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Torgerson C, Ahmadi H, Choupan J, Fan CC, Blosnich JR, Herting MM. Sex, gender diversity, and brain structure in early adolescence. Hum Brain Mapp 2024; 45:e26671. [PMID: 38590252 PMCID: PMC11002534 DOI: 10.1002/hbm.26671] [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: 07/28/2023] [Revised: 02/27/2024] [Accepted: 03/13/2024] [Indexed: 04/10/2024] Open
Abstract
There remains little consensus about the relationship between sex and brain structure, particularly in early adolescence. Moreover, few pediatric neuroimaging studies have analyzed both sex and gender as variables of interest-many of which included small sample sizes and relied on binary definitions of gender. The current study examined gender diversity with a continuous felt-gender score and categorized sex based on X and Y allele frequency in a large sample of children ages 9-11 years old (N = 7195). Then, a statistical model-building approach was employed to determine whether gender diversity and sex independently or jointly relate to brain morphology, including subcortical volume, cortical thickness, gyrification, and white matter microstructure. Additional sensitivity analyses found that male versus female differences in gyrification and white matter were largely accounted for by total brain volume, rather than sex per se. The model with sex, but not gender diversity, was the best-fitting model in 60.1% of gray matter regions and 61.9% of white matter regions after adjusting for brain volume. The proportion of variance accounted for by sex was negligible to small in all cases. While models including felt-gender explained a greater amount of variance in a few regions, the felt-gender score alone was not a significant predictor on its own for any white or gray matter regions examined. Overall, these findings demonstrate that at ages 9-11 years old, sex accounts for a small proportion of variance in brain structure, while gender diversity is not directly associated with neurostructural diversity.
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Affiliation(s)
- Carinna Torgerson
- Department of Population and Public Health SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Hedyeh Ahmadi
- Department of Population and Public Health SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Jeiran Choupan
- Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Chun Chieh Fan
- Center for Population Neuroscience and GeneticsLaureate Institute for Brain ResearchTulsaOklahomaUSA
- Department of Radiology, School of MedicineUniversity of CaliforniaSan DiegoCaliforniaUSA
| | - John R. Blosnich
- Suzanne Dworak‐Peck School of Social WorkUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Megan M. Herting
- Department of Population and Public Health SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
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2
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Guichet C, Banjac S, Achard S, Mermillod M, Baciu M. Modeling the neurocognitive dynamics of language across the lifespan. Hum Brain Mapp 2024; 45:e26650. [PMID: 38553863 PMCID: PMC10980845 DOI: 10.1002/hbm.26650] [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: 07/17/2023] [Revised: 02/08/2024] [Accepted: 02/26/2024] [Indexed: 04/02/2024] Open
Abstract
Healthy aging is associated with a heterogeneous decline across cognitive functions, typically observed between language comprehension and language production (LP). Examining resting-state fMRI and neuropsychological data from 628 healthy adults (age 18-88) from the CamCAN cohort, we performed state-of-the-art graph theoretical analysis to uncover the neural mechanisms underlying this variability. At the cognitive level, our findings suggest that LP is not an isolated function but is modulated throughout the lifespan by the extent of inter-cognitive synergy between semantic and domain-general processes. At the cerebral level, we show that default mode network (DMN) suppression coupled with fronto-parietal network (FPN) integration is the way for the brain to compensate for the effects of dedifferentiation at a minimal cost, efficiently mitigating the age-related decline in LP. Relatedly, reduced DMN suppression in midlife could compromise the ability to manage the cost of FPN integration. This may prompt older adults to adopt a more cost-efficient compensatory strategy that maintains global homeostasis at the expense of LP performances. Taken together, we propose that midlife represents a critical neurocognitive juncture that signifies the onset of LP decline, as older adults gradually lose control over semantic representations. We summarize our findings in a novel synergistic, economical, nonlinear, emergent, cognitive aging model, integrating connectomic and cognitive dimensions within a complex system perspective.
