Neuroimaging techniques are now widely used to study human cognition. The functional associations between brain areas have become a standard proxy to describe how cognitive processes are distributed across the brain network. Among the many analysis tools available, dynamic models of brain activity have been developed to overcome the limitations of original connectivity measures such as functional connectivity. This goes in line with the many efforts devoted to the assessment of directional interactions between brain areas from the observed neuroimaging activity. This opinion article provides an overview of our model-based whole-brain effective connectivity to analyze fMRI data, while discussing the pros and cons of our approach with respect to other established approaches. Our framework relies on the multivariate Ornstein-Uhlenbeck (MOU) process and is thus referred to as MOU-EC. Once tuned, the model provides a directed connectivity estimate that reflects the dynamical state of BOLD activity, which can be used to explore cognition. We illustrate this approach using two applications on task-evoked fMRI data. First, as a connectivity measure, MOU-EC can be used to extract biomarkers for task-specific brain coordination, understood as the patterns of areas exchanging information. The multivariate nature of connectivity measures raises several challenges for whole-brain analysis, for which machine-learning tools present some advantages over statistical testing. Second, we show how to interpret changes in MOU-EC connections in a collective and model-based manner, bridging with network analysis. Our framework provides a comprehensive set of tools that open exciting perspectives to study distributed cognition, as well as neuropathologies.
Atypical antipsychotic drugs (APDs) used to treat positive and negative symptoms in schizophrenia block serotonin receptors 5-HT 2A R and dopamine receptors D 2 R and stimulate 5-HT 1A R directly or indirectly. However, the exact cellular mechanisms mediating their therapeutic actions remain unresolved. We recorded neural activity in the prefrontal cortex (PFC) and hippocampus (HPC) of freely-moving mice before and after acute administration of 5-HT 1A R, 5-HT 2A R and D 2 R selective agonists and antagonists and atypical APD risperidone. We then investigated the contribution of the three receptors to the actions of risperidone on brain activity via statistical modeling and pharmacological reversal (risperidone + 5-HT 1A R antagonist WAY-100635, risperidone + 5-HT 2A/2C R agonist DOI, risperidone + D 2 R agonist quinpirole). Risperidone, 5-HT 1A R agonism with 8-OH-DPAT, 5-HT 2A R antagonism with M100907, and D 2 R antagonism with haloperidol reduced locomotor activity of mice that correlated with a suppression of neural spiking, power of theta and gamma oscillations in PFC and HPC, and reduction of PFC-HPC theta phase synchronization. By contrast, activation of 5-HT 2A R with DOI enhanced high-gamma oscillations in PFC and PFC-HPC high gamma functional connectivity, likely related to its hallucinogenic effects. Together, power changes, regression modeling and pharmacological reversals suggest an important role of 5-HT 1A R agonism and 5-HT 2A R antagonism in risperidone-induced alterations of delta, beta and gamma oscillations, while D 2 R antagonism may contribute to risperidone-mediated changes in delta oscillations. This study provides novel insight into the neural mechanisms for widely prescribed psychiatric medication targeting the serotonin and dopamine systems in two regions involved in the pathophysiology of schizophrenia.
The direction of functional information flow in the sensory thalamocortical circuit may play a role in stimulus perception, but, surprisingly, this process is poorly understood. We addressed this problem by evaluating a directional information measure between simultaneously recorded neurons from somatosensory thalamus (ventral posterolateral nucleus, VPL) and somatosensory cortex (S1) sharing the same cutaneous receptive field while monkeys judged the presence or absence of a tactile stimulus. During stimulus presence, feed-forward information (VPL → S1) increased as a function of the stimulus amplitude, while pure feed-back information (S1 → VPL) was unaffected. In parallel, zero-lag interaction emerged with increasing stimulus amplitude, reflecting externally driven thalamocortical synchronization during stimulus processing. Furthermore, VPL → S1 information decreased during error trials. Also, VPL → S1 and zero-lag interaction decreased when monkeys were not required to report the stimulus presence. These findings provide evidence that both the direction of information flow and the instant synchronization in the sensory thalamocortical circuit play a role in stimulus perception.
The present method may be used to improve epileptogenic identification as well as pinpoint additional regions that are functionally altered during ictal events.
