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Taylor WD, Ajilore O, Karim HT, Butters MA, Krafty R, Boyd BD, Banihashemi L, Szymkowicz SM, Ryan C, Hassenstab J, Landman BA, Andreescu C. Assessing depression recurrence, cognitive burden, and neurobiological homeostasis in late life: Design and rationale of the REMBRANDT Study. J Mood Anxiety Disord 2024; 5:100038. [PMID: 38523701 PMCID: PMC10959248 DOI: 10.1016/j.xjmad.2023.100038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
Background Late-life depression is characterized by disability, cognitive impairment and decline, and a high risk of recurrence following remission. Aside from past psychiatric history, prognostic neurobiological and clinical factors influencing recurrence risk are unclear. Moreover, it is unclear if cognitive impairment predisposes to recurrence, or whether recurrent episodes may accelerate brain aging and cognitive decline. The purpose of the REMBRANDT study (Recurrence markers, cognitive burden, and neurobiological homeostasis in late-life depression) is to better elucidate these relationships and identify phenotypic, cognitive, environmental, and neurobiological factors contributing to and predictive of depression recurrence. Methods Across three sites, REMBRANDT will enroll 300 depressed elders who will receive antidepressant treatment. The goal is to enroll 210 remitted depressed participants and 75 participants with no mental health history into a two-year longitudinal phase focusing on depression recurrence. Participants are evaluated every 2 months with deeper assessments occurring every 8 months, including structural and functional neuroimaging, environmental stress assessments, deep symptom phenotyping, and two weeks of 'burst' ecological momentary assessments to elucidate variability in symptoms and cognitive performance. A broad neuropsychological test battery is completed at the beginning and end of the longitudinal study. Significance REMBRANDT will improve our understanding of how alterations in neural circuits and cognition that persist during remission contribute to depression recurrence vulnerability. It will also elucidate how these processes may contribute to cognitive impairment and decline. This project will obtain deep phenotypic data that will help identify vulnerability and resilience factors that can help stratify individual clinical risk.
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Affiliation(s)
- Warren D. Taylor
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, TN
- Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois-Chicago, Chicago, IL
| | - Helmet T. Karim
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, PA
| | - Meryl A. Butters
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, PA
| | - Robert Krafty
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA
| | - Brian D. Boyd
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, TN
| | - Layla Banihashemi
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, PA
| | - Sarah M. Szymkowicz
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, TN
| | - Claire Ryan
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, TN
| | - Jason Hassenstab
- Departments of Neurology and Psychiatry, Washington University in St. Louis, St. Louis, MO
| | - Bennett A. Landman
- Departments of Computer Science, Electrical Engineering, and Biomedical Engineering, Vanderbilt University; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center
| | - Carmen Andreescu
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, PA
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Gao C, Kim ME, Lee HH, Yang Q, Khairi NM, Kanakaraj P, Newlin NR, Archer DB, Jefferson AL, Taylor WD, Boyd BD, Beason-Held LL, Resnick SM, Huo Y, Van Schaik KD, Schilling KG, Moyer D, Išgum I, Landman BA. Predicting Age from White Matter Diffusivity with Residual Learning. ArXiv 2024:arXiv:2311.03500v2. [PMID: 37986731 PMCID: PMC10659451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.
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Affiliation(s)
- Chenyu Gao
- Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
| | - Michael E Kim
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Ho Hin Lee
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Qi Yang
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Nazirah Mohd Khairi
- Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
| | | | - Nancy R Newlin
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Derek B Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, USA
| | - Angela L Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Medicine, Vanderbilt University Medical Center, Nashville, USA
| | - Warren D Taylor
- Dept. of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, USA
| | - Brian D Boyd
- Vanderbilt Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, USA
| | - Yuankai Huo
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Katherine D Van Schaik
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
| | - Kurt G Schilling
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
| | - Daniel Moyer
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Ivana Išgum
- Dept. of Biomedical Engineering and Physics, Dept. of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Bennett A Landman
- Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
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Ahmed R, Boyd BD, Elson D, Albert K, Begnoche P, Kang H, Landman BA, Szymkowicz SM, Andrews P, Vega J, Taylor WD. Influences of resting-state intrinsic functional brain connectivity on the antidepressant treatment response in late-life depression. Psychol Med 2023; 53:6261-6270. [PMID: 36482694 PMCID: PMC10250562 DOI: 10.1017/s0033291722003579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 09/04/2022] [Accepted: 10/24/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Late-life depression (LLD) is characterized by differences in resting state functional connectivity within and between intrinsic functional networks. This study examined whether clinical improvement to antidepressant medications is associated with pre-randomization functional connectivity in intrinsic brain networks. METHODS Participants were 95 elders aged 60 years or older with major depressive disorder. After clinical assessments and baseline MRI, participants were randomized to escitalopram or placebo with a two-to-one allocation for 8 weeks. Non-remitting participants subsequently entered an 8-week trial of open-label bupropion. The main clinical outcome was depression severity measured by MADRS. Resting state functional connectivity was measured between a priori key seeds in the default mode (DMN), cognitive control, and limbic networks. RESULTS In primary analyses of blinded data, lower post-treatment MADRS score was associated with higher resting connectivity between: (a) posterior cingulate cortex (PCC) and left medial prefrontal cortex; (b) PCC and subgenual anterior cingulate cortex (ACC); (c) right medial PFC and subgenual ACC; (d) right orbitofrontal cortex and left hippocampus. Lower post-treatment MADRS was further associated with lower connectivity between: (e) the right orbitofrontal cortex and left amygdala; and (f) left dorsolateral PFC and left dorsal ACC. Secondary analyses associated mood improvement on escitalopram with anterior DMN hub connectivity. Exploratory analyses of the bupropion open-label trial associated improvement with subgenual ACC, frontal, and amygdala connectivity. CONCLUSIONS Response to antidepressants in LLD is related to connectivity in the DMN, cognitive control and limbic networks. Future work should focus on clinical markers of network connectivity informing prognosis. REGISTRATION ClinicalTrials.gov NCT02332291.
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Affiliation(s)
- Ryan Ahmed
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
| | - Brian D. Boyd
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
| | - Damian Elson
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
| | - Kimberly Albert
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
| | - Patrick Begnoche
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A. Landman
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Sarah M. Szymkowicz
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
| | - Patricia Andrews
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
| | - Jennifer Vega
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
| | - Warren D. Taylor
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, The Vanderbilt Center for Cognitive Medicine, Nashville, TN, USA
- Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Health System, Nashville, TN, USA
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Bao S, Boyd BD, Kanakaraj P, Ramadass K, Meyer FAC, Liu Y, Duett WE, Huo Y, Lyu I, Zald DH, Smith SA, Rogers BP, Landman BA. Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for Large-Scale Medical Image Analysis. J Digit Imaging 2022; 35:1576-1589. [PMID: 35922700 PMCID: PMC9712842 DOI: 10.1007/s10278-022-00679-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 06/21/2022] [Accepted: 07/08/2022] [Indexed: 01/01/2023] Open
Abstract
A robust medical image computing infrastructure must host massive multimodal archives, perform extensive analysis pipelines, and execute scalable job management. An emerging data format standard, the Brain Imaging Data Structure (BIDS), introduces complexities for interfacing with XNAT archives. Moreover, workflow integration is combinatorically problematic when matching large amount of processing to large datasets. Historically, workflow engines have been focused on refining workflows themselves instead of actual job generation. However, such an approach is incompatible with data centric architecture that hosts heterogeneous medical image computing. Distributed automation for XNAT toolkit (DAX) provides large-scale image storage and analysis pipelines with an optimized job management tool. Herein, we describe developments for DAX that allows for integration of XNAT and BIDS standards. We also improve DAX's efficiencies of diverse containerized workflows in a high-performance computing (HPC) environment. Briefly, we integrate YAML configuration processor scripts to abstract workflow data inputs, data outputs, commands, and job attributes. Finally, we propose an online database-driven mechanism for DAX to efficiently identify the most recent updated sessions, thereby improving job building efficiency on large projects. We refer the proposed overall DAX development in this work as DAX-1 (DAX version 1). To validate the effectiveness of the new features, we verified (1) the efficiency of converting XNAT data to BIDS format and the correctness of the conversion using a collection of BIDS standard containerized neuroimaging workflows, (2) how YAML-based processor simplified configuration setup via a sequence of application pipelines, and (3) the productivity of DAX-1 on generating actual HPC processing jobs compared with earlier DAX baseline method. The empirical results show that (1) DAX-1 converting XNAT data to BIDS has similar speed as accessing XNAT data only; (2) YAML can integrate to the DAX-1 with shallow learning curve for users, and (3) DAX-1 reduced the job/assessor generation latency by finding recent modified sessions. Herein, we present approaches for efficiently integrating XNAT and modern image formats with a scalable workflow engine for the large-scale dataset access and processing.
