Dynamical interactions reconfigure the gradient of cortical timescales

Sorrentino, Pierpaolo; Rabuffo, Giovanni; Baselice, Fabio; Lopez, Emahnuel Troisi; Liparoti, Marianna; Quarantelli, Mario; Sorrentino, Giuseppe; Bernard, Christophe; Jirsa, Viktor

doi:10.1162/netn_a_00270

Cited by 11 publications

(11 citation statements)

References 40 publications

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“…Recent growing empirical studies has been shifting toward studying the temporal reconfiguration of functional connectivity and dynamic properties of the brain [9], [37], suggesting that the spatial and temporal properties of neural activity interact through several spatiotemporal scales [5], [9], in parallel with an increase of new approaches focused on temporally static spatial topography (e.g., spatial cortical gradients) of brain connectivity [15], [38], [39]. Gradient-based approaches provide an organizational framework for capturing the complex large-scale structural and functional organization of the brain a) b) c) HC SZ HC-SZ [39], [40]; However, brain activity is ever changing and the functional topography may change accordingly [41].…”

Section: Discussionmentioning

confidence: 99%

A method for estimating dynamic functional network connectivity gradients (dFNG) from ICA captures smooth inter-network modulation.

Soleimani,

Iraji,

Belger

et al. 2024

Preprint

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Dynamic functional network connectivity (dFNC) analysis is a widely used approach for studying brain function and offering insight into how brain networks evolve over time. Typically, dFNC studies utilized fixed spatial maps and evaluate transient changes in coupling among time courses estimated from independent component analysis (ICA). This manuscript presents a complementary approach that relaxes this assumption by spatially reordering the components dynamically at each timepoint to optimize for a smooth gradient in the FNC (i.e., a smooth gradient among ICA connectivity values). Several methods are presented to summarize dynamic FNC gradients (dFNGs) over time, starting with static FNC gradients (sFNGs), then exploring the reordering properties as well as the dynamics of the gradients themselves. We then apply this approach to a dataset of schizophrenia (SZ) patients and healthy controls (HC). Functional dysconnectivity between different brain regions has been reported in schizophrenia, yet the neural mechanisms behind it remain elusive. Using resting state fMRI and ICA on a dataset consisting of 151 schizophrenia patients and 160 age and gender-matched healthy controls, we extracted 53 intrinsic connectivity networks (ICNs) for each subject using a fully automated spatially constrained ICA approach. We develop several summaries of our functional network connectivity gradient analysis, both in a static sense, computed as the Pearson correlation coefficient between full time series, and a dynamic sense, computed using a sliding window approach followed by reordering based on the computed gradient, and evaluate group differences. Static connectivity analysis revealed significantly stronger connectivity between subcortical (SC), auditory (AUD) and visual (VIS) networks in patients, as well as hypoconnectivity in sensorimotor (SM) network relative to controls. sFNG analysis highlighted distinctive clustering patterns in patients and HCs along cognitive control (CC)/ default mode network (DMN), SC/ AUD/ SM/ cerebellar (CB), and VIS gradients. Furthermore, we observed significant differences in the sFNGs between groups in SC and CB domains. dFNG analysis suggested that SZ patients spend significantly more time in a SC/ CB state based on the first gradient, while HCs favor the DMN state. For the second gradient, however, patients exhibited significantly higher activity in CB/ VIS domains, contrasting with HCs DMN engagement. The gradient synchrony analysis conveyed more shifts between SM/ SC networks and transmodal CC/ DMN networks in patients. In addition, the dFNG coupling revealed distinct connectivity patterns between SC, SM and CB centroids in SZ patients compared to HCs. To recap, our results advance our understanding of brain network modulation by examining smooth connectivity trajectories. This provides a more complete spatiotemporal summary of the data, contributing to the growing body of current literature regarding the functional dysconnectivity in schizophrenia patients. By employing dFNG, we highlight a new perspective to capture large scale fluctuations across the brain while maintaining the convenience of brain networks and low dimensional summary measures.

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Section: Discussionmentioning

confidence: 99%

A method for estimating dynamic functional network connectivity gradients (dFNG) from ICA captures smooth inter-network modulation.

Soleimani,

Iraji,

Belger

et al. 2024

Preprint

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“…This hierarchical structure is preserved during the course of mammalian evolution, despite the increase of brain volume (62); it is characteristic of individual brains, and alterations in its organization lead to mental and neurological disease (65). However, differently than structural features, brain function is highly dynamic and the topography of timescales can vary over time both in rest (66), and task (67). For instance, visual stimuli presentation induces a decrease in the amplitude of the alpha band (8-12Hz), while mental calculation or working memory tasks increase the activity in this same frequency band (7,68).…”

Section: Neuronal Avalanches Association To Cortical Timescalesmentioning

confidence: 99%

“…For instance, visual stimuli presentation induces a decrease in the amplitude of the alpha band (8-12Hz), while mental calculation or working memory tasks increase the activity in this same frequency band (7,68). While several measures for the signals timescale have been proposed (64,66), here for illustrative purposes we evaluated the timescales as the area τ before the first minimum; Fig.S4.A, left). Even in the same recording channel, the decay of the auto-correlation can change when observed in different time windows (Fig.S4.A, right).…”

Section: Neuronal Avalanches Association To Cortical Timescalesmentioning

confidence: 99%

“…While several measures for the signals timescale have been proposed (64,66), here for illustrative purposes we evaluated the timescales as the area τ before the first minimum; Fig.S4.A, left). Even in the same recording channel, the decay of the auto-correlation can change when observed in different time windows (Fig.S4.A, right).…”

