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Schmitt JE, DeBevits JJ, Roalf DR, Ruparel K, Gallagher RS, Gur RC, Alexander-Bloch A, Eom TY, Alam S, Steinberg J, Akers W, Khairy K, Crowley TB, Emanuel B, Zakharenko SS, McDonald-McGinn DM, Gur RE. A Comprehensive Analysis of Cerebellar Volumes in the 22q11.2 Deletion Syndrome. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:79-90. [PMID: 34848384 PMCID: PMC9162086 DOI: 10.1016/j.bpsc.2021.11.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 10/12/2021] [Accepted: 11/08/2021] [Indexed: 01/17/2023]
Abstract
BACKGROUND The presence of a 22q11.2 microdeletion (22q11.2 deletion syndrome [22q11DS]) ranks among the greatest known genetic risk factors for the development of psychotic disorders. There is emerging evidence that the cerebellum is important in the pathophysiology of psychosis. However, there is currently limited information on cerebellar neuroanatomy in 22q11DS specifically. METHODS High-resolution 3T magnetic resonance imaging was acquired in 79 individuals with 22q11DS and 70 typically developing control subjects (N = 149). Lobar and lobule-level cerebellar volumes were estimated using validated automated segmentation algorithms, and subsequently group differences were compared. Hierarchical clustering, principal component analysis, and graph theoretical models were used to explore intercerebellar relationships. Cerebrocerebellar structural connectivity with cortical thickness was examined via linear regression models. RESULTS Individuals with 22q11DS had, on average, 17.3% smaller total cerebellar volumes relative to typically developing subjects (p < .0001). The lobules of the superior posterior cerebellum (e.g., VII and VIII) were particularly affected in 22q11DS. However, all cerebellar lobules were significantly smaller, even after adjusting for total brain volumes (all cerebellar lobules p < .0002). The superior posterior lobule was disproportionately associated with cortical thickness in the frontal lobes and cingulate cortex, brain regions known be affected in 22q11DS. Exploratory analyses suggested that the superior posterior lobule, particularly Crus I, may be associated with psychotic symptoms in 22q11DS. CONCLUSIONS The cerebellum is a critical but understudied component of the 22q11DS neuroendophenotype.
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Affiliation(s)
- J Eric Schmitt
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania; Division of Neuroradiology, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
| | - John J DeBevits
- Division of Neuroradiology, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - David R Roalf
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania
| | - Kosha Ruparel
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania
| | - R Sean Gallagher
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania
| | - Ruben C Gur
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania
| | - Aaron Alexander-Bloch
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania
| | - Tae-Yeon Eom
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Shahinur Alam
- Center for Bioimage Informatics, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Jeffrey Steinberg
- Center for Bioimage Informatics, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Walter Akers
- Center for Bioimage Informatics, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Khaled Khairy
- Center for In Vivo Imaging and Therapeutics, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - T Blaine Crowley
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Beverly Emanuel
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Stanislav S Zakharenko
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Donna M McDonald-McGinn
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Raquel E Gur
- Brain Behavior Laboratory, Neurodevelopment and Psychosis Section, Department of Psychiatry, Philadelphia, Pennsylvania; Division of Neuroradiology, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
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Mareckova K, Miles A, Liao Z, Andryskova L, Brazdil M, Paus T, Nikolova YS. Prenatal stress and its association with amygdala-related structural covariance patterns in youth. Neuroimage Clin 2022; 34:102976. [PMID: 35316668 PMCID: PMC8938327 DOI: 10.1016/j.nicl.2022.102976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 02/05/2022] [Accepted: 02/26/2022] [Indexed: 11/27/2022]
Abstract
Prenatal exposure to stress predicts amygdala degree centrality in young adulthood. High (vs. low) stress group showed lower structural covariance degree of amygdala. These effects were particularly significant in men. Global network parameters did not drive these effects.
