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Cabral L, Calabro FJ, Foran W, Parr AC, Ojha A, Rasmussen J, Ceschin R, Panigrahy A, Luna B. Multivariate and regional age-related change in basal ganglia iron in neonates. Cereb Cortex 2024; 34:bhad456. [PMID: 38059685 DOI: 10.1093/cercor/bhad456] [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: 07/05/2023] [Revised: 10/31/2023] [Accepted: 11/01/2023] [Indexed: 12/08/2023] Open
Abstract
In the perinatal period, reward and cognitive systems begin trajectories, influencing later psychiatric risk. The basal ganglia is important for reward and cognitive processing but early development has not been fully characterized. To assess age-related development, we used a measure of basal ganglia physiology, specifically brain tissue iron, obtained from nT2* signal in resting-state functional magnetic resonance imaging (rsfMRI), associated with dopaminergic processing. We used data from the Developing Human Connectome Project (n = 464) to assess how moving from the prenatal to the postnatal environment affects rsfMRI nT2*, modeling gestational and postnatal age separately for basal ganglia subregions in linear models. We did not find associations with tissue iron and gestational age [range: 24.29-42.29] but found positive associations with postnatal age [range:0-17.14] in the pallidum and putamen, but not the caudate. We tested if there was an interaction between preterm birth and postnatal age, finding early preterm infants (GA < 35 wk) had higher iron levels and changed less over time. To assess multivariate change, we used support vector regression to predict age from voxel-wise-nT2* maps. We could predict postnatal but not gestational age when maps were residualized for the other age term. This provides evidence subregions differentially change with postnatal experience and preterm birth may disrupt trajectories.
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Affiliation(s)
- Laura Cabral
- Department of Radiology University of Pittsburgh, Pittsburgh, PA 15224, United States
| | - Finnegan J Calabro
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, United States
- Department of Bioengineering, University of Pittsburgh, 15213, United States
| | - Will Foran
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - Ashley C Parr
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - Amar Ojha
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA 15213, United States
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - Jerod Rasmussen
- Development, Health and Disease Research Program, University of California, Irvine, CA 92697, United States
- Department of Pediatrics, University of California, Irvine, CA 92697, United States
| | - Rafael Ceschin
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15224, United States
| | - Ashok Panigrahy
- Department of Radiology University of Pittsburgh, Pittsburgh, PA 15224, United States
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, United States
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Spatiotemporal Developmental Gradient of Thalamic Morphology, Microstructure, and Connectivity fromthe Third Trimester to Early Infancy. J Neurosci 2023; 43:559-570. [PMID: 36639904 PMCID: PMC9888512 DOI: 10.1523/jneurosci.0874-22.2022] [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: 05/08/2022] [Revised: 10/19/2022] [Accepted: 11/26/2022] [Indexed: 12/12/2022] Open
Abstract
Thalamus is a critical component of the limbic system that is extensively involved in both basic and high-order brain functions. However, how the thalamic structure and function develops at macroscopic and microscopic scales during the perinatal period development is not yet well characterized. Here, we used multishell high-angular resolution diffusion MRI of 144 preterm-born and full-term infants in both sexes scanned at 32-44 postmenstrual weeks (PMWs) from the Developing Human Connectome Project database to investigate the thalamic development in morphology, microstructure, associated connectivity, and subnucleus division. We found evident anatomic expansion and linear increases of fiber integrity in the lateral side of thalamus compared with the medial part. The tractography results indicated that thalamic connection to the frontal cortex developed later than the other thalamocortical connections (parieto-occipital, motor, somatosensory, and temporal). Using a connectivity-based segmentation strategy, we revealed that functional partitions of thalamic subdivisions were formed at 32 PMWs or earlier, and the partition developed toward the adult pattern in a lateral-to-medial pattern. Collectively, these findings revealed faster development of the lateral thalamus than the central part as well as a posterior-to-anterior developmental gradient of thalamocortical connectivity from the third trimester to early infancy.SIGNIFICANCE STATEMENT This is the first study that characterizes the spatiotemporal developmental pattern of thalamus during the third trimester to early infancy. We found that thalamus develops in a lateral-to-medial pattern for both thalamic microstructures and subdivisions; and thalamocortical connectivity develops in a posterior-to-anterior gradient that thalamofrontal connectivity appears later than the other thalamocortical connections. These findings may enrich our understanding of the developmental principles of thalamus and provide references for the atypical brain growth in neurodevelopmental disorders.
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Kanel D, Vanes LD, Ball G, Hadaya L, Falconer S, Counsell SJ, Edwards AD, Nosarti C. OUP accepted manuscript. Brain Commun 2022; 4:fcac009. [PMID: 35178519 PMCID: PMC8846580 DOI: 10.1093/braincomms/fcac009] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/04/2021] [Accepted: 01/24/2022] [Indexed: 11/13/2022] Open
Abstract
Very preterm children are more likely to exhibit difficulties in socio-emotional processing than their term-born peers. Emerging socio-emotional problems may be partly due to alterations in limbic system development associated with infants’ early transition to extrauterine life. The amygdala is a key structure in this system and plays a critical role in various aspects of socio-emotional development, including emotion regulation. The current study tested the hypothesis that amygdala resting-state functional connectivity at term-equivalent age would be associated with socio-emotional outcomes in childhood. Participants were 129 very preterm infants (<33 weeks' gestation) who underwent resting-state functional MRI at term and received a neurodevelopmental assessment at 4–7 years (median = 4.64). Using the left and right amygdalae as seed regions, we investigated associations between whole-brain seed-based functional connectivity and three socio-emotional outcome factors which were derived using exploratory factor analysis (Emotion Moderation, Social Function and Empathy), controlling for sex, neonatal sickness, post-menstrual age at scan and social risk. Childhood Emotion Moderation scores were significantly associated with neonatal resting-state functional connectivity of the right amygdala with right parahippocampal gyrus and right middle occipital gyrus, as well as with functional connectivity of the left amygdala with the right thalamus. No significant associations were found between amygdalar resting-state functional connectivity and either Social Function or Empathy scores. The current findings show that amygdalar functional connectivity assessed at term is associated with later socio-emotional outcomes in very preterm children.
