Biology subjects

Pinzuti, E.

Publications and source records attributed to Pinzuti, E..

4 recordsLinked to original sources

bioRxiv2023

Understanding the neural mechanisms of emotion-cognition interaction via high resolution mapping in space, time, frequency, and information transfer

Interactions between emotional and cognitive processing play a critical role in determining human behavior. The ability to efficiently control these interactions is essential for goal-directed behavior. Previous research on emotion-cognition interaction in humans has primarily focused on its neural topography using techniques with either low temporal resolution (fMRI) or low spatial resolution (ERP), leaving the underlying neuronal mechanisms and their behavioral implications poorly understood. Here, we used EEG recordings from a large cohort of human participants (N=121) and employed source-reconstruction with finite-element head modeling during an emotional Flanker task. Our findings reveal a neural interaction between emotional interference and cognitive control within the pars triangularis of the right inferior frontal gyrus (rIFG). These results align spatially and conceptually with meta-analytic evidence from fMRI studies on interference inhibition processes, supporting the idea that the rIFG is crucial for interference inhibition, regardless of the interfering information type. Notably, the neural interaction between emotion and cognition in the rIFG was transient, peaking during the transition from emotional to cognitive processing, and was most pronounced in the beta-frequency band. Furthermore, conditional Granger-causality analysis of information flow indicated that the pars triangularis of the rIFG communicated with other functional subdivisions of the rIFG and parieto-occipital areas (such as the Precuneus and V2) in a frequency-specific manner. This information flow predicted individual emotional and cognitive interference effects on behavior. Overall, our findings highlight the functional and temporal segregation and integration of emotion, cognition, and their interaction in the rIFG. Furthermore, rIFGs top-down modulation of visuo-attentional areas, ultimately shaping behavioral performance, underscores the importance of stimulus interference inhibition for emotion-cognition interaction. Author summaryUnderstanding the interplay between emotion and cognition is crucial for understanding human behavior. Our study investigates the role of transient oscillatory activity using source-localized EEG in a large cohort. We identify a spatial overlap of the interaction of emotion and cognition processing in the rIFGs pars triangularis, aligning closely with localizations indicated by meta-analytic fMRI evidence. This interaction, primarily in the beta-frequency (around 20Hz) band, is transient, peaking during the shift from emotional to cognitive processing. Granger-causality analysis reveals frequency-specific communication patterns within the rIFG and parieto-occipital areas, predicting individual emotional and cognitive interference effects. Our findings elucidate the functional and temporal dynamics of emotion-cognition interaction within the rIFG, providing insights into the nuanced neural processes shaping human behavior.

neuroscience↗

bioRxiv2022

Information theoretic evidence for layer- and frequency-specific changes in cortical information processing under anesthesia

Nature relies on highly distributed computation for the processing of information in nervous systems across the entire animal kingdom. Such distributed computation can be more easily understood if decomposed into the three elementary components of information processing, i.e. storage, transfer and modification, and rigorous information theoretic measures for these components exist. However, the distributed computation is often also linked to neural dynamics exhibiting distinct rhythms. Thus, it would be beneficial to associate the above components of information processing with distinct rhythmic processes where possible. Here we focus on the storage of information in neural dynamics and introduce a novel spectrally-resolved measure of active information storage (AIS). Drawing on intracortical recordings of neural activity under anesthesia before and after loss of consciousness (LOC) we show that anesthesia-related modulation of AIS is highly specific to different frequency bands and that these frequency-specific effects differ across cortical layers and brain regions. We found that in the high/low gamma band the effects of anesthesia result in AIS modulation only in the supergranular layers, while in the alpha/beta band the strongest decrease in AIS can be seen at infragranular layers. Finally, we show that the increase of spectral power at multiple frequencies, in particular at alpha and delta bands in frontal areas, that is often observed during LOC ( anteriorization) also impacts local information processing - but in a frequency specific way: Increases in isoflurane concentration induced a decrease in AIS in the alpha frequencies, while they increased AIS in the delta frequency range < 2Hz. Thus, the analysis of spectrally-resolved AIS provides valuable additional insights into changes in cortical information processing under anaesthesia. Author SummaryWhile describing information processing in digital computers is somewhat straightforward and accessible (e.g. how much information is stored in a hard disk or which modification of information a CPU is executing), quantifying the widely distributed information processing in a biological neural system is much more challenging. In neural systems separating the components of distributed information processing - information transfer, storage and modification - helps with this task, but requires accurate mathematical definitions of these components of information processing. These definitions of distributed information processing quantities have become available only very recently. Of the three component processes mentioned above information storage, in particular, has been used with great success to analyze information processing in swarms, and to evolve, and optimize artificial information processing systems. The analysis of information storage has also already proven to be useful for the analysis of biological neural systems. Since in such systems, information processing seems to be often carried out by rhythmic neural activity with different frequencies, a measure of the frequency-specific components of the active information storage is needed. Here introduce such a measure and study how isoflurane anesthesia affects the local information processing in the ferret prefrontal and primary visual areas around loss of consciousness. We found that the modulation of active information storage by isoflurane is specific to frequency, layers and area, and that the analysis of frequency-specific active information storage provides insights not captured by more traditional descriptions of neural activity.