Background Prenatal stress influences brain development and mood disorder vulnerability. Brain structural covariance network (SCN) properties based on inter-regional volumetric correlations may reflect developmentally-mediated shared plasticity among regions. Childhood trauma is associated with amygdala-centric SCN reorganization patterns, however, the impact of prenatal stress on SCN properties remains unknown. Methods The study included participants from the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) with archival prenatal stress data and structural MRI acquired in young adulthood (age 23–24). SCNs were constructed based on Freesurfer-extracted volumes of 7 subcortical and 34 cortical regions. We compared amygdala degree centrality, a measure of hubness, between those exposed to high vs. low (median split) prenatal stress, defined by maternal reports of stressful life events during the first (n = 93, 57% female) and second (n = 125, 54% female) half of pregnancy. Group differences were tested across network density thresholds (5–40%) using 10,000 permutations, with sex and intracranial volume as covariates, followed by sex-specific analyses. Finally, we sought to replicate our results in an independent all-male sample (n = 450, age 18–20) from the Avon Longitudinal Study of Parents and Children (ALSPAC). Results The high-stress during the first half of pregnancy ELSPAC group showed lower amygdala degree particularly in men, who demonstrated this difference at 10 consecutive thresholds, with no significant differences in global network properties. At the lowest significant density threshold, amygdala volume was positively correlated with hippocampus, putamen, rostral anterior and posterior cingulate, transverse temporal, and pericalcarine cortex in the low-stress (p(FDR) < 0.027), but not the high-stress (p(FDR) > 0.882) group. Although amygdala degree was nominally lower across thresholds in the high-stress ALSPAC group, these results were not significant. Conclusion Unlike childhood trauma, prenatal stress may shift SCN towards a less amygdala-centric SCN pattern, particularly in men. These findings did not replicate in an all-male ALSPAC sample, possibly due to the sample’s younger age and lower prenatal stress exposure.
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Affiliation(s)
- Klara Mareckova
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada; Brain and Mind Research, Central European Institute of Technology, Masaryk University, Brno, Czech Republic.
| | - Amy Miles
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
| | - Zhijie Liao
- Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Lenka Andryskova
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Milan Brazdil
- Brain and Mind Research, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Tomas Paus
- Department of Psychology, University of Toronto, Toronto, ON, Canada; Departments of Psychiatry and Neuroscience and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Yuliya S Nikolova
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada.
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Kikinis Z, Makris N, Sydnor VJ, Bouix S, Pasternak O, Coman IL, Antshel KM, Fremont W, Kubicki MR, Shenton ME, Kates WR, Rathi Y. Abnormalities in gray matter microstructure in young adults with 22q11.2 deletion syndrome. NEUROIMAGE-CLINICAL 2018; 21:101611. [PMID: 30522971 PMCID: PMC6411601 DOI: 10.1016/j.nicl.2018.101611] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 11/19/2018] [Accepted: 11/25/2018] [Indexed: 11/30/2022]
Abstract
BACKGROUND 22q11.2 Deletion Syndrome (22q11DS) is a genetic, neurodevelopmental disorder characterized by a chromosomal deletion and a distinct cognitive profile. Although abnormalities in the macrostructure of the cortex have been identified in individuals with 22q11DS, it is not known if there are additional microstructural changes in gray matter regions in this syndrome, and/or if such microstructural changes are associated with cognitive functioning. METHODS This study employed a novel diffusion MRI measure, the Heterogeneity of Fractional Anisotropy (HFA), to examine variability in the microstructural organization of the cortex in healthy young adults (N = 30) and those with 22q11DS (N = 56). Diffusion MRI, structural MRI, clinical and cognitive data were acquired. RESULTS Compared to controls, individuals with 22q11DS evinced increased HFA in cortical association (p = .003, d = 0.86) and paralimbic (p < .0001, d = 1.2) brain areas, whereas no significant differences were found between the two groups in primary cortical brain areas. Additionally, increased HFA of the right paralimbic area was associated with poorer performance on tests of response inhibition, i.e., the Stroop Test (rho = -0.37 p = .005) and the Gordon Diagnostic System Vigilance Commission (rho = -0.41 p = .002) in the 22q11DS group. No significant correlations were found between HFA and cognitive abilities in the healthy control group. CONCLUSIONS These findings suggest that cortical microstructural disorganization may be a neural correlate of response inhibition in individuals with 22q11DS. Given that the migration pattern of neural crest cells is disrupted at the time of early brain development in 22q11DS, we hypothesize that these neural alterations may be neurodevelopmental in origin, and reflect cortical dysfunction associated with cognitive deficits.
