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Nicholson S, Russo AW, Brewer K, Bien H, Tobyne SM, Eloyan A, Klawiter EC. The effect of ibudilast on thalamic volume in progressive multiple sclerosis. Mult Scler 2023; 29:1819-1830. [PMID: 37947294 PMCID: PMC10841081 DOI: 10.1177/13524585231204710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
BACKGROUND Thalamic volume loss is known to be associated with clinical and cognitive disability in progressive multiple sclerosis (PMS). OBJECTIVE To investigate the treatment effect of ibudilast on thalamic atrophy more than 96 weeks in the phase 2 trial in progressive(MS Secondary and Primary Progressive Ibudilast NeuroNEXT Trial in Multiple Sclerosis [SPRINT-MS]). METHODS A total of 231 participants were randomized to either ibudilast (n = 114) or placebo (n = 117). Thalamic volume change was computed using Bayesian Sequence Adaptive Multimodal Segmentation tool (SAMseg) incorporating T1, fluid-attenuated inversion recovery (FLAIR), and fractional anisotropy maps and analyzed with a mixed-effects repeated-measures model. RESULTS There was no significant difference in thalamic volumes between treatment groups. On exploratory analysis, participants with primary progressive multiple sclerosis (PPMS) on placebo had a 0.004% greater rate of thalamic atrophy than PPMS participants on ibudilast (p = 0.058, 95% confidence interval (CI) = -0.008 to <0.001). Greater reductions in thalamic volumes at more than 96 weeks were associated with worsening multiple sclerosis functional composite (MSFC-4) scores (p = 0.002) and worsening performance on the symbol digit modality test (SDMT) (p < 0.001). CONCLUSION In a phase 2 trial evaluating ibudilast in PMS, no treatment effect was demonstrated in preventing thalamic atrophy. Participants with PPMS exhibited a treatment effect that trended toward significance. Longitudinal changes in thalamic volume were related to worsening of physical and cognitive disability, highlighting this outcome's clinical importance.
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Affiliation(s)
- Showly Nicholson
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrew W Russo
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kristina Brewer
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Heidi Bien
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sean M Tobyne
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ani Eloyan
- Department of Biostatistics, Brown University, Providence, RI, USA
| | - Eric C Klawiter
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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2
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Strelnikov D, Alijanpourotaghsara A, Piroska M, Szalontai L, Forgo B, Jokkel Z, Persely A, Hernyes A, Kozak LR, Szabo A, Maurovich-Horvat P, Tarnoki DL, Tarnoki AD. Heritability of Subcortical Grey Matter Structures. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:1687. [PMID: 36422226 PMCID: PMC9696305 DOI: 10.3390/medicina58111687] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 11/10/2022] [Accepted: 11/17/2022] [Indexed: 02/03/2024]
Abstract
Background and Objectives: Subcortical grey matter structures play essential roles in cognitive, affective, social, and motoric functions in humans. Their volume changes with age, and decreased volumes have been linked with many neuropsychiatric disorders. The aim of our study was to examine the heritability of six subcortical brain volumes (the amygdala, caudate nucleus, pallidum, putamen, thalamus, and nucleus accumbens) and four general brain volumes (the total intra-cranial volume and the grey matter, white matter, and cerebrospinal fluid (CSF) volume) in twins. Materials and Methods: A total of 118 healthy adult twins from the Hungarian Twin Registry (86 monozygotic and 32 dizygotic; median age 50 ± 27 years) underwent brain magnetic resonance imaging. Two automated volumetry pipelines, Computational Anatomy Toolbox 12 (CAT12) and volBrain, were used to calculate the subcortical and general brain volumes from three-dimensional T1-weighted images. Age- and sex-adjusted monozygotic and dizygotic intra-pair correlations were calculated, and the univariate ACE model was applied. Pearson's correlation test was used to compare the results obtained by the two pipelines. Results: The age- and sex-adjusted heritability estimates, using CAT12 for the amygdala, caudate nucleus, pallidum, putamen, and nucleus accumbens, were between 0.75 and 0.95. The thalamus volume was more strongly influenced by common environmental factors (C = 0.45-0.73). The heritability estimates, using volBrain, were between 0.69 and 0.92 for the nucleus accumbens, pallidum, putamen, right amygdala, and caudate nucleus. The left amygdala and thalamus were more strongly influenced by common environmental factors (C = 0.72-0.85). A strong correlation between CAT12 and volBrain (r = 0.74-0.94) was obtained for all volumes. Conclusions: The majority of examined subcortical volumes appeared to be strongly heritable. The thalamus was more strongly influenced by common environmental factors when investigated with both segmentation methods. Our results underline the importance of identifying the relevant genes responsible for variations in the subcortical structure volume and associated diseases.