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Affiliation(s)
- Dana Kanel
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Lucy D. Vanes
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Gareth Ball
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - Laila Hadaya
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Shona Falconer
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Serena J. Counsell
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | | | - Chiara Nosarti
- Correspondence to: Chiara Nosarti Centre for the Developing Brain School of Bioengineering and Imaging Sciences King’s College London and Evelina Children’s Hospital London SE1 7EH, UK E-mail:
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Lao Y, Cao M, Yang Y, Kishan AU, Yang W, Wang Y, Sheng K. Bladder surface dose modeling in prostate cancer radiotherapy: An analysis of motion-induced variations and the cumulative dose across the treatment. Med Phys 2021; 48:8024-8036. [PMID: 34734414 DOI: 10.1002/mp.15326] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 09/15/2021] [Accepted: 10/21/2021] [Indexed: 01/04/2023] Open
Abstract
PURPOSE To introduce a novel surface-based dose mapping method to improve quantitative bladder dosimetric assessment in prostate cancer (PC) radiotherapy. METHODS Based on the planning and daily pre and postfraction MRIs of 12 PC patients, bladder surface models (SMs) were generated on manually delineated contours and regionally aligned via surface-based registration. Subsequently, bladder surface dose models (SDMs) were created using face-wise dose sampling. To determine the bladder intrafractional and interfractional motion and dose variation, we performed a pose analysis between pre and postfraction bladder SMs, as well as surface mapping for fractional SMs. Discrepancies between the received dose, accumulated from daily SDMs, and the planned dose were then assessed on the corresponding SDMs. Complementary to the surface dose mapping, dose surface histogram (DSH)-based comparisons were also performed. RESULTS The intrafraction pose analysis revealed a significant (p < 0.05) bladder expansion, as well as an anterior/superior drift during the treatment. The intrafraction motion substantially altered dose to mid-bladder body, but not the bladder surface areas distal to or contiguous with the target. A similar pattern of dose variations was also detected by interfraction comparisons. With surface registration to the common SM, the cumulative bladder dose significantly differs from the planned dose. The discrepancy is evident in the mid-posterior range that corresponds to a mid- to high-dose region. The received DSH significantly differs from the planned DSH after permutation correction (p = 0.0122), while the overall surface-based comparison after multiple comparison correction is nonsignificant (p = 0.0800). CONCLUSIONS We developed a novel surface-based intra and interdose mapping framework applied to a unique daily MR dataset for image-guided radiotherapy. The framework identified significant intrafraction bladder positional changes, localized the intra and interfraction variations, and quantified planned versus received dose differences on the bladder surface. The result indicates the importance of adopting the motion-integrated bladder SDM for bladder dose management.
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Affiliation(s)
- Yi Lao
- Department of Radiation Oncology, University of California, Los Angeles, California, USA
| | - Minsong Cao
- Department of Radiation Oncology, University of California, Los Angeles, California, USA
| | - Yingli Yang
- Department of Radiation Oncology, University of California, Los Angeles, California, USA
| | - Amar U Kishan
- Department of Radiation Oncology, University of California, Los Angeles, California, USA
| | - Wensha Yang
- Department of Radiation Oncology, University of Southern California, Los Angeles, California, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, Arizona, USA
| | - Ke Sheng
- Department of Radiation Oncology, University of California, Los Angeles, California, USA
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Lapidaire W, Clark C, Fewtrell MS, Lucas A, Leeson P, Lewandowski AJ. The Preterm Heart-Brain Axis in Young Adulthood: The Impact of Birth History and Modifiable Risk Factors. J Clin Med 2021; 10:jcm10061285. [PMID: 33808886 PMCID: PMC8003804 DOI: 10.3390/jcm10061285] [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: 02/04/2021] [Revised: 03/11/2021] [Accepted: 03/16/2021] [Indexed: 11/20/2022] Open
Abstract
People born preterm are at risk of developing both cardiac and brain abnormalities. We aimed to investigate whether cardiovascular physiology may directly affect brain structure in young adulthood and whether cardiac changes are associated with modifiable biomarkers. Forty-eight people born preterm, followed since birth, underwent cardiac MRI at age 25.1 ± 1.4 years and brain MRI at age 33.4 ± 1.0 years. Term born controls were recruited at both time points for comparison. Cardiac left and right ventricular stroke volume, left and right ventricular end diastolic volume and right ventricular ejection fraction were significantly different between preterm and term born controls and associated with subcortical brain volumes and fractional anisotropy in the corpus callosum in the preterm group. This suggests that cardiovascular abnormalities in young adults born preterm are associated with potentially adverse future brain health. Associations between left ventricular stroke volume indexed to body surface area and right putamen volumes, as well as left ventricular end diastolic length and left thalamus volumes, remained significant when adjusting for early life factors related to prematurity. Although no significant associations were found between modifiable biomarkers and cardiac physiology, this highlights that cardiovascular health interventions may also be important for brain health in preterm born adults.
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Affiliation(s)
- Winok Lapidaire
- Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK
- UCL GOS Institute of Child Health, University College London, London WC1N 1EH, UK
| | - Chris Clark
- UCL GOS Institute of Child Health, University College London, London WC1N 1EH, UK
| | - Mary S Fewtrell
- UCL GOS Institute of Child Health, University College London, London WC1N 1EH, UK
| | - Alan Lucas
- UCL GOS Institute of Child Health, University College London, London WC1N 1EH, UK
| | - Paul Leeson
- Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK
| | - Adam J Lewandowski
- Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK
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Fu Y, Zhang J, Li Y, Shi J, Zou Y, Guo H, Li Y, Yao Z, Wang Y, Hu B. A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder. Prog Neuropsychopharmacol Biol Psychiatry 2021; 104:109989. [PMID: 32512131 PMCID: PMC9632410 DOI: 10.1016/j.pnpbp.2020.109989] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 05/19/2020] [Accepted: 05/30/2020] [Indexed: 10/24/2022]
Abstract
Autism spectrum disorder (ASD) is accompanied with widespread impairment in social-emotional functioning. Classification of ASD using sensitive morphological features derived from structural magnetic resonance imaging (MRI) of the brain may help us to better understand ASD-related mechanisms and improve related automatic diagnosis. Previous studies using T1 MRI scans in large heterogeneous ABIDE dataset with typical development (TD) controls reported poor classification accuracies (around 60%). This may because they only considered surface-based morphometry (SBM) as scalar estimates (such as cortical thickness and surface area) and ignored the neighboring intrinsic geometry information among features. In recent years, the shape-related SBM achieves great success in discovering the disease burden and progression of other brain diseases. However, when focusing on local geometry information, its high dimensionality requires careful treatment in its application to machine learning. To address the above challenges, we propose a novel pipeline for ASD classification, which mainly includes the generation of surface-based features, patch-based surface sparse coding and dictionary learning, Max-pooling and ensemble classifiers based on adaptive optimizers. The proposed pipeline may leverage the sensitivity of brain surface morphometry statistics and the efficiency of sparse coding and Max-pooling. By introducing only the surface features of bilateral hippocampus that derived from 364 male subjects with ASD and 381 age-matched TD males, this pipeline outperformed five recent MRI-based ASD classification studies with >80% accuracy in discriminating individuals with ASD from TD controls. Our results suggest shape-related SBM features may further boost the classification performance of MRI between ASD and TD.
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Affiliation(s)
- Yu Fu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Yuan Li
- School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong Province, China
| | - Jie Shi
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Ying Zou
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Hanning Guo
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yongchao Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Zhijun Yao
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China.
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China.
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Yao Z, Fu Y, Wu J, Zhang W, Yu Y, Zhang Z, Wu X, Wang Y, Hu B. Morphological changes in subregions of hippocampus and amygdala in major depressive disorder patients. Brain Imaging Behav 2020; 14:653-667. [PMID: 30519998 PMCID: PMC6551316 DOI: 10.1007/s11682-018-0003-1] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Despite many neuroimaging studies in the past years, the neuroanatomical substrates of major depressive disorder (MDD) subcortical structures are still not well understood. Since hippocampus and amygdala are the two vital subcortical structures that most susceptible to MDD, finding the evidence of morphological changes in their subregions may bring some new insights for MDD research. Combining structural magnetic resonance imaging (MRI) with novel morphometry analysis methods, we recruited 25 MDD patients and 28 healthy controls (HC), and investigated their volume and morphological differences in hippocampus and amygdala. Relative to volumetric method, our methods detected more significant global morphological atrophies (p<0.05). More precisely, subiculum and cornu ammonis (CA) 1 subregions of bilateral hippocampus, lateral (LA) and basolateral ventromedial (BLVM) of left amygdala and LA, BLVM, central (CE), amygdalostriatal transition area (ASTR), anterior cortical (ACO) and anterior amygdaloid area (AAA) of right amygdala were demonstrated prone to atrophy. Correlation analyses between each subject's surface eigenvalues and Hamilton Depression Scale (HAMD) were then performed. Correlation results showed that atrophy areas in hippocampus and amygdala have slight tendencies of expanding into other subregions with the development of MDD. Finally, we performed group morphometric analysis and drew the atrophy and expansion areas between MDD-Medicated group (only 19 medicated subjects in MDD group were included) and HC group, found some preliminary evidence about subregional morphological resilience of hippocampus and amygdala. These findings revealed new pathophysiologic patterns in the subregions of hippocampus and amygdala, which can help with subsequent smaller-scale MDD research.
