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Magalhães TNC, Hicks TH, Jackson TB, Ballard HK, Herrejon IA, Bernard JA. Sex-steroid hormones relate to cerebellar structure and functional connectivity across adulthood. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.24.600454. [PMID: 38979355 PMCID: PMC11230255 DOI: 10.1101/2024.06.24.600454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Aging involves complex biological changes that affect disease susceptibility and aging trajectories. Although females typically live longer than males, they have a higher susceptibility to diseases like Alzheimer's, speculated to be influenced by menopause, and reduced ovarian hormone production. Understanding sex-specific differences is crucial for personalized medical interventions and gender equality in health. Our study aims to elucidate sex differences in regional cerebellar structure and connectivity during normal aging by investigating both structural and functional connectivity variations, with a focus on investigating these differences in the context of sex-steroid hormones. The study included 138 participants (mean age = 57(13.3) years, age range = 35-86 years, 54% women). The cohort was divided into three groups: 38 early middle-aged individuals (EMA) (mean age = 41(4.7) years), 48 late middle-aged individuals (LMA) (mean age = 58(4) years), and 42 older adults (OA) (mean age = 72(6.3) years). All participants underwent MRI scans, and saliva samples were collected for sex-steroid hormone quantification (17β-estradiol (E), progesterone (P), and testosterone (T)). We found less connectivity in females between Lobule I-IV and the cuneus, and greater connectivity in females between Crus I, Crus II, and the precuneus with increased age. Higher 17β-estradiol levels were linked to greater connectivity in Crus I and Crus II cerebellar subregions. Analyzing all participants together, testosterone was associated with both higher and lower connectivity in Lobule I-IV and Crus I, respectively, while higher progesterone levels were linked to lower connectivity in females. Structural differences were observed, with EMA males having larger volumes compared to LMA and OA groups, particularly in the right I-IV, right Crus I, right V, and right VI. EMA females showed higher volumes in the right lobules V and VI. These results highlight the significant role of sex hormones in modulating cerebellar connectivity and structure across adulthood, emphasizing the need to consider sex and hormonal status in neuroimaging studies to better understand age-related cognitive decline and neurological disorders.
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Affiliation(s)
- Thamires N C Magalhães
- Department of Psychological and Brain Sciences, Texas A&M University, College Station, Texas, United States of America
| | - Tracey H Hicks
- Department of Psychological and Brain Sciences, Texas A&M University, College Station, Texas, United States of America
| | - T Bryan Jackson
- Vanderbilt Memory & Alzheimer's Center, Nashville, Tennessee, United States of America
| | - Hannah K Ballard
- Department of Psychological Sciences, William Marsh Rice University, Houston, Texas, United States of America
| | - Ivan A Herrejon
- Department of Psychological and Brain Sciences, Texas A&M University, College Station, Texas, United States of America
| | - Jessica A Bernard
- Department of Psychological and Brain Sciences, Texas A&M University, College Station, Texas, United States of America
- Department of Psychological Sciences, William Marsh Rice University, Houston, Texas, United States of America
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2
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Strike LT, Kerestes R, McMahon KL, de Zubicaray GI, Harding IH, Medland SE. Heritability of cerebellar subregion volumes in adolescent and young adult twins. Hum Brain Mapp 2024; 45:e26717. [PMID: 38798116 PMCID: PMC11128777 DOI: 10.1002/hbm.26717] [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] [Received: 01/02/2024] [Revised: 04/23/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024] Open
Abstract
Twin studies have found gross cerebellar volume to be highly heritable. However, whether fine-grained regional volumes within the cerebellum are similarly heritable is still being determined. Anatomical MRI scans from two independent datasets (QTIM: Queensland Twin IMaging, N = 798, mean age 22.1 years; QTAB: Queensland Twin Adolescent Brain, N = 396, mean age 11.3 years) were combined with an optimised and automated cerebellum parcellation algorithm to segment and measure 28 cerebellar regions. We show that the heritability of regional volumetric measures varies widely across the cerebellum (h 2 $$ {h}^2 $$ 47%-91%). Additionally, the good to excellent test-retest reliability for a subsample of QTIM participants suggests that non-genetic variance in cerebellar volumes is due primarily to unique environmental influences rather than measurement error. We also show a consistent pattern of strong associations between the volumes of homologous left and right hemisphere regions. Associations were predominantly driven by genetic effects shared between lobules, with only sparse contributions from environmental effects. These findings are consistent with similar studies of the cerebrum and provide a first approximation of the upper bound of heritability detectable by genome-wide association studies.
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Affiliation(s)
- Lachlan T. Strike
- Psychiatric Genetics, QIMR Berghofer Medical Research InstituteBrisbaneAustralia
- School of Psychology and Counselling, Faculty of HealthQueensland University of TechnologyKelvin GroveQueenslandAustralia
- School of Biomedical Sciences, Faculty of MedicineUniversity of QueenslandBrisbaneAustralia
| | - Rebecca Kerestes
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneAustralia
| | - Katie L. McMahon
- School of Clinical Sciences, Centre for Biomedical TechnologiesQueensland University of TechnologyBrisbaneQueenslandAustralia
| | - Greig I. de Zubicaray
- School of Psychology and Counselling, Faculty of HealthQueensland University of TechnologyKelvin GroveQueenslandAustralia
| | - Ian H. Harding
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneAustralia
- Cerebellum and Neurodegeneration, QIMR Berghofer Medical Research InstituteBrisbaneAustralia
| | - Sarah E. Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research InstituteBrisbaneAustralia
- School of Psychology and Counselling, Faculty of HealthQueensland University of TechnologyKelvin GroveQueenslandAustralia
- School of PsychologyUniversity of QueenslandBrisbaneAustralia
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Sobootian DJ, Bronzlik P, Spineli LM, Becker LS, Winther HB, Bueltmann E. Convolutional Neural Network for Fully Automated Cerebellar Volumetry in Children in Comparison to Manual Segmentation and Developmental Trajectory of Cerebellar Volumes. CEREBELLUM (LONDON, ENGLAND) 2024; 23:1074-1085. [PMID: 37833550 PMCID: PMC11102395 DOI: 10.1007/s12311-023-01609-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/25/2023] [Indexed: 10/15/2023]
Abstract
The purpose of this study was to develop a fully automated and reliable volumetry of the cerebellum of children during infancy and childhood using deep learning algorithms in comparison to manual segmentation. In addition, the clinical usefulness of measuring the cerebellar volume is shown. One hundred patients (0 to 16.3 years old) without infratentorial signal abnormalities on conventional MRI were retrospectively selected from our pool of pediatric MRI examinations. Based on a routinely acquired 3D T1-weighted magnetization prepared rapid gradient echo (MPRAGE) sequence, the cerebella were manually segmented using ITK-SNAP. The data set of all 100 cases was divided into four splits (four-fold cross-validation) to train the network (NN) to delineate the boundaries of the cerebellum. First, the accuracy of the newly created neural network was compared with the manual segmentation. Secondly, age-related volume changes were investigated. Our trained NN achieved an excellent Spearman correlation coefficient of 0.99, a Dice Coefficient of 95.0 ± 2.1%, and an intersection over union (IoU) of 90.6 ± 3.8%. Cerebellar volume increased continuously with age, showing an exponentially rapid growth within the first year of life. Using a convolutional neural network, it was possible to achieve reliable, fully automated cerebellar volume measurements in childhood and infancy, even when based on a relatively small cohort. In this preliminary study, age-dependent cerebellar volume changes could be acquired.
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Affiliation(s)
- Daria Juliane Sobootian
- Institute of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Paul Bronzlik
- Institute of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Loukia M Spineli
- Midwifery Research and Education Unit, Hannover Medical School, Hannover, Germany
| | - Lena Sophie Becker
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Hinrich Boy Winther
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Eva Bueltmann
- Institute of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany.
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Fenoy AJ, Chu ZD, Ritter RJ, Conner CR, Kralik SF. Evaluating functional connectivity differences between DBS ON/OFF states in essential tremor. Neurotherapeutics 2024; 21:e00375. [PMID: 38824101 PMCID: PMC11301224 DOI: 10.1016/j.neurot.2024.e00375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 05/05/2024] [Accepted: 05/13/2024] [Indexed: 06/03/2024] Open
Abstract
Deep brain stimulation (DBS) targeting the ventral intermediate (Vim) nucleus of the thalamus is an effective treatment for essential tremor (ET). We studied 15 ET patients undergoing DBS to a major input/output tract of the Vim, the dentato-rubro-thalamic tract (DRTt), using resting state functional MRI (rsfMRI) to evaluate connectivity differences between DBS ON and OFF and elucidate significant regions most influential in impacting tremor control and/or concomitant gait ataxia. Anatomical/functional 1.5T MRIs were acquired and replicated for each DBS state. Tremor severity and gait ataxia severity were scored with DBS ON at optimal stimulation parameters and immediately upon DBS OFF. Whole brain analysis was performed using dual regression analysis followed by randomized permutation testing for multiple correction comparison. Regions of interest (ROI) analysis was also performed. All 15 patients had tremor improvement between DBS ON/OFF (p < 0.001). Whole brain analysis revealed significant connectivity changes between states in the left pre-central gyrus and left supplemental motor area. Group analysis of ROIs revealed that, with threshold p < 0.05, in DBS ON vs. OFF both tremor duration and tremor improvement were significantly correlated to changes in connectivity. A sub-group analysis of patients with greater ataxia had significantly decreased functional connectivity between multiple ROIs in the cortex and cerebellum when DBS was ON compared to OFF. Stimulation of the DRTt and concordant improvement of tremor resulted in connectivity changes seen in multiple regions outside the motor network; when combined with both structural and electrophysiologic connectivity, this may help to serve as a biomarker to improve DBS targeting and possibly predict outcome.
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Affiliation(s)
- Albert J Fenoy
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA; Departments of Neurosurgery and Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA.
| | - Zili D Chu
- Edward B. Singleton Department of Radiology, Baylor College of Medicine at Texas Children's Hospital, Houston, TX, USA
| | - Robert J Ritter
- Department of Neurosurgery, McGovern School of Medicine, UTHealth Houston, Houston, TX, USA
| | - Christopher R Conner
- Division of Neurosurgery, Dept. of Surgery, University of Connecticut, Hartford, CT, USA
| | - Stephen F Kralik
- Edward B. Singleton Department of Radiology, Baylor College of Medicine at Texas Children's Hospital, Houston, TX, USA
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Kenyon KH, Strik M, Noffs G, Morgan A, Kolbe S, Harding IH, Vogel AP, Boonstra FMC, van der Walt A. Volumetric and diffusion MRI abnormalities associated with dysarthria in multiple sclerosis. Brain Commun 2024; 6:fcae177. [PMID: 38846538 PMCID: PMC11154149 DOI: 10.1093/braincomms/fcae177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 04/16/2024] [Accepted: 05/21/2024] [Indexed: 06/09/2024] Open
Abstract
Up to half of all people with multiple sclerosis experience communication difficulties due to dysarthria, a disorder that impacts the motor aspects of speech production. Dysarthria in multiple sclerosis is linked to cerebellar dysfunction, disease severity and lesion load, but the neuroanatomical substrates of these symptoms remain unclear. In this study, 52 participants with multiple sclerosis and 14 age- and sex-matched healthy controls underwent structural and diffusion MRI, clinical assessment of disease severity and cerebellar dysfunction and a battery of motor speech tasks. Assessments of regional brain volume and white matter integrity, and their relationships with clinical and speech measures, were undertaken. White matter tracts of interest included the interhemispheric sensorimotor tract, cerebello-thalamo-cortical tract and arcuate fasciculus, based on their roles in motor and speech behaviours. Volumetric analyses were targeted to Broca's area, Wernicke's area, the corpus callosum, thalamus and cerebellum. Our results indicated that multiple sclerosis participants scored worse on all motor speech tasks. Fixel-based diffusion MRI analyses showed significant evidence of white matter tract atrophy in each tract of interest. Correlational analyses further indicated that higher speech naturalness-a perceptual measure of dysarthria-and lower reading rate were associated with axonal damage in the interhemispheric sensorimotor tract and left arcuate fasciculus in people with multiple sclerosis. Axonal damage in all tracts of interest also correlated with clinical scales sensitive to cerebellar dysfunction. Participants with multiple sclerosis had lower volumes of the thalamus and corpus callosum compared with controls, although no brain volumetrics correlated with measures of dysarthria. These findings indicate that axonal damage, particularly when measured using diffusion metrics, underpin dysarthria in multiple sclerosis.
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Affiliation(s)
- Katherine H Kenyon
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, VIC 3004, Australia
- Centre for Neuroscience of Speech, University of Melbourne, Parkville, VIC 3052, Australia
| | - Myrte Strik
- Spinoza Centre for Neuroimaging, Netherlands Institute for Neuroscience, Royal Academy for Arts and Sciences, KNAW, Amsterdam 1105 BK, The Netherlands
- Melbourne Brain Centre Imaging Unit, Department of Radiology, University of Melbourne, Parkville, VIC 3052, Australia
| | - Gustavo Noffs
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, VIC 3004, Australia
- Centre for Neuroscience of Speech, University of Melbourne, Parkville, VIC 3052, Australia
- Department of Neurology, Royal Melbourne Hospital, Parkville, VIC 3052, Australia
- Redenlab Inc, Melbourne, VIC 3000, Australia
| | - Angela Morgan
- Murdoch Children’s Research Institute, Genomic Medicine, Speech and Language Group, Parkville 3052, Australia
- Department of Speech Pathology and Audiology, University of Melbourne, Parkville 3052, Australia
| | - Scott Kolbe
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Ian H Harding
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Adam P Vogel
- Centre for Neuroscience of Speech, University of Melbourne, Parkville, VIC 3052, Australia
- Melbourne Brain Centre Imaging Unit, Department of Radiology, University of Melbourne, Parkville, VIC 3052, Australia
- Redenlab Inc, Melbourne, VIC 3000, Australia
- Division of Translational Genomics of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen 72076, Germany
- Center for Neurology, University Hospital Tübingen, Tübingen 72076, Germany
- The Bionics Institute, East Melbourne, VIC 3002, Australia
| | - Frederique M C Boonstra
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Anneke van der Walt
- Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, VIC 3004, Australia
- Spinoza Centre for Neuroimaging, Netherlands Institute for Neuroscience, Royal Academy for Arts and Sciences, KNAW, Amsterdam 1105 BK, The Netherlands
- The Bionics Institute, East Melbourne, VIC 3002, Australia
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6
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Sefik E, Duan K, Li Y, Sholar B, Evans L, Pincus J, Ammar Z, Murphy MM, Klaiman C, Saulnier CA, Pulver SL, Goldman-Yassen AE, Guo Y, Walker EF, Li L, Mulle JG, Shultz S. Structural deviations of the posterior fossa and the cerebellum and their cognitive links in a neurodevelopmental deletion syndrome. Mol Psychiatry 2024:10.1038/s41380-024-02584-8. [PMID: 38744992 DOI: 10.1038/s41380-024-02584-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 04/16/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024]
Abstract
High-impact genetic variants associated with neurodevelopmental disorders provide biologically-defined entry points for mechanistic investigation. The 3q29 deletion (3q29Del) is one such variant, conferring a 40-100-fold increased risk for schizophrenia, as well as high risk for autism and intellectual disability. However, the mechanisms leading to neurodevelopmental disability remain largely unknown. Here, we report the first in vivo quantitative neuroimaging study in individuals with 3q29Del (N = 24) and neurotypical controls (N = 1608) using structural MRI. Given prior radiology reports of posterior fossa abnormalities in 3q29Del, we focused our investigation on the cerebellum and its tissue-types and lobules. Additionally, we compared the prevalence of cystic/cyst-like malformations of the posterior fossa between 3q29Del and controls and examined the association between neuroanatomical findings and quantitative traits to probe gene-brain-behavior relationships. 3q29Del participants had smaller cerebellar cortex volumes than controls, before and after correction for intracranial volume (ICV). An anterior-posterior gradient emerged in finer grained lobule-based and voxel-wise analyses. 3q29Del participants also had larger cerebellar white matter volumes than controls following ICV-correction and displayed elevated rates of posterior fossa arachnoid cysts and mega cisterna magna findings independent of cerebellar volume. Cerebellar white matter and subregional gray matter volumes were associated with visual-perception and visual-motor integration skills as well as IQ, while cystic/cyst-like malformations yielded no behavioral link. In summary, we find that abnormal development of cerebellar structures may represent neuroimaging-based biomarkers of cognitive and sensorimotor function in 3q29Del, adding to the growing evidence identifying cerebellar pathology as an intersection point between syndromic and idiopathic forms of neurodevelopmental disabilities.
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Affiliation(s)
- Esra Sefik
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Kuaikuai Duan
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Yiheng Li
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Brittney Sholar
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA
| | - Lindsey Evans
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA
| | - Jordan Pincus
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA
| | - Zeena Ammar
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA
| | - Melissa M Murphy
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Cheryl Klaiman
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA
| | - Celine A Saulnier
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Neurodevelopmental Assessment & Consulting Services, Atlanta, GA, USA
| | - Stormi L Pulver
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA
| | - Adam E Goldman-Yassen
- Department of Radiology, Children's Healthcare of Atlanta, Atlanta, GA, USA
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Elaine F Walker
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Longchuan Li
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA
| | - Jennifer G Mulle
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA.
| | - Sarah Shultz
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA.
- Marcus Autism Center, Children's Healthcare of Atlanta and Emory University School of Medicine, Atlanta, GA, USA.
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Rice LC, Rochowiak RN, Plotkin MR, Rosch KS, Mostofsky SH, Crocetti D. Sex Differences and Behavioral Associations with Typically Developing Pediatric Regional Cerebellar Gray Matter Volume. CEREBELLUM (LONDON, ENGLAND) 2024; 23:589-600. [PMID: 37382829 PMCID: PMC10986327 DOI: 10.1007/s12311-023-01569-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/15/2023] [Indexed: 06/30/2023]
Abstract
The cerebellum contributes to motor and higher-order control throughout neurodevelopment, with marked growth during childhood. Few studies have investigated differential associations of cerebellar morphometry with function in males and females. The present study examines sex differences in regional cerebellar gray matter volume (GMV) and the moderating effect of sex on the relationship between GMV and motor, cognitive, and emotional functions in a large cohort of typically developing (TD) children. Participants included 371 TD children (123 females, age 8-12 years). A convolutional neural network-based approach was employed for cerebellar parcellation. Volumes were harmonized using ComBat to adjust for hardware-induced variations. Regression analyses examined the effect of sex on GMV and whether sex moderated the relationship between GMV and motor, cognitive, and emotional functions. Males showed larger GMV in right lobules I-V, bilateral lobules VI, crus II/VIIb, and VIII, left lobule X, and vermis regions I-V and VIII-X. Greater motor function correlated with less vermis VI-VII GMV in females. Greater cognitive function correlated with greater left lobule VI GMV in females and less left lobule VI GMV in males. Finally, greater internalizing symptoms correlated with greater bilateral lobule IX GMV in females but less in males. These findings reveal sexually dimorphic patterns of cerebellar structure and associations with motor, cognitive, and emotional functions. Males generally show larger GMV than females. Larger GMV was associated with better cognitive functioning for females and better motor/emotional functioning for males.
