Brain decoding: Opportunities and challenges for pattern recognition

Ville, Dimitri Van De; Lee, Seong–Whan

doi:10.1016/j.patcog.2011.06.001

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…In this ideal-type, Big Data are understood in terms of a range of analytical techniques, such as pattern-recognition, data mining and machine learning (Amato et al., 2013). The editorials have a positive tone and describe ways in which these Big Data techniques can aid healthcare, for example by predicting disease outcomes and increasing the understanding of the causes of diseases (Belgrave et al., 2014; Van De Ville and Lee, 2012). The editorials typically discuss how analytic techniques should be used and how they can be improved.…”

Section: Resultsmentioning

confidence: 99%

“…Editorials in this discourse place a high value on experimentation. For example, innovative studies in which Big Data techniques are used for brain decoding and the development of clinical decision support systems are presented (Najarian et al., 2013; Van De Ville and Lee, 2012). Using Big Data techniques for these purposes is by no means standard practice, but by trying out and experimenting with data analytic processes, the techniques are improved.…”

Section: Resultsmentioning

confidence: 99%

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Conceptualizations of Big Data and their epistemological claims in healthcare: A discourse analysis

Wehrens

Bont

2018

Big Data & Society

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In recent years, the healthcare field welcomed an emerging field of practices captured under the umbrella term 'Big Data'. This term is surrounded with positive rhetoric and promises about the ability to analyse real-world data quickly and comprehensively. Such rhetoric is highly consequential in shaping debates on Big Data. While the fields of Science and Technology Studies and Critical Data Studies have been instrumental in elaborating the neglected and problematic dimensions of Big Data, it remains an open question how and to what extent such insights become embedded in other fields. In this paper, we analyse the epistemological claims that accompany Big Data in the healthcare domain. We systematically searched scientific literature and selected 206 editorials as these reflect on developments in the domain. Through an interpretive analysis, we construct five ideal-typical discourses that all frame Big Data in specific ways. Three of the discourses (the modernist, instrumentalist and pragmatist) frame Big Data in positive terms and disseminate a compelling rhetoric. Metaphors of 'capturing', 'illuminating' and 'harnessing' data presume that Big Data are benign and leading to valid knowledge. The scientist and critical-interpretive discourses question the objectivity and effectivity claims of Big Data. Metaphors of 'selecting' and 'constructing' data illustrate another political message, framing Big Data as limited. We conclude that work in the critical-interpretive discourse has not broadly infiltrated the medical domain. Ways to better integrate aspects of the discourse in the healthcare domain are urgently needed.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Conceptualizations of Big Data and their epistemological claims in healthcare: A discourse analysis

Wehrens

Bont

2018

Big Data & Society

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“…SVM or RVM) to neuroimaging data (Bray et al 2009; Cheng et al 2012; De Martino et al 2008; Duchesnay et al 2007; Duchesnay et al 2004; Duff et al 2011; Franke et al 2010; Liu et al 2012; Lohmann et al 2007; Mitchell et al 2004; Mwangi et al 2012a; Norman et al 2006; Pereira et al 2009; Rizk-Jackson et al 2011; Schrouff et al 2013; Valente et al 2011; Van De Ville and Lee, 2012). Other than mitigating the curse-of-dimensionality effect as above, the feature reduction process may also facilitate a deeper understanding of the scientific question of interest.…”

Section: 0 Introductionmentioning

confidence: 99%

A Review of Feature Reduction Techniques in Neuroimaging

2013

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Machine learning techniques are increasingly being used in making relevant predictions and inferences on individual subjects neuroimaging scan data. Previous studies have mostly focused on categorical discrimination of patients and matched healthy controls and more recently, on prediction of individual continuous variables such as clinical scores or age. However, these studies are greatly hampered by the large number of predictor variables (voxels) and low observations (subjects) also known as the curse-of-dimensionality or small-n-large-p problem. As a result, feature reduction techniques such as feature subset selection and dimensionality reduction are used to remove redundant predictor variables and experimental noise, a process which mitigates the curse-of-dimensionality and small-n-large-p effects. Feature reduction is an essential step before training a machine learning model to avoid overfitting and therefore improving model prediction accuracy and generalization ability. In this review, we discuss feature reduction techniques used with machine learning in neuroimaging studies.

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“…In functional neuroimaging studies, the application of machine learning techniques has recently become a popular method for decoding stimulus-related information at the level of the single response to external stimuli [1] , [2] . Most of the machine learning techniques applied to neuroimaging data are intrinsically multivariate and therefore particularly suitable when the main differences between experimental conditions are not in the strength of the activity at specific brain regions, but rather in the configuration or relative spatial locations of simultaneously activated areas (for reviews see [3] – [7] ).…”

Section: Introductionmentioning

confidence: 99%

Decoding Sequence Learning from Single-Trial Intracranial EEG in Humans

et al. 2011

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We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG) after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM) that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and one after a night of sleep) in two patients with depth electrodes implanted in several brain areas. They performed a visuomotor sequence (serial reaction time task, SRTT) using the fingers of their non-dominant hand. Our results show that the decoding algorithm correctly classified single iEEG trials from the trained sequence as belonging to either the initial training phase (day 1, before sleep) or a later consolidated phase (day 2, after sleep), whereas it failed to do so for trials belonging to a control condition (pseudo-random sequence). Accurate single-trial classification was achieved by taking advantage of the distributed pattern of neural activity. However, across all the contacts the hippocampus contributed most significantly to the classification accuracy for both patients, and one fronto-striatal contact for one patient. Together, these human intracranial findings demonstrate that a multivariate decoding approach can detect learning-related changes at the level of single-trial iEEG. Because it allows an unbiased identification of brain sites contributing to a behavioral effect (or experimental condition) at the level of single subject, this approach could be usefully applied to assess the neural correlates of other complex cognitive functions in patients implanted with multiple electrodes.

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Brain decoding: Opportunities and challenges for pattern recognition

Cited by 11 publications

References 12 publications

Conceptualizations of Big Data and their epistemological claims in healthcare: A discourse analysis

Conceptualizations of Big Data and their epistemological claims in healthcare: A discourse analysis

A Review of Feature Reduction Techniques in Neuroimaging

Decoding Sequence Learning from Single-Trial Intracranial EEG in Humans

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