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Affiliation(s)
- Zhijun Yao
- School of Information Science and Engineering, Lanzhou University, P.O. Box 730000, Lanzhou, China
| | - Yu Fu
- School of Information Science and Engineering, Lanzhou University, P.O. Box 730000, Lanzhou, China
| | - Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809, Tempe, AZ, 85287, USA
| | - Wenwen Zhang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Yue Yu
- School of Information Science and Engineering, Lanzhou University, P.O. Box 730000, Lanzhou, China
| | - Zicheng Zhang
- School of Information Science and Engineering, Lanzhou University, P.O. Box 730000, Lanzhou, China
| | - Xia Wu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
- College of Information Science and Technology, Beijing Normal University, P.O. Box 100000, Beijing, China.
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809, Tempe, AZ, 85287, USA.
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, P.O. Box 730000, Lanzhou, China.
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Paquette N, Gajawelli N, Lepore N. Structural neuroimaging. HANDBOOK OF CLINICAL NEUROLOGY 2020; 174:251-264. [PMID: 32977882 DOI: 10.1016/b978-0-444-64148-9.00018-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Characterizing the neuroanatomical correlates of brain development is essential in understanding brain-behavior relationships and neurodevelopmental disorders. Advances in brain MRI acquisition protocols and image processing techniques have made it possible to detect and track with great precision anatomical brain development and pediatric neurologic disorders. In this chapter, we provide a brief overview of the modern neuroimaging techniques for pediatric brain development and review key normal brain development studies. Characteristic disorders affecting neurodevelopment in childhood, such as prematurity, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), epilepsy, and brain cancer, and key neuroanatomical findings are described and then reviewed. Large datasets of typically developing children and children with various neurodevelopmental conditions are now being acquired to help provide the biomarkers of such impairments. While there are still several challenges in imaging brain structures specific to the pediatric populations, such as subject cooperation and tissues contrast variability, considerable imaging research is now being devoted to solving these problems and improving pediatric data analysis.
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Affiliation(s)
- Natacha Paquette
- CIBORG Lab, Department of Radiology, Children's Hospital of Los Angeles and University of Southern California, Los Angeles, CA, United States
| | - Niharika Gajawelli
- CIBORG Lab, Department of Radiology, Children's Hospital of Los Angeles and University of Southern California, Los Angeles, CA, United States
| | - Natasha Lepore
- CIBORG Lab, Department of Radiology, Children's Hospital of Los Angeles and University of Southern California, Los Angeles, CA, United States.
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Dong Q, Zhang J, Li Q, Wang J, Leporé N, Thompson PM, Caselli RJ, Ye J, Wang Y. Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images. J Alzheimers Dis 2020; 75:971-992. [PMID: 32390615 PMCID: PMC7427104 DOI: 10.3233/jad-190973] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer's disease (AD) research. Convolutional neural networks (CNN) have been proved to be powerful for various computer vision research by refining reliable and high-level feature maps from image patches. OBJECTIVE A key challenge in applying CNN to neuroimaging research is the limited labeled samples with high dimensional features. Another challenge is how to improve the prediction accuracy by joint analysis of multiple data sources (i.e., multiple time points or multiple biomarkers). To address these two challenges, we propose a novel multi-task learning framework based on CNN. METHODS First, we pre-trained CNN on the ImageNet dataset and transferred the knowledge from the pre-trained model to neuroimaging representation. We used this deep model as feature extractor to generate high-level feature maps of different tasks. Then a novel unsupervised learning method, termed Multi-task Stochastic Coordinate Coding (MSCC), was proposed for learning sparse features of multi-task feature maps by using shared and individual dictionaries. Finally, Lasso regression was performed on these multi-task sparse features to predict AD progression measured by the Mini-Mental State Examination (MMSE) and the Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-Cog). RESULTS We applied this novel CNN-MSCC system on the Alzheimer's Disease Neuroimaging Initiative dataset to predict future MMSE/ADAS-Cog scales. We found our method achieved superior performances compared with seven other methods. CONCLUSION Our work may add new insights into data augmentation and multi-task deep model research and facilitate the adoption of deep models in neuroimaging research.
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Affiliation(s)
- Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Qingyang Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Junwen Wang
- Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, 85259, USA
| | - Natasha Leporé
- Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | | | - Jieping Ye
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
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Tortora D, Severino M, Di Biase C, Malova M, Parodi A, Minghetti D, Traggiai C, Uccella S, Boeri L, Morana G, Rossi A, Ramenghi LA. Early Pain Exposure Influences Functional Brain Connectivity in Very Preterm Neonates. Front Neurosci 2019; 13:899. [PMID: 31507370 PMCID: PMC6716476 DOI: 10.3389/fnins.2019.00899] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Accepted: 08/12/2019] [Indexed: 11/13/2022] Open
Abstract
Background Early exposure to nociceptive events may cause brain structural alterations in preterm neonates, with long-lasting consequences on neurodevelopmental outcome. Little is known on the extent to which early pain may affect brain connectivity. We aim to evaluate brain functional connectivity changes in preterm neonate that underwent multiple invasive procedures during the postnatal period, and to correlate them with the neurodevelopmental outcome at 24 months. Methods In this prospective case-control study, we collected information about exposure to painful events during the early postnatal period and resting-state BOLD-fMRI data at term equivalent age from two groups of preterm neonate: 33 subjected to painful procedures during the neonatal intensive care (mean gestational age 27.9 ± 1.8 weeks) and 13 who did not require invasive procedures (average gestational age 31.2 ± 2.1 weeks). A data-driven principal-component-based multivariate pattern analysis (MVPA) was used to investigate the effect of early pain exposure on brain functional connectivity, and the relationship between connectivity changes and neurodevelopmental outcome at 24 months, assessed with Griffiths, Developmental Scale-Revised: 0-2. Results Early pain was associated with decreased functional connectivity between thalami and bilateral somatosensory cortex, and between the right insular cortex and ipsilateral amygdala and hippocampal regions, with a more evident effect in preterm neonate undergoing more invasive procedures. Functional connectivity of the right thalamocortical pathway was related to neuromotor outcome at 24 months (P = 0.003). Conclusion Early exposure to pain is associated with abnormal functional connectivity of developing networks involved in the modulation of noxious stimuli in preterm neonate, contributing to the neurodevelopmental consequence of preterm birth.