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Affiliation(s)
- Zora Kikinis
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Zora Kikinis, 1249 Boylston Street, Boston, MA 02215, USA.
| | - Nikos Makris
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Zora Kikinis, 1249 Boylston Street, Boston, MA 02215, USA; Departments of Psychiatry and Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Valerie J Sydnor
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Zora Kikinis, 1249 Boylston Street, Boston, MA 02215, USA
| | - Sylvain Bouix
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Zora Kikinis, 1249 Boylston Street, Boston, MA 02215, USA
| | - Ofer Pasternak
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Zora Kikinis, 1249 Boylston Street, Boston, MA 02215, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ioana L Coman
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA; Department of Computer Science, SUNY Oswego, Oswego, NY, USA
| | - Kevin M Antshel
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA; Department of Psychology, Syracuse University, Syracuse, NY, USA
| | - Wanda Fremont
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Marek R Kubicki
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Zora Kikinis, 1249 Boylston Street, Boston, MA 02215, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Martha E Shenton
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Zora Kikinis, 1249 Boylston Street, Boston, MA 02215, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; VA Boston Healthcare System, Brockton, MA, USA
| | - Wendy R Kates
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Yogesh Rathi
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Zora Kikinis, 1249 Boylston Street, Boston, MA 02215, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Yee Y, Fernandes DJ, French L, Ellegood J, Cahill LS, Vousden DA, Spencer Noakes L, Scholz J, van Eede MC, Nieman BJ, Sled JG, Lerch JP. Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity. Neuroimage 2018; 179:357-372. [PMID: 29782994 DOI: 10.1016/j.neuroimage.2018.05.028] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 04/13/2018] [Accepted: 05/10/2018] [Indexed: 12/14/2022] Open
Abstract
An organizational pattern seen in the brain, termed structural covariance, is the statistical association of pairs of brain regions in their anatomical properties. These associations, measured across a population as covariances or correlations usually in cortical thickness or volume, are thought to reflect genetic and environmental underpinnings. Here, we examine the biological basis of structural volume covariance in the mouse brain. We first examined large scale associations between brain region volumes using an atlas-based approach that parcellated the entire mouse brain into 318 regions over which correlations in volume were assessed, for volumes obtained from 153 mouse brain images via high-resolution MRI. We then used a seed-based approach and determined, for 108 different seed regions across the brain and using mouse gene expression and connectivity data from the Allen Institute for Brain Science, the variation in structural covariance data that could be explained by distance to seed, transcriptomic similarity to seed, and connectivity to seed. We found that overall, correlations in structure volumes hierarchically clustered into distinct anatomical systems, similar to findings from other studies and similar to other types of networks in the brain, including structural connectivity and transcriptomic similarity networks. Across seeds, this structural covariance was significantly explained by distance (17% of the variation, up to a maximum of 49% for structural covariance to the visceral area of the cortex), transcriptomic similarity (13% of the variation, up to maximum of 28% for structural covariance to the primary visual area) and connectivity (15% of the variation, up to a maximum of 36% for structural covariance to the intermediate reticular nucleus in the medulla) of covarying structures. Together, distance, connectivity, and transcriptomic similarity explained 37% of structural covariance, up to a maximum of 63% for structural covariance to the visceral area. Additionally, this pattern of explained variation differed spatially across the brain, with transcriptomic similarity playing a larger role in the cortex than subcortex, while connectivity explains structural covariance best in parts of the cortex, midbrain, and hindbrain. These results suggest that both gene expression and connectivity underlie structural volume covariance, albeit to different extents depending on brain region, and this relationship is modulated by distance.
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Affiliation(s)
- Yohan Yee
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada.
| | - Darren J Fernandes
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Leon French
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Jacob Ellegood
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Lindsay S Cahill
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Dulcie A Vousden
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | | | - Jan Scholz
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Matthijs C van Eede
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Brian J Nieman
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - John G Sled
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Jason P Lerch
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
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