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
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...
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| Veröffentlicht in: | Frontiers in neuroscience Jg. 17; S. 1128646 |
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| Abstract | 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.
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.
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.
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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| AbstractList | 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.IntroductionTraumatic 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.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.MethodsFunctional 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.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.ResultsThe 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.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.DiscussionFindings 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. IntroductionTraumatic 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.MethodsFunctional 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.ResultsThe 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.DiscussionFindings 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. 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. 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. 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. 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. |
| Author | Wu, Kai Halperin, Jeffery M. Cao, Meng Li, Xiaobo |
| AuthorAffiliation | 1 Department of Biomedical Engineering, New Jersey Institute of Technology , Newark, NJ , United States 4 Department of Electrical and Computer Engineering, New Jersey Institute of Technology , Newark, NJ , United States 2 School of Biomedical Sciences and Engineering, South China University of Technology , Guangzhou , China 3 Department of Psychology, Queens College, City University of New York , New York, NY , United States |
| AuthorAffiliation_xml | – name: 2 School of Biomedical Sciences and Engineering, South China University of Technology , Guangzhou , China – name: 1 Department of Biomedical Engineering, New Jersey Institute of Technology , Newark, NJ , United States – name: 4 Department of Electrical and Computer Engineering, New Jersey Institute of Technology , Newark, NJ , United States – name: 3 Department of Psychology, Queens College, City University of New York , New York, NY , United States |
| Author_xml | – sequence: 1 givenname: Meng surname: Cao fullname: Cao, Meng – sequence: 2 givenname: Kai surname: Wu fullname: Wu, Kai – sequence: 3 givenname: Jeffery M. surname: Halperin fullname: Halperin, Jeffery M. – sequence: 4 givenname: Xiaobo surname: Li fullname: Li, Xiaobo |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36937671$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.neuroimage.2012.01.021 10.1162/089892903321208196 10.1007/978-3-030-80519-7 10.1097/00004583-200010000-00002 10.1016/0028-3932(71)90067-4 10.1007/s00221-008-1642-z 10.1001/jamapediatrics.2018.2847 10.1016/j.ijdevneu.2012.01.003 10.1038/srep22959 10.1017/S1355617719000444 10.1016/j.neuroimage.2021.118870 10.1089/neu.2014.3534 10.1371/journal.pcbi.0030017 10.1523/JNEUROSCI.4793-12.2013 10.1017/S1355617708080545 10.1126/science.1138071 10.1016/j.neunet.2015.09.014 10.1016/S0140-6736(74)91639-0 10.1007/s11682-012-9150-y 10.1177/1073858413514136 10.1177/1545968316675430 10.1109/ICACA.2016.7887916 10.1002/acn3.50951 10.1126/science.1127647 10.1016/j.cortex.2022.05.014 10.1371/journal.pone.0087357 10.1038/nrn2575 10.1002/hbm.25705 10.3233/PRM-150350 10.1016/j.neuroimage.2008.11.007 10.1016/j.rehab.2019.09.003 10.1016/j.pmrj.2014.04.004 10.1006/nimg.2001.0978 10.1371/journal.pone.0215520 10.3233/PRM-2009-0093 10.1016/j.bpsc.2019.11.007 10.1371/journal.pone.0267456 10.1016/j.neuroimage.2015.10.019 10.1089/neu.2015.4012 10.1155/2015/104282 10.1176/appi.books.9780890425596 10.2147/NDT.S239013 