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Nabulsi L, Chandio BQ, McPhilemy G, Martyn FM, Roberts G, Hallahan B, Dannlowski U, Kircher T, Haarman B, Mitchell P, McDonald C, Cannon DM, Andreassen OA, Ching CRK, Thompson PM. Multi-Site Statistical Mapping of Along-Tract Microstructural Abnormalities in Bipolar Disorder with Diffusion MRI Tractometry. bioRxiv 2023:2023.08.17.553762. [PMID: 37662230 PMCID: PMC10473593 DOI: 10.1101/2023.08.17.553762] [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: 09/05/2023]
Abstract
Investigating alterations in brain circuitry associated with bipolar disorder (BD) may offer a valuable approach to discover brain biomarkers for genetic and interventional studies of the disorder and related mental illnesses. Some diffusion MRI studies report evidence of microstructural abnormalities in white matter regions of interest, but we lack a fine-scale spatial mapping of brain microstructural differences along tracts in BD. We also lack large-scale studies that integrate tractometry data from multiple sites, as larger datasets can greatly enhance power to detect subtle effects and assess whether effects replicate across larger international datasets. In this multisite diffusion MRI study, we used BUndle ANalytics (BUAN, Chandio 2020), a recently developed analytic approach for tractography, to extract, map, and visualize profiles of microstructural abnormalities on 3D models of fiber tracts in 148 participants with BD and 259 healthy controls from 6 independent scan sites. Modeling site differences as random effects, we investigated along-tract white matter (WM) microstructural differences between diagnostic groups. QQ plots showed that group differences were gradually enhanced as more sites were added. Using the BUAN pipeline, BD was associated with lower mean fractional anisotropy (FA) in fronto-limbic, interhemispheric, and posterior pathways; higher FA was also noted in posterior bundles, relative to controls. By integrating tractography and anatomical information, BUAN effectively captures unique effects along white matter (WM) tracts, providing valuable insights into anatomical variations that may assist in the classification of diseases.
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Affiliation(s)
- Leila Nabulsi
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA 90292, USA
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Bramsh Q Chandio
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA 90292, USA
| | - Genevieve McPhilemy
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Fiona M Martyn
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Gloria Roberts
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Brian Hallahan
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Benno Haarman
- Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Philip Mitchell
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Colm McDonald
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Dara M Cannon
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA 90292, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA 90292, USA
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Domain L, Guillery M, Linz N, König A, Batail JM, David R, Corouge I, Bannier E, Ferré JC, Dondaine T, Drapier D, Robert GH. Multimodal MRI cerebral correlates of verbal fluency switching and its impairment in women with depression. Neuroimage Clin 2022; 33:102910. [PMID: 34942588 PMCID: PMC8713114 DOI: 10.1016/j.nicl.2021.102910] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 12/03/2021] [Accepted: 12/04/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND The search of biomarkers in the field of depression requires easy implementable tests that are biologically rooted. Qualitative analysis of verbal fluency tests (VFT) are good candidates, but its cerebral correlates are unknown. METHODS We collected qualitative semantic and phonemic VFT scores along with grey and white matter anatomical MRI of depressed (n = 26) and healthy controls (HC, n = 25) women. Qualitative VFT variables are the "clustering score" (i.e. the ability to produce words within subcategories) and the "switching score" (i.e. the ability to switch between clusters). The clustering and switching scores were automatically calculated using a data-driven approach. Brain measures were cortical thickness (CT) and fractional anisotropy (FA). We tested for associations between CT, FA and qualitative VFT variables within each group. RESULTS Patients had reduced switching VFT scores compared to HC. Thicker cortex was associated with better switching score in semantic VFT bilaterally in the frontal (superior, rostral middle and inferior gyri), parietal (inferior parietal lobule including the supramarginal gyri), temporal (transverse and fusiform gyri) and occipital (lingual gyri) lobes in the depressed group. Positive association between FA and the switching score in semantic VFT was retrieved in depressed patients within the corpus callosum, right inferior fronto-occipital fasciculus, right superior longitudinal fasciculus extending to the anterior thalamic radiation (all p < 0.05, corrected). CONCLUSION Together, these results suggest that automatic qualitative VFT scores are associated with brain anatomy and reinforce its potential use as a surrogate for depression cerebral bases.
