151
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Maffei C, Gilmore N, Snider SB, Foulkes AS, Bodien YG, Yendiki A, Edlow BL. Automated detection of axonal damage along white matter tracts in acute severe traumatic brain injury. Neuroimage Clin 2022; 37:103294. [PMID: 36529035 PMCID: PMC9792957 DOI: 10.1016/j.nicl.2022.103294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 12/12/2022] [Accepted: 12/13/2022] [Indexed: 12/15/2022]
Abstract
New techniques for individualized assessment of white matter integrity are needed to detect traumatic axonal injury (TAI) and predict outcomes in critically ill patients with acute severe traumatic brain injury (TBI). Diffusion MRI tractography has the potential to quantify white matter microstructure in vivo and has been used to characterize tract-specific changes following TBI. However, tractography is not routinely used in the clinical setting to assess the extent of TAI, in part because focal lesions reduce the robustness of automated methods. Here, we propose a pipeline that combines automated tractography reconstructions of 40 white matter tracts with multivariate analysis of along-tract diffusion metrics to assess the presence of TAI in individual patients with acute severe TBI. We used the Mahalanobis distance to identify abnormal white matter tracts in each of 18 patients with acute severe TBI as compared to 33 healthy subjects. In all patients for which a FreeSurfer anatomical segmentation could be obtained (17 of 18 patients), including 13 with focal lesions, the automated pipeline successfully reconstructed a mean of 37.5 ± 2.1 white matter tracts without the need for manual intervention. A mean of 2.5 ± 2.1 tracts resulted in partial or failed reconstructions and needed to be reinitialized upon visual inspection. The pipeline detected at least one abnormal tract in all patients (mean: 9.1 ± 7.9) and accurately discriminated between patients and controls (AUC: 0.91). The number and neuroanatomic location of abnormal tracts varied across patients and levels of consciousness. The premotor, temporal, and parietal sections of the corpus callosum were the most commonly damaged tracts (in 10, 9, and 8 patients, respectively), consistent with prior histopathological studies of TAI. TAI measures were not associated with concurrent behavioral measures of consciousness. In summary, we provide proof-of-principle evidence that an automated tractography pipeline has translational potential to detect and quantify TAI in individual patients with acute severe TBI.
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Affiliation(s)
- Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
| | - Natalie Gilmore
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Samuel B Snider
- Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrea S Foulkes
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Yelena G Bodien
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Brian L Edlow
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
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152
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Cui Y, Tang TY, Lu CQ, Ju S. Insulin Resistance and Cognitive Impairment: Evidence From Neuroimaging. J Magn Reson Imaging 2022; 56:1621-1649. [PMID: 35852470 DOI: 10.1002/jmri.28358] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/02/2022] [Accepted: 07/05/2022] [Indexed: 01/04/2023] Open
Abstract
Insulin is a peptide well known for its role in regulating glucose metabolism in peripheral tissues. Emerging evidence from human and animal studies indicate the multifactorial role of insulin in the brain, such as neuronal and glial metabolism, glucose regulation, and cognitive processes. Insulin resistance (IR), defined as reduced sensitivity to the action of insulin, has been consistently proposed as an important risk factor for developing neurodegeneration and cognitive impairment. Although the exact mechanism of IR-related cognitive impairment still awaits further elucidation, neuroimaging offers a versatile set of novel contrasts to reveal the subtle cerebral abnormalities in IR. These imaging contrasts, including but not limited to brain volume, white matter (WM) microstructure, neural function and brain metabolism, are expected to unravel the nature of the link between IR, cognitive decline, and brain abnormalities, and their changes over time. This review summarizes the current neuroimaging studies with multiparametric techniques, focusing on the cerebral abnormalities related to IR and therapeutic effects of IR-targeting treatments. According to the results, brain regions associated with IR pathophysiology include the medial temporal lobe, hippocampus, prefrontal lobe, cingulate cortex, precuneus, occipital lobe, and the WM tracts across the globe. Of these, alterations in the temporal lobe are highly reproducible across different imaging modalities. These structures have been known to be vulnerable to Alzheimer's disease (AD) pathology and are critical in cognitive processes such as memory and executive functioning. Comparing to asymptomatic subjects, results are more mixed in patients with metabolic disorders such as type 2 diabetes and obesity, which might be attributed to a multifactorial mechanism. Taken together, neuroimaging, especially MRI, is beneficial to reveal early abnormalities in cerebral structure and function in insulin-resistant brain, providing important evidence to unravel the underlying neuronal substrate that reflects the cognitive decline in IR. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Ying Cui
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Tian-Yu Tang
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Chun-Qiang Lu
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Shenghong Ju
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
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153
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Modabbernia A, Whalley HC, Glahn DC, Thompson PM, Kahn RS, Frangou S. Systematic evaluation of machine learning algorithms for neuroanatomically-based age prediction in youth. Hum Brain Mapp 2022; 43:5126-5140. [PMID: 35852028 PMCID: PMC9812239 DOI: 10.1002/hbm.26010] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/25/2022] [Accepted: 06/27/2022] [Indexed: 01/15/2023] Open
Abstract
Application of machine learning (ML) algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain-age). The choice of the ML approach in estimating brain-age in youth is important because age-related brain changes in this age-group are dynamic. However, the comparative performance of the available ML algorithms has not been systematically appraised. To address this gap, the present study evaluated the accuracy (mean absolute error [MAE]) and computational efficiency of 21 machine learning algorithms using sMRI data from 2105 typically developing individuals aged 5-22 years from five cohorts. The trained models were then tested in two independent holdout datasets, one comprising 4078 individuals aged 9-10 years and another comprising 594 individuals aged 5-21 years. The algorithms encompassed parametric and nonparametric, Bayesian, linear and nonlinear, tree-based, and kernel-based models. Sensitivity analyses were performed for parcellation scheme, number of neuroimaging input features, number of cross-validation folds, number of extreme outliers, and sample size. Tree-based models and algorithms with a nonlinear kernel performed comparably well, with the latter being especially computationally efficient. Extreme Gradient Boosting (MAE of 1.49 years), Random Forest Regression (MAE of 1.58 years), and Support Vector Regression (SVR) with Radial Basis Function (RBF) Kernel (MAE of 1.64 years) emerged as the three most accurate models. Linear algorithms, with the exception of Elastic Net Regression, performed poorly. Findings of the present study could be used as a guide for optimizing methodology when quantifying brain-age in youth.
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Affiliation(s)
| | - Heather C. Whalley
- Division of PsychiatryUniversity of Edinburgh, Kennedy Tower, Royal Edinburgh HospitalEdinburghUK
| | - David C. Glahn
- Boston Children's Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Rene S. Kahn
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Sophia Frangou
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverBritish ColumbiaCanada
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154
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Sui YV, Masurkar AV, Rusinek H, Reisberg B, Lazar M. Cortical myelin profile variations in healthy aging brain: A T1w/T2w ratio study. Neuroimage 2022; 264:119743. [PMID: 36368498 PMCID: PMC9904172 DOI: 10.1016/j.neuroimage.2022.119743] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 10/31/2022] [Accepted: 11/07/2022] [Indexed: 11/09/2022] Open
Abstract
Demyelination is observed in both healthy aging and age-related neurodegenerative disorders. While the significance of myelin within the cortex is well acknowledged, studies focused on intracortical demyelination and depth-specific structural alterations in normal aging are lacking. Using the recently available Human Connectome Project Aging dataset, we investigated intracortical myelin in a normal aging population using the T1w/T2w ratio. To capture the fine changes across cortical depths, we employed a surface-based approach by constructing cortical profiles traveling perpendicularly through the cortical ribbon and sampling T1w/T2w values. The curvatures of T1w/T2w cortical profiles may be influenced by differences in local myeloarchitecture and other tissue properties, which are known to vary across cortical regions. To quantify the shape of these profiles, we parametrized the level of curvature using a nonlinearity index (NLI) that measures the deviation of the profile from a straight line. We showed that NLI exhibited a steep decline in aging that was independent of local cortical thinning. Further examination of the profiles revealed that lower T1w/T2w near the gray-white matter boundary and superficial cortical depths were major contributors to the apparent NLI variations with age. These findings suggest that demyelination and changes in other T1w/T2w related tissue properties in normal aging may be depth-specific and highlight the potential of NLI as a unique marker of microstructural alterations within the cerebral cortex.
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Affiliation(s)
- Yu Veronica Sui
- Department of Radiology, NYU Grossman School of Medicine, 660 1st Ave, rm440, New York, NY 10016, USA,Corresponding author. (Y.V. Sui)
| | - Arjun V. Masurkar
- Department of Neurology, Center for Cognitive Neurology, NYU Grossman School of Medicine, New York, NY, USA,Department of Neuroscience and Physiology, NYU Grossman School of Medicine, New York, NY, USA,Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, USA
| | - Henry Rusinek
- Department of Radiology, NYU Grossman School of Medicine, 660 1st Ave, rm440, New York, NY 10016, USA,Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | - Barry Reisberg
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | - Mariana Lazar
- Department of Radiology, NYU Grossman School of Medicine, 660 1st Ave, rm440, New York, NY 10016, USA
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155
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Nicolaisen-Sobesky E, Mihalik A, Kharabian-Masouleh S, Ferreira FS, Hoffstaedter F, Schwender H, Maleki Balajoo S, Valk SL, Eickhoff SB, Yeo BTT, Mourao-Miranda J, Genon S. A cross-cohort replicable and heritable latent dimension linking behaviour to multi-featured brain structure. Commun Biol 2022; 5:1297. [PMID: 36435870 PMCID: PMC9701210 DOI: 10.1038/s42003-022-04244-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 11/09/2022] [Indexed: 11/28/2022] Open
Abstract
Identifying associations between interindividual variability in brain structure and behaviour requires large cohorts, multivariate methods, out-of-sample validation and, ideally, out-of-cohort replication. Moreover, the influence of nature vs nurture on brain-behaviour associations should be analysed. We analysed associations between brain structure (grey matter volume, cortical thickness, and surface area) and behaviour (spanning cognition, emotion, and alertness) using regularized canonical correlation analysis and a machine learning framework that tests the generalisability and stability of such associations. The replicability of brain-behaviour associations was assessed in two large, independent cohorts. The load of genetic factors on these associations was analysed with heritability and genetic correlation. We found one heritable and replicable latent dimension linking cognitive-control/executive-functions and positive affect to brain structural variability in areas typically associated with higher cognitive functions, and with areas typically associated with sensorimotor functions. These results revealed a major axis of interindividual behavioural variability linking to a whole-brain structural pattern.
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Affiliation(s)
- Eliana Nicolaisen-Sobesky
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany.
| | - Agoston Mihalik
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Shahrzad Kharabian-Masouleh
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Fabio S Ferreira
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Holger Schwender
- Mathematical Institute, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Somayeh Maleki Balajoo
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sofie L Valk
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Research Group "Cognitive Neurogenetics", Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Centre for Translational MR Research, Centre for Sleep & Cognition, N.1 Institute for Health, Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Janaina Mourao-Miranda
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany.
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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156
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Wu J, Li J, Eickhoff SB, Hoffstaedter F, Hanke M, Yeo BTT, Genon S. Cross-cohort replicability and generalizability of connectivity-based psychometric prediction patterns. Neuroimage 2022; 262:119569. [PMID: 35985618 PMCID: PMC9611632 DOI: 10.1016/j.neuroimage.2022.119569] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 08/04/2022] [Accepted: 08/15/2022] [Indexed: 11/20/2022] Open
Abstract
An increasing number of studies have investigated the relationships between inter-individual variability in brain regions' connectivity and behavioral phenotypes, making use of large population neuroimaging datasets. However, the replicability of brain-behavior associations identified by these approaches remains an open question. In this study, we examined the cross-dataset replicability of brain-behavior association patterns for fluid cognition and openness predictions using a previously developed region-wise approach, as well as using a standard whole-brain approach. Overall, we found moderate similarity in patterns for fluid cognition predictions across cohorts, especially in the Human Connectome Project Young Adult, Human Connectome Project Aging, and Enhanced Nathan Kline Institute Rockland Sample cohorts, but low similarity in patterns for openness predictions. In addition, we assessed the generalizability of prediction models in cross-dataset predictions, by training the model in one dataset and testing in another. Making use of the region-wise prediction approach, we showed that first, a moderate extent of generalizability could be achieved with fluid cognition prediction, and that, second, a set of common brain regions related to fluid cognition across cohorts could be identified. Nevertheless, the moderate replicability and generalizability could only be achieved in specific contexts. Thus, we argue that replicability and generalizability in connectivity-based prediction remain limited and deserve greater attention in future studies.