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Affiliation(s)
- Shunxing Bao
- Computer Science, Vanderbilt University, Nashville, TN USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN USA
| | - Brian D. Boyd
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | | | | | | | - Yuqian Liu
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
| | - William E. Duett
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
| | - Yuankai Huo
- Computer Science, Vanderbilt University, Nashville, TN USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN USA
- Data Science Institute, Vanderbilt University, Nashville, TN USA
| | - Ilwoo Lyu
- Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - David H. Zald
- Department of Psychology, Vanderbilt University, Nashville, TN USA
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ USA
| | - Seth A. Smith
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN USA
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | - Baxter P. Rogers
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN USA
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | - Bennett A. Landman
- Computer Science, Vanderbilt University, Nashville, TN USA
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN USA
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5
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Ahmed R, Ryan C, Christman S, Elson D, Bermudez C, Landman BA, Szymkowicz SM, Boyd BD, Kang H, Taylor WD. Structural MRI-Based Measures of Accelerated Brain Aging do not Moderate the Acute Antidepressant Response in Late-Life Depression. Am J Geriatr Psychiatry 2022; 30:1015-1025. [PMID: 34949526 PMCID: PMC9142760 DOI: 10.1016/j.jagp.2021.11.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/14/2021] [Accepted: 11/21/2021] [Indexed: 01/22/2023]
Abstract
OBJECTIVE Late-life depression (LLD) is characterized by accelerated biological aging. Accelerated brain aging, estimated from structural magnetic resonance imaging (sMRI) data by a machine learning algorithm, is associated with LLD diagnosis, poorer cognitive performance, and disability. We hypothesized that accelerated brain aging moderates the antidepressant response. DESIGN AND INTERVENTIONS Following MRI, participants entered an 8-week randomized, controlled trial of escitalopram. Nonremitting participants then entered an open-label 8-week trial of bupropion. PARTICIPANTS Ninety-five individuals with LLD. MEASUREMENTS A machine learning algorithm estimated each participant's brain age from sMRI data. This was used to calculate the brain-age gap (BAG), or how estimated age differed from chronological age. Secondary sMRI measures of aging pathology included white matter hyperintensity (WMH) volumes and hippocampal volumes. Mixed models examined the relationship between sMRI measures and change in depression severity. Initial analyses tested for a moderating effect of MRI measures on change in depression severity with escitalopram. Subsequent analyses tested for the effect of MRI measures on change in depression severity over time across trials. RESULTS In the blinded initial phase, BAG was not significantly associated with a differential response to escitalopram over time. BAG was also not associated with a change in depression severity over time across both arms in the blinded phase or in the subsequent open-label bupropion phase. We similarly did not observe effects of WMH volume or hippocampal volume on change in depression severity over time. CONCLUSION sMRI markers of accelerated brain aging were not associated with treatment response in this sequential antidepressant trial.
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Affiliation(s)
- Ryan Ahmed
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Claire Ryan
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Seth Christman
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Damian Elson
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Camilo Bermudez
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Bennett A Landman
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Sarah M Szymkowicz
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Brian D Boyd
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Hakmook Kang
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Warren D Taylor
- School of Medicine (RA), Vanderbilt University, Nashville, TN; Department of Psychiatry and Behavioral Sciences (SC, DE, BAL, SMS, BDB, WDT), Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Engineering (CB, BAL), Vanderbilt University, Nashville TN; Department of Electrical Engineering and Computer Science (BAL), Vanderbilt University, Nashville, TN; Department of Biostatistics (HK), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT), Veterans Affairs Tennessee Valley Health System, Nashville, TN.
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6
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Begnoche JP, Schilling KG, Boyd BD, Cai LY, Taylor WD, Landman BA. EPI susceptibility correction introduces significant differences far from local areas of high distortion. Magn Reson Imaging 2022; 92:1-9. [DOI: 10.1016/j.mri.2022.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 05/01/2022] [Accepted: 05/22/2022] [Indexed: 11/16/2022]
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7
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Albert KM, Boyd BD, Taylor WD, Newhouse PA. Differential effects of estradiol on neural and emotional stress response in postmenopausal women with remitted Major Depressive Disorder. J Affect Disord 2021; 293:355-362. [PMID: 34233228 PMCID: PMC8349860 DOI: 10.1016/j.jad.2021.06.040] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 05/30/2021] [Accepted: 06/20/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND Estrogen fluctuations throughout the lifespan may contribute to major depressive disorder (MDD) risk in women through effects on brain networks important in stress responding, and mood regulation. Although there is evidence to support ovarian hormone treatment for peri-menopausal depression, postmenopausal use has not been well examined. The objective of this study was to investigate whether estrogen modulation of the neural and emotional cognitive responses to stress differs between postmenopausal women with and without MDD history. METHODS 60 postmenopausal women completed an fMRI psychosocial stress task, after receiving no drug or 3 months of daily estradiol (E2). fMRI activity and subjective mood response were examined. RESULTS In women without a history of MDD, E2 was associated with a more negative mood response to stress and less activity in emotional regulation regions. In women with a history of MDD, E2 was associated with a less negative mood response to stress and less activity in emotion perception regions. LIMITATIONS This study was limited by open-label estradiol administration and inclusion of participants using antidepressants. CONCLUSIONS These results support a differential effect of estrogen on emotional and neural responses to psychosocial stress in postmenopausal women with MDD history and may reflect a shift in brain activity patterns related to emotion processing following menopause.
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Affiliation(s)
- Kimberly M. Albert
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Brian D. Boyd
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Warren D. Taylor
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States,Geriatric Research, Education, and Clinical Center, Tennessee Valley VA Health System, Nashville TN, United States
| | - Paul A. Newhouse
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States,Geriatric Research, Education, and Clinical Center, Tennessee Valley VA Health System, Nashville TN, United States
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Zhou M, Boyd BD, Taylor WD, Kang H. Double-wavelet transform for multi-subject resting state functional magnetic resonance imaging data. Stat Med 2021; 40:6762-6776. [PMID: 34596260 DOI: 10.1002/sim.9209] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 08/18/2021] [Accepted: 09/12/2021] [Indexed: 11/11/2022]
Abstract
Conventional regions of interest (ROIs)-level resting state fMRI (functional magnetic resonance imaging) response analyses do not rigorously model the underlying spatial correlation within each ROI. This can result in misleading inference. Moreover, they tend to estimate the temporal covariance matrix with the assumption of stationary time series, which may not always be valid. To overcome these limitations, we propose a double-wavelet approach that simplifies temporal and spatial covariance structure because wavelet coefficients are approximately uncorrelated under mild regularity conditions. This property allows us to analyze much larger dimensions of spatial and temporal resting-state fMRI data with reasonable computational burden. Another advantage of our double-wavelet approach is that it does not require the stationarity assumption. Simulation studies show that our method reduced false positive and false negative rates by properly taking into account spatial and temporal correlations in data. We also demonstrate advantages of our method by using resting-state fMRI data to study the difference in resting-state functional connectivity between healthy subjects and patients with major depressive disorder.
