1
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Savcisens G, Eliassi-Rad T, Hansen LK, Mortensen LH, Lilleholt L, Rogers A, Zettler I, Lehmann S. Using sequences of life-events to predict human lives. Nat Comput Sci 2024; 4:43-56. [PMID: 38177491 DOI: 10.1038/s43588-023-00573-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 11/15/2023] [Indexed: 01/06/2024]
Abstract
Here we represent human lives in a way that shares structural similarity to language, and we exploit this similarity to adapt natural language processing techniques to examine the evolution and predictability of human lives based on detailed event sequences. We do this by drawing on a comprehensive registry dataset, which is available for Denmark across several years, and that includes information about life-events related to health, education, occupation, income, address and working hours, recorded with day-to-day resolution. We create embeddings of life-events in a single vector space, showing that this embedding space is robust and highly structured. Our models allow us to predict diverse outcomes ranging from early mortality to personality nuances, outperforming state-of-the-art models by a wide margin. Using methods for interpreting deep learning models, we probe the algorithm to understand the factors that enable our predictions. Our framework allows researchers to discover potential mechanisms that impact life outcomes as well as the associated possibilities for personalized interventions.
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Affiliation(s)
| | - Tina Eliassi-Rad
- Network Science Institute, Northeastern University, Boston, MA, USA
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Lars Kai Hansen
- DTU Compute, Technical University of Denmark, Lyngby, Denmark
| | - Laust Hvas Mortensen
- Data Science Lab, Statistics Denmark, Copenhagen, Denmark
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Lau Lilleholt
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
- Copenhagen Center for Social Data Science (SODAS), University of Copenhagen, Copenhagen, Denmark
| | - Anna Rogers
- Computer Science Department, IT University of Copenhagen, Copenhagen, Denmark
| | - Ingo Zettler
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
- Copenhagen Center for Social Data Science (SODAS), University of Copenhagen, Copenhagen, Denmark
| | - Sune Lehmann
- DTU Compute, Technical University of Denmark, Lyngby, Denmark.
- Copenhagen Center for Social Data Science (SODAS), University of Copenhagen, Copenhagen, Denmark.
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2
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Albers KJ, Liptrot MG, Ambrosen KS, Røge R, Herlau T, Andersen KW, Siebner HR, Hansen LK, Dyrby TB, Madsen KH, Schmidt MN, Mørup M. Uncovering Cortical Units of Processing From Multi-Layered Connectomes. Front Neurosci 2022; 16:836259. [PMID: 35360166 PMCID: PMC8960198 DOI: 10.3389/fnins.2022.836259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 02/09/2022] [Indexed: 11/13/2022] Open
Abstract
Modern diffusion and functional magnetic resonance imaging (dMRI/fMRI) provide non-invasive high-resolution images from which multi-layered networks of whole-brain structural and functional connectivity can be derived. Unfortunately, the lack of observed correspondence between the connectivity profiles of the two modalities challenges the understanding of the relationship between the functional and structural connectome. Rather than focusing on correspondence at the level of connections we presently investigate correspondence in terms of modular organization according to shared canonical processing units. We use a stochastic block-model (SBM) as a data-driven approach for clustering high-resolution multi-layer whole-brain connectivity networks and use prediction to quantify the extent to which a given clustering accounts for the connectome within a modality. The employed SBM assumes a single underlying parcellation exists across modalities whilst permitting each modality to possess an independent connectivity structure between parcels thereby imposing concurrent functional and structural units but different structural and functional connectivity profiles. We contrast the joint processing units to their modality specific counterparts and find that even though data-driven structural and functional parcellations exhibit substantial differences, attributed to modality specific biases, the joint model is able to achieve a consensus representation that well accounts for both the functional and structural connectome providing improved representations of functional connectivity compared to using functional data alone. This implies that a representation persists in the consensus model that is shared by the individual modalities. We find additional support for this viewpoint when the anatomical correspondence between modalities is removed from the joint modeling. The resultant drop in predictive performance is in general substantial, confirming that the anatomical correspondence of processing units is indeed present between the two modalities. Our findings illustrate how multi-modal integration admits consensus representations well-characterizing each individual modality despite their biases and points to the importance of multi-layered connectomes as providing supplementary information regarding the brain's canonical processing units.
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Affiliation(s)
- Kristoffer Jon Albers
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- *Correspondence: Kristoffer Jon Albers
| | - Matthew G. Liptrot
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Karen Sandø Ambrosen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Rasmus Røge
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Tue Herlau
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Kasper Winther Andersen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Hartwig R. Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
- Department of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lars Kai Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Tim B. Dyrby
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Kristoffer H. Madsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Mikkel N. Schmidt
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Morten Mørup
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- Morten Mørup
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3
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Abstract
Preparing thermal states on a quantum computer can have a variety of applications, from simulating many-body quantum systems to training machine learning models. Variational circuits have been proposed for this task on near-term quantum computers, but several challenges remain, such as finding a scalable cost-function, avoiding the need of purification, and mitigating noise effects. We propose a new algorithm for thermal state preparation that tackles those three challenges by exploiting the noise of quantum circuits. We consider a variational architecture containing a depolarizing channel after each unitary layer, with the ability to directly control the level of noise. We derive a closed-form approximation for the free-energy of such circuit and use it as a cost function for our variational algorithm. By evaluating our method on a variety of Hamiltonians and system sizes, we find several systems for which the thermal state can be approximated with a high fidelity. However, we also show that the ability for our algorithm to learn the thermal state strongly depends on the temperature: while a high fidelity can be obtained for high and low temperatures, we identify a specific range for which the problem becomes more challenging. We hope that this first study on noise-assisted thermal state preparation will inspire future research on exploiting noise in variational algorithms.
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Affiliation(s)
- Jonathan Foldager
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800, Kongens Lyngby, Denmark.
| | - Arthur Pesah
- Department of Physics and Astronomy, University College London, London, WC1E 6BT, UK
| | - Lars Kai Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
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4
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Tuckute G, Hansen ST, Kjaer TW, Hansen LK. Real-Time Decoding of Attentional States Using Closed-Loop EEG Neurofeedback. Neural Comput 2021; 33:967-1004. [PMID: 33513324 DOI: 10.1162/neco_a_01363] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/16/2020] [Indexed: 11/04/2022]
Abstract
Sustained attention is a cognitive ability to maintain task focus over extended periods of time (Mackworth, 1948; Chun, Golomb, & Turk-Browne, 2011). In this study, scalp electroencephalography (EEG) signals were processed in real time using a 32 dry-electrode system during a sustained visual attention task. An attention training paradigm was implemented, as designed in DeBettencourt, Cohen, Lee, Norman, and Turk-Browne (2015) in which the composition of a sequence of blended images is updated based on the participant's decoded attentional level to a primed image category. It was hypothesized that a single neurofeedback training session would improve sustained attention abilities. Twenty-two participants were trained on a single neurofeedback session with behavioral pretraining and posttraining sessions within three consecutive days. Half of the participants functioned as controls in a double-blinded design and received sham neurofeedback. During the neurofeedback session, attentional states to primed categories were decoded in real time and used to provide a continuous feedback signal customized to each participant in a closed-loop approach. We report a mean classifier decoding error rate of 34.3% (chance = 50%). Within the neurofeedback group, there was a greater level of task-relevant attentional information decoded in the participant's brain before making a correct behavioral response than before an incorrect response. This effect was not visible in the control group (interaction p=7.23e-4), which strongly indicates that we were able to achieve a meaningful measure of subjective attentional state in real time and control participants' behavior during the neurofeedback session. We do not provide conclusive evidence whether the single neurofeedback session per se provided lasting effects in sustained attention abilities. We developed a portable EEG neurofeedback system capable of decoding attentional states and predicting behavioral choices in the attention task at hand. The neurofeedback code framework is Python based and open source, and it allows users to actively engage in the development of neurofeedback tools for scientific and translational use.
