tumors from medical images for radiation treatment planning. One way to estimate these uncertainties is through the computation of posteriors of Bayesian models, which is computationally prohibitive for many practical applications. On the other hand, most computationally efficient methods fail to estimate label uncertainty. We therefore propose in this paper the Active Mean Fields (AMF) approach, a technique based on Bayesian modeling that uses a mean-field approximation to efficiently compute a segmentation and its corresponding uncertainty. Based on a variational formulation, the resulting convex model combines any label-likelihood measure with a prior on the length of the segmentation boundary. A specific implementation of that model is the Chan-Vese segmentation model (CV), in which the binary segmentation task is defined by a Gaussian likelihood and a prior regularizing the length of the segmentation boundary. Furthermore, the Euler-Lagrange equations derived from the AMF model are equivalent to those of the popular Rudin-Osher-Fatemi (ROF) model for image denoising. Solutions to the AMF model can thus be implemented by directly utilizing highly-efficient ROF solvers on log-likelihood ratio fields. We qualitatively assess the approach on synthetic data as well as on real natural and medical images. For a quantitative evaluation, we apply our approach to the icgbench dataset.

    View details for DOI 10.1137/16M1058601

    View details for Web of Science ID 000412157400004

    View details for PubMedID 29051796

    View details for PubMedCentralID PMC5642306

  • 3D Motion Modeling and Reconstruction of Left Ventricle Wall in Cardiac MRI. Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH Yang, D., Wu, P., Tan, C., Pohl, K. M., Axel, L., Metaxas, D. 2017; 10263: 481–92

    Abstract

    The analysis of left ventricle (LV) wall motion is a critical step for understanding cardiac functioning mechanisms and clinical diagnosis of ventricular diseases. We present a novel approach for 3D motion modeling and analysis of LV wall in cardiac magnetic resonance imaging (MRI). First, a fully convolutional network (FCN) is deployed to initialize myocardium contours in 2D MR slices. Then, we propose an image registration algorithm to align MR slices in space and minimize the undesirable motion artifacts from inconsistent respiration. Finally, a 3D deformable model is applied to recover the shape and motion of myocardium wall. Utilizing the proposed approach, we can visually analyze 3D LV wall motion, evaluate cardiac global function, and diagnose ventricular diseases.

    View details for DOI 10.1007/978-3-319-59448-4_46

    View details for PubMedID 28664198

    View details for PubMedCentralID PMC5484578

  • Structural brain anomalies in healthy adolescents in the NCANDA cohort: relation to neuropsychological test performance, sex, and ethnicity. Brain imaging and behavior Sullivan, E. V., Lane, B., Kwon, D., Meloy, M. J., Tapert, S. F., Brown, S. A., Colrain, I. M., Baker, F. C., De Bellis, M. D., Clark, D. B., Nagel, B. J., Pohl, K. M., Pfefferbaum, A. 2016: -?

    Abstract

    Structural MRI of volunteers deemed "normal" following clinical interview provides a window into normal brain developmental morphology but also reveals unexpected dysmorphology, commonly known as "incidental findings." Although unanticipated, these anatomical findings raise questions regarding possible treatment that could even ultimately require neurosurgical intervention, which itself carries significant risk but may not be indicated if the anomaly is nonprogressive or of no functional consequence. Neuroradiological readings of 833 structural MRI from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) cohort found an 11.8 % incidence of brain structural anomalies, represented proportionately across the five collection sites and ethnic groups. Anomalies included 26 mega cisterna magna, 15 subarachnoid cysts, 12 pineal cysts, 12 white matter dysmorphologies, 5 tonsillar ectopias, 5 prominent perivascular spaces, 5 gray matter heterotopias, 4 pituitary masses, 4 excessively large or asymmetrical ventricles, 4 cavum septum pellucidum, 3 developmental venous anomalies, 1 exceptionally large midsagittal vein, and single cases requiring clinical followup: cranio-cervical junction stenosis, parietal cortical mass, and Chiari I malformation. A case of possible demyelinating disorder (e.g., neuromyelitis optica or multiple sclerosis) newly emerged at the 1-year NCANDA followup, requiring clinical referral. Comparing test performance of the 98 anomalous cases with 619 anomaly-free no-to-low alcohol consuming adolescents revealed significantly lower scores on speed measures of attention and motor functions; these differences were not attributed to any one anomaly subgroup. Further, we devised an automated approach for quantifying posterior fossa CSF volumes for detection of mega cisterna magna, which represented 26.5 % of clinically identified anomalies. Automated quantification fit a Gaussian distribution with a rightward skew. Using a 3SD cut-off, quantification identified 22 of the 26 clinically-identified cases, indicating that cases with percent of CSF in the posterior-inferior-middle aspect of the posterior fossa ≥3SD merit further review, and support complementing clinical readings with objective quantitative analysis. Discovery of asymptomatic brain structural anomalies, even when no clinical action is indicated, can be disconcerting to the individual and responsible family members, raising a disclosure dilemma: refrain from relating the incidental findings to avoid unnecessary alarm or anxiety; or alternatively, relate the neuroradiological findings as "normal variants" to the study volunteers and family, thereby equipping them with knowledge for the future should they have the occasion for a brain scan following an illness or accident that the incidental findings predated the later event.

    View details for PubMedID 27722828

  • Adolescent Development of Cortical and White Matter Structure in the NCANDA Sample: Role of Sex, Ethnicity, Puberty, and Alcohol Drinking. Cerebral cortex Pfefferbaum, A., Rohlfing, T., Pohl, K. M., Lane, B., Chu, W., Kwon, D., Nolan Nichols, B., Brown, S. A., Tapert, S. F., Cummins, K., Thompson, W. K., Brumback, T., Meloy, M. J., Jernigan, T. L., Dale, A., Colrain, I. M., Baker, F. C., Prouty, D., De Bellis, M. D., Voyvodic, J. T., Clark, D. B., Luna, B., Chung, T., Nagel, B. J., Sullivan, E. V. 2016; 26 (10): 4101-4121

