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Wang MB, Rahmani F, Benzinger TLS, Raji CA. Edge Density Imaging Identifies White Matter Biomarkers of Late-Life Obesity and Cognition. Aging Dis 2024; 15:1899-1912. [PMID: 37196133 PMCID: PMC11272213 DOI: 10.14336/ad.2022.1210] [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: 08/13/2022] [Accepted: 12/10/2022] [Indexed: 05/19/2023] Open
Abstract
Alzheimer disease (AD) and obesity are related to disruptions in the white matter (WM) connectome. We examined the link between the WM connectome and obesity and AD through edge-density imaging/index (EDI), a tractography-based method that characterizes the anatomical embedding of tractography connections. A total of 60 participants, 30 known to convert from normal cognition or mild-cognitive impairment to AD within a minimum of 24 months of follow up, were selected from the Alzheimer disease Neuroimaging Initiative (ADNI). Diffusion-weighted MR images from the baseline scans were used to extract fractional anisotropy (FA) and EDI maps that were subsequently averaged using deterministic WM tractography based on the Desikan-Killiany atlas. Multiple linear and logistic regression analysis were used to identify the weighted sum of tract-specific FA or EDI indices that maximized correlation to body-mass-index (BMI) or conversion to AD. Participants from the Open Access Series of Imaging Studies (OASIS) were used as an independent validation for the BMI findings. The edge-density rich, periventricular, commissural and projection fibers were among the most important WM tracts linking BMI to FA as well as to EDI. WM fibers that contributed significantly to the regression model related to BMI overlapped with those that predicted conversion; specifically in the frontopontine, corticostriatal, and optic radiation pathways. These results were replicated by testing the tract-specific coefficients found using ADNI in the OASIS-4 dataset. WM mapping with EDI enables identification of an abnormal connectome implicated in both obesity and conversion to AD.
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Affiliation(s)
- Maxwell Bond Wang
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
- Medical Scientist Training Program, University of Pittsburgh/Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Farzaneh Rahmani
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA.
- Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), Washington University, St. Louis, Missouri, USA.
| | - Tammie L. S Benzinger
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA.
- Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), Washington University, St. Louis, Missouri, USA.
| | - Cyrus A Raji
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA.
- Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), Washington University, St. Louis, Missouri, USA.
- Department of Neurology, Washington University in Saint Louis, St. Louis, Missouri, USA
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2
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Di Sarno L, Caroselli A, Tonin G, Graglia B, Pansini V, Causio FA, Gatto A, Chiaretti A. Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives. Biomedicines 2024; 12:1220. [PMID: 38927427 PMCID: PMC11200597 DOI: 10.3390/biomedicines12061220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/19/2024] [Accepted: 05/28/2024] [Indexed: 06/28/2024] Open
Abstract
The dawn of Artificial intelligence (AI) in healthcare stands as a milestone in medical innovation. Different medical fields are heavily involved, and pediatric emergency medicine is no exception. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies. We examined PubMed and Cochrane Library from inception up to April 2024. Key applications include triage optimization, predictive models for traumatic brain injury assessment, and computerized sepsis prediction systems. In each of these domains, AI models outperformed standard methods. The main barriers to a widespread adoption include technological challenges, but also ethical issues, age-related differences in data interpretation, and the paucity of comprehensive datasets in the pediatric context. Future feasible research directions should address the validation of models through prospective datasets with more numerous sample sizes of patients. Furthermore, our analysis shows that it is essential to tailor AI algorithms to specific medical needs. This requires a close partnership between clinicians and developers. Building a shared knowledge platform is therefore a key step.
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Affiliation(s)
- Lorenzo Di Sarno
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
| | - Anya Caroselli
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
| | - Giovanna Tonin
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.T.); (V.P.)
| | - Benedetta Graglia
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
| | - Valeria Pansini
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.T.); (V.P.)
| | - Francesco Andrea Causio
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
- Section of Hygiene and Public Health, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Antonio Gatto
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.T.); (V.P.)