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Affiliation(s)
- Laura C Rice
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, 716 N. Broadway, Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Rebecca N Rochowiak
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, 716 N. Broadway, Baltimore, MD, 21205, USA
| | - Micah R Plotkin
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, 716 N. Broadway, Baltimore, MD, 21205, USA
| | - Keri S Rosch
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, 716 N. Broadway, Baltimore, MD, 21205, USA
- Neuropsychology Department, Kennedy Krieger Institute, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Stewart H Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, 716 N. Broadway, Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Deana Crocetti
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, 716 N. Broadway, Baltimore, MD, 21205, USA.
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Lee DA, Lee HJ, Park KM. Altered cerebellar volumes and intrinsic cerebellar networks in patients with transient global amnesia. Brain Imaging Behav 2024; 18:315-323. [PMID: 38057649 DOI: 10.1007/s11682-023-00833-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/26/2023] [Indexed: 12/08/2023]
Abstract
This study aimed to investigate the differences in cerebellar volumes and intrinsic cerebellar networks between patients with transient global amnesia (TGA) and healthy controls. We retrospectively enrolled patients with TGA and age- and sex-matched healthy controls. We used three-dimensional T1-weighted imaging at the time of TGA diagnosis to obtain cerebellar volumes, and the intrinsic cerebellar network was calculated by applying graph theory based on cerebellar volumes. The nodes were defined as individual cerebellar volumes, and edges as partial correlations, controlling for the effects of age and sex. The cerebellar volumes and intrinsic cerebellar networks were compared between the two groups. We enrolled 44 patients with TGA and 47 healthy controls. The volume of the left cerebellar white matter in patients with TGA was significantly lower than that in healthy controls (1.0328 vs. 1.0753%, p = 0.0094). In addition, there were significant differences in intrinsic cerebellar networks between the two groups. The small-worldness index in patients with TGA was higher than that in the healthy controls (0.951 vs. 0.880, p = 0.038). In the correlation analysis, the volumes of the right cerebellar cortex and lobules VIIIB were significantly correlated with age in patients with TGA (r = -0.323, p = 0.033; r = -0.313, p = 0.038, respectively). Patients with TGA exhibit alterations in cerebellar volumes and intrinsic cerebellar networks compared with healthy controls. These findings may contribute to a better understanding of the pathophysiology of the TGA.
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Affiliation(s)
- Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-Ro 875, Haeundae-Gu, Busan, Republic of Korea
| | - Ho-Joon Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-Ro 875, Haeundae-Gu, Busan, Republic of Korea.
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Kerestes R, Perry A, Vivash L, O’Brien TJ, Alvim MK, Arienzo D, Aventurato ÍK, Ballerini A, Baltazar GF, Bargalló N, Bender B, Brioschi R, Bürkle E, Caligiuri ME, Cendes F, de Tisi J, Duncan JS, Engel JP, Foley S, Fortunato F, Gambardella A, Giacomini T, Guerrini R, Hall G, Hamandi K, Ives-Deliperi V, João RB, Keller SS, Kleiser B, Labate A, Lenge M, Marotta C, Martin P, Mascalchi M, Meletti S, Owens-Walton C, Parodi CB, Pascual-Diaz S, Powell D, Rao J, Rebsamen M, Reiter J, Riva A, Rüber T, Rummel C, Scheffler F, Severino M, Silva LS, Staba RJ, Stein DJ, Striano P, Taylor PN, Thomopoulos SI, Thompson PM, Tortora D, Vaudano AE, Weber B, Wiest R, Winston GP, Yasuda CL, Zheng H, McDonald CR, Sisodiya SM, Harding IH. Patterns of subregional cerebellar atrophy across epilepsy syndromes: An ENIGMA-Epilepsy study. Epilepsia 2024; 65:1072-1091. [PMID: 38411286 PMCID: PMC11120093 DOI: 10.1111/epi.17881] [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] [Received: 06/20/2023] [Revised: 12/26/2023] [Accepted: 01/03/2024] [Indexed: 02/28/2024]
Abstract
OBJECTIVE The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current corticocentric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural magnetic resonance imaging in 1602 adults with epilepsy and 1022 healthy controls across 22 sites from the global ENIGMA-Epilepsy working group. METHODS A state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions. Linear mixed models compared total and regional cerebellar volume in (1) all epilepsies, (2) temporal lobe epilepsy with hippocampal sclerosis (TLE-HS), (3) nonlesional temporal lobe epilepsy, (4) genetic generalized epilepsy, and (5) extratemporal focal epilepsy (ETLE). Relationships were examined for cerebellar volume versus age at seizure onset, duration of epilepsy, phenytoin treatment, and cerebral cortical thickness. RESULTS Across all epilepsies, reduced total cerebellar volume was observed (d = .42). Maximum volume loss was observed in the corpus medullare (dmax = .49) and posterior lobe gray matter regions, including bilateral lobules VIIB (dmax = .47), crus I/II (dmax = .39), VIIIA (dmax = .45), and VIIIB (dmax = .40). Earlier age at seizure onset (η ρ max 2 = .05) and longer epilepsy duration (η ρ max 2 = .06) correlated with reduced volume in these regions. Findings were most pronounced in TLE-HS and ETLE, with distinct neuroanatomical profiles observed in the posterior lobe. Phenytoin treatment was associated with reduced posterior lobe volume. Cerebellum volume correlated with cerebral cortical thinning more strongly in the epilepsy cohort than in controls. SIGNIFICANCE We provide robust evidence of deep cerebellar and posterior lobe subregional gray matter volume loss in patients with chronic epilepsy. Volume loss was maximal for posterior subregions implicated in nonmotor functions, relative to motor regions of both the anterior and posterior lobe. Associations between cerebral and cerebellar changes, and variability of neuroanatomical profiles across epilepsy syndromes argue for more precise incorporation of cerebellar subregional damage into neurobiological models of epilepsy.
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Affiliation(s)
- Rebecca Kerestes
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Andrew Perry
- Monash Bioinformatics Platform, Monash University, Melbourne, VIC, Australia
| | - Lucy Vivash
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Terence J. O’Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Medicine and Neurology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
| | - Marina K.M. Alvim
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Donatello Arienzo
- Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
| | - Ítalo K. Aventurato
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Alice Ballerini
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Gabriel F. Baltazar
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Núria Bargalló
- Magnetic Resonance Image Core Facility, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
- Department of Radiology of Center of Image Diagnosis (CDIC), Hospital Clinic de Barcelona, Barcelona, Spain
- CIBERSAM, Madrid, Spain
| | - Benjamin Bender
- Department of Radiology, Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, Tübingen, Germany
| | - Ricardo Brioschi
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Eva Bürkle
- Department of Radiology, Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, Tübingen, Germany
| | - Maria Eugenia Caligiuri
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University “Magna Græcia” of Catanzaro, Catanzaro, Italy
| | - Fernando Cendes
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - John S. Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - Jerome P. Engel
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
| | - Francesco Fortunato
- Institute of Neurology, Department of Medical and Surgical Sciences, University “Magna Græcia” of Catanzaro, Catanzaro, Italy
| | - Antonio Gambardella
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University “Magna Græcia” of Catanzaro, Catanzaro, Italy
- Institute of Neurology, Department of Medical and Surgical Sciences, University “Magna Græcia” of Catanzaro, Catanzaro, Italy
| | - Thea Giacomini
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
| | - Renzo Guerrini
- Meyer Children’s Hospital IRCCS, Florence, Italy
- University of Florence, Florence, Italy
| | - Gerard Hall
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
- The Welsh Epilepsy Unit, Department of Neurology, University Hospital of Wales, Cardiff, UK
| | | | - Rafael B. João
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Simon S. Keller
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Benedict Kleiser
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Angelo Labate
- Neurophysiopatology and Movement Disorders Clinic, University of Messina, Messina, Italy
- Regional Epilepsy Center, University of Messina, Messina, Italy
| | - Matteo Lenge
- Meyer Children’s Hospital IRCCS, Florence, Italy
| | | | - Pascal Martin
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Mario Mascalchi
- ‘Mario Serio’ Department of Clinical and Experimental Medical Sciences, University of Florence, Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Study, Prevention and network in Oncology of the Tuscany Region, Florence, Italy
| | - Stefano Meletti
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Neurology Unit, OCB Hospital, Azienda Ospedaliera-Universitaria Modena, Modena, Italy
| | - Conor Owens-Walton
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | | | - Saül Pascual-Diaz
- Magnetic Resonance Image Core Facility, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - David Powell
- Monash Bioinformatics Platform, Monash University, Melbourne, VIC, Australia
| | - Jun Rao
- Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
| | - Michael Rebsamen
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Johannes Reiter
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
| | | | - Theodor Rüber
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Christian Rummel
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Freda Scheffler
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | | | - Lucas S. Silva
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Richard J. Staba
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Dan J. Stein
- SMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Pasquale Striano
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
- IRCCS Istituto ‘Giannina Gaslini’, Genoa, Italy
| | - Peter N. Taylor
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | | | - Anna Elisabetta Vaudano
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Neurology Unit, OCB Hospital, Azienda Ospedaliera-Universitaria Modena, Modena, Italy
| | - Bernd Weber
- Institute of Experimental Epileptology and Cognition Research, University of Bonn, Bonn, Germany
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Gavin P. Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Epilepsy Society MRI Unit, Chalfont St Peter, UK
- Department of Medicine (Division of Neurology), Queen’s University Kingston, ON, Canada
| | - Clarissa L. Yasuda
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Hong Zheng
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Carrie R. McDonald
- Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Sanjay M. Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Chalfont Centre for Epilepsy, Bucks, UK
| | - Ian H. Harding
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
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10
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Gaiser C, van der Vliet R, de Boer AAA, Donchin O, Berthet P, Devenyi GA, Mallar Chakravarty M, Diedrichsen J, Marquand AF, Frens MA, Muetzel RL. Population-wide cerebellar growth models of children and adolescents. Nat Commun 2024; 15:2351. [PMID: 38499518 PMCID: PMC10948906 DOI: 10.1038/s41467-024-46398-2] [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] [Received: 08/15/2023] [Accepted: 02/22/2024] [Indexed: 03/20/2024] Open
Abstract
In the past, the cerebellum has been best known for its crucial role in motor function. However, increasingly more findings highlight the importance of cerebellar contributions in cognitive functions and neurodevelopment. Using a total of 7240 neuroimaging scans from 4862 individuals, we describe and provide detailed, openly available models of cerebellar development in childhood and adolescence (age range: 6-17 years), an important time period for brain development and onset of neuropsychiatric disorders. Next to a traditionally used anatomical parcellation of the cerebellum, we generated growth models based on a recently proposed functional parcellation. In both, we find an anterior-posterior growth gradient mirroring the age-related improvements of underlying behavior and function, which is analogous to cerebral maturation patterns and offers evidence for directly related cerebello-cortical developmental trajectories. Finally, we illustrate how the current approach can be used to detect cerebellar abnormalities in clinical samples.
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Affiliation(s)
- Carolin Gaiser
- Department of Neuroscience, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, The Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC - Sophia Children's Hospital, University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - Rick van der Vliet
- Department of Neuroscience, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, The Netherlands
- Department of Neurology, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, The Netherlands
- Department of Clinical Genetics, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - Augustijn A A de Boer
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - Opher Donchin
- Department of Biomedical Engineering, Ben-Gurion University of the Negev, Be'er Sheva, Israel
- Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Pierre Berthet
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Gabriel A Devenyi
- Cerebral Imaging Centre, Douglas Research Centre, McGill University, Montreal, Canada
- Department of Psychiatry, McGill University, Montreal, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Research Centre, McGill University, Montreal, Canada
- Department of Psychiatry, McGill University, Montreal, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Canada
| | - Jörn Diedrichsen
- Western Institute of Neuroscience, Western University, London, Ontario, Canada
- Department of Statistical and Actuarial Sciences, Western University, London, Ontario, Canada
- Department of Computer Science, Western University, London, Ontario, Canada
| | - Andre F Marquand
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - Maarten A Frens
- Department of Neuroscience, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, The Netherlands.
| | - Ryan L Muetzel
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC - Sophia Children's Hospital, University Medical Centre Rotterdam, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, The Netherlands
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11
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Kim J, Lee HJ, Lee DA, Park KM. Cerebellar volumes and the intrinsic cerebellar network in patients with obstructive sleep apnea. Sleep Breath 2024; 28:301-309. [PMID: 37710027 DOI: 10.1007/s11325-023-02916-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 08/28/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023]
Abstract
PURPOSE This research aimed to explore changes in both cerebellar volume and the intrinsic cerebellar network in patients with obstructive sleep apnea (OSA). METHODS Newly diagnosed OSA patients and healthy controls were included in the study. All participants underwent three-dimensional T1-weighted imaging using a 3-T MRI scanner. Cerebellar volumes, both overall and subdivided, were quantified using the ACAPULCO program. The intrinsic cerebellar network was assessed using the BRAPH program, which applied graph theory to the cerebellar volume subdivision. Comparisons were drawn between the patients with OSA and healthy controls. RESULTS The study revealed that the 26 patients with OSA exhibited a notably lower total cerebellar volume compared to the 28 healthy controls (8.330 vs. 9.068%, p < 0.001). The volume of the left lobule VIIB was reduced in patients with OSA compared to healthy controls (0.339 vs. 0.407%, p = 0.001). Among patients with OSA, there was a negative correlation between the volume of the left lobule X and apnea-hypopnea index during non-rapid eye movement sleep (r = - 0.536, p = 0.005). However, no significant differences were observed in the intrinsic cerebellar network between patients and healthy controls. CONCLUSION This study established that patients with OSA exhibited decreased total cerebellar volumes and particularly reduced volumes in subdivisions such as the left lobule VIIB compared to healthy controls. These findings suggest potential involvement of the cerebellum in the underlying mechanisms of OSA.
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Affiliation(s)
- Jinseung Kim
- Department of Family Medicine, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Ho-Joon Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-Ro 875, Haeundae-Gu, 48108, Busan, Republic of Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-Ro 875, Haeundae-Gu, 48108, Busan, Republic of Korea.
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12
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Huggins AA, Baird CL, Briggs M, Laskowitz S, Hussain A, Fouda S, Haswell C, Sun D, Salminen LE, Jahanshad N, Thomopoulos SI, Veltman DJ, Frijling JL, Olff M, van Zuiden M, Koch SBJ, Nawjin L, Wang L, Zhu Y, Li G, Stein DJ, Ipser J, Seedat S, du Plessis S, van den Heuvel LL, Suarez-Jimenez B, Zhu X, Kim Y, He X, Zilcha-Mano S, Lazarov A, Neria Y, Stevens JS, Ressler KJ, Jovanovic T, van Rooij SJH, Fani N, Hudson AR, Mueller SC, Sierk A, Manthey A, Walter H, Daniels JK, Schmahl C, Herzog JI, Říha P, Rektor I, Lebois LAM, Kaufman ML, Olson EA, Baker JT, Rosso IM, King AP, Liberzon I, Angstadt M, Davenport ND, Sponheim SR, Disner SG, Straube T, Hofmann D, Qi R, Lu GM, Baugh LA, Forster GL, Simons RM, Simons JS, Magnotta VA, Fercho KA, Maron-Katz A, Etkin A, Cotton AS, O'Leary EN, Xie H, Wang X, Quidé Y, El-Hage W, Lissek S, Berg H, Bruce S, Cisler J, Ross M, Herringa RJ, Grupe DW, Nitschke JB, Davidson RJ, Larson CL, deRoon-Cassini TA, Tomas CW, Fitzgerald JM, Blackford JU, Olatunji BO, Kremen WS, Lyons MJ, Franz CE, Gordon EM, May G, Nelson SM, Abdallah CG, Levy I, Harpaz-Rotem I, Krystal JH, Dennis EL, Tate DF, Cifu DX, Walker WC, Wilde EA, Harding IH, Kerestes R, Thompson PM, Morey R. Smaller total and subregional cerebellar volumes in posttraumatic stress disorder: a mega-analysis by the ENIGMA-PGC PTSD workgroup. Mol Psychiatry 2024; 29:611-623. [PMID: 38195980 PMCID: PMC11153161 DOI: 10.1038/s41380-023-02352-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 11/22/2023] [Accepted: 11/27/2023] [Indexed: 01/11/2024]
Abstract
Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p-FDR < 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status. These findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in higher-order cognitive and emotional processes, far beyond its historical association with vestibulomotor function. Further examination of the cerebellum in trauma-related psychopathology will help to clarify how cerebellar structure and function may disrupt cognitive and affective processes at the center of translational models for PTSD.
