151
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Huckins JF, daSilva AW, Wang R, Wang W, Hedlund EL, Murphy EI, Lopez RB, Rogers C, Holtzheimer PE, Kelley WM, Heatherton TF, Wagner DD, Haxby JV, Campbell AT. Fusing Mobile Phone Sensing and Brain Imaging to Assess Depression in College Students. Front Neurosci 2019; 13:248. [PMID: 30949024 PMCID: PMC6437560 DOI: 10.3389/fnins.2019.00248] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2018] [Accepted: 03/04/2019] [Indexed: 12/17/2022] Open
Abstract
As smartphone usage has become increasingly prevalent in our society, so have rates of depression, particularly among young adults. Individual differences in smartphone usage patterns have been shown to reflect individual differences in underlying affective processes such as depression (Wang et al., 2018). In the current study, a positive relationship was identified between smartphone screen time (e.g., phone unlock duration) and resting-state functional connectivity (RSFC) between the subgenual cingulate cortex (sgCC), a brain region implicated in depression and antidepressant treatment response, and regions of the ventromedial/orbitofrontal cortex (OFC), such that increased phone usage was related to stronger connectivity between these regions. This cluster was subsequently used to constrain subsequent analyses looking at individual differences in depressive symptoms in the same cohort and observed partial replication in a separate cohort. Similar analyses were subsequently performed on metrics of circadian rhythm consistency showing a negative relationship between connectivity of the sgCC and OFC. The data and analyses presented here provide relatively simplistic preliminary analyses which replicate and provide an initial step in combining functional brain activity and smartphone usage patterns to better understand issues related to mental health. Smartphones are a prevalent part of modern life and the usage of mobile sensing data from smartphones promises to be an important tool for mental health diagnostics and neuroscience research.
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Affiliation(s)
- Jeremy F. Huckins
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Alex W. daSilva
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Rui Wang
- Department of Computer Science, Dartmouth College, Hanover, NH, United States
| | - Weichen Wang
- Department of Computer Science, Dartmouth College, Hanover, NH, United States
| | - Elin L. Hedlund
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Eilis I. Murphy
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Richard B. Lopez
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Courtney Rogers
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Paul E. Holtzheimer
- National Center for PTSD, White River Junction, VT, United States
- Department of Psychiatry, Dartmouth–Hitchcock Medical Center, Lebanon, NH, United States
| | - William M. Kelley
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Todd F. Heatherton
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Dylan D. Wagner
- Department of Psychology, The Ohio State University, Columbus, OH, United States
| | - James V. Haxby
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Andrew T. Campbell
- Department of Computer Science, Dartmouth College, Hanover, NH, United States
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152
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Anderson AN, King JB, Anderson JS. Neuroimaging in Psychiatry and Neurodevelopment: why the emperor has no clothes. Br J Radiol 2019; 92:20180910. [PMID: 30864835 DOI: 10.1259/bjr.20180910] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Neuroimaging has been a dominant force in guiding research into psychiatric and neurodevelopmental disorders for decades, yet researchers have been unable to formulate sensitive or specific imaging tests for these conditions. The search for neuroimaging biomarkers has been constrained by limited reproducibility of imaging techniques, limited tools for evaluating neurochemistry, heterogeneity of patient populations not defined by brain-based phenotypes, limited exploration of temporal components of brain function, and relatively few studies evaluating developmental and longitudinal trajectories of brain function. Opportunities for development of clinically impactful imaging metrics include longer duration functional imaging data sets, new engineering approaches to mitigate suboptimal spatiotemporal resolution, improvements in image post-processing and analysis strategies, big data approaches combined with data sharing of multisite imaging samples, and new techniques that allow dynamical exploration of brain function across multiple timescales. Despite narrow clinical impact of neuroimaging methods, there is reason for optimism that imaging will contribute to diagnosis, prognosis, and treatment monitoring for psychiatric and neurodevelopmental disorders in the near future.
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Affiliation(s)
| | - Jace B King
- 2Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT
| | - Jeffrey S Anderson
- 2Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT
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153
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Urchs S, Armoza J, Moreau C, Benhajali Y, St-Aubin J, Orban P, Bellec P. MIST: A multi-resolution parcellation of functional brain networks. ACTA ACUST UNITED AC 2019. [DOI: 10.12688/mniopenres.12767.2] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The functional architecture of the brain is organized across multiple levels of spatial resolutions, from distributed networks to the localized areas they are made of. A brain parcellation that defines functional nodes at multiple resolutions is required to investigate the functional connectome across these scales. Here we present the Multiresolution Intrinsic Segmentation Template (MIST), a multi-resolution group level parcellation of the cortical, subcortical and cerebellar gray matter. The individual MIST parcellations match other published group parcellations in internal homogeneity and reproducibility and perform very well in real-world application benchmarks. In addition, the MIST parcellations are fully annotated and provide a hierarchical decomposition of functional brain networks across nine resolutions (7 to 444 functional parcels). We hope that the MIST parcellation will accelerate research in brain connectivity across resolutions. Because visualizing multiresolution parcellations is challenging, we provide an interactive web interface to explore the MIST. The MIST is also available through the popular nilearn toolbox.
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154
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Kiar G, Brown ST, Glatard T, Evans AC. A Serverless Tool for Platform Agnostic Computational Experiment Management. Front Neuroinform 2019; 13:12. [PMID: 30890927 PMCID: PMC6411646 DOI: 10.3389/fninf.2019.00012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 02/15/2019] [Indexed: 01/22/2023] Open
Abstract
Neuroscience has been carried into the domain of big data and high performance computing (HPC) on the backs of initiatives in data collection and an increasingly compute-intensive tools. While managing HPC experiments requires considerable technical acumen, platforms, and standards have been developed to ease this burden on scientists. While web-portals make resources widely accessible, data organizations such as the Brain Imaging Data Structure and tool description languages such as Boutiques provide researchers with a foothold to tackle these problems using their own datasets, pipelines, and environments. While these standards lower the barrier to adoption of HPC and cloud systems for neuroscience applications, they still require the consolidation of disparate domain-specific knowledge. We present Clowdr, a lightweight tool to launch experiments on HPC systems and clouds, record rich execution records, and enable the accessible sharing and re-launch of experimental summaries and results. Clowdr uniquely sits between web platforms and bare-metal applications for experiment management by preserving the flexibility of do-it-yourself solutions while providing a low barrier for developing, deploying and disseminating neuroscientific analysis.
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Affiliation(s)
- Gregory Kiar
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Shawn T. Brown
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Tristan Glatard
- Department of Computer Science, Concordia University, Montreal, QC, Canada
| | - Alan C. Evans
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
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155
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Zuo XN, Biswal BB, Poldrack RA. Editorial: Reliability and Reproducibility in Functional Connectomics. Front Neurosci 2019; 13:117. [PMID: 30842722 PMCID: PMC6391345 DOI: 10.3389/fnins.2019.00117] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2018] [Accepted: 01/31/2019] [Indexed: 11/13/2022] Open
Affiliation(s)
- Xi-Nian Zuo
- Key Laboratory of Brain and Education, Nanning Normal University, Nanning, China
- Department of Psychology, University of Chinese Academy of Science, Beijing, China
- CAS Key Laboratory of Behavioral Sciences, Institute of Psychology, Beijing, China
- Magnetic Resonance Imaging Research Center, CAS Institute of Psychology, Beijing, China
- Research Center for Lifespan Development of Mind and Brain, CAS Institute of Psychology, Beijing, China
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Bharat B. Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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156
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Xia Y, Chen Q, Shi L, Li M, Gong W, Chen H, Qiu J. Tracking the dynamic functional connectivity structure of the human brain across the adult lifespan. Hum Brain Mapp 2019; 40:717-728. [PMID: 30515914 PMCID: PMC6865727 DOI: 10.1002/hbm.24385] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Accepted: 08/27/2018] [Indexed: 12/12/2022] Open
Abstract
The transition from early adulthood to the elder is marked by functional and structural brain transformations. Many previous studies examined the correlation between the functional connectivity (FC) and aging using resting-state fMRI. Results showed that the changes in FC are linked to aging as well as the cognitive ability changes. However, some researchers proposed that the FC is not static but dynamic changes during the resting-state fMRI scan. In this study, we examined the correlation between the resting-state dynamic functional network connectivity and age using resting scan data of 434 subjects. The results suggested: (a) age is negatively associated with variability of dynamic functional network connectivity state; (b) the dwell time of each age range spends in each state is different; (c) the dynamic graph metrics curve of each age ranges is different and 19-30 age range has the largest average global efficiency and average local efficiency; (d) some dynamic functional network connectivity measures were correlated to the certain cognitive ability. Overall, the results suggested the changes in dynamic functional network connectivity measures may be a characteristic of the aging process and in further investigations may provide a deep understanding of the aging process.
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Affiliation(s)
- Yunman Xia
- Key Laboratory of Cognition and Personality (Ministry of Education)ChongqingChina
- School of Psychology, Southwest UniversityChongqingChina
| | - Qunlin Chen
- Key Laboratory of Cognition and Personality (Ministry of Education)ChongqingChina
- School of Psychology, Southwest UniversityChongqingChina
| | - Liang Shi
- Key Laboratory of Cognition and Personality (Ministry of Education)ChongqingChina
- School of Psychology, Southwest UniversityChongqingChina
| | - MengZe Li
- Key Laboratory of Cognition and Personality (Ministry of Education)ChongqingChina
- School of Psychology, Southwest UniversityChongqingChina
| | - Weikang Gong
- Key Laboratory of Computational BiologyCASMPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of SciencesShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
| | - Hong Chen
- Key Laboratory of Cognition and Personality (Ministry of Education)ChongqingChina
- School of Psychology, Southwest UniversityChongqingChina
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (Ministry of Education)ChongqingChina
- School of Psychology, Southwest UniversityChongqingChina
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157
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The individual functional connectome is unique and stable over months to years. Neuroimage 2019; 189:676-687. [PMID: 30721751 DOI: 10.1016/j.neuroimage.2019.02.002] [Citation(s) in RCA: 126] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 01/30/2019] [Accepted: 02/01/2019] [Indexed: 12/30/2022] Open
Abstract
Functional connectomes computed from fMRI provide a means to characterize individual differences in the patterns of BOLD synchronization across regions of the entire brain. Using four resting-state fMRI datasets with a wide range of ages, we show that individual differences of the functional connectome are stable across 3 months to 1-2 years (and even detectable at above-chance levels across 3 years). Medial frontal and frontoparietal networks appear to be both unique and stable, resulting in high ID rates, as did a combination of these two networks. We conduct analyses demonstrating that these results are not driven by head motion. We also show that edges contributing the most to a successful ID tend to connect nodes in the frontal and parietal cortices, while edges contributing the least tend to connect cross-hemispheric homologs. Our results demonstrate that the functional connectome is stable across years and that high ID rates are not an idiosyncratic aspect of a specific dataset, but rather reflect stable individual differences in the functional connectivity of the brain.