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Affiliation(s)
- Greta Tuckute
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark, and Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, U.S.A.,
| | - Sofie Therese Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark,
| | - Troels Wesenberg Kjaer
- Department of Neurology, Zealand University Hospital, 4000 Roskilde, Denmark, and Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark,
| | - Lars Kai Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark,
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5
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Tuckute G, Hansen ST, Pedersen N, Steenstrup D, Hansen LK. Single-Trial Decoding of Scalp EEG under Natural Conditions. Comput Intell Neurosci 2019; 2019:9210785. [PMID: 31143206 PMCID: PMC6501266 DOI: 10.1155/2019/9210785] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 02/12/2019] [Accepted: 02/24/2019] [Indexed: 12/04/2022]
Abstract
There is significant current interest in decoding mental states from electroencephalography (EEG) recordings. EEG signals are subject-specific, are sensitive to disturbances, and have a low signal-to-noise ratio, which has been mitigated by the use of laboratory-grade EEG acquisition equipment under highly controlled conditions. In the present study, we investigate single-trial decoding of natural, complex stimuli based on scalp EEG acquired with a portable, 32 dry-electrode sensor system in a typical office setting. We probe generalizability by a leave-one-subject-out cross-validation approach. We demonstrate that support vector machine (SVM) classifiers trained on a relatively small set of denoised (averaged) pseudotrials perform on par with classifiers trained on a large set of noisy single-trial samples. We propose a novel method for computing sensitivity maps of EEG-based SVM classifiers for visualization of EEG signatures exploited by the SVM classifiers. Moreover, we apply an NPAIRS resampling framework for estimation of map uncertainty, and thus show that effect sizes of sensitivity maps for classifiers trained on small samples of denoised data and large samples of noisy data are similar. Finally, we demonstrate that the average pseudotrial classifier can successfully predict the class of single trials from withheld subjects, which allows for fast classifier training, parameter optimization, and unbiased performance evaluation in machine learning approaches for brain decoding.
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Affiliation(s)
- Greta Tuckute
- Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
| | - Sofie Therese Hansen
- Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
| | - Nicolai Pedersen
- Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
| | - Dea Steenstrup
- Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
| | - Lars Kai Hansen
- Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
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6
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Poulsen A, Leonetti JM, Hansen LK, Kouider S. Tracking difficulty in a helicopter simulator: EEG complexity as a marker for mental workload. Front Hum Neurosci 2018. [DOI: 10.3389/conf.fnhum.2018.227.00090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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7
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Troelsgaard R, Hansen LK. Sequence Classification Using Third-Order Moments. Neural Comput 2017; 30:216-236. [PMID: 29162004 DOI: 10.1162/neco_a_01033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Model-based classification of sequence data using a set of hidden Markov models is a well-known technique. The involved score function, which is often based on the class-conditional likelihood, can, however, be computationally demanding, especially for long data sequences. Inspired by recent theoretical advances in spectral learning of hidden Markov models, we propose a score function based on third-order moments. In particular, we propose to use the Kullback-Leibler divergence between theoretical and empirical third-order moments for classification of sequence data with discrete observations. The proposed method provides lower computational complexity at classification time than the usual likelihood-based methods. In order to demonstrate the properties of the proposed method, we perform classification of both simulated data and empirical data from a human activity recognition study.
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Affiliation(s)
- Rasmus Troelsgaard
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby 2860, Denmark
| | - Lars Kai Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby 2860, Denmark
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8
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Hansen ST, Hansen LK. Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior. Neuroimage 2016; 148:274-283. [PMID: 27986607 DOI: 10.1016/j.neuroimage.2016.12.030] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Revised: 11/11/2016] [Accepted: 12/11/2016] [Indexed: 11/17/2022] Open
Abstract
Electroencephalography (EEG) can capture brain dynamics in high temporal resolution. By projecting the scalp EEG signal back to its origin in the brain also high spatial resolution can be achieved. Source localized EEG therefore has potential to be a very powerful tool for understanding the functional dynamics of the brain. Solving the inverse problem of EEG is however highly ill-posed as there are many more potential locations of the EEG generators than EEG measurement points. Several well-known properties of brain dynamics can be exploited to alleviate this problem. More short ranging connections exist in the brain than long ranging, arguing for spatially focal sources. Additionally, recent work (Delorme et al., 2012) argues that EEG can be decomposed into components having sparse source distributions. On the temporal side both short and long term stationarity of brain activation are seen. We summarize these insights in an inverse solver, the so-called "Variational Garrote" (Kappen and Gómez, 2013). Using a Markov prior we can incorporate flexible degrees of temporal stationarity. Through spatial basis functions spatially smooth distributions are obtained. Sparsity of these are inherent to the Variational Garrote solver. We name our method the MarkoVG and demonstrate its ability to adapt to the temporal smoothness and spatial sparsity in simulated EEG data. Finally a benchmark EEG dataset is used to demonstrate MarkoVG's ability to recover non-stationary brain dynamics.
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Affiliation(s)
- Sofie Therese Hansen
- Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, DK-2800 Kgs. Lyngby, Denmark.
| | - Lars Kai Hansen
- Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, DK-2800 Kgs. Lyngby, Denmark.
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9
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Twigg SRF, Ousager LB, Miller KA, Zhou Y, Elalaoui SC, Sefiani A, Bak GS, Hove H, Hansen LK, Fagerberg CR, Tajir M, Wilkie AOM. Acromelic frontonasal dysostosis and ZSWIM6 mutation: phenotypic spectrum and mosaicism. Clin Genet 2016; 90:270-5. [PMID: 26706854 PMCID: PMC5025718 DOI: 10.1111/cge.12721] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2015] [Revised: 12/22/2015] [Accepted: 12/22/2015] [Indexed: 11/24/2022]
Abstract
Acromelic frontonasal dysostosis (AFND) is a distinctive and rare frontonasal malformation that presents in combination with brain and limb abnormalities. A single recurrent heterozygous missense substitution in ZSWIM6, encoding a protein of unknown function, was previously shown to underlie this disorder in four unrelated cases. Here we describe four additional individuals from three families, comprising two sporadic subjects (one of whom had no limb malformation) and a mildly affected female with a severely affected son. In the latter family we demonstrate parental mosaicism through deep sequencing of DNA isolated from a variety of tissues, which each contain different levels of mutation. This has important implications for genetic counselling.
