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Bischoff-Grethe A, Stoner SA, Riley EP, Moore EM. Subcortical volume in middle-aged adults with fetal alcohol spectrum disorders. Brain Commun 2024; 6:fcae273. [PMID: 39229493 PMCID: PMC11369821 DOI: 10.1093/braincomms/fcae273] [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: 01/06/2024] [Revised: 05/06/2024] [Accepted: 08/28/2024] [Indexed: 09/05/2024] Open
Abstract
Studies of youth and young adults with prenatal alcohol exposure (PAE) have most consistently reported reduced volumes of the corpus callosum, cerebellum and subcortical structures. However, it is unknown whether this continues into middle adulthood or if individuals with PAE may experience premature volumetric decline with aging. Forty-eight individuals with fetal alcohol spectrum disorders (FASD) and 28 healthy comparison participants aged 30 to 65 participated in a 3T MRI session that resulted in usable T1-weighted and T2-weighted structural images. Primary analyses included volumetric measurements of the caudate, putamen, pallidum, cerebellum and corpus callosum using FreeSurfer software. Analyses were conducted examining both raw volumetric measurements and subcortical volumes adjusted for overall intracranial volume (ICV). Models tested for main effects of age, sex and group, as well as interactions of group with age and group with sex. We found the main effects for group; all regions were significantly smaller in participants with FASD for models using raw volumes (P's < 0.001) as well as for models using volumes adjusted for ICV (P's < 0.046). Although there were no significant interactions of group with age, females with FASD had smaller corpus callosum volumes relative to both healthy comparison females and males with FASD (P's < 0.001). As seen in children and adolescents, adults aged 30 to 65 with FASD showed reduced volumes of subcortical structures relative to healthy comparison adults, suggesting persistent impact of PAE. Moreover, the observed volumetric reduction of the corpus callosum in females with FASD could suggest more rapid degeneration, which may have implications for cognition as these individuals continue to age.
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Affiliation(s)
| | - Susan A Stoner
- Department of Psychiatry and Behavioral Sciences, Fetal Alcohol and Drug Unit, University of Washington School of Medicine, Seattle, Washington 98105, USA
| | - Edward P Riley
- Department of Psychology, Center for Behavioral Teratology, San Diego State University, San Diego, CA, 92120, USA
| | - Eileen M Moore
- Department of Psychology, Center for Behavioral Teratology, San Diego State University, San Diego, CA, 92120, USA
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2
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Herlin B, Uszynski I, Chauvel M, Dupont S, Poupon C. Sex-related variability of white matter tracts in the whole HCP cohort. Brain Struct Funct 2024; 229:1713-1735. [PMID: 39012482 PMCID: PMC11374878 DOI: 10.1007/s00429-024-02833-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 07/06/2024] [Indexed: 07/17/2024]
Abstract
Behavioral differences between men and women have been studied extensively, as have differences in brain anatomy. However, most studies have focused on differences in gray matter, while white matter has been much less studied. We conducted a comprehensive study of 77 deep white matter tracts to analyze their volumetric and microstructural variability between men and women in the full Human Connectome Project (HCP) cohort of 1065 healthy individuals aged 22-35 years. We found a significant difference in total brain volume between men and women (+ 12.6% in men), consistent with the literature. 16 tracts showed significant volumetric differences between men and women, one of which stood out due to a larger effect size: the corpus callosum genu, which was larger in women (+ 7.3% in women, p = 5.76 × 10-19). In addition, we found several differences in microstructural parameters between men and women, both using standard Diffusion Tensor Imaging (DTI) parameters and more complex microstructural parameters from the Neurite Orientation Dispersion and Density Imaging (NODDI) model, with the tracts showing the greatest differences belonging to motor (cortico-spinal tracts, cortico-cerebellar tracts) or limbic (cingulum, fornix, thalamo-temporal radiations) systems. These microstructural differences may be related to known behavioral differences between the sexes in timed motor performance, aggressiveness/impulsivity, and social cognition.
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Affiliation(s)
- B Herlin
- BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-Sur-Yvette, France.
- Rehabilitation Unit, AP-HP, Pitié-Salpêtrière Hospital, Paris, France.
- Université Paris Sorbonne, Paris, France.
| | - I Uszynski
- BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-Sur-Yvette, France
| | - M Chauvel
- BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-Sur-Yvette, France
| | - S Dupont
- Reference Center for Rare Epilepsies, Department of Neurology, Epileptology Unit, AP-HP, Pitié-Salpêtrière Hospital, Paris, France
- Rehabilitation Unit, AP-HP, Pitié-Salpêtrière Hospital, Paris, France
- Paris Brain Institute (ICM), Sorbonne-Université, Inserm U1127, CNRS 7225, Paris, France
- Université Paris Sorbonne, Paris, France
| | - C Poupon
- BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-Sur-Yvette, France
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Huang AS, Kang K, Vandekar S, Rogers BP, Heckers S, Woodward ND. Lifespan development of thalamic nuclei and characterizing thalamic nuclei abnormalities in schizophrenia using normative modeling. Neuropsychopharmacology 2024; 49:1518-1527. [PMID: 38480909 PMCID: PMC11319674 DOI: 10.1038/s41386-024-01837-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 02/13/2024] [Accepted: 02/21/2024] [Indexed: 03/18/2024]
Abstract
Thalamic abnormalities have been repeatedly implicated in the pathophysiology of schizophrenia and other neurodevelopmental disorders. Uncovering the etiology of thalamic abnormalities and how they may contribute to illness phenotypes faces at least two obstacles. First, the typical developmental trajectories of thalamic nuclei and their association with cognition across the lifespan are largely unknown. Second, modest effect sizes indicate marked individual differences and pose a significant challenge to personalized medicine. To address these knowledge gaps, we characterized the development of thalamic nuclei volumes using normative models generated from the Human Connectome Project Lifespan datasets (5-100+ years), then applied them to an independent clinical cohort to determine the frequency of thalamic volume deviations in people with schizophrenia (17-61 years). Normative models revealed diverse non-linear age effects across the lifespan. Association nuclei exhibited negative age effects during youth but stabilized in adulthood until turning negative again with older age. Sensorimotor nuclei volumes remained relatively stable through youth and adulthood until also turning negative with older age. Up to 18% of individuals with schizophrenia exhibited abnormally small (i.e., below the 5th centile) mediodorsal and pulvinar volumes, and the degree of deviation, but not raw volumes, correlated with the severity of cognitive impairment. While case-control differences are robust, only a minority of patients demonstrate unusually small thalamic nuclei volumes. Normative modeling enables the identification of these individuals, which is a necessary step toward precision medicine.
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Affiliation(s)
- Anna S Huang
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Kaidi Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Stephan Heckers
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Neil D Woodward
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
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Young TR, Kumar VJ, Saranathan M. Normative Modeling of Thalamic Nuclear Volumes and Characterization of Lateralized Volume Alterations in Alzheimer's Disease Versus Schizophrenia. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00241-6. [PMID: 39182722 DOI: 10.1016/j.bpsc.2024.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 08/13/2024] [Accepted: 08/13/2024] [Indexed: 08/27/2024]
Abstract
BACKGROUND Thalamic nuclei facilitate a wide range of complex behaviors, emotions, and cognition and have been implicated in neuropsychiatric disorders including Alzheimer's disease (AD) and schizophrenia. The aim of this work was to establish novel normative models of thalamic nuclear volumes and their laterality indices and investigate their changes in schizophrenia and AD. METHODS Volumes of bilateral whole thalami and 10 thalamic nuclei were generated from T1 MRI data using a state-of-the-art novel segmentation method in healthy control subjects (n=2374) and early mild cognitive impairment (MCI, n=211), late MCI (n=113), AD (n=88), and schizophrenia (n=168). Normative models for each nucleus were generated from healthy control subjects while controlling for sex, intracranial volume, and site. Extreme z-score deviations (|z|>1.96) and z-score distributions were compared across phenotypes. Z-scores were associated with clinical descriptors. RESULTS Increased infranormal and decreased supranormal z-scores were observed in schizophrenia and AD. Z-score shifts representing reduced volumes were observed in most nuclei in schizophrenia and AD with strong overlap in the bilateral pulvinar, medial dorsal, and centromedian nuclei. Shifts were larger in AD with evidence of a left-sided preference in early MCI while a predilection for right thalamic nuclei was observed in schizophrenia. The right medial dorsal nucleus was associated with disorganized thought and daily auditory verbal hallucinations. CONCLUSION In AD, thalamic nuclei are more severely and symmetrically affected while in schizophrenia, the right thalamic nuclei are more affected. We highlight the right medial dorsal nucleus, which may mediate multiple symptoms of schizophrenia and is affected early in the disease course.
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Affiliation(s)
- Taylor R Young
- Department of Psychiatry, University of Massachusetts Chan Medical School, Worcester, MA; Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA.
| | | | - Manojkumar Saranathan
- Department of Radiology, University of Massachusetts Chan Medical School, Worcester, MA
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Quintana GR, Pfaus JG. Do Sex and Gender Have Separate Identities? ARCHIVES OF SEXUAL BEHAVIOR 2024; 53:2957-2975. [PMID: 39105983 PMCID: PMC11335805 DOI: 10.1007/s10508-024-02933-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 06/07/2024] [Accepted: 06/09/2024] [Indexed: 08/07/2024]
Abstract
The largely binary nature of biological sex and its conflation with the socially constructed concept of gender has created much strife in the last few years. The notion of gender identity and its differences and similarities with sex have fostered much scientific and legal confusion and disagreement. Settling the debate can have significant repercussions for science, medicine, legislation, and people's lives. The present review addresses this debate though different levels of analysis (i.e., genetic, anatomical, physiological, behavioral, and sociocultural), and their implications and interactions. We propose a rationale where both perspectives coexist, where diversity is the default, establishing a delimitation to the conflation between sex and gender, while acknowledging their interaction. Whereas sex in humans and other mammals is a biological reality that is largely binary and based on genes, chromosomes, anatomy, and physiology, gender is a sociocultural construct that is often, but not always, concordant with a person' sex, and can span a multitude of expressions.
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Affiliation(s)
- Gonzalo R Quintana
- Departamento de Psicología y Filosofía, Facultad de Ciencias Sociales, Universidad de Tarapacá, Arica, Arica y Parinacota, Chile
| | - James G Pfaus
- Department of Psychology and Life Sciences, Charles University, Prague, 18200, Czech Republic.
- Center for Sexual Health and Intervention, Czech National Institute of Mental Health, Klecany, Czech Republic.
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Lu JJ, Ma J, Wu JJ, Zhen XM, Xiang YT, Lu HY, Zheng MX, Hua XY, Xu JG. Tongue coating-dependent superior temporal sulcus remodeling in amnestic mild cognitive impairment. Brain Res Bull 2024; 214:110995. [PMID: 38844172 DOI: 10.1016/j.brainresbull.2024.110995] [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: 03/29/2024] [Revised: 04/23/2024] [Accepted: 06/01/2024] [Indexed: 06/10/2024]
Abstract
Tongue coating affects cognition, and cognitive decline at early stage also showed relations to functional and structural remodeling of superior temporal sulcus (STS) in amnestic mild cognitive impairment (aMCI). The potential correlation between disparate cognitive manifestations in aMCI patients with different tongue coatings, and corresponding mechanisms of STS remodeling remains uncharted. In this case-control study, aMCI patients were divided into thin coating (n = 18) and thick coating (n = 21) groups. All participants underwent neuropsychological evaluations and multimodal magnetic resonance imaging. Group comparisons were conducted in clinical assessments and neuroimaging measures of banks of the STS (bankssts). Generalized linear models were constructed to explore relationships between neuroimaging measures and cognition. aMCI patients in the thick coating group exhibited significantly poorer immediate and delayed recall and slower information processing speed (IPS) (P < 0.05), and decreased functional connectivity (FC) of bilateral bankssts with frontoparietal cortices (P < 0.05, AlphaSim corrected) compared to the thin coating group. It was found notable correlations between cognition encompassing recall and IPS, and FC of bilateral bankssts with frontoparietal cortices (P < 0.05, Bonferroni's correction), as well as interaction effects of group × regional homogeneity (ReHo) of right bankssts on the first immediate recall (P < 0.05, Bonferroni's correction). aMCI patients with thick coating exhibited poor cognitive performance, which might be attributed to decreased FC seeding from bankssts. Our findings strengthen the understanding of brain reorganization of STS via which tongue coating status impacts cognition in patients with aMCI.
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Affiliation(s)
- Juan-Juan Lu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jie Ma
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiao-Min Zhen
- Department of Neurology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yun-Ting Xiang
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Hao-Yu Lu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Mou-Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China.
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7
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Na S, Kim T, Song IU, Hong YJ, Kim SH. Cortex-to-caudate volume ratio as a predictor of cognitive decline in Alzheimer's disease and mild cognitive impairment. J Neurol Sci 2024; 462:123113. [PMID: 38941706 DOI: 10.1016/j.jns.2024.123113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/24/2024] [Accepted: 06/24/2024] [Indexed: 06/30/2024]
Abstract
BACKGROUND Brain and cortical atrophy play crucial roles in supporting the clinical diagnosis of Alzheimer's disease (AD). This study hypothesized that the ratios of brain or cortical volume to subcortical gray matter structure volumes are potential imaging markers for cognitive alterations in AD dementia and amnestic mild cognitive impairment (aMCI). METHODS Seventy-seven subjects diagnosed with AD dementia or aMCI underwent baseline neuropsychological testing, 2-year follow-up cognitive assessments, and high-resolution T1-weighted MRI scans. Total brain/cortical volume and subcortical gray matter structure volumes were automatically segmented and measured. Univariate and multiple linear regression analyses were conducted to determine the associations between volumetric ratios and interval changes in cognitive scores. RESULTS The ratio of cortical volume to caudate volume showed the most significant association with changes in MoCA (B = 0.132, SE = 0.042, p = 0.002), MMSE (B = 0.140, SE = 0.040, p = 0.001), and CDR-SOB (B = -0.013, SE = 0.005, p = 0.007) scores over the 2-year follow-up period. These associations remained significant after adjusting for various covariates. Similar associations were observed for the ratios of cortical volume to putamen and globus pallidum volumes. CONCLUSIONS The cortex-to-caudate volume ratio is significantly associated with cognitive decline in AD dementia and aMCI. This ratio may serve as a useful biomarker for monitoring disease progression and predicting cognitive outcomes. Our findings highlight the importance of considering the relative atrophy of cortical and subcortical structures in understanding AD pathology.
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Affiliation(s)
- Seunghee Na
- Department of Neurology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Taewon Kim
- Department of Neurology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
| | - In-Uk Song
- Department of Neurology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yun Jeong Hong
- Department of Neurology, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Seong-Hoon Kim
- Department of Neurology, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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Décarie-Labbé L, Dialahy IZ, Corriveau-Lecavalier N, Mellah S, Belleville S. Examining the relationship between brain activation and proxies of disease severity using quantile regression in individuals at risk of Alzheimer's disease. Cortex 2024; 173:234-247. [PMID: 38432175 DOI: 10.1016/j.cortex.2024.01.011] [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: 05/20/2023] [Revised: 10/27/2023] [Accepted: 01/25/2024] [Indexed: 03/05/2024]
Abstract
Previous studies have reported a pattern of hyperactivation in the pre-dementia phase of Alzheimer's disease (AD), followed by hypoactivation in later stages of the disease. This pattern was modeled as an inverse U-shape function between activation and markers of disease severity. In this study, we used quantile regression to model the association between task-related brain activation in AD signature regions and three markers of disease severity (hippocampal volume, cortical thickness, and associative memory). This approach offers distinct advantages over standard regression models as it analyzes the relationship between brain activation and disease severity across various levels of brain activation. Participants were 54 older adults with subjective cognitive decline+ (SCD+) or mild cognitive impairment (MCI) from the CIMA-Q cohort. The analysis revealed an inverse U-shape quadratic function depicting the relationship between disease severity markers and the activation of the left superior parietal region, while a linear relationship was observed for activation of the hippocampal and temporal regions. Quantile differences were observed for temporal and parietal activation, with more pronounced effects observed in the higher quantiles of activation. When comparing quantiles, we found that higher quantile of activation featured a greater number of individuals with SCD+ compared to mild cognitive impairment (MCI). Results are globally consistent with the presence of an inverse-U shape function of activation in relation to disease severity. They study also underscores the utility of employing quantile regression modeling as the modeling approach revealed the presence of non-homogeneous effects across various quantiles.