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Affiliation(s)
- L Domain
- Universitary Department of Psychiatry, Centre Hospitalier Guillaume Régnier, Rennes, France
| | - M Guillery
- Universitary Department of Psychiatry, Centre Hospitalier Guillaume Régnier, Rennes, France
| | - N Linz
- ki:elements, Saarbrücken, Germany
| | - A König
- Stars Team, Institut National de Recherche en Informatique et en Automatique (INRIA), Sophia Antipolis, France; CoBTeK (Cognition-Behaviour-Technology) Lab, FRIS-University Côte d'Azur, Nice, France
| | - J M Batail
- Universitary Department of Psychiatry, Centre Hospitalier Guillaume Régnier, Rennes, France
| | - R David
- Old-age Psychiatry DEPARTMENT, Geriatry Division, University of Nice, France
| | - I Corouge
- U1228 Empenn, UMR 6074, IRISA, University of Rennes 1, France
| | - E Bannier
- U1228 Empenn, UMR 6074, IRISA, University of Rennes 1, France
| | - J C Ferré
- U1228 Empenn, UMR 6074, IRISA, University of Rennes 1, France
| | - T Dondaine
- Univ. Lille, Inserm, CHU Lille, LilNCog, Lille Neuroscience & Cognition, F-59000 Lille, France
| | - D Drapier
- Universitary Department of Psychiatry, Centre Hospitalier Guillaume Régnier, Rennes, France
| | - G H Robert
- Universitary Department of Psychiatry, Centre Hospitalier Guillaume Régnier, Rennes, France; U1228 Empenn, UMR 6074, IRISA, University of Rennes 1, France
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Pandey AK, Ardekani BA, Kamarajan C, Zhang J, Chorlian DB, Byrne KNH, Pandey G, Meyers JL, Kinreich S, Stimus A, Porjesz B. Lower Prefrontal and Hippocampal Volume and Diffusion Tensor Imaging Differences Reflect Structural and Functional Abnormalities in Abstinent Individuals with Alcohol Use Disorder. Alcohol Clin Exp Res 2018; 42:1883-1896. [PMID: 30118142 DOI: 10.1111/acer.13854] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [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: 12/31/2017] [Accepted: 07/25/2018] [Indexed: 01/18/2023]
Abstract
BACKGROUND Alcohol use disorder (AUD) is known to have adverse effects on brain structure and function. Multimodal assessments investigating volumetric, diffusion, and cognitive characteristics may facilitate understanding of the consequences of long-term alcohol use on brain circuitry, their structural impairment patterns, and their impact on cognitive function in AUD. METHODS Voxel- and surface-based volumetric estimations, diffusion tensor imaging (DTI), and neuropsychological tests were performed on 60 individuals: 30 abstinent individuals with AUD (DSM-IV) and 30 healthy controls. Group differences in the volumes of cortical and subcortical regions, fractional anisotropy (FA), axial and radial diffusivities (AD and RD, respectively), and performance on neuropsychological tests were analyzed, and the relationship among significantly different measures was assessed using canonical correlation. RESULTS AUD participants had significantly smaller volumes in left pars orbitalis, right medial orbitofrontal, right caudal middle frontal, and bilateral hippocampal regions, lower FA in 9 white matter (WM) regions, and higher FA in left thalamus, compared to controls. In AUD, lower FA in 6 of 9 WM regions was due to higher RD and due to lower AD in the left external capsule. AUD participants scored lower on problem-solving ability, visuospatial memory span, and working memory. Positive correlations of prefrontal cortical, left hippocampal volumes, and FA in 4 WM regions with visuospatial memory performance and negative correlation with lower problem-solving ability were observed. Significant positive correlation between age and FA was observed in bilateral putamen. CONCLUSIONS Findings showed specific structural brain abnormalities to be associated with visuospatial memory and problem-solving ability-related impairments observed in AUD. Higher RD in 6 WM regions suggests demyelination, and lower AD in left external capsule suggests axonal loss in AUD. The positive correlation between FA and age in bilateral putamen may reflect accumulation of iron depositions with increasing age.
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Affiliation(s)
- Ashwini Kumar Pandey
- Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, New York
| | - Babak Assai Ardekani
- Computational Neuroimaging Laboratories of the Center for Biomedical Imaging and Neuromodulation (C-BIN), The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York
| | - Chella Kamarajan
- Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, New York
| | - Jian Zhang
- Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, New York
| | - David Balin Chorlian
- Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, New York
| | - Kelly Nicole-Helen Byrne
- Computational Neuroimaging Laboratories of the Center for Biomedical Imaging and Neuromodulation (C-BIN), The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York
| | - Gayathri Pandey
- Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, New York
| | - Jacquelyn Leigh Meyers
- Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, New York
| | - Sivan Kinreich
- Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, New York
| | - Arthur Stimus
- Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, New York
| | - Bernice Porjesz
- Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, New York
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A Yassine I, M Eldeeb W, A Gad K, A Ashour Y, A Yassine I, O Hosny A. Cognitive functions, electroencephalographic and diffusion tensor imaging changes in children with active idiopathic epilepsy. Epilepsy Behav 2018; 84:135-141. [PMID: 29800799 DOI: 10.1016/j.yebeh.2018.04.024] [Citation(s) in RCA: 2] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2018] [Revised: 03/12/2018] [Accepted: 04/29/2018] [Indexed: 10/16/2022]
Abstract
INTRODUCTION Neurocognitive impairment represents one of the most common comorbidities occurring in children with idiopathic epilepsy. Diagnosis of the idiopathic form of epilepsy requires the absence of any macrostructural abnormality in the conventional MRI. Though changes can be seen at the microstructural level imaged using advanced techniques such as the Diffusion Tensor Imaging (DTI). AIM OF THE WORK The aim of this work is to study the correlation between the microstructural white matter DTI findings, the electroencephalographic changes and the cognitive dysfunction in children with active idiopathic epilepsy. METHODS A comparative cross-sectional study, included 60 children with epilepsy based on the Stanford-Binet 5th Edition Scores was conducted. Patients were equally assigned to normal cognitive function or cognitive dysfunction groups. The history of the epileptic condition was gathered via personal interviews. All patients underwent brain Electroencephalography (EEG) and DTI, which was analyzed using FSL. RESULTS The Fractional Anisotropy (FA) was significantly higher whereas the Mean Diffusivity (MD) was significantly lower in the normal cognitive function group than in the cognitive dysfunction group. This altered microstructure was related to the degree of the cognitive performance of the studied children with epilepsy. The microstructural alterations of the neural fibers in children with epilepsy and cognitive dysfunction were significantly related to the younger age of onset of epilepsy, the poor control of the clinical seizures, and the use of multiple antiepileptic medications. CONCLUSION Children with epilepsy and normal cognitive functions differ in white matter integrity, measured using DTI, compared with children with cognitive dysfunction. These changes have important cognitive consequences.
