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Andica C, Kamagata K, Aoki S. Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging. Anat Sci Int 2023:10.1007/s12565-023-00715-9. [PMID: 37017902 DOI: 10.1007/s12565-023-00715-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 03/09/2023] [Indexed: 04/06/2023]
Abstract
White matter bundle segmentation using diffusion magnetic resonance imaging fiber tractography enables detailed evaluation of individual white matter tracts three-dimensionally, and plays a crucial role in studying human brain anatomy, function, development, and diseases. Manual extraction of streamlines utilizing a combination of the inclusion and exclusion of regions of interest can be considered the current gold standard for extracting white matter bundles from whole-brain tractograms. However, this is a time-consuming and operator-dependent process with limited reproducibility. Several automated approaches using different strategies to reconstruct the white matter tracts have been proposed to address the issues of time, labor, and reproducibility. In this review, we discuss few of the most well-validated approaches that automate white matter bundle segmentation with an end-to-end pipeline, including TRActs Constrained by UnderLying Anatomy (TRACULA), Automated Fiber Quantification, and TractSeg.
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Affiliation(s)
- Christina Andica
- Faculty of Health Data Science, Juntendo University, 6-8-1 Hinode, Urayasu, Chiba, 279-0013, Japan.
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Shigeki Aoki
- Faculty of Health Data Science, Juntendo University, 6-8-1 Hinode, Urayasu, Chiba, 279-0013, Japan
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
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2
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Patt E, Singhania A, Roberts AE, Morton SU. The Genetics of Neurodevelopment in Congenital Heart Disease. Can J Cardiol 2023; 39:97-114. [PMID: 36183910 DOI: 10.1016/j.cjca.2022.09.026] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 09/23/2022] [Accepted: 09/25/2022] [Indexed: 02/07/2023] Open
Abstract
Congenital heart disease (CHD) is the most common birth anomaly, affecting almost 1% of infants. Neurodevelopmental delay is the most common extracardiac feature in people with CHD. Many factors may contribute to neurodevelopmental risk, including genetic factors, CHD physiology, and the prenatal/postnatal environment. Damaging variants are most highly enriched among individuals with extracardiac anomalies or neurodevelopmental delay in addition to CHD, indicating that genetic factors have an impact beyond cardiac tissues in people with CHD. Potential sources of genetic risk include large deletions or duplications that affect multiple genes, such as 22q11 deletion syndrome, single genes that alter both heart and brain development, such as CHD7, and common variants that affect neurodevelopmental resiliency, such as APOE. Increased use of genome-sequencing technologies in studies of neurodevelopmental outcomes in people with CHD will improve our ability to detect relevant genes and variants. Ultimately, such knowledge can lead to improved and more timely intervention of learning support for affected children.
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Affiliation(s)
- Eli Patt
- Harvard Medical School, Boston, Massachusetts, USA
| | - Asmita Singhania
- School of Medical Sciences, University of Manchester, Manchester, United Kingdom
| | - Amy E Roberts
- Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Sarah U Morton
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA; Division of Newborn Medicine, Boston Children's Hospital, Boston, Massachusetts, USA.