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Grants
- R01 MH105535 NIMH NIH HHS
- WA 1539/8-2 Deutsche Forschungsgemeinschaft (German Research Foundation)
- UL1TR000454 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- K01MH118467 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- IK2 RX000709 RRD VA
- R01MH106574 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- I01 RX002172 RRD VA
- K23MH090366 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- R01MH105535 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- P41 EB015922 NIBIB NIH HHS
- I01 RX002174 RRD VA
- W81XWH-10-1-0925 U.S. Department of Defense (United States Department of Defense)
- R56 MH071537 NIMH NIH HHS
- 20ZDA079 National Natural Science Foundation of China (National Science Foundation of China)
- P30 HD003352 NICHD NIH HHS
- K01 MH122774 NIMH NIH HHS
- I01 RX003444 RRD VA
- IK2 RX002922 RRD VA
- 31971020 National Natural Science Foundation of China (National Science Foundation of China)
- R21 MH098212 NIMH NIH HHS
- R01 MH113574 NIMH NIH HHS
- K12 HD085850 NICHD NIH HHS
- M01RR00039 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- 1IK2CX001680 U.S. Department of Veterans Affairs (Department of Veterans Affairs)
- R01 MH071537 NIMH NIH HHS
- R21 MH106998 NIMH NIH HHS
- I01 RX003442 RRD VA
- IK2 CX001680 CSRD VA
- R01 AG064955 NIA NIH HHS
- HD071982 U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
- MH098212 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- 14848 Michael J. Fox Foundation for Parkinson's Research (Michael J. Fox Foundation)
- I01 CX001135 CSRD VA
- 1IK2RX000709 U.S. Department of Veterans Affairs (Department of Veterans Affairs)
- R21 MH112956 NIMH NIH HHS
- W81XWH-08-2-0038 United States Department of Defense | United States Army | Army Medical Command | Congressionally Directed Medical Research Programs (CDMRP)
- K01 MH118428 NIMH NIH HHS
- HD085850 U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
- R01 MH105355 NIMH NIH HHS
- M01 RR000039 NCRR NIH HHS
- I01 RX003443 RRD VA
- R01 MH111671 NIMH NIH HHS
- R01 MH106574 NIMH NIH HHS
- R01 MH116147 NIMH NIH HHS
- M01RR00039 U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- 1K2RX002922 U.S. Department of Veterans Affairs (Department of Veterans Affairs)
- I01 RX001880 RRD VA
- K01MH122774 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- I01 RX000622 RRD VA
- R01MH111671 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- I01 RX002171 RRD VA
- R21MH098198 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- I01 HX003155 HSRD VA
- U54 EB020403 NIBIB NIH HHS
- R01 MH117601 NIMH NIH HHS
- I01 RX001774 RRD VA
- R01AG050595 U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- I01 CX002097 CSRD VA
- I01 RX002076 RRD VA
- R01 MH119227 NIMH NIH HHS
- SFB/TRR 58: C06, C07 Deutsche Forschungsgemeinschaft (German Research Foundation)
- R21MH106998 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- U21A20364 National Natural Science Foundation of China (National Science Foundation of China)
- R01MH117601 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- BK20221554 Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)
- UL1 TR000454 NCATS NIH HHS
- R01 MH107382 NIMH NIH HHS
- I01 CX001246 CSRD VA
- R01MH105355 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- R56 AG058854 NIA NIH HHS
- R01MH107382 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- R21MH112956 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- 40-00812-98-10041 ZonMw (Netherlands Organisation for Health Research and Development)
- T32 MH018931 NIMH NIH HHS
- R01 AG076838 NIA NIH HHS
- K23 MH101380 NIMH NIH HHS
- R21 MH102634 NIMH NIH HHS
- K01MH118428 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- R01 MH043454 NIMH NIH HHS
- I01 RX002170 RRD VA
- MH071537 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- R01 HD071982 NICHD NIH HHS
- K23 MH090366 NIMH NIH HHS
- I01 RX002173 RRD VA
- R61 NS120249 NINDS NIH HHS
- R61NS120249 U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)
- I01RX000622 U.S. Department of Veterans Affairs (Department of Veterans Affairs)
- 27040 Brain and Behavior Research Foundation (Brain & Behavior Research Foundation)
- W81XWH-12-2-0012 U.S. Department of Defense (United States Department of Defense)
- K01 MH118467 NIMH NIH HHS
- I01 CX002096 CSRD VA
- I01 CX001820 CSRD VA
- P50 U.S. Department of Health & Human Services | NIH | National Institute on Alcohol Abuse and Alcoholism (NIAAA)
- R01AG059874 U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- MH101380 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- I01 RX001135 RRD VA
- DA 1222/4-1 Deutsche Forschungsgemeinschaft (German Research Foundation)
- R01 MH096987 NIMH NIH HHS
- 1184403 Department of Health | National Health and Medical Research Council (NHMRC)
- R01MH110483 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- R01MH096987 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- R01MH119227 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- R21MH102634 U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
- R01AG022381 U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- R01 AG022381 NIA NIH HHS
- R01 AG050595 NIA NIH HHS
- R01 AG059874 NIA NIH HHS
- VA Mid-Atlantic MIRECC
- Michael J. Fox Foundation for Parkinson’s Research (Michael J. Fox Foundation)
- Amsterdam Academic Medical Center grant
- South African Medical Research Council (SAMRC)
- Ghent University Special Research Fund (BOF) 01J05415
- Julia Kasparian Fund for Neuroscience Research
- McLean Hospital Trauma Scholars Fund, Barlow Family Fund, Julia Kasparian Fund for Neuroscience Research
- Foundation for the Social Development Project of Jiangsu No. BE2022705
- Center for Brain and Behavior Research Pilot Grant, South Dakota Governor’s Research Center Grant
- Center for Brain and Behavior Research Pilot Grant, South Dakota Governor ’s Research Center Grant
- Fondation Pierre Deniker pour la Recherche et la Prévention en Santé Mentale (Fondation Pierre Deniker pour la Recherche & la Prévention en Santé Mentale)
- PHRC, SFR FED4226
- Dana Foundation (Charles A. Dana Foundation)
- UW | Institute for Clinical and Translational Research, University of Wisconsin, Madison (UW Institute for Clinical and Translational Research)
- National Science Foundation (NSF)
- US VA VISN17 Center of Excellence Pilot funding
- VA National Center for PTSD, Beth K and Stuart Yudofsky Chair in the Neuropsychiatry of Military Post Traumatic Stress Syndrome
- US VA National Center for PTSD, NCATS
- This work was supported by the Assistant Secretary of Defense for Health Affairs endorsed by the Department of Defense, through the Psychological Health/Traumatic Brain Injury Research Program Long-Term Impact of Military-Relevant Brain Injury Consortium (LIMBIC) Award/W81XWH-18-PH/TBIRP-LIMBIC under Awards No. W81XWH1920067 and W81XWH-13-2-0095, and by the U.S. Department of Veterans Affairs Awards No. I01 CX002097, I01 CX002096, I01 CX001820, I01 HX003155, I01 RX003444, I01 RX003443, I01 RX003442, I01 CX001135, I01 CX001246, I01 RX001774, I01 RX 001135, I01 RX 002076, I01 RX 001880, I01 RX 002172, I01 RX 002173, I01 RX 002171, I01 RX 002174, and I01 RX 002170. The U.S. Army Medical Research Acquisition Activity, 839 Chandler Street, Fort Detrick MD 21702-5014 is the awarding and administering acquisition office.
- HFP90-020
- VA VISN6 MIRECC
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Affiliation(s)
- Ashley A Huggins
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA.
- Department of Veteran Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA.
| | - C Lexi Baird
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
- Department of Veteran Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA
| | - Melvin Briggs
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
- Department of Veteran Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA
| | - Sarah Laskowitz
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
- Department of Veteran Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA
| | - Ahmed Hussain
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
- Department of Veteran Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA
| | - Samar Fouda
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
- Department of Veteran Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA
- Department of Psychiatry & Behavioral Sciences, Duke School of Medicine, Durham, NC, USA
| | - Courtney Haswell
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
- Department of Veteran Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA
| | - Delin Sun
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
- Department of Veteran Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA
- Department of Psychology, The Education University of Hong Kong, Ting Kok, Hong Kong
| | - Lauren E Salminen
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Dick J Veltman
- Amsterdam UMC Vrije Universiteit, Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Jessie L Frijling
- Amsterdam UMC University of Amsterdam, Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Miranda Olff
- Amsterdam UMC University of Amsterdam, Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- ARQ National Psychotrauma Centre, Diemen, The Netherlands
| | - Mirjam van Zuiden
- Amsterdam UMC University of Amsterdam, Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Saskia B J Koch
- Amsterdam UMC University of Amsterdam, Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Centre for Cognitive Neuroimaging, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Laura Nawjin
- Amsterdam UMC Vrije Universiteit, Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Amsterdam UMC University of Amsterdam, Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Li Wang
- Laboratory for Traumatic Stress Studies, Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Ye Zhu
- Laboratory for Traumatic Stress Studies, Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Gen Li
- Laboratory for Traumatic Stress Studies, Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Center for Global Health Equity, New York University Shanghai, Shanghai, China
| | - Dan J Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Jonathan Ipser
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Soraya Seedat
- Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
- South African Medical Research Council Unit on the Genomics of Brain Disorders (GBD), Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - Stefan du Plessis
- Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
- South African Medical Research Council Unit on the Genomics of Brain Disorders (GBD), Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - Leigh L van den Heuvel
- Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
- South African Medical Research Council Unit on the Genomics of Brain Disorders (GBD), Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | | | - Xi Zhu
- Department of Psychiatry, Columbia University Medical Center, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Yoojean Kim
- New York State Psychiatric Institute, New York, NY, USA
| | - Xiaofu He
- Department of Psychiatry, Columbia University Medical Center, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | | | - Amit Lazarov
- Department of Psychiatry, Columbia University Medical Center, New York, NY, USA
- Tel-Aviv University, Tel Aviv-Yafo, Israel
| | - Yuval Neria
- Department of Psychiatry, Columbia University Medical Center, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Kerry J Ressler
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University School of Medicine, Detroit, MI, USA
| | - Sanne J H van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Negar Fani
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Anna R Hudson
- Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium
| | - Sven C Mueller
- Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium
| | - Anika Sierk
- University Medical Centre Charité, Berlin, Germany
| | | | | | - Judith K Daniels
- Department of Clinical Psychology, University of Groningen, Groningen, The Netherlands
| | - Christian Schmahl
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Julia I Herzog
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Pavel Říha
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
- CEITEC-Central European Institute of Technology, Multimodal and Functional Neuroimaging Research Group, Masaryk University, Brno, Czech Republic
| | - Ivan Rektor
- CEITEC-Central European Institute of Technology, Multimodal and Functional Neuroimaging Research Group, Masaryk University, Brno, Czech Republic
| | - Lauren A M Lebois
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Harvard University, Belmont, MA, USA
| | - Milissa L Kaufman
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Division of Women's Mental Health, McLean Hospital, Belmont, MA, USA
| | - Elizabeth A Olson
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Harvard University, Belmont, MA, USA
| | - Justin T Baker
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
| | - Isabelle M Rosso
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Harvard University, Belmont, MA, USA
| | - Anthony P King
- Department of Psychiatry and Behavioral Health, Institute for Behavioral Medicine Research, The Ohio State University, Columbus, OH, USA
| | - Isreal Liberzon
- Department of Psychiatry, Texas A&M University, Bryan, Texas, USA
| | - Mike Angstadt
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas D Davenport
- Minneapolis VA Health Care System, Minneapolis, MN, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Scott R Sponheim
- Minneapolis VA Health Care System, Minneapolis, MN, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Seth G Disner
- Minneapolis VA Health Care System, Minneapolis, MN, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Thomas Straube
- Institute of Medical Psychology and Systems Neuroscience, University of Münster, Münster, Germany
| | - David Hofmann
- Institute of Medical Psychology and Systems Neuroscience, University of Münster, Münster, Germany
| | - Rongfeng Qi
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China
| | - Guang Ming Lu
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China
| | - Lee A Baugh
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, SD, USA
- Center for Brain and Behavior Research, University of South Dakota, Vermillion, SD, USA
- Sioux Falls VA Health Care System, Sioux Falls, SD, USA
| | - Gina L Forster
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, SD, USA
- Center for Brain and Behavior Research, University of South Dakota, Vermillion, SD, USA
- Brain Health Research Centre, Department of Anatomy, University of Otago, Dunedin, New Zealand
| | - Raluca M Simons
- Center for Brain and Behavior Research, University of South Dakota, Vermillion, SD, USA
- Department of Psychology, University of South Dakota, Vermillion, SD, USA
- Disaster Mental Health Institute, Vermillion, SD, USA
| | - Jeffrey S Simons
- Sioux Falls VA Health Care System, Sioux Falls, SD, USA
- Department of Psychology, University of South Dakota, Vermillion, SD, USA
| | - Vincent A Magnotta
- Departments of Radiology, Psychiatry, and Biomedical Engineering, University of Iowa, Iowa City, IA, USA
| | - Kelene A Fercho
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, SD, USA
- Center for Brain and Behavior Research, University of South Dakota, Vermillion, SD, USA
- Sioux Falls VA Health Care System, Sioux Falls, SD, USA
- Civil Aerospace Medical Institute, US Federal Aviation Administration, Oklahoma City, OK, USA
| | - Adi Maron-Katz
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Andrew S Cotton
- Department of Psychiatry, University of Toledo, Toledo, OH, USA
| | - Erin N O'Leary
- Department of Psychiatry, University of Toledo, Toledo, OH, USA
| | - Hong Xie
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
| | - Xin Wang
- Department of Psychiatry, University of Toledo, Toledo, OH, USA
| | - Yann Quidé
- School of Psychology, University of New South Wales (UNSW) Sydney, Sydney, NSW, Australia
- Neuroscience Research Australia, Randwick, NSW, Australia
| | - Wissam El-Hage
- UMR1253, Université de Tours, Inserm, Tours, France
- CIC1415, CHRU de Tours, Inserm, Tours, France
| | - Shmuel Lissek
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Hannah Berg
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Steven Bruce
- Department of Psychological Sciences, Center for Trauma Recovery University of Missouri-St. Louis, St. Louis, MO, USA
| | - Josh Cisler
- Department of Psychiatry, University of Texas at Austin, Austin, TX, USA
| | - Marisa Ross
- Northwestern Neighborhood and Network Initiative, Northwestern University Institute for Policy Research, Evanston, IL, USA
| | - Ryan J Herringa
- School of Medicine and Public Health, University of Wisconsin Madison, Madison, WI, USA
| | - Daniel W Grupe
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA
| | - Jack B Nitschke
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Richard J Davidson
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA
| | - Christine L Larson
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
| | - Terri A deRoon-Cassini
- Division of Trauma and Acute Care Surgery, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
- Comprehensive Injury Center, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Carissa W Tomas
- Comprehensive Injury Center, Medical College of Wisconsin, Milwaukee, WI, USA
- Division of Epidemiology and Social Sciences, Institute of Health and Equity, Medical College of Wisconsin Milwaukee, Milwaukee, WI, USA
| | | | - Jennifer Urbano Blackford
- Munroe-Meyer Institute, University of Nebraska Medical Center, Omaha, NE, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bunmi O Olatunji
- Department of Psychology, Vanderbilt University, Nashville, TN, USA
| | - William S Kremen
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Michael J Lyons
- Dept. of Psychological & Brain Sciences, Boston University, Boston, MA, USA
| | - Carol E Franz
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Evan M Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Geoffrey May
- Veterans Integrated Service Network-17 Center of Excellence for Research on Returning War Veterans, Waco, TX, USA
- Department of Psychology and Neuroscience, Baylor University, Waco, TX, USA
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
- Department of Psychiatry and Behavioral Science, Texas A&M University Health Science Center, Bryan, TX, USA
| | - Steven M Nelson
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Masonic Institute for the Developing Brain, Minneapolis, MN, USA
| | - Chadi G Abdallah
- Department of Psychiatry, Baylor College of Medicine, Houston, TX, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Ifat Levy
- Departments of Comparative Medicine, Neuroscience and Psychology, Wu Tsai Institute, Yale University, New Haven, CT, USA
- Division of Clinical Neuroscience, National Center for PTSD, West Haven, CT, USA
| | - Ilan Harpaz-Rotem
- Division of Clinical Neuroscience, National Center for PTSD, West Haven, CT, USA
- Departments of Psychiatry and of Psychology, Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - John H Krystal
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Division of Clinical Neuroscience, National Center for PTSD, West Haven, CT, USA
| | - Emily L Dennis
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - David F Tate
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - David X Cifu
- Department of Physical Medicine and Rehabilitation, Virginia Commonwealth University, Richmond, VA, USA
| | - William C Walker
- Department of Physical Medicine and Rehabilitation, Virginia Commonwealth University, Richmond, VA, USA
- Veterans Affairs (VA) Richmond Health Care, Richmond, VA, USA
| | - Elizabeth A Wilde
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
| | - Ian H Harding
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Vic, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Vic, Australia
| | - Rebecca Kerestes
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Vic, Australia
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Rajendra Morey
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
- Department of Veteran Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA
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13
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Fernandez L, Burmester A, Duque JD, Silk TJ, Hyde CE, Kirkovski M, Enticott PG, Caeyenberghs K. Examination of Cerebellar Grey-Matter Volume in Children with Neurodevelopmental Disorders: a Coordinated Analysis Using the ACAPULCO Algorithm. CEREBELLUM (LONDON, ENGLAND) 2023; 22:1243-1249. [PMID: 36482028 DOI: 10.1007/s12311-022-01503-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/30/2022] [Indexed: 12/13/2022]
Abstract
Alterations in cerebellar morphology relative to controls have been identified in children with autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and developmental coordination disorder (DCD). However, it is not clear if common cerebellar regions are affected in each neurodevelopmental disorder and whether cerebellar morphological changes reflect a generic developmental vulnerability, or disorder-specific characteristic. The present study concatenated anatomical MRI scans from five existing cohorts, resulting in data from 252 children between the age of 7 and 12 years (ASD = 58, ADHD = 86, DCD = 22, Controls = 86). The ACAPULCO processing pipeline for cerebellar segmentation was conducted on T1-weighted images. A voxel-wise approach with general linear model was used to compare grey-matter volume of the 27 cerebellar lobules between each clinical group and controls. Our findings revealed that the ADHD group showed lower grey-matter volume in the left Crus I - part of the executive/non-motor portion of the cerebellum, relative to controls (p = 0.02). This no longer remained significant after controlling for medication status. There were no regions of significant differences in volume of the cerebellar lobules in ASD or DCD compared to controls. Future work will conduct harmonisation of behavioural data (cognitive and motor outcomes) across cohorts, enabling more advanced analyses to identify symptom cluster across neurodevelopmental disorders.
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Affiliation(s)
- Lara Fernandez
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, 3220, Australia.
