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Abstract
Recent advances in connectomics have led to a synthesis of perspectives regarding the brain's functional organization that reconciles classical concepts of localized specialization with an appreciation for properties that emerge from interactions across distributed functional networks. This provides a more comprehensive framework for understanding neural mechanisms of normal cognition and disease. Although fMRI has not become a routine clinical tool, research has already had important influences on clinical concepts guiding diagnosis and patient management. Here we review illustrative examples. Studies demonstrating the network plasticity possible in adults and the global consequences of even focal brain injuries or disease both have had substantial impact on modern concepts of disease evolution and expression. Applications of functional connectomics in studies of clinical populations are challenging traditional disease classifications and helping to clarify biological relationships between clinical syndromes (and thus also ways of extending indications for, or "re-purposing," current treatments). Large datasets from prospective, longitudinal studies promise to enable the discovery and validation of functional connectomic biomarkers with the potential to identify people at high risk of disease before clinical onset, at a time when treatments may be most effective. Studies of pain and consciousness have catalyzed reconsiderations of approaches to clinical management, but also have stimulated debate about the clinical meaningfulness of differences in internal perceptual or cognitive states inferred from functional connectomics or other physiological correlates. By way of a closing summary, we offer a personal view of immediate challenges and potential opportunities for clinically relevant applications of fMRI-based functional connectomics.
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Affiliation(s)
- Paul M Matthews
- Division of Brain Sciences, Department of Medicine and Centre for Neurotechnology, Imperial College London, London WC12 0NN, UK.
| | - Adam Hampshire
- Division of Brain Sciences, Department of Medicine and Centre for Neurotechnology, Imperial College London, London WC12 0NN, UK
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202
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Topologically convergent and divergent functional connectivity patterns in unmedicated unipolar depression and bipolar disorder. Transl Psychiatry 2017; 7:e1165. [PMID: 28675389 PMCID: PMC5538109 DOI: 10.1038/tp.2017.117] [Citation(s) in RCA: 81] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Revised: 04/06/2017] [Accepted: 04/25/2017] [Indexed: 01/08/2023] Open
Abstract
Bipolar disorder (BD), particularly BD II, is frequently misdiagnosed as unipolar depression (UD), leading to inappropriate treatment and poor clinical outcomes. Although depressive symptoms may be expressed similarly in UD and BD, the similarities and differences in the architecture of brain functional networks between the two disorders are still unknown. In this study, we hypothesized that UD and BD II patients would show convergent and divergent patterns of disrupted topological organization of the functional connectome, especially in the default mode network (DMN) and the limbic network. Brain resting-state functional magnetic resonance imaging (fMRI) data were acquired from 32 UD-unmedicated patients, 31 unmedicated BD II patients (current episode depressed) and 43 healthy subjects. Using graph theory, we systematically studied the topological organization of their whole-brain functional networks at the following three levels: whole brain, modularity and node. First, both the UD and BD II patients showed increased characteristic path length and decreased global efficiency compared with the controls. Second, both the UD and BD II patients showed disrupted intramodular connectivity within the DMN and limbic system network. Third, decreased nodal characteristics (nodal strength and nodal efficiency) were found predominantly in brain regions in the DMN, limbic network and cerebellum of both the UD and BD II patients, whereas differences between the UD and BD II patients in the nodal characteristics were also observed in the precuneus and temporal pole. Convergent deficits in the topological organization of the whole brain, DMN and limbic networks may reflect overlapping pathophysiological processes in unipolar and bipolar depression. Our discovery of divergent regional connectivity that supports emotion processing could help to identify biomarkers that will aid in differentiating these disorders.
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203
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O'Connor D, Potler NV, Kovacs M, Xu T, Ai L, Pellman J, Vanderwal T, Parra LC, Cohen S, Ghosh S, Escalera J, Grant-Villegas N, Osman Y, Bui A, Craddock RC, Milham MP. The Healthy Brain Network Serial Scanning Initiative: a resource for evaluating inter-individual differences and their reliabilities across scan conditions and sessions. Gigascience 2017; 6:1-14. [PMID: 28369458 PMCID: PMC5466711 DOI: 10.1093/gigascience/giw011] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Accepted: 12/05/2016] [Indexed: 01/08/2023] Open
Abstract
Background Although typically measured during the resting state, a growing literature is illustrating the ability to map intrinsic connectivity with functional MRI during task and naturalistic viewing conditions. These paradigms are drawing excitement due to their greater tolerability in clinical and developing populations and because they enable a wider range of analyses (e.g., inter-subject correlations). To be clinically useful, the test-retest reliability of connectivity measured during these paradigms needs to be established. This resource provides data for evaluating test-retest reliability for full-brain connectivity patterns detected during each of four scan conditions that differ with respect to level of engagement (rest, abstract animations, movie clips, flanker task). Data are provided for 13 participants, each scanned in 12 sessions with 10 minutes for each scan of the four conditions. Diffusion kurtosis imaging data was also obtained at each session. Findings Technical validation and demonstrative reliability analyses were carried out at the connection-level using the Intraclass Correlation Coefficient and at network-level representations of the data using the Image Intraclass Correlation Coefficient. Variation in intrinsic functional connectivity across sessions was generally found to be greater than that attributable to scan condition. Between-condition reliability was generally high, particularly for the frontoparietal and default networks. Between-session reliabilities obtained separately for the different scan conditions were comparable, though notably lower than between-condition reliabilities. Conclusions This resource provides a test-bed for quantifying the reliability of connectivity indices across subjects, conditions and time. The resource can be used to compare and optimize different frameworks for measuring connectivity and data collection parameters such as scan length. Additionally, investigators can explore the unique perspectives of the brain's functional architecture offered by each of the scan conditions.
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Affiliation(s)
- David O'Connor
- Center for the Developing Brain, Child Mind Institute, New York, NY.,Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY
| | | | - Meagan Kovacs
- Center for the Developing Brain, Child Mind Institute, New York, NY
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY
| | - Lei Ai
- Center for the Developing Brain, Child Mind Institute, New York, NY
| | - John Pellman
- Center for the Developing Brain, Child Mind Institute, New York, NY.,Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY
| | | | | | - Samantha Cohen
- The Graduate Center of the City University of New York, New York, NY
| | | | - Jasmine Escalera
- Center for the Developing Brain, Child Mind Institute, New York, NY
| | | | - Yael Osman
- Center for the Developing Brain, Child Mind Institute, New York, NY
| | - Anastasia Bui
- Center for the Developing Brain, Child Mind Institute, New York, NY
| | - R Cameron Craddock
- Center for the Developing Brain, Child Mind Institute, New York, NY.,Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY.,Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY
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204
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Madan CR, Kensinger EA. Test-retest reliability of brain morphology estimates. Brain Inform 2017; 4:107-121. [PMID: 28054317 PMCID: PMC5413592 DOI: 10.1007/s40708-016-0060-4] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 12/26/2016] [Indexed: 12/17/2022] Open
Abstract
Metrics of brain morphology are increasingly being used to examine inter-individual differences, making it important to evaluate the reliability of these structural measures. Here we used two open-access datasets to assess the intersession reliability of three cortical measures (thickness, gyrification, and fractal dimensionality) and two subcortical measures (volume and fractal dimensionality). Reliability was generally good, particularly with the gyrification and fractal dimensionality measures. One dataset used a sequence previously optimized for brain morphology analyses and had particularly high reliability. Examining the reliability of morphological measures is critical before the measures can be validly used to investigate inter-individual differences.
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Affiliation(s)
- Christopher R Madan
- Department of Psychology, Boston College, McGuinn 300, 140 Commonwealth Ave., Chestnut Hill, MA, 02467, USA.
| | - Elizabeth A Kensinger
- Department of Psychology, Boston College, McGuinn 300, 140 Commonwealth Ave., Chestnut Hill, MA, 02467, USA
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205
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Sankar T, Park MTM, Jawa T, Patel R, Bhagwat N, Voineskos AN, Lozano AM, Chakravarty MM, the Alzheimer's Disease Neuroimaging Initiative. Your algorithm might think the hippocampus grows in Alzheimer's disease: Caveats of longitudinal automated hippocampal volumetry. Hum Brain Mapp 2017; 38:2875-2896. [PMID: 28295799 PMCID: PMC5447460 DOI: 10.1002/hbm.23559] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Revised: 01/31/2017] [Accepted: 02/27/2017] [Indexed: 11/10/2022] Open
Abstract
Hippocampal atrophy rate-measured using automated techniques applied to structural MRI scans-is considered a sensitive marker of disease progression in Alzheimer's disease, frequently used as an outcome measure in clinical trials. Using publicly accessible data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we examined 1-year hippocampal atrophy rates generated by each of five automated or semiautomated hippocampal segmentation algorithms in patients with Alzheimer's disease, subjects with mild cognitive impairment, or elderly controls. We analyzed MRI data from 398 and 62 subjects available at baseline and at 1 year at MRI field strengths of 1.5 T and 3 T, respectively. We observed a high rate of hippocampal segmentation failures across all algorithms and diagnostic categories, with only 50.8% of subjects at 1.5 T and 58.1% of subjects at 3 T passing stringent segmentation quality control. We also found that all algorithms identified several subjects (between 2.94% and 48.68%) across all diagnostic categories showing increases in hippocampal volume over 1 year. For any given algorithm, hippocampal "growth" could not entirely be explained by excluding patients with flawed hippocampal segmentations, scan-rescan variability, or MRI field strength. Furthermore, different algorithms did not uniformly identify the same subjects as hippocampal "growers," and showed very poor concordance in estimates of magnitude of hippocampal volume change over time (intraclass correlation coefficient 0.319 at 1.5 T and 0.149 at 3 T). This precluded a meaningful analysis of whether hippocampal "growth" represents a true biological phenomenon. Taken together, our findings suggest that longitudinal hippocampal volume change should be interpreted with considerable caution as a biomarker. Hum Brain Mapp 38:2875-2896, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Tejas Sankar
- Division of Neurosurgery, Department of SurgeryUniversity of AlbertaAlbertaCanada
| | - Min Tae M. Park
- Cerebral Imaging CentreDouglas Mental Health University InstituteMontrealQuebecCanada
- Schulich School of Medicine and DentistryWestern UniversityLondonOntarioCanada
| | - Tasha Jawa
- Division of NeurosurgeryUniversity of TorontoTorontoOntarioCanada
| | - Raihaan Patel
- Cerebral Imaging CentreDouglas Mental Health University InstituteMontrealQuebecCanada
- Department of Biological and Biomedical EngineeringMcGill UniversityMontrealQuebecCanada
| | - Nikhil Bhagwat
- Cerebral Imaging CentreDouglas Mental Health University InstituteMontrealQuebecCanada
- Kimel Family Translational Imaging Genetics Research LaboratoryCampbell Family Mental Health Institute, Centre for Addiction and Mental HealthTorontoOntarioCanada
- Department of Biological and Biomedical EngineeringMcGill UniversityMontrealQuebecCanada
- Institute of Biomaterials and Biomedical EngineeringUniversity of TorontoTorontoOntarioCanada
| | - Aristotle N. Voineskos
- Kimel Family Translational Imaging Genetics Research LaboratoryCampbell Family Mental Health Institute, Centre for Addiction and Mental HealthTorontoOntarioCanada
- Department of PsychiatryUniversity of TorontoTorontoOntarioCanada
| | - Andres M. Lozano
- Division of NeurosurgeryUniversity of TorontoTorontoOntarioCanada
| | - M. Mallar Chakravarty
- Cerebral Imaging CentreDouglas Mental Health University InstituteMontrealQuebecCanada
- Department of PsychiatryMcGill UniversityMontrealQuebecCanada
- Department of Biological and Biomedical EngineeringMcGill UniversityMontrealQuebecCanada
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206
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Kiar G, Gorgolewski KJ, Kleissas D, Roncal WG, Litt B, Wandell B, Poldrack RA, Wiener M, Vogelstein RJ, Burns R, Vogelstein JT. Science in the cloud (SIC): A use case in MRI connectomics. Gigascience 2017; 6:1-10. [PMID: 28327935 PMCID: PMC5467033 DOI: 10.1093/gigascience/gix013] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Revised: 02/13/2017] [Accepted: 03/01/2017] [Indexed: 01/08/2023] Open
Abstract
Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift from data collection to data analysis. Unfortunately, lack of standardized sharing mechanisms and practices often make reproducing or extending scientific results very difficult. With the creation of data organization structures and tools that drastically improve code portability, we now have the opportunity to design such a framework for communicating extensible scientific discoveries. Our proposed solution leverages these existing technologies and standards, and provides an accessible and extensible model for reproducible research, called 'science in the cloud' (SIC). Exploiting scientific containers, cloud computing, and cloud data services, we show the capability to compute in the cloud and run a web service that enables intimate interaction with the tools and data presented. We hope this model will inspire the community to produce reproducible and, importantly, extensible results that will enable us to collectively accelerate the rate at which scientific breakthroughs are discovered, replicated, and extended.