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Affiliation(s)
- Jianxiao Wu
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany.
| | - Jingwei Li
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany
| | - Michael Hanke
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore City, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore City, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore City, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore City, Singapore; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Sarah Genon
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany.
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157
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Liu Q, Ely BA, Stern ER, Xu J, Kim JW, Pick DG, Alonso CM, Gabbay V. Neural function underlying reward expectancy and attainment in adolescents with diverse psychiatric symptoms. Neuroimage Clin 2022; 36:103258. [PMID: 36451362 PMCID: PMC9668660 DOI: 10.1016/j.nicl.2022.103258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 10/31/2022] [Accepted: 11/02/2022] [Indexed: 11/08/2022]
Abstract
Reward dysfunction has been hypothesized to play a key role in the development of psychiatric conditions during adolescence. To help capture the complexity of reward function in youth, we used the Reward Flanker fMRI Task, which enabled us to examine neural activity during expectancy and attainment of both certain and uncertain rewards. Participants were 84 psychotropic-medication-free adolescents, including 67 with diverse psychiatric conditions and 17 healthy controls. Functional MRI used high-resolution acquisition and high-fidelity processing techniques modeled after the Human Connectome Project. Analyses examined neural activation during reward expectancy and attainment, and their associations with clinical measures of depression, anxiety, and anhedonia severity, with results controlled for family-wise errors using non-parametric permutation tests. As anticipated, reward expectancy activated regions within the fronto-striatal reward network, thalamus, occipital lobe, superior parietal lobule, temporoparietal junction, and cerebellum. Unexpectedly, however, reward attainment was marked by widespread deactivation in many of these same regions, which we further explored using cosine similarity analysis. Across all subjects, striatum and thalamus activation during reward expectancy negatively correlated with anxiety severity, while activation in numerous cortical and subcortical regions during reward attainment positively correlated with both anxiety and depression severity. These findings highlight the complexity and dynamic nature of neural reward processing in youth.
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Affiliation(s)
- Qi Liu
- Department of Psychiatry & Behavioral Science, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Benjamin A Ely
- Department of Psychiatry & Behavioral Science, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Emily R Stern
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, United States; Department of Psychiatry, New York University School of Medicine, New York, NY, United States
| | - Junqian Xu
- Departments of Radiology and Psychiatry, Baylor College of Medicine, Houston, TX, United States
| | - Joo-Won Kim
- Departments of Radiology and Psychiatry, Baylor College of Medicine, Houston, TX, United States
| | - Danielle G Pick
- Department of Psychiatry & Behavioral Science, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Carmen M Alonso
- Department of Psychiatry & Behavioral Science, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Vilma Gabbay
- Department of Psychiatry & Behavioral Science, Albert Einstein College of Medicine, Bronx, NY, United States; Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, United States.
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158
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Hui ES. Advanced Diffusion
MRI
of Stroke Recovery. J Magn Reson Imaging 2022; 57:1312-1319. [PMID: 36378071 DOI: 10.1002/jmri.28523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 11/16/2022] Open
Abstract
There is an urgent need for ways to improve our understanding of poststroke recovery to inform the development of novel rehabilitative interventions, and improve the clinical management of stroke patients. Supported by the notion that predictive information on poststroke recovery is embedded not only in the individual brain regions, but also the connections throughout the brain, majority of previous investigations have focused on the relationship between brain functional connections and post-stroke deficit and recovery. However, considering the fact that it is the static anatomical brain connections that constrain and facilitate the dynamic functional brain connections, the microstructures and structural connections of the brain may potentially be better alternatives to the functional MRI-based biomarkers of stroke recovery. This review, therefore, seeks to provide an overview of the basic concept and applications of two recently proposed advanced diffusion MRI techniques, namely lesion network mapping and fixel-based morphometry, that may be useful for the investigation of stroke recovery at the local and global levels of the brain. This review will also highlight the application of some of other emerging advanced diffusion MRI techniques that warrant further investigation in the context of stroke recovery research.
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Affiliation(s)
- Edward S. Hui
- Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Shatin Hong Kong China
- Department of Psychiatry The Chinese University of Hong Kong Shatin Hong Kong China
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159
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Stanford WC, Mucha PJ, Dayan E. A robust core architecture of functional brain networks supports topological resilience and cognitive performance in middle- and old-aged adults. Proc Natl Acad Sci U S A 2022; 119:e2203682119. [PMID: 36282912 PMCID: PMC9636938 DOI: 10.1073/pnas.2203682119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 09/21/2022] [Indexed: 11/18/2022] Open
Abstract
Aging is associated with gradual changes in cognition, yet some individuals exhibit protection against age-related cognitive decline. The topological characteristics of brain networks that promote protection against cognitive decline in aging are unknown. Here, we investigated whether the robustness and resilience of brain networks, queried via the delineation of the brain's core network structure, relate to age and cognitive performance in a cross-sectional dataset of healthy middle- and old-aged adults (n = 478, ages 40 to 90 y). First, we decomposed each subject's functional brain network using k-shell decomposition and found that age was negatively associated with robust core network structures. Next, we perturbed these networks, via attack simulations, and found that resilience of core brain network nodes also declined in relationship to age. We then partitioned our dataset into middle- (ages 40 to 65 y, n = 300) and old- (ages 65 to 90 y, n = 178) aged subjects and observed that older individuals had less robust core connectivity and resilience. Following these analyses, we found that episodic memory was positively related to robust connectivity and core resilience, particularly within the default node, limbic, and frontoparietal control networks. Importantly, we found that age-related differences in episodic memory were positively related to core resilience, which indicates a potential role for core resilience in protection against cognitive decline. Together, these findings suggest that robust core connectivity and resilience of brain networks could facilitate high cognitive performance in aging.
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Affiliation(s)
- William C. Stanford
- Biological and Biomedical Sciences Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
| | - Peter J. Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH 03755
| | - Eran Dayan
- Biological and Biomedical Sciences Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
- Department of Radiology, Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
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160
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Warrington S, Thompson E, Bastiani M, Dubois J, Baxter L, Slater R, Jbabdi S, Mars RB, Sotiropoulos SN. Concurrent mapping of brain ontogeny and phylogeny within a common space: Standardized tractography and applications. SCIENCE ADVANCES 2022; 8:eabq2022. [PMID: 36260675 PMCID: PMC9581484 DOI: 10.1126/sciadv.abq2022] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 08/31/2022] [Indexed: 06/16/2023]
Abstract
Developmental and evolutionary effects on brain organization are complex, yet linked, as evidenced by the correspondence in cortical area expansion across these vastly different time scales. However, it is still not possible to study concurrently the ontogeny and phylogeny of cortical areal connections, which is arguably more relevant to brain function than allometric measurements. Here, we propose a novel framework that allows the integration of structural connectivity maps from humans (adults and neonates) and nonhuman primates (macaques) onto a common space. We use white matter bundles to anchor the common space and use the uniqueness of cortical connection patterns to these bundles to probe area specialization. This enabled us to quantitatively study divergences and similarities in connectivity over evolutionary and developmental scales, to reveal brain maturation trajectories, including the effect of premature birth, and to translate cortical atlases between diverse brains. Our findings open new avenues for an integrative approach to imaging neuroanatomy.
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Affiliation(s)
- Shaun Warrington
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Elinor Thompson
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Matteo Bastiani
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Jessica Dubois
- Université Paris Cité, Inserm, NeuroDiderot Unit, Paris, France
- University Paris-Saclay, CEA, NeuroSpin, Gif-sur-Yvette, France
| | - Luke Baxter
- Department of Paediatrics, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Rebeccah Slater
- Department of Paediatrics, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Rogier B. Mars
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Stamatios N. Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, UK
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161
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Li W, Fan L, Shi W, Lu Y, Li J, Luo N, Wang H, Chu C, Ma L, Song M, Li K, Cheng L, Cao L, Jiang T. Brainnetome atlas of preadolescent children based on anatomical connectivity profiles. Cereb Cortex 2022; 33:5264-5275. [PMID: 36255322 PMCID: PMC10151881 DOI: 10.1093/cercor/bhac415] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/17/2022] [Accepted: 09/18/2022] [Indexed: 11/13/2022] Open
Abstract
During the preadolescent period, when the cerebral thickness, curvature, and myelin are constantly changing, the brain's regionalization patterns underwent persistent development, contributing to the continuous improvements of various higher cognitive functions. Using a brain atlas to study the development of these functions has attracted much attention. However, the brains of children do not always have the same topological patterns as those of adults. Therefore, age-specific brain mapping is particularly important, serving as a basic and indispensable tool to study the normal development of children. In this study, we took advantage of longitudinal data to create the brain atlas specifically for preadolescent children. The resulting human Child Brainnetome Atlas, with 188 cortical and 36 subcortical subregions, provides a precise period-specific and cross-validated version of the brain atlas that is more appropriate for adoption in the preadolescent period. In addition, we compared and illustrated for regions with different topological patterns in the child and adult atlases, providing a topologically consistent reference for subsequent research studying child and adolescent development.
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Affiliation(s)
- Wen Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Weiyang Shi
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Yuheng Lu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Jin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Congying Chu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Ming Song
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Kaixin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Luqi Cheng
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, No.1 Jinji Road, Qixing District, Guilin 541004, China.,Research Center for Augmented Intelligence, Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Yuhang District, Hangzhou 311100, China
| | - Long Cao
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,Research Center for Augmented Intelligence, Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Yuhang District, Hangzhou 311100, China
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162
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Hoopes A, Mora JS, Dalca AV, Fischl B, Hoffmann M. SynthStrip: skull-stripping for any brain image. Neuroimage 2022; 260:119474. [PMID: 35842095 PMCID: PMC9465771 DOI: 10.1016/j.neuroimage.2022.119474] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 06/17/2022] [Accepted: 07/11/2022] [Indexed: 01/18/2023] Open
Abstract
The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams. Despite their abundance, popular classical skull-stripping methods are usually tailored to images with specific acquisition properties, namely near-isotropic resolution and T1-weighted (T1w) MRI contrast, which are prevalent in research settings. As a result, existing tools tend to adapt poorly to other image types, such as stacks of thick slices acquired with fast spin-echo (FSE) MRI that are common in the clinic. While learning-based approaches for brain extraction have gained traction in recent years, these methods face a similar burden, as they are only effective for image types seen during the training procedure. To achieve robust skull-stripping across a landscape of imaging protocols, we introduce SynthStrip, a rapid, learning-based brain-extraction tool. By leveraging anatomical segmentations to generate an entirely synthetic training dataset with anatomies, intensity distributions, and artifacts that far exceed the realistic range of medical images, SynthStrip learns to successfully generalize to a variety of real acquired brain images, removing the need for training data with target contrasts. We demonstrate the efficacy of SynthStrip for a diverse set of image acquisitions and resolutions across subject populations, ranging from newborn to adult. We show substantial improvements in accuracy over popular skull-stripping baselines - all with a single trained model. Our method and labeled evaluation data are available at https://w3id.org/synthstrip.
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Affiliation(s)
- Andrew Hoopes
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13(th) St, Charlestown, MA, USA
| | - Jocelyn S Mora
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13(th) St, Charlestown, MA, USA
| | - Adrian V Dalca
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13(th) St, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, 25 Shattuck St, Boston, MA, USA; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13(th) St, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, 25 Shattuck St, Boston, MA, USA; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA, USA; Harvard-MIT Division of Health Sciences and Technology, 77 Massachusetts Ave, Cambridge, MA, USA
| | - Malte Hoffmann
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13(th) St, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, 25 Shattuck St, Boston, MA, USA.