    Abstract

    Brain structural development continues throughout adolescence, when experimentation with alcohol is often initiated. To parse contributions from biological and environmental factors on neurodevelopment, this study used baseline National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) magnetic resonance imaging (MRI) data, acquired in 674 adolescents meeting no/low alcohol or drug use criteria and 134 adolescents exceeding criteria. Spatial integrity of images across the 5 recruitment sites was assured by morphological scaling using Alzheimer's disease neuroimaging initiative phantom-derived volume scalar metrics. Clinical MRI readings identified structural anomalies in 11.4%. Cortical volume and thickness were smaller and white matter volumes were larger in older than in younger adolescents. Effects of sex (male > female) and ethnicity (majority > minority) were significant for volume and surface but minimal for cortical thickness. Adjusting volume and area for supratentorial volume attenuated or removed sex and ethnicity effects. That cortical thickness showed age-related decline and was unrelated to supratentorial volume is consistent with the radial unit hypothesis, suggesting a universal neural development characteristic robust to sex and ethnicity. Comparison of NCANDA with PING data revealed similar but flatter, age-related declines in cortical volumes and thickness. Smaller, thinner frontal, and temporal cortices in the exceeds-criteria than no/low-drinking group suggested untoward effects of excessive alcohol consumption on brain structural development.

    View details for DOI 10.1093/cercor/bhv205

    View details for PubMedID 26408800

  • Joint Data Harmonization and Group Cardinality Constrained Classification. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Zhang, Y., Park, S. H., Pohl, K. M. 2016; 9900: 282–90

    Abstract

    To boost the power of classifiers, studies often increase the size of existing samples through the addition of independently collected data sets. Doing so requires harmonizing the data for demographic and acquisition differences based on a control cohort before performing disease specific classification. The initial harmonization often mitigates group differences negatively impacting classification accuracy. To preserve cohort separation, we propose the first model unifying linear regression for data harmonization with a logistic regression for disease classification. Learning to harmonize data is now an adaptive process taking both disease and control data into account. Solutions within that model are confined by group cardinality to reduce the risk of overfitting (via sparsity), to explicitly account for the impact of disease on the inter-dependency of regions (by grouping them), and to identify disease specific patterns (by enforcing sparsity via the l0-'norm'). We test those solutions in distinguishing HIV-Associated Neurocognitive Disorder from Mild Cognitive Impairment of two independently collected, neuroimage data sets; each contains controls and samples from one disease. Our classifier is impartial to acquisition difference between the data sets while being more accurate in diseases seperation than sequential learning of harmonization and classification parameters, and non-sparsity based logistic regressors.

    View details for PubMedID 28758167

  • Extracting patterns of morphometry distinguishing HIV associated neurodegeneration from mild cognitive impairment via group cardinality constrained classification. Human brain mapping Zhang, Y., Kwon, D., Esmaeili-Firidouni, P., Pfefferbaum, A., Sullivan, E. V., Javitz, H., Valcour, V., Pohl, K. M. 2016

    Abstract

    HIV-Associated Neurocognitive Disorder (HAND) is the most common constellation of cognitive dysfunctions in chronic HIV infected patients age 60 or older in the U.S. Only few published methods assist in distinguishing HAND from other forms of age-associated cognitive decline, such as Mild Cognitive Impairment (MCI). In this report, a data-driven, nonparameteric model to identify morphometric patterns separating HAND from MCI due to non-HIV conditions in this older age group was proposed. This model enhanced the potential for group separation by combining a smaller, longitudinal data set containing HAND samples with a larger, public data set including MCI cases. Using cross-validation, a linear model on healthy controls to harmonize the volumetric scores extracted from MRIs for demographic and acquisition differences between the two independent, disease-specific data sets was trained. Next, patterns distinguishing HAND from MCI via a group sparsity constrained logistic classifier were identified. Unlike existing approaches, our classifier directly solved the underlying minimization problem by decoupling the minimization of the logistic regression function from enforcing the group sparsity constraint. The extracted patterns consisted of eight regions that distinguished HAND from MCI on a significant level while being indifferent to differences in demographics and acquisition between the two sets. Individually selecting regions through conventional morphometric group analysis resulted in a larger number of regions that were less accurate. In conclusion, simultaneously analyzing all brain regions and time points for disease specific patterns contributed to distinguishing with high accuracy HAND-related impairment from cognitive impairment found in the HIV uninfected, MCI cohort. Hum Brain Mapp 37:4523-4538, 2016. © 2016 Wiley Periodicals, Inc.

    View details for DOI 10.1002/hbm.23326

    View details for PubMedID 27489003

  • Cognitive, Emotion Control, and Motor Performance of Adolescents in the NCANDA Study: Contributions From Alcohol Consumption, Age, Sex, Ethnicity, and Family History of Addiction NEUROPSYCHOLOGY Sullivan, E. V., Brumback, T., Tapert, S. F., Fama, R., Prouty, D., Brown, S. A., Cummins, K., Thompson, W. K., Colrain, I. M., Baker, F. C., De Bellis, M. D., Hooper, S. R., Clark, D. B., Chung, T., Nagel, B. J., Nichols, B. N., Rohlfing, T., Chu, W., Pohl, K. M., Pfefferbaum, A. 2016; 30 (4): 449-473

    Abstract

    To investigate development of cognitive and motor functions in healthy adolescents and to explore whether hazardous drinking affects the normal developmental course of those functions.Participants were 831 adolescents recruited across 5 United States sites of the National Consortium on Alcohol and NeuroDevelopment in Adolescence 692 met criteria for no/low alcohol exposure, and 139 exceeded drinking thresholds. Cross-sectional, baseline data were collected with computerized and traditional neuropsychological tests assessing 8 functional domains expressed as composite scores. General additive modeling evaluated factors potentially modulating performance (age, sex, ethnicity, socioeconomic status, and pubertal developmental stage).Older no/low-drinking participants achieved better scores than younger ones on 5 accuracy composites (general ability, abstraction, attention, emotion, and balance). Speeded responses for attention, motor speed, and general ability were sensitive to age and pubertal development. The exceeds-threshold group (accounting for age, sex, and other demographic factors) performed significantly below the no/low-drinking group on balance accuracy and on general ability, attention, episodic memory, emotion, and motor speed scores and showed evidence for faster speed at the expense of accuracy. Delay Discounting performance was consistent with poor impulse control in the younger no/low drinkers and in exceeds-threshold drinkers regardless of age.Higher achievement with older age and pubertal stage in general ability, abstraction, attention, emotion, and balance suggests continued functional development through adolescence, possibly supported by concurrently maturing frontal, limbic, and cerebellar brain systems. Determination of whether low scores by the exceeds-threshold group resulted from drinking or from other preexisting factors requires longitudinal study. (PsycINFO Database Record