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Affiliation(s)
- S R F Twigg
- Clinical Genetics Group, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - L B Ousager
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | - K A Miller
- Clinical Genetics Group, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Y Zhou
- Clinical Genetics Group, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - S C Elalaoui
- Human Genomics Center, Faculty of Medicine and Pharmacy of Rabat, Rabat, Morocco.,Department of Medical Genetics, National Institute of Health, Rabat, Morocco
| | - A Sefiani
- Human Genomics Center, Faculty of Medicine and Pharmacy of Rabat, Rabat, Morocco.,Department of Medical Genetics, National Institute of Health, Rabat, Morocco
| | - G S Bak
- Department of Obstetrics and Gynecology, Odense University Hospital, Odense, Denmark
| | - H Hove
- Department of Clinical Genetics, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - L K Hansen
- Department of Paediatrics, Hans Christian Andersen Children's Hospital, Odense University Hospital, Odense, Denmark
| | - C R Fagerberg
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | - M Tajir
- Human Genomics Center, Faculty of Medicine and Pharmacy of Rabat, Rabat, Morocco.,Department of Medical Genetics, National Institute of Health, Rabat, Morocco
| | - A O M Wilkie
- Clinical Genetics Group, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
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10
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Abstract
Correlated component analysis as proposed by Dmochowski, Sajda, Dias, and Parra (2012) is a tool for investigating brain process similarity in the responses to multiple views of a given stimulus. Correlated components are identified under the assumption that the involved spatial networks are identical. Here we propose a hierarchical probabilistic model that can infer the level of universality in such multiview data, from completely unrelated representations, corresponding to canonical correlation analysis, to identical representations as in correlated component analysis. This new model, which we denote Bayesian correlated component analysis, evaluates favorably against three relevant algorithms in simulated data. A well-established benchmark EEG data set is used to further validate the new model and infer the variability of spatial representations across multiple subjects.
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Affiliation(s)
- Simon Kamronn
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Copenhagen 2800, Denmark
| | - Andreas Trier Poulsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Copenhagen 2800, Denmark
| | - Lars Kai Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Copenhagen 2800, Denmark
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11
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Knudsen GM, Jensen PS, Erritzoe D, Baaré WFC, Ettrup A, Fisher PM, Gillings N, Hansen HD, Hansen LK, Hasselbalch SG, Henningsson S, Herth MM, Holst KK, Iversen P, Kessing LV, Macoveanu J, Madsen KS, Mortensen EL, Nielsen FÅ, Paulson OB, Siebner HR, Stenbæk DS, Svarer C, Jernigan TL, Strother SC, Frokjaer VG. The Center for Integrated Molecular Brain Imaging (Cimbi) database. Neuroimage 2015; 124:1213-1219. [PMID: 25891375 DOI: 10.1016/j.neuroimage.2015.04.025] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2015] [Revised: 04/08/2015] [Accepted: 04/09/2015] [Indexed: 01/07/2023] Open
Abstract
We here describe a multimodality neuroimaging containing data from healthy volunteers and patients, acquired within the Lundbeck Foundation Center for Integrated Molecular Brain Imaging (Cimbi) in Copenhagen, Denmark. The data is of particular relevance for neurobiological research questions related to the serotonergic transmitter system with its normative data on the serotonergic subtype receptors 5-HT1A, 5-HT1B, 5-HT2A, and 5-HT4 and the 5-HT transporter (5-HTT), but can easily serve other purposes. The Cimbi database and Cimbi biobank were formally established in 2008 with the purpose to store the wealth of Cimbi-acquired data in a highly structured and standardized manner in accordance with the regulations issued by the Danish Data Protection Agency as well as to provide a quality-controlled resource for future hypothesis-generating and hypothesis-driven studies. The Cimbi database currently comprises a total of 1100 PET and 1000 structural and functional MRI scans and it holds a multitude of additional data, such as genetic and biochemical data, and scores from 17 self-reported questionnaires and from 11 neuropsychological paper/computer tests. The database associated Cimbi biobank currently contains blood and in some instances saliva samples from about 500 healthy volunteers and 300 patients with e.g., major depression, dementia, substance abuse, obesity, and impulsive aggression. Data continue to be added to the Cimbi database and biobank.
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Affiliation(s)
- Gitte M Knudsen
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, DK-2100 Copenhagen, Denmark.
| | - Peter S Jensen
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark
| | - David Erritzoe
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark
| | - William F C Baaré
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, DK-2650 Hvidovre, Denmark
| | - Anders Ettrup
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark
| | - Patrick M Fisher
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark
| | - Nic Gillings
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; PET and Cyclotron Unit, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark
| | - Hanne D Hansen
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark
| | - Lars Kai Hansen
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; DTU Compute, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Steen G Hasselbalch
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, DK-2100 Copenhagen, Denmark
| | - Susanne Henningsson
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, DK-2650 Hvidovre, Denmark
| | - Matthias M Herth
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; PET and Cyclotron Unit, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2100 Copenhagen, Denmark
| | - Klaus K Holst
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Department of Biostatistics, University of Copenhagen, DK-1014 Copenhagen, Denmark
| | - Pernille Iversen
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, DK-2650 Hvidovre, Denmark
| | - Lars V Kessing
- Psychiatric Center Copenhagen, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, DK-2100 Copenhagen, Denmark
| | - Julian Macoveanu
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, DK-2650 Hvidovre, Denmark; Psychiatric Center Copenhagen, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Kathrine Skak Madsen
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, DK-2650 Hvidovre, Denmark
| | - Erik L Mortensen
- Department of Public Health and Center for Healthy Aging, University of Copenhagen, DK-2200 Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, DK-2100 Copenhagen, Denmark
| | - Finn Årup Nielsen
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; DTU Compute, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Olaf B Paulson
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, DK-2650 Hvidovre, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, DK-2100 Copenhagen, Denmark
| | - Hartwig R Siebner
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, DK-2650 Hvidovre, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg, DK-2400 Copenhagen, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg, DK-2400 Copenhagen, Denmark
| | - Dea S Stenbæk
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark
| | - Claus Svarer
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark
| | - Terry L Jernigan
- Center for Human Development, University of California, San Diego, La Jolla, CA 92093, USA
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Vibe G Frokjaer
- Center for Integrated Molecular Brain Imaging, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, DK-2100 Copenhagen, Denmark; Psychiatric Center Copenhagen, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
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12
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Abstract
Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis/Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher's iris data set and Howells' craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian.