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Affiliation(s)
| | - Sonja Banjac
- Université Grenoble Alpes, CNRS LPNC UMR 5105GrenobleFrance
| | - Sophie Achard
- LJK, UMR CNRS 5224, Université Grenoble AlpesGrenobleFrance
| | | | - Monica Baciu
- Université Grenoble Alpes, CNRS LPNC UMR 5105GrenobleFrance
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3
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Carozza S, Holmes J, Akarca D, Astle DE. Global topology of human connectome is insensitive to early life environments - A prospective longitudinal study of the general population. Dev Sci 2024:e13490. [PMID: 38494672 DOI: 10.1111/desc.13490] [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: 05/02/2023] [Revised: 01/26/2024] [Accepted: 01/28/2024] [Indexed: 03/19/2024]
Abstract
The widely acknowledged detrimental impact of early adversity on child development has driven efforts to understand the underlying mechanisms that may mediate these effects within the developing brain. Recent efforts have begun to move beyond associating adversity with the morphology of individual brain regions towards determining if and how adversity might shape their interconnectivity. However, whether adversity effects a global shift in the organisation of whole-brain networks remains unclear. In this study, we assessed this possibility using parental questionnaire and diffusion imaging data from The Avon Longitudinal Study of Parents and Children (ALSPAC, N = 913), a prospective longitudinal study spanning more than 20 years. We tested whether a wide range of adversities-including experiences of abuse, domestic violence, physical and emotional cruelty, poverty, neglect, and parental separation-measured by questionnaire within the first seven years of life were significantly associated with the tractography-derived connectome in young adulthood. We tested this across multiple measures of organisation and using a computational model that simulated the wiring economy of the brain. We found no significant relationships between early exposure to any form of adversity and the global organisation of the structural connectome in young adulthood. We did detect local differences in the medial prefrontal cortex, as well as an association between weaker brain wiring constraints and greater externalising behaviour in adolescence. Our results indicate that further efforts are necessary to delimit the magnitude and functional implications of adversity-related differences in connectomic organization. RESEARCH HIGHLIGHTS: Diverse prospective measures of the early-life environment do not predict the organisation of the DTI tractography-derived connectome in young adulthood Wiring economy of the connectome is weakly associated with externalising in adolescence, but not internalising or cognitive ability Further work is needed to establish the scope and significance of global adversity-related differences in the structural connectome.
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Affiliation(s)
- Sofia Carozza
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Joni Holmes
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- School of Psychology, University of East Anglia, Norwich, UK
| | - Danyal Akarca
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Duncan E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
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4
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Zhu AH, Nir TM, Javid S, Villalon-Reina JE, Rodrigue AL, Strike LT, de Zubicaray GI, McMahon KL, Wright MJ, Medland SE, Blangero J, Glahn DC, Kochunov P, Håberg AK, Thompson PM, Jahanshad N. Lifespan reference curves for harmonizing multi-site regional brain white matter metrics from diffusion MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.22.581646. [PMID: 38463962 PMCID: PMC10925090 DOI: 10.1101/2024.02.22.581646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Age-related white matter (WM) microstructure maturation and decline occur throughout the human lifespan, complementing the process of gray matter development and degeneration. Here, we create normative lifespan reference curves for global and regional WM microstructure by harmonizing diffusion MRI (dMRI)-derived data from ten public datasets (N = 40,898 subjects; age: 3-95 years; 47.6% male). We tested three harmonization methods on regional diffusion tensor imaging (DTI) based fractional anisotropy (FA), a metric of WM microstructure, extracted using the ENIGMA-DTI pipeline. ComBat-GAM harmonization provided multi-study trajectories most consistent with known WM maturation peaks. Lifespan FA reference curves were validated with test-retest data and used to assess the effect of the ApoE4 risk factor for dementia in WM across the lifespan. We found significant associations between ApoE4 and FA in WM regions associated with neurodegenerative disease even in healthy individuals across the lifespan, with regional age-by-genotype interactions. Our lifespan reference curves and tools to harmonize new dMRI data to the curves are publicly available as eHarmonize (https://github.com/ahzhu/eharmonize).