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163
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Sanders AFP, Baum GL, Harms MP, Kandala S, Bookheimer SY, Dapretto M, Somerville LH, Thomas KM, Van Essen DC, Yacoub E, Barch DM. Developmental trajectories of cortical thickness by functional brain network: The roles of pubertal timing and socioeconomic status. Dev Cogn Neurosci 2022; 57:101145. [PMID: 35944340 PMCID: PMC9386024 DOI: 10.1016/j.dcn.2022.101145] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 07/20/2022] [Accepted: 08/04/2022] [Indexed: 11/20/2022] Open
Abstract
The human cerebral cortex undergoes considerable changes during development, with cortical maturation patterns reflecting regional heterogeneity that generally progresses in a posterior-to-anterior fashion. However, the organizing principles that govern cortical development remain unclear. In the current study, we characterized age-related differences in cortical thickness (CT) as a function of sex, pubertal timing, and two dissociable indices of socioeconomic status (i.e., income-to-needs and maternal education) in the context of functional brain network organization, using a cross-sectional sample (n = 789) diverse in race, ethnicity, and socioeconomic status from the Lifespan Human Connectome Project in Development (HCP-D). We found that CT generally followed a linear decline from 5 to 21 years of age, except for three functional networks that displayed nonlinear trajectories. We found no main effect of sex or age by sex interaction for any network. Earlier pubertal timing was associated with reduced mean CT and CT in seven networks. We also found a significant age by maternal education interaction for mean CT across cortex and CT in the dorsal attention network, where higher levels of maternal education were associated with steeper age-related decreases in CT. Taken together, our results suggest that these biological and environmental variations may impact the emerging functional connectome.
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Affiliation(s)
- Ashley F P Sanders
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA.
| | - Graham L Baum
- Department of Psychology, Harvard University, Cambridge, MA 02138, USA
| | - Michael P Harms
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Sridhar Kandala
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles School of Medicine, Los Angeles, CA 90095, USA
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles School of Medicine, Los Angeles, CA 90095, USA
| | - Leah H Somerville
- Department of Psychology, Harvard University, Cambridge, MA 02138, USA
| | - Kathleen M Thomas
- Institute of Child Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - David C Van Essen
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
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164
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Nadvar N, Stiles N, Choupan J, Patel V, Ameri H, Shi Y, Liu Z, Jonides J, Weiland J. Sight restoration reverses blindness-induced cross-modal functional connectivity changes between the visual and somatosensory cortex at rest. Front Neurosci 2022; 16:902866. [PMID: 36213743 PMCID: PMC9539921 DOI: 10.3389/fnins.2022.902866] [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: 03/23/2022] [Accepted: 08/29/2022] [Indexed: 11/28/2022] Open
Abstract
Resting-state functional connectivity (rsFC) has been used to assess the effect of vision loss on brain plasticity. With the emergence of vision restoration therapies, rsFC analysis provides a means to assess the functional changes following sight restoration. Our study demonstrates a partial reversal of blindness-induced rsFC changes in Argus II retinal prosthesis patients compared to those with severe retinitis pigmentosa (RP). For 10 healthy control (HC), 10 RP, and 7 Argus II subjects, four runs of resting-state functional magnetic resonance imaging (fMRI) per subject were included in our study. rsFC maps were created with the primary visual cortex (V1) as the seed. The rsFC group contrast maps for RP > HC, Argus II > RP, and Argus II > HC revealed regions in the post-central gyrus (PostCG) with significant reduction, significant enhancement, and no significant changes in rsFC to V1 for the three contrasts, respectively. These findings were also confirmed by the respective V1-PostCG ROI-ROI analyses between test groups. Finally, the extent of significant rsFC to V1 in the PostCG region was 5,961 in HC, 0 in RP, and 842 mm3 in Argus II groups. Our results showed a reduction of visual-somatosensory rsFC following blindness, consistent with previous findings. This connectivity was enhanced following sight recovery with Argus II, representing a reversal of changes in cross-modal functional plasticity as manifested during rest, despite the rudimentary vision obtained by Argus II patients. Future investigation with a larger number of test subjects into this rare condition can further unveil the profound ability of our brain to reorganize in response to vision restoration.
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Affiliation(s)
- Negin Nadvar
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Noelle Stiles
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Jeiran Choupan
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Vivek Patel
- Irvine School of Medicine, The University of California, Irvine, Irvine, CA, United States
| | - Hossein Ameri
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Yonggang Shi
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Zhongming Liu
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States
| | - John Jonides
- Department of Psychology, University of Michigan, Ann Arbor, MI, United States
| | - James Weiland
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, United States
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165
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Cheng H, Vinci-Booher S, Wang J, Caron B, Wen Q, Newman S, Pestilli F. Denoising diffusion weighted imaging data using convolutional neural networks. PLoS One 2022; 17:e0274396. [PMID: 36108272 PMCID: PMC9477507 DOI: 10.1371/journal.pone.0274396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 08/26/2022] [Indexed: 11/17/2022] Open
Abstract
Diffusion weighted imaging (DWI) with multiple, high b-values is critical for extracting tissue microstructure measurements; however, high b-value DWI images contain high noise levels that can overwhelm the signal of interest and bias microstructural measurements. Here, we propose a simple denoising method that can be applied to any dataset, provided a low-noise, single-subject dataset is acquired using the same DWI sequence. The denoising method uses a one-dimensional convolutional neural network (1D-CNN) and deep learning to learn from a low-noise dataset, voxel-by-voxel. The trained model can then be applied to high-noise datasets from other subjects. We validated the 1D-CNN denoising method by first demonstrating that 1D-CNN denoising resulted in DWI images that were more similar to the noise-free ground truth than comparable denoising methods, e.g., MP-PCA, using simulated DWI data. Using the same DWI acquisition but reconstructed with two common reconstruction methods, i.e. SENSE1 and sum-of-square, to generate a pair of low-noise and high-noise datasets, we then demonstrated that 1D-CNN denoising of high-noise DWI data collected from human subjects showed promising results in three domains: DWI images, diffusion metrics, and tractography. In particular, the denoised images were very similar to a low-noise reference image of that subject, more than the similarity between repeated low-noise images (i.e. computational reproducibility). Finally, we demonstrated the use of the 1D-CNN method in two practical examples to reduce noise from parallel imaging and simultaneous multi-slice acquisition. We conclude that the 1D-CNN denoising method is a simple, effective denoising method for DWI images that overcomes some of the limitations of current state-of-the-art denoising methods, such as the need for a large number of training subjects and the need to account for the rectified noise floor.
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Affiliation(s)
- Hu Cheng
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States of America
- Program of Neuroscience, Indiana University, Bloomington, IN, United States of America
| | - Sophia Vinci-Booher
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States of America
- Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, United States of America
| | - Jian Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Bradley Caron
- Department of Psychology, Center for Perceptual Systems and Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, TX, United States of America
| | - Qiuting Wen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States of America
| | - Sharlene Newman
- Alabama Life Research Institute, The University of Alabama, Tuscaloosa, AL, United States of America
| | - Franco Pestilli
- Department of Psychology, Center for Perceptual Systems and Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, TX, United States of America
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166
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Zhao Y, Wang M, Hu K, Wang Q, Lou J, Fan L, Liu B. The development of cortical functional hierarchy is associated with the molecular organization of prenatal/postnatal periods. Cereb Cortex 2022; 33:4248-4261. [PMID: 36069939 DOI: 10.1093/cercor/bhac340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 07/14/2022] [Accepted: 08/02/2022] [Indexed: 11/14/2022] Open
Abstract
The human cerebral cortex conforms to specific functional hierarchies facilitating information processing and higher-order cognition. Prior studies in adults have unveiled a dominant functional hierarchy spanning from sensorimotor regions to transmodal regions, which is also present in younger cohorts. However, how the functional hierarchy develops and the underlying molecular mechanisms remain to be investigated. Here, we set out to investigate the developmental patterns of the functional hierarchy for preschool children (#scans = 141, age = 2.41-6.90 years) using a parsimonious general linear model and the underlying biological mechanisms by combining the neuroimaging developmental pattern with two separate transcriptomic datasets (i.e. Allen Human Brain Atlas and BrainSpan Atlas). Our results indicated that transmodal regions were further segregated from sensorimotor regions and that such changes were potentially driven by two gene clusters with distinct enrichment profiles, namely prenatal gene cluster and postnatal gene cluster. Additionally, we found similar developmental profiles manifested in subsequent developmental periods by conducting identical analyses on the Human Connectome Projects in Development (#scans = 638, age = 5.58-21.92 years) and Philadelphia Neurodevelopment Cohort datasets (#scans = 795, age = 8-21 years), driven by concordant two gene clusters. Together, these findings illuminate a comprehensive developmental principle of the functional hierarchy and the underpinning molecular factors, and thus may shed light on the potential pathobiology of neurodevelopmental disorders.
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Affiliation(s)
- Yuxin Zhao
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Meng Wang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ke Hu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qi Wang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jing Lou
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Chinese Institute for Brain Research, Beijing 102206, China
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167
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Glasser MF, Coalson TS, Harms MP, Xu J, Baum GL, Autio JA, Auerbach EJ, Greve DN, Yacoub E, Van Essen DC, Bock NA, Hayashi T. Empirical transmit field bias correction of T1w/T2w myelin maps. Neuroimage 2022; 258:119360. [PMID: 35697132 PMCID: PMC9483036 DOI: 10.1016/j.neuroimage.2022.119360] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 06/01/2022] [Accepted: 06/04/2022] [Indexed: 12/30/2022] Open
Abstract
T1-weighted divided by T2-weighted (T1w/T2w) myelin maps were initially developed for neuroanatomical analyses such as identifying cortical areas, but they are increasingly used in statistical comparisons across individuals and groups with other variables of interest. Existing T1w/T2w myelin maps contain radiofrequency transmit field (B1+) biases, which may be correlated with these variables of interest, leading to potentially spurious results. Here we propose two empirical methods for correcting these transmit field biases using either explicit measures of the transmit field or alternatively a 'pseudo-transmit' approach that is highly correlated with the transmit field at 3T. We find that the resulting corrected T1w/T2w myelin maps are both better neuroanatomical measures (e.g., for use in cross-species comparisons), and more appropriate for statistical comparisons of relative T1w/T2w differences across individuals and groups (e.g., sex, age, or body-mass-index) within a consistently acquired study at 3T. We recommend that investigators who use the T1w/T2w approach for mapping cortical myelin use these B1+ transmit field corrected myelin maps going forward.