- Harding Lab, Department of Neuroscience, Central Clinical School, Monash University, Clayton, VIC, 3800, Australia.
| | - Alex Burmester
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, 3220, Australia
| | - Juan Dominguez Duque
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, 3220, Australia
| | - Timothy J Silk
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, 3220, Australia
| | - Christian E Hyde
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, 3220, Australia
| | - Melissa Kirkovski
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, 3220, Australia
- Institute for Health and Sport, Victoria University, Melbourne, 3000, Australia
| | - Peter G Enticott
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, 3220, Australia
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, 3220, Australia
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14
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Park KM, Kim KT, Lee DA, Cho YW. Redefining the role of the cerebellum in restless legs syndrome. Sleep Med 2023; 112:256-261. [PMID: 37925852 DOI: 10.1016/j.sleep.2023.10.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/12/2023] [Accepted: 10/28/2023] [Indexed: 11/07/2023]
Abstract
OBJECTIVES We aimed to investigate alterations in cerebellar volume and the intrinsic cerebellar network in patients with restless legs syndrome (RLS). METHODS We recruited 69 patients with RLS and 50 healthy controls who underwent brain magnetic resonance imaging (MRI), including three-dimensional T1-weighted imaging. The total cerebellar volume and cerebellar volume subdivisions were analyzed through automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization program. The intrinsic cerebellar network was calculated through brain analysis using a graph theory program based on the cerebellar volume subdivisions. The cerebellar volumes and intrinsic cerebellar networks were compared between patients with RLS and healthy controls. RESULTS The cerebellar volume and subdivisions in patients with RLS did not show significant differences compared to those in healthy controls. However, significant alterations were observed in the intrinsic cerebellar network of patients with RLS. Increased mean clustering coefficient (0.185 vs. 0.114; p = 0.047) and small-worldness index (0.927 vs. 0.800; p = 0.047) were observed in patients with RLS. Additionally, total cerebellar volume was negatively correlated with the Pittsburgh Sleep Quality Index (r = -0.398; p = 0.020) and Insomnia Severity Index (ISI; r = -0.396; p = 0.011). Negative correlations were additionally observed between the right X lobule volume and the International Restless Legs Syndrome Severity Scale (r = -0.425; p = 0.008) and between the vermis VIII volume and ISI score (r = -0.399; p = 0.011). CONCLUSION We demonstrated alterations in the intrinsic cerebellar network in patients with RLS compared with healthy controls, showing increased connectivity with increased segregation in patients with RLS. This suggests a potential role of the cerebellum in RLS pathophysiology.
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Affiliation(s)
- Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea
| | - Keun Tae Kim
- Department of Neurology, Keimyung University School of Medicine, Daegu, South Korea
| | - Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea
| | - Yong Won Cho
- Department of Neurology, Keimyung University School of Medicine, Daegu, South Korea.
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15
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Kerestes R, Laansma MA, Owens-Walton C, Perry A, van Heese EM, Al-Bachari S, Anderson TJ, Assogna F, Aventurato ÍK, van Balkom TD, Berendse HW, van den Berg KR, Mphys RB, Brioschi R, Carr J, Cendes F, Clark LR, Dalrymple-Alford JC, Dirkx MF, Druzgal J, Durrant H, Emsley HC, Garraux G, Haroon HA, Helmich RC, van den Heuvel OA, João RB, Johansson ME, Khachatryan S, Lochner C, McMillan CT, Melzer TR, Mosley P, Newman B, Opriessnig P, Parkes LM, Pellicano C, Piras F, Pitcher TL, Poston KL, Rango M, Roos A, Rummel C, Schmidt R, Schwingenschuh P, Silva LS, Smith V, Squarcina L, Stein DJ, Tavadyan Z, Tsai CC, Vecchio D, Vriend C, Wang JJ, Wiest R, Yasuda CL, Young CB, Jahanshad N, Thompson PM, van der Werf YD, Harding IH. Cerebellar Volume and Disease Staging in Parkinson's Disease: An ENIGMA-PD Study. Mov Disord 2023; 38:2269-2281. [PMID: 37964373 PMCID: PMC10754393 DOI: 10.1002/mds.29611] [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] [Received: 05/29/2023] [Revised: 08/14/2023] [Accepted: 09/11/2023] [Indexed: 11/16/2023] Open
Abstract
BACKGROUND Increasing evidence points to a pathophysiological role for the cerebellum in Parkinson's disease (PD). However, regional cerebellar changes associated with motor and non-motor functioning remain to be elucidated. OBJECTIVE To quantify cross-sectional regional cerebellar lobule volumes using three dimensional T1-weighted anatomical brain magnetic resonance imaging from the global ENIGMA-PD working group. METHODS Cerebellar parcellation was performed using a deep learning-based approach from 2487 people with PD and 1212 age and sex-matched controls across 22 sites. Linear mixed effects models compared total and regional cerebellar volume in people with PD at each Hoehn and Yahr (HY) disease stage, to an age- and sex- matched control group. Associations with motor symptom severity and Montreal Cognitive Assessment scores were investigated. RESULTS Overall, people with PD had a regionally smaller posterior lobe (dmax = -0.15). HY stage-specific analyses revealed a larger anterior lobule V bilaterally (dmax = 0.28) in people with PD in HY stage 1 compared to controls. In contrast, smaller bilateral lobule VII volume in the posterior lobe was observed in HY stages 3, 4, and 5 (dmax = -0.76), which was incrementally lower with higher disease stage. Within PD, cognitively impaired individuals had lower total cerebellar volume compared to cognitively normal individuals (d = -0.17). CONCLUSIONS We provide evidence of a dissociation between anterior "motor" lobe and posterior "non-motor" lobe cerebellar regions in PD. Whereas less severe stages of the disease are associated with larger motor lobe regions, more severe stages of the disease are marked by smaller non-motor regions. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Rebecca Kerestes
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Max A. Laansma
- Amsterdam UMC, Dept. Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands
| | - Conor Owens-Walton
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Andrew Perry
- Monash Bioinformatics Platform, Monash University, Melbourne, VIC, Australia
| | - Eva M. van Heese
- Amsterdam UMC, Dept. Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands
| | - Sarah Al-Bachari
- Faculty of Health and Medicine, The University of Lancaster, Lancaster, UK
| | - Tim J. Anderson
- Department of Medicine, University of Otago, Christchurch, Christchurch, New Zealand
- New Zealand Brain Research Institute, Christchurch, New Zealand
- Neurology Department, Te Wahtu Ora - Health New Zealand Waitaha Canterbury, Christchurch, New Zew Zealand
| | - Francesca Assogna
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Ítalo K. Aventurato
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Tim D. van Balkom
- Amsterdam UMC, Dept. Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands
- Amsterdam UMC, Dept. Psychiatry, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Henk W. Berendse
- Amsterdam UMC, Dept. Neurology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Kevin R.E. van den Berg
- Department of Neurology and Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Rebecca Betts Mphys
- School of Physics and Astronomy, Faculty of Science and Engineering, The University of Manchester, Manchester, UK
| | - Ricardo Brioschi
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
| | - Jonathan Carr
- Division of Neurology, Tygerberg Hospital and Stellenbosch University, Cape Town, South Africa
| | - Fernando Cendes
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Lyles R. Clark
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - John C. Dalrymple-Alford
- New Zealand Brain Research Institute, Christchurch, New Zealand
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Michiel F. Dirkx
- Department of Neurology and Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Jason Druzgal
- Department of Radiology and Medical Imaging, University of Virginia, USA
| | - Helena Durrant
- School of Physics and Astronomy, Faculty of Science and Engineering, The University of Manchester, Manchester, UK
| | - Hedley C.A. Emsley
- Lancaster Medical School, Lancaster University, Lancaster, UK
- Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Gaëtan Garraux
- GIGA-CRC in vivo imaging, University of Liège, Belgium
- Department of Neurology, CHU Liège, Liège, Belgium
| | - Hamied A. Haroon
- Division of Psychology, Communication & Human Neuroscience, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Rick C. Helmich
- Department of Neurology and Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Odile A. van den Heuvel
- Amsterdam UMC, Dept. Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands
- Amsterdam UMC, Dept. Psychiatry, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Rafael B. João
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Martin E. Johansson
- Department of Neurology and Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Samson Khachatryan
- Department of Neurology and Neurosurgery, National Institute of Health, Yerevan, Armenia
- Centers for Sleep and Movement Disorders, Somnus Neurology Clinic, Yerevan, Armenia
| | - Christine Lochner
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
| | - Corey T. McMillan
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Tracy R. Melzer
- Department of Medicine, University of Otago, Christchurch, Christchurch, New Zealand
- New Zealand Brain Research Institute, Christchurch, New Zealand
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Philip Mosley
- Clinical Brain Networks Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- The Australian eHealth Research Centre, CSIRO Health and Biosecurity, Brisbane, Queensland, Australia
| | - Benjamin Newman
- Department of Radiology and Medical Imaging, University of Virginia, USA
| | - Peter Opriessnig
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Laura M. Parkes
- Division of Psychology, Communication and Human Neuroscience, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Northern Care Alliance & University of Manchester, Manchester, UK
| | - Clelia Pellicano
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Toni L. Pitcher
- Department of Medicine, University of Otago, Christchurch, Christchurch, New Zealand
- New Zealand Brain Research Institute, Christchurch, New Zealand
| | - Kathleen L. Poston
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA, USA
| | - Mario Rango
- Excellence Center for Advanced MR Techniques and Parkinson’s Disease Center, Neurology unit, Fondazione IRCCS Cà Granda Maggiore Policlinico Hospital, University of Milan, Milan, Italy
- Dept of Neurosciences, Neurology Unit, Fondazione Ca’ Granda, IRCCS, Ospedale Policlinico, Univeristy of Milan, Milano, Italy
| | - Annerine Roos
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Christian Rummel
- Support Center for Advanced Neuroimaging, (SCAN) University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Reinhold Schmidt
- Department of Neurology, Medical University of Graz, Graz, Austria
| | | | - Lucas S. Silva
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Viktorija Smith
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA, USA
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Dan J. Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Zaruhi Tavadyan
- Department of Neurology and Neurosurgery, National Institute of Health, Yerevan, Armenia
- Centers for Sleep and Movement Disorders, Somnus Neurology Clinic, Yerevan, Armenia
| | - Chih-Chien Tsai
- Healthy Aging Research Center, Chang Gung University, Taoyuan, Taiwan
| | - Daniela Vecchio
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Chris Vriend
- Amsterdam UMC, Dept. Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam UMC, Dept. Psychiatry, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain imaging, Amsterdam, the Netherlands
| | - Jiun-Jie Wang
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan City, Taiwan
- Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Keelung Branch Keelung City, Taiwan
- Healthy Ageing Research Center, ChangGung University, Taiwan
- Department of Chemical Engineering, Ming-Chi University of Technology, New Taipei City, Taiwan
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, (SCAN) University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Clarissa L. Yasuda
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Christina B. Young
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Ysbrand D. van der Werf
- Amsterdam UMC, Dept. Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands
| | - Ian H. Harding
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
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16
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Keleher F, Lindsey HM, Kerestes R, Amiri H, Asarnow RF, Babikian T, Bartnik-Olson B, Bigler ED, Caeyenberghs K, Esopenko C, Ewing-Cobbs L, Giza CC, Goodrich-Hunsaker NJ, Hodges CB, Hoskinson KR, Irimia A, Königs M, Max JE, Newsome MR, Olsen A, Ryan NP, Schmidt AT, Stein DJ, Suskauer SJ, Ware AL, Wheeler AL, Zielinski BA, Thompson PM, Harding IH, Tate DF, Wilde EA, Dennis EL. Multimodal Analysis of Secondary Cerebellar Alterations After Pediatric Traumatic Brain Injury. JAMA Netw Open 2023; 6:e2343410. [PMID: 37966838 PMCID: PMC10652147 DOI: 10.1001/jamanetworkopen.2023.43410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 10/04/2023] [Indexed: 11/16/2023] Open
Abstract
Importance Traumatic brain injury (TBI) is known to cause widespread neural disruption in the cerebrum. However, less is known about the association of TBI with cerebellar structure and how such changes may alter executive functioning. Objective To investigate alterations in subregional cerebellum volume and cerebral white matter microstructure after pediatric TBI and examine subsequent changes in executive function. Design, Setting, and Participants This retrospective cohort study combined 12 data sets (collected between 2006 and 2020) from 9 sites in the Enhancing Neuroimaging Genetics Through Meta-Analysis Consortium Pediatric TBI working group in a mega-analysis of cerebellar structure. Participants with TBI or healthy controls (some with orthopedic injury) were recruited from trauma centers, clinics, and institutional trauma registries, some of which were followed longitudinally over a period of 0.7 to 1.9 years. Healthy controls were recruited from the surrounding community. Data analysis occurred from October to December 2022. Exposure Accidental mild complicated-severe TBI (msTBI) for those in the TBI group. Some controls received a diagnosis of orthopedic injury. Main Outcomes and Measures Volume of 18 cerebellar lobules and vermal regions were estimated from 3-dimensional T1-weighted magnetic resonance imaging (MRI) scans. White matter organization in 28 regions of interest was assessed with diffusion tensor MRI. Executive function was measured by parent-reported scores from the Behavior Rating Inventory of Executive Functioning. Results A total of 598 children and adolescents (mean [SD] age, 14.05 [3.06] years; range, 5.45-19.70 years; 386 male participants [64.5%]; 212 female participants [35.5%]) were included in the study, with 314 participants in the msTBI group, and 284 participants in the non-TBI group (133 healthy individuals and 151 orthopedically injured individuals). Significantly smaller total cerebellum volume (d = -0.37; 95% CI, -0.52 to -0.22; P < .001) and subregional cerebellum volumes (eg, corpus medullare; d = -0.43; 95% CI, -0.58 to -0.28; P < .001) were observed in the msTBI group. These alterations were primarily seen in participants in the chronic phase (ie, >6 months postinjury) of injury (total cerebellar volume, d = -0.55; 95% CI, -0.75 to -0.35; P < .001). Smaller cerebellum volumes were associated with higher scores on the Behavior Rating Inventory of Executive Functioning Global Executive Composite score (β = -208.9 mm3; 95% CI, -319.0 to -98.0 mm3; P = .008) and Metacognition Index score (β = -202.5 mm3; 95% CI, -319.0 to -85.0 mm3; P = .02). In a subset of 185 participants with longitudinal data, younger msTBI participants exhibited cerebellum volume reductions (β = 0.0052 mm3; 95% CI, 0.0013 to 0.0090 mm3; P = .01), and older participants slower growth rates. Poorer white matter organization in the first months postinjury was associated with decreases in cerebellum volume over time (β=0.52 mm3; 95% CI, 0.19 to 0.84 mm3; P = .005). Conclusions and Relevance In this cohort study of pediatric msTBI, our results demonstrated robust cerebellar volume alterations associated with pediatric TBI, localized to the posterior lobe. Furthermore, longitudinal cerebellum changes were associated with baseline diffusion tensor MRI metrics, suggesting secondary cerebellar atrophy. These results provide further understanding of secondary injury mechanisms and may point to new opportunities for intervention.
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Affiliation(s)
- Finian Keleher
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City
| | - Hannah M. Lindsey
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, Utah
| | - Rebecca Kerestes
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Houshang Amiri
- Institute of Neuropharmacology, Neuroscience Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Robert F. Asarnow
- Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
- Brain Research Institute, University of California, Los Angeles
- Department of Psychology, University of California, Los Angeles
| | - Talin Babikian
- Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
- Steve Tisch BrainSPORT Program, University of California, Los Angeles
| | - Brenda Bartnik-Olson
- Department of Radiology, Loma Linda University Medical Center, Loma Linda, California
| | - Erin D. Bigler
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City
- Department of Psychology, Brigham Young University, Provo, Utah
- Neuroscience Center, Brigham Young University, Provo, Utah
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Victoria, Australia
| | - Carrie Esopenko
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Linda Ewing-Cobbs
- Children’s Learning Institute, Department of Pediatrics, University of Texas Health Science Center at Houston
| | - Christopher C. Giza
- Steve Tisch BrainSPORT Program, University of California, Los Angeles
- Division of Neurology, Department of Pediatrics, Mattel Children’s Hospital University of California, Los Angeles
- Department of Neurosurgery, David Geffen School of Medicine at the University of California, Los Angeles
| | - Naomi J. Goodrich-Hunsaker
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, Utah
- Department of Psychology, Brigham Young University, Provo, Utah
| | - Cooper B. Hodges
- Department of Psychology, Brigham Young University, Provo, Utah
- School of Social and Behavioral Sciences, Andrews University, Berrien Springs, Michigan
| | - Kristen R. Hoskinson
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, Ohio
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles
| | - Marsh Königs
- Emma Neuroscience Group, Emma Children’s Hospital, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Jeffrey E. Max
- Department of Psychiatry, University of California, San Diego, La Jolla
- Department of Psychiatry, Rady Children’s Hospital, San Diego, California
| | - Mary R. Newsome
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, Texas
| | - Alexander Olsen
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Rehabilitation, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- NorHEAD-Norwegian Centre for Headache Research, Trondheim, Norway
| | - Nicholas P. Ryan
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Victoria, Australia
- Department of Clinical Sciences, Murdoch Children’s Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Adam T. Schmidt
- Department of Psychological Sciences, Texas Tech University, Lubbock
| | - Dan J. Stein
- South African Medical Research Council Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Cape Town University, Cape Town, South Africa
- South African Medical Research Council Unit on Risk and Resilience in Mental Disorders, Neuroscience Institute, Cape Town University, Cape Town, South Africa
| | - Stacy J. Suskauer
- Kennedy Krieger Institute, Baltimore, Maryland
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Ashley L. Ware
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City
- Department of Psychology, Georgia State University, Atlanta
| | - Anne L. Wheeler
- Neuroscience and Mental Health Program, Hospital for Sick Children, Toronto, Ontario, Canada
- Physiology Department, University of Toronto, Toronto, Ontario, Canada
| | - Brandon A. Zielinski
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City
- Department of Pediatrics, University of Florida, Gainesville
- Department of Pediatrics, University of Utah School of Medicine, Salt Lake City
- Department of Neurology, University of Florida, Gainesville
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey
- Department of Neurology, University of Southern California, Los Angeles
- Department of Pediatrics, University of Southern California, Los Angeles
- Department of Psychiatry, University of Southern California, Los Angeles
- Department of Radiology, University of Southern California, Los Angeles
- Department of Engineering, University of Southern California, Los Angeles
- Department of Ophthalmology, University of Southern California, Los Angeles
| | - Ian H. Harding
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - David F. Tate
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, Utah
- Department of Psychology, Brigham Young University, Provo, Utah
| | - Elisabeth A. Wilde
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, Utah
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, Texas
| | - Emily L. Dennis
- TBI and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, Utah
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17
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Kerestes R, Perry A, Vivash L, O'Brien TJ, Alvim MKM, Arienzo D, Aventurato ÍK, Ballerini A, Baltazar GF, Bargalló N, Bender B, Brioschi R, Bürkle E, Caligiuri ME, Cendes F, de Tisi J, Duncan JS, Engel JP, Foley S, Fortunato F, Gambardella A, Giacomini T, Guerrini R, Hall G, Hamandi K, Ives-Deliperi V, João RB, Keller SS, Kleiser B, Labate A, Lenge M, Marotta C, Martin P, Mascalchi M, Meletti S, Owens-Walton C, Parodi CB, Pascual-Diaz S, Powell D, Rao J, Rebsamen M, Reiter J, Riva A, Rüber T, Rummel C, Scheffler F, Severino M, Silva LS, Staba RJ, Stein DJ, Striano P, Taylor PN, Thomopoulos SI, Thompson PM, Tortora D, Vaudano AE, Weber B, Wiest R, Winston GP, Yasuda CL, Zheng H, McDonald CR, Sisodiya SM, Harding IH. Patterns of subregional cerebellar atrophy across epilepsy syndromes: An ENIGMA-Epilepsy study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.21.562994. [PMID: 37961570 PMCID: PMC10634708 DOI: 10.1101/2023.10.21.562994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Objective The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current cortico-centric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural MRI in 1,602 adults with epilepsy and 1,022 healthy controls across twenty-two sites from the global ENIGMA-Epilepsy working group. Methods A state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions. Linear mixed models compared total and regional cerebellar volume in i) all epilepsies; ii) temporal lobe epilepsy with hippocampal sclerosis (TLE-HS); iii) non-lesional temporal lobe epilepsy (TLE-NL); iv) genetic generalised epilepsy; and (v) extra-temporal focal epilepsy (ETLE). Relationships were examined for cerebellar volume versus age at seizure onset, duration of epilepsy, phenytoin treatment, and cerebral cortical thickness. Results Across all epilepsies, reduced total cerebellar volume was observed (d=0.42). Maximum volume loss was observed in the corpus medullare (dmax=0.49) and posterior lobe grey matter regions, including bilateral lobules VIIB (dmax= 0.47), Crus I/II (dmax= 0.39), VIIIA (dmax=0.45) and VIIIB (dmax=0.40). Earlier age at seizure onset (ηρ2max=0.05) and longer epilepsy duration (ηρ2max=0.06) correlated with reduced volume in these regions. Findings were most pronounced in TLE-HS and ETLE with distinct neuroanatomical profiles observed in the posterior lobe. Phenytoin treatment was associated with reduced posterior lobe volume. Cerebellum volume correlated with cerebral cortical thinning more strongly in the epilepsy cohort than in controls. Significance We provide robust evidence of deep cerebellar and posterior lobe subregional grey matter volume loss in patients with chronic epilepsy. Volume loss was maximal for posterior subregions implicated in non-motor functions, relative to motor regions of both the anterior and posterior lobe. Associations between cerebral and cerebellar changes, and variability of neuroanatomical profiles across epilepsy syndromes argue for more precise incorporation of cerebellum subregions into neurobiological models of epilepsy.