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Affiliation(s)
- Laurie Décarie-Labbé
- Research Center, Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada; Department of Psychology, Université de Montréal, Montreal, Quebec, Canada
| | - Isaora Zefania Dialahy
- Research Center, Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada
| | | | - Samira Mellah
- Research Center, Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada
| | - Sylvie Belleville
- Research Center, Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada; Department of Psychology, Université de Montréal, Montreal, Quebec, Canada.
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Ge R, Yu Y, Qi YX, Fan YN, Chen S, Gao C, Haas SS, New F, Boomsma DI, Brodaty H, Brouwer RM, Buckner R, Caseras X, Crivello F, Crone EA, Erk S, Fisher SE, Franke B, Glahn DC, Dannlowski U, Grotegerd D, Gruber O, Hulshoff Pol HE, Schumann G, Tamnes CK, Walter H, Wierenga LM, Jahanshad N, Thompson PM, Frangou S. Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation. Lancet Digit Health 2024; 6:e211-e221. [PMID: 38395541 PMCID: PMC10929064 DOI: 10.1016/s2589-7500(23)00250-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 10/04/2023] [Accepted: 12/01/2023] [Indexed: 02/25/2024]
Abstract
The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3-90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.
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Affiliation(s)
- Ruiyang Ge
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yuetong Yu
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yi Xuan Qi
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yu-Nan Fan
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Shiyu Chen
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Chuntong Gao
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Faye New
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dorret I Boomsma
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, Netherlands
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, University of New South Wales, Sydney, NSW, Australia
| | - Rachel M Brouwer
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit, Amsterdam, Netherlands
| | - Randy Buckner
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Xavier Caseras
- Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, Wales, UK
| | - Fabrice Crivello
- Groupe d'Imagerie Neurofonctionnelle-Institut des Maladies Neurodégénératives, Université de Bordeaux, CNRS UMR 5293, Bordeaux, France
| | - Eveline A Crone
- Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands
| | - Susanne Erk
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
| | - Barbara Franke
- Departments of Human Genetics, Psychiatry and Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - David C Glahn
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
| | - Udo Dannlowski
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Hilleke E Hulshoff Pol
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, Netherlands
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-inspired Intelligence, Fudan University, Shanghai, China; PONS Centre, Department of Psychiatry and Clinical Neuroscience, CCM, Charite Universitätsmedizin Berlin, Berlin, Germany
| | | | - Henrik Walter
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lara M Wierenga
- Brain and Development Research Center, Leiden University, Leiden, Netherlands
| | - Neda Jahanshad
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA
| | - Paul M Thompson
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA
| | - Sophia Frangou
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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10
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Choi YY, Lee JJ, Te Nijenhuis J, Choi KY, Park J, Ok J, Choo IH, Kim H, Song MK, Choi SM, Cho SH, Choe Y, Kim BC, Lee KH. Multi-Ethnic Norms for Volumes of Subcortical and Lobar Brain Structures Measured by Neuro I: Ethnicity May Improve the Diagnosis of Alzheimer's Disease1. J Alzheimers Dis 2024; 99:223-240. [PMID: 38640153 DOI: 10.3233/jad-231182] [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] [Indexed: 04/21/2024]
Abstract
Background We previously demonstrated the validity of a regression model that included ethnicity as a novel predictor for predicting normative brain volumes in old age. The model was optimized using brain volumes measured with a standard tool FreeSurfer. Objective Here we further verified the prediction model using newly estimated brain volumes from Neuro I, a quantitative brain analysis system developed for Korean populations. Methods Lobar and subcortical volumes were estimated from MRI images of 1,629 normal Korean and 786 Caucasian subjects (age range 59-89) and were predicted in linear regression from ethnicity, age, sex, intracranial volume, magnetic field strength, and scanner manufacturers. Results In the regression model predicting the new volumes, ethnicity was again a substantial predictor in most regions. Additionally, the model-based z-scores of regions were calculated for 428 AD patients and the matched controls, and then employed for diagnostic classification. When the AD classifier adopted the z-scores adjusted for ethnicity, the diagnostic accuracy has noticeably improved (AUC = 0.85, ΔAUC = + 0.04, D = 4.10, p < 0.001). Conclusions Our results suggest that the prediction model remains robust across different measurement tool, and ethnicity significantly contributes to the establishment of norms for brain volumes and the development of a diagnostic system for neurodegenerative diseases.
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Affiliation(s)
- Yu Yong Choi
- Gwangju Alzheimer's & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
- Department of Neurology, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Jang Jae Lee
- Gwangju Alzheimer's & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
| | - Jan Te Nijenhuis
- Gwangju Alzheimer's & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
| | - Kyu Yeong Choi
- Gwangju Alzheimer's & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
| | | | | | - Il Han Choo
- Department of Neuropsychiatry, Chosun University School of Medicine and Hospital, Gwangju, Republic of Korea
| | - Hoowon Kim
- Gwangju Alzheimer's & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
- Department of Neurology, Chosun University School of Medicine and Hospital, Gwangju, Republic of Korea
| | - Min-Kyung Song
- Department of Neurology, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Seong-Min Choi
- Department of Neurology, Chonnam National University Hospital, Gwangju, Republic of Korea
- Department of Neurology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Soo Hyun Cho
- Department of Neurology, Chonnam National University Hospital, Gwangju, Republic of Korea
- Department of Neurology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Youngshik Choe
- Korea Brain Research Institute, Daegu, Republic of Korea
| | - Byeong C Kim
- Department of Neurology, Chonnam National University Hospital, Gwangju, Republic of Korea
- Department of Neurology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Kun Ho Lee
- Gwangju Alzheimer's & Related Dementia Cohort Research Center, Chosun University, Gwangju, Republic of Korea
- Neurozen Inc., Seoul, Republic of Korea
- Korea Brain Research Institute, Daegu, Republic of Korea
- Department of Biomedical Science, Chosun University, Gwangju, Republic of Korea
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11
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Ge R, Yu Y, Qi YX, Fan YV, Chen S, Gao C, Haas SS, Modabbernia A, New F, Agartz I, Asherson P, Ayesa-Arriola R, Banaj N, Banaschewski T, Baumeister S, Bertolino A, Boomsma DI, Borgwardt S, Bourque J, Brandeis D, Breier A, Brodaty H, Brouwer RM, Buckner R, Buitelaar JK, Cannon DM, Caseras X, Cervenka S, Conrod PJ, Crespo-Facorro B, Crivello F, Crone EA, de Haan L, de Zubicaray GI, Di Giorgio A, Erk S, Fisher SE, Franke B, Frodl T, Glahn DC, Grotegerd D, Gruber O, Gruner P, Gur RE, Gur RC, Harrison BJ, Hatton SN, Hickie I, Howells FM, Pol HEH, Huyser C, Jernigan TL, Jiang J, Joska JA, Kahn RS, Kalnin AJ, Kochan NA, Koops S, Kuntsi J, Lagopoulos J, Lazaro L, Lebedeva IS, Lochner C, Martin NG, Mazoyer B, McDonald BC, McDonald C, McMahon KL, Nakao T, Nyberg L, Piras F, Portella MJ, Qiu J, Roffman JL, Sachdev PS, Sanford N, Satterthwaite TD, Saykin AJ, Schumann G, Sellgren CM, Sim K, Smoller JW, Soares J, Sommer IE, Spalletta G, Stein DJ, Tamnes CK, Thomopolous SI, Tomyshev AS, Tordesillas-Gutiérrez D, Trollor JN, van ’t Ent D, van den Heuvel OA, van Erp TGM, van Haren NEM, Vecchio D, Veltman DJ, Walter H, Wang Y, Weber B, Wei D, Wen W, Westlye LT, Wierenga LM, Williams SCR, Wright MJ, Medland S, Wu MJ, Yu K, Jahanshad N, Thompson PM, Frangou S. Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.30.523509. [PMID: 38076938 PMCID: PMC10705253 DOI: 10.1101/2023.01.30.523509] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/).
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Affiliation(s)
- Ruiyang Ge
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yuetong Yu
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yi Xuan Qi
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yunan Vera Fan
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Shiyu Chen
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Chuntong Gao
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Faye New
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
| | - Philip Asherson
- Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry Center, King's College London, London, UK
| | - Rosa Ayesa-Arriola
- Department of Psychiatry, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute (IDIVAL), Santander, Spain
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Germany
| | - Sarah Baumeister
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Germany
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Stefan Borgwardt
- Translational Psychiatry Unit, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Josiane Bourque
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Daniel Brandeis
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Germany
- Department of Child and Adolescent Psychiatry, University of Zürich, Zurich, Switzerland
| | - Alan Breier
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Henry Brodaty
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Rachel M Brouwer
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Randy Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jan K Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Dara M Cannon
- Clinical Neuroimaging Laboratory, National University of Ireland Galway, Galway, Ireland
| | - Xavier Caseras
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Simon Cervenka
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
- Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden
| | - Patricia J Conrod
- Department of Psychiatry and Addiction, Université de Montréal, CHU Ste Justine, Montréal, Canada
| | - Benedicto Crespo-Facorro
- University Hospital Virgen del Rocio, Seville, Spain; Department of Psychiatry, University of Seville, Institute of Biomedicine of Seville (IBIS), Seville, Spain
- Mental Health Research Networking Center (CIBERSAM), Madrid, Spain
| | - Fabrice Crivello
- Institut des Maladies Neurodégénératives, Université de Bordeaux, Bordeaux, France
| | - Eveline A Crone
- Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Institute of Psychology, Leiden University, Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Liewe de Haan
- Department of Psychiatry, Amsterdam UMC, Amsterdam, The Netherlands
| | - Greig I de Zubicaray
- School of Psychology & Counselling, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Annabella Di Giorgio
- Laboratory of Biological Psychiatry, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Susanne Erk
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Thomas Frodl
- University Clinics and Clinics for Psychiatry, Psychotherapy and Psychosomatic Medicine, RWTH Aachen University, Aachen, Germany
| | - David C Glahn
- Department of Psychiatry, Tommy Fuss Center for Neuropsychiatric Disease Research Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Dominik Grotegerd
- Department of Psychiatry and Psychotherapy, University of Muenster, Muenster, Germany
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Patricia Gruner
- Department of Psychiatry, Yale University, New Haven, Connecticut, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ben J Harrison
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Melbourne, Australia
| | - Sean N Hatton
- Center for Multimodal Imaging and Genetics, University of California San Diego, La jolla, California, USA
| | - Ian Hickie
- Brain and Mind Centre, University of Sydney, Sydney, Australia
| | - Fleur M Howells
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Hilleke E Hulshoff Pol
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Psychology, Utrecht University, Utrecht, The Netherlands
| | - Chaim Huyser
- Department of Child and Adolescent Psychiatry, Academic Medical Centre/De Bascule, Amsterdam, The Netherlands
| | - Terry L Jernigan
- Center for Human Development, Departments of Cognitive Science, Psychiatry, and Radiology, University of California, San Diego, USA
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - John A Joska
- Department of Neuropsychiatry, University of Cape Town, Cape Town, South Africa
| | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrew J Kalnin
- Department of Radiology, The Ohio State University College of Medicine, Columbus, Ohio, USA
| | - Nicole A Kochan
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Sanne Koops
- Department of Psychiatry and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jonna Kuntsi
- Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry Center, King's College London, London, UK
| | - Jim Lagopoulos
- Sunshine Coast Mind and Neuroscience - Thompson Institute, University of the Sunshine Coast, Queensland, Australia
| | - Luisa Lazaro
- Department of Child and Adolescent Psychiatry and Psychology, Hospital Clínic Barcelona, Barcelona, Spain
| | | | - Christine Lochner
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - Nicholas G Martin
- Queensland Institute of Medical Research, Berghofer Medical Research Institute, Brisbane, Australia
| | - Bernard Mazoyer
- Institut des Maladies Neurodégénératives, Université de Bordeaux, Bordeaux, France
| | - Brenna C McDonald
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Colm McDonald
- Centre for Neuroimaging & Cognitive Genomics (NICOG), NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - Katie L McMahon
- School of Clinical Sciences, Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, Australia
| | - Tomohiro Nakao
- Department of Neuropsychiatry, Kyushu University, Fukuoka, Japan
| | - Lars Nyberg
- Department of Radiation Sciences, Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden; Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Maria J Portella
- Mental Health Research Networking Center (CIBERSAM), Madrid, Spain
- Department of Psychiatry, Hospital de la Santa Creu iSant Pau, Institutd' Investigació Biomèdica SantPau, Universitat Autònomade Barcelona (UAB), Barcelona, Spain
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Southwest University, Ministry of Education, Chongqing, PR China
- Faculty of Psychology, Southwest University, Chongqing, PR China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, PR China
| | - Joshua L Roffman
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Nicole Sanford
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology, and Neuroscience, Social, Genetic & Developmental Psychiatry Centre, King's College London, London, UK; Institute for Science and Technology of Brain-inspired Intelligence, Fudan University, Shanghai, PR China; Centre for Population Neuroscience and Stratified Medicine (PONS), Charite Mental Health, Department of Psychiatry and Psychotherapy, CCM, Charite Universitätsmedizin Berlin, Berlin, Germany
| | - Carl M Sellgren
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
- Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden
| | - Kang Sim
- Institute of Mental Health, Singapore
| | - Jordan W Smoller
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jair Soares
- University of Texas Health Harris County Psychiatric Center, Houston, Texas, USA
| | - Iris E Sommer
- Department of Biomedical Sciences of Cells and Systems, Rijksuniversiteit Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Dan J Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Christian K Tamnes
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Sophia I Thomopolous
- Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck USC School of Medicine, Marina del Rey, California, USA
| | | | - Diana Tordesillas-Gutiérrez
- Department of Radiology, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute (IDIVAL), Santander, Spain; Advanced Computing and e-Science, Instituto de Física de Cantabria (UC-CSIC), Santander, Spain
| | - Julian N Trollor
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
- Department of Developmental Disability Neuropsychiatry, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Dennis van ’t Ent
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Odile A van den Heuvel
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Anatomy & Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Theo GM van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, California, USA
| | - Neeltje EM van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Daniela Vecchio
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Henrik Walter
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Yang Wang
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Bernd Weber
- Institute for Experimental Epileptology and Cognition Research, University of Bonn Germany, Bonn, Germany; University Hospital Bonn, Bonn, Germany
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality, Southwest University, Ministry of Education, Chongqing, PR China
- Faculty of Psychology, Southwest University, Chongqing, PR China
| | - Wei Wen
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Lara M Wierenga
- Institute of Psychology, Leiden University, Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Steven CR Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Sarah Medland
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Mon-Ju Wu
- Department of Psychiatry and Behavioral Science, University of Texas Health Science Center, Houston, Texas, USA
| | - Kevin Yu
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Neda Jahanshad
- Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck USC School of Medicine, Marina del Rey, California, USA
| | - Paul M Thompson
- Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck USC School of Medicine, Marina del Rey, California, USA
| | - Sophia Frangou
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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12
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Xu X, Lin L, Wu S, Sun S. Exploring Successful Cognitive Aging: Insights Regarding Brain Structure, Function, and Demographics. Brain Sci 2023; 13:1651. [PMID: 38137099 PMCID: PMC10741933 DOI: 10.3390/brainsci13121651] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 11/24/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023] Open
Abstract
In the realm of cognitive science, the phenomenon of "successful cognitive aging" stands as a hallmark of individuals who exhibit cognitive abilities surpassing those of their age-matched counterparts. However, it is paramount to underscore a significant gap in the current research, which is marked by a paucity of comprehensive inquiries that deploy substantial sample sizes to methodically investigate the cerebral biomarkers and contributory elements underpinning this cognitive success. It is within this context that our present study emerges, harnessing data derived from the UK Biobank. In this study, a highly selective cohort of 1060 individuals aged 65 and above was meticulously curated from a larger pool of 17,072 subjects. The selection process was guided by their striking cognitive resilience, ascertained via rigorous evaluation encompassing both generic and specific cognitive assessments, compared to their peers within the same age stratum. Notably, the cognitive abilities of the chosen participants closely aligned with the cognitive acumen commonly observed in middle-aged individuals. Our study leveraged a comprehensive array of neuroimaging-derived metrics, obtained from three Tesla MRI scans (T1-weighted images, dMRI, and resting-state fMRI). The metrics included image-derived phenotypes (IDPs) that addressed grey matter morphology, the strength of brain network connectivity, and the microstructural attributes of white matter. Statistical analyses were performed employing ANOVA, Mann-Whitney U tests, and chi-square tests to evaluate the distinctive aspects of IDPs pertinent to the domain of successful cognitive aging. Furthermore, these analyses aimed to elucidate lifestyle practices that potentially underpin the maintenance of cognitive acumen throughout the aging process. Our findings unveiled a robust and compelling association between heightened cognitive aptitude and the integrity of white matter structures within the brain. Furthermore, individuals who exhibited successful cognitive aging demonstrated markedly enhanced activity in the cerebral regions responsible for auditory perception, voluntary motor control, memory retention, and emotional regulation. These advantageous cognitive attributes were mirrored in the health-related lifestyle choices of the surveyed cohort, characterized by elevated educational attainment, a lower incidence of smoking, and a penchant for moderate alcohol consumption. Moreover, they displayed superior grip strength and enhanced walking speeds. Collectively, these findings furnish valuable insights into the multifaceted determinants of successful cognitive aging, encompassing both neurobiological constituents and lifestyle practices. Such comprehensive comprehension significantly contributes to the broader discourse on aging, thereby establishing a solid foundation for the formulation of targeted interventions aimed at fostering cognitive well-being among aging populations.