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158
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Abstract
The availability of cloud computing services has enabled the widespread adoption of the "software as a service" (SaaS) approach for software distribution, which utilizes network-based access to applications running on centralized servers. In this paper we apply the SaaS approach to neuroimaging-based age prediction. Our system, named "NAPR" (Neuroanatomical Age Prediction using R), provides access to predictive modeling software running on a persistent cloud-based Amazon Web Services (AWS) compute instance. The NAPR framework allows external users to estimate the age of individual subjects using cortical thickness maps derived from their own locally processed T1-weighted whole brain MRI scans. As a demonstration of the NAPR approach, we have developed two age prediction models that were trained using healthy control data from the ABIDE, CoRR, DLBS and NKI Rockland neuroimaging datasets (total N = 2367, age range 6-89 years). The provided age prediction models were trained using (i) relevance vector machines and (ii) Gaussian processes machine learning methods applied to cortical thickness surfaces obtained using Freesurfer v5.3. We believe that this transparent approach to out-of-sample evaluation and comparison of neuroimaging age prediction models will facilitate the development of improved age prediction models and allow for robust evaluation of the clinical utility of these methods.
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Affiliation(s)
- Heath R Pardoe
- Comprehensive Epilepsy Center, New York University School of Medicine, 223 East 34th St, New York, NY, 10016, USA.
| | - Ruben Kuzniecky
- Comprehensive Epilepsy Center, New York University School of Medicine, 223 East 34th St, New York, NY, 10016, USA
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159
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Makowski C, Lepage M, Evans AC. Head motion: the dirty little secret of neuroimaging in psychiatry. J Psychiatry Neurosci 2019; 44:62-68. [PMID: 30565907 PMCID: PMC6306289 DOI: 10.1503/jpn.180022] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Psychiatry is at a crossroads when choosing final samples for analysis of neuroimaging data. Many patient populations exhibit significantly increased motion in the scanner compared with healthy controls, suggesting that more patients would need to be excluded to obtain a clean sample. However, this need is often overshadowed by the extensive amount of time and effort required to recruit these valuable and uncommon samples. This commentary sheds light on the impact of motion on imaging studies, drawing examples from psychiatric patient samples to better understand how head motion can confound interpretation of clinically oriented questions. We discuss the impact of even subtle motion artifacts on the interpretation of results as well as how different levels of stringency in quality control can affect findings within nearly identical samples. We also summarize recent initiatives toward harmonization of quality-control procedures as well as tools to prospectively and retrospectively correct for motion artifacts.
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Affiliation(s)
- Carolina Makowski
- From the McGill Centre for Integrative Neuroscience, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Que., Canada (Makowski, Evans); and the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Verdun, Que., Canada (Makowski, Lepage)
| | - Martin Lepage
- From the McGill Centre for Integrative Neuroscience, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Que., Canada (Makowski, Evans); and the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Verdun, Que., Canada (Makowski, Lepage)
| | - Alan C. Evans
- From the McGill Centre for Integrative Neuroscience, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Que., Canada (Makowski, Evans); and the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Verdun, Que., Canada (Makowski, Lepage)
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160
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Development of Neuroimaging-Based Biomarkers in Psychiatry. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1192:159-195. [PMID: 31705495 DOI: 10.1007/978-981-32-9721-0_9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This chapter presents an overview of accumulating neuroimaging data with emphasis on translational potential. The subject will be described in the context of three disease states, i.e., schizophrenia, bipolar disorder, and major depressive disorder, and for three clinical goals, i.e., disease risk assessment, subtyping, and treatment decision.
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161
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162
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Mohaddes Z, Das S, Abou-Haidar R, Safi-Harab M, Blader D, Callegaro J, Henri-Bellemare C, Tunteng JF, Evans L, Campbell T, Lo D, Morin PE, Whitehead V, Chertkow H, Evans AC. National Neuroinformatics Framework for Canadian Consortium on Neurodegeneration in Aging (CCNA). Front Neuroinform 2018; 12:85. [PMID: 30622468 PMCID: PMC6308193 DOI: 10.3389/fninf.2018.00085] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Accepted: 10/31/2018] [Indexed: 01/29/2023] Open
Abstract
The Canadian Institutes for Health Research (CIHR) launched the "International Collaborative Research Strategy for Alzheimer's Disease" as a signature initiative, focusing on Alzheimer's Disease (AD) and related neurodegenerative disorders (NDDs). The Canadian Consortium for Neurodegeneration and Aging (CCNA) was subsequently established to coordinate and strengthen Canadian research on AD and NDDs. To facilitate this research, CCNA uses LORIS, a modular data management system that integrates acquisition, storage, curation, and dissemination across multiple modalities. Through an unprecedented national collaboration studying various groups of dementia-related diagnoses, CCNA aims to investigate and develop proactive treatment strategies to improve disease prognosis and quality of life of those affected. However, this constitutes a unique technical undertaking, as heterogeneous data collected from sites across Canada must be uniformly organized, stored, and processed in a consistent manner. Currently clinical, neuropsychological, imaging, genomic, and biospecimen data for 509 CCNA subjects have been uploaded to LORIS. In addition, data validation is handled through a number of quality control (QC) measures such as double data entry (DDE), conflict flagging and resolution, imaging protocol checks, and visual imaging quality validation. Site coordinators are also notified of incidental findings found in MRI reads or biosample analyses. Data is then disseminated to CCNA researchers via a web-based Data-Querying Tool (DQT). This paper will detail the wide array of capabilities handled by LORIS for CCNA, aiming to provide the necessary neuroinformatic infrastructure for this nation-wide investigation of healthy and diseased aging.
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Affiliation(s)
- Zia Mohaddes
- McGill Centre for Integrative Neuroscience, Montreal, QC, Canada
- Montreal Neurological Institute, Montreal, QC, Canada
| | - Samir Das
- McGill Centre for Integrative Neuroscience, Montreal, QC, Canada
- Montreal Neurological Institute, Montreal, QC, Canada
| | - Rida Abou-Haidar
- McGill Centre for Integrative Neuroscience, Montreal, QC, Canada
- Montreal Neurological Institute, Montreal, QC, Canada
| | - Mouna Safi-Harab
- McGill Centre for Integrative Neuroscience, Montreal, QC, Canada
- Montreal Neurological Institute, Montreal, QC, Canada
| | - David Blader
- McGill Centre for Integrative Neuroscience, Montreal, QC, Canada
- Montreal Neurological Institute, Montreal, QC, Canada
| | - Jessica Callegaro
- McGill Centre for Integrative Neuroscience, Montreal, QC, Canada
- Montreal Neurological Institute, Montreal, QC, Canada
| | - Charlie Henri-Bellemare
- McGill Centre for Integrative Neuroscience, Montreal, QC, Canada
- Montreal Neurological Institute, Montreal, QC, Canada
| | - Jingla-Fri Tunteng
- McGill Centre for Integrative Neuroscience, Montreal, QC, Canada
- Montreal Neurological Institute, Montreal, QC, Canada
| | - Leigh Evans
- McGill Centre for Integrative Neuroscience, Montreal, QC, Canada
- Montreal Neurological Institute, Montreal, QC, Canada
| | - Tara Campbell
- McGill Centre for Integrative Neuroscience, Montreal, QC, Canada
- Montreal Neurological Institute, Montreal, QC, Canada
| | - Derek Lo
- McGill Centre for Integrative Neuroscience, Montreal, QC, Canada
- Montreal Neurological Institute, Montreal, QC, Canada
| | - Pierre-Emmanuel Morin
- Centre de recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada
| | | | - Howard Chertkow
- Lady Davis Institute, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Alan C. Evans
- McGill Centre for Integrative Neuroscience, Montreal, QC, Canada
- Montreal Neurological Institute, Montreal, QC, Canada
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163
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Hu CP, Jiang X, Jeffrey R, Zuo XN. Open science as a better gatekeeper for science and society: a perspective from neurolaw. Sci Bull (Beijing) 2018; 63:1529-1531. [PMID: 36751069 DOI: 10.1016/j.scib.2018.11.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Chuan-Peng Hu
- Deutsches Resilienz Zentrum (DRZ) & Neuroimaging Center (NIC), University Medical Center of the Johannes Gutenberg University, Mainz 55131, Germany.
| | - Xiaoming Jiang
- Department of Psychology, Tongji University, Shanghai 200092, China
| | - Ricky Jeffrey
- Language Center, International Campus, Zhejiang University, Haining 314400, China
| | - Xi-Nian Zuo
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, the Chinese Academy of Sciences, Beijing 100101, China.
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164
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Yamashita M, Yoshihara Y, Hashimoto R, Yahata N, Ichikawa N, Sakai Y, Yamada T, Matsukawa N, Okada G, Tanaka SC, Kasai K, Kato N, Okamoto Y, Seymour B, Takahashi H, Kawato M, Imamizu H. A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity. eLife 2018; 7:38844. [PMID: 30526859 PMCID: PMC6324880 DOI: 10.7554/elife.38844] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 12/08/2018] [Indexed: 11/24/2022] Open
Abstract
Working memory deficits are present in many neuropsychiatric diseases with diagnosis-related severity. However, it is unknown whether this common behavioral abnormality is a continuum explained by a neural mechanism shared across diseases or a set of discrete dysfunctions. Here, we performed predictive modeling to examine working memory ability (WMA) as a function of normative whole-brain connectivity across psychiatric diseases. We built a quantitative model for letter three-back task performance in healthy participants, using resting state functional magnetic resonance imaging (rs-fMRI). This normative model was applied to independent participants (N = 965) including four psychiatric diagnoses. Individual’s predicted WMA significantly correlated with a measured WMA in both healthy population and schizophrenia. Our predicted effect size estimates on WMA impairment were comparable to previous meta-analysis results. These results suggest a general association between brain connectivity and working memory ability applicable commonly to health and psychiatric diseases.