Section: Neuronal Avalanches Association To Cortical Timescalesmentioning

confidence: 99%

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Neuronal Avalanches in Naturalistic Speech and Music Listening

Neri,

Runfola,

te Rietmolen

et al. 2023

Preprint

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Neuronal avalanches are cascade-like events ubiquitously observed across imaging modalities and scales. Aperiodic timing and topographic distribution of these events have been related to the systemic physiology of brain states. However, it is still unknown whether neuronal avalanches are correlates of cognition, or purely reflect physiological properties. In this work, we investigate this question by analyzing intracranial recordings of epileptic participants during rest and passive listening of naturalistic speech and music stimuli. During speech or music listening, but not rest, participants’ brains “tick” together, as the timing of neuronal avalanches is stimulus-driven and hence correlated across participants. Auditory regions are strongly participating in coordinated neuronal avalanches, but also associative regions, indicating both the specificity and distributivity of cognitive processing. The subnetworks where such processing takes place during speech and music largely overlap, especially in auditory regions, but also diverge in associative cortical sites. Finally, differential pathways of avalanche propagation across auditory and non-auditory regions differentiate brain network dynamics during speech, music and rest. Overall, these results highlight the potential of neuronal avalanches as a neural index of cognition.Author’s summaryNeuronal avalanches consist of collective network events propagating across the brain in short-lived and aperiodic instances. These salient events have garnered a great interest for studying the physics of cortical dynamics, and bear potential for studying brain data also in purely neuroscientific contexts. In this work we investigated neuronal avalanches to index cognition, analyzing an intracranial stereo electroencephalography (iEEG) dataset during speech, music listening and resting state in epileptic patients. We show that neuronal avalanches are consistently driven by music and speech stimuli: avalanches co-occur in participants listening to the same auditory stimulus; avalanche topography differs from resting state, presenting partial similarities during speech and music; avalanche propagation changes during speech, music, and rest conditions, especially along the pathways between auditory and non auditory regions. Our work underlines the distributed nature of auditory stimulus processing, supporting neuronal avalanches as a valuable and computationally advantageous framework for the study of cognition in humans.

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“…In particular, neuronal avalanches (NA) belong to the framework of criticality, which lends itself nicely to the investigation of the microscopic dynamical behavior of neuronal assemblies and its (non-linear) relation to large-scale alterations such as, in the case of epilepsy, seizure initiation, propagation and termination (Hagemann et al, 2021; Liu et al, 2023; Moosavi & Truccolo, 2023). Recent findings suggest that NA spreading on the large scale represents a more stable and reliable feature for the individualized investigation and task classification of brain dynamics (Corsi et al, 2023; Polverino et al, 2022; Sorrentino, Troisi Lopez, et al, 2023). Relevantly, our previous work has highlighted the altered spreading of NA and its relation to brain morphology in temporal lobe epilepsy (TLE), using the source-derived high-density EEG (hdEEG) activity at rest (Duma et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Neuronal avalanches in temporal lobe epilepsy as a diagnostic tool: a noninvasive investigation of intrinsic resting state dynamics

Corsi,

Lopez,

Sorrentino

et al. 2023

Preprint

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Background and ObjectivesThe epilepsy diagnosis still represents a complex process, with misdiagnosis reaching 40%. Here, we aimed at building an automatable workflow, to help the clinicians in the diagnostic process, differentiating between controls and a population of patients with temporal lobe epilepsy (TLE). While primarily interested in correctly classifying the participants, we used data features providing hints on the underlying pathophysiological processes. Specifically, we hypothesized that neuronal avalanches (NA) may represent a feature that encapsulates the rich brain dynamics better than the classically used functional connectivity measures (Imaginary Coherence; ImCoh).MethodsWe recorded 10 minutes of resting state activity with high-density scalp electroencephalography (hdEEG; 128 channels). We analyzed large-scale activation bursts (NA) from source activation, to capture altered dynamics. Then, we used machine-learning algorithms to classify epilepsy patients vs. controls, and we described the goodness of the classification as well as the effect of the durations of the data segments on the performance.ResultsUsing a support vector machine (SVM), we reached a classification accuracy of 0.87 ± 0.10 (SD) and an area under the curve (AUC) of 0.94 ± 0.06. The use of avalanches-derived features, generated a mean increase of 16% in the accuracy of diagnosis prediction, compared to ImCoh. Investigating the main features informing the model, we observed that the dynamics of the entorhinal cortex, superior and inferior temporal gyri, cingulate cortex and prefrontal dorsolateral cortex were informing the model with NA. Finally, we studied the time-dependent accuracy in the classification. While the classification performance grows with the duration of the data length, there are specific lengths, at 30s and 180s at which the classification performance becomes steady, with intermediate lengths showing greater variability. Classification accuracy reached a plateau at 5 minutes of recording.DiscussionWe showed that NA represents a better EEG feature for an automated epilepsy identification, being related with neuronal dynamics of pathology-relevant brain areas. Furthermore, the presence of specific durations and the performance plateau might be interpreted as the manifestation of the specific intrinsic neuronal timescales altered in epilepsy. The study represents a potentially automatable and noninvasive workflow aiding the clinicians in the diagnosis.

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Dynamical interactions reconfigure the gradient of cortical timescales

Cited by 11 publications

References 40 publications

A method for estimating dynamic functional network connectivity gradients (dFNG) from ICA captures smooth inter-network modulation.

A method for estimating dynamic functional network connectivity gradients (dFNG) from ICA captures smooth inter-network modulation.

Neuronal Avalanches in Naturalistic Speech and Music Listening

Neuronal avalanches in temporal lobe epilepsy as a diagnostic tool: a noninvasive investigation of intrinsic resting state dynamics

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