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Affiliation(s)
- Alyssa H Zhu
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
| | - Talia M Nir
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Shayan Javid
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
| | - Julio E Villalon-Reina
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Amanda L Rodrigue
- Department of Psychiatry and Behavioral Science, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lachlan T Strike
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Queensland University of Technology, Brisbane, QLD, Australia
| | | | - Katie L McMahon
- Queensland University of Technology, Brisbane, QLD, Australia
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Queensland University of Technology, Brisbane, QLD, Australia
- School of Psychology, `, Brisbane, QLD, Australia
| | - John Blangero
- Department of Human Genetics, University of Texas Rio Grande Valley, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - David C Glahn
- Department of Psychiatry and Behavioral Science, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Peter Kochunov
- Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Asta K Håberg
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of MiDtT National Research Center, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Paul M Thompson
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
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5
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Sullivan-Toole H, Jobson KR, Hoffman LJ, Stewart LC, Olson IR, Olino TM. Adolescents at risk for depression show increased white matter microstructure with age across diffuse areas of the brain. Dev Cogn Neurosci 2023; 64:101307. [PMID: 37813039 PMCID: PMC10570597 DOI: 10.1016/j.dcn.2023.101307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 08/22/2023] [Accepted: 09/23/2023] [Indexed: 10/11/2023] Open
Abstract
Maternal history of depression is a strong predictor of depression in offspring and linked to structural and functional alterations in the developing brain. However, very little work has examined differences in white matter in adolescents at familial risk for depression. In a sample aged 9-14 (n = 117), we used tract-based spatial statistics (TBSS) to examine differences in white matter microstructure between adolescents with (n = 42) and without (n = 75) maternal history of depression. Microstructure was indexed using fractional anisotropy (FA). Threshold-free cluster enhancement was applied and cluster maps were thresholded at whole-brain family-wise error < .05. There was no significant main effect of risk status on FA. However, there was a significant interaction between risk status and age, such that large and diffuse portions of the white matter skeleton showed relatively increased FA with age for youth with a maternal history of depression compared to those without. Most tracts identified by the interaction were robust to controlling for sex, youth internalizing, in-scanner motion, neighborhood SES, and intra-cranial volume, evidence that maternal depression is a unique predictor of white matter alterations in youth. Widespread increases in FA with age may correspond to a global pattern of accelerated brain maturation in youth at risk for depression.
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Affiliation(s)
| | - Katie R Jobson
- Department of Psychology and Neuroscience, Temple University, USA
| | - Linda J Hoffman
- Department of Psychology and Neuroscience, Temple University, USA
| | | | - Ingrid R Olson
- Department of Psychology and Neuroscience, Temple University, USA
| | - Thomas M Olino
- Department of Psychology and Neuroscience, Temple University, USA
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6
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Flinkenflügel K, Meinert S, Thiel K, Winter A, Goltermann J, Strathausen L, Brosch K, Stein F, Thomas-Odenthal F, Evermann U, Wroblewski A, Usemann P, Pfarr JK, Grotegerd D, Hahn T, Leehr EJ, Dohm K, Bauer J, Jamalabadi H, Straube B, Alexander N, Jansen A, Nenadić I, Krug A, Kircher T, Dannlowski U. Negative Stressful Life Events and Social Support Are Associated With White Matter Integrity in Depressed Patients and Healthy Control Participants: A Diffusion Tensor Imaging Study. Biol Psychiatry 2023; 94:650-660. [PMID: 37028741 DOI: 10.1016/j.biopsych.2023.03.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/23/2023] [Accepted: 03/24/2023] [Indexed: 04/09/2023]
Abstract
BACKGROUND Negative stressful life events and deprivation of social support play critical roles in the development and maintenance of major depressive disorder (MDD). The present study aimed to investigate in a large sample of patients with MDD and healthy control participants (HCs) whether these effects are also reflected in white matter (WM) integrity. METHODS In this diffusion tensor imaging study, 793 patients with MDD and 793 age- and sex-matched HCs were drawn from the Marburg-Münster Affective Disorders Cohort Study (MACS) and completed the Life Events Questionnaire (LEQ) and Social Support Questionnaire (SSQ). Generalized linear models were performed to test voxelwise associations between fractional anisotropy (FA) and diagnosis (analysis 1), LEQ (analysis 2), and SSQ (analysis 3). We examined whether SSQ interacts with LEQ on FA or is independently associated with improved WM integrity (analysis 4). RESULTS Patients with MDD showed lower FA in several frontotemporal association fibers compared with HCs (pTFCE-FWE = .028). Across both groups, LEQ correlated negatively with FA in widely distributed WM tracts (pTFCE-FWE = .023), while SSQ correlated positively with FA in the corpus callosum (pTFCE-FWE = .043). Modeling the combined association of both variables on FA revealed significant-and antagonistic-main effects of LEQ (pTFCE-FWE = .031) and SSQ (pTFCE-FWE = .037), but no interaction of SSQ × LEQ. CONCLUSIONS Our results indicate that negative stressful life events and social support are both related to WM integrity in opposing directions. The associations did not differ between patients with MDD and HCs, suggesting more general, rather than depression-specific, mechanisms. Furthermore, social support appears to contribute to improved WM integrity independent of stressful life events.
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Affiliation(s)
- Kira Flinkenflügel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Lea Strathausen
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Brosch
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Ulrika Evermann
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Adrian Wroblewski
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Paula Usemann
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Katharina Dohm
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jochen Bauer
- Department of Radiology, University of Münster, Münster, Germany
| | - Hamidreza Jamalabadi
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany
| | - Benjamin Straube
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Nina Alexander
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Andreas Jansen
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany; Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany
| | - Tilo Kircher
- Department of Psychiatry und Psychotherapy, University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.