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Affiliation(s)
- Xinze Xu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (X.X.); (S.W.); (S.S.)
| | - Lan Lin
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (X.X.); (S.W.); (S.S.)
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Shuicai Wu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (X.X.); (S.W.); (S.S.)
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Shen Sun
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (X.X.); (S.W.); (S.S.)
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
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13
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Wylie GR, Genova HM, Yao B, Chiaravalloti N, Román CAF, Sandroff BM, DeLuca J. Evaluating the effects of brain injury, disease and tasks on cognitive fatigue. Sci Rep 2023; 13:20166. [PMID: 37978235 PMCID: PMC10656417 DOI: 10.1038/s41598-023-46918-y] [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: 05/11/2023] [Accepted: 11/07/2023] [Indexed: 11/19/2023] Open
Abstract
Because cognitive fatigue (CF) is common and debilitating following brain injury or disease we investigated the relationships among CF, behavioral performance, and cerebral activation within and across populations by combining the data from two cross-sectional studies. Individuals with multiple sclerosis (MS) were included to model CF resulting from neurological disease; individuals who had sustained a traumatic brain injury (TBI) were included to model CF resulting from neurological insult; both groups were compared with a control group (Controls). CF was induced while neuroimaging data was acquired using two different tasks. CF significantly differed between the groups, with the clinical groups reporting more CF than Controls-a difference that was statistically significant for the TBI group and trended towards significance for the MS group. The accrual of CF did not differ across the three groups; and CF ratings were consistent across tasks. Increasing CF was associated with longer response time for all groups. The brain activation in the caudate nucleus and the thalamus was consistently correlated with CF in all three groups, while more dorsally in the caudate, activation differed across the groups. These results suggest the caudate and thalamus to be central to CF while more dorsal aspects of the caudate may be sensitive to damage associated with particular types of insult.
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Affiliation(s)
- Glenn R Wylie
- Rocco Ortenzio Neuroimaging Center, Kessler Foundation, 1199 Pleasant Valley Way, West Orange, NJ, 07052, USA.
- Department of Physical Medicine and Rehabilitation, Rutgers University, New Jersey Medical School, Newark, USA.
- Department of Veterans' Affairs, The War Related Illness and Injury Center, East Orange Campus, East Orange, NJ, 07018, USA.
| | - Helen M Genova
- Rocco Ortenzio Neuroimaging Center, Kessler Foundation, 1199 Pleasant Valley Way, West Orange, NJ, 07052, USA
- Department of Physical Medicine and Rehabilitation, Rutgers University, New Jersey Medical School, Newark, USA
| | - Bing Yao
- Rocco Ortenzio Neuroimaging Center, Kessler Foundation, 1199 Pleasant Valley Way, West Orange, NJ, 07052, USA
- Department of Physical Medicine and Rehabilitation, Rutgers University, New Jersey Medical School, Newark, USA
| | - Nancy Chiaravalloti
- Rocco Ortenzio Neuroimaging Center, Kessler Foundation, 1199 Pleasant Valley Way, West Orange, NJ, 07052, USA
- Department of Physical Medicine and Rehabilitation, Rutgers University, New Jersey Medical School, Newark, USA
| | - Cristina A F Román
- Rocco Ortenzio Neuroimaging Center, Kessler Foundation, 1199 Pleasant Valley Way, West Orange, NJ, 07052, USA
| | - Brian M Sandroff
- Rocco Ortenzio Neuroimaging Center, Kessler Foundation, 1199 Pleasant Valley Way, West Orange, NJ, 07052, USA
- Department of Physical Medicine and Rehabilitation, Rutgers University, New Jersey Medical School, Newark, USA
| | - John DeLuca
- Rocco Ortenzio Neuroimaging Center, Kessler Foundation, 1199 Pleasant Valley Way, West Orange, NJ, 07052, USA
- Department of Physical Medicine and Rehabilitation, Rutgers University, New Jersey Medical School, Newark, USA
- Department of Neurology, Rutgers University, New Jersey Medical School, Newark, NJ, 07101, USA
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14
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Christova P, Georgopoulos AP. Changes of gray matter volumes of subcortical regions across the lifespan: a Human Connectome Project study. J Neurophysiol 2023; 130:1303-1308. [PMID: 37850792 PMCID: PMC11068360 DOI: 10.1152/jn.00283.2023] [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: 09/19/2023] [Accepted: 10/11/2023] [Indexed: 10/19/2023] Open
Abstract
We assessed changes in gray matter volume (GMV) of nine subcortical regions (accumbens, amygdala, brainstem, caudate, cerebellar cortex, pallidum, putamen, thalamus, and ventral diencephalon) across the lifespan in a large sample of participants in the Human Connectome Project (n = 2,458, 5-90 yr old, 1,113 males and 1,345 females). 3T MRI data were acquired using a harmonized protocol and were processed in an identical way for all brains. GMVs of individual regions were adjusted for estimated total intracranial volume and regressed against age. We found highly statistically significant changes in GMV with age (P < 0.001) that were distinct among areas and mostly consistent between sexes, as follows. 1) The GMVs of accumbens, caudate, putamen, and cerebellum decreased with age in a linear fashion. The rate of decrease was steeper in males than in females for all regions. 2) The GMVs of the amygdala, pallidum, thalamus, ventral diencephalon, and brainstem changed with age in a quadratic fashion, i.e., increasing first and decreasing afterward. The estimated age at the peak (vertex) of the parabola was 51.8 yr for the brainstem and 28.0-37.9 yr for the other regions. The peak occurred earlier in males than in females, by an average of 8 yr, with the exception of the brainstem, where the age at the peak was very similar in both sexes. These results confirm previous findings and offer new insights into region-specific age-related changes in subcortical brain GMVs.NEW & NOTEWORTHY We report mixed effects of age on subcortical grey matter volume (GMV) during lifespan (n = 2458, 5-90 yr old, 1113 male, 1345 female). Striatal and cerebellar GMVs decreased linearly with age, more steeply in males. In contrast, GMVs of the amygdala, pallidum, thalamus, ventral diencephalon, and brainstem changed in a quadratic fashion, increasing first and decreasing afterward, with males peaking earlier than females in all regions but the brainstem where they peaked at nearly the same time.
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Affiliation(s)
- Peka Christova
- Brain Sciences Center, Department of Veterans Affairs Health Care System, The Neuroimaging Research Group, Minneapolis, Minnesota, United States
- Department of Neuroscience, University of Minnesota Medical School, Minneapolis, Minnesota, United States
| | - Apostolos P Georgopoulos
- Brain Sciences Center, Department of Veterans Affairs Health Care System, The Neuroimaging Research Group, Minneapolis, Minnesota, United States
- Department of Neuroscience, University of Minnesota Medical School, Minneapolis, Minnesota, United States
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15
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Kheloui S, Jacmin-Park S, Larocque O, Kerr P, Rossi M, Cartier L, Juster RP. Sex/gender differences in cognitive abilities. Neurosci Biobehav Rev 2023; 152:105333. [PMID: 37517542 DOI: 10.1016/j.neubiorev.2023.105333] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 07/09/2023] [Accepted: 07/27/2023] [Indexed: 08/01/2023]
Abstract
Sex/gender differences in cognitive sciences are riddled by conflicting perspectives. At the center of debates are clinical, social, and political perspectives. Front and center, evolutionary and biological perspectives have often focused on 'nature' arguments, while feminist and constructivist views have often focused on 'nurture arguments regarding cognitive sex differences. In the current narrative review, we provide a comprehensive overview regarding the origins and historical advancement of these debates while providing a summary of the results in the field of sexually polymorphic cognition. In so doing, we attempt to highlight the importance of using transdisciplinary perspectives which help bridge disciplines together to provide a refined understanding the specific factors that drive sex differences a gender diversity in cognitive abilities. To summarize, biological sex (e.g., birth-assigned sex, sex hormones), socio-cultural gender (gender identity, gender roles), and sexual orientation each uniquely shape the cognitive abilities reviewed. To date, however, few studies integrate these sex and gender factors together to better understand individual differences in cognitive functioning. This has potential benefits if a broader understanding of sex and gender factors are systematically measured when researching and treating numerous conditions where cognition is altered.
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Affiliation(s)
- Sarah Kheloui
- Department of Psychiatry and Addiction, University of Montreal, Montreal, Quebec, Canada; Department of Psychology, Université du Québec à Montréal, Montreal, Quebec, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Canada; Center on Sex⁎Gender, Allostasis and Resilience, Canada
| | - Silke Jacmin-Park
- Department of Psychology, University of Montreal, Montreal, Quebec, Canada; Department of Psychology, Université du Québec à Montréal, Montreal, Quebec, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Canada; Center on Sex⁎Gender, Allostasis and Resilience, Canada
| | - Ophélie Larocque
- Department of Psychology, University of Montreal, Montreal, Quebec, Canada; Department of Psychology, Université du Québec à Montréal, Montreal, Quebec, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Canada; Center on Sex⁎Gender, Allostasis and Resilience, Canada
| | - Philippe Kerr
- Department of Psychiatry and Addiction, University of Montreal, Montreal, Quebec, Canada; Department of Psychology, Université du Québec à Montréal, Montreal, Quebec, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Canada; Center on Sex⁎Gender, Allostasis and Resilience, Canada
| | - Mathias Rossi
- Department of Psychiatry and Addiction, University of Montreal, Montreal, Quebec, Canada; Department of Psychology, Université du Québec à Montréal, Montreal, Quebec, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Canada; Center on Sex⁎Gender, Allostasis and Resilience, Canada
| | - Louis Cartier
- Department of Psychiatry and Addiction, University of Montreal, Montreal, Quebec, Canada; Department of Psychology, Université du Québec à Montréal, Montreal, Quebec, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Canada; Center on Sex⁎Gender, Allostasis and Resilience, Canada
| | - Robert-Paul Juster
- Department of Psychiatry and Addiction, University of Montreal, Montreal, Quebec, Canada; Department of Psychology, Université du Québec à Montréal, Montreal, Quebec, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Canada; Center on Sex⁎Gender, Allostasis and Resilience, Canada.
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16
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Bozek J, Griffanti L, Lau S, Jenkinson M. Normative models for neuroimaging markers: Impact of model selection, sample size and evaluation criteria. Neuroimage 2023; 268:119864. [PMID: 36621581 DOI: 10.1016/j.neuroimage.2023.119864] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 12/13/2022] [Accepted: 01/03/2023] [Indexed: 01/07/2023] Open
Abstract
Modelling population reference curves or normative modelling is increasingly used with the advent of large neuroimaging studies. In this paper we assess the performance of fitting methods from the perspective of clinical applications and investigate the influence of the sample size. Further, we evaluate linear and non-linear models for percentile curve estimation and highlight how the bias-variance trade-off manifests in typical neuroimaging data. We created plausible ground truth distributions of hippocampal volumes in the age range of 45 to 80 years, as an example application. Based on these distributions we repeatedly simulated samples for sizes between 50 and 50,000 data points, and for each simulated sample we fitted a range of normative models. We compared the fitted models and their variability across repetitions to the ground truth, with specific focus on the outer percentiles (1st, 5th, 10th) as these are the most clinically relevant. Our results quantify the expected decreasing trend in variance of the volume estimates with increasing sample size. However, bias in the volume estimates only decreases a modest amount, without much improvement at large sample sizes. The uncertainty of model performance is substantial for what would often be considered large samples in a neuroimaging context and rises dramatically at the ends of the age range, where fewer data points exist. Flexible models perform better across sample sizes, especially for non-linear ground truth. Surprisingly large samples of several thousand data points are needed to accurately capture outlying percentiles across the age range for applications in research and clinical settings. Performance evaluation methods should assess both bias and variance. Furthermore, caution is needed when attempting to go near the ends of the age range captured by the source data set and, as is a well known general principle, extrapolation beyond the age range should always be avoided. To help with such evaluations of normative models we have made our code available to guide researchers developing or utilising normative models.
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Affiliation(s)
- Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, Warneford Hospital, University of Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, United Kingdom
| | - Stephan Lau
- Australian Institute for Machine Learning, School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, SA, Australia; South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, United Kingdom; Australian Institute for Machine Learning, School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, SA, Australia; South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia.
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17
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Haddad E, Pizzagalli F, Zhu AH, Bhatt RR, Islam T, Ba Gari I, Dixon D, Thomopoulos SI, Thompson PM, Jahanshad N. Multisite test-retest reliability and compatibility of brain metrics derived from FreeSurfer versions 7.1, 6.0, and 5.3. Hum Brain Mapp 2023; 44:1515-1532. [PMID: 36437735 PMCID: PMC9921222 DOI: 10.1002/hbm.26147] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 10/19/2022] [Accepted: 10/19/2022] [Indexed: 11/29/2022] Open
Abstract
Automatic neuroimaging processing tools provide convenient and systematic methods for extracting features from brain magnetic resonance imaging scans. One tool, FreeSurfer, provides an easy-to-use pipeline to extract cortical and subcortical morphometric measures. There have been over 25 stable releases of FreeSurfer, with different versions used across published works. The reliability and compatibility of regional morphometric metrics derived from the most recent version releases have yet to be empirically assessed. Here, we used test-retest data from three public data sets to determine within-version reliability and between-version compatibility across 42 regional outputs from FreeSurfer versions 7.1, 6.0, and 5.3. Cortical thickness from v7.1 was less compatible with that of older versions, particularly along the cingulate gyrus, where the lowest version compatibility was observed (intraclass correlation coefficient 0.37-0.61). Surface area of the temporal pole, frontal pole, and medial orbitofrontal cortex, also showed low to moderate version compatibility. We confirm low compatibility between v6.0 and v5.3 of pallidum and putamen volumes, while those from v7.1 were compatible with v6.0. Replication in an independent sample showed largely similar results for measures of surface area and subcortical volumes, but had lower overall regional thickness reliability and compatibility. Batch effect correction may adjust for some inter-version effects when most sites are run with one version, but results vary when more sites are run with different versions. Age associations in a quality controlled independent sample (N = 106) revealed version differences in results of downstream statistical analysis. We provide a reference to highlight the regional metrics that may yield recent version-related inconsistencies in published findings. An interactive viewer is provided at http://data.brainescience.org/Freesurfer_Reliability/.
