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Frässle S, Aponte EA, Bollmann S, Brodersen KH, Do CT, Harrison OK, Harrison SJ, Heinzle J, Iglesias S, Kasper L, Lomakina EI, Mathys C, Müller-Schrader M, Pereira I, Petzschner FH, Raman S, Schöbi D, Toussaint B, Weber LA, Yao Y, Stephan KE. TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry. Front Psychiatry 2021; 12:680811. [PMID: 34149484 PMCID: PMC8206497 DOI: 10.3389/fpsyt.2021.680811] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/10/2021] [Indexed: 12/26/2022] Open
Abstract
Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.
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Affiliation(s)
- Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Eduardo A. Aponte
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Saskia Bollmann
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Charlestown, MA, United States
| | - Kay H. Brodersen
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Cao T. Do
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Olivia K. Harrison
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- School of Pharmacy, University of Otago, Dunedin, New Zealand
| | - Samuel J. Harrison
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Jakob Heinzle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Sandra Iglesias
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Lars Kasper
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Techna Institute, University Health Network, Toronto, ON, Canada
| | - Ekaterina I. Lomakina
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Christoph Mathys
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Interacting Minds Center, Aarhus University, Aarhus, Denmark
| | - Matthias Müller-Schrader
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Inês Pereira
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Frederike H. Petzschner
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Sudhir Raman
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Dario Schöbi
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Birte Toussaint
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Lilian A. Weber
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Yu Yao
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Klaas E. Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
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Frässle S, Lomakina EI, Kasper L, Manjaly ZM, Leff A, Pruessmann KP, Buhmann JM, Stephan KE. A generative model of whole-brain effective connectivity. Neuroimage 2018; 179:505-529. [DOI: 10.1016/j.neuroimage.2018.05.058] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Revised: 05/16/2018] [Accepted: 05/24/2018] [Indexed: 12/17/2022] Open
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Frässle S, Lomakina EI, Razi A, Friston KJ, Buhmann JM, Stephan KE. Regression DCM for fMRI. Neuroimage 2017; 155:406-421. [PMID: 28259780 DOI: 10.1016/j.neuroimage.2017.02.090] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [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: 11/06/2016] [Revised: 01/25/2017] [Accepted: 02/28/2017] [Indexed: 12/13/2022] Open
Abstract
The development of large-scale network models that infer the effective (directed) connectivity among neuronal populations from neuroimaging data represents a key challenge for computational neuroscience. Dynamic causal models (DCMs) of neuroimaging and electrophysiological data are frequently used for inferring effective connectivity but are presently restricted to small graphs (typically up to 10 regions) in order to keep model inversion computationally feasible. Here, we present a novel variant of DCM for functional magnetic resonance imaging (fMRI) data that is suited to assess effective connectivity in large (whole-brain) networks. The approach rests on translating a linear DCM into the frequency domain and reformulating it as a special case of Bayesian linear regression. This paper derives regression DCM (rDCM) in detail and presents a variational Bayesian inversion method that enables extremely fast inference and accelerates model inversion by several orders of magnitude compared to classical DCM. Using both simulated and empirical data, we demonstrate the face validity of rDCM under different settings of signal-to-noise ratio (SNR) and repetition time (TR) of fMRI data. In particular, we assess the potential utility of rDCM as a tool for whole-brain connectomics by challenging it to infer effective connection strengths in a simulated whole-brain network comprising 66 regions and 300 free parameters. Our results indicate that rDCM represents a computationally highly efficient approach with promising potential for inferring whole-brain connectivity from individual fMRI data.