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Affiliation(s)
- Roger P. Woods
- Departments of Neurology and of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, California, United States of America
- * E-mail:
| | - Lars Kai Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Stephen Strother
- Rotman Research Institute, Baycrest, and Department of Medical Biophysics, University of Toronto, Toronto, Canada
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13
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14
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Andersen KW, Madsen KH, Siebner HR, Schmidt MN, Mørup M, Hansen LK. Non-parametric Bayesian graph models reveal community structure in resting state fMRI. Neuroimage 2014; 100:301-15. [PMID: 24914522 DOI: 10.1016/j.neuroimage.2014.05.083] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2013] [Revised: 05/16/2014] [Accepted: 05/30/2014] [Indexed: 10/25/2022] Open
Abstract
Modeling of resting state functional magnetic resonance imaging (rs-fMRI) data using network models is of increasing interest. It is often desirable to group nodes into clusters to interpret the communication patterns between nodes. In this study we consider three different nonparametric Bayesian models for node clustering in complex networks. In particular, we test their ability to predict unseen data and their ability to reproduce clustering across datasets. The three generative models considered are the Infinite Relational Model (IRM), Bayesian Community Detection (BCD), and the Infinite Diagonal Model (IDM). The models define probabilities of generating links within and between clusters and the difference between the models lies in the restrictions they impose upon the between-cluster link probabilities. IRM is the most flexible model with no restrictions on the probabilities of links between clusters. BCD restricts the between-cluster link probabilities to be strictly lower than within-cluster link probabilities to conform to the community structure typically seen in social networks. IDM only models a single between-cluster link probability, which can be interpreted as a background noise probability. These probabilistic models are compared against three other approaches for node clustering, namely Infomap, Louvain modularity, and hierarchical clustering. Using 3 different datasets comprising healthy volunteers' rs-fMRI we found that the BCD model was in general the most predictive and reproducible model. This suggests that rs-fMRI data exhibits community structure and furthermore points to the significance of modeling heterogeneous between-cluster link probabilities.
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Affiliation(s)
- Kasper Winther Andersen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Matematiktorvet, Bygning 303 B, 2800 Kgs. Lyngby, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Kettegaard Alle 30, 2650 Hvidovre, Denmark.
| | - Kristoffer H Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Kettegaard Alle 30, 2650 Hvidovre, Denmark.
| | - Hartwig Roman Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Kettegaard Alle 30, 2650 Hvidovre, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200 København N, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg, Bispebjerg Bakke 23, 2400 København NV, Denmark.
| | - Mikkel N Schmidt
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Matematiktorvet, Bygning 303 B, 2800 Kgs. Lyngby, Denmark.
| | - Morten Mørup
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Matematiktorvet, Bygning 303 B, 2800 Kgs. Lyngby, Denmark.
| | - Lars Kai Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Matematiktorvet, Bygning 303 B, 2800 Kgs. Lyngby, Denmark.
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15
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Shamshina JL, Gurau G, Block LE, Hansen LK, Dingee C, Walters A, Rogers RD. Chitin-calcium alginate composite fibers for wound care dressings spun from ionic liquid solution. J Mater Chem B 2014; 2:3924-3936. [PMID: 32261644 DOI: 10.1039/c4tb00329b] [Citation(s) in RCA: 85] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Chitin-calcium alginate composite fibers were prepared from a solution of high molecular weight chitin extracted from shrimp shells and alginic acid in the ionic liquid 1-ethyl-3-methylimidazolium acetate by dry-jet wet spinning into an aqueous bath saturated with CaCO3. The fibers exhibited a significant proportion of the individual properties of both calcium alginate and chitin. Ultimate stress values were close to values obtained for calcium alginate fibers, and the absorption capacities measured were consistent with those reported for current wound care dressings. Wound healing studies (rat model, histological evaluation) indicated that chitin-calcium alginate covered wound sites underwent normal wound healing with re-epithelialization and that coverage of the dermal fibrosis with hyperplastic epidermis was consistently complete after only 7 days of treatment. Using a single patch per wound per animal during the entire study, all rat wounds achieved 95-99% closure by day 10 with complete wound closure by day 14.
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Affiliation(s)
- J L Shamshina
- Center for Green Manufacturing and Department of Chemistry, The University of Alabama, Tuscaloosa, AL 35487, USA.
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16
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Konvalinka I, Bauer M, Stahlhut C, Hansen LK, Roepstorff A, Frith CD. Frontal alpha oscillations distinguish leaders from followers: multivariate decoding of mutually interacting brains. Neuroimage 2014; 94:79-88. [PMID: 24631790 DOI: 10.1016/j.neuroimage.2014.03.003] [Citation(s) in RCA: 131] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2013] [Revised: 02/22/2014] [Accepted: 03/04/2014] [Indexed: 01/08/2023] Open
Abstract
Successful social interactions rely upon the abilities of two or more people to mutually exchange information in real-time, while simultaneously adapting to one another. The neural basis of social cognition has mostly been investigated in isolated individuals, and more recently using two-person paradigms to quantify the neuronal dynamics underlying social interaction. While several studies have shown the relevance of understanding complementary and mutually adaptive processes, the neural mechanisms underlying such coordinative behavioral patterns during joint action remain largely unknown. Here, we employed a synchronized finger-tapping task while measuring dual-EEG from pairs of human participants who either mutually adjusted to each other in an interactive task or followed a computer metronome. Neurophysiologically, the interactive condition was characterized by a stronger suppression of alpha and low-beta oscillations over motor and frontal areas in contrast to the non-interactive computer condition. A multivariate analysis of two-brain activity to classify interactive versus non-interactive trials revealed asymmetric patterns of the frontal alpha-suppression in each pair, during both task anticipation and execution, such that only one member showed the frontal component. Analysis of the behavioral data showed that this distinction coincided with the leader-follower relationship in 8/9 pairs, with the leaders characterized by the stronger frontal alpha-suppression. This suggests that leaders invest more resources in prospective planning and control. Hence our results show that the spontaneous emergence of leader-follower relationships in dyadic interactions can be predicted from EEG recordings of brain activity prior to and during interaction. Furthermore, this emphasizes the importance of investigating complementarity in joint action.
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Affiliation(s)
- Ivana Konvalinka
- Center of Functionally Integrative Neuroscience, Aarhus University, 8000 Aarhus, Denmark; Section for Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.
| | - Markus Bauer
- Wellcome Trust Centre for Neuroimaging, University College London, WC1N 3BG London, UK; School of Psychology, University of Nottingham, NG7 2RD Nottingham, UK
| | - Carsten Stahlhut
- Section for Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Lars Kai Hansen
- Section for Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Andreas Roepstorff
- Center of Functionally Integrative Neuroscience, Aarhus University, 8000 Aarhus, Denmark; Interacting Minds Centre, Aarhus University, 8000 Aarhus, Denmark
| | - Chris D Frith
- Center of Functionally Integrative Neuroscience, Aarhus University, 8000 Aarhus, Denmark; Wellcome Trust Centre for Neuroimaging, University College London, WC1N 3BG London, UK
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17
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Stopczynski A, Stahlhut C, Larsen JE, Petersen MK, Hansen LK. The smartphone brain scanner: a portable real-time neuroimaging system. PLoS One 2014; 9:e86733. [PMID: 24505263 PMCID: PMC3914802 DOI: 10.1371/journal.pone.0086733] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2013] [Accepted: 12/15/2013] [Indexed: 12/05/2022] Open
Abstract
Combining low-cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. Here we present the technical details and validation of a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system – Smartphone Brain Scanner – combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully portable system for real-time 3D EEG imaging. We discuss the benefits and challenges, including technical limitations as well as details of real-time reconstruction of 3D images of brain activity. We present examples of brain activity captured in a simple experiment involving imagined finger tapping, which shows that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using an off-the-shelf consumer neuroheadset is lower than the signal obtained using high-density standard EEG equipment, we propose mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings.
