Wavelet correlation between subjects: A time-scale data driven analysis for brain mapping using fMRI

Lessa, Patrícia Silva; Sato, João Ricardo; Cardoso, Ellison Fernando; Neto, C. Griese; Valadares, Ana Paula; Amaro, Edson

doi:10.1016/j.jneumeth.2010.09.005

Cited by 10 publications

(7 citation statements)

References 29 publications

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“…High-resolution scanning would allow identifying correlated activity of a smaller number of neurons across subjects, but it may reduce ISC as it also increases the effects of anatomical variability. It is thus important to investigate in designs that have greater statistical power but which are still applicable to exploratory approaches such as wavelet correlation (Lessa et al, 2011 ).…”

Section: Discussionmentioning

confidence: 99%

“…with no predefined fixation points) of complex scenes of moving stimuli with a longer duration that is closer to real life than precisely parameterised stimuli. Intersubject correlation (ISC) is one method alongside other recent developments in neuroscience such as independent component analysis (Bartels & Zeki, 2004 ; Wolf, Dziobek, & Heekeren, 2010 ), Wavelet correlation (Lessa et al, 2011 ), or event boundary analysis (Zacks et al, 2001 ; Zacks, Speer, Swallow, & Maley, 2010 ), which enable analyses of fMRI data recorded while presenting stimuli that fulfil these criteria (Hasson, Nir, Levy, Fuhrmann, & Malach, 2004 ).…”

Section: Introductionmentioning

confidence: 99%

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Uni- and Multisensory Brain Areas are Synchronised across Spectators When Watching Unedited Dance Recordings

et al. 2013

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The superior temporal sulcus (STS) and gyrus (STG) are commonly identified to be functionally relevant for multisensory integration of audiovisual (AV) stimuli. However, most neuroimaging studies on AV integration used stimuli of short duration in explicit evaluative tasks. Importantly though, many of our AV experiences are of a long duration and ambiguous. It is unclear if the enhanced activity in audio, visual, and AV brain areas would also be synchronised over time across subjects when they are exposed to such multisensory stimuli. We used intersubject correlation to investigate which brain areas are synchronised across novices for uni- and multisensory versions of a 6-min 26-s recording of an unfamiliar, unedited Indian dance recording (Bharatanatyam). In Bharatanatyam, music and dance are choreographed together in a highly intermodal-dependent manner. Activity in the middle and posterior STG was significantly correlated between subjects and showed also significant enhancement for AV integration when the functional magnetic resonance signals were contrasted against each other using a general linear model conjunction analysis. These results extend previous studies by showing an intermediate step of synchronisation for novices: while there was a consensus across subjects' brain activity in areas relevant for unisensory processing and AV integration of related audio and visual stimuli, we found no evidence for synchronisation of higher level cognitive processes, suggesting these were idiosyncratic.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uni- and Multisensory Brain Areas are Synchronised across Spectators When Watching Unedited Dance Recordings

et al. 2013

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“…Therefore, these areas can be identified by testing for correlated BOLD time-courses between subjects (“synchronization”). This approach has now been employed with a variety of stimuli and tasks (e.g., palm trees task, Seghier and Price, 2009; face processing, Lessa et al, 2011; story comprehension, Lerner et al, 2011; movie watching, Kauppi et al, 2010; representation of action-schemas, Hanson et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Sensory processing during viewing of cinematographic material: Computational modeling and functional neuroimaging

2013

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The investigation of brain activity using naturalistic, ecologically-valid stimuli is becoming an important challenge for neuroscience research. Several approaches have been proposed, primarily relying on data-driven methods (e.g. independent component analysis, ICA). However, data-driven methods often require some post-hoc interpretation of the imaging results to draw inferences about the underlying sensory, motor or cognitive functions. Here, we propose using a biologically-plausible computational model to extract (multi-)sensory stimulus statistics that can be used for standard hypothesis-driven analyses (general linear model, GLM). We ran two separate fMRI experiments, which both involved subjects watching an episode of a TV-series. In Exp 1, we manipulated the presentation by switching on-and-off color, motion and/or sound at variable intervals, whereas in Exp 2, the video was played in the original version, with all the consequent continuous changes of the different sensory features intact. Both for vision and audition, we extracted stimulus statistics corresponding to spatial and temporal discontinuities of low-level features, as well as a combined measure related to the overall stimulus saliency. Results showed that activity in occipital visual cortex and the superior temporal auditory cortex co-varied with changes of low-level features. Visual saliency was found to further boost activity in extra-striate visual cortex plus posterior parietal cortex, while auditory saliency was found to enhance activity in the superior temporal cortex. Data-driven ICA analyses of the same datasets also identified "sensory" networks comprising visual and auditory areas, but without providing specific information about the possible underlying processes, e.g., these processes could relate to modality, stimulus features and/or saliency. We conclude that the combination of computational modeling and GLM enables the tracking of the impact of bottom-up signals on brain activity during viewing of complex and dynamic multisensory stimuli, beyond the capability of purely data-driven approaches.

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“…A few papers recently published have reported on the effectiveness of WT applied to the EEG signal for representing various aspects of non-stationary signals such as trends, discontinuities, and repeated patterns where other signal processing approaches fail or are not as effective (Adeli et al, 2003;Asaduzzaman et al, 2010;Guo et al, 2009;Lessa, 2011), but there are still some problems with classical EEG analysis and classification (Arab et al, 2010;Bauer et al, 2008;Oehler et al, 2009). It is important to emphasize the algorithm for classification of EEG signals based on WT and Patterns Recognize Techniques.…”

Section: Introductionmentioning

confidence: 99%

Energy Distribution of EEG Signal Components by Wavelet Transform

Omerhodžić¹,

Avdaković²,

Nuhanovic³

et al. 2012

Wavelet Transforms and Their Recent Applications in Biology and Geoscience

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Wavelet correlation between subjects: A time-scale data driven analysis for brain mapping using fMRI

Cited by 10 publications

References 29 publications

Uni- and Multisensory Brain Areas are Synchronised across Spectators When Watching Unedited Dance Recordings

Uni- and Multisensory Brain Areas are Synchronised across Spectators When Watching Unedited Dance Recordings

Sensory processing during viewing of cinematographic material: Computational modeling and functional neuroimaging

Energy Distribution of EEG Signal Components by Wavelet Transform

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