Browsing by Subject "VISUAL WORKING-MEMORY"

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  • Wang, Sheng H.; Lobier, Muriel; Siebenhühner, Felix; Puoliväli, Tuomas; Palva, Satu; Palva, J. Matias (2018)
    It has not been well documented that MEG/EEG functional connectivity graphs estimated with zero-lag-free interaction metrics are severely confounded by a multitude of spurious interactions (SI), i.e., the false-positive “ghosts” of true interactions [1,2]. These SI are caused by the multivariate linear mixing between sources, and thus they pose a severe challenge to the validity of connectivity analysis. Due to the complex nature of signal mixing and the SI problem, there is a need to intuitively demonstrate how the SI are discovered and how they can be attenuated using a novel approach that we termed hyperedge bundling. Here we provide a dataset with software with which the readers can perform simulations in order to better understand the theory and the solution to SI. We include the supplementary material of [1] that is not directly relevant to the hyperedge bundling per se but reflects important properties of the MEG source model and the functional connectivity graphs. For example, the gyri of dorsal-lateral cortices are the most accurately modeled areas; the sulci of inferior temporal, frontal and the insula have the least modeling accuracy. Importantly, we found the interaction estimates are heavily biased by the modeling accuracy between regions, which means the estimates cannot be straightforwardly interpreted as the coupling between brain regions. This raise a red flag that the conventional method of thresholding graphs by estimate values is rather suboptimal: because the measured topology of the graph reflects the geometric property of source-model instead of the cortical interactions under investigation.
  • Riekki, Tapani; Salmi, Juha; Svedholm-Häkkinen, Annika M.; Lindeman, Marjaana (2018)
    According to the Empathizing-Systemizing theory (E-S Theory), individual differences in how people understand the physical world (systemizing) and the social world (empathizing), are two continuums in the general population with several implications, from vocational interests to skills in the social and physical domains. The underlying mechanisms of intuitive physics performance among individuals with strong systemizing and weak empathizing (systemizers) are, however, unknown. Our results affirm higher intuitive physics skills in healthy adult systemizers (N=36), and further reveal the brain mechanisms that are characteristic for those individuals in carrying out such tasks. When the participants performed intuitive physics tasks during functional magnetic resonance imaging, combined higher systemizing and lower empathizing was associated with stronger activations in parts of the default mode network (DMN, cuneus and posterior cingulate gyrus), middle occipital gyrus, and parahippocampal region. The posterior cingulate gyrus and parahippocampal gyrus were specifically associated with systemizing "brain type" even after controlling for task performance, while especially in the parietal cortex, the activation changes were simply explained by higher task performance. We therefore suggest that utilization of DMN-parahippocampal complex, suggested to play a role in internalizing and activating long-term spatial memory representations, is the factor that distinguishes systemizers from empathizers with the opposite "brain type" in intuitive physics tasks.
  • Rouhinen, Santeri; Siebenhühner, Felix; Palva, J. Matias; Palva, Satu (2020)
    The capacity of visual attention determines how many visual objects may be perceived at any moment. This capacity can be investigated with multiple object tracking (MOT) tasks, which have shown that it varies greatly between individuals. The neuronal mechanisms underlying capacity limits have remained poorly understood. Phase synchronization of cortical oscillations coordinates neuronal communication within the fronto-parietal attention network and between the visual regions during endogenous visual attention. We tested a hypothesis that attentional capacity is predicted by the strength of pretarget synchronization within attention-related cortical regions. We recorded cortical activity with magneto- and electroencephalography (M/EEG) while measuring attentional capacity with MOT tasks and identified large-scale synchronized networks from source-reconstructed M/EEG data. Individual attentional capacity was correlated with load-dependent strengthening of theta (3-8 Hz), alpha (8-10 Hz), and gamma-band (30-120 Hz) synchronization that connected the visual cortex with posterior parietal and prefrontal cortices. Individual memory capacity was also preceded by crossfrequency phase-phase and phase-amplitude coupling of alpha oscillation phase with beta and gamma oscillations. Our results show that good attentional capacity is preceded by efficient dynamic functional coupling and decoupling within brain regions and across frequencies, which may enable efficient communication and routing of information between sensory and attentional systems.