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Affiliation(s)
- Gregory Kiar
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
| | | | - Dean Kleissas
- Johns Hopkins University Applied Physics Lab, Columbia, MD, USA
| | - William Gray Roncal
- Johns Hopkins University Applied Physics Lab, Columbia, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Brian Wandell
- Department of Psychology, Stanford University, Stanford, CA, USA
- Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA, USA
| | | | - Martin Wiener
- Department of Psychology, George Mason University, Fairfax, VA, USA
| | | | - Randal Burns
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Joshua T. Vogelstein
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
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207
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Gilmore RO, Diaz MT, Wyble BA, Yarkoni T. Progress toward openness, transparency, and reproducibility in cognitive neuroscience. Ann N Y Acad Sci 2017; 1396:5-18. [PMID: 28464561 PMCID: PMC5545750 DOI: 10.1111/nyas.13325] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Revised: 01/23/2017] [Accepted: 01/28/2017] [Indexed: 11/27/2022]
Abstract
Accumulating evidence suggests that many findings in psychological science and cognitive neuroscience may prove difficult to reproduce; statistical power in brain imaging studies is low and has not improved recently; software errors in analysis tools are common and can go undetected for many years; and, a few large-scale studies notwithstanding, open sharing of data, code, and materials remain the rare exception. At the same time, there is a renewed focus on reproducibility, transparency, and openness as essential core values in cognitive neuroscience. The emergence and rapid growth of data archives, meta-analytic tools, software pipelines, and research groups devoted to improved methodology reflect this new sensibility. We review evidence that the field has begun to embrace new open research practices and illustrate how these can begin to address problems of reproducibility, statistical power, and transparency in ways that will ultimately accelerate discovery.
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Affiliation(s)
| | - Michele T. Diaz
- Department of Psychology, The Pennsylvania State University
- Social, Life, & Engineering Sciences Imaging Center
| | - Brad A. Wyble
- Department of Psychology, The Pennsylvania State University
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208
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Dimitriadis SI, Salis C, Tarnanas I, Linden DE. Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs). Front Neuroinform 2017; 11:28. [PMID: 28491032 PMCID: PMC5405139 DOI: 10.3389/fninf.2017.00028] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Accepted: 03/29/2017] [Indexed: 12/25/2022] Open
Abstract
The human brain is a large-scale system of functionally connected brain regions. This system can be modeled as a network, or graph, by dividing the brain into a set of regions, or "nodes," and quantifying the strength of the connections between nodes, or "edges," as the temporal correlation in their patterns of activity. Network analysis, a part of graph theory, provides a set of summary statistics that can be used to describe complex brain networks in a meaningful way. The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. The adaptation of both bivariate (mutual information) and multivariate (Granger causality) connectivity estimators to quantify the synchronization between multichannel recordings yields a fully connected, weighted, (a)symmetric functional connectivity graph (FCG), representing the associations among all brain areas. The aforementioned procedure leads to an extremely dense network of tens up to a few hundreds of weights. Therefore, this FCG must be filtered out so that the "true" connectivity pattern can emerge. Here, we compared a large number of well-known topological thresholding techniques with the novel proposed data-driven scheme based on orthogonal minimal spanning trees (OMSTs). OMSTs filter brain connectivity networks based on the optimization between the global efficiency of the network and the cost preserving its wiring. We demonstrated the proposed method in a large EEG database (N = 101 subjects) with eyes-open (EO) and eyes-closed (EC) tasks by adopting a time-varying approach with the main goal to extract features that can totally distinguish each subject from the rest of the set. Additionally, the reliability of the proposed scheme was estimated in a second case study of fMRI resting-state activity with multiple scans. Our results demonstrated clearly that the proposed thresholding scheme outperformed a large list of thresholding schemes based on the recognition accuracy of each subject compared to the rest of the cohort (EEG). Additionally, the reliability of the network metrics based on the fMRI static networks was improved based on the proposed topological filtering scheme. Overall, the proposed algorithm could be used across neuroimaging and multimodal studies as a common computationally efficient standardized tool for a great number of neuroscientists and physicists working on numerous of projects.
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Affiliation(s)
- Stavros I. Dimitriadis
- Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff UniversityCardiff, UK
- Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff UniversityCardiff, UK
- School of Psychology, Cardiff UniversityCardiff, UK
- Neuroinformatics.GRoup, School of Psychology, Cardiff UniversityCardiff, UK
| | - Christos Salis
- Department of Informatics and Telecommunications Engineering, University of Western MacedoniaKozani, Greece
| | - Ioannis Tarnanas
- Health-IS Lab, Chair of Information Management, ETH ZurichZurich, Switzerland
- 3rd Department of Neurology, Medical School, Aristotle University of ThessalonikiThessaloniki, Greece
| | - David E. Linden
- Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff UniversityCardiff, UK
- Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff UniversityCardiff, UK
- Neuroscience and Mental Health Research Institute (NMHRI), School of Medicine, Cardiff UniversityCardiff, UK
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209
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Dansereau C, Benhajali Y, Risterucci C, Pich EM, Orban P, Arnold D, Bellec P. Statistical power and prediction accuracy in multisite resting-state fMRI connectivity. Neuroimage 2017; 149:220-232. [DOI: 10.1016/j.neuroimage.2017.01.072] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Revised: 01/27/2017] [Accepted: 01/30/2017] [Indexed: 12/29/2022] Open
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210
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Keshavan A, Datta E, M McDonough I, Madan CR, Jordan K, Henry RG. Mindcontrol: A web application for brain segmentation quality control. Neuroimage 2017; 170:365-372. [PMID: 28365419 DOI: 10.1016/j.neuroimage.2017.03.055] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 03/01/2017] [Accepted: 03/27/2017] [Indexed: 12/12/2022] Open
Abstract
Tissue classification plays a crucial role in the investigation of normal neural development, brain-behavior relationships, and the disease mechanisms of many psychiatric and neurological illnesses. Ensuring the accuracy of tissue classification is important for quality research and, in particular, the translation of imaging biomarkers to clinical practice. Assessment with the human eye is vital to correct various errors inherent to all currently available segmentation algorithms. Manual quality assurance becomes methodologically difficult at a large scale - a problem of increasing importance as the number of data sets is on the rise. To make this process more efficient, we have developed Mindcontrol, an open-source web application for the collaborative quality control of neuroimaging processing outputs. The Mindcontrol platform consists of a dashboard to organize data, descriptive visualizations to explore the data, an imaging viewer, and an in-browser annotation and editing toolbox for data curation and quality control. Mindcontrol is flexible and can be configured for the outputs of any software package in any data organization structure. Example configurations for three large, open-source datasets are presented: the 1000 Functional Connectomes Project (FCP), the Consortium for Reliability and Reproducibility (CoRR), and the Autism Brain Imaging Data Exchange (ABIDE) Collection. These demo applications link descriptive quality control metrics, regional brain volumes, and thickness scalars to a 3D imaging viewer and editing module, resulting in an easy-to-implement quality control protocol that can be scaled for any size and complexity of study.
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Affiliation(s)
- Anisha Keshavan
- Department of Neurology, University California, San Francisco, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, San Francisco, USA.
| | - Esha Datta
- Department of Neurology, University California, San Francisco, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, San Francisco, USA.
| | | | | | - Kesshi Jordan
- Department of Neurology, University California, San Francisco, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, San Francisco, USA.
| | - Roland G Henry
- Department of Neurology, University California, San Francisco, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, San Francisco, USA; Department of Radiology and Biomedical Imaging, University California, San Francisco, CA, USA.
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211
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Ciric R, Wolf DH, Power JD, Roalf DR, Baum GL, Ruparel K, Shinohara RT, Elliott MA, Eickhoff SB, Davatzikos C, Gur RC, Gur RE, Bassett DS, Satterthwaite TD. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. Neuroimage 2017; 154:174-187. [PMID: 28302591 DOI: 10.1016/j.neuroimage.2017.03.020] [Citation(s) in RCA: 696] [Impact Index Per Article: 87.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 03/08/2017] [Accepted: 03/10/2017] [Indexed: 01/08/2023] Open
Abstract
Since initial reports regarding the impact of motion artifact on measures of functional connectivity, there has been a proliferation of participant-level confound regression methods to limit its impact. However, many of the most commonly used techniques have not been systematically evaluated using a broad range of outcome measures. Here, we provide a systematic evaluation of 14 participant-level confound regression methods in 393 youths. Specifically, we compare methods according to four benchmarks, including the residual relationship between motion and connectivity, distance-dependent effects of motion on connectivity, network identifiability, and additional degrees of freedom lost in confound regression. Our results delineate two clear trade-offs among methods. First, methods that include global signal regression minimize the relationship between connectivity and motion, but result in distance-dependent artifact. In contrast, censoring methods mitigate both motion artifact and distance-dependence, but use additional degrees of freedom. Importantly, less effective de-noising methods are also unable to identify modular network structure in the connectome. Taken together, these results emphasize the heterogeneous efficacy of existing methods, and suggest that different confound regression strategies may be appropriate in the context of specific scientific goals.
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Affiliation(s)
- Rastko Ciric
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel H Wolf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jonathan D Power
- Department of Psychiatry, Weill Cornell Medical College, NY, NY, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Graham L Baum
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kosha Ruparel
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mark A Elliott
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Germany
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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212
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Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Sci Data 2017; 4:170010. [PMID: 28291247 PMCID: PMC5349246 DOI: 10.1038/sdata.2017.10] [Citation(s) in RCA: 372] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Accepted: 01/05/2017] [Indexed: 12/27/2022] Open
Abstract
The second iteration of the Autism Brain Imaging Data Exchange (ABIDE II) aims to enhance the scope of brain connectomics research in Autism Spectrum Disorder (ASD). Consistent with the initial ABIDE effort (ABIDE I), that released 1112 datasets in 2012, this new multisite open-data resource is an aggregate of resting state functional magnetic resonance imaging (MRI) and corresponding structural MRI and phenotypic datasets. ABIDE II includes datasets from an additional 487 individuals with ASD and 557 controls previously collected across 16 international institutions. The combination of ABIDE I and ABIDE II provides investigators with 2156 unique cross-sectional datasets allowing selection of samples for discovery and/or replication. This sample size can also facilitate the identification of neurobiological subgroups, as well as preliminary examinations of sex differences in ASD. Additionally, ABIDE II includes a range of psychiatric variables to inform our understanding of the neural correlates of co-occurring psychopathology; 284 diffusion imaging datasets are also included. It is anticipated that these enhancements will contribute to unraveling key sources of ASD heterogeneity.
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213
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Sato JR, White TP, Biazoli CE. Commentary: A test-retest dataset for assessing long-term reliability of brain morphology and resting-state brain activity. Front Neurosci 2017; 11:85. [PMID: 28275335 PMCID: PMC5319983 DOI: 10.3389/fnins.2017.00085] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Accepted: 02/07/2017] [Indexed: 12/24/2022] Open
Affiliation(s)
- João R Sato
- Centre of Mathematics, Computation and Cognition, Universidade Federal do ABC Santo Andre, Brazil
| | - Thomas P White
- School of Psychology, University of Birmingham Birmingham, UK
| | - Claudinei E Biazoli
- Centre of Mathematics, Computation and Cognition, Universidade Federal do ABC Santo Andre, Brazil
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214
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Marchitelli R, Collignon O, Jovicich J. Test–Retest Reproducibility of the Intrinsic Default Mode Network: Influence of Functional Magnetic Resonance Imaging Slice-Order Acquisition and Head-Motion Correction Methods. Brain Connect 2017; 7:69-83. [DOI: 10.1089/brain.2016.0450] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Affiliation(s)
- Rocco Marchitelli
- Center for Mind/Brain Sciences (CIMEC), University of Trento, Rovereto, Italy
- I.R.C.C.S. SDN, Naples, Italy
| | - Olivier Collignon
- Center for Mind/Brain Sciences (CIMEC), University of Trento, Rovereto, Italy
- Institute of Research in Psychology (IPSY) and in Neuroscience (IoNS), University of Louvain, Louvain-la-Neuve, Belgium
| | - Jorge Jovicich
- Center for Mind/Brain Sciences (CIMEC), University of Trento, Rovereto, Italy
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215
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Wang S, Peterson DJ, Gatenby JC, Li W, Grabowski TJ, Madhyastha TM. Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion MRI. Front Neuroinform 2017; 11:17. [PMID: 28270762 PMCID: PMC5318394 DOI: 10.3389/fninf.2017.00017] [Citation(s) in RCA: 113] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 02/09/2017] [Indexed: 11/24/2022] Open
Abstract
Correction of echo planar imaging (EPI)-induced distortions (called “unwarping”) improves anatomical fidelity for diffusion magnetic resonance imaging (MRI) and functional imaging investigations. Commonly used unwarping methods require the acquisition of supplementary images during the scanning session. Alternatively, distortions can be corrected by nonlinear registration to a non-EPI acquired structural image. In this study, we compared reliability using two methods of unwarping: (1) nonlinear registration to a structural image using symmetric normalization (SyN) implemented in Advanced Normalization Tools (ANTs); and (2) unwarping using an acquired field map. We performed this comparison in two different test-retest data sets acquired at differing sites (N = 39 and N = 32). In both data sets, nonlinear registration provided higher test-retest reliability of the output fractional anisotropy (FA) maps than field map-based unwarping, even when accounting for the effect of interpolation on the smoothness of the images. In general, field map-based unwarping was preferable if and only if the field maps were acquired optimally.
