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Ng C, Huang P, Cho Y, Lee P, Liu Y, Chang T. Frontoparietal and salience network synchronizations during nonsymbolic magnitude processing predict brain age and mathematical performance in youth. Hum Brain Mapp 2024; 45:e26777. [PMID: 39046114 PMCID: PMC11267564 DOI: 10.1002/hbm.26777] [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: 08/18/2023] [Revised: 06/03/2024] [Accepted: 06/19/2024] [Indexed: 07/25/2024] Open
Abstract
The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify markers for functional impairments and atypical development. Among human cognitive systems, nonsymbolic magnitude representations serve as a foundational building block for future success in mathematical learning and achievement for individuals. Using task-based frontoparietal (FPN) and salience network (SN) features during nonsymbolic magnitude processing alongside machine learning algorithms, we developed a framework to construct brain age prediction models for participants aged 7-30. Our study revealed differential developmental profiles in the synchronization within and between FPN and SN networks. Specifically, we observed a linear increase in FPN connectivity, concomitant with a decline in SN connectivity across the age span. A nonlinear U-shaped trajectory in the connectivity between the FPN and SN was discerned, revealing reduced FPN-SN synchronization among adolescents compared to both pediatric and adult cohorts. Leveraging the Gradient Boosting machine learning algorithm and nested fivefold stratified cross-validation with independent training datasets, we demonstrated that functional connectivity measures of the FPN and SN nodes predict chronological age, with a correlation coefficient of .727 and a mean absolute error of 2.944 between actual and predicted ages. Notably, connectivity within the FPN emerged as the most contributing feature for age prediction. Critically, a more matured brain age estimate is associated with better arithmetic performance. Our findings shed light on the intricate developmental changes occurring in the neural networks supporting magnitude representations. We emphasize brain age estimation as a potent tool for understanding cognitive development and its relationship to mathematical abilities across the critical developmental period of youth. PRACTITIONER POINTS: This study investigated the prolonged changes in the brain's architecture across childhood, adolescence, and adulthood, with a focus on task-state frontoparietal and salience networks. Distinct developmental pathways were identified: frontoparietal synchronization strengthens consistently throughout development, while salience network connectivity diminishes with age. Furthermore, adolescents show a unique dip in connectivity between these networks. Leveraging advanced machine learning methods, we accurately predicted individuals' ages based on these brain circuits, with a more mature estimated brain age correlating with better math skills.
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Affiliation(s)
- Chan‐Tat Ng
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
| | - Po‐Hsien Huang
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
| | - Yi‐Cheng Cho
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
| | - Pei‐Hong Lee
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
| | - Yi‐Chang Liu
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
| | - Ting‐Ting Chang
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
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2
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Zhao L, Zhu D, Wang X, Liu X, Li T, Wang B, Yao Z, Zheng W, Hu B. An Attention-Based Hemispheric Relation Inference Network for Perinatal Brain Age Prediction. IEEE J Biomed Health Inform 2024; 28:4483-4493. [PMID: 38857141 DOI: 10.1109/jbhi.2024.3411620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024]
Abstract
Brain anatomical age is an effective feature to assess the status of the brain, such as atypical development and aging. Although some deep learning models have been developed for estimating infant brain age, the performance of these models was unsatisfactory because few of them considered the developmental characteristics of brain anatomy during the perinatal period-the most rapid and complex developmental stage across the lifespan. The present study proposed an attention-based hemispheric relation inference network (HRINet) that takes advantage of the nature of brain structural lateralization during early development. This model captures the inter-hemispheric relationship using a graph attention mechanism and transmits lateralization information as features to describe the interactive development between bilateral hemispheres. The HRINet was used to estimate the brain age of 531 preterm and full-term neonates from the Developing Human Connectome Project (dHCP) database based on two metrics (mean curvature and sulcal depth) characterizing the folding morphology of the cortex. Our results showed that the HRINet outperformed other benchmark models in fitting the perinatal brain age, with mean absolute error of 0.53 and determination coefficient of 0.89. We also verified the generalizability of the HRINet on an extra independent dataset collected from the Gansu Provincial Maternity and Child-care Hospital. Furthermore, by applying the best-performing model to an independent dataset consisting of 47 scans of preterm infants at term-equivalent age, we showed that the predicted age was significantly lower than the chronological age, suggesting a delayed development of premature brains. Our results demonstrate the effectiveness and generalizability of the HRINet in estimating infant brain age, providing promising clinical applications for assessing neonatal brain maturity.
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3
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Damera SR, De Asis-Cruz J, Cook KM, Kapse K, Spoehr E, Murnick J, Basu S, Andescavage N, Limperopoulos C. Regional homogeneity as a marker of sensory cortex dysmaturity in preterm infants. iScience 2024; 27:109662. [PMID: 38665205 PMCID: PMC11043889 DOI: 10.1016/j.isci.2024.109662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 01/23/2024] [Accepted: 04/01/2024] [Indexed: 04/28/2024] Open
Abstract
Atypical perinatal sensory experience in preterm infants is thought to increase their risk of neurodevelopmental disabilities by altering the development of the sensory cortices. Here, we used resting-state fMRI data from preterm and term-born infants scanned between 32 and 48 weeks post-menstrual age to assess the effect of early ex-utero exposure on sensory cortex development. Specifically, we utilized a measure of local correlated-ness called regional homogeneity (ReHo). First, we demonstrated that the brain-wide distribution of ReHo mirrors the known gradient of cortical maturation. Next, we showed that preterm birth differentially reduces ReHo across the primary sensory cortices. Finally, exploratory analyses showed that the reduction of ReHo in the primary auditory cortex of preterm infants is related to increased risk of autism at 18 months. In sum, we show that local connectivity within sensory cortices has different developmental trajectories, is differentially affected by preterm birth, and may be associated with later neurodevelopment.
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Affiliation(s)
- Srikanth R. Damera
- Developing Brain Institute, Children’s National, 111 Michigan Avenue NW, Washington, DC 20010, USA
| | - Josepheen De Asis-Cruz
- Developing Brain Institute, Children’s National, 111 Michigan Avenue NW, Washington, DC 20010, USA
| | - Kevin M. Cook
- Developing Brain Institute, Children’s National, 111 Michigan Avenue NW, Washington, DC 20010, USA
| | - Kushal Kapse
- Developing Brain Institute, Children’s National, 111 Michigan Avenue NW, Washington, DC 20010, USA
| | - Emma Spoehr
- Developing Brain Institute, Children’s National, 111 Michigan Avenue NW, Washington, DC 20010, USA
| | - Jon Murnick
- Developing Brain Institute, Children’s National, 111 Michigan Avenue NW, Washington, DC 20010, USA
| | - Sudeepta Basu
- Developing Brain Institute, Children’s National, 111 Michigan Avenue NW, Washington, DC 20010, USA
| | - Nickie Andescavage
- Developing Brain Institute, Children’s National, 111 Michigan Avenue NW, Washington, DC 20010, USA
| | - Catherine Limperopoulos
- Developing Brain Institute, Children’s National, 111 Michigan Avenue NW, Washington, DC 20010, USA
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4
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Zhao H, Cai H, Liu M. Transformer based multi-modal MRI fusion for prediction of post-menstrual age and neonatal brain development analysis. Med Image Anal 2024; 94:103140. [PMID: 38461655 DOI: 10.1016/j.media.2024.103140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/23/2023] [Accepted: 03/05/2024] [Indexed: 03/12/2024]
Abstract
The brain development during the perinatal period is characterized by rapid changes in both structure and function, which have significant impact on the cognitive and behavioral abilities later in life. Accurate assessment of brain age is a crucial indicator for brain development maturity and can help predict the risk of neonatal pathology. However, evaluating neonatal brains using magnetic resonance imaging (MRI) is challenging due to its complexity, multi-dimension, and noise with subtle alterations. In this paper, we propose a multi-modal deep learning framework based on transformers for precise post-menstrual age (PMA) estimation and brain development analysis using T2-weighted structural MRI (T2-sMRI) and diffusion MRI (dMRI) data. First, we build a two-stream dense network to learn modality-specific features from T2-sMRI and dMRI of brain individually. Then, a transformer module based on self-attention mechanism integrates these features for PMA prediction and preterm/term classification. Finally, saliency maps on brain templates are used to enhance the interpretability of results. Our method is evaluated on the multi-modal MRI dataset of the developing Human Connectome Project (dHCP), which contains 592 neonates, including 478 term-born and 114 preterm-born subjects. The results demonstrate that our method achieves a 0.5-week mean absolute error (MAE) in PMA estimation for term-born subjects. Notably, preterm-born subjects exhibit delayed brain development, worsening with increasing prematurity. Our method also achieves 95% accuracy in classification of term-born and preterm-born subjects, revealing significant group differences.
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Affiliation(s)
- Haiyan Zhao
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Hongjie Cai
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Manhua Liu
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai, China.
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5
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Sun H, Mehta S, Khaitova M, Cheng B, Hao X, Spann M, Scheinost D. Brain age prediction and deviations from normative trajectories in the neonatal connectome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590811. [PMID: 38712238 PMCID: PMC11071351 DOI: 10.1101/2024.04.23.590811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Structural and functional connectomes undergo rapid changes during the third trimester and the first month of postnatal life. Despite progress, our understanding of the developmental trajectories of the connectome in the perinatal period remains incomplete. Brain age prediction uses machine learning to estimate the brain's maturity relative to normative data. The difference between the individual's predicted and chronological age-or brain age gap (BAG)-represents the deviation from these normative trajectories. Here, we assess brain age prediction and BAGs using structural and functional connectomes for infants in the first month of life. We used resting-state fMRI and DTI data from 611 infants (174 preterm; 437 term) from the Developing Human Connectome Project (dHCP) and connectome-based predictive modeling to predict postmenstrual age (PMA). Structural and functional connectomes accurately predicted PMA for term and preterm infants. Predicted ages from each modality were correlated. At the network level, nearly all canonical brain networks-even putatively later developing ones-generated accurate PMA prediction. Additionally, BAGs were associated with perinatal exposures and toddler behavioral outcomes. Overall, our results underscore the importance of normative modeling and deviations from these models during the perinatal period.
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Irzan H, Hütel M, O'Reilly H, Ourselin S, Marlow N, Melbourne A. Multi-source multi-modal markers for Bayesian Networks: Application to the extremely preterm born brain. Med Image Anal 2024; 92:103037. [PMID: 38056163 PMCID: PMC7616207 DOI: 10.1016/j.media.2023.103037] [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: 02/02/2022] [Revised: 10/26/2023] [Accepted: 11/15/2023] [Indexed: 12/08/2023]
Abstract
The preterm phenotype results from the interplay of multiple disorders affecting the brain and cognitive outcomes. Accurately characterising these interactions can reveal prematurity markers. Bayesian Networks (BNs) are powerful tools to disentangle these relationships, as they inherently measure associations between variables while mitigating confounding factors. We present Modified PC-HC (MPC-HC), a Bayesian Network (BN) structural learning algorithm. MPC-HC employs statistical testing and search-and-score techniques to explore equivalent classes. We employ MPC-HC to estimate BNs for extremely preterm (EP) young adults and full-term controls. Using MRI measurements and cognitive performance markers, we investigate predictive relationships and mutual influences through predictions and sensitivity analysis. We assess the confidence in the estimated BN structures using bootstrapping. Furthermore, MPC-HC's validation involves assessing its ability to recover benchmark BN structures. MPC-HC achieves an average prediction accuracy of 72.5% compared to 62.5% of PC, 64.5% of MMHC, and 71.5% of HC, while it outperforms PC, MMHC, and HC algorithms in reconstructing the true structure of benchmark BNs. The sensitivity analysis shows that MRI measurements mainly affect EP cognitive scores. Our work has two key contributions: first, the introduction and validation of a new BN structure learning method. Second, demonstrating the potential of BNs in modelling variable relationships, predicting variables of interest, modelling uncertainty, and evaluating how variables impact each other. Finally, we demonstrate this by characterising complex phenotypes, such as preterm birth, and discovering results consistent with literature findings.
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Affiliation(s)
- Hassna Irzan
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE17EU, UK; Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E6BT, UK.
| | - Michael Hütel
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE17EU, UK
| | - Helen O'Reilly
- Institute for Women's Health, University College London, London, WC1E6HU, UK; Department of Psychology, University College Dublin, Dublin, D04C1P1, Ireland
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE17EU, UK
| | - Neil Marlow
- Institute for Women's Health, University College London, London, WC1E6HU, UK
| | - Andrew Melbourne
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE17EU, UK
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7
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Guan S, Jiang R, Meng C, Biswal B. Brain age prediction across the human lifespan using multimodal MRI data. GeroScience 2024; 46:1-20. [PMID: 37733220 PMCID: PMC10828281 DOI: 10.1007/s11357-023-00924-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 08/22/2023] [Indexed: 09/22/2023] Open
Abstract
Measuring differences between an individual's age and biological age with biological information from the brain have the potential to provide biomarkers of clinically relevant neurological syndromes that arise later in human life. To explore the effect of multimodal brain magnetic resonance imaging (MRI) features on the prediction of brain age, we investigated how multimodal brain imaging data improved age prediction from more imaging features of structural or functional MRI data by using partial least squares regression (PLSR) and longevity data sets (age 6-85 years). First, we found that the age-predicted values for each of these ten features ranged from high to low: cortical thickness (R = 0.866, MAE = 7.904), all seven MRI features (R = 0.8594, MAE = 8.24), four features in structural MRI (R = 0.8591, MAE = 8.24), fALFF (R = 0.853, MAE = 8.1918), gray matter volume (R = 0.8324, MAE = 8.931), three rs-fMRI feature (R = 0.7959, MAE = 9.744), mean curvature (R = 0.7784, MAE = 10.232), ReHo (R = 0.7833, MAE = 10.122), ALFF (R = 0.7517, MAE = 10.844), and surface area (R = 0.719, MAE = 11.33). In addition, the significance of the volume and size of brain MRI data in predicting age was also studied. Second, our results suggest that all multimodal imaging features, except cortical thickness, improve brain-based age prediction. Third, we found that the left hemisphere contributed more to the age prediction, that is, the left hemisphere showed a greater weight in the age prediction than the right hemisphere. Finally, we found a nonlinear relationship between the predicted age and the amount of MRI data. Combined with multimodal and lifespan brain data, our approach provides a new perspective for chronological age prediction and contributes to a better understanding of the relationship between brain disorders and aging.