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Affiliation(s)
- Domenico Tortora
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | | | - Carlo Di Biase
- Neonatal Intensive Care Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Maryia Malova
- Neonatal Intensive Care Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Alessandro Parodi
- Neonatal Intensive Care Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Diego Minghetti
- Neonatal Intensive Care Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Cristina Traggiai
- Neonatal Intensive Care Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Sara Uccella
- Child Neuropsychiatry Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Luca Boeri
- Child Neuropsychiatry Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Giovanni Morana
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Andrea Rossi
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
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Holme Nielsen C, Bladt Brandt A, Thymann T, Obelitz-Ryom K, Jiang P, Vanden Hole C, van Ginneken C, Pankratova S, Sangild PT. Rapid Postnatal Adaptation of Neurodevelopment in Pigs Born Late Preterm. Dev Neurosci 2019; 40:586-600. [PMID: 31141813 DOI: 10.1159/000499127] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 02/26/2019] [Indexed: 11/19/2022] Open
Abstract
Preterm birth interrupts intrauterine brain growth and maturation and may induce a delay in postnatal neurodevelopment. Such developmental delays can result from the reduced fetal age at birth, together with the clinical compli-cations of preterm birth (e.g., hypoxia, ischemia, and inflammation). We hypothesized that late preterm birth, inducing only mild clinical complications, has minimal effects on brain-related outcomes such as motor function and behavior. Using the pig as a model for late preterm infants, piglets were cesarean delivered preterm (90%, 106 days gestation) or at full term, reared by identical procedures, and euthanized for tissue collection at birth or after 11 days (e.g., term-corrected age for preterm pigs). Clinical variables and both structural and functional brain endpoints were assessed. The preterm pigs were slow to get on their feet, gained less weight (-30%), and had a higher cerebral hydration level and blood-to-cerebrospinal fluid permeability than the term pigs. At term-corrected age (11 days), the absolute weight of the brain and the weights of its regions were similar between 11-day-old preterm and newborn term pigs, and both were lower than in 11-day-old term pigs. Postnatally, physical activity and movements in an open field were similar, except that preterm pigs showed a reduced normalized stride length and increased normalized maximum stride height. Perinatal brain growth is closely associated with advancing postconceptional age in pigs, and late preterm birth is initially associated with impaired brain growth and physical activity. Postnatally, neuromuscular functions mature rapidly and become similar to those in term pigs, even before term-corrected age. Neuromuscular functions and behavior may show rapid postnatal adaptation to late preterm birth in both pigs and infants.
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Affiliation(s)
- Charlotte Holme Nielsen
- Comparative Pediatrics and Nutrition, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anne Bladt Brandt
- Comparative Pediatrics and Nutrition, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Thymann
- Comparative Pediatrics and Nutrition, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Karina Obelitz-Ryom
- Comparative Pediatrics and Nutrition, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Pingping Jiang
- Comparative Pediatrics and Nutrition, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Chris van Ginneken
- Department of Veterinary Sciences, University of Antwerp, Antwerp, Belgium
| | - Stanislava Pankratova
- Comparative Pediatrics and Nutrition, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Per Torp Sangild
- Comparative Pediatrics and Nutrition, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark, .,Department of Pediatrics and Adolescent Medicine, Rigshospitalet, Copenhagen, Denmark,
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12
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Dong Q, Zhang W, Wu J, Li B, Schron EH, McMahon T, Shi J, Gutman BA, Chen K, Baxter LC, Thompson PM, Reiman EM, Caselli RJ, Wang Y. Applying surface-based hippocampal morphometry to study APOE-E4 allele dose effects in cognitively unimpaired subjects. NEUROIMAGE-CLINICAL 2019; 22:101744. [PMID: 30852398 PMCID: PMC6411498 DOI: 10.1016/j.nicl.2019.101744] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 02/02/2019] [Accepted: 03/02/2019] [Indexed: 11/30/2022]
Abstract
Apolipoprotein E (APOE) e4 is the major genetic risk factor for late-onset Alzheimer's disease (AD). The dose-dependent impact of this allele on hippocampal volumes has been documented, but its influence on general hippocampal morphology in cognitively unimpaired individuals is still elusive. Capitalizing on the study of a large number of cognitively unimpaired late middle aged and older adults with two, one and no APOE-e4 alleles, the current study aims to characterize the ability of our automated surface-based hippocampal morphometry algorithm to distinguish between these three levels of genetic risk for AD and demonstrate its superiority to a commonly used hippocampal volume measurement. We examined the APOE-e4 dose effect on cross-sectional hippocampal morphology analysis in a magnetic resonance imaging (MRI) database of 117 cognitively unimpaired subjects aged between 50 and 85 years (mean = 57.4, SD = 6.3), including 36 heterozygotes (e3/e4), 37 homozygotes (e4/e4) and 44 non-carriers (e3/e3). The proposed automated framework includes hippocampal surface segmentation and reconstruction, higher-order hippocampal surface correspondence computation, and hippocampal surface deformation analysis with multivariate statistics. In our experiments, the surface-based method identified APOE-e4 dose effects on the left hippocampal morphology. Compared to the widely-used hippocampal volume measure, our hippocampal morphometry statistics showed greater statistical power by distinguishing cognitively unimpaired subjects with two, one, and no APOE-e4 alleles. Our findings mirrored previous studies showing that APOE-e4 has a dose effect on the acceleration of brain structure deformities. The results indicated that the proposed surface-based hippocampal morphometry measure is a potential preclinical AD imaging biomarker for cognitively unimpaired individuals. Applied surface-based hippocampal morphometry on cognitively unimpaired subjects. Our study identified APOE-e4 dose effects on cognitively unimpaired subjects. Surface-based hippocampal morphometry outperformed the hippocampal volume measure. Surface-based hippocampal morphometry may be a potential preclinical AD biomarker.
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Affiliation(s)
- Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Wen Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Bolun Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Travis McMahon
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Boris A Gutman
- Armour College of Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Leslie C Baxter
- Human Brain Imaging Laboratory, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | | | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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13
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Lao Y, David J, Torosian A, Placencio V, Wang Y, Hendifar A, Yang W, Tuli R. Combined morphologic and metabolic pipeline for Positron emission tomography/computed tomography based radiotherapy response evaluation in locally advanced pancreatic adenocarcinoma. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2019; 9:28-34. [PMID: 32190750 PMCID: PMC7079767 DOI: 10.1016/j.phro.2018.12.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
A novel morphologic and metabolic combined pipeline for PA response evaluation. The derived metric outperformed traditional imaging metrics in risk stratification. May serve as a new image biomarker to characterize heterogeneous tumor response.
Background and purpose Adaptive radiation planning for pancreatic adenocarcinoma (PA) relies on accurate treatment response assessment, while traditional response evaluation criteria inefficiently characterize tumors with complex morphological features or intrinsically low metabolism. To better assess treatment response of PA, we quantify and compare regional morphological and metabolic features of the 3D pre- and post-radiation therapy (RT) tumor models. Materials and methods Thirty-one PA patients with pre and post-RT Positron emission tomography/computed tomography (PET/CT) scans were evaluated. 3D meshes of pre- and post-RT tumors were generated and registered to establish vertex-wise correspondence. To assess tumor response, Mahalanobis distances (Mdist|Fusion) between pre- and post-RT tumor surfaces with anatomic and metabolic fused vectors were calculated for each patient. Mdist|Fusion was evaluated by overall survival (OS) prediction and survival risk classification. As a comparison, the same analyses were conducted on traditional imaging/physiological predictors, and distances measurements based on metabolic and morphological features only. Results Among all the imaging/physiological parameters, Mdist|Fusion was shown to be the best predictor of OS (HR = 0.52, p = 0.008), while other parameters failed to reach significance. Moreover, Mdist|Fusion outperformed traditional morphologic and metabolic measurements in patient risk stratification, either alone (HR = 11.51, p < 0.001) or combined with age (HR = 9.04, p < 0.001). Conclusions We introduced a PET/CT-based novel morphologic and metabolic pipeline for response evaluation in locally advanced PA. The fused Mdist|Fusion outperformed traditional morphologic, metabolic, and physiological measurements in OS prediction and risk stratification. The novel fusion model may serve as a new imaging-marker to more accurately characterize the heterogeneous tumor RT response.
