BackgroundInformation theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses.ResultsHere we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies.ConclusionThe new toolbox presented here implements fast and data-robust computations of the most relevant quantities used in information theoretic analysis of neural data. The toolbox can be easily used within Matlab, the environment used by most neuroscience laboratories for the acquisition, preprocessing and plotting of neural data. It can therefore significantly enlarge the domain of application of information theory to neuroscience, and lead to new discoveries about the neural code.
Previous studies have shown that healthy participants learn to control local brain activity with operant training by using real-time functional magnetic resonance imaging (rt-fMRI). Very little data exist, however, on the dynamics of interaction between critical brain regions during rt-fMRI-based training. Here, we examined self-regulation of stimulus-elicited insula activation and performed a psychophysiological interaction (PPI) analysis of real-time self-regulation data. During voluntary up-regulation of the left anterior insula in the presence of threat-related pictures, differential activations were observed in the ventrolateral prefrontal cortex, the frontal operculum, the middle cingulate cortex and the right insula. Down-regulation in comparison to no-regulation revealed additional activations in right superior temporal cortex, right inferior parietal cortex and right middle frontal cortex. There was a significant learning effect over sessions during up-regulation, documented by a significant improvement of anterior insula control over time. Connectivity analysis revealed that successful up-regulation of the activity in left anterior insula while viewing aversive pictures was directly modulated by dorsomedial and ventrolateral prefrontal cortex. Down-regulation of activity was more difficult to achieve and no learning effect was observed. More extensive training might be necessary for successful down-regulation. These findings illustrate the functional interactions between different brain areas during regulation of anterior insula activity in the presence of threat-related stimuli.
When people wakefully rest in the functional MRI scanner, their minds wander, and they engage a so-called default mode (DM) of neural processing that is relatively suppressed when attention is focused on the outside world. Accruing evidence suggests that DM brain systems activated during rest are also important for active, internally focused psychosocial mental processing, for example, when recalling personal memories, imagining the future, and feeling social emotions with moral connotations. Here the authors review evidence for the DM and relations to psychological functioning, including associations with mental health and cognitive abilities like reading comprehension and divergent thinking. This article calls for research into the dimensions of internally focused thought, ranging from free-form daydreaming and off-line consolidation to intensive, effortful abstract thinking, especially with socioemotional relevance. It is argued that the development of some socioemotional skills may be vulnerable to disruption by environmental distraction, for example, from certain educational practices or overuse of social media. The authors hypothesize that high environmental attention demands may bias youngsters to focus on the concrete, physical, and immediate aspects of social situations and self, which may be more compatible with external attention. They coin the term constructive internal reflection and advocate educational practices that promote effective balance between external attention and internal reflection.
Inducing and experiencing emotions about others’ mental and physical circumstances is thought to involve self-relevant processing and personal memories of similar experiences. The hippocampus is important for self-referential processing during recall and prospection; however, its contributions during social emotions have not been systematically investigated. We use event-related averaging and Granger causal connectivity mapping to investigate hippocampal contributions during the processing of varieties of admiration and compassion pertaining to protagonists’ mental versus physical circumstances (admiration for virtue, AV, versus for skill; compassion for social/psychological pain, CSP, versus for physical pain). Data were collected using a multistep emotion induction paradigm that included psychosocial interviews, BOLD fMRI and simultaneous psychophysiological recording. Given that mnemonic demands were equivalent among conditions, we tested whether: (1) the hippocampi would be recruited more strongly and for a longer duration during the processing of AV and CSP; (2) connectivity between the hippocampi and cortical systems involved in visceral somatosensation/emotional feeling, social cognitive, and self-related processing would be more extensive during AV and CSP. Results elucidate the hippocampus’ facilitative role in inducing and sustaining appropriate emotional reactions, the importance of self-related processing during social emotions, and corroborate the conception that varieties of emotional processing pertaining to others’ mental and physical situations engage at least partially distinct neural mechanisms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.