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Affiliation(s)
- Rebecca Kerestes
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Andrew Perry
- Monash Bioinformatics Platform, Monash University, Melbourne, VIC, Australia
| | - Lucy Vivash
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Terence J O'Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Medicine and Neurology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
| | - Marina K M Alvim
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Donatello Arienzo
- Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
| | - Ítalo K Aventurato
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Alice Ballerini
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Gabriel F Baltazar
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Núria Bargalló
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
- Department of Radiology of Center of Image Diagnosis (CDIC), Hospital Clinic de Barcelona, Barcelona, Spain
- CIBERSAM, Madrid, Spain
| | - Benjamin Bender
- Department of Radiology, Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, Tübingen, Germany
| | - Ricardo Brioschi
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Eva Bürkle
- Department of Radiology, Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, Tübingen, Germany
| | - Maria Eugenia Caligiuri
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Græcia" of Catanzaro, Catanzaro, Italy
| | - Fernando Cendes
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - Jerome P Engel
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
| | - Francesco Fortunato
- Institute of Neurology, Department of Medical and Surgical Sciences, University "Magna Græcia" of Catanzaro, Catanzaro, Italy
| | - Antonio Gambardella
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Græcia" of Catanzaro, Catanzaro, Italy
- Institute of Neurology, Department of Medical and Surgical Sciences, University "Magna Græcia" of Catanzaro, Catanzaro, Italy
| | - Thea Giacomini
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
| | - Renzo Guerrini
- Functional and Epilepsy Neurosurgery Unit, Neurosurgery Department, Children's Hospital A. Meyer-University of Florence, Florence, Italy
| | - Gerard Hall
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
- The Welsh Epilepsy Unit, Department of Neurology, University Hospital of Wales, Cardiff, UK
| | | | - Rafael B João
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Simon S Keller
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Benedict Kleiser
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Angelo Labate
- Neurophysiopatology and Movement Disorders Clinic, University of Messina, Messina, Italy
- Regional Epilepsy Center, University of Messina, Messina, Italy
| | - Matteo Lenge
- Functional and Epilepsy Neurosurgery Unit, Neurosurgery Department, Children's Hospital A. Meyer-University of Florence, Florence, Italy
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and Laboratories, Children's Hospital A. Meyer-University of Florence, Florence, Italy
| | | | - Pascal Martin
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Mario Mascalchi
- 'Mario Serio' Department of Clinical and Experimental Medical Sciences, University of Florence, Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Study, Prevention and network in Oncology of the Tuscany Region, Florence, Italy
| | - Stefano Meletti
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Neurology Unit, OCB Hospital, Azienda Ospedaliera-Universitaria Modena, Modena, Italy
| | - Conor Owens-Walton
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | | | - Saül Pascual-Diaz
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - David Powell
- Monash Bioinformatics Platform, Monash University, Melbourne, VIC, Australia
| | - Jun Rao
- Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
| | - Michael Rebsamen
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Johannes Reiter
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | | | - Theodor Rüber
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Christian Rummel
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Freda Scheffler
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | | | - Lucas S Silva
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Richard J Staba
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Dan J Stein
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Monash Bioinformatics Platform, Monash University, Melbourne, VIC, Australia
- Department of Medicine and Neurology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
- Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
- Department of Radiology of Center of Image Diagnosis (CDIC), Hospital Clinic de Barcelona, Barcelona, Spain
- CIBERSAM, Madrid, Spain
- Department of Radiology, Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, Tübingen, Germany
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Græcia" of Catanzaro, Catanzaro, Italy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
- Institute of Neurology, Department of Medical and Surgical Sciences, University "Magna Græcia" of Catanzaro, Catanzaro, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
- Functional and Epilepsy Neurosurgery Unit, Neurosurgery Department, Children's Hospital A. Meyer-University of Florence, Florence, Italy
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- The Welsh Epilepsy Unit, Department of Neurology, University Hospital of Wales, Cardiff, UK
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Neurophysiopatology and Movement Disorders Clinic, University of Messina, Messina, Italy
- Regional Epilepsy Center, University of Messina, Messina, Italy
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and Laboratories, Children's Hospital A. Meyer-University of Florence, Florence, Italy
- Department of Neurology, Alfred Health, Melbourne, VIC, Australia
- 'Mario Serio' Department of Clinical and Experimental Medical Sciences, University of Florence, Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Study, Prevention and network in Oncology of the Tuscany Region, Florence, Italy
- Neurology Unit, OCB Hospital, Azienda Ospedaliera-Universitaria Modena, Modena, Italy
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
- IRCCS Istituto 'Giannina Gaslini', Genova, Italy
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Institute of Experimental Epileptology and Cognition Research, University of Bonn, Bonn, Germany
- Epilepsy Society MRI Unit, Chalfont St Peter, UK
- Department of Medicine (Division of Neurology), Queen's University Kingston, ON, Canada
- Chalfont Centre for Epilepsy, Bucks, UK
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Pasquale Striano
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
- IRCCS Istituto 'Giannina Gaslini', Genova, Italy
| | - Peter N Taylor
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | | | - Anna Elisabetta Vaudano
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Neurology Unit, OCB Hospital, Azienda Ospedaliera-Universitaria Modena, Modena, Italy
| | - Bernd Weber
- Institute of Experimental Epileptology and Cognition Research, University of Bonn, Bonn, Germany
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Epilepsy Society MRI Unit, Chalfont St Peter, UK
- Department of Medicine (Division of Neurology), Queen's University Kingston, ON, Canada
| | - Clarissa L Yasuda
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Hong Zheng
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Carrie R McDonald
- Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
| | - Sanjay M Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Chalfont Centre for Epilepsy, Bucks, UK
| | - Ian H Harding
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
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18
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de Oliveira CM, Leotti VB, Polita S, Anes M, Cappelli AH, Rocha AG, Ecco G, Bolzan G, Kersting N, Duarte JA, Saraiva-Pereira ML, Junior MCF, Rezende TJR, Jardim LB. The longitudinal progression of MRI changes in pre-ataxic carriers of SCA3/MJD. J Neurol 2023; 270:4276-4287. [PMID: 37193796 PMCID: PMC10187509 DOI: 10.1007/s00415-023-11763-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/02/2023] [Accepted: 05/03/2023] [Indexed: 05/18/2023]
Abstract
BACKGROUND The natural history of magnetic resonance imaging (MRI) in pre-ataxic stages of spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) is not well known. We report cross-sectional and longitudinal data obtained at this stage. METHODS Baseline (follow-up) observations included 32 (17) pre-ataxic carriers (SARA < 3) and 20 (12) related controls. The mutation length was used to estimate the time to onset (TimeTo) of gait ataxia. Clinical scales and MRIs were performed at baseline and after a median (IQR) of 30 (7) months. Cerebellar volumetries (ACAPULCO), deep gray-matter (T1-Multiatlas), cortical thickness (FreeSurfer), cervical spinal cord area (SCT) and white matter (DTI-Multiatlas) were assessed. Baseline differences between groups were described; variables that presented a p < 0.1 after Bonferroni correction were assessed longitudinally, using TimeTo and study time. For TimeTo strategy, corrections for age, sex and intracranial volume were done with Z-score progression. A significance level of 5% was adopted. RESULTS SCT at C1 level distinguished pre-ataxic carriers from controls. DTI measures of the right inferior cerebellar peduncle (ICP), bilateral middle cerebellar peduncles (MCP) and bilateral medial lemniscus (ML), also distinguished pre-ataxic carriers from controls, and progressed over TimeTo, with effect sizes varying from 0.11 to 0.20, larger than those of the clinical scales. No MRI variable showed progression over study time. DISCUSSION DTI parameters of the right ICP, left MCP and right ML were the best biomarkers for the pre-ataxic stage of SCA3/MJD. TimeTo is an interesting timescale, since it captured the longitudinal worsening of these structures.
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Affiliation(s)
- Camila Maria de Oliveira
- Programa de Pós-Graduação em Ciências Médicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Vanessa Bielefeldt Leotti
- Departamento de Estatística, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Programa de Pós-Graduação em Epidemiologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Sandra Polita
- Serviço de Radiologia, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Mauricio Anes
- Serviço de Física Médica e Radioproteção, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Amanda Henz Cappelli
- Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Gabriela Ecco
- Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Gabriela Bolzan
- Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Programa de Pós-Graduação em Genética e Biologia Molecular, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Nathalia Kersting
- Programa de Pós-Graduação em Ciências Médicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Juliana Avila Duarte
- Programa de Pós-Graduação em Ciências Médicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Serviço de Radiologia, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Maria-Luiza Saraiva-Pereira
- Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Programa de Pós-Graduação em Genética e Biologia Molecular, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Serviço de Genética Médica, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Departamento de Bioquímica, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Marcondes Cavalcante França Junior
- Departamento de Neurologia, Faculdade de Ciências Médicas, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
- Neuroimaging Laboratory, Rua Vital Brasil, 89-99, Cidade Universitária "Zeferino Vaz", Campinas, SP, 13083-888, Brazil
| | - Thiago Junqueira Ribeiro Rezende
- Departamento de Neurologia, Faculdade de Ciências Médicas, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil.
- Neuroimaging Laboratory, Rua Vital Brasil, 89-99, Cidade Universitária "Zeferino Vaz", Campinas, SP, 13083-888, Brazil.
- Medical Genetics Service, Hospital de Clínicas de Porto Alegre, Rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil.
| | - Laura Bannach Jardim
- Programa de Pós-Graduação em Ciências Médicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
- Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.
- Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
- Programa de Pós-Graduação em Genética e Biologia Molecular, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
- Serviço de Genética Médica, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.
- Departamento de Medicina Interna, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
- Medical Genetics Service, Hospital de Clínicas de Porto Alegre, Rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil.
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19
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Lee DA, Lee HJ, Park KM. Cerebellar Volume Reduction in Patients with Isolated REM Sleep Behavior Disorder: Evidence of a Potential Role of the Cerebellum. Eur Neurol 2023; 86:341-347. [PMID: 37527632 DOI: 10.1159/000533297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 07/24/2023] [Indexed: 08/03/2023]
Abstract
INTRODUCTION In this study, we aimed to investigate changes in the total cerebellar volume, subdivisions of the cerebellar volume, and intrinsic cerebellar network in patients with isolated rapid eye movement (REM) sleep behavior disorder (iRBD) compared to healthy controls. METHODS We enrolled patients with newly diagnosed iRBD and healthy controls who had no structural lesions according to their brain MRI. All participants underwent three-dimensional T1-weighted imaging. We obtained the total cerebellar volume and subdivisions of the cerebellar volume using the ACAPULCO program and calculated the intrinsic cerebellar network using a BRAPH program based on the subdivisions of the cerebellar volume by applying a graph theory. We compared the cerebellar volumes and intrinsic cerebellar network between the patients with iRBD and healthy controls. RESULTS In total, we enrolled 43 patients with iRBD and 47 healthy controls. Total cerebellar volume in patients with iRBD was lower than that in the healthy controls (8.4637 vs. 9.0863%, p = 0.0001). There were significant differences in the subdivisions of cerebellar volume between the groups. The volumes of the right and left lobule VIIB in the patients with iRBD were lower than those in the healthy controls (right, 0.3495 vs. 0.4025%, p = 0.0009; left, 0.3561 vs. 0.4293%, p < 0.0001). However, the other cerebellar volumes, such as the corpus meullare and vermis, were not different between the groups. The intrinsic cerebellar network was not different between the patients with iRBD and healthy controls. CONCLUSION We found decreased total cerebellar volumes and subdivisions of the cerebellar volume, particularly in the right and left lobule VIIB, in patients with iRBD compared to healthy controls. The present results suggest that the cerebellum may play a potential role in the pathogenesis of iRBD.
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Affiliation(s)
- Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Ho-Joon Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
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20
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Hughes DE, Kunitoki K, Elyounssi S, Luo M, Bazer OM, Hopkinson CE, Dowling KF, Doyle AE, Dunn EC, Eryilmaz H, Gilman JM, Holt DJ, Valera EM, Smoller JW, Cecil CAM, Tiemeier H, Lee PH, Roffman JL. Genetic patterning for child psychopathology is distinct from that for adults and implicates fetal cerebellar development. Nat Neurosci 2023; 26:959-969. [PMID: 37202553 PMCID: PMC7614744 DOI: 10.1038/s41593-023-01321-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 03/29/2023] [Indexed: 05/20/2023]
Abstract
Childhood psychiatric symptoms are often diffuse but can coalesce into discrete mental illnesses during late adolescence. We leveraged polygenic scores (PGSs) to parse genomic risk for childhood symptoms and to uncover related neurodevelopmental mechanisms with transcriptomic and neuroimaging data. In independent samples (Adolescent Brain Cognitive Development, Generation R) a narrow cross-disorder neurodevelopmental PGS, reflecting risk for attention deficit hyperactivity disorder, autism, depression and Tourette syndrome, predicted psychiatric symptoms through early adolescence with greater sensitivity than broad cross-disorder PGSs reflecting shared risk across eight psychiatric disorders, the disorder-specific PGS individually or two other narrow cross-disorder (Compulsive, Mood-Psychotic) scores. Neurodevelopmental PGS-associated genes were preferentially expressed in the cerebellum, where their expression peaked prenatally. Further, lower gray matter volumes in cerebellum and functionally coupled cortical regions associated with psychiatric symptoms in mid-childhood. These findings demonstrate that the genetic underpinnings of pediatric psychiatric symptoms differ from those of adult illness, and implicate fetal cerebellar developmental processes that endure through childhood.
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Affiliation(s)
- Dylan E Hughes
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Keiko Kunitoki
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Safia Elyounssi
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Mannan Luo
- Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, the Netherlands
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Oren M Bazer
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Casey E Hopkinson
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Kevin F Dowling
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Medical Scientist Training Program, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA
| | - Alysa E Doyle
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Erin C Dunn
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center on the Developing Child at Harvard University, Cambridge, MA, USA
| | - Hamdi Eryilmaz
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jodi M Gilman
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Daphne J Holt
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Eve M Valera
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jordan W Smoller
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Charlotte A M Cecil
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC-Sophia, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC-Sophia, Rotterdam, the Netherlands
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Phil H Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Joshua L Roffman
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.
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21
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Wang Y, Feng A, Xue Y, Zuo L, Liu Y, Blitz AM, Luciano MG, Carass A, Prince JL. AUTOMATED VENTRICLE PARCELLATION AND EVAN'S RATIO COMPUTATION IN PRE- AND POST-SURGICAL VENTRICULOMEGALY. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2023; 2023:10.1109/isbi53787.2023.10230729. [PMID: 38013948 PMCID: PMC10679954 DOI: 10.1109/isbi53787.2023.10230729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Normal pressure hydrocephalus (NPH) is a brain disorder associated with enlarged ventricles and multiple cognitive and motor symptoms. The degree of ventricular enlargement can be measured using magnetic resonance images (MRIs) and characterized quantitatively using the Evan's ratio (ER). Automatic computation of ER is desired to avoid the extra time and variations associated with manual measurements on MRI. Because shunt surgery is often used to treat NPH, it is necessary that this process be robust to image artifacts caused by the shunt and related implants. In this paper, we propose a 3D regions-of-interest aware (ROI-aware) network for segmenting the ventricles. The method achieves state-of-the-art performance on both pre-surgery MRIs and post-surgery MRIs with artifacts. Based on our segmentation results, we also describe an automated approach to compute ER from these results. Experimental results on multiple datasets demonstrate the potential of the proposed method to assist clinicians in the diagnosis and management of NPH.