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Affiliation(s)
- Yi Lao
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, USA
| | - John David
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Arman Torosian
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Veronica Placencio
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, USA.,School of Computing, Informatics, Decision Systems and Engineering, Arizona State University, Tempe, AZ, USA
| | - Yalin Wang
- School of Computing, Informatics, Decision Systems and Engineering, Arizona State University, Tempe, AZ, USA
| | - Andrew Hendifar
- Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Wensha Yang
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Richard Tuli
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, USA
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14
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Lao Y, David J, Mirhadi A, Lepore N, Sandler H, Wang Y, Tuli R, Yang W. Discriminating lung adenocarcinoma from lung squamous cell carcinoma using respiration-induced tumor shape changes. Phys Med Biol 2018; 63:215027. [PMID: 30403196 DOI: 10.1088/1361-6560/aae7f1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Based on 4D-CT, we aimed to characterize the pattern of morphological changes in lung tumors during respiration, and investigated its potential in non-invasively differentiating lung adenocarcinoma (AC) and squamous cell carcinoma (SCC). We applied a 3D surface analysis on 22 tumors (13 AC, 9 SCC) to investigate the tumor regional morphological fluctuations in response to respiration phases. Tumor surface vertices among ten respiratory phases were matched using surface-based registration, and the shape descriptors (ρ and detJ) were calculated and tracked across respiration stages in a regionally aligned scenario. Pair-wise group comparisons were performed between lung AC and SCC subtypes, in terms of ratios of maximal shape changes as well as correlation coefficients between tumor shape and respiratory stage indicators from the lung. AC type tumors had averaged larger surface measurements at exhale than at inhale, and these surface measurements were negatively correlated with lung volumes across respiratory stages. In contrast, SCC type tumors had averaged smaller surface measurements at exhale than at inhale, and the correlations with lung volumes were positive. The group differences in maximal shape changes as well as correlations were both statistically significant (p < 0.05). We developed a non-invasive lung tumor sub-type detection pipeline based on respiration-induced tumor surface deformation. Significant differences in deformation patterns were detected between lung AC and SCC. The derived surface measurements may potentially serve as a new non-invasive imaging biomarker of lung cancer subtypes.
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Affiliation(s)
- Yi Lao
- Department of Radiation Oncology, Cedars Sinai Medical Center, Los Angeles, CA, United States of America
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15
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Sun C, Yang F, Wang C, Wang Z, Zhang Y, Ming D, Du J. Mutual Information-Based Brain Network Analysis in Post-stroke Patients With Different Levels of Depression. Front Hum Neurosci 2018; 12:285. [PMID: 30065639 PMCID: PMC6056615 DOI: 10.3389/fnhum.2018.00285] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 06/25/2018] [Indexed: 11/15/2022] Open
Abstract
Post-stroke depression (PSD) is the most common stroke-related emotional disorder, and it severely affects the recovery process. However, more than half cases are not correctly diagnosed. This study was designed to develop a new method to assess PSD using EEG signal to analyze the specificity of PSD patients' brain network. We have 107 subjects attended in this study (72 stabilized stroke survivors and 35 non-depressed healthy subjects). A Hamilton Depression Rating Scale (HDRS) score was determined for all subjects before EEG data collection. According to HDRS score, the 72 patients were divided into 3 groups: post-stroke non-depression (PSND), post-stroke mild depression (PSMD) and post-stroke depression (PSD). Mutual information (MI)-based graph theory was used to analyze brain network connectivity. Statistical analysis of brain network characteristics was made with a threshold of 10-30% of the strongest MIs. The results showed significant weakened interhemispheric connections and lower clustering coefficient in post-stroke depressed patients compared to those in healthy controls. Stroke patients showed a decreasing trend in the connection between the parietal-occipital and the frontal area as the severity of the depression increased. PSD subjects showed abnormal brain network connectivity and network features based on EEG, suggesting that MI-based brain network may have the potential to assess the severity of depression post stroke.
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Affiliation(s)
- Changcheng Sun
- Rehabilitation Medical Department, Tianjin Union Medical Centre, Tianjin, China
| | - Fei Yang
- Department of Health and Exercise Science, Tianjin University of Sport, Tianjin, China
| | - Chunfang Wang
- Rehabilitation Medical Department, Tianjin Union Medical Centre, Tianjin, China
| | - Zhonghan Wang
- Rehabilitation Medical Department, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Ying Zhang
- Rehabilitation Medical Department, Tianjin Union Medical Centre, Tianjin, China
| | - Dong Ming
- Department of Biomedical Engineering, College of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China
| | - Jingang Du
- Rehabilitation Medical Department, Tianjin Union Medical Centre, Tianjin, China
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16
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Vlasova R, Dirks H, Dean D, O'Muircheartaigh J, Gonzalez S, Nelson MD, Deoni S, Lepore N. Contribution to speech development of the right anterior putamen revealed with multivariate tensor-based morphometry. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:3085-3087. [PMID: 29060550 DOI: 10.1109/embc.2017.8037509] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In our previous study1, we suggested that the difference between tensor-based metrics in the anterior part of the right putamen between 21 and 18 months age groups associated with speech development during this ages. Here we used a correlational analysis between verbal scores and determinant of the Jacobian matrix to confirm our hypothesis. Significant correlations in anterior part of the right putamen between verbal scores and surface metric were revealed in the 18 and 21 age groups.
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17
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Paquette N, Shi J, Wang Y, Lao Y, Ceschin R, Nelson MD, Panigrahy A, Lepore N. Ventricular shape and relative position abnormalities in preterm neonates. NEUROIMAGE-CLINICAL 2017. [PMID: 28649491 PMCID: PMC5470570 DOI: 10.1016/j.nicl.2017.05.025] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Recent neuroimaging findings have highlighted the impact of premature birth on subcortical development and morphological changes in the deep grey nuclei and ventricular system. To help characterize subcortical microstructural changes in preterm neonates, we recently implemented a multivariate tensor-based method (mTBM). This method allows to precisely measure local surface deformation of brain structures in infants. Here, we investigated ventricular abnormalities and their spatial relationships with surrounding subcortical structures in preterm neonates. We performed regional group comparisons on the surface morphometry and relative position of the lateral ventricles between 19 full-term and 17 preterm born neonates at term-equivalent age. Furthermore, a relative pose analysis was used to detect individual differences in translation, rotation, and scale of a given brain structure with respect to an average. Our mTBM results revealed broad areas of alterations on the frontal horn and body of the left ventricle, and narrower areas of differences on the temporal horn of the right ventricle. A significant shift in the rotation of the left ventricle was also found in preterm neonates. Furthermore, we located significant correlations between morphology and pose parameters of the lateral ventricles and that of the putamen and thalamus. These results show that regional abnormalities on the surface and pose of the ventricles are also associated with alterations on the putamen and thalamus. The complementarity of the information provided by the surface and pose analysis may help to identify abnormal white and grey matter growth, hinting toward a pattern of neural and cellular dysmaturation.
