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Model-based feature construction for multivariate decoding

NEUROIMAGE 2011年第2期56卷 601-615页

作者： Brodersen, Kay H. Haiss, Florent Ong, Cheng Soon Jung, Fabienne Tittgemeyer, Marc Buhmann, Joachim M. Weber, Bruno Stephan, Klaas E. Univ Zurich Inst Empir Res Econ Lab Social & Neural Syst Res CH-8006 Zurich Switzerland Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Univ Zurich Inst Pharmacol & Toxicol CH-8006 Zurich Switzerland Max Planck Inst Neurol Res Cologne Germany UCL Wellcome Trust Ctr Neuroimaging London WC1E 6BT England

Conventional decoding methods in neuroscience aim to predict discrete brain states from multivariate correlates of neural activity. This approach faces two important challenges. First, a small number of examples are typically represented by a much larger number of features, making it hard to select the few informative features that allow for accurate predictions. Second, accuracy estimates and information maps often remain descriptive and can be hard to interpret. In this paper, we propose a model-based decoding approach that addresses both challenges from a new angle. Our method involves (i) inverting a dynamic causal model of neurophysiological data in a trial-by-trial fashion;(ii) training and testing a discriminative classifier on a strongly reduced feature space derived from trial-wise estimates of the model parameters;and (iii) reconstructing the separating hyperplane. Since the approach is model-based, it provides a principled dimensionality reduction of the feature space;in addition, if the model is neurobiologically plausible, decoding results may offer a mechanistically meaningful interpretation. The proposed method can be used in conjunction with a variety of modelling approaches and brain data, and supports decoding of either trial or subject labels. Moreover, it can supplement evidence-based approaches for model-based decoding and enable structural model selection in cases where Bayesian model selection cannot be applied. Here, we illustrate its application using dynamic causal modelling (DCM) of electrophysiological recordings in rodents. We demonstrate that the approach achieves significant above-chance performance and, at the same time, allows for a neurobiological interpretation of the results. (C) 2010 Elsevier Inc. All rights reserved.

关键词： multivariate decoding Classification Feature selection Dynamic causal modelling DCM Bayesian model selection Structural model selection Feature extraction

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Characterizing multivariate decoding models based on correlated EEG spectral features

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CLINICAL NEUROPHYSIOLOGY 2013年第7期124卷 1297-1302页

作者： McFarland, Dennis J. New York State Dept Hlth Wadsworth Ctr Lab Neural Injury & Repair Albany NY 12201 USA

Objective: multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods: Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results: The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions: Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance: While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. (C) 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

关键词： Sensorimotor rhythm multivariate decoding Multicollinearity Brain-computer interface

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Deconstructing multivariate decoding for the study of brain function

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NEUROIMAGE 2018年第PartA期180卷 4-18页

作者： Hebart, Martin N. Baker, Chris I. NIMH Sect Learning & Plast Lab Brain & Cognit NIH Bethesda MD 20892 USA

multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function.

关键词： multivariate decoding multivariate analysis multivariate pattern analysis Encoding decoding fMRI Prediction

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Frontal alpha oscillations distinguish leaders from followers: multivariate decoding of mutually interacting brains

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NEUROIMAGE 2014年 94卷 79-88页

作者： Konvalinka, Ivana Bauer, Markus Stahlhut, Carsten Hansen, Lars Kai Roepstorff, Andreas Frith, Chris D. Aarhus Univ Ctr Funct Integrat Neurosci DK-8000 Aarhus Denmark Tech Univ Denmark Sect Cognit Syst Dept Appl Math & Comp Sci DK-2800 Lyngby Denmark UCL Wellcome Trust Ctr Neuroimaging London WC1N 3BG England Univ Nottingham Sch Psychol Nottingham NG7 2RD England Aarhus Univ Interacting Minds Ctr DK-8000 Aarhus Denmark

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. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Social interaction Dual EEG Interpersonal coordination multivariate decoding Leader-follower dynamics

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Context influence on speech perception: evidence for acoustic-level mechanism across the voice onset time continuum

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NeuroImage 2025年 310卷 121140-121140页

作者： Liu, Shun Liu, Xiqin Department of Education Hunan First Normal University Changsha China School of Foreign Languages South China University of Technology Guangzhou China Department of Psychology University of Jyvaskyla Jyväskylä Finland

Contextual information plays a significant role in shaping our perception of speech, yet it remains uncertain at which level of processing such information integrates with acoustic cues. A key area of debate is whether top-down information influences acoustic encoding within the lower levels of the speech-processing hierarchy. This study employed a machine learning algorithm to decode the voice onset time (VOT) of speech and investigated how the gender of the speaker of a precursor sentence impacted subsequent speech perception. Using EEG recordings, we examined neural responses to a VOT continuum following male and female voices. Our results reveal that a linear representation of the VOT continuum emerged at an early EEG time window and that gender-based contextual cues modulated speech perception at this stage. Notably, since context information was not involved in the decoding procedure itself, we conclude that this modulation reflected the true effects of context on the perception of VOT. Moreover, the contextual influence extended across the entire VOT continuum, not just at specific sounds, suggesting a broad and consistent modulation of speech perception by gender-based context. These findings support the idea of a general acoustic-level mechanism through which contextual information influences the early stage of speech processing, contributing to ongoing debates about the interaction between top-down and bottom-up processes in speech perception. © 2025

