Biology subjects

Marusic, P.

Publications and source records attributed to Marusic, P..

7 recordsLinked to original sources

bioRxiv2026

Temporal order of activations and interactions during arithmetic calculations measured by intracranial electrophysiological recordings in the human brain

Arithmetic requires complex and fast processes orchestrated within a large-scale network spanning multiple brain regions. However, reports on the networks temporal dynamics are scarce. Here, we present data from intracranial EEG (iEEG) of 20 subjects (epilepsy surgery candidates) performing a sequential three-operand arithmetic task. Utilizing the high temporal and spatial resolution of iEEG, we analysed changes in high-gamma band (HGB; 52-120 Hz) activity and functional connectivity assessed by phase-locking value (PLV) in the delta (0.1-3 Hz) and theta (3-7 Hz) frequency bands. Strong and transient HGB activations peaked first in the ventral occipito-temporal cortex, followed by a more gradual increase in the lateral parietal, sensorimotor, and frontal cortices, accompanied by deactivations in default mode network areas. The connectivity patterns were more extensive during calculation than number recognition, with the theta PLV peaking [~]150 ms earlier than the delta PLV. Earliest connectivity appeared, surprisingly, between ventral temporal and frontal regions at [~]100-200 ms, evolving into a robust pattern among key network nodes at [~]200-400 ms after the presentation of each operand. The presented results elucidate information flow within the putative arithmetic network during calculation in the human brain, offering high-temporal-resolution insights into its functional architecture.

neuroscience↗

bioRxiv2025

Choosing the best or avoiding the worst: complementary and opponent value signals in the human brain

Whether the brain encodes the subjective value of pleasant and unpleasant situations via a single integrated circuit or through distinct pathways remains unresolved. It is also unclear how neural value signals guide choices when the context involves choosing the most favorable option or avoiding the most unfavorable one. To address these questions, we recorded intracerebral activity from 27 participants as they first rated pleasant and unpleasant scenarios and then made binary choices across all combinations of item domain (pleasant/unpleasant) and choice frame (choose best/worst). Ventromedial prefrontal cortex (vmPFC) broadband gamma activity (BGA, 50-150 Hz) tracked the value of pleasant items during rating and pre-choice, correlating positively with the most pleasant item irrespective of choice frame. Similarly, anterior insula (aIns) BGA tracked the value of unpleasant items during rating and pre-choice, correlating negatively with the most unpleasant item irrespective of choice frame. The difference in relative value correlations between the vmPFC and aIns was larger for choices involving a conflict between item domain and choice frame, consistent with slower response times and choices less aligned with value differences in these trials. These findings reveal dissociable yet complementary neural systems for pleasant and unpleasant valuation and highlight how such signals may guide flexible decision-making across contexts.

neuroscience↗

bioRxiv2024

Neural dynamics of visual streams interactions during memory-guided actions investigated by intracranial EEG

The dorsal and ventral visual streams play distinct roles in visual processing for action: the dorsal stream is assumed to support real-time actions, while the ventral stream facilitates memory-guided actions. As the recent evidence suggests a more integrated function of these streams, we investigated the neural dynamics and functional connectivity between them during memory-guided actions using intracranial EEG. We tracked neural activity in the inferior parietal lobule in the dorsal stream, and ventral temporal cortex in the ventral stream as well as hippocampus during a delayed action task. We found increased alpha power in both streams during the delay, indicating their role in maintaining visual information. We also observed an increase in theta band synchronization between the inferior parietal lobule and ventral temporal cortex, and between the inferior parietal lobule and hippocampus during the delay. Our study provides unique electrophysiological evidence for close interactions between dorsal and ventral streams, supporting an integrated processing model.

neuroscience↗

bioRxiv2024

Antagonistic behavior of brain networks mediated by low-frequency oscillations: electrophysiological dynamics during internal-external attention switching

Antagonistic activity of brain networks likely plays a fundamental role in how the brain optimizes its performance by efficient allocation of computational resources. A prominent example involves externally/internally oriented attention tasks, implicating two anticorrelated, intrinsic brain networks: the default mode network (DMN) and the dorsal attention network (DAN). To elucidate electrophysiological underpinnings and causal interplay during attention switching, we recorded intracranial EEG (iEEG) from 25 epilepsy patients with electrode contacts localized in the DMN and DAN. We show antagonistic network dynamics of activation-related changes in high-frequency (> 50 Hz) and low-frequency (< 30 Hz) power. The temporal profile of information flow between the networks estimated by effective connectivity suggests that the activated network inhibits the other one, gating its activity by increasing the amplitude of the low-frequency oscillations. Insights about inter-network communication may have profound implications for various brain disorders in which these dynamics are compromised.

