Stimulus-Related Independent Component and Voxel-Wise Analysis of Human Brain Activity during Free Viewing of a Feature Film

Lahnakoski, Juha M.; Salmi, Juha; Jä̈askëlainen, Iiro P.; Lampinen, Jouko; Glerean, Enrico; Tikka, Pia; Sams, Mikko

doi:10.1371/journal.pone.0035215

Cited by 53 publications

(52 citation statements)

References 36 publications

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“…To our knowledge, this is the first demonstration of reinforcement enhancing effects of nicotine in humans via a source other than cigarette smoking. Nicotine’s effects on striatum may be relevant, as visual rewards may differentially activate striatum compared to others (Aharon et al, 2001; Lahnakoski et al, 2012), including monetary reward (Thut et al, 1997). Although speculative, enhancing reinforcement from video reward could partly help account for some of the growing appeal of nicotine e-cigarette use, especially if that use often occurs just before or during “leisure” activities, as is common with tobacco smoking (e.g., Hatsukami et al, 1990; Raiff et al, 2012; van Gucht et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

Reinforcement enhancing effects of acute nicotine via electronic cigarettes

Perkins

Karelitz

Michael

2015

Drug and Alcohol Dependence

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Background Recent human studies confirm animal research showing that nicotine enhances reinforcement from rewards unrelated to nicotine. These effects of acute nicotine via tobacco smoking may also occur when consumed from non-tobacco products. Methods We assessed acute effects of nicotine via electronic cigarettes (“e-cigarettes”) on responding reinforced by music, video, or monetary rewards, or for no reward (control). In a fully within-subjects design, adult dependent smokers (N=28) participated in three similar experimental sessions, each following overnight abstinence (verified by CO≤10 ppm). Varying only in e-cigarette condition, sessions involved controlled exposure to a nicotine (labeled “36 mg/ml”) or placebo (“0”) e-cigarette, or no e-cigarette use. A fourth session involved smoking one’s own tobacco cigarette brand after no abstinence, specifically to compare responses under typical nicotine satiation with these acute e-cigarette conditions after abstinence. Results Reinforced responding for video reward, but not the other rewards, was greater due to use of the nicotine versus placebo e-cigarette (i.e., nicotine per se), while no differences were found between the placebo e-cigarette and no e-cigarette conditions (i.e., e-cigarette use per se). For nicotine via tobacco smoking, responding compared to the nicotine e-cigarette was similar for video but greater for music, while both video and music reward were enhanced relative to the non-nicotine conditions (placebo and no e-cigarette). Conclusions Acute nicotine from a non-tobacco product has some reinforcement enhancing effects in humans, in a manner partly consistent with nicotine via tobacco smoking and perhaps contributing to the rising popularity of nicotine e-cigarette use.

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Section: Discussionmentioning

confidence: 99%

Reinforcement enhancing effects of acute nicotine via electronic cigarettes

Perkins

Karelitz

Michael

2015

Drug and Alcohol Dependence

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“…Standard preprocessing steps were performed with FSL (for more details, see Lahnakoski et al, 2012) and all subjects were co-registered to the MNI 152 two-millimeter template. We performed two types of analyses using ROIs and whole-brain data.…”

Section: Data and Comparison Of Methodsmentioning

confidence: 99%

Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity

et al. 2012

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Functional brain activity and connectivity have been studied by calculating intersubject and seed-based correlations of hemodynamic data acquired with functional magnetic resonance imaging (fMRI). To inspect temporal dynamics, these correlation measures have been calculated over sliding time windows with necessary restrictions on the length of the temporal window that compromises the temporal resolution. Here, we show that it is possible to increase temporal resolution by using instantaneous phase synchronization (PS) as a measure of dynamic (time-varying) functional connectivity. We applied PS on an fMRI dataset obtained while 12 healthy volunteers watched a feature film. Narrow frequency band (0.04-0.07 Hz) was used in the PS analysis to avoid artifactual results. We defined three metrics for computing time-varying functional connectivity and time-varying intersubject reliability based on estimation of instantaneous PS across the subjects: (1) seed-based PS, (2) intersubject PS, and (3) intersubject seed-based PS. Our findings show that these PS-based metrics yield results consistent with both seed-based correlation and intersubject correlation methods when inspected over the whole time series, but provide an important advantage of maximal single-TR temporal resolution. These metrics can be applied both in studies with complex naturalistic stimuli (e.g., watching a movie or listening to music in the MRI scanner) and more controlled (e.g., event-related or blocked design) paradigms. A MATLAB toolbox FUNPSY (http://becs .aalto.fi/bml/software.html) is openly available for using these metrics in fMRI data analysis.