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Affiliation(s)
- Minchun Zhou
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Brian D Boyd
- The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Warren D Taylor
- The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee.,The Geriatric Research, Education, and Clinical Center (GRECC), Department of Veterans Affairs Medical Center, Tennessee Valley Healthcare System, Nashville, Tennessee
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee.,Center for Quantitative Science, Vanderbilt University Medical Center, Nashville, Tennessee
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Cai LY, Yang Q, Hansen CB, Nath V, Ramadass K, Johnson GW, Conrad BN, Boyd BD, Begnoche JP, Beason-Held LL, Shafer AT, Resnick SM, Taylor WD, Price GR, Morgan VL, Rogers BP, Schilling KG, Landman BA. PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images. Magn Reson Med 2021; 86:456-470. [PMID: 33533094 PMCID: PMC8387107 DOI: 10.1002/mrm.28678] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 12/19/2020] [Accepted: 12/22/2020] [Indexed: 12/31/2022]
Abstract
PURPOSE Diffusion weighted MRI imaging (DWI) is often subject to low signal-to-noise ratios (SNRs) and artifacts. Recent work has produced software tools that can correct individual problems, but these tools have not been combined with each other and with quality assurance (QA). A single integrated pipeline is proposed to perform DWI preprocessing with a spectrum of tools and produce an intuitive QA document. METHODS The proposed pipeline, built around the FSL, MRTrix3, and ANTs software packages, performs DWI denoising; inter-scan intensity normalization; susceptibility-, eddy current-, and motion-induced artifact correction; and slice-wise signal drop-out imputation. To perform QA on the raw and preprocessed data and each preprocessing operation, the pipeline documents qualitative visualizations, quantitative plots, gradient verifications, and tensor goodness-of-fit and fractional anisotropy analyses. RESULTS Raw DWI data were preprocessed and quality checked with the proposed pipeline and demonstrated improved SNRs; physiologic intensity ratios; corrected susceptibility-, eddy current-, and motion-induced artifacts; imputed signal-lost slices; and improved tensor fits. The pipeline identified incorrect gradient configurations and file-type conversion errors and was shown to be effective on externally available datasets. CONCLUSIONS The proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI-focused software packages with intuitive QA.
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Affiliation(s)
- Leon Y. Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Qi Yang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Colin B. Hansen
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Vishwesh Nath
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Graham W. Johnson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Benjamin N. Conrad
- Neuroscience Graduate Program, Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, TN, USA
| | - Brian D. Boyd
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John P. Begnoche
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lori L. Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Andrea T. Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Warren D. Taylor
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Gavin R. Price
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, TN, USA
| | - Victoria L. Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Baxter P. Rogers
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Kurt G. Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Bennett A. Landman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
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10
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Taylor WD, Boyd BD, Elson D, Andrews P, Albert K, Vega J, Newhouse PA, Woodward ND, Kang H, Shokouhi S. Preliminary Evidence That Cortical Amyloid Burden Predicts Poor Response to Antidepressant Medication Treatment in Cognitively Intact Individuals With Late-Life Depression. Am J Geriatr Psychiatry 2021; 29:448-457. [PMID: 33032927 PMCID: PMC8004530 DOI: 10.1016/j.jagp.2020.09.019] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 09/21/2020] [Accepted: 09/24/2020] [Indexed: 01/01/2023]
Abstract
OBJECTIVE Amyloid accumulation, the pathological hallmark of Alzheimer's disease, may predispose some older adults to depression and cognitive decline. Deposition of amyloid also occurs prior to the development of cognitive decline. It is unclear whether amyloid influences antidepressant outcomes in cognitively intact depressed elders. DESIGN A pharmacoimaging trial utilizing florbetapir (18F) PET scanning followed by 2 sequential 8-week antidepressant medication trials. PARTICIPANTS Twenty-seven depressed elders who were cognitively intact on screening. MEASUREMENTS AND INTERVENTIONS After screening, diagnostic testing, assessment of depression severity and neuropsychological assessment, participants completed florbetapir (18F) PET scanning. They were then randomized to receive escitalopram or placebo for 8 weeks in a double-blinded two-to-one allocation rate. Individuals who did not respond to initial treatment transitioned to a second open-label trial of bupropion for another 8 weeks. RESULTS Compared with 22 amyloid-negative participants, 5 amyloid-positive participants exhibited significantly less change in depression severity and a lower likelihood of remission. In the initial blinded trial, 4 of 5 amyloid-positive participants were nonremitters (80%), while only 18% (4 of 22) of amyloid-negative participants did not remit (p = 0.017; Fisher's Exact test). In separate models adjusting for key covariates, both positive amyloid status (t = 3.07, 21 df, p = 0.003) and higher cortical amyloid binding by standard uptake value ratio (t = 2.62, 21 df, p = 0.010) were associated with less improvement in depression severity. Similar findings were observed when examining change in depression status across both antidepressant trials. CONCLUSIONS In this preliminary study, amyloid status predicted poor antidepressant response to sequential antidepressant treatment. Alternative treatment approaches may be needed for amyloid-positive depressed elders.
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Affiliation(s)
- Warren D Taylor
- The Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences (WDT, BDB, PA, KA, JV, PAN, NDW, HK, SS), Vanderbilt University Medical Center, Nashville, TN; Geriatric Research, Education, and Clinical Center (WDT, PAN), Veterans Affairs Tennessee Valley Health System, Nashville, TN.
| | - Brian D Boyd
- The Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Damian Elson
- The Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Patricia Andrews
- The Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Kimberly Albert
- The Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Jennifer Vega
- The Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Paul A Newhouse
- The Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN,Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Health System, Nashville, TN
| | - Neil D. Woodward
- The Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Sepideh Shokouhi
- The Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
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11
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Conley AC, Key AP, Taylor WD, Albert KM, Boyd BD, Vega JN, Newhouse PA. EEG as a Functional Marker of Nicotine Activity: Evidence From a Pilot Study of Adults With Late-Life Depression. Front Psychiatry 2021; 12:721874. [PMID: 35002791 PMCID: PMC8732868 DOI: 10.3389/fpsyt.2021.721874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 11/15/2021] [Indexed: 11/13/2022] Open
Abstract
Late-life depression (LLD) is a debilitating condition that is associated with poor response to antidepressant medications and deficits in cognitive performance. Nicotinic cholinergic stimulation has emerged as a potentially effective candidate to improve cognitive performance in patients with cognitive impairment. Previous studies of nicotinic stimulation in animal models and human populations with cognitive impairment led to examining potential cognitive and mood effects of nicotinic stimulation in older adults with LLD. We report results from a pilot study of transdermal nicotine in LLD testing whether nicotine treatment would enhance cognitive performance and mood. The study used electroencephalography (EEG) recordings as a tool to test for potential mechanisms underlying the effect of nicotine. Eight non-smoking participants with LLD completed EEG recordings at baseline and after 12 weeks of transdermal nicotine treatment (NCT02816138). Nicotine augmentation treatment was associated with improved performance on an auditory oddball task. Analysis of event-related oscillations showed that nicotine treatment was associated with reduced beta desynchronization at week 12 for both standard and target trials. The change in beta power on standard trials was also correlated with improvement in mood symptoms. This pilot study provides preliminary evidence for the impact of nicotine in modulating cortical activity and improving mood in depressed older adults and shows the utility of using EEG as a marker of functional engagement in nicotinic interventions in clinical geriatric patients.