关键词： Acoustic level EEG multivariate decoding Speech perception Top-down effect

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Effects of adaptation on numerosity decoding in the human brain

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NEUROIMAGE 2016年 143卷 364-377页

作者： Castaldi, E. Aagten-Murphy, D. Tosetti, M. Burr, D. Morrone, M. C. Univ Pisa Dept Translat Res New Technol Med & Surg I-56100 Pisa Italy Univ Florence Dept Neurosci Psychol Pharmacol & Child Hlth Florence Italy Stella Maris Sci Inst Pisa Italy IRCCS Stella Maris Lab Med Phys & Biotechnol Magnet Resonance Pisa Italy IMAGO7 Fdn Pisa Italy CNR Inst Neurosci Pisa Italy

Psychophysical studies have shown that numerosity is a sensory attribute susceptible to adaptation. Neuroimaging studies have reported that, at least for relatively low numbers, numerosity can be accurately discriminated in the intra-parietal sulcus. Here we developed a novel rapid adaptation paradigm where adapting and test stimuli are separated by pauses sufficient to dissociate their BOLD activity. We used multivariate pattern recognition to classify brain activity evoked by non-symbolic numbers over a wide range (20-80), both before and after psychophysical adaptation to the highest numerosity. Adaptation caused underestimation of all lower numerosities, and decreased slightly the average BOLD responses in V1 and IPS. Using support vector machine, we showed that the BOLD response of IPS, but not in V1, classified numerosity well, both when tested before and after adaptation. However, there was no transfer from training pre-adaptation responses to testing post-adaptation, and vice versa, indicating that adaptation changes the neuronal representation of the numerosity. Interestingly, decoding was more accurate after adaptation, and the amount of improvement correlated with the amount of perceptual underestimation of numerosity across subjects. These results suggest that numerosity adaptation acts directly on IPS, rather than indirectly via other low-level stimulus parameters analysis, and that adaptation improves the capacity to discriminate numerosity. (C) 2016 The Authors. Published by Elsevier Inc.

关键词： Number perception Adaptation fMRI multivariate decoding Parietal cortex

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Cortical sensitivity to natural scene structure

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HUMAN BRAIN MAPPING 2020年第5期41卷 1286-1295页

作者： Kaiser, Daniel Haeberle, Greta Cichy, Radoslaw M. Univ York Dept Psychol York YO10 5DD N Yorkshire England Free Univ Berlin Dept Educ & Psychol Berlin Germany Humboldt Univ Einstein Ctr Neurosci Berlin Berlin Germany Humboldt Univ Berlin Sch Mind & Brain Berlin Germany Humboldt Univ Bernstein Ctr Computat Neurosci Berlin Berlin Germany

Natural scenes are inherently structured, with meaningful objects appearing in predictable locations. Human vision is tuned to this structure: When scene structure is purposefully jumbled, perception is strongly impaired. Here, we tested how such perceptual effects are reflected in neural sensitivity to scene structure. During separate fMRI and EEG experiments, participants passively viewed scenes whose spatial structure (i.e., the position of scene parts) and categorical structure (i.e., the content of scene parts) could be intact or jumbled. Using multivariate decoding, we show that spatial (but not categorical) scene structure profoundly impacts on cortical processing: Scene-selective responses in occipital and parahippocampal cortices (fMRI) and after 255 ms (EEG) accurately differentiated between spatially intact and jumbled scenes. Importantly, this differentiation was more pronounced for upright than for inverted scenes, indicating genuine sensitivity to spatial structure rather than sensitivity to low-level attributes. Our findings suggest that visual scene analysis is tightly linked to the spatial structure of our natural environments. This link between cortical processing and scene structure may be crucial for rapidly parsing naturalistic visual inputs.