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Affiliation(s)
| | | | - Michael P Harms
- Psychiatry, Washington University Medical School, St. Louis, MO, United States
| | - Junqian Xu
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States; Departments of Radiology and Psychiatry, Baylor College of Medicine, Houston, TX, United States
| | - Graham L Baum
- Department of Psychology, Harvard University, Cambridge, MA, United States
| | - Joonas A Autio
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Edward J Auerbach
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
| | - Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
| | | | - Nicholas A Bock
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, ON, Canada
| | - Takuya Hayashi
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
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168
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Bourke NJ, Demarchi C, De Simoni S, Samra R, Patel MC, Kuczynski A, Mok Q, Wimalasundera N, Vargha-Khadem F, Sharp DJ. Brain volume abnormalities and clinical outcomes following paediatric traumatic brain injury. Brain 2022; 145:2920-2934. [PMID: 35798350 PMCID: PMC9420021 DOI: 10.1093/brain/awac130] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 03/09/2022] [Accepted: 03/12/2022] [Indexed: 11/25/2022] Open
Abstract
Long-term outcomes are difficult to predict after paediatric traumatic brain injury. The presence or absence of focal brain injuries often do not explain cognitive, emotional and behavioural disabilities that are common and disabling. In adults, traumatic brain injury produces progressive brain atrophy that can be accurately measured and is associated with cognitive decline. However, the effect of paediatric traumatic brain injury on brain volumes is more challenging to measure because of its interaction with normal brain development. Here we report a robust approach to the individualized estimation of brain volume following paediatric traumatic brain injury and investigate its relationship to clinical outcomes. We first used a large healthy control dataset (n > 1200, age 8-22) to describe the healthy development of white and grey matter regions through adolescence. Individual estimates of grey and white matter regional volume were then generated for a group of moderate/severe traumatic brain injury patients injured in childhood (n = 39, mean age 13.53 ± 1.76, median time since injury = 14 months, range 4-168 months) by comparing brain volumes in patients to age-matched controls. Patients were individually classified as having low or normal brain volume. Neuropsychological and neuropsychiatric outcomes were assessed using standardized testing and parent/carer assessments. Relative to head size, grey matter regions decreased in volume during normal adolescence development whereas white matter tracts increased in volume. Traumatic brain injury disrupted healthy brain development, producing reductions in both grey and white matter brain volumes after correcting for age. Of the 39 patients investigated, 11 (28%) had at least one white matter tract with reduced volume and seven (18%) at least one area of grey matter with reduced volume. Those classified as having low brain volume had slower processing speed compared to healthy controls, emotional impairments, higher levels of apathy, increased anger and learning difficulties. In contrast, the presence of focal brain injury and microbleeds were not associated with an increased risk of these clinical impairments. In summary, we show how brain volume abnormalities after paediatric traumatic brain injury can be robustly calculated from individual T1 MRI using a large normative dataset that allows the effects of healthy brain development to be controlled for. Using this approach, we show that volumetric abnormalities are common after moderate/severe traumatic brain injury in both grey and white matter regions, and are associated with higher levels of cognitive, emotional and behavioural abnormalities that are common after paediatric traumatic brain injury.
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Affiliation(s)
- Niall J Bourke
- Department of Brain Sciences, Imperial College London, London, UK.,UK Dementia Research Institute, Care Research and Technology Centre, Imperial College London, London, UK
| | - Célia Demarchi
- Department of Brain Sciences, Imperial College London, London, UK.,UK Dementia Research Institute, Care Research and Technology Centre, Imperial College London, London, UK.,Clinical Neuropsychology, Department of Psychological Services, Great Ormond Street Hospital, London, UK
| | - Sara De Simoni
- King's College London, Department of Psychology, Institute of Psychiatry Psychology and Neuroscience, De Crespigny Park, London SE5 8AF, UK
| | - Ravjeet Samra
- Department of Brain Sciences, Imperial College London, London, UK
| | - Maneesh C Patel
- Imaging Department, Imperial College Healthcare NHS Trust, Charing Cross Hospital, London W6 8RF, UK
| | - Adam Kuczynski
- Clinical Neuropsychology, Department of Psychological Services, Great Ormond Street Hospital, London, UK
| | - Quen Mok
- Department of Paediatric Critical Care, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Neil Wimalasundera
- Paediatric Rehabilitation, Royal Children's Hospital, Melbourne, Australia
| | - Fareneh Vargha-Khadem
- Cognitive Neuroscience and Neuropsychiatry, UCL Great Ormond Street Institute of Child Health, London, UK
| | - David J Sharp
- Department of Brain Sciences, Imperial College London, London, UK.,UK Dementia Research Institute, Care Research and Technology Centre, Imperial College London, London, UK
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169
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A multifaceted gradient in human cerebellum of structural and functional development. Nat Neurosci 2022; 25:1129-1133. [PMID: 35982153 DOI: 10.1038/s41593-022-01136-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 07/11/2022] [Indexed: 11/08/2022]
Abstract
The organization of the basic tissue and functional properties of the cerebellum across development is unknown. Combining several large datasets, we demonstrate in the human cerebellum a static tissue gradient in adults that mirrors a similar growth-rate gradient across development. Quantitative tissue metrics corroborate unique densities of certain lipids and proteins among lobules, and cerebellar structural development closely follows cerebellar functional properties through childhood.
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170
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Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI. Neuroimage 2022; 257:119327. [PMID: 35636227 PMCID: PMC9453851 DOI: 10.1016/j.neuroimage.2022.119327] [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: 03/11/2022] [Revised: 05/06/2022] [Accepted: 05/19/2022] [Indexed: 01/25/2023] Open
Abstract
Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.
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171
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Baum GL, Flournoy JC, Glasser MF, Harms MP, Mair P, Sanders AFP, Barch DM, Buckner RL, Bookheimer S, Dapretto M, Smith S, Thomas KM, Yacoub E, Van Essen DC, Somerville LH. Graded Variation in T1w/T2w Ratio during Adolescence: Measurement, Caveats, and Implications for Development of Cortical Myelin. J Neurosci 2022; 42:5681-5694. [PMID: 35705486 PMCID: PMC9302463 DOI: 10.1523/jneurosci.2380-21.2022] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 04/07/2022] [Accepted: 06/04/2022] [Indexed: 01/22/2023] Open
Abstract
Adolescence is characterized by the maturation of cortical microstructure and connectivity supporting complex cognition and behavior. Axonal myelination influences brain connectivity during development by enhancing neural signaling speed and inhibiting plasticity. However, the maturational timing of cortical myelination during human adolescence remains poorly understood. Here, we take advantage of recent advances in high-resolution cortical T1w/T2w mapping methods, including principled correction of B1+ transmit field effects, using data from the Human Connectome Project in Development (HCP-D; N = 628, ages 8-21). We characterize microstructural changes relevant to myelination by estimating age-related differences in T1w/T2w throughout the cerebral neocortex from childhood to early adulthood. We apply Bayesian spline models and clustering analysis to demonstrate graded variation in age-dependent cortical T1w/T2w differences that are correlated with the sensorimotor-association (S-A) axis of cortical organization reported by others. In sensorimotor areas, T1w/T2w ratio measures start at high levels at early ages, increase at a fast pace, and decelerate at later ages (18-21). In intermediate multimodal areas along the S-A axis, T1w/T2w starts at intermediate levels and increases linearly at an intermediate pace. In transmodal/paralimbic association areas, T1w/T2w starts at low levels and increases linearly at the slowest pace. These data provide evidence for graded variation of the T1w/T2w ratio along the S-A axis that may reflect cortical myelination changes during adolescence underlying the development of complex information processing and psychological functioning. We discuss the implications of these results as well as caveats in interpreting magnetic resonance imaging (MRI)-based estimates of myelination.SIGNIFICANCE STATEMENT Myelin is a lipid membrane that is essential to healthy brain function. Myelin wraps axons to increase neural signaling speed, enabling complex neuronal functioning underlying learning and cognition. Here, we characterize the developmental timing of myelination across the cerebral cortex during adolescence using a noninvasive proxy measure, T1w/T2w mapping. Our results provide new evidence demonstrating graded variation across the cortex in the timing of T1w/T2w changes during adolescence, with rapid T1w/T2w increases in lower-order sensory areas and gradual T1w/T2w increases in higher-order association areas. This spatial pattern of microstructural brain development closely parallels the sensorimotor-to-association axis of cortical organization and plasticity during ontogeny.
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Affiliation(s)
- Graham L Baum
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA, 02138
| | - John C Flournoy
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA, 02138
| | - Matthew F Glasser
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA, 63110
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA, 63110
| | - Michael P Harms
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA, 63110
| | - Patrick Mair
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA, 02138
| | - Ashley F P Sanders
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA, 63110
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA, 63110
- Department of Psychological and Brain Sciences, Washington University, St. Louis, MO, USA, MO 63130
| | - Randy L Buckner
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA, 02138
| | - Susan Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA, 90095
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA, 90095
| | - Stephen Smith
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom, OX3 9DU
| | - Kathleen M Thomas
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA, 55455
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA, 55455
| | - David C Van Essen
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA, 63110
| | - Leah H Somerville
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA, 02138
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172
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A longitudinal resource for studying connectome development and its psychiatric associations during childhood. Sci Data 2022; 9:300. [PMID: 35701428 PMCID: PMC9197863 DOI: 10.1038/s41597-022-01329-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 04/20/2022] [Indexed: 12/14/2022] Open
Abstract
Most psychiatric disorders are chronic, associated with high levels of disability and distress, and present during pediatric development. Scientific innovation increasingly allows researchers to probe brain-behavior relationships in the developing human. As a result, ambitions to (1) establish normative pediatric brain development trajectories akin to growth curves, (2) characterize reliable metrics for distinguishing illness, and (3) develop clinically useful tools to assist in the diagnosis and management of mental health and learning disorders have gained significant momentum. To this end, the NKI-Rockland Sample initiative was created to probe lifespan development as a large-scale multimodal dataset. The NKI-Rockland Sample Longitudinal Discovery of Brain Development Trajectories substudy (N = 369) is a 24- to 30-month multi-cohort longitudinal pediatric investigation (ages 6.0-17.0 at enrollment) carried out in a community-ascertained sample. Data include psychiatric diagnostic, medical, behavioral, and cognitive phenotyping, as well as multimodal brain imaging (resting fMRI, diffusion MRI, morphometric MRI, arterial spin labeling), genetics, and actigraphy. Herein, we present the rationale, design, and implementation of the Longitudinal Discovery of Brain Development Trajectories protocol.
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173
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Gao P, Dong HM, Liu SM, Fan XR, Jiang C, Wang YS, Margulies D, Li HF, Zuo XN. A Chinese multi-modal neuroimaging data release for increasing diversity of human brain mapping. Sci Data 2022; 9:286. [PMID: 35680932 PMCID: PMC9184635 DOI: 10.1038/s41597-022-01413-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 05/24/2022] [Indexed: 11/09/2022] Open
Abstract
The big-data use is becoming a standard practice in the neuroimaging field through data-sharing initiatives. It is important for the community to realize that such open science effort must protect personal, especially facial information when raw neuroimaging data are shared. An ideal tool for the face anonymization should not disturb subsequent brain tissue extraction and further morphological measurements. Using the high-resolution head images from magnetic resonance imaging (MRI) of 215 healthy Chinese, we discovered and validated a template effect on the face anonymization. Improved facial anonymization was achieved when the Chinese head templates but not the Western templates were applied to obscure the faces of Chinese brain images. This finding has critical implications for international brain imaging data-sharing. To facilitate the further investigation of potential culture-related impacts on and increase diversity of data-sharing for the human brain mapping, we released the 215 Chinese multi-modal MRI data into a database for imaging Chinese young brains, namely’I See your Brains (ISYB)’, to the public via the Science Data Bank (10.11922/sciencedb.00740). Measurement(s) | brain imaging measurements | Technology Type(s) | magnetic resonance imaging | Factor Type(s) | multimodal neuroimaging metrics | Sample Characteristic - Organism | Homo | Sample Characteristic - Environment | magnetic | Sample Characteristic - Location | North China |
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Affiliation(s)
- Peng Gao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Hao-Ming Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.,National Basic Science Data Center, Beijing, 100109, China
| | - Si-Man Liu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xue-Ru Fan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Chao Jiang
- School of Psychology, Capital Normal University, Beijing, 100048, China
| | - Yin-Shan Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Daniel Margulies
- Centre National de la Recherche Scientifique, Frontlab, Brain and Spinal Cord Institute, Paris, UMR 7225, France
| | - Hai-Fang Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China.
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China. .,National Basic Science Data Center, Beijing, 100109, China. .,Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China. .,Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China. .,Key Laboratory of Brain and Education, School of Education Science, Nanning Normal University, Nanning, 530001, China.