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Affiliation(s)
- Masahiro Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
| | - Yujiro Yoshihara
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ryuichiro Hashimoto
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Noriaki Yahata
- Department of Youth Mental Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Molecular Imaging Center, National Institute of Radiological Sciences, Chiba, Japan
| | - Naho Ichikawa
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yuki Sakai
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Takashi Yamada
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Noriko Matsukawa
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Saori C Tanaka
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
| | - Kiyoto Kasai
- Department of Youth Mental Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Nobumasa Kato
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Ben Seymour
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.,Center for Information and Neural Networks, National Institute of Information and Communications Technology, Osaka, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
| | - Hiroshi Imamizu
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Department of Psychology, The University of Tokyo, Tokyo, Japan
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165
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Vakli P, Deák-Meszlényi RJ, Hermann P, Vidnyánszky Z. Transfer learning improves resting-state functional connectivity pattern analysis using convolutional neural networks. Gigascience 2018; 7:5160132. [PMID: 30395218 PMCID: PMC6283213 DOI: 10.1093/gigascience/giy130] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 10/24/2018] [Indexed: 12/15/2022] Open
Abstract
Background Deep learning is gaining importance in the prediction of cognitive states and brain pathology based on neuroimaging data. Including multiple hidden layers in artificial neural networks enables unprecedented predictive power; however, the proper training of deep neural networks requires thousands of exemplars. Collecting this amount of data is not feasible in typical neuroimaging experiments. A handy solution to this problem, which has largely fallen outside the scope of deep learning applications in neuroimaging, is to repurpose deep networks that have already been trained on large datasets by fine-tuning them to target datasets/tasks with fewer exemplars. Here, we investigated how this method, called transfer learning, can aid age category classification and regression based on brain functional connectivity patterns derived from resting-state functional magnetic resonance imaging. We trained a connectome-convolutional neural network on a larger public dataset and then examined how the knowledge learned can be used effectively to perform these tasks on smaller target datasets collected with a different type of scanner and/or imaging protocol and pre-processing pipeline. Results Age classification on the target datasets benefitted from transfer learning. Significant improvement (∼9%–13% increase in accuracy) was observed when the convolutional layers’ weights were initialized based on the values learned on the public dataset and then fine-tuned to the target datasets. Transfer learning also appeared promising in improving the otherwise poor prediction of chronological age. Conclusions Transfer learning is a plausible solution to adapt convolutional neural networks to neuroimaging data with few exemplars and different data acquisition and pre-processing protocols.
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Affiliation(s)
- Pál Vakli
- Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2., 1117 Budapest, Hungary
| | - Regina J Deák-Meszlényi
- Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2., 1117 Budapest, Hungary.,Department of Cognitive Science, Budapest University of Technology and Economics, Egry József utca 1., 1111 Budapest, Hungary
| | - Petra Hermann
- Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2., 1117 Budapest, Hungary
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2., 1117 Budapest, Hungary.,Department of Cognitive Science, Budapest University of Technology and Economics, Egry József utca 1., 1111 Budapest, Hungary
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166
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Ciric R, Rosen AFG, Erus G, Cieslak M, Adebimpe A, Cook PA, Bassett DS, Davatzikos C, Wolf DH, Satterthwaite TD. Mitigating head motion artifact in functional connectivity MRI. Nat Protoc 2018; 13:2801-2826. [PMID: 30446748 PMCID: PMC8161527 DOI: 10.1038/s41596-018-0065-y] [Citation(s) in RCA: 180] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Participant motion during functional magnetic resonance image (fMRI) acquisition produces spurious signal fluctuations that can confound measures of functional connectivity. Without mitigation, motion artifact can bias statistical inferences about relationships between connectivity and individual differences. To counteract motion artifact, this protocol describes the implementation of a validated, high-performance denoising strategy that combines a set of model features, including physiological signals, motion estimates, and mathematical expansions, to target both widespread and focal effects of subject movement. This protocol can be used to reduce motion-related variance to near zero in studies of functional connectivity, providing up to a 100-fold improvement over minimal-processing approaches in large datasets. Image denoising requires 40 min to 4 h of computing per image, depending on model specifications and data dimensionality. The protocol additionally includes instructions for assessing the performance of a denoising strategy. Associated software implements all denoising and diagnostic procedures, using a combination of established image-processing libraries and the eXtensible Connectivity Pipeline (XCP) software.
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Affiliation(s)
- Rastko Ciric
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Adon F G Rosen
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew Cieslak
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Azeez Adebimpe
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Philip A Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel H Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
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167
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Woletz M, Hoffmann A, Tik M, Sladky R, Lanzenberger R, Robinson S, Windischberger C. Beware detrending: Optimal preprocessing pipeline for low-frequency fluctuation analysis. Hum Brain Mapp 2018; 40:1571-1582. [PMID: 30430691 PMCID: PMC6587723 DOI: 10.1002/hbm.24468] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Revised: 09/21/2018] [Accepted: 10/30/2018] [Indexed: 12/19/2022] Open
Abstract
Resting‐state functional magnetic resonance imaging (rs‐fMRI) offers the possibility to assess brain function independent of explicit tasks and individual performance. This absence of explicit stimuli in rs‐fMRI makes analyses more susceptible to nonneural signal fluctuations than task‐based fMRI. Data preprocessing is a critical procedure to minimise contamination by artefacts related to motion and physiology. We herein investigate the effects of different preprocessing strategies on the amplitude of low‐frequency fluctuations (ALFFs) and its fractional counterpart, fractional ALFF (fALFF). Sixteen artefact reduction schemes based on nuisance regression are applied to data from 82 subjects acquired at 1.5 T, 30 subjects at 3 T, and 23 subjects at 7 T, respectively. In addition, we examine test–retest variance and effects of bias correction. In total, 569 data sets are included in this study. Our results show that full artefact reduction reduced test–retest variance by up to 50%. Polynomial detrending of rs‐fMRI data has a positive effect on group‐level t‐values for ALFF but, importantly, a negative effect for fALFF. We show that the normalisation process intrinsic to fALFF calculation causes the observed reduction and introduce a novel measure for low‐frequency fluctuations denoted as high‐frequency ALFF (hfALFF). We demonstrate that hfALFF values are not affected by the negative detrending effects seen in fALFF data. Still, highest grey matter (GM) group‐level t‐values were obtained for fALFF data without detrending, even when compared to an exploratory detrending approach based on autocorrelation measures. From our results, we recommend the use of full nuisance regression including polynomial detrending in ALFF data, but to refrain from using polynomial detrending in fALFF data. Such optimised preprocessing increases GM group‐level t‐values by up to 60%.
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Affiliation(s)
- Michael Woletz
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - André Hoffmann
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Martin Tik
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Ronald Sladky
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Simon Robinson
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Christian Windischberger
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
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168
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Shan Y, Wang YS, Zhang M, Rong DD, Zhao ZL, Cao YX, Wang PP, Deng ZZ, Ma QF, Li KC, Zuo XN, Lu J. Homotopic Connectivity in Early Pontine Infarction Predicts Late Motor Recovery. Front Neurol 2018; 9:907. [PMID: 30429821 PMCID: PMC6220368 DOI: 10.3389/fneur.2018.00907] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Accepted: 10/08/2018] [Indexed: 11/13/2022] Open
Abstract
Connectivity-based methods are essential to explore brain reorganization after a stroke and to provide meaningful predictors for late motor recovery. We aim to investigate the homotopic connectivity alterations during a 180-day follow-up of patients with pontine infarction to find an early biomarker for late motor recovery prediction. In our study, resting-state functional MRI was performed in 15 patients (11 males, 4 females, age: 57.87 ± 6.50) with unilateral pontine infarction and impaired motor function during a period of 6 months (7, 14, 30, 90, and 180 days after stroke onset). Clinical neurological assessments were performed using the Fugl–Meyer scale (FM).15 matched healthy volunteers were also recruited. Whole-brain functional homotopy in each individual scan was measured by voxel-mirrored homotopic connectivity (VMHC) values. Group-level analysis was performed between stroke patients and normal controls. A Pearson correlation was performed to evaluate correlations between early VMHC and the subsequent 4 visits for behavioral measures during day 14 to day 180. We found in early stroke (within 7 days after onset), decreased VMHC was detected in the bilateral precentral and postcentral gyrus and precuneus/posterior cingulate cortex (PCC), while increased VMHC was found in the hippocampus/amygdala and frontal pole (P < 0.01). During follow-up, VMHC in the precentral and postcentral gyrus increased to the normal level from day 90, while VMHC in the precuneus/PCC presented decreased intensity during all time points (P < 0.05). The hippocampus/amygdala and frontal pole presented a higher level of VMHC during all time points (P < 0.05). Negative correlation was found between early VMHC in the hippocampus/amygdala with FM on day 14 (r = −0.59, p = 0.021), day 30 (r = −0.643, p = 0.01), day 90 (r = −0.693, p = 0.004), and day 180 (r = −0.668, p = 0.007). Furthermore, early VMHC in the frontal pole was negatively correlated with FM scores on day 30 (r = −0.662, p = 0.013), day 90 (r = −0.606, p = 0.017), and day 180 (r = −0.552, p = 0.033). Our study demonstrated the potential utility of early homotopic connectivity for prediction of late motor recovery in pontine infarction.
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Affiliation(s)
- Yi Shan
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Yin-Shan Wang
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.,Key Laboratory of Behavioral Science, Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Miao Zhang
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Dong-Dong Rong
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Zhi-Lian Zhao
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Yan-Xiang Cao
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Pei-Pei Wang
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Zheng-Zheng Deng
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.,Key Laboratory of Behavioral Science, Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Qing-Feng Ma
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Kun-Cheng Li
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Xi-Nian Zuo
- Key Laboratory of Behavioral Science, Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China.,Department of Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China
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169
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ten Kate M, Ingala S, Schwarz AJ, Fox NC, Chételat G, van Berckel BNM, Ewers M, Foley C, Gispert JD, Hill D, Irizarry MC, Lammertsma AA, Molinuevo JL, Ritchie C, Scheltens P, Schmidt ME, Visser PJ, Waldman A, Wardlaw J, Haller S, Barkhof F. Secondary prevention of Alzheimer's dementia: neuroimaging contributions. Alzheimers Res Ther 2018; 10:112. [PMID: 30376881 PMCID: PMC6208183 DOI: 10.1186/s13195-018-0438-z] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 10/10/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND In Alzheimer's disease (AD), pathological changes may arise up to 20 years before the onset of dementia. This pre-dementia window provides a unique opportunity for secondary prevention. However, exposing non-demented subjects to putative therapies requires reliable biomarkers for subject selection, stratification, and monitoring of treatment. Neuroimaging allows the detection of early pathological changes, and longitudinal imaging can assess the effect of interventions on markers of molecular pathology and rates of neurodegeneration. This is of particular importance in pre-dementia AD trials, where clinical outcomes have a limited ability to detect treatment effects within the typical time frame of a clinical trial. We review available evidence for the use of neuroimaging in clinical trials in pre-dementia AD. We appraise currently available imaging markers for subject selection, stratification, outcome measures, and safety in the context of such populations. MAIN BODY Amyloid positron emission tomography (PET) is a validated in-vivo marker of fibrillar amyloid plaques. It is appropriate for inclusion in trials targeting the amyloid pathway, as well as to monitor treatment target engagement. Amyloid PET, however, has limited ability to stage the disease and does not perform well as a prognostic marker within the time frame of a pre-dementia AD trial. Structural magnetic resonance imaging (MRI), providing markers of neurodegeneration, can improve the identification of subjects at risk of imminent decline and hence play a role in subject inclusion. Atrophy rates (either hippocampal or whole brain), which can be reliably derived from structural MRI, are useful in tracking disease progression and have the potential to serve as outcome measures. MRI can also be used to assess comorbid vascular pathology and define homogeneous groups for inclusion or for subject stratification. Finally, MRI also plays an important role in trial safety monitoring, particularly the identification of amyloid-related imaging abnormalities (ARIA). Tau PET to measure neurofibrillary tangle burden is currently under development. Evidence to support the use of advanced MRI markers such as resting-state functional MRI, arterial spin labelling, and diffusion tensor imaging in pre-dementia AD is preliminary and requires further validation. CONCLUSION We propose a strategy for longitudinal imaging to track early signs of AD including quantitative amyloid PET and yearly multiparametric MRI.