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Affiliation(s)
- Yuli Wang
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, USA
| | - Anqi Feng
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, USA
| | - Yuan Xue
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| | - Lianrui Zuo
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, USA
| | - Yihao Liu
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| | - Ari M Blitz
- Department of Radiology, Case Western Reserve University School of Medicine, USA
| | - Mark G Luciano
- Department of Neurosurgery, Johns Hopkins School of Medicine, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| | - Jerry L Prince
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, USA
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
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22
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Lee HJ, Lee DA, Park KM. Altered Cerebellar Volumes and Intrinsic Cerebellar Network in Juvenile Myoclonic Epilepsy. Acta Neurol Scand 2023. [DOI: 10.1155/2023/7907887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Objectives. This study is aimed at investigating the alterations in cerebellar volumes and intrinsic cerebellar network in patients with juvenile myoclonic epilepsy (JME) in comparison with healthy controls. Methods. Patients newly diagnosed with JME and healthy controls were enrolled. Three-dimensional T1-weighted imaging was conducted, and no structural lesions were found on brain magnetic resonance imaging. Cerebellar volumes were obtained using the ACAPULCO program, while the intrinsic cerebellar network was evaluated by applying graph theory using the BRAPH program. The nodes were defined as individual cerebellar volumes and edges as partial correlations, controlling for the effects of age and sex. Cerebellar volumes and intrinsic cerebellar networks were compared between the two groups. Results. Forty-five patients with JME and 45 healthy controls were enrolled. Compared with the healthy controls, the patients with JME had significantly lower volumes of the right and left cerebellar white matter (3.33 vs. 3.48%,
; 3.35 vs. 3.49%,
), corpus medullare (0.99 vs. 1.03%,
), and left lobule V (0.19 vs. 0.22%,
). The intrinsic cerebellar networks also showed significant differences between the two groups. The small-worldness index in the patients with JME was significantly lower than that in the healthy controls (0.771 vs. 0.919,
). Conclusion. The cerebellar volumes and intrinsic cerebellar network demonstrated alterations in the patients with JME when compared with those of the healthy controls. Our study results provide evidence that the cerebellum may play a role in the pathogenesis of JME.
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23
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Kerestes R, Cummins H, Georgiou-Karistianis N, Selvadurai LP, Corben LA, Delatycki MB, Egan GF, Harding IH. Reduced cerebello-cerebral functional connectivity correlates with disease severity and impaired white matter integrity in Friedreich ataxia. J Neurol 2023; 270:2360-2369. [PMID: 36859626 PMCID: PMC10130106 DOI: 10.1007/s00415-023-11637-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 02/07/2023] [Accepted: 02/19/2023] [Indexed: 03/03/2023]
Abstract
Friedreich ataxia (FRDA) is a rare, inherited neurodegenerative disease characterised in most cases by progressive and debilitating motor dysfunction. Degeneration of cerebellar white matter pathways have been previously reported, alongside indications of cerebello-cerebral functional alterations. In this work, we examine resting-state functional connectivity changes within cerebello-cerebral circuits, and their associations with disease severity (Scale for the Assessment and Rating of Ataxia [SARA]), psychomotor function (speeded and paced finger tapping), and white matter integrity (diffusion tensor imaging) in 35 adults with FRDA and 45 age and sex-matched controls. Voxel-wise seed-based functional connectivity was assessed for three cerebellar cortical regions (anterior lobe, lobules I-V; superior posterior lobe, lobules VI-VIIB; inferior posterior lobe, lobules VIIIA-IX) and two dentate nucleus seeds (dorsal and ventral). Compared to controls, people with FRDA showed significantly reduced connectivity between the anterior cerebellum and bilateral pre/postcentral gyri, and between the superior posterior cerebellum and left dorsolateral PFC. Greater disease severity correlated with lower connectivity in these circuits. Lower anterior cerebellum-motor cortex functional connectivity also correlated with slower speeded finger tapping and less fractional anisotropy in the superior cerebellar peduncles, internal capsule, and precentral white matter in the FRDA cohort. There were no significant between-group differences in inferior posterior cerebellar or dentate nucleus connectivity. This study indicates that altered cerebello-cerebral functional connectivity is associated with functional status and white matter damage in cerebellar efferent pathways in people with FRDA, particularly in motor circuits.
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Affiliation(s)
- Rebecca Kerestes
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Hannah Cummins
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Nellie Georgiou-Karistianis
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Louisa P Selvadurai
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Louise A Corben
- Bruce Lefroy Centre for Genetic Health Research, Murdoch Children's Research Institute, Melbourne, Australia
| | - Martin B Delatycki
- Bruce Lefroy Centre for Genetic Health Research, Murdoch Children's Research Institute, Melbourne, Australia
| | - Gary F Egan
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia.,Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3800, Australia
| | - Ian H Harding
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia. .,Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3800, Australia.
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24
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Cabeza-Ruiz R, Velázquez-Pérez L, Pérez-Rodríguez R, Reetz K. ConvNets for automatic detection of polyglutamine SCAs from brain MRIs: state of the art applications. Med Biol Eng Comput 2023; 61:1-24. [PMID: 36385616 DOI: 10.1007/s11517-022-02714-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 10/26/2022] [Indexed: 11/17/2022]
Abstract
Polyglutamine spinocerebellar ataxias (polyQ SCAs) are a group of neurodegenerative diseases, clinically and genetically heterogeneous, characterized by loss of balance and motor coordination due to dysfunction of the cerebellum and its connections. The diagnosis of each type of polyQ SCA, alongside with genetic tests, includes medical images analysis, and its automation may help specialists to distinguish between each type. Convolutional neural networks (ConvNets or CNNs) have been recently used for medical image processing, with outstanding results. In this work, we present the main clinical and imaging features of polyglutamine SCAs, and the basics of CNNs. Finally, we review studies that have used this approach to automatically process brain medical images and may be applied to SCAs detection. We conclude by discussing the possible limitations and opportunities of using ConvNets for SCAs diagnose in the future.
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Affiliation(s)
| | - Luis Velázquez-Pérez
- Cuban Academy of Sciences, La Habana, Cuba
- Center for the Research and Rehabilitation of Hereditary Ataxias, Holguín, Cuba
| | - Roberto Pérez-Rodríguez
- CAD/CAM Study Center, University of Holguín, Holguín, Cuba
- Cuban Academy of Sciences, La Habana, Cuba
| | - Kathrin Reetz
- Department of Neurology, RWTH Aachen University, Aachen, Germany
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25
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van Veenhuijzen K, Westeneng HJ, Tan HHG, Nitert AD, van der Burgh HK, Gosselt I, van Es MA, Nijboer TCW, Veldink JH, van den Berg LH. Longitudinal Effects of Asymptomatic C9orf72 Carriership on Brain Morphology. Ann Neurol 2022; 93:668-680. [PMID: 36511398 DOI: 10.1002/ana.26572] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 11/18/2022] [Accepted: 11/28/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVE We investigated effects of C9orf72 repeat expansion and gene expression on longitudinal cerebral changes before symptom onset. METHODS We enrolled 79 asymptomatic family members (AFMs) from 9 families with C9orf72 repeat expansion. Twenty-eight AFMs carried the mutation (C9+). Participants had up to 3 magnetic resonance imaging (MRI) scans, after which we compared motor cortex and motor tracts between C9+ and C9- AFMs using mixed effects models, incorporating kinship to correct for familial relations and lessen effects of other genetic factors. We also compared cortical, subcortical, cerebellar, and connectome structural measurements in a hypothesis-free analysis. We correlated regional C9orf72 expression in donor brains with the pattern of cortical thinning in C9+ AFMs using meta-regression. For comparison, we included 42 C9+ and 439 C9- patients with amyotrophic lateral sclerosis (ALS) in this analysis. RESULTS C9+ AFM motor cortex had less gyrification and was thinner than in C9- AFMs, without differences in motor tracts. Whole brain analysis revealed thinner cortex and less gyrification in parietal, occipital, and temporal regions, smaller thalami and right hippocampus, and affected frontotemporal connections. Thinning of bilateral precentral, precuneus, and left superior parietal cortex was faster in C9+ than in C9- AFMs. Higher C9orf72 expression correlated with thinner cortex in both C9+ AFMs and C9+ ALS patients. INTERPRETATION In asymptomatic C9orf72 repeat expansion carriers, brain MRI reveals widespread features suggestive of impaired neurodevelopment, along with faster decline of motor and parietal cortex than found in normal aging. C9orf72 expression might play a role in cortical development, and consequently explain the specific brain abnormalities of mutation carriers. ANN NEUROL 2022.
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Affiliation(s)
- Kevin van Veenhuijzen
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Henk-Jan Westeneng
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Harold H G Tan
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Abram D Nitert
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Hannelore K van der Burgh
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Isabel Gosselt
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.,Center of Excellence for Rehabilitation Medicine, Brain Center, University Medical Center Utrecht and De Hoogstraat Rehabilitation, Utrecht, the Netherlands
| | - Michael A van Es
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Tanja C W Nijboer
- Department of Experimental Psychology, Utrecht University, Utrecht, the Netherlands
| | - Jan H Veldink
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Leonard H van den Berg
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
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26
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Faber J, Kügler D, Bahrami E, Heinz LS, Timmann D, Ernst TM, Deike-Hofmann K, Klockgether T, van de Warrenburg B, van Gaalen J, Reetz K, Romanzetti S, Oz G, Joers JM, Diedrichsen J, Reuter M, Garcia-Moreno H, Jacobi H, Jende J, de Vries J, Povazan M, Barker PB, Steiner KM, Krahe J. CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation. Neuroimage 2022; 264:119703. [PMID: 36349595 PMCID: PMC9771831 DOI: 10.1016/j.neuroimage.2022.119703] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 10/18/2022] [Indexed: 11/07/2022] Open
Abstract
Quantifying the volume of the cerebellum and its lobes is of profound interest in various neurodegenerative and acquired diseases. Especially for the most common spinocerebellar ataxias (SCA), for which the first antisense oligonculeotide-base gene silencing trial has recently started, there is an urgent need for quantitative, sensitive imaging markers at pre-symptomatic stages for stratification and treatment assessment. This work introduces CerebNet, a fully automated, extensively validated, deep learning method for the lobular segmentation of the cerebellum, including the separation of gray and white matter. For training, validation, and testing, T1-weighted images from 30 participants were manually annotated into cerebellar lobules and vermal sub-segments, as well as cerebellar white matter. CerebNet combines FastSurferCNN, a UNet-based 2.5D segmentation network, with extensive data augmentation, e.g. realistic non-linear deformations to increase the anatomical variety, eliminating additional preprocessing steps, such as spatial normalization or bias field correction. CerebNet demonstrates a high accuracy (on average 0.87 Dice and 1.742mm Robust Hausdorff Distance across all structures) outperforming state-of-the-art approaches. Furthermore, it shows high test-retest reliability (average ICC >0.97 on OASIS and Kirby) as well as high sensitivity to disease effects, including the pre-ataxic stage of spinocerebellar ataxia type 3 (SCA3). CerebNet is compatible with FreeSurfer and FastSurfer and can analyze a 3D volume within seconds on a consumer GPU in an end-to-end fashion, thus providing an efficient and validated solution for assessing cerebellum sub-structure volumes. We make CerebNet available as source-code (https://github.com/Deep-MI/FastSurfer).
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Affiliation(s)
- Jennifer Faber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany,Department of Neurology, University Hospital Bonn, Germany
| | - David Kügler
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Emad Bahrami
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany,Computer Science Department, University Bonn, Bonn, Germany
| | - Lea-Sophie Heinz
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Dagmar Timmann
- Department of Neurology, Center for Translational Neuro, and Behavioral Sciences (C-TNBS), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Thomas M. Ernst
- Department of Neurology, Center for Translational Neuro, and Behavioral Sciences (C-TNBS), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | | | - Thomas Klockgether
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany,Department of Neurology, University Hospital Bonn, Germany
| | - Bart van de Warrenburg
- Department of Neurology, Donders Institute for Brain, Cognition, and Behaviour, Radboud university medical center, Nijmegen, The Netherlands
| | - Judith van Gaalen
- Department of Neurology, Donders Institute for Brain, Cognition, and Behaviour, Radboud university medical center, Nijmegen, The Netherlands
| | - Kathrin Reetz
- Department of Neurology, RWTH Aachen University, Germany,JARA-Brain Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich, Germany
| | | | - Gulin Oz
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - James M. Joers
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Jorn Diedrichsen
- Departments of Computer Science and Statistical and Actuarial Sciences, Western University, London, ON, Canada
| | - ESMI MRI Study GroupGiuntiPaola1Garcia-MorenoHector1JacobiHeike3JendeJohann4de VriesJeroen5PovazanMichal6BarkerPeter B.6SteinerKatherina Marie8KraheJanna9Ataxia Centre, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology & National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UKDepartment of Neurology, University Hospital of Heidelberg, Heidelberg, GermanyDepartment of Neuroradiology, Heidelberg University Hospital, Heidelberg, GermanyDepartment of Neurology, Expertise Center Movement Disorders Groningen, University Medical Center Groningen, University of Groningen, The NetherlandsJohns Hopkins University School of Medicine, Baltimore, MD, U.S.Department of Neurology, Center for Translational Neuro, and Behavioral Sciences (C-TNBS), University Hospital Essen, University of Duisburg-Essen, Essen, GermanyDepartment of Neurology, RWTH Aachen University, Germany
| | - Martin Reuter
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany,A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA,Department of Radiology, Harvard Medical School, Boston, MA, USA,Corresponding author.
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27
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Tan HHG, Westeneng H, Nitert AD, van Veenhuijzen K, Meier JM, van der Burgh HK, van Zandvoort MJE, van Es MA, Veldink JH, van den Berg LH. MRI Clustering Reveals Three ALS Subtypes With Unique Neurodegeneration Patterns. Ann Neurol 2022; 92:1030-1045. [PMID: 36054734 PMCID: PMC9826424 DOI: 10.1002/ana.26488] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 08/18/2022] [Accepted: 08/18/2022] [Indexed: 01/11/2023]
Abstract
OBJECTIVE The purpose of this study was to identify subtypes of amyotrophic lateral sclerosis (ALS) by comparing patterns of neurodegeneration using brain magnetic resonance imaging (MRI) and explore their phenotypes. METHODS We performed T1-weighted and diffusion tensor imaging in 488 clinically well-characterized patients with ALS and 338 control subjects. Measurements of whole-brain cortical thickness and white matter connectome fractional anisotropy were adjusted for disease-unrelated variation. A probabilistic network-based clustering algorithm was used to divide patients into subgroups of similar neurodegeneration patterns. Clinical characteristics and cognitive profiles were assessed for each subgroup. In total, 512 follow-up scans were used to validate clustering results longitudinally. RESULTS The clustering algorithm divided patients with ALS into 3 subgroups of 187, 163, and 138 patients. All subgroups displayed involvement of the precentral gyrus and are characterized, respectively, by (1) pure motor involvement (pure motor cluster [PM]), (2) orbitofrontal and temporal involvement (frontotemporal cluster [FT]), and (3) involvement of the posterior cingulate cortex, parietal white matter, temporal operculum, and cerebellum (cingulate-parietal-temporal cluster [CPT]). These subgroups had significantly distinct clinical profiles regarding male-to-female ratio, age at symptom onset, and frequency of bulbar symptom onset. FT and CPT revealed higher rates of cognitive impairment on the Edinburgh cognitive and behavioral ALS screen (ECAS). Longitudinally, clustering remained stable: at 90.4% of their follow-up visits, patients clustered in the same subgroup as their baseline visit. INTERPRETATION ALS can manifest itself in 3 main patterns of cerebral neurodegeneration, each associated with distinct clinical characteristics and cognitive profiles. Besides the pure motor and frontotemporal dementia (FTD)-like variants of ALS, a new neuroimaging phenotype has emerged, characterized by posterior cingulate, parietal, temporal, and cerebellar involvement. ANN NEUROL 2022;92:1030-1045.
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Affiliation(s)
- Harold H. G. Tan
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Henk‐Jan Westeneng
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Abram D. Nitert
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Kevin van Veenhuijzen
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Jil M. Meier
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Hannelore K. van der Burgh
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Martine J. E. van Zandvoort
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands,Department of Experimental PsychologyUtrecht UniversityUtrechtThe Netherlands
| | - Michael A. van Es
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Jan H. Veldink
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Leonard H. van den Berg
- Department of Neurology, UMC Utrecht Brain Center University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
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Wang Y, Li ST, Huang J, Lai QQ, Guo YF, Huang YH, Li YZ. Cardiac MRI segmentation of the atria based on UU-NET. Front Cardiovasc Med 2022; 9:1011916. [PMID: 36505371 PMCID: PMC9731285 DOI: 10.3389/fcvm.2022.1011916] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 11/04/2022] [Indexed: 11/25/2022] Open
Abstract
Background and objective In today's society, people's work pressure, coupled with irregular diet, lack of exercise and other bad lifestyle, resulting in frequent cardiovascular diseases. Medical imaging has made great progress in modern society, among which the role of MRI in cardiovascular field is self-evident. Based on this research background, how to process cardiac MRI quickly and accurately by computer has been extensively discussed. By comparing and analyzing several traditional image segmentation and deep learning image segmentation, this paper proposes the left and right atria segmentation algorithm of cardiac MRI based on UU-NET network. Methods In this paper, an atrial segmentation algorithm for cardiac MRI images in UU-NET network is proposed. Firstly, U-shaped upper and lower sampling modules are constructed by using residual theory, which are used as encoders and decoders of the model. Then, the modules are interconnected to form multiple paths from input to output to increase the information transmission capacity of the model. Results The segmentation method based on UU-NET network has achieved good results proposed in this paper, compared with the current mainstream image segmentation algorithm results have been improved to a certain extent. Through the analysis of the experimental results, the image segmentation algorithm based on UU-NET network on the data set, its performance in the verification set and online set is higher than other grid models. The DSC in the verification set is 96.7%, and the DSC in the online set is 96.7%, which is nearly one percentage point higher than the deconvolution neural network model. The hausdorff distance (HD) is 1.2 mm. Compared with other deep learning models, it is significantly improved (about 3 mm error is reduced), and the time is 0.4 min. Conclusion The segmentation algorithm based on UU-NET improves the segmentation accuracy obviously compared with other segmentation models. Our technique will be able to help diagnose and treat cardiac complications.