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Affiliation(s)
- Sijia Wang
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's HospitalShanghai, China; Department of Radiology, University of WashingtonSeattle, WA, USA
| | | | - J C Gatenby
- Department of Radiology, University of Washington Seattle, WA, USA
| | - Wenbin Li
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital Shanghai, China
| | - Thomas J Grabowski
- Department of Radiology, University of WashingtonSeattle, WA, USA; Department of Neurology, University of WashingtonSeattle, WA, USA
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216
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Meszlényi RJ, Hermann P, Buza K, Gál V, Vidnyánszky Z. Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping. Front Neurosci 2017; 11:75. [PMID: 28261052 PMCID: PMC5313507 DOI: 10.3389/fnins.2017.00075] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Accepted: 02/01/2017] [Indexed: 12/24/2022] Open
Abstract
Traditional resting-state network concept is based on calculating linear dependence of spontaneous low frequency fluctuations of the BOLD signals of different brain areas, which assumes temporally stable zero-lag synchrony across regions. However, growing amount of experimental findings suggest that functional connectivity exhibits dynamic changes and a complex time-lag structure, which cannot be captured by the static zero-lag correlation analysis. Here we propose a new approach applying Dynamic Time Warping (DTW) distance to evaluate functional connectivity strength that accounts for non-stationarity and phase-lags between the observed signals. Using simulated fMRI data we found that DTW captures dynamic interactions and it is less sensitive to linearly combined global noise in the data as compared to traditional correlation analysis. We tested our method using resting-state fMRI data from repeated measurements of an individual subject and showed that DTW analysis results in more stable connectivity patterns by reducing the within-subject variability and increasing robustness for preprocessing strategies. Classification results on a public dataset revealed a superior sensitivity of the DTW analysis to group differences by showing that DTW based classifiers outperform the zero-lag correlation and maximal lag cross-correlation based classifiers significantly. Our findings suggest that analysing resting-state functional connectivity using DTW provides an efficient new way for characterizing functional networks.
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Affiliation(s)
- Regina J Meszlényi
- Department of Cognitive Science, Budapest University of Technology and EconomicsBudapest, Hungary; Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of SciencesBudapest, Hungary
| | - Petra Hermann
- 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
| | - Viktor Gál
- Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of Sciences Budapest, Hungary
| | - Zoltán Vidnyánszky
- Department of Cognitive Science, Budapest University of Technology and EconomicsBudapest, Hungary; Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of SciencesBudapest, Hungary
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217
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Keshavan A, Madan C, Datta E, McDonough I. Mindcontrol: Organize, quality control, annotate, edit, and collaborate on neuroimaging processing results. RESEARCH IDEAS AND OUTCOMES 2017. [DOI: 10.3897/rio.3.e12276] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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218
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Functional connectomics from a "big data" perspective. Neuroimage 2017; 160:152-167. [PMID: 28232122 DOI: 10.1016/j.neuroimage.2017.02.031] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2016] [Revised: 01/21/2017] [Accepted: 02/13/2017] [Indexed: 01/10/2023] Open
Abstract
In the last decade, explosive growth regarding functional connectome studies has been observed. Accumulating knowledge has significantly contributed to our understanding of the brain's functional network architectures in health and disease. With the development of innovative neuroimaging techniques, the establishment of large brain datasets and the increasing accumulation of published findings, functional connectomic research has begun to move into the era of "big data", which generates unprecedented opportunities for discovery in brain science and simultaneously encounters various challenging issues, such as data acquisition, management and analyses. Big data on the functional connectome exhibits several critical features: high spatial and/or temporal precision, large sample sizes, long-term recording of brain activity, multidimensional biological variables (e.g., imaging, genetic, demographic, cognitive and clinic) and/or vast quantities of existing findings. We review studies regarding functional connectomics from a big data perspective, with a focus on recent methodological advances in state-of-the-art image acquisition (e.g., multiband imaging), analysis approaches and statistical strategies (e.g., graph theoretical analysis, dynamic network analysis, independent component analysis, multivariate pattern analysis and machine learning), as well as reliability and reproducibility validations. We highlight the novel findings in the application of functional connectomic big data to the exploration of the biological mechanisms of cognitive functions, normal development and aging and of neurological and psychiatric disorders. We advocate the urgent need to expand efforts directed at the methodological challenges and discuss the direction of applications in this field.
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219
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Liu W, Wei D, Chen Q, Yang W, Meng J, Wu G, Bi T, Zhang Q, Zuo XN, Qiu J. Longitudinal test-retest neuroimaging data from healthy young adults in southwest China. Sci Data 2017; 4:170017. [PMID: 28195583 PMCID: PMC5308199 DOI: 10.1038/sdata.2017.17] [Citation(s) in RCA: 97] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Accepted: 12/22/2016] [Indexed: 11/10/2022] Open
Abstract
Multimodal magnetic resonance imaging (mMRI) has been widely used to map the structure and function of the human brain, as well as its behavioral associations. However, to date, a large sample with a long-term longitudinal design and a narrow age-span has been lacking for the assessment of test-retest reliability and reproducibility of brain-behavior correlations, as well as the development of novel causal insights into these correlational findings. Here we describe the SLIM dataset, which includes brain and behavioral data across a long-term retest-duration within three and a half years, mMRI scans provided a set of structural, diffusion and resting-state functional MRI images, along with rich samples of behavioral assessments addressed-demographic, cognitive and emotional information. Together with the Consortium for Reliability and Reproducibility (CoRR), the SLIM is expected to accelerate the reproducible sciences of the human brain by providing an open resource for brain-behavior discovery sciences with big-data approaches.
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Affiliation(s)
- Wei Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China.,Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen 6525 EZ, The Netherlands
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Qunlin Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Wenjing Yang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Jie Meng
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Guorong Wu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Taiyong Bi
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Qinglin Zhang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Xi-Nian Zuo
- Faculty of Psychology, Southwest University, Chongqing 400715, China.,Key Laboratory of Behavioral Science, Laboratory for Functional Connectome and Development and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.,Department of Psychology, School of Education Science, Guangxi Teachers Education University, Nanning 530000, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
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220
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Poldrack RA, Baker CI, Durnez J, Gorgolewski KJ, Matthews PM, Munafò MR, Nichols TE, Poline JB, Vul E, Yarkoni T. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat Rev Neurosci 2017; 18:115-126. [PMID: 28053326 PMCID: PMC6910649 DOI: 10.1038/nrn.2016.167] [Citation(s) in RCA: 825] [Impact Index Per Article: 103.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions.
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Affiliation(s)
- Russell A Poldrack
- Department of Psychology and Stanford Center for Reproducible Neuroscience, Stanford University, Stanford, California 94305, USA
| | - Chris I Baker
- Laboratory of Brain and Cognition, National Institute of Mental Health, US National Institutes of Health, Maryland 20892, USA
| | - Joke Durnez
- Department of Psychology and Stanford Center for Reproducible Neuroscience, Stanford University, Stanford, California 94305, USA
- Institut National de Recherche en Informatique et en Automatique (INRIA) Parietal, Neurospin, Building 145, CEA Saclay, 91191 Gif sur Yvette, France
| | - Krzysztof J Gorgolewski
- Department of Psychology and Stanford Center for Reproducible Neuroscience, Stanford University, Stanford, California 94305, USA
| | - Paul M Matthews
- Division of Brain Sciences, Department of Medicine, Imperial College Hammersmith Hospital, London W12 0NN, UK
| | - Marcus R Munafò
- Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1BN, UK
- UK Centre for Tobacco and Alcohol Studies, School of Experimental Psychology, University of Bristol, Bristol BS8 1TU, UK
| | - Thomas E Nichols
- Department of Statistics and WMG, University of Warwick, Coventry CV4 7AL, UK
| | - Jean-Baptiste Poline
- Helen Wills Neuroscience Institute, Henry H. Wheeler Jr. Brain Imaging Center, University of California, 132 Barker Hall 210S, Berkeley, California 94720-3192, USA
| | - Edward Vul
- Department of Psychology, University of California, San Diego, San Diego, California 92093, USA
| | - Tal Yarkoni
- Department of Psychology, University of Texas at Austin, Austin, Texas 78712, USA
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221
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Klein A, Ghosh SS, Bao FS, Giard J, Häme Y, Stavsky E, Lee N, Rossa B, Reuter M, Chaibub Neto E, Keshavan A. Mindboggling morphometry of human brains. PLoS Comput Biol 2017; 13:e1005350. [PMID: 28231282 PMCID: PMC5322885 DOI: 10.1371/journal.pcbi.1005350] [Citation(s) in RCA: 365] [Impact Index Per Article: 45.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2016] [Accepted: 01/08/2017] [Indexed: 01/01/2023] Open
Abstract
Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, while more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle's algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available.
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Affiliation(s)
- Arno Klein
- Child Mind Institute, New York, New York, United States of America
| | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Otolaryngology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Forrest S. Bao
- Department of Electrical and Computer Engineering, University of Akron, Akron, Ohio, United States of America
| | | | - Yrjö Häme
- Columbia University, New York, New York, United States of America
| | - Eliezer Stavsky
- Columbia University, New York, New York, United States of America
| | - Noah Lee
- Columbia University, New York, New York, United States of America
| | - Brian Rossa
- TankThink Labs, Boston, Massachusetts, United States of America
| | - Martin Reuter
- Harvard Medical School, Cambridge, Massachusetts, United States of America
| | | | - Anisha Keshavan
- University of California San Francisco, San Francisco, California, United States of America
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222
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Ghinda CD, Duffau H. Network Plasticity and Intraoperative Mapping for Personalized Multimodal Management of Diffuse Low-Grade Gliomas. Front Surg 2017; 4:3. [PMID: 28197403 PMCID: PMC5281570 DOI: 10.3389/fsurg.2017.00003] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Accepted: 01/16/2017] [Indexed: 01/07/2023] Open
Abstract
Gliomas are the most frequent primary brain tumors and include a variety of different histological tumor types and malignancy grades. Recent achievements in terms of molecular and imaging fields have created an unprecedented opportunity to perform a comprehensive interdisciplinary assessment of the glioma pathophysiology, with direct implications in terms of the medical and surgical treatment strategies available for patients. The current paradigm shift considers glioma management in a comprehensive perspective that takes into account the intricate connectivity of the cerebral networks. This allowed significant improvement in the outcome of patients with lesions previously considered inoperable. The current review summarizes the current theoretical framework integrating the adult human brain plasticity and functional reorganization within a dynamic individualized treatment strategy for patients affected by diffuse low-grade gliomas. The concept of neuro-oncology as a brain network surgery has major implications in terms of the clinical management and ensuing outcomes, as indexed by the increased survival and quality of life of patients managed using such an approach.
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Affiliation(s)
- Cristina Diana Ghinda
- Department of Neurosurgery, The Ottawa Hospital, Ottawa Hospital Research Institute, Ottawa, ON, Canada; Neuroscience Division, University of Ottawa, Ottawa, ON, Canada
| | - Hugues Duffau
- Department of Neurosurgery, Hôpital Gui de Chauliac, Montpellier University Medical Center, Montpellier, France; Brain Plasticity, Stem Cells and Glial Tumors Team, National Institute for Health and Medical Research (INSERM), Montpellier, France
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223
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Dickie DA, Shenkin SD, Anblagan D, Lee J, Blesa Cabez M, Rodriguez D, Boardman JP, Waldman A, Job DE, Wardlaw JM. Whole Brain Magnetic Resonance Image Atlases: A Systematic Review of Existing Atlases and Caveats for Use in Population Imaging. Front Neuroinform 2017; 11:1. [PMID: 28154532 PMCID: PMC5244468 DOI: 10.3389/fninf.2017.00001] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 01/04/2017] [Indexed: 11/17/2022] Open
Abstract
Brain MRI atlases may be used to characterize brain structural changes across the life course. Atlases have important applications in research, e.g., as registration and segmentation targets to underpin image analysis in population imaging studies, and potentially in future in clinical practice, e.g., as templates for identifying brain structural changes out with normal limits, and increasingly for use in surgical planning. However, there are several caveats and limitations which must be considered before successfully applying brain MRI atlases to research and clinical problems. For example, the influential Talairach and Tournoux atlas was derived from a single fixed cadaveric brain from an elderly female with limited clinical information, yet is the basis of many modern atlases and is often used to report locations of functional activation. We systematically review currently available whole brain structural MRI atlases with particular reference to the implications for population imaging through to emerging clinical practice. We found 66 whole brain structural MRI atlases world-wide. The vast majority were based on T1, T2, and/or proton density (PD) structural sequences, had been derived using parametric statistics (inappropriate for brain volume distributions), had limited supporting clinical or cognitive data, and included few younger (>5 and <18 years) or older (>60 years) subjects. To successfully characterize brain structural features and their changes across different stages of life, we conclude that whole brain structural MRI atlases should include: more subjects at the upper and lower extremes of age; additional structural sequences, including fluid attenuation inversion recovery (FLAIR) and T2* sequences; a range of appropriate statistics, e.g., rank-based or non-parametric; and detailed cognitive and clinical profiles of the included subjects in order to increase the relevance and utility of these atlases.