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7
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Newman BT, Patrie JT, Druzgal TJ. An intracellular isotropic diffusion signal is positively associated with pubertal development in white matter. Dev Cogn Neurosci 2023; 63:101301. [PMID: 37717292 PMCID: PMC10511341 DOI: 10.1016/j.dcn.2023.101301] [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: 01/31/2023] [Revised: 08/14/2023] [Accepted: 09/13/2023] [Indexed: 09/19/2023] Open
Abstract
Puberty is a key event in adolescent development that involves significant, hormone-driven changes to many aspects of physiology including the brain. Understanding how the brain responds during this time period is important for evaluating neuronal developments that affect mental health throughout adolescence and the adult lifespan. This study examines diffusion MRI scans from the cross-sectional ABCD Study baseline cohort, a large multi-site study containing thousands of participants, to describe the relationship between pubertal development and brain microstructure. Using advanced, 3-tissue constrained spherical deconvolution methods, this study is able to describe multiple tissue compartments beyond only white matter (WM) axonal qualities. After controlling for age, sex, brain volume, subject handedness, scanning site, and sibling relationships, we observe a positive relationship between an isotropic, intracellular diffusion signal fraction and pubertal development across a majority of regions of interest (ROIs) in the WM skeleton. We also observe regional effects from an intracellular anisotropic signal fraction compartment and extracellular isotropic free water-like compartment in several ROIs. This cross-sectional work suggests that changes in pubertal status are associated with a complex response from brain tissue that cannot be completely described by traditional methods focusing only on WM axonal properties.
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Affiliation(s)
- Benjamin T Newman
- Department of Radiology and Medical Imaging, School of Medicine, University of Virginia, USA.
| | - James T Patrie
- Department of Public Health Sciences, School of Medicine, University of Virginia, USA
| | - T Jason Druzgal
- Department of Radiology and Medical Imaging, School of Medicine, University of Virginia, USA
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8
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Siqueira Pinto M, Winzeck S, Kornaropoulos EN, Richter S, Paolella R, Correia MM, Glocker B, Williams G, Vik A, Posti JP, Haberg A, Stenberg J, Guns PJ, den Dekker AJ, Menon DK, Sijbers J, Van Dyck P, Newcombe VFJ. Use of Support Vector Machines Approach via ComBat Harmonized Diffusion Tensor Imaging for the Diagnosis and Prognosis of Mild Traumatic Brain Injury: A CENTER-TBI Study. J Neurotrauma 2023; 40:1317-1338. [PMID: 36974359 DOI: 10.1089/neu.2022.0365] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023] Open
Abstract
The prediction of functional outcome after mild traumatic brain injury (mTBI) is challenging. Conventional magnetic resonance imaging (MRI) does not do a good job of explaining the variance in outcome, as many patients with incomplete recovery will have normal-appearing clinical neuroimaging. More advanced quantitative techniques such as diffusion MRI (dMRI), can detect microstructural changes not otherwise visible, and so may offer a way to improve outcome prediction. In this study, we explore the potential of linear support vector classifiers (linearSVCs) to identify dMRI biomarkers that can predict recovery after mTBI. Simultaneously, the harmonization of fractional anisotropy (FA) and mean diffusivity (MD) via ComBat was evaluated and compared for the classification performances of the linearSVCs. We included dMRI scans of 179 mTBI patients and 85 controls from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI), a multi-center prospective cohort study, up to 21 days post-injury. Patients were dichotomized according to their Extended Glasgow Outcome Scale (GOSE) scores at 6 months into complete (n = 92; GOSE = 8) and incomplete (n = 87; GOSE <8) recovery. FA and MD maps were registered to a common space and harmonized via the ComBat algorithm. LinearSVCs were applied to distinguish: (1) mTBI patients from controls and (2) mTBI patients with complete from those with incomplete recovery. The linearSVCs were trained on (1) age and sex only, (2) non-harmonized, (3) two-category-harmonized ComBat, and (4) three-category-harmonized ComBat FA and MD images combined with age and sex. White matter FA and MD voxels and regions of interest (ROIs) within the John Hopkins University (JHU) atlas were examined. Recursive feature elimination was used to identify the 10% most discriminative voxels or the 10 most discriminative ROIs for each implementation. mTBI patients displayed significantly higher MD and lower FA values than controls for the discriminative voxels and ROIs. For the analysis between mTBI patients and controls, the three-category-harmonized ComBat FA and MD voxel-wise linearSVC provided significantly higher classification scores (81.4% accuracy, 93.3% sensitivity, 80.3% F1-score, and 0.88 area under the curve [AUC], p < 0.05) compared with the classification based on age and sex only and the ROI approaches (accuracies: 59.8% and 64.8%, respectively). Similar to the analysis between mTBI patients and controls, the three-category-harmonized ComBat FA and MD maps voxelwise approach yields statistically significant prediction scores between mTBI patients with complete and those with incomplete recovery (71.8% specificity, 66.2% F1-score and 0.71 AUC, p < 0.05), which provided a modest increase in the classification score (accuracy: 66.4%) compared with the classification based on age and sex only and ROI-wise approaches (accuracy: 61.4% and 64.7%, respectively). This study showed that ComBat harmonized FA and MD may provide additional information for diagnosis and prognosis of mTBI in a multi-modal machine learning approach. These findings demonstrate that dMRI may assist in the early detection of patients at risk of incomplete recovery from mTBI.
