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Einspieler C, Bos AF, Spittle AJ, Bertoncelli N, Burger M, Peyton C, Toldo M, Utsch F, Zhang D, Marschik PB. The General Movement Optimality Score-Revised (GMOS-R) with Socioeconomically Stratified Percentile Ranks. J Clin Med 2024; 13:2260. [PMID: 38673533 PMCID: PMC11050782 DOI: 10.3390/jcm13082260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 04/04/2024] [Accepted: 04/07/2024] [Indexed: 04/28/2024] Open
Abstract
Background: The general movement optimality score (GMOS) quantifies the details of general movements (GMs). We recently conducted psychometric analyses of the GMOS and developed a revised scoresheet. Consequently, the GMOS-Revised (GMOS-R) instrument necessitated validation using new percentile ranks. This study aimed to provide these percentile ranks for the GMOS-R and to investigate whether sex, preterm birth, or the infant's country of birth and residence affected the GMOS-R distribution. Methods: We applied the GMOS-R to an international sample of 1983 infants (32% female, 44% male, and 24% not disclosed), assessed in the extremely and very preterm period (10%), moderate (12%) and late (22%) preterm periods, at term (25%), and post-term age (31%). Data were grouped according to the World Bank's classification into lower- and upper-middle-income countries (LMICs and UMICs; 26%) or high-income countries (HICs; 74%), respectively. Results: We found that sex and preterm or term birth did not affect either GM classification or the GMOS-R, but the country of residence did. A lower median GMOS-R for infants with normal or poor-repertoire GMs from LMICs and UMICs compared with HICs suggests the use of specific percentile ranks for LMICs and UMICs vs. HICs. Conclusion: For clinical and scientific use, we provide a freely available GMOS-R scoring sheet, with percentile ranks reflecting socioeconomic stratification.
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Affiliation(s)
- Christa Einspieler
- Interdisciplinary Developmental Neuroscience—iDN, Division of Phoniatrics, Medical University of Graz, 8010 Graz, Austria
| | - Arend F. Bos
- Division of Neonatology, Department of Pediatrics, Beatrix Children’s Hospital, University Medical Center Groningen, University of Groningen, 9712 GZ Groningen, The Netherlands
| | - Alicia J. Spittle
- Department of Physiotherapy, Melbourne School of Health Sciences, University of Melbourne, Melbourne, VIC 3010, Australia;
| | - Natascia Bertoncelli
- Neonatal Intensive Care Unit, Department of Medical and Surgical Sciences of Mothers, Children and Adults, University Hospital of Modena, 41124 Modena, Italy;
| | - Marlette Burger
- Physiotherapy Division, Department of Health and Rehabilitation Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa;
| | - Colleen Peyton
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA;
| | - Moreno Toldo
- Department of Medical Rehabilitation, Kiran Society for Rehabilitation and Education of Children with Disabilities, Varanasi 221011, India;
| | - Fabiana Utsch
- Reabilitação Infantil, Rede SARAH de Hospitais de Reabilitação, Belo Horizonte 30510-000, Brazil;
| | - Dajie Zhang
- Interdisciplinary Developmental Neuroscience—iDN, Division of Phoniatrics, Medical University of Graz, 8010 Graz, Austria
- Child and Adolescent Psychiatry, Center for Psychosocial Medicine, University Hospital Heidelberg, Ruprecht-Karls University, 69115 Heidelberg, Germany
| | - Peter B. Marschik
- Interdisciplinary Developmental Neuroscience—iDN, Division of Phoniatrics, Medical University of Graz, 8010 Graz, Austria
- Child and Adolescent Psychiatry, Center for Psychosocial Medicine, University Hospital Heidelberg, Ruprecht-Karls University, 69115 Heidelberg, Germany
- Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Leibniz-ScienceCampus Primate Cognition, 37075 Göttingen, Germany
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women’s and Children’s Health, Karolinska Institutet, 171 77 Stockholm, Sweden
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The General Movements Motor Optimality Score in High-Risk Infants: A Systematic Scoping Review. Pediatr Phys Ther 2023; 35:2-26. [PMID: 36288244 DOI: 10.1097/pep.0000000000000969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE The aim of this systematic scoping review was to explore the use of the motor optimality score in the fidgety movement period in clinical practice, and to investigate evidence for the motor optimality score in predicting neurodevelopmental outcomes. SUMMARY OF KEY POINTS Thirty-seven studies, with 3662 infants, were included. Studies were conceptualized and charted into 4 categories based on the motor optimality score: prediction, outcome measure, descriptive, or psychometric properties. The most represented populations were preterm or low-birth-weight infants (16 studies), infants with cerebral palsy or neurological concerns (5 studies), and healthy or term-born infants (4 studies). CONCLUSION The motor optimality score has the potential to add value to existing tools used to predict risk of adverse neurodevelopmental outcomes. Further research is needed regarding the reliability and validity of the motor optimality score to support increased use of this tool in clinical practice. What this adds to the evidence : The motor optimality score has potential to improve the prediction of adverse neurodevelopmental outcomes. Further research on validity and reliability of the motor optimality score is needed; however, a revised version, the motor optimality score-R (with accompanying manual) will likely contribute to more consistency in the reporting of the motor optimality score in future.