    View details for DOI 10.1037/neu0000259

    View details for PubMedID 26752122

  • Harmonizing DTI measurements across scanners to examine the development of white matter microstructure in 803 adolescents of the NCANDA study NEUROIMAGE Pohl, K. M., Sullivan, E. V., Rohlfing, T., Chu, W., Kwon, D., Nichols, B. N., Zhang, Y., Brown, S. A., Tapert, S. F., Cummins, K., Thompson, W. K., Brumback, T., Colrain, I. M., Baker, F. C., Prouty, D., De Bellis, M. D., Voyvodic, J. T., Clark, D. B., Schirda, C., Nagel, B. J., Pfefferbaum, A. 2016; 130: 194-213

    Abstract

    Neurodevelopment continues through adolescence, with notable maturation of white matter tracts comprising regional fiber systems progressing at different rates. To identify factors that could contribute to regional differences in white matter microstructure development, large samples of youth spanning adolescence to young adulthood are essential to parse these factors. Recruitment of adequate samples generally relies on multi-site consortia but comes with the challenge of merging data acquired on different platforms. In the current study, diffusion tensor imaging (DTI) data were acquired on GE and Siemens systems through the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), a multi-site study designed to track the trajectories of regional brain development during a time of high risk for initiating alcohol consumption. This cross-sectional analysis reports baseline Tract-Based Spatial Statistic (TBSS) of regional fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (L1), and radial diffusivity (LT) from the five consortium sites on 671 adolescents who met no/low alcohol or drug consumption criteria and 132 adolescents with a history of exceeding consumption criteria. Harmonization of DTI metrics across manufacturers entailed the use of human-phantom data, acquired multiple times on each of three non-NCANDA participants at each site's MR system, to determine a manufacturer-specific correction factor. Application of the correction factor derived from human phantom data measured on MR systems from different manufacturers reduced the standard deviation of the DTI metrics for FA by almost a half, enabling harmonization of data that would have otherwise carried systematic error. Permutation testing supported the hypothesis of higher FA and lower diffusivity measures in older adolescents and indicated that, overall, the FA, MD, and L1 of the boys were higher than those of the girls, suggesting continued microstructural development notable in the boys. The contribution of demographic and clinical differences to DTI metrics was assessed with General Additive Models (GAM) testing for age, sex, and ethnicity differences in regional skeleton mean values. The results supported the primary study hypothesis that FA skeleton mean values in the no/low-drinking group were highest at different ages. When differences in intracranial volume were covaried, FA skeleton mean reached a maximum at younger ages in girls than boys and varied in magnitude with ethnicity. Our results, however, did not support the hypothesis that youth who exceeded exposure criteria would have lower FA or higher diffusivity measures than the no/low-drinking group; detecting the effects of excessive alcohol consumption during adolescence on DTI metrics may require longitudinal study.

    View details for DOI 10.1016/j.neuroimage.2016.01.061

    View details for Web of Science ID 000372745600018

    View details for PubMedCentralID PMC4808415

  • Harmonizing DTI measurements across scanners to examine the development of white matter microstructure in 803 adolescents of the NCANDA study. NeuroImage Pohl, K. M., Sullivan, E. V., Rohlfing, T., Chu, W., Kwon, D., Nichols, B. N., Zhang, Y., Brown, S. A., Tapert, S. F., Cummins, K., Thompson, W. K., Brumback, T., Colrain, I. M., Baker, F. C., Prouty, D., De Bellis, M. D., Voyvodic, J. T., Clark, D. B., Schirda, C., Nagel, B. J., Pfefferbaum, A. 2016; 130: 194-213

    Abstract

    Neurodevelopment continues through adolescence, with notable maturation of white matter tracts comprising regional fiber systems progressing at different rates. To identify factors that could contribute to regional differences in white matter microstructure development, large samples of youth spanning adolescence to young adulthood are essential to parse these factors. Recruitment of adequate samples generally relies on multi-site consortia but comes with the challenge of merging data acquired on different platforms. In the current study, diffusion tensor imaging (DTI) data were acquired on GE and Siemens systems through the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), a multi-site study designed to track the trajectories of regional brain development during a time of high risk for initiating alcohol consumption. This cross-sectional analysis reports baseline Tract-Based Spatial Statistic (TBSS) of regional fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (L1), and radial diffusivity (LT) from the five consortium sites on 671 adolescents who met no/low alcohol or drug consumption criteria and 132 adolescents with a history of exceeding consumption criteria. Harmonization of DTI metrics across manufacturers entailed the use of human-phantom data, acquired multiple times on each of three non-NCANDA participants at each site's MR system, to determine a manufacturer-specific correction factor. Application of the correction factor derived from human phantom data measured on MR systems from different manufacturers reduced the standard deviation of the DTI metrics for FA by almost a half, enabling harmonization of data that would have otherwise carried systematic error. Permutation testing supported the hypothesis of higher FA and lower diffusivity measures in older adolescents and indicated that, overall, the FA, MD, and L1 of the boys were higher than those of the girls, suggesting continued microstructural development notable in the boys. The contribution of demographic and clinical differences to DTI metrics was assessed with General Additive Models (GAM) testing for age, sex, and ethnicity differences in regional skeleton mean values. The results supported the primary study hypothesis that FA skeleton mean values in the no/low-drinking group were highest at different ages. When differences in intracranial volume were covaried, FA skeleton mean reached a maximum at younger ages in girls than boys and varied in magnitude with ethnicity. Our results, however, did not support the hypothesis that youth who exceeded exposure criteria would have lower FA or higher diffusivity measures than the no/low-drinking group; detecting the effects of excessive alcohol consumption during adolescence on DTI metrics may require longitudinal study.