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Affiliation(s)
- Alexander C Conley
- Department of Psychiatry, Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Alexandra P Key
- Department of Psychiatry, Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.,Vanderbilt Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Warren D Taylor
- Department of Psychiatry, Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.,Department of Veterans Affairs Medical Center, Geriatric Research, Education and Clinical Center, Tennessee Valley Healthcare System, Nashville, TN, United States
| | - Kimberly M Albert
- Department of Psychiatry, Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Brian D Boyd
- Department of Psychiatry, Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jennifer N Vega
- Department of Psychiatry, Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Paul A Newhouse
- Department of Psychiatry, Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.,Department of Veterans Affairs Medical Center, Geriatric Research, Education and Clinical Center, Tennessee Valley Healthcare System, Nashville, TN, United States
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12
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Taylor WD, Deng Y, Boyd BD, Donahue MJ, Albert K, McHugo M, Gandelman JA, Landman BA. Medial temporal lobe volumes in late-life depression: effects of age and vascular risk factors. Brain Imaging Behav 2020; 14:19-29. [PMID: 30251182 DOI: 10.1007/s11682-018-9969-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Substantial work associates late-life depression with hippocampal pathology. However, there is less information about differences in hippocampal subfields and other connected temporal lobe regions and how these regions may be influenced by vascular factors. Individuals aged 60 years or older with and without a DSM-IV diagnosis of Major Depressive Disorder completed clinical assessments and 3 T cranial MRI using a protocol allowing for automated measurement of medial temporal lobe subfield volumes. A subset also completed pseudo-continuous arterial spin labeling, allowing for the measurement of hippocampal cerebral blood flow. In 59 depressed and 21 never-depressed elders (mean age = 66.4 years, SD = 5.8y, range 60-86y), the depressed group did not exhibit statistically significant volumetric differences for the total hippocampus or hippocampal subfields but did exhibit significantly smaller volumes of the perirhinal cortex, specifically in the BA36 region. Additionally, age had a greater effect in the depressed group on volumes of the cornu ammonis, entorhinal cortex, and BA36 region. Finally, both clinical and radiological markers of vascular risk were associated with smaller BA36 volumes, while reduced hippocampal blood flow was associated with smaller hippocampal and cornu ammonis volumes. In conclusion, while we did not observe group differences in hippocampal regions, we observed group differences and an effect of vascular pathology on the BA36 region, part of the perirhinal cortex. This is a critical region exhibiting atrophy in prodromal Alzheimer's disease. Moreover, the observed greater effect of age in the depressed groups is concordant with past longitudinal studies reporting greater hippocampal atrophy in late-life depression.
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Affiliation(s)
- Warren D Taylor
- The Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, 1601 23rd Avenue South, Nashville, TN, 37212, USA. .,Geriatric Research, Education and Clinical Center, Department of Veterans Affairs Medical Center, Tennessee Valley Healthcare System, Nashville, TN, 37212, USA.
| | - Yi Deng
- The Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, 1601 23rd Avenue South, Nashville, TN, 37212, USA
| | - Brian D Boyd
- The Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, 1601 23rd Avenue South, Nashville, TN, 37212, USA
| | - Manus J Donahue
- The Department of Radiology and Radiological Science, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
| | - Kimberly Albert
- The Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, 1601 23rd Avenue South, Nashville, TN, 37212, USA
| | - Maureen McHugo
- The Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, 1601 23rd Avenue South, Nashville, TN, 37212, USA
| | | | - Bennett A Landman
- The Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, 1601 23rd Avenue South, Nashville, TN, 37212, USA.,The Department of Radiology and Radiological Science, Vanderbilt University Medical Center, Nashville, TN, 37212, USA.,The Department of Electrical Engineering, Vanderbilt University, Nashville, TN, 37212, USA
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13
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Conley AC, Albert KM, Boyd BD, Kim SG, Shokouhi S, McDonald BC, Saykin AJ, Dumas JA, Newhouse PA. Cognitive complaints are associated with smaller right medial temporal gray-matter volume in younger postmenopausal women. Menopause 2020; 27:1220-1227. [PMID: 33110037 PMCID: PMC9153070 DOI: 10.1097/gme.0000000000001613] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Menopause is associated with increasing cognitive complaints and older women are at increased risk of developing Alzheimer disease compared to men. However, there is difficulty in early markers of risk using objective performance measures. We investigated the impact of subjective cognitive complaints on the cortical structure in a sample of younger postmenopausal women. METHODS Data for this cross-sectional study were drawn from the baseline visit of a longer double-blind study examining estrogen-cholinergic interactions in normal postmenopausal women. Structural Magnetic Resonance Imaging was acquired on 44 women, aged 50-60 years and gray-matter volume was defined by voxel-based morphometry. Subjective measures of cognitive complaints and postmenopausal symptoms were obtained as well as tests of verbal episodic and working memory performance. RESULTS Increased levels of cognitive complaints were associated with lower gray-matter volume in the right medial temporal lobe (r = -0.445, P < 0.002, R = 0.2). Increased depressive symptoms and somatic complaints were also related to increased cognitive complaints and smaller medial temporal volumes but did not mediate the effect of cognitive complaints. In contrast, there was no association between performance on the memory tasks and subjective cognitive ratings, or medial temporal lobe volume. CONCLUSIONS The findings of the present study indicate that the level of reported cognitive complaints in postmenopausal women may be associated with reduced gray-matter volume which may be associated with cortical changes that may increase risk of future cognitive decline. : Video Summary:http://links.lww.com/MENO/A626.
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Affiliation(s)
- Alexander C. Conley
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN
| | - Kimberly M. Albert
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN
| | - Brian D. Boyd
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN
| | - Shin-Gyeom Kim
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN
- Department of Neuropsychiatry, Soonchunhyang University, Bucheon Hospital, Republic of Korea
| | - Sepideh Shokouhi
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN
| | - Brenna C. McDonald
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN
| | - Andrew J. Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN
| | - Julie A. Dumas
- Clinical Neuroscience Research Unit, Department of Psychiatry, University of Vermont College of Medicine, Burlington, VT
| | - Paul A. Newhouse
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN
- Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Health System, Nashville, TN
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14
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Wang R, Albert KM, Taylor WD, Boyd BD, Blaber J, Lyu I, Landman BA, Vega J, Shokouhi S, Kang H. A bayesian approach to examining default mode network functional connectivity and cognitive performance in major depressive disorder. Psychiatry Res Neuroimaging 2020; 301:111102. [PMID: 32447185 PMCID: PMC7369149 DOI: 10.1016/j.pscychresns.2020.111102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 04/23/2020] [Accepted: 04/24/2020] [Indexed: 10/24/2022]
Abstract
To reconcile the inconsistency of the association between the resting-state functional connectivity (RSFC) and cognitive performance in healthy and depressed groups due to high variance of both measures, we proposed a Bayesian spatio-temporal model to precisely and accurately estimate the RSFC in depressed and nondepressed participants. This model was employed to estimate spatially-adjusted functional connectivity (saFC) in the extended default mode network (DMN) that was hypothesized to correlate with cognitive performance in both depressed and nondepressed. Multiple linear regression models were used to study the relationship between DMN saFC and cognitive performance scores measured in the following four cognitive domains while adjusting for age, sex, and education. In ROI pairs including the posterior cingulate (PCC) and anterior cingulate (ACC) cortex regions, the relationship between connectivity and cognition was found only with the Bayesian approach. Moreover, only the Bayesian approach was able to detect a significant diagnostic difference in the association in ROI pairs, including both PCC and ACC regions, due to smaller variance for the saFC estimator. The results confirm that a reliable and precise saFC estimator, based on the Bayesian model, can foster scientific discovery that may not be feasible with the conventional ROI-based FC estimator (denoted as 'AVG-FC').
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Affiliation(s)
- Rui Wang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA
| | - Kimberly M Albert
- The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
| | - Warren D Taylor
- The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN, 37212, USA; Geriatric Research, Education and Clinical Center, Department of Veterans Affairs Medical Center, Tennessee Valley Healthcare System, Nashville, TN, 37212, USA
| | - Brian D Boyd
- The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
| | - Justin Blaber
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, 37212, USA
| | - Ilwoo Lyu
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, 37212, USA
| | - Bennett A Landman
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, 37212, USA; Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Jennifer Vega
- The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
| | - Sepideh Shokouhi
- The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA; Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
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15
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Vega JN, Taylor WD, Gandelman JA, Boyd BD, Newhouse PA, Shokouhi S, Albert KM. Persistent Intrinsic Functional Network Connectivity Alterations in Middle-Aged and Older Women With Remitted Depression. Front Psychiatry 2020; 11:62. [PMID: 32153440 PMCID: PMC7047962 DOI: 10.3389/fpsyt.2020.00062] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 01/24/2020] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND In younger adults, residual alterations in functional neural networks persist during remitted depression. However, there are fewer data for midlife and older adults at risk of recurrence. Such residual network alterations may contribute to vulnerability to recurrence. This study examined intrinsic network functional connectivity in midlife and older women with remitted depression. METHODS A total of 69 women (24 with a history of depression, 45 with no psychiatric history) over 50 years of age completed 3T fMRI with resting-state acquisition. Participants with remitted depression met DSM-IV-TR criteria for an episode in the last 10 years but not the prior year. Whole-brain seed-to-voxel resting-state functional connectivity analyses examined the default mode network (DMN), executive control network (ECN), and salience network (SN), plus bilateral hippocampal seeds. All analyses were adjusted for age and used cluster-level correction for multiple comparisons with FDR < 0.05 and a height threshold of p < 0.001, uncorrected. RESULTS Women with a history of depression exhibited decreased functional connectivity between the SN (right insula seed) and ECN regions, specifically the left superior frontal gyrus. They also exhibited increased functional connectivity between the left hippocampus and the left postcentral gyrus. We did not observe any group differences in functional connectivity for DMN or ECN seeds. CONCLUSIONS Remitted depression in women is associated with connectivity differences between the SN and ECN and between the hippocampus and the postcentral gyrus, a region involved in interoception. Further work is needed to determine whether these findings are related to functional alterations or are predictive of recurrence.