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Affiliation(s)
- Elizabeth Haddad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Fabrizio Pizzagalli
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA.,Department of Neurosciences, University of Turin, Turin, Italy
| | - Alyssa H Zhu
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Ravi R Bhatt
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Tasfiya Islam
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Iyad Ba Gari
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Daniel Dixon
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
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18
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Du X, Wei L, Yang B, Long S, Wang J, Sun A, Jiang Y, Qiao Z, Wang H, Wang Y. Cortical and subcortical morphological alteration in Angelman syndrome. J Neurodev Disord 2023; 15:7. [PMID: 36788499 PMCID: PMC9930225 DOI: 10.1186/s11689-022-09469-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 11/28/2022] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Angelman syndrome (AS) is a neurodevelopmental disorder with serious seizures. We aim to explore the brain morphometry of patients with AS and figure out whether the seizure is associated with brain development. METHODS Seventy-three patients and 26 healthy controls (HC) underwent high-resolution structural brain MRI. Group differences between the HC group and the AS group and also between AS patients with seizure (AS-Se) and age-matched AS patients with non-seizure (AS-NSe) were compared. The voxel-based and surface-based morphometry analyses were used in our study. Gray matter volume, cortical thickness (CTH), and local gyrification index (LGI) were assessed to analyze the cortical and subcortical structure alteration in the AS brain. RESULTS Firstly, compared with the HC group, children with AS were found to have a significant decrease in gray matter volume in the subcortical nucleus, cortical, and cerebellum. However, the gray matter volume of AS patients in the inferior precuneus was significantly increased. Secondly, patients with AS had significantly increased LGI in the whole brain as compared with HC. Thirdly, the comparison of AS-Se and the AS-NSe groups revealed a significant decrease in caudate volume in the AS-Se group. Lastly, we further selected the caudate and the precuneus as ROIs for volumetric analysis, the AS group showed significantly increased LGI in the precuneus and reduced CTH in the right precuneus. Between the AS-Se and the AS-NSe groups, the AS-Se group exhibited significantly lower density in the caudate, while only the CTH in the left precuneus showed a significant difference. CONCLUSIONS These results revealed cortical and subcortical morphological alterations in patients with AS, including globally the decreased brain volume in the subcortical nucleus, the increased gray matter volume of precuneus, and the whole-brain increase of LGI and reduction of CTH. The abnormal brain pattern was more serious in patients with seizures, suggesting that the occurrence of seizures may be related to abnormal brain changes.
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Affiliation(s)
- Xiaonan Du
- Department of Neurology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.,Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Lei Wei
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Baofeng Yang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Shasha Long
- Department of Neurology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China
| | - Ji Wang
- Department of Neurology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China
| | - Aiqi Sun
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Yonghui Jiang
- Department of Genetics and Paediatrics, Yale School of Medicine, CT, New Haven, China
| | - Zhongwei Qiao
- Department of Radiology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China
| | - He Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China. .,Human Phenome Institute, Fudan University, Shanghai, China. .,Key Laboratory of Computational Neuroscience and BrainInspired Intelligence (Fudan University), Ministry of Education, Shanghai, USA.
| | - Yi Wang
- Department of Neurology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
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Brain Macro-Structural Alterations in Aging Rats: A Longitudinal Lifetime Approach. Cells 2023; 12:cells12030432. [PMID: 36766774 PMCID: PMC9914014 DOI: 10.3390/cells12030432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/25/2023] [Accepted: 01/26/2023] [Indexed: 02/03/2023] Open
Abstract
Aging is accompanied by macro-structural alterations in the brain that may relate to age-associated cognitive decline. Animal studies could allow us to study this relationship, but so far it remains unclear whether their structural aging patterns correspond to those in humans. Therefore, by applying magnetic resonance imaging (MRI) and deformation-based morphometry (DBM), we longitudinally screened the brains of male RccHan:WIST rats for structural changes across their average lifespan. By combining dedicated region of interest (ROI) and voxel-wise approaches, we observed an increase in their global brain volume that was superimposed by divergent local morphologic alterations, with the largest aging effects in early and middle life. We detected a modality-dependent vulnerability to shrinkage across the visual, auditory, and somato-sensory cortical areas, whereas the piriform cortex showed partial resistance. Furthermore, shrinkage emerged in the amygdala, subiculum, and flocculus as well as in frontal, parietal, and motor cortical areas. Strikingly, we noticed the preservation of ectorhinal, entorhinal, retrosplenial, and cingulate cortical regions, which all represent higher-order brain areas and extraordinarily grew with increasing age. We think that the findings of this study will further advance aging research and may contribute to the establishment of interventional approaches to preserve cognitive health in advanced age.
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Lee-Hughes R, Lancaster TM. Cumulative Impact of Morphometric Features in Schizophrenia in Two Independent Samples. SCHIZOPHRENIA BULLETIN OPEN 2023; 4:sgad031. [PMID: 39145335 PMCID: PMC11207677 DOI: 10.1093/schizbullopen/sgad031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Schizophrenia and bipolar disorder share a common structural brain alteration profile. However, there is considerable between- and within-diagnosis variability in these features, which may underestimate informative individual differences. Using a recently established morphometric risk score (MRS) approach, we aim to provide confirmation that individual MRS scores are higher in individuals with a psychosis diagnosis, helping to parse individual heterogeneity. Using the Human Connectome Project Early Psychosis (N = 124), we estimate MRS for psychosis and specifically for bipolar/schizophrenia using T1-weighted MRI data and prior meta-analysis effect sizes. We confirm associations in an independent replication sample (N = 69). We assess (1) the impact of diagnosis on these MRS, (2) compare effect sizes of MRS to all individual, cytoarchitecturally defined brain regions, and (3) perform negative control analyses to assess MRS specificity. The MRS specifically for SCZ was higher in the whole psychosis group (Cohen's d = 0.56; P = 0.003) and outperformed any single region of interest in standardized mean difference (ZMRS>75 ROIS = 2.597; P = 0.009) and correlated with previously reported effect sizes (PSPIN/SHUFFLE < 0.005). MRS without Enhancing Neuroimaging Genomics through Meta-Analysis weights did not delineate groups with empirically null associations (t = 2.29; P = 0.02). We replicate MRS specifically for SCZ associations in the independent sample. Akin to polygenic risk scoring and individual allele effect sizes, these observations suggest that assessing the combined impact of regional structural alterations may be more informative than any single cytoarchitecturally constrained cortical region, where well-powered, meta-analytical samples are informative in the delineation of diagnosis and within psychosis case differences, in smaller independent samples.
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21
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Caillaud M, Maltezos S, Hudon C, Mellah S, Belleville S. Hippocampal Volume and Episodic Associative Memory Identify Memory Risk in Subjective Cognitive Decline Individuals in the CIMA-Q Cohort, Regardless of Cognitive Reserve Level and APOE4 Status. J Alzheimers Dis 2023; 94:1047-1056. [PMID: 37355896 PMCID: PMC10473077 DOI: 10.3233/jad-230131] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/16/2023] [Indexed: 06/26/2023]
Abstract
BACKGROUND Subjective cognitive decline (SCD) was proposed to identify older adults who complain about their memory but perform within a normal range on standard neuropsychological tests. Persons with SCD are at increased risk of dementia meaning that some SCD individuals experience subthreshold memory decline due to an underlying progression of Alzheimer's disease (AD). OBJECTIVE Our main goal was to determine whether hippocampal volume and APOE4, which represent typical AD markers, predict inter-individual differences in memory performance among SCD individuals and can be used to identify a meaningful clinical subgroup. METHODS Neuropsychological assessment, structural MRI, and genetic testing for APOE4 were administered to one hundred and twenty-five older adults over the age of 65 from the CIMAQ cohort: 66 SCD, 29 individuals with mild cognitive impairment (MCI), and 30 cognitively intact controls (CTRLS). Multiple regression models were first used to identify which factor (hippocampal volume, APOE4 allele, or cognitive reserve) best predicted inter-individual differences in a Face-name association memory task within the SCD group. RESULTS Hippocampal volume was found to be the only and best predictor of memory performance. We then compared the demographic, clinical and cognitive characteristics of two SCD subgroups, one with small hippocampal volume (SCD/SH) and another with normal hippocampal volume (SCD/NH), with MCI and CTRLS. SCD/SH were comparable to MCI on neuropsychological tasks evaluating memory (i.e., test of delayed word recall), whereas SCD/NH were comparable to CTRLS. CONCLUSION Thus, using hippocampal volume allows identification of an SCD subgroup with a cognitive profile consistent with a higher risk of conversion to AD.
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Affiliation(s)
- Marie Caillaud
- Research Centre, Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada
- Department of Psychology, Université de Montréal, Montréal, Québec, Canada
| | - Samantha Maltezos
- Research Centre, Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada
- Department of Psychology, Université de Montréal, Montréal, Québec, Canada
| | - Carol Hudon
- CERVO Brain Research Centre, Institut Universitaire en Santé Mentale de Québec, Québec City, Québec, Canada
- VITAM Research Centre, Centre Intégré Universitaire en Santé et Services Sociaux de la Capitale-Nationale, Québec City, Québec, Canada
- Department of Psychology, Université de Laval, Québec City, Québec, Canada
| | - Samira Mellah
- Research Centre, Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada
| | - the Consortium for the Early Identification of Alzheimer’s Disease-Quebec
- Research Centre, Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada
- Department of Psychology, Université de Montréal, Montréal, Québec, Canada
- CERVO Brain Research Centre, Institut Universitaire en Santé Mentale de Québec, Québec City, Québec, Canada
- VITAM Research Centre, Centre Intégré Universitaire en Santé et Services Sociaux de la Capitale-Nationale, Québec City, Québec, Canada
- Department of Psychology, Université de Laval, Québec City, Québec, Canada
| | - Sylvie Belleville
- Research Centre, Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada
- Department of Psychology, Université de Montréal, Montréal, Québec, Canada
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22
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Mouches P, Wilms M, Aulakh A, Langner S, Forkert ND. Multimodal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors. Front Neurol 2022; 13:979774. [PMID: 36588902 PMCID: PMC9794870 DOI: 10.3389/fneur.2022.979774] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 11/16/2022] [Indexed: 12/15/2022] Open
Abstract
Introduction The difference between the chronological and biological brain age, called the brain age gap (BAG), has been identified as a promising biomarker to detect deviation from normal brain aging and to indicate the presence of neurodegenerative diseases. Moreover, the BAG has been shown to encode biological information about general health, which can be measured through cardiovascular risk factors. Current approaches for biological brain age estimation, and therefore BAG estimation, either depend on hand-crafted, morphological measurements extracted from brain magnetic resonance imaging (MRI) or on direct analysis of brain MRI images. The former can be processed with traditional machine learning models while the latter is commonly processed with convolutional neural networks (CNNs). Using a multimodal setting, this study aims to compare both approaches in terms of biological brain age prediction accuracy and biological information captured in the BAG. Methods T1-weighted MRI, containing brain tissue information, and magnetic resonance angiography (MRA), providing information about brain arteries, from 1,658 predominantly healthy adults were used. The volumes, surface areas, and cortical thickness of brain structures were extracted from the T1-weighted MRI data, while artery density and thickness within the major blood flow territories and thickness of the major arteries were extracted from MRA data. Independent multilayer perceptron and CNN models were trained to estimate the brain age from the hand-crafted features and image data, respectively. Next, both approaches were fused to assess the benefits of combining image data and hand-crafted features for brain age prediction. Results The combined model achieved a mean absolute error of 4 years between the chronological and predicted biological brain age. Among the independent models, the lowest mean absolute error was observed for the CNN using T1-weighted MRI data (4.2 years). When evaluating the BAGs obtained using the different approaches and imaging modalities, diverging associations between cardiovascular risk factors were found. For example, BAGs obtained from the CNN models showed an association with systolic blood pressure, while BAGs obtained from hand-crafted measurements showed greater associations with obesity markers. Discussion In conclusion, the use of more diverse sources of data can improve brain age estimation modeling and capture more diverse biological deviations from normal aging.
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Affiliation(s)
- Pauline Mouches
- Biomedical Engineering Program, University of Calgary, Calgary, AB, Canada,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada,Department of Radiology, University of Calgary, Calgary, AB, Canada,*Correspondence: Pauline Mouches
| | - Matthias Wilms
- Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Agampreet Aulakh
- Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Sönke Langner
- Institute for Diagnostic Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Nils D. Forkert
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada,Department of Radiology, University of Calgary, Calgary, AB, Canada,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
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23
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Chen Y, Zuo Y, Kang S, Pan L, Jiang S, Yan A, Li L. Using fractal dimension analysis to assess the effects of normal aging and sex on subregional cortex alterations across the lifespan from a Chinese dataset. Cereb Cortex 2022; 33:5289-5296. [PMID: 36300622 DOI: 10.1093/cercor/bhac417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 09/23/2022] [Accepted: 09/25/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Fractal dimension (FD) is used to quantify brain structural complexity and is more sensitive to morphological variability than other cortical measures. However, the effects of normal aging and sex on FD are not fully understood. In this study, age- and sex-related differences in FD were investigated in a sample of 448 adults age of 19–80 years from a Chinese dataset. The FD was estimated with the surface-based morphometry (SBM) approach, sex differences were analyzed on a vertex level, and correlations between FD and age were examined. Generalized additive models (GAMs) were used to characterize the trajectories of age-related changes in 68 regions based on the Desikan–Killiany atlas. The SBM results showed sex differences in the entire sample and 3 subgroups defined by age. GAM results demonstrated that the FD values of 51 regions were significantly correlated with age. The trajectories of changes can be classified into 4 main patterns. Our results indicate that sex differences in FD are evident across developmental stages. Age-related trajectories in FD are not homogeneous across the cerebral cortex. Our results extend previous findings and provide a foundation for future investigation of the underlying mechanism.
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Affiliation(s)
- Yiyong Chen
- Ningbo University School of Medicine, , Ningbo, 315211, Zhejiang, PR China
| | - Yizhi Zuo
- Nanjing Medical University Human Anatomy Department, , Nanjing, 211166, Jiangsu, PR China
| | - Shaofang Kang
- Ningbo University College of Teacher Education, , Ningbo, 315211, Zhejiang, PR China
| | - Liliang Pan
- Ningbo University School of Medicine, , Ningbo, 315211, Zhejiang, PR China
| | - Siyu Jiang
- Ningbo University School of Medicine, , Ningbo, 315211, Zhejiang, PR China
| | - Aohui Yan
- Ningbo University School of Medicine, , Ningbo, 315211, Zhejiang, PR China
| | - Lin Li
- Nanjing Medical University Human Anatomy Department, , Nanjing, 211166, Jiangsu, PR China
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24
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Caetano I, Amorim L, Castanho TC, Coelho A, Ferreira S, Portugal-Nunes C, Soares JM, Gonçalves N, Sousa R, Reis J, Lima C, Marques P, Moreira PS, Rodrigues AJ, Santos NC, Morgado P, Esteves M, Magalhães R, Picó-Pérez M, Sousa N. Association of amygdala size with stress perception: Findings of a transversal study across the lifespan. Eur J Neurosci 2022; 56:5287-5298. [PMID: 36017669 DOI: 10.1111/ejn.15809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 08/17/2022] [Accepted: 08/20/2022] [Indexed: 12/14/2022]
Abstract
Daily routines are getting increasingly stressful. Interestingly, associations between stress perception and amygdala volume, a brain region implicated in emotional behaviour, have been observed in both younger and older adults. Life stress, on the other hand, has become pervasive and is no longer restricted to a specific age group or life stage. As a result, it is vital to consider stress as a continuum across the lifespan. In this study, we investigated the relationship between perceived stress and amygdala size in 272 healthy participants with a broad age range. Participants were submitted to a structural magnetic resonance imaging (MRI) to extract amygdala volume, and the Perceived Stress Scale (PSS) scores were used as the independent variable in volumetric regressions. We found that perceived stress is positively associated with the right amygdala volume throughout life.