    View details for DOI 10.1016/j.neuroimage.2016.01.061

    View details for PubMedID 26872408

  • The National Consortium on Alcohol and Neuro-Development in Adolescence (NCANDA): A Multisite Study of Adolescent Development and Substance Use JOURNAL OF STUDIES ON ALCOHOL AND DRUGS Brown, S. A., Brumback, T. Y., Tomlinson, K., Cummins, K., Thompson, W. K., Nagel, B. J., De Bellis, M. D., Hooper, S. R., Clark, D. B., Chung, T., Hasler, B. P., Colrain, I. M., Baker, F. C., Prouty, D., Pfefferbaum, A., Sullivan, E. V., Pohl, K. M., Rohlfing, T., Nichols, B. N., Chu, W., Tapert, S. F. 2015; 76 (6): 895-908

    Abstract

    During adolescence, neurobiological maturation occurs concurrently with social and interpersonal changes, including the initiation of alcohol and other substance use. The National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) is designed to disentangle the complex relationships between onset, escalation, and desistance of alcohol use and changes in neurocognitive functioning and neuromaturation.A sample of 831 youth, ages 12-21 years, was recruited at five sites across the United States, oversampling those at risk for alcohol use problems. Most (83%) had limited or no history of alcohol or other drug use, and a smaller portion (17%) exceeded drinking thresholds. A comprehensive assessment of biological development, family background, psychiatric symptomatology, and neuropsychological functioning-in addition to anatomical, diffusion, and functional brain magnetic resonance imaging-was completed at baseline.The NCANDA sample of youth is nationally representative of sex and racial/ethnic groups. More than 50% have at least one risk characteristic for subsequent heavy drinking (e.g., family history, internalizing or externalizing symptoms). As expected, those who exceeded drinking thresholds (n = 139) differ from those who did not (n = 692) on identified factors associated with early alcohol use and problems.NCANDA successfully recruited a large sample of adolescents and comprehensively assessed psychosocial functioning across multiple domains. Based on the sample's risk profile, NCANDA is well positioned to capture the transition into drinking and alcohol problems in a large portion of the cohort, as well as to help disentangle the associations between alcohol use, neurobiological maturation, and neurocognitive development and functioning.

    View details for PubMedID 26562597

  • Neuroinformatics Software Applications Supporting Electronic Data Capture, Management, and Sharing for the Neuroimaging Community NEUROPSYCHOLOGY REVIEW Nichols, B. N., Pohl, K. M. 2015; 25 (3): 356-368

    Abstract

    Accelerating insight into the relation between brain and behavior entails conducting small and large-scale research endeavors that lead to reproducible results. Consensus is emerging between funding agencies, publishers, and the research community that data sharing is a fundamental requirement to ensure all such endeavors foster data reuse and fuel reproducible discoveries. Funding agency and publisher mandates to share data are bolstered by a growing number of data sharing efforts that demonstrate how information technologies can enable meaningful data reuse. Neuroinformatics evaluates scientific needs and develops solutions to facilitate the use of data across the cognitive and neurosciences. For example, electronic data capture and management tools designed to facilitate human neurocognitive research can decrease the setup time of studies, improve quality control, and streamline the process of harmonizing, curating, and sharing data across data repositories. In this article we outline the advantages and disadvantages of adopting software applications that support these features by reviewing the tools available and then presenting two contrasting neuroimaging study scenarios in the context of conducting a cross-sectional and a multisite longitudinal study.

    View details for DOI 10.1007/s11065-015-9293-x

    View details for Web of Science ID 000360912800010

    View details for PubMedID 26267019

  • Solving Logistic Regression with Group Cardinality Constraints for Time Series Analysis Zhang, Y., Pohl, K. M., Navab, N., Hornegger, J., Wells, W. M., Frangi, A. F. SPRINGER INT PUBLISHING AG. 2015: 459–66

    Abstract

    We propose an algorithm to distinguish 3D+t images of healthy from diseased subjects by solving logistic regression based on cardinality constrained, group sparsity. This method reduces the risk of overfitting by providing an elegant solution to identifying anatomical regions most impacted by disease. It also ensures that consistent identification across the time series by grouping each image feature across time and counting the number of non-zero groupings. While popular in medical imaging, group cardinality constrained problems are generally solved by relaxing counting with summing over the groupings. We instead solve the original problem by generalizing a penalty decomposition algorithm, which alternates between minimizing a logistic regression function with a regularizer based on the Frobenius norm and enforcing sparsity. Applied to 86 cine MRIs of healthy cases and subjects with Tetralogy of Fallot (TOF), our method correctly identifies regions impacted by TOF and obtains a statistically significant higher classification accuracy than logistic regression without and relaxed grouped sparsity constraint.

    View details for PubMedID 27610425

  • White matter microstructural recovery with abstinence and decline with relapse in alcohol dependence interacts with normal ageing: a controlled longitudinal DTI study. The lancet. Psychiatry Pfefferbaum, A., Rosenbloom, M. J., Chu, W., Sassoon, S. A., Rohlfing, T., Pohl, K. M., Zahr, N. M., Sullivan, E. V. 2014; 1 (3): 202-212