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Affiliation(s)
- Jennifer N Vega
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Warren D Taylor
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States.,Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Health System, Nashville, TN, United States
| | - Jason A Gandelman
- Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Brian D Boyd
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Paul A Newhouse
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States.,Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Health System, Nashville, TN, United States
| | - Sepideh Shokouhi
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Kimberly M Albert
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
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16
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Huo Y, Blaber J, Damon SM, Boyd BD, Bao S, Parvathaneni P, Noguera CB, Chaganti S, Nath V, Greer JM, Lyu I, French WR, Newton AT, Rogers BP, Landman BA. Towards Portable Large-Scale Image Processing with High-Performance Computing. J Digit Imaging 2019; 31:304-314. [PMID: 29725960 PMCID: PMC5959833 DOI: 10.1007/s10278-018-0080-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
High-throughput, large-scale medical image computing demands tight integration of high-performance computing (HPC) infrastructure for data storage, job distribution, and image processing. The Vanderbilt University Institute for Imaging Science (VUIIS) Center for Computational Imaging (CCI) has constructed a large-scale image storage and processing infrastructure that is composed of (1) a large-scale image database using the eXtensible Neuroimaging Archive Toolkit (XNAT), (2) a content-aware job scheduling platform using the Distributed Automation for XNAT pipeline automation tool (DAX), and (3) a wide variety of encapsulated image processing pipelines called “spiders.” The VUIIS CCI medical image data storage and processing infrastructure have housed and processed nearly half-million medical image volumes with Vanderbilt Advanced Computing Center for Research and Education (ACCRE), which is the HPC facility at the Vanderbilt University. The initial deployment was natively deployed (i.e., direct installations on a bare-metal server) within the ACCRE hardware and software environments, which lead to issues of portability and sustainability. First, it could be laborious to deploy the entire VUIIS CCI medical image data storage and processing infrastructure to another HPC center with varying hardware infrastructure, library availability, and software permission policies. Second, the spiders were not developed in an isolated manner, which has led to software dependency issues during system upgrades or remote software installation. To address such issues, herein, we describe recent innovations using containerization techniques with XNAT/DAX which are used to isolate the VUIIS CCI medical image data storage and processing infrastructure from the underlying hardware and software environments. The newly presented XNAT/DAX solution has the following new features: (1) multi-level portability from system level to the application level, (2) flexible and dynamic software development and expansion, and (3) scalable spider deployment compatible with HPC clusters and local workstations.
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Affiliation(s)
- Yuankai Huo
- Electrical Engineering, Vanderbilt University, 2201 West End Ave, Nashville, TN, 37235, USA.
| | - Justin Blaber
- Electrical Engineering, Vanderbilt University, 2201 West End Ave, Nashville, TN, 37235, USA.,Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Stephen M Damon
- Electrical Engineering, Vanderbilt University, 2201 West End Ave, Nashville, TN, 37235, USA.,Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Brian D Boyd
- Electrical Engineering, Vanderbilt University, 2201 West End Ave, Nashville, TN, 37235, USA
| | - Shunxing Bao
- Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Prasanna Parvathaneni
- Electrical Engineering, Vanderbilt University, 2201 West End Ave, Nashville, TN, 37235, USA
| | | | | | - Vishwesh Nath
- Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Jasmine M Greer
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Ilwoo Lyu
- Computer Science, Vanderbilt University, Nashville, TN, USA
| | - William R French
- Advanced Computing Center for Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Allen T Newton
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Baxter P Rogers
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.,Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.,Psychiatry, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A Landman
- Electrical Engineering, Vanderbilt University, 2201 West End Ave, Nashville, TN, 37235, USA.,Computer Science, Vanderbilt University, Nashville, TN, USA.,Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.,Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
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17
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Albert KM, Potter GG, Boyd BD, Kang H, Taylor WD. Brain network functional connectivity and cognitive performance in major depressive disorder. J Psychiatr Res 2019; 110:51-56. [PMID: 30594024 PMCID: PMC6360105 DOI: 10.1016/j.jpsychires.2018.11.020] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 10/23/2018] [Accepted: 11/21/2018] [Indexed: 11/26/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is one of the most prevalent and debilitating psychiatric disorders. Cognitive complaints are commonly reported in MDD and cognitive impairment is a criterion item for MDD diagnosis. As cognitive processes are increasingly understood as the consequences of distributed interactions between brain regions, a network-based approach may provide novel information about the neurobiological basis of cognitive deficits in MDD. METHODS 51 Depressed (MDD, n = 23) and non-depressed (control, n = 28) adult participants completed neuropsychological testing and resting-state fMRI (rsfMRI). Cognitive domain scores (processing speed, working memory, episodic memory, and executive function) were calculated. Anatomical regions of interests were entered as seeds for functional connectivity analyses in: default mode (DMN), salience, and executive control (ECN) networks. Partial correlations controlling for age and sex were conducted for cognitive domain scores and functional connectivity in clusters with significant differences between groups. RESULTS Significant rsfMRI differences between groups were identified in multiple clusters in the DMN and ECN. Greater positive connectivity within the ECN and between ECN and DMN regions was associated with poorer episodic memory performance in the Non-Depressed group but better performance in the MDD group. Greater connectivity within the DMN was associated with better episodic and working memory performance in the Non-Depressed group but worse performance in the MDD group. CONCLUSIONS These results provide evidence that cognitive performance in MDD may be associated with aberrant functional connectivity in cognitive networks and suggest patterns of alternate brain function that may support cognitive processes in MDD.
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Affiliation(s)
- Kimberly M. Albert
- 1. The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center
| | | | - Brian D. Boyd
- 1. The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center
| | - Hakmook Kang
- 3. Department of Biostatistics, Vanderbilt University Medical Center
| | - Warren D. Taylor
- 1. The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center,4. GRECC, VA, Tennessee Valley Healthcare System
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18
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Gandelman JA, Albert K, Boyd BD, Park JW, Riddle M, Woodward ND, Kang H, Landman BA, Taylor WD. Intrinsic Functional Network Connectivity Is Associated With Clinical Symptoms and Cognition in Late-Life Depression. Biol Psychiatry Cogn Neurosci Neuroimaging 2018; 4:160-170. [PMID: 30392844 DOI: 10.1016/j.bpsc.2018.09.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 08/13/2018] [Accepted: 09/01/2018] [Indexed: 01/15/2023]
Abstract
BACKGROUND Late-life depression (LLD) has been associated with alterations in intrinsic functional networks, best characterized in the default mode network (DMN), cognitive control network (CCN), and salience network. However, these findings often derive from small samples, and it is not well understood how network findings relate to clinical and cognitive symptomatology. METHODS We studied 100 older adults (n = 79 with LLD, n = 21 nondepressed) and collected resting-state functional magnetic resonance imaging, clinical measures of depression, and performance on cognitive tests. We selected canonical network regions for each intrinsic functional network (DMN, CCN, and salience network) as seeds in seed-to-voxel analysis. We compared connectivity between the depressed and nondepressed groups and correlated connectivity with depression severity among depressed subjects. We then investigated whether the observed connectivity findings were associated with greater severity of common neuropsychiatric symptoms or poorer cognitive performance. RESULTS LLD was characterized by decreased DMN connectivity to the frontal pole, a CCN region (Wald χ21 = 22.33, p < .001). No significant group differences in connectivity were found for the CCN or salience network. However, in the LLD group, increased CCN connectivity was associated with increased depression severity (Wald χ21 > 20.14, p < .001), greater anhedonia (Wald χ21 = 7.02, p = .008) and fatigue (Wald χ21 = 6.31, p = .012), and poorer performance on tests of episodic memory (Wald χ21 > 4.65, p < .031), executive function (Wald χ21 = 7.18, p = .007), and working memory (Wald χ21 > 4.29, p < .038). CONCLUSIONS LLD is characterized by differences in DMN connectivity, while CCN connectivity is associated with LLD symptomology, including poorer performance in several cognitive domains.