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Affiliation(s)
- Inês Caetano
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal
| | - Liliana Amorim
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal.,Association P5 Digital Medical Center (ACMP5), Braga, Portugal
| | - Teresa Costa Castanho
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal.,Association P5 Digital Medical Center (ACMP5), Braga, Portugal
| | - Ana Coelho
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal
| | - Sónia Ferreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal
| | - Carlos Portugal-Nunes
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal.,CECAV-Veterinary and Animal Science Research Centre, Vila Real, Portugal
| | - José Miguel Soares
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal
| | - Nuno Gonçalves
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal
| | - Rui Sousa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal.,Departamento de Psiquiatria e Saúde Mental, Centro Hospitalar Tondela-Viseu, Viseu, Portugal
| | - Joana Reis
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal
| | - Catarina Lima
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal
| | - Paulo Marques
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal
| | - Pedro Silva Moreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal
| | - Ana João Rodrigues
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal
| | - Nadine Correia Santos
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal
| | - Pedro Morgado
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal
| | - Madalena Esteves
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal
| | - Ricardo Magalhães
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal
| | - Maria Picó-Pérez
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal
| | - Nuno Sousa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), Braga, Portugal.,Association P5 Digital Medical Center (ACMP5), Braga, Portugal
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25
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Sandroff BM, Motl RW, Román CAF, Wylie GR, DeLuca J, Cutter GR, Benedict RHB, Dwyer MG, Zivadinov R. Thalamic atrophy moderates associations among aerobic fitness, cognitive processing speed, and walking endurance in persons with multiple sclerosis. J Neurol 2022; 269:5531-5540. [PMID: 35718819 PMCID: PMC9474622 DOI: 10.1007/s00415-022-11205-9] [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: 03/15/2022] [Revised: 05/18/2022] [Accepted: 05/20/2022] [Indexed: 01/03/2023]
Abstract
BACKGROUND AND OBJECTIVES Thalamic atrophy (TA) represents a biomarker of neurodegeneration and associated dysfunction/decline in physical and cognitive functioning among persons with multiple sclerosis (MS). Aerobic fitness, as an end point of exercise training, represents a promising target for restoring function in MS, but it is unknown if such effects differ by TA. This cross-sectional study examined whether aerobic fitness was differentially associated with cognitive processing speed and walking endurance in persons with MS who present with and without TA. METHODS 44 fully ambulatory persons with MS completed a graded exercise test for measuring aerobic fitness (VO2peak) and underwent 3T MRI for measuring TA, the Symbol Digit Modalities Test (SDMT), and the 6-min walk (6MW). We performed Spearman correlations (rs) among VO2peak, SDMT, and 6MW scores overall, and in persons with and without TA. We applied Fisher's z-test for comparing correlations based on TA status. RESULTS When controlling for age, EDSS score, and global MRI measures of atrophy, VO2peak was strongly associated with SDMT scores (prs = 0.74, p < 0.01) and 6MW performance (prs = 0.77, p < 0.01) in persons with TA, whereas VO2peak was not associated with SDMT scores (prs = - 0.01, p = 0.99) or 6MW performance (prs = 0.25, p = 0.38) in those without TA. The correlations between VO2peak and SDMT (z = 2.86, p < 0.01) and VO2peak and 6MW (z = 2.33, p = 0.02) were significantly stronger in the TA group. DISCUSSION This study provides initial evidence of strong, selective associations among aerobic fitness, cognitive processing speed, and walking endurance in persons with TA as a biomarker for MS-related neurodegeneration. Such data support TA as a moderator of the association among aerobic fitness, cognitive processing speed, and walking endurance in persons with MS. Future research should carefully consider the role of TA when designing trials of aerobic exercise, cognition, and mobility in MS.
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Affiliation(s)
- Brian M Sandroff
- Center for Neuropsychology and Neuroscience Research, Kessler Foundation, 1199 Pleasant Valley Way, West Orange, NJ, 07052, USA.
- Rutgers New Jersey Medical School, Newark, NJ, USA.
| | | | - Cristina A F Román
- Center for Neuropsychology and Neuroscience Research, Kessler Foundation, 1199 Pleasant Valley Way, West Orange, NJ, 07052, USA
- Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Glenn R Wylie
- Center for Neuropsychology and Neuroscience Research, Kessler Foundation, 1199 Pleasant Valley Way, West Orange, NJ, 07052, USA
- Rutgers New Jersey Medical School, Newark, NJ, USA
| | - John DeLuca
- Center for Neuropsychology and Neuroscience Research, Kessler Foundation, 1199 Pleasant Valley Way, West Orange, NJ, 07052, USA
- Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Gary R Cutter
- University of Alabama at Birmingham, Birmingham, AL, USA
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26
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Reynier KA, Giudice JS, Chernyavskiy P, Forman JL, Panzer MB. Quantifying the Effect of Sex and Neuroanatomical Biomechanical Features on Brain Deformation Response in Finite Element Brain Models. Ann Biomed Eng 2022; 50:1510-1519. [PMID: 36121528 DOI: 10.1007/s10439-022-03084-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 09/11/2022] [Indexed: 11/30/2022]
Abstract
Recent automotive epidemiology studies have concluded that females have significantly higher odds of sustaining a moderate brain injury or concussion than males in a frontal crash after controlling for multiple crash and occupant variables. Differences in neuroanatomical features, such as intracranial volume (ICV), have been shown between male and female subjects, but how these sex-specific neuroanatomical differences affect brain deformation is unknown. This study used subject-specific finite element brain models, generated via registration-based morphing using both male and female magnetic resonance imaging scans, to investigate sex differences of a variety of neuroanatomical features and their effect on brain deformation; additionally, this study aimed to determine the relative importance of these neuroanatomical features and sex on brain deformation metrics for a single automotive loading environment. Based on the Bayesian linear mixed models, sex had a significant effect on ICV, white matter volume and gray matter volume, as well as a section of cortical gray matter regions' thicknesses and volumes; however, after these neuroanatomical features were accounted for in the statistical model, sex was not a significant factor in predicting brain deformation. ICV had the highest relative effect on the brain deformation metrics assessed. Therefore, ICV should be considered when investigating both brain injury biomechanics and injury risk.
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Affiliation(s)
- Kristen A Reynier
- Department of Mechanical and Aerospace Engineering, Center for Applied Biomechanics, University of Virginia, 4040 Lewis and Clark Drive, Charlottesville, VA, 22911, USA
| | - J Sebastian Giudice
- Department of Mechanical and Aerospace Engineering, Center for Applied Biomechanics, University of Virginia, 4040 Lewis and Clark Drive, Charlottesville, VA, 22911, USA
| | - Pavel Chernyavskiy
- Department of Public Health Sciences, University of Virginia, P.O. Box 800717, Charlottesville, VA, 22908, USA
| | - Jason L Forman
- Department of Mechanical and Aerospace Engineering, Center for Applied Biomechanics, University of Virginia, 4040 Lewis and Clark Drive, Charlottesville, VA, 22911, USA
| | - Matthew B Panzer
- Department of Mechanical and Aerospace Engineering, Center for Applied Biomechanics, University of Virginia, 4040 Lewis and Clark Drive, Charlottesville, VA, 22911, USA.
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27
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Kim Y, Varosanec M, Kosa P, Bielekova B. Confounder-adjusted MRI-based predictors of multiple sclerosis disability. FRONTIERS IN RADIOLOGY 2022; 2:971157. [PMID: 37492673 PMCID: PMC10365278 DOI: 10.3389/fradi.2022.971157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 08/02/2022] [Indexed: 07/27/2023]
Abstract
Introduction Both aging and multiple sclerosis (MS) cause central nervous system (CNS) atrophy. Excess brain atrophy in MS has been interpreted as "accelerated aging." Current paper tests an alternative hypothesis: MS causes CNS atrophy by mechanism(s) different from physiological aging. Thus, subtracting effects of physiological confounders on CNS structures would isolate MS-specific effects. Methods Standardized brain MRI and neurological examination were acquired prospectively in 646 participants enrolled in ClinicalTrials.gov Identifier: NCT00794352 protocol. CNS volumes were measured retrospectively, by automated Lesion-TOADS algorithm and by Spinal Cord Toolbox, in a blinded fashion. Physiological confounders identified in 80 healthy volunteers were regressed out by stepwise multiple linear regression. MS specificity of confounder-adjusted MRI features was assessed in non-MS cohort (n = 158). MS patients were randomly split into training (n = 277) and validation (n = 131) cohorts. Gradient boosting machine (GBM) models were generated in MS training cohort from unadjusted and confounder-adjusted CNS volumes against four disability scales. Results Confounder adjustment highlighted MS-specific progressive loss of CNS white matter. GBM model performance decreased substantially from training to cross-validation, to independent validation cohorts, but all models predicted cognitive and physical disability with low p-values and effect sizes that outperform published literature based on recent meta-analysis. Models built from confounder-adjusted MRI predictors outperformed models from unadjusted predictors in the validation cohort. Conclusion GBM models from confounder-adjusted volumetric MRI features reflect MS-specific CNS injury, and due to stronger correlation with clinical outcomes compared to brain atrophy these models should be explored in future MS clinical trials.
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28
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Wilms M, Bannister JJ, Mouches P, MacDonald ME, Rajashekar D, Langner S, Forkert ND. Invertible Modeling of Bidirectional Relationships in Neuroimaging With Normalizing Flows: Application to Brain Aging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2331-2347. [PMID: 35324436 DOI: 10.1109/tmi.2022.3161947] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Many machine learning tasks in neuroimaging aim at modeling complex relationships between a brain's morphology as seen in structural MR images and clinical scores and variables of interest. A frequently modeled process is healthy brain aging for which many image-based brain age estimation or age-conditioned brain morphology template generation approaches exist. While age estimation is a regression task, template generation is related to generative modeling. Both tasks can be seen as inverse directions of the same relationship between brain morphology and age. However, this view is rarely exploited and most existing approaches train separate models for each direction. In this paper, we propose a novel bidirectional approach that unifies score regression and generative morphology modeling and we use it to build a bidirectional brain aging model. We achieve this by defining an invertible normalizing flow architecture that learns a probability distribution of 3D brain morphology conditioned on age. The use of full 3D brain data is achieved by deriving a manifold-constrained formulation that models morphology variations within a low-dimensional subspace of diffeomorphic transformations. This modeling idea is evaluated on a database of MR scans of more than 5000 subjects. The evaluation results show that our bidirectional brain aging model (1) accurately estimates brain age, (2) is able to visually explain its decisions through attribution maps and counterfactuals, (3) generates realistic age-specific brain morphology templates, (4) supports the analysis of morphological variations, and (5) can be utilized for subject-specific brain aging simulation.
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29
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Liu J, Kelly E, Bielekova B. Current Status and Future Opportunities in Modeling Clinical Characteristics of Multiple Sclerosis. Front Neurol 2022; 13:884089. [PMID: 35720098 PMCID: PMC9198703 DOI: 10.3389/fneur.2022.884089] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 04/25/2022] [Indexed: 12/15/2022] Open
Abstract
Development of effective treatments requires understanding of disease mechanisms. For diseases of the central nervous system (CNS), such as multiple sclerosis (MS), human pathology studies and animal models tend to identify candidate disease mechanisms. However, these studies cannot easily link the identified processes to clinical outcomes, such as MS severity, required for causality assessment of candidate mechanisms. Technological advances now allow the generation of thousands of biomarkers in living human subjects, derived from genes, transcripts, medical images, and proteins or metabolites in biological fluids. These biomarkers can be assembled into computational models of clinical value, provided such models are generalizable. Reproducibility of models increases with the technical rigor of the study design, such as blinding, control implementation, the use of large cohorts that encompass the entire spectrum of disease phenotypes and, most importantly, model validation in independent cohort(s). To facilitate the growth of this important research area, we performed a meta-analysis of publications (n = 302) that model MS clinical outcomes extracting effect sizes, while also scoring the technical quality of the study design using predefined criteria. Finally, we generated a Shiny-App-based website that allows dynamic exploration of the data by selective filtering. On average, the published studies fulfilled only one of the seven criteria of study design rigor. Only 15.2% of the studies used any validation strategy, and only 8% used the gold standard of independent cohort validation. Many studies also used small cohorts, e.g., for magnetic resonance imaging (MRI) and blood biomarker predictors, the median sample size was <100 subjects. We observed inverse relationships between reported effect sizes and the number of study design criteria fulfilled, expanding analogous reports from non-MS fields, that studies that fail to limit bias overestimate effect sizes. In conclusion, the presented meta-analysis represents a useful tool for researchers, reviewers, and funders to improve the design of future modeling studies in MS and to easily compare new studies with the published literature. We expect that this will accelerate research in this important area, leading to the development of robust models with proven clinical value.
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Affiliation(s)
| | | | - Bibiana Bielekova
- Neuroimmunological Diseases Section (NDS), National Institute of Allergy and Infectious Diseases (NIAID) of the National Institutes of Health (NIH), Bethesda, MD, United States
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30
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Niu Y, Sun J, Wang B, Yang Y, Wen X, Xiang J. Trajectories of brain entropy across lifetime estimated by resting state functional magnetic resonance imaging. Hum Brain Mapp 2022; 43:4359-4369. [PMID: 35615859 PMCID: PMC9435012 DOI: 10.1002/hbm.25959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 04/07/2022] [Accepted: 04/10/2022] [Indexed: 11/25/2022] Open
Abstract
The human brain is a complex system of interconnected brain regions that form functional networks with differing roles in cognition and behavior. However, the trajectories of these functional networks across development are unclear and designing a metric to track the complex trajectory of these characteristics throughout the lifespan is challenging. Here, permutation entropy (PE) was used to examine age‐related variations in functional magnetic resonance imaging (fMRI) in healthy subjects aged 6–85 from global, network, and nodal perspectives. The global PE followed an inverted U‐shaped trajectory that peaked at approximately age 40. The trajectory of the motor and somatosensory functional network was more consistent with a linear model and increased with age; other functional networks showed inverted U‐shaped trajectories that peaked between 25 and 52 years of age. All nodes showed inverted U‐shaped trajectories. Using cluster analysis, the peak ages of nodes were grouped into three clusters (at 24, 38, and 51 years). Overall, we characterized four aging trajectories: networks with a linear increase, early peak age, intermediate peak age, and older peak age. These findings suggest possible complexity in trajectories at critical age points regarding changes in related functional brain networks.
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Affiliation(s)
- Yan Niu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Sun
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yanli Yang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Xin Wen
- College of Software, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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31
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De Stefano N, Battaglini M, Pareto D, Cortese R, Zhang J, Oesingmann N, Prados F, Rocca MA, Valsasina P, Vrenken H, Gandini Wheeler-Kingshott CAM, Filippi M, Barkhof F, Rovira À. MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies. Neuroimage Clin 2022; 34:102972. [PMID: 35245791 PMCID: PMC8892169 DOI: 10.1016/j.nicl.2022.102972] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 02/22/2022] [Accepted: 02/23/2022] [Indexed: 11/24/2022]
Abstract
Sharing data from cooperative studies is essential to develop new biomarkers in MS. Differences in MRI acquisition, analysis, storage represent a substantial constraint. We review the state of the art and developments in the harmonization of MRI. We provide recommendations to harmonize large MRI datasets in the MS field.
There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources.