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Affiliation(s)
- Sihai Guan
- College of Electronic and Information, Southwest Minzu University, Chengdu, 610041, China.
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu, 610041, China.
| | - Runzhou Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Medical Equipment Department, Xiangyang No. 1 People's Hospital, Xiangyang, 441000, China
| | - Chun Meng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bharat Biswal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
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Keles E, Bagci U. The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review. NPJ Digit Med 2023; 6:220. [PMID: 38012349 PMCID: PMC10682088 DOI: 10.1038/s41746-023-00941-5] [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: 01/29/2023] [Accepted: 10/05/2023] [Indexed: 11/29/2023] Open
Abstract
Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.
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Affiliation(s)
- Elif Keles
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA.
| | - Ulas Bagci
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA
- Northwestern University, Department of Biomedical Engineering, Chicago, IL, USA
- Department of Electrical and Computer Engineering, Chicago, IL, USA
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Hu L, Wan Q, Huang L, Tang J, Huang S, Chen X, Bai X, Kong L, Deng J, Liang H, Liu G, Liu H, Lu L. MRI-based brain age prediction model for children under 3 years old using deep residual network. Brain Struct Funct 2023; 228:1771-1784. [PMID: 37603065 DOI: 10.1007/s00429-023-02686-z] [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: 03/08/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023]
Abstract
Early identification and intervention of abnormal brain development individual subjects are of great significance, especially during the earliest and most active stage of brain development in children aged under 3. Neuroimage-based brain's biological age has been associated with health, ability, and remaining life. However, the existing brain age prediction models based on neuroimage are predominantly adult-oriented. Here, we collected 658 T1-weighted MRI scans from 0 to 3 years old healthy controls and developed an accurate brain age prediction model for young children using deep learning techniques with high accuracy in capturing age-related changes. The performance of the deep learning-based model is comparable to that of the SVR-based model, showcasing remarkable precision and yielding a noteworthy correlation of 91% between the predicted brain age and the chronological age. Our results demonstrate the accuracy of convolutional neural network (CNN) brain-predicted age using raw T1-weighted MRI data with minimum preprocessing necessary. We also applied our model to children with low birth weight, premature delivery history, autism, and ADHD, and discovered that the brain age was delayed in children with extremely low birth weight (less than 1000 g) while ADHD may cause accelerated aging of the brain. Our child-specific brain age prediction model can be a valuable quantitative tool to detect abnormal brain development and can be helpful in the early identification and intervention of age-related brain disorders.
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Affiliation(s)
- Lianting Hu
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Qirong Wan
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, 4330060, Hubei, China
| | - Li Huang
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China
| | - Jiajie Tang
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China
| | - Shuai Huang
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Xuanhui Chen
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Xiaohe Bai
- School of Physical Sciences, University of California San Diego, La Jolla, San Diego, CA, 92093, USA
| | - Lingcong Kong
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Jingyi Deng
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China
| | - Huiying Liang
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Guangjian Liu
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Hongsheng Liu
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China.
| | - Long Lu
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China.
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China.
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Blanchett R, Chen Y, Aguate F, Xia K, Cornea E, Burt SA, de Los Campos G, Gao W, Gilmore JH, Knickmeyer RC. Genetic and environmental factors influencing neonatal resting-state functional connectivity. Cereb Cortex 2023; 33:4829-4843. [PMID: 36190430 PMCID: PMC10110449 DOI: 10.1093/cercor/bhac383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 08/24/2022] [Accepted: 08/25/2022] [Indexed: 11/14/2022] Open
Abstract
Functional magnetic resonance imaging has been used to identify complex brain networks by examining the correlation of blood-oxygen-level-dependent signals between brain regions during the resting state. Many of the brain networks identified in adults are detectable at birth, but genetic and environmental influences governing connectivity within and between these networks in early infancy have yet to be explored. We investigated genetic influences on neonatal resting-state connectivity phenotypes by generating intraclass correlations and performing mixed effects modeling to estimate narrow-sense heritability on measures of within network and between-network connectivity in a large cohort of neonate twins. We also used backwards elimination regression and mixed linear modeling to identify specific demographic and medical history variables influencing within and between network connectivity in a large cohort of typically developing twins and singletons. Of the 36 connectivity phenotypes examined, only 6 showed narrow-sense heritability estimates greater than 0.10, with none being statistically significant. Demographic and obstetric history variables contributed to between- and within-network connectivity. Our results suggest that in early infancy, genetic factors minimally influence brain connectivity. However, specific demographic and medical history variables, such as gestational age at birth and maternal psychiatric history, may influence resting-state connectivity measures.
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Affiliation(s)
- Reid Blanchett
- Genetics and Genome Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Yuanyuan Chen
- Biomedical Imaging Research Institute, Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Fernando Aguate
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Kai Xia
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - S Alexandra Burt
- Department of Psychology, Michigan State University, East Lansing, MI 48824, USA
| | - Gustavo de Los Campos
- Departments of Epidemiology and Biostatistics and Statistics and Probability, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Wei Gao
- Biomedical Imaging Research Institute, Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Rebecca C Knickmeyer
- Department of Pediatrics and Human Development, Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI 48824, USA
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Zhang W, Yang C, Cao Z, Li Z, Zhuo L, Tan Y, He Y, Yao L, Zhou Q, Gong Q, Sweeney JA, Shi F, Lui S. Detecting individuals with severe mental illness using artificial intelligence applied to magnetic resonance imaging. EBioMedicine 2023; 90:104541. [PMID: 36996601 PMCID: PMC10063405 DOI: 10.1016/j.ebiom.2023.104541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 03/10/2023] [Accepted: 03/10/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND Identifying individuals at risk for severe mental illness (SMI) is crucial for prevention and early intervention strategies. While MRI shows potential for case identification even before illness onset, no practical model for mental health risk monitoring has been developed. This study aims to develop an initial version of an efficient and practical model for mental health screening among at-risk populations. METHODS A deep learning model known as Multiple Instance Learning (MIL) was adopted to train and test a SMI detection model with clinical MRI scans of 14,915 patients with SMI (age 32.98 ± 12.01 years, 9102 women) and 4538 healthy controls (age 40.60 ± 10.95 years, 2424 women) in the primary dataset. Validation analysis was conducted in an independent dataset with 290 patients (age 28.08 ± 10.95 years, 169 women) and 310 healthy participants (age 33.55 ± 11.09 years, 165 women). Another three machine learning models of ResNet, DenseNet and EfficientNet were used for comparison. We also recruited 148 individuals receiving high-stress medical school education to characterize the potential real-world utility of the MIL model in detecting risk of mental illness. FINDINGS Similar performance of successful differentiation of individuals with SMI and healthy controls was observed for the MIL model (AUC: 0.82) and other models (ResNet, DenseNet, EfficientNet, 0.83, 0.81, and 0.80 respectively). MIL had better generalization in the validation test than other models (AUC: 0.82 vs 0.59, 0.66 and 0.59), and less drop-off in performance from 3.0T to 1.5T scanners. The MIL model did better in predicting clinician ratings of distress than self-ratings with questionnaires (84% vs 22%) in the medical student sample. Brain regions that contributed to SMI identification were mainly neocortical, including right precuneus, bilateral temporal regions, left precentral/postcentral gyrus, bilateral medial prefrontal cortex and right cerebellum. INTERPRETATION Our digital model based on brief clinical MRI protocols identified individual SMI patients with good accuracy and high sensitivity, suggesting that with incremental improvements the approach may offer potentially useful aid for early identification and intervention to prevent illness onset in vulnerable at-risk populations. FUNDING This study was supported by the National Natural Science Foundation of China, National Key Technologies R&D Program of China, and Sichuan Science and Technology Program.
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Huang CC, Chiang HF, Hsieh CC, Chou CL, Jhou ZY, Hou TY, Shaw JS. Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke. Tomography 2023; 9:647-656. [PMID: 36961011 PMCID: PMC10037617 DOI: 10.3390/tomography9020052] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 03/11/2023] [Accepted: 03/13/2023] [Indexed: 03/18/2023] Open
Abstract
BACKGROUND Collateral status is an important predictor for the outcome of acute ischemic stroke with large vessel occlusion. Multiphase computed-tomography angiography (mCTA) is useful to evaluate the collateral status, but visual evaluation of this examination is time-consuming. This study aims to use an artificial intelligence (AI) technique to develop an automatic AI prediction model for the collateral status of mCTA. METHODS This retrospective study enrolled subjects with acute ischemic stroke receiving endovascular thrombectomy between January 2015 and June 2020 in a tertiary referral hospital. The demographic data and images of mCTA were collected. The collateral status of all mCTA was visually evaluated. Images at the basal ganglion and supraganglion levels of mCTA were selected to produce AI models using the convolutional neural network (CNN) technique to automatically predict the collateral status of mCTA. RESULTS A total of 82 subjects were enrolled. There were 57 cases randomly selected for the training group and 25 cases for the validation group. In the training group, there were 40 cases with a positive collateral result (good or intermediate) and 17 cases with a negative collateral result (poor). In the validation group, there were 21 cases with a positive collateral result and 4 cases with a negative collateral result. During training for the CNN prediction model, the accuracy of the training group could reach 0.999 ± 0.015, whereas the prediction model had a performance of 0.746 ± 0.008 accuracy on the validation group. The area under the ROC curve was 0.7. CONCLUSIONS This study suggests that the application of the AI model derived from mCTA images to automatically evaluate the collateral status is feasible.
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Affiliation(s)
- Chun-Chao Huang
- Department of Radiology, MacKay Memorial Hospital, Taipei 104217, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City 252005, Taiwan
| | - Hsin-Fan Chiang
- Department of Radiology, MacKay Memorial Hospital, Taipei 104217, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City 252005, Taiwan
- Mackay Junior College of Medicine, Nursing, and Management, Taipei 112021, Taiwan
| | - Cheng-Chih Hsieh
- Department of Radiology, MacKay Memorial Hospital, Taipei 104217, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City 252005, Taiwan
- Mackay Junior College of Medicine, Nursing, and Management, Taipei 112021, Taiwan
| | - Chao-Liang Chou
- Department of Medicine, MacKay Medical College, New Taipei City 252005, Taiwan
- Department of Neurology, MacKay Memorial Hospital, Taipei 104217, Taiwan
| | - Zong-Yi Jhou
- Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei 106344, Taiwan
| | - Ting-Yi Hou
- Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei 106344, Taiwan
| | - Jin-Siang Shaw
- Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei 106344, Taiwan
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13
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Nielsen AN, Kaplan S, Meyer D, Alexopoulos D, Kenley JK, Smyser TA, Wakschlag LS, Norton ES, Raghuraman N, Warner BB, Shimony JS, Luby JL, Neil JJ, Petersen SE, Barch DM, Rogers CE, Sylvester CM, Smyser CD. Maturation of large-scale brain systems over the first month of life. Cereb Cortex 2023; 33:2788-2803. [PMID: 35750056 PMCID: PMC10016041 DOI: 10.1093/cercor/bhac242] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 04/29/2022] [Accepted: 05/23/2022] [Indexed: 01/14/2023] Open
Abstract
The period immediately after birth is a critical developmental window, capturing rapid maturation of brain structure and a child's earliest experiences. Large-scale brain systems are present at delivery, but how these brain systems mature during this narrow window (i.e. first weeks of life) marked by heightened neuroplasticity remains uncharted. Using multivariate pattern classification techniques and functional connectivity magnetic resonance imaging, we detected robust differences in brain systems related to age in newborns (n = 262; R2 = 0.51). Development over the first month of life occurred brain-wide, but differed and was more pronounced in brain systems previously characterized as developing early (i.e. sensorimotor networks) than in those characterized as developing late (i.e. association networks). The cingulo-opercular network was the only exception to this organizing principle, illuminating its early role in brain development. This study represents a step towards a normative brain "growth curve" that could be used to identify atypical brain maturation in infancy.
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Affiliation(s)
- Ashley N Nielsen
- Department of Neurology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Sydney Kaplan
- Department of Neurology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Dominique Meyer
- Department of Neurology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Dimitrios Alexopoulos
- Department of Neurology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Jeanette K Kenley
- Department of Neurology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Tara A Smyser
- Department of Psychiatry, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Lauren S Wakschlag
- Institute for Innovations and Developmental Sciences, Northwestern University, 420 E Superior, Chicago, IL, 60611, USA
- Department of Medical Social Sciences, Northwestern University, 420 E Superior, Chicago, IL, 60611, USA
- Feinberg School of Medicine, Northwestern University, 420 E Superior, Chicago, IL, 60611, USA
| | - Elizabeth S Norton
- Institute for Innovations and Developmental Sciences, Northwestern University, 420 E Superior, Chicago, IL, 60611, USA
- Department of Medical Social Sciences, Northwestern University, 420 E Superior, Chicago, IL, 60611, USA
- Department of Communication Sciences and Disorders, Northwestern University, 420 E Superior, Chicago, IL, 60611, USA
| | - Nandini Raghuraman
- Department of Obstetrics and Gynecology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Barbara B Warner
- Department of Pediatrics, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Joshua S Shimony
- Department of Radiology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Joan L Luby
- Department of Psychiatry, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Jeffery J Neil
- Department of Neurology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Department of Radiology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Steven E Petersen
- Department of Neurology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Department of Radiology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Department of Psychological and Brain Sciences, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Cynthia E Rogers
- Department of Communication Sciences and Disorders, Northwestern University, 420 E Superior, Chicago, IL, 60611, USA
- Department of Radiology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Christopher D Smyser
- Department of Neurology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Department of Pediatrics, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
- Department of Radiology, Washington University in St. Louis, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
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Porter A, Nielsen A, Dorn M, Dworetsky A, Edmonds D, Gratton C. Masked features of task states found in individual brain networks. Cereb Cortex 2023; 33:2879-2900. [PMID: 35802477 PMCID: PMC10016040 DOI: 10.1093/cercor/bhac247] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 05/24/2022] [Accepted: 05/24/2022] [Indexed: 11/14/2022] Open
Abstract
Completing complex tasks requires that we flexibly integrate information across brain areas. While studies have shown how functional networks are altered during different tasks, this work has generally focused on a cross-subject approach, emphasizing features that are common across people. Here we used extended sampling "precision" fMRI data to test the extent to which task states generalize across people or are individually specific. We trained classifiers to decode state using functional network data in single-person datasets across 5 diverse task states. Classifiers were then tested on either independent data from the same person or new individuals. Individualized classifiers were able to generalize to new participants. However, classification performance was significantly higher within a person, a pattern consistent across model types, people, tasks, feature subsets, and even for decoding very similar task conditions. Notably, these findings also replicated in a new independent dataset. These results suggest that individual-focused approaches can uncover robust features of brain states, including features obscured in cross-subject analyses. Individual-focused approaches have the potential to deepen our understanding of brain interactions during complex cognition.