    Abstract

    Alcohol dependence exacts a toll on brain white matter microstructure, which has the potential of repair with prolonged sobriety. Diffusion tensor imaging (DTI) enables in-vivo quantification of tissue constituents and localisation of tracts potentially affected in alcohol dependence and its recovery. We did an extended longitudinal study of alcoholism's trajectory of effect on selective fibre bundles with sustained sobriety or decline with relapse.Participants were drawn from a longitudinal, 1·5T DTI database of 841 scans of individuals with various medical or neuropsychiatric conditions and normal ageing. Participants diagnosed with alcohol dependence had to meet the criteria from DSM-IV for alcohol dependence. Controls were screened and free of any DSM-IV axis I diagnosis, including being without history of alcohol or drug abuse or dependence. Tract-based spatial statistics (TBSS) quantified white matter integrity throughout the brain in 47 alcohol-dependent individuals and 56 controls examined 2-5 times over 1-8 year intervals. We identified regions showing group differences with a white matter atlas. For macrostructural comparison, we measured corpus callosum and centrum semiovale volumes on MRI.This study took place in the USA, between June 23, 2000, and Sept 6, 2011. TBSS identified a large cluster (threshold p<0·001), where controls showed significant fractional anisotropy (FA) decrease with ageing and alcohol-dependent individuals had significantly lower FA than controls regardless of age. Over the examination interval, 27 (57%) alcohol-dependent individuals abstained, ten (21%) relapsed into light drinking, and ten (21%) relapsed into heavy drinking (>5 kg of alcohol/year). Despite abnormally low FA, age trajectories of the abstainers were positive and progressing toward normality, whereas those of the relapsers and controls were negative. Axial diffusivity (lower values indexing myelin integrity) was abnormally high in the total alcohol-dependent group; however, the abstainers' slopes paralleled those of controls, whereas the heavy-drinking relapsers' slopes showed accelerated ageing. Callosal genu and body microstructure but not macrostructure showed untoward alcohol-related effects. Affected projection and association tracts had an anterior and superior neuroanatomical distribution.Return to heavy drinking resulted in accelerating microstructural white matter damage. Despite evidence for damage, alcohol-dependent individuals maintaining sobriety over extended periods showed improvement in brain fibre tract integrity reflective of fibre reorganisation and myelin restoration, indicative of a neural mechanism explaining recovery.US National Institute on Alcohol Abuse and Alcoholism (AA012388, AA017168, AA005965, AA013521-INIA).

    View details for DOI 10.1016/S2215-0366(14)70301-3

    View details for PubMedID 26360732

  • White matter microstructural recovery with abstinence and decline with relapse in alcohol dependence interacts with normal ageing: a controlled longitudinal DTI study LANCET PSYCHIATRY Pfefferbaum, A., Rosenbloom, M. J., Chu, W., Sassoon, S. A., Rohlfing, T., Pohl, K. M., Zahr, N. M., Sullivan, E. V. 2014; 1 (3): 202-212

    Abstract

    Alcohol dependence exacts a toll on brain white matter microstructure, which has the potential of repair with prolonged sobriety. Diffusion tensor imaging (DTI) enables in-vivo quantification of tissue constituents and localisation of tracts potentially affected in alcohol dependence and its recovery. We did an extended longitudinal study of alcoholism's trajectory of effect on selective fibre bundles with sustained sobriety or decline with relapse.Participants were drawn from a longitudinal, 1·5T DTI database of 841 scans of individuals with various medical or neuropsychiatric conditions and normal ageing. Participants diagnosed with alcohol dependence had to meet the criteria from DSM-IV for alcohol dependence. Controls were screened and free of any DSM-IV axis I diagnosis, including being without history of alcohol or drug abuse or dependence. Tract-based spatial statistics (TBSS) quantified white matter integrity throughout the brain in 47 alcohol-dependent individuals and 56 controls examined 2-5 times over 1-8 year intervals. We identified regions showing group differences with a white matter atlas. For macrostructural comparison, we measured corpus callosum and centrum semiovale volumes on MRI.This study took place in the USA, between June 23, 2000, and Sept 6, 2011. TBSS identified a large cluster (threshold p<0·001), where controls showed significant fractional anisotropy (FA) decrease with ageing and alcohol-dependent individuals had significantly lower FA than controls regardless of age. Over the examination interval, 27 (57%) alcohol-dependent individuals abstained, ten (21%) relapsed into light drinking, and ten (21%) relapsed into heavy drinking (>5 kg of alcohol/year). Despite abnormally low FA, age trajectories of the abstainers were positive and progressing toward normality, whereas those of the relapsers and controls were negative. Axial diffusivity (lower values indexing myelin integrity) was abnormally high in the total alcohol-dependent group; however, the abstainers' slopes paralleled those of controls, whereas the heavy-drinking relapsers' slopes showed accelerated ageing. Callosal genu and body microstructure but not macrostructure showed untoward alcohol-related effects. Affected projection and association tracts had an anterior and superior neuroanatomical distribution.Return to heavy drinking resulted in accelerating microstructural white matter damage. Despite evidence for damage, alcohol-dependent individuals maintaining sobriety over extended periods showed improvement in brain fibre tract integrity reflective of fibre reorganisation and myelin restoration, indicative of a neural mechanism explaining recovery.US National Institute on Alcohol Abuse and Alcoholism (AA012388, AA017168, AA005965, AA013521-INIA).

    View details for Web of Science ID 000343703500022

  • Regional Manifold Learning for Disease Classification IEEE TRANSACTIONS ON MEDICAL IMAGING Ye, D. H., Desjardins, B., Hamm, J., Litt, H., Pohl, K. M. 2014; 33 (6): 1236-1247

    Abstract

    While manifold learning from images itself has become widely used in medical image analysis, the accuracy of existing implementations suffers from viewing each image as a single data point. To address this issue, we parcellate images into regions and then separately learn the manifold for each region. We use the regional manifolds as low-dimensional descriptors of high-dimensional morphological image features, which are then fed into a classifier to identify regions affected by disease. We produce a single ensemble decision for each scan by the weighted combination of these regional classification results. Each weight is determined by the regional accuracy of detecting the disease. When applied to cardiac magnetic resonance imaging of 50 normal controls and 50 patients with reconstructive surgery of Tetralogy of Fallot, our method achieves significantly better classification accuracy than approaches learning a single manifold across the entire image domain.

    View details for DOI 10.1109/TMI.2014.2305751

    View details for Web of Science ID 000337125400003

    View details for PubMedCentralID PMC5450500

  • PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration IEEE TRANSACTIONS ON MEDICAL IMAGING Kwon, D., Niethammer, M., Akbari, H., Bilello, M., Davatzikos, C., Pohl, K. M. 2014; 33 (3): 651-667

    Abstract

    We propose a new method for deformable registration of pre-operative and post-recurrence brain MR scans of glioma patients. Performing this type of intra-subject registration is challenging as tumor, resection, recurrence, and edema cause large deformations, missing correspondences, and inconsistent intensity profiles between the scans. To address this challenging task, our method, called PORTR, explicitly accounts for pathological information. It segments tumor, resection cavity, and recurrence based on models specific to each scan. PORTR then uses the resulting maps to exclude pathological regions from the image-based correspondence term while simultaneously measuring the overlap between the aligned tumor and resection cavity. Embedded into a symmetric registration framework, we determine the optimal solution by taking advantage of both discrete and continuous search methods. We apply our method to scans of 24 glioma patients. Both quantitative and qualitative analysis of the results clearly show that our method is superior to other state-of-the-art approaches.

