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Boardman JP, Andrew R, Bastin ME, Battersby C, Batty GD, Cábez MB, Cox SR, Hall J, Ingledow L, Marioni RE, Modi N, Murphy L, Quigley AJ, Reynolds RM, Richardson H, Stock SJ, Thrippleton MJ, Tsanas A, Whalley HC. Preterm birth as a determinant of neurodevelopment and cognition in children (PRENCOG): protocol for an exposure-based cohort study in the UK. BMJ Open 2024; 14:e085365. [PMID: 39284691 PMCID: PMC11409314 DOI: 10.1136/bmjopen-2024-085365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/20/2024] Open
Abstract
INTRODUCTION Preterm birth (PTB) is strongly associated with encephalopathy of prematurity (EoP) and neurocognitive impairment. The biological axes linking PTB with atypical brain development are uncertain. We aim to elucidate the roles of neuroendocrine stress activation and immune dysregulation in linking PTB with EoP. METHODS AND ANALYSIS PRENCOG (PREterm birth as a determinant of Neurodevelopment and COGnition in children: mechanisms and causal evidence) is an exposure-based cohort study at the University of Edinburgh. Three hundred mother-infant dyads comprising 200 preterm births (gestational age, GA <32 weeks, exposed) and 100 term births (GA >37 weeks, non-exposed), will be recruited between January 2023 and December 2027. We will collect parental and infant medical, demographic, socioeconomic characteristics and biological data which include placental tissue, umbilical cord blood, maternal and infant hair, infant saliva, infant dried blood spots, faecal material, and structural and diffusion MRI. Infant biosamples will be collected between birth and 44 weeks GA.EoP will be characterised by MRI using morphometric similarity networks (MSNs), hierarchical complexity (HC) and magnetisation transfer saturation imaging (MTsat). We will conduct: first, multivariable regressions and statistical association assessments to test how PTB-associated risk factors (PTB-RFs) relate to MSNs, HC and or MTsat; second, structural equation modelling to investigate neuroendocrine stress activation and immune dysregulation as mediators of PTB-RFs on features of EoP. PTB-RF selection will be informed by the variables that predict real-world educational outcomes, ascertained by linking the UK National Neonatal Research Database with the National Pupil Database. ETHICS AND DISSEMINATION A favourable ethical opinion has been given by the South East Scotland Research Ethics Committee 02 (23/SS/0067) and NHS Lothian Research and Development (2023/0150). Results will be reported to the Medical Research Council, in scientific media, via stakeholder partners and on a website in accessible language (https://www.ed.ac.uk/centre-reproductive-health/prencog).
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Affiliation(s)
- James P Boardman
- Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Ruth Andrew
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Cheryl Battersby
- Section of Neonatal Medicine, Imperial College London, London, UK
| | - G David Batty
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Manuel Blesa Cábez
- Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Jill Hall
- Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | | | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, UK
| | - Neena Modi
- Section of Neonatal Medicine, Imperial College London, London, UK
| | - Lee Murphy
- Edinburgh Clinical Research Facility, University of Edinburgh, Edinburgh, UK
| | | | - Rebecca M Reynolds
- Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Hilary Richardson
- School of Philosophy, Psychology, and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - Sarah J Stock
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK
| | | | - Athanasios Tsanas
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK
- The Alan Turing Institute, London, UK
| | - Heather C Whalley
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
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Jiménez-Sánchez L, Blesa Cábez M, Vaher K, Corrigan A, Thrippleton MJ, Bastin ME, Quigley AJ, Fletcher-Watson S, Boardman JP. Infant attachment does not depend on neonatal amygdala and hippocampal structure and connectivity. Dev Cogn Neurosci 2024; 67:101387. [PMID: 38692007 PMCID: PMC11070590 DOI: 10.1016/j.dcn.2024.101387] [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: 12/18/2023] [Revised: 03/13/2024] [Accepted: 04/17/2024] [Indexed: 05/03/2024] Open
Abstract
Infant attachment is an antecedent of later socioemotional abilities, which can be adversely affected by preterm birth. The structural integrity of amygdalae and hippocampi may subserve attachment in infancy. We aimed to investigate associations between neonatal amygdalae and hippocampi structure and their whole-brain connections and attachment behaviours at nine months of age in a sample of infants enriched for preterm birth. In 133 neonates (median gestational age 32 weeks, range 22.14-42.14), we calculated measures of amygdala and hippocampal structure (volume, fractional anisotropy, mean diffusivity, neurite dispersion index, orientation dispersion index) and structural connectivity, and coded attachment behaviours (distress, fretfulness, attentiveness to caregiver) from responses to the Still-Face Paradigm at nine months. After multiple comparisons correction, there were no significant associations between neonatal amygdala or hippocampal structure and structural connectivity and attachment behaviours: standardised β values - 0.23 to 0.18, adjusted p-values > 0.40. Findings indicate that the neural basis of infant attachment in term and preterm infants is not contingent on the structure or connectivity of the amygdalae and hippocampi in the neonatal period, which implies that it is more widely distributed in early life and or that network specialisation takes place in the months after hospital discharge.
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Affiliation(s)
- Lorena Jiménez-Sánchez
- Translational Neuroscience PhD Programme, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Salvesen Mindroom Research Centre, University of Edinburgh, Edinburgh, UK
| | - Manuel Blesa Cábez
- Centre for Reproductive Health, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Kadi Vaher
- Centre for Reproductive Health, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Amy Corrigan
- Centre for Reproductive Health, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK
| | | | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Alan J Quigley
- Department of Radiology, Royal Hospital for Children and Young People, Edinburgh, UK
| | - Sue Fletcher-Watson
- Salvesen Mindroom Research Centre, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - James P Boardman
- Centre for Reproductive Health, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
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Jang YH, Ham J, Kasani PH, Kim H, Lee JY, Lee GY, Han TH, Kim BN, Lee HJ. Predicting 2-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity. Sci Rep 2024; 14:9331. [PMID: 38653988 DOI: 10.1038/s41598-024-58682-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
The neurodevelopmental outcomes of preterm infants can be stratified based on the level of prematurity. We explored brain structural networks in extremely preterm (EP; < 28 weeks of gestation) and very-to-late (V-LP; ≥ 28 and < 37 weeks of gestation) preterm infants at term-equivalent age to predict 2-year neurodevelopmental outcomes. Using MRI and diffusion MRI on 62 EP and 131 V-LP infants, we built a multimodal feature set for volumetric and structural network analysis. We employed linear and nonlinear machine learning models to predict the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) scores, assessing predictive accuracy and feature importance. Our findings revealed that models incorporating local connectivity features demonstrated high predictive performance for BSID-III subsets in preterm infants. Specifically, for cognitive scores in preterm (variance explained, 17%) and V-LP infants (variance explained, 17%), and for motor scores in EP infants (variance explained, 15%), models with local connectivity features outperformed others. Additionally, a model using only local connectivity features effectively predicted language scores in preterm infants (variance explained, 15%). This study underscores the value of multimodal feature sets, particularly local connectivity, in predicting neurodevelopmental outcomes, highlighting the utility of machine learning in understanding microstructural changes and their implications for early intervention.
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Affiliation(s)
- Yong Hun Jang
- Department of Translational Medicine, Hanyang University Graduate School of Biomedical Science and Engineering, Seoul, Republic of Korea
| | - Jusung Ham
- Department of Communication Sciences and Disorders, University of Iowa, Iowa City, IA, 52242, USA
| | - Payam Hosseinzadeh Kasani
- Department of Pediatrics, Hanyang University Hospital, Hanyang University College of Medicine, 222-1, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Hyuna Kim
- Department of Translational Medicine, Hanyang University Graduate School of Biomedical Science and Engineering, Seoul, Republic of Korea
| | - Joo Young Lee
- Department of Translational Medicine, Hanyang University Graduate School of Biomedical Science and Engineering, Seoul, Republic of Korea
| | - Gang Yi Lee
- Department of Translational Medicine, Hanyang University Graduate School of Biomedical Science and Engineering, Seoul, Republic of Korea
| | - Tae Hwan Han
- Division of Neurology, Department of Pediatrics, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Bung-Nyun Kim
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyun Ju Lee
- Department of Pediatrics, Hanyang University Hospital, Hanyang University College of Medicine, 222-1, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea.
- Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Seoul, Republic of Korea.
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Li H, Li H, Ma L, Polina D. Revealing brain's cognitive process deeply: a study of the consistent EEG patterns of audio-visual perceptual holistic. Front Hum Neurosci 2024; 18:1377233. [PMID: 38601801 PMCID: PMC11004307 DOI: 10.3389/fnhum.2024.1377233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 03/14/2024] [Indexed: 04/12/2024] Open
Abstract
Introduction To investigate the brain's cognitive process and perceptual holistic, we have developed a novel method that focuses on the informational attributes of stimuli. Methods We recorded EEG signals during visual and auditory perceptual cognition experiments and conducted ERP analyses to observe specific positive and negative components occurring after 400ms during both visual and auditory perceptual processes. These ERP components represent the brain's perceptual holistic processing activities, which we have named Information-Related Potentials (IRPs). We combined IRPs with machine learning methods to decode cognitive processes in the brain. Results Our experimental results indicate that IRPs can better characterize information processing, particularly perceptual holism. Additionally, we conducted a brain network analysis and found that visual and auditory perceptual holistic processing share consistent neural pathways. Discussion Our efforts not only demonstrate the specificity, significance, and reliability of IRPs but also reveal their great potential for future brain mechanism research and BCI applications.
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Affiliation(s)
| | - Haifeng Li
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
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Thng G, Shen X, Stolicyn A, Adams MJ, Yeung HW, Batziou V, Conole ELS, Buchanan CR, Lawrie SM, Bastin ME, McIntosh AM, Deary IJ, Tucker-Drob EM, Cox SR, Smith KM, Romaniuk L, Whalley HC. A comprehensive hierarchical comparison of structural connectomes in Major Depressive Disorder cases v. controls in two large population samples. Psychol Med 2024:1-12. [PMID: 38497116 DOI: 10.1017/s0033291724000643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
BACKGROUND The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity. METHODS We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case-control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection. RESULTS In UKB, reductions in network efficiency were observed in MDD cases globally (d = -0.076, pFDR = 0.033), across all tiers (d = -0.069 to -0.079, pFDR = 0.020), and in hubs (d = -0.080 to -0.113, pFDR = 0.013-0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample. CONCLUSION Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.
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Affiliation(s)
- Gladi Thng
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Xueyi Shen
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Aleks Stolicyn
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Mark J Adams
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Hon Wah Yeung
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Venia Batziou
- Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh, UK
| | - Eleanor L S Conole
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
| | - Colin R Buchanan
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE), Edinburgh, UK
| | - Stephen M Lawrie
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE), Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Generation Scotland, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas, Austin, TX, USA
- Population Research Center and Center on Aging and Population Sciences, University of Texas, Austin, TX, USA
| | - Simon R Cox
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE), Edinburgh, UK
| | - Keith M Smith
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK
| | - Liana Romaniuk
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Heather C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Generation Scotland, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
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6
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Galdi P, Cabez MB, Farrugia C, Vaher K, Williams LZJ, Sullivan G, Stoye DQ, Quigley AJ, Makropoulos A, Thrippleton MJ, Bastin ME, Richardson H, Whalley H, Edwards AD, Bajada CJ, Robinson EC, Boardman JP. Feature similarity gradients detect alterations in the neonatal cortex associated with preterm birth. Hum Brain Mapp 2024; 45:e26660. [PMID: 38488444 PMCID: PMC10941526 DOI: 10.1002/hbm.26660] [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: 09/28/2023] [Revised: 01/18/2024] [Accepted: 02/29/2024] [Indexed: 03/18/2024] Open
Abstract
The early life environment programmes cortical architecture and cognition across the life course. A measure of cortical organisation that integrates information from multimodal MRI and is unbound by arbitrary parcellations has proven elusive, which hampers efforts to uncover the perinatal origins of cortical health. Here, we use the Vogt-Bailey index to provide a fine-grained description of regional homogeneities and sharp variations in cortical microstructure based on feature gradients, and we investigate the impact of being born preterm on cortical development at term-equivalent age. Compared with term-born controls, preterm infants have a homogeneous microstructure in temporal and occipital lobes, and the medial parietal, cingulate, and frontal cortices, compared with term infants. These observations replicated across two independent datasets and were robust to differences that remain in the data after matching samples and alignment of processing and quality control strategies. We conclude that cortical microstructural architecture is altered in preterm infants in a spatially distributed rather than localised fashion.
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Affiliation(s)
- Paola Galdi
- MRC Centre for Reproductive HealthUniversity of EdinburghEdinburghUK
- School of InformaticsUniversity of EdinburghEdinburghUK
| | | | - Christine Farrugia
- Faculty of EngineeringUniversity of MaltaVallettaMalta
- University of Malta Magnetic Resonance Imaging Platform (UMRI)VallettaMalta
| | - Kadi Vaher
- MRC Centre for Reproductive HealthUniversity of EdinburghEdinburghUK
| | - Logan Z. J. Williams
- Centre for the Developing BrainKing's College LondonLondonUK
- School of Biomedical Engineering and Imaging ScienceKing's College LondonLondonUK
| | - Gemma Sullivan
- MRC Centre for Reproductive HealthUniversity of EdinburghEdinburghUK
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - David Q. Stoye
- MRC Centre for Reproductive HealthUniversity of EdinburghEdinburghUK
| | | | | | | | - Mark E. Bastin
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - Hilary Richardson
- School of Philosophy, Psychology and Language SciencesUniversity of EdinburghEdinburghUK
| | - Heather Whalley
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
- Centre for Genomic and Experimental MedicineUniversity of EdinburghEdinburghUK
| | - A. David Edwards
- Centre for the Developing BrainKing's College LondonLondonUK
- MRC Centre for Neurodevelopmental DisordersKing's College LondonLondonUK
| | - Claude J. Bajada
- University of Malta Magnetic Resonance Imaging Platform (UMRI)VallettaMalta
- Department of Physiology and Biochemistry, Faculty of Medicine and SurgeryUniversity of MaltaVallettaMalta
| | - Emma C. Robinson
- Centre for the Developing BrainKing's College LondonLondonUK
- School of Biomedical Engineering and Imaging ScienceKing's College LondonLondonUK
| | - James P. Boardman
- MRC Centre for Reproductive HealthUniversity of EdinburghEdinburghUK
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
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Fan L, Li Y, Huang ZG, Zhang W, Wu X, Liu T, Wang J. Low-frequency repetitive transcranial magnetic stimulation alters the individual functional dynamical landscape. Cereb Cortex 2023; 33:9583-9598. [PMID: 37376783 DOI: 10.1093/cercor/bhad228] [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: 04/26/2023] [Revised: 06/05/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive approach to modulate brain activity and behavior in humans. Still, how individual resting-state brain dynamics after rTMS evolves across different functional configurations is rarely studied. Here, using resting state fMRI data from healthy subjects, we aimed to examine the effects of rTMS to individual large-scale brain dynamics. Using Topological Data Analysis based Mapper approach, we construct the precise dynamic mapping (PDM) for each participant. To reveal the relationship between PDM and canonical functional representation of the resting brain, we annotated the graph using relative activation proportion of a set of large-scale resting-state networks (RSNs) and assigned the single brain volume to corresponding RSN-dominant or a hub state (not any RSN was dominant). Our results show that (i) low-frequency rTMS could induce changed temporal evolution of brain states; (ii) rTMS didn't alter the hub-periphery configurations underlined resting-state brain dynamics; and (iii) the rTMS effects on brain dynamics differ across the left frontal and occipital lobe. In conclusion, low-frequency rTMS significantly alters the individual temporo-spatial dynamics, and our finding further suggested a potential target-dependent alteration of brain dynamics. This work provides a new perspective to comprehend the heterogeneous effect of rTMS.
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Affiliation(s)
- Liming Fan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Zi-Gang Huang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Wenlong Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Xiaofeng Wu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Tian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
- The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, China
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Conole ELS, Vaher K, Cabez MB, Sullivan G, Stevenson AJ, Hall J, Murphy L, Thrippleton MJ, Quigley AJ, Bastin ME, Miron VE, Whalley HC, Marioni RE, Boardman JP, Cox SR. Immuno-epigenetic signature derived in saliva associates with the encephalopathy of prematurity and perinatal inflammatory disorders. Brain Behav Immun 2023; 110:322-338. [PMID: 36948324 DOI: 10.1016/j.bbi.2023.03.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/12/2023] [Accepted: 03/16/2023] [Indexed: 03/24/2023] Open
Abstract
BACKGROUND Preterm birth is closely associated with a phenotype that includes brain dysmaturation and neurocognitive impairment, commonly termed Encephalopathy of Prematurity (EoP), of which systemic inflammation is considered a key driver. DNA methylation (DNAm) signatures of inflammation from peripheral blood associate with poor brain imaging outcomes in adult cohorts. However, the robustness of DNAm inflammatory scores in infancy, their relation to comorbidities of preterm birth characterised by inflammation, neonatal neuroimaging metrics of EoP, and saliva cross-tissue applicability are unknown. METHODS Using salivary DNAm from 258 neonates (n = 155 preterm, gestational age at birth 23.28 - 34.84 weeks, n = 103 term, gestational age at birth 37.00 - 42.14 weeks), we investigated the impact of a DNAm surrogate for C-reactive protein (DNAm CRP) on brain structure and other clinically defined inflammatory exposures. We assessed i) if DNAm CRP estimates varied between preterm infants at term equivalent age and term infants, ii) how DNAm CRP related to different types of inflammatory exposure (maternal, fetal and postnatal) and iii) whether elevated DNAm CRP associated with poorer measures of neonatal brain volume and white matter connectivity. RESULTS Higher DNAm CRP was linked to preterm status (-0.0107 ± 0.0008, compared with -0.0118 ± 0.0006 among term infants; p < 0.001), as well as perinatal inflammatory diseases, including histologic chorioamnionitis, sepsis, bronchopulmonary dysplasia, and necrotising enterocolitis (OR range |2.00 | to |4.71|, p < 0.01). Preterm infants with higher DNAm CRP scores had lower brain volume in deep grey matter, white matter, and hippocampi and amygdalae (β range |0.185| to |0.218|). No such associations were observed for term infants. Association magnitudes were largest for measures of white matter microstructure among preterms, where elevated epigenetic inflammation associated with poorer global measures of white matter integrity (β range |0.206| to |0.371|), independent of other confounding exposures. CONCLUSIONS Inflammatory-related DNAm captures the allostatic load of inflammatory burden in preterm infants. Such DNAm measures complement biological and clinical metrics when investigating the determinants of neurodevelopmental differences.
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Affiliation(s)
- Eleanor L S Conole
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK; Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK.
| | - Kadi Vaher
- MRC Centre for Reproductive Health, Queen's Medical Research Institute, Edinburgh BioQuarter, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Manuel Blesa Cabez
- MRC Centre for Reproductive Health, Queen's Medical Research Institute, Edinburgh BioQuarter, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Gemma Sullivan
- MRC Centre for Reproductive Health, Queen's Medical Research Institute, Edinburgh BioQuarter, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Anna J Stevenson
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Jill Hall
- MRC Centre for Reproductive Health, Queen's Medical Research Institute, Edinburgh BioQuarter, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Lee Murphy
- Edinburgh Clinical Research Facility, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK; Edinburgh Clinical Research Facility, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Alan J Quigley
- Imaging Department, Royal Hospital for Children and Young People, Edinburgh, EH16 4TJ, UK
| | - Mark E Bastin
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Veronique E Miron
- MRC Centre for Reproductive Health, Queen's Medical Research Institute, Edinburgh BioQuarter, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Heather C Whalley
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - James P Boardman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK; MRC Centre for Reproductive Health, Queen's Medical Research Institute, Edinburgh BioQuarter, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Simon R Cox
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK.
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9
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Mckinnon K, Galdi P, Blesa-Cábez M, Sullivan G, Vaher K, Corrigan A, Hall J, Jiménez-Sánchez L, Thrippleton M, Bastin ME, Quigley AJ, Valavani E, Tsanas A, Richardson H, Boardman JP. Association of Preterm Birth and Socioeconomic Status With Neonatal Brain Structure. JAMA Netw Open 2023; 6:e2316067. [PMID: 37256618 PMCID: PMC10233421 DOI: 10.1001/jamanetworkopen.2023.16067] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 04/17/2023] [Indexed: 06/01/2023] Open
Abstract
Importance Preterm birth and socioeconomic status (SES) are associated with brain structure in childhood, but the relative contributions of each during the neonatal period are unknown. Objective To investigate associations of birth gestational age (GA) and SES with neonatal brain morphology by testing 3 hypotheses: GA and SES are associated with brain morphology; associations between SES and brain morphology vary with GA; and associations between SES and brain structure and morphology depend on how SES is operationalized. Design, Setting, and Participants This cohort study recruited participants from November 2016 to September 2021 at a single center in the United Kingdom. Participants were 170 extremely and very preterm infants and 91 full-term or near-term infants. Exclusion criteria were major congenital malformation, chromosomal abnormality, congenital infection, cystic periventricular leukomalacia, hemorrhagic parenchymal infarction, and posthemorrhagic ventricular dilatation. Exposures Birth GA and SES, operationalized at the neighborhood level (using the Scottish Index of Multiple Deprivation), the family level (using parental education and occupation), and subjectively (World Health Organization Quality of Life measure). Main Outcomes and Measures Brain volume (85 parcels) and 5 whole-brain cortical morphology measures (gyrification index, thickness, sulcal depth, curvature, surface area) at term-equivalent age (median [range] age, 40 weeks, 5 days [36 weeks, 2 days to 45 weeks, 6 days] and 42 weeks [38 weeks, 2 days to 46 weeks, 1 day] for preterm and full-term infants, respectively). Results Participants were 170 extremely and very preterm infants (95 [55.9%] male; 4 of 166 [2.4%] Asian, 145 of 166 [87.3%] White) and 91 full-term or near-term infants (50 [54.9%] male; 3 of 86 [3.5%] Asian, 78 of 86 [90.7%] White infants) with median (range) birth GAs of 30 weeks, 0 days (22 weeks, 1 day, to 32 weeks, 6 days) and 39 weeks, 4 days (36 weeks, 3 days, to 42 weeks, 1 day), respectively. In fully adjusted models, birth GA was associated with a higher proportion of brain volumes (27 of 85 parcels [31.8%]; β range, -0.20 to 0.24) than neighborhood-level SES (1 of 85 parcels [1.2%]; β = 0.17 [95% CI, -0.16 to 0.50]) or family-level SES (maternal education: 4 of 85 parcels [4.7%]; β range, 0.09 to 0.15; maternal occupation: 1 of 85 parcels [1.2%]; β = 0.06 [95% CI, 0.02 to 0.11] respectively). There were interactions between GA and both family-level and subjective SES measures on regional brain volumes. Birth GA was associated with cortical surface area (β = 0.10 [95% CI, 0.02 to 0.18]) and gyrification index (β = 0.16 [95% CI, 0.07 to 0.25]); no SES measure was associated with cortical measures. Conclusions and Relevance In this cohort study of UK infants, birth GA and SES were associated with neonatal brain morphology, but low GA had more widely distributed associations with neonatal brain structure than SES. Further work is warranted to elucidate the mechanisms underlying the association of both GA and SES with early brain development.
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Affiliation(s)
- Katie Mckinnon
- Medical Research Council Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Paola Galdi
- Medical Research Council Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Manuel Blesa-Cábez
- Medical Research Council Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Gemma Sullivan
- Medical Research Council Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Kadi Vaher
- Medical Research Council Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Amy Corrigan
- Medical Research Council Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Jill Hall
- Medical Research Council Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Michael Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E. Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Alan J. Quigley
- Department of Radiology, Royal Hospital for Children and Young People, Edinburgh, United Kingdom
| | - Evdoxia Valavani
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Athanasios Tsanas
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
- Alan Turing Institute, London, United Kingdom
| | - Hilary Richardson
- School of Philosophy, Psychology, and Language Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - James P. Boardman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
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10
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Sullivan G, Vaher K, Blesa M, Galdi P, Stoye DQ, Quigley AJ, Thrippleton MJ, Norrie J, Bastin ME, Boardman JP. Breast Milk Exposure is Associated With Cortical Maturation in Preterm Infants. Ann Neurol 2023; 93:591-603. [PMID: 36412221 DOI: 10.1002/ana.26559] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 11/14/2022] [Accepted: 11/18/2022] [Indexed: 11/23/2022]
Abstract
OBJECTIVE Breast milk exposure is associated with improved neurocognitive outcomes following preterm birth but the neural substrates linking breast milk with outcome are uncertain. We tested the hypothesis that high versus low breast milk exposure in preterm infants results in cortical morphology that more closely resembles that of term-born infants. METHODS We studied 135 preterm (<32 weeks' gestation) and 77 term infants. Feeding data were collected from birth until hospital discharge and brain magnetic resonance imaging (MRI) was performed at term-equivalent age. Cortical indices (volume, thickness, surface area, gyrification index, sulcal depth, and curvature) and diffusion parameters (fractional anisotropy [FA], mean diffusivity [MD], radial diffusivity [RD], axial diffusivity [AD], neurite density index [NDI], and orientation dispersion index [ODI]) were compared between preterm infants who received exclusive breast milk for <75% of inpatient days, preterm infants who received exclusive breast milk for ≥75% of inpatient days and term-born controls. To investigate a dose response effect, we performed linear regression using breast milk exposure quartile weighted by propensity scores. RESULTS In preterm infants, high breast milk exposure was associated with reduced cortical gray matter volume (d = 0.47, 95% confidence interval [CI] = 0.14 to 0.94, p = 0.014), thickness (d = 0.42, 95% CI = 0.08 to 0.84, p = 0.039), and RD (d = 0.38, 95% CI = 0.002 to 0.77, p = 0.039), and increased FA (d = -0.38, 95% CI = -0.74 to -0.01, p = 0.037) after adjustment for age at MRI, which was similar to the cortical phenotype observed in term-born controls. Breast milk exposure quartile was associated with cortical volume (ß = -0.192, 95% CI = -0.342 to -0.042, p = 0.017), FA (ß = 0.223, 95% CI = 0.075 to 0.372, p = 0.007), and RD (ß = -0.225, 95% CI = -0.373 to -0.076, p = 0.007) following adjustment for age at birth, age at MRI, and weighted by propensity scores, suggesting a dose effect. INTERPRETATION High breast milk exposure following preterm birth is associated with a cortical imaging phenotype that more closely resembles the brain morphology of term-born infants and effects appear to be dose-dependent. ANN NEUROL 2023;93:591-603.
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Affiliation(s)
- Gemma Sullivan
- MRC Centre for Reproductive Health, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Kadi Vaher
- MRC Centre for Reproductive Health, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Manuel Blesa
- MRC Centre for Reproductive Health, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Paola Galdi
- MRC Centre for Reproductive Health, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - David Q Stoye
- MRC Centre for Reproductive Health, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Alan J Quigley
- Department of Radiology, Royal Hospital for Children and Young People, Edinburgh, UK
| | - Michael J Thrippleton
- Edinburgh Imaging, University of Edinburgh, Edinburgh, UK.,Centre for Clinical Brain Sciences, Chancellor's Building, University of Edinburgh, Edinburgh, UK
| | - John Norrie
- Usher Institute, Edinburgh Clinical Trials Unit, University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, Chancellor's Building, University of Edinburgh, Edinburgh, UK
| | - James P Boardman
- MRC Centre for Reproductive Health, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK.,Centre for Clinical Brain Sciences, Chancellor's Building, University of Edinburgh, Edinburgh, UK
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11
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Uchitel J, Blanco B, Collins-Jones L, Edwards A, Porter E, Pammenter K, Hebden J, Cooper RJ, Austin T. Cot-side imaging of functional connectivity in the developing brain during sleep using wearable high-density diffuse optical tomography. Neuroimage 2023; 265:119784. [PMID: 36464095 DOI: 10.1016/j.neuroimage.2022.119784] [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: 06/08/2022] [Revised: 11/16/2022] [Accepted: 11/30/2022] [Indexed: 12/03/2022] Open
Abstract
Studies of cortical function in newborn infants in clinical settings are extremely challenging to undertake with traditional neuroimaging approaches. Partly in response to this challenge, functional near-infrared spectroscopy (fNIRS) has become an increasingly common clinical research tool but has significant limitations including a low spatial resolution and poor depth specificity. Moreover, the bulky optical fibres required in traditional fNIRS approaches present significant mechanical challenges, particularly for the study of vulnerable newborn infants. A new generation of wearable, modular, high-density diffuse optical tomography (HD-DOT) technologies has recently emerged that overcomes many of the limitations of traditional, fibre-based and low-density fNIRS measurements. Driven by the development of this new technology, we have undertaken the first cot-side study of newborn infants using wearable HD-DOT in a clinical setting. We use this technology to study functional brain connectivity (FC) in newborn infants during sleep and assess the effect of neonatal sleep states, active sleep (AS) and quiet sleep (QS), on resting state FC. Our results demonstrate that it is now possible to obtain high-quality functional images of the neonatal brain in the clinical setting with few constraints. Our results also suggest that sleep states differentially affect FC in the neonatal brain, consistent with prior reports.
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Affiliation(s)
- Julie Uchitel
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, UCL, London, UK; Department of Pediatrics, University of Cambridge, Cambridge, UK.
| | - Borja Blanco
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, UCL, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - Liam Collins-Jones
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Andrea Edwards
- Neonatal Intensive Care Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Emma Porter
- Neonatal Intensive Care Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Kelle Pammenter
- Neonatal Intensive Care Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Jem Hebden
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Robert J Cooper
- DOT-HUB, Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Topun Austin
- Neonatal Intensive Care Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
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12
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Abstract
Neonatal care is becoming increasingly complex with large amounts of rich, routinely recorded physiological, diagnostic and outcome data. Artificial intelligence (AI) has the potential to harness this vast quantity and range of information and become a powerful tool to support clinical decision making, personalised care, precise prognostics, and enhance patient safety. Current AI approaches in neonatal medicine include tools for disease prediction and risk stratification, neurological diagnostic support and novel image recognition technologies. Key to the integration of AI in neonatal medicine is the understanding of its limitations and a standardised critical appraisal of AI tools. Barriers and challenges to this include the quality of datasets used, performance assessment, and appropriate external validation and clinical impact studies. Improving digital literacy amongst healthcare professionals and cross-disciplinary collaborations are needed to harness the full potential of AI to help take the next significant steps in improving neonatal outcomes for high-risk infants.
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13
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Smith KM, Starr JM, Escudero J, Ibañez A, Parra MA. Abnormal Functional Hierarchies of EEG Networks in Familial and Sporadic Prodromal Alzheimer's Disease During Visual Short-Term Memory Binding. FRONTIERS IN NEUROIMAGING 2022; 1:883968. [PMID: 37555153 PMCID: PMC10406202 DOI: 10.3389/fnimg.2022.883968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/16/2022] [Indexed: 08/10/2023]
Abstract
Alzheimer's Disease (AD) shows both complex alterations of functional dependencies between brain regions and a decreased ability to perform Visual Short-Term Memory Binding (VSTMB) tasks. Recent advances in network neuroscience toward understanding the complexity of hierarchical brain function here enables us to establish a link between these two phenomena. Here, we study data on two types of dementia at Mild Cognitive Impairment (MCI) stage-familial AD patients (E280A mutation of the presenilin-1 gene) and elderly MCI patients at high risk of sporadic AD, both with age-matched controls. We analyzed Electroencephalogram (EEG) signals recorded during the performance of Visual Short-Term Memory (VSTM) tasks by these participants. Functional connectivity was computed using the phase-lag index in Alpha and Beta; and network analysis was employed using network indices of hierarchical spread (degree variance) and complexity. Hierarchical characteristics of EEG functional connectivity networks revealed abnormal patterns in familial MCI VSTMB function and sporadic MCI VSTMB function. The middle-aged familial MCI binding network displayed a larger degree variance in lower Beta compared to healthy controls (p = 0.0051, Cohen's d = 1.0124), while the elderly sporadic MCI binding network displayed greater hierarchical complexity in Alpha (p = 0.0140, Cohen's d = 1.1627). Characteristics in healthy aging were not shown to differ. These results indicate that activity in MCI exhibits cross-frequency network reorganization characterized by increased heterogeneity of node roles in the functional hierarchy. Aging itself is not found to cause VSTM functional hierarchy differences.
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Affiliation(s)
- Keith M. Smith
- Department of Physics and Mathematics, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom
| | - John M. Starr
- Alzheimer Scotland Dementia Research Centre, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Javier Escudero
- School of Engineering, Institute for Digital Communications, University of Edinburgh, Edinburgh, United Kingdom
| | - Agustin Ibañez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
- Global Brain Health Institute, UCSF, San Francisco, CA, United States
- Trinity College Institute of Neuroscience, Trintity College Dublin, Dublin, Ireland
| | - Mario A. Parra
- Trinity College Institute of Neuroscience, Trintity College Dublin, Dublin, Ireland
- School of Psychological Sciences and Health, University of Strathclyde, Glasgow, United Kingdom
- Neuroprogressive and Dementia Network, NHS Scotland, Glasgow, United Kingdom
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14
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Craig BT, Kinney-Lang E, Hilderley AJ, Carlson HL, Kirton A. Structural connectivity of the sensorimotor network within the non-lesioned hemisphere of children with perinatal stroke. Sci Rep 2022; 12:3866. [PMID: 35264665 PMCID: PMC8907195 DOI: 10.1038/s41598-022-07863-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 02/21/2022] [Indexed: 11/09/2022] Open
Abstract
Perinatal stroke occurs early in life and often leads to a permanent, disabling weakness to one side of the body. To test the hypothesis that non-lesioned hemisphere sensorimotor network structural connectivity in children with perinatal stroke is different from controls, we used diffusion imaging and graph theory to explore structural topology between these populations. Children underwent diffusion and anatomical 3T MRI. Whole-brain tractography was constrained using a brain atlas creating an adjacency matrix containing connectivity values. Graph theory metrics including betweenness centrality, clustering coefficient, and both neighbourhood and hierarchical complexity of sensorimotor nodes were compared to controls. Relationships between these connectivity metrics and validated sensorimotor assessments were explored. Eighty-five participants included 27 with venous stroke (mean age = 11.5 ± 3.7 years), 26 with arterial stroke (mean age = 12.7 ± 4.0 years), and 32 controls (mean age = 13.3 ± 3.6 years). Non-lesioned primary motor (M1), somatosensory (S1) and supplementary motor (SMA) areas demonstrated lower betweenness centrality and higher clustering coefficient in stroke groups. Clustering coefficient of M1, S1, and SMA were inversely associated with clinical motor function. Hemispheric betweenness centrality and clustering coefficient were higher in stroke groups compared to controls. Hierarchical and average neighbourhood complexity across the hemisphere were lower in stroke groups. Developmental plasticity alters the connectivity of key nodes within the sensorimotor network of the non-lesioned hemisphere following perinatal stroke and contributes to clinical disability.
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Affiliation(s)
- Brandon T Craig
- Calgary Pediatric Stroke Program, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Eli Kinney-Lang
- Calgary Pediatric Stroke Program, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Alicia J Hilderley
- Calgary Pediatric Stroke Program, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Helen L Carlson
- Calgary Pediatric Stroke Program, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Adam Kirton
- Calgary Pediatric Stroke Program, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. .,Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. .,Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. .,Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. .,Department of Clinical Neuroscience, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
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15
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Hu H, Cusack R, Naci L. OUP accepted manuscript. Brain Commun 2022; 4:fcac071. [PMID: 35425900 PMCID: PMC9006044 DOI: 10.1093/braincomms/fcac071] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 12/29/2021] [Accepted: 03/16/2022] [Indexed: 11/12/2022] Open
Abstract
One of the great frontiers of consciousness science is understanding how early consciousness arises in the development of the human infant. The reciprocal relationship between the default mode network and fronto-parietal networks—the dorsal attention and executive control network—is thought to facilitate integration of information across the brain and its availability for a wide set of conscious mental operations. It remains unknown whether the brain mechanism of conscious awareness is instantiated in infants from birth. To address this gap, we investigated the development of the default mode and fronto-parietal networks and of their reciprocal relationship in neonates. To understand the effect of early neonate age on these networks, we also assessed neonates born prematurely or before term-equivalent age. We used the Developing Human Connectome Project, a unique Open Science dataset which provides a large sample of neonatal functional MRI data with high temporal and spatial resolution. Resting state functional MRI data for full-term neonates (n = 282, age 41.2 weeks ± 12 days) and preterm neonates scanned at term-equivalent age (n = 73, 40.9 weeks ± 14.5 days), or before term-equivalent age (n = 73, 34.6 weeks ± 13.4 days), were obtained from the Developing Human Connectome Project, and for a reference adult group (n = 176, 22–36 years), from the Human Connectome Project. For the first time, we show that the reciprocal relationship between the default mode and dorsal attention network was present at full-term birth or term-equivalent age. Although different from the adult networks, the default mode, dorsal attention and executive control networks were present as distinct networks at full-term birth or term-equivalent age, but premature birth was associated with network disruption. By contrast, neonates before term-equivalent age showed dramatic underdevelopment of high-order networks. Only the dorsal attention network was present as a distinct network and the reciprocal network relationship was not yet formed. Our results suggest that, at full-term birth or by term-equivalent age, infants possess key features of the neural circuitry that enables integration of information across diverse sensory and high-order functional modules, giving rise to conscious awareness. Conversely, they suggest that this brain infrastructure is not present before infants reach term-equivalent age. These findings improve understanding of the ontogeny of high-order network dynamics that support conscious awareness and of their disruption by premature birth.
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Affiliation(s)
- Huiqing Hu
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Rhodri Cusack
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
- Correspondence to: Lorina Naci School of Psychology Trinity College Institute of Neuroscience Global Brain Health Institute Trinity College Dublin Dublin, Ireland E-mail:
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16
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Oldham S, Ball G, Fornito A. Early and late development of hub connectivity in the human brain. Curr Opin Psychol 2021; 44:321-329. [PMID: 34896927 DOI: 10.1016/j.copsyc.2021.10.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/14/2021] [Accepted: 10/28/2021] [Indexed: 12/28/2022]
Abstract
Human brain networks undergo pronounced changes during development. The emergence of highly connected hub regions that can support integrated brain function is central to this maturational process, with these areas undergoing a particularly protracted period of development that extends into adulthood. The location of cortical network hubs emerges early but connections to and from hubs continue to strengthen throughout childhood and adolescence. Patterns of functional coupling in cortical association hubs are immature and incomplete at birth, but gradually strengthen during development. Early establishment of hub connectivity may provide a stable substrate that is refined by changes in tissue organization and microstructure, resulting in the emergence of complex functional dynamics by adulthood.
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Affiliation(s)
- Stuart Oldham
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia; Developmental Imaging, Murdoch Children's Research Institute, Victoria, Australia.
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
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17
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Valdés Hernández MDC, Smith K, Bastin ME, Nicole Amft E, Ralston SH, Wardlaw JM, Wiseman SJ. Brain network reorganisation and spatial lesion distribution in systemic lupus erythematosus. Lupus 2020; 30:285-298. [PMID: 33307988 PMCID: PMC7854491 DOI: 10.1177/0961203320979045] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Objective This work investigates network organisation of brain structural connectivity
in systemic lupus erythematosus (SLE) relative to healthy controls and its
putative association with lesion distribution and disease indicators. Methods White matter hyperintensity (WMH) segmentation and connectomics were
performed in 47 patients with SLE and 47 healthy age-matched controls from
structural and diffusion MRI data. Network nodes were divided into
hierarchical tiers based on numbers of connections. Results were compared
between patients and controls to assess for differences in brain network
organisation. Voxel-based analyses of the spatial distribution of WMH in
relation to network measures and SLE disease indicators were conducted. Results Despite inter-individual differences in brain network organization observed
across the study sample, the connectome networks of SLE patients had larger
proportion of connections in the peripheral nodes. SLE patients had
statistically larger numbers of links in their networks with generally
larger fractional anisotropy weights (i.e. a measure of white matter
integrity) and less tendency to aggregate than those of healthy controls.
The voxels exhibiting connectomic differences were coincident with WMH
clusters, particularly the left hemisphere’s intersection between the
anterior limb of the internal and external capsules. Moreover, these voxels
also associated more strongly with disease indicators. Conclusion Our results indicate network differences reflective of compensatory
reorganization of the neural circuits, reflecting adaptive or extended
neuroplasticity in SLE.
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Affiliation(s)
- Maria Del C Valdés Hernández
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Keith Smith
- Usher Institute for Population Health Science and Informatics, University of Edinburgh, Edinburgh, UK.,Health Data Research UK, London, UK
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - E Nicole Amft
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Stuart H Ralston
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, UK
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Stewart J Wiseman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
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