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Affiliation(s)
- Imane A Yassine
- Neurology Department, Faculty of Medicine, Suez Canal University, Ismailia, Egypt.
| | - Waleed M Eldeeb
- Neurology Department, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
| | - Khaled A Gad
- Diagnostic Radiology Department, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
| | - Yossri A Ashour
- Neurology Department, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
| | - Inas A Yassine
- Systems and Biomedical Engineering Department, Faculty of Engineering, Cairo University, Egypt
| | - Ahmed O Hosny
- Neurology Department, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
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Cropley VL, Klauser P, Lenroot RK, Bruggemann J, Sundram S, Bousman C, Pereira A, Di Biase MA, Weickert TW, Weickert CS, Pantelis C, Zalesky A. Accelerated Gray and White Matter Deterioration With Age in Schizophrenia. Am J Psychiatry 2017; 174:286-295. [PMID: 27919183 DOI: 10.1176/appi.ajp.2016.16050610] [Citation(s) in RCA: 123] [Impact Index Per Article: 17.6] [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] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Although brain changes in schizophrenia have been proposed to mirror those found with advancing age, the trajectory of gray matter and white matter changes during the disease course remains unclear. The authors sought to measure whether these changes in individuals with schizophrenia remain stable, are accelerated, or are diminished with age. METHOD Gray matter volume and fractional anisotropy were mapped in 326 individuals diagnosed with schizophrenia or schizoaffective disorder and in 197 healthy comparison subjects aged 20-65 years. Polynomial regression was used to model the influence of age on gray matter volume and fractional anisotropy at a whole-brain and voxel level. Between-group differences in gray matter volume and fractional anisotropy were regionally localized across the lifespan using permutation testing and cluster-based inference. RESULTS Significant loss of gray matter volume was evident in schizophrenia, progressively worsening with age to a maximal loss of 8% in the seventh decade of life. The inferred rate of gray matter volume loss was significantly accelerated in schizophrenia up to middle age and plateaued thereafter. In contrast, significant reductions in fractional anisotropy emerged in schizophrenia only after age 35, and the rate of fractional anisotropy deterioration with age was constant and best modeled with a straight line. The slope of this line was 60% steeper in schizophrenia relative to comparison subjects, indicating a significantly faster rate of white matter deterioration with age. The rates of reduction of gray matter volume and fractional anisotropy were significantly faster in males than in females, but an interaction between sex and diagnosis was not evident. CONCLUSIONS The findings suggest that schizophrenia is characterized by an initial, rapid rate of gray matter loss that slows in middle life, followed by the emergence of a deficit in white matter that progressively worsens with age at a constant rate.
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Affiliation(s)
- Vanessa L Cropley
- From the Melbourne Neuropsychiatry Centre, Department of Psychiatry, and the Department of Electrical and Electronic Engineering, University of Melbourne, and Melbourne Health, Melbourne; the Brain and Mental Health Laboratory, School of Psychological Sciences, Monash Biomedical Imaging, and the Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, Australia; the Schizophrenia Research Institute and Neuroscience Research Australia, Randwick, Australia; the Molecular Psychopharmacology Laboratory, Florey Institute of Neuroscience and Mental Health, Parkville, Australia; the Faculty of Health, Arts, and Design, Swinburne University of Technology, Hawthorn, Australia; and the School of Psychiatry, Faculty of Medicine, University of New South Wales, Kensington, Australia
| | - Paul Klauser
- From the Melbourne Neuropsychiatry Centre, Department of Psychiatry, and the Department of Electrical and Electronic Engineering, University of Melbourne, and Melbourne Health, Melbourne; the Brain and Mental Health Laboratory, School of Psychological Sciences, Monash Biomedical Imaging, and the Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, Australia; the Schizophrenia Research Institute and Neuroscience Research Australia, Randwick, Australia; the Molecular Psychopharmacology Laboratory, Florey Institute of Neuroscience and Mental Health, Parkville, Australia; the Faculty of Health, Arts, and Design, Swinburne University of Technology, Hawthorn, Australia; and the School of Psychiatry, Faculty of Medicine, University of New South Wales, Kensington, Australia
| | - Rhoshel K Lenroot
- From the Melbourne Neuropsychiatry Centre, Department of Psychiatry, and the Department of Electrical and Electronic Engineering, University of Melbourne, and Melbourne Health, Melbourne; the Brain and Mental Health Laboratory, School of Psychological Sciences, Monash Biomedical Imaging, and the Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, Australia; the Schizophrenia Research Institute and Neuroscience Research Australia, Randwick, Australia; the Molecular Psychopharmacology Laboratory, Florey Institute of Neuroscience and Mental Health, Parkville, Australia; the Faculty of Health, Arts, and Design, Swinburne University of Technology, Hawthorn, Australia; and the School of Psychiatry, Faculty of Medicine, University of New South Wales, Kensington, Australia
| | - Jason Bruggemann