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Affiliation(s)
- Maíra Siqueira Pinto
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Stefan Winzeck
- BioMedIA Group, Department of Computing, Imperial College London, London, United Kingdom
- Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Evgenios N Kornaropoulos
- Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Sophie Richter
- Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Roberto Paolella
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
- Icometrix, Leuven, Belgium
| | - Marta M Correia
- MRC Cognition and Brain Sciences Unit, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Ben Glocker
- BioMedIA Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Guy Williams
- Wolfson Brain Imaging Centre, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Anne Vik
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Jussi P Posti
- Department of Neurosurgery and Turku Brain Injury Center, Turku University Hospital and University of Turku, Turku, Finland
| | - Asta Haberg
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Jonas Stenberg
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | | | - Arnold J den Dekker
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - David K Menon
- Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Jan Sijbers
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Pieter Van Dyck
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium
- mVISION, University of Antwerp, Antwerp, Belgium
| | - Virginia F J Newcombe
- Division of Anaesthesia, Department of Medicine, Department of Neurosciences, University of Cambridge, Cambridge, United Kingdom
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Korbmacher M, Gurholt TP, de Lange AMG, van der Meer D, Beck D, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Bio-psycho-social factors' associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants. Front Psychol 2023; 14:1117732. [PMID: 37359862 PMCID: PMC10288151 DOI: 10.3389/fpsyg.2023.1117732] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 04/27/2023] [Indexed: 06/28/2023] Open
Abstract
Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age (N = 35,749, 44.6-82.8 years of age). Bio-psycho-social factors could uniquely explain a small proportion of the brain age variance, in a similar pattern across diffusion approaches: cognitive scores, life satisfaction, health and lifestyle factors adding to the variance explained, but not socio-demographics. Consistent brain age associations across models were found for waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception. Furthermore, we found large variability in sex and ethnicity group differences in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors, and to observe bio-psycho-social factor interactions' influence on brain age in future studies.
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Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
| | - Tiril P. Gurholt
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Ann-Marie G. de Lange
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Dani Beck
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
| | - Arvid Lundervold
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I. Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
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Meisler SL, Gabrieli JDE. Fiber-specific structural properties relate to reading skills in children and adolescents. eLife 2022; 11:e82088. [PMID: 36576253 PMCID: PMC9815823 DOI: 10.7554/elife.82088] [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: 07/22/2022] [Accepted: 12/21/2022] [Indexed: 12/29/2022] Open
Abstract
Recent studies suggest that the cross-sectional relationship between reading skills and white matter microstructure, as indexed by fractional anisotropy, is not as robust as previously thought. Fixel-based analyses yield fiber-specific micro- and macrostructural measures, overcoming several shortcomings of the traditional diffusion tensor model. We ran a whole-brain analysis investigating whether the product of fiber density and cross-section (FDC) related to single-word reading skills in a large, open, quality-controlled dataset of 983 children and adolescents ages 6-18. We also compared FDC between participants with (n = 102) and without (n = 570) reading disabilities. We found that FDC positively related to reading skills throughout the brain, especially in left temporoparietal and cerebellar white matter, but did not differ between reading proficiency groups. Exploratory analyses revealed that among metrics from other diffusion models - diffusion tensor imaging, diffusion kurtosis imaging, and neurite orientation dispersion and density imaging - only the orientation dispersion and neurite density indexes from NODDI were associated (inversely) with reading skills. The present findings further support the importance of left-hemisphere dorsal temporoparietal white matter tracts in reading. Additionally, these results suggest that future DWI studies of reading and dyslexia should be designed to benefit from advanced diffusion models, include cerebellar coverage, and consider continuous analyses that account for individual differences in reading skill.
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Affiliation(s)
- Steven Lee Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard Medical SchoolBostonUnited States
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