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Affiliation(s)
- Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland.
| | - Ekaterina I Lomakina
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland; Department of Computer Science, ETH Zurich, 8032 Zurich, Switzerland
| | - Adeel Razi
- Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom; Department of Electronic Engineering, NED University of Engineering & Technology, Karachi, Pakistan
| | - Karl J Friston
- Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom
| | - Joachim M Buhmann
- Department of Computer Science, ETH Zurich, 8032 Zurich, Switzerland
| | - Klaas E Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom
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Lomakina EI, Paliwal S, Diaconescu AO, Brodersen KH, Aponte EA, Buhmann JM, Stephan KE. Inversion of hierarchical Bayesian models using Gaussian processes. Neuroimage 2015; 118:133-45. [DOI: 10.1016/j.neuroimage.2015.05.084] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Revised: 05/08/2015] [Accepted: 05/29/2015] [Indexed: 10/23/2022] Open
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Mathys CD, Lomakina EI, Daunizeau J, Iglesias S, Brodersen KH, Friston KJ, Stephan KE. Uncertainty in perception and the Hierarchical Gaussian Filter. Front Hum Neurosci 2014; 8:825. [PMID: 25477800 PMCID: PMC4237059 DOI: 10.3389/fnhum.2014.00825] [Citation(s) in RCA: 198] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2014] [Accepted: 09/27/2014] [Indexed: 12/02/2022] Open
Abstract
In its full sense, perception rests on an agent's model of how its sensory input comes about and the inferences it draws based on this model. These inferences are necessarily uncertain. Here, we illustrate how the Hierarchical Gaussian Filter (HGF) offers a principled and generic way to deal with the several forms that uncertainty in perception takes. The HGF is a recent derivation of one-step update equations from Bayesian principles that rests on a hierarchical generative model of the environment and its (in)stability. It is computationally highly efficient, allows for online estimates of hidden states, and has found numerous applications to experimental data from human subjects. In this paper, we generalize previous descriptions of the HGF and its account of perceptual uncertainty. First, we explicitly formulate the extension of the HGF's hierarchy to any number of levels; second, we discuss how various forms of uncertainty are accommodated by the minimization of variational free energy as encoded in the update equations; third, we combine the HGF with decision models and demonstrate the inversion of this combination; finally, we report a simulation study that compared four optimization methods for inverting the HGF/decision model combination at different noise levels. These four methods (Nelder–Mead simplex algorithm, Gaussian process-based global optimization, variational Bayes and Markov chain Monte Carlo sampling) all performed well even under considerable noise, with variational Bayes offering the best combination of efficiency and informativeness of inference. Our results demonstrate that the HGF provides a principled, flexible, and efficient—but at the same time intuitive—framework for the resolution of perceptual uncertainty in behaving agents.
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Affiliation(s)
- Christoph D Mathys
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London London, UK ; Max Planck UCL Centre for Computational Psychiatry and Ageing Research London, UK ; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland ; Laboratory for Social and Neural Systems Research (SNS Lab), Department of Economics, University of Zurich Zurich, Switzerland
| | - Ekaterina I Lomakina
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland ; Laboratory for Social and Neural Systems Research (SNS Lab), Department of Economics, University of Zurich Zurich, Switzerland ; Department of Computer Science, ETH Zurich Zurich, Switzerland
| | - Jean Daunizeau
- Institut du Cerveau et de la Moelle Épinière, Hôpital Pitié Salpêtrière Paris, France
| | - Sandra Iglesias
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland ; Laboratory for Social and Neural Systems Research (SNS Lab), Department of Economics, University of Zurich Zurich, Switzerland
| | - Kay H Brodersen
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland ; Laboratory for Social and Neural Systems Research (SNS Lab), Department of Economics, University of Zurich Zurich, Switzerland
| | - Karl J Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London London, UK
| | - Klaas E Stephan
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London London, UK ; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland ; Laboratory for Social and Neural Systems Research (SNS Lab), Department of Economics, University of Zurich Zurich, Switzerland
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Diaconescu AO, Mathys C, Weber LAE, Daunizeau J, Kasper L, Lomakina EI, Fehr E, Stephan KE. Inferring on the intentions of others by hierarchical Bayesian learning. PLoS Comput Biol 2014; 10:e1003810. [PMID: 25187943 PMCID: PMC4154656 DOI: 10.1371/journal.pcbi.1003810] [Citation(s) in RCA: 102] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2014] [Accepted: 07/14/2014] [Indexed: 11/18/2022] Open
Abstract
Inferring on others' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to “player” or “adviser” roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser's incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser's intentions were presently stable. Finally, our model of the players' inference predicted the players' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers' helpfulness and the advisers' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition. The ability to decode another person's intentions is a critical component of social interactions. This is particularly important when we have to make decisions based on someone else's advice. Our research proposes that this complex cognitive skill (social learning) can be translated into a mathematical model, which prescribes a mechanism for mentally simulating another person's intentions. This study demonstrates that this process can be parsimoniously described as the deployment of hierarchical learning. In other words, participants learn about two quantities: the intentions of the person they interact with and the veracity of the recommendations they offer. As participants become more and more confident about their representation of the other's intentions, they make decisions more in accordance with the advice they receive. Importantly, our modeling framework captures individual differences in the social learning process: The estimated “learning fingerprint” can predict other aspects of participants' behavior, such as their perspective-taking abilities and their explicit ratings of the adviser's level of trustworthiness. The present modeling approach can be further applied in the context of psychiatry to identify maladaptive learning processes in disorders where social learning processes are particularly impaired, such as schizophrenia.