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Affiliation(s)
- Alexis Porter
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
| | - Ashley Nielsen
- Department of Neurology, Washington University in St. Louis, 1 Brookings Dr, St. Louis, MO 63130, United States
| | - Megan Dorn
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
| | - Ally Dworetsky
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
| | - Donnisa Edmonds
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
| | - Caterina Gratton
- Department of Psychology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
- Department of Neurology, Northwestern University, 633 Clark St, Evanston, IL 60208, United States
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Cai H, Li A, Yu G, Yang X, Liu M. Brain Age Prediction in Developing Childhood with Multimodal Magnetic Resonance Images. Neuroinformatics 2023; 21:5-19. [PMID: 35962180 DOI: 10.1007/s12021-022-09596-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/01/2022] [Indexed: 11/30/2022]
Abstract
It is well known that brain development is very fast and complex in the early childhood with age-based neurological and physiological changes of brain structure and function. The brain maturity is an important indicator for evaluating the normal development of children. In this paper, we propose a multimodal regression framework to combine the features from structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI) data for age prediction of children. First, three types of features are extracted from sMRI and DTI data. Second, we propose to combine the sparse coding and Q-Learning for feature selection from each modality. Finally, the ensemble regression is performed by random forest based on proximity measures to fuse multimodal features for age prediction. The proposed method is evaluated on 212 participants, including 76 young children less than 2 years old and 136 children aged from 2-15 years old recruited from Shanghai Children's Hospital. The results show that integrating multimodal features has achieved the highest accuracies with the root mean squared error (RMSE) of 0.208 years and mean absolute error (MAE) of 0.150 years for age prediction of young children (0-2), and RMSE of 1.666 years and MAE of 1.087 years for older children (2-15). We have shown that the selected features by Q-Learning can consistently improve the prediction accuracy. The comparison of prediction results demonstrates that the proposed method performs better than other competing methods.
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Affiliation(s)
- Hongjie Cai
- School of EIEE, Shanghai Jiao Tong University, Shanghai, China
| | - Aojie Li
- School of EIEE, Shanghai Jiao Tong University, Shanghai, China
| | - Guangjun Yu
- Department of Child Health Care, Shanghai Children's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200062, China
| | - Xiujun Yang
- Department of Radiology, Shanghai Children's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200062, China.
| | - Manhua Liu
- School of EIEE, Shanghai Jiao Tong University, Shanghai, China. .,MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.
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16
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Bedside tracking of functional autonomic age in preterm infants. Pediatr Res 2022:10.1038/s41390-022-02376-2. [PMID: 36376508 DOI: 10.1038/s41390-022-02376-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 09/27/2022] [Accepted: 10/23/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Preterm birth predisposes infants to adverse outcomes that, without early intervention, impacts their long-term health. To assist bedside monitoring, we developed a tool to track the autonomic maturation of the preterm by assessing heart rate variability (HRV) changes during intensive care. METHODS Electrocardiogram (ECG) recordings were longitudinally recorded in 67 infants (26-38 weeks postmenstrual age (PMA)). Supervised machine learning was used to generate a functional autonomic age (FAA), by combining 50 computed HRV features from successive 5-minute ECG epochs (median of 23 epochs per infant). Performance of the FAA was assessed by correlation to PMA, clinical outcomes and the infant's functional brain age (FBA), an index of maturation derived from the electroencephalogram. RESULTS The FAA was strongly correlated to PMA (r = 0.86, 95% CI: 0.83-0.93) with a mean absolute error (MAE) of 1.66 weeks and also accurately estimated FBA (MAE = 1.58 weeks, n = 54 infants). The relationship between PMA and FAA was not confounded by neurodevelopmental outcome (p = 0.18, n = 45), sex (p = 0.88, n = 56), patent ductus arteriosus (p = 0.08, n = 56), IVH (p = 0.63, n = 56) or body weight at birth (p = 0.95, n = 56). CONCLUSIONS The FAA, an index derived from the ubiquitous ECG signal, offers direct avenues towards estimating autonomic maturation at the bedside during intensive care monitoring. IMPACT The development of a tool to track functional autonomic age in preterm infants based on heart rate variability features in the electrocardiogram provides a rapid and specialized view of autonomic maturation at the bedside. Functional autonomic age is linked closely to postmenstrual age and central nervous system function response, as determined by its relationship to functional brain age from the electroencephalogram. Tracking functional autonomic age during neonatal intensive care unit monitoring offers a unique insight into cardiovascular health in infants born extremely preterm and their maturational trajectories to term age.
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17
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Cyr PEP, Lean RE, Kenley JK, Kaplan S, Meyer DE, Neil JJ, Alexopoulos D, Brady RG, Shimony JS, Rodebaugh TL, Rogers CE, Smyser CD. Neonatal motor functional connectivity and motor outcomes at age two years in very preterm children with and without high-grade brain injury. Neuroimage Clin 2022; 36:103260. [PMID: 36451363 PMCID: PMC9668638 DOI: 10.1016/j.nicl.2022.103260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 10/09/2022] [Accepted: 11/02/2022] [Indexed: 11/08/2022]
Abstract
Preterm-born children have high rates of motor impairments, but mechanisms for early identification remain limited. We hypothesized that neonatal motor system functional connectivity (FC) would relate to motor outcomes at age two years; currently, this relationship is not yet well-described in very preterm (VPT; born <32 weeks' gestation) infants with and without brain injury. We recruited 107 VPT infants - including 55 with brain injury (grade III-IV intraventricular hemorrhage, cystic periventricular leukomalacia, post-hemorrhagic hydrocephalus) - and collected FC data at/near term-equivalent age (35-45 weeks postmenstrual age). Correlation coefficients were used to calculate the FC between bilateral motor and visual cortices and thalami. At two years corrected-age, motor outcomes were assessed with the Bayley Scales of Infant and Toddler Development, 3rd edition. Multiple imputation was used to estimate missing data, and regression models related FC measures to motor outcomes. Within the brain-injured group only, interhemispheric motor cortex FC was positively related to gross motor outcomes. Thalamocortical and visual FC were not related to motor scores. This suggests neonatal alterations in motor system FC may provide prognostic information about impairments in children with brain injury.
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Affiliation(s)
- Peppar E P Cyr
- Washington University School of Medicine, Department of Neurology, United States.
| | - Rachel E Lean
- Washington University School of Medicine, Department of Psychiatry, United States
| | - Jeanette K Kenley
- Washington University School of Medicine, Department of Neurology, United States
| | - Sydney Kaplan
- Washington University School of Medicine, Department of Neurology, United States
| | - Dominique E Meyer
- Washington University School of Medicine, Department of Neurology, United States
| | - Jeffery J Neil
- Washington University School of Medicine, Department of Neurology, United States
| | | | - Rebecca G Brady
- Washington University School of Medicine, Department of Neurology, United States
| | - Joshua S Shimony
- Washington University School of Medicine, Mallinckrodt Institute of Radiology, United States
| | - Thomas L Rodebaugh
- Washington University in St. Louis, Department of Psychology, United States
| | - Cynthia E Rogers
- Washington University School of Medicine, Department of Psychiatry, United States; Washington University School of Medicine, Department of Pediatrics, United States
| | - Christopher D Smyser
- Washington University School of Medicine, Department of Neurology, United States; Washington University School of Medicine, Mallinckrodt Institute of Radiology, United States; Washington University School of Medicine, Department of Pediatrics, United States
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18
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Li Y, Zhang X, Nie J, Zhang G, Fang R, Xu X, Wu Z, Hu D, Wang L, Zhang H, Lin W, Li G. Brain Connectivity Based Graph Convolutional Networks and Its Application to Infant Age Prediction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2764-2776. [PMID: 35500083 PMCID: PMC10041448 DOI: 10.1109/tmi.2022.3171778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Infancy is a critical period for the human brain development, and brain age is one of the indices for the brain development status associated with neuroimaging data. The difference between the predicted age based on neuroimaging and the chronological age can provide an important early indicator of deviation from the normal developmental trajectory. In this study, we utilize the Graph Convolutional Network (GCN) to predict the infant brain age based on resting-state fMRI data. The brain connectivity obtained from rs-fMRI can be represented as a graph with brain regions as nodes and functional connections as edges. However, since the brain connectivity is a fully connected graph with features on edges, current GCN cannot be directly used for it is a node-based method for sparse graphs. Hence, we propose an edge-based Graph Path Convolution (GPC) method, which aggregates the information from different paths and can be naturally applied on dense graphs. We refer the whole model as Brain Connectivity Graph Convolutional Networks (BC-GCN). Further, two upgraded network structures are proposed by including the residual and attention modules, referred as BC-GCN-Res and BC-GCN-SE to emphasize the information of the original data and enhance influential channels. Moreover, we design a two-stage coarse-to-fine framework, which determines the age group first and then predicts the age using group-specific BC-GCN-SE models. To avoid accumulated errors from the first stage, a cross-group training strategy is adopted for the second stage regression models. We conduct experiments on infant fMRI scans from 6 to 811 days of age. The coarse-to-fine framework shows significant improvements when being applied to several models (reducing error over 10 days). Comparing with state-of-the-art methods, our proposed model BC-GCN-SE with coarse-to-fine framework reduces the mean absolute error of the prediction from >70 days to 49.9 days. The code is now available at https://github.com/SCUT-Xinlab/BC-GCN.
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Kardan O, Kaplan S, Wheelock MD, Feczko E, Day TKM, Miranda-Domínguez Ó, Meyer D, Eggebrecht AT, Moore LA, Sung S, Chamberlain TA, Earl E, Snider K, Graham A, Berman MG, Uğurbil K, Yacoub E, Elison JT, Smyser CD, Fair DA, Rosenberg MD. Resting-state functional connectivity identifies individuals and predicts age in 8-to-26-month-olds. Dev Cogn Neurosci 2022; 56:101123. [PMID: 35751994 PMCID: PMC9234342 DOI: 10.1016/j.dcn.2022.101123] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 05/20/2022] [Accepted: 06/13/2022] [Indexed: 11/23/2022] Open
Abstract
Resting-state functional connectivity (rsFC) measured with fMRI has been used to characterize functional brain maturation in typically and atypically developing children and adults. However, its reliability and utility for predicting development in infants and toddlers is less well understood. Here, we use fMRI data from the Baby Connectome Project study to measure the reliability and uniqueness of rsFC in infants and toddlers and predict age in this sample (8-to-26 months old; n = 170). We observed medium reliability for within-session infant rsFC in our sample, and found that individual infant and toddler's connectomes were sufficiently distinct for successful functional connectome fingerprinting. Next, we trained and tested support vector regression models to predict age-at-scan with rsFC. Models successfully predicted novel infants' age within ± 3.6 months error and a prediction R2 = .51. To characterize the anatomy of predictive networks, we grouped connections into 11 infant-specific resting-state functional networks defined in a data-driven manner. We found that connections between regions of the same network-i.e. within-network connections-predicted age significantly better than between-network connections. Looking ahead, these findings can help characterize changes in functional brain organization in infancy and toddlerhood and inform work predicting developmental outcome measures in this age range.