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Affiliation(s)
- David Alexander Dickie
- Brain Research Imaging Centre, Neuroimaging Sciences, Centre for Clinical Brain Sciences, Royal Infirmary of Edinburgh, The University of EdinburghEdinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationGlasgow, UK
| | - Susan D. Shenkin
- Brain Research Imaging Centre, Neuroimaging Sciences, Centre for Clinical Brain Sciences, Royal Infirmary of Edinburgh, The University of EdinburghEdinburgh, UK
- Geriatric Medicine Unit, Royal Infirmary of Edinburgh, The University of EdinburghEdinburgh, UK
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, The University of EdinburghEdinburgh, UK
| | - Devasuda Anblagan
- Brain Research Imaging Centre, Neuroimaging Sciences, Centre for Clinical Brain Sciences, Royal Infirmary of Edinburgh, The University of EdinburghEdinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationGlasgow, UK
- MRC Centre for Reproductive Health, Queen's Medical Research InstituteEdinburgh, UK
| | - Juyoung Lee
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of TübingenTübingen, Germany
| | - Manuel Blesa Cabez
- MRC Centre for Reproductive Health, Queen's Medical Research InstituteEdinburgh, UK
| | - David Rodriguez
- Brain Research Imaging Centre, Neuroimaging Sciences, Centre for Clinical Brain Sciences, Royal Infirmary of Edinburgh, The University of EdinburghEdinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationGlasgow, UK
| | - James P. Boardman
- MRC Centre for Reproductive Health, Queen's Medical Research InstituteEdinburgh, UK
| | - Adam Waldman
- Brain Research Imaging Centre, Neuroimaging Sciences, Centre for Clinical Brain Sciences, Royal Infirmary of Edinburgh, The University of EdinburghEdinburgh, UK
| | - Dominic E. Job
- Brain Research Imaging Centre, Neuroimaging Sciences, Centre for Clinical Brain Sciences, Royal Infirmary of Edinburgh, The University of EdinburghEdinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationGlasgow, UK
| | - Joanna M. Wardlaw
- Brain Research Imaging Centre, Neuroimaging Sciences, Centre for Clinical Brain Sciences, Royal Infirmary of Edinburgh, The University of EdinburghEdinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationGlasgow, UK
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, The University of EdinburghEdinburgh, UK
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224
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Teipel SJ, Wohlert A, Metzger C, Grimmer T, Sorg C, Ewers M, Meisenzahl E, Klöppel S, Borchardt V, Grothe MJ, Walter M, Dyrba M. Multicenter stability of resting state fMRI in the detection of Alzheimer's disease and amnestic MCI. Neuroimage Clin 2017; 14:183-194. [PMID: 28180077 PMCID: PMC5279697 DOI: 10.1016/j.nicl.2017.01.018] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Revised: 11/30/2016] [Accepted: 01/17/2017] [Indexed: 12/26/2022]
Abstract
BACKGROUND In monocentric studies, patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia exhibited alterations of functional cortical connectivity in resting-state functional MRI (rs-fMRI) analyses. Multicenter studies provide access to large sample sizes, but rs-fMRI may be particularly sensitive to multiscanner effects. METHODS We used data from five centers of the "German resting-state initiative for diagnostic biomarkers" (psymri.org), comprising 367 cases, including AD patients, MCI patients and healthy older controls, to assess the influence of the distributed acquisition on the group effects. We calculated accuracy of group discrimination based on whole brain functional connectivity of the posterior cingulate cortex (PCC) using pooled samples as well as second-level analyses across site-specific group contrast maps. RESULTS We found decreased functional connectivity in AD patients vs. controls, including clusters in the precuneus, inferior parietal cortex, lateral temporal cortex and medial prefrontal cortex. MCI subjects showed spatially similar, but less pronounced, differences in PCC connectivity when compared to controls. Group discrimination accuracy for AD vs. controls (MCI vs. controls) in the test data was below 76% (72%) based on the pooled analysis, and even lower based on the second level analysis stratified according to scanner. Only a subset of quality measures was useful to detect relevant scanner effects. CONCLUSIONS Multicenter rs-fMRI analysis needs to employ strict quality measures, including visual inspection of all the data, to avoid seriously confounded group effects. While pending further confirmation in biomarker stratified samples, these findings suggest that multicenter acquisition limits the use of rs-fMRI in AD and MCI diagnosis.
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Affiliation(s)
- Stefan J. Teipel
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany
- DZNE, German Center for Neurodegenerative Diseases, Rostock, Germany
| | - Alexandra Wohlert
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany
| | - Coraline Metzger
- Institute of Cognitive Neurology and Dementia Research (IKND), Department of Psychiatry and Psychotherapy, Otto von Guericke University, Germany and German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Timo Grimmer
- Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Christian Sorg
- Department of Neuroradiology of Klinikum rechts der Isar, Technische Universität München, Department of Psychiatry of Klinikum rechts der Isar, TUM-Neuroimaging Center, Einsteinstr. 1, 81675 Munich, Germany
| | - Michael Ewers
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Eva Meisenzahl
- Department of Psychiatry, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Stefan Klöppel
- Department of Psychiatry and Psychotherapy, Section of Gerontopsychiatry and Neuropsychology, Faculty of Medicine, University of Freiburg, Germany
- University Hospital of Old Age Psychiatry, Bern, Switzerland
| | - Viola Borchardt
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Department of Psychiatry, University Tübingen, Germany
| | - Michel J. Grothe
- DZNE, German Center for Neurodegenerative Diseases, Rostock, Germany
| | - Martin Walter
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Department of Psychiatry, University Tübingen, Germany
| | - Martin Dyrba
- DZNE, German Center for Neurodegenerative Diseases, Rostock, Germany
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225
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Das S, Glatard T, Rogers C, Saigle J, Paiva S, MacIntyre L, Safi-Harab M, Rousseau ME, Stirling J, Khalili-Mahani N, MacFarlane D, Kostopoulos P, Rioux P, Madjar C, Lecours-Boucher X, Vanamala S, Adalat R, Mohaddes Z, Fonov VS, Milot S, Leppert I, Degroot C, Durcan TM, Campbell T, Moreau J, Dagher A, Collins DL, Karamchandani J, Bar-Or A, Fon EA, Hoge R, Baillet S, Rouleau G, Evans AC. Cyberinfrastructure for Open Science at the Montreal Neurological Institute. Front Neuroinform 2017; 10:53. [PMID: 28111547 PMCID: PMC5216036 DOI: 10.3389/fninf.2016.00053] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 12/01/2016] [Indexed: 12/20/2022] Open
Abstract
Data sharing is becoming more of a requirement as technologies mature and as global research and communications diversify. As a result, researchers are looking for practical solutions, not only to enhance scientific collaborations, but also to acquire larger amounts of data, and to access specialized datasets. In many cases, the realities of data acquisition present a significant burden, therefore gaining access to public datasets allows for more robust analyses and broadly enriched data exploration. To answer this demand, the Montreal Neurological Institute has announced its commitment to Open Science, harnessing the power of making both clinical and research data available to the world (Owens, 2016a,b). As such, the LORIS and CBRAIN (Das et al., 2016) platforms have been tasked with the technical challenges specific to the institutional-level implementation of open data sharing, including: Comprehensive linking of multimodal data (phenotypic, clinical, neuroimaging, biobanking, and genomics, etc.)Secure database encryption, specifically designed for institutional and multi-project data sharing, ensuring subject confidentiality (using multi-tiered identifiers).Querying capabilities with multiple levels of single study and institutional permissions, allowing public data sharing for all consented and de-identified subject data.Configurable pipelines and flags to facilitate acquisition and analysis, as well as access to High Performance Computing clusters for rapid data processing and sharing of software tools.Robust Workflows and Quality Control mechanisms ensuring transparency and consistency in best practices.Long term storage (and web access) of data, reducing loss of institutional data assets.Enhanced web-based visualization of imaging, genomic, and phenotypic data, allowing for real-time viewing and manipulation of data from anywhere in the world.Numerous modules for data filtering, summary statistics, and personalized and configurable dashboards. Implementing the vision of Open Science at the Montreal Neurological Institute will be a concerted undertaking that seeks to facilitate data sharing for the global research community. Our goal is to utilize the years of experience in multi-site collaborative research infrastructure to implement the technical requirements to achieve this level of public data sharing in a practical yet robust manner, in support of accelerating scientific discovery.
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Affiliation(s)
- Samir Das
- McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada
- Montreal Neurological InstituteMontreal, QC, Canada
| | - Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia UniversityMontreal, QC, Canada
| | - Christine Rogers
- McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada
- Montreal Neurological InstituteMontreal, QC, Canada
| | - John Saigle
- Montreal Neurological InstituteMontreal, QC, Canada
| | - Santiago Paiva
- McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological InstituteMontreal, QC, Canada
| | - Leigh MacIntyre
- McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada
- Montreal Neurological InstituteMontreal, QC, Canada
| | - Mouna Safi-Harab
- McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada
- Montreal Neurological InstituteMontreal, QC, Canada
| | - Marc-Etienne Rousseau
- McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada
- Montreal Neurological InstituteMontreal, QC, Canada
| | - Jordan Stirling
- McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada
- Montreal Neurological InstituteMontreal, QC, Canada
| | - Najmeh Khalili-Mahani
- McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada
- Montreal Neurological InstituteMontreal, QC, Canada
- Department of Computer Science and Software Engineering, Concordia UniversityMontreal, QC, Canada
| | - David MacFarlane
- McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada
- Montreal Neurological InstituteMontreal, QC, Canada
| | - Penelope Kostopoulos
- McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada
- Montreal Neurological InstituteMontreal, QC, Canada
| | - Pierre Rioux
- McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada
- Montreal Neurological InstituteMontreal, QC, Canada
| | - Cecile Madjar
- Douglas Mental Health University HospitalMontreal, QC, Canada
| | - Xavier Lecours-Boucher
- McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada
- Montreal Neurological InstituteMontreal, QC, Canada
| | | | - Reza Adalat
- McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada
- Montreal Neurological InstituteMontreal, QC, Canada
| | - Zia Mohaddes
- McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada
- Montreal Neurological InstituteMontreal, QC, Canada
| | - Vladimir S. Fonov
- Montreal Neurological InstituteMontreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological InstituteMontreal, QC, Canada
| | - Sylvain Milot
- Montreal Neurological InstituteMontreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological InstituteMontreal, QC, Canada
| | - Ilana Leppert
- Montreal Neurological InstituteMontreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological InstituteMontreal, QC, Canada
| | | | | | - Tara Campbell
- McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada
- Montreal Neurological InstituteMontreal, QC, Canada
| | - Jeremy Moreau
- Montreal Neurological InstituteMontreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological InstituteMontreal, QC, Canada
| | - Alain Dagher
- McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological InstituteMontreal, QC, Canada
| | - D. Louis Collins
- Montreal Neurological InstituteMontreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological InstituteMontreal, QC, Canada
| | | | - Amit Bar-Or
- Montreal Neurological InstituteMontreal, QC, Canada
| | | | - Rick Hoge
- Montreal Neurological InstituteMontreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological InstituteMontreal, QC, Canada
| | - Sylvain Baillet
- Montreal Neurological InstituteMontreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological InstituteMontreal, QC, Canada
| | - Guy Rouleau
- Montreal Neurological InstituteMontreal, QC, Canada
| | - Alan C. Evans
- McGill Centre for Integrative Neuroscience, Montreal Neurological InstituteMontreal, QC, Canada
- Montreal Neurological InstituteMontreal, QC, Canada
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226
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Zuo XN, He Y, Betzel RF, Colcombe S, Sporns O, Milham MP. Human Connectomics across the Life Span. Trends Cogn Sci 2016; 21:32-45. [PMID: 27865786 DOI: 10.1016/j.tics.2016.10.005] [Citation(s) in RCA: 144] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Revised: 10/10/2016] [Accepted: 10/17/2016] [Indexed: 01/19/2023]
Abstract
Connectomics has enhanced our understanding of neurocognitive development and decline by the integration of network sciences into studies across different stages of the human life span. However, these studies commonly occurred independently, missing the opportunity to test integrated models of the dynamical brain organization across the entire life span. In this review article, we survey empirical findings in life-span connectomics and propose a generative framework for computationally modeling the connectome over the human life span. This framework highlights initial findings that across the life span, the human connectome gradually shifts from an 'anatomically driven' organization to one that is more 'topological'. Finally, we consider recent advances that are promising to provide an integrative and systems perspective of human brain plasticity as well as underscore the pitfalls and challenges.