10.1097/HTR.0000000000000432 10.1017/S1355617710001414 10.3390/brainsci11101348 10.1038/nature14539 10.1016/j.neuroimage.2009.10.003 10.1007/s00247-020-04743-9 10.1542/peds.2015-0437 10.1016/j.neuron.2007.05.031 10.1037/t15171-000 10.1097/01.chi.0000173292.05817.f8 10.1080/10705510903439003 10.1097/HTR.0000000000000567 10.1016/j.cortex.2021.07.005 10.1016/j.nicl.2012.09.011 10.2753/MTP1069-6679190202 10.1002/hbm.23614 10.1002/hbm.23741 10.1089/brain.2020.0866 10.1038/s41467-022-32420-y 10.1007/s11682-017-9673-3 10.1126/science.289.5482.1206 10.1186/s12963-015-0037-1 10.1089/neu.2017.5265 10.1016/j.wneu.2016.03.045 10.1023/A:1010933404324 10.1016/j.nicl.2015.02.002 10.1097/HTR.0000000000000550 10.1016/j.neuroimage.2015.06.092 10.1016/j.patcog.2022.108562 10.1016/j.jaac.2012.08.013 10.1006/nimg.2001.0931 10.2217/iim.10.21 10.1016/j.acn.2007.03.004 10.3389/fneur.2021.734329 |
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| Keywords | autoencoder graph theory traumatic brain injury diffusion tensor imaging attention deficits functional magnetic resonance imaging semi-supervised deep learning technique pediatric |
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| References | Cao (B16) 2022; 17 Noh (B56) 2017; 30 Teasdale (B68) 1974; 2 Breiman (B10) 2001; 45 Wozniak (B79) 2007; 22 Ross (B61) 2014; 9 Ceh (B18) 2021; 143 Li (B47) 2012; 51 Iyer (B31) 2019; 6 Tamez-Pena (B67) 2021; 12 Andersson (B3) 2016; 125 Hansen (B28) 2022; 13 Fischl (B23) 2012; 62 Achard (B2) 2007; 3 Mechelli (B52) 2003; 15 Katsuki (B34) 2014; 20 Buschman (B12) 2007; 315 Nielsen (B55) 2020; 5 Rubinov (B63) 2010; 52 LeCun (B46) 2015; 521 Konigs (B39) 2018; 12 Konigs (B40) 2017; 38 Bullmore (B11) 2009; 10 Ashourvan (B4) 2019; 14 Cao (B17) 2013; 33 Fisher (B24) 2019; 20 Kingma (B37) 2014 Wilde (B77); 30 Dennis (B21) 2015; 7 Girard (B25) 2017; 38 Vinckier (B72) 2007; 55 Jones (B32) 2010; 2 Cao (B15); 11 Abadi (B1) 2016 Sun (B66) 2020; 16 Conners (B19) 2008 Kaufman (B35) 2000 Max (B50) 2005; 44 Benedek (B8) 2016; 6 Oldfield (B57) 1971; 9 Wechsler (B75) 2011 LeBlond (B45) 2019; 25 Henseler (B29) 2010; 17 Pereira (B58) 2009; 45 Yuan (B80) 2017; 31 Dennis (B20) 2016; 33 Audhkhasi (B6) 2016; 78 Konigs (B38) 2015; 136 Rossi (B62) 2009; 192 Strazzer (B65) 2015; 2015 Narad (B54) 2019; 35 Ware (B74) 2022; 43 Lumba-Brown (B48) 2018; 172 Kramer (B41) 2008; 14 Muthukrishnan (B53) 2016 Cao (B14); 11 Macaluso (B49) 2000; 289 Smith (B64) 2015; 119 Kurowski (B42) 2009; 2 Raji (B60) 2020; 50 Wilde (B76); 6 Woolrich (B78) 2001; 14 Le Fur (B44) 2019; 63 Zhang (B81) 2022; 249 Polinder (B59) 2015; 13 Kurowski (B43) 2019; 34 Caeyenberghs (B13) 2012; 1 Keenan (B36) 2018; 35 Tlustos (B69) 2011; 17 Kamal (B33) 2022; 126 Mayer (B51) 2015; 32 Wade (B73) 2020; 35 Hinton (B30) 2006; 313 Tzourio-Mazoyer (B71) 2002; 15 Backeljauw (B7) 2014; 6 Hair (B26) 2021 Hair (B27) 2011; 19 Dewan (B22) 2016; 91 Botchway (B9) 2022; 154 Tlustos (B70) 2015; 8 Association (B5) 2013 |
| References_xml | – volume: 62 start-page: 774 year: 2012 ident: B23 article-title: FreeSurfer. publication-title: Neuroimage doi: 10.1016/j.neuroimage.2012.01.021 – volume: 15 start-page: 260 year: 2003 ident: B52 article-title: Neuroimaging studies of word and pseudoword reading: consistencies, inconsistencies, and limitations. publication-title: J. Cogn. Neurosci. doi: 10.1162/089892903321208196 – year: 2021 ident: B26 publication-title: Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. doi: 10.1007/978-3-030-80519-7 – year: 2000 ident: B35 publication-title: K-Sads-Pl. doi: 10.1097/00004583-200010000-00002 – volume: 9 start-page: 97 year: 1971 ident: B57 article-title: The assessment and analysis of handedness: the Edinburgh inventory. publication-title: Neuropsychologia doi: 10.1016/0028-3932(71)90067-4 – volume: 192 start-page: 489 year: 2009 ident: B62 article-title: The prefrontal cortex and the executive control of attention. publication-title: Exp Brain Res doi: 10.1007/s00221-008-1642-z – volume: 172 year: 2018 ident: B48 article-title: Diagnosis and management of mild traumatic brain injury in children: a systematic