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Affiliation(s)
| | - Sydney Kaplan
- Washington University in St. Louis School of Medicine, USA
| | | | | | | | | | | | | | | | | | | | - Eric Earl
- Oregon Health & Science University, USA
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Chen JV, Chaudhari G, Hess CP, Glenn OA, Sugrue LP, Rauschecker AM, Li Y. Deep Learning to Predict Neonatal and Infant Brain Age from Myelination on Brain MRI Scans. Radiology 2022; 305:678-687. [DOI: 10.1148/radiol.211860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Joshua Vic Chen
- From the School of Medicine (J.V.C., G.C.) and Department of Radiology and Biomedical Imaging (C.P.H., O.A.G., L.P.S., A.M.R., Y.L.), University of California, San Francisco, 505 Parnassus Avenue, M-391, San Francisco, CA 94143-0628
| | - Gunvant Chaudhari
- From the School of Medicine (J.V.C., G.C.) and Department of Radiology and Biomedical Imaging (C.P.H., O.A.G., L.P.S., A.M.R., Y.L.), University of California, San Francisco, 505 Parnassus Avenue, M-391, San Francisco, CA 94143-0628
| | - Christopher P. Hess
- From the School of Medicine (J.V.C., G.C.) and Department of Radiology and Biomedical Imaging (C.P.H., O.A.G., L.P.S., A.M.R., Y.L.), University of California, San Francisco, 505 Parnassus Avenue, M-391, San Francisco, CA 94143-0628
| | - Orit A. Glenn
- From the School of Medicine (J.V.C., G.C.) and Department of Radiology and Biomedical Imaging (C.P.H., O.A.G., L.P.S., A.M.R., Y.L.), University of California, San Francisco, 505 Parnassus Avenue, M-391, San Francisco, CA 94143-0628
| | - Leo P. Sugrue
- From the School of Medicine (J.V.C., G.C.) and Department of Radiology and Biomedical Imaging (C.P.H., O.A.G., L.P.S., A.M.R., Y.L.), University of California, San Francisco, 505 Parnassus Avenue, M-391, San Francisco, CA 94143-0628
| | - Andreas M. Rauschecker
- From the School of Medicine (J.V.C., G.C.) and Department of Radiology and Biomedical Imaging (C.P.H., O.A.G., L.P.S., A.M.R., Y.L.), University of California, San Francisco, 505 Parnassus Avenue, M-391, San Francisco, CA 94143-0628
| | - Yi Li
- From the School of Medicine (J.V.C., G.C.) and Department of Radiology and Biomedical Imaging (C.P.H., O.A.G., L.P.S., A.M.R., Y.L.), University of California, San Francisco, 505 Parnassus Avenue, M-391, San Francisco, CA 94143-0628
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Radhakrishnan R, Vishnubhotla RV, Guckien Z, Zhao Y, Sokol GM, Haas DM, Sadhasivam S. Thalamocortical functional connectivity in infants with prenatal opioid exposure correlates with severity of neonatal opioid withdrawal syndrome. Neuroradiology 2022; 64:1649-1659. [PMID: 35410397 DOI: 10.1007/s00234-022-02939-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 03/28/2022] [Indexed: 11/26/2022]
Abstract
PURPOSE Prenatal opioid exposure (POE) is a growing public health concern due to its associated adverse outcomes including neonatal opioid withdrawal syndrome (NOWS). The aim of this study was to assess alterations in thalamic functional connectivity in neonates with POE using resting-state functional magnetic resonance imaging (rs-fMRI) and identify whether these altered connectivity measures were associated with NOWS severity. METHODS In this prospective, IRB-approved study, we performed rs-fMRI in 19 infants with POE and 20 healthy control infants without POE. Following standard pre-processing, we performed seed-based functional connectivity analysis with the right and left thalamus as the regions of interest. We performed post hoc analysis in the prenatal opioid exposure group to identify associations of altered thalamocortical connectivity with severity of NOWS. P value of < .05 was considered statistically significant. RESULTS There were several regions of significantly altered thalamic to cortical functional connectivity in infants with POE compared to the healthy infants. Distinct regions of thalamocortical functional connectivity correlated with maximum modified Finnegan score. Association between thalamocortical connectivity and severity of NOWS was nominally modified by maternal psychological conditions and polysubstance use. CONCLUSION Our findings reveal prenatal opioid exposure-related alterations in thalamic functional connectivity in the infant brain that are correlated with severity of NOWS. Future studies may benefit from evaluation of thalamocortical resting state functional connectivity in infants with POE to help stratify risk of long term neurodevelopmental outcomes.
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Affiliation(s)
- Rupa Radhakrishnan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 705 Riley Hospital Drive, Indianapolis, IN, 46202, USA.
| | - Ramana V Vishnubhotla
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 705 Riley Hospital Drive, Indianapolis, IN, 46202, USA
| | - Zoe Guckien
- Indiana University School of Medicine, Indianapolis, IN, USA
| | - Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Gregory M Sokol
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - David M Haas
- Department of Obstetrics and Gynecology, Indiana University School of Medicine, Indianapolis, IN, USA
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22
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Onofrj V, Chiarelli AM, Wise R, Colosimo C, Caulo M. Interaction of the salience network, ventral attention network, dorsal attention network and default mode network in neonates and early development of the bottom-up attention system. Brain Struct Funct 2022; 227:1843-1856. [DOI: 10.1007/s00429-022-02477-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 02/23/2022] [Indexed: 11/29/2022]
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23
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Pringle C, Kilday JP, Kamaly-Asl I, Stivaros SM. The role of artificial intelligence in paediatric neuroradiology. Pediatr Radiol 2022; 52:2159-2172. [PMID: 35347371 PMCID: PMC9537195 DOI: 10.1007/s00247-022-05322-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/22/2021] [Accepted: 02/11/2022] [Indexed: 01/17/2023]
Abstract
Imaging plays a fundamental role in the managing childhood neurologic, neurosurgical and neuro-oncological disease. Employing multi-parametric MRI techniques, such as spectroscopy and diffusion- and perfusion-weighted imaging, to the radiophenotyping of neuroradiologic conditions is becoming increasingly prevalent, particularly with radiogenomic analyses correlating imaging characteristics with molecular biomarkers of disease. However, integration into routine clinical practice remains elusive. With modern multi-parametric MRI now providing additional data beyond anatomy, informing on histology, biology and physiology, such metric-rich information can present as information overload to the treating radiologist and, as such, information relevant to an individual case can become lost. Artificial intelligence techniques are capable of modelling the vast radiologic, biological and clinical datasets that accompany childhood neurologic disease, such that this information can become incorporated in upfront prognostic modelling systems, with artificial intelligence techniques providing a plausible approach to this solution. This review examines machine learning approaches than can be used to underpin such artificial intelligence applications, with exemplars for each machine learning approach from the world literature. Then, within the specific use case of paediatric neuro-oncology, we examine the potential future contribution for such artificial intelligence machine learning techniques to offer solutions for patient care in the form of decision support systems, potentially enabling personalised medicine within this domain of paediatric radiologic practice.
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Affiliation(s)
- Catherine Pringle
- Children’s Brain Tumour Research Network (CBTRN), Royal Manchester Children’s Hospital, Manchester, UK ,Division of Informatics, Imaging, and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, UK
| | - John-Paul Kilday
- Children’s Brain Tumour Research Network (CBTRN), Royal Manchester Children’s Hospital, Manchester, UK ,The Centre for Paediatric, Teenage and Young Adult Cancer, Institute of Cancer Sciences, University of Manchester, Manchester, UK
| | - Ian Kamaly-Asl
- Children’s Brain Tumour Research Network (CBTRN), Royal Manchester Children’s Hospital, Manchester, UK ,The Centre for Paediatric, Teenage and Young Adult Cancer, Institute of Cancer Sciences, University of Manchester, Manchester, UK
| | - Stavros Michael Stivaros
- Division of Informatics, Imaging, and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, UK. .,Department of Paediatric Radiology, Royal Manchester Children's Hospital, Central Manchester University Hospitals NHS Foundation Trust, Oxford Road, Manchester, M13 9WL, UK. .,The Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
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24
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Clausen AN, Fercho KA, Monsour M, Disner S, Salminen L, Haswell CC, Rubright EC, Watts AA, Buckley MN, Maron-Katz A, Sierk A, Manthey A, Suarez-Jimenez B, Olatunji BO, Averill CL, Hofmann D, Veltman DJ, Olson EA, Li G, Forster GL, Walter H, Fitzgerald J, Théberge J, Simons JS, Bomyea JA, Frijling JL, Krystal JH, Baker JT, Phan KL, Ressler K, Han LKM, Nawijn L, Lebois LAM, Schmaal L, Densmore M, Shenton ME, van Zuiden M, Stein M, Fani N, Simons RM, Neufeld RWJ, Lanius R, van Rooij S, Koch SBJ, Bonomo S, Jovanovic T, deRoon-Cassini T, Ely TD, Magnotta VA, He X, Abdallah CG, Etkin A, Schmahl C, Larson C, Rosso IM, Blackford JU, Stevens JS, Daniels JK, Herzog J, Kaufman ML, Olff M, Davidson RJ, Sponheim SR, Mueller SC, Straube T, Zhu X, Neria Y, Baugh LA, Cole JH, Thompson PM, Morey RA. Assessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups. Brain Behav 2022; 12:e2413. [PMID: 34907666 PMCID: PMC8785613 DOI: 10.1002/brb3.2413] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 09/03/2021] [Accepted: 10/15/2021] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. METHOD Adult subjects (N = 2229; 56.2% male) aged 18-69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA-PGC PTSD sites underwent T1-weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel-wise (brainageR) and region-of-interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age - chronological age) controlling for chronological age, sex, and scan site. RESULTS BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three-way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages. DISCUSSION Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age-related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan.
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Affiliation(s)
- Ashley N Clausen
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, North Carolina, USA.,Duke University Brain Imaging and Analysis Center, Durham, North Carolina, USA.,Kansas City VA Medical Center, Kansas City, Missouri, USA
| | - Kelene A Fercho
- Center for Brain and Behavior Research, University of South Dakota, Vermillion, South Dakota, USA.,Civil Aerospace Medical Institute, US Federal Aviation Administration, Oklahoma City, Oklahoma, USA.,Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, South Dakota, USA.,Sioux Falls VA Health Care System, Sioux Falls, South Dakota, USA
| | - Molly Monsour
- Duke University Brain Imaging and Analysis Center, Durham, North Carolina, USA
| | - Seth Disner
- Center for Brain and Behavior Research, University of South Dakota, Vermillion, South Dakota, USA.,Department of Psychiatry, University of Minnesota Medical School, Minneapolis, Minnesota, USA.,Minneapolis VA Health Care System, Minneapolis, Minnesota, USA
| | - Lauren Salminen
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Courtney C Haswell
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, North Carolina, USA.,Duke University Brain Imaging and Analysis Center, Durham, North Carolina, USA
| | - Emily Clarke Rubright
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, North Carolina, USA.,Duke University Brain Imaging and Analysis Center, Durham, North Carolina, USA
| | - Amanda A Watts
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, North Carolina, USA.,Duke University Brain Imaging and Analysis Center, Durham, North Carolina, USA
| | - M Nicole Buckley
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, North Carolina, USA.,Duke University Brain Imaging and Analysis Center, Durham, North Carolina, USA
| | - Adi Maron-Katz
- Department of Psychiatry and Behavioral Sciences, Stanford University of Medicine, Stanford, California, USA
| | - Anika Sierk
- University Medical Centre Charite, Berlin, Germany
| | | | - Benjamin Suarez-Jimenez
- Columbia University Medical Center, Manhattan, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| | - Bunmi O Olatunji
- Department of Psychology, Vanderbilt University, Nashville, Tennessee, USA
| | - Christopher L Averill
- Clinical Neuroscience Division, National Center for PTSD, West Haven, Connecticut, USA.,Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - David Hofmann
- Institute of Medical Psychology and Systems Neuroscience, University of Muenster, Muenster, Germany
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam University Medical Centers, Location VU University Medical Center, VU University, Amsterdam, The Netherlands
| | - Elizabeth A Olson
- Harvard Medical School, Boston, Massachusetts, USA.,McLean Hospital, Belmont, Massachusetts, USA
| | - Gen Li
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.,Laboratory for Traumatic Stress Studies, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Gina L Forster
- Brain Health Research Centre, Department of Anatomy, University of Otago, Dunedin, New Zealand.,Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, South Dakota, USA
| | | | | | - Jean Théberge
- Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada.,Department of Psychiatry, Western University, London, Ontario, Canada.,Imaging Division, Lawson Health Research Institute, London, Ontario, Canada
| | - Jeffrey S Simons
- Center for Brain and Behavior Research, University of South Dakota, Vermillion, South Dakota, USA.,Sioux Falls VA Health Care System, Sioux Falls, South Dakota, USA
| | - Jessica A Bomyea
- UC San Diego Department of Psychiatry, San Deigo, California, USA.,VA San Diego Healthcare System Center of Excellence for Stress and Mental Health, San Deigo, California, USA
| | - Jessie L Frijling
- Department of Psychiatry, Amsterdam University Medical Centers, Location Academic Medical Center, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, The Netherlands
| | - John H Krystal
- Clinical Neuroscience Division, National Center for PTSD, West Haven, Connecticut, USA.,Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Justin T Baker
- Institute for Technology in Psychiatry, McLean Hospital, Harvard University, Belmont, Massachusetts, USA
| | - K Luan Phan
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, Ohio, USA
| | - Kerry Ressler
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA.,Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, Massachusetts, USA
| | - Laura K M Han
- Department of Psychiatry, Amsterdam University Medical Centers, VU University Medical Center, GGZ inGeest, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Laura Nawijn
- Department of Psychiatry, Amsterdam University Medical Centers, Location Academic Medical Center, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, The Netherlands.,Department of Psychiatry, Amsterdam University Medical Centers, Location VU University Medical Center, VU University, Amsterdam, The Netherlands
| | - Lauren A M Lebois
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA.,Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, Massachusetts, USA
| | - Lianne Schmaal
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia.,Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, Australia
| | - Maria Densmore
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - Martha E Shenton
- Department of Psychiatry, VA Boston Healthcare System, Brockton, Massachusetts, USA.,Departments of Psychiatry & Radiology, Harvard Medical School, Boston, Massachusetts, USA.,Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Mirjam van Zuiden
- Department of Psychiatry, Amsterdam University Medical Centers, Location Academic Medical Center, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, The Netherlands
| | - Murray Stein
- UC San Diego Department of Family Medicine and Public Health, San Deigo, California, USA.,UC San Diego Department of Psychiatry, San Deigo, California, USA
| | - Negar Fani
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Raluca M Simons
- Center for Brain and Behavior Research, University of South Dakota, Vermillion, South Dakota, USA.,Department of Psychology, University of South Dakota, Vermillion, South Dakota, USA
| | - Richard W J Neufeld
- Department of Neuroscience, Western University, London, Ontario, Canada.,Department of Psychiatry, Western University, London, Ontario, Canada.,Department of Psychology, University of British Columbia, Okanagan, Canada.,Department of Psychology, Western University, London, Ontario, Canada
| | - Ruth Lanius
- Department of Neuroscience, Western University, London, Ontario, Canada.,Department of Psychiatry, Western University, London, Ontario, Canada
| | - Sanne van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Saskia B J Koch
- Department of Psychiatry, Amsterdam University Medical Centers, Location Academic Medical Center, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, The Netherlands.,Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Serena Bonomo
- New York State Psychiatric Institute, New York, New York, USA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, Michigan, USA
| | | | - Timothy D Ely
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Vincent A Magnotta
- Departments of Radiology, Psychiatry and Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Xiaofu He
- Columbia University Medical Center, Manhattan, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| | - Chadi G Abdallah
- Clinical Neuroscience Division, National Center for PTSD, West Haven, Connecticut, USA.,Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.,Michael E, DeBakey VA Medical Center, Houston, Texas, USA.,Menninger Department of Psychiatry, Baylor College of Medicine, Houston, Texas, USA
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University of Medicine, Stanford, California, USA.,Wu Tsai Neuroscience Institute, Stanford University, Stanford, California, USA
| | - Christian Schmahl
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | | | - Isabelle M Rosso
- Harvard Medical School, Boston, Massachusetts, USA.,McLean Hospital, Belmont, Massachusetts, USA
| | - Jennifer Urbano Blackford
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, Tennessee, USA
| | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
| | | | - Julia Herzog
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Milissa L Kaufman
- Division of Women's Mental Health, McLean Hospital, Belmont, Massachusetts, USA
| | - Miranda Olff
- ARQ National Psychotrauma Centrum, Diemen, The Netherlands.,Department of Psychiatry, Amsterdam University Medical Centers, Location Academic Medical Center, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, The Netherlands
| | - Richard J Davidson
- Center for Healthy Minds, Departments of Psychology and Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Scott R Sponheim
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, Minnesota, USA.,Minneapolis VA Health Care System, Minneapolis, Minnesota, USA
| | - Sven C Mueller
- Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium.,Department of Personality, Psychological Assessment and Treatment, University of Deusto, Bilbao, Spain
| | - Thomas Straube
- Institute of Medical Psychology and Systems Neuroscience, University of Muenster, Muenster, Germany
| | - Xi Zhu
- Columbia University Medical Center, Manhattan, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| | - Yuval Neria
- Columbia University Medical Center, Manhattan, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| | - Lee A Baugh
- Center for Brain and Behavior Research, University of South Dakota, Vermillion, South Dakota, USA.,Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, South Dakota, USA.,Sioux Falls VA Health Care System, Sioux Falls, South Dakota, USA
| | - James H Cole
- Centre for Medical Image Computing, Computer Science, University College London, London, UK.,Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Rajendra A Morey
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, North Carolina, USA.,Duke University Brain Imaging and Analysis Center, Durham, North Carolina, USA.,Kansas City VA Medical Center, Kansas City, Missouri, USA.,ARQ National Psychotrauma Centrum, Diemen, The Netherlands.,Department of Psychiatry, Amsterdam University Medical Centers, VU University Medical Center, GGZ inGeest, Amsterdam Neuroscience, Amsterdam, The Netherlands
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25
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Mazzu-Nascimento T, Evangelista DN, Abubakar O, Roscani MG, Aguilar RS, Chachá SGF, Rosa PRD, Silva DF. Smartphone-Based Screening for Cardiovascular Diseases: A Trend? INTERNATIONAL JOURNAL OF CARDIOVASCULAR SCIENCES 2021. [DOI: 10.36660/ijcs.20210096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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26
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Abstract
Advances in neuroimaging have increasingly enabled researchers to investigate whether alterations in brain development commonly identified in preterm infants underlie their high risk of long-term neurodevelopmental impairment, including sensory, motor, cognitive, and psychiatric deficits. This review begins by examining the growing body of literature utilizing advanced magnetic resonance imaging (MRI) techniques to probe structural (via diffusion MRI) and functional (via resting state-functional MRI) connectivity development in the preterm brain during the neonatal period, both in the presence and absence of brain injury. It then details the recent work linking neonatal brain connectivity measures to neurodevelopmental and psychiatric outcomes in prematurely-born cohorts. Finally, building upon the recent substantive growth in the utilization of these neuroimaging modalities, it concludes by highlighting areas in which continued optimization of age-specific acquisition and analysis techniques for these data remains necessary, efforts fundamental to advancing the field toward establishing individual-level predictive capabilities in this high-risk population.
