Abstract
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.
Highlights
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]
We report the findings in two patients, patient M.R. and patient C.S. who completed the experimental protocol without any major epileptic activity over the whole period of recording
Additional t-tests were performed whenever interactions were significant. Both patients showed a main effect of Day (patient M.R.: F(1,152) = 4.89, p,0.05; patient C.S.: F(1,152) = 15.72, p,0.001) due to the reaction time (RT) being faster on the second than the first day
Summary
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]. [14,15,16,17]) studies have emphasized the role of widespread activation patterns underlying many human cognitive functions, shifting the focus from the description of specialized brain regions to a network perspective. Because these methods typically exploit high-dimensional data, they often offer a valid alternative to the preselection of a subset of the available data prior to the analysis. In intracranial electroencephalography studies in humans, the use of a very small subset of the data, i.e. highly localized recording sites, is preferred because it gets around the problem of variations in the patterns of surgical implantation across patients (e.g. [18,19,20])
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