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Affiliation(s)
- Mara ten Kate
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - Adam J. Schwarz
- Takeda Pharmaceuticals Comparny, Cambridge, MA USA
- Eli Lilly and Company, Indianapolis, Indiana USA
| | - Nick C. Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Gaël Chételat
- Institut National de la Santé et de la Recherche Médicale, Inserm UMR-S U1237, Université de Caen-Normandie, GIP Cyceron, Caen, France
| | - Bart N. M. van Berckel
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - Michael Ewers
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | | | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | | | | | - Adriaan A. Lammertsma
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | - Craig Ritchie
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Philip Scheltens
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | | | - Pieter Jelle Visser
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | - Adam Waldman
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Joanna Wardlaw
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Sven Haller
- Affidea Centre de Diagnostic Radiologique de Carouge, Geneva, Switzerland
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
- Insititutes of Neurology and Healthcare Engineering, University College London, London, UK
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170
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Dickie EW, Ameis SH, Shahab S, Calarco N, Smith DE, Miranda D, Viviano JD, Voineskos AN. Personalized Intrinsic Network Topography Mapping and Functional Connectivity Deficits in Autism Spectrum Disorder. Biol Psychiatry 2018; 84:278-286. [PMID: 29703592 PMCID: PMC6076333 DOI: 10.1016/j.biopsych.2018.02.1174] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Revised: 02/23/2018] [Accepted: 02/27/2018] [Indexed: 12/17/2022]
Abstract
BACKGROUND Recent advances in techniques using functional magnetic resonance imaging data demonstrate individually specific variation in brain architecture in healthy individuals. To our knowledge, the effects of individually specific variation in complex brain disorders have not been previously reported. METHODS We developed a novel approach (Personalized Intrinsic Network Topography, PINT) for localizing individually specific resting-state networks using conventional resting-state functional magnetic resonance imaging scans. Using cross-sectional data from participants with autism spectrum disorder (ASD; n = 393) and typically developing (TD) control participants (n = 496) across 15 sites, we tested: 1) effect of diagnosis and age on the variability of intrinsic network locations and 2) whether prior findings of functional connectivity differences in persons with ASD compared with TD persons remain after PINT application. RESULTS We found greater variability in the spatial locations of resting-state networks within individuals with ASD compared with those in TD individuals. For TD persons, variability decreased from childhood into adulthood and increased in late life, following a U-shaped pattern that was not present in those with ASD. Comparison of intrinsic connectivity between groups revealed that the application of PINT decreased the number of hypoconnected regions in ASD. CONCLUSIONS Our results provide a new framework for measuring altered brain functioning in neurodevelopmental disorders that may have implications for tracking developmental course, phenotypic heterogeneity, and ultimately treatment response. We underscore the importance of accounting for individual variation in the study of complex brain disorders.
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Affiliation(s)
- Erin W Dickie
- Kimel Family Translational Imaging Genetics Research Laboratory, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada
| | - Stephanie H Ameis
- Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Margaret and Wallace McCain Centre for Child, Youth, and Family Mental Health, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Centre for Brain and Mental Health, the Hospital for Sick Children, Toronto, Canada
| | - Saba Shahab
- Kimel Family Translational Imaging Genetics Research Laboratory, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Navona Calarco
- Kimel Family Translational Imaging Genetics Research Laboratory, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada
| | - Dawn E Smith
- Kimel Family Translational Imaging Genetics Research Laboratory, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada
| | - Dayton Miranda
- Kimel Family Translational Imaging Genetics Research Laboratory, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada
| | - Joseph D Viviano
- Kimel Family Translational Imaging Genetics Research Laboratory, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada
| | - Aristotle N Voineskos
- Kimel Family Translational Imaging Genetics Research Laboratory, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, University of Toronto, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada.
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171
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Combining Non-negative Matrix Factorization and Sparse Coding for Functional Brain Overlapping Community Detection. Cognit Comput 2018. [DOI: 10.1007/s12559-018-9585-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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172
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Guo H, Yan P, Cheng C, Li Y, Chen J, Xu Y, Xiang J. fMRI classification method with multiple feature fusion based on minimum spanning tree analysis. Psychiatry Res Neuroimaging 2018; 277:14-27. [PMID: 29793077 DOI: 10.1016/j.pscychresns.2018.05.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 05/08/2018] [Accepted: 05/09/2018] [Indexed: 01/07/2023]
Abstract
Resting state functional brain networks have been widely studied in brain disease research. Conventional network analysis methods are hampered by differences in network size, density and normalization. Minimum spanning tree (MST) analysis has been recently suggested to ameliorate these limitations. Moreover, common MST analysis methods involve calculating quantifiable attributes and selecting these attributes as features in the classification. However, a disadvantage of these methods is that information about the topology of the network is not fully considered, limiting further improvement of classification performance. To address this issue, we propose a novel method combining brain region and subgraph features for classification, utilizing two feature types to quantify two properties of the network. We experimentally validated our proposed method using a major depressive disorder (MDD) patient dataset. The results indicated that MSTs of MDD patients were more similar to random networks and exhibited significant differences in certain regions involved in the limbic-cortical-striatal-pallidal-thalamic (LCSPT) circuit, which is considered to be a major pathological circuit of depression. Moreover, we demonstrated that this novel classification method could effectively improve classification accuracy and provide better interpretability. Overall, the current study demonstrated that different forms of feature representation provide complementary information.
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Affiliation(s)
- Hao Guo
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, PR China; National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, PR China.
| | - Pengpeng Yan
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, PR China
| | - Chen Cheng
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, PR China; National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, PR China
| | - Yao Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, PR China
| | - Junjie Chen
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, PR China
| | - Yong Xu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, PR China
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, PR China
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173
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Milham MP, Craddock RC, Son JJ, Fleischmann M, Clucas J, Xu H, Koo B, Krishnakumar A, Biswal BB, Castellanos FX, Colcombe S, Di Martino A, Zuo XN, Klein A. Assessment of the impact of shared brain imaging data on the scientific literature. Nat Commun 2018; 9:2818. [PMID: 30026557 PMCID: PMC6053414 DOI: 10.1038/s41467-018-04976-1] [Citation(s) in RCA: 84] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 06/05/2018] [Indexed: 01/14/2023] Open
Abstract
Data sharing is increasingly recommended as a means of accelerating science by facilitating collaboration, transparency, and reproducibility. While few oppose data sharing philosophically, a range of barriers deter most researchers from implementing it in practice. To justify the significant effort required for sharing data, funding agencies, institutions, and investigators need clear evidence of benefit. Here, using the International Neuroimaging Data-sharing Initiative, we present a case study that provides direct evidence of the impact of open sharing on brain imaging data use and resulting peer-reviewed publications. We demonstrate that openly shared data can increase the scale of scientific studies conducted by data contributors, and can recruit scientists from a broader range of disciplines. These findings dispel the myth that scientific findings using shared data cannot be published in high-impact journals, suggest the transformative power of data sharing for accelerating science, and underscore the need for implementing data sharing universally.
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Affiliation(s)
- Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA.
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, New York, 10962, NY, USA.
| | - R Cameron Craddock
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, New York, 10962, NY, USA
| | - Jake J Son
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA
| | - Michael Fleischmann
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA
| | - Jon Clucas
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA
| | - Helen Xu
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA
| | - Bonhwang Koo
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA
| | - Anirudh Krishnakumar
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA
- Centre de Recherches Interdisciplinaires, INSERM U1001, Dpt Frontières du Vivant et de l'Apprendre, University Paris Descartes, Sorbonne Paris Cité, Paris, 75014, France
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, 07102, NJ, USA
| | - F Xavier Castellanos
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, New York, 10962, NY, USA
- Department of Child and Adolescent Psychiatry, Hassenfeld Children's Hospital at NYU Langone, New York, 10016, NY, USA
| | - Stan Colcombe
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, New York, 10962, NY, USA
| | - Adriana Di Martino
- Department of Child and Adolescent Psychiatry, Hassenfeld Children's Hospital at NYU Langone, New York, 10016, NY, USA
| | - Xi-Nian Zuo
- Department of Psychology, University of Chinese Academy of Sciences (CAS), Beijing, 100049, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
- Research Center for Lifespan Development of Mind and Brain (CLIMB) and Magnetic Resonance Imaging Research Center, Institute of Psychology, Beijing, 100101, China
- Key Laboratory for Brain and Education Sciences, Guangxi Teachers Education University, Nanning, 530001, China
| | - Arno Klein
- Center for the Developing Brain, Child Mind Institute, New York, 10022, NY, USA
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174
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Abstract
In this paper we describe an open-access collection of multimodal neuroimaging data in schizophrenia for release to the community. Data were acquired from approximately 100 patients with schizophrenia and 100 age-matched controls during rest as well as several task activation paradigms targeting a hierarchy of cognitive constructs. Neuroimaging data include structural MRI, functional MRI, diffusion MRI, MR spectroscopic imaging, and magnetoencephalography. For three of the hypothesis-driven projects, task activation paradigms were acquired on subsets of ~200 volunteers which examined a range of sensory and cognitive processes (e.g., auditory sensory gating, auditory/visual multisensory integration, visual transverse patterning). Neuropsychological data were also acquired and genetic material via saliva samples were collected from most of the participants and have been typed for both genome-wide polymorphism data as well as genome-wide methylation data. Some results are also presented from the individual studies as well as from our data-driven multimodal analyses (e.g., multimodal examinations of network structure and network dynamics and multitask fMRI data analysis across projects). All data will be released through the Mind Research Network's collaborative informatics and neuroimaging suite (COINS).