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Affiliation(s)
- Arkadiusz Stopczynski
- Section for Cognitive Systems, DTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark
- * E-mail:
| | - Carsten Stahlhut
- Section for Cognitive Systems, DTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Jakob Eg Larsen
- Section for Cognitive Systems, DTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Michael Kai Petersen
- Section for Cognitive Systems, DTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Lars Kai Hansen
- Section for Cognitive Systems, DTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark
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18
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Abrahamsen TJ, Hansen LK. Variance inflation in high dimensional Support Vector Machines. Pattern Recognit Lett 2013. [DOI: 10.1016/j.patrec.2013.08.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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19
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Stopczynski A, Stahlhut C, Petersen MK, Larsen JE, Jensen CF, Ivanova MG, Andersen TS, Hansen LK. Smartphones as pocketable labs: visions for mobile brain imaging and neurofeedback. Int J Psychophysiol 2013; 91:54-66. [PMID: 23994206 DOI: 10.1016/j.ijpsycho.2013.08.007] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2013] [Revised: 07/24/2013] [Accepted: 08/12/2013] [Indexed: 10/26/2022]
Abstract
Mobile brain imaging solutions, such as the Smartphone Brain Scanner, which combines low cost wireless EEG sensors with open source software for real-time neuroimaging, may transform neuroscience experimental paradigms. Normally subject to the physical constraints in labs, neuroscience experimental paradigms can be transformed into dynamic environments allowing for the capturing of brain signals in everyday contexts. Using smartphones or tablets to access text or images may enable experimental design capable of tracing emotional responses when shopping or consuming media, incorporating sensorimotor responses reflecting our actions into brain machine interfaces, and facilitating neurofeedback training over extended periods. Even though the quality of consumer neuroheadsets is still lower than laboratory equipment and susceptible to environmental noise, we show that mobile neuroimaging solutions, like the Smartphone Brain Scanner, complemented by 3D reconstruction or source separation techniques may support a range of neuroimaging applications and thus become a valuable addition to high-end neuroimaging solutions.
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Affiliation(s)
- Arkadiusz Stopczynski
- Technical University of Denmark, Department of Applied Mathematics and Computer Science, Section for Cognitive Systems, Building 303B, DK-2800 Kgs. Lyngby, Denmark.
| | - Carsten Stahlhut
- Technical University of Denmark, Department of Applied Mathematics and Computer Science, Section for Cognitive Systems, Building 303B, DK-2800 Kgs. Lyngby, Denmark.
| | - Michael Kai Petersen
- Technical University of Denmark, Department of Applied Mathematics and Computer Science, Section for Cognitive Systems, Building 303B, DK-2800 Kgs. Lyngby, Denmark.
| | - Jakob Eg Larsen
- Technical University of Denmark, Department of Applied Mathematics and Computer Science, Section for Cognitive Systems, Building 303B, DK-2800 Kgs. Lyngby, Denmark.
| | - Camilla Falk Jensen
- Technical University of Denmark, Department of Applied Mathematics and Computer Science, Section for Cognitive Systems, Building 303B, DK-2800 Kgs. Lyngby, Denmark.
| | - Marieta Georgieva Ivanova
- Technical University of Denmark, Department of Applied Mathematics and Computer Science, Section for Cognitive Systems, Building 303B, DK-2800 Kgs. Lyngby, Denmark.
| | - Tobias S Andersen
- Technical University of Denmark, Department of Applied Mathematics and Computer Science, Section for Cognitive Systems, Building 303B, DK-2800 Kgs. Lyngby, Denmark.
| | - Lars Kai Hansen
- Technical University of Denmark, Department of Applied Mathematics and Computer Science, Section for Cognitive Systems, Building 303B, DK-2800 Kgs. Lyngby, Denmark.
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20
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Stahlhut C, Attias HT, Stopczynski A, Petersen MK, Larsen JE, Hansen LK. An evaluation of EEG scanner's dependence on the imaging technique, forward model computation method, and array dimensionality. Annu Int Conf IEEE Eng Med Biol Soc 2013; 2012:1538-41. [PMID: 23366196 DOI: 10.1109/embc.2012.6346235] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
EEG source reconstruction involves solving an inverse problem that is highly ill-posed and dependent on a generally fixed forward propagation model. In this contribution we compare a low and high density EEG setup's dependence on correct forward modeling. Specifically, we examine how different forward models affect the source estimates obtained using four inverse solvers Minimum-Norm, LORETA, Minimum-Variance Adaptive Beamformer, and Sparse Bayesian Learning.
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Affiliation(s)
- Carsten Stahlhut
- DTU Informatics, Technical University of Denmark, DK-2800 Kgs. Lyngby. C. Stahlhut.
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21
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Dragusin R, Petcu P, Lioma C, Larsen B, Jørgensen HL, Cox IJ, Hansen LK, Ingwersen P, Winther O. Specialized tools are needed when searching the web for rare disease diagnoses. Rare Dis 2013; 1:e25001. [PMID: 25002998 PMCID: PMC3932942 DOI: 10.4161/rdis.25001] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2013] [Accepted: 05/10/2013] [Indexed: 11/21/2022] Open
Abstract
In our recent paper, we study web search as an aid in the process of diagnosing rare diseases. To answer the question of how well Google Search and PubMed perform, we created an evaluation framework with 56 diagnostic cases and made our own specialized search engine, FindZebra (findzebra.com). FindZebra uses a set of publicly available curated sources on rare diseases and an open-source information retrieval system, Indri. Our evaluation and the feedback received after the publication of our paper both show that FindZebra outperforms Google Search and PubMed. In this paper, we summarize the original findings and the response to FindZebra, discuss why Google Search is not designed for specialized tasks and outline some of the current trends in using web resources and social media for medical diagnosis.