| | - Antonio Chiaretti
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
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3
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Cai LT, Moon J, Camacho PB, Anderson AT, Chwa WJ, Sutton BP, Markowitz AJ, Palacios EM, Rodriguez A, Manley GT, Shankar S, Bremer PT, Mukherjee P, Madduri RK. MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline. Neuroinformatics 2024; 22:177-191. [PMID: 38446357 DOI: 10.1007/s12021-024-09650-0] [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] [Accepted: 01/29/2024] [Indexed: 03/07/2024]
Abstract
Large-scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that requires many software packages with complex dependencies and high computational costs. We developed MaPPeRTrac, an edge-centric tractography pipeline that simplifies and accelerates this process in a wide range of high-performance computing (HPC) environments. It fully automates either probabilistic or deterministic tractography, starting from a subject's magnetic resonance imaging (MRI) data, including structural and diffusion MRI images, to the edge density image (EDI) of their structural connectomes. Dependencies are containerized with Singularity (now called Apptainer) and decoupled from code to enable rapid prototyping and modification. Data derivatives are organized with the Brain Imaging Data Structure (BIDS) to ensure that they are findable, accessible, interoperable, and reusable following FAIR principles. The pipeline takes full advantage of HPC resources using the Parsl parallel programming framework, resulting in the creation of connectome datasets of unprecedented size. MaPPeRTrac is publicly available and tested on commercial and scientific hardware, so it can accelerate brain connectome research for a broader user community. MaPPeRTrac is available at: https://github.com/LLNL/mappertrac .
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Affiliation(s)
- Lanya T Cai
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., San Francisco, CA, 94107, USA
| | - Joseph Moon
- Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA, 94550, USA
| | - Paul B Camacho
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N Mathews Ave, Urbana, IL, 61801, USA
| | - Aaron T Anderson
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N Mathews Ave, Urbana, IL, 61801, USA
| | - Won Jong Chwa
- Department of Radiology, Washington University in St. Louis, 510 S Kingshighway Blvd, St. Louis, MO, 63110, USA
| | - Bradley P Sutton
- Bioengineering Department, University of Illinois at Urbana-Champaign, 506 S Mathews Ave, Urbana, IL, 61801, USA
| | - Amy J Markowitz
- Department of Neurosurgery, University of California, San Francisco, 400 Parnassus Ave, San Francisco, CA, 94143, USA
| | - Eva M Palacios
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., San Francisco, CA, 94107, USA
| | - Alexis Rodriguez
- Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL, 60439, USA
| | - Geoffrey T Manley
- Department of Neurosurgery, University of California, San Francisco, 400 Parnassus Ave, San Francisco, CA, 94143, USA
| | - Shivsundaram Shankar
- Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA, 94550, USA
| | - Peer-Timo Bremer
- Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA, 94550, USA
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., San Francisco, CA, 94107, USA.
| | - Ravi K Madduri
- Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL, 60439, USA.
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4
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Klein RM, Good SR, Christie JJ. Changes in the Networks of Attention across the Lifespan: A Graphical Meta-Analysis. J Intell 2024; 12:19. [PMID: 38392175 PMCID: PMC10889552 DOI: 10.3390/jintelligence12020019] [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: 09/08/2023] [Revised: 11/24/2023] [Accepted: 01/12/2024] [Indexed: 02/24/2024] Open
Abstract
Three Posnerian networks of attention (alerting, orienting, and executive control) have been distinguished on the bases of behavioural, neuropsychological, and neuroscientific evidence. Here, we examined the trajectories of these networks throughout the human lifespan using the various Attention Network Tests (ANTs), which were specifically developed to measure the efficacy of these networks. The ANT Database was used to identify relevant research, resulting in the inclusion of 36 publications. We conducted a graphical meta-analysis using network scores from each study, based on reaction time plotted as a function of age group. Evaluation of attentional networks from childhood to early adulthood suggests that the alerting network develops relatively quickly, and reaches near-adult level by the age of 12. The developmental pattern of the orienting network seems to depend on the information value of the spatial cues. Executive control network scores show a consistent decrease (improvement) with age in childhood. During adulthood (ages 19-75), changes in alerting depend on the modality of the warning signal, while a moderate increase in orienting scores was seen with increasing age. Whereas executive control scores, as measured in reaction time, increase (deterioration) from young adulthood into later adulthood an opposite trend is seen when scores are based on error rates.