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174
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Zhu X, Liu Y, Habeck CG, Stern Y, Lee S, For-The-Alzheimer's-Disease-Neuroimaging-Initiative. Transfer learning for cognitive reserve quantification. Neuroimage 2022; 258:119353. [PMID: 35667639 PMCID: PMC9271605 DOI: 10.1016/j.neuroimage.2022.119353] [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: 03/15/2022] [Accepted: 05/31/2022] [Indexed: 11/30/2022] Open
Abstract
Cognitive reserve (CR) has been introduced to explain individual differences in susceptibility to cognitive or functional impairment in the presence of age or pathology. We developed a deep learning model to quantify the CR as residual variance in memory performance using the Structural Magnetic Resonance Imaging (sMRI) data from a lifespan healthy cohort. The generalizability of the sMRI-based deep learning model was tested in two independent healthy and Alzheimer's cohorts using transfer learning framework. Structural MRIs were collected from three cohorts: 495 healthy adults (age: 20-80) from RANN, 620 healthy adults (age: 36-100) from lifespan Human Connectome Project Aging (HCPA), and 941 adults (age: 55-92) from Alzheimer's Disease Neuroimaging Initiative (ADNI). Region of interest (ROI)-specific cortical thickness and volume measures were extracted using the Desikan-Killiany Atlas. CR was quantified by residuals which subtract the predicted memory from the true memory. Cascade neural network (CNN) models were used to train RANN dataset for memory prediction. Transfer learning was applied to transfer the T1 imaging-based model from source domain (RANN) to the target domains (HCPA or ADNI). The CNN model trained on the RANN dataset exhibited strong linear correlation between true and predicted memory based on the T1 cortical thickness and volume predictors. In addition, the model generated from healthy lifespan data (RANN) was able to generalize to an independent healthy lifespan data (HCPA) and older demented participants (ADNI) across different scanner types. The estimated CR was correlated with CR proxies such education and IQ across all three datasets. The current findings suggest that the transfer learning approach is an effective way to generalize the residual-based CR estimation. It is applicable to various diseases and may flexibly incorporate different imaging modalities such as fMRI and PET, making it a promising tool for scientific and clinical purposes.
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Affiliation(s)
- Xi Zhu
- Department of Psychiatry, Columbia University Irving Medical Center, New York, USA; New York State Psychiatric Institute, New York, USA
| | - Yi Liu
- Department of Biostatistics, Columbia University Irving Medical Center, New York, USA
| | - Christian G Habeck
- Cognitive Neuroscience Division, Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, New York, USA
| | - Yaakov Stern
- New York State Psychiatric Institute, New York, USA; Cognitive Neuroscience Division, Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, New York, USA
| | - Seonjoo Lee
- Department of Psychiatry, Columbia University Irving Medical Center, New York, USA; Department of Biostatistics, Columbia University Irving Medical Center, New York, USA.
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175
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Rainer LJ, Kronbichler M, Kuchukhidze G, Trinka E, Langthaler PB, Kronbichler L, Said-Yuerekli S, Kirschner M, Zimmermann G, Höfler J, Schmid E, Braun M. Emotional Word Processing in Patients With Juvenile Myoclonic Epilepsy. Front Neurol 2022; 13:875950. [PMID: 35720080 PMCID: PMC9201996 DOI: 10.3389/fneur.2022.875950] [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: 02/14/2022] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
Objective According to Panksepp's hierarchical emotion model, emotion processing relies on three functionally and neuroanatomically distinct levels. These levels comprise subcortical networks (primary level), the limbic system (secondary level), and the neocortex (tertiary level) and are suggested to serve differential emotional processing. We aimed to validate and extend previous evidence of discrete and dimensional emotion processing in patient with juvenile myoclonic epilepsy (JME). Methods We recorded brain activity of patients with JME and healthy controls in response to lexical decisions to words reflecting the discrete emotion fear and the affective dimension negativity previously suggested to rely on different brain regions and to reflect different levels of processing. In all study participants, we tested verbal cognitive functions, as well as the relationship of psychiatric conditions, seizure types and duration of epilepsy and emotional word processing. Results In support of the hierarchical emotion model, we found an interaction of discrete emotion and affective dimensional processing in the right amygdala likely to reflect secondary level processing. Brain activity related to affective dimensional processing was found in the right inferior frontal gyrus and is suggested to reflect tertiary level processing. Psychiatric conditions, type of seizure nor mono- vs. polytherapy and duration of epilepsy within patients did not have any effect on the processing of emotional words. In addition, no differences in brain activity or response times between patients and controls were observed, despite neuropsychological testing revealed slightly decreased verbal intelligence, verbal fluency and reading speed in patients with JME. Significance These results were interpreted to be in line with the hierarchical emotion model and to highlight the amygdala's role in processing biologically relevant stimuli, as well as to suggest a semantic foundation of affective dimensional processing in prefrontal cortex. A lack of differences in brain activity of patients with JME and healthy controls in response to the emotional content of words could point to unaffected implicit emotion processing in patients with JME.
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Affiliation(s)
- Lucas Johannes Rainer
- Department of Neurology, Christian-Doppler Medical Centre, Paracelsus Medical University, Centre for Cognitive Neuroscience Salzburg, Member of the European Reference Network, Epicare, Salzburg, Austria
- Neuroscience Institute, Christian-Doppler Medical Centre, Paracelsus Medical University, Centre for Cognitive Neuroscience, Salzburg, Austria
- Department of Psychiatry, Psychotherapy & Psychosomatics, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | - Martin Kronbichler
- Neuroscience Institute, Christian-Doppler Medical Centre, Paracelsus Medical University, Centre for Cognitive Neuroscience, Salzburg, Austria
- Department of Psychology, Naturwissenschaftliche Fakultaet, Centre for Cognitive Neuroscience, Paris-Lodron University, Salzburg, Austria
| | - Giorgi Kuchukhidze
- Department of Neurology, Christian-Doppler Medical Centre, Paracelsus Medical University, Centre for Cognitive Neuroscience Salzburg, Member of the European Reference Network, Epicare, Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian-Doppler Medical Centre, Paracelsus Medical University, Centre for Cognitive Neuroscience Salzburg, Member of the European Reference Network, Epicare, Salzburg, Austria
- Neuroscience Institute, Christian-Doppler Medical Centre, Paracelsus Medical University, Centre for Cognitive Neuroscience, Salzburg, Austria
- Department of Public Health, Health Services Research and Health Technology Assessment, UMIT–University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria
- Karl-Landsteiner Institute for Neurorehabilitation and Space Neurology, Salzburg, Austria
| | - Patrick Benjamin Langthaler
- Department of Neurology, Christian-Doppler Medical Centre, Paracelsus Medical University, Centre for Cognitive Neuroscience Salzburg, Member of the European Reference Network, Epicare, Salzburg, Austria
- Department of Mathematics, Paris-Lodron University, Naturwissenschaftliche Fakultaet, Salzburg, Austria
| | - Lisa Kronbichler
- Neuroscience Institute, Christian-Doppler Medical Centre, Paracelsus Medical University, Centre for Cognitive Neuroscience, Salzburg, Austria
- Department of Psychiatry, Psychotherapy & Psychosomatics, Christian-Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
| | - Sarah Said-Yuerekli
- Department of Neurology, Christian-Doppler Medical Centre, Paracelsus Medical University, Centre for Cognitive Neuroscience Salzburg, Member of the European Reference Network, Epicare, Salzburg, Austria
- Department of Psychology, Naturwissenschaftliche Fakultaet, Centre for Cognitive Neuroscience, Paris-Lodron University, Salzburg, Austria
| | - Margarita Kirschner
- Department of Neurology, Christian-Doppler Medical Centre, Paracelsus Medical University, Centre for Cognitive Neuroscience Salzburg, Member of the European Reference Network, Epicare, Salzburg, Austria
| | - Georg Zimmermann
- Team Biostatistics and Big Medical Data, IDA Lab Salzburg, Paracelsus Medical University, Salzburg, Austria
- Research and Innovation Management, Paracelsus Medical University, Salzburg, Austria
| | - Julia Höfler
- Department of Neurology, Christian-Doppler Medical Centre, Paracelsus Medical University, Centre for Cognitive Neuroscience Salzburg, Member of the European Reference Network, Epicare, Salzburg, Austria
| | - Elisabeth Schmid
- Department of Neurology, Christian-Doppler Medical Centre, Paracelsus Medical University, Centre for Cognitive Neuroscience Salzburg, Member of the European Reference Network, Epicare, Salzburg, Austria
| | - Mario Braun
- Department of Psychology, Naturwissenschaftliche Fakultaet, Centre for Cognitive Neuroscience, Paris-Lodron University, Salzburg, Austria
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176
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Gbadeyan O, Teng J, Prakash RS. Predicting response time variability from task and resting-state functional connectivity in the aging brain. Neuroimage 2022; 250:118890. [PMID: 35007719 PMCID: PMC9063711 DOI: 10.1016/j.neuroimage.2022.118890] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 12/23/2021] [Accepted: 01/06/2022] [Indexed: 12/22/2022] Open
Abstract
Aging is associated with declines in a host of cognitive functions, including attentional control, inhibitory control, episodic memory, processing speed, and executive functioning. Theoretical models attribute the age-related decline in cognitive functioning to deficits in goal maintenance and attentional inhibition. Despite these well-documented declines in executive control resources, older adults endorse fewer episodes of mind-wandering when assessed using task-embedded thought probes. Furthermore, previous work on the neural basis of mind-wandering has mostly focused on young adults with studies predominantly focusing on the activity and connectivity of a select few canonical networks. However, whole-brain functional networks associated with mind-wandering in aging have not yet been characterized. In this study, using response time variability-the trial-to-trial fluctuations in behavioral responses-as an indirect marker of mind-wandering or an "out-of-the-zone" attentional state representing suboptimal behavioral performance, we show that brain-based predictive models of response time variability can be derived from whole-brain task functional connectivity. In contrast, models derived from resting-state functional connectivity alone did not predict individual response time variability. Finally, we show that despite successful within-sample prediction of response time variability, our models did not generalize to predict response time variability in independent cohorts of older adults with resting-state connectivity. Overall, our findings provide evidence for the utility of task-based functional connectivity in predicting individual response time variability in aging. Future research is needed to derive more robust and generalizable models.
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Affiliation(s)
- Oyetunde Gbadeyan
- Department of Psychology, The Ohio State University, 139 Psychology Building, 1835 Neil Avenue, Columbus, OH 43210, USA
| | - James Teng
- Department of Psychology, The Ohio State University, 139 Psychology Building, 1835 Neil Avenue, Columbus, OH 43210, USA
| | - Ruchika Shaurya Prakash
- Department of Psychology, The Ohio State University, 139 Psychology Building, 1835 Neil Avenue, Columbus, OH 43210, USA; Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA.
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177
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Petrican R, Fornito A, Jones N. Psychological Resilience and Neurodegenerative Risk: A Connectomics-Transcriptomics Investigation in Healthy Adolescent and Middle-Aged Females. Neuroimage 2022; 255:119209. [PMID: 35429627 DOI: 10.1016/j.neuroimage.2022.119209] [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: 09/14/2021] [Revised: 04/05/2022] [Accepted: 04/11/2022] [Indexed: 11/25/2022] Open
Abstract
Adverse life events can inflict substantial long-term damage, which, paradoxically, has been posited to stem from initially adaptative responses to the challenges encountered in one's environment. Thus, identification of the mechanisms linking resilience against recent stressors to longer-term psychological vulnerability is key to understanding optimal functioning across multiple timescales. To address this issue, our study tested the relevance of neuro-reproductive maturation and senescence, respectively, to both resilience and longer-term risk for pathologies characterised by accelerated brain aging, specifically, Alzheimer's Disease (AD). Graph theoretical and partial least squares analyses were conducted on multimodal imaging, reported biological aging and recent adverse experience data from the Lifespan Human Connectome Project (HCP). Availability of reproductive maturation/senescence measures restricted our investigation to adolescent (N =178) and middle-aged (N=146) females. Psychological resilience was linked to age-specific brain senescence patterns suggestive of precocious functional development of somatomotor and control-relevant networks (adolescence) and earlier aging of default mode and salience/ventral attention systems (middle adulthood). Biological aging showed complementary associations with the neural patterns relevant to resilience in adolescence (positive relationship) versus middle-age (negative relationship). Transcriptomic and expression quantitative trait locus data analyses linked the neural aging patterns correlated with psychological resilience in middle adulthood to gene expression patterns suggestive of increased AD risk. Our results imply a partially antagonistic relationship between resilience against proximal stressors and longer-term psychological adjustment in later life. They thus underscore the importance of fine-tuning extant views on successful coping by considering the multiple timescales across which age-specific processes may unfold.