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175
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Phan TV, Sima DM, Beelen C, Vanderauwera J, Smeets D, Vandermosten M. Evaluation of methods for volumetric analysis of pediatric brain data: The child metrix pipeline versus adult-based approaches. NEUROIMAGE-CLINICAL 2018; 19:734-744. [PMID: 30003026 PMCID: PMC6040578 DOI: 10.1016/j.nicl.2018.05.030] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 05/04/2018] [Accepted: 05/22/2018] [Indexed: 12/18/2022]
Abstract
Pediatric brain volumetric analysis based on Magnetic Resonance Imaging (MRI) is of particular interest in order to understand the typical brain development and to characterize neurodevelopmental disorders at an early age. However, it has been shown that the results can be biased due to head motion, inherent to pediatric data, and due to the use of methods based on adult brain data that are not able to accurately model the anatomical disparity of pediatric brains. To overcome these issues, we proposed childmetrix, a tool developed for the analysis of pediatric neuroimaging data that uses an age-specific atlas and a probabilistic model-based approach in order to segment the gray matter (GM) and white matter (WM). The tool was extensively validated on 55 scans of children between 5 and 6 years old (including 13 children with developmental dyslexia) and 10 pairs of test-retest scans of children between 6 and 8 years old and compared with two state-of-the-art methods using an adult atlas, namely icobrain (applying a probabilistic model-based segmentation) and Freesurfer (applying a surface model-based segmentation). The results obtained with childmetrix showed a better reproducibility of GM and WM segmentations and a better robustness to head motion in the estimation of GM volume compared to Freesurfer. Evaluated on two subjects, childmetrix showed good accuracy with 82-84% overlap with manual segmentation for both GM and WM, thereby outperforming the adult-based methods (icobrain and Freesurfer), especially for the subject with poor quality data. We also demonstrated that the adult-based methods needed double the number of subjects to detect significant morphological differences between dyslexics and typical readers. Once further developed and validated, we believe that childmetrix would provide appropriate and reliable measures for the examination of children's brain.
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Affiliation(s)
- Thanh Vân Phan
- icometrix, Research and Development, Leuven, Belgium; Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium.
| | - Diana M Sima
- icometrix, Research and Development, Leuven, Belgium
| | - Caroline Beelen
- Parenting and Special Education Research Unit, Faculty of Psychology and Educational Science, KU Leuven, Leuven, Belgium
| | - Jolijn Vanderauwera
- Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium; Parenting and Special Education Research Unit, Faculty of Psychology and Educational Science, KU Leuven, Leuven, Belgium
| | - Dirk Smeets
- icometrix, Research and Development, Leuven, Belgium
| | - Maaike Vandermosten
- Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium
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176
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Extracting orthogonal subject- and condition-specific signatures from fMRI data using whole-brain effective connectivity. Neuroimage 2018; 178:238-254. [PMID: 29753842 PMCID: PMC6057306 DOI: 10.1016/j.neuroimage.2018.04.070] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Revised: 04/16/2018] [Accepted: 04/30/2018] [Indexed: 12/19/2022] Open
Abstract
The study of brain communication based on fMRI data is often limited because such measurements are a mixture of session-to-session variability with subject- and condition-related information. Disentangling these contributions is crucial for real-life applications, in particular when only a few recording sessions are available. The present study aims to define a reliable standard for the extraction of multiple signatures from fMRI data, while verifying that they do not mix information about the different modalities (e.g., subjects and conditions such as tasks performed by them). In particular, condition-specific signatures should not be contaminated by subject-related information, since they aim to generalize over subjects. Practically, signatures correspond to subnetworks of directed interactions between brain regions (typically 100 covering the whole brain) supporting the subject and condition identification for single fMRI sessions. The key for robust prediction is using effective connectivity instead of functional connectivity. Our method demonstrates excellent generalization capabilities for subject identification in two datasets, using only a few sessions per subject as reference. Using another dataset with resting state and movie viewing, we show that the two signatures related to subjects and tasks correspond to distinct subnetworks, which are thus topologically orthogonal. Our results set solid foundations for applications tailored to individual subjects, such as clinical diagnostic.
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177
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Wu J, Ngo GH, Greve D, Li J, He T, Fischl B, Eickhoff SB, Yeo BTT. Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems. Hum Brain Mapp 2018; 39:3793-3808. [PMID: 29770530 PMCID: PMC6239990 DOI: 10.1002/hbm.24213] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 04/07/2018] [Accepted: 05/02/2018] [Indexed: 12/21/2022] Open
Abstract
The results of most neuroimaging studies are reported in volumetric (e.g., MNI152) or surface (e.g., fsaverage) coordinate systems. Accurate mappings between volumetric and surface coordinate systems can facilitate many applications, such as projecting fMRI group analyses from MNI152/Colin27 to fsaverage for visualization or projecting resting‐state fMRI parcellations from fsaverage to MNI152/Colin27 for volumetric analysis of new data. However, there has been surprisingly little research on this topic. Here, we evaluated three approaches for mapping data between MNI152/Colin27 and fsaverage coordinate systems by simulating the above applications: projection of group‐average data from MNI152/Colin27 to fsaverage and projection of fsaverage parcellations to MNI152/Colin27. Two of the approaches are currently widely used. A third approach (registration fusion) was previously proposed, but not widely adopted. Two implementations of the registration fusion (RF) approach were considered, with one implementation utilizing the Advanced Normalization Tools (ANTs). We found that RF‐ANTs performed the best for mapping between fsaverage and MNI152/Colin27, even for new subjects registered to MNI152/Colin27 using a different software tool (FSL FNIRT). This suggests that RF‐ANTs would be useful even for researchers not using ANTs. Finally, it is worth emphasizing that the most optimal approach for mapping data to a coordinate system (e.g., fsaverage) is to register individual subjects directly to the coordinate system, rather than via another coordinate system. Only in scenarios where the optimal approach is not possible (e.g., mapping previously published results from MNI152 to fsaverage), should the approaches evaluated in this manuscript be considered. In these scenarios, we recommend RF‐ANTs (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/registration/Wu2017_RegistrationFusion).
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Affiliation(s)
- Jianxiao Wu
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore City, Singapore
| | - Gia H Ngo
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore City, Singapore
| | - Douglas Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jingwei Li
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore City, Singapore
| | - Tong He
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore City, Singapore
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.,Harvard-MIT Division of Health Sciences and Technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts
| | - Simon B Eickhoff
- Medical Faculty, Heinrich-Heine University Düsseldorf, Institute for Systems Neuroscience, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore City, Singapore.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Center for Cognitive Neuroscience, Duke-NUS Medical School, Singapore, Singapore
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178
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An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. Neuroimage 2018; 171:415-436. [DOI: 10.1016/j.neuroimage.2017.12.073] [Citation(s) in RCA: 445] [Impact Index Per Article: 63.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Revised: 12/21/2017] [Accepted: 12/21/2017] [Indexed: 12/19/2022] Open
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179
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Zuo XN, He Y, Su X, Hou XH, Weng X, Li Q. Developmental population neuroscience: emerging from ICHBD. Sci Bull (Beijing) 2018; 63:331-332. [PMID: 36658866 DOI: 10.1016/j.scib.2018.01.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Affiliation(s)
- Xi-Nian Zuo
- Key Laboratory for Brain and Education Sciences, Guangxi Teachers Education University, Nanning 530001, China; 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; CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China.
| | - Ye He
- Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Xuequan Su
- Key Laboratory for Brain and Education Sciences, Guangxi Teachers Education University, Nanning 530001, China; Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiao-Hui Hou
- Key Laboratory for Brain and Education Sciences, Guangxi Teachers Education University, Nanning 530001, China; School of Education Sciences, Guangxi Teachers Education University, Nanning 530299, China
| | - Xuchu Weng
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Qiang Li
- Key Laboratory for Brain and Education Sciences, Guangxi Teachers Education University, Nanning 530001, China; School of Education Sciences, Guangxi Teachers Education University, Nanning 530299, China.
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180
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Temporal reliability of ultra-high field resting-state MRI for single-subject sensorimotor and language mapping. Neuroimage 2018; 168:499-508. [DOI: 10.1016/j.neuroimage.2016.11.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 10/29/2016] [Accepted: 11/12/2016] [Indexed: 11/19/2022] Open
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181
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Müller-Oehring EM, Kwon D, Nagel BJ, Sullivan EV, Chu W, Rohlfing T, Prouty D, Nichols BN, Poline JB, Tapert SF, Brown SA, Cummins K, Brumback T, Colrain IM, Baker FC, De Bellis MD, Voyvodic JT, Clark DB, Pfefferbaum A, Pohl KM. Influences of Age, Sex, and Moderate Alcohol Drinking on the Intrinsic Functional Architecture of Adolescent Brains. Cereb Cortex 2018; 28:1049-1063. [PMID: 28168274 PMCID: PMC6059181 DOI: 10.1093/cercor/bhx014] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Revised: 12/16/2016] [Indexed: 12/11/2022] Open
Abstract
The transition from adolescent to adult cognition and emotional control requires neurodevelopmental maturation likely involving intrinsic functional networks (IFNs). Normal neurodevelopment may be vulnerable to disruption from environmental insult such as alcohol consumption commonly initiated during adolescence. To test potential disruption to IFN maturation, we used resting-state functional magnetic resonance imaging (rs-fMRI) in 581 no-to-low alcohol-consuming and 117 moderate-to-high-drinking youth. Functional seed-to-voxel connectivity analysis assessed age, sex, and moderate alcohol drinking on default-mode, executive-control, salience, reward, and emotion networks and tested cognitive and motor coordination correlates of network connectivity. Among no-to-low alcohol-consuming adolescents, executive-control frontolimbicstriatal connectivity was stronger in older than younger adolescents, particularly boys, and predicted better ability in balance, memory, and impulse control. Connectivity patterns in moderate-to-high-drinking youth were tested mainly in late adolescence when drinking was initiated. Implicated was the emotion network with attenuated connectivity to default-mode network regions. Our cross-sectional rs-fMRI findings from this large cohort of adolescents show sexual dimorphism in connectivity and suggest neurodevelopmental rewiring toward stronger and spatially more distributed executive-control networking in older than younger adolescents. Functional network rewiring in moderate-to-high-drinking adolescents may impede maturation of affective and self-reflection systems and obscure maturation of complex social and emotional behaviors.