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Affiliation(s)
- Yi Wang
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Shu-Ting Li
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Jing Huang
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Qing-Quan Lai
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Yi-Fan Guo
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China
| | - Yin-Hui Huang
- Department of Neurology, Jinjiang Municipal Hospital, Quanzhou, China,*Correspondence: Yuan-Zhe Li
| | - Yuan-Zhe Li
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China,Yin-Hui Huang
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29
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A multifaceted gradient in human cerebellum of structural and functional development. Nat Neurosci 2022; 25:1129-1133. [PMID: 35982153 DOI: 10.1038/s41593-022-01136-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 07/11/2022] [Indexed: 11/08/2022]
Abstract
The organization of the basic tissue and functional properties of the cerebellum across development is unknown. Combining several large datasets, we demonstrate in the human cerebellum a static tissue gradient in adults that mirrors a similar growth-rate gradient across development. Quantitative tissue metrics corroborate unique densities of certain lipids and proteins among lobules, and cerebellar structural development closely follows cerebellar functional properties through childhood.
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30
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Cerebellar Contributions to Motor and Cognitive Control in Multiple Sclerosis ✰✰✰. Arch Phys Med Rehabil 2022; 103:1592-1599. [PMID: 34998712 DOI: 10.1016/j.apmr.2021.12.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 10/25/2021] [Accepted: 12/13/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE To evaluate relationships between specific cerebellar regions and common clinical measures of motor and cognitive function in persons with multiple sclerosis (PwMS). DESIGN Cross-sectional. SETTING Laboratory. PARTICIPANTS Twenty-nine PwMS and 28 age- and sex-matched controls without multiple sclerosis (MS) (N=57). INTERVENTIONS Not applicable. MAIN OUTCOME MEASURES Both diffusion and lobule magnetic resonance imaging analyses and common clinical measures of motor and cognitive function were used to examine structure-function relationships in the cerebellum. RESULTS PwMS demonstrate significantly worse motor and cognitive function than controls, including weaker strength, slower walking, and poorer performance on the Symbol Digit Modalities Test, but demonstrate no differences in cerebellar volume. However, PwMS demonstrate significantly worse diffusivity (mean diffusivity: P=.0003; axial diffusivity: P=.0015; radial diffusivity: P=.0005; fractional anisotropy: P=.016) of the superior cerebellar peduncle, the primary output of the cerebellum. Increased volume of the motor lobules (I-V, VIII) was significantly related to better motor (P<.022) and cognitive (P=.046) performance, and increased volume of the cognitive lobules (VI-VII) was also related to better motor (P<.032) and cognitive (P=.008) performance, supporting the role of the cerebellum in both motor and cognitive functioning. CONCLUSIONS These data highlight the contributions of the cerebellum to both motor and cognitive function in PwMS. Using novel neuroimaging techniques to examine structure-function relationships in PwMS improves our understanding of individualized differences in this heterogeneous group and may provide an avenue for targeted, individualized rehabilitation aimed at improving cerebellar dysfunction in MS.
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Nejad A, Masoudnia S, Nazem-Zadeh MR. A Fast and Memory-Efficient Brain MRI Segmentation Framework for Clinical Applications. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2140-2143. [PMID: 36086643 DOI: 10.1109/embc48229.2022.9871715] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Current segmentation tools of brain MRI provide quantitative structural information for diagnosing neurological disorders. However, their clinical application is generally limited due to high memory usage and time consumption. Although 3D CNN-based segmentation methods have recently achieved the state-of-the-art and come up with timely available results, they heavily require high memory GPUs. In this paper, we customize a memory-efficient (GPU) brain structure segmentation framework, named FLBS, based on nnU-nets which enables our framework to adapt its architecture based on memory constraints dynamically. To further reduce the need for memory, we also reduce multi-label brain segmentation to the fusion of sequential single-label segmentations. In the first step, single label patches are extracted from the T1w and segmentation maps by locating the approximate area of each structure on the MNI305 template, including the safety margin. These considerations not only decrease the hardware usage but also maintains comparable computational time. Moreover, the target brain structures are customizable based on the specific clinical applications. We evaluate the performance in terms of Dice coefficient, runtime, and GPU requirement on OASIS-3 and CoRR-BNU1 datasets. The validation results show our comparable accuracies with state-of-the-arts and confirm the generalizability on unseen datasets while significantly reducing GPU requirements and maintaining runtime duration. Our framework is also executable on a budget GPU with a minimum requirement of 4G RAM. Clinical Relevance- We develop a memory-efficient deep Brain MRI segmentation tool that significantly reduces the hardware requirement of MRI segmentation while maintaining comparable accuracy and time. These advantages make FLBS suitable for widespread use in clinical applications especially for clinics with a limited budget. We plan to release the framework as a part of a free clinical brain imaging analysis tool. The code for this framework is publicly available.
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Convolutional Neural Networks for Segmenting Cerebellar Fissures from Magnetic Resonance Imaging. SENSORS 2022; 22:s22041345. [PMID: 35214268 PMCID: PMC8963095 DOI: 10.3390/s22041345] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/08/2022] [Accepted: 02/08/2022] [Indexed: 02/06/2023]
Abstract
The human cerebellum plays an important role in coordination tasks. Diseases such as spinocerebellar ataxias tend to cause severe damage to the cerebellum, leading patients to a progressive loss of motor coordination. The detection of such damages can help specialists to approximate the state of the disease, as well as to perform statistical analysis, in order to propose treatment therapies for the patients. Manual segmentation of such patterns from magnetic resonance imaging is a very difficult and time-consuming task, and is not a viable solution if the number of images to process is relatively large. In recent years, deep learning techniques such as convolutional neural networks (CNNs or convnets) have experienced an increased development, and many researchers have used them to automatically segment medical images. In this research, we propose the use of convolutional neural networks for automatically segmenting the cerebellar fissures from brain magnetic resonance imaging. Three models are presented, based on the same CNN architecture, for obtaining three different binary masks: fissures, cerebellum with fissures, and cerebellum without fissures. The models perform well in terms of precision and efficiency. Evaluation results show that convnets can be trained for such purposes, and could be considered as additional tools in the diagnosis and characterization of neurodegenerative diseases.
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Shao M, Zuo L, Carass A, Zhuo J, Gullapalli RP, Prince JL. Evaluating the impact of MR image harmonization on thalamus deep network segmentation. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12032:120320H. [PMID: 35514535 PMCID: PMC9070007 DOI: 10.1117/12.2613159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Medical image segmentation is one of the core tasks of medical image analysis. Automatic segmentation of brain magnetic resonance images (MRIs) can be used to visualize and track changes of the brain's anatomical structures that may occur due to normal aging or disease. Machine learning techniques are widely used in automatic structure segmentation. However, the contrast variation between the training and testing data makes it difficult for segmentation algorithms to generate consistent results. To address this problem, an image-to-image translation technique called MR image harmonization can be used to match the contrast between different data sets. It is important for the harmonization to transform image intensity while maintaining the underlying anatomy. In this paper, we present a 3D U-Net algorithm to segment the thalamus from multiple MR image modalities and investigate the impact of harmonization on the segmentation algorithm. Manual delineations of thalamic nuclei on two data sets are available. However, we aim to analyze the thalamus in another large data set where ground truth labels are lacking. We trained two segmentation networks, one with unharmonized images and the other with harmonized images, on one data set with manual labels, and compared their performances on the other data set with manual labels. These two data groups were diagnosed with two brain disorders and were acquired with similar imaging protocols. The harmonization target is the large data set without manual labels, which also has a different imaging protocol. The networks trained on unharmonized and harmonized data showed no significant difference when evaluating on the other data set; demonstrating that image harmonization can maintain the anatomy and does not affect the segmentation task. The two networks were evaluated on the harmonization target data set and the network trained on harmonized data showed significant improvement over the network trained on unharmonized data. Therefore, the network trained on harmonized data provides the potential to process large amounts of data from other sites, even in the absence of site-specific training data.
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Affiliation(s)
- Muhan Shao
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Lianrui Zuo
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institute of Health, Baltimore, MD 21224, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jiachen Zhuo
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Rao P. Gullapalli
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
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McKinney WS, Kelly SE, Unruh KE, Shafer RL, Sweeney JA, Styner M, Mosconi MW. Cerebellar Volumes and Sensorimotor Behavior in Autism Spectrum Disorder. Front Integr Neurosci 2022; 16:821109. [PMID: 35592866 PMCID: PMC9113114 DOI: 10.3389/fnint.2022.821109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Background Sensorimotor issues are common in autism spectrum disorder (ASD), though their neural bases are not well understood. The cerebellum is vital to sensorimotor control and reduced cerebellar volumes in ASD have been documented. Our study examined the extent to which cerebellar volumes are associated with multiple sensorimotor behaviors in ASD. Materials and Methods Fifty-eight participants with ASD and 34 typically developing (TD) controls (8-30 years) completed a structural MRI scan and precision grip testing, oculomotor testing, or both. Force variability during precision gripping as well as absolute error and trial-to-trial error variability of visually guided saccades were examined. Volumes of cerebellar lobules, vermis, and white matter were quantified. The relationships between each cerebellar region of interest (ROI) and force variability, saccade error, and saccade error variability were examined. Results Relative to TD controls, individuals with ASD showed increased force variability. Individuals with ASD showed a reduced volume of cerebellar vermis VI-VII relative to TD controls. Relative to TD females, females with ASD showed a reduced volume of bilateral cerebellar Crus II/lobule VIIB. Increased volume of Crus I was associated with increased force variability. Increased volume of vermal lobules VI-VII was associated with reduced saccade error for TD controls but not individuals with ASD. Increased right lobule VIII and cerebellar white matter volumes as well as reduced right lobule VI and right lobule X volumes were associated with greater ASD symptom severity. Reduced volumes of right Crus II/lobule VIIB were associated with greater ASD symptom severity in only males, while reduced volumes of right Crus I were associated with more severe restricted and repetitive behaviors only in females. Conclusion Our finding that increased force variability in ASD is associated with greater cerebellar Crus I volumes indicates that disruption of sensory feedback processing supported by Crus I may contribute to skeletomotor differences in ASD. Results showing that volumes of vermal lobules VI-VII are associated with saccade precision in TD but not ASD implicates atypical organization of the brain systems supporting oculomotor control in ASD. Associations between volumes of cerebellar subregions and ASD symptom severity suggest cerebellar pathological processes may contribute to multiple developmental challenges in ASD.
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Affiliation(s)
- Walker S. McKinney
- Schiefelbusch Institute for Life Span Studies and Kansas Center for Autism Research and Training (K-CART), University of Kansas, Lawrence, KS, United States
- Clinical Child Psychology Program, University of Kansas, Lawrence, KS, United States
| | - Shannon E. Kelly
- Schiefelbusch Institute for Life Span Studies and Kansas Center for Autism Research and Training (K-CART), University of Kansas, Lawrence, KS, United States
- Department of Psychology, University of Kansas, Lawrence, KS, United States
| | - Kathryn E. Unruh
- Schiefelbusch Institute for Life Span Studies and Kansas Center for Autism Research and Training (K-CART), University of Kansas, Lawrence, KS, United States
| | - Robin L. Shafer
- Schiefelbusch Institute for Life Span Studies and Kansas Center for Autism Research and Training (K-CART), University of Kansas, Lawrence, KS, United States
| | - John A. Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Martin Styner
- Department of Psychiatry and Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Matthew W. Mosconi
- Schiefelbusch Institute for Life Span Studies and Kansas Center for Autism Research and Training (K-CART), University of Kansas, Lawrence, KS, United States
- Clinical Child Psychology Program, University of Kansas, Lawrence, KS, United States
- Department of Psychology, University of Kansas, Lawrence, KS, United States
- *Correspondence: Matthew W. Mosconi,
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Decuyper M, Maebe J, Van Holen R, Vandenberghe S. Artificial intelligence with deep learning in nuclear medicine and radiology. EJNMMI Phys 2021; 8:81. [PMID: 34897550 PMCID: PMC8665861 DOI: 10.1186/s40658-021-00426-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 11/19/2021] [Indexed: 12/19/2022] Open
Abstract
The use of deep learning in medical imaging has increased rapidly over the past few years, finding applications throughout the entire radiology pipeline, from improved scanner performance to automatic disease detection and diagnosis. These advancements have resulted in a wide variety of deep learning approaches being developed, solving unique challenges for various imaging modalities. This paper provides a review on these developments from a technical point of view, categorizing the different methodologies and summarizing their implementation. We provide an introduction to the design of neural networks and their training procedure, after which we take an extended look at their uses in medical imaging. We cover the different sections of the radiology pipeline, highlighting some influential works and discussing the merits and limitations of deep learning approaches compared to other traditional methods. As such, this review is intended to provide a broad yet concise overview for the interested reader, facilitating adoption and interdisciplinary research of deep learning in the field of medical imaging.
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Affiliation(s)
- Milan Decuyper
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Jens Maebe
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Roel Van Holen
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Stefaan Vandenberghe
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
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Zuo L, Dewey BE, Liu Y, He Y, Newsome SD, Mowry EM, Resnick SM, Prince JL, Carass A. Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory. Neuroimage 2021; 243:118569. [PMID: 34506916 PMCID: PMC10473284 DOI: 10.1016/j.neuroimage.2021.118569] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 08/11/2021] [Accepted: 09/07/2021] [Indexed: 01/19/2023] Open
Abstract
In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes pulse sequence-based contrast variations in MR images from site to site, which impedes consistent measurements in automatic analyses. In this paper, we propose an unsupervised MR image harmonization approach, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), which aims to alleviate contrast variations in multi-site MR imaging. Designed using information bottleneck theory, CALAMITI learns a globally disentangled latent space containing both anatomical and contrast information, which permits harmonization. In contrast to supervised harmonization methods, our approach does not need a sample population to be imaged across sites. Unlike traditional unsupervised harmonization approaches which often suffer from geometry shifts, CALAMITI better preserves anatomy by design. The proposed method is also able to adapt to a new testing site with a straightforward fine-tuning process. Experiments on MR images acquired from ten sites show that CALAMITI achieves superior performance compared with other harmonization approaches.
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Affiliation(s)
- Lianrui Zuo
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institute of Health, Baltimore, MD 20892, USA.
| | - Blake E Dewey
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA
| | - Yihao Liu
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA
| | - Yufan He
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA
| | - Scott D Newsome
- Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Ellen M Mowry
- Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institute of Health, Baltimore, MD 20892, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA
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Beliveau V, Nørgaard M, Birkl C, Seppi K, Scherfler C. Automated segmentation of deep brain nuclei using convolutional neural networks and susceptibility weighted imaging. Hum Brain Mapp 2021; 42:4809-4822. [PMID: 34322940 PMCID: PMC8449109 DOI: 10.1002/hbm.25604] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 07/08/2021] [Accepted: 07/13/2021] [Indexed: 01/10/2023] Open
Abstract
The advent of susceptibility-sensitive MRI techniques, such as susceptibility weighted imaging (SWI), has enabled accurate in vivo visualization and quantification of iron deposition within the human brain. Although previous approaches have been introduced to segment iron-rich brain regions, such as the substantia nigra, subthalamic nucleus, red nucleus, and dentate nucleus, these methods are largely unavailable and manual annotation remains the most used approach to label these regions. Furthermore, given their recent success in outperforming other segmentation approaches, convolutional neural networks (CNN) promise better performances. The aim of this study was thus to evaluate state-of-the-art CNN architectures for the labeling of deep brain nuclei from SW images. We implemented five CNN architectures and considered ensembles of these models. Furthermore, a multi-atlas segmentation model was included to provide a comparison not based on CNN. We evaluated two prediction strategies: individual prediction, where a model is trained independently for each region, and combined prediction, which simultaneously predicts multiple closely located regions. In the training dataset, all models performed with high accuracy with Dice coefficients ranging from 0.80 to 0.95. The regional SWI intensities and volumes from the models' labels were strongly correlated with those obtained from manual labels. Performances were reduced on the external dataset, but were higher or comparable to the intrarater reliability and most models achieved significantly better results compared to multi-atlas segmentation. CNNs can accurately capture the individual variability of deep brain nuclei and represent a highly useful tool for their segmentation from SW images.
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Affiliation(s)
- Vincent Beliveau
- Department of NeurologyMedical University of InnsbruckInnsbruckAustria
- Neuroimaging Research Core FacilityMedical University of InnsbruckInnsbruckAustria
| | - Martin Nørgaard
- Neurobiology Research Unit & CIMBICopenhagen University HospitalCopenhagenDenmark
- Center for Reproducible Neuroscience, Department of PsychologyStanford UniversityStanfordCaliforniaUSA
| | - Christoph Birkl
- Department of NeuroradiologyMedical University of InnsbruckInnsbruckAustria
| | - Klaus Seppi
- Department of NeurologyMedical University of InnsbruckInnsbruckAustria
- Neuroimaging Research Core FacilityMedical University of InnsbruckInnsbruckAustria
| | - Christoph Scherfler
- Department of NeurologyMedical University of InnsbruckInnsbruckAustria
- Neuroimaging Research Core FacilityMedical University of InnsbruckInnsbruckAustria
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Morimoto C, Nakamura Y, Kuwabara H, Abe O, Kasai K, Yamasue H, Koike S. Unique Morphometric Features of the Cerebellum and Cerebellocerebral Structural Correlation Between Autism Spectrum Disorder and Schizophrenia. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2021; 1:219-228. [PMID: 36325298 PMCID: PMC9616290 DOI: 10.1016/j.bpsgos.2021.05.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 05/13/2021] [Accepted: 05/23/2021] [Indexed: 12/13/2022] Open
Abstract
Background Although cerebellar morphological involvement has been increasingly recognized in autism spectrum disorder (ASD) and schizophrenia (SZ), the extent to which there are morphological differences between them has not been definitively quantified. Furthermore, although previous studies have demonstrated increased anatomical cerebellocerebral correlations in both conditions, differences between their associations have not been well characterized. Methods We compared cerebellar volume between males with ASD (n = 31), males with SZ (n = 28), and typically developing males (n = 49). A total of 31 cerebellar subregions were investigated with the cerebellum segmented into their constituent lobules, in gray matter (GM) and white matter (WM) separately. Additionally, structural correlations with the contralateral cerebrum were analyzed for each cerebellar lobule. Results We found significantly larger WM volume in the bilateral lobules VI and Crus I in the ASD group than in other groups. While WM or GM volumes of these right lobules had positive associations with ASD symptoms, there was a negative association between GM volume of the right Crus I and SZ symptoms. We further observed, in the ASD group specifically, significant correlations between WM of the right lobule VI and WM of the left frontal pole (r = 0.67) and between GM of the right lobule VI and the left caudate (r = 0.60). Conclusions Our findings support evidence that cerebellar morphology is involved in ASD and SZ with different mechanisms. Furthermore, this study showed that these biological differences require consideration when determining diagnostic criteria and treatment for these disorders.