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Affiliation(s)
- Raluca Petrican
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom.
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Natalie Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
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178
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Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 84] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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179
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A comprehensive investigation of physiologic noise modeling in resting state fMRI; time shifted cardiac noise in EPI and its removal without external physiologic signal measures. Neuroimage 2022; 254:119136. [PMID: 35346840 DOI: 10.1016/j.neuroimage.2022.119136] [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: 11/11/2021] [Revised: 02/18/2022] [Accepted: 03/22/2022] [Indexed: 11/23/2022] Open
Abstract
Hemodynamic cardiac and respiratory-cycle fluctuations are a source of unwanted non-neuronal signal components, often called physiologic noise, in resting state (rs-) fMRI studies. Here, we use image-based retrospective correction of physiological motion (RETROICOR) with externally measured physiologic signals to investigate cardiac and respiratory hemodynamic phase functions reflected in rs-fMRI data. We find that the cardiac phase function is time shifted locally, while the respiratory phase function is described as single, fixed phase form across the brain. In light of these findings, we propose an update to Physiologic EStimation by Temporal ICA (PESTICA), our publically available software package that estimates physiologic signals when external physiologic measures are not available. This update incorporates: 1) auto-selection of slicewise physiologic regressors and generation of physiologic fixed phase regressors with total slices/TR sampling rate, 2) Fourier series expansion of the cardiac fixed phase regressor to account for time delayed cardiac noise 3) removal of cardiac and respiratory noise in imaging data. We compare the efficacy of the updated method to RETROICOR.
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180
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Tian Q, Li Z, Fan Q, Polimeni JR, Bilgic B, Salat DH, Huang SY. SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI. Neuroimage 2022; 253:119033. [PMID: 35240299 DOI: 10.1016/j.neuroimage.2022.119033] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 02/15/2022] [Accepted: 02/21/2022] [Indexed: 12/12/2022] Open
Abstract
Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and white matter tracts. Nonetheless, the noise in the diffusion-weighted images (DWIs) decreases the accuracy and precision of DTI derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR). Deep learning-based image denoising using convolutional neural networks (CNNs) has superior performance but often requires additional high-SNR data for supervising the training of CNNs, which reduces the feasibility of supervised learning-based denoising in practice. In this work, we develop a self-supervised deep learning-based method entitled "SDnDTI" for denoising DTI data, which does not require additional high-SNR data for training. Specifically, SDnDTI divides multi-directional DTI data into many subsets of six DWI volumes and transforms DWIs from each subset to along the same diffusion-encoding directions through the diffusion tensor model, generating multiple repetitions of DWIs with identical image contrasts but different noise observations. SDnDTI removes noise by first denoising each repetition of DWIs using a deep 3-dimensional CNN with the average of all repetitions with higher SNR as the training target, following the same approach as normal supervised learning based denoising methods, and then averaging CNN-denoised images for achieving higher SNR. The denoising efficacy of SDnDTI is demonstrated in terms of the similarity of output images and resultant DTI metrics compared to the ground truth generated using substantially more DWI volumes on two datasets with different spatial resolutions, b-values and numbers of input DWI volumes provided by the Human Connectome Project (HCP) and the Lifespan HCP in Aging. The SDnDTI results preserve image sharpness and textural details and substantially improve upon those from the raw data. The results of SDnDTI are comparable to those from supervised learning-based denoising and outperform those from state-of-the-art conventional denoising algorithms including BM4D, AONLM and MPPCA. By leveraging domain knowledge of diffusion MRI physics, SDnDTI makes it easier to use CNN-based denoising methods in practice and has the potential to benefit a wider range of research and clinical applications that require accelerated DTI acquisition and high-quality DTI data for mapping of tissue microstructure, fiber tracts and structural connectivity in the living human brain.
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Affiliation(s)
- Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States.
| | - Ziyu Li
- Department of Biomedical Engineering, Tsinghua University, Beijing, PR China
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - David H Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
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181
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Hoffmann M, Billot B, Greve DN, Iglesias JE, Fischl B, Dalca AV. SynthMorph: Learning Contrast-Invariant Registration Without Acquired Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:543-558. [PMID: 34587005 PMCID: PMC8891043 DOI: 10.1109/tmi.2021.3116879] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to contrast introduced by magnetic resonance imaging (MRI). While classical registration methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning-based techniques are fast at test time but limited to registering images with contrasts and geometric content similar to those seen during training. We propose to remove this dependency on training data by leveraging a generative strategy for diverse synthetic label maps and images that exposes networks to a wide range of variability, forcing them to learn more invariant features. This approach results in powerful networks that accurately generalize to a broad array of MRI contrasts. We present extensive experiments with a focus on 3D neuroimaging, showing that this strategy enables robust and accurate registration of arbitrary MRI contrasts even if the target contrast is not seen by the networks during training. We demonstrate registration accuracy surpassing the state of the art both within and across contrasts, using a single model. Critically, training on arbitrary shapes synthesized from noise distributions results in competitive performance, removing the dependency on acquired data of any kind. Additionally, since anatomical label maps are often available for the anatomy of interest, we show that synthesizing images from these dramatically boosts performance, while still avoiding the need for real intensity images. Our code is available at doic https://w3id.org/synthmorph.
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182
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Fan Q, Eichner C, Afzali M, Mueller L, Tax CMW, Davids M, Mahmutovic M, Keil B, Bilgic B, Setsompop K, Lee HH, Tian Q, Maffei C, Ramos-Llordén G, Nummenmaa A, Witzel T, Yendiki A, Song YQ, Huang CC, Lin CP, Weiskopf N, Anwander A, Jones DK, Rosen BR, Wald LL, Huang SY. Mapping the Human Connectome using Diffusion MRI at 300 mT/m Gradient Strength: Methodological Advances and Scientific Impact. Neuroimage 2022; 254:118958. [PMID: 35217204 DOI: 10.1016/j.neuroimage.2022.118958] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 01/27/2022] [Accepted: 01/31/2022] [Indexed: 12/20/2022] Open
Abstract
Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in Continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength dMRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for dMRI and where the field is headed in the coming years.
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Affiliation(s)
- Qiuyun Fan
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Cornelius Eichner
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, LS2 9JT, UK
| | - Lars Mueller
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, LS2 9JT, UK
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Image Sciences Institute, University Medical Center (UMC) Utrecht, Utrecht, Netherlands
| | - Mathias Davids
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Mirsad Mahmutovic
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | | | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Yi-Qiao Song
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA USA
| | - Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China; Shanghai Changning Mental Health Center, Shanghai, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.; Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Alfred Anwander
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States.
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183
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Dutt RK, Hannon K, Easley TO, Griffis JC, Zhang W, Bijsterbosch JD. Mental health in the UK Biobank: A roadmap to self-report measures and neuroimaging correlates. Hum Brain Mapp 2022; 43:816-832. [PMID: 34708477 PMCID: PMC8720192 DOI: 10.1002/hbm.25690] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 09/10/2021] [Accepted: 10/11/2021] [Indexed: 11/09/2022] Open
Abstract
The UK Biobank (UKB) is a highly promising dataset for brain biomarker research into population mental health due to its unprecedented sample size and extensive phenotypic, imaging, and biological measurements. In this study, we aimed to provide a shared foundation for UKB neuroimaging research into mental health with a focus on anxiety and depression. We compared UKB self-report measures and revealed important timing effects between scan acquisition and separate online acquisition of some mental health measures. To overcome these timing effects, we introduced and validated the Recent Depressive Symptoms (RDS-4) score which we recommend for state-dependent and longitudinal research in the UKB. We furthermore tested univariate and multivariate associations between brain imaging-derived phenotypes (IDPs) and mental health. Our results showed a significant multivariate relationship between IDPs and mental health, which was replicable. Conversely, effect sizes for individual IDPs were small. Test-retest reliability of IDPs was stronger for measures of brain structure than for measures of brain function. Taken together, these results provide benchmarks and guidelines for future UKB research into brain biomarkers of mental health.
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Affiliation(s)
- Rosie K Dutt
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Kayla Hannon
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Ty O Easley
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Joseph C Griffis
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Wei Zhang
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Janine D Bijsterbosch
- Department of RadiologyWashington University School of MedicineSaint LouisMissouriUSA
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184
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HashemizadehKolowri S, Chen RR, Adluru G, DiBella EVR. Jointly estimating parametric maps of multiple diffusion models from undersampled q-space data: A comparison of three deep learning approaches. Magn Reson Med 2022; 87:2957-2971. [PMID: 35081261 DOI: 10.1002/mrm.29162] [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: 08/03/2021] [Revised: 12/27/2021] [Accepted: 01/03/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE While advanced diffusion techniques have been found valuable in many studies, their clinical availability has been hampered partly due to their long scan times. Moreover, each diffusion technique can only extract a few relevant microstructural features. Using multiple diffusion methods may help to better understand the brain microstructure, which requires multiple expensive model fittings. In this work, we compare deep learning (DL) approaches to jointly estimate parametric maps of multiple diffusion representations/models from highly undersampled q-space data. METHODS We implement three DL approaches to jointly estimate parametric maps of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and multi-compartment spherical mean technique (SMT). A per-voxel q-space deep learning (1D-qDL), a per-slice convolutional neural network (2D-CNN), and a 3D-patch-based microstructure estimation with sparse coding using a separable dictionary (MESC-SD) network are considered. RESULTS The accuracy of estimated diffusion maps depends on the q-space undersampling, the selected network architecture, and the region and the parameter of interest. The smallest errors are observed for the MESC-SD network architecture (less than 10 % normalized RMSE in most brain regions). CONCLUSION Our experiments show that DL methods are very efficient tools to simultaneously estimate several diffusion maps from undersampled q-space data. These methods can significantly reduce both the scan ( ∼ 6-fold) and processing times ( ∼ 25-fold) for estimating advanced parametric diffusion maps while achieving a reasonable accuracy.
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Affiliation(s)
| | - Rong-Rong Chen
- Electrical and Computer Engineering Department, University of Utah, Salt Lake City, UT, USA
| | - Ganesh Adluru
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA.,Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Edward V R DiBella
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA.,Electrical and Computer Engineering Department, University of Utah, Salt Lake City, UT, USA.,Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
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185
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Bridging gaps between images and data: a systematic update on imaging biobanks. Eur Radiol 2022; 32:3173-3186. [PMID: 35001159 DOI: 10.1007/s00330-021-08431-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 10/01/2021] [Accepted: 10/22/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND OBJECTIVE The systematic collection of medical images combined with imaging biomarkers and patient non-imaging data is the core concept of imaging biobanks, a key element for fuelling the development of modern precision medicine. Our purpose is to review the existing image repositories fulfilling the criteria for imaging biobanks. METHODS Pubmed, Scopus and Web of Science were searched for articles published in English from January 2010 to July 2021 using a combination of the terms: "imaging" AND "biobanks" and "imaging" AND "repository". Moreover, the Community Research and Development Information Service (CORDIS) database ( https://cordis.europa.eu/projects ) was searched using the terms: "imaging" AND "biobanks", also including collections, projects, project deliverables, project publications and programmes. RESULTS Of 9272 articles retrieved, only 54 related to biobanks containing imaging data were finally selected, of which 33 were disease-oriented (61.1%) and 21 population-based (38.9%). Most imaging biobanks were European (26/54, 48.1%), followed by American biobanks (20/54, 37.0%). Among disease-oriented biobanks, the majority were focused on neurodegenerative (9/33, 27.3%) and oncological diseases (9/33, 27.3%). The number of patients enrolled ranged from 240 to 3,370,929, and the imaging modality most frequently involved was MRI (40/54, 74.1%), followed by CT (20/54, 37.0%), PET (13/54, 24.1%), and ultrasound (12/54, 22.2%). Most biobanks (38/54, 70.4%) were accessible under request. CONCLUSIONS Imaging biobanks can serve as a platform for collecting, sharing and analysing medical images integrated with imaging biomarkers, biological and clinical data. A relatively small proportion of current biobanks also contain images and can thus be classified as imaging biobanks. KEY POINTS • Imaging biobanks are a powerful tool for large-scale collection and processing of medical images integrated with imaging biomarkers and patient non-imaging data. • Most imaging biobanks retrieved were European, disease-oriented and accessible under user request. • While many biobanks have been developed so far, only a relatively small proportion of them are imaging biobanks.