- From the Melbourne Neuropsychiatry Centre, Department of Psychiatry, and the Department of Electrical and Electronic Engineering, University of Melbourne, and Melbourne Health, Melbourne; the Brain and Mental Health Laboratory, School of Psychological Sciences, Monash Biomedical Imaging, and the Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, Australia; the Schizophrenia Research Institute and Neuroscience Research Australia, Randwick, Australia; the Molecular Psychopharmacology Laboratory, Florey Institute of Neuroscience and Mental Health, Parkville, Australia; the Faculty of Health, Arts, and Design, Swinburne University of Technology, Hawthorn, Australia; and the School of Psychiatry, Faculty of Medicine, University of New South Wales, Kensington, Australia
| | - Suresh Sundram
- From the Melbourne Neuropsychiatry Centre, Department of Psychiatry, and the Department of Electrical and Electronic Engineering, University of Melbourne, and Melbourne Health, Melbourne; the Brain and Mental Health Laboratory, School of Psychological Sciences, Monash Biomedical Imaging, and the Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, Australia; the Schizophrenia Research Institute and Neuroscience Research Australia, Randwick, Australia; the Molecular Psychopharmacology Laboratory, Florey Institute of Neuroscience and Mental Health, Parkville, Australia; the Faculty of Health, Arts, and Design, Swinburne University of Technology, Hawthorn, Australia; and the School of Psychiatry, Faculty of Medicine, University of New South Wales, Kensington, Australia
| | - Chad Bousman
- From the Melbourne Neuropsychiatry Centre, Department of Psychiatry, and the Department of Electrical and Electronic Engineering, University of Melbourne, and Melbourne Health, Melbourne; the Brain and Mental Health Laboratory, School of Psychological Sciences, Monash Biomedical Imaging, and the Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, Australia; the Schizophrenia Research Institute and Neuroscience Research Australia, Randwick, Australia; the Molecular Psychopharmacology Laboratory, Florey Institute of Neuroscience and Mental Health, Parkville, Australia; the Faculty of Health, Arts, and Design, Swinburne University of Technology, Hawthorn, Australia; and the School of Psychiatry, Faculty of Medicine, University of New South Wales, Kensington, Australia
| | - Avril Pereira
- From the Melbourne Neuropsychiatry Centre, Department of Psychiatry, and the Department of Electrical and Electronic Engineering, University of Melbourne, and Melbourne Health, Melbourne; the Brain and Mental Health Laboratory, School of Psychological Sciences, Monash Biomedical Imaging, and the Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, Australia; the Schizophrenia Research Institute and Neuroscience Research Australia, Randwick, Australia; the Molecular Psychopharmacology Laboratory, Florey Institute of Neuroscience and Mental Health, Parkville, Australia; the Faculty of Health, Arts, and Design, Swinburne University of Technology, Hawthorn, Australia; and the School of Psychiatry, Faculty of Medicine, University of New South Wales, Kensington, Australia
| | - Maria A Di Biase
- From the Melbourne Neuropsychiatry Centre, Department of Psychiatry, and the Department of Electrical and Electronic Engineering, University of Melbourne, and Melbourne Health, Melbourne; the Brain and Mental Health Laboratory, School of Psychological Sciences, Monash Biomedical Imaging, and the Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, Australia; the Schizophrenia Research Institute and Neuroscience Research Australia, Randwick, Australia; the Molecular Psychopharmacology Laboratory, Florey Institute of Neuroscience and Mental Health, Parkville, Australia; the Faculty of Health, Arts, and Design, Swinburne University of Technology, Hawthorn, Australia; and the School of Psychiatry, Faculty of Medicine, University of New South Wales, Kensington, Australia
| | - Thomas W Weickert
- From the Melbourne Neuropsychiatry Centre, Department of Psychiatry, and the Department of Electrical and Electronic Engineering, University of Melbourne, and Melbourne Health, Melbourne; the Brain and Mental Health Laboratory, School of Psychological Sciences, Monash Biomedical Imaging, and the Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, Australia; the Schizophrenia Research Institute and Neuroscience Research Australia, Randwick, Australia; the Molecular Psychopharmacology Laboratory, Florey Institute of Neuroscience and Mental Health, Parkville, Australia; the Faculty of Health, Arts, and Design, Swinburne University of Technology, Hawthorn, Australia; and the School of Psychiatry, Faculty of Medicine, University of New South Wales, Kensington, Australia
| | - Cynthia Shannon Weickert
- From the Melbourne Neuropsychiatry Centre, Department of Psychiatry, and the Department of Electrical and Electronic Engineering, University of Melbourne, and Melbourne Health, Melbourne; the Brain and Mental Health Laboratory, School of Psychological Sciences, Monash Biomedical Imaging, and the Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, Australia; the Schizophrenia Research Institute and Neuroscience Research Australia, Randwick, Australia; the Molecular Psychopharmacology Laboratory, Florey Institute of Neuroscience and Mental Health, Parkville, Australia; the Faculty of Health, Arts, and Design, Swinburne University of Technology, Hawthorn, Australia; and the School of Psychiatry, Faculty of Medicine, University of New South Wales, Kensington, Australia
| | - Christos Pantelis