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Affiliation(s)
- David Strelnikov
- Medical Imaging Centre, Semmelweis University, 1082 Budapest, Hungary
| | | | - Marton Piroska
- Medical Imaging Centre, Semmelweis University, 1082 Budapest, Hungary
| | - Laszlo Szalontai
- Medical Imaging Centre, Semmelweis University, 1082 Budapest, Hungary
| | - Bianka Forgo
- Department of Radiology, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
| | - Zsofia Jokkel
- Medical Imaging Centre, Semmelweis University, 1082 Budapest, Hungary
| | - Alíz Persely
- Medical Imaging Centre, Semmelweis University, 1082 Budapest, Hungary
| | - Anita Hernyes
- Medical Imaging Centre, Semmelweis University, 1082 Budapest, Hungary
| | | | - Adam Szabo
- Medical Imaging Centre, Semmelweis University, 1082 Budapest, Hungary
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Singh M, Pahl E, Wang S, Carass A, Lee J, Prince JL. Accurate Estimation of Total Intracranial Volume in MRI using a Multi-tasked Image-to-Image Translation Network. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2021; 11596. [PMID: 34548736 DOI: 10.1117/12.2582264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Total intracranial volume (TIV) is the volume enclosed inside the cranium, inclusive of the meninges and the brain. TIV is extensively used to correct variations in inter-subject head size for the evaluation of neurodegenerative diseases. In this work, we present an automatic method to generate a TIV mask from MR images while synthesizing a CT image to be used in subsequent analysis. In addition, we propose an alternative way to obtain ground truth TIV masks using a semi-manual approach, which results in significant time savings. We train a conditional generative adversarial network (cGAN) using 2D MR slices to realize our tasks. The quantitative evaluation showed that the model was able to synthesize CT and generate TIV masks that closely approximate the reference images. This study also provides a comparison of the described method against skull stripping tools that output a mask enclosing the cranial volume, using MRI scan. In particular, highlighting the deficiencies in using such tools to approximate the volume using MRI scan.
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Affiliation(s)
- Mallika Singh
- Dept. of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21218
| | - Eleanor Pahl
- Dept. of Aerospace Engineering, Embry-Riddle Aeronautical University (ERAU), Prescott, AZ 86301
| | - Shangxian Wang
- Dept. of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218
| | - Aaron Carass
- Dept. of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218
| | - Junghoon Lee
- Dept. of Radiation Oncology and Molecular Radiation Sciences, The Johns Hopkins School of Medicine, Baltimore, MD 21231
| | - Jerry L Prince
- Dept. of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21218.,Dept. of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218
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4
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de Sitter A, Burggraaff J, Bartel F, Palotai M, Liu Y, Simoes J, Ruggieri S, Schregel K, Ropele S, Rocca MA, Gasperini C, Gallo A, Schoonheim MM, Amann M, Yiannakas M, Pareto D, Wattjes MP, Sastre-Garriga J, Kappos L, Filippi M, Enzinger C, Frederiksen J, Uitdehaag B, Guttmann CRG, Barkhof F, Vrenken H. Development and evaluation of a manual segmentation protocol for deep grey matter in multiple sclerosis: Towards accelerated semi-automated references. NEUROIMAGE-CLINICAL 2021; 30:102659. [PMID: 33882422 PMCID: PMC8082260 DOI: 10.1016/j.nicl.2021.102659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 03/19/2021] [Accepted: 03/31/2021] [Indexed: 10/25/2022]
Abstract
BACKGROUND Deep grey matter (dGM) structures, particularly the thalamus, are clinically relevant in multiple sclerosis (MS). However, segmentation of dGM in MS is challenging; labeled MS-specific reference sets are needed for objective evaluation and training of new methods. OBJECTIVES This study aimed to (i) create a standardized protocol for manual delineations of dGM; (ii) evaluate the reliability of the protocol with multiple raters; and (iii) evaluate the accuracy of a fast-semi-automated segmentation approach (FASTSURF). METHODS A standardized manual segmentation protocol for caudate nucleus, putamen, and thalamus was created, and applied by three raters on multi-center 3D T1-weighted MRI scans of 23 MS patients and 12 controls. Intra- and inter-rater agreement was assessed through intra-class correlation coefficient (ICC); spatial overlap through Jaccard Index (JI) and generalized conformity index (CIgen). From sparse delineations, FASTSURF reconstructed full segmentations; accuracy was assessed both volumetrically and spatially. RESULTS All structures showed excellent agreement on expert manual outlines: intra-rater JI > 0.83; inter-rater ICC ≥ 0.76 and CIgen ≥ 0.74. FASTSURF reproduced manual references excellently, with ICC ≥ 0.97 and JI ≥ 0.92. CONCLUSIONS The manual dGM segmentation protocol showed excellent reproducibility within and between raters. Moreover, combined with FASTSURF a reliable reference set of dGM segmentations can be produced with lower workload.