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Affiliation(s)
| | - Kimberly Albert
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Brian D Boyd
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jung Woo Park
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Meghan Riddle
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Neil D Woodward
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Bennett A Landman
- Department of Electrical Engineering, Vanderbilt University, Nashville, Tennessee
| | - Warren D Taylor
- Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee; Geriatric Research, Education and Clinical Center, Department of Veterans Affairs Medical Center, Tennessee Valley Healthcare System, Nashville, Tennessee.
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19
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Gandelman JA, Kang H, Antal A, Albert K, Boyd BD, Conley AC, Newhouse P, Taylor WD. Transdermal Nicotine for the Treatment of Mood and Cognitive Symptoms in Nonsmokers With Late-Life Depression. J Clin Psychiatry 2018; 79:18m12137. [PMID: 30192444 PMCID: PMC6129985 DOI: 10.4088/jcp.18m12137] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 02/27/2018] [Indexed: 01/12/2023]
Abstract
OBJECTIVE Late-life depression (LLD) is characterized by poor antidepressant response and cognitive dysfunction. This study examined whether transdermal nicotine benefits mood symptoms and cognitive performance in LLD. METHODS In a 12-week open-label outpatient study conducted between November 2016 and August 2017, transdermal nicotine was given to 15 nonsmoking older adults (≥ 60 years of age). Eligible participants met DSM-IV-TR criteria for major depressive disorder with ≥ 15 on the Montgomery-Asberg Depression Rating scale (MADRS) and endorsed subjective cognitive impairment. Transdermal nicotine patches were applied daily and titrated in a rigid dose escalation strategy to a maximum dose of 21.0 mg/d, allowing dose reductions for tolerability. The primary mood outcome was MADRS change measured every 3 weeks, with response defined as ≥ 50% improvement from baseline and remission as MADRS score ≤ 8. The primary cognitive outcome was the Conners Continuous Performance Test (CPT), a test of attention. RESULTS Robust rates of response (86.7%; 13/15 subjects) and remission (53.3%; 8/15 subjects) were observed. There was a significant decrease in MADRS scores over the study (β = -1.51, P < .001), with improvement seen as early as 3 weeks (Bonferroni-adjusted P value = .004). We also observed improvement in apathy and rumination. We did not observe improvement on the CPT but did observe improvement in subjective cognitive performance and signals of potential drug effects on secondary cognitive measures of working memory, episodic memory, and self-referential emotional processing. Overall, transdermal nicotine was well tolerated, although 6 participants could not reach the maximum targeted dose. CONCLUSIONS Nicotine may be a promising therapy for depressed mood and cognitive performance in LLD. A definitive placebo-controlled trial and establishment of longer-term safety are necessary before clinical usage. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT02816138.
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Affiliation(s)
| | - Hakmook Kang
- The Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Ashleigh Antal
- The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
| | - Kimberly Albert
- The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
| | - Brian D Boyd
- The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
| | - Alexander C Conley
- The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
| | - Paul Newhouse
- The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN, 37212, USA,Geriatric Research, Education and Clinical Center, Department of Veterans Affairs Medical Center, Tennessee Valley Healthcare System, Nashville, TN, 37212, USA
| | - Warren D Taylor
- Vanderbilt University, 1601 23rd Ave South, Nashville, TN 37212. .,Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Geriatric Research, Education and Clinical Center, Department of Veterans Affairs Medical Center, Tennessee Valley Healthcare System, Nashville, Tennessee, USA
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20
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Hinton KE, Lahey BB, Villalta-Gil V, Boyd BD, Yvernault BC, Werts KB, Plassard AJ, Applegate B, Woodward ND, Landman BA, Zald DH. Right Fronto-Subcortical White Matter Microstructure Predicts Cognitive Control Ability on the Go/No-go Task in a Community Sample. Front Hum Neurosci 2018; 12:127. [PMID: 29706875 PMCID: PMC5908979 DOI: 10.3389/fnhum.2018.00127] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 03/19/2018] [Indexed: 01/27/2023] Open
Abstract
Go/no-go tasks are widely used to index cognitive control. This construct has been linked to white matter microstructure in a circuit connecting the right inferior frontal gyrus (IFG), subthalamic nucleus (STN), and pre-supplementary motor area. However, the specificity of this association has not been tested. A general factor of white matter has been identified that is related to processing speed. Given the strong processing speed component in successful performance on the go/no-go task, this general factor could contribute to task performance, but the general factor has often not been accounted for in past studies of cognitive control. Further, studies on cognitive control have generally employed small unrepresentative case-control designs. The present study examined the relationship between go/no-go performance and white matter microstructure in a large community sample of 378 subjects that included participants with a range of both clinical and subclinical nonpsychotic psychopathology. We found that white matter microstructure properties in the right IFG-STN tract significantly predicted task performance, and remained significant after controlling for dimensional psychopathology. The general factor of white matter only reached statistical significance when controlling for dimensional psychopathology. Although the IFG-STN and general factor tracts were highly correlated, when both were included in the model, only the IFG-STN remained a significant predictor of performance. Overall, these findings suggest that while a general factor of white matter can be identified in a young community sample, white matter microstructure properties in the right IFG-STN tract show a specific relationship to cognitive control. The findings highlight the importance of examining both specific and general correlates of cognition, especially in tasks with a speeded component.
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Affiliation(s)
- Kendra E Hinton
- Department of Psychological Sciences, Vanderbilt University, Nashville, TN, United States
| | - Benjamin B Lahey
- Department of Public Health Sciences, University of Chicago, Chicago, IL, United States
| | - Victoria Villalta-Gil
- Department of Psychological Sciences, Vanderbilt University, Nashville, TN, United States
| | - Brian D Boyd
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | | | - Katherine B Werts
- Department of Psychological Sciences, Vanderbilt University, Nashville, TN, United States
| | - Andrew J Plassard
- School of Engineering, Vanderbilt University, Nashville, TN, United States
| | - Brooks Applegate
- Department of Educational Leadership, Research and Technology, Western Michigan University, Kalamazoo, MI, United States
| | - Neil D Woodward
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Bennett A Landman
- School of Engineering, Vanderbilt University, Nashville, TN, United States
| | - David H Zald
- Department of Psychological Sciences, Vanderbilt University, Nashville, TN, United States
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21
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Daou MAZ, Boyd BD, Donahue MJ, Albert K, Taylor WD. Anterior-posterior gradient differences in lobar and cingulate cortex cerebral blood flow in late-life depression. J Psychiatr Res 2018; 97:1-7. [PMID: 29156413 PMCID: PMC5742550 DOI: 10.1016/j.jpsychires.2017.11.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 10/10/2017] [Accepted: 11/10/2017] [Indexed: 02/08/2023]
Abstract
Vascular pathology is common in late-life depression, contributing to changes in cerebral function. We examined whether late-life depression was associated with differences in cerebral blood flow (CBF) and whether such differences were related to vascular risk and cerebrovascular pathology, specifically white matter hyperintensity (WMH) volumes. Twenty-three depressed elders and 20 age- and sex-matched elders with no psychiatric history completed cranial 3T MRI. MRI procedures included a pseudo-continuous Arterial Spin Labeling (pcASL) acquisition obtained while on room air and during a hypercapnia challenge allowing for calculation of cerebrovascular reactivity (CVR). Brain segmentation identified frontal, temporal, parietal and cingulate sub-regions in which CBF and CVR were calculated. The depressed group exhibited an anterior-posterior gradient in CBF, with lower CBF throughout the frontal lobe but higher CBF in the parietal lobe, temporal lobe, thalamus and hippocampus. A similar anterior to posterior gradient was observed in the cingulate cortex, with anterior regions exhibiting lower CBF and posterior regions exhibiting higher CBF. We did not observe any group differences in CVR measures. We did not observe significant relationships between CBF and CVR with vascular risk or WMH volumes, aside from an isolated finding associating higher WMH volumes with lower CBF in the rostral anterior cingulate cortex. Decreased anterior CBF in depressed elders might reflect decreased metabolic activity in these regions, while increased posterior CBF may represent either compensatory processes or different activity of posterior intrinsic functional networks. Future work should examine how these findings are related to compensatory changes with aging.