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Affiliation(s)
- Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Rosa Cortese
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Jian Zhang
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | | | - Ferran Prados
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, United Kingdom; e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Hugo Vrenken
- Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Brain MRI 3T Research Center, C. Mondino National Neurological Institute, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy; Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Frederik Barkhof
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, United Kingdom; Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
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32
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Treit S, Stolz E, Rickard JN, McCreary CR, Bagshawe M, Frayne R, Lebel C, Emery D, Beaulieu C. Lifespan Volume Trajectories From Non–harmonized T1–Weighted MRI Do Not Differ After Site Correction Based on Traveling Human Phantoms. Front Neurol 2022; 13:826564. [PMID: 35614930 PMCID: PMC9124864 DOI: 10.3389/fneur.2022.826564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/31/2022] [Indexed: 11/18/2022] Open
Abstract
Multi–site imaging consortiums strive to increase participant numbers by pooling data across sites, but scanner related differences can bias results. This study combines data from three research MRI centers, including three different scanner models from two vendors, to examine non–harmonized T1–weighted brain imaging protocols in two cohorts. First, 23 human traveling phantoms were scanned twice each at all three sites (six scans per person; 138 scans total) to quantify within–participant variability of brain volumes (total brain, white matter, gray matter, lateral ventricles, thalamus, caudate, putamen and globus pallidus), and to calculate site–specific correction factors for each structure. Sample size calculations were used to determine the number of traveling phantoms needed to achieve effect sizes for observed differences to help guide future studies. Next, cross–sectional lifespan volume trajectories were examined in 856 healthy participants (5—91 years of age) scanned at these sites. Cross–sectional trajectories of volume versus age for each structure were then compared before and after application of traveling phantom based site–specific correction factors, as well as correction using the open–source method ComBat. Although small systematic differences between sites were observed in the traveling phantom analysis, correction for site using either method had little impact on the lifespan trajectories. Only white matter had small but significant differences in the intercept parameter after ComBat correction (but not traveling phantom based correction), while no other fits differed. This suggests that age–related changes over the lifespan outweigh systematic differences between scanners for volumetric analysis. This work will help guide pooling of multisite datasets as well as meta–analyses of data from non–harmonized protocols.
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Affiliation(s)
- Sarah Treit
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
- *Correspondence: Sarah Treit
| | - Emily Stolz
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
| | - Julia N. Rickard
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
| | - Cheryl R. McCreary
- Departments of Radiology and Clinical Neurosciences, Foothills Medical Centre, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Mercedes Bagshawe
- Department of Radiology, Alberta Children's Hospital, University of Calgary, Calgary, AB, Canada
| | - Richard Frayne
- Departments of Radiology and Clinical Neurosciences, Foothills Medical Centre, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Catherine Lebel
- Department of Radiology, Alberta Children's Hospital, University of Calgary, Calgary, AB, Canada
| | - Derek Emery
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
| | - Christian Beaulieu
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
- Christian Beaulieu
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33
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Mouches P, Wilms M, Rajashekar D, Langner S, Forkert ND. Multimodal biological brain age prediction using magnetic resonance imaging and angiography with the identification of predictive regions. Hum Brain Mapp 2022; 43:2554-2566. [PMID: 35138012 PMCID: PMC9057090 DOI: 10.1002/hbm.25805] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 01/24/2022] [Accepted: 01/25/2022] [Indexed: 02/06/2023] Open
Abstract
Biological brain age predicted using machine learning models based on high-resolution imaging data has been suggested as a potential biomarker for neurological and cerebrovascular diseases. In this work, we aimed to develop deep learning models to predict the biological brain age using structural magnetic resonance imaging and angiography datasets from a large database of 2074 adults (21-81 years). Since different imaging modalities can provide complementary information, combining them might allow to identify more complex aging patterns, with angiography data, for instance, showing vascular aging effects complementary to the atrophic brain tissue changes seen in T1-weighted MRI sequences. We used saliency maps to investigate the contribution of cortical, subcortical, and arterial structures to the prediction. Our results show that combining T1-weighted and angiography MR data led to a significantly improved brain age prediction accuracy, with a mean absolute error of 3.85 years comparing the predicted and chronological age. The most predictive brain regions included the lateral sulcus, the fourth ventricle, and the amygdala, while the brain arteries contributing the most to the prediction included the basilar artery, the middle cerebral artery M2 segments, and the left posterior cerebral artery. Our study proposes a framework for brain age prediction using multimodal imaging, which gives accurate predictions and allows identifying the most predictive regions for this task, which can serve as a surrogate for the brain regions that are most affected by aging.
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Affiliation(s)
- Pauline Mouches
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Biomedical Engineering Program, University of Calgary, Calgary, Alberta, Canada
| | - Matthias Wilms
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Deepthi Rajashekar
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Biomedical Engineering Program, University of Calgary, Calgary, Alberta, Canada
| | - Sönke Langner
- Institute for Diagnostic Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Nils D Forkert
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
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Miletić S, Bazin PL, Isherwood SJS, Keuken MC, Alkemade A, Forstmann BU. Charting human subcortical maturation across the adult lifespan with in vivo 7 T MRI. Neuroimage 2022; 249:118872. [PMID: 34999202 DOI: 10.1016/j.neuroimage.2022.118872] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 12/20/2021] [Accepted: 01/03/2022] [Indexed: 12/26/2022] Open
Abstract
The human subcortex comprises hundreds of unique structures. Subcortical functioning is crucial for behavior, and disrupted function is observed in common neurodegenerative diseases. Despite their importance, human subcortical structures continue to be difficult to study in vivo. Here we provide a detailed account of 17 prominent subcortical structures and ventricles, describing their approximate iron and myelin contents, morphometry, and their age-related changes across the normal adult lifespan. The results provide compelling insights into the heterogeneity and intricate age-related alterations of these structures. They also show that the locations of many structures shift across the lifespan, which is of direct relevance for the use of standard magnetic resonance imaging atlases. The results further our understanding of subcortical morphometry and neuroimaging properties, and of normal aging processes which ultimately can improve our understanding of neurodegeneration.
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Affiliation(s)
- Steven Miletić
- University of Amsterdam, Department of Psychology, Integrative Model-based Cognitive Neuroscience research unit (IMCN), Nieuwe Achtergracht 129B, Amsterdam 1001 NK, the Netherlands.
| | - Pierre-Louis Bazin
- University of Amsterdam, Department of Psychology, Integrative Model-based Cognitive Neuroscience research unit (IMCN), Nieuwe Achtergracht 129B, Amsterdam 1001 NK, the Netherlands; Max Planck Institute for Human Cognitive and Brain Sciences, Departments of Neurophysics and Neurology, Stephanstraße 1A, Leipzig, Germany
| | - Scott J S Isherwood
- University of Amsterdam, Department of Psychology, Integrative Model-based Cognitive Neuroscience research unit (IMCN), Nieuwe Achtergracht 129B, Amsterdam 1001 NK, the Netherlands
| | - Max C Keuken
- University of Amsterdam, Department of Psychology, Integrative Model-based Cognitive Neuroscience research unit (IMCN), Nieuwe Achtergracht 129B, Amsterdam 1001 NK, the Netherlands
| | - Anneke Alkemade
- University of Amsterdam, Department of Psychology, Integrative Model-based Cognitive Neuroscience research unit (IMCN), Nieuwe Achtergracht 129B, Amsterdam 1001 NK, the Netherlands
| | - Birte U Forstmann
- University of Amsterdam, Department of Psychology, Integrative Model-based Cognitive Neuroscience research unit (IMCN), Nieuwe Achtergracht 129B, Amsterdam 1001 NK, the Netherlands.
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35
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Drouin SM, McFall GP, Potvin O, Bellec P, Masellis M, Duchesne S, Dixon RA. Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes. J Alzheimers Dis 2022; 88:97-115. [PMID: 35570482 PMCID: PMC9277685 DOI: 10.3233/jad-215289] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/11/2022] [Indexed: 11/15/2022]
Abstract
BACKGROUND Hippocampal atrophy is a well-known biomarker of neurodegeneration, such as that observed in Alzheimer's disease (AD). Although distributions of hippocampal volume trajectories for asymptomatic individuals often reveal substantial heterogeneity, it is unclear whether interpretable trajectory classes can be objectively detected and used for prediction analyses. OBJECTIVE To detect and predict hippocampal trajectory classes in a computationally competitive context using established AD-related risk factors/biomarkers. METHODS We used biomarker/risk factor and longitudinal MRI data in asymptomatic adults from the AD Neuroimaging Initiative (n = 351; Mean = 75 years; 48.7% female). First, we applied latent class growth analyses to left (LHC) and right (RHC) hippocampal trajectory distributions to identify distinct classes. Second, using random forest analyses, we tested 38 multi-modal biomarkers/risk factors for their relative importance in discriminating the lower (potentially elevated atrophy risk) from the higher (potentially reduced risk) class. RESULTS For both LHC and RHC trajectory distribution analyses, we observed three distinct trajectory classes. Three biomarkers/risk factors predicted membership in LHC and RHC lower classes: male sex, higher education, and lower plasma Aβ1-42. Four additional factors selectively predicted membership in the lower LHC class: lower plasma tau and Aβ1-40, higher depressive symptomology, and lower body mass index. CONCLUSION Data-driven analyses of LHC and RHC trajectories detected three classes underlying the heterogeneous distributions. Machine learning analyses determined three common and four unique biomarkers/risk factors discriminating the higher and lower LHC/RHC classes. Our sequential analytic approach produced evidence that the dynamics of preclinical hippocampal trajectories can be predicted by AD-related biomarkers/risk factors from multiple modalities.
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Affiliation(s)
- Shannon M. Drouin
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
| | - G. Peggy McFall
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| | | | - Pierre Bellec
- Département de Psychologie, Université de Montréal, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada
| | - Mario Masellis
- Hurvitz Brain Sciences Research Program, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Medicine (Neurology), University of Toronto, Toronto, ON, Canada
| | - Simon Duchesne
- CERVO Brain Research Centre, Quebec, QC, Canada
- Radiology and Nuclear Medicine Department, Université Laval, Quebec, QC, Canada
| | - Roger A. Dixon
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
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36
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Reserve and Maintenance in the Aging Brain: A Longitudinal Study of Healthy Older Adults. eNeuro 2022; 9:ENEURO.0455-21.2022. [PMID: 35045976 PMCID: PMC8856699 DOI: 10.1523/eneuro.0455-21.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/22/2021] [Accepted: 01/03/2022] [Indexed: 11/24/2022] Open
Abstract
The aging brain undergoes structural changes even in very healthy individuals. Quantifying these changes could help disentangle pathologic changes from those associated with the normal human aging process. Using longitudinal magnetic resonance imaging (MRI) data from 227 carefully selected healthy human cohort with age ranging from 50 to 80 years old at baseline scan, we quantified age-related volumetric changes in the brain of healthy human older adults. Longitudinally, the rates of tissue loss in total gray matter (GM) and white matter (WM) were 2497.5 and 2579.8 mm3 per year, respectively. Across the whole brain, the rates of GM decline varied with regions in the frontal and parietal lobes having faster rates of decline, whereas some regions in the occipital and temporal lobes appeared relatively preserved. In contrast, cross-sectional changes were mainly observed in the temporal-occipital regions. Similar longitudinal atrophic changes were also observed in subcortical regions including thalamus, hippocampus, putamen, and caudate, whereas the pallidum showed an increasing volume with age. Overall, regions maturing late in development (frontal, parietal) are more vulnerable to longitudinal decline, whereas those that fully mature in the early stage (temporal, occipital) are mainly affected by cross-sectional changes in healthy older cohort. This may suggest that, for a successful healthy aging, the former needs to be maximally developed at an earlier age to compensate for the longitudinal decline later in life and the latter to remain relatively preserved even in old age, consistent with both concepts of reserve and brain maintenance.
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37
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Fraser MA, Walsh EI, Shaw ME, Anstey KJ, Cherbuin N. Longitudinal Effects of Physical Activity Change on Hippocampal Volumes over up to 12 Years in Middle and Older Age Community-Dwelling Individuals. Cereb Cortex 2021; 32:2705-2716. [PMID: 34671805 DOI: 10.1093/cercor/bhab375] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/11/2021] [Accepted: 09/12/2021] [Indexed: 12/17/2022] Open
Abstract
The objectives of this study were to investigate the long-term associations between changes in physical activity levels and hippocampal volumes over time, while considering the influence of age, sex, and APOE-ε4 genotype. We investigated the effects of change in physical activity on hippocampal volumes in 411 middle age (mean age = 47.2 years) and 375 older age (mean age = 63.1 years) adults followed up to 12 years. An annual volume decrease was observed in the left (middle age: 0.46%; older age: 0.51%) but not in the right hippocampus. Each additional 10 metabolic equivalents (METs, ~2 h of moderate exercise) increase in weekly physical activity was associated with 0.33% larger hippocampal volume in middle age (equivalent to ~1 year of typical aging). In older age, each additional MET was associated with 0.05% larger hippocampal volume; however, the effects declined with time by 0.005% per year. For older age APOE-ε4 carriers, each additional MET was associated with a 0.10% increase in hippocampal volume. No sex effects of physical activity change were found. Increasing physical activity has long-term positive effects on hippocampal volumes and appears especially beneficial for older APOE-ε4 carriers. To optimize healthy brain aging, physical activity programs should focus on creating long-term exercise habits.
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Affiliation(s)
- Mark A Fraser
- Centre for Research on Ageing, Health and Wellbeing, Research School of Population Health, Australian National University, Canberra, Australian Capital Territory 2601, Australia
| | - Erin I Walsh
- Centre for Research on Ageing, Health and Wellbeing, Research School of Population Health, Australian National University, Canberra, Australian Capital Territory 2601, Australia.,Population Health Exchange, Research School of Population Health, Australian National University, Canberra, Australian Capital Territory 2601, Australia
| | - Marnie E Shaw
- ANU College of Engineering & Computer Science, Australian National University, Canberra, Australian Capital Territory 2600, Australia
| | - Kaarin J Anstey
- Centre for Research on Ageing, Health and Wellbeing, Research School of Population Health, Australian National University, Canberra, Australian Capital Territory 2601, Australia.,Ageing Futures Institute, University of New South Wales, Sydney, New South Wales 2052, Australia.,Neuroscience Research Australia, Sydney, New South Wales 2031, Australia
| | - Nicolas Cherbuin
- Centre for Research on Ageing, Health and Wellbeing, Research School of Population Health, Australian National University, Canberra, Australian Capital Territory 2601, Australia
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38
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A rare familial rearrangement of chromosomes 9 and 15 associated with intellectual disability: a clinical and molecular study. Mol Cytogenet 2021; 14:47. [PMID: 34607577 PMCID: PMC8489072 DOI: 10.1186/s13039-021-00565-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 03/09/2021] [Indexed: 11/22/2022] Open
Abstract
Background There are many reports on rearrangements occurring separately in the regions of chromosomes 9p and 15q affected in the case under study. 15q duplication syndrome is caused by the presence of at least one extra maternally derived copy of the Prader–Willi/Angelman critical region. Trisomy 9p is the fourth most frequent chromosome anomaly with a clinically recognizable syndrome often accompanied by intellectual disability. Here we report a new case of a patient with maternally derived unique complex sSMC resulting in partial trisomy of both chromosomes 9 and 15 associated with intellectual disability. Case presentation We characterise a supernumerary derivative chromosome 15: 47,XY,+der(15)t(9;15)(p21.2;q13.2), likely resulting from 3:1 malsegregation during maternal gametogenesis. Chromosomal analysis showed that a phenotypically normal mother is a carrier of balanced translocation t(9;15)(p21.1;q13.2). Her 7-year-old son showed signs of intellectual disability and a number of physical abnormalities including bilateral cryptorchidism and congenital megaureter. The child’s magnetic resonance imaging showed changes in brain volume and in structural and functional connectivity revealing phenotypic changes caused by the presence of the extra chromosome material, whereas the mother’s brain MRI was normal. Sequence analyses of the microdissected der(15) chromosome detected two breakpoint regions: HSA9:25,928,021-26,157,441 (9p21.2 band) and HSA15:30,552,104-30,765,905 (15q13.2 band). The breakpoint region on chromosome HSA9 is poor in genetic features with several areas of high homology with the breakpoint region on chromosome 15. The breakpoint region on HSA15 is located in the area of a large segmental duplication. Conclusions We discuss the case of these phenotypic and brain MRI features in light of reported signatures for 9p partial trisomy and 15 duplication syndromes and analyze how the genomic characteristics of the found breakpoint regions have contributed to the origin of the derivative chromosome. We recommend MRI for all patients with a developmental delay, especially in cases with identified rearrangements, to accumulate more information on brain phenotypes related to chromosomal syndromes. Supplementary Information The online version contains supplementary material available at 10.1186/s13039-021-00565-y.