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Affiliation(s)
- Raymond M Klein
- Department of Psychology & Neuroscience, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Samantha R Good
- Department of Psychology & Neuroscience, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - John J Christie
- Department of Psychology & Neuroscience, Dalhousie University, Halifax, NS B3H 4R2, Canada
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5
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Teng J, Liu W, Mi C, Zhang H, Shi J, Li N. Extracting the most discriminating functional connections in mild traumatic brain injury based on machine learning. Neurosci Lett 2023; 810:137311. [PMID: 37236344 DOI: 10.1016/j.neulet.2023.137311] [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: 04/10/2023] [Revised: 05/17/2023] [Accepted: 05/21/2023] [Indexed: 05/28/2023]
Abstract
BACKGROUND Mild traumatic brain injury (mTBI) is characterized as brain microstructural damage, which may cause a wide range of brain functional disturbances and emotional problems. Brain network analysis based on machine learning is an important means of neuroimaging research. Obtaining the most discriminating functional connection is of great significance to analyze the pathological mechanism of mTBI. METHODS To better obtain the most discriminating features of functional connection networks, this study proposes a hierarchical feature selection pipeline (HFSP) composed of Variance Filtering (VF), Lasso, and Principal Component Analysis (PCA). Ablation experiments indicate that each module plays a positive role in classification, validating the robustness and reliability of the HFSP. Furthermore, the HFSP is compared with recursive feature elimination (RFE), elastic net (EN), and locally linear embedding (LLE), verifying its superiority. In addition, this study also utilizes random forest (RF), SVM, Bayesian, linear discriminant analysis (LDA), and logistic regression (LR) as classifiers to evaluate the generalizability of HFSP. RESULTS The results show that the indexes obtained from RF are the highest, with accuracy = 89.74%, precision = 91.26%, recall = 89.74%, and F1 score = 89.42%. The HFSP selects 25 pairs of the most discriminating functional connections, mainly distributed in the frontal lobe, occipital lobe, and cerebellum. Nine brain regions show the largest node degree. LIMITATIONS The number of samples is small. This study only includes acute mTBI. CONCLUSIONS The HFSP is a useful tool for extracting discriminating functional connections and may contribute to the diagnostic processes.
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Affiliation(s)
- Jing Teng
- The School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, Beijing, China.
| | - Wuyi Liu
- The School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, Beijing, China.
| | - Chunlin Mi
- The School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, Beijing, China.
| | - Honglei Zhang
- The School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, Beijing, China.
| | - Jian Shi
- Department of Critical Care Medicine and Hematology, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan Province, China; Department of Spine Surgery, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan Province, China.
| | - Na Li
- Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan Province, China.
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6
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Ware AL, Onicas AI, Abdeen N, Beauchamp MH, Beaulieu C, Bjornson BH, Craig W, Dehaes M, Deschenes S, Doan Q, Freedman SB, Goodyear BG, Gravel J, Ledoux AA, Zemek R, Yeates KO, Lebel C. Altered longitudinal structural connectome in paediatric mild traumatic brain injury: an Advancing Concussion Assessment in Paediatrics study. Brain Commun 2023; 5:fcad173. [PMID: 37324241 PMCID: PMC10265725 DOI: 10.1093/braincomms/fcad173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 04/18/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023] Open
Abstract
Advanced diffusion-weighted imaging techniques have increased understanding of the neuropathology of paediatric mild traumatic brain injury (i.e. concussion). Most studies have examined discrete white-matter pathways, which may not capture the characteristically subtle, diffuse and heterogenous effects of paediatric concussion on brain microstructure. This study compared the structural connectome of children with concussion to those with mild orthopaedic injury to determine whether network metrics and their trajectories across time post-injury differentiate paediatric concussion from mild traumatic injury more generally. Data were drawn from of a large study of outcomes in paediatric concussion. Children aged 8-16.99 years were recruited from five paediatric emergency departments within 48 h of sustaining a concussion (n = 360; 56% male) or mild orthopaedic injury (n = 196; 62% male). A reliable change score was used to classify children with concussion into two groups: concussion with or without persistent symptoms. Children completed 3 T MRI at post-acute (2-33 days) and/or chronic (3 or 6 months, via random assignment) post-injury follow-ups. Diffusion-weighted images were used to calculate the diffusion tensor, conduct deterministic whole-brain fibre tractography and compute connectivity matrices in native (diffusion) space for 90 supratentorial regions. Weighted adjacency matrices were constructed using average fractional anisotropy and used to calculate global and local (regional) graph theory metrics. Linear mixed effects modelling was performed to compare groups, correcting for multiple comparisons. Groups did not differ in global network metrics. However, the clustering coefficient, betweenness centrality and efficiency of the insula, cingulate, parietal, occipital and subcortical regions differed among groups, with differences moderated by time (days) post-injury, biological sex and age at time of injury. Post-acute differences were minimal, whereas more robust alterations emerged at 3 and especially 6 months in children with concussion with persistent symptoms, albeit differently by sex and age. In the largest neuroimaging study to date, post-acute regional network metrics distinguished concussion from mild orthopaedic injury and predicted symptom recovery 1-month post-injury. Regional network parameters alterations were more robust and widespread at chronic timepoints than post-acutely after concussion. Results suggest that increased regional and local subnetwork segregation (modularity) and inefficiency occurs across time after concussion, emerging after post-concussive symptom resolve in most children. These differences persist up to 6 months after concussion, especially in children who showed persistent symptoms. While prognostic, the small to modest effect size of group differences and the moderating effects of sex likely would preclude effective clinical application in individual patients.