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186
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Leaver AM, Gonzalez S, Vasavada M, Kubicki A, Jog M, Wang DJJ, Woods RP, Espinoza R, Gollan J, Parrish T, Narr KL. Modulation of Brain Networks during MR-Compatible Transcranial Direct Current Stimulation. Neuroimage 2022; 250:118874. [PMID: 35017127 PMCID: PMC9623807 DOI: 10.1016/j.neuroimage.2022.118874] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 11/16/2021] [Accepted: 01/04/2022] [Indexed: 10/19/2022] Open
Abstract
Transcranial direct current stimulation (tDCS) can influence performance on behavioral tasks and improve symptoms of brain conditions. Yet, it remains unclear precisely how tDCS affects brain function and connectivity. Here, we measured changes in functional connectivity (FC) metrics in blood-oxygenation-level-dependent (BOLD) fMRI data acquired during MR-compatible tDCS in a whole-brain analysis with corrections for false discovery rate. Volunteers (n=64) received active tDCS, sham tDCS, and rest (no stimulation), using one of three previously established electrode tDCS montages targeting left dorsolateral prefrontal cortex (DLPFC, n=37), lateral temporoparietal area (LTA, n=16), or superior temporal cortex (STC, n=11). In brain networks where simulated E field was highest in each montage, connectivity with remote nodes decreased during active tDCS. During active DLPFC-tDCS, connectivity decreased between a fronto-parietal network and subgenual ACC, while during LTA-tDCS connectivity decreased between an auditory-somatomotor network and frontal operculum. Active DLPFC-tDCS was also associated with increased connectivity within an orbitofrontal network overlapping subgenual ACC. Irrespective of montage, FC metrics increased in sensorimotor and attention regions during both active and sham tDCS, which may reflect the cognitive-perceptual demands of tDCS. Taken together, these results indicate that tDCS may have both intended and unintended effects on ongoing brain activity, stressing the importance of including sham, stimulation-absent, and active comparators in basic science and clinical trials of tDCS.
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Affiliation(s)
- Amber M Leaver
- Department of Radiology, Northwestern University, Chicago, IL, 60611; Department of Neurology, University of California Los Angeles, Los Angeles, CA, 90095.
| | - Sara Gonzalez
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, 90095
| | - Megha Vasavada
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, 90095
| | - Antoni Kubicki
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, 90095
| | - Mayank Jog
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, 90095
| | - Danny J J Wang
- Department of Neurology, University of Southern California, Los Angeles CA 90033
| | - Roger P Woods
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, 90095; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095
| | - Randall Espinoza
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095
| | - Jacqueline Gollan
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, 60611
| | - Todd Parrish
- Department of Radiology, Northwestern University, Chicago, IL, 60611
| | - Katherine L Narr
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, 90095; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 90095
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187
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Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain. Neuroinformatics 2022; 20:109-137. [PMID: 33974213 PMCID: PMC8111663 DOI: 10.1007/s12021-021-09519-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/07/2021] [Indexed: 02/06/2023]
Abstract
We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.
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188
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Allen EJ, St-Yves G, Wu Y, Breedlove JL, Prince JS, Dowdle LT, Nau M, Caron B, Pestilli F, Charest I, Hutchinson JB, Naselaris T, Kay K. A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence. Nat Neurosci 2022; 25:116-126. [PMID: 34916659 DOI: 10.1038/s41593-021-00962-x] [Citation(s) in RCA: 81] [Impact Index Per Article: 40.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 10/12/2021] [Indexed: 11/09/2022]
Abstract
Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. Here we present the Natural Scenes Dataset (NSD), in which high-resolution functional magnetic resonance imaging responses to tens of thousands of richly annotated natural scenes were measured while participants performed a continuous recognition task. To optimize data quality, we developed and applied novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we used NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality and breadth, NSD opens new avenues of inquiry in cognitive neuroscience and artificial intelligence.
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Affiliation(s)
- Emily J Allen
- Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Ghislain St-Yves
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Yihan Wu
- Graduate Program in Cognitive Science, University of Minnesota, Minneapolis, MN, USA
| | - Jesse L Breedlove
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Jacob S Prince
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Psychology, Harvard University, Cambridge, MA, USA
| | - Logan T Dowdle
- Department of Neuroscience, Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
- Department of Neurosurgery, Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Matthias Nau
- National Institute of Mental Health (NIMH), Bethesda MD, USA
| | - Brad Caron
- Program in Neuroscience, Indiana University, Bloomington IN, USA
- Program in Vision Science, Indiana University, Bloomington IN, USA
| | - Franco Pestilli
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Center for Perceptual Systems, University of Texas at Austin, Austin, TX, USA
- Institute for Neuroscience, University of Texas at Austin, Austin, TX, USA
| | - Ian Charest
- Center for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
- cerebrUM, Département de Psychologie, Université de Montréal, Montréal QC, Canada
| | | | - Thomas Naselaris
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Kendrick Kay
- Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA.
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189
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Rutherford S, Sturmfels P, Angstadt M, Hect J, Wiens J, van den Heuvel MI, Scheinost D, Sripada C, Thomason M. Automated Brain Masking of Fetal Functional MRI with Open Data. Neuroinformatics 2022; 20:173-185. [PMID: 34129169 PMCID: PMC9437772 DOI: 10.1007/s12021-021-09528-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/18/2021] [Indexed: 01/09/2023]
Abstract
Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing.
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Affiliation(s)
- Saige Rutherford
- Donders Institute, Radboud University Medical Center, Nijmegen, The Netherlands.
- Department of Psychiatry, University of Michigan, MI, Ann Arbor, USA.
| | - Pascal Sturmfels
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Mike Angstadt
- Department of Psychiatry, University of Michigan, MI, Ann Arbor, USA
| | - Jasmine Hect
- Department of Psychology, Wayne State University, Detroit, MI, USA
| | - Jenna Wiens
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | | | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Chandra Sripada
- Department of Psychiatry, University of Michigan, MI, Ann Arbor, USA
| | - Moriah Thomason
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA
- Department of Population Health, New York University School of Medicine, New York, NY, USA
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190
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Bernard JA. Understanding cerebellar function through network perspectives: A review of resting-state connectivity of the cerebellum. PSYCHOLOGY OF LEARNING AND MOTIVATION 2022. [DOI: 10.1016/bs.plm.2022.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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191
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What's New and What's Next in Diffusion MRI Preprocessing. Neuroimage 2021; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on “what’s new” since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on “Mapping the Connectome” in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on “what’s next” in dMRI preprocessing.
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192
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Tozzi L, Anene ET, Gotlib IH, Wintermark M, Kerr AB, Wu H, Seok D, Narr KL, Sheline YI, Whitfield-Gabrieli S, Williams LM. Convergence, preliminary findings and future directions across the four human connectome projects investigating mood and anxiety disorders. Neuroimage 2021; 245:118694. [PMID: 34732328 PMCID: PMC8727513 DOI: 10.1016/j.neuroimage.2021.118694] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/11/2021] [Accepted: 10/29/2021] [Indexed: 12/31/2022] Open
Abstract
In this paper we provide an overview of the rationale, methods, and preliminary results of the four Connectome Studies Related to Human Disease investigating mood and anxiety disorders. The first study, "Dimensional connectomics of anxious misery" (HCP-DAM), characterizes brain-symptom relations of a transdiagnostic sample of anxious misery disorders. The second study, "Human connectome Project for disordered emotional states" (HCP-DES), tests a hypothesis-driven model of brain circuit dysfunction in a sample of untreated young adults with symptoms of depression and anxiety. The third study, "Perturbation of the treatment resistant depression connectome by fast-acting therapies" (HCP-MDD), quantifies alterations of the structural and functional connectome as a result of three fast-acting interventions: electroconvulsive therapy, serial ketamine therapy, and total sleep deprivation. Finally, the fourth study, "Connectomes related to anxiety and depression in adolescents" (HCP-ADA), investigates developmental trajectories of subtypes of anxiety and depression in adolescence. The four projects use comparable and standardized Human Connectome Project magnetic resonance imaging (MRI) protocols, including structural MRI, diffusion-weighted MRI, and both task and resting state functional MRI. All four projects also conducted comprehensive and convergent clinical and neuropsychological assessments, including (but not limited to) demographic information, clinical diagnoses, symptoms of mood and anxiety disorders, negative and positive affect, cognitive function, and exposure to early life stress. The first round of analyses conducted in the four projects offered novel methods to investigate relations between functional connectomes and self-reports in large datasets, identified new functional correlates of symptoms of mood and anxiety disorders, characterized the trajectory of connectome-symptom profiles over time, and quantified the impact of novel treatments on aberrant connectivity. Taken together, the data obtained and reported by the four Connectome Studies Related to Human Disease investigating mood and anxiety disorders describe a rich constellation of convergent biological, clinical, and behavioral phenotypes that span the peak ages for the onset of emotional disorders. These data are being prepared for open sharing with the scientific community following screens for quality by the Connectome Coordinating Facility (CCF). The CCF also plans to release data from all projects that have been pre-processed using identical state-of-the-art pipelines. The resultant dataset will give researchers the opportunity to pool complementary data across the four projects to study circuit dysfunctions that may underlie mood and anxiety disorders, to map cohesive relations among circuits and symptoms, and to probe how these relations change as a function of age and acute interventions. This large and combined dataset may also be ideal for using data-driven analytic approaches to inform neurobiological targets for future clinical trials and interventions focused on clinical or behavioral outcomes.
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Affiliation(s)
- Leonardo Tozzi
- Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Esther T Anene
- Psychiatry, Neurology, Radiology, University of Pennsylvania, Philadelphia PA, USA
| | | | | | - Adam B Kerr
- Center for Cognitive and Neurobiological Imaging, Stanford University, CA, USA; Electrical Engineering, Stanford University, CA, USA
| | - Hua Wu
- Electrical Engineering, Stanford University, CA, USA
| | - Darsol Seok
- Department of Psychiatry, University of Pennsylvania, Philadelphia PA, USA
| | - Katherine L Narr
- Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA
| | - Yvette I Sheline
- Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA.
| | | | - Leanne M Williams
- Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
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193
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Markett S, Nothdurfter D, Focsa A, Reuter M, Jawinski P. Attention networks and the intrinsic network structure of the human brain. Hum Brain Mapp 2021; 43:1431-1448. [PMID: 34882908 PMCID: PMC8837576 DOI: 10.1002/hbm.25734] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 11/15/2021] [Accepted: 11/24/2021] [Indexed: 11/09/2022] Open
Abstract
Attention network theory distinguishes three independent systems, each supported by its own distributed network: an alerting network to deploy attentional resources in anticipation, an orienting network to direct attention to a cued location, and a control network to select relevant information at the expense of concurrently available information. Ample behavioral and neuroimaging evidence supports the dissociation of the three attention domains. The strong assumption that each attentional system is realized through a separable network, however, raises the question how these networks relate to the intrinsic network structure of the brain. Our understanding of brain networks has advanced majorly in the past years due to the increasing focus on brain connectivity. The brain is intrinsically organized into several large‐scale networks whose modular structure persists across task states. Existing proposals on how the presumed attention networks relate to intrinsic networks rely mostly on anecdotal and partly contradictory arguments. We addressed this issue by mapping different attention networks at the level of cifti‐grayordinates. Resulting group maps were compared to the group‐level topology of 23 intrinsic networks, which we reconstructed from the same participants' resting state fMRI data. We found that all attention domains recruited multiple and partly overlapping intrinsic networks and converged in the dorsal fronto‐parietal and midcingulo‐insular network. While we observed a preference of each attentional domain for its own set of intrinsic networks, implicated networks did not match well to those proposed in the literature. Our results indicate a necessary refinement of the attention network theory.