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Petanjek Z, Banovac I, Sedmak D, Hladnik A. Dendritic Spines: Synaptogenesis and Synaptic Pruning for the Developmental Organization of Brain Circuits. ADVANCES IN NEUROBIOLOGY 2023; 34:143-221. [PMID: 37962796 DOI: 10.1007/978-3-031-36159-3_4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Synaptic overproduction and elimination is a regular developmental event in the mammalian brain. In the cerebral cortex, synaptic overproduction is almost exclusively correlated with glutamatergic synapses located on dendritic spines. Therefore, analysis of changes in spine density on different parts of the dendritic tree in identified classes of principal neurons could provide insight into developmental reorganization of specific microcircuits.The activity-dependent stabilization and selective elimination of the initially overproduced synapses is a major mechanism for generating diversity of neural connections beyond their genetic determination. The largest number of overproduced synapses was found in the monkey and human cerebral cortex. The highest (exceeding adult values by two- to threefold) and most protracted overproduction (up to third decade of life) was described for associative layer IIIC pyramidal neurons in the human dorsolateral prefrontal cortex.Therefore, the highest proportion and extraordinarily extended phase of synaptic spine overproduction is a hallmark of neural circuitry in human higher-order associative areas. This indicates that microcircuits processing the most complex human cognitive functions have the highest level of developmental plasticity. This finding is the backbone for understanding the effect of environmental impact on the development of the most complex, human-specific cognitive and emotional capacities, and on the late onset of human-specific neuropsychiatric disorders, such as autism and schizophrenia.
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Affiliation(s)
- Zdravko Petanjek
- Department of Anatomy and Clinical Anatomy, School of Medicine, University of Zagreb, Zagreb, Croatia.
- Department of Neuroscience, Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia.
- Center of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb, Croatia.
| | - Ivan Banovac
- Department of Anatomy and Clinical Anatomy, School of Medicine, University of Zagreb, Zagreb, Croatia
- Department of Neuroscience, Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
- Center of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Dora Sedmak
- Department of Anatomy and Clinical Anatomy, School of Medicine, University of Zagreb, Zagreb, Croatia
- Department of Neuroscience, Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
- Center of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Ana Hladnik
- Department of Anatomy and Clinical Anatomy, School of Medicine, University of Zagreb, Zagreb, Croatia
- Department of Neuroscience, Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
- Center of Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb, Croatia
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Vasung L, Xu J, Abaci-Turk E, Zhou C, Holland E, Barth WH, Barnewolt C, Connolly S, Estroff J, Golland P, Feldman HA, Adalsteinsson E, Grant PE. Cross-Sectional Observational Study of Typical in utero Fetal Movements Using Machine Learning. Dev Neurosci 2022; 45:105-114. [PMID: 36538911 PMCID: PMC10233700 DOI: 10.1159/000528757] [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: 08/09/2022] [Accepted: 11/14/2022] [Indexed: 12/24/2022] Open
Abstract
Early variations of fetal movements are the hallmark of a healthy developing central nervous system. However, there are no automatic methods to quantify the complex 3D motion of the developing fetus in utero. The aim of this prospective study was to use machine learning (ML) on in utero MRI to perform quantitative kinematic analysis of fetal limb movement, assessing the impact of maternal, placental, and fetal factors. In this cross-sectional, observational study, we used 76 sets of fetal (24-40 gestational weeks [GW]) blood oxygenation level-dependent (BOLD) MRI scans of 52 women (18-45 years old) during typical pregnancies. Pregnant women were scanned for 5-10 min while breathing room air (21% O2) and for 5-10 min while breathing 100% FiO2 in supine and/or lateral position. BOLD acquisition time was 20 min in total with effective temporal resolution approximately 3 s. To quantify upper and lower limb kinematics, we used a 3D convolutional neural network previously trained to track fetal key points (wrists, elbows, shoulders, ankles, knees, hips) on similar BOLD time series. Tracking was visually assessed, errors were manually corrected, and the absolute movement time (AMT) for each joint was calculated. To identify variables that had a significant association with AMT, we constructed a mixed-model ANOVA with interaction terms. Fetuses showed significantly longer duration of limb movements during maternal hyperoxia. We also found a significant centrifugal increase of AMT across limbs and significantly longer AMT of upper extremities <31 GW and longer AMT of lower extremities >35 GW. In conclusion, using ML we successfully quantified complex 3D fetal limb motion in utero and across gestation, showing maternal factors (hyperoxia) and fetal factors (gestational age, joint) that impact movement. Quantification of fetal motion on MRI is a potential new biomarker of fetal health and neuromuscular development.