review. publication-title: JAMA Pediatr. doi: 10.1001/jamapediatrics.2018.2847 – volume: 30 start-page: 267 ident: B77 article-title: Longitudinal changes in cortical thickness in children after traumatic brain injury and their relation to behavioral regulation and emotional control. publication-title: Int. J. Dev. Neurosci. doi: 10.1016/j.ijdevneu.2012.01.003 – volume: 6 year: 2016 ident: B8 article-title: Brain mechanisms associated with internally directed attention and self-generated thought. publication-title: Sci. Rep. doi: 10.1038/srep22959 – volume: 25 start-page: 740 year: 2019 ident: B45 article-title: Influence of methylphenidate on long-term neuropsychological and everyday executive functioning after traumatic brain injury in children with secondary attention problems. publication-title: J. Int. Neuropsychol. Soc. doi: 10.1017/S1355617719000444 – volume: 249 year: 2022 ident: B81 article-title: Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review. publication-title: Neuroimage doi: 10.1016/j.neuroimage.2021.118870 – volume: 32 start-page: 723 year: 2015 ident: B51 article-title: Gray matter abnormalities in pediatric mild traumatic brain injury. publication-title: J. Neurotrauma doi: 10.1089/neu.2014.3534 – volume: 3 year: 2007 ident: B2 article-title: Efficiency and cost of economical brain functional networks. publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.0030017 – volume: 20 start-page: 1 year: 2019 ident: B24 article-title: All models are wrong, but many are useful: learning a variable’s importance by studying an entire class of prediction models simultaneously. publication-title: J. Mach. Learn. Res. – volume: 33 start-page: 10676 year: 2013 ident: B17 article-title: Probabilistic diffusion tractography and graph theory analysis reveal abnormal white matter structural connectivity networks in drug-naive boys with attention deficit/hyperactivity disorder. publication-title: J. Neurosci. doi: 10.1523/JNEUROSCI.4793-12.2013 – volume: 14 start-page: 424 year: 2008 ident: B41 article-title: Long-term neural processing of attention following early childhood traumatic brain injury: fMRI and neurobehavioral outcomes. publication-title: J. Int. Neuropsychol. Soc. doi: 10.1017/S1355617708080545 – volume: 315 start-page: 1860 year: 2007 ident: B12 article-title: Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. publication-title: Science doi: 10.1126/science.1138071 – volume: 78 start-page: 15 year: 2016 ident: B6 article-title: Noise-enhanced convolutional neural networks. publication-title: Neural Netw. doi: 10.1016/j.neunet.2015.09.014 – volume: 2 start-page: 81 year: 1974 ident: B68 article-title: Assessment of coma and impaired consciousness. A practical scale. publication-title: Lancet doi: 10.1016/S0140-6736(74)91639-0 – volume: 6 start-page: 404 ident: B76 article-title: Diffusion tensor imaging in moderate-to-severe pediatric traumatic brain injury: changes within an 18 month post-injury interval. publication-title: Brain Imaging Behav. doi: 10.1007/s11682-012-9150-y – volume: 20 start-page: 509 year: 2014 ident: B34 article-title: Bottom-up and top-down attention: different processes and overlapping neural systems. publication-title: Neuroscientist doi: 10.1177/1073858413514136 – volume: 31 start-page: 190 year: 2017 ident: B80 article-title: Changes in structural connectivity following a cognitive intervention in children with traumatic brain injury. publication-title: Neurorehabil. Neural. Repair. doi: 10.1177/1545968316675430 – start-page: 18 year: 2016 ident: B53 article-title: LASSO: A feature selection technique in predictive modeling for machine learning publication-title: Proceedings of the 2016 IEEE International Conference on Advances in Computer Applications (ICACA) doi: 10.1109/ICACA.2016.7887916 – volume: 6 start-page: 2544 year: 2019 ident: B31 article-title: Default mode network anatomy and function is linked to pediatric concussion recovery. publication-title: Ann. Clin. Transl. Neurol. doi: 10.1002/acn3.50951 – volume: 313 start-page: 504 year: 2006 ident: B30 article-title: Reducing the