- From the Melbourne Neuropsychiatry Centre, Department of Psychiatry, and the Department of Electrical and Electronic Engineering, University of Melbourne, and Melbourne Health, Melbourne; the Brain and Mental Health Laboratory, School of Psychological Sciences, Monash Biomedical Imaging, and the Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, Australia; the Schizophrenia Research Institute and Neuroscience Research Australia, Randwick, Australia; the Molecular Psychopharmacology Laboratory, Florey Institute of Neuroscience and Mental Health, Parkville, Australia; the Faculty of Health, Arts, and Design, Swinburne University of Technology, Hawthorn, Australia; and the School of Psychiatry, Faculty of Medicine, University of New South Wales, Kensington, Australia
| | - Andrew Zalesky
- From the Melbourne Neuropsychiatry Centre, Department of Psychiatry, and the Department of Electrical and Electronic Engineering, University of Melbourne, and Melbourne Health, Melbourne; the Brain and Mental Health Laboratory, School of Psychological Sciences, Monash Biomedical Imaging, and the Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, Australia; the Schizophrenia Research Institute and Neuroscience Research Australia, Randwick, Australia; the Molecular Psychopharmacology Laboratory, Florey Institute of Neuroscience and Mental Health, Parkville, Australia; the Faculty of Health, Arts, and Design, Swinburne University of Technology, Hawthorn, Australia; and the School of Psychiatry, Faculty of Medicine, University of New South Wales, Kensington, Australia
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Arshad M, Stanley JA, Raz N. Adult age differences in subcortical myelin content are consistent with protracted myelination and unrelated to diffusion tensor imaging indices. Neuroimage 2016; 143:26-39. [PMID: 27561713 DOI: 10.1016/j.neuroimage.2016.08.047] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [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: 05/22/2016] [Revised: 08/15/2016] [Accepted: 08/21/2016] [Indexed: 01/23/2023] Open
Abstract
Post mortem studies suggest protracted myelination of subcortical white matter into the middle age followed by gradual decline in the late adulthood. To date, however, establishing the proposed inverted-U pattern of age-myelin association proved difficult, as the most common method of investigating white matter, diffusion tensor imaging (DTI), usually reveals only linear associations between DTI indices and age among healthy adults. Here we use a novel method of estimating Myelin Water Fraction (MWF) based on modeling the short spin-spin (T2) relaxation component from multi-echo T2 relaxation imaging data and assess subcortical myelin content within six white matter tracts in a sample of healthy adults (N=61, age 18-84 years). Myelin content evidenced a quadratic relationship with age, in accord with the pattern observed postmortem studies. In contrast, DTI-derived indices that are frequently cited as proxies for myelination, fractional anisotropy (FA) and radial diffusivity (RD), exhibited linear or null relationships with age. Furthermore, the magnitude of age differences in MWF varied across the white matter tracts. Myelin content estimated by MWF was unrelated to FA and correlated with RD only in the splenium. These findings are consistent with the notion that myelination continues throughout the young adulthood into the middle age. The results demonstrate that single-tensor DTI cannot serve as a source of specific proxies for myelination of white matter tracts.
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Affiliation(s)
- Muzamil Arshad
- Department of Psychiatry & Behavioral Neurosciences, School of Medicine, Wayne State University, Detroit, MI, United States; Institute of Gerontology, Wayne State University, Detroit, MI, United States
| | - Jeffrey A Stanley
- Department of Psychiatry & Behavioral Neurosciences, School of Medicine, Wayne State University, Detroit, MI, United States
| | - Naftali Raz
- Institute of Gerontology, Wayne State University, Detroit, MI, United States; Department of Psychology, Wayne State University, Detroit, MI, United States.
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Perkins TJ, Stokes MA, McGillivray JA, Mussap AJ, Cox IA, Maller JJ, Bittar RG. Increased left hemisphere impairment in high-functioning autism: a tract based spatial statistics study. Psychiatry Res 2014; 224:119-23. [PMID: 25159311 DOI: 10.1016/j.pscychresns.2014.08.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [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: 01/08/2014] [Revised: 06/23/2014] [Accepted: 08/04/2014] [Indexed: 10/24/2022]
Abstract
There is evidence emerging from Diffusion Tensor Imaging (DTI) research that autism spectrum disorders (ASD) are associated with greater impairment in the left hemisphere. Although this has been quantified with volumetric region of interest analyses, it has yet to be tested with white matter integrity analysis. In the present study, tract based spatial statistics was used to contrast white matter integrity of 12 participants with high-functioning autism or Aspergers syndrome (HFA/AS) with 12 typically developing individuals. Fractional Anisotropy (FA) was examined, in addition to axial, radial and mean diffusivity (AD, RD and MD). In the left hemisphere, participants with HFA/AS demonstrated significantly reduced FA in predominantly thalamic and fronto-parietal pathways and increased RD. Symmetry analyses confirmed that in the HFA/AS group, WM disturbance was significantly greater in the left compared to right hemisphere. These findings contribute to a growing body of literature suggestive of reduced FA in ASD, and provide preliminary evidence for RD impairments in the left hemisphere.