Abstract-Based on the hypothesis-testing method, we derive lower bounds on the average error probability of finite-length joint source-channel coding. The extension of the meta-converse bound of channel coding to joint source-channel coding depends on the codebook and the decoding rule and thus, it is a priori computationally challenging. Weaker versions of this general bound recover known converses in the literature and provide computationally feasible expressions.
Cognitive processing requires the ability to flexibly integrate and process information across large brain networks. More information is needed on how brain networks dynamically reorganize to allow such broad communication across many different brain regions in order to integrate the necessary information. Here, we use intracranial EEG to record neural activity from 12 epileptic patients while they perform three cognitive tasks in order to study how the functional connectivity changes to facilitate communication across the underlying network spanning many different brain regions. At the topological level, this facilitation is characterized by measures of integration and segregation. Across all patients, we found significant increases in integration and decreases in segregation during cognitive processing, especially in the gamma band (50-90 Hz). Accordingly, we also found significantly higher level of global synchronization and functional connectivity during the execution of the cognitive task, again particularly in the gamma band. More importantly, we demonstrate here for the first time that the modulations at the level of functional connectivity facilitating communication across the network were not caused by changes in the level of the underlying oscillations but caused by a rearrangement of the mutual synchronisation between the different nodes as proposed by the "Communication Through Coherence" Theory.
Neural correlations during a cognitive task are central to study brain information processing and computation. However, they have been poorly analyzed due to the difficulty of recording simultaneous single neurons during task performance. In the present work, we quantified neural directional correlations using spike trains that were simultaneously recorded in sensory, premotor, and motor cortical areas of two monkeys during a somatosensory discrimination task. Upon modeling spike trains as binary time series, we used a nonparametric Bayesian method to estimate pairwise directional correlations between many pairs of neurons throughout different stages of the task, namely, perception, working memory, decision making, and motor report. We find that solving the task involves feedforward and feedback correlation paths linking sensory and motor areas during certain task intervals. Specifically, information is communicated by task-driven neural correlations that are significantly delayed across secondary somatosensory cortex, premotor, and motor areas when decision making takes place. Crucially, when sensory comparison is no longer requested for task performance, a major proportion of directional correlations consistently vanish across all cortical areas.vibrotactile discrimination | large-scale cortical networks | spike-train analysis | information theory | decision making T he problem of neural communication in the brain has been little explored traditionally due to the need for simultaneous recordings (1). The arrival of new techniques to record both neural population activity and single-neuron action potentials offers new prospects to study this problem (2, 3). Recently, population recordings have motivated a large number of works on multiunit interactions, including the study of interactions between local field potentials (LFPs) (4-6), LFPs and multiunit activity (5), and LFPs and neuronal spikes (7), but less attention has been paid to interactions between single-unit recordings (8). However, the analysis of simultaneous spike trains becomes critical because it is generally assumed that neurons are key units in distributing information across brain areas (9).An ideal paradigm to study neural communication is the somatosensory discrimination task designed by Romo and coworkers (10). In this task, a trained monkey discriminates the difference in frequency between two mechanical vibrations delivered sequentially to one fingertip (Fig. 1A). Essentially, the monkey must hold the first stimulus frequency (f 1) in working memory, must compare the second stimulus frequency (f 2) with the memory trace of f1 to form a decision of whether f 2 > f 1 or f 2 < f 1, and must postpone the decision until a sensory cue triggers the motor report (11). At the end of every trial, the monkey is rewarded with a drop of liquid for correct discriminations. Previous work on this task has analyzed how single-neuron responses across sensory and motor areas linearly correlate with stimuli and the decision report during the key stages of the ...
Epileptic seizures are known to follow specific changes in brain dynamics. While some algorithms can nowadays robustly detect these changes, a clear understanding of the mechanism by which these alterations occur and generate seizures is still lacking. Here, we provide crossvalidated evidence that such changes are initiated by an alteration of physiological network state dynamics. Specifically, our analysis of long intracranial electroencephalography (iEEG) recordings from a group of 10 patients identifies a critical phase of a few hours in which time-dependent network states become less variable ("degenerate"), and this phase is followed by a global functional connectivity reduction before seizure onset. This critical phase is characterized by an abnormal occurrence of highly correlated network instances and is shown to be particularly associated with the activity of the resected regions in patients with validated postsurgical outcome. Our approach characterizes preseizure network dynamics as a cascade of 2 sequential events providing new insights into seizure prediction and control.
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