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Affiliation(s)
- Xi-Nian Zuo
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Magnetic Resonance Imaging Research Center, Institute of Psychology, Beijing, China; Lifespan Connectomics and Behavior Team, Institute of Psychology, Beijing, China; Key Laboratory for Brain and Education Sciences, Guangxi Teachers Education University, Nanning, Guangxi, China; Center for Longevity Research, Guangxi Teachers Education University, Nanning, Guangxi, China.
| | - Ye He
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Magnetic Resonance Imaging Research Center, Institute of Psychology, Beijing, China; Lifespan Connectomics and Behavior Team, Institute of Psychology, Beijing, China; Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Richard F Betzel
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Stan Colcombe
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, SC, USA
| | - Olaf Sporns
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Michael P Milham
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, SC, USA; Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
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227
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McDonald AR, Muraskin J, Dam NTV, Froehlich C, Puccio B, Pellman J, Bauer CCC, Akeyson A, Breland MM, Calhoun VD, Carter S, Chang TP, Gessner C, Gianonne A, Giavasis S, Glass J, Homann S, King M, Kramer M, Landis D, Lieval A, Lisinski J, Mackay-Brandt A, Miller B, Panek L, Reed H, Santiago C, Schoell E, Sinnig R, Sital M, Taverna E, Tobe R, Trautman K, Varghese B, Walden L, Wang R, Waters AB, Wood DC, Castellanos FX, Leventhal B, Colcombe SJ, LaConte S, Milham MP, Craddock RC. The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository. Neuroimage 2016; 146:157-170. [PMID: 27836708 DOI: 10.1016/j.neuroimage.2016.10.048] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Revised: 10/14/2016] [Accepted: 10/30/2016] [Indexed: 01/18/2023] Open
Abstract
This data descriptor describes a repository of openly shared data from an experiment to assess inter-individual differences in default mode network (DMN) activity. This repository includes cross-sectional functional magnetic resonance imaging (fMRI) data from the Multi Source Interference Task, to assess DMN deactivation, the Moral Dilemma Task, to assess DMN activation, a resting state fMRI scan, and a DMN neurofeedback paradigm, to assess DMN modulation, along with accompanying behavioral and cognitive measures. We report technical validation from n=125 participants of the final targeted sample of 180 participants. Each session includes acquisition of one whole-brain anatomical scan and whole-brain echo-planar imaging (EPI) scans, acquired during the aforementioned tasks and resting state. The data includes several self-report measures related to perseverative thinking, emotion regulation, and imaginative processes, along with a behavioral measure of rapid visual information processing. Technical validation of the data confirms that the tasks deactivate and activate the DMN as expected. Group level analysis of the neurofeedback data indicates that the participants are able to modulate their DMN with considerable inter-subject variability. Preliminary analysis of behavioral responses and specifically self-reported sleep indicate that as many as 73 participants may need to be excluded from an analysis depending on the hypothesis being tested. The present data are linked to the enhanced Nathan Kline Institute, Rockland Sample and builds on the comprehensive neuroimaging and deep phenotyping available therein. As limited information is presently available about individual differences in the capacity to directly modulate the default mode network, these data provide a unique opportunity to examine DMN modulation ability in relation to numerous phenotypic characteristics.
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Affiliation(s)
- Amalia R McDonald
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Jordan Muraskin
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Nicholas T Van Dam
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Child Mind Institute, New York, NY, USA
| | | | - Benjamin Puccio
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | | | - Clemens C C Bauer
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Boston, MA, USA
| | - Alexis Akeyson
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Melissa M Breland
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico, USA; Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico, USA
| | - Steven Carter
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Tiffany P Chang
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Chelsea Gessner
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Alyssa Gianonne
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | | | - Jamie Glass
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Steven Homann
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Margaret King
- The Mind Research Network, Albuquerque, New Mexico, USA
| | - Melissa Kramer
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Drew Landis
- The Mind Research Network, Albuquerque, New Mexico, USA
| | - Alexis Lieval
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | | | - Anna Mackay-Brandt
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Columbia University, Cognitive Neuroscience Division, Taub Institute and GH Sergeivesky Center, New York, NY, USA
| | | | - Laura Panek
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Hayley Reed
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | | | - Eszter Schoell
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Richard Sinnig
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Melissa Sital
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Elise Taverna
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Russell Tobe
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Kristin Trautman
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Betty Varghese
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Lauren Walden
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Runtang Wang
- The Mind Research Network, Albuquerque, New Mexico, USA
| | - Abigail B Waters
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Dylan C Wood
- The Mind Research Network, Albuquerque, New Mexico, USA
| | - F Xavier Castellanos
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; The Child Study Center, NYU Langone Medical Center, New York, NY, USA
| | - Bennett Leventhal
- Department of Psychiatry, University of California - San Francisco, San Francisco, CA, USA
| | | | - Stephen LaConte
- Virginia Tech Carilion Research Institute, Roanoke, VA, USA; School of Biomedical Engineering and Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA; Departments of Emergency Medicine and Emergency Radiology, Virginia Tech Carilion School of Medicine, Roanoke, VA, USA
| | - Michael P Milham
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Child Mind Institute, New York, NY, USA
| | - R Cameron Craddock
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Child Mind Institute, New York, NY, USA.
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228
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Optimized implementations of voxel-wise degree centrality and local functional connectivity density mapping in AFNI. Gigascience 2016. [DOI: 10.1186/s13742-016-0147-0-d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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229
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Noble S, Scheinost D, Finn ES, Shen X, Papademetris X, McEwen SC, Bearden CE, Addington J, Goodyear B, Cadenhead KS, Mirzakhanian H, Cornblatt BA, Olvet DM, Mathalon DH, McGlashan TH, Perkins DO, Belger A, Seidman LJ, Thermenos H, Tsuang MT, van Erp TGM, Walker EF, Hamann S, Woods SW, Cannon TD, Constable RT. Multisite reliability of MR-based functional connectivity. Neuroimage 2016; 146:959-970. [PMID: 27746386 DOI: 10.1016/j.neuroimage.2016.10.020] [Citation(s) in RCA: 121] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2016] [Revised: 10/10/2016] [Accepted: 10/12/2016] [Indexed: 11/26/2022] Open
Abstract
Recent years have witnessed an increasing number of multisite MRI functional connectivity (fcMRI) studies. While multisite studies provide an efficient way to accelerate data collection and increase sample sizes, especially for rare clinical populations, any effects of site or MRI scanner could ultimately limit power and weaken results. Little data exists on the stability of functional connectivity measurements across sites and sessions. In this study, we assess the influence of site and session on resting state functional connectivity measurements in a healthy cohort of traveling subjects (8 subjects scanned twice at each of 8 sites) scanned as part of the North American Prodrome Longitudinal Study (NAPLS). Reliability was investigated in three types of connectivity analyses: (1) seed-based connectivity with posterior cingulate cortex (PCC), right motor cortex (RMC), and left thalamus (LT) as seeds; (2) the intrinsic connectivity distribution (ICD), a voxel-wise connectivity measure; and (3) matrix connectivity, a whole-brain, atlas-based approach to assessing connectivity between nodes. Contributions to variability in connectivity due to subject, site, and day-of-scan were quantified and used to assess between-session (test-retest) reliability in accordance with Generalizability Theory. Overall, no major site, scanner manufacturer, or day-of-scan effects were found for the univariate connectivity analyses; instead, subject effects dominated relative to the other measured factors. However, summaries of voxel-wise connectivity were found to be sensitive to site and scanner manufacturer effects. For all connectivity measures, although subject variance was three times the site variance, the residual represented 60-80% of the variance, indicating that connectivity differed greatly from scan to scan independent of any of the measured factors (i.e., subject, site, and day-of-scan). Thus, for a single 5min scan, reliability across connectivity measures was poor (ICC=0.07-0.17), but increased with increasing scan duration (ICC=0.21-0.36 at 25min). The limited effects of site and scanner manufacturer support the use of multisite studies, such as NAPLS, as a viable means of collecting data on rare populations and increasing power in univariate functional connectivity studies. However, the results indicate that aggregation of fcMRI data across longer scan durations is necessary to increase the reliability of connectivity estimates at the single-subject level.
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Affiliation(s)
- Stephanie Noble
- Yale University, Interdepartmental Neuroscience Program, New Haven, CT, USA.
| | - Dustin Scheinost
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Emily S Finn
- Yale University, Interdepartmental Neuroscience Program, New Haven, CT, USA
| | - Xilin Shen
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Xenophon Papademetris
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA; Yale University, Department of Biomedical Engineering, New Haven, CT, USA
| | - Sarah C McEwen
- University of California, Los Angeles, Departments of Psychology and Psychiatry, Los Angeles, CA, USA
| | - Carrie E Bearden
- University of California, Los Angeles, Departments of Psychology and Psychiatry, Los Angeles, CA, USA
| | - Jean Addington
- University of Calgary, Department of Psychiatry, Calgary, Alberta, Canada
| | - Bradley Goodyear
- University of Calgary, Departments of Radiology, Clinical Neurosciences and Psychiatry, Calgary, Alberta, Canada
| | - Kristin S Cadenhead
- University of California, San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Heline Mirzakhanian
- University of California, San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Barbara A Cornblatt
- Zucker Hillside Hospital, Department of Psychiatry Research, Glen Oaks, NY, USA
| | - Doreen M Olvet
- Zucker Hillside Hospital, Department of Psychiatry Research, Glen Oaks, NY, USA
| | - Daniel H Mathalon
- University of California, San Francisco, Department of Psychiatry, San Francisco, CA, USA
| | | | - Diana O Perkins
- Yale University, Department of Psychiatry, New Haven, CT, USA
| | - Aysenil Belger
- University of North Carolina, Chapel Hill, Department of Psychiatry, Chapel Hill, NC, USA
| | - Larry J Seidman
- Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Heidi Thermenos
- Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Ming T Tsuang
- University of California, San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Theo G M van Erp
- University of California, Irvine, Department of Psychiatry and Human Behavior, Irvine, CA, USA
| | - Elaine F Walker
- Emory University, Department of Psychology, Atlanta, GA, USA
| | - Stephan Hamann
- Emory University, Department of Psychology, Atlanta, GA, USA
| | - Scott W Woods
- Yale University, Department of Psychiatry, New Haven, CT, USA
| | - Tyrone D Cannon
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, USA
| | - R Todd Constable
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
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230
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Abstract
Much effort has been made to understand the organizational principles of human brain function using functional magnetic resonance imaging (fMRI) methods, among which resting-state fMRI (rfMRI) is an increasingly recognized technique for measuring the intrinsic dynamics of the human brain. Functional connectivity (FC) with rfMRI is the most widely used method to describe remote or long-distance relationships in studies of cerebral cortex parcellation, interindividual variability, and brain disorders. In contrast, local or short-distance functional interactions, especially at a scale of millimeters, have rarely been investigated or systematically reviewed like remote FC, although some local FC algorithms have been developed and applied to the discovery of brain-based changes under neuropsychiatric conditions. To fill this gap between remote and local FC studies, this review will (1) briefly survey the history of studies on organizational principles of human brain function; (2) propose local functional homogeneity as a network centrality to characterize multimodal local features of the brain connectome; (3) render a neurobiological perspective on local functional homogeneity by linking its temporal, spatial, and individual variability to information processing, anatomical morphology, and brain development; and (4) discuss its role in performing connectome-wide association studies and identify relevant challenges, and recommend its use in future brain connectomics studies.
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Affiliation(s)
- Lili Jiang
- Key Laboratory of Behavioral Science, Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Xi-Nian Zuo
- Key Laboratory of Behavioral Science, Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Faculty of Psychology, Southwest University, Beibei, Chongqing, China
- Department of Psychology, School of Education Science, Guangxi Teachers Education University, Nanning, Guangxi, China
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231
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Moyer D, Gutman BA, Faskowitz J, Jahanshad N, Thompson PM. A Continuous Model of Cortical Connectivity. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2016; 9900:157-165. [PMID: 29147688 PMCID: PMC5684891 DOI: 10.1007/978-3-319-46720-7_19] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
We present a continuous model for structural brain connectivity based on the Poisson point process. The model treats each stream-line curve in a tractography as an observed event in connectome space, here a product space of cortical white matter boundaries. 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 connectivty. We further present empirical results that suggest the "discrete" connectomes derived from our model have substantially higher test-retest reliability compared to standard methods.
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Affiliation(s)
- Daniel Moyer
- Imaging Genetics Center, University of Southern California
| | - Boris A Gutman
- Imaging Genetics Center, University of Southern California
| | | | - Neda Jahanshad
- Imaging Genetics Center, University of Southern California
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232
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Roberts JA, Perry A, Roberts G, Mitchell PB, Breakspear M. Consistency-based thresholding of the human connectome. Neuroimage 2016; 145:118-129. [PMID: 27666386 DOI: 10.1016/j.neuroimage.2016.09.053] [Citation(s) in RCA: 123] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 08/23/2016] [Accepted: 09/21/2016] [Indexed: 12/25/2022] Open
Abstract
Densely seeded probabilistic tractography yields weighted networks that are nearly fully connected, hence containing many spurious fibers. It is thus necessary to prune spurious connections from probabilistically-derived networks to obtain a more reliable overall estimate of the connectivity. A standard method is to threshold by weight, keeping only the strongest edges. Here, by measuring the consistency of edge weights across subjects, we propose a new thresholding method that aims to reduce the rate of false-positives in group-averaged connectivity matrices. Close inspection of the relationship between consistency, weight, and distance suggests that the most consistent edges are in fact those that are strong for their length, rather than simply strong overall. Hence retaining the most consistent edges preserves more long-distance connections than traditional weight-based thresholding, which penalizes long connections for being weak regardless of anatomy. By comparing our thresholded networks to mouse and macaque tracer data, we also show that consistency-based thresholding exhibits the species-invariant exponential decay of connection weights with distance, while weight-based thresholding does not. We also show that consistency-based thresholding can be used to identify highly consistent and highly inconsistent subnetworks across subjects, enabling more nuanced analyses of group-level connectivity than just the mean connectivity.