neuroscience↗

bioRxiv2020

Measuring spectrally-resolved information transfer for sender- and receiver-specific frequencies

Information transfer, measured by transfer entropy, is a key component of distributed computation. It is therefore important to understand the pattern of information transfer in order to unravel the distributed computational algorithms of a system. Since in many natural systems distributed computation is thought to rely on rhythmic processes a frequency resolved measure of information transfer is highly desirable. Here, we present a novel algorithm, and its efficient implementation, to identify separately frequencies sending and receiving information in a network. Our approach relies on the invertible maximum overlap discrete wavelet transform (MODWT) for the creation of surrogate data in the computation of transfer entropy and entirely avoids filtering of the original signals. The approach thereby avoids well-known problems due to phase shifts or the ineffectiveness of filtering in the information theoretic setting. We also show that measuring frequency-resolved information transfer is a partial information decomposition problem that cannot be fully resolved to date and discuss the implications of this issue. Last, we evaluate the performance of our algorithm on simulated data and apply it to human magnetoencephalography (MEG) recordings and to local field potential recordings in the ferret. In human MEG we demonstrate top-down information flow in temporal cortex from very high frequencies (above 100Hz) to both similarly high frequencies and to frequencies around 20Hz, i.e. a complex spectral configuration of cortical information transmission that has not been described before. In the ferret we show that the prefrontal cortex sends information at low frequencies (4-8 Hz) to early visual cortex (V1), while V1 receives the information at high frequencies (> 125 Hz). Author SummarySystems in nature that perform computations typically consist of a large number of relatively simple but interacting parts. In human brains, for example, billions of neurons work together to enable our cognitive abilities. This well-orchestrated teamwork requires information to be exchanged very frequently. In many cases this exchange happens rhythmically and, therefore, it seems beneficial for our understanding of physical systems if we could link the information exchange to specific rhythms. We here present a method to determine which rhythms send, and which rhythms receive information. Since many rhythms can interact at both sender and receiver side, we show that the interpretation of results always needs to consider that the above problem is tightly linked to partial information decomposition - an intriguing problem from information theory only solved recently, and only partly. We applied our novel method to information transfer in the human inferior temporal cortex, a brain region relevant for object perception, and unexpectedly found information transfer originating at very high frequencies at 100Hz and then forking to be received at both similarly high but also much lower frequencies around 20Hz. These results overturn the current standard assumption that low frequencies send information to high frequencies.

neuroscience↗

bioRxiv2020

Cortical network mechanisms of response inhibition

Both the right inferior frontal gyrus (rIFG) and the pre-supplementary motor area (pre-SMA) are crucial for successful response inhibition. However, the particular functional roles of those two regions have been controversially debated for more than a decade now. It is unclear whether the rIFG directly initiates stopping or serves an attentional function, whereas the stopping is triggered by the pre-SMA. The current multimodal MEG/fMRI study sought to clarify the role and temporal activation order of both regions in response inhibition using a selective stopping task. This task dissociates inhibitory from attentional processes. Our results reliably reveal a temporal precedence of rIFG over pre-SMA. Moreover, connectivity during response inhibition is directed from rIFG to pre-SMA and predicts stopping performance. Response inhibition is implemented via beta-band oscillations. Our findings support the hypothesis that response inhibition is initiated by the rIFG as a form of attention-independent top-down control.

neuroscience↗