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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., Author Summary Brain connectivity measures have been increasingly used to study cognition and neuropathologies with neuroimaging data. Many methods have been developed with particular objectives, in particular predictability to obtain biomarkers (i.e., which brain regions exhibit changes in their interactions across conditions) and interpretability (relating the connectivity measures back to the biology). In this article we present our framework for whole-brain effective connectivity that aims to find the equilibrium between these two desired properties, relying on a dynamic model that can be fitted to fMRI time series. Meanwhile, we compare it with previous work. Concretely, we show how machine learning can be used to extract biomarkers and how network-oriented analysis can be used to interpret the model estimates in terms of brain dynamics.

The brain can be regarded as an information processing system in which neurons store and propagate information about external stimuli and internal processes. Therefore, estimating interactions between neural activity at the cellular scale has significant implications in understanding how neuronal circuits encode and communicate information across brain areas to generate behavior. While the number of simultaneously recorded neurons is growing exponentially, current methods relying only on pairwise statistical dependencies still suffer from a number of conceptual and technical challenges that preclude experimental breakthroughs describing neural information flows. In this review, we examine the evolution of the field over the years, starting from descriptive statistics to model-based and model-free approaches. Then, we discuss in detail the Granger Causality framework, which includes many popular state-of-the-art methods and we highlight some of its limitations from a conceptual and practical estimation perspective. Finally, we discuss directions for future research, including the development of theoretical information flow models and the use of dimensionality reduction techniques to extract relevant interactions from large-scale recording datasets.

Movement-related theta oscillations in rodent hippocampus coordinate forward sweeps of location-specific neural activity that could be used to evaluate spatial trajectories online. This raises the possibility that increases in human hippocampal theta power accompany the evaluation of upcoming spatial choices. To test this hypothesis, we measured hippocampal oscillations during a spatial planning task that closely resembles a perceptual decision-making paradigm. In this task, female and male participants searched visually for the shortest path between a start and goal location in novel mazes that contained multiple choice points, and were subsequently asked to make a spatial decision at one of those choice points. During the search/planning period, we observed ~3-6 Hz hippocampal theta power increases that were negatively correlated with subsequent decision speed, where decision speed correlated with choice accuracy. Notably, hippocampal theta power increases were preferentially induced by planning in mazes containing a sequence of choices that were initially straightforward and subsequently ambiguous. These results implicate the hippocampal theta rhythm in the searches of deep decision trees, with a particular bottleneck form.

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. This work was supported by Dirección General de Asuntos del Personal Académico de la Universidad Nacional Autónoma de México Grants PAPIIT- IN202716 and PAPIIT-IN210819, Consejo Nacional de Ciencia y Tecnología Grant 240892 (to R.R.), Spanish Research Project PSI2016-75688-P [Agencia Estatal de Investigación/Fondo Europeo de Desarrollo Regional (AEI/FEDER), European Union] (to G.D.), