1
|
Sibilia F, Jost-Mousseau C, Banaschewski T, Barker GJ, Büchel C, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Ittermann B, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Poustka L, Millenet S, Fröhner JH, Smolka MN, Walter H, Whelan R, Schumann G, Bokde AL. The relationship between negative life events and cortical structural connectivity in adolescents. IBRO Neurosci Rep 2024; 16:201-210. [PMID: 38348392 PMCID: PMC10859284 DOI: 10.1016/j.ibneur.2024.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 01/27/2024] [Indexed: 02/15/2024] Open
Abstract
Adolescence is a crucial period for physical and psychological development. The impact of negative life events represents a risk factor for the onset of neuropsychiatric disorders. This study aims to investigate the relationship between negative life events and structural brain connectivity, considering both graph theory and connectivity strength. A group (n = 487) of adolescents from the IMAGEN Consortium was divided into Low and High Stress groups. Brain networks were extracted at an individual level, based on morphological similarity between grey matter regions with regions defined using an atlas-based region of interest (ROI) approach. Between-group comparisons were performed with global and local graph theory measures in a range of sparsity levels. The analysis was also performed in a larger sample of adolescents (n = 976) to examine linear correlations between stress level and network measures. Connectivity strength differences were investigated with network-based statistics. Negative life events were not found to be a factor influencing global network measures at any sparsity level. At local network level, between-group differences were found in centrality measures of the left somato-motor network (a decrease of betweenness centrality was seen at sparsity 5%), of the bilateral central visual and the left dorsal attention network (increase of degree at sparsity 10% at sparsity 30% respectively). Network-based statistics analysis showed an increase in connectivity strength in the High stress group in edges connecting the dorsal attention, limbic and salience networks. This study suggests negative life events alone do not alter structural connectivity globally, but they are associated to connectivity properties in areas involved in emotion and attention.
Collapse
Affiliation(s)
- Francesca Sibilia
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Coline Jost-Mousseau
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- Paris Institute of Technology for Life, Food and Environmental Sciences, Paris, France
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Gareth J. Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Christian Büchel
- University Medical Centre Hamburg-Eppendorf, House W34, 3.OG, Martinistr. 52, 20246, Hamburg, Germany
| | - Sylvane Desrivières
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, VT, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes – Sorbonne Paris Cité; and Maison de Solenn, Paris, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes; and AP-HP.Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité; and Psychiatry Department 91G16, Orsay Hospital, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
| | | | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Sabina Millenet
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Juliane H. Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N. Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Gunter Schumann
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Arun L.W. Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - IMAGEN Consortium
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- Paris Institute of Technology for Life, Food and Environmental Sciences, Paris, France
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
- University Medical Centre Hamburg-Eppendorf, House W34, 3.OG, Martinistr. 52, 20246, Hamburg, Germany
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, VT, USA
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
- Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes – Sorbonne Paris Cité; and Maison de Solenn, Paris, France
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes; and AP-HP.Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité; and Psychiatry Department 91G16, Orsay Hospital, France
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| |
Collapse
|
2
|
Imms P, Chowdhury NF, Chaudhari NN, Amgalan A, Poudel G, Caeyenberghs K, Irimia A. Prediction of cognitive outcome after mild traumatic brain injury from acute measures of communication within brain networks. Cortex 2024; 171:397-412. [PMID: 38103453 PMCID: PMC10922490 DOI: 10.1016/j.cortex.2023.10.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 09/04/2023] [Accepted: 10/20/2023] [Indexed: 12/19/2023]
Abstract
A considerable but ill-defined proportion of patients with mild traumatic brain injury (mTBI) experience persistent cognitive sequelae; the ability to identify such individuals early can help their neurorehabilitation. Here we tested the hypothesis that acute measures of efficient communication within brain networks are associated with patients' risk for unfavorable cognitive outcome six months after mTBI. Diffusion and T1-weighted magnetic resonance imaging, alongside cognitive measures, were obtained to map connectomes both one week and six months post injury in 113 adult patients with mTBI (71 males). For task-related brain networks, communication measures (characteristic path length, global efficiency, navigation efficiency) were moderately correlated with changes in cognition. Taking into account the covariance of age and sex, more unfavorable communication within networks were associated with worse outcomes within cognitive domains frequently impacted by mTBI (episodic and working memory, verbal fluency, inductive reasoning, and processing speed). Individuals with more unfavorable outcomes had significantly longer and less efficient pathways within networks supporting verbal fluency (all t > 2.786, p < .006), highlighting the vulnerability of language to mTBI. Participants in whom a task-related network was relatively inefficient one week post injury were up to eight times more likely to have unfavorable cognitive outcome pertaining to that task. Our findings suggest that communication measures within task-related networks identify mTBI patients who are unlikely to develop persistent cognitive deficits after mTBI. Our approach and findings can help to stratify mTBI patients according to their expected need for follow-up and/or neurorehabilitation.
Collapse
Affiliation(s)
- Phoebe Imms
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA USA.
| | - Nahian F Chowdhury
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA USA.
| | - Nikhil N Chaudhari
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA USA; Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA USA.
| | - Anar Amgalan
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA USA.
| | - Govinda Poudel
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, Australia.
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne Burwood Campus, Burwood, VIC, Australia.
| | - Andrei Irimia
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA USA; Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA USA; Department of Quantitative & Computational Biology, Dana and David Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, CA USA.
| |
Collapse
|
3
|
Wang Y, Guo L, Wang R, Wang Y, Duan F, Zhan Y, Cheng J, Sun X, Tang Z. Abnormal Topological Organization of White Matter Structural Networks in Normal Tension Glaucoma Revealed via Diffusion Tensor Tractography. Brain Sci 2023; 13:1597. [PMID: 38002558 PMCID: PMC10669977 DOI: 10.3390/brainsci13111597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/10/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Normal tension glaucoma (NTG) is considered a neurodegenerative disease with glaucomatous damage extending to diffuse brain areas. Therefore, this study aims to explore the abnormalities in the NTG structural network to help in the early diagnosis and course evaluation of NTG. METHODS The structural networks of 46 NTG patients and 19 age- and sex-matched healthy controls were constructed using diffusion tensor imaging, followed by graph theory analysis and correlation analysis of small-world properties with glaucoma clinical indicators. In addition, the network-based statistical analysis (NBS) method was used to compare structural network connectivity differences between NTG patients and healthy controls. RESULTS Structural brain networks in both NTG and NC groups exhibited small-world properties. However, the small-world index in the severe NTG group was reduced and correlated with a mean deviation of the visual field (MDVF) and retinal nerve fiber layer (RNFL) thickness. When compared to healthy controls, degree centrality and nodal efficiency in visual brain areas were significantly decreased, and betweenness centrality and nodal local efficiency in both visual and nonvisual brain areas were also significantly altered in NTG patients (all p < 0.05, FDR corrected). Furthermore, NTG patients exhibited increased structural connectivity in the occipitotemporal area, with the left fusiform gyrus (FFG.L) as the hub (p < 0.05). CONCLUSIONS NTG exhibited altered global properties and local properties of visual and cognitive-emotional brain areas, with enhanced structural connections within the occipitotemporal area. Moreover, the disrupted small-world properties of white matter might be imaging biomarkers for assessing NTG progression.
Collapse
Affiliation(s)
- Yin Wang
- Department of Radiology, Eye & ENT Hospital of Fudan University, Fudan University, Shanghai 200031, China (F.D.)
| | - Linying Guo
- Department of Radiology, Eye & ENT Hospital of Fudan University, Fudan University, Shanghai 200031, China (F.D.)
| | - Rong Wang
- Department of Radiology, Huashan Hospital of Fudan University, Fudan University, Shanghai 200040, China
| | - Yuzhe Wang
- Department of Radiology, Zhongshan Hospital of Fudan University, Fudan University, Shanghai 200032, China; (Y.W.)
| | - Fei Duan
- Department of Radiology, Eye & ENT Hospital of Fudan University, Fudan University, Shanghai 200031, China (F.D.)
| | - Yang Zhan
- Department of Radiology, Zhongshan Hospital of Fudan University, Fudan University, Shanghai 200032, China; (Y.W.)
| | - Jingfeng Cheng
- Department of Radiology, Eye & ENT Hospital of Fudan University, Fudan University, Shanghai 200031, China (F.D.)
| | - Xinghuai Sun
- Department of Ophthalmology & Visual Science, Eye & ENT Hospital of Fudan University, Fudan University, Shanghai 200031, China;
| | - Zuohua Tang
- Department of Radiology, Eye & ENT Hospital of Fudan University, Fudan University, Shanghai 200031, China (F.D.)
| |
Collapse
|
4
|
Madole JW, Buchanan CR, Rhemtulla M, Ritchie SJ, Bastin ME, Deary IJ, Cox SR, Tucker-Drob EM. Strong intercorrelations among global graph-theoretic indices of structural connectivity in the human brain. Neuroimage 2023; 275:120160. [PMID: 37169117 DOI: 10.1016/j.neuroimage.2023.120160] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/06/2023] [Accepted: 05/08/2023] [Indexed: 05/13/2023] Open
Abstract
Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and neurodegenerative diseases. In N = 8,185 human structural connectomes from UK Biobank, we examined the extent to which 11 commonly-used global graph-theoretic metrics index distinct versus overlapping information with respect to interindividual differences in brain organization. Using unthresholded, FA-weighted networks we found that all metrics other than Participation Coefficient were highly intercorrelated, both with each other (mean |r| = 0.788) and with a topologically-naïve summary index of brain structure (mean edge weight; mean |r| = 0.873). In a series of sensitivity analyses, we found that overlap between metrics is influenced by the sparseness of the network and the magnitude of variation in edge weights. Simulation analyses representing a range of population network structures indicated that individual differences in global graph metrics may be intrinsically difficult to separate from mean edge weight. In particular, Closeness, Characteristic Path Length, Global Efficiency, Clustering Coefficient, and Small Worldness were nearly perfectly collinear with one another (mean |r| = 0.939) and with mean edge weight (mean |r| = 0.952) across all observed and simulated conditions. Global graph-theoretic measures are valuable for their ability to distill a high-dimensional system of neural connections into summary indices of brain organization, but they may be of more limited utility when the goal is to index separable components of interindividual variation in specific properties of the human structural connectome.
Collapse
Affiliation(s)
- James W Madole
- Department of Psychology, University of Texas at Austin, Austin, TX, USA; VA Puget Sound Health Care System, Seattle Division, Seattle, WA, USA.
| | - Colin R Buchanan
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Mijke Rhemtulla
- Department of Psychology, University of California, Davis, CA, USA
| | - Stuart J Ritchie
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - Mark E Bastin
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, USA; Population Research Center and Center on Aging and Population Sciences, University of Texas at Austin, Austin, TX, USA
| |
Collapse
|
5
|
Mirza-Davies A, Foley S, Caseras X, Baker E, Holmans P, Escott-Price V, Jones DK, Harrison JR, Messaritaki E. The impact of genetic risk for Alzheimer's disease on the structural brain networks of young adults. Front Neurosci 2022; 16:987677. [PMID: 36532292 PMCID: PMC9748570 DOI: 10.3389/fnins.2022.987677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 11/09/2022] [Indexed: 12/02/2022] Open
Abstract
Introduction We investigated the structural brain networks of 562 young adults in relation to polygenic risk for Alzheimer's disease, using magnetic resonance imaging (MRI) and genotype data from the Avon Longitudinal Study of Parents and Children. Methods Diffusion MRI data were used to perform whole-brain tractography and generate structural brain networks for the whole-brain connectome, and for the default mode, limbic and visual subnetworks. The mean clustering coefficient, mean betweenness centrality, characteristic path length, global efficiency and mean nodal strength were calculated for these networks, for each participant. The connectivity of the rich-club, feeder and local connections was also calculated. Polygenic risk scores (PRS), estimating each participant's genetic risk, were calculated at genome-wide level and for nine specific disease pathways. Correlations were calculated between the PRS and (a) the graph theoretical metrics of the structural networks and (b) the rich-club, feeder and local connectivity of the whole-brain networks. Results In the visual subnetwork, the mean nodal strength was negatively correlated with the genome-wide PRS (r = -0.19, p = 1.4 × 10-3), the mean betweenness centrality was positively correlated with the plasma lipoprotein particle assembly PRS (r = 0.16, p = 5.5 × 10-3), and the mean clustering coefficient was negatively correlated with the tau-protein binding PRS (r = -0.16, p = 0.016). In the default mode network, the mean nodal strength was negatively correlated with the genome-wide PRS (r = -0.14, p = 0.044). The rich-club and feeder connectivities were negatively correlated with the genome-wide PRS (r = -0.16, p = 0.035; r = -0.15, p = 0.036). Discussion We identified small reductions in brain connectivity in young adults at risk of developing Alzheimer's disease in later life.
Collapse
Affiliation(s)
- Anastasia Mirza-Davies
- School of Medicine, University Hospital Wales, Cardiff University, Cardiff, United Kingdom
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Xavier Caseras
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Emily Baker
- UK Dementia Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Peter Holmans
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Valentina Escott-Price
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
- UK Dementia Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Judith R. Harrison
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- Institute for Translational and Clinical Research, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Eirini Messaritaki
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- BRAIN Biomedical Research Unit, School of Medicine, Cardiff University, Cardiff, United Kingdom
| |
Collapse
|
6
|
Sipes BS, Jakary A, Li Y, Max JE, Yang TT, Tymofiyeva O. Resting state brain subnetwork relates to prosociality and compassion in adolescents. Front Psychol 2022; 13:1012745. [PMID: 36337478 PMCID: PMC9632179 DOI: 10.3389/fpsyg.2022.1012745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 10/04/2022] [Indexed: 11/30/2022] Open
Abstract
Adolescence is a crucial time for social development, especially for helping (prosocial) and compassionate behaviors; yet brain networks involved in adolescent prosociality and compassion currently remain underexplored. Here, we sought to evaluate a recently proposed domain-general developmental (Do-GooD) network model of prosocial cognition by relating adolescent functional and structural brain networks with prosocial and compassionate disposition. We acquired resting state fMRI and diffusion MRI from 95 adolescents (ages 14–19 years; 46 males; 49 females) along with self-report questionnaires assessing prosociality and compassion. We then applied the Network-Based Statistic (NBS) to inductively investigate whether there is a significant subnetwork related to prosociality and compassion while controlling for age and sex. Based on the Do-GooD model, we expected that this subnetwork would involve connectivity to the ventromedial prefrontal cortex (VMPFC) from three domain-general networks, the default mode network (DMN), the salience network, and the control network, as well as from the DMN to the mirror neuron systems. NBS revealed a significant functional (but not structural) subnetwork related to prosociality and compassion connecting 31 regions (p = 0.02), showing DMN and DLPFC connectivity to the VMPFC; DMN connectivity to mirror neuron systems; and connectivity between the DMN and cerebellum. These findings largely support and extend the Do-GooD model of prosocial cognition in adolescents by further illuminating network-based relationships that have the potential to advance our understanding of brain mechanisms of prosociality.
Collapse
Affiliation(s)
- Benjamin S. Sipes
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Angela Jakary
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Yi Li
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Jeffrey E. Max
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
- Rady Children’s Hospital San Diego, San Diego, CA, United States
| | - Tony T. Yang
- Department of Psychiatry and Behavioral Sciences, The Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Olga Tymofiyeva
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
- *Correspondence: Olga Tymofiyeva,
| |
Collapse
|
7
|
De Brito Robalo BM, Vlegels N, Leemans A, Reijmer YD, Biessels GJ. Impact of thresholding on the consistency and sensitivity of diffusion MRI-based brain networks in patients with cerebral small vessel disease. Brain Behav 2022; 12:e2523. [PMID: 35413156 PMCID: PMC9120729 DOI: 10.1002/brb3.2523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 12/21/2021] [Accepted: 01/25/2022] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION Thresholding of low-weight connections of diffusion MRI-based brain networks has been proposed to remove false-positive connections. It has been previously established that this yields more reproducible scan-rescan network architecture in healthy subjects. In patients with brain disease, network measures are applied to assess inter-individual variation and changes over time. Our aim was to investigate whether thresholding also achieves improved consistency in network architecture in patients, while maintaining sensitivity to disease effects for these applications. METHODS We applied fixed-density and absolute thresholding on brain networks in patients with cerebral small vessel disease (SVD, n = 86; ≈24 months follow-up), as a clinically relevant exemplar condition. In parallel, we applied the same methods in healthy young subjects (n = 44; scan-rescan interval ≈4 months) as a frame of reference. Consistency of network architecture was assessed with dice similarity of edges and intraclass correlation coefficient (ICC) of edge-weights and hub-scores. Sensitivity to disease effects in patients was assessed by evaluating interindividual variation, changes over time, and differences between those with high and low white matter hyperintensity burden, using correlation analyses and mixed ANOVA. RESULTS Compared to unthresholded networks, both thresholding methods generated more consistent architecture over time in patients (unthresholded: dice = .70; ICC: .70-.78; thresholded: dice = .77; ICC: .73-.83). However, absolute thresholding created fragmented nodes. Similar observations were made in the reference group. Regarding sensitivity to disease effects in patients, fixed-density thresholds that were optimal in terms of consistency (densities: .10-.30) preserved interindividual variation in global efficiency and node strength as well as the sensitivity to detect effects of time and group. Absolute thresholding produced larger fluctuations of interindividual variation. CONCLUSIONS Our results indicate that thresholding of low-weight connections, particularly when using fixed-density thresholding, results in more consistent network architecture in patients with longer rescan intervals, while preserving sensitivity to disease effects.
Collapse
Affiliation(s)
- Bruno M De Brito Robalo
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Naomi Vlegels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Alexander Leemans
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Yael D Reijmer
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|
8
|
Chen Q, Baran TM, Turnbull A, Zhang Z, Rebok GW, Lin FV. Increased segregation of structural brain networks underpins enhanced broad cognitive abilities of cognitive training. Hum Brain Mapp 2021; 42:3202-3215. [PMID: 33955088 PMCID: PMC8193523 DOI: 10.1002/hbm.25428] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 02/26/2021] [Accepted: 03/08/2021] [Indexed: 12/11/2022] Open
Abstract
A major challenge in the cognitive training field is inducing broad, far-transfer training effects. Thus far, little is known about the neural mechanisms underlying broad training effects. Here, we tested a set of competitive hypotheses regarding the role of brain integration versus segregation underlying the broad training effect. We retrospectively analyzed data from a randomized controlled trial comparing neurocognitive effects of vision-based speed of processing training (VSOP) and an active control consisting of mental leisure activities (MLA) in older adults with MCI. We classified a subset of participants in the VSOP as learners, who showed improvement in executive function and episodic memory. The other participants in the VSOP (i.e., VSOP non-learners) and a subset of participants in the MLA (i.e., MLA non-learners) served as controls. Structural brain networks were constructed from diffusion tensor imaging. Clustering coefficients (CCs) and characteristic path lengths were computed as measures of segregation and integration, respectively. Learners showed significantly greater global CCs after intervention than controls. Nodal CCs were selectively enhanced in cingulate cortex, parietal regions, striatum, and thalamus. Among VSOP learners, those with more severe baseline neurodegeneration had greater improvement in segregation after training. Our findings suggest broad training effects are related to enhanced segregation in selective brain networks, providing insight into cognitive training related neuroplasticity.
Collapse
Affiliation(s)
- Quanjing Chen
- Elaine C. Hubbard Center for Nursing Research on Aging, School of NursingUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Department of Psychiatry, School of Medicine and DentistryUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Timothy M. Baran
- Department of Biomedical EngineeringUniversity of RochesterRochesterNew YorkUSA
- Department of Imaging SciencesUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Adam Turnbull
- Elaine C. Hubbard Center for Nursing Research on Aging, School of NursingUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Department of Imaging SciencesUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Zhengwu Zhang
- Department of Biostatistics and Computational BiologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - George W. Rebok
- Department of Mental HealthJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Feng Vankee Lin
- Elaine C. Hubbard Center for Nursing Research on Aging, School of NursingUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Department of Psychiatry, School of Medicine and DentistryUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Department of Brain and Cognitive SciencesUniversity of RochesterRochesterNew YorkUSA
- Department of Neuroscience, School of Medicine and DentistryUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Department of Neurology, School of Medicine and DentistryUniversity of Rochester Medical CenterRochesterNew YorkUSA
| |
Collapse
|
9
|
Dimitriadis SI, Messaritaki E, K Jones D. The impact of graph construction scheme and community detection algorithm on the repeatability of community and hub identification in structural brain networks. Hum Brain Mapp 2021; 42:4261-4280. [PMID: 34170066 PMCID: PMC8356981 DOI: 10.1002/hbm.25545] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 05/14/2021] [Indexed: 12/20/2022] Open
Abstract
A critical question in network neuroscience is how nodes cluster together to form communities, to form the mesoscale organisation of the brain. Various algorithms have been proposed for identifying such communities, each identifying different communities within the same network. Here, (using test–retest data from the Human Connectome Project), the repeatability of thirty‐three community detection algorithms, each paired with seven different graph construction schemes were assessed. Repeatability of community partition depended heavily on both the community detection algorithm and graph construction scheme. Hard community detection algorithms (in which each node is assigned to only one community) outperformed soft ones (in which each node can belong to more than one community). The highest repeatability was observed for the fast multi‐scale community detection algorithm paired with a graph construction scheme that combines nine white matter metrics. This pair also gave the highest similarity between representative group community affiliation and individual community affiliation. Connector hubs had higher repeatability than provincial hubs. Our results provide a workflow for repeatable identification of structural brain networks communities, based on the optimal pairing of community detection algorithm and graph construction scheme.
Collapse
Affiliation(s)
- Stavros I Dimitriadis
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.,Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.,School of Psychology, Cardiff University, Cardiff, UK.,Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK.,Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK.,MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, UK
| | - Eirini Messaritaki
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.,Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.,School of Psychology, Cardiff University, Cardiff, UK.,BRAIN Biomedical Research Unit, Cardiff University, Cardiff, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.,School of Psychology, Cardiff University, Cardiff, UK
| |
Collapse
|
10
|
Ho TC, Walker JC, Teresi GI, Kulla A, Kirshenbaum JS, Gifuni AJ, Singh MK, Gotlib IH. Default mode and salience network alterations in suicidal and non-suicidal self-injurious thoughts and behaviors in adolescents with depression. Transl Psychiatry 2021; 11:38. [PMID: 33436537 PMCID: PMC7804956 DOI: 10.1038/s41398-020-01103-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 10/23/2020] [Accepted: 11/10/2020] [Indexed: 12/27/2022] Open
Abstract
Suicidal ideation (SI) and non-suicidal self-injury (NSSI) are two distinct yet often co-occurring risk factors for suicide deaths in adolescents. Elucidating the neurobiological patterns that specifically characterize SI and NSSI in adolescents is needed to inform the use of these markers in intervention studies and to develop brain-based treatment targets. Here, we clinically assessed 70 adolescents-49 adolescents with depression and 21 healthy controls-to determine SI and NSSI history. Twenty-eight of the depressed adolescents had a history of SI and 29 had a history of NSSI (20 overlapping). All participants underwent a resting-state fMRI scan. We compared groups in network coherence of subdivisions of the central executive network (CEN), default mode network (DMN), and salience network (SN). We also examined group differences in between-network connectivity and explored brain-behavior correlations. Depressed adolescents with SI and with NSSI had lower coherence in the ventral DMN compared to those without SI or NSSI, respectively, and healthy controls (all ps < 0.043, uncorrected). Depressed adolescents with NSSI had lower coherence in the anterior DMN and in insula-SN (all ps < 0.030, uncorrected), and higher CEN-DMN connectivity compared to those without NSSI and healthy controls (all ps < 0.030, uncorrected). Lower network coherence in all DMN subnetworks and insula-SN were associated with higher past-month SI and NSSI (all ps < 0.001, uncorrected). Thus, in our sample, both SI and NSSI are related to brain networks associated with difficulties in self-referential processing and future planning, while NSSI specifically is related to brain networks associated with disruptions in interoceptive awareness.
Collapse
Affiliation(s)
- Tiffany C Ho
- Department of Psychiatry and Behavioral Sciences; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
| | - Johanna C Walker
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Giana I Teresi
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Artenisa Kulla
- Department of Psychology, Stanford University, Stanford, CA, USA
| | | | - Anthony J Gifuni
- Department of Psychology, Stanford University, Stanford, CA, USA
- Department of Psychiatry and Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Manpreet K Singh
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, CA, USA
| |
Collapse
|
11
|
Tymofiyeva O, Henje E, Yuan JP, Huang CY, Connolly CG, Ho TC, Bhandari S, Parks KC, Sipes BS, Yang TT, Xu D. Reduced anxiety and changes in amygdala network properties in adolescents with training for awareness, resilience, and action (TARA). Neuroimage Clin 2020; 29:102521. [PMID: 33316764 PMCID: PMC7735968 DOI: 10.1016/j.nicl.2020.102521] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 11/13/2020] [Accepted: 11/30/2020] [Indexed: 12/18/2022]
Abstract
Mindfulness-based approaches show promise to improve emotional health in youth and may help treat and prevent adolescent depression and anxiety. However, there is a fundamental gap in understanding the neural reorganization that takes place as a result of such interventions. The Training for Awareness, Resilience, and Action (TARA) program, initially developed for depressed adolescents, uses a framework drawn from neuroscience, mindfulness, yoga, and modern psychotherapeutic techniques to promote emotional health. The goal of this study was to assess the effects of the TARA training on emotional health and structural white matter brain networks in healthy youth. We analyzed data from 23 adolescents who underwent the 12-week TARA training in a controlled within-subject study design and whose brain networks were assessed using diffusion MRI connectomics. Compared to the control time period, adolescents showed a significant decrease in anxiety symptoms with TARA (Cohen's d = -0.961, p = 0.006); moreover, the node strength of the Right Amygdala decreased significantly after TARA (Cohen's d = -1.026, p = 0.004). Post-hoc analyses indicated that anxiety at baseline before TARA was positively correlated with Right Amygdala node strength (r = 0.672, p = 0.001). While change in Right Amygdala node strength with TARA was not correlated with change in anxiety (r = 0.146, p = 0.51), it was associated with change in depression subscale of Anhedonia / Negative Affect (r = 0.575, p = 0.004, exploratory analysis), possibly due to overlapping constructs captured in our anxiety and depression scales. Our results suggest that increased structural connectivity of Right Amygdala may underlie increased anxiety in adolescents and be lowered through anxiety-reducing training such as TARA. The results of this study contribute to our understanding of the neural mechanisms of TARA and may facilitate neuroscience-based prevention and treatment of adolescent anxiety and depression.
Collapse
Affiliation(s)
- Olga Tymofiyeva
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, 1700 4th Street, BH102, San Francisco, CA 94143, USA.
| | - Eva Henje
- Department of Psychiatry and Behavioral Sciences, The Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94143, USA; Department of Clinical Science/Child- and Adolescent Psychiatry, Umeå University, SE-901 87 Umeå, Sweden
| | - Justin P Yuan
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, 1700 4th Street, BH102, San Francisco, CA 94143, USA; Department of Psychology, Stanford University, 450 Jane Stanford Way, Bldg 420, Jordan Hall, Stanford, CA 94305-2130, USA
| | - Chiung-Yu Huang
- Department of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, San Francisco, CA 94143, USA
| | - Colm G Connolly
- Department of Psychiatry and Behavioral Sciences, The Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94143, USA; Department of Biomedical Sciences, Florida State University College of Medicine, 1115 West Call Street, Tallahassee, FL 32306, USA
| | - Tiffany C Ho
- Department of Psychiatry and Behavioral Sciences, The Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94143, USA
| | - Sarina Bhandari
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, 1700 4th Street, BH102, San Francisco, CA 94143, USA
| | - Kendall C Parks
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, 1700 4th Street, BH102, San Francisco, CA 94143, USA
| | - Benjamin S Sipes
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, 1700 4th Street, BH102, San Francisco, CA 94143, USA
| | - Tony T Yang
- Department of Psychiatry and Behavioral Sciences, The Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94143, USA
| | - Duan Xu
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, 1700 4th Street, BH102, San Francisco, CA 94143, USA
| |
Collapse
|
12
|
Yang F, Zhang J, Fan L, Liao M, Wang Y, Chen C, Zhai T, Zhang Y, Li L, Su L, Dai Z. White matter structural network disturbances in first-episode, drug-naïve adolescents with generalized anxiety disorder. J Psychiatr Res 2020; 130:394-404. [PMID: 32889357 DOI: 10.1016/j.jpsychires.2020.08.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 07/12/2020] [Accepted: 08/09/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND Previous studies have suggested that individuals with generalized anxiety disorder (GAD) would show inefficient whole-brain communication and dysconnectivity in the fronto-parietal-subcortical sub-networks in the white matter (WM) structural network. However, these hypotheses have yet to be tested. METHODS Individual WM structural networks were constructed based on diffusion MRI data and deterministic tractography in 34 first-episode, medication-naïve adolescents with GAD and 27 healthy controls (HCs). Graph theory was applied to investigate the topological organization alterations of the structural network. RESULTS GAD patients showed disrupted small-world configurations (i.e., increased path length and decreased clustering coefficient) and hub organization (i.e., less connection strength in the feeder and local connections). A decreased connection strength was found in a GAD-related sub-network (mainly involving the frontal-subcortical circuits), which was able to distinguish GAD patients from HCs with higher accuracy (area under the curve of 0.96, sensitivity of 94%, specificity of 89%) than clinical scores and other topological alternations. LIMITATIONS The current study just compared GAD patients with HCs based on a small sample, leaving whether the alternations found here are specific to GAD still an open question. Future studies are recommended to recruit patients with other anxiety disorders (e.g., social anxiety disorder) and/or comorbid mood disorders to identify the GAD-specific WM alterations using a larger sample. CONCLUSIONS Our findings highlight the disruption of the topological organization of the whole-brain WM structural network (especially the frontal-subcortical circuits) in GAD, and suggest the potential of using structural connectivity of the GAD-related sub-network as a biomarker for GAD patients.
Collapse
Affiliation(s)
- Fan Yang
- Guangdong Mental Health Center, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jinbo Zhang
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Linlin Fan
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, United States
| | - Mei Liao
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yuyin Wang
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Chang Chen
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Tianyi Zhai
- Department of Psychiatry, Guangzhou Huiai Hospital, Guangzhou, China
| | - Yan Zhang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Lingjiang Li
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Linyan Su
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou, China.
| |
Collapse
|
13
|
Tymofiyeva O, Zhou VX, Lee CM, Xu D, Hess CP, Yang TT. MRI Insights Into Adolescent Neurocircuitry-A Vision for the Future. Front Hum Neurosci 2020; 14:237. [PMID: 32733218 PMCID: PMC7359264 DOI: 10.3389/fnhum.2020.00237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 05/29/2020] [Indexed: 11/13/2022] Open
Abstract
Adolescence is the time of onset of many psychiatric disorders. Half of pediatric patients present with comorbid psychiatric disorders that complicate both their medical and psychiatric care. Currently, diagnosis and treatment decisions are based on symptoms. The field urgently needs brain-based diagnosis and personalized care. Neuroimaging can shed light on how aberrations in brain circuits might underlie psychiatric disorders and their development in adolescents. In this perspective article, we summarize recent MRI literature that provides insights into development of psychiatric disorders in adolescents. We specifically focus on studies of brain structural and functional connectivity. Ninety-six included studies demonstrate the potential of MRI to assess psychiatrically relevant constructs, diagnose psychiatric disorders, predict their development or predict response to treatment. Limitations of the included studies are discussed, and recommendations for future research are offered. We also present a vision for the role that neuroimaging may play in pediatrics and primary care in the future: a routine neuropsychological and neuropsychiatric imaging (NPPI) protocol for adolescent patients, which would include a 30-min brain scan, a quality control and safety read of the scan, followed by computer-based calculation of the structural and functional brain network metrics that can be compared to the normative data by the pediatrician. We also perform a cost-benefit analysis to support this vision and provide a roadmap of the steps required for this vision to be implemented.
Collapse
Affiliation(s)
- Olga Tymofiyeva
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Vivian X Zhou
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Chuan-Mei Lee
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.,Clinical Excellence Research Center, Stanford University, Stanford, CA, United States
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Christopher P Hess
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Tony T Yang
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| |
Collapse
|
14
|
Osmanlıoğlu Y, Alappatt JA, Parker D, Verma R. Connectomic consistency: a systematic stability analysis of structural and functional connectivity. J Neural Eng 2020; 17:045004. [PMID: 32428883 PMCID: PMC7584380 DOI: 10.1088/1741-2552/ab947b] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
OBJECTIVE Connectomics, the study of brain connectivity, has become an indispensable tool in neuroscientific research as it provides insights into brain organization. Connectomes are generated using different modalities such as diffusion MRI to capture structural organization of the brain or functional MRI to elaborate brain's functional organization. Understanding links between structural and functional organizations is crucial in explaining how observed behavior emerges from the underlying neurobiological mechanisms. Many studies have investigated how these two organizations relate to each other; however, we still lack a comparative understanding on how much variation should be expected in the two modalities, both between people and within a single person across scans. APPROACH In this study, we systematically analyzed the consistency of connectomes, that is the similarity between connectomes in terms of individual connections between brain regions and in terms of overall network topology. We present a comprehensive study of consistency in connectomes for a single subject examined longitudinally and across a large cohort of subjects cross-sectionally, in structure and function separately. Within structural connectomes, we compared connectomes generated by different tracking algorithms, parcellations, edge weighting schemes, and edge pruning techniques. In functional connectomes, we compared full, positive, and negative connectivity separately along with thresholding of weak edges. We evaluated consistency using correlation (incorporating information at the level of individual edges) and graph matching accuracy (evaluating connectivity at the level of network topology). We also examined the consistency of connectomes that are generated using different communication schemes. MAIN RESULTS Our results demonstrate varying degrees of consistency for the two modalities, with structural connectomes showing higher consistency than functional connectomes. Moreover, we observed a wide variation in consistency depending on how connectomes are generated. SIGNIFICANCE Our study sets a reference point for consistency of connectome types, which is especially important for structure-function coupling studies in evaluating mismatches between modalities.
Collapse
Affiliation(s)
- Yusuf Osmanlıoğlu
- Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, United States of America
| | - Jacob A Alappatt
- Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, United States of America
| | - Drew Parker
- Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, United States of America
| | - Ragini Verma
- Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, United States of America
| |
Collapse
|
15
|
Structural networks in children with autism spectrum disorder with regression: A graph theory study. Behav Brain Res 2020; 378:112262. [DOI: 10.1016/j.bbr.2019.112262] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 09/21/2019] [Accepted: 09/24/2019] [Indexed: 12/16/2022]
|
16
|
Tymofiyeva O, Yuan JP, Huang CY, Connolly CG, Henje Blom E, Xu D, Yang TT. Application of machine learning to structural connectome to predict symptom reduction in depressed adolescents with cognitive behavioral therapy (CBT). NEUROIMAGE-CLINICAL 2019; 23:101914. [PMID: 31491813 PMCID: PMC6627980 DOI: 10.1016/j.nicl.2019.101914] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 06/14/2019] [Accepted: 06/29/2019] [Indexed: 12/29/2022]
Abstract
Purpose Adolescent major depressive disorder (MDD) is a highly prevalent, incapacitating and costly illness. Many depressed teens do not improve with cognitive behavioral therapy (CBT), a first-line treatment for adolescent MDD, and face devastating consequences of increased risk of suicide and many negative health outcomes. “Who will improve with CBT?” is a crucial question that remains unanswered, and treatment planning for adolescent depression remains biologically unguided. The purpose of this study was to utilize machine learning applied to patients' brain imaging data in order to help predict depressive symptom reduction with CBT. Methods We applied supervised machine learning to diffusion MRI-based structural connectome data in order to predict symptom reduction in 30 depressed adolescents after three months of CBT. A set of 21 attributes was chosen, including the baseline depression score, age, gender, two global network properties, and node strengths of brain regions previously implicated in depression. The practical and robust J48 pruned tree classifier was utilized with a 10-fold cross-validation. Results The classification resulted in an 83% accuracy of predicting depressive symptom reduction. The resulting tree of size seven with only three attributes highlights the role of the right thalamus in predicting depressive symptom reduction with CBT. Additional analysis showed a significant negative correlation between the change in the depressive symptoms and the node strength of the right thalamus. Conclusions Our results demonstrate that a machine learning algorithm that exclusively uses structural connectome data and the baseline depression score can predict with a high accuracy depressive symptom reduction in adolescent MDD with CBT. This knowledge can help improve treatment planning for adolescent depression. Machine learning predicted symptom reduction in depressed teens with 83% accuracy. Resulting prunned classification tree size was 7, with only 3 attributes. Change in depression symptoms correlated with node strength of the right thalamus.
Collapse
Affiliation(s)
- Olga Tymofiyeva
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, 1700 4th Street, BH102, San Francisco, CA 94143, USA.
| | - Justin P Yuan
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, 1700 4th Street, BH102, San Francisco, CA 94143, USA
| | - Chiung-Yu Huang
- Department of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, San Francisco, CA 94143, USA
| | - Colm G Connolly
- Department of Psychiatry and the Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94143, USA; Department of Biomedical Sciences, Florida State University College of Medicine, 1115 West Call Street, Tallahassee, FL 32306, USA
| | - Eva Henje Blom
- Department of Psychiatry and the Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94143, USA; Department of Clinical Science/Child- and Adolescent Psychiatry, Umeå University, SE-901 87 Umeå, Sweden
| | - Duan Xu
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, 1700 4th Street, BH102, San Francisco, CA 94143, USA
| | - Tony T Yang
- Department of Psychiatry and the Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94143, USA
| |
Collapse
|
17
|
Optimization of graph construction can significantly increase the power of structural brain network studies. Neuroimage 2019; 199:495-511. [PMID: 31176831 PMCID: PMC6693529 DOI: 10.1016/j.neuroimage.2019.05.052] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 04/08/2019] [Accepted: 05/19/2019] [Indexed: 12/31/2022] Open
Abstract
Structural brain networks derived from diffusion magnetic resonance imaging data have been used extensively to describe the human brain, and graph theory has allowed quantification of their network properties. Schemes used to construct the graphs that represent the structural brain networks differ in the metrics they use as edge weights and the algorithms they use to define the network topologies. In this work, twenty graph construction schemes were considered. The schemes use the number of streamlines, the fractional anisotropy, the mean diffusivity or other attributes of the tracts to define the edge weights, and either an absolute threshold or a data-driven algorithm to define the graph topology. The test-retest data of the Human Connectome Project were used to compare the reproducibility of the graphs and their various attributes (edges, topologies, graph theoretical metrics) derived through those schemes, for diffusion images acquired with three different diffusion weightings. The impact of the scheme on the statistical power of the study and on the number of participants required to detect a difference between populations or an effect of an intervention was also calculated. The reproducibility of the graphs and their attributes depended heavily on the graph construction scheme. Graph reproducibility was higher for schemes that used thresholding to define the graph topology, while data-driven schemes performed better at topology reproducibility (mean similarities of 0.962 and 0.984 respectively, for graphs derived from diffusion images with b=2000 s/mm2). Additionally, schemes that used thresholding resulted in better reproducibility for local graph theoretical metrics (intra-class correlation coefficients (ICC) of the order of 0.8), compared to data-driven schemes. Thresholded and data-driven schemes resulted in high (0.86 or higher) ICCs only for schemes that use exclusively the number of streamlines to construct the graphs. Crucially, the number of participants required to detect a difference between populations or an effect of an intervention could change by a factor of two or more depending on the scheme used, affecting the power of studies to reveal the effects of interest.
Collapse
|
18
|
Qin J, Sui J, Ni H, Wang S, Zhang F, Zhou Z, Tian L. The Shared and Distinct White Matter Networks Between Drug-Naive Patients With Obsessive-Compulsive Disorder and Schizophrenia. Front Neurosci 2019; 13:96. [PMID: 30846924 PMCID: PMC6393388 DOI: 10.3389/fnins.2019.00096] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2018] [Accepted: 01/28/2019] [Indexed: 12/17/2022] Open
Abstract
Background: Obsessive-compulsive disorder (OCD) and schizophrenia (SZ) as two severe mental disorders share many clinical symptoms, and have a tight association on the psychopathological level. However, the neurobiological substrates between these two diseases remain unclear. To the best of our knowledge, no study has directly compared OCD with SZ from the perspective of white matter (WM) networks. Methods: Graph theory and network-based statistic methods were applied to diffusion MRI to investigate and compare the WM topological characteristics among 29 drug-naive OCDs, 29 drug-naive SZs, and 65 demographically-matched healthy controls (NC). Results: Compared to NCs, OCDs showed the alterations of nodal efficiency and strength in orbitofrontal (OFG) and middle frontal gyrus (MFG), while SZs exhibited widely-distributed abnormalities involving the OFG, MFG, fusiform gyrus, heschl gyrus, calcarine, lingual gyrus, putamen, and thalamus, and most of these regions also showed a significant difference from OCDs. Moreover, SZs had significantly fewer connections in striatum and visual/auditory cortices than OCDs. The right putamen consistently showed significant differences between both disorders on nodal characteristics and structural connectivity. Conclusions: SZ and OCD present different level of anatomical impairment and some distinct topological patterns, and the former has more serious and more widespread disruptions. The significant differences between both disorders are observed in many regions involving the frontal, temporal, occipital, and subcortical regions. Particularly, putamen may serve as a potential imaging marker to distinguish these two disorders and may be the key difference in their pathological changes.
Collapse
Affiliation(s)
- Jiaolong Qin
- The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jing Sui
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, China
| | - Huangjing Ni
- Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Shuai Wang
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China
- Wuxi Tongren International Rehabilitation Hospital, Wuxi, China
| | - Fuquan Zhang
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China
- Wuxi Tongren International Rehabilitation Hospital, Wuxi, China
| | - Zhenhe Zhou
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China
- Wuxi Tongren International Rehabilitation Hospital, Wuxi, China
| | - Lin Tian
- The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China
- Wuxi Tongren International Rehabilitation Hospital, Wuxi, China
| |
Collapse
|