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Affiliation(s)
- James A Roberts
- Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Herston, QLD 4006, Australia; Centre for Integrative Brain Function, QIMR Berghofer Medical Research Institute, Herston, QLD 4006, Australia.
| | - Alistair Perry
- Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Herston, QLD 4006, Australia; Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia
| | - Gloria Roberts
- School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia; Black Dog Institute, Prince of Wales Hospital, Hospital Road, Randwick, NSW 2031, Australia
| | - Philip B Mitchell
- School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia; Black Dog Institute, Prince of Wales Hospital, Hospital Road, Randwick, NSW 2031, Australia
| | - Michael Breakspear
- Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Herston, QLD 4006, Australia; Centre for Integrative Brain Function, QIMR Berghofer Medical Research Institute, Herston, QLD 4006, Australia; Metro North Mental Health Service, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia
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233
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Savalia NK, Agres PF, Chan MY, Feczko EJ, Kennedy KM, Wig GS. Motion-related artifacts in structural brain images revealed with independent estimates of in-scanner head motion. Hum Brain Mapp 2016; 38:472-492. [PMID: 27634551 PMCID: PMC5217095 DOI: 10.1002/hbm.23397] [Citation(s) in RCA: 129] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Revised: 08/17/2016] [Accepted: 08/30/2016] [Indexed: 12/17/2022] Open
Abstract
Motion‐contaminated T1‐weighted (T1w) magnetic resonance imaging (MRI) results in misestimates of brain structure. Because conventional T1w scans are not collected with direct measures of head motion, a practical alternative is needed to identify potential motion‐induced bias in measures of brain anatomy. Head movements during functional MRI (fMRI) scanning of 266 healthy adults (20–89 years) were analyzed to reveal stable features of in‐scanner head motion. The magnitude of head motion increased with age and exhibited within‐participant stability across different fMRI scans. fMRI head motion was then related to measurements of both quality control (QC) and brain anatomy derived from a T1w structural image from the same scan session. A procedure was adopted to “flag” individuals exhibiting excessive head movement during fMRI or poor T1w quality rating. The flagging procedure reliably reduced the influence of head motion on estimates of gray matter thickness across the cortical surface. Moreover, T1w images from flagged participants exhibited reduced estimates of gray matter thickness and volume in comparison to age‐ and gender‐matched samples, resulting in inflated effect sizes in the relationships between regional anatomical measures and age. Gray matter thickness differences were noted in numerous regions previously reported to undergo prominent atrophy with age. Recommendations are provided for mitigating this potential confound, and highlight how the procedure may lead to more accurate measurement and comparison of anatomical features. Hum Brain Mapp 38:472–492, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Neil K Savalia
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas
| | - Phillip F Agres
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas
| | - Micaela Y Chan
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas
| | - Eric J Feczko
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon.,Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon
| | - Kristen M Kennedy
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas
| | - Gagan S Wig
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas.,Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas
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234
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Xu T, Opitz A, Craddock RC, Wright MJ, Zuo XN, Milham MP. Assessing Variations in Areal Organization for the Intrinsic Brain: From Fingerprints to Reliability. Cereb Cortex 2016; 26:4192-4211. [PMID: 27600846 PMCID: PMC5066830 DOI: 10.1093/cercor/bhw241] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2016] [Revised: 07/15/2016] [Accepted: 07/15/2016] [Indexed: 01/02/2023] Open
Abstract
Resting state fMRI (R-fMRI) is a powerful in-vivo tool for examining the functional architecture of the human brain. Recent studies have demonstrated the ability to characterize transitions between functionally distinct cortical areas through the mapping of gradients in intrinsic functional connectivity (iFC) profiles. To date, this novel approach has primarily been applied to iFC profiles averaged across groups of individuals, or in one case, a single individual scanned multiple times. Here, we used a publically available R-fMRI dataset, in which 30 healthy participants were scanned 10 times (10 min per session), to investigate differences in full-brain transition profiles (i.e., gradient maps, edge maps) across individuals, and their reliability. 10-min R-fMRI scans were sufficient to achieve high accuracies in efforts to "fingerprint" individuals based upon full-brain transition profiles. Regarding test-retest reliability, the image-wise intraclass correlation coefficient (ICC) was moderate, and vertex-level ICC varied depending on region; larger durations of data yielded higher reliability scores universally. Initial application of gradient-based methodologies to a recently published dataset obtained from twins suggested inter-individual variation in areal profiles might have genetic and familial origins. Overall, these results illustrate the utility of gradient-based iFC approaches for studying inter-individual variation in brain function.
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Affiliation(s)
- Ting Xu
- Key Laboratory of Behavioral Sciences and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing100101, China.,Center for the Developing Brain, Child Mind Institute, New York, NY10022, USA.,Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY10962, USA
| | - Alexander Opitz
- Center for the Developing Brain, Child Mind Institute, New York, NY10022, USA.,Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY10962, USA
| | - R Cameron Craddock
- Center for the Developing Brain, Child Mind Institute, New York, NY10022, USA.,Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY10962, USA
| | - Margaret J Wright
- Queensland Brain Institute and Centre for Advanced Imaging, University of Queensland, St Lucia, QLD 4072, Australia
| | - Xi-Nian Zuo
- Key Laboratory of Behavioral Sciences and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing100101, China
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY10022, USA.,Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY10962, USA
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235
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Honor LB, Haselgrove C, Frazier JA, Kennedy DN. Data Citation in Neuroimaging: Proposed Best Practices for Data Identification and Attribution. Front Neuroinform 2016; 10:34. [PMID: 27570508 PMCID: PMC4981598 DOI: 10.3389/fninf.2016.00034] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Accepted: 07/26/2016] [Indexed: 01/04/2023] Open
Abstract
Data sharing and reuse, while widely accepted as good ideas, have been slow to catch on in any concrete and consistent way. One major hurdle within the scientific community has been the lack of widely accepted standards for citing that data, making it difficult to track usage and measure impact. Within the neuroimaging community, there is a need for a way to not only clearly identify and cite datasets, but also to derive new aggregate sets from multiple sources while clearly maintaining lines of attribution. This work presents a functional prototype of a system to integrate Digital Object Identifiers (DOI) and a standardized metadata schema into a XNAT-based repository workflow, allowing for identification of data at both the project and image level. These item and source level identifiers allow any newly defined combination of images, from any number of projects, to be tagged with a new group-level DOI that automatically inherits the individual attributes and provenance information of its constituent parts. This system enables the tracking of data reuse down to the level of individual images. The implementation of this type of data identification system would impact researchers and data creators, data hosting facilities, and data publishers, but the benefit of having widely accepted standards for data identification and attribution would go far toward making data citation practical and advantageous.
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Affiliation(s)
- Leah B Honor
- Lamar Soutter Library, University of Massachusetts Medical School Worcester MA, USA
| | - Christian Haselgrove
- Eunice Kennedy Shriver Center, University of Massachusetts Medical SchoolWorcester MA, USA; Child and Adolescent NeuroDevelopment Initiative, Department of Psychiatry, University of Massachusetts Medical SchoolWorcester MA, USA
| | - Jean A Frazier
- Eunice Kennedy Shriver Center, University of Massachusetts Medical SchoolWorcester MA, USA; Child and Adolescent NeuroDevelopment Initiative, Department of Psychiatry, University of Massachusetts Medical SchoolWorcester MA, USA
| | - David N Kennedy
- Eunice Kennedy Shriver Center, University of Massachusetts Medical SchoolWorcester MA, USA; Child and Adolescent NeuroDevelopment Initiative, Department of Psychiatry, University of Massachusetts Medical SchoolWorcester MA, USA
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236
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Size matters to function: Brain volume correlates with intrinsic brain activity across healthy individuals. Neuroimage 2016; 139:271-278. [PMID: 27355434 DOI: 10.1016/j.neuroimage.2016.06.046] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Revised: 06/19/2016] [Accepted: 06/24/2016] [Indexed: 11/23/2022] Open
Abstract
A fundamental issue in neuroscience is to understand the structural substrates of neural activities. Intrinsic brain activity has been increasingly recognized as an important functional activity mode and is tightly linked with various cognitive functions. Structurally, cognitive functions have also shown a relation with brain volume/size. Therefore, an association between intrinsic brain activities and brain volume/size can be hypothesized, and brain volume/size may impact intrinsic brain activity in human brains. The present study aimed to explicitly investigate this brain structure-function relationship using two large independent cohorts of 176 and 236 young adults. Structural-MRI was performed to estimate the brain volume, and resting-state functional-MRI was applied to extract the amplitude of low-frequency fluctuations (ALFF), an imaging measure of intrinsic brain activity. Intriguingly, our results revealed a robust linear correlation between whole-brain size and ALFF. Moreover, specific brain lobes/regions, including the frontal lobe, the left middle frontal gyrus, anterior cingulate gyrus, Rolandic operculum, and insula, also showed a reliable, positive volume-ALFF correlation in the two cohorts. These findings offer direct, empirical evidence of a strong association between brain size/volume and intrinsic brain activity, as well as provide novel insight into the structural substrates of the intrinsic brain activity of the human brain.
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237
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Tian X, Wei D, Du X, Wang K, Yang J, Liu W, Meng J, Liu H, Liu G, Qiu J. Assessment of trait anxiety and prediction of changes in state anxiety using functional brain imaging: A test–retest study. Neuroimage 2016; 133:408-416. [DOI: 10.1016/j.neuroimage.2016.03.024] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2015] [Revised: 02/22/2016] [Accepted: 03/11/2016] [Indexed: 12/27/2022] Open
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238
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Mišić B, Sporns O. From regions to connections and networks: new bridges between brain and behavior. Curr Opin Neurobiol 2016; 40:1-7. [PMID: 27209150 DOI: 10.1016/j.conb.2016.05.003] [Citation(s) in RCA: 159] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Accepted: 05/04/2016] [Indexed: 12/11/2022]
Abstract
Connections and interactions among distributed brain areas are increasingly recognized as the basis for cognitive operations and a diverse repertoire of behaviors. Analytic advances have allowed for brain connectivity to be represented and quantified at multiple levels: from single connections to communities and networks. This review traces the trajectory of network neuroscience, focusing on how connectivity patterns can be related to cognition and behavior. As recent initiatives for open science provide access to imaging and phenotypic data with great detail and depth, we argue that approaches capable of directly modeling multivariate relationships between brain and behavior will become increasingly important in the field.
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Affiliation(s)
- Bratislav Mišić
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada H3A 2B4; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA.
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239
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Castellanos FX, Aoki Y. Intrinsic Functional Connectivity in Attention-Deficit/Hyperactivity Disorder: A Science in Development. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2016; 1:253-261. [PMID: 27713929 DOI: 10.1016/j.bpsc.2016.03.004] [Citation(s) in RCA: 89] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Functional magnetic resonance imaging (fMRI) without an explicit task, i.e., resting state fMRI, of individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) is growing rapidly. Early studies were unaware of the vulnerability of this method to even minor degrees of head motion, a major concern in the field. Recent efforts are implementing various strategies to address this source of artifact along with a growing set of analytical tools. Availability of the ADHD-200 Consortium dataset, a large-scale multi-site repository, is facilitating increasingly sophisticated approaches. In parallel, investigators are beginning to explicitly test the replicability of published findings. In this narrative review, we sketch out broad, overarching hypotheses being entertained while noting methodological uncertainties. Current hypotheses implicate the interplay of default, cognitive control (frontoparietal) and attention (dorsal, ventral, salience) networks in ADHD; functional connectivities of reward-related and amygdala-related circuits are also supported as substrates for dimensional aspects of ADHD. Before these can be further specified and definitively tested, we assert the field must take on the challenge of mapping the "topography" of the analytical space, i.e., determining the sensitivities of results to variations in acquisition, analysis, demographic and phenotypic parameters. Doing so with openly available datasets will provide the needed foundation for delineating typical and atypical developmental trajectories of brain structure and function in neurodevelopmental disorders including ADHD when applied to large-scale multi-site prospective longitudinal studies such as the forthcoming Adolescent Brain Cognitive Development study.