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Affiliation(s)
- Andreea O. Diaconescu
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland
- * E-mail:
| | - Christoph Mathys
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
| | - Lilian A. E. Weber
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland
| | - Jean Daunizeau
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
- Institut du Cerveau et de la Moelle épinière (ICM), Hôpital Pitié Salpêtrière, Paris, France
| | - Lars Kasper
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland
| | - Ekaterina I. Lomakina
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Ernst Fehr
- Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland
| | - Klaas E. Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
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Brodersen KH, Wiech K, Lomakina EI, Lin CS, Buhmann JM, Bingel U, Ploner M, Stephan KE, Tracey I. Decoding the perception of pain from fMRI using multivariate pattern analysis. Neuroimage 2012; 63:1162-70. [PMID: 22922369 PMCID: PMC3532598 DOI: 10.1016/j.neuroimage.2012.08.035] [Citation(s) in RCA: 115] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2012] [Revised: 08/11/2012] [Accepted: 08/13/2012] [Indexed: 11/27/2022] Open
Abstract
Pain is known to comprise sensory, cognitive, and affective aspects. Despite numerous previous fMRI studies, however, it remains open which spatial distribution of activity is sufficient to encode whether a stimulus is perceived as painful or not. In this study, we analyzed fMRI data from a perceptual decision-making task in which participants were exposed to near-threshold laser pulses. Using multivariate analyses on different spatial scales, we investigated the predictive capacity of fMRI data for decoding whether a stimulus had been perceived as painful. Our analysis yielded a rank order of brain regions: during pain anticipation, activity in the periaqueductal gray (PAG) and orbitofrontal cortex (OFC) afforded the most accurate trial-by-trial discrimination between painful and non-painful experiences; whereas during the actual stimulation, primary and secondary somatosensory cortex, anterior insula, dorsolateral and ventrolateral prefrontal cortex, and OFC were most discriminative. The most accurate prediction of pain perception from the stimulation period, however, was enabled by the combined activity in pain regions commonly referred to as the ‘pain matrix’. Our results demonstrate that the neural representation of (near-threshold) pain is spatially distributed and can be best described at an intermediate spatial scale. In addition to its utility in establishing structure-function mappings, our approach affords trial-by-trial predictions and thus represents a step towards the goal of establishing an objective neuronal marker of pain perception.
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Affiliation(s)
- Kay H Brodersen
- Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Nuffield Division Anaesthetics, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.