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Silva AI, Ehrhart F, Ulfarsson MO, Stefansson H, Stefansson K, Wilkinson LS, Hall J, Linden DEJ. Neuroimaging Findings in Neurodevelopmental Copy Number Variants: Identifying Molecular Pathways to Convergent Phenotypes. Biol Psychiatry 2022; 92:341-361. [PMID: 35659384 DOI: 10.1016/j.biopsych.2022.03.018] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 03/22/2022] [Accepted: 03/25/2022] [Indexed: 12/15/2022]
Abstract
Genomic copy number variants (CNVs) are associated with a high risk of neurodevelopmental disorders. A growing body of genetic studies suggests that these high-risk genetic variants converge in common molecular pathways and that common pathways also exist across clinically distinct disorders, such as schizophrenia and autism spectrum disorder. A key question is how common molecular mechanisms converge into similar clinical outcomes. We review emerging evidence for convergent cognitive and brain phenotypes across distinct CNVs. Multiple CNVs were shown to have similar effects on core sensory, cognitive, and motor traits. Emerging data from multisite neuroimaging studies have provided valuable information on how these CNVs affect brain structure and function. However, most of these studies examined one CNV at a time, making it difficult to fully understand the proportion of shared brain effects. Recent studies have started to combine neuroimaging data from multiple CNV carriers and identified similar brain effects across CNVs. Some early findings also support convergence in CNV animal models. Systems biology, through integration of multilevel data, provides new insights into convergent molecular mechanisms across genetic risk variants (e.g., altered synaptic activity). However, the link between such key molecular mechanisms and convergent psychiatric phenotypes is still unknown. To better understand this link, we need new approaches that integrate human molecular data with neuroimaging, cognitive, and animal model data, while taking into account critical developmental time points. Identifying risk mechanisms across genetic loci can elucidate the pathophysiology of neurodevelopmental disorders and identify new therapeutic targets for cross-disorder applications.
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Affiliation(s)
- Ana I Silva
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands; Neuroscience and Mental Health Research Institute, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom.
| | - Friederike Ehrhart
- Department of Bioinformatics, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Magnus O Ulfarsson
- deCODE genetics, Amgen, Reykjavik, Iceland; Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland
| | | | | | - Lawrence S Wilkinson
- Neuroscience and Mental Health Research Institute, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom; School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Jeremy Hall
- Neuroscience and Mental Health Research Institute, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - David E J Linden
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands; Neuroscience and Mental Health Research Institute, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom.
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4
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Räsänen N, Tiihonen J, Koskuvi M, Lehtonen Š, Koistinaho J. The iPSC perspective on schizophrenia. Trends Neurosci 2021; 45:8-26. [PMID: 34876311 DOI: 10.1016/j.tins.2021.11.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/29/2021] [Accepted: 11/10/2021] [Indexed: 12/17/2022]
Abstract
Over a decade of schizophrenia research using human induced pluripotent stem cell (iPSC)-derived neural models has provided substantial data describing neurobiological characteristics of the disorder in vitro. Simultaneously, translation of the results into general mechanistic concepts underlying schizophrenia pathophysiology has been trailing behind. Given that modeling brain function using cell cultures is challenging, the gap between the in vitro models and schizophrenia as a clinical disorder has remained wide. In this review, we highlight reproducible findings and emerging trends in recent schizophrenia-related iPSC studies. We illuminate the relevance of the results in the context of human brain development, with a focus on processes coinciding with critical developmental periods for schizophrenia.
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Affiliation(s)
- Noora Räsänen
- Neuroscience Center, University of Helsinki, Helsinki, Finland
| | - Jari Tiihonen
- Neuroscience Center, University of Helsinki, Helsinki, Finland; Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden; Center for Psychiatric Research, Stockholm City Council, Stockholm, Sweden; Department of Forensic Psychiatry, University of Eastern Finland, Niuvanniemi Hospital, Kuopio, Finland
| | - Marja Koskuvi
- Neuroscience Center, University of Helsinki, Helsinki, Finland; A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Šárka Lehtonen
- Neuroscience Center, University of Helsinki, Helsinki, Finland; A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Jari Koistinaho
- Neuroscience Center, University of Helsinki, Helsinki, Finland; A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.