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39
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MacDonald ME, Pike GB. MRI of healthy brain aging: A review. NMR IN BIOMEDICINE 2021; 34:e4564. [PMID: 34096114 DOI: 10.1002/nbm.4564] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 05/08/2021] [Accepted: 05/11/2021] [Indexed: 06/12/2023]
Abstract
We present a review of the characterization of healthy brain aging using MRI with an emphasis on morphology, lesions, and quantitative MR parameters. A scope review found 6612 articles encompassing the keywords "Brain Aging" and "Magnetic Resonance"; papers involving functional MRI or not involving imaging of healthy human brain aging were discarded, leaving 2246 articles. We first consider some of the biogerontological mechanisms of aging, and the consequences of aging in terms of cognition and onset of disease. Morphological changes with aging are reviewed for the whole brain, cerebral cortex, white matter, subcortical gray matter, and other individual structures. In general, volume and cortical thickness decline with age, beginning in mid-life. Prevalent silent lesions such as white matter hyperintensities, microbleeds, and lacunar infarcts are also observed with increasing frequency. The literature regarding quantitative MR parameter changes includes T1 , T2 , T2 *, magnetic susceptibility, spectroscopy, magnetization transfer, diffusion, and blood flow. We summarize the findings on how each of these parameters varies with aging. Finally, we examine how the aforementioned techniques have been used for age prediction. While relatively large in scope, we present a comprehensive review that should provide the reader with sound understanding of what MRI has been able to tell us about how the healthy brain ages.
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Affiliation(s)
- M Ethan MacDonald
- Department of Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada
- Departments of Radiology and Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
- Healthy Brain Aging Laboratory, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - G Bruce Pike
- Departments of Radiology and Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
- Healthy Brain Aging Laboratory, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
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40
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Bouchard AE, Dickler M, Renauld E, Lenglos C, Ferland F, Rouillard C, Leblond J, Fecteau S. Brain morphometry in adults with gambling disorder. J Psychiatr Res 2021; 141:66-73. [PMID: 34175744 DOI: 10.1016/j.jpsychires.2021.06.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 05/20/2021] [Accepted: 06/15/2021] [Indexed: 10/21/2022]
Abstract
Little is known regarding the brain substrates of Gambling Disorder, including surface brain morphometry, and whether these are linked to the clinical profile. A better understanding of the brain substrates will likely help determine targets to treat patients. Hence, the aim of this study was two-fold, that is to examine surface-based morphometry in 17 patients with gambling disorder as compared to norms of healthy individuals (2713 and 2790 subjects for cortical and subcortical anatomical scans, respectively) and to assess the clinical relevance of morphometry in patients with Gambling Disorder. This study measured brain volume, surface and thickness in Gambling Disorder. We compared these measures to those of a normative database that controlled for factors such as age and sex. We also tested for correlations with gambling-related behaviors, such as gambling severity and duration, impulsivity, and depressive symptoms (assessed using the South Oaks Gambling Screen, years of gambling, Barratt Impulsiveness Scale, and Beck Depression Inventory, respectively). Patients displayed thinner prefrontal and parietal cortices, greater volume and thickness of the occipital and the entorhinal cortices, and greater volume of subcortical regions as compared to the norms of healthy individuals. There were positive correlations between surface area of occipital regions and depressive symptoms. This work contributes to better characterize the brain substrates of Gambling Disorder, which appear to resemble those of substance use disorders and Internet Gaming Disorder.
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Affiliation(s)
- Amy E Bouchard
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, 2325 rue de l'Université, Quebec City, Quebec, G1V 0A6, Canada; CERVO Brain Research Centre, Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, 2301 avenue D'Estimauville, Quebec City, Quebec, G1E 1T2, Canada.
| | - Maya Dickler
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, 2325 rue de l'Université, Quebec City, Quebec, G1V 0A6, Canada; CERVO Brain Research Centre, Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, 2301 avenue D'Estimauville, Quebec City, Quebec, G1E 1T2, Canada.
| | - Emmanuelle Renauld
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, 2325 rue de l'Université, Quebec City, Quebec, G1V 0A6, Canada; CERVO Brain Research Centre, Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, 2301 avenue D'Estimauville, Quebec City, Quebec, G1E 1T2, Canada.
| | - Christophe Lenglos
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, 2325 rue de l'Université, Quebec City, Quebec, G1V 0A6, Canada; CERVO Brain Research Centre, Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, 2301 avenue D'Estimauville, Quebec City, Quebec, G1E 1T2, Canada.
| | - Francine Ferland
- Centre de réadaptation en dépendance du CIUSSS de la Capitale-Nationale, 2525 chemin de la Canardière, Quebec City, Quebec, G1J 2G3, Canada.
| | - Claude Rouillard
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, 2325 rue de l'Université, Quebec City, Quebec, G1V 0A6, Canada; Axe Neurosciences, Centre de recherche du CHU de Québec, 2705 boul. Laurier, Quebec City, Quebec, G1V 4G2, Canada.
| | - Jean Leblond
- Centre interdisciplinaire de recherche en réadaptation et intégration sociale, 525 boul. Wilfrid-Hamel, Quebec City, Quebec, G1M 2S8, Canada.
| | - Shirley Fecteau
- Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, 2325 rue de l'Université, Quebec City, Quebec, G1V 0A6, Canada; CERVO Brain Research Centre, Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, 2301 avenue D'Estimauville, Quebec City, Quebec, G1E 1T2, Canada.
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Choi YY, Lee JJ, Choi KY, Choi US, Seo EH, Choo IH, Kim H, Song MK, Choi SM, Cho SH, Choe Y, Kim BC, Lee KH. Multi-Racial Normative Data for Lobar and Subcortical Brain Volumes in Old Age: Korean and Caucasian Norms May Be Incompatible With Each Other †. Front Aging Neurosci 2021; 13:675016. [PMID: 34413763 PMCID: PMC8369368 DOI: 10.3389/fnagi.2021.675016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 06/15/2021] [Indexed: 11/17/2022] Open
Abstract
Brain aging is becoming an increasingly important topic, and the norms of brain structures are essential for diagnosing neurodegenerative diseases. However, previous studies of the aging brain have mostly focused on Caucasians, not East Asians. The aim of this paper was to examine ethnic differences in the aging process of brain structures or to determine to what extent ethnicity affects the normative values of lobar and subcortical volumes in clinically normal elderly and the diagnosis in multi-racial patients with Alzheimer's disease (AD). Lobar and subcortical volumes were measured using FreeSurfer from MRI data of 1,686 normal Koreans (age range 59–89) and 851 Caucasian, non-Hispanic subjects in the ADNI and OASIS datasets. The regression models were designed to predict brain volumes, including ethnicity, age, sex, intracranial volume (ICV), magnetic field strength (MFS), and MRI scanner manufacturers as independent variables. Ethnicity had a significant effect for all lobar (|β| > 0.20, p < 0.001) and subcortical regions (|β| > 0.08, p < 0.001) except left pallidus and bilateral ventricles. To demonstrate the validity of the z-score for AD diagnosis, 420 patients and 420 normal controls were selected evenly from the Korean and Caucasian datasets. The four validation groups divided by race and diagnosis were matched on age and sex using a propensity score matching. We analyzed whether and to what extent the ethnicity adjustment improved the diagnostic power of the logistic regression model that was built using the only z-scores of six regions: bilateral temporal cortices, hippocampi, and amygdalae. The performance of the classifier after ethnicity adjustment was significantly improved compared with the classifier before ethnicity adjustment (ΔAUC = 0.10, D = 7.80, p < 0.001; AUC comparison test using bootstrap). Korean AD dementia patients may not be classified by Caucasian norms of brain volumes because the brain regions vulnerable to AD dementia are bigger in normal Korean elderly peoples. Therefore, ethnicity is an essential factor in establishing normative data for regional volumes in brain aging and applying it to the diagnosis of neurodegenerative diseases.
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Affiliation(s)
- Yu Yong Choi
- Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center, Chosun University, Gwangju, South Korea.,Biomedical Technology Center, Chosun University Hospital, Gwangju, South Korea
| | - Jang Jae Lee
- Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center, Chosun University, Gwangju, South Korea
| | - Kyu Yeong Choi
- Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center, Chosun University, Gwangju, South Korea
| | - Uk-Su Choi
- Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center, Chosun University, Gwangju, South Korea
| | - Eun Hyun Seo
- Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center, Chosun University, Gwangju, South Korea
| | - Il Han Choo
- Department of Neuropsychiatry, Chosun University School of Medicine and Hospital, Gwangju, South Korea
| | - Hoowon Kim
- Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center, Chosun University, Gwangju, South Korea.,Biomedical Technology Center, Chosun University Hospital, Gwangju, South Korea.,Department of Neurology, Chosun University School of Medicine and Hospital, Gwangju, South Korea
| | - Min-Kyung Song
- Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, South Korea
| | - Seong-Min Choi
- Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, South Korea
| | - Soo Hyun Cho
- Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, South Korea
| | | | - Byeong C Kim
- Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, South Korea
| | - Kun Ho Lee
- Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center, Chosun University, Gwangju, South Korea.,Korea Brain Research Institute, Daegu, South Korea.,Department of Biomedical Science, Chosun University, Gwangju, South Korea.,Neurozen Inc., Seoul, South Korea
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Iglesias JE, Billot B, Balbastre Y, Tabari A, Conklin J, Gilberto González R, Alexander DC, Golland P, Edlow BL, Fischl B. Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast. Neuroimage 2021; 237:118206. [PMID: 34048902 PMCID: PMC8354427 DOI: 10.1016/j.neuroimage.2021.118206] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 05/20/2021] [Accepted: 05/24/2021] [Indexed: 12/14/2022] Open
Abstract
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well-typically requiring T1-weighted images (e.g., MP-RAGE scans). This limitation prevents the analysis of millions of MRI scans acquired with large inter-slice spacing in clinical settings every year. In turn, the inability to quantitatively analyze these scans hinders the adoption of quantitative neuro imaging in healthcare, and also precludes research studies that could attain huge sample sizes and hence greatly improve our understanding of the human brain. Recent advances in convolutional neural networks (CNNs) are producing outstanding results in super-resolution and contrast synthesis of MRI. However, these approaches are very sensitive to the specific combination of contrast, resolution and orientation of the input images, and thus do not generalize to diverse clinical acquisition protocols - even within sites. In this article, we present SynthSR, a method to train a CNN that receives one or more scans with spaced slices, acquired with different contrast, resolution and orientation, and produces an isotropic scan of canonical contrast (typically a 1 mm MP-RAGE). The presented method does not require any preprocessing, beyond rigid coregistration of the input scans. Crucially, SynthSR trains on synthetic input images generated from 3D segmentations, and can thus be used to train CNNs for any combination of contrasts, resolutions and orientations without high-resolution real images of the input contrasts. We test the images generated with SynthSR in an array of common downstream analyses, and show that they can be reliably used for subcortical segmentation and volumetry, image registration (e.g., for tensor-based morphometry), and, if some image quality requirements are met, even cortical thickness morphometry. The source code is publicly available at https://github.com/BBillot/SynthSR.
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Affiliation(s)
- Juan Eugenio Iglesias
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, USA.
| | - Benjamin Billot
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Yaël Balbastre
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Azadeh Tabari
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Department of Radiology, Massachusetts General Hospital, Boston, USA
| | - John Conklin
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Department of Radiology, Massachusetts General Hospital, Boston, USA
| | - R Gilberto González
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Neuroradiology Division, Massachusetts General Hospital, Boston, USA
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, USA
| | - Brian L Edlow
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA
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De Francesco S, Galluzzi S, Vanacore N, Festari C, Rossini PM, Cappa SF, Frisoni GB, Redolfi A. Norms for Automatic Estimation of Hippocampal Atrophy and a Step Forward for Applicability to the Italian Population. Front Neurosci 2021; 15:656808. [PMID: 34262425 PMCID: PMC8273578 DOI: 10.3389/fnins.2021.656808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 06/03/2021] [Indexed: 11/23/2022] Open
Abstract
INTRODUCTION Hippocampal volume is one of the main biomarkers of Alzheimer's Dementia (AD). Over the years, advanced tools that performed automatic segmentation of Magnetic Resonance Imaging (MRI) T13D scans have been developed, such as FreeSurfer (FS) and ACM-Adaboost (AA). Hippocampal volume is considered abnormal when it is below the 5th percentile of the normative population. The aim of this study was to set norms, established from the Alzheimer's Disease Neuroimaging Initiative (ADNI) population, for hippocampal volume measured with FS v.6.0 and AA tools in the neuGRID platform (www.neugrid2.eu) and demonstrate their applicability for the Italian population. METHODS Norms were set from a large group of 545 healthy controls belonging to ADNI. For each pipeline, subjects with segmentation errors were discarded, resulting in 532 valid segmentations for FS and 421 for AA (age range 56-90 years). The comparability of ADNI and the Italian Brain Normative Archive (IBNA), representative of the Italian general population, was assessed testing clinical variables, neuropsychological scores and normalized hippocampal volumes. Finally, percentiles were validated using the Italian Alzheimer's disease Repository Without Borders (ARWiBo) as external independent data set to evaluate FS and AA generalizability. RESULTS Hippocampal percentiles were checked with the chi-square goodness of fit test. P-values were not significant, showing that FS and AA algorithm distributions fitted the data well. Clinical, neuropsychological and volumetric features were similar in ADNI and IBNA (p > 0.01). Hippocampal volumes measured with both FS and AA were associated with age (p < 0.001). The 5th percentile thresholds, indicating left/right hippocampal atrophy were respectively: (i) below 3,223/3,456 mm3 at 56 years and 2,506/2,415 mm3 at 90 years for FS; (ii) below 4,583/4,873 mm3 at 56 years and 3,831/3,870 mm3 at 90 years for AA. The average volumes computed on 100 cognitively intact healthy controls (CN) selected from ARWiBo were close to the 50th percentiles, while those for 100 AD patients were close to the abnormal percentiles. DISCUSSION Norms generated from ADNI through the automatic FS and AA segmentation tools may be used as normative references for Italian patients with suspected AD.
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Affiliation(s)
- Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Samantha Galluzzi
- Laboratory of Alzheimer’s Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Nicola Vanacore
- National Center for Disease Prevention and Health Promotion, National Institute of Health, Rome, Italy
| | - Cristina Festari
- Laboratory of Alzheimer’s Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Paolo Maria Rossini
- Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
| | - Stefano F. Cappa
- IRCCS Mondino Foundation, Pavia, Italy
- IUSS Cognitive Neuroscience (ICoN) Center, University School for Advanced Studies, Pavia, Italy
| | - Giovanni B. Frisoni
- Laboratory of Alzheimer’s Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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44
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Dadar M, Potvin O, Camicioli R, Duchesne S. Beware of white matter hyperintensities causing systematic errors in FreeSurfer gray matter segmentations! Hum Brain Mapp 2021; 42:2734-2745. [PMID: 33783933 PMCID: PMC8127151 DOI: 10.1002/hbm.25398] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 02/19/2021] [Accepted: 02/19/2021] [Indexed: 12/11/2022] Open
Abstract
Volumetric estimates of subcortical and cortical structures, extracted from T1-weighted MRIs, are widely used in many clinical and research applications. Here, we investigate the impact of the presence of white matter hyperintensities (WMHs) on FreeSurfer gray matter (GM) structure volumes and its possible bias on functional relationships. T1-weighted images from 1,077 participants (4,321 timepoints) from the Alzheimer's Disease Neuroimaging Initiative were processed with FreeSurfer version 6.0.0. WMHs were segmented using a previously validated algorithm on either T2-weighted or Fluid-attenuated inversion recovery images. Mixed-effects models were used to assess the relationships between overlapping WMHs and GM structure volumes and overall WMH burden, as well as to investigate whether such overlaps impact associations with age, diagnosis, and cognitive performance. Participants with higher WMH volumes had higher overlaps with GM volumes of bilateral caudate, cerebral cortex, putamen, thalamus, pallidum, and accumbens areas (p < .0001). When not corrected for WMHs, caudate volumes increased with age (p < .0001) and were not different between cognitively healthy individuals and age-matched probable Alzheimer's disease patients. After correcting for WMHs, caudate volumes decreased with age (p < .0001), and Alzheimer's disease patients had lower caudate volumes than cognitively healthy individuals (p < .01). Uncorrected caudate volume was not associated with ADAS13 scores, whereas corrected lower caudate volumes were significantly associated with poorer cognitive performance (p < .0001). Presence of WMHs leads to systematic inaccuracies in GM segmentations, particularly for the caudate, which can also change clinical associations. While specifically measured for the Freesurfer toolkit, this problem likely affects other algorithms.