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Affiliation(s)
- Alexandra de Sitter
- Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, NL, Netherlands
| | - Jessica Burggraaff
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, NL, Netherlands.
| | - Fabian Bartel
- Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, NL, Netherlands
| | - Miklos Palotai
- Center for Neurological Imaging, Department of radiology, Brigham and Women's Hospital, Harvard Medical School Boston, MA, USA
| | - Yaou Liu
- Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, NL, Netherlands
| | - Jorge Simoes
- Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, NL, Netherlands
| | - Serena Ruggieri
- Department of Human Neurosciences, "Sapienza" University of Rome, Rome, IT, Italy; Department of Neurosciences, San Camillo Forlanini Hospital, Rome, IT, Italy
| | - Katharina Schregel
- Center for Neurological Imaging, Department of radiology, Brigham and Women's Hospital, Harvard Medical School Boston, MA, USA; Institute of Neuroradiology, University Medical Center Goettingen, Goettingen, DE, Germany
| | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Graz, AT, Austria
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, United States; Neurology Unit, San Raffaele Scientific Institute, UniSR, Milan, IT, Italy
| | - Claudio Gasperini
- Department of Neurosciences, San Camillo Forlanini Hospital, Rome, IT, Italy
| | - Antonio Gallo
- Division of Neurology and 3T MRI Research Center, Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, IT, Italy
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, NL, Netherlands
| | - Michael Amann
- Medical Image Analysis Center (MIAC), United States; Neurologic Clinic and Policlinic and Neuroradiology, Department of Biomedical Engineering, University Hospital Basel, Basel, CH, Switzerland
| | - Marios Yiannakas
- Department of Neuroinflammation, Institute of Neurology, UCL, London, UK
| | - Deborah Pareto
- Section of Neuroradiology and MRI Unit, Department of Radiology, University Hospital Valld'Hebron, Autonomous University of Barcelona, Barcelona, ES, Spain
| | - Mike P Wattjes
- Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, NL, Netherlands; Deptartment of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, DE, Germany
| | - Jaume Sastre-Garriga
- Department of Neurology, University Hospital iValld'Hebron, Autonomous University of Barcelona, Barcelona, ES, Spain
| | - Ludwig Kappos
- Neurologic Clinic and Policlinic and Neuroradiology, Department of Biomedical Engineering, University Hospital Basel, Basel, CH, Switzerland
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, United States; Neurology Unit, San Raffaele Scientific Institute, UniSR, Milan, IT, Italy; Neurophysiology Unit, San Raffaele Scientific Institute, Italy; Vita-Salute San Raffaele University, Milan, IT, Italy
| | - Christian Enzinger
- Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology, Medical University of Graz, Graz, AT, Austria
| | - Jette Frederiksen
- Department of Neurology, Glostrup University Hospital, Copenhagen, DK, Denmark
| | - Bernard Uitdehaag
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, NL, Netherlands
| | - Charles R G Guttmann
- Center for Neurological Imaging, Department of radiology, Brigham and Women's Hospital, Harvard Medical School Boston, MA, USA
| | - Frederik Barkhof
- Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, NL, Netherlands; Institutes of Neurology & Healthcare Engineering, UCL, London, UK
| | - Hugo Vrenken
- Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, NL, Netherlands
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Amin M, Ontaneda D. Thalamic Injury and Cognition in Multiple Sclerosis. Front Neurol 2021; 11:623914. [PMID: 33613423 PMCID: PMC7892763 DOI: 10.3389/fneur.2020.623914] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 12/30/2020] [Indexed: 11/13/2022] Open
Abstract
Multiple sclerosis (MS) produces demyelination and degeneration in both gray and white matter. Both cortical and deep gray matter injury is observed during the course of MS. Among deep gray matter structures, the thalamus has received special attention, as it undergoes volume loss in different MS subtypes and is involved in the earliest form of the disease, radiologically isolated syndrome. The thalamus plays an important role as an information relay center, and involvement of the thalamus in MS has been associated with a variety of clinical manifestations in MS, including fatigue, movement disorders, pain, and cognitive impairment (CI). Similar to thalamic volume loss, CI is seen from the earliest stages of MS and is potentially one of the most debilitating manifestations of the disease. The thalamus, particularly the dorsomedial nucleus as part of the basolateral limbic circuit and anterior thalamic nuclei through connections with the prefrontal cortex, has been shown to be involved in CI. Specifically, several cognitive performance measures such as processing speed and memory correlate with thalamic volume. Thalamic atrophy is one of the most important predictors of CI in MS, and both thalamic volume, diffusion tensor imaging measures, and functional activation correlate with the degree of CI in MS. Although the exact mechanism of thalamic atrophy is not well-understood, it is hypothesized to be secondary to degeneration following white matter injury resulting in secondary neurodegeneration and neuronal loss. The thalamus may represent an ideal biomarker for studies aiming to test neuroprotective or restorative therapies aimed at cognition.