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Affiliation(s)
- Eva M Müller-Oehring
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Dongjin Kwon
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Bonnie J Nagel
- Departments of Psychiatry and Behavioral Neuroscience, Oregon Health & Sciences University, Portland, OR 97239, USA
| | - Edith V Sullivan
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Weiwei Chu
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
| | - Torsten Rohlfing
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
| | - Devin Prouty
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
| | - B Nolan Nichols
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jean-Baptiste Poline
- Henry H. Wheeler, Jr. Brain Imaging Center, Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA 94720, USA
| | - Susan F Tapert
- Department of Psychiatry, University of California, La Jolla, San Diego, CA 92093, USA
| | - Sandra A Brown
- Department of Psychiatry, University of California, La Jolla, San Diego, CA 92093, USA
| | - Kevin Cummins
- Department of Psychiatry, University of California, La Jolla, San Diego, CA 92093, USA
| | - Ty Brumback
- Department of Psychiatry, University of California, La Jolla, San Diego, CA 92093, USA
| | - Ian M Colrain
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
| | - Fiona C Baker
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
| | - Michael D De Bellis
- Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC 27710, USA
| | - James T Voyvodic
- Department of Radiology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Duncan B Clark
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Adolf Pfefferbaum
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kilian M Pohl
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
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182
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Chen G, Taylor PA, Haller SP, Kircanski K, Stoddard J, Pine DS, Leibenluft E, Brotman MA, Cox RW. Intraclass correlation: Improved modeling approaches and applications for neuroimaging. Hum Brain Mapp 2018; 39:1187-1206. [PMID: 29218829 PMCID: PMC5807222 DOI: 10.1002/hbm.23909] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 11/20/2017] [Accepted: 11/29/2017] [Indexed: 12/21/2022] Open
Abstract
Intraclass correlation (ICC) is a reliability metric that gauges similarity when, for example, entities are measured under similar, or even the same, well-controlled conditions, which in MRI applications include runs/sessions, twins, parent/child, scanners, sites, and so on. The popular definitions and interpretations of ICC are usually framed statistically under the conventional ANOVA platform. Here, we provide a comprehensive overview of ICC analysis in its prior usage in neuroimaging, and we show that the standard ANOVA framework is often limited, rigid, and inflexible in modeling capabilities. These intrinsic limitations motivate several improvements. Specifically, we start with the conventional ICC model under the ANOVA platform, and extend it along two dimensions: first, fixing the failure in ICC estimation when negative values occur under degenerative circumstance, and second, incorporating precision information of effect estimates into the ICC model. These endeavors lead to four modeling strategies: linear mixed-effects (LME), regularized mixed-effects (RME), multilevel mixed-effects (MME), and regularized multilevel mixed-effects (RMME). Compared to ANOVA, each of these four models directly provides estimates for fixed effects and their statistical significances, in addition to the ICC estimate. These new modeling approaches can also accommodate missing data and fixed effects for confounding variables. More importantly, we show that the MME and RMME approaches offer more accurate characterization and decomposition among the variance components, leading to more robust ICC computation. Based on these theoretical considerations and model performance comparisons with a real experimental dataset, we offer the following general-purpose recommendations. First, ICC estimation through MME or RMME is preferable when precision information (i.e., weights that more accurately allocate the variances in the data) is available for the effect estimate; when precision information is unavailable, ICC estimation through LME or the RME is the preferred option. Second, even though the absolute agreement version, ICC(2,1), is presently more popular in the field, the consistency version, ICC(3,1), is a practical and informative choice for whole-brain ICC analysis that achieves a well-balanced compromise when all potential fixed effects are accounted for. Third, approaches for clear, meaningful, and useful result reporting in ICC analysis are discussed. All models, ICC formulations, and related statistical testing methods have been implemented in an open source program 3dICC, which is publicly available as part of the AFNI suite. Even though our work here focuses on the whole-brain level, the modeling strategy and recommendations can be equivalently applied to other situations such as voxel, region, and network levels.
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Affiliation(s)
- Gang Chen
- Scientific and Statistical Computing CoreNational Institute of Mental Health, National Institutes of HealthBethesdaMD
| | - Paul A. Taylor
- Scientific and Statistical Computing CoreNational Institute of Mental Health, National Institutes of HealthBethesdaMD
| | - Simone P. Haller
- Section on Mood Dysregulation and Neuroscience, Emotion and Development BranchNational Institute of Mental HealthBethesdaMD
| | - Katharina Kircanski
- Section on Mood Dysregulation and Neuroscience, Emotion and Development BranchNational Institute of Mental HealthBethesdaMD
| | - Joel Stoddard
- Division of Child and Adolescent Psychiatry, Department of PsychiatryUniversity of Colorado School of MedicineAuroraColorado
| | - Daniel S. Pine
- Section on Development and Affective Neuroscience, Emotion and Development BranchNational Institute of Mental HealthBethesdaMD
| | - Ellen Leibenluft
- Section on Mood Dysregulation and Neuroscience, Emotion and Development BranchNational Institute of Mental HealthBethesdaMD
| | - Melissa A. Brotman
- Section on Mood Dysregulation and Neuroscience, Emotion and Development BranchNational Institute of Mental HealthBethesdaMD
| | - Robert W. Cox
- Scientific and Statistical Computing CoreNational Institute of Mental Health, National Institutes of HealthBethesdaMD
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183
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Rosenberg MD, Casey BJ, Holmes AJ. Prediction complements explanation in understanding the developing brain. Nat Commun 2018; 9:589. [PMID: 29467408 PMCID: PMC5821815 DOI: 10.1038/s41467-018-02887-9] [Citation(s) in RCA: 116] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 01/05/2018] [Indexed: 11/08/2022] Open
Abstract
A central aim of human neuroscience is understanding the neurobiology of cognition and behavior. Although we have made significant progress towards this goal, reliance on group-level studies of the developed adult brain has limited our ability to explain population variability and developmental changes in neural circuitry and behavior. In this review, we suggest that predictive modeling, a method for predicting individual differences in behavior from brain features, can complement descriptive approaches and provide new ways to account for this variability. Highlighting the outsized scientific and clinical benefits of prediction in developmental populations including adolescence, we show that predictive brain-based models are already providing new insights on adolescent-specific risk-related behaviors. Together with large-scale developmental neuroimaging datasets and complementary analytic approaches, predictive modeling affords us the opportunity and obligation to identify novel treatment targets and individually tailor the course of interventions for developmental psychopathologies that impact so many young people today.
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Affiliation(s)
| | - B J Casey
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
- Department of Psychiatry, Yale University, New Haven, CT, 06511, USA
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184
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Reliable new measures capturing low-frequency fluctuations from resting-state functional MRI. Neuroreport 2018; 29:197-202. [PMID: 29240648 DOI: 10.1097/wnr.0000000000000951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Resting-state functional MRI (rsfMRI) is one of the most important neuroimaging modalities for investigating alterations in the resting-state networks of the human brain, given that abnormal neural activity during the resting state is associated with neurological disorders. However, neuroimaging results obtained from rsfMRI have rarely been replicated with repeated measurements. Therefore, we aimed to develop new measures to extract highly reliable and reproducible functional neuroimaging metrics from rsfMRI data. Preprocessed rsfMRI data from 30 patients with 10 sessions of rsfMRI scans taken within 1 month were obtained from the Consortium for Reliability and Reproducibility. We developed a time-domain measure to capture low-frequency fluctuation (LFF) using a general linear model with three different periodic regressors: boxcar, triangular, and sinusoidal functions. Then, test-retest reliability for the proposed methods was evaluated using the intraclass correlation (ICC). Our approaches for evaluating LFF from rsfMRI data significantly identified the default mode network areas (corrected P<0.05). The regression model with the sinusoidal basis function produced the most reliable results (ICC=0.6) compared with the boxcar (ICC=0.32) or triangular (ICC=0.34) functions. Taken together, the proposed methods successfully identified the default mode network regions. In addition, our results suggest that new functional metrics aiming to extract LFF components by modeling rsfMRI time-series data might provide a reliable biomarker to identify neurological disorders accompanying abnormal functional activity.
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185
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186
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Dimitriadis SI, Drakesmith M, Bells S, Parker GD, Linden DE, Jones DK. Improving the Reliability of Network Metrics in Structural Brain Networks by Integrating Different Network Weighting Strategies into a Single Graph. Front Neurosci 2017; 11:694. [PMID: 29311775 PMCID: PMC5742099 DOI: 10.3389/fnins.2017.00694] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 11/27/2017] [Indexed: 11/21/2022] Open
Abstract
Structural brain networks estimated from diffusion MRI (dMRI) via tractography have been widely studied in healthy controls and patients with neurological and psychiatric diseases. However, few studies have addressed the reliability of derived network metrics both node-specific and network-wide. Different network weighting strategies (NWS) can be adopted to weight the strength of connection between two nodes yielding structural brain networks that are almost fully-weighted. Here, we scanned five healthy participants five times each, using a diffusion-weighted MRI protocol and computed edges between 90 regions of interest (ROI) from the Automated Anatomical Labeling (AAL) template. The edges were weighted according to nine different methods. We propose a linear combination of these nine NWS into a single graph using an appropriate diffusion distance metric. We refer to the resulting weighted graph as an Integrated Weighted Structural Brain Network (ISWBN). Additionally, we consider a topological filtering scheme that maximizes the information flow in the brain network under the constraint of the overall cost of the surviving connections. We compared each of the nine NWS and the ISWBN based on the improvement of: (a) intra-class correlation coefficient (ICC) of well-known network metrics, both node-wise and per network level; and (b) the recognition accuracy of each subject compared to the remainder of the cohort, as an attempt to access the uniqueness of the structural brain network for each subject, after first applying our proposed topological filtering scheme. Based on a threshold where the network level ICC should be >0.90, our findings revealed that six out of nine NWS lead to unreliable results at the network level, while all nine NWS were unreliable at the node level. In comparison, our proposed ISWBN performed as well as the best performing individual NWS at the network level, and the ICC was higher compared to all individual NWS at the node level. Importantly, both network and node-wise ICCs of network metrics derived from the topologically filtered ISBWN (ISWBNTF), were further improved compared to the non-filtered ISWBN. Finally, in the recognition accuracy tests, we assigned each single ISWBNTF to the correct subject. We also applied our methodology to a second dataset of diffusion-weighted MRI in healthy controls and individuals with psychotic experience. Following a binary classification scheme, the classification performance based on ISWBNTF outperformed the nine different weighting strategies and the ISWBN. Overall, these findings suggest that the proposed methodology results in improved characterization of genuine between-subject differences in connectivity leading to the possibility of network-based structural phenotyping.
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Affiliation(s)
- Stavros I Dimitriadis
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom.,Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom.,School of Psychology, Cardiff University, Cardiff, United Kingdom.,Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom.,Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Mark Drakesmith
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom.,Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Sonya Bells
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom.,School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Greg D Parker
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom.,School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - David E Linden
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom.,Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom.,MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom.,Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
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187
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An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci Data 2017; 4:170181. [PMID: 29257126 PMCID: PMC5735921 DOI: 10.1038/sdata.2017.181] [Citation(s) in RCA: 340] [Impact Index Per Article: 42.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Accepted: 10/11/2017] [Indexed: 11/23/2022] Open
Abstract
Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5–21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eye-tracking, voice and video recordings, genetics and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n=664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis).