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Affiliation(s)
- Rebecca G Brenner
- Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue Campus Box 8111, St. Louis, MO 63110, United States
| | - Muriah D Wheelock
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Jeffrey J Neil
- Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue Campus Box 8111, St. Louis, MO 63110, United States; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, United States
| | - Christopher D Smyser
- Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue Campus Box 8111, St. Louis, MO 63110, United States; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, United States.
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27
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Shi Z, Langleben DD, O'Brien CP, Childress AR, Wiers CE. Multivariate pattern analysis links drug use severity to distributed cortical hypoactivity during emotional inhibitory control in opioid use disorder. Neuroimage Clin 2021; 32:102806. [PMID: 34525436 PMCID: PMC8436158 DOI: 10.1016/j.nicl.2021.102806] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 08/17/2021] [Accepted: 08/20/2021] [Indexed: 11/22/2022]
Abstract
Opioid use disorder (OUD) is characterized by emotional and cognitive impairements that are associated with poor treatment outcomes. The present study investigated the neural mechanism underlying emotion evaluation and inhibitory control using an affective go/no-go (AGN) task and its association with drug use severity and craving in patients with OUD. Twenty-six recently detoxified patients with OUD underwent functional magnetic resonance imaging (fMRI) while performing the AGN task that required response to frequently presented appetitive stimuli ("go") and inhibition of response to infrequently presented aversive stimuli ("no-go"). The fMRI session was immediately followed by an injection of extended-release opioid antagonist naltrexone (XR-NTX). Participants' opioid craving was assessed immediately before fMRI and 10 ± 2 days after XR-NTX injection. Multivariate pattern analysis (MVPA) showed that drug use severity was associated with distributed brain hypoactivity in response to aversive no-go stimuli, with particularly large negative contributions from the cognitive control and dorsal attention brain networks. While drug use severity and its associated MVPA brain response pattern were both correlated with opioid craving at baseline, only the brain response pattern predicted craving during XR-NTX treatment. Our findings point to widespread functional hypoactivity in the brain networks underlying emotional inhibitory control in OUD. Such a distributed pattern is consistent with the multifaceted nature of OUD, which affects multiple brain networks. It also highlights the utility of the multivariate approach in uncovering large-scale cortical substrates associated with clinical severity in complex psychiatric disorders and in predicting treatment response.
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Affiliation(s)
- Zhenhao Shi
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA.
| | - Daniel D Langleben
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Charles P O'Brien
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Anna Rose Childress
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Corinde E Wiers
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
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28
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Leon C, Cabon S, Patural H, Gascoin G, Flamant C, Roue JM, Favrais G, Beuchee A, Pladys P, Carrault G. Evaluation of maturation in preterm infants through an ensemble machine learning algorithm using physiological signals. IEEE J Biomed Health Inform 2021; 26:400-410. [PMID: 34185652 DOI: 10.1109/jbhi.2021.3093096] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This study was designed to test if heart rate variability (HRV) data from preterm and full-term infants could be used to estimate their functional maturational age (FMA), using a machine learning model. We propose that the FMA, and its deviation from the postmenstrual age (PMA) of the infants could inform physicians about the progress of the maturation of the infants. The HRV data was acquired from 50 healthy infants, born between 25 and 41 weeks of gestational age, who did not present any signs of abnormal maturation relative to their age group during the period of observation. The HRV features were used as input for a machine learning model that uses filtering and genetic algorithms for feature selection, and an ensemble machine learning (EML) algorithm, which combines linear and random forest regressions, to produce as output a FMA. Using HRV data, the FMA had a mean absolute error of 0.93 weeks, 95% CI [0.78, 1.08], compared to the PMA. These results demonstrate that HRV features of newborn infants can be used by an EML model to estimate their FMA. This method was also generalized using respiration rate variability (RRV) and bradycardia data, obtaining similar results. The FMA, predicted either by HRV, RRV or bradycardia, and its deviation from the true PMA of the infants, could be used as a surrogate measure of the maturational age of the infants, which could potentially be monitored non-invasively and in real-time in the setting of neonatal intensive care units.
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29
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Tataranno ML, Vijlbrief DC, Dudink J, Benders MJNL. Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury. Front Pediatr 2021; 9:634092. [PMID: 34095022 PMCID: PMC8171663 DOI: 10.3389/fped.2021.634092] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 04/14/2021] [Indexed: 11/27/2022] Open
Abstract
Despite advances in neonatal care to prevent neonatal brain injury and neurodevelopmental impairment, predicting long-term outcome in neonates at risk for brain injury remains difficult. Early prognosis is currently based on cranial ultrasound (CUS), MRI, EEG, NIRS, and/or general movements assessed at specific ages, and predicting outcome in an individual (precision medicine) is not yet possible. New algorithms based on large databases and machine learning applied to clinical, neuromonitoring, and neuroimaging data and genetic analysis and assays measuring multiple biomarkers (omics) can fulfill the needs of modern neonatology. A synergy of all these techniques and the use of automatic quantitative analysis might give clinicians the possibility to provide patient-targeted decision-making for individualized diagnosis, therapy, and outcome prediction. This review will first focus on common neonatal neurological diseases, associated risk factors, and most common treatments. After that, we will discuss how precision medicine and machine learning (ML) approaches could change the future of prediction and prognosis in this field.
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Affiliation(s)
| | | | | | - Manon J. N. L. Benders
- Department of Neonatology, Wilhelmina Children's Hospital/University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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Chiarelli AM, Sestieri C, Navarra R, Wise RG, Caulo M. Distinct effects of prematurity on MRI metrics of brain functional connectivity, activity, and structure: Univariate and multivariate analyses. Hum Brain Mapp 2021; 42:3593-3607. [PMID: 33955622 PMCID: PMC8249887 DOI: 10.1002/hbm.25456] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 04/09/2021] [Accepted: 04/11/2021] [Indexed: 12/27/2022] Open
Abstract
Premature birth affects the developmental trajectory of the brain during a period of intense maturation with possible lifelong consequences. To better understand the effect of prematurity on brain structure and function, we performed blood‐oxygen‐level dependent (BOLD) and anatomical magnetic resonance imaging (MRI) at 40 weeks of postmenstrual age on 88 newborns with variable gestational age (GA) at birth and no evident radiological alterations. We extracted measures of resting‐state functional connectivity and activity in a set of 90 cortical and subcortical brain regions through the evaluation of BOLD correlations between regions and of fractional amplitude of low‐frequency fluctuation (fALFF) within regions, respectively. Anatomical information was acquired through the assessment of regional volumes. We performed univariate analyses on each metric to examine the association with GA at birth, the spatial distribution of the effects, and the consistency across metrics. Moreover, a data‐driven multivariate analysis (i.e., Machine Learning) framework exploited the high dimensionality of the data to assess the sensitivity of each metric to the effect of premature birth. Prematurity was associated with bidirectional alterations of functional connectivity and regional volume and, to a lesser extent, of fALFF. Notably, the effects of prematurity on functional connectivity were spatially diffuse, mainly within cortical regions, whereas effects on regional volume and fALFF were more focal, involving subcortical structures. While the two analytical approaches delivered consistent results, the multivariate analysis was more sensitive in capturing the complex pattern of prematurity effects. Future studies might apply multivariate frameworks to identify premature infants at risk of a negative neurodevelopmental outcome.
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Affiliation(s)
- Antonio M Chiarelli
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. D'Annunzio of Chieti-Pescara; Institute for Advanced Biomedical Technologies, Chieti, Italy
| | - Carlo Sestieri
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. D'Annunzio of Chieti-Pescara; Institute for Advanced Biomedical Technologies, Chieti, Italy
| | - Riccardo Navarra
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. D'Annunzio of Chieti-Pescara; Institute for Advanced Biomedical Technologies, Chieti, Italy
| | - Richard G Wise
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. D'Annunzio of Chieti-Pescara; Institute for Advanced Biomedical Technologies, Chieti, Italy
| | - Massimo Caulo
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. D'Annunzio of Chieti-Pescara; Institute for Advanced Biomedical Technologies, Chieti, Italy
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31
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Kelly CE, Thompson DK, Cooper M, Pham J, Nguyen TD, Yang JY, Ball G, Adamson C, Murray AL, Chen J, Inder TE, Cheong JL, Doyle LW, Anderson PJ. White matter tracts related to memory and emotion in very preterm children. Pediatr Res 2021; 89:1452-1460. [PMID: 32920605 PMCID: PMC7954988 DOI: 10.1038/s41390-020-01134-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 08/10/2020] [Accepted: 08/18/2020] [Indexed: 11/09/2022]
Abstract
BACKGROUND Very preterm (VP) children are at risk of memory and emotional impairments; however, the neural correlates remain incompletely defined. This study investigated the effect of VP birth on white matter tracts traditionally related to episodic memory and emotion. METHODS The cingulum, fornix, uncinate fasciculus, medial forebrain bundle and anterior thalamic radiation were reconstructed using tractography in 144 VP children and 33 full-term controls at age 7 years. RESULTS Compared with controls, VP children had higher axial, radial, and mean diffusivities and neurite orientation dispersion, and lower volume and neurite density in the fornix, along with higher neurite orientation dispersion in the medial forebrain bundle. Support vector classification models based on tract measures significantly classified VP children and controls. Higher fractional anisotropy and lower diffusivities in the cingulum, uncinate fasciculus, medial forebrain bundle and anterior thalamic radiation were associated with better episodic memory, independent of key perinatal risk factors. Support vector regression models using tract measures did not predict episodic memory and emotional outcomes. CONCLUSIONS Altered tract structure is related to adverse episodic memory outcomes in VP children, but further research is required to determine the ability of tract structure to predict outcomes of individual children. IMPACT We studied white matter fibre tracts thought to be involved in episodic memory and emotion in VP and full-term children using diffusion magnetic resonance imaging and machine learning. VP children have altered fornix and medial forebrain bundle structure compared with full-term children. Altered tract structure can be detected using machine learning, which accurately classified VP and full-term children using tract data. Altered cingulum, uncinate fasciculus, medial forebrain bundle and anterior thalamic radiation structure was associated with poorer episodic memory skills using linear regression. The ability of tract structure to predict episodic memory and emotional outcomes of individual children based on support vector regression was limited.