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Affiliation(s)
- Chie Morimoto
- Department of Neuropsychiatry, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Yuko Nakamura
- UTokyo Center for Integrative Science of Human Behaviour, Graduate School of Art and Sciences, University of Tokyo, Tokyo, Japan
| | - Hitoshi Kuwabara
- Department of Psychiatry, Hamamatsu University School of Medicine, Hamamatsu City, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Center for Evolutionary Cognitive Science, Graduate School of Art and Sciences, University of Tokyo, Tokyo, Japan
- International Research Center for Neurointelligence, University of Tokyo Institutes for Advanced Study, University of Tokyo, Tokyo, Japan
- UTokyo Institute for Diversity and Adaptation of Human Mind, University of Tokyo, Tokyo, Japan
| | - Hidenori Yamasue
- Department of Psychiatry, Hamamatsu University School of Medicine, Hamamatsu City, Japan
| | - Shinsuke Koike
- UTokyo Center for Integrative Science of Human Behaviour, Graduate School of Art and Sciences, University of Tokyo, Tokyo, Japan
- Center for Evolutionary Cognitive Science, Graduate School of Art and Sciences, University of Tokyo, Tokyo, Japan
- International Research Center for Neurointelligence, University of Tokyo Institutes for Advanced Study, University of Tokyo, Tokyo, Japan
- UTokyo Institute for Diversity and Adaptation of Human Mind, University of Tokyo, Tokyo, Japan
- Address correspondence to Shinsuke Koike, M.D., Ph.D.
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39
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Williams CM, Peyre H, Toro R, Ramus F. Neuroanatomical norms in the UK Biobank: The impact of allometric scaling, sex, and age. Hum Brain Mapp 2021; 42:4623-4642. [PMID: 34268815 PMCID: PMC8410561 DOI: 10.1002/hbm.25572] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 06/03/2021] [Accepted: 06/11/2021] [Indexed: 12/18/2022] Open
Abstract
Few neuroimaging studies are sufficiently large to adequately describe population‐wide variations. This study's primary aim was to generate neuroanatomical norms and individual markers that consider age, sex, and brain size, from 629 cerebral measures in the UK Biobank (N = 40,028). The secondary aim was to examine the effects and interactions of sex, age, and brain allometry—the nonlinear scaling relationship between a region and brain size (e.g., total brain volume)—across cerebral measures. Allometry was a common property of brain volumes, thicknesses, and surface areas (83%) and was largely stable across age and sex. Sex differences occurred in 67% of cerebral measures (median |β| = .13): 37% of regions were larger in males and 30% in females. Brain measures (49%) generally decreased with age, although aging effects varied across regions and sexes. While models with an allometric or linear covariate adjustment for brain size yielded similar significant effects, omitting brain allometry influenced reported sex differences in variance. Finally, we contribute to the reproducibility of research on sex differences in the brain by replicating previous studies examining cerebral sex differences. This large‐scale study advances our understanding of age, sex, and brain allometry's impact on brain structure and provides data for future UK Biobank studies to identify the cerebral regions that covary with specific phenotypes, independently of sex, age, and brain size.
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Affiliation(s)
- Camille Michèle Williams
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, Paris, France
| | - Hugo Peyre
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, Paris, France.,INSERM UMR 1141, Paris Diderot University, Paris, France.,Department of Child and Adolescent Psychiatry, Robert Debré Hospital, APHP, Paris, France
| | - Roberto Toro
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR 3571 CNRS, Paris, France.,Center for Research and Interdisciplinarity (CRI), INSERM U1284, Paris, France.,Université de Paris, Paris, France
| | - Franck Ramus
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, Paris, France
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40
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Matos PCAAP, Rezende TJR, Schmitt GS, Bonadia LC, Reis F, Martinez ARM, de Lima FD, Bueno MGDA, Tomaselli PJ, Cendes F, Pedroso JL, Barsottini OGP, Marques W, França M. Brain Structural Signature of RFC1-Related Disorder. Mov Disord 2021; 36:2634-2641. [PMID: 34241918 DOI: 10.1002/mds.28711] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/01/2021] [Accepted: 06/16/2021] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND The cerebellar ataxia, neuropathy, and vestibular areflexia syndrome was initially described in the early 1990s as a late-onset slowly progressive condition. Its underlying genetic cause was recently mapped to the RFC1 gene, and additional reports have expanded on the phenotypic manifestations related to RFC1, although little is known about the pattern and extent of structural brain abnormalities in this condition. OBJECTIVE The aim is to characterize the structural signature of brain damage in RFC1-related disorder, correlating the findings with clinical symptoms and normal brain RFC1 expression. METHODS We recruited 22 individuals with molecular confirmation of RFC1 expansions and submitted them to high-resolution 3T magnetic resonance imaging scans. We performed multimodal analyses to assess separately cerebral and cerebellar abnormalities within gray and white matter (WM). The results were compared with a group of 22 age- and sex-matched controls. RESULTS The mean age and disease duration of patients were 62.8 and 10.9 years, respectively. Ataxia, sensory neuronopathy, and vestibular areflexia were the most frequent manifestations, but parkinsonism and pyramidal signs were also noticed. We found that RFC1-related disorder is characterized by widespread and relatively symmetric cerebellar and basal ganglia atrophy. There is brainstem volumetric reduction along all its segments. Cerebral WM is also involved-mostly the corpus callosum and deep tracts, but cerebral cortical damage is rather restricted. CONCLUSION This study adds new relevant insights into the pathophysiological mechanisms of RFC1-related disorder. It should no longer be considered a purely cerebellar and sensory pathway disorder. Basal ganglia and deep cerebral WM are additional targets of damage. © 2021 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Paula Camila A A P Matos
- Division of General Neurology and Ataxia Unit, Department of Neurology, Federal University of São Paulo (UNIFESP), São Paulo, Brazil
| | - Thiago J R Rezende
- Department of Neurology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | - Gabriel S Schmitt
- Department of Neurology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | - Luciana Cardoso Bonadia
- Department of Medical Genetics, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | - Fabiano Reis
- Department of Radiology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | - Alberto R M Martinez
- Department of Neurology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | - Fabrício D de Lima
- Department of Neurology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | | | - Pedro José Tomaselli
- Department of Neuroscience and Behavioural Science, School of Medicine, University of São Paulo (USP) of Ribeirão Preto, Ribeirão Preto, Brazil
| | - Fernando Cendes
- Department of Neurology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
| | - José Luiz Pedroso
- Division of General Neurology and Ataxia Unit, Department of Neurology, Federal University of São Paulo (UNIFESP), São Paulo, Brazil
| | - Orlando G P Barsottini
- Division of General Neurology and Ataxia Unit, Department of Neurology, Federal University of São Paulo (UNIFESP), São Paulo, Brazil
| | - Wilson Marques
- Department of Neuroscience and Behavioural Science, School of Medicine, University of São Paulo (USP) of Ribeirão Preto, Ribeirão Preto, Brazil
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Zhou SK, Greenspan H, Davatzikos C, Duncan JS, van Ginneken B, Madabhushi A, Prince JL, Rueckert D, Summers RM. A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2021; 109:820-838. [PMID: 37786449 PMCID: PMC10544772 DOI: 10.1109/jproc.2021.3054390] [Citation(s) in RCA: 202] [Impact Index Per Article: 67.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.
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Affiliation(s)
- S Kevin Zhou
- School of Biomedical Engineering, University of Science and Technology of China and Institute of Computing Technology, Chinese Academy of Sciences
| | - Hayit Greenspan
- Biomedical Engineering Department, Tel-Aviv University, Israel
| | - Christos Davatzikos
- Radiology Department and Electrical and Systems Engineering Department, University of Pennsylvania, USA
| | - James S Duncan
- Departments of Biomedical Engineering and Radiology & Biomedical Imaging, Yale University
| | | | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University and Louis Stokes Cleveland Veterans Administration Medical Center, USA
| | - Jerry L Prince
- Electrical and Computer Engineering Department, Johns Hopkins University, USA
| | - Daniel Rueckert
- Klinikum rechts der Isar, TU Munich, Germany and Department of Computing, Imperial College, UK
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Hett K, Lyu I, Trujillo P, Lopez AM, Aumann M, Larson KE, Hedera P, Dawant B, Landman BA, Claassen DO, Oguz I. Anatomical texture patterns identify cerebellar distinctions between essential tremor and Parkinson's disease. Hum Brain Mapp 2021; 42:2322-2331. [PMID: 33755270 PMCID: PMC8090778 DOI: 10.1002/hbm.25331] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 11/25/2020] [Accepted: 12/16/2020] [Indexed: 01/15/2023] Open
Abstract
Voxel-based morphometry is an established technique to study focal structural brain differences in neurologic disease. More recently, texture-based analysis methods have enabled a pattern-based assessment of group differences, at the patch level rather than at the voxel level, allowing a more sensitive localization of structural differences between patient populations. In this study, we propose a texture-based approach to identify structural differences between the cerebellum of patients with Parkinson's disease (n = 280) and essential tremor (n = 109). We analyzed anatomical differences of the cerebellum among patients using two features: T1-weighted MRI intensity, and a texture-based similarity feature. Our results show anatomical differences between groups that are localized to the inferior part of the cerebellar cortex. Both the T1-weighted intensity and texture showed differences in lobules VIII and IX, vermis VIII and IX, and middle peduncle, but the texture analysis revealed additional differences in the dentate nucleus, lobules VI and VII, vermis VI and VII. This comparison emphasizes how T1-weighted intensity and texture-based methods can provide a complementary anatomical structure analysis. While texture-based similarity shows high sensitivity for gray matter differences, T1-weighted intensity shows sensitivity for the detection of white matter differences.
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Affiliation(s)
- Kilian Hett
- Department of Electrical Engineering and Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
| | - Ilwoo Lyu
- Department of Electrical Engineering and Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
| | - Paula Trujillo
- Department of NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Alexander M. Lopez
- Department of NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Megan Aumann
- Department of NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Kathleen E. Larson
- Department of Electrical Engineering and Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
| | - Peter Hedera
- Department of NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA,Department of NeurologyUniversity of LouisvilleLouisvilleKentuckyUSA
| | - Benoit Dawant
- Department of Electrical Engineering and Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
| | - Bennett A. Landman
- Department of Electrical Engineering and Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
| | - Daniel O. Claassen
- Department of NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Ipek Oguz
- Department of Electrical Engineering and Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
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Li H, Zhang H, Johnson H, Long JD, Paulsen JS, Oguz I. MRI subcortical segmentation in neurodegeneration with cascaded 3D CNNs. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2021; 11596:115960W. [PMID: 34873359 PMCID: PMC8643361 DOI: 10.1117/12.2582005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The subcortical structures of the brain are relevant for many neurodegenerative diseases like Huntington's disease (HD). Quantitative segmentation of these structures from magnetic resonance images (MRIs) has been studied in clinical and neuroimaging research. Recently, convolutional neural networks (CNNs) have been successfully used for many medical image analysis tasks, including subcortical segmentation. In this work, we propose a 2-stage cascaded 3D subcortical segmentation framework, with the same 3D CNN architecture for both stages. Attention gates, residual blocks and output adding are used in our proposed 3D CNN. In the first stage, we apply our model to downsampled images to output a coarse segmentation. Next, we crop the extended subcortical region from the original image based on this coarse segmentation, and we input the cropped region to the second CNN to obtain the final segmentation. Left and right pairs of thalamus, caudate, pallidum and putamen are considered in our segmentation. We use the Dice coefficient as our metric and evaluate our method on two datasets: the publicly available IBSR dataset and a subset of the PREDICT-HD database, which includes healthy controls and HD subjects. We train our models on only healthy control subjects and test on both healthy controls and HD subjects to examine model generalizability. Compared with the state-of-the-art methods, our method has the highest mean Dice score on all considered subcortical structures (except the thalamus on IBSR), with more pronounced improvement for HD subjects. This suggests that our method may have better ability to segment MRIs of subjects with neurodegenerative disease.
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Affiliation(s)
- Hao Li
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235
| | - Huahong Zhang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235
| | - Hans Johnson
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242
| | - Jeffrey D. Long
- Departments of Psychiatry and Biostatistics, University of Iowa, Iowa City, IA 52242
| | - Jane S. Paulsen
- Department of Neurology, University of Wisconsin, Madison, WI 53705
| | - Ipek Oguz
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235
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Sörös P, Wölk L, Bantel C, Bräuer A, Klawonn F, Witt K. Replicability, Repeatability, and Long-term Reproducibility of Cerebellar Morphometry. THE CEREBELLUM 2021; 20:439-453. [PMID: 33421018 PMCID: PMC8213608 DOI: 10.1007/s12311-020-01227-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 12/15/2020] [Indexed: 01/09/2023]
Abstract
To identify robust and reproducible methods of cerebellar morphometry that can be used in future large-scale structural MRI studies, we investigated the replicability, repeatability, and long-term reproducibility of three fully automated software tools: FreeSurfer, CEREbellum Segmentation (CERES), and automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization (ACAPULCO). Replicability was defined as computational replicability, determined by comparing two analyses of the same high-resolution MRI data set performed with identical analysis software and computer hardware. Repeatability was determined by comparing the analyses of two MRI scans of the same participant taken during two independent MRI sessions on the same day for the Kirby-21 study. Long-term reproducibility was assessed by analyzing two MRI scans of the same participant in the longitudinal OASIS-2 study. We determined percent difference, the image intraclass correlation coefficient, the coefficient of variation, and the intraclass correlation coefficient between two analyses. Our results show that CERES and ACAPULCO use stochastic algorithms that result in surprisingly high differences between identical analyses for ACAPULCO and small differences for CERES. Changes between two consecutive scans from the Kirby-21 study were less than ± 5% in most cases for FreeSurfer and CERES (i.e., demonstrating high repeatability). As expected, long-term reproducibility was lower than repeatability for all software tools. In summary, CERES is an accurate, as demonstrated before, and reproducible tool for fully automated segmentation and parcellation of the cerebellum. We conclude with recommendations for the assessment of replicability, repeatability, and long-term reproducibility in future studies on cerebellar structure.
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Affiliation(s)
- Peter Sörös
- Department of Neurology, Carl von Ossietzky University of Oldenburg, Heiligengeisthöfe 4, 26121, Oldenburg, Germany.
- Research Center Neurosensory Science, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany.
| | - Louise Wölk
- Department of Neurology, Carl von Ossietzky University of Oldenburg, Heiligengeisthöfe 4, 26121, Oldenburg, Germany
| | - Carsten Bantel
- Research Center Neurosensory Science, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany
- Anesthesiology, Critical Care, Emergency Medicine, and Pain Management, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany
| | - Anja Bräuer
- Research Center Neurosensory Science, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany
- Department of Anatomy, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany
| | - Frank Klawonn
- Biostatistics, Helmholtz Centre for Infection Research, Braunschweig, Germany
- Department of Computer Science, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany
| | - Karsten Witt
- Department of Neurology, Carl von Ossietzky University of Oldenburg, Heiligengeisthöfe 4, 26121, Oldenburg, Germany
- Research Center Neurosensory Science, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany
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Singh MK, Singh KK. A Review of Publicly Available Automatic Brain Segmentation Methodologies, Machine Learning Models, Recent Advancements, and Their Comparison. Ann Neurosci 2021; 28:82-93. [PMID: 34733059 PMCID: PMC8558983 DOI: 10.1177/0972753121990175] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 01/04/2021] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND The noninvasive study of the structure and functions of the brain using neuroimaging techniques is increasingly being used for its clinical and research perspective. The morphological and volumetric changes in several regions and structures of brains are associated with the prognosis of neurological disorders such as Alzheimer's disease, epilepsy, schizophrenia, etc. and the early identification of such changes can have huge clinical significance. The accurate segmentation of three-dimensional brain magnetic resonance images into tissue types (i.e., grey matter, white matter, cerebrospinal fluid) and brain structures, thus, has huge importance as they can act as early biomarkers. The manual segmentation though considered the "gold standard" is time-consuming, subjective, and not suitable for bigger neuroimaging studies. Several automatic segmentation tools and algorithms have been developed over the years; the machine learning models particularly those using deep convolutional neural network (CNN) architecture are increasingly being applied to improve the accuracy of automatic methods. PURPOSE The purpose of the study is to understand the current and emerging state of automatic segmentation tools, their comparison, machine learning models, their reliability, and shortcomings with an intent to focus on the development of improved methods and algorithms. METHODS The study focuses on the review of publicly available neuroimaging tools, their comparison, and emerging machine learning models particularly those based on CNN architecture developed and published during the last five years. CONCLUSION Several software tools developed by various research groups and made publicly available for automatic segmentation of the brain show variability in their results in several comparison studies and have not attained the level of reliability required for clinical studies. The machine learning models particularly three dimensional fully convolutional network models can provide a robust and efficient alternative with relation to publicly available tools but perform poorly on unseen datasets. The challenges related to training, computation cost, reproducibility, and validation across distinct scanning modalities for machine learning models need to be addressed.
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Affiliation(s)
| | - Krishna Kumar Singh
- Symbiosis Centre for Information
Technology, Hinjawadi, Pune, Maharashtra, India
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Han S, An Y, Carass A, Prince JL, Resnick SM. Longitudinal analysis of regional cerebellum volumes during normal aging. Neuroimage 2020; 220:117062. [PMID: 32592850 PMCID: PMC10683793 DOI: 10.1016/j.neuroimage.2020.117062] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 06/07/2020] [Accepted: 06/14/2020] [Indexed: 01/23/2023] Open
Abstract
Some cross-sectional studies suggest reduced cerebellar volumes with aging, but there have been few longitudinal studies of age changes in cerebellar subregions in cognitively healthy older adults. In this work, 2,023 magnetic resonance (MR) images of 822 cognitively normal participants from the Baltimore Longitudinal Study of Aging (BLSA) were analyzed. Participants ranged in age from 50 to 95 years (mean 70.7 years) at the baseline assessment. Follow-up intervals were 1-9 years (mean 3.7 years) for participants with two or more visits. We used a recently developed cerebellum parcellation algorithm based on convolutional neural networks to divide the cerebellum into 28 subregions. Linear mixed effects models were applied to the volume of each cerebellar subregion to investigate cross-sectional and longitudinal age effects, as well as effects of sex and their interactions, after adjusting for intracranial volume. Our findings suggest spatially varying atrophy patterns across the cerebellum with respect to age and sex both cross-sectionally and longitudinally.
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Affiliation(s)
- Shuo Han
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, 20892, USA.
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, 20892, USA
| | - 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
| | - Jerry L Prince
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA; 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
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, 20892, USA
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