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Affiliation(s)
- Thomas John Perkins
- Cognitive Neuroscience Unit, Department of Psychology, School of Psychology, Faculty of Health, Deakin University, 221 Burwood Highway, Victoria, Australia.
| | - Mark Andrew Stokes
- Cognitive Neuroscience Unit, Department of Psychology, School of Psychology, Faculty of Health, Deakin University, 221 Burwood Highway, Victoria, Australia
| | - Jane Anne McGillivray
- Cognitive Neuroscience Unit, Department of Psychology, School of Psychology, Faculty of Health, Deakin University, 221 Burwood Highway, Victoria, Australia
| | - Alexander Julien Mussap
- Cognitive Neuroscience Unit, Department of Psychology, School of Psychology, Faculty of Health, Deakin University, 221 Burwood Highway, Victoria, Australia
| | - Ivanna Anne Cox
- Cognitive Neuroscience Unit, Department of Psychology, School of Psychology, Faculty of Health, Deakin University, 221 Burwood Highway, Victoria, Australia
| | - Jerome Joseph Maller
- Monash Alfred Psychiatry Research Centre, The Alfred and Monash University Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia
| | - Richard Garth Bittar
- Cognitive Neuroscience Unit, Department of Psychology, School of Psychology, Faculty of Health, Deakin University, 221 Burwood Highway, Victoria, Australia; Precision Brain Spine and Pain Centre, Australia; Department of Neurosurgery, Royal Melbourne Hospital, Australia
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8
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Whitford TJ, Lee SW, Oh JS, de Luis-Garcia R, Savadjiev P, Alvarado JL, Westin CF, Niznikiewicz M, Nestor PG, McCarley RW, Kubicki M, Shenton ME. Localized abnormalities in the cingulum bundle in patients with schizophrenia: a Diffusion Tensor tractography study. Neuroimage Clin 2014; 5:93-9. [PMID: 25003032 PMCID: PMC4081981 DOI: 10.1016/j.nicl.2014.06.003] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2014] [Revised: 05/27/2014] [Accepted: 06/07/2014] [Indexed: 02/02/2023]
Abstract
The cingulum bundle (CB) connects gray matter structures of the limbic system and as such has been implicated in the etiology of schizophrenia. There is growing evidence to suggest that the CB is actually comprised of a conglomeration of discrete sub-connections. The present study aimed to use Diffusion Tensor tractography to subdivide the CB into its constituent sub-connections, and to investigate the structural integrity of these sub-connections in patients with schizophrenia and matched healthy controls. Diffusion Tensor Imaging scans were acquired from 24 patients diagnosed with chronic schizophrenia and 26 matched healthy controls. Deterministic tractography was used in conjunction with FreeSurfer-based regions-of-interest to subdivide the CB into 5 sub-connections (I1 to I5). The patients with schizophrenia exhibited subnormal levels of FA in two cingulum sub-connections, specifically the fibers connecting the rostral and caudal anterior cingulate gyrus (I1) and the fibers connecting the isthmus of the cingulate with the parahippocampal cortex (I4). Furthermore, while FA in the I1 sub-connection was correlated with the severity of patients' positive symptoms (specifically hallucinations and delusions), FA in the I4 sub-connection was correlated with the severity of patients' negative symptoms (specifically affective flattening and anhedonia/asociality). These results support the notion that the CB is a conglomeration of structurally interconnected yet functionally distinct sub-connections, of which only a subset are abnormal in patients with schizophrenia. Furthermore, while acknowledging the fact that the present study only investigated the CB, these results suggest that the positive and negative symptoms of schizophrenia may have distinct neurobiological underpinnings. Cingulum bundle was divided into 5 sub-regions using DTI tractography. Fractional Anisotropy of these 5 sub-regions was assessed in schizophrenia patients. Schizophrenia patients exhibited FA reductions in only 2 of 5 cingulum sub-regions. One sub-region correlated with positive symptoms and other with negative symptoms.