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Affiliation(s)
- N Paquette
- Department of Radiology, University of Southern California and Children's Hospital of Los Angeles, CA, USA
| | - J Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Y Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Y Lao
- Department of Radiology, University of Southern California and Children's Hospital of Los Angeles, CA, USA
| | - R Ceschin
- Department of Radiology, Children's Hospital of Pittsburgh UPMC, Pittsburgh, PA, USA
| | - M D Nelson
- Department of Radiology, University of Southern California and Children's Hospital of Los Angeles, CA, USA
| | - A Panigrahy
- Department of Radiology, Children's Hospital of Pittsburgh UPMC, Pittsburgh, PA, USA
| | - N Lepore
- Department of Radiology, University of Southern California and Children's Hospital of Los Angeles, CA, USA.
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18
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Lao Y, Dion LA, Gilbert G, Bouchard MF, Rocha G, Wang Y, Leporé N, Saint-Amour D. Mapping the basal ganglia alterations in children chronically exposed to manganese. Sci Rep 2017; 7:41804. [PMID: 28155922 PMCID: PMC5290534 DOI: 10.1038/srep41804] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 12/30/2016] [Indexed: 01/24/2023] Open
Abstract
Chronic manganese (Mn) exposure is associated with neuromotor and neurocognitive deficits, but the exact mechanism of Mn neurotoxicity is still unclear. With the advent of magnetic resonance imaging (MRI), in-vivo analysis of brain structures has become possible. Among different sub-cortical structures, the basal ganglia (BG) has been investigated as a putative anatomical biomarker in MR-based studies of Mn toxicity. However, previous investigations have yielded inconsistent results in terms of regional MR signal intensity changes. These discrepancies may be due to the subtlety of brain alterations caused by Mn toxicity, coupled to analysis techniques that lack the requisite detection power. Here, based on brain MRI, we apply a 3D surface-based morphometry method on 3 bilateral basal ganglia structures in school-age children chronically exposed to Mn through drinking water to investigate the effect of Mn exposure on brain anatomy. Our method successfully pinpointed significant enlargement of many areas of the basal ganglia structures, preferentially affecting the putamen. Moreover, these areas showed significant correlations with fine motor performance, indicating a possible link between altered basal ganglia neurodevelopment and declined motor performance in high Mn exposed children.
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Affiliation(s)
- Yi Lao
- CIBORG laboratory, Department of Radiology, Children's Hospital Los Angeles, Los Angeles CA, USA.,Department of Biomedical Engineering, University of Southern California, Los Angeles CA, USA
| | - Laurie-Anne Dion
- Department of Psychology, Université du Québec à Montréal, Montréal, QC, Canada
| | - Guillaume Gilbert
- Department of radiology, Université de Montréal, Montréal, QC, Canada.,MR Clinical Science, Philips Healthcare, Montreal, Quebec, Canada
| | - Maryse F Bouchard
- Sainte-Justine Hospital Research Centre and Department of Occupational and Environmental Health, Université de Montréal, Montréal, QC, Canada
| | - Gabriel Rocha
- Department of Biomedical Engineering, University of Southern California, Los Angeles CA, USA
| | - Yalin Wang
- School of Computing, Informatics, Decision Systems and Engineering, Arizona State University, Tempe, Arizona, USA
| | - Natasha Leporé
- CIBORG laboratory, Department of Radiology, Children's Hospital Los Angeles, Los Angeles CA, USA.,Department of Biomedical Engineering, University of Southern California, Los Angeles CA, USA
| | - Dave Saint-Amour
- Department of Psychology, Université du Québec à Montréal, Montréal, QC, Canada
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19
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Botellero VL, Skranes J, Bjuland KJ, Håberg AK, Lydersen S, Brubakk AM, Indredavik MS, Martinussen M. A longitudinal study of associations between psychiatric symptoms and disorders and cerebral gray matter volumes in adolescents born very preterm. BMC Pediatr 2017; 17:45. [PMID: 28143492 PMCID: PMC5286868 DOI: 10.1186/s12887-017-0793-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Accepted: 01/17/2017] [Indexed: 12/13/2022] Open
Abstract
Background Being born preterm with very low birthweight (VLBW ≤ 1500 g) poses a risk for cortical and subcortical gray matter (GM) abnormalities, as well as for having more psychiatric problems during childhood and adolescence than term-born individuals. The aim of this study was to investigate the relationship between cortical and subcortical GM volumes and the course of psychiatric disorders during adolescence in VLBW individuals. Methods We followed VLBW individuals and term-born controls (birth weight ≥10th percentile) from 15 (VLBW;controls n = 40;56) to 19 (n = 44;60) years of age. Of these, 30;37 individuals were examined longitudinally. Cortical and subcortical GM volumes were extracted from MRPRAGE images obtained with the same 1.5 T MRI scanner at both time points and analyzed at each time point with the longitudinal stream of the FreeSurfer software package 5.3.0. All participants underwent clinical interviews and were assessed for psychiatric symptoms and diagnosis (Schedule for Affective Disorders and Schizophrenia for School-age Children, Children’s Global Assessment Scale, Attention-Deficit/Hyperactivity Disorder Rating Scale-IV). VLBW adolescents were divided into two groups according to diagnostic status from 15 to 19 years of age: persisting/developing psychiatric diagnosis or healthy/becoming healthy. Results Reduction in subcortical GM volume at 15 and 19 years, not including the thalamus, was limited to VLBW adolescents with persisting/developing diagnosis during adolescence, whereas VLBW adolescents in the healthy/becoming healthy group had similar subcortical GM volumes to controls. Moreover, across the entire VLBW group, poorer psychosocial functioning was predicted by smaller subcortical GM volumes at both time points and with reduced GM volume in the thalamus and the parietal and occipital cortex at 15 years. Inattention problems were predicted by smaller GM volumes in the parietal and occipital cortex. Conclusions GM volume reductions in the parietal and occipital cortex as well as smaller thalamic and subcortical GM volumes were associated with the higher rates of psychiatric symptoms found across the entire VLBW group. Significantly smaller subcortical GM volumes in VLBW individuals compared with term-born peers might pose a risk for developing and maintaining psychiatric diagnoses during adolescence. Future research should explore the possible role of reduced cortical and subcortical GM volumes in the pathogenesis of psychiatric illness in VLBW adolescents. Electronic supplementary material The online version of this article (doi:10.1186/s12887-017-0793-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Violeta L Botellero
- Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology, Medical Technology Research Center, P.O. Box 8905, NO-7491, Trondheim, Norway.