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Affiliation(s)
- Ashley L Ware
- Correspondence to: Ashley L. Ware, PhD Department of Psychology, Georgia State University 140 Decatur Street SE, Atlanta, GA 30303, USA E-mail:
| | - Adrian I Onicas
- Department of Psychology, University of Calgary, Calgary, AB T2N 0V2, Canada
- Computer Vision Group, Sano Centre for Computational Medicine, Kraków 30-054, Poland
| | - Nishard Abdeen
- Department of Radiology, Children’s Hospital of Eastern Ontario Research Institute, University of Ottawa,Ottawa, ON, Canada K1H 8L1
| | - Miriam H Beauchamp
- Department of Psychology, University of Montreal and CHU Sainte-Justine Hospital Research Center, Montréal, QC, Canada H3C 3J7
| | - Christian Beaulieu
- Department of Biomedical Engineering, 1098 Research Transition Facility, University of Alberta, Edmonton, AB, Canada T6G 2V2
| | - Bruce H Bjornson
- Division of Neurology, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada V6H 3V4
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada V6H 3V4
| | - William Craig
- University of Alberta and Stollery Children’s Hospital, Edmonton, AB, Canada T6G 1C9
| | - Mathieu Dehaes
- Department of Radiology, Radio-oncology and Nuclear Medicine, Institute of Biomedical Engineering, University of Montreal, Montréal, QC, Canada H3T1J4
- CHU Sainte-Justine Research Center, Montréal, QC, Canada H3T1C5
| | - Sylvain Deschenes
- CHU Sainte-Justine Research Center, Montréal, QC, Canada H3T1C5
- Department of Radiology, Radio-oncology and Nuclear Medicine, University of Montreal, Montréal, QC, CHU Sainte-Justine Research Center, Montréal, QC, Canada H3T1C5
| | - Quynh Doan
- Department of Pediatrics University of British Columbia, BC Children’s Hospital Research Institute, Vancouver, BC, Canada V5Z 4H4
| | - Stephen B Freedman
- Departments of Pediatric and Emergency Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada T3B 6A8
| | - Bradley G Goodyear
- Alberta Children's Hospital Research Institute and Hotchkiss Brain Institute, University of Calgary, AB T2N 0V2, Canada
- Department of Radiology, University of Calgary, Calgary, AB T2N 0V2, Canada
| | - Jocelyn Gravel
- Pediatric Emergency Department, CHU Sainte-Justine, Montréal, QC H3T1C5, Canada
- Department of Pediatric, Université de Montréal, Montréal, QC H3T 1C5, Canada
| | - Andrée-Anne Ledoux
- Department of Cellular Molecular Medicine, University of Ottawa, Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada K1H8L1
| | - Roger Zemek
- Department of Pediatrics and Emergency Medicine, University of Ottawa, Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada K1H8L1
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Yeung HW, Stolicyn A, Buchanan CR, Tucker‐Drob EM, Bastin ME, Luz S, McIntosh AM, Whalley HC, Cox SR, Smith K. Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes. Hum Brain Mapp 2023; 44:1913-1933. [PMID: 36541441 PMCID: PMC9980898 DOI: 10.1002/hbm.26182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 11/11/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.