    View details for DOI 10.1109/TMI.2013.2293478

    View details for Web of Science ID 000332599500005

    View details for PubMedID 24595340

  • AUTO-ENCODING OF DISCRIMINATING MORPHOMETRY FROM CARDIAC MRI Ye, D., Desjardins, B., Ferrari, V., Metaxas, D., Pohl, K. M., IEEE IEEE. 2014: 217–21

    Abstract

    We propose a fully-automatic morphometric encoding targeted towards differentiating diseased from healthy cardiac MRI. Existing encodings rely on accurate segmentations of each scan. Segmentation generally includes labour-intensive editing and increases the risk associated with intra- and inter-rater variability. Our morphometric framework only requires the segmentation of a template scan. This template is non-rigidly registered to the other scans. We then confine the resulting deformation maps to the regions outlined by the segmentations. We learn a manifold for each region and identify the most informative coordinates with respect to distinguishing diseased from healthy scans. Compared with volumetric measurements and a deformation-based score, this encoding is much more accurate in capturing morphometric patterns distinguishing healthy subjects from those with Tetralogy of Fallot, diastolic dysfunction, and hypertrophic cardiomyopathy.

    View details for PubMedID 28593032

    View details for PubMedCentralID PMC5459374

  • WESD-Weighted Spectral Distance for Measuring Shape Dissimilarity IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Konukoglu, E., Glocker, B., Criminisi, A., Pohl, K. M. 2013; 35 (9): 2284–97

    Abstract

    This paper presents a new distance for measuring shape dissimilarity between objects. Recent publications introduced the use of eigenvalues of the Laplace operator as compact shape descriptors. Here, we revisit the eigenvalues to define a proper distance, called Weighted Spectral Distance (WESD), for quantifying shape dissimilarity. The definition of WESD is derived through analyzing the heat trace. This analysis provides the proposed distance with an intuitive meaning and mathematically links it to the intrinsic geometry of objects. We analyze the resulting distance definition, present and prove its important theoretical properties. Some of these properties include: 1) WESD is defined over the entire sequence of eigenvalues yet it is guaranteed to converge, 2) it is a pseudometric, 3) it is accurately approximated with a finite number of eigenvalues, and 4) it can be mapped to the [0,1) interval. Last, experiments conducted on synthetic and real objects are presented. These experiments highlight the practical benefits of WESD for applications in vision and medical image analysis.

    View details for DOI 10.1109/TPAMI.2012.275

    View details for Web of Science ID 000322029000017

    View details for PubMedID 23868785

    View details for PubMedCentralID PMC5513679

  • FLOOR: Fusing Locally Optimal Registrations Ye, D., Hamm, J., Desjardins, B., Pohl, K. M., Mori, K., Sakuma, Sato, Y., Barillot, C., Navab, N. SPRINGER-VERLAG BERLIN. 2013: 195–202

    Abstract

    Most registration algorithms, such as Demons, align two scans by iteratively finding the deformation minimizing the image dissimilarity at each location and smoothing this minimum across the image domain. These methods generally get stuck in local minima, are negatively impacted by missing correspondences between the images, and require careful tuning of the smoothing parameters to achieve optimal results. In this paper, we propose to improve on those issues by choosing the minimum from a set of candidates. Our method generates such candidates by running the registration algorithm multiple times varying the setting of the smoothing and the image domain. We iteratively refine those candidates by fusing them with the outcome of alternative approaches and locally adapting the smoothing parameters. We implement our algorithm based on Demons and find alternative minima via manifold learning. Compared to those two methods, our 600 pairwise registrations of cardiac MRIs significantly better handle the large shape variations of the heart and the different field of views captured by scans.

    View details for Web of Science ID 000333633500025

    View details for PubMedID 24505761

  • Collaborative Multi Organ Segmentation by Integrating Deformable and Graphical Models Uzunbas, M., Chen, C., Zhang, S., Pohl, K. M., Li, K., Metaxas, D., Sakuma, Barillot, C., Navab, N. SPRINGER-VERLAG BERLIN. 2013: 157–64

    Abstract

    Organ segmentation is a challenging problem on which significant progress has been made. Deformable models (DM) and graphical models (GM) are two important categories of optimization based image segmentation methods. Efforts have been made on integrating two types of models into one framework. However, previous methods are not designed for segmenting multiple organs simultaneously and accurately. In this paper, we propose a hybrid multi organ segmentation approach by integrating DM and GM in a coupled optimization framework. Specifically, we show that region-based deformable models can be integrated with Markov Random Fields (MRF), such that multiple models' evolutions are driven by a maximum a posteriori (MAP) inference. It brings global and local deformation constraints into a unified framework for simultaneous segmentation of multiple objects in an image. We validate this proposed method on two challenging problems of multi organ segmentation, and the results are promising.

    View details for Web of Science ID 000342835100020

    View details for PubMedID 24579136

    View details for PubMedCentralID PMC5809157

  • Extracting evolving pathologies via spectral clustering. Information processing in medical imaging : proceedings of the ... conference Bernardis, E., Pohl, K. M., Davatzikos, C. 2013; 23: 680–91

    Abstract

    A bottleneck in the analysis of longitudinal MR scans with white matter brain lesions is the temporally consistent segmentation of the pathology. We identify pathologies in 3D+t(ime) within a spectral graph clustering framework. Our clustering approach simultaneously segments and tracks the evolving lesions by identifying characteristic image patterns at each time-point and voxel correspondences across time-points. For each 3D image, our method constructs a graph where weights between nodes capture the likeliness of two voxels belonging to the same region. Based on these weights, we then establish rough correspondences between graph nodes at different time-points along estimated pathology evolution directions. We combine the graphs by aligning the weights to a reference time-point, thus integrating temporal information across the 3D images, and formulate the 3D+t segmentation problem as a binary partitioning of this graph. The resulting segmentation is very robust to local intensity fluctuations and yields better results than segmentations generated for each time-point.