| | - Jon Skranes
- Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology, Medical Technology Research Center, P.O. Box 8905, NO-7491, Trondheim, Norway.,Department of Pediatrics, Sørlandet Hospital, Arendal, Norway
| | - Knut Jørgen Bjuland
- Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology, Medical Technology Research Center, P.O. Box 8905, NO-7491, Trondheim, Norway
| | - Asta Kristine Håberg
- Department of Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Medical Imaging, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Stian Lydersen
- Regional Center for Child and Youth Mental Health and Child Welfare, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ann-Mari Brubakk
- Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology, Medical Technology Research Center, P.O. Box 8905, NO-7491, Trondheim, Norway.,Department of Pediatrics, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Marit S Indredavik
- Regional Center for Child and Youth Mental Health and Child Welfare, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Child and Adolescent Psychiatry, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Marit Martinussen
- Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology, Medical Technology Research Center, P.O. Box 8905, NO-7491, Trondheim, Norway.,Department of Gynecology and Obstetrics, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
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20
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Lao Y, Nguyen B, Tsao S, Gajawelli N, Law M, Chui H, Weiner M, Wang Y, Leporé N. A T1 and DTI fused 3D corpus callosum analysis in MCI subjects with high and low cardiovascular risk profile. Neuroimage Clin 2016; 14:298-307. [PMID: 28210541 PMCID: PMC5299209 DOI: 10.1016/j.nicl.2016.12.027] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 12/13/2016] [Accepted: 12/20/2016] [Indexed: 01/08/2023]
Abstract
Understanding the extent to which vascular disease and its risk factors are associated with prodromal dementia, notably Alzheimer's disease (AD), may enhance predictive accuracy as well as guide early interventions. One promising avenue to determine this relationship consists of looking for reliable and sensitive in-vivo imaging methods capable of characterizing the subtle brain alterations before the clinical manifestations. However, little is known from the imaging perspective about how risk factors such as vascular disease influence AD progression. Here, for the first time, we apply an innovative T1 and DTI fusion analysis of 3D corpus callosum (CC) on mild cognitive impairment (MCI) populations with different levels of vascular profile, aiming to de-couple the vascular factor in the prodromal AD stage. Our new fusion method successfully increases the detection power for differentiating MCI subjects with high from low vascular risk profiles, as well as from healthy controls. MCI subjects with high and low vascular risk profiles showed differed alteration patterns in the anterior CC, which may help to elucidate the inter-wired relationship between MCI and vascular risk factors.
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Affiliation(s)
- Yi Lao
- CIBORG Laboratory, Department of Radiology, Children's Hospital Los Angeles, USA
- Department of Biomedical Engineering, University of Southern California, USA
| | - Binh Nguyen
- CIBORG Laboratory, Department of Radiology, Children's Hospital Los Angeles, USA
| | - Sinchai Tsao
- CIBORG Laboratory, Department of Radiology, Children's Hospital Los Angeles, USA
| | - Niharika Gajawelli
- CIBORG Laboratory, Department of Radiology, Children's Hospital Los Angeles, USA
- Department of Biomedical Engineering, University of Southern California, USA
| | - Meng Law
- Department of Biomedical Engineering, University of Southern California, USA
- Department of Radiology, Keck School of Medicine, University of Southern California, USA
| | - Helena Chui
- Department of Biomedical Engineering, University of Southern California, USA
- Department of Radiology, Keck School of Medicine, University of Southern California, USA
| | - Michael Weiner
- Department of Radiology, University of California, San Francisco, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, USA
| | - Natasha Leporé
- CIBORG Laboratory, Department of Radiology, Children's Hospital Los Angeles, USA
- Department of Biomedical Engineering, University of Southern California, USA
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21
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Chai Y, Lao Y, Li Y, Ji C, O'Neil S, Wang Y, Lepore N, Wood J. Multivariate surface-based analysis of corpus callosum in patients with sickle cell disease. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2016; 10160:101600A. [PMID: 31178616 PMCID: PMC6554202 DOI: 10.1117/12.2257399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Sickle cell disease (SCD) is a genetic hematological disease in which the hemoglobin molecule in red blood cells is abnormal. It is closely associated with many symptoms, including pain, anemia, chest syndrome and neurocognitive impairment. One of the most debilitating symptoms is elevated risk for cerebro-vascular accidents. The corpus callosum (CC), as the largest and most prominent white matter (WM) structure in the brain, can reflect the chronic cerebrovascular damage resulting from silent strokes or infarctions in asymptomatic SCD patients. While a lot of studies have reported WM alterations in this cohort, little is known about the shape deformation of the CC. Here we perform the first surface morphometry analysis of the CC in SCD patients using four different shape metrics on T1-weighted magnetic resonance images. We detect regional surface morphological differences in the CC between 11 patients and 10 healthy control subjects. Differences are located in the genu, posterior midbody and splenium, potentially casting light on the anatomical substrates underlying neuropsychological test differences between the SCD and control groups.
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Affiliation(s)
- Yaqiong Chai
- CIBORG laboratory, Department of Radiology, Children's Hospital Los Angeles, CA, USA
- Department of Radiology, University of Southern California, CA, USA
- Department of Biomedical Engineering, University of Southern California, CA, USA
| | - Yi Lao
- CIBORG laboratory, Department of Radiology, Children's Hospital Los Angeles, CA, USA
- Department of Radiology, University of Southern California, CA, USA
- Department of Biomedical Engineering, University of Southern California, CA, USA
| | - Yicen Li
- Department of Electrical Engineering, University of Southern California, CA, USA
| | - Chaoran Ji
- Department of Electrical Engineering, University of Southern California, CA, USA
| | - Sharon O'Neil
- CIBORG laboratory, Department of Radiology, Children's Hospital Los Angeles, CA, USA
- Department of Radiology, University of Southern California, CA, USA
- Department of Biomedical Engineering, University of Southern California, CA, USA
- Department of Electrical Engineering, University of Southern California, CA, USA
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
- Division of Cardiology, Children's Hospital Los Angeles, CA, USA
| | - Yalin Wang
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Natasha Lepore
- CIBORG laboratory, Department of Radiology, Children's Hospital Los Angeles, CA, USA
- Department of Radiology, University of Southern California, CA, USA
- Department of Biomedical Engineering, University of Southern California, CA, USA
| | - John Wood
- Division of Cardiology, Children's Hospital Los Angeles, CA, USA
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22
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Vlasova R, Gajawelli N, Wang Y, Dirks H, Dean D, O'Muircheartaigh J, Lao Y, Yoon J, Nelson MD, Deoni S, Lepore N. Putamen Development in Children 12 to 21 Months Old. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2016; 10160. [PMID: 31178618 DOI: 10.1117/12.2257278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
We studied the developmental trajectory of the putamen in 13-21 months old children using multivariate surface tensor-based morphometry. Our results indicate surface changes between 12 and 15 months' age groups in the middle superior part the left putamen. The growth of the left putamen at earlier ages slows down after 15 months. The most important surface changes were detected in the right putamen between 18 and 21 months and were located in the anterior part of the structure. Our results demonstrate the heterochronic growth of the right and left putamen related to different functional subregions within putamen. Our results are compatible with previous studies devoted to total putamen volume changes during normal development.