neuroscience↗

bioRxiv2022

Tackling the challenges of group network inference from intracranial EEG data

Intracranial EEG (iEEG) data is a powerful way to map brain function, characterized by high temporal and spatial resolution, allowing the study of interactions among neuronal populations that orchestrate cognitive processing. However, the statistical inference and analysis of brain networks using iEEG data faces many challenges related to its sparse brain coverage, and its inhomogeneity across patients. We review these challenges and develop a methodological pipeline for estimation of network structure not obtainable from any single patient, illustrated on the inference of the interaction among visual streams using a dataset of 27 human iEEG recordings from a visual experiment employing visual scene stimuli. 100 ms sliding window and multiple band-pass filtered signals are used to provide temporal and spectral resolution. For the connectivity analysis we showcase two connectivity measures reflecting different types of interaction between regions of interest (ROI): Phase Locking Value as a symmetric measure of synchrony, and Directed Transfer Function - asymmetric measure describing causal interaction. For each two channels, initial uncorrected significance testing at p<0.05 for every time-frequency point is carried out by comparison of the data-derived connectivity to a baseline surrogate-based null distribution, providing a binary time-frequency connectivity map. For each ROI pair, a connectivity density map is obtained by averaging across all pairs of channels spanning them, effectively agglomerating data across relevant channels and subjects. Finally, the difference of the mean map value after and before the stimulation is compared to the same statistic in surrogate data to assess link significance. The analysis confirmed the function of the parieto-medial temporal pathway, mediating visuospatial information between dorsal and ventral visual streams during visual scene analysis. Moreover, we observed the anterior hippocampal connectivity with more posterior areas in the medial temporal lobe, and found the reciprocal information flow between early processing areas and medial place area. To summarize, we developed an approach for estimating network connectivity, dealing with the challenge of sparse individual coverage of intracranial EEG electrodes. Its application provided new insights into the interaction between the dorsal and ventral visual streams, one of the iconic dualities in human cognition.

neuroscience↗

bioRxiv2021

Intracerebral mechanisms explaining the impact of incidental feedback on mood state and risky choice

Identifying factors whose fluctuations are associated with choice inconsistency is a major issue for rational decision theory. Here, we investigated the neuro-computational mechanisms through which mood fluctuations may bias human choice behavior. Intracerebral EEG data were collected in a large group of participants (n = 30), while they were performing interleaved quiz and choice tasks. Neural baseline activity preceding choice onset was confronted first to mood level, estimated by a computational model integrating the feedbacks received in the quiz task, and then to the weighting of option attributes, in a computational model predicting risk attitude in the choice task. Results showed that 1) elevated broadband gamma activity (BGA) in the ventromedial prefrontal cortex (vmPFC) and dorsal anterior insula (daIns) was respectively signaling periods of high and low mood, 2) increased vmPFC and daIns BGA respectively promoted and tempered risk taking by overweighting gain versus loss prospects. Thus, incidental feedbacks induce brain states that correspond to different moods and bias the comparison of safe and risky options. More generally, these findings might explain why people experiencing positive (or negative) outcome in some part of their life tend to expect success (or failure) in any other.

neuroscience↗

bioRxiv2017

Global and Local Dynamics of High-Gamma Activity Underlying Error Processing in the Human Brain Revealed by Noninvasive and Intracranial EEG

Error detection in motor behavior is a fundamental cognitive function heavily relying on cortical information processing. Neural activity in the high-gamma frequency band (HGB) closely reflects such local cortical processing, but little is known about its role in error processing, particularly in the healthy human brain. Here we characterize the error-related response of the human brain based on data obtained with noninvasive EEG optimized for HGB mapping in 31 healthy subjects (15 females, 16 males), and additional intracranial EEG data from 9 epilepsy patients (4 females, 5 males). Our findings reveal a comprehensive picture of the global and local dynamics of error-related HGB activity in the human brain. On the global level as reflected in the noninvasive EEG, the error-related response started with an early component dominated by anterior brain regions, followed by a shift to parietal regions, and a subsequent phase characterized by sustained parietal HGB activity. This phase lasted for more than 1 s after the error onset. On the local level reflected in the intracranial EEG, a cascade of both transient and sustained error-related responses involved an even more extended network, spanning beyond frontal and parietal regions to the insula and the hippocampus. HGB mapping appeared especially well suited to investigate late, sustained components of the error response, possibly linked to downstream functional stages such as error-related learning and behavioral adaptation. Our findings establish the basic spatio-temporal properties of HGB activity as a neural correlate of error processing, complementing traditional error-related potential studies.\n\nSignificance StatementThere is great interest to understand how the human brain reacts to errors in goal-directed behavior. An important index of cortical and subcortical information processing is fast oscillatory brain activity, particularly in the high-gamma band (above 50 Hz). Here we show that it is possible to detect signatures of errors in event-related high-gamma responses with noninvasive techniques, characterize these responses comprehensively, and validate the EEG procedure for the detection of such signals. In addition, we demonstrate the added value of intracranial recordings pinpointing the fine-grained spatio-temporal patterns in error-related brain networks. We anticipate that the optimized noninvasive EEG techniques as described here will be helpful in many areas of cognitive neuroscience where fast oscillatory brain activity is of interest.

neuroscience↗