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Affiliation(s)
- Radu Dragusin
- DTU Compute; Technical University of Denmark; Lyngby, Denmark ; Department of Computer Science; University of Copenhagen; Copenhagen, Denmark
| | - Paula Petcu
- DTU Compute; Technical University of Denmark; Lyngby, Denmark ; FindWise; Copenhagen, Denmark
| | - Christina Lioma
- DTU Compute; Technical University of Denmark; Lyngby, Denmark ; Department of Computer Science; University of Copenhagen; Copenhagen, Denmark
| | - Birger Larsen
- Information Systems and Interaction Design; Royal School of Library and Information Science; Copenhagen, Denmark
| | - Henrik L Jørgensen
- Department of Clinical Biochemistry; Bispebjerg Hospital; Copenhagen, Denmark
| | - Ingemar J Cox
- DTU Compute; Technical University of Denmark; Lyngby, Denmark ; Department of Computer Science; University College London; London, UK
| | - Lars Kai Hansen
- DTU Compute; Technical University of Denmark; Lyngby, Denmark
| | - Peter Ingwersen
- Information Systems and Interaction Design; Royal School of Library and Information Science; Copenhagen, Denmark
| | - Ole Winther
- DTU Compute; Technical University of Denmark; Lyngby, Denmark
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22
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Rasmussen PM, Abrahamsen TJ, Madsen KH, Hansen LK. Nonlinear denoising and analysis of neuroimages with kernel principal component analysis and pre-image estimation. Neuroimage 2012; 60:1807-18. [PMID: 22305952 DOI: 10.1016/j.neuroimage.2012.01.096] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2011] [Revised: 01/15/2012] [Accepted: 01/18/2012] [Indexed: 10/14/2022] Open
Abstract
We investigate the use of kernel principal component analysis (PCA) and the inverse problem known as pre-image estimation in neuroimaging: i) We explore kernel PCA and pre-image estimation as a means for image denoising as part of the image preprocessing pipeline. Evaluation of the denoising procedure is performed within a data-driven split-half evaluation framework. ii) We introduce manifold navigation for exploration of a nonlinear data manifold, and illustrate how pre-image estimation can be used to generate brain maps in the continuum between experimentally defined brain states/classes. We base these illustrations on two fMRI BOLD data sets - one from a simple finger tapping experiment and the other from an experiment on object recognition in the ventral temporal lobe.
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23
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Abstract
We propose kernel parallel analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel principal component analysis (KPCA). Parallel analysis is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also tune the Gaussian kernel scale of radial basis function based KPCA. We evaluate kPA for denoising of simulated data and the U.S. postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio of the denoised data.
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24
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Hansen LK. Book Review: "Time Series Prediction: Forecasting the Future and Understanding the Past", Eds. Andreas S. Weigend and Neil A. Gershenfeld. Int J Neural Syst 2011. [DOI: 10.1142/s0129065794000359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- Lars Kai Hansen
- CONNECT, Electronics Institute, Technical University of Denmark, B349, DK-2800 Lyngby, Denmark
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25
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Abrahamsen TJ, Hansen LK. Sparse non-linear denoising: Generalization performance and pattern reproducibility in functional MRI. Pattern Recognit Lett 2011. [DOI: 10.1016/j.patrec.2011.08.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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26
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Petersen MK, Stahlhut C, Stopczynski A, Larsen JE, Hansen LK. Smartphones Get Emotional: Mind Reading Images and Reconstructing the Neural Sources. Affective Computing and Intelligent Interaction 2011. [DOI: 10.1007/978-3-642-24571-8_72] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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27
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Hansen LK, Arvidsson A, Nielsen FA, Colleoni E, Etter M. Good Friends, Bad News - Affect and Virality in Twitter. Communications in Computer and Information Science 2011. [DOI: 10.1007/978-3-642-22309-9_5] [Citation(s) in RCA: 154] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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28
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Rasmussen PM, Madsen KH, Lund TE, Hansen LK. Visualization of nonlinear kernel models in neuroimaging by sensitivity maps. Neuroimage 2010; 55:1120-31. [PMID: 21168511 DOI: 10.1016/j.neuroimage.2010.12.035] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2010] [Revised: 11/26/2010] [Accepted: 12/02/2010] [Indexed: 11/30/2022] Open
Abstract
There is significant current interest in decoding mental states from neuroimages. In this context kernel methods, e.g., support vector machines (SVM) are frequently adopted to learn statistical relations between patterns of brain activation and experimental conditions. In this paper we focus on visualization of such nonlinear kernel models. Specifically, we investigate the sensitivity map as a technique for generation of global summary maps of kernel classification models. We illustrate the performance of the sensitivity map on functional magnetic resonance (fMRI) data based on visual stimuli. We show that the performance of linear models is reduced for certain scan labelings/categorizations in this data set, while the nonlinear models provide more flexibility. We show that the sensitivity map can be used to visualize nonlinear versions of kernel logistic regression, the kernel Fisher discriminant, and the SVM, and conclude that the sensitivity map is a versatile and computationally efficient tool for visualization of nonlinear kernel models in neuroimaging.
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29
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de Nijs R, Miranda MJ, Hansen LK, Hanson LG. Motion correction of single-voxel spectroscopy by independent component analysis applied to spectra from nonanesthetized pediatric subjects. Magn Reson Med 2009; 62:1147-54. [DOI: 10.1002/mrm.22129] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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30
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Wilkowski B, Szewczyk MM, Hansen LK. Bridging the gap between coordinate- and keyword- based search of neuroscientific databases by UMLS-assisted semantic keyword extraction. Neuroimage 2009. [DOI: 10.1016/s1053-8119(09)71750-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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31
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Christensen BE, Hansen LK, Kristensen JK, Videbaek A. Splenectomy in haematology. Indications, results, and complications in 41 cases. Scand J Haematol 2009; 7:247-60. [PMID: 5529312 DOI: 10.1111/j.1600-0609.1970.tb01896.x] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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32
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33
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Adali T, Wang ZJ, McKeown MJ, Ciuciu P, Hansen LK, Cichocki A, Calhoun VD. Introduction to the Issue on fMRI Analysis for Human Brain Mapping. IEEE J Sel Top Signal Process 2008; 2:813. [PMID: 20119499 PMCID: PMC2812927 DOI: 10.1109/jstsp.2008.2009263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Affiliation(s)
- Tülay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland-Baltimore County, Baltimore, MD 21250 USA
| | - Z. Jane Wang
- ECE Department, University of British Columbia, Vancouver, BC Canada
| | - Martin J. McKeown
- Faculty of Medicine (Neurology), University of British Columbia, Vancouver, BC Canada
| | | | - Lars Kai Hansen
- Informatics and Mathematical Modelling, Technical University of Denmark, Lyngby, Denmark
| | | | - Vince D. Calhoun
- The MIND Research Network and the ECE Department, University of New Mexico, Albuquerque, NM 87131 USA
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34
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Mørup M, Hansen LK, Arnfred SM, Lim LH, Madsen KH. Shift-invariant multilinear decomposition of neuroimaging data. Neuroimage 2008; 42:1439-50. [DOI: 10.1016/j.neuroimage.2008.05.062] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2008] [Revised: 04/25/2008] [Accepted: 05/30/2008] [Indexed: 10/21/2022] Open
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35
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Abstract
There is a increasing interest in analysis of large-scale multiway data. The concept of multiway data refers to arrays of data with more than two dimensions, that is, taking the form of tensors. To analyze such data, decomposition techniques are widely used. The two most common decompositions for tensors are the Tucker model and the more restricted PARAFAC model. Both models can be viewed as generalizations of the regular factor analysis to data of more than two modalities. Nonnegative matrix factorization (NMF), in conjunction with sparse coding, has recently been given much attention due to its part-based and easy interpretable representation. While NMF has been extended to the PARAFAC model, no such attempt has been done to extend NMF to the Tucker model. However, if the tensor data analyzed are nonnegative, it may well be relevant to consider purely additive (i.e., nonnegative) Tucker decompositions). To reduce ambiguities of this type of decomposition, we develop updates that can impose sparseness in any combination of modalities, hence, proposed algorithms for sparse nonnegative Tucker decompositions (SN-TUCKER). We demonstrate how the proposed algorithms are superior to existing algorithms for Tucker decompositions when the data and interactions can be considered nonnegative. We further illustrate how sparse coding can help identify what model (PARAFAC or Tucker) is more appropriate for the data as well as to select the number of components by turning off excess components. The algorithms for SN-TUCKER can be downloaded from Mørup (2007).