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Brain microstructural abnormalities in 22q11.2 deletion syndrome: A systematic review of diffusion tensor imaging studies. Eur Neuropsychopharmacol 2021; 52:96-135. [PMID: 34358796 DOI: 10.1016/j.euroneuro.2021.07.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 07/06/2021] [Accepted: 07/12/2021] [Indexed: 01/16/2023]
Abstract
22q11.2 deletion syndrome (22q11DS) is a severe genetic syndrome characterized by cognitive deficits and neuropsychiatric disorders, particularly schizophrenia. Neuroimaging alterations have been extensively reported in 22q11DS, both in gray and white matter structures. However, a considerable variability among the results affects the generalizability of the findings to date. Herein, we reviewed diffusion tensor imaging (DTI) findings in 22q11DS, their association with psychosis and cognition, and the implications of DTI studies on neurodevelopment in 22q11DS. We also investigated differences between 22q11DS and schizophrenic patients without 22q11DS. Using an online search of PubMed and Embase, we identified studies investigating DTI findings in 22q11DS. After selecting eligible studies in accordance with the preferred reporting items for systematic reviews and meta-analyses guideline, we included thirty-one studies. Overall, 22q11DS patients show altered structural connectivity and disrupted microstructural organization of most cortical and subcortical structures and white matter tracts. Moreover, despite a significant heterogeneity in the results, reduced diffusivity measures and elevated fractional anisotropy were observed. However controversial, compared to typically developing children, 22q11DS patients reached the peak of fractional anisotropy (FA) and the trough of radial diffusivity (RD) at an older age, which shows neurodevelopmental delay. DTI measures were also associated with psychotic symptoms and cognitive deficits. In conclusion, this study provides a comprehensive review of microstructural alterations in 22q11DS. Future larger investigations on this syndrome could potentially lead to the detection of early diagnostic imaging markers for genetically induced schizophrenia, thus improving the treatment and, ultimately, the outcome.
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Bagautdinova J, Zöller D, Schaer M, Padula MC, Mancini V, Schneider M, Eliez S. Altered cortical thickness development in 22q11.2 deletion syndrome and association with psychotic symptoms. Mol Psychiatry 2021; 26:7671-7678. [PMID: 34253864 PMCID: PMC8873018 DOI: 10.1038/s41380-021-01209-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 06/15/2021] [Accepted: 06/23/2021] [Indexed: 02/06/2023]
Abstract
Schizophrenia has been extensively associated with reduced cortical thickness (CT), and its neurodevelopmental origin is increasingly acknowledged. However, the exact timing and extent of alterations occurring in preclinical phases remain unclear. With a high prevalence of psychosis, 22q11.2 deletion syndrome (22q11DS) is a neurogenetic disorder that represents a unique opportunity to examine brain maturation in high-risk individuals. In this study, we quantified trajectories of CT maturation in 22q11DS and examined the association of CT development with the emergence of psychotic symptoms. Longitudinal structural MRI data with 1-6 time points were collected from 324 participants aged 5-35 years (N = 148 22q11DS, N = 176 controls), resulting in a total of 636 scans (N = 334 22q11DS, N = 302 controls). Mixed model regression analyses were used to compare CT trajectories between participants with 22q11DS and controls. Further, CT trajectories were compared between participants with 22q11DS who developed (N = 61, 146 scans), or remained exempt of (N = 47; 98 scans) positive psychotic symptoms during development. Compared to controls, participants with 22q11DS showed widespread increased CT, focal reductions in the posterior cingulate gyrus and superior temporal gyrus (STG), and accelerated cortical thinning during adolescence, mainly in frontotemporal regions. Within 22q11DS, individuals who developed psychotic symptoms showed exacerbated cortical thinning in the right STG. Together, these findings suggest that genetic predisposition for psychosis is associated with increased CT starting from childhood and altered maturational trajectories of CT during adolescence, affecting predominantly frontotemporal regions. In addition, accelerated thinning in the STG may represent an early biomarker associated with the emergence of psychotic symptoms.
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Affiliation(s)
- Joëlle Bagautdinova
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
| | - Daniela Zöller
- grid.8591.50000 0001 2322 4988Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland ,grid.5333.60000000121839049Medical Image Processing Laboratory, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland ,grid.8591.50000 0001 2322 4988Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Marie Schaer
- grid.8591.50000 0001 2322 4988Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Maria Carmela Padula
- grid.8591.50000 0001 2322 4988Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Valentina Mancini
- grid.8591.50000 0001 2322 4988Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Maude Schneider
- grid.8591.50000 0001 2322 4988Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland ,grid.8591.50000 0001 2322 4988Clinical Psychology Unit for Intellectual and Developmental Disabilities, Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
| | - Stephan Eliez
- grid.8591.50000 0001 2322 4988Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland
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