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Affiliation(s)
- Moein Amin
- Neurological Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Daniel Ontaneda
- Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic, Cleveland, OH, United States
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6
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Burggraaff J, Liu Y, Prieto JC, Simoes J, de Sitter A, Ruggieri S, Brouwer I, Lissenberg-Witte BI, Rocca MA, Valsasina P, Ropele S, Gasperini C, Gallo A, Pareto D, Sastre-Garriga J, Enzinger C, Filippi M, De Stefano N, Ciccarelli O, Hulst HE, Wattjes MP, Barkhof F, Uitdehaag BMJ, Vrenken H, Guttmann CRG. Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study. NEUROIMAGE-CLINICAL 2020; 29:102549. [PMID: 33401136 PMCID: PMC7787946 DOI: 10.1016/j.nicl.2020.102549] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 12/09/2020] [Accepted: 12/20/2020] [Indexed: 01/05/2023]
Abstract
BACKGROUND AND RATIONALE Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining. METHODS Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor. RESULTS In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ρ=(-0.42)-(-0.76); p-values < 0.001). All methods significantly distinguished CI from CP MS patients, except manual outlines of the left thalamus (p = 0.23). Poorer global neuropsychological test performance was significantly associated with smaller thalamus volumes bilaterally using all methods. Vendor significantly affected the findings. CONCLUSION Automated and manual thalamus segmentation consistently demonstrated an association between thalamus atrophy and cognitive impairment in MS. However, a proportional bias in smaller thalami and choice of MRI acquisition system might impact the effect size of these findings.
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Affiliation(s)
- Jessica Burggraaff
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1118, 1081 HV Amsterdam, The Netherlands.
| | - Yao Liu
- Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1118, 1081 HV Amsterdam, The Netherlands.
| | - Juan C Prieto
- Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 1249 Boylston Street, Boston, MA 02215, USA.
| | - Jorge Simoes
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1118, 1081 HV Amsterdam, The Netherlands.
| | - Alexandra de Sitter
- Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1118, 1081 HV Amsterdam, The Netherlands.
| | - Serena Ruggieri
- Department of Human Neurosciences, "Sapienza" University of Rome, Piazzale Aldo Moro, 5, 00185 Roma RM, Italy; Department of Neurosciences, San Camillo Forlanini Hospital, Circonvallazione Gianicolense, 87, 00152 Roma RM, Italy.
| | - Iman Brouwer
- Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1118, 1081 HV Amsterdam, The Netherlands.
| | - Birgit I Lissenberg-Witte
- Department of Epidemiology and Biostatistics, Amsterdam UMC, Location VUmc, De Boelelaan 1089a, 1081 HV Amsterdam, the Netherlands.
| | - Mara A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, and Neurology Unit, San Raffaele Scientific Institute, Via Olgettina, 58, 20132 Milano MI, Italy; Neurology Unit, San Raffaele Scientific Institute, Via Olgettina, 58, 20132 Milano MI, Italy.
| | - Paola Valsasina
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, and Neurology Unit, San Raffaele Scientific Institute, Via Olgettina, 58, 20132 Milano MI, Italy.
| | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, 8036 Graz, Austria.
| | - Claudio Gasperini
- Department of Neurosciences, San Camillo Forlanini Hospital, Circonvallazione Gianicolense, 87, 00152 Roma RM, Italy.
| | - Antonio Gallo
- Division of Neurology and 3T MRI Research Center, Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Viale Abramo Lincoln, 5, 81100 Caserta, CE, Napoli, Italy.
| | - Deborah Pareto
- Section of Neuroradiology and MRI Unit, Department of Radiology, University Hospital iValld'Hebron, Autonomous University of Barcelona, Passeig de la Vall d'Hebron 119-129, 08035 Barcelona, Spain.
| | - Jaume Sastre-Garriga
- Department of Neurology, University Hospital iValld'Hebron, Autonomous University of Barcelona, Passeig de la Vall d'Hebron 119-129, 08035 Barcelona, Spain.
| | - Christian Enzinger
- Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 22, 8036 Graz, Austria.