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Affiliation(s)
- Hon Wah Yeung
- Department of PsychiatryUniversity of EdinburghEdinburghUK
| | - Aleks Stolicyn
- Department of PsychiatryUniversity of EdinburghEdinburghUK
| | - Colin R. Buchanan
- Department of PsychologyUniversity of EdinburghEdinburghUK
- Lothian Birth Cohorts, University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE)EdinburghUK
| | - Elliot M. Tucker‐Drob
- Department of PsychologyUniversity of TexasAustinTexasUSA
- Population Research Center and Center on Aging and Population SciencesUniversity of Texas at AustinAustinTexasUSA
| | - Mark E. Bastin
- Lothian Birth Cohorts, University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE)EdinburghUK
- Centre for Clinical Brain ScienceUniversity of EdinburghEdinburghUK
| | - Saturnino Luz
- Edinburgh Medical SchoolUsher Institute, The University of EdinburghEdinburghUK
| | - Andrew M. McIntosh
- Department of PsychiatryUniversity of EdinburghEdinburghUK
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Molecular Medicine, University of EdinburghEdinburghUK
| | | | - Simon R. Cox
- Department of PsychologyUniversity of EdinburghEdinburghUK
- Lothian Birth Cohorts, University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE)EdinburghUK
| | - Keith Smith
- Department of Physics and MathematicsNottingham Trent UniversityNottinghamUK
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8
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Cao M, Wu K, Halperin JM, Li X. Abnormal structural and functional network topological properties associated with left prefrontal, parietal, and occipital cortices significantly predict childhood TBI-related attention deficits: A semi-supervised deep learning study. Front Neurosci 2023; 17:1128646. [PMID: 36937671 PMCID: PMC10017753 DOI: 10.3389/fnins.2023.1128646] [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: 12/21/2022] [Accepted: 02/17/2023] [Indexed: 03/06/2023] Open
Abstract
Introduction Traumatic brain injury (TBI) is a major public health concern in children. Children with TBI have elevated risk in developing attention deficits. Existing studies have found that structural and functional alterations in multiple brain regions were linked to TBI-related attention deficits in children. Most of these existing studies have utilized conventional parametric models for group comparisons, which have limited capacity in dealing with large-scale and high dimensional neuroimaging measures that have unknown nonlinear relationships. Nevertheless, none of these existing findings have been successfully implemented to clinical practice for guiding diagnoses and interventions of TBI-related attention problems. Machine learning techniques, especially deep learning techniques, are able to handle the multi-dimensional and nonlinear information to generate more robust predictions. Therefore, the current research proposed to construct a deep learning model, semi-supervised autoencoder, to investigate the topological alterations in both structural and functional brain networks in children with TBI and their predictive power for post-TBI attention deficits. Methods Functional magnetic resonance imaging data during sustained attention processing task and diffusion tensor imaging data from 110 subjects (55 children with TBI and 55 group-matched controls) were used to construct the functional and structural brain networks, respectively. A total of 60 topological properties were selected as brain features for building the model. Results The model was able to differentiate children with TBI and controls with an average accuracy of 82.86%. Functional and structural nodal topological properties associated with left frontal, inferior temporal, postcentral, and medial occipitotemporal regions served as the most important brain features for accurate classification of the two subject groups. Post hoc regression-based machine learning analyses in the whole study sample showed that among these most important neuroimaging features, those associated with left postcentral area, superior frontal region, and medial occipitotemporal regions had significant value for predicting the elevated inattentive and hyperactive/impulsive symptoms. Discussion Findings of this study suggested that deep learning techniques may have the potential to help identifying robust neurobiological markers for post-TBI attention deficits; and the left superior frontal, postcentral, and medial occipitotemporal regions may serve as reliable targets for diagnosis and interventions of TBI-related attention problems in children.