关键词： EEG fMRI multivariate decoding scene representation spatial structure visual perception

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The Neural Representation of Voluntary Task-Set Selection in Dynamic Environments

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CEREBRAL CORTEX 2015年第12期25卷 4715-4726页

作者： Wisniewski, David Reverberi, Carlo Tusche, Anita Haynes, John-Dylan Charite Bernstein Ctr Computat Neurosci Berlin D-10115 Berlin Germany Charite Berlin Ctr Adv Neuroimaging D-10115 Berlin Germany Charite Cluster Excellence NeuroCure D-10115 Berlin Germany Charite Dept Neurol D-10115 Berlin Germany Tech Univ Dresden SFB Volit & Cognit Control 940 D-01062 Dresden Germany Humboldt Univ Berlin Sch Mind & Brain D-10099 Berlin Germany Humboldt Univ Dept Psychol D-10099 Berlin Germany Univ Milano Bicocca Dept Psychol Milan Italy Milan Ctr Neurosci Milan Italy Max Planck Inst Human Cognit & Brain Sci Dept Social Neurosci Leipzig Germany CALTECH Div Humanities & Social Sci Pasadena CA 91125 USA

When choosing actions, humans have to balance carefully between different task demands. On the one hand, they should perform tasks repeatedly to avoid frequent and effortful switching between different tasks. On the other hand, subjects have to retain their flexibility to adapt to changes in external task demands such as switching away from an increasingly difficult task. Here, we developed a difficulty-based choice task to investigate how subjects voluntarily select task-sets in predictably changing environments. Subjects were free to choose 1 of the 3 task-sets on a trial-by-trial basis, while the task difficulty changed dynamically over time. Subjects self-sequenced their behavior in this environment while we measured brain responses with functional magnetic resonance imaging (fMRI). Using multivariate decoding, we found that task choices were encoded in the medial prefrontal cortex (dorso-medial prefrontal cortex, dmPFC, and dorsal anterior cingulate cortex, dACC). The same regions were found to encode task difficulty, a major factor influencing choices. Importantly, the present paradigm allowed us to disentangle the neural code for task choices and task difficulty, ensuring that activation patterns in dmPFC/dACC independently encode these 2 factors. This finding provides new evidence for the importance of the dmPFC/dACC for task-selection and motivational functions in highly dynamic environments.

关键词： fMRI multivariate decoding prefrontal cortex task difficulty task-set

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decoding the representation of learned social roles in the human brain

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CORTEX 2013年第9期49卷 2484-2493页

作者： Eger, Evelyn Moretti, Laura Dehaene, Stanislas Sirigu, Angela INSERM U992 Gif Yuette France CEA Gif Yuette France Univ Paris 11 Orsay France CNRS U5229 Bron France Coll France F-75231 Paris France

Humans as social beings are profoundly affected by exclusion. Short experiences with people differing in their degree of prosocial behaviour can induce reliable preferences for including partners, but the neural mechanisms of this learning remain unclear. Here, we asked participants to play a short social interaction game based on "cyber-ball" where one fictive partner included and another excluded the subject, thus defining social roles (includer - "good", excluder - "bad"). We then used multivariate pattern recognition on high-resolution functional magnetic resonance imaging (fMRI) data acquired before and after this game to test whether neural responses to the partners' and neutral control faces during a perceptual task reflect their learned social valence. Support vector classification scores revealed a learning-related increase in neural discrimination of social status in anterior insula and anterior cingulate regions, which was mainly driven by includer faces becoming distinguishable from excluder and control faces. Thus, face-evoked responses in anterior insula and anterior cing-ulate cortex contain fine-grained information shaped by prior social interactions that allow for categorisation of faces according to their learned social status. These lasting traces of social experience in cortical areas important for emotional and social processing could provide a substrate of how social inclusion shapes future behaviour and promotes cooperative interactions between individuals. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Functional magnetic resonance imaging multivariate decoding Face processing Social cognition Learning

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The neural code of reward anticipation in human orbitofrontal cortex

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2010年第13期107卷 6010-6015页

作者： Kahnt, Thorsten Heinzle, Jakob Park, Soyoung Q. Haynes, John-Dylan Charite Univ Med Berlin Bernstein Ctr Computat Neurosci D-10115 Berlin Germany Max Planck Inst Human Cognit & Brain Sci D-04303 Leipzig Germany Humboldt Univ Berlin Sch Mind & Brain D-10099 Berlin Germany Free Univ Berlin D-14195 Berlin Germany Otto Von Guericke Univ Dept Neurol D-39120 Magdeburg Germany

An optimal choice among alternative behavioral options requires precise anticipatory representations of their possible outcomes. A fundamental question is how such anticipated outcomes are represented in the brain. Reward coding at the level of single cells in the orbitofrontal cortex (OFC) follows a more heterogeneous coding scheme than suggested by studies using functional MRI (fMRI) in humans. Using a combination of multivariate pattern classification and fMRI we show that the reward value of sensory cues can be decoded from distributed fMRI patterns in the OFC. This distributed representation is compatible with previous reports from animal electrophysiology that show that reward is encoded by different neural populations with opposing coding schemes. Importantly, the fMRI patterns representing specific values during anticipation are similar to those that emerge during the receipt of reward. Furthermore, we show that the degree of this coding similarity is related to subjects' ability to use value information to guide behavior. These findings narrow the gap between reward coding in humans and animals and corroborate the notion that value representations in OFC are independent of whether reward is anticipated or actually received.

关键词： expected reward value functional MRI multivariate decoding distributed coding decision making

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