dimensionality of data with neural networks. publication-title: Science doi: 10.1126/science.1127647 – volume: 154 start-page: 89 year: 2022 ident: B9 article-title: Resting-state network organisation in children with traumatic brain injury. publication-title: Cortex doi: 10.1016/j.cortex.2022.05.014 – volume: 9 year: 2014 ident: B61 article-title: Mutual information between discrete and continuous data sets. publication-title: PLoS One doi: 10.1371/journal.pone.0087357 – volume: 10 start-page: 186 year: 2009 ident: B11 article-title: Complex brain networks: graph theoretical analysis of structural and functional systems. publication-title: Nat. Rev. Neurosci. doi: 10.1038/nrn2575 – volume: 43 start-page: 1032 year: 2022 ident: B74 article-title: Structural connectome differences in pediatric mild traumatic brain and orthopedic injury. publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.25705 – volume: 8 start-page: 321 year: 2015 ident: B70 article-title: Neural substrates of inhibitory and emotional processing in adolescents with traumatic brain injury. publication-title: J. Pediatr. Rehabil. Med. doi: 10.3233/PRM-150350 – volume: 45 start-page: S199 year: 2009 ident: B58 article-title: Machine learning classifiers and fMRI: a tutorial overview. publication-title: Neuroimage doi: 10.1016/j.neuroimage.2008.11.007 – volume: 63 start-page: 270 year: 2019 ident: B44 article-title: Executive functions and attention 7years after severe childhood traumatic brain injury: Results of the Traumatisme Grave de l’Enfant (TGE) cohort. publication-title: Ann. Phys. Rehabil. Med. doi: 10.1016/j.rehab.2019.09.003 – volume: 6 start-page: 814 year: 2014 ident: B7 article-title: Interventions for attention problems after pediatric traumatic brain injury: what is the evidence? publication-title: PM R doi: 10.1016/j.pmrj.2014.04.004 – volume: 15 start-page: 273 year: 2002 ident: B71 article-title: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. publication-title: Neuroimage doi: 10.1006/nimg.2001.0978 – volume: 14 year: 2019 ident: B4 article-title: Multi-scale detection of hierarchical community architecture in structural and functional brain networks. publication-title: PLoS One doi: 10.1371/journal.pone.0215520 – volume: 30 start-page: 5115 year: 2017 ident: B56 article-title: Regularizing deep neural networks by noise: Its interpretation and optimization. publication-title: Adv. Neural Inform. Process. Syst. – volume: 2 start-page: 273 year: 2009 ident: B42 article-title: Correlation of diffusion tensor imaging with executive function measures after early childhood traumatic brain injury. publication-title: J. Pediatr. Rehabil. Med. doi: 10.3233/PRM-2009-0093 – volume: 5 start-page: 791 year: 2020 ident: B55 article-title: Machine learning with neuroimaging: evaluating its applications in psychiatry. publication-title: Biol. Psychiatry Cogn. Neurosci. Neuroimaging doi: 10.1016/j.bpsc.2019.11.007 – volume: 17 year: 2022 ident: B16 article-title: GAT-FD: An integrated MATLAB toolbox for graph theoretical analysis of task-related functional dynamics. publication-title: PLoS One doi: 10.1371/journal.pone.0267456 – volume: 125 start-page: 1063 year: 2016 ident: B3 article-title: An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. publication-title: Neuroimage doi: 10.1016/j.neuroimage.2015.10.019 – volume: 33 start-page: 840 year: 2016 ident: B20 article-title: Tensor-based morphometry reveals volumetric deficits in moderate = severe pediatric traumatic brain injury. publication-title: J. Neurotrauma doi: 10.1089/neu.2015.4012 – volume: 2015 year: 2015 ident: B65 article-title: Altered recruitment of the attention network is associated with disability and cognitive impairment in pediatric patients with acquired brain injury. publication-title: Neural Plast. doi: 10.1155/2015/104282 – year: 2013 ident: B5 publication-title: Diagnostic and statistical manual of mental disorders (DSM-5®). doi: 10.1176/appi.books.9780890425596 – volume: 16 start-page: 691 year: 2020 ident: B66 article-title: Differentiating boys with ADHD from those with typical