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188
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Liu XL, Li L, Li JN, Rong JH, Liu B, Hu ZX. Reliability of Glutamate Quantification in Human Nucleus Accumbens Using Proton Magnetic Resonance Spectroscopy at a 70-cm Wide-Bore Clinical 3T MRI System. Front Neurosci 2017; 11:686. [PMID: 29259538 PMCID: PMC5723319 DOI: 10.3389/fnins.2017.00686] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Accepted: 11/22/2017] [Indexed: 12/29/2022] Open
Abstract
The human nucleus accumbens is a challenging region to study using proton magnetic resonance spectroscopy (1H-MRS) on a 70-cm wide-bore clinical 3T MRI system. The aim of this study was to investigate the reliability for quantitative measurement of glutamate concentration in the nucleus accumbens using a 70-cm wide-bore clinical 3T MRI. 1H-MRS of the nucleus accumbens was acquired using the Point-Resolved Spectroscopic Sequence (PRESS) with echo time of 40 ms from 10 healthy volunteers (5 female; age range: 18–30 years) on two separate visits (a baseline, and 1-month time point). The Java-based Magnetic Resonance User Interface (jMRUI) software package was used to quantitatively measure the absolute metabolite concentrations. The test-retest reliability and reproducibility were assessed using intraclass correlations coefficients (ICC), and coefficients of variation (CV). Glutamate concentrations were similar across visits (P = 0.832). Reproducibility measures for all metabolites were good with CV ranging from 7.8 to 14.0%. The ICC values of all metabolites for the intra-class measures were excellent (ICC > 0.8), except that the reliability for Glx (glutamate + glutamine) was good (ICC = 0.768). Pearson correlations for all metabolites were all highly significant (r = 0.636–0.788, P < 0.05). In conclusion, the short-echo-time PRESS can reliably obtain high quality glutamate spectrum from a ~3.4 cm3 voxel of the nucleus accumbens using a 70-cm wide-bore clinical 3T MRI.
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Affiliation(s)
- Xi-Long Liu
- Department of Radiology, Guangdong Provincial Corps Hospital of Chinese People's Armed Police Forces, Guangzhou Medical University, Guangzhou, China
| | - Long Li
- Department of Radiology, Guangdong Provincial Corps Hospital of Chinese People's Armed Police Forces, Guangzhou Medical University, Guangzhou, China
| | - Jian-Neng Li
- Department of Radiology, Guangdong Provincial Corps Hospital of Chinese People's Armed Police Forces, Guangzhou Medical University, Guangzhou, China
| | - Jia-Hui Rong
- Department of Radiology, Guangdong Provincial Corps Hospital of Chinese People's Armed Police Forces, Guangzhou Medical University, Guangzhou, China
| | - Bo Liu
- Department of Radiology, Guangdong Provincial Corps Hospital of Chinese People's Armed Police Forces, Guangzhou Medical University, Guangzhou, China
| | - Ze-Xuan Hu
- Department of Radiology, Guangdong Provincial Corps Hospital of Chinese People's Armed Police Forces, Guangzhou Medical University, Guangzhou, China
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189
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Urchs S, Armoza J, Benhajali Y, St-Aubin J, Orban P, Bellec P. MIST: A multi-resolution parcellation of functional brain networks. ACTA ACUST UNITED AC 2017. [DOI: 10.12688/mniopenres.12767.1] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Functional brain connectomics investigates functional connectivity between distinct brain parcels. There is an increasing interest to investigate connectivity across several levels of spatial resolution, from networks down to localized areas. Here we present the Multiresolution Intrinsic Segmentation Template (MIST), a multi-resolution parcellation of the cortical, subcortical and cerebellar gray matter. We provide annotated functional parcellations at nine resolutions from 7 to 444 functional parcels. The MIST parcellations compare well with prior work in terms of homogeneity and generalizability. We found that parcels at higher resolutions largely fell within the boundaries of larger parcels at lower resolutions. This allowed us to provide an overlap based pseudo-hierarchical decomposition tree that relates parcels across resolutions in a meaningful way. We provide an interactive web interface to explore the MIST parcellations and also made it accessible in the neuroimaging library nilearn. We believe that the MIST parcellation will facilitate future investigations of the multiresolution basis of brain function.
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190
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Li X, Gan JQ, Wang H. Collective sparse symmetric non-negative matrix factorization for identifying overlapping communities in resting-state brain functional networks. Neuroimage 2017; 166:259-275. [PMID: 29117581 DOI: 10.1016/j.neuroimage.2017.11.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Accepted: 11/01/2017] [Indexed: 12/31/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) provides a valuable tool to study spontaneous brain activity. Using rs-fMRI, researchers have extensively studied the organization of the brain functional network and found several consistent communities consisting of functionally connected but spatially separated brain regions across subjects. However, increasing evidence in many disciplines has shown that most realistic complex networks have overlapping community structure. Only recently has the overlapping community structure drawn increasing interest in the domain of brain network studies. Another issue is that the inter-subject variability is often not directly reflected in the process of community detection at the group level. In this paper, we propose a novel method called collective sparse symmetric non-negative matrix factorization (cssNMF) to address these issues. The cssNMF approach identifies the group-level overlapping communities across subjects and in the meantime preserves the information of individual variation in brain functional network organization. To comprehensively validate cssNMF, a simulated fMRI dataset with ground-truth, a real rs-fMRI dataset with two repeated sessions and another different real rs-fMRI dataset have been used for performance comparison in the experiment. Experimental results show that the proposed cssNMF method accurately and stably identifies group-level overlapping communities across subjects as well as individual differences in network organization with neurophysiologically meaningful interpretations. This research extends our understanding of the common underlying community structures and individual differences in community strengths in brain functional network organization.
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Affiliation(s)
- Xuan Li
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, PR China; School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
| | - John Q Gan
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, PR China; School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
| | - Haixian Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, PR China.
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191
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Meszlényi RJ, Buza K, Vidnyánszky Z. Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture. Front Neuroinform 2017; 11:61. [PMID: 29089883 PMCID: PMC5651030 DOI: 10.3389/fninf.2017.00061] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 10/03/2017] [Indexed: 01/05/2023] Open
Abstract
Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.
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Affiliation(s)
- Regina J Meszlényi
- Department of Cognitive Science, Budapest University of Technology and Economics, Budapest, Hungary.,Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary
| | - Krisztian Buza
- Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary.,Knowledge Discovery and Machine Learning, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Zoltán Vidnyánszky
- Department of Cognitive Science, Budapest University of Technology and Economics, Budapest, Hungary.,Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary
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192
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Di X, Biswal BB. Psychophysiological Interactions in a Visual Checkerboard Task: Reproducibility, Reliability, and the Effects of Deconvolution. Front Neurosci 2017; 11:573. [PMID: 29089865 PMCID: PMC5651039 DOI: 10.3389/fnins.2017.00573] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 10/02/2017] [Indexed: 11/18/2022] Open
Abstract
Psychophysiological interaction (PPI) is a regression based method to study task modulated brain connectivity. Despite its popularity in functional MRI (fMRI) studies, its reliability and reproducibility have not been evaluated. We investigated reproducibility and reliability of PPI effects during a simple visual task, and examined the effect of deconvolution on the PPI results. A large open-access dataset was analyzed (n = 138), where a visual task was scanned twice with repetition times (TRs) of 645 and 1,400 ms, respectively. We first replicated our previous results by using the left and right middle occipital gyrus as seeds. Then regions of interest (ROI)-wise analysis was performed among 20 visual-related thalamic and cortical regions, and negative PPI effects were found between many ROIs with the posterior fusiform gyrus as a hub region. Both the seed-based and ROI-wise results were similar between the two runs and between the two PPI methods with and without deconvolution. The non-deconvolution method and the short TR run in general had larger effect sizes and greater extents. However, the deconvolution method performed worse in the 645 ms TR run than the 1,400 ms TR run in the voxel-wise analysis. Given the general similar results between the two methods and the uncertainty of deconvolution, we suggest that deconvolution may be not necessary for PPI analysis on block-designed data. Lastly, intraclass correlations (ICC) between the two runs were much lower for the PPI effects than the activation main effects, which raise cautions on performing inter-subject correlations and group comparisons on PPI effects.
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Affiliation(s)
- Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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193
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Zhou X, Wu T, Yu J, Lei X. Sleep Deprivation Makes the Young Brain Resemble the Elderly Brain: A Large-Scale Brain Networks Study. Brain Connect 2017; 7:58-68. [PMID: 27733049 DOI: 10.1089/brain.2016.0452] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Decreased cognition performance and impaired brain function are similar results of sleep deprivation (SD) and aging, according to mounted supporting evidence. Some investigators even proposed SD as a model of aging. However, few direct comparisons were ever explored between the effects of SD and aging by network module analysis with the resting-state functional magnetic resonance imaging. In this study, both within-module and between-module (BT) connectivities were calculated in the whole brain to describe a complete picture of brain networks' functional connectivity among three groups (young normal sleep, young SD, and old group). The results showed that the BT connectivities in subcortical and cerebellar networks were significantly declined in both the young SD group and old group. There were six other networks, that is, ventral attention, dorsal attention, default mode, auditory, cingulo-opercular, and memory retrieval networks, significantly influenced by aging. Therefore, we speculated that the effects of SD on the young group can be regarded as a simplified model of aging. Moreover, this provided a possible explanation, that is, the old were more tolerable for SD than the young. However, SD may not be a considerable model for aging when discussing the brain regions related to those SD-uninfluenced networks.