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194
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Chen LZ, Holmes AJ, Zuo XN, Dong Q. Neuroimaging brain growth charts: A road to mental health. PSYCHORADIOLOGY 2021; 1:272-286. [PMID: 35028568 PMCID: PMC8739332 DOI: 10.1093/psyrad/kkab022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 12/03/2021] [Accepted: 12/17/2021] [Indexed: 12/30/2022]
Abstract
Mental disorders are common health concerns and contribute to a heavy global burden on our modern society. It is challenging to identify and treat them timely. Neuroimaging evidence suggests the incidence of various psychiatric and behavioral disorders is closely related to the atypical development of brain structure and function. The identification and understanding of atypical brain development provide chances for clinicians to detect mental disorders earlier, perhaps even prior to onset, and treat them more precisely. An invaluable and necessary method in identifying and monitoring atypical brain development are growth charts of typically developing individuals in the population. The brain growth charts can offer a series of standard references on typical neurodevelopment, representing an important resource for the scientific and medical communities. In the present paper, we review the relationship between mental disorders and atypical brain development from a perspective of normative brain development by surveying the recent progress in the development of brain growth charts, including four aspects on growth chart utility: 1) cohorts, 2) measures, 3) mechanisms, and 4) clinical translations. In doing so, we seek to clarify the challenges and opportunities in charting brain growth, and to promote the application of brain growth charts in clinical practice.
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Affiliation(s)
- Li-Zhen Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT 06511, USA
- Department of Psychiatry, Yale University, New Haven, CT 06511, USA
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- National Basic Science Data Center, Beijing 100190, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
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195
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Liu S, Wang YS, Zhang Q, Zhou Q, Cao LZ, Jiang C, Zhang Z, Yang N, Dong Q, Zuo XN. Chinese Color Nest Project : An accelerated longitudinal brain-mind cohort. Dev Cogn Neurosci 2021; 52:101020. [PMID: 34653938 PMCID: PMC8517840 DOI: 10.1016/j.dcn.2021.101020] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 10/02/2021] [Accepted: 10/07/2021] [Indexed: 12/12/2022] Open
Abstract
The ongoing Chinese Color Nest Project (CCNP) was established to create normative charts for brain structure and function across the human lifespan, and link age-related changes in brain imaging measures to psychological assessments of behavior, cognition, and emotion using an accelerated longitudinal design. In the initial stage, CCNP aims to recruit 1520 healthy individuals (6-90 years), which comprises three phases: developing (devCCNP: 6-18 years, N = 480), maturing (matCCNP: 20-60 years, N = 560) and aging (ageCCNP: 60-84 years, N = 480). In this paper, we present an overview of the devCCNP, including study design, participants, data collection and preliminary findings. The devCCNP has acquired data with three repeated measurements from 2013 to 2017 in Southwest University, Chongqing, China (CCNP-SWU, N = 201). It has been accumulating baseline data since July 2018 and the second wave data since September 2020 in Chinese Academy of Sciences, Beijing, China (CCNP-CAS, N = 168). Each participant in devCCNP was followed up for 2.5 years at 1.25-year intervals. The devCCNP obtained longitudinal neuroimaging, biophysical, social, behavioral and cognitive data via MRI, parent- and self-reported questionnaires, behavioral assessments, and computer tasks. Additionally, data were collected on children's learning, daily life and emotional states during the COVID-19 pandemic in 2020. We address data harmonization across the two sites and demonstrated its promise of characterizing the growth curves for the overall brain morphometry using multi-center longitudinal data. CCNP data will be shared via the National Science Data Bank and requests for further information on collaboration and data sharing are encouraged.
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Affiliation(s)
- Siman Liu
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yin-Shan Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qing Zhang
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Quan Zhou
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Li-Zhi Cao
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chao Jiang
- School of Psychology, Capital Normal University, Beijing 100048, China
| | - Zhe Zhang
- Department of Psychology, College of Education, Hebei Normal University, Shijiazhuang 05024, Hebei, China
| | - Ning Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Xi-Nian Zuo
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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196
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Elam JS, Glasser MF, Harms MP, Sotiropoulos SN, Andersson JLR, Burgess GC, Curtiss SW, Oostenveld R, Larson-Prior LJ, Schoffelen JM, Hodge MR, Cler EA, Marcus DM, Barch DM, Yacoub E, Smith SM, Ugurbil K, Van Essen DC. The Human Connectome Project: A retrospective. Neuroimage 2021; 244:118543. [PMID: 34508893 PMCID: PMC9387634 DOI: 10.1016/j.neuroimage.2021.118543] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/13/2021] [Accepted: 08/30/2021] [Indexed: 01/21/2023] Open
Abstract
The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium.
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Affiliation(s)
| | | | - Michael P Harms
- Washington University School of Medicine, St. Louis, MO, USA
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre & NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, School of Medicine, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | | | | | | | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands
| | | | - Jan-Mathijs Schoffelen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands
| | - Michael R Hodge
- Washington University School of Medicine, St. Louis, MO, USA
| | - Eileen A Cler
- Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel M Marcus
- Washington University School of Medicine, St. Louis, MO, USA
| | - Deanna M Barch
- Washington University School of Medicine, St. Louis, MO, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
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197
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Li B, Jang I, Riphagen J, Almaktoum R, Yochim KM, Ances BM, Bookheimer SY, Salat DH. Identifying individuals with Alzheimer's disease-like brains based on structural imaging in the Human Connectome Project Aging cohort. Hum Brain Mapp 2021; 42:5535-5546. [PMID: 34582057 PMCID: PMC8559490 DOI: 10.1002/hbm.25626] [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: 07/22/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 11/28/2022] Open
Abstract
Given the difficulty in factoring out typical age effects from subtle Alzheimer's disease (AD) effects on brain structure, identification of very early, as well as younger preclinical “at‐risk” individuals has unique challenges. We examined whether age‐correction procedures could be used to better identify individuals at very early potential risk from adults who did not have any existing cognitive diagnosis. First, we obtained cross‐sectional age effects for each structural feature using data from a selected portion of the Human Connectome Project Aging (HCP‐A) cohort. After age detrending, we weighted AD structural deterioration with patterns quantified from data of the Alzheimer's Disease Neuroimaging Initiative. Support vector machine was then used to classify individuals with brains that most resembled atrophy in AD across the entire HCP‐A sample. Additionally, we iteratively adjusted the pipeline by removing individuals classified as AD‐like from the HCP‐A cohort to minimize atypical brain structural contributions to the age detrending. The classifier had a mean cross‐validation accuracy of 94.0% for AD recognition. It also could identify mild cognitive impairment with more severe AD‐specific biomarkers and worse cognition. In an independent HCP‐A cohort, 8.8% were identified as AD‐like, and they trended toward worse cognition. An “AD risk” score derived from the machine learning models also significantly correlated with cognition. This work provides a proof of concept for the potential to use structural brain imaging to identify asymptomatic individuals at young ages who show structural brain patterns similar to AD and are potentially at risk for a future clinical disorder.
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Affiliation(s)
- Binyin Li
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ikbeom Jang
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Joost Riphagen
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.,Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, The Netherlands
| | - Randa Almaktoum
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Kathryn Morrison Yochim
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Beau M Ances
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, University of California, Los Angeles, California, USA
| | - David H Salat
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, Massachusetts, USA
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198
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Large, open datasets for human connectomics research: Considerations for reproducible and responsible data use. Neuroimage 2021; 244:118579. [PMID: 34536537 DOI: 10.1016/j.neuroimage.2021.118579] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 08/27/2021] [Accepted: 09/14/2021] [Indexed: 12/19/2022] Open
Abstract
Large, open datasets have emerged as important resources in the field of human connectomics. In this review, the evolution of data sharing involving magnetic resonance imaging is described. A summary of the challenges and progress in conducting reproducible data analyses is provided, including description of recent progress made in the development of community guidelines and recommendations, software and data management tools, and initiatives to enhance training and education. Finally, this review concludes with a discussion of ethical conduct relevant to analyses of large, open datasets and a researcher's responsibility to prevent further stigmatization of historically marginalized racial and ethnic groups. Moving forward, future work should include an enhanced emphasis on the social determinants of health, which may further contextualize findings among diverse population-based samples. Leveraging the progress to date and guided by interdisciplinary collaborations, the future of connectomics promises to be an impressive era of innovative research, yielding a more inclusive understanding of brain structure and function.
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199
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Qi T, Schaadt G, Friederici AD. Associated functional network development and language abilities in children. Neuroimage 2021; 242:118452. [PMID: 34358655 PMCID: PMC8463838 DOI: 10.1016/j.neuroimage.2021.118452] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 07/14/2021] [Accepted: 08/03/2021] [Indexed: 11/26/2022] Open
Abstract
During childhood, the brain is gradually converging to the efficient functional architecture observed in adults. How the brain's functional architecture evolves with age, particularly in young children, is however, not well understood. We examined the functional connectivity of the core language regions, in association with cortical growth and language abilities, in 175 young children in the age range of 4 to 9 years. We analyzed the brain's developmental changes using resting-state functional and T1-weighted structural magnetic resonance imaging data. The results showed increased functional connectivity strength with age between the pars triangularis of the left inferior frontal gyrus and left temporoparietal regions (cohen's d = 0.54, CI: 0.24 - 0.84), associated with children's language abilities. Stronger functional connectivity between bilateral prefrontal and temporoparietal regions was associated with better language abilities regardless of age. In addition, the stronger functional connectivity between the left inferior frontal and temporoparietal regions was associated with larger surface area and thinner cortical thickness in these regions, which in turn was associated with superior language abilities. Thus, using functional and structural brain indices, coupled with behavioral measures, we elucidate the association of functional language network development, language ability, and cortical growth, thereby adding to our understanding of the neural basis of language acquisition in young children.
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Affiliation(s)
- Ting Qi
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Gesa Schaadt
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Education and Psychology, Free University of Berlin, Berlin, Germany
| | - Angela D Friederici
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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200
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Bijsterbosch JD, Valk SL, Wang D, Glasser MF. Recent developments in representations of the connectome. Neuroimage 2021; 243:118533. [PMID: 34469814 PMCID: PMC8842504 DOI: 10.1016/j.neuroimage.2021.118533] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 07/16/2021] [Accepted: 08/28/2021] [Indexed: 02/03/2023] Open
Abstract
Research into the human connectome (i.e., all connections in the human brain) with the use of resting state functional MRI has rapidly increased in popularity in recent years, especially with the growing availability of large-scale neuroimaging datasets. The goal of this review article is to describe innovations in functional connectome representations that have come about in the past 8 years, since the 2013 NeuroImage special issue on 'Mapping the Connectome'. In the period, research has shifted from group-level brain parcellations towards the characterization of the individualized connectome and of relationships between individual connectomic differences and behavioral/clinical variation. Achieving subject-specific accuracy in parcel boundaries while retaining cross-subject correspondence is challenging, and a variety of different approaches are being developed to meet this challenge, including improved alignment, improved noise reduction, and robust group-to-subject mapping approaches. Beyond the interest in the individualized connectome, new representations of the data are being studied to complement the traditional parcellated connectome representation (i.e., pairwise connections between distinct brain regions), such as methods that capture overlapping and smoothly varying patterns of connectivity ('gradients'). These different connectome representations offer complimentary insights into the inherent functional organization of the brain, but challenges for functional connectome research remain. Interpretability will be improved by future research towards gaining insights into the neural mechanisms underlying connectome observations obtained from functional MRI. Validation studies comparing different connectome representations are also needed to build consensus and confidence to proceed with clinical trials that may produce meaningful clinical translation of connectome insights.
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Affiliation(s)
- Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA.
| | - Sofie L Valk
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; INM-7, Forschungszentrum Jülich, Jülich, Germany
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Matthew F Glasser
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA; Department of Neuroscience, Washington University School of Medicine, Saint Louis, Missouri, 63110, USA
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