Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analysing fMRI data. Although decoding methods have been extensively applied in Brain Computing Interfaces (BCI), these methods have only recently been applied to time-series neuroimaging data such as MEG and EEG to address experimental questions in Cognitive Neuroscience. In a tutorial-style review, we describe a broad set of options to inform future time-series decoding studies from a Cognitive Neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to 'decode' different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalisation, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time-series decoding experiments.Comment: 64 pages, 15 figure

Studying Category-Based Visual Attention and Mental Imagery in the Human Brain Using Local Field Potentials

Cerebral processing of visual stimuli is characterized by a complex circuitry involved in processing visual inputs while simultaneously contextualizing, categorizing or modulating these inputs by higher-order cortical centers. The sensory input and the cognitive control over this input can potentially be differentiated spatially, i.e. different brain regions, or spectrally, i.e. different frequencies of neuronal activity. In this work, we used object-category based visual tasks to investigate the spectral and spatial patterns of encoding of visual inputs and cognitive control at the level of visual attention and mental imagery using local field potentials in human subjects across widely distributed recording sites and a broad frequency spectrum(1-100Hz). Using PCA, we demonstrate that during a task involving both visual attention to an object category with varied observed category a broadband, and two narrowband low-frequency explained the main variance in the data. When comparing response to attended versus seen categories, we did not observe a spatial difference in location of encoding sites, but using decoding models, the broadband signal decodes vision better than attention in visual cortex and vision and attention equally in the temporal lobe. However, narrowband delta-theta decodes the best for both vision and attention, and alpha-beta differentially decodes for attention better than vision. Using an alternate task that involves image memorization, imagery, and passive viewing, we demonstrate that the main power spectral modulation among those mental states are represented by a broadband, gamma band, and low-frequency band. Encoding sites were similarly spatially widely distributed. Decoding models showed that gamma band predicts attentive viewing and mental imagery with best accuracy. Both broadband and low frequency band accurately decode for passive and attentive viewing. Our findings demonstrate that encoding of vision, attention and mental imagery is not dependent on a single spectral domain and optimal decoding of visual processes should consider the co-contribution of narrowband and broadband spectral patterns to account for the different co-occurring top-down and bottom-up processes. Therefore, although gamma-significantly studied in vision-is involved in visual working-memory tasks, both broadband and low-frequency narrowband patterns of neuronal activity co-participate in visual processing in the context of object-based visual attention and mental imagery

Decoding the memorization of individual stimuli with direct human brain recordings

Contains fulltext : 115385.pdf (Publisher’s version ) (Closed access)Through decades of research, neuroscientists and clinicians have identified an array of brain areas that each activate when a person views a certain category of stimuli. However, we do not have a detailed understanding of how the brain represents individual stimuli within a category. Here we used direct human brain recordings and machine-learning algorithms to characterize the distributed patterns that distinguish specific cognitive states. Epilepsy patients with surgically implanted electrodes performed a working-memory task and we used machine-learning algorithms to predict the identity of each viewed stimulus. We found that the brain's representation of stimulus-specific information is distributed across neural activity at multiple frequencies, electrodes, and timepoints. Stimulus-specific neuronal activity was most prominent in the high-gamma (65-128 Hz) and theta/alpha (4-16 Hz) bands, but the properties of these signals differed significantly between individuals and for novel stimuli compared to common ones. Our findings are helpful for understanding the neural basis of memory and developing brain-computer interfaces by showing that the brain distinguishes specific cognitive states by diverse spatiotemporal patterns of neuronal.10 p

Analisi multivariata di dati EEG per lo studio dell'inibizione di ritorno

La presente tesi si propone di esaminare l’applicazione della Multivariate pattern analysis (MVPA) su dati EEG per lo studio dell’inibizione di ritorno. L’inibizione di ritorno è un fenomeno prodotto dall’orientamento automatico dell’attenzione che si manifesta solo quando l’intervallo fra il segnale esogeno e lo stimolo target è maggiore di 200 millisecondi. In questo caso, i tempi di reazione sono rallentati quando lo stimolo-target compare nella stessa posizione del segnale esogeno. Questo processo è importante per la regolazione del comportamento e può essere influenzato da diversi fattori, come l’età e le condizioni neurologiche. Nonostante i potenziali evento-relati (ERP) siano ampiamente utilizzati negli studi sull’orientamento e controllo dell’attenzione visuo-spaziale le tecniche di analisi di questi dati risultano ancora non del tutto adeguate. L’applicazione dell’MVPA permette di ovviare ad alcuni dei limiti dell’analisi classica univariata, consentendo, tramite l’utilizzo di classificatori, di analizzare i dati con una maggiore sensibilità e accuratezza, tenendo conto dei cambiamenti che avvengono contemporaneamente fra le diverse variabili. In questo studio il disegno sperimentale prevedeva due condizioni, in una in cui lo stimolo-target appariva nella stessa posizione del segnale esogeno, nell’atra, invece, lo stimolo target appariva in una posizione differente. L’applicazione dell’MVPA consiste nell’addestrare classificatori ad ogni intervallo temporale in grado di classificare sulla base dei dati EEG a disposizione se il soggetto si trova in una o nell’atra condizione. L’accuratezza calcolata di ogni modello funge, poi, da indicatore al fine di comprendere se in quel determinato istante c’è una differenza nei correlati neuronali in grado di guidare il classificatore nel discernere le due condizioni. Questo disegno viene poi ripetuto per tre sotto condizioni che dipendono dal lasso di tempo che intercorre fra la presentazione del segnale esogeno e la presentazione del target (250 ms, 400 ms, 500 ms). I risultati hanno mostrato che l'accuratezza di classificazione è significativamente superiore al caso in diversi intervalli temporali, permettendo di identificare la dinamica temporale con cui l'attività elettrica cerebrale discrimina le due condizioni.This thesis aims to examine the application of Multivariate pattern analysis (MVPA) on EEG data to study return inhibition. Return inhibition is a phenomenon produced by the automatic orientation of attention that occurs only when the interval between the exogenous signal and the target stimulus is greater than 200 milliseconds. In this case, reaction times are slowed down when the stimulus-target appears at the same location as the exogenous signal. This process is important for the regulation of behavior and can be influenced by several factors, such as age and neurological conditions. Although event-related potentials (ERPs) are widely used in studies of orientation and control of visuospatial attention the techniques for analyzing these data are still not fully adequate. The application of MVPA makes it possible to overcome some of the limitations of classical univariate analysis by allowing, through the use of classifiers, the data to be analyzed with greater sensitivity and accuracy, taking into account changes occurring simultaneously among different variables. In this study, the experimental design involved two conditions, in one in which the stimulus-target appeared in the same location as the exogenous signal, and in the other, the target stimulus appeared in a different location. The application of MVPA is to train classifiers at each time interval that can classify on the basis of the available EEG data whether the subject is in one or the other condition. The calculated accuracy of each pattern then serves as an indicator in order to understand whether at that particular instant there is a difference in the neuronal correlates that can guide the classifier in discerning the two conditions. This design is then repeated for three subconditions depending on the time lapse between exogenous signal presentation and target presentation (250 ms, 400 ms, 500 ms). The results showed that classification accuracy is significantly higher than chance in different time intervals, allowing identification of the temporal dynamics by which brain electrical activity discriminates between the two conditions