    View details for PubMedID 24684009

  • Multinomial probabilistic fiber representation for connectivity driven clustering. Information processing in medical imaging : proceedings of the ... conference Tunç, B., Smith, A. R., Wasserman, D., Pennec, X., Wells, W. M., Verma, R., Pohl, K. M. 2013; 23: 730–41

    Abstract

    The clustering of fibers into bundles is an important task in studying the structure and function of white matter. Existing technology mostly relies on geometrical features, such as the shape of fibers, and thus only provides very limited information about the neuroanatomical function of the brain. We advance this issue by proposing a multinomial representation of fibers decoding their connectivity to gray matter regions. We then simplify the clustering task by first deriving a compact encoding of our representation via the logit transformation. Furthermore, we define a distance between fibers that is in theory invariant to parcellation biases and is equivalent to a family of Riemannian metrics on the simplex of multinomial probabilities. We apply our method to longitudinal scans of two healthy subjects showing high reproducibility of the resulting fiber bundles without needing to register the corresponding scans to a common coordinate system. We confirm these qualitative findings via a simple statistical analyse of the fiber bundles.

    View details for PubMedID 24684013

    View details for PubMedCentralID PMC3974202

  • Discriminative Segmentation-Based Evaluation Through Shape Dissimilarity IEEE TRANSACTIONS ON MEDICAL IMAGING Konukoglu, E., Glocker, B., Ye, D., Criminisi, A., Pohl, K. M. 2012; 31 (12): 2278–89

    Abstract

    Segmentation-based scores play an important role in the evaluation of computational tools in medical image analysis. These scores evaluate the quality of various tasks, such as image registration and segmentation, by measuring the similarity between two binary label maps. Commonly these measurements blend two aspects of the similarity: pose misalignments and shape discrepancies. Not being able to distinguish between these two aspects, these scores often yield similar results to a widely varying range of different segmentation pairs. Consequently, the comparisons and analysis achieved by interpreting these scores become questionable. In this paper, we address this problem by exploring a new segmentation-based score, called normalized Weighted Spectral Distance (nWSD), that measures only shape discrepancies using the spectrum of the Laplace operator. Through experiments on synthetic and real data we demonstrate that nWSD provides additional information for evaluating differences between segmentations, which is not captured by other commonly used scores. Our results demonstrate that when jointly used with other scores, such as Dice's similarity coefficient, the additional information provided by nWSD allows richer, more discriminative evaluations. We show for the task of registration that through this addition we can distinguish different types of registration errors. This allows us to identify the source of errors and discriminate registration results which so far had to be treated as being of similar quality in previous evaluation studies.

    View details for DOI 10.1109/TMI.2012.2216281

    View details for Web of Science ID 000313690600010

    View details for PubMedID 22955890

    View details for PubMedCentralID PMC5507673

  • GLISTR: Glioma Image Segmentation and Registration IEEE TRANSACTIONS ON MEDICAL IMAGING Gooya, A., Pohl, K. M., Bilello, M., Cirillo, L., Biros, G., Melhem, E. R., Davatzikos, C. 2012; 31 (10): 1941–54

    Abstract

    We present a generative approach for simultaneously registering a probabilistic atlas of a healthy population to brain magnetic resonance (MR) scans showing glioma and segmenting the scans into tumor as well as healthy tissue labels. The proposed method is based on the expectation maximization (EM) algorithm that incorporates a glioma growth model for atlas seeding, a process which modifies the original atlas into one with tumor and edema adapted to best match a given set of patient's images. The modified atlas is registered into the patient space and utilized for estimating the posterior probabilities of various tissue labels. EM iteratively refines the estimates of the posterior probabilities of tissue labels, the deformation field and the tumor growth model parameters. Hence, in addition to segmentation, the proposed method results in atlas registration and a low-dimensional description of the patient scans through estimation of tumor model parameters. We validate the method by automatically segmenting 10 MR scans and comparing the results to those produced by clinical experts and two state-of-the-art methods. The resulting segmentations of tumor and edema outperform the results of the reference methods, and achieve a similar accuracy from a second human rater. We additionally apply the method to 122 patients scans and report the estimated tumor model parameters and their relations with segmentation and registration results. Based on the results from this patient population, we construct a statistical atlas of the glioma by inverting the estimated deformation fields to warp the tumor segmentations of patients scans into a common space.

    View details for DOI 10.1109/TMI.2012.2210558

    View details for Web of Science ID 000310149700010

    View details for PubMedID 22907965

    View details for PubMedCentralID PMC4371551

  • Temporal Shape Analysis via the Spectral Signature Bernardis, E., Konukoglu, E., Ou, Y., Metaxas, D. N., Desjardins, B., Pohl, K. M., Ayache, N., Delingette, H., Golland, P., Mori, K. SPRINGER-VERLAG BERLIN. 2012: 49–56

    Abstract

    In this paper, we adapt spectral signatures for capturing morphological changes over time. Advanced techniques for capturing temporal shape changes frequently rely on first registering the sequence of shapes and then analyzing the corresponding set of high dimensional deformation maps. Instead, we propose a simple encoding motivated by the observation that small shape deformations lead to minor refinements in the spectral signature composed of the eigenvalues of the Laplace operator. The proposed encoding does not require registration, since spectral signatures are invariant to pose changes. We apply our representation to the shapes of the ventricles extracted from 22 cine MR scans of healthy controls and Tetralogy of Fallot patients. We then measure the accuracy score of our encoding by training a linear classifier, which outperforms the same classifier based on volumetric measurements.

    View details for Web of Science ID 000371316700007

    View details for PubMedID 23286031

  • Regional Manifold Learning for Deformable Registration of Brain MR Images Ye, D., Hamm, J., Kwon, D., Davatzikos, C., Pohl, K. M., Ayache, N., Delingette, H., Golland, P., Mori, K. SPRINGER-VERLAG BERLIN. 2012: 131–38

    Abstract

    We propose a method for deformable registration based on learning the manifolds of individual brain regions. Recent publications on registration of medical images advocate the use of manifold learning in order to confine the search space to anatomically plausible deformations. Existing methods construct manifolds based on a single metric over the entire image domain thus frequently miss regional brain variations. We address this issue by first learning manifolds for specific regions and then computing region-specific deformations from these manifolds. We then determine deformations for the entire image domain by learning the global manifold in such a way that it preserves the region-specific deformations. We evaluate the accuracy of our method by applying it to the LPBA40 dataset and measuring the overlap of the deformed segmentations. The result shows significant improvement in registration accuracy on cortex regions compared to other state of the art methods.