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“…Despite the apparent complexity and unrestrained nature of movies, consistent and synchronized brain activity patterns across movie viewers have been demonstrated with functional magnetic resonance imaging (fMRI; e.g. Hasson et al, 2004, Bartels and Zeki, 2004aBartels and Zeki, 2004b;J€ a€ askel€ ainen et al, 2008;Lahnakoski et al, 2012;Nummenmaa et al, 2012;Pamilo et al, 2012;Kauttonen et al, 2015), and more recently with magnetoencephalography (MEG; Betti et al, 2013;Lankinen et al, 2014Lankinen et al, , 2016Chang et al, 2015) and electroencephalography (EEG; Whittingstall et al, 2010;Dmochowski et al, 2012Dmochowski et al, , 2014Bridwell et al, 2015;Chang et al, 2015;Ki et al, 2016;Cohen and Parra, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…J€ a€ askel€ ainen et al, 2008;Golland et al, 2007;Kauppi et al, 2010Kauppi et al, , 2017Nummenmaa et al, 2012;Lahnakoski et al, 2012). Compared with the strong across-viewers correlations in BOLD signals (up to 0.78 in Kauppi et al, 2010), the correspondingly calculated intersubject correlations of MEG or EEG signals are usually weaker (typically less than 0.1) both at sensor (Bridwell et al, 2015) and source level (Suppanen, 2014;Chang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Consistency and similarity of MEG- and fMRI-signal time courses during movie viewing

et al. 2018

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Movie viewing allows human perception and cognition to be studied in complex, real-life-like situations in a brain-imaging laboratory. Previous studies with functional magnetic resonance imaging (fMRI) and with magneto-and electroencephalography (MEG and EEG) have demonstrated consistent temporal dynamics of brain activity across movie viewers. However, little is known about the similarities and differences of fMRI and MEG or EEG dynamics during such naturalistic situations.We thus compared MEG and fMRI responses to the same 15-min black-and-white movie in the same eight subjects who watched the movie twice during both MEG and fMRI recordings. We analyzed intra-and intersubject voxel-wise correlations within each imaging modality as well as the correlation of the MEG envelopes and fMRI signals. The fMRI signals showed voxel-wise within-and between-subjects correlations up to r ¼ 0.66 and r ¼ 0.37, respectively, whereas these correlations were clearly weaker for the envelopes of band-pass filtered (7 frequency bands below 100 Hz) MEG signals (within-subjects correlation r < 0.14 and between-subjects r < 0.05). Direct MEG-fMRI voxel-wise correlations were unreliable. Notably, applying a spatial-filtering approach to the MEG data uncovered consistent canonical variates that showed considerably stronger (up to r ¼ 0.25) betweensubjects correlations than the univariate voxel-wise analysis. Furthermore, the envelopes of the time courses of these variates up to about 10 Hz showed association with fMRI signals in a general linear model. Similarities between envelopes of MEG canonical variates and fMRI voxel time-courses were seen mostly in occipital, but also in temporal and frontal brain regions, whereas intra-and intersubject correlations for MEG and fMRI separately were strongest only in the occipital areas.In contrast to the conventional univariate analysis, the spatial-filtering approach was able to uncover associations between the MEG envelopes and fMRI time courses, shedding light on the similarities of hemodynamic and electromagnetic brain activities during movie viewing. IntroductionA practical and ecologically valid approach to probe the neural underpinnings of perception and social cognition is to use movies as stimuli in neuroimaging experiments. Mimicking everyday situations around us, movies can provoke a wide spectrum of sensory, social, and emotional percepts that may be difficult to elicit using the highly controlled repetitive stimuli typically employed in conventional brain-imaging experiments. Despite the apparent complexity and unrestrained nature of movies, consistent and synchronized brain activity patterns across movie viewers have been demonstrated with functional magnetic resonance imaging (fMRI; e.g. Hasson et al.,

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Stimulus-Related Independent Component and Voxel-Wise Analysis of Human Brain Activity during Free Viewing of a Feature Film

Cited by 53 publications

References 36 publications

Reinforcement enhancing effects of acute nicotine via electronic cigarettes

Reinforcement enhancing effects of acute nicotine via electronic cigarettes

Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity

Consistency and similarity of MEG- and fMRI-signal time courses during movie viewing

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