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Affiliation(s)
- Claire E. Kelly
- Victorian Infant Brain Studies (VIBeS), Murdoch Children’s Research Institute, Melbourne, Australia,Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia,Corresponding author: Claire Kelly, Victorian Infant Brain Studies (VIBeS), Murdoch Children’s Research Institute, The Royal Children’s Hospital, 50 Flemington Road, Parkville, Victoria, Australia, 3052.
| | - Deanne K. Thompson
- Victorian Infant Brain Studies (VIBeS), Murdoch Children’s Research Institute, Melbourne, Australia,Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia,Florey Institute of Neuroscience and Mental Health, Melbourne, Australia,Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Malcolm Cooper
- Victorian Infant Brain Studies (VIBeS), Murdoch Children’s Research Institute, Melbourne, Australia,Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Jenny Pham
- Victorian Infant Brain Studies (VIBeS), Murdoch Children’s Research Institute, Melbourne, Australia,Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Thanh D. Nguyen
- Victorian Infant Brain Studies (VIBeS), Murdoch Children’s Research Institute, Melbourne, Australia,Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Joseph Y.M. Yang
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia,Department of Paediatrics, The University of Melbourne, Melbourne, Australia,Neuroscience Advanced Clinical Imaging Suite (NACIS), Department of Neurosurgery, The Royal Children’s Hospital, Melbourne, Australia,Neuroscience Research, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Gareth Ball
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Chris Adamson
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Andrea L. Murray
- Victorian Infant Brain Studies (VIBeS), Murdoch Children’s Research Institute, Melbourne, Australia,Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Jian Chen
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Terrie E. Inder
- Department of Pediatric Newborn Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Jeanie L.Y. Cheong
- Victorian Infant Brain Studies (VIBeS), Murdoch Children’s Research Institute, Melbourne, Australia,Newborn Research, The Royal Women’s Hospital, Melbourne, Australia,Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, Australia
| | - Lex W. Doyle
- Victorian Infant Brain Studies (VIBeS), Murdoch Children’s Research Institute, Melbourne, Australia,Department of Paediatrics, The University of Melbourne, Melbourne, Australia,Newborn Research, The Royal Women’s Hospital, Melbourne, Australia,Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, Australia
| | - Peter J. Anderson
- Victorian Infant Brain Studies (VIBeS), Murdoch Children’s Research Institute, Melbourne, Australia,Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Melbourne, Australia
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32
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Dubois J, Alison M, Counsell SJ, Hertz‐Pannier L, Hüppi PS, Benders MJ. MRI of the Neonatal Brain: A Review of Methodological Challenges and Neuroscientific Advances. J Magn Reson Imaging 2021; 53:1318-1343. [PMID: 32420684 PMCID: PMC8247362 DOI: 10.1002/jmri.27192] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 04/24/2020] [Accepted: 04/24/2020] [Indexed: 01/04/2023] Open
Abstract
In recent years, exploration of the developing brain has become a major focus for researchers and clinicians in an attempt to understand what allows children to acquire amazing and unique abilities, as well as the impact of early disruptions (eg, prematurity, neonatal insults) that can lead to a wide range of neurodevelopmental disorders. Noninvasive neuroimaging methods such as MRI are essential to establish links between the brain and behavioral changes in newborns and infants. In this review article, we aim to highlight recent and representative studies using the various techniques available: anatomical MRI, quantitative MRI (relaxometry, diffusion MRI), multiparametric approaches, and functional MRI. Today, protocols use 1.5 or 3T MRI scanners, and specialized methodologies have been put in place for data acquisition and processing to address the methodological challenges specific to this population, such as sensitivity to motion. MR sequences must be adapted to the brains of newborns and infants to obtain relevant good soft-tissue contrast, given the small size of the cerebral structures and the incomplete maturation of tissues. The use of age-specific image postprocessing tools is also essential, as signal and contrast differ from the adult brain. Appropriate methodologies then make it possible to explore multiple neurodevelopmental mechanisms in a precise way, and assess changes with age or differences between groups of subjects, particularly through large-scale projects. Although MRI measurements only indirectly reflect the complex series of dynamic processes observed throughout development at the molecular and cellular levels, this technique can provide information on brain morphology, structural connectivity, microstructural properties of gray and white matter, and on the functional architecture. Finally, MRI measures related to clinical, behavioral, and electrophysiological markers have a key role to play from a diagnostic and prognostic perspective in the implementation of early interventions to avoid long-term disabilities in children. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Jessica Dubois
- University of ParisNeuroDiderot, INSERM,ParisFrance
- UNIACT, NeuroSpin, CEA; Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Marianne Alison
- University of ParisNeuroDiderot, INSERM,ParisFrance
- Department of Pediatric RadiologyAPHP, Robert‐Debré HospitalParisFrance
| | - Serena J. Counsell
- Centre for the Developing BrainSchool of Biomedical Engineering & Imaging Sciences, King's College LondonLondonUK
| | - Lucie Hertz‐Pannier
- University of ParisNeuroDiderot, INSERM,ParisFrance
- UNIACT, NeuroSpin, CEA; Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Petra S. Hüppi
- Division of Development and Growth, Department of Woman, Child and AdolescentUniversity Hospitals of GenevaGenevaSwitzerland
| | - Manon J.N.L. Benders
- Department of NeonatologyUniversity Medical Center Utrecht, Utrecht UniversityUtrechtthe Netherlands
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Wang F, Yin Y, Yang Y, Liang T, Huang T, He C, Hu J, Zhang J, Yang Y, Xing Q, Zhang T, Liu H. Connectome-based prediction of brain age in Rolandic epilepsy: a protocol for a multicenter cross-sectional study. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:511. [PMID: 33850908 PMCID: PMC8039653 DOI: 10.21037/atm-21-574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Rolandic epilepsy (RE) is a common pediatric idiopathic partial epilepsy syndrome. Children with RE display varying degrees of cognitive impairment. In epilepsy, age-related neuroanatomic and cognitive changes differ greatly from those observed in the healthy brain, and may be defined as accelerated brain aging. Connectome-based predictive modeling (CPM) is a recently developed machine learning approach that uses whole-brain connectivity measured with neuroimaging data ("neural fingerprints") to predict brain-behavior relationships. The aim of the study will be to develop and validate a CPM for predicting brain age in patients with RE. METHODS A multicenter, cross-sectional study will be conducted in 5 Chinese hospitals. A total of 100 RE patients (including 50 patients receiving anti-epileptic drugs and 50 drug-naïve patients) and 100 healthy children will be recruited to undergo a neuropsychological test using the Wechsler Intelligence Scale. Magnetic resonance images will also be collected. CPM will be applied to predict the brain age of children with RE based on brain functional connectivity. DISCUSSION The findings of the study will facilitate our understanding of developmental changes in the brain in children with RE and could also be an important milestone in the journey toward developing effective early interventions for this disorder. TRIAL REGISTRATION The study has been registered with Chinese Clinical Trial Registry (ChiCTR2000032984).
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Affiliation(s)
- Fuqin Wang
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Yu Yin
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Yang Yang
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Ting Liang
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Tingting Huang
- Department of Radiology, the First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Cheng He
- Department of Radiology, Chongqing University Central Hospital, Chongqing, China
| | - Jie Hu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Jingjing Zhang
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Yanli Yang
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Qianlu Xing
- Department of Pediatrics, the Second Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Tijiang Zhang
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Heng Liu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
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34
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Turesky TK, Vanderauwera J, Gaab N. Imaging the rapidly developing brain: Current challenges for MRI studies in the first five years of life. Dev Cogn Neurosci 2021; 47:100893. [PMID: 33341534 PMCID: PMC7750693 DOI: 10.1016/j.dcn.2020.100893] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 10/21/2020] [Accepted: 12/05/2020] [Indexed: 12/20/2022] Open
Abstract
Rapid and widespread changes in brain anatomy and physiology in the first five years of life present substantial challenges for developmental structural, functional, and diffusion MRI studies. One persistent challenge is that methods best suited to earlier developmental stages are suboptimal for later stages, which engenders a trade-off between using different, but age-appropriate, methods for different developmental stages or identical methods across stages. Both options have potential benefits, but also biases, as pipelines for each developmental stage can be matched on methods or the age-appropriateness of methods, but not both. This review describes the data acquisition, processing, and analysis challenges that introduce these potential biases and attempts to elucidate decisions and make recommendations that would optimize developmental comparisons.
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Affiliation(s)
- Ted K Turesky
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Jolijn Vanderauwera
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Psychological Sciences Research Institute, Université Catholique De Louvain, Louvain-la-Neuve, Belgium; Institute of Neuroscience, Université Catholique De Louvain, Louvain-la-Neuve, Belgium
| | - Nadine Gaab
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
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35
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Zhang W, Nery FG, Tallman MJ, Patino LR, Adler CM, Strawn JR, Fleck DE, Barzman DH, Sweeney JA, Strakowski SM, Lui S, DelBello MP. Individual prediction of symptomatic converters in youth offspring of bipolar parents using proton magnetic resonance spectroscopy. Eur Child Adolesc Psychiatry 2021; 30:55-64. [PMID: 32008167 DOI: 10.1007/s00787-020-01483-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 01/23/2020] [Indexed: 02/05/2023]
Abstract
Children of individuals with bipolar disorder (bipolar offspring) are at increased risk for developing mood disorders, but strategies to predict mood episodes are unavailable. In this study, we used support vector machine (SVM) to characterize the potential of proton magnetic resonance spectroscopy (1H-MRS) in predicting the first mood episode in youth bipolar offspring. From a longitudinal neuroimaging study, 19 at-risk youth who developed their first mood episode (converters), and 19 without mood episodes during follow-up (non-converters) were selected and matched for age, sex and follow-up time. Baseline 1H-MRS data were obtained from anterior cingulate cortex (ACC) and bilateral ventrolateral prefrontal cortex (VLPFC). Glutamate (Glu), myo-inositol (mI), choline (Cho), N-acetyl aspartate (NAA), and phosphocreatine plus creatine (PCr + Cr) levels were calculated. SVM with a linear kernel was adopted to classify converters and non-converters based on their baseline metabolites. SVM allowed the significant classification of converters and non-converters across all regions for Cho (accuracy = 76.0%), but not for other metabolites. Considering all metabolites within each region, SVM allowed the significant classification of converters and non-converters for left VLPFC (accuracy = 76.5%), but not for right VLPFC or ACC. The combined mI, PCr + Cr, and Cho from left VLPFC achieved the highest accuracy differentiating converters from non-converters (79.0%). Our findings from this exploratory study suggested that 1H-MRS levels of mI, Cho, and PCr + Cr from left VLPFC might be useful to predict the development of first mood episode in youth bipolar offspring using machine learning. Future studies that prospectively examine and validate these metabolites as predictors of mood episodes in high-risk individuals are necessary.
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Affiliation(s)
- Wenjing Zhang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Fabiano G Nery
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - Maxwell J Tallman
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - L Rodrigo Patino
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - Caleb M Adler
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - Jeffrey R Strawn
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - David E Fleck
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - Drew H Barzman
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - Stephen M Strakowski
- Department of Psychiatry, Dell Medical School, University of Texas At Austin, Austin, TX, 78712, USA
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Melissa P DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
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36
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Ouyang M, Peng Q, Jeon T, Heyne R, Chalak L, Huang H. Diffusion-MRI-based regional cortical microstructure at birth for predicting neurodevelopmental outcomes of 2-year-olds. eLife 2020; 9:58116. [PMID: 33350380 PMCID: PMC7755384 DOI: 10.7554/elife.58116] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 12/06/2020] [Indexed: 12/11/2022] Open
Abstract
Cerebral cortical architecture at birth encodes regionally differential dendritic arborization and synaptic formation. It underlies behavioral emergence of 2-year-olds. Brain changes in 0-2 years are most dynamic across the lifespan. Effective prediction of future behavior with brain microstructure at birth will reveal structural basis of behavioral emergence in typical development and identify biomarkers for early detection and tailored intervention in atypical development. Here we aimed to evaluate the neonate whole-brain cortical microstructure quantified by diffusion MRI for predicting future behavior. We found that individual cognitive and language functions assessed at the age of 2 years were robustly predicted by neonate cortical microstructure using support vector regression. Remarkably, cortical regions contributing heavily to the prediction models exhibited distinctive functional selectivity for cognition and language. These findings highlight regional cortical microstructure at birth as a potential sensitive biomarker in predicting future neurodevelopmental outcomes and identifying individual risks of brain disorders.
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Affiliation(s)
- Minhui Ouyang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States
| | - Qinmu Peng
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
| | - Tina Jeon
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States
| | - Roy Heyne
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, United States
| | - Lina Chalak
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, United States
| | - Hao Huang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
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Boggini T, Pozzoli S, Schiavolin P, Erario R, Mosca F, Brambilla P, Fumagalli M. Cumulative procedural pain and brain development in very preterm infants: A systematic review of clinical and preclinical studies. Neurosci Biobehav Rev 2020; 123:320-336. [PMID: 33359095 DOI: 10.1016/j.neubiorev.2020.12.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 12/05/2020] [Accepted: 12/15/2020] [Indexed: 01/16/2023]
Abstract
Very preterm infants may manifest neurodevelopmental impairments, even in the absence of brain lesions. Pathogenesis is complex and multifactorial. Evidence suggests a role of early adversities on neurodevelopmental outcomes, via epigenetic regulation and changes in brain architecture. In this context, we focused on cumulative pain exposure which preterm neonates experience in neonatal intensive care unit (NICU). We systematically searched for: i) evidence linking pain with brain development and exploring the potential pathogenetic role of epigenetics; ii) preclinical research supporting clinical observational studies. Nine clinical neuroimaging studies, during neonatal or school age, mostly from the same research group, revealed volume reduction of white and gray matter structures in association with postnatal pain exposure. Three controlled animal studies mimicking NICU settings found increased cell death or apoptosis; nevertheless, eligible groups were limited in size. Epigenetic modulation (SLC6A4 promoter methylation) was identified in only two clinical trials. We call for additional research and, although knowledge gaps, we also point out the urgent need of minimizing painful procedures in NICUs.