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Affiliation(s)
- Xinqi Zhou
- 1 Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University , Chongqing, China .,2 Key Laboratory of Cognition and Personality of Ministry of Education , Chongqing, China
| | - Taoyu Wu
- 1 Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University , Chongqing, China .,2 Key Laboratory of Cognition and Personality of Ministry of Education , Chongqing, China
| | - Jing Yu
- 1 Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University , Chongqing, China .,2 Key Laboratory of Cognition and Personality of Ministry of Education , Chongqing, China
| | - Xu Lei
- 1 Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University , Chongqing, China .,2 Key Laboratory of Cognition and Personality of Ministry of Education , Chongqing, China
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194
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Chen X, Lu B, Yan CG. Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes. Hum Brain Mapp 2017; 39:300-318. [PMID: 29024299 DOI: 10.1002/hbm.23843] [Citation(s) in RCA: 244] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 10/01/2017] [Accepted: 10/02/2017] [Indexed: 12/18/2022] Open
Abstract
Concerns regarding reproducibility of resting-state functional magnetic resonance imaging (R-fMRI) findings have been raised. Little is known about how to operationally define R-fMRI reproducibility and to what extent it is affected by multiple comparison correction strategies and sample size. We comprehensively assessed two aspects of reproducibility, test-retest reliability and replicability, on widely used R-fMRI metrics in both between-subject contrasts of sex differences and within-subject comparisons of eyes-open and eyes-closed (EOEC) conditions. We noted permutation test with Threshold-Free Cluster Enhancement (TFCE), a strict multiple comparison correction strategy, reached the best balance between family-wise error rate (under 5%) and test-retest reliability/replicability (e.g., 0.68 for test-retest reliability and 0.25 for replicability of amplitude of low-frequency fluctuations (ALFF) for between-subject sex differences, 0.49 for replicability of ALFF for within-subject EOEC differences). Although R-fMRI indices attained moderate reliabilities, they replicated poorly in distinct datasets (replicability < 0.3 for between-subject sex differences, < 0.5 for within-subject EOEC differences). By randomly drawing different sample sizes from a single site, we found reliability, sensitivity and positive predictive value (PPV) rose as sample size increased. Small sample sizes (e.g., < 80 [40 per group]) not only minimized power (sensitivity < 2%), but also decreased the likelihood that significant results reflect "true" effects (PPV < 0.26) in sex differences. Our findings have implications for how to select multiple comparison correction strategies and highlight the importance of sufficiently large sample sizes in R-fMRI studies to enhance reproducibility. Hum Brain Mapp 39:300-318, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Bin Lu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.,Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Child and Adolescent Psychiatry, NYU Langone Medical Center, School of Medicine, New York, NY, USA
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195
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Moyer D, Gutman BA, Faskowitz J, Jahanshad N, Thompson PM. Continuous representations of brain connectivity using spatial point processes. Med Image Anal 2017; 41:32-39. [PMID: 28487128 PMCID: PMC5559296 DOI: 10.1016/j.media.2017.04.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Revised: 04/15/2017] [Accepted: 04/27/2017] [Indexed: 01/25/2023]
Abstract
We present a continuous model for structural brain connectivity based on the Poisson point process. The model treats each streamline curve in a tractography as an observed event in connectome space, here the product space of the gray matter/white matter interfaces. We approximate the model parameter via kernel density estimation. To deal with the heavy computational burden, we develop a fast parameter estimation method by pre-computing associated Legendre products of the data, leveraging properties of the spherical heat kernel. We show how our approach can be used to assess the quality of cortical parcellations with respect to connectivity. We further present empirical results that suggest that "discrete" connectomes derived from our model have substantially higher test-retest reliability compared to standard methods. In this, the expanded form of this paper for journal publication, we also explore parcellation free analysis techniques that avoid the use of explicit partitions of the cortical surface altogether. We provide an analysis of sex effects on our proposed continuous representation, demonstrating the utility of this approach.
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Affiliation(s)
- Daniel Moyer
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, United States; Information Sciences Institute, University of Southern California, United States; Department of Computer Science, University of Southern California, United States.
| | - Boris A Gutman
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, United States
| | - Joshua Faskowitz
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, United States; Department of Psychological and Brain Sciences, Indiana University, United States
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, United States
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, United States.
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196
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Abstract
The ability to map brain networks in living individuals is fundamental in efforts to chart the relation between human behavior, health and disease. Advances in network neuroscience may benefit from developing new frameworks for mapping brain connectomes. We present a framework to encode structural brain connectomes and diffusion-weighted magnetic resonance (dMRI) data using multidimensional arrays. The framework integrates the relation between connectome nodes, edges, white matter fascicles and diffusion data. We demonstrate the utility of the framework for in vivo white matter mapping and anatomical computing by evaluating 1,490 connectomes, thirteen tractography methods, and three data sets. The framework dramatically reduces storage requirements for connectome evaluation methods, with up to 40x compression factors. Evaluation of multiple, diverse datasets demonstrates the importance of spatial resolution in dMRI. We measured large increases in connectome resolution as function of data spatial resolution (up to 52%). Moreover, we demonstrate that the framework allows performing anatomical manipulations on white matter tracts for statistical inference and to study the white matter geometrical organization. Finally, we provide open-source software implementing the method and data to reproduce the results.
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Affiliation(s)
- Cesar F Caiafa
- Department of Psychological and, Brain Sciences Indiana University Bloomington, IN, 47405, USA
- Instituto Argentino de Radioastronomía (IAR), CONICET CCT, La Plata Villa Elisa, 1894, Argentina
- Facultad de Ingeniería - Departamento de Computación, UBA Buenos Aires, C1063ACV, Argentina
| | - Franco Pestilli
- Department of Psychological and, Brain Sciences Indiana University Bloomington, IN, 47405, USA.
- Department of Intelligent Systems, Engineering Indiana University Bloomington, IN, 47405, USA.
- Department of Computer Science, Indiana University Bloomington, IN, 47405, USA.
- Program in Neuroscience Indiana University Bloomington, IN, 47405, USA.
- Program in Cognitive Science Indiana University Bloomington, IN, 47405, USA.
- School of Optometry Indiana University Bloomington, IN, 47405, USA.
- Indiana Network Science Institute Indiana University Bloomington, IN, 47405, USA.
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197
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Madan CR. Advances in Studying Brain Morphology: The Benefits of Open-Access Data. Front Hum Neurosci 2017; 11:405. [PMID: 28824407 PMCID: PMC5543094 DOI: 10.3389/fnhum.2017.00405] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 07/21/2017] [Indexed: 12/20/2022] Open
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198
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Zhang H, Chen X, Zhang Y, Shen D. Test-Retest Reliability of "High-Order" Functional Connectivity in Young Healthy Adults. Front Neurosci 2017; 11:439. [PMID: 28824362 PMCID: PMC5539178 DOI: 10.3389/fnins.2017.00439] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 07/18/2017] [Indexed: 12/16/2022] Open
Abstract
Functional connectivity (FC) has become a leading method for resting-state functional magnetic resonance imaging (rs-fMRI) analysis. However, the majority of the previous studies utilized pairwise, temporal synchronization-based FC. Recently, high-order FC (HOFC) methods were proposed with the idea of computing "correlation of correlations" to capture high-level, more complex associations among the brain regions. There are two types of HOFC. The first type is topographical profile similarity-based HOFC (tHOFC) and its variant, associated HOFC (aHOFC), for capturing different levels of HOFC. Instead of measuring the similarity of the original rs-fMRI signals with the traditional FC (low-order FC, or LOFC), tHOFC measures the similarity of LOFC profiles (i.e., a set of LOFC values between a region and all other regions) between each pair of brain regions. The second type is dynamics-based HOFC (dHOFC) which defines the quadruple relationship among every four brain regions by first calculating two pairwise dynamic LOFC "time series" and then measuring their temporal synchronization (i.e., temporal correlation of the LOFC fluctuations, not the BOLD fluctuations). Applications have shown the superiority of HOFC in both disease biomarker detection and individualized diagnosis than LOFC. However, no study has been carried out for the assessment of test-retest reliability of different HOFC metrics. In this paper, we systematically evaluate the reliability of the two types of HOFC methods using test-retest rs-fMRI data from 25 (12 females, age 24.48 ± 2.55 years) young healthy adults with seven repeated scans (with interval = 3-8 days). We found that all HOFC metrics have satisfactory reliability, specifically (1) fair-to-good for tHOFC and aHOFC, and (2) fair-to-moderate for dHOFC with relatively strong connectivity strength. We further give an in-depth analysis of the biological meanings of each HOFC metric and highlight their differences compared to the LOFC from the aspects of cross-level information exchanges, within-/between-network connectivity, and modulatory connectivity. In addition, how the dynamic analysis parameter (i.e., sliding window length) affects dHOFC reliability is also investigated. Our study reveals unique functional associations characterized by the HOFC metrics. Guidance and recommendations for future applications and clinical research using HOFC are provided. This study has made a further step toward unveiling more complex human brain connectome.
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Affiliation(s)
- Han Zhang
- Department of Radiology and Brain Research Imaging Center, University of North Carolina at Chapel HillChapel Hill, NC, United States
| | - Xiaobo Chen
- Department of Radiology and Brain Research Imaging Center, University of North Carolina at Chapel HillChapel Hill, NC, United States
| | - Yu Zhang
- Department of Radiology and Brain Research Imaging Center, University of North Carolina at Chapel HillChapel Hill, NC, United States
| | - Dinggang Shen
- Department of Radiology and Brain Research Imaging Center, University of North Carolina at Chapel HillChapel Hill, NC, United States
- Department of Brain and Cognitive Engineering, Korea UniversitySeoul, South Korea
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199
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Tomasi DG, Shokri-Kojori E, Volkow ND. Temporal Evolution of Brain Functional Connectivity Metrics: Could 7 Min of Rest be Enough? Cereb Cortex 2017; 27:4153-4165. [PMID: 27522070 PMCID: PMC6059168 DOI: 10.1093/cercor/bhw227] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2016] [Revised: 06/26/2016] [Accepted: 06/28/2016] [Indexed: 01/10/2023] Open
Abstract
Unaccounted temporal dynamics of resting-state functional connectivity (FC) metrics challenges their potential as biomarkers for clinical applications in neuroscience. Here we studied the scan time required to reach stable values for various FC metrics including seed-voxel correlations and spatial independent component analyses (sICA), and for the local functional connectivity density (lFCD), a graph theory metric. By increasing the number of time points included in the analysis, we assessed the effects of scan time on convergence of accuracy, sensitivity, specificity, reproducibility, and reliability of these FC metrics. The necessary scan time to attenuate the effects of the temporal dynamics by 80% varied across connectivity metrics and was shorter for lFCD (7 min) than for FC (11 min) or for sICA (10 min). Findings suggest that the scan time required to achieve stable FC is metric-dependent, with lFCD being the most resilient metric to the effects of temporal dynamics. Thus, the lFCD metric could be particularly useful for pediatric and patient populations who may not tolerate long scans.
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Affiliation(s)
- Dardo G. Tomasi
- National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892-1013, USA
| | - Ehsan Shokri-Kojori
- National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892-1013, USA
| | - Nora D. Volkow
- National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD 20892-1013, USA
- National Institute on Drug Abuse, Bethesda, MD 20892-9561, USA
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200
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Huang H, Wang J, Seger C, Lu M, Deng F, Wu X, He Y, Niu C, Wang J, Huang R. Long-term intensive gymnastic training induced changes in intra- and inter-network functional connectivity: an independent component analysis. Brain Struct Funct 2017; 223:131-144. [DOI: 10.1007/s00429-017-1479-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Accepted: 07/17/2017] [Indexed: 01/08/2023]
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