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Affiliation(s)
- Margarita Abi Zeid Daou
- The Center for Cognitive Medicine, Department of Psychiatry and
Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, 37212,
USA
| | - Brian D. Boyd
- The Center for Cognitive Medicine, Department of Psychiatry and
Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, 37212,
USA
| | - Manus J. Donahue
- The Department of Radiology and Radiological Science, Vanderbilt
University Medical Center, Nashville, TN, 37212, USA
| | - Kimberly Albert
- The Center for Cognitive Medicine, Department of Psychiatry and
Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, 37212,
USA
| | - Warren D. Taylor
- The Center for Cognitive Medicine, Department of Psychiatry and
Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, 37212,
USA,Geriatric Research, Education and Clinical Center, Department of
Veterans Affairs Medical Center, Tennessee Valley Healthcare System, Nashville, TN,
37212, USA
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22
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Park JH, Jeon BH, Lee JS, Newhouse PA, Taylor WD, Boyd BD, Kim KW, Kim MD. CADASIL as a Useful Medical Model and Genetic Form of Vascular Depression. Am J Geriatr Psychiatry 2017; 25:719-727. [PMID: 28434675 DOI: 10.1016/j.jagp.2017.03.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Revised: 02/06/2017] [Accepted: 03/14/2017] [Indexed: 11/19/2022]
Abstract
OBJECTIVE The main magnetic resonance imaging (MRI) findings of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) are white matter hyperintensities (WMHs), lacunar infarctions, and cerebral microbleeds (CMBs). The purpose of this study was to investigate the effects of these three neuroimaging markers of CADASIL on depression to determine whether CADASIL is a useful medical model supporting the vascular depression hypothesis. METHODS Eighty-four subjects with CADASIL, aged 34-86 years, participated in this study. They underwent comprehensive clinical evaluation, including 3T MRI and genotyping of NOTCH3. The effects of WMH, lacunar infarctions, and CMBs were analyzed by path analyses and multivariate logistic regression analyses. RESULTS Patients with CADASIL exhibited frequencies of 17.9% for major depressive disorder (MDD) and 10.7% for minor depressive disorder. The frequency of MDD increased from 5.0% to 46.2% as WMH volume increased from first quartile to fourth quartile. WMH volume (OR: 1.03, 95% CI: 1.003-1.06) in patients with CADASIL was associated with the current depressive disorder. Path analyses demonstrated that only WMH volume was associated with the Korean version of the short form Geriatric Depression Scale score, Center for Epidemiologic Studies Depression Scale score, and 17-item Hamilton depression scale score. The effects of lacunar infarctions and CMBs on depression were not significant in path analyses and multivariate logistic regression analyses. CONCLUSIONS This study demonstrates that WMHs are closely associated with depression in patients with CADASIL. This supports that CADASIL might be a useful medical model and genetic form of vascular depression.
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Affiliation(s)
- Joon Hyuk Park
- Department of Psychiatry, Jeju National University School of Medicine, Jeju National University Hospital, Jejudo, Republic of Korea
| | - Bong-Hee Jeon
- Department of Psychiatry, Naju National Hospital, Naju, Republic of Korea
| | - Jung Seok Lee
- Department of Neurology, Jeju National University School of Medicine, Jeju National University Hospital, Jejudo, Republic of Korea
| | - Paul A Newhouse
- Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN, USA; Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Warren D Taylor
- Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN, USA; Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Brian D Boyd
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Ki Woong Kim
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea; Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Brain and Cognitive Science, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Moon-Doo Kim
- Department of Psychiatry, Jeju National University School of Medicine, Jeju National University Hospital, Jejudo, Republic of Korea.
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23
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Damon SM, Boyd BD, Plassard AJ, Taylor W, Landman BA. DAX - The Next Generation: Towards One Million Processes on Commodity Hardware. Proc SPIE Int Soc Opt Eng 2017; 2017. [PMID: 28919661 DOI: 10.1117/12.2254371] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Large scale image processing demands a standardized way of not only storage but also a method for job distribution and scheduling. The eXtensible Neuroimaging Archive Toolkit (XNAT) is one of several platforms that seeks to solve the storage issues. Distributed Automation for XNAT (DAX) is a job control and distribution manager. Recent massive data projects have revealed several bottlenecks for projects with >100,000 assessors (i.e., data processing pipelines in XNAT). In order to address these concerns, we have developed a new API, which exposes a direct connection to the database rather than REST API calls to accomplish the generation of assessors. This method, consistent with XNAT, keeps a full history for auditing purposes. Additionally, we have optimized DAX to keep track of processing status on disk (called DISKQ) rather than on XNAT, which greatly reduces load on XNAT by vastly dropping the number of API calls. Finally, we have integrated DAX into a Docker container with the idea of using it as a Docker controller to launch Docker containers of image processing pipelines. Using our new API, we reduced the time to create 1,000 assessors (a sub-cohort of our case project) from 65040 seconds to 229 seconds (a decrease of over 270 fold). DISKQ, using pyXnat, allows launching of 400 jobs in under 10 seconds which previously took 2,000 seconds. Together these updates position DAX to support projects with hundreds of thousands of scans and to run them in a time-efficient manner.
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Affiliation(s)
- Stephen M Damon
- Electrical Engineering, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235
| | - Brian D Boyd
- Psychiatry, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235
| | - Andrew J Plassard
- Electrical Engineering, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235
| | - Warren Taylor
- Psychiatry, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235
| | - Bennett A Landman
- Electrical Engineering, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235.,Psychiatry, Vanderbilt University, 2301 Vanderbilt Place, Nashville, TN USA 37235
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24
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Weinstein JJ, Rogers BP, Taylor WD, Boyd BD, Cowan RL, Shelton KM, Salomon RM. Effects of acute tryptophan depletion on raphé functional connectivity in depression. Psychiatry Res 2015; 234:164-71. [PMID: 26411798 PMCID: PMC4631618 DOI: 10.1016/j.pscychresns.2015.08.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2014] [Revised: 07/21/2015] [Accepted: 08/31/2015] [Indexed: 10/23/2022]
Abstract
Depression remains a great societal burden and a major treatment challenge. Most antidepressant medications target serotonergic raphé nuclei. Acute tryptophan depletion (ATD) modulates serotonin function. To better understand the raphé's role in mood networks, we studied raphé functional connectivity in depression. Fifteen depressed patients were treated with sertraline for 12 weeks and scanned during ATD and sham conditions. Based on our previous findings in a separate cohort, resting state MRI functional connectivity between raphé and other depression-related regions (ROIs) was analyzed in narrow frequency bands. ATD decreased raphé functional connectivity with the bilateral thalamus within 0.025-0.05 Hz, and also decreased raphé functional connectivity with the right pregenual anterior cingulate cortex within 0.05-0.1 Hz. Using the control broadband filter 0.01-0.1 Hz, no significant differences in raphé-ROI functional connectivity were observed. Post-hoc analysis by remission status suggested increased raphé functional connectivity with left pregenual anterior cingulate cortex in remitters (n=10) and decreased raphé functional connectivity with left thalamus in non-remitters (n=5), both within 0.025-0.05 Hz. Reducing serotonin function appears to alter coordination of these mood-related networks in specific, low frequency ranges. For examination of effects of reduced serotonin function on mood-related networks, specific low frequency BOLD fMRI signals can identify regions implicated in neural circuitry and may enable clinically-relevant interpretation of functional connectivity measures. The biological significance of these low frequency signals detected in the raphé merits further study.