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Affiliation(s)
- F Xavier Castellanos
- The Child Study Center at NYU Langone Medical Center, New York, NY; Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY
| | - Yuta Aoki
- The Child Study Center at NYU Langone Medical Center, New York, NY
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240
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Shah LM, Cramer JA, Ferguson MA, Birn RM, Anderson JS. Reliability and reproducibility of individual differences in functional connectivity acquired during task and resting state. Brain Behav 2016; 6:e00456. [PMID: 27069771 PMCID: PMC4814225 DOI: 10.1002/brb3.456] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2015] [Revised: 02/22/2016] [Accepted: 02/24/2016] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVES Application of fMRI connectivity metrics as diagnostic biomarkers at the individual level will require reliability, sensitivity and specificity to longitudinal changes in development, aging, neurocognitive, and behavioral performance and pathologies. Such metrics have not been well characterized for recent advances in BOLD acquisition. EXPERIMENTAL DESIGN Analysis of multiband BOLD data from the HCP 500 Subjects Release was performed with FIX ICA and with WM, CSF and motion parameter regression. Analysis with ROIs covering the gray matter at 5 mm resolution was performed to assess functional connectivity. ROIs in key areas were used to demonstrate statistical differences between specific connections. Reproducibility of group-mean functional connectivity and for single connections for individuals was evaluated for both resting state and task acquisitions. PRINCIPAL OBSERVATIONS Systematic differences in group-mean connectivity were demonstrated during task and rest and during different tasks, although individual differences in connectivity were maintained. Reproducibility of a single connection for a subject and across subjects for resting and task acquisition was demonstrated to be a linear function of the square root of imaging time. Randomly removing up to 50% of time points had little effect on reliability, while truncating an acquisition was associated with decreased reliability. Reliability was highest within the cortex, and lowest for deep gray nuclei, gray-white junction, and near large sulci. CONCLUSIONS This study found systematic differences in group-mean connectivity acquired during task and rest acquitisions and preserved individual differences in connectivity due to intrinsic differences in an individual's brain activity and structural brain architecture. We also show that longer scan times are needed to acquire data on single subjects for information on connections between specific ROIs. Longer scans may be facilitated by acquisition during task paradigms, which will systematically affect functional connectivity but may preserve individual differences in connectivity on top of task modulations.
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Affiliation(s)
- Lubdha M. Shah
- Department of RadiologyUniversity of UtahSalt Lake CityUtah84132
| | - Justin A. Cramer
- Department of RadiologyUniversity of UtahSalt Lake CityUtah84132
| | | | - Rasmus M. Birn
- Department of PsychiatryUniversity of WisconsinMadisonWisconsin 53705
| | - Jeffrey S. Anderson
- Department of RadiologyUniversity of UtahSalt Lake CityUtah84132
- Department of BioengineeringUniversity of UtahSalt Lake CityUtah84132
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241
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Abstract
Changes in brain activity accompanying shifts in vigilance and arousal can interfere with the study of other intrinsic and task-evoked characteristics of brain function. However, the difficulty of tracking and modeling the arousal state during functional MRI (fMRI) typically precludes the assessment of arousal-dependent influences on fMRI signals. Here we combine fMRI, electrophysiology, and the monitoring of eyelid behavior to demonstrate an approach for tracking continuous variations in arousal level from fMRI data. We first characterize the spatial distribution of fMRI signal fluctuations that track a measure of behavioral arousal; taking this pattern as a template, and using the local field potential as a simultaneous and independent measure of cortical activity, we observe that the time-varying expression level of this template in fMRI data provides a close approximation of electrophysiological arousal. We discuss the potential benefit of these findings for increasing the sensitivity of fMRI as a cognitive and clinical biomarker.
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242
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Marchitelli R, Minati L, Marizzoni M, Bosch B, Bartrés-Faz D, Müller BW, Wiltfang J, Fiedler U, Roccatagliata L, Picco A, Nobili F, Blin O, Bombois S, Lopes R, Bordet R, Sein J, Ranjeva JP, Didic M, Gros-Dagnac H, Payoux P, Zoccatelli G, Alessandrini F, Beltramello A, Bargalló N, Ferretti A, Caulo M, Aiello M, Cavaliere C, Soricelli A, Parnetti L, Tarducci R, Floridi P, Tsolaki M, Constantinidis M, Drevelegas A, Rossini PM, Marra C, Schönknecht P, Hensch T, Hoffmann KT, Kuijer JP, Visser PJ, Barkhof F, Frisoni GB, Jovicich J. Test-retest reliability of the default mode network in a multi-centric fMRI study of healthy elderly: Effects of data-driven physiological noise correction techniques. Hum Brain Mapp 2016; 37:2114-32. [PMID: 26990928 DOI: 10.1002/hbm.23157] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Revised: 02/16/2016] [Accepted: 02/17/2016] [Indexed: 12/31/2022] Open
Abstract
Understanding how to reduce the influence of physiological noise in resting state fMRI data is important for the interpretation of functional brain connectivity. Limited data is currently available to assess the performance of physiological noise correction techniques, in particular when evaluating longitudinal changes in the default mode network (DMN) of healthy elderly participants. In this 3T harmonized multisite fMRI study, we investigated how different retrospective physiological noise correction (rPNC) methods influence the within-site test-retest reliability and the across-site reproducibility consistency of DMN-derived measurements across 13 MRI sites. Elderly participants were scanned twice at least a week apart (five participants per site). The rPNC methods were: none (NPC), Tissue-based regression, PESTICA and FSL-FIX. The DMN at the single subject level was robustly identified using ICA methods in all rPNC conditions. The methods significantly affected the mean z-scores and, albeit less markedly, the cluster-size in the DMN; in particular, FSL-FIX tended to increase the DMN z-scores compared to others. Within-site test-retest reliability was consistent across sites, with no differences across rPNC methods. The absolute percent errors were in the range of 5-11% for DMN z-scores and cluster-size reliability. DMN pattern overlap was in the range 60-65%. In particular, no rPNC method showed a significant reliability improvement relative to NPC. However, FSL-FIX and Tissue-based physiological correction methods showed both similar and significant improvements of reproducibility consistency across the consortium (ICC = 0.67) for the DMN z-scores relative to NPC. Overall these findings support the use of rPNC methods like tissue-based or FSL-FIX to characterize multisite longitudinal changes of intrinsic functional connectivity. Hum Brain Mapp 37:2114-2132, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Rocco Marchitelli
- Center for Mind/Brain Sciences (CIMEC), University of Trento, Rovereto, Italy
| | - Ludovico Minati
- Center for Mind/Brain Sciences (CIMEC), University of Trento, Rovereto, Italy.,Scientific Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Moira Marizzoni
- LENITEM Laboratory of Epidemiology, Neuroimaging, & Telemedicine-IRCCS San Giovanni Di Dio-FBF, Brescia, Italy
| | - Beatriz Bosch
- Alzheimer's Disease and Other Cognitive Disorders Unit, Department of Neurology, Hospital Clínic, and IDIBAPS, Barcelona, Spain
| | - David Bartrés-Faz
- Department of Psychiatry and Clinical Psychobiology, Universitat De Barcelona and IDIBAPS, Barcelona, Spain
| | - Bernhard W Müller
- LVR-Clinic for Psychiatry and Psychotherapy, Institutes and Clinics of the University Duisburg-Essen, Essen, Germany
| | - Jens Wiltfang
- LVR-Clinic for Psychiatry and Psychotherapy, Institutes and Clinics of the University Duisburg-Essen, Essen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center (UMG), Georg August University, Göttingen, Germany
| | - Ute Fiedler
- LVR-Clinic for Psychiatry and Psychotherapy, Institutes and Clinics of the University Duisburg-Essen, Essen, Germany
| | - Luca Roccatagliata
- Department of Neuroradiology, IRCSS San Martino University Hospital and IST, Genoa, Italy.,Department of Health Sciences, University of Genoa, Genoa, Italy
| | - Agnese Picco
- Department of Neuroscience, Ophthalmology, Genetics and Mother-Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Flavio Nobili
- Department of Neuroscience, Ophthalmology, Genetics and Mother-Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Oliver Blin
- Pharmacology, Assistance Publique - Hôpitaux De Marseille, Aix-Marseille University-CNRS, UMR, Marseille, 7289, France
| | - Stephanie Bombois
- University of Lille, INSERM, CHU Lille, U1171 - Degenerative and Vascular Cognitive Disorders, Lille, France
| | - Renaud Lopes
- University of Lille, INSERM, CHU Lille, U1171 - Degenerative and Vascular Cognitive Disorders, Lille, France
| | - Régis Bordet
- University of Lille, INSERM, CHU Lille, U1171 - Degenerative and Vascular Cognitive Disorders, Lille, France
| | - Julien Sein
- CRMBM-CEMEREM, UMR 7339, Aix Marseille Université-CNRS, Marseille, France
| | | | - Mira Didic
- APHM, CHU Timone, Service De Neurologie Et Neuropsychologie, Marseille, France.,Aix-Marseille Université, INSERM INS UMR_S 1106, Marseille, 13005, France
| | - Hélène Gros-Dagnac
- INSERM, Imagerie Cérébrale Et Handicaps Neurologiques, UMR 825, Toulouse, France.,Université De Toulouse, UPS, Imagerie Cérébrale Et Handicaps Neurologiques, UMR 825, CHU Purpan, Place Du Dr Baylac, Toulouse Cedex 9, France
| | - Pierre Payoux
- INSERM, Imagerie Cérébrale Et Handicaps Neurologiques, UMR 825, Toulouse, France.,Université De Toulouse, UPS, Imagerie Cérébrale Et Handicaps Neurologiques, UMR 825, CHU Purpan, Place Du Dr Baylac, Toulouse Cedex 9, France
| | | | | | | | - Núria Bargalló
- Department of Neuroradiology and Magnetic Resonace Image Core Facility, Hospital Clínic De Barcelona, IDIBAPS, Barcelona, Spain
| | - Antonio Ferretti
- Department of Neuroscience Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti, Italy.,Institute for Advanced Biomedical Technologies (ITAB), University "G. d'Annunzio" of Chieti, Italy
| | - Massimo Caulo
- Department of Neuroscience Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti, Italy.,Institute for Advanced Biomedical Technologies (ITAB), University "G. d'Annunzio" of Chieti, Italy
| | | | | | - Andrea Soricelli
- IRCCS SDN, Naples, Italy.,University of Naples Parthenope, Naples, Italy
| | - Lucilla Parnetti
- Section of Neurology, Centre for Memory Disturbances, University of Perugia, Perugia, Italy
| | | | - Piero Floridi
- Perugia General Hospital, Neuroradiology Unit, Perugia, Italy
| | - Magda Tsolaki
- 3rd Department of Neurology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Antonios Drevelegas
- Interbalkan Medical Center of Thessaloniki, Thessaloniki, Greece.,Department of Radiology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Paolo Maria Rossini
- Department of Geriatrics, Neuroscience & Orthopaedics, Catholic University, Policlinic Gemelli, Rome, Italy.,IRCSS S.Raffaele Pisana, Rome, Italy
| | - Camillo Marra
- Center for Neuropsychological Research, Catholic University, Rome, Italy
| | - Peter Schönknecht
- Department of Psychiatry, University Hospital Leipzig, Leipzig, Germany
| | - Tilman Hensch
- Department of Psychiatry, University Hospital Leipzig, Leipzig, Germany
| | | | - Joost P Kuijer
- Department of Physics and Medical Technology, VU University Medical Center, Amsterdam, the Netherlands
| | - Pieter Jelle Visser
- Alzheimer Centre and Department of Neurology, Vrije Universiteit University Medical Center, Amsterdam, the Netherlands.,Department of Psychiatry and Neuropsychology, Alzheimer Center Limburg, University of Maastricht, Maastricht, the Netherlands
| | - Frederik Barkhof
- Alzheimer Centre and Department of Neurology, Vrije Universiteit University Medical Center, Amsterdam, the Netherlands
| | - Giovanni B Frisoni
- LENITEM Laboratory of Epidemiology, Neuroimaging, & Telemedicine-IRCCS San Giovanni Di Dio-FBF, Brescia, Italy.,Memory Clinic and LANVIE, Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Jorge Jovicich
- Center for Mind/Brain Sciences (CIMEC), University of Trento, Rovereto, Italy
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243
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Panta SR, Wang R, Fries J, Kalyanam R, Speer N, Banich M, Kiehl K, King M, Milham M, Wager TD, Turner JA, Plis SM, Calhoun VD. A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets. Front Neuroinform 2016; 10:9. [PMID: 27014049 PMCID: PMC4791544 DOI: 10.3389/fninf.2016.00009] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Accepted: 02/22/2016] [Indexed: 11/21/2022] Open
Abstract
In this paper we propose a web-based approach for quick visualization of big data from brain magnetic resonance imaging (MRI) scans using a combination of an automated image capture and processing system, nonlinear embedding, and interactive data visualization tools. We draw upon thousands of MRI scans captured via the COllaborative Imaging and Neuroinformatics Suite (COINS). We then interface the output of several analysis pipelines based on structural and functional data to a t-distributed stochastic neighbor embedding (t-SNE) algorithm which reduces the number of dimensions for each scan in the input data set to two dimensions while preserving the local structure of data sets. Finally, we interactively display the output of this approach via a web-page, based on data driven documents (D3) JavaScript library. Two distinct approaches were used to visualize the data. In the first approach, we computed multiple quality control (QC) values from pre-processed data, which were used as inputs to the t-SNE algorithm. This approach helps in assessing the quality of each data set relative to others. In the second case, computed variables of interest (e.g., brain volume or voxel values from segmented gray matter images) were used as inputs to the t-SNE algorithm. This approach helps in identifying interesting patterns in the data sets. We demonstrate these approaches using multiple examples from over 10,000 data sets including (1) quality control measures calculated from phantom data over time, (2) quality control data from human functional MRI data across various studies, scanners, sites, (3) volumetric and density measures from human structural MRI data across various studies, scanners and sites. Results from (1) and (2) show the potential of our approach to combine t-SNE data reduction with interactive color coding of variables of interest to quickly identify visually unique clusters of data (i.e., data sets with poor QC, clustering of data by site) quickly. Results from (3) demonstrate interesting patterns of gray matter and volume, and evaluate how they map onto variables including scanners, age, and gender. In sum, the proposed approach allows researchers to rapidly identify and extract meaningful information from big data sets. Such tools are becoming increasingly important as datasets grow larger.