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Affiliation(s)
- Meng Cao
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China
| | - Jeffery M. Halperin
- Department of Psychology, Queens College, City University of New York, New York, NY, United States
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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9
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Tjønndal A, Røsten S. Safeguarding Athletes Against Head Injuries Through Advances in Technology: A Scoping Review of the Uses of Machine Learning in the Management of Sports-Related Concussion. Front Sports Act Living 2022; 4:837643. [PMID: 35520095 PMCID: PMC9067303 DOI: 10.3389/fspor.2022.837643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Sports injury prevention is an important part of the athlete welfare and safeguarding research field. In sports injury prevention, sport-related concussion (SRC) has proved to be one of the most difficult and complex injuries to manage in terms of prevention, diagnosis, classification, treatment and rehabilitation. SRC can cause long-term health issues and is a commonly reported injury in both adult and youth athletes around the world. Despite increased knowledge of the prevalence of SRC, very few tools are available for diagnosing SRC in athletic settings. Recent technological innovations have resulted in different machine learning and deep learning methodologies being tested to improve the management of this complex sports injury. The purpose of this article is to summarize and map the existing research literature on the use of machine learning in the management of SRC, ascertain where there are gaps in the existing research and identify recommendations for future research. This is explored through a scoping review. A systematic search in the three electronic databases SPORTDiscus, PubMed and Scopus identified an initial 522 studies, of which 24 were included in the final review, the majority of which focused on machine learning for the prediction and prevention of SRC (N = 10), or machine learning for the diagnosis and classification of SRC (N = 11). Only 3 studies explored machine learning approaches for the treatment and rehabilitation of SRC. A main finding is that current research highlights promising practical uses (e.g., more accurate and rapid injury assessment or return-to-sport participation criteria) of machine learning in the management of SRC. The review also revealed a narrow research focus in the existing literature. As current research is primarily conducted on male adolescents or adults from team sports in North America there is an urgent need to include wider demographics in more diverse samples and sports contexts in the machine learning algorithms. If research datasets continue to be based on narrow samples of athletes, the development of any new diagnostic and predictive tools for SRC emerging from this research will be at risk. Today, these risks appear to mainly affect the health and safety of female athletes.
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10
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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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11
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Ware AL, Yeates KO, Geeraert B, Long X, Beauchamp MH, Craig W, Doan Q, Freedman SB, Goodyear BG, Zemek R, Lebel C. Structural connectome differences in pediatric mild traumatic brain and orthopedic injury. Hum Brain Mapp 2021; 43:1032-1046. [PMID: 34748258 PMCID: PMC8764485 DOI: 10.1002/hbm.25705] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 09/23/2021] [Accepted: 10/18/2021] [Indexed: 01/06/2023] Open
Abstract
Sophisticated network‐based approaches such as structural connectomics may help to detect a biomarker of mild traumatic brain injury (mTBI) in children. This study compared the structural connectome of children with mTBI or mild orthopedic injury (OI) to that of typically developing (TD) children. Children aged 8–16.99 years with mTBI (n = 83) or OI (n = 37) were recruited from the emergency department and completed 3T diffusion MRI 2–20 days postinjury. TD children (n = 39) were recruited from the community and completed diffusion MRI. Graph theory metrics were calculated for the binarized average fractional anisotropy among 90 regions. Multivariable linear regression and linear mixed effects models were used to compare groups, with covariates age, hemisphere, and sex, correcting for multiple comparisons. The two injury groups did not differ on graph theory metrics, but both differed from TD children in global metrics (local network efficiency: TD > OI, mTBI, d = 0.49; clustering coefficient: TD < OI, mTBI, d = 0.49) and regional metrics for the fusiform gyrus (lower degree centrality and nodal efficiency: TD > OI, mTBI, d = 0.80 to 0.96; characteristic path length: TD < OI, mTBI, d = −0.75 to −0.90) and in the superior and middle orbital frontal gyrus, paracentral lobule, insula, and thalamus (clustering coefficient: TD > OI, mTBI, d = 0.66 to 0.68). Both mTBI and OI demonstrated reduced global and regional network efficiency and segregation as compared to TD children. Findings suggest a general effect of childhood injury that could reflect pre‐ and postinjury factors that can alter brain structure. An OI group provides a more conservative comparison group than TD children for structural neuroimaging research in pediatric mTBI.
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Affiliation(s)
- Ashley L Ware
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada.,Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Keith Owen Yeates
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada
| | - Bryce Geeraert
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
| | - Xiangyu Long
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
| | - Miriam H Beauchamp
- Department of Psychology, University of Montreal & CHU Sainte-Justine Hospital Research Center, Montreal, Quebec, Canada
| | - William Craig
- University of Alberta and Stollery Children's Hospital, Edmonton, Alberta, Canada
| | - Quynh Doan
- Pediatric Emergency Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Stephen B Freedman
- Department of Emergency Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Bradley G Goodyear
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
| | - Roger Zemek
- Department of Pediatrics and Emergency Medicine, University of Ottawa, Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Catherine Lebel
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
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