development based on whole-brain functional connections using a machine learning approach. publication-title: Neuropsychiatr. Dis. Treat. doi: 10.2147/NDT.S239013 – volume: 34 start-page: E1 year: 2019 ident: B43 article-title: Benefits of methylphenidate for long-term attention problems after traumatic brain injury in childhood: a randomized. double-masked, placebo-controlled, dose-titration, crossover trial. publication-title: J. Head Trauma Rehabil. doi: 10.1097/HTR.0000000000000432 – volume: 17 start-page: 181 year: 2011 ident: B69 article-title: Neural correlates of interference control in adolescents with traumatic brain injury: functional magnetic resonance imaging study of the counting stroop task. publication-title: J. Int. Neuropsychol. Soc. doi: 10.1017/S1355617710001414 – volume: 11 ident: B14 article-title: Abnormal functional network topology and its dynamics during sustained attention processing significantly implicate post-TBI attention deficits in children. publication-title: Brain Sci. doi: 10.3390/brainsci11101348 – volume: 521 start-page: 436 year: 2015 ident: B46 article-title: Deep learning. publication-title: Nature doi: 10.1038/nature14539 – volume: 52 start-page: 1059 year: 2010 ident: B63 article-title: Complex network measures of brain connectivity: uses and interpretations. publication-title: Neuroimage doi: 10.1016/j.neuroimage.2009.10.003 – volume: 50 start-page: 1594 year: 2020 ident: B60 article-title: Connectome mapping with edge density imaging differentiates pediatric mild traumatic brain injury from typically developing controls: proof of concept. publication-title: Pediatr. Radiol. doi: 10.1007/s00247-020-04743-9 – year: 2016 ident: B1 article-title: Tensorflow: Large-scale machine learning on heterogeneous distributed systems. publication-title: arXiv [preprint] – volume: 136 start-page: 534 year: 2015 ident: B38 article-title: Pediatric traumatic brain injury and attention deficit. publication-title: Pediatrics doi: 10.1542/peds.2015-0437 – volume: 55 start-page: 143 year: 2007 ident: B72 article-title: Hierarchical coding of letter strings in the ventral stream: dissecting the inner organization of the visual word-form system. publication-title: Neuron doi: 10.1016/j.neuron.2007.05.031 – year: 2011 ident: B75 publication-title: Wechsler Abbreviated Scale of Intelligence–Second Edition (WASI-II). doi: 10.1037/t15171-000 – volume: 44 start-page: 1041 year: 2005 ident: B50 article-title: Predictors of secondary attention-deficit/hyperactivity disorder in children and adolescents 6 to 24 months after traumatic brain injury. publication-title: J. Am. Acad. Child Adolesc. Psychiatry doi: 10.1097/01.chi.0000173292.05817.f8 – volume: 17 start-page: 82 year: 2010 ident: B29 article-title: A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. publication-title: Struct. Eq. Model. doi: 10.1080/10705510903439003 – volume: 35 start-page: E393 year: 2020 ident: B73 article-title: Behavior problems following childhood TBI: the role of sex, age, and time since injury. publication-title: J. Head Trauma Rehabil. doi: 10.1097/HTR.0000000000000567 – volume: 143 start-page: 29 year: 2021 ident: B18 article-title: Neurophysiological indicators of internal attention: An fMRI-eye-tracking coregistration study. publication-title: Cortex doi: 10.1016/j.cortex.2021.07.005 – volume: 1 start-page: 106 year: 2012 ident: B13 article-title: Brain connectivity and postural control in young traumatic brain injury patients: A diffusion MRI based network analysis. publication-title: Neuroimage Clin. doi: 10.1016/j.nicl.2012.09.011 – volume: 19 start-page: 139 year: 2011 ident: B27 article-title: PLS-SEM: Indeed a silver bullet. publication-title: J. Market. Theor. Pract. doi: 10.2753/MTP1069-6679190202 – volume: 38 start-page: 3603 year: 2017 ident: B40 article-title: The structural connectome of children with traumatic brain injury. publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.23614 – volume: 38 start-page: 5485 year: 2017 ident: B25 article-title: AxTract: Toward microstructure informed tractography. publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.23741 – year: 2014 ident: B37 article-title: Adam: A method for stochastic optimization. publication-title: arXiv [preprint] – volume: 11 start-page: 651 ident: B15 article-title: Topological aberrance of structural brain network provides quantitative substrates of post-traumatic brain injury attention deficits in children. publication-title: Brain Connect. doi: 10.1089/brain.2020.0866 – year: 2008 ident: B19 publication-title: Conners 3. – volume: 13 year: 2022 ident: B28 article-title: Local molecular and global connectomic contributions to cross-disorder cortical abnormalities. publication-title: Nat. Commun. doi: 10.1038/s41467-022-32420-y – volume: 12 start-page: 29 year: 2018 ident: B39 article-title: Relevance of neuroimaging for neurocognitive and behavioral outcome after pediatric traumatic brain injury. publication-title: Brain Imaging Behav. doi: 10.1007/s11682-017-9673-3 – volume: 289 start-page: 1206 year: 2000 ident: B49 article-title: Modulation of human visual cortex by crossmodal spatial attention. publication-title: Science doi: 10.1126/science.289.5482.1206 – volume: 13 year: 2015 ident: B59 article-title: Health-related quality of life after TBI: a systematic review of study design, instruments, measurement properties, and outcome. publication-title: Popul. Health Metr. doi: 10.1186/s12963-015-0037-1 – volume: 35 start-page: 286 year: 2018 ident: B36 article-title: Psychosocial and executive function recovery trajectories one year after pediatric traumatic brain injury: the influence of age and injury severity. publication-title: J. Neurotrauma doi: 10.1089/neu.2017.5265 – volume: 91 year: 2016 ident: B22 article-title: Epidemiology of global pediatric traumatic brain injury: qualitative review. publication-title: World Neurosurg. doi: 10.1016/j.wneu.2016.03.045 – volume: 45 start-page: 5 year: 2001 ident: B10 article-title: Random forests. publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 7 start-page: 493 year: 2015 ident: B21 article-title: White matter disruption in moderate/severe pediatric traumatic brain injury: advanced tract-based analyses. publication-title: Neuroimage Clin. doi: 10.1016/j.nicl.2015.02.002 – volume: 35 start-page: E271 year: 2019 ident: B54 article-title: Impact of Secondary ADHD on long-term outcomes after early childhood traumatic brain injury. publication-title: J. Head Trauma Rehabil. doi: 10.1097/HTR.0000000000000550 – volume: 119 start-page: 338 year: 2015 ident: B64 article-title: SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. publication-title: Neuroimage doi: 10.1016/j.neuroimage.2015.06.092 – volume: 126 year: 2022 ident: B33 article-title: Super-encoder with cooperative autoencoder networks. publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2022.108562 – volume: 51 year: 2012 ident: B47 article-title: Atypical pulvinar-cortical pathways during sustained attention performance in children with attention-deficit/hyperactivity disorder. publication-title: J. Am. Acad. Child. Adolesc. Psychiatry doi: 10.1016/j.jaac.2012.08.013 – volume: 14 start-page: 1370 year: 2001 ident: B78 article-title: Temporal autocorrelation in univariate linear modeling of FMRI data. publication-title: Neuroimage doi: 10.1006/nimg.2001.0931 – volume: 2 year: 2010 ident: B32 article-title: Challenges and limitations of quantifying brain connectivity in vivo with diffusion MRI. publication-title: Imaging Med. doi: 10.2217/iim.10.21 – volume: 22 start-page: 555 year: 2007 ident: B79 article-title: Neurocognitive and neuroimaging correlates of pediatric traumatic brain injury: a diffusion tensor imaging (DTI) study. publication-title: Arch. Clin. Neuropsychol. doi: 10.1016/j.acn.2007.03.004 – volume: 12 year: 2021 ident: B67 article-title: Post-concussive mTBI in Student Athletes: MRI features and machine learning. publication-title: Front. Neurol. doi: 10.3389/fneur.2021.734329 |
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| Title | 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 |
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