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Affiliation(s)
- Morten Mørup
- Technical University of Denmark, 2800 Kongens Lyngby, Denmark.
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Arnfred SM, Hansen LK, Parnas J, Mørup M. Regularity increases middle latency evoked and late induced beta brain response following proprioceptive stimulation. Brain Res 2008; 1218:114-31. [DOI: 10.1016/j.brainres.2008.03.057] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2007] [Revised: 03/17/2008] [Accepted: 03/19/2008] [Indexed: 10/22/2022]
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Lehmann S, Schwartz M, Hansen LK. Biclique communities. Phys Rev E Stat Nonlin Soft Matter Phys 2008; 78:016108. [PMID: 18764021 DOI: 10.1103/physreve.78.016108] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2007] [Revised: 04/17/2008] [Indexed: 05/26/2023]
Abstract
We present a method for detecting communities in bipartite networks. Based on an extension of the k -clique community detection algorithm, we demonstrate how modular structure in bipartite networks presents itself as overlapping bicliques. If bipartite information is available, the biclique community detection algorithm retains all of the advantages of the k -clique algorithm, but avoids discarding important structural information when performing a one-mode projection of the network. Further, the biclique community detection algorithm provides a level of flexibility by incorporating independent clique thresholds for each of the nonoverlapping node sets in the bipartite network.
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Affiliation(s)
- Sune Lehmann
- Center for Complex Network Research and Department of Physics, Northeastern University, Boston, Massachusetts 02115, USA
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38
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Abstract
Akathisia is a side-effect that continues to affect a large percentage of psychiatric patients though the focus over recent years has moved away from extrapyramidal side-effects to metabolic problems. As cognitive behavioural therapy has become an integrated part of the management plan for psychotic patients an increased understanding of patients' interpretation of their symptoms and side-effects are encouraged. This case series illustrates different views that patients take on medication induced side-effects.
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Affiliation(s)
- L K Hansen
- Hampshire Partnership Trust, Fordingbridge Bridge Wing, Fordingbridge, Hants, UK.
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Abstract
Nonlinear hemodynamic models express the BOLD (blood oxygenation level dependent) signal as a nonlinear, parametric functional of the temporal sequence of local neural activity. Several models have been proposed for both the neural activity and the hemodynamics. We compare two such combined models: the original balloon model with a square-pulse neural model (Friston, Mechelli, Turner, & Price, 2000) and an extended balloon model with a more sophisticated neural model (Buxton, Uludag, Dubowitz, & Liu, 2004). We learn the parameters of both models using a Bayesian approach, where the distribution of the parameters conditioned on the data is estimated using Markov chain Monte Carlo techniques. Using a split-half resampling procedure (Strother, Anderson, & Hansen, 2002), we compare the generalization abilities of the models as well as their reproducibility, for both synthetic and real data, recorded from two different visual stimulation paradigms. The results show that the simple model is the better one for these data.
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Dyrby TB, Rostrup E, Baaré WFC, van Straaten ECW, Barkhof F, Vrenken H, Ropele S, Schmidt R, Erkinjuntti T, Wahlund LO, Pantoni L, Inzitari D, Paulson OB, Hansen LK, Waldemar G. Segmentation of age-related white matter changes in a clinical multi-center study. Neuroimage 2008; 41:335-45. [PMID: 18394928 DOI: 10.1016/j.neuroimage.2008.02.024] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2007] [Revised: 02/10/2008] [Accepted: 02/14/2008] [Indexed: 11/19/2022] Open
Abstract
Age-related white matter changes (WMC) are thought to be a marker of vascular pathology, and have been associated with motor and cognitive deficits. In the present study, an optimized artificial neural network was used as an automatic segmentation method to produce probabilistic maps of WMC in a clinical multi-center study. The neural network uses information from T1- and T2-weighted and fluid attenuation inversion recovery (FLAIR) magnetic resonance (MR) scans, neighboring voxels and spatial location. Generalizability of the neural network was optimized by including the Optimal Brain Damage (OBD) pruning method in the training stage. Six optimized neural networks were produced to investigate the impact of different input information on WMC segmentation. The automatic segmentation method was applied to MR scans of 362 non-demented elderly subjects from 11 centers in the European multi-center study Leukoaraiosis And Disability (LADIS). Semi-manually delineated WMC were used for validating the segmentation produced by the neural networks. The neural network segmentation demonstrated high consistency between subjects and centers, making it a promising technique for large studies. For WMC volumes less than 10 ml, an increasing discrepancy between semi-manual and neural network segmentation was observed using the similarity index (SI) measure. The use of all three image modalities significantly improved cross-center generalizability compared to neural networks using the FLAIR image only. Expert knowledge not available to the neural networks was a minor source of discrepancy, while variation in MR scan quality constituted the largest source of error.
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Affiliation(s)
- Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital, Hvidovre, Denmark.
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Hansen LK. Multivariate strategies in functional magnetic resonance imaging. Brain Lang 2007; 102:186-91. [PMID: 17223190 DOI: 10.1016/j.bandl.2006.12.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2006] [Revised: 11/12/2006] [Accepted: 12/07/2006] [Indexed: 05/13/2023]
Abstract
We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a 'mind reading' predictive multivariate fMRI model.
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Affiliation(s)
- Lars Kai Hansen
- Informatics and Mathematical Modelling, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.
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Lukic AS, Wernick MN, Yang Y, Hansen LK, Arfanakis K, Strother SC. Effect of spatial alignment transformations in PCA and ICA of functional neuroimages. IEEE Trans Med Imaging 2007; 26:1058-68. [PMID: 17695126 DOI: 10.1109/tmi.2007.896928] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
It has been previously observed that independent component analysis (ICA), if applied to data pooled in a particular way, may lessen the need for spatial alignment of scans in a functional neuroimaging study. In this paper, we seek to determine analytically the conditions under which this observation is true, not only for spatial ICA, but also for temporal ICA and for principal component analysis (PCA). In each case, we find conditions that the spatial alignment operator must satisfy to ensure invariance of the results. We illustrate our findings using functional magnetic-resonance imaging (fMRI) data. Our analysis is applicable to both intersubject and intrasubject spatial normalization.
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Abstract
The open source toolbox 'ERPWAVELAB' is developed for multi-channel time-frequency analysis of event related activity of EEG and MEG data. The toolbox provides tools for data analysis and visualization of the most commonly used measures of time-frequency transformed event related data as well as data decomposition through non-negative matrix and multi-way (tensor) factorization. The decompositions provided can accommodate additional dimensions like subjects, conditions or repeats and as such they are perfected for group analysis. Furthermore, the toolbox enables tracking of phase locked activity from one channel-time-frequency instance to another as well as tools for artifact rejection in the time-frequency domain. Several other features are highlighted. ERPWAVELAB can freely be downloaded from www.erpwavelab.org, requires EEGLAB [Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Meth 2004;134:9-21] and runs under MATLAB (The Mathworks, Inc.).