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, and Neurology Unit, San Raffaele Scientific Institute, Via Olgettina, 58, 20132 Milano MI, Italy; Neurology Unit, San Raffaele Scientific Institute, Via Olgettina, 58, 20132 Milano MI, Italy; Neurophysiology Unit, San Raffaele Scientific Institute, and (14)Vita-Salute San Raffaele University, Via Olgettina, 58, 20132 Milano, MI, Italy; Department of Neurological and Behavioural Sciences, University of Siena, 53100 Siena SI, Italy.
| | - Nicola De Stefano
- Department of Neurological and Behavioural Sciences, University of Siena, 53100 Siena SI, Italy.
| | - Olga Ciccarelli
- Department of Neuroinflammation UCL, Queen Square Institute of Neurology UCL, Queen Square, London WC1N 3BG, United Kingdom.
| | - Hanneke E Hulst
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1108, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands.
| | - Mike P Wattjes
- Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1118, 1081 HV Amsterdam, The Netherlands; Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Carl-Neuberg-Straße, 30625 Hannover, Germany.
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1118, 1081 HV Amsterdam, The Netherlands; Institutes of Neurology & Healthcare Engineering, UCL, 235 Euston Rd, Bloomsbury, London NW1 2BU, United Kingdom.
| | - Bernard M J Uitdehaag
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1118, 1081 HV Amsterdam, The Netherlands.
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1118, 1081 HV Amsterdam, The Netherlands.
| | - Charles R G Guttmann
- Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 1249 Boylston Street, Boston, MA 02215, USA.
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7
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Majdi MS, Keerthivasan MB, Rutt BK, Zahr NM, Rodriguez JJ, Saranathan M. Automated thalamic nuclei segmentation using multi-planar cascaded convolutional neural networks. Magn Reson Imaging 2020; 73:45-54. [PMID: 32828985 DOI: 10.1016/j.mri.2020.08.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 07/25/2020] [Accepted: 08/17/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE To develop a fast and accurate convolutional neural network based method for segmentation of thalamic nuclei. METHODS A cascaded multi-planar scheme with a modified residual U-Net architecture was used to segment thalamic nuclei on conventional and white-matter-nulled (WMn) magnetization prepared rapid gradient echo (MPRAGE) data. A single network was optimized to work with images from healthy controls and patients with multiple sclerosis (MS) and essential tremor (ET), acquired at both 3 T and 7 T field strengths. WMn-MPRAGE images were manually delineated by a trained neuroradiologist using the Morel histological atlas as a guide to generate reference ground truth labels. Dice similarity coefficient and volume similarity index (VSI) were used to evaluate performance. Clinical utility was demonstrated by applying this method to study the effect of MS on thalamic nuclei atrophy. RESULTS Segmentation of each thalamus into twelve nuclei was achieved in under a minute. For 7 T WMn-MPRAGE, the proposed method outperforms current state-of-the-art on patients with ET with statistically significant improvements in Dice for five nuclei (increase in the range of 0.05-0.18) and VSI for four nuclei (increase in the range of 0.05-0.19), while performing comparably for healthy and MS subjects. Dice and VSI achieved using 7 T WMn-MPRAGE data are comparable to those using 3 T WMn-MPRAGE data. For conventional MPRAGE, the proposed method shows a statistically significant Dice improvement in the range of 0.14-0.63 over FreeSurfer for all nuclei and disease types. Effect of noise on network performance shows robustness to images with SNR as low as half the baseline SNR. Atrophy of four thalamic nuclei and whole thalamus was observed for MS patients compared to healthy control subjects, after controlling for the effect of parallel imaging, intracranial volume, gender, and age (p < 0.004). CONCLUSION The proposed segmentation method is fast, accurate, performs well across disease types and field strengths, and shows great potential for improving our understanding of thalamic nuclei involvement in neurological diseases.