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Brodersen KH, Schofield TM, Leff AP, Ong CS, Lomakina EI, Buhmann JM, Stephan KE. Generative embedding for model-based classification of fMRI data. PLoS Comput Biol 2011; 7:e1002079. [PMID: 21731479 PMCID: PMC3121683 DOI: 10.1371/journal.pcbi.1002079] [Citation(s) in RCA: 129] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2011] [Accepted: 04/20/2011] [Indexed: 01/22/2023] Open
Abstract
Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups. Neurological and psychiatric spectrum disorders are typically defined in terms of particular symptom sets, despite increasing evidence that the same symptom may be caused by very different pathologies. Pathophysiological classification and effective treatment of such disorders will increasingly require a mechanistic understanding of inter-individual differences and clinical tools for making accurate diagnostic inference in individual patients. Previous classification studies have shown that functional magnetic resonance imaging (fMRI) can be used to differentiate between healthy controls and neurological or psychiatric patients. However, these studies are typically based on descriptive patterns and indirect measures of neural activity, and they rarely afford mechanistic insights into the underlying condition. In this paper, we address this challenge by proposing a classification approach that rests on a model of brain function and exploits the rich discriminative information encoded in directed interregional connection strengths. Based on an fMRI dataset acquired from moderately aphasic patients and healthy controls, we illustrate that our approach enables more accurate classification and deeper mechanistic insights about disease processes than conventional classification methods.
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Affiliation(s)
- Kay H Brodersen
- Department of Computer Science, ETH Zurich, Zurich, Switzerland.
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Balabin RM, Safieva RZ, Lomakina EI. Near-infrared (NIR) spectroscopy for motor oil classification: From discriminant analysis to support vector machines. Microchem J 2011. [DOI: 10.1016/j.microc.2010.12.007] [Citation(s) in RCA: 94] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Balabin RM, Lomakina EI. Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data. Analyst 2011; 136:1703-12. [PMID: 21350755 DOI: 10.1039/c0an00387e] [Citation(s) in RCA: 127] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.
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Affiliation(s)
- Roman M Balabin
- Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland.
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Balabin RM, Lomakina EI. Support vector machine regression (LS-SVM)—an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data? Phys Chem Chem Phys 2011; 13:11710-8. [DOI: 10.1039/c1cp00051a] [Citation(s) in RCA: 139] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Balabin RM, Safieva RZ, Lomakina EI. Gasoline classification using near infrared (NIR) spectroscopy data: comparison of multivariate techniques. Anal Chim Acta 2010; 671:27-35. [PMID: 20541639 DOI: 10.1016/j.aca.2010.05.013] [Citation(s) in RCA: 104] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2010] [Revised: 05/06/2010] [Accepted: 05/07/2010] [Indexed: 11/19/2022]
Abstract
Near infrared (NIR) spectroscopy is a non-destructive (vibrational spectroscopy based) measurement technique for many multicomponent chemical systems, including products of petroleum (crude oil) refining and petrochemicals, food products (tea, fruits, e.g., apples, milk, wine, spirits, meat, bread, cheese, etc.), pharmaceuticals (drugs, tablets, bioreactor monitoring, etc.), and combustion products. In this paper we have compared the abilities of nine different multivariate classification methods: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), soft independent modeling of class analogy (SIMCA), partial least squares (PLS) classification, K-nearest neighbor (KNN), support vector machines (SVM), probabilistic neural network (PNN), and multilayer perceptron (ANN-MLP) - for gasoline classification. Three sets of near infrared (NIR) spectra (450, 415, and 345 spectra) were used for classification of gasolines into 3, 6, and 3 classes, respectively, according to their source (refinery or process) and type. The 14,000-8000 cm(-1) NIR spectral region was chosen. In all cases NIR spectroscopy was found to be effective for gasoline classification purposes, when compared with nuclear magnetic resonance (NMR) spectroscopy or gas chromatography (GC). KNN, SVM, and PNN techniques for classification were found to be among the most effective ones. Artificial neural network (ANN-MLP) approach based on principal component analysis (PCA), which was believed to be efficient, has shown much worse results. We hope that the results obtained in this study will help both further chemometric (multivariate data analysis) investigations and investigations in the sphere of applied vibrational (infrared/IR, near-IR, and Raman) spectroscopy of sophisticated multicomponent systems.
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Affiliation(s)
- Roman M Balabin
- Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland.
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Balabin RM, Lomakina EI. Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies. J Chem Phys 2009; 131:074104. [DOI: 10.1063/1.3206326] [Citation(s) in RCA: 115] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
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Balabin RM, Safieva RZ, Lomakina EI. Universal technique for optimization of neural network training parameters: gasoline near infrared data example. Neural Comput Appl 2008. [DOI: 10.1007/s00521-008-0213-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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