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Affiliation(s)
- Morten Mørup
- Informatics and Mathematical Modelling, Technical University of Denmark, Richard Petersens Plads, Building 321, DK-2800 Kongens Lyngby, Denmark.
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Lambert P, Sloth E, Smith B, Hansen LK, Koefoed-Nielsen J, Tønnesen E, Larsson A. Does a positive end-expiratory pressure-induced reduction in stroke volume indicate preload responsiveness? An experimental study. Acta Anaesthesiol Scand 2007; 51:415-25. [PMID: 17378779 DOI: 10.1111/j.1399-6576.2007.01248.x] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
BACKGROUND Increases in positive end-expiratory pressure (PEEP) are often associated with cardiovascular depression, responding to fluid loading. Therefore, we hypothesized that if stroke volume (SV) is reduced by an increase in PEEP this reduction is an indicator of hypovolemia or preload responsiveness, i.e. that SV would increase by fluid administration at zero end-expiratory pressure (ZEEP). The relationship between the cardiovascular response to different PEEP levels and fluid load as well as the relation between change in SV as a result of change in preload (Frank-Starling relationship) were evaluated in a porcine model. In addition, other measures of fluid status were assessed. METHODS Eight, 20-22 kg, anesthetized, mechanically ventilated pigs were subjected to 0, 10, and 20 cm H(2)O PEEP at 10% (of estimated blood volume) hypovolemia, normo- and 10% hypervolemia, and to ZEEP at 20% hypervolemia. SV, cardiac output, intrathoracic blood volume and airway, esophageal, vascular pressures, stroke volume variations, left ventricular end-diastolic and end-systolic areas and respiratory variations in the diameter of the inferior vena cava were obtained. RESULTS At hypovolemia and normovolemia, 10 cm H(2)O PEEP induced a significant decrease in SV, while no change occurred at 10% hypervolemia. SV measured at ZEEP increased from hypovolemia to normovolemia and 10% hypervolemia, while no change was found between 10% and 20% hypervolemia. The sensitivity and specificity decrease in SV by PEEP indicating an increase in SV by fluids was 60-88% and 67%, respectively, depending on the volemic (preload) levels. CONCLUSION Although the overall results suggest that a change in SV by PEEP might predict preload responsiveness, the individual response of SV by 10 cm H(2)O PEEP and of the successive fluid administration seemed to be dependent on where on the Frank-Starling curve the heart function was located.
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Affiliation(s)
- P Lambert
- Department of Anesthesia and Intensive Care Medicine, Center for Cardiovascular Research, Aalborg Hospital, Arhus University Hospitals, Aalborg, Denmark
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Abstract
We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model.
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Affiliation(s)
- Mads Dyrholm
- Intelligent Signal Processing Group, Informatics and Mathematical Modelling, Technical University of Denmark, 2800 Lyngby, Denmark.
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48
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Abstract
A proprioceptive stimulus consisting of a weight change of a handheld load has recently been shown to elicit an evoked potential. Previously, somatosensory gamma oscillations have only been evoked by electrical stimuli. We conjectured that a natural proprioceptive stimulus also would be able to evoke gamma oscillations. EEG was recorded using 64 channels in 14 healthy subjects. In each of three runs a stimulus of 100 g load increment in each hand was presented in 120 trials. Data were wavelet transformed and runs collapsed. Inter-trial phase coherence (ITPC) was computed as the best measure of precision of evoked oscillations. The window of interest was 25-75 Hz and 0-200 ms. ITPC maxima were determined by visual inspection and these results were compared to results of multi-way factor analyses. The predicted 40 Hz activity was observed 60 ms after stimulus onset in the parietal region contralateral to stimulus side and additionally an unexpected 20 Hz activity was observed slightly lateralized in the frontal central region. The gamma phase locking may be a manifestation of early somatosensory feature integration. The analyses suggest that the high frequency activity consists of two distinct underlying oscillatory processes, in need of further investigation.
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Affiliation(s)
- Sidse M Arnfred
- Department of Psychiatry, Hvidovre Hospital, University Hospital of Copenhagen, Brøndbyøstervej 160, 2605 Brøndby, Denmark.
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Regenberg B, Grotkjær T, Winther O, Fausbøll A, Åkesson M, Bro C, Hansen LK, Brunak S, Nielsen J. Growth-rate regulated genes have profound impact on interpretation of transcriptome profiling in Saccharomyces cerevisiae. Genome Biol 2007; 7:R107. [PMID: 17105650 PMCID: PMC1794586 DOI: 10.1186/gb-2006-7-11-r107] [Citation(s) in RCA: 179] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2006] [Revised: 09/04/2006] [Accepted: 11/14/2006] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Growth rate is central to the development of cells in all organisms. However, little is known about the impact of changing growth rates. We used continuous cultures to control growth rate and studied the transcriptional program of the model eukaryote Saccharomyces cerevisiae, with generation times varying between 2 and 35 hours. RESULTS A total of 5930 transcripts were identified at the different growth rates studied. Consensus clustering of these revealed that half of all yeast genes are affected by the specific growth rate, and that the changes are similar to those found when cells are exposed to different types of stress (>80% overlap). Genes with decreased transcript levels in response to faster growth are largely of unknown function (>50%) whereas genes with increased transcript levels are involved in macromolecular biosynthesis such as those that encode ribosomal proteins. This group also covers most targets of the transcriptional activator RAP1, which is also known to be involved in replication. A positive correlation between the location of replication origins and the location of growth-regulated genes suggests a role for replication in growth rate regulation. CONCLUSION Our data show that the cellular growth rate has great influence on transcriptional regulation. This, in turn, implies that one should be cautious when comparing mutants with different growth rates. Our findings also indicate that much of the regulation is coordinated via the chromosomal location of the affected genes, which may be valuable information for the control of heterologous gene expression in metabolic engineering.
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Affiliation(s)
- Birgitte Regenberg
- Institut für Molekulare Biowissenschaften, Johann Wolfgang Goethe-Universität, Max-von-Laue-Str. 9, 60438 Frankfurt am Main, Germany
| | - Thomas Grotkjær
- Center for Microbial Biotechnology, BioCentrum-DTU, Building 223, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Ole Winther
- Informatics and Mathematical Modelling, Building 321, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Anders Fausbøll
- Center for Biological Sequence Analysis, BioCentrum-DTU, Building 208, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Mats Åkesson
- Center for Microbial Biotechnology, BioCentrum-DTU, Building 223, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Christoffer Bro
- Center for Microbial Biotechnology, BioCentrum-DTU, Building 223, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Lars Kai Hansen
- Informatics and Mathematical Modelling, Building 321, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Søren Brunak
- Center for Biological Sequence Analysis, BioCentrum-DTU, Building 208, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Jens Nielsen
- Center for Microbial Biotechnology, BioCentrum-DTU, Building 223, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
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Affiliation(s)
- Finn Arup Nielsen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet.
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