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Affiliation(s)
- Tiziana Boggini
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, NICU, Milan, Italy.
| | - Sara Pozzoli
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Neurosciences and Mental Health, Milan, Italy
| | - Paola Schiavolin
- University of Milan, Department of Clinical Sciences and Community Health, Milan, Italy
| | - Raffaele Erario
- University of Milan, Department of Pathophysiology and Transplantation, Milan, Italy
| | - Fabio Mosca
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, NICU, Milan, Italy; University of Milan, Department of Clinical Sciences and Community Health, Milan, Italy
| | - Paolo Brambilla
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Neurosciences and Mental Health, Milan, Italy; University of Milan, Department of Pathophysiology and Transplantation, Milan, Italy
| | - Monica Fumagalli
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, NICU, Milan, Italy; University of Milan, Department of Clinical Sciences and Community Health, Milan, Italy
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38
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Chriskos P, Frantzidis CA, Papanastasiou E, Bamidis PD. Applications of Convolutional Neural Networks in neurodegeneration and physiological aging. Int J Psychophysiol 2020; 159:1-10. [PMID: 33202245 DOI: 10.1016/j.ijpsycho.2020.08.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 07/29/2020] [Accepted: 08/25/2020] [Indexed: 12/19/2022]
Abstract
The process of aging is linked with significant changes in a human's physiological organization and structure. This is more evident in the case of the brain whose functions generally vary between young and old individuals. Detecting such patterns can be of significant importance especially during the Mild Cognitive Impairment (MCI) stage which is a transition state before the clinical onset of dementia. Intervening in that stage may delay or eventually prevent dementia onset. In this paper we propose a new methodology based in electroencephalographic (EEG) recordings, aiming to classify individuals into healthy, pathological (patients diagnosed with MCI or Mild Dementia) and young, old groups (healthy individuals over and under 50 years of age) through functional connectivity and macro-architecture features. These features are calculated on the estimated brain region activations through the inverse problem solution, enabling us to transform the sensor level EEG recordings through an appropriate transformation matrix. Afterwards, Synchronization Likelihood and Relative Wavelet Entropy values were calculated along with the graph metrics corresponding to the functional connectivity values, as well as the relative energy contributions of five EEG bands (delta, theta, alpha, beta and gamma). These features were organized in Red, Green, Blue (RGB) image-like data structures. Therefore, it was possible to classify each individual into one of the two groups per experiment employing Convolutional Neural Networks. From the maximum classification accuracy achieved on the test set, 90.48% for the pathological aging group and 91.19% for the physiological aging, it is evident that the proposed approach is capable of providing adequate health and age group classification.
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Affiliation(s)
- Panteleimon Chriskos
- Laboratory of Medical Physics, Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Christos A Frantzidis
- Laboratory of Medical Physics, Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
| | - Emmanouil Papanastasiou
- Laboratory of Medical Physics, Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Panagiotis D Bamidis
- Laboratory of Medical Physics, Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
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39
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Stevenson NJ, Oberdorfer L, Tataranno ML, Breakspear M, Colditz PB, de Vries LS, Benders MJNL, Klebermass-Schrehof K, Vanhatalo S, Roberts JA. Automated cot-side tracking of functional brain age in preterm infants. Ann Clin Transl Neurol 2020; 7:891-902. [PMID: 32368863 PMCID: PMC7318094 DOI: 10.1002/acn3.51043] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 03/20/2020] [Indexed: 12/14/2022] Open
Abstract
Objective A major challenge in the care of preterm infants is the early identification of compromised neurological development. While several measures are routinely used to track anatomical growth, there is a striking lack of reliable and objective tools for tracking maturation of early brain function; a cornerstone of lifelong neurological health. We present a cot‐side method for measuring the functional maturity of the newborn brain based on routinely available neurological monitoring with electroencephalography (EEG). Methods We used a dataset of 177 EEG recordings from 65 preterm infants to train a multivariable prediction of functional brain age (FBA) from EEG. The FBA was validated on an independent set of 99 EEG recordings from 42 preterm infants. The difference between FBA and postmenstrual age (PMA) was evaluated as a predictor for neurodevelopmental outcome. Results The FBA correlated strongly with the PMA of an infant, with a median prediction error of less than 1 week. Moreover, individual babies follow well‐defined individual trajectories. The accuracy of the FBA applied to the validation set was statistically equivalent to the training set accuracy. In a subgroup of infants with repeated EEG recordings, a persistently negative predicted age difference was associated with poor neurodevelopmental outcome. Interpretation The FBA enables the tracking of functional neurodevelopment in preterm infants. This establishes proof of principle for growth charts for brain function, a new tool to assist clinical management and identify infants who will benefit most from early intervention.
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Affiliation(s)
- Nathan J Stevenson
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Lisa Oberdorfer
- Department of Pediatrics, Division of Neonatology, Pediatric Intensive Care and Neuropediatrics, Medical University of Vienna, Vienna, Austria
| | - Maria-Luisa Tataranno
- Department of Neonatology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Michael Breakspear
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia.,Priority Research Center for Mind and Brain, University of Newcastle, Newcastle, NSW, 2305, Australia
| | - Paul B Colditz
- Centre for Clinical Research, Faculty of Medicine, University of Queensland, Brisbane, QLD, 4029, Australia
| | - Linda S de Vries
- Department of Neonatology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Manon J N L Benders
- Department of Neonatology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Katrin Klebermass-Schrehof
- Department of Pediatrics, Division of Neonatology, Pediatric Intensive Care and Neuropediatrics, Medical University of Vienna, Vienna, Austria
| | - Sampsa Vanhatalo
- Department of Children's Clinical Neurophysiology, BABA Center, Pediatric Research Center, Children's Hospital, HUS Medical Imaging Center, Helsinki University Central Hospital, University of Helsinki, Finland
| | - James A Roberts
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
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Peyton C, Einspieler C, Fjørtoft T, Adde L, Schreiber MD, Drobyshevsky A, Marks JD. Correlates of Normal and Abnormal General Movements in Infancy and Long-Term Neurodevelopment of Preterm Infants: Insights from Functional Connectivity Studies at Term Equivalence. J Clin Med 2020; 9:E834. [PMID: 32204407 PMCID: PMC7141532 DOI: 10.3390/jcm9030834] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 03/14/2020] [Accepted: 03/17/2020] [Indexed: 12/19/2022] Open
Abstract
Preterm infants born before 32 weeks gestation have increased risks for neurodevelopmental impairment at two years of age. How brain function differs between preterm infants with normal or impaired development is unknown. However, abnormal spontaneous motor behavior at 12-15 weeks post-term age is associated with neurodevelopmental impairment. We imaged brain blood oxygen level-dependent signals at term-equivalent age in 62 infants born at <32 weeks gestation and explored whether resting state functional connectivity (rsFC) differed with performances on the General Movement Assessment (GMA) at 12-15 weeks, and Bayley III scores at two years of corrected age. Infants with aberrant general movements exhibited decreased rsFC between the basal ganglia and regions in parietal and frontotemporal lobes. Infants with normal Bayley III cognitive scores exhibited increased rsFC between the basal ganglia and association cortices in parietal and occipital lobes compared with cognitively impaired children. Infants with normal motor scores exhibited increased rsFC between the basal ganglia and visual cortices, compared with children with motor impairment. Thus, the presence of abnormal general movements is associated with region-specific differences in rsFC at term. The association of abnormal long-term neurodevelopmental outcomes with decreased rsFC between basal ganglia and sub-score specific cortical regions may provide biomarkers of neurodevelopmental trajectory and outcome.
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Affiliation(s)
- Colleen Peyton
- Department of Pediatrics, University of Chicago, Chicago, IL 60422, USA
- Department of Physical Therapy and Human Movement Science and the Department of Pediatrics, Northwestern University, Chicago, IL 60611, USA
| | - Christa Einspieler
- Research Unit IDN, Interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, Graz 8036, Austria;
| | - Toril Fjørtoft
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway; (T.F.); (L.A.)
- Clinics of Clinical Services, St. Olavs Hospital, Trondheim University Hospital, 7006 Trondheim, Norway
| | - Lars Adde
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway; (T.F.); (L.A.)
- Clinics of Clinical Services, St. Olavs Hospital, Trondheim University Hospital, 7006 Trondheim, Norway
| | | | | | - Jeremy D. Marks
- Department of Pediatrics, University of Chicago, Chicago, IL 60422, USA
- Department of Neurology, University of Chicago, Chicago, IL 60422, USA
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Kassani PH, Gossmann A, Wang YP. Multimodal Sparse Classifier for Adolescent Brain Age Prediction. IEEE J Biomed Health Inform 2020; 24:336-344. [PMID: 31265424 PMCID: PMC9037951 DOI: 10.1109/jbhi.2019.2925710] [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: 11/14/2023]
Abstract
The study of healthy brain development helps to better understand both brain transformation and connectivity patterns, which happen during childhood to adulthood. This study presents a sparse machine learning solution across whole-brain functional connectivity measures of three datasets, derived from resting state functional magnetic resonance imaging (rs-fMRI) and two task fMRI data including a working memory n-back task (nb-fMRI) and an emotion identification task (em-fMRI). The fMRI data are collected from the Philadelphia Neurodevelopmental Cohort (PNC) for the prediction of brain age in adolescents. Due to extremely large variable-to-instance ratio of PNC data, a high-dimensional matrix with several irrelevant and highly correlated features is generated, and hence a sparse learning approach is necessary to extract effective features from fMRI data. We propose a sparse learner based on the residual errors along the estimation of an inverse problem for extreme learning machine (ELM). Our proposed method is able to overcome the overlearning problem by pruning several redundant features and their corresponding output weights. The proposed multimodal sparse ELM classifier based on residual errors is highly competitive in terms of classification accuracy compared to its counterparts such as conventional ELM, and sparse Bayesian learning ELM.
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42
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Galdi P, Blesa M, Stoye DQ, Sullivan G, Lamb GJ, Quigley AJ, Thrippleton MJ, Bastin ME, Boardman JP. Neonatal morphometric similarity mapping for predicting brain age and characterizing neuroanatomic variation associated with preterm birth. Neuroimage Clin 2020; 25:102195. [PMID: 32044713 PMCID: PMC7016043 DOI: 10.1016/j.nicl.2020.102195] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 01/14/2020] [Accepted: 01/21/2020] [Indexed: 01/01/2023]
Abstract
Multi-contrast MRI captures information about brain macro- and micro-structure which can be combined in an integrated model to obtain a detailed "fingerprint" of the anatomical properties of an individual's brain. Inter-regional similarities between features derived from structural and diffusion MRI, including regional volumes, diffusion tensor metrics, neurite orientation dispersion and density imaging measures, can be modelled as morphometric similarity networks (MSNs). Here, individual MSNs were derived from 105 neonates (59 preterm and 46 term) who were scanned between 38 and 45 weeks postmenstrual age (PMA). Inter-regional similarities were used as predictors in a regression model of age at the time of scanning and in a classification model to discriminate between preterm and term infant brains. When tested on unseen data, the regression model predicted PMA at scan with a mean absolute error of 0.70 ± 0.56 weeks, and the classification model achieved 92% accuracy. We conclude that MSNs predict chronological brain age accurately; and they provide a data-driven approach to identify networks that characterise typical maturation and those that contribute most to neuroanatomic variation associated with preterm birth.
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Affiliation(s)
- Paola Galdi
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK.
| | - Manuel Blesa
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - David Q Stoye
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Gemma Sullivan
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Gillian J Lamb
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Alan J Quigley
- Department of Radiology, Royal Hospital for Sick Children, Edinburgh EH9 1LF, UK
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK; Edinburgh Imaging, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - James P Boardman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
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Luby J, Allen N, Estabrook R, Pine DS, Rogers C, Krogh-Jespersen S, Norton ES, Wakschlag L. Mapping infant neurodevelopmental precursors of mental disorders: How synthetic cohorts & computational approaches can be used to enhance prediction of early childhood psychopathology. Behav Res Ther 2019; 123:103484. [PMID: 31734549 PMCID: PMC7667707 DOI: 10.1016/j.brat.2019.103484] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 09/04/2019] [Accepted: 09/25/2019] [Indexed: 12/22/2022]
Abstract
Bridging advances in neurodevelopmental assessment and the established onset of common psychopathologies in early childhood with epidemiological data science and computational methods holds much promise for identifying risk for mental disorders as early as infancy. In particular, we propose the development of a mental health risk algorithm for the early detection of mental disorders with the potential for high public health impact that applies and adapts methods innovated in and successfully applied to early detection of cardiovascular risk. Specifically, we propose methods to advance risk prediction of early developmental psychopathology by creating synthetic cohorts that contain complete behavioral and neural data in the first years of life, as the basis for a robust and generalizable risk algorithm. The application of computational approaches within synthetic cohorts, an approach increasingly applied in psychiatry, may be particularly well suited to advancing risk prediction in early childhood mental health. We propose new research directions using these methods to generate an early childhood mental health risk calculator that could significantly advance early mental health risk detection to direct preventive intervention and/or need for more intensive assessment within a pragmatic framework for maximal clinical utility. The availability of such a tool in early childhood, a period of high neuroplasticity, holds promise to reduce the burden of mental disorder by identifying risk early in the clinical sequence and delivering prevention that targets the neurodevelopmental vulnerability phase.
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Affiliation(s)
- Joan Luby
- Washington University School of Medicine, 4444 Forest Park Avenue, St. Louis, MO, 63108, USA.
| | - Norrina Allen
- Northwestern University Feinberg School of Medicine & Institute for Innovations in Developmental Sciences, 633 N. St Clair, 19th Floor, Chicago, IL, 60611, USA
| | - Ryne Estabrook
- Northwestern University Feinberg School of Medicine & Institute for Innovations in Developmental Sciences, 633 N. St Clair, 19th Floor, Chicago, IL, 60611, USA
| | - Daniel S Pine
- National Institute of Mental Health (NIMH) Intramural Research Program, Building 15K, Room 110, MSC 2670, Bethesda, MD, 20814, USA
| | - Cynthia Rogers
- Washington University School of Medicine, 4444 Forest Park Avenue, St. Louis, MO, 63108, USA
| | - Sheila Krogh-Jespersen
- Northwestern University Feinberg School of Medicine & Institute for Innovations in Developmental Sciences, 633 N. St Clair, 19th Floor, Chicago, IL, 60611, USA
| | - Elizabeth S Norton
- Northwestern University, Department of Communication Sciences and Disorders, 2240 Campus Drive, Evanston, IL, 60208, USA
| | - Lauren Wakschlag
- Northwestern University Feinberg School of Medicine & Institute for Innovations in Developmental Sciences, 633 N. St Clair, 19th Floor, Chicago, IL, 60611, USA
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Nielsen AN, Barch DM, Petersen SE, Schlaggar BL, Greene DJ. Machine Learning With Neuroimaging: Evaluating Its Applications in Psychiatry. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 5:791-798. [PMID: 31982357 DOI: 10.1016/j.bpsc.2019.11.007] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 10/29/2019] [Accepted: 11/17/2019] [Indexed: 01/08/2023]
Abstract
Psychiatric disorders are complex, involving heterogeneous symptomatology and neurobiology that rarely involves the disruption of single, isolated brain structures. In an attempt to better describe and understand the complexities of psychiatric disorders, investigators have increasingly applied multivariate pattern classification approaches to neuroimaging data and in particular supervised machine learning methods. However, supervised machine learning approaches also come with unique challenges and trade-offs, requiring additional study design and interpretation considerations. The goal of this review is to provide a set of best practices for evaluating machine learning applications to psychiatric disorders. We discuss how to evaluate two common efforts: 1) making predictions that have the potential to aid in diagnosis, prognosis, and treatment and 2) interrogating the complex neurophysiological mechanisms underlying psychopathology. We focus here on machine learning as applied to functional connectivity with magnetic resonance imaging, as an example to ground discussion. We argue that for machine learning classification to have translational utility for individual-level predictions, investigators must ensure that the classification is clinically informative, independent of confounding variables, and appropriately assessed for both performance and generalizability. We contend that shedding light on the complex mechanisms underlying psychiatric disorders will require consideration of the unique utility, interpretability, and reliability of the neuroimaging features (e.g., regions, networks, connections) identified from machine learning approaches. Finally, we discuss how the rise of large, multisite, publicly available datasets may contribute to the utility of machine learning approaches in psychiatry.