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Affiliation(s)
- Jodi J. Weinstein
- Department of Psychiatry, Vanderbilt University Medical Center
(VUMC), Nashville, TN, USA,Correspondence to: Columbia University Medical Center,
Department of Psychiatry and New York State Psychiatric Institute, 1051 Riverside Drive,
New York, NY 10032, USA
| | - Baxter P. Rogers
- Department of Psychiatry, Vanderbilt University Medical Center
(VUMC), Nashville, TN, USA,Department of Radiology and Radiological Sciences, VUMC,
Nashville, TN, USA,Department of Biomedical Engineering, Vanderbilt University
| | - Warren D. Taylor
- Department of Psychiatry, Vanderbilt University Medical Center
(VUMC), Nashville, TN, USA,The Geriatric Research, Education, and Clinical Center (GRECC),
VA Medical Center, Tennessee Valley Healthcare System, USA
| | - Brian D. Boyd
- Department of Psychiatry, Vanderbilt University Medical Center
(VUMC), Nashville, TN, USA
| | - Ronald L. Cowan
- Department of Psychiatry, Vanderbilt University Medical Center
(VUMC), Nashville, TN, USA,Department of Radiology and Radiological Sciences, VUMC,
Nashville, TN, USA
| | - K. Maureen Shelton
- Department of Psychiatry, Vanderbilt University Medical Center
(VUMC), Nashville, TN, USA
| | - Ronald M. Salomon
- Psychiatric Research Institute, University of Arkansas for
Medical Sciences, Little Rock, AR, USA,Correspondence to: University of Arkansas Medical School
Psychiatric Research Institute, 4301 West Markham Street, Slot 554, Little Rock, AR 72205,
USA. (J.J. Weinstein)
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25
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Harrigan RL, Yvernault BC, Boyd BD, Damon SM, Gibney KD, Conrad BN, Phillips NS, Rogers BP, Gao Y, Landman BA. Vanderbilt University Institute of Imaging Science Center for Computational Imaging XNAT: A multimodal data archive and processing environment. Neuroimage 2015; 124:1097-1101. [PMID: 25988229 DOI: 10.1016/j.neuroimage.2015.05.021] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Revised: 05/07/2015] [Accepted: 05/08/2015] [Indexed: 11/25/2022] Open
Abstract
The Vanderbilt University Institute for Imaging Science (VUIIS) Center for Computational Imaging (CCI) has developed a database built on XNAT housing over a quarter of a million scans. The database provides framework for (1) rapid prototyping, (2) large scale batch processing of images and (3) scalable project management. The system uses the web-based interfaces of XNAT and REDCap to allow for graphical interaction. A python middleware layer, the Distributed Automation for XNAT (DAX) package, distributes computation across the Vanderbilt Advanced Computing Center for Research and Education high performance computing center. All software are made available in open source for use in combining portable batch scripting (PBS) grids and XNAT servers.
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Affiliation(s)
- Robert L Harrigan
- Electrical Engineering, Vanderbilt University, Nashville, TN 37235, USA.
| | | | - Brian D Boyd
- Psychiatry, Vanderbilt University, Nashville, TN 37235, USA
| | - Stephen M Damon
- Electrical Engineering, Vanderbilt University, Nashville, TN 37235, USA
| | - Kyla David Gibney
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37235, USA
| | - Benjamin N Conrad
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37235, USA; Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37235, USA
| | - Nicholas S Phillips
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37235, USA; Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37235, USA
| | - Baxter P Rogers
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37235, USA; Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37235, USA; Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Psychiatry, Vanderbilt University, Nashville, TN 37235, USA
| | - Yurui Gao
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37235, USA; Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
| | - Bennett A Landman
- Electrical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Institute of Imaging Science, Vanderbilt University, Nashville, TN 37235, USA; Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37235, USA; Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
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26
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Chang CC, Yu SC, McQuoid DR, Messer DF, Taylor WD, Singh K, Boyd BD, Krishnan KRR, MacFall JR, Steffens DC, Payne ME. Reduction of dorsolateral prefrontal cortex gray matter in late-life depression. Psychiatry Res 2011; 193:1-6. [PMID: 21596532 PMCID: PMC3105213 DOI: 10.1016/j.pscychresns.2011.01.003] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2010] [Revised: 01/07/2011] [Accepted: 01/07/2011] [Indexed: 11/30/2022]
Abstract
Postmortem studies have documented abnormalities in the dorsolateral prefrontal cortex (dlPFC) in depressed subjects. In this study we used magnetic resonance imaging to test for dlPFC volume differences between older depressed and non-depressed individuals. Eighty-eight subjects meeting DSM IV criteria for major depressive disorder and thirty-five control subjects completed clinical evaluations and cranial 3T magnetic resonance imaging. After tissue types were identified using an automated segmentation process, the dlPFC was measured in both hemispheres using manual delineation based on anatomical landmarks. Depressed subjects had significantly lower gray matter in the left and right dorsolateral prefrontal cortex (standardized to cerebral parenchyma) after controlling for age and sex. Our study confirmed the reduction of dorsolateral prefrontal cortex in elderly depressed subjects, especially in the gray matter. These regional abnormalities may be associated with psychopathological changes in late-life depression.
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Affiliation(s)
- Cheng-Chen Chang
- The Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, United States of America, The Neuropsychiatric Imaging Research Laboratory, Duke University Medical Center, Durham, North Carolina, United States of America, The Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan, ROC, Department of Psychiatry, Changhua Christian Hospital, Changhua, Taiwan, ROC
| | - Shun-Chieh Yu
- The Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, United States of America, The Neuropsychiatric Imaging Research Laboratory, Duke University Medical Center, Durham, North Carolina, United States of America, Department of General Psychiatry, Yu-Li Hospital, Hualien, Taiwan, ROC
| | - Douglas R. McQuoid
- The Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Denise F. Messer
- Formerly of Duke University Medical Center (The Department of Psychiatry and Behavioral Sciences, and the Neuropsychiatric Imaging Research Laboratory)
| | - Warren D. Taylor
- The Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, United States of America, The Neuropsychiatric Imaging Research Laboratory, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Kulpreet Singh
- The Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, United States of America, The Neuropsychiatric Imaging Research Laboratory, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Brian D. Boyd
- The Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, United States of America, The Neuropsychiatric Imaging Research Laboratory, Duke University Medical Center, Durham, North Carolina, United States of America
| | - K. Ranga R. Krishnan
- The Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, United States of America, The Duke-NUS Graduate Medical School, Singapore
| | - James R. MacFall
- The Neuropsychiatric Imaging Research Laboratory, Duke University Medical Center, Durham, North Carolina, United States of America, The Department of Radiology, Duke University Medical Center, Durham, North Carolina, United States of America
| | - David C. Steffens
- The Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, United States of America, The Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Martha E. Payne
- The Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, United States of America, The Neuropsychiatric Imaging Research Laboratory, Duke University Medical Center, Durham, North Carolina, United States of America,Corresponding Author: Martha E. Payne, PhD, Duke University Medical Center, 2200 West Main Street, Suite B210, Durham, NC 27705 USA, Tel.: (919) 416-7543, Fax: (919) 416-7547,
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Ellis NR, Deacon JR, Harris LA, Poor A, Angers D, Diorio MS, Watkins RS, Boyd BD, Cavalier AR. Learning, memory, and transfer in profoundly, severely, and moderately mentally retarded persons. Am J Ment Defic 1982; 87:186-196. [PMID: 7124831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Discrimination learning, memory, and transfer capacity were assessed in representative samples of institutionalized retarded persons in order to provide information on trainability. The 56 subjects were selected from moderately, severely, and two levels of profoundly retarded adults. They learned and relearned three successive two-choice discrimination problems. Generally, the higher functioning subjects, defined by IQ and adaptive behavior learned more rapidly than did the lower functioning subjects. Forgetting was related to IQ/adaptive behavior level. Interproblem transfer was negligible at all levels of retardation, but ceiling effects may have obscured positive transfer in the higher functioning groups. Backward learning curves revealed large differences between lower and higher functioning persons in the presolution trials, but once learning began even profoundly retarded subjects solved these problems as rapidly as did the moderately retarded subjects. Ten of the 56 subjects failed to learn all three problems.
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