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Affiliation(s)
- Roza Vlasova
- CIBORG Lab, Department of Radiology, Children's Hospital Los Angeles, CA, USA
| | - Niharika Gajawelli
- CIBORG Lab, Department of Radiology, Children's Hospital Los Angeles, CA, USA.,Department of Biomedical Engineering, University of Southern California, CA, USA
| | - Yalin Wang
- Department of Computer Science, Arizona State University, AZ, USA
| | - Holly Dirks
- Department of Biomedical Engineering, Brown University, RI, USA
| | - Douglas Dean
- Department of Biomedical Engineering, Brown University, RI, USA
| | | | - Yi Lao
- CIBORG Lab, Department of Radiology, Children's Hospital Los Angeles, CA, USA.,Department of Biomedical Engineering, University of Southern California, CA, USA
| | - James Yoon
- CIBORG Lab, Department of Radiology, Children's Hospital Los Angeles, CA, USA.,Department of Biological Sciences, University of Southern California, CA, USA
| | - Marvin D Nelson
- Department of Radiology, University of Southern California, CA, USA.,Department of Radiology, Children's Hospital Los Angeles, CA, USA
| | - Sean Deoni
- Department of Pediatric Radiology Research, Children's Hospital Colorado, CO, USA.,Department of Biomedical Engineering, Brown University, RI, USA
| | - Natasha Lepore
- CIBORG Lab, Department of Radiology, Children's Hospital Los Angeles, CA, USA.,Department of Biomedical Engineering, University of Southern California, CA, USA.,Department of Radiology, University of Southern California, CA, USA
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23
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Panigrahy A, Lee V, Ceschin R, Zuccoli G, Beluk N, Khalifa O, Votava-Smith JK, DeBrunner M, Munoz R, Domnina Y, Morell V, Wearden P, De Toledo JS, Devine W, Zahid M, Lo CW. Brain Dysplasia Associated with Ciliary Dysfunction in Infants with Congenital Heart Disease. J Pediatr 2016; 178:141-148.e1. [PMID: 27574995 PMCID: PMC5085835 DOI: 10.1016/j.jpeds.2016.07.041] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Revised: 06/03/2016] [Accepted: 07/27/2016] [Indexed: 12/11/2022]
Abstract
OBJECTIVE To test for associations between abnormal respiratory ciliary motion (CM) and brain abnormalities in infants with congenital heart disease (CHD) STUDY DESIGN: We recruited 35 infants with CHD preoperatively and performed nasal tissue biopsy to assess respiratory CM by videomicroscopy. Cranial ultrasound scan and brain magnetic resonance imaging were obtained pre- and/or postoperatively and systematically reviewed for brain abnormalities. Segmentation was used to quantitate cerebrospinal fluid and regional brain volumes. Perinatal and perioperative clinical variables were collected. RESULTS A total of 10 (28.5%) patients with CHD had abnormal CM. Abnormal CM was not associated with brain injury but was correlated with increased extraaxial cerebrospinal fluid volume (P < .001), delayed brain maturation (P < .05), and a spectrum of subtle dysplasia including the hippocampus (P < .0078) and olfactory bulb (P < .034). Abnormal CM was associated with higher composite dysplasia score (P < .001), and both were correlated with elevated preoperative serum lactate (P < .001). CONCLUSIONS Abnormal respiratory CM in infants with CHD is associated with a spectrum of brain dysplasia. These findings suggest that ciliary defects may play a role in brain dysplasia in patients with CHD and have the potential to prognosticate neurodevelopmental risks.
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Affiliation(s)
- Ashok Panigrahy
- Department of Pediatric Radiology, Childrens Hospital of Pittsburgh of University of Pittsburgh Medical Center and University of Pittsburgh School of Medicine, Pittsburgh, PA; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA.
| | - Vincent Lee
- Department of Pediatric Radiology, Childrens Hospital of Pittsburgh of UPMC and University of Pittsburgh School of Medicine
| | - Rafael Ceschin
- Department of Pediatric Radiology, Childrens Hospital of Pittsburgh of UPMC and University of Pittsburgh School of Medicine,Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA
| | - Giulio Zuccoli
- Department of Pediatric Radiology, Childrens Hospital of Pittsburgh of UPMC and University of Pittsburgh School of Medicine
| | - Nancy Beluk
- Department of Pediatric Radiology, Childrens Hospital of Pittsburgh of UPMC and University of Pittsburgh School of Medicine
| | - Omar Khalifa
- Dept. of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Jodie K Votava-Smith
- Department of Pediatric, Division of Cardiology, Childrens Hospital of Los Angeles., Los Angeles, CA
| | - Mark DeBrunner
- Division of Pediatric Cardiology, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Ricardo Munoz
- Cardiac Intensive Care Division, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Yuliya Domnina
- Cardiac Intensive Care Division, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Victor Morell
- Division of Pediatric Cardiothoracic Surgery, Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Peter Wearden
- Division of Pediatric Cardiothoracic Surgery, Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Joan Sanchez De Toledo
- Cardiac Intensive Care Division, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - William Devine
- Dept. of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Maliha Zahid
- Dept. of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Cecilia W. Lo
- Dept. of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA
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24
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Influence of APOE Genotype on Hippocampal Atrophy over Time - An N=1925 Surface-Based ADNI Study. PLoS One 2016; 11:e0152901. [PMID: 27065111 PMCID: PMC4827849 DOI: 10.1371/journal.pone.0152901] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Accepted: 03/21/2016] [Indexed: 11/25/2022] Open
Abstract
The apolipoprotein E (APOE) e4 genotype is a powerful risk factor for late-onset Alzheimer’s disease (AD). In the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, we previously reported significant baseline structural differences in APOE e4 carriers relative to non-carriers, involving the left hippocampus more than the right—a difference more pronounced in e4 homozygotes than heterozygotes. We now examine the longitudinal effects of APOE genotype on hippocampal morphometry at 6-, 12- and 24-months, in the ADNI cohort. We employed a new automated surface registration system based on conformal geometry and tensor-based morphometry. Among different hippocampal surfaces, we computed high-order correspondences, using a novel inverse-consistent surface-based fluid registration method and multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance. At each time point, using Hotelling’s T2 test, we found significant morphological deformation in APOE e4 carriers relative to non-carriers in the full cohort as well as in the non-demented (pooled MCI and control) subjects at each follow-up interval. In the complete ADNI cohort, we found greater atrophy of the left hippocampus than the right, and this asymmetry was more pronounced in e4 homozygotes than heterozygotes. These findings, combined with our earlier investigations, demonstrate an e4 dose effect on accelerated hippocampal atrophy, and support the enrichment of prevention trial cohorts with e4 carriers.
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25
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Shi J, Wang Y, Lao Y, Ceschin R, Mi L, Nelson MD, Panigrahy A, Leporé N. Abnormal Ventricular Development in Preterm Neonates with Visually Normal MRIs. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2015; 9681. [PMID: 31178622 DOI: 10.1117/12.2213297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Children born preterm are at risk for a wide range of neurocognitive and neurobehavioral disorders. Some of these may stem from early brain abnormalities at the neonatal age. Hence, a precise characterization of neonatal neuroanatomy may help inform treatment strategies. In particular, the ventricles are often enlarged in neurocognitive disorders, due to atrophy of surrounding tissues. Here we present a new pipeline for the detection of morphological and relative pose differences in the ventricles of premature neonates compared to controls. To this end, we use a new hyperbolic Ricci flow based mapping of the ventricular surfaces of each subjects to the Poincaré disk. Resulting surfaces are then registered to a template, and a between group comparison is performed using mulitvariate tensor-based morphometry. We also statistically compare the relative pose of the ventricles within the brain between the two groups, by performing a Procrustes alignment between each subject's ventricles and an average shape. For both types of analyses, differences were found in the left ventricles between the two groups.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Yi Lao
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA.,CIBORG Laboratory, Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Rafael Ceschin
- Department of Radiology, Children's Hospital of Pittsburgh UPMC, Pittsburgh, PA, USA
| | - Liang Mi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Marvin D Nelson
- Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA.,Department of Radiology, University of Southern California, CA, USA
| | - Ashok Panigrahy
- Department of Radiology, Children's Hospital of Pittsburgh UPMC, Pittsburgh, PA, USA.,Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Natasha Leporé
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA.,CIBORG Laboratory, Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA.,Department of Radiology, University of Southern California, CA, USA
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