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Affiliation(s)
- Mohammad S Majdi
- Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States of America
| | - Mahesh B Keerthivasan
- Department of Medical Imaging, University of Arizona, Tucson, AZ, United States of America; Siemens Healthcare, Tucson, AZ, USA
| | - Brian K Rutt
- Department of Radiology, Stanford University, Stanford, CA, United States of America
| | - Natalie M Zahr
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States of America
| | - Jeffrey J Rodriguez
- Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States of America
| | - Manojkumar Saranathan
- Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States of America; Department of Medical Imaging, University of Arizona, Tucson, AZ, United States of America.
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8
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Alemán-Gómez Y, Najdenovska E, Roine T, Fartaria MJ, Canales-Rodríguez EJ, Rovó Z, Hagmann P, Conus P, Do KQ, Klauser P, Steullet P, Baumann PS, Bach Cuadra M. Partial-volume modeling reveals reduced gray matter in specific thalamic nuclei early in the time course of psychosis and chronic schizophrenia. Hum Brain Mapp 2020; 41:4041-4061. [PMID: 33448519 PMCID: PMC7469814 DOI: 10.1002/hbm.25108] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 04/22/2020] [Accepted: 06/14/2020] [Indexed: 12/20/2022] Open
Abstract
The structural complexity of the thalamus, due to its mixed composition of gray and white matter, make it challenging to disjoint and quantify each tissue contribution to the thalamic anatomy. This work promotes the use of partial‐volume‐based over probabilistic‐based tissue segmentation approaches to better capture thalamic gray matter differences between patients at different stages of psychosis (early and chronic) and healthy controls. The study was performed on a cohort of 23 patients with schizophrenia, 41 with early psychosis and 69 age and sex‐matched healthy subjects. Six tissue segmentation approaches were employed to obtain the gray matter concentration/probability images. The statistical tests were applied at three different anatomical scales: whole thalamus, thalamic subregions and voxel‐wise. The results suggest that the partial volume model estimation of gray matter is more sensitive to detect atrophies within the thalamus of patients with psychosis. However all the methods detected gray matter deficit in the pulvinar, particularly in early stages of psychosis. This study demonstrates also that the gray matter decrease varies nonlinearly with age and between nuclei. While a gray matter loss was found in the pulvinar of patients in both stages of psychosis, reduced gray matter in the mediodorsal was only observed in early psychosis subjects. Finally, our analyses point to alterations in a sub‐region comprising the lateral posterior and ventral posterior nuclei. The obtained results reinforce the hypothesis that thalamic gray matter assessment is more reliable when the tissues segmentation method takes into account the partial volume effect.
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Affiliation(s)
- Yasser Alemán-Gómez
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Switzerland
| | - Elena Najdenovska
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Timo Roine
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Switzerland
| | - Mário João Fartaria
- Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Erick J Canales-Rodríguez
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,FIDMAG Germanes Hospitalàries Research Foundation, Sant Boi de Llobregat, Barcelona, Spain
| | - Zita Rovó
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Patric Hagmann
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Philippe Conus
- Service of General Psychiatry, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Kim Q Do
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Paul Klauser
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Pascal Steullet
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Philipp S Baumann
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Service of General Psychiatry, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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9
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Carass A, Roy S, Gherman A, Reinhold JC, Jesson A, Arbel T, Maier O, Handels H, Ghafoorian M, Platel B, Birenbaum A, Greenspan H, Pham DL, Crainiceanu CM, Calabresi PA, Prince JL, Roncal WRG, Shinohara RT, Oguz I. Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis. Sci Rep 2020; 10:8242. [PMID: 32427874 PMCID: PMC7237671 DOI: 10.1038/s41598-020-64803-w] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 04/20/2020] [Indexed: 11/09/2022] Open
Abstract
The Sørensen-Dice index (SDI) is a widely used measure for evaluating medical image segmentation algorithms. It offers a standardized measure of segmentation accuracy which has proven useful. However, it offers diminishing insight when the number of objects is unknown, such as in white matter lesion segmentation of multiple sclerosis (MS) patients. We present a refinement for finer grained parsing of SDI results in situations where the number of objects is unknown. We explore these ideas with two case studies showing what can be learned from our two presented studies. Our first study explores an inter-rater comparison, showing that smaller lesions cannot be reliably identified. In our second case study, we demonstrate fusing multiple MS lesion segmentation algorithms based on the insights into the algorithms provided by our analysis to generate a segmentation that exhibits improved performance. This work demonstrates the wealth of information that can be learned from refined analysis of medical image segmentations.
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Affiliation(s)
- Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA.
- Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Snehashis Roy
- CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, 20817, USA
| | - Adrian Gherman
- Department of Biostatistics, The Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Jacob C Reinhold
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Andrew Jesson
- Centre For Intelligent Machines, McGill University, Montréal, QC, H3A 0E9, Canada
| | - Tal Arbel
- Centre For Intelligent Machines, McGill University, Montréal, QC, H3A 0E9, Canada
| | - Oskar Maier
- Institute of Medical Informatics, University of Lübeck, 23538, Lübeck, Germany
| | - Heinz Handels
- Institute of Medical Informatics, University of Lübeck, 23538, Lübeck, Germany
| | - Mohsen Ghafoorian
- Institute for Computing and Information Sciences, Radboud University, 6525, HP, Nijmegen, Netherlands
| | - Bram Platel
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6525, GA, Nijmegen, Netherlands
| | - Ariel Birenbaum
- Department of Electrical Engineering, Tel-Aviv University, Tel-Aviv, 69978, Israel
| | - Hayit Greenspan
- Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv, 69978, Israel
| | - Dzung L Pham
- CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, 20817, USA
| | - Ciprian M Crainiceanu
- Department of Biostatistics, The Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Peter A Calabresi
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - William R Gray Roncal
- Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics & Epidemiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ipek Oguz
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, 37203, USA
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10
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Hyatt CS, Owens MM, Crowe ML, Carter NT, Lynam DR, Miller JD. The quandary of covarying: A brief review and empirical examination of covariate use in structural neuroimaging studies on psychological variables. Neuroimage 2019; 205:116225. [PMID: 31568872 DOI: 10.1016/j.neuroimage.2019.116225] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 07/12/2019] [Accepted: 09/23/2019] [Indexed: 12/17/2022] Open
Abstract
Although covarying for potential confounds or nuisance variables is common in psychological research, relatively little is known about how the inclusion of covariates may influence the relations between psychological variables and indices of brain structure. In Part 1 of the current study, we conducted a descriptive review of relevant articles from the past two years of NeuroImage in order to identify the most commonly used covariates in work of this nature. Age, sex, and intracranial volume were found to be the most commonly used covariates, although the number of covariates used ranged from 0 to 14, with 37 different covariate sets across the 68 models tested. In Part 2, we used data from the Human Connectome Project to investigate the degree to which the addition of common covariates altered the relations between individual difference variables (i.e., personality traits, psychopathology, cognitive tasks) and regional gray matter volume (GMV), as well as the statistical significance of values associated with these effect sizes. Using traditional and random sampling approaches, our results varied widely, such that some covariate sets influenced the relations between the individual difference variables and GMV very little, while the addition of other covariate sets resulted in a substantially different pattern of results compared to models with no covariates. In sum, these results suggest that the use of covariates should be critically examined and discussed as part of the conversation on replicability in structural neuroimaging. We conclude by recommending that researchers pre-register their analytic strategy and present information on how relations differ based on the inclusion of covariates.
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Affiliation(s)
| | - Max M Owens
- University of Georgia, USA; University of Vermont, USA
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11
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Fritz NE, Eloyan A, Glaister J, Dewey BE, Al-Louzi O, Costello MG, Chen M, Prince JL, Calabresi PA, Zackowski KM. Quantitative vibratory sensation measurement is related to sensory cortical thickness in MS. Ann Clin Transl Neurol 2019; 6:586-595. [PMID: 30911581 PMCID: PMC6414478 DOI: 10.1002/acn3.734] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 12/20/2018] [Accepted: 01/17/2019] [Indexed: 11/16/2022] Open
Abstract
Objective Vibratory sensation is a quantifiable measure of physical dysfunction and is often related to spinal cord pathology; however, its association with relevant brain areas has not been fully explored. Our objective was to establish a cortical structural substrate for vibration sensation. Methods Eighty‐four individuals with multiple sclerosis (MS) (n = 54 relapsing, n = 30 progressive) and 28 controls participated in vibratory sensation threshold quantification at the great toe and a 3T MRI evaluating volume of the thalamus and cortical thickness primary and secondary sensory cortices. Results After controlling for age, sex, and disability level, vibratory sensation thresholds were significantly related to cortical thickness of the anterior cingulate (P = 0.041), parietal operculum (P = 0.022), and inferior frontal gyrus pars operculum (P = 0.044), pars orbitalis (P = 0.007), and pars triangularis (P = 0.029). Within the progressive disease subtype, there were significant relationships between vibratory sensation and thalamic volume (P = 0.039) as well as reduced inferior frontal gyrus pars operculum (P = 0.014) and pars orbitalis (P = 0.005) cortical thickness. Conclusions The data show significant independent relationships between quantitative vibratory sensation and measures of primary and secondary sensory cortices. Quantitative clinical measurement of vibratory sensation reflects pathological changes in spatially distinct brain areas and may supplement information captured by brain atrophy measures. Without overt relapses, monitoring decline in progressive forms of MS has proved challenging; quantitative clinical assessment may provide a tool to examine pathological decline in this cohort. These data suggest that quantitative clinical assessment may be a reliable way to examine pathological decline and have broader relevance to progressive forms of MS.