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Affiliation(s)
- Thomas J. Whitford
- School of Psychology, University of New South Wales, Sydney, NSW, Australia
- Psychiatry Neuroimaging Laboratory, Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sun Woo Lee
- Psychiatry Neuroimaging Laboratory, Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, School of Medicine, Chungnam National University, Daejeon, South Korea
| | - Jungsu S. Oh
- Psychiatry Neuroimaging Laboratory, Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Rodrigo de Luis-Garcia
- Laboratory of Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Laboratorio de Procesado de Imagen, Universidad de Valladolid, Spain
| | - Peter Savadjiev
- Psychiatry Neuroimaging Laboratory, Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory of Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jorge L. Alvarado
- Psychiatry Neuroimaging Laboratory, Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Carl-Fredrik Westin
- Laboratory of Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Margaret Niznikiewicz
- Clinical Neuroscience Division, Laboratory of Neuroscience, Department of Psychiatry, Boston Veterans Affairs Healthcare System, Brockton Division, Brockton, MA, USA and Harvard Medical School, Boston, MA, USA
| | - Paul G. Nestor
- Clinical Neuroscience Division, Laboratory of Neuroscience, Department of Psychiatry, Boston Veterans Affairs Healthcare System, Brockton Division, Brockton, MA, USA and Harvard Medical School, Boston, MA, USA
- Boston Veterans Affairs Healthcare System, Brockton Division, Brockton, MA, USA
- Department of Psychology, University of Massachusetts, Boston, MA, USA
| | - Robert W. McCarley
- Clinical Neuroscience Division, Laboratory of Neuroscience, Department of Psychiatry, Boston Veterans Affairs Healthcare System, Brockton Division, Brockton, MA, USA and Harvard Medical School, Boston, MA, USA
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Boston Veterans Affairs Healthcare System, Brockton Division, Brockton, MA, USA
- Corresponding author at: Psychiatry Neuroimaging Laboratory, Departments of Psychiatry and Radiology, Harvard Medical School, Boston, MA, USA. Tel.: + 1 617 525 6117.
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Schwarz CG, Reid RI, Gunter JL, Senjem ML, Przybelski SA, Zuk SM, Whitwell JL, Vemuri P, Josephs KA, Kantarci K, Thompson PM, Petersen RC, Jack CR. Improved DTI registration allows voxel-based analysis that outperforms tract-based spatial statistics. Neuroimage 2014; 94:65-78. [PMID: 24650605 PMCID: PMC4137565 DOI: 10.1016/j.neuroimage.2014.03.026] [Citation(s) in RCA: 143] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2013] [Revised: 02/06/2014] [Accepted: 03/10/2014] [Indexed: 01/21/2023] Open
Abstract
Tract-Based Spatial Statistics (TBSS) is a popular software pipeline to coregister sets of diffusion tensor Fractional Anisotropy (FA) images for performing voxel-wise comparisons. It is primarily defined by its skeleton projection step intended to reduce effects of local misregistration. A white matter “skeleton” is computed by morphological thinning of the inter-subject mean FA, and then all voxels are projected to the nearest location on this skeleton. Here we investigate several enhancements to the TBSS pipeline based on recent advances in registration for other modalities, principally based on groupwise registration with the ANTS-SyN algorithm. We validate these enhancements using simulation experiments with synthetically-modified images. When used with these enhancements, we discover that TBSS's skeleton projection step actually reduces algorithm accuracy, as the improved registration leaves fewer errors to warrant correction, and the effects of this projection's compromises become stronger than those of its benefits. In our experiments, our proposed pipeline without skeleton projection is more sensitive for detecting true changes and has greater specificity in resisting false positives from misregistration. We also present comparative results of the proposed and traditional methods, both with and without the skeleton projection step, on three real-life datasets: two comparing differing populations of Alzheimer's disease patients to matched controls, and one comparing progressive supranuclear palsy patients to matched controls. The proposed pipeline produces more plausible results according to each disease's pathophysiology.
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Affiliation(s)
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Jeffrey L Gunter
- Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Matthew L Senjem
- Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Scott A Przybelski
- Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Samantha M Zuk
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | | | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Keith A Josephs
- Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging Informatics, USC Keck School of Medicine, Los Angeles, CA, USA; Departments of Neurology, Psychiatry, Radiology, Engineering, and Ophthalmology, USC Keck School of Medicine, Los Angeles, CA, USA
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA
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Asman AJ, Lauzon CB, Landman BA. Robust Inter-Modality Multi-Atlas Segmentation for PACS-based DTI Quality Control. Proc SPIE Int Soc Opt Eng 2013; 8674. [PMID: 24379940 DOI: 10.1117/12.2007587] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [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
Anatomical contexts (spatial labels) are critical for interpretation of medical imaging content. Numerous approaches have been devised for segmentation, query, and retrieval within the Picture Archive and Communication System (PACS) framework. To date, application-based methods for anatomical localization and tissue classification have yielded the most successful results, but these approaches typically rely upon the availability of standardized imaging sequences. With the ever expanding scope of PACS archives - including multiple imaging modalities, multiple image types within a modality, and multi-site efforts, it is becoming increasingly burdensome to devise a specific method for each data type. To address the challenge of generalizing segmentations from one modality to another, we consider multi-atlas segmentation to transfer label information from labeled T1-weighted MRI data to unlabeled B0 data collected in a diffusion tensor imaging (DTI) experiment. The label transfer approach is fully automated and enables a generalizable cross-modality segmentation method. Herein, we propose a multi-tier multi-atlas segmentation framework for the segmentation of previously unlabeled imaging modalities (e.g., B0 images for DTI analysis). We show that this approach can be used to construct informed structure-wise noise estimates for fractional anisotropy (FA) measurements of DTI. Although this label transfer methodology is demonstrated in the context of quality control of DTI images, the proposed framework is applicable to any application where the segmentation of unlabeled modalities is limited due to the current collection of available atlases.