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Affiliation(s)
- Ashley N Nielsen
- Institute for Innovations in Developmental Sciences, Northwestern University, Chicago, Illinois; Department of Medical Social Sciences, Northwestern University, Chicago, Illinois.
| | - Deanna M Barch
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri; Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Steven E Petersen
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri; Department of Neuroscience, Washington University School of Medicine, St. Louis, Missouri; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Bradley L Schlaggar
- Kennedy Krieger Institute, Baltimore, Maryland; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland; Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Deanna J Greene
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
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Uddin M, Wang Y, Woodbury-Smith M. Artificial intelligence for precision medicine in neurodevelopmental disorders. NPJ Digit Med 2019; 2:112. [PMID: 31799421 PMCID: PMC6872596 DOI: 10.1038/s41746-019-0191-0] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 10/29/2019] [Indexed: 12/23/2022] Open
Abstract
The ambition of precision medicine is to design and optimize the pathway for diagnosis, therapeutic intervention, and prognosis by using large multidimensional biological datasets that capture individual variability in genes, function and environment. This offers clinicians the opportunity to more carefully tailor early interventions- whether treatment or preventative in nature-to each individual patient. Taking advantage of high performance computer capabilities, artificial intelligence (AI) algorithms can now achieve reasonable success in predicting risk in certain cancers and cardiovascular disease from available multidimensional clinical and biological data. In contrast, less progress has been made with the neurodevelopmental disorders, which include intellectual disability (ID), autism spectrum disorder (ASD), epilepsy and broader neurodevelopmental disorders. Much hope is pinned on the opportunity to quantify risk from patterns of genomic variation, including the functional characterization of genes and variants, but this ambition is confounded by phenotypic and etiologic heterogeneity, along with the rare and variable penetrant nature of the underlying risk variants identified so far. Structural and functional brain imaging and neuropsychological and neurophysiological markers may provide further dimensionality, but often require more development to achieve sensitivity for diagnosis. Herein, therefore, lies a precision medicine conundrum: can artificial intelligence offer a breakthrough in predicting risks and prognosis for neurodevelopmental disorders? In this review we will examine these complexities, and consider some of the strategies whereby artificial intelligence may overcome them.
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Affiliation(s)
- Mohammed Uddin
- Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE
- 2The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON Canada
| | - Yujiang Wang
- 3Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
- 4School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Marc Woodbury-Smith
- 2The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON Canada
- 3Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
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Khosla M, Jamison K, Kuceyeski A, Sabuncu MR. Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction. Neuroimage 2019; 199:651-662. [PMID: 31220576 PMCID: PMC6777738 DOI: 10.1016/j.neuroimage.2019.06.012] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 05/30/2019] [Accepted: 06/04/2019] [Indexed: 11/16/2022] Open
Abstract
The specificity and sensitivity of resting state functional MRI (rs-fMRI) measurements depend on preprocessing choices, such as the parcellation scheme used to define regions of interest (ROIs). In this study, we critically evaluate the effect of brain parcellations on machine learning models applied to rs-fMRI data. Our experiments reveal an intriguing trend: On average, models with stochastic parcellations consistently perform as well as models with widely used atlases at the same spatial scale. We thus propose an ensemble learning strategy to combine the predictions from models trained on connectivity data extracted using different (e.g., stochastic) parcellations. We further present an implementation of our ensemble learning strategy with a novel 3D Convolutional Neural Network (CNN) approach. The proposed CNN approach takes advantage of the full-resolution 3D spatial structure of rs-fMRI data and fits non-linear predictive models. Our ensemble CNN framework overcomes the limitations of traditional machine learning models for connectomes that often rely on region-based summary statistics and/or linear models. We showcase our approach on a classification (autism patients versus healthy controls) and a regression problem (prediction of subject's age), and report promising results.
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Affiliation(s)
- Meenakshi Khosla
- School of Electrical and Computer Engineering, Cornell University, USA
| | | | - Amy Kuceyeski
- Radiology, Weill Cornell Medical College, USA; Brain and Mind Research Institute, Weill Cornell Medical College, USA
| | - Mert R Sabuncu
- School of Electrical and Computer Engineering, Cornell University, USA; Nancy E. & Peter C. Meinig School of Biomedical Engineering, Cornell University, USA.
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Attallah O, Sharkas MA, Gadelkarim H. Fetal Brain Abnormality Classification from MRI Images of Different Gestational Age. Brain Sci 2019; 9:brainsci9090231. [PMID: 31547368 PMCID: PMC6770437 DOI: 10.3390/brainsci9090231] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 08/30/2019] [Accepted: 09/08/2019] [Indexed: 02/07/2023] Open
Abstract
Magnetic resonance imaging (MRI) is a common imaging technique used extensively to study human brain activities. Recently, it has been used for scanning the fetal brain. Amongst 1000 pregnant women, 3 of them have fetuses with brain abnormality. Hence, the primary detection and classification are important. Machine learning techniques have a large potential in aiding the early detection of these abnormalities, which correspondingly could enhance the diagnosis process and follow up plans. Most research focused on the classification of abnormal brains in a primary age has been for newborns and premature infants, with fewer studies focusing on images for fetuses. These studies associated fetal scans to scans after birth for the detection and classification of brain defects early in the neonatal age. This type of brain abnormality is named small for gestational age (SGA). This article proposes a novel framework for the classification of fetal brains at an early age (before the fetus is born). As far as we could know, this is the first study to classify brain abnormalities of fetuses of widespread gestational ages (GAs). The study incorporates several machine learning classifiers, such as diagonal quadratic discriminates analysis (DQDA), K-nearest neighbour (K-NN), random forest, naïve Bayes, and radial basis function (RBF) neural network classifiers. Moreover, several bagging and Adaboosting ensembles models have been constructed using random forest, naïve Bayes, and RBF network classifiers. The performances of these ensembles have been compared with their individual models. Our results show that our novel approach can successfully identify and classify numerous types of defects within MRI images of the fetal brain of various GAs. Using the KNN classifier, we were able to achieve the highest classification accuracy and area under receiving operating characteristics of 95.6% and 99% respectively. In addition, ensemble classifiers improved the results of their respective individual models.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications, College of Engineering and Technology, Arab Academy for Science and Technology and Maritime Transport, Alexandria, P.O. Box 1029, Egypt.
| | - Maha A Sharkas
- Department of Electronics and Communications, College of Engineering and Technology, Arab Academy for Science and Technology and Maritime Transport, Alexandria, P.O. Box 1029, Egypt.
| | - Heba Gadelkarim
- Department of Electronics and Communications, College of Engineering and Technology, Arab Academy for Science and Technology and Maritime Transport, Alexandria, P.O. Box 1029, Egypt.
- Department of Computer and Communication Engineering (SSP), Faculty of Engineering, Alexandria University, Alexandria 21526, Egypt.
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Maternal pomegranate juice intake and brain structure and function in infants with intrauterine growth restriction: A randomized controlled pilot study. PLoS One 2019; 14:e0219596. [PMID: 31433809 PMCID: PMC6703683 DOI: 10.1371/journal.pone.0219596] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 03/24/2019] [Indexed: 12/16/2022] Open
Abstract
Polyphenol-rich pomegranate juice has been shown to have benefit as a neuroprotectant in animal models of neonatal hypoxic-ischemia. No published studies have investigated maternal polyphenol administration as a potential neuroprotectant in at-risk newborns, such as those with intrauterine growth restriction (IUGR). This was a randomized, placebo-controlled, double-blind pilot study to investigate the impact of maternal pomegranate juice intake in pregnancies with IUGR, on newborn brain structure and function at term-equivalent age (TEA). Mothers with IUGR at 24-34 weeks' gestation were recruited from Barnes-Jewish Hospital obstetrical clinic. Consented mothers were randomized to treatment (8 oz. pomegranate juice) or placebo (8 oz. polyphenol-free juice) and continued to take juice daily from enrollment until delivery (mean 20.1 and 27.1 days, respectively). Infants underwent brain MRI at TEA (36-41 weeks' gestation). Brain measures were compared between groups including: brain injury score, brain metrics, brain volumes, diffusion tensor imaging and resting state functional connectivity. Statistical analyses were undertaken as modified intention-to-treat (including randomized participants who received their allocated intervention and whose infants received brain MRI) and per-protocol (including participants who strictly adhered to the protocol, based on metabolite status). Seventy-seven mothers were randomized to treatment (n = 40) or placebo (n = 37). Of these, 28 and 27 infants, respectively, underwent term-equivalent MRI. There were no group differences in brain injury, metrics or volumes. However, treatment subjects displayed reduced diffusivity within the anterior and posterior limbs of the internal capsule compared with placebo. Resting state functional connectivity demonstrated increased correlation and covariance within several networks in treatment subjects, with alterations most apparent in the visual network in per-protocol analyses. Direct effects on health were not found. In conclusion, maternal pomegranate juice intake in pregnancies with known IUGR was associated with altered white matter organization and functional connectivity in the infant brain, suggesting differences in brain structure and function following in utero pomegranate juice exposure, warranting continued investigation. Clinical trial registration. NCT00788866, registered November 11, 2008, initial participant enrollment August 21, 2012.
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Zhao L, Gong J, Xi Y, Xu M, Li C, Kang X, Yin Y, Qin W, Yin H, Shi M. MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma. Eur Radiol 2019; 30:537-546. [PMID: 31372781 DOI: 10.1007/s00330-019-06211-x] [Citation(s) in RCA: 96] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 03/21/2019] [Accepted: 04/02/2019] [Indexed: 01/30/2023]
Abstract
OBJECTIVES To establish and validate a radiomics nomogram for prediction of induction chemotherapy (IC) response and survival in nasopharyngeal carcinoma (NPC) patients. METHODS One hundred twenty-three NPC patients (100 in training and 23 in validation cohort) with multi-MR images were enrolled. A radiomics nomogram was established by integrating the clinical data and radiomics signature generated by support vector machine. RESULTS The radiomics signature consisting of 19 selected features from the joint T1-weighted (T1-WI), T2-weighted (T2-WI), and contrast-enhanced T1-weighted MRI images (T1-C) showed good prognostic performance in terms of evaluating IC response in two cohorts. The radiomics nomogram established by integrating the radiomics signature with clinical data outperformed clinical nomogram alone (C-index in validation cohort, 0.863 vs 0.549; p < 0.01). Decision curve analysis demonstrated the clinical utility of the radiomics nomogram. Survival analysis showed that IC responders had significant better PFS (progression-free survival) than non-responders (3-year PFS 84.81% vs 39.75%, p < 0.001). Low-risk groups defined by radiomics signature had significant better PFS than high-risk groups (3-year PFS 76.24% vs 48.04%, p < 0.05). CONCLUSIONS Multiparametric MRI-based radiomics could be helpful for personalized risk stratification and treatment in NPC patients receiving IC. KEY POINTS • MRI Radiomics can predict IC response and survival in non-endemic NPC. • Radiomics signature in combination with clinical data showed excellent predictive performance. • Radiomics signature could separate patients into two groups with different prognosis.
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Affiliation(s)
- Lina Zhao
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Jie Gong
- Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, 710126, China
| | - Yibin Xi
- Department of Radiology, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Man Xu
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Chen Li
- Department of Radiology, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Xiaowei Kang
- Department of Radiology, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Yutian Yin
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Wei Qin
- Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, 710126, China.
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Mei Shi
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University, Xi'an, China.
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Wheelock MD, Culver JP, Eggebrecht AT. High-density diffuse optical tomography for imaging human brain function. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2019; 90:051101. [PMID: 31153254 PMCID: PMC6533110 DOI: 10.1063/1.5086809] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Accepted: 04/14/2019] [Indexed: 05/08/2023]
Abstract
This review describes the unique opportunities and challenges for noninvasive optical mapping of human brain function. Diffuse optical methods offer safe, portable, and radiation free alternatives to traditional technologies like positron emission tomography or functional magnetic resonance imaging (fMRI). Recent developments in high-density diffuse optical tomography (HD-DOT) have demonstrated capabilities for mapping human cortical brain function over an extended field of view with image quality approaching that of fMRI. In this review, we cover fundamental principles of the diffusion of near infrared light in biological tissue. We discuss the challenges involved in the HD-DOT system design and implementation that must be overcome to acquire the signal-to-noise necessary to measure and locate brain function at the depth of the cortex. We discuss strategies for validation of the sensitivity, specificity, and reliability of HD-DOT acquired maps of cortical brain function. We then provide a brief overview of some clinical applications of HD-DOT. Though diffuse optical measurements of neurophysiology have existed for several decades, tremendous opportunity remains to advance optical imaging of brain function to address a crucial niche in basic and clinical neuroscience: that of bedside and minimally constrained high fidelity imaging of brain function.
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Affiliation(s)
- Muriah D. Wheelock
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | | | - Adam T. Eggebrecht
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
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