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Affiliation(s)
- Sandeep R Panta
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Runtang Wang
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Jill Fries
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Ravi Kalyanam
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Nicole Speer
- Intermountain Neuroimaging Consortium, University of Boulder Colorado Boulder, CO, USA
| | - Marie Banich
- Intermountain Neuroimaging Consortium, University of Boulder Colorado Boulder, CO, USA
| | - Kent Kiehl
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA; Department of Psychology, University of New MexicoAlbuquerque, NM, USA
| | - Margaret King
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Michael Milham
- The Child Mind Institute and The Nathan Kline Institute New York, NY, USA
| | - Tor D Wager
- Intermountain Neuroimaging Consortium, University of Boulder Colorado Boulder, CO, USA
| | - Jessica A Turner
- Department of Psychology, Georgia Tech University Atlanta, GA, USA
| | - Sergey M Plis
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA; Department of Electrical & Computer Engineering, University of New MexicoAlbuquerque, NM, USA
| | - Vince D Calhoun
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA; Department of Electrical & Computer Engineering, University of New MexicoAlbuquerque, NM, USA
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244
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Huang L, Huang T, Zhen Z, Liu J. A test-retest dataset for assessing long-term reliability of brain morphology and resting-state brain activity. Sci Data 2016; 3:160016. [PMID: 26978040 PMCID: PMC4792176 DOI: 10.1038/sdata.2016.16] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2015] [Accepted: 02/19/2016] [Indexed: 11/09/2022] Open
Abstract
We present a test-retest dataset for evaluation of long-term reliability of measures from structural and resting-state functional magnetic resonance imaging (sMRI and rfMRI) scans. The repeated scan dataset was collected from 61 healthy adults in two sessions using highly similar imaging parameters at an interval of 103-189 days. However, as the imaging parameters were not completely identical, the reliability estimated from this dataset shall reflect the lower bounds of the true reliability of sMRI/rfMRI measures. Furthermore, in conjunction with other test-retest datasets, our dataset may help explore the impact of different imaging parameters on reliability of sMRI/rfMRI measures, which is especially critical for assessing datasets collected from multiple centers. In addition, intelligence quotient (IQ) was measured for each participant using Raven's Advanced Progressive Matrices. The data can thus be used for purposes other than assessing reliability of sMRI/rfMRI alone. For example, data from each single session could be used to associate structural and functional measures of the brain with the IQ metrics to explore brain-IQ association.
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Affiliation(s)
- Lijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning &IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Taicheng Huang
- State Key Laboratory of Cognitive Neuroscience and Learning &IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Zonglei Zhen
- State Key Laboratory of Cognitive Neuroscience and Learning &IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Jia Liu
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, Beijing 100875, China
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245
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Yang Z, Zuo XN, McMahon KL, Craddock RC, Kelly C, de Zubicaray GI, Hickie I, Bandettini PA, Castellanos FX, Milham MP, Wright MJ. Genetic and Environmental Contributions to Functional Connectivity Architecture of the Human Brain. Cereb Cortex 2016; 26:2341-2352. [PMID: 26891986 PMCID: PMC4830303 DOI: 10.1093/cercor/bhw027] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
One of the grand challenges faced by neuroscience is to delineate the determinants of interindividual variation in the comprehensive structural and functional connection matrices that comprise the human connectome. At present, this endeavor appears most tractable at the macroanatomic scale, where intrinsic brain activity exhibits robust patterns of synchrony that recapitulate core functional circuits at the individual level. Here, we use a classical twin study design to examine the heritability of intrinsic functional network properties in 101 twin pairs, including network activity (i.e., variance of a network's specific temporal fluctuations) and internetwork coherence (i.e., correlation between networks' specific temporal fluctuations). Five of 7 networks exhibited significantly heritable (23.3–65.2%) network activity, 6 of the 21 internetwork coherences were significantly heritable (25.6–42.0%), and 11 of the 21 internetwork coherences were significantly influenced by common environmental factors (18.0–47.1%). These results suggest that the source of interindividual variation in functional connectome has a modular architecture: individual modules represented by intrinsic connectivity networks are genetic controlled, while environmental factors influence the interplays between the modules. This work further provides network-specific hypotheses for discovery of the specific genetic and environmental factors influencing functional specialization and integration of the human brain.
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Affiliation(s)
- Zhi Yang
- Key Laboratory of Behavioral Sciences and MRI Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Xi-Nian Zuo
- Key Laboratory of Behavioral Sciences and MRI Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Katie L McMahon
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
| | - R Cameron Craddock
- Child Mind Institute, New York, NY, USA.,Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Clare Kelly
- Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the NYU Child Study Center, New York, NY, USA
| | | | - Ian Hickie
- Brain and Mind Research Institute, University of Sydney, Sydney, Australia
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - F Xavier Castellanos
- Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the NYU Child Study Center, New York, NY, USA
| | - Michael P Milham
- Child Mind Institute, New York, NY, USA.,Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Margaret J Wright
- Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, QLD, Australia
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246
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Roy S, Carass A, Pacheco J, Bilgel M, Resnick SM, Prince JL, Pham DL. Temporal filtering of longitudinal brain magnetic resonance images for consistent segmentation. Neuroimage Clin 2016; 11:264-275. [PMID: 26958465 PMCID: PMC4773508 DOI: 10.1016/j.nicl.2016.02.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2015] [Revised: 01/13/2016] [Accepted: 02/12/2016] [Indexed: 01/13/2023]
Abstract
Longitudinal analysis of magnetic resonance images of the human brain provides knowledge of brain changes during both normal aging as well as the progression of many diseases. Previous longitudinal segmentation methods have either ignored temporal information or have incorporated temporal consistency constraints within the algorithm. In this work, we assume that some anatomical brain changes can be explained by temporal transitions in image intensities. Once the images are aligned in the same space, the intensities of each scan at the same voxel constitute a temporal (or 4D) intensity trend at that voxel. Temporal intensity variations due to noise or other artifacts are corrected by a 4D intensity-based filter that smooths the intensity values where appropriate, while preserving real anatomical changes such as atrophy. Here smoothing refers to removal of sudden changes or discontinuities in intensities. Images processed with the 4D filter can be used as a pre-processing step to any segmentation method. We show that such a longitudinal pre-processing step produces robust and consistent longitudinal segmentation results, even when applying 3D segmentation algorithms. We compare with state-of-the-art 4D segmentation algorithms. Specifically, we experimented on three longitudinal datasets containing 4-12 time-points, and showed that the 4D temporal filter is more robust and has more power in distinguishing between healthy subjects and those with dementia, mild cognitive impairment, as well as different phenotypes of multiple sclerosis.
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Affiliation(s)
- Snehashis Roy
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation for the Advancement of Military Medicine, United States,Corresponding author.
| | - Aaron Carass
- Image Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, United States,Department of Computer Science, Johns Hopkins University, United States
| | - Jennifer Pacheco
- Laboratory of Behavioral Neuroscience, National Institute on Aging, United States
| | - Murat Bilgel
- Image Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, United States,Laboratory of Behavioral Neuroscience, National Institute on Aging, United States
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, United States
| | - Jerry L. Prince
- Image Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, United States
| | - Dzung L. Pham
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation for the Advancement of Military Medicine, United States
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248
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Abstract
OBJECTIVES Clinical neuroscience is increasingly turning to imaging the human brain for answers to a range of questions and challenges. To date, the majority of studies have focused on the neural basis of current psychiatric symptoms, which can facilitate the identification of neurobiological markers for diagnosis. However, the increasing availability and feasibility of using imaging modalities, such as diffusion imaging and resting-state fMRI, enable longitudinal mapping of brain development. This shift in the field is opening the possibility of identifying predictive markers of risk or prognosis, and also represents a critical missing element for efforts to promote personalized or individualized medicine in psychiatry (i.e., stratified psychiatry). METHODS The present work provides a selective review of potentially high-yield populations for longitudinal examination with MRI, based upon our understanding of risk from epidemiologic studies and initial MRI findings. RESULTS Our discussion is organized into three topic areas: (1) practical considerations for establishing temporal precedence in psychiatric research; (2) readiness of the field for conducting longitudinal MRI, particularly for neurodevelopmental questions; and (3) illustrations of high-yield populations and time windows for examination that can be used to rapidly generate meaningful and useful data. Particular emphasis is placed on the implementation of time-appropriate, developmentally informed longitudinal designs, capable of facilitating the identification of biomarkers predictive of risk and prognosis. CONCLUSIONS Strategic longitudinal examination of the brain at-risk has the potential to bring the concepts of early intervention and prevention to psychiatry.
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249
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Brain structure-function associations identified in large-scale neuroimaging data. Brain Struct Funct 2016; 221:4459-4474. [PMID: 26749003 PMCID: PMC5102954 DOI: 10.1007/s00429-015-1177-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2015] [Accepted: 12/19/2015] [Indexed: 12/19/2022]
Abstract
The relationships between structural and functional measures of the human brain remain largely unknown. A majority of our limited knowledge regarding structure–function associations has been obtained through comparisons between specific groups of patients and healthy controls. Unfortunately, a direct and complete view of the associations across multiple structural and functional metrics in normal population is missing. We filled this gap by learning cross-individual co-variance among structural and functional measures using large-scale neuroimaging datasets. A discover-confirm scheme was applied to two independent samples (N = 184 and N = 340) of multi-modal neuroimaging datasets. A data mining tool, gRAICAR, was employed in the discover stage to generate quantitative and unbiased hypotheses of the co-variance among six functional and six structural imaging metrics. These hypotheses were validated using an independent dataset in the confirm stage. Fifteen multi-metric co-variance units, representing different co-variance relationships among the 12 metrics, were reliable across the two sets of neuroimaging datasets. The reliable co-variance units were summarized into a database, where users can select any location on the cortical map of any metric to examine the co-varying maps with the other 11 metrics. This database characterized the six functional metrics based on their co-variance with structural metrics, and provided a detailed reference to connect previous findings using different metrics and to predict maps of unexamined metrics. Gender, age, and handedness were associated to the co-variance units, and a sub-study of schizophrenia demonstrated the usefulness of the co-variance database.
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250
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Boubela RN, Kalcher K, Huf W, Našel C, Moser E. Big Data Approaches for the Analysis of Large-Scale fMRI Data Using Apache Spark and GPU Processing: A Demonstration on Resting-State fMRI Data from the Human Connectome Project. Front Neurosci 2016; 9:492. [PMID: 26778951 PMCID: PMC4701924 DOI: 10.3389/fnins.2015.00492] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2015] [Accepted: 12/10/2015] [Indexed: 11/29/2022] Open
Abstract
Technologies for scalable analysis of very large datasets have emerged in the domain of internet computing, but are still rarely used in neuroimaging despite the existence of data and research questions in need of efficient computation tools especially in fMRI. In this work, we present software tools for the application of Apache Spark and Graphics Processing Units (GPUs) to neuroimaging datasets, in particular providing distributed file input for 4D NIfTI fMRI datasets in Scala for use in an Apache Spark environment. Examples for using this Big Data platform in graph analysis of fMRI datasets are shown to illustrate how processing pipelines employing it can be developed. With more tools for the convenient integration of neuroimaging file formats and typical processing steps, big data technologies could find wider endorsement in the community, leading to a range of potentially useful applications especially in view of the current collaborative creation of a wealth of large data repositories including thousands of individual fMRI datasets.
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Affiliation(s)
- Roland N. Boubela
- Center for Medical Physics and Biomedical Engineering, Medical University of ViennaVienna, Austria
- MR Centre of Excellence, Medical University of ViennaVienna, Austria
| | - Klaudius Kalcher
- Center for Medical Physics and Biomedical Engineering, Medical University of ViennaVienna, Austria
- MR Centre of Excellence, Medical University of ViennaVienna, Austria
| | - Wolfgang Huf
- Center for Medical Physics and Biomedical Engineering, Medical University of ViennaVienna, Austria
- MR Centre of Excellence, Medical University of ViennaVienna, Austria
| | - Christian Našel
- Department of Radiology, Tulln Hospital, Karl Landsteiner University of Health SciencesTulln, Austria
| | - Ewald Moser
- Center for Medical Physics and Biomedical Engineering, Medical University of ViennaVienna, Austria
- MR Centre of Excellence, Medical University of ViennaVienna, Austria
- Brain Behaviour Laboratory, Department of Psychiatry, University of Pennsylvania Medical CenterPhiladelphia, PA, USA
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