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Affiliation(s)
- Lana Vasung
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Junshen Xu
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA
| | - Esra Abaci-Turk
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Cindy Zhou
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Elizabeth Holland
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - William H Barth
- Department of Obstetrics and Gynecology, Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Carol Barnewolt
- Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Susan Connolly
- Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Judy Estroff
- Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Polina Golland
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, Massachusetts, USA
| | - Henry A Feldman
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
- Institutional Centers for Clinical and Translational Research, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Elfar Adalsteinsson
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, Massachusetts, USA
| | - P Ellen Grant
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
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Kostović I, Radoš M, Kostović-Srzentić M, Krsnik Ž. Fundamentals of the Development of Connectivity in the Human Fetal Brain in Late Gestation: From 24 Weeks Gestational Age to Term. J Neuropathol Exp Neurol 2021; 80:393-414. [PMID: 33823016 PMCID: PMC8054138 DOI: 10.1093/jnen/nlab024] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
During the second half of gestation, the human cerebrum undergoes pivotal histogenetic events that underlie functional connectivity. These include the growth, guidance, selection of axonal pathways, and their first engagement in neuronal networks. Here, we characterize the spatiotemporal patterns of cerebral connectivity in extremely preterm (EPT), very preterm (VPT), preterm and term babies, focusing on magnetic resonance imaging (MRI) and histological data. In the EPT and VPT babies, thalamocortical axons enter into the cortical plate creating the electrical synapses. Additionally, the subplate zone gradually resolves in the preterm and term brain in conjunction with the growth of associative pathways leading to the activation of large-scale neural networks. We demonstrate that specific classes of axonal pathways within cerebral compartments are selectively vulnerable to temporally nested pathogenic factors. In particular, the radial distribution of axonal lesions, that is, radial vulnerability, is a robust predictor of clinical outcome. Furthermore, the subplate tangential nexus that we can visualize using MRI could be an additional marker as pivotal in the development of cortical connectivity. We suggest to direct future research toward the identification of sensitive markers of earlier lesions, the elucidation of genetic mechanisms underlying pathogenesis, and better long-term follow-up using structural and functional MRI.
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Affiliation(s)
- Ivica Kostović
- From the Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Scientific Centre of Excellence for Basic, Clinical and Translational Neuroscience, Zagreb, Croatia
| | - Milan Radoš
- From the Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Scientific Centre of Excellence for Basic, Clinical and Translational Neuroscience, Zagreb, Croatia.,Polyclinic "Neuron", Zagreb, Croatia
| | - Mirna Kostović-Srzentić
- From the Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Scientific Centre of Excellence for Basic, Clinical and Translational Neuroscience, Zagreb, Croatia.,Department of Health Psychology, University of Applied Health Sciences, Zagreb, Croatia.,Croatian Institute for Brain Research, Center of Research Excellence for Basic, Clinical and Translational Neuroscience, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Željka Krsnik
- From the Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Scientific Centre of Excellence for Basic, Clinical and Translational Neuroscience, Zagreb, Croatia
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