Functional Subdivision of Group-ICA Results of fMRI Data Collected during Cinema Viewing

Pamilo, Siina; Malinen, Sanna; Hlushchuk, Yevhen; Seppä, Mika; Tikka, Pia; Hari, Riitta

doi:10.1371/journal.pone.0042000

Cited by 33 publications

(33 citation statements)

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“…2009; Tikka et al. 2012). However, besides the low LFPR, the component's location adjacent to the anterior insula, a region prone to respiration‐related artifacts (Shulman et al.…”

Section: Discussionmentioning

confidence: 99%

Dynamic interactions of the cortical networks during thought suppression

et al. 2016

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ObjectivesThought suppression has spurred extensive research in clinical and preclinical fields, particularly with regard to the paradoxical aspects of this behavior. However, the involvement of the brain's inhibitory system in the dynamics underlying the continuous effort to suppress thoughts has yet to be clarified. This study aims to provide a unified perspective for the volitional suppression of internal events incorporating the current understanding of the brain's inhibitory system.Materials and MethodsTwenty healthy volunteers underwent functional magnetic resonance imaging while they performed thought suppression blocks alternating with visual imagery blocks. The whole dataset was decomposed by group‐independent component analysis into 30 components. After discarding noise components, the 20 valid components were subjected to further analysis of their temporal properties including task‐relatedness and between‐component residual correlation.ResultsCombining a long task period and a data‐driven approach, we observed a right‐side‐dominant, lateral frontoparietal network to be strongly suppression related. This network exhibited increased fluctuation during suppression, which is compatible with the well‐known difficulty of suppression maintenance.ConclusionsBetween‐network correlation provided further insight into the coordinated engagement of the executive control and dorsal attention networks, as well as the reciprocal activation of imagery‐related components, thus revealing neural substrates associated with the rivalry between intrusive thoughts and the suppression process.

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“…2009; Tikka et al. 2012). However, besides the low LFPR, the component's location adjacent to the anterior insula, a region prone to respiration‐related artifacts (Shulman et al.…”

Section: Discussionmentioning

confidence: 99%

Dynamic interactions of the cortical networks during thought suppression

et al. 2016

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“…Moreover, the details of analysis may determine whether one sees robust wide-spread networks or their task-related division to subnetworks (Pamilo et al, 2012).…”

Section: Neural Substrates Of Reading Other Mindsmentioning

confidence: 99%

Centrality of Social Interaction in Human Brain Function

Hari

Henriksson

Malinen

et al. 2015

Neuron

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People are embedded in social interaction that shapes their brains throughout lifetime. Instead of emerging from lower-level cognitive functions, social interaction could be the default mode via which humans communicate with their environment. Should this hypothesis be true, it would have profound implications on how we think about brain functions and how we dissect and simulate them. We suggest that the research on the brain basis of social cognition and interaction should move from passive spectator science to studies including engaged participants and simultaneous recordings from the brains of the interacting persons.

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“…Some earlier such studies include a look at human visual cortex responses, namely in V1, with presentation of a patterned-flash photic stimulation , imaging of the human primary motor cortex during task activation , generation (Hinke et al, 1993). More recent studies have led to developments in our understanding of many things from group-ICA results of data collected during cinema viewing (Pamilo et al, 2012) to frontalamygdala connectivity alterations during emotion down-regulation in Bipolar I Disorder (Townsend et al, 2012).…”

Section: -Abstractmentioning

confidence: 99%

Evaluation and calibration of functional network modeling methods based on known anatomical connections

et al. 2013

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1-Abstract EnglishRecent studies have identified large scale brain networks based on the spatio-temporal structure of spontaneous fluctuations in resting-state fMRI data. It is expected that functional connectivity based on resting-state data is reflective of-but not identical to the underlying anatomical connectivity.However, which functional connectivity analysis methods reliably predict the network structure remains unclear. Here we tested and compared network connectivity analysis methods by applying them to fMRI restingstate time-series obtained from the human visual cortex. The methods evaluated here are those previously tested against simulated data in Smith et al. (Neuroimage, 2011).To this end, we defined regions within retinotopic visual areas V1, V2, and V3 according to their eccentricity in the visual field, delineating central, intermediate, and peripheral eccentricity regions of interest (ROIs). These ROIs served as nodes in the models we study. We based our evaluation -anatomical connectivity in the monkey visual cortex. For each evaluated method, we computed the fractional rate of detecting connections known to -th percentile of the 4 distribution of interaction magnitudes of those connections not expected to exist.Under optimal conditions, including session duration of 68 minutes, a relatively small network consisting of 9 nodes and artifact-free regression of the global effect, each of the top methods predicted the expected connections with 75%-83% c-sensitivity. Partial Correlation performed best (PCorr; 83%), followed by Regularized Inverse Covariance (ICOV; 79%), Bayesian Network methods (BayesNet; 77%), Correlation (75%), andGeneral Synchronization measures (75%). With decreased session duration, these top methods saw decreases in c-sensitivities, achieving 66%-78% and 60%-70% for 34 and 17 minute sessions, respectively. With a short resting-state fMRI scan of 8.5 minutes (TR = 2s), none of the methods predicted the real network well, with ICOV (53%) and PCorr (51%) performing best. With increased complexity of the network from 9 to 36 nodes, multivariate methods including PCorr and BayesNet saw a decrease in performance. However, this decrease became small when using data from a long (68 minutes) session. Artifact-free regression of the global effect significantly increased the c-sensitivity of all top-performing methods. In an overall evaluation across all tests we performed, PCorr, ICOV and BayesNet set themselves somewhat above all other methods. 5We propose that data-based calibration based on known anatomical connections be integrated into future network studies, in order to maximize sensitivity and reduce false positives. Studies have also demonstrated changes in fMRI-measured brain activity when the subjects are in a state of rest. Importantly, it has been shown that these spontaneous (i.e. without any external stimulation)fluctuations in fMRI signals demonstrate functional connectivity over large parts of the human brain (Xiong et al., 1999). These large parts of the brain correspo...

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Functional Subdivision of Group-ICA Results of fMRI Data Collected during Cinema Viewing

Cited by 33 publications

References 46 publications

Dynamic interactions of the cortical networks during thought suppression

Dynamic interactions of the cortical networks during thought suppression

Centrality of Social Interaction in Human Brain Function

Evaluation and calibration of functional network modeling methods based on known anatomical connections

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