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Affiliation(s)
- Nora E Fritz
- Center for Movement Studies Kennedy Krieger Institute Baltimore Maryland.,Department of Physical Medicine and Rehabilitation Johns Hopkins School of Medicine Baltimore Maryland.,Program in Physical Therapy and Department of Neurology Wayne State University Detroit Michigan
| | - Ani Eloyan
- Department of Biostatistics Brown University Providence Rhode Island
| | - Jeffrey Glaister
- Department of Electrical and Computer Engineering Johns Hopkins University Baltimore Maryland
| | - Blake E Dewey
- Department of Electrical and Computer Engineering Johns Hopkins University Baltimore Maryland.,F.M. Kirby Center for Functional Brain Imaging Kennedy Krieger Institute Baltimore Maryland
| | - Omar Al-Louzi
- Department of Neurology Massachusetts General Hospital Brigham and Women's Hospital Harvard Medical School Boston Massachusetts.,Department of Neurology Johns Hopkins School of Medicine Baltimore Maryland
| | - M Gabriela Costello
- Center for Movement Studies Kennedy Krieger Institute Baltimore Maryland.,Department of Physical Medicine and Rehabilitation Johns Hopkins School of Medicine Baltimore Maryland
| | - Min Chen
- Department of Electrical and Computer Engineering Johns Hopkins University Baltimore Maryland.,Department of Radiology University of Pennsylvania Philadelphia Pennsylvania
| | - Jerry L Prince
- Department of Electrical and Computer Engineering Johns Hopkins University Baltimore Maryland
| | - Peter A Calabresi
- Department of Neurology Johns Hopkins School of Medicine Baltimore Maryland
| | - Kathleen M Zackowski
- Center for Movement Studies Kennedy Krieger Institute Baltimore Maryland.,Department of Physical Medicine and Rehabilitation Johns Hopkins School of Medicine Baltimore Maryland.,Department of Neurology Johns Hopkins School of Medicine Baltimore Maryland
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12
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Xue SW, Wang D, Tan Z, Wang Y, Lian Z, Sun Y, Hu X, Wang X, Zhou X. Disrupted Brain Entropy And Functional Connectivity Patterns Of Thalamic Subregions In Major Depressive Disorder. Neuropsychiatr Dis Treat 2019; 15:2629-2638. [PMID: 31571880 PMCID: PMC6750201 DOI: 10.2147/ndt.s220743] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 09/04/2019] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Entropy analysis of resting-state functional magnetic resonance imaging (R-fMRI) has recently been adopted to characterize brain temporal dynamics in some neuropsychological or psychiatric diseases. Thalamus-related dysfunction might be a potential trait marker of major depressive disorder (MDD), but the abnormal changes in the thalamus based on R-fMRI are still unclear from the perspective of brain temporal dynamics. The aim of this study was to identify local entropy changes and subregional connectivity patterns of the thalamus in MDD patients. PATIENTS AND METHODS We measured the sample entropy of the R-fMRI data from 46 MDD patients and 32 matched healthy controls. We employed the Louvain method for the module detection algorithm to automatically identify a functional parcellation of the thalamus and then examined the whole-brain subregional connectivity patterns. RESULTS The results indicated that the MDD patients had decreased entropy in the bilateral thalami compared with healthy controls. Increased functional connectivity between the thalamic subregions and the medial part of the superior frontal gyrus (mSFG) was found in MDD patients. CONCLUSION This study showed new evidence about sample entropy changes in MDD patients. The functional connectivity alterations that were widely distributed across almost all the thalamic subregions with the mSFG in MDD suggest a general involvement independent of the location and function of the subregions.
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Affiliation(s)
- Shao-Wei Xue
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
| | - Donglin Wang
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
| | - Zhonglin Tan
- Department of Psychiatry, Hangzhou Seventh People's Hospital, Hangzhou 310013, People's Republic of China
| | - Yan Wang
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
| | - Zhenzhen Lian
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
| | - Yunkai Sun
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
| | - Xiaojiao Hu
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
| | - Xiaole Wang
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
| | - Xin Zhou
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
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