    View details for Web of Science ID 000371317400017

    View details for PubMedID 23286123

    View details for PubMedCentralID PMC5459478

  • Validation of DRAMMS among 12 Popular Methods in Cross-Subject Cardiac MRI Registration. Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- ) Ou, Y., Ye, D. H., Pohl, K. M., Davatzikos, C. 2012; 7359: 209–19

    Abstract

    Cross-subject image registration is the building block for many cardiac studies. In the literature, it is often handled by voxel-wise registration methods. However, studies are lacking to show which methods are more accurate and stable in this context. Aiming at answering this question, this paper evaluates 12 popular registration methods and validates a recently developed method DRAMMS [16] in the context of cross-subject cardiac registration. Our dataset consists of short-axis end-diastole cardiac MR images from 24 subjects, in which non-cardiac structures are removed. Each registration method was applied to all 552 image pairs. Registration accuracy is approximated by Jaccard overlap between deformed expert annotation of source image and the corresponding expert annotation of target image. This accuracy surrogate is further correlated with deformation aggressiveness, which is reflected by minimum, maximum and range of Jacobian determinants. Our study shows that DRAMMS [16] scores high in accuracy and well balances accuracy and aggressiveness in this dataset, followed by ANTs [13], MI-FFD [14], Demons [15], and ART [12]. Our findings in cross-subject cardiac registrations echo those findings in brain image registrations [7].

    View details for DOI 10.1007/978-3-642-31340-0_22

    View details for PubMedID 28603787

    View details for PubMedCentralID PMC5462118

  • COMBINING REGIONAL METRICS FOR DISEASE-RELATED BRAIN POPULATION ANALYSIS Ye, D., Hamm, J., Pohl, K. M., IEEE IEEE. 2012: 1515–18

    Abstract

    In this paper, we present a new metric combining regional measurements to improve image based population studies that use manifold learning techniques. These studies currently rely on a single score over the whole brain image domain. Thus, they require large amount of training data to uncover spatially complex variation in the whole brain impacted by diseases. We reduce the impact of this issue by first computing pairwise measurements in local regions separately and then combining regional measurements into a single pairwise metric. We apply the new metric to learn the manifold of ADNI data and evaluate the resulting morphological representation by fitting multiple linear regression models to the mini-mental state examination (MMSE) score. The regression models show that the morphological representations from the proposed metric achieves higher estimation accuracy of MMSE score compared to those from the conventional global scores.

    View details for Web of Science ID 000312384100392

    View details for PubMedID 28593031

    View details for PubMedCentralID PMC5459375

  • Automatic Segmentation of Breast Carcinomas from DCE-MRI using a Statistical Learning Algorithm Jayender, J., Vosburgh, K. G., Gombos, E., Ashraf, A., Kontos, D., Gavenonis, S. C., Jolesz, F. A., Pohl, K., IEEE IEEE. 2012: 122–25

    Abstract

    Segmenting regions of high angiogenic activity corresponding to malignant tumors from DCE-MRI is a time-consuming task requiring processing of data in 4 dimensions. Quantitative analyses developed thus far are highly sensitive to external factors and are valid only under certain operating assumptions, which need not be valid for breast carcinomas. In this paper, we have developed a novel Statistical Learning Algorithm for Tumor Segmentation (SLATS) for automatically segmenting cancer from a region selected by the user on DCE-MRI. In this preliminary study, SLATS appears to demonstrate high accuracy (78%) and sensitivity (100%) in segmenting cancers from DCE-MRI when compared to segmentations performed by an expert radiologist. This may be a useful tool for delineating tumors for image-guided interventions.

    View details for Web of Science ID 000312384100031

    View details for PubMedID 28603582

    View details for PubMedCentralID PMC5464330

  • SEGMENTATION OF MYOCARDIUM USING DEFORMABLE REGIONS AND GRAPH CUTS Uzunbas, M., Zhang, S., Pohl, K. M., Metaxas, D., Axel, L., IEEE IEEE. 2012: 254–57

    Abstract

    Deformable models and graph cuts are two standard image segmentation techniques. Combining some of their benefits, we introduce a new segmentation system for (semi-) automatic delineation of epicardium and endocardium of Left Ventricle of the heart in Magnetic Resonance Images (MRI). Specifically, a temporal information among consecutive phases is exploited via a coupling between deformable models and graph cuts which provides automated accurate cues for graph cuts and also good initialization scheme for deformable model that ultimately leads to more accurate and smooth segmentation results with lower interaction costs than using only graph cut segmentation. In addition, we define deformable model as a region defined by two nested contours and segment epicardium and endocardium in an unified way by optimizing single energy functional. This approach provides inherent coherency among the two contours thus leads to more accurate results than deforming separate contours for each target. We show promising results on the challenging problems of left ventricle segmentation.

    View details for Web of Science ID 000312384100064

    View details for PubMedID 28603583

    View details for PubMedCentralID PMC5463182

  • NONRIGID VOLUME REGISTRATION USING SECOND-ORDER MRF MODEL Kwon, D., Yun, I., Pohl, K. M., Davatzikos, C., Lee, S., IEEE IEEE. 2012: 708–11

    Abstract

    In this paper, we introduce a nonrigid registration method using a Markov Random Field (MRF) energy model with second-order smoothness priors. The registration determines an optimal labeling of the MRF energy model where the label corresponds to a 3D displacement vector. In the MRF energy model, spatial relationships are constructed between nodes using second-order smoothness priors. This model improves limitations of first-order spatial priors which cannot fully incorporate the natural smoothness of deformations. Specifically, the second-order smoothness priors can generate desired smoother displacement vector fields and do not suffer from fronto-parallel effects commonly occurred in first-order priors. The usage of second-order priors in the energy model enables this method to produce more accurate registration results. In the experiments, we will show comparative results using uni- and multi-modal Brain MRI volumes.

    View details for Web of Science ID 000312384100178

    View details for PubMedID 28626513

    View details for PubMedCentralID PMC5470541