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Affiliation(s)
- Andrew J Asman
- Electrical Engineering, Vanderbilt University, Nashville, TN, USA 37235
| | - Carolyn B Lauzon
- Electrical Engineering, Vanderbilt University, Nashville, TN, USA 37235
| | - Bennett A Landman
- Electrical Engineering, Vanderbilt University, Nashville, TN, USA 37235 ; Biomedical Engineering, Vanderbilt University, Nashville, TN, USA 37235
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11
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Green CR, Lebel C, Rasmussen C, Beaulieu C, Reynolds JN. Diffusion tensor imaging correlates of saccadic reaction time in children with fetal alcohol spectrum disorder. Alcohol Clin Exp Res 2013; 37:1499-507. [PMID: 23551175 DOI: 10.1111/acer.12132] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [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: 12/01/2011] [Accepted: 01/22/2013] [Indexed: 11/28/2022]
Abstract
BACKGROUND Eye movement tasks provide a simple method for inferring structural or functional brain deficits in neurodevelopmental disorders. Oculomotor control is impaired in children with fetal alcohol spectrum disorder (FASD), yet the neuroanatomical substrates underlying this are not known. Regions of white matter have been shown by diffusion tensor imaging (DTI) to be different in FASD and thus may play a role in the delayed saccadic eye movements. The objective of this study was to correlate oculomotor performance with regional measures of DTI-derived white matter anisotropy in children with FASD. METHODS Fourteen children (8 to 13 years) with FASD were recruited for oculomotor assessment and DTI. Eye movement control was evaluated using the pro- and antisaccade tasks, in which subjects look at (prosaccade) or away from (antisaccade) a peripheral target. Saccadic reaction time (SRT; time for subjects to move their eyes after the target appears) and direction errors (saccades made in the incorrect direction relative to the instruction) were measured and correlated to fractional anisotropy (FA) on a voxel-by-voxel basis across the whole brain white matter. RESULTS A significant positive correlation was observed between antisaccade SRT and FA in a large cluster containing anterior and posterior sections of the corpus callosum just to the right of the midline; prosaccade SRT and FA correlated positively in the genu of the corpus callosum and the right inferior longitudinal fasciculus (ILF), and correlated negatively in the left cerebellum. CONCLUSIONS The negative correlation for prosaccade SRT and cerebellum demonstrated that individuals with slower reaction times had lower FA values relative to their faster responding counterparts, a finding that implicates cerebellar dysfunction as a significant contributor to deficits in oculomotor control. The higher FA in the corpus callosum and ILF corresponding to longer reaction times for both pro- and antisaccade was opposite to what was expected, but nonetheless implies that altered brain structure in these regions underlies deficits in oculomotor control.
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Affiliation(s)
- Courtney R Green
- The Centre for Neuroscience Studies , Queen's University, Kingston, ON, Canada; Department of Biomedical and Molecular Sciences , Queen's University, Kingston, ON, Canada
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Soleimanifard S, Abd-Elmoniem KZ, Agarwal HK, Tomas MS, Sasano T, Vonken E, Youssef A, Abraham MR, Abraham TP, Prince JL. IDENTIFICATION OF MYOCARDIAL INFARCTION USING THREE-DIMENSIONAL STRAIN TENSOR FRACTIONAL ANISOTROPY. Proc IEEE Int Symp Biomed Imaging 2010; 2010:468-471. [PMID: 24443666 PMCID: PMC3892898 DOI: 10.1109/isbi.2010.5490309] [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: 06/03/2023]
Abstract
Accurate localization of myocardial viability is important in diagnosis of infarction. Regional strain function provides excessive information for clinical decision making but comparison of strain tensor profiles across differing tissue types is usually difficult due to multivariate nature of tensors. It is desirable to describe tensors with simplified scalar indices which are more mathematically and statistically intuitive. In this work, anisotropy of tensors in healthy and experimental infarct regions in a large animal model is assessed and compared to directional components of strain tensors which are currently the most popular indices in active use. Myocardial strain tensors are computed using zHARP, a magnetic resonance (MR) tagging technique that provides quantification of cardiac function with direct computation of three-dimensional tensors from two-dimensional short axis MR images. Fractional anisotropy of strain tensors shows high correlation with late gadolinium enhanced images and is capable of discrimination between healthy and infarcted regions.
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Affiliation(s)
- Sahar Soleimanifard
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, M.D., USA
| | - Khaled Z Abd-Elmoniem
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, M.D., USA
| | - Harsh K Agarwal
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, M.D., USA
| | - Miguel S Tomas
- Department of Cardiology, School of Medicine, Johns Hopkins University, Baltimore, M.D., USA
| | - Tetsuo Sasano
- Department of Cardiology, School of Medicine, Johns Hopkins University, Baltimore, M.D., USA
| | - Evertjan Vonken
- Department of Cardiology, School of Medicine, Johns Hopkins University, Baltimore, M.D., USA
| | - Amr Youssef
- Department of Cardiology, School of Medicine, Johns Hopkins University, Baltimore, M.D., USA
| | - M Roselle Abraham
- Department of Cardiology, School of Medicine, Johns Hopkins University, Baltimore, M.D., USA
| | - Theodore P Abraham
- Department of Cardiology, School of Medicine, Johns Hopkins University, Baltimore, M.D., USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, M.D., USA
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