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Affiliation(s)
- Mahsa Dadar
- CERVO Brain Research CenterCentre intégré universitaire santé et services sociaux de la Capitale NationaleQuébecQuebecCanada
| | - Olivier Potvin
- CERVO Brain Research CenterCentre intégré universitaire santé et services sociaux de la Capitale NationaleQuébecQuebecCanada
| | - Richard Camicioli
- Department of Medicine, Division of NeurologyUniversity of AlbertaEdmontonAlbertaCanada
| | - Simon Duchesne
- CERVO Brain Research CenterCentre intégré universitaire santé et services sociaux de la Capitale NationaleQuébecQuebecCanada
- Department of Radiology and Nuclear Medicine, Faculty of MedicineUniversité LavalQuébecQuebecCanada
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45
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Schwarz AJ. The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders. Neurotherapeutics 2021; 18:686-708. [PMID: 33846962 PMCID: PMC8423963 DOI: 10.1007/s13311-021-01027-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2021] [Indexed: 12/11/2022] Open
Abstract
Imaging biomarkers play a wide-ranging role in clinical trials for neurological disorders. This includes selecting the appropriate trial participants, establishing target engagement and mechanism-related pharmacodynamic effect, monitoring safety, and providing evidence of disease modification. In the early stages of clinical drug development, evidence of target engagement and/or downstream pharmacodynamic effect-especially with a clear relationship to dose-can provide confidence that the therapeutic candidate should be advanced to larger and more expensive trials, and can inform the selection of the dose(s) to be further tested, i.e., to "de-risk" the drug development program. In these later-phase trials, evidence that the therapeutic candidate is altering disease-related biomarkers can provide important evidence that the clinical benefit of the compound (if observed) is grounded in meaningful biological changes. The interpretation of disease-related imaging markers, and comparability across different trials and imaging tools, is greatly improved when standardized outcome measures are defined. This standardization should not impinge on scientific advances in the imaging tools per se but provides a common language in which the results generated by these tools are expressed. PET markers of pathological protein aggregates and structural imaging of brain atrophy are common disease-related elements across many neurological disorders. However, PET tracers for pathologies beyond amyloid β and tau are needed, and the interpretability of structural imaging can be enhanced by some simple considerations to guard against the possible confound of pseudo-atrophy. Learnings from much-studied conditions such as Alzheimer's disease and multiple sclerosis will be beneficial as the field embraces rarer diseases.
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Affiliation(s)
- Adam J Schwarz
- Takeda Pharmaceuticals Ltd., 40 Landsdowne Street, Cambridge, MA, 02139, USA.
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46
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Dump the "dimorphism": Comprehensive synthesis of human brain studies reveals few male-female differences beyond size. Neurosci Biobehav Rev 2021; 125:667-697. [PMID: 33621637 DOI: 10.1016/j.neubiorev.2021.02.026] [Citation(s) in RCA: 148] [Impact Index Per Article: 49.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 01/01/2021] [Accepted: 02/16/2021] [Indexed: 12/21/2022]
Abstract
With the explosion of neuroimaging, differences between male and female brains have been exhaustively analyzed. Here we synthesize three decades of human MRI and postmortem data, emphasizing meta-analyses and other large studies, which collectively reveal few reliable sex/gender differences and a history of unreplicated claims. Males' brains are larger than females' from birth, stabilizing around 11 % in adults. This size difference accounts for other reproducible findings: higher white/gray matter ratio, intra- versus interhemispheric connectivity, and regional cortical and subcortical volumes in males. But when structural and lateralization differences are present independent of size, sex/gender explains only about 1% of total variance. Connectome differences and multivariate sex/gender prediction are largely based on brain size, and perform poorly across diverse populations. Task-based fMRI has especially failed to find reproducible activation differences between men and women in verbal, spatial or emotion processing due to high rates of false discovery. Overall, male/female brain differences appear trivial and population-specific. The human brain is not "sexually dimorphic."
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Alexander R, Aragón OR, Bookwala J, Cherbuin N, Gatt JM, Kahrilas IJ, Kästner N, Lawrence A, Lowe L, Morrison RG, Mueller SC, Nusslock R, Papadelis C, Polnaszek KL, Helene Richter S, Silton RL, Styliadis C. The neuroscience of positive emotions and affect: Implications for cultivating happiness and wellbeing. Neurosci Biobehav Rev 2021; 121:220-249. [PMID: 33307046 DOI: 10.1016/j.neubiorev.2020.12.002] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Revised: 11/10/2020] [Accepted: 12/06/2020] [Indexed: 02/07/2023]
Abstract
This review paper provides an integrative account regarding neurophysiological correlates of positive emotions and affect that cumulatively contribute to the scaffolding for happiness and wellbeing in humans and other animals. This paper reviews the associations among neurotransmitters, hormones, brain networks, and cognitive functions in the context of positive emotions and affect. Consideration of lifespan developmental perspectives are incorporated, and we also examine the impact of healthy social relationships and environmental contexts on the modulation of positive emotions and affect. The neurophysiological processes that implement positive emotions are dynamic and modifiable, and meditative practices as well as flow states that change patterns of brain function and ultimately support wellbeing are also discussed. This review is part of "The Human Affectome Project" (http://neuroqualia.org/background.php), and in order to advance a primary aim of the Human Affectome Project, we also reviewed relevant linguistic dimensions and terminology that characterizes positive emotions and wellbeing. These linguistic dimensions are discussed within the context of the neuroscience literature with the overarching goal of generating novel recommendations for advancing neuroscience research on positive emotions and wellbeing.
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Affiliation(s)
- Rebecca Alexander
- Neuroscience Research Australia, Randwick, Sydney, NSW, 2031, Australia; Australian National University, Canberra, ACT, 2601, Australia
| | - Oriana R Aragón
- Yale University, 2 Hillhouse Ave, New Haven, CT, 06520, USA; Clemson University, 252 Sirrine Hall, Clemson, SC, 29634, USA
| | - Jamila Bookwala
- Department of Psychology and Program in Aging Studies, Lafayette College, 730 High Road, Easton, PA, USA
| | - Nicolas Cherbuin
- Centre for Research on Ageing, Health, and Wellbeing, Australian National University, Canberra, ACT, 2601, Australia
| | - Justine M Gatt
- Neuroscience Research Australia, Randwick, Sydney, NSW, 2031, Australia; School of Psychology, University of New South Wales, Randwick, Sydney, NSW, 2031, Australia
| | - Ian J Kahrilas
- Department of Psychology, Loyola University Chicago, 1032 W. Sheridan Road, Chicago, IL, 60660, USA
| | - Niklas Kästner
- Department of Behavioural Biology, University of Münster, Badestraße 13, 48149, Münster, Germany
| | - Alistair Lawrence
- Scotland's Rural College, King's Buildings, Edinburgh, EH9 3JG, United Kingdom; The Roslin Institute, University of Edinburgh, Easter Bush, EH25 9RG, United Kingdom
| | - Leroy Lowe
- Neuroqualia (NGO), Truro, NS, B2N 1X5, Canada
| | - Robert G Morrison
- Department of Psychology, Loyola University Chicago, 1032 W. Sheridan Road, Chicago, IL, 60660, USA
| | - Sven C Mueller
- Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium; Department of Personality, Psychological Assessment and Treatment, University of Deusto, Bilbao, Spain
| | - Robin Nusslock
- Department of Psychology and Institute for Policy Research, Northwestern University, 2029 Sheridan Road, Evanston, IL, 60208, USA
| | - Christos Papadelis
- Jane and John Justin Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St, Fort Worth, TX, 76104, USA; Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kelly L Polnaszek
- Department of Psychology, Loyola University Chicago, 1032 W. Sheridan Road, Chicago, IL, 60660, USA
| | - S Helene Richter
- Department of Behavioural Biology, University of Münster, Badestraße 13, 48149, Münster, Germany
| | - Rebecca L Silton
- Department of Psychology, Loyola University Chicago, 1032 W. Sheridan Road, Chicago, IL, 60660, USA; Institute for Innovations in Developmental Sciences, Northwestern University, 633 N. Saint Clair, Chicago, IL, 60611, USA.
| | - Charis Styliadis
- Neuroscience of Cognition and Affection group, Lab of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece
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Corriveau‐Lecavalier N, Duchesne S, Gauthier S, Hudon C, Kergoat M, Mellah S, Belleville S. A quadratic function of activation in individuals at risk of Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 12:e12139. [PMID: 33521234 PMCID: PMC7817778 DOI: 10.1002/dad2.12139] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 11/04/2020] [Accepted: 11/06/2020] [Indexed: 01/14/2023]
Abstract
INTRODUCTION Brain activation is hypothesized to form an inverse U-shape in prodromal Alzheimer's disease (AD), with hyperactivation in the early phase, followed by hypoactivation. METHODS Using task-related functional magnetic resonance imaging (fMRI), we tested the inverse U-shape hypothesis with polynomial regressions and between-group comparisons in individuals with subjective cognitive decline plus (SCD+; smaller hippocampal volumes compared to a group of healthy controls without SCD and/or apolipoprotein E [APOE] ε4 allele) or mild cognitive impairment (MCI). RESULTS A quadratic function modeled the relationship between proxies of disease severity (neurodegeneration, memory performance) and left superior parietal activation. Linear negative functions modeled the relationship between neurodegeneration and left hippocampal/right inferior temporal activation. Group comparison indicated presence of hyperactivation in SCD+ and hypoactivation in MCI in the left superior parietal lobule, relative to healthy controls. DISCUSSION These findings support the presence of an inverse U-shape model of activation and suggest that hyperactivation might represent a biomarker of the early AD stages.
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Affiliation(s)
- Nick Corriveau‐Lecavalier
- Research CenterInstitut universitaire de gériatrie de MontréalMontrealCanada
- Department of PsychologyUniversité de MontréalMontrealCanada
| | - Simon Duchesne
- CERVO Research CenterQuebec CityCanada
- Department of RadiologyUniversité LavalQuebec CityCanada
- Institut universitaire en santé mentaleQuebec CityCanada
| | - Serge Gauthier
- Douglas Hospital Research CenterMontrealCanada
- McGill University Research Centre for Studies in AgingMontrealCanada
| | - Carol Hudon
- CERVO Research CenterQuebec CityCanada
- École de psychologieUniversité LavalQuebec CityCanada
| | | | - Samira Mellah
- Research CenterInstitut universitaire de gériatrie de MontréalMontrealCanada
| | - Sylvie Belleville
- Research CenterInstitut universitaire de gériatrie de MontréalMontrealCanada
- Department of PsychologyUniversité de MontréalMontrealCanada
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Clinically Applicable Quantitative Magnetic Resonance Morphologic Measurements of Grey Matter Changes in the Human Brain. Brain Sci 2021; 11:brainsci11010055. [PMID: 33466559 PMCID: PMC7824828 DOI: 10.3390/brainsci11010055] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 12/18/2020] [Accepted: 12/21/2020] [Indexed: 11/17/2022] Open
Abstract
(1) Purpose: Quantitative magnetic resonance imaging (qMRI) measurements can be used to sensitively estimate brain morphological alterations and may support clinical diagnosis of neurodegenerative diseases (ND). We aimed to establish a normative reference database for a clinical applicable quantitative MR morphologic measurement on neurodegenerative changes in patients; (2) Methods: Healthy subjects (HCs, n = 120) with an evenly distribution between 21 to 70 years and amyotrophic lateral sclerosis (ALS) patients (n = 11, mean age = 52.45 ± 6.80 years), as an example of ND patients, underwent magnetic resonance imaging (MRI) examinations under routine diagnostic conditions. Regional cortical thickness (rCTh) in 68 regions of interest (ROIs) and subcortical grey matter volume (SGMV) in 14 ROIs were determined from all subjects by using Computational Anatomy Toolbox. Those derived from HCs were analyzed to determine age-related differences and subsequently used as reference to estimate ALS-related alterations; (3) Results: In HCs, the rCTh (in 49/68 regions) and the SGMV (in 9/14 regions) in elderly subjects were less than those in younger subjects and exhibited negative linear correlations to age (p < 0.0007 for rCTh and p < 0.004 for SGMV). In comparison to age- and sex-matched HCs, the ALS patients revealed significant decreases of rCTh in eight ROIs, majorly located in frontal and temporal lobes; (4) Conclusion: The present study proves an overall grey matter decline with normal ageing as reported previously. The provided reference may be used for detection of grey matter alterations in neurodegenerative diseases that are not apparent in standard MR scans, indicating the potential of using qMRI as an add-on diagnostic tool in a clinical setting.
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50
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Kim REY, Lee M, Kang DW, Wang SM, Kim NY, Lee MK, Lim HK, Kim D. Deep Learning-Based Segmentation to Establish East Asian Normative Volumes Using Multisite Structural MRI. Diagnostics (Basel) 2020; 11:diagnostics11010013. [PMID: 33374745 PMCID: PMC7824436 DOI: 10.3390/diagnostics11010013] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 12/18/2020] [Accepted: 12/21/2020] [Indexed: 11/16/2022] Open
Abstract
Normative brain magnetic resonance imaging (MRI) is essential to interpret the state of an individual's brain health. However, a normative study is often expensive for small research groups. Although several attempts have been made to establish brain MRI norms, the focus has been limited to certain age ranges. This study aimed to establish East Asian normative brain data using multi-site MRI and determine the robustness of these data for clinical research. Normative MRI was gathered covering a wide range of cognitively normal East Asian populations (age: 18-96 years) from two open sources and three research sites. Eight sub-regional volumes were extracted in the left and right hemispheres using an in-house deep learning-based tool. Repeated measure consistency and multicenter reliability were determined using intraclass correlation coefficients and compared to a widely used tool, FreeSurfer. Our results showed highly consistent outcomes with high reliability across sites. Our method outperformed FreeSurfer in repeated measure consistency for most structures and multicenter reliability for all structures. The normative MRI we constructed was able to identify sub-regional differences in mild cognitive impairments and dementia after covariate adjustments. Our investigation suggests it is possible to provide a sound normative reference for neurodegenerative or aging research.
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Affiliation(s)
- Regina E. Y. Kim
- Research Institute, NEUROPHET Inc., Seoul 06247, Korea; (R.E.Y.K.); (M.L.)
- Institute of Human Genomic Study, College of Medicine, Korea University, Seoul 15355, Korea
- Department of Psychiatry, University of Iowa, Iowa City, IA 52240, USA
| | - Minho Lee
- Research Institute, NEUROPHET Inc., Seoul 06247, Korea; (R.E.Y.K.); (M.L.)
| | - Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea;
| | - Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 07345, Korea; (S.-M.W.); (N.-Y.K.)
| | - Nak-Young Kim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 07345, Korea; (S.-M.W.); (N.-Y.K.)
| | - Min Kyoung Lee
- Department of Radiology, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea;
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 07345, Korea; (S.-M.W.); (N.-Y.K.)
- Correspondence: (H.K.L.); (D.K.); Tel.: +82-70-5223-4414 (D.K.)
| | - Donghyeon Kim
- Research Institute, NEUROPHET Inc., Seoul 06247, Korea; (R.E.Y.K.); (M.L.)
- Correspondence: (H.K.L.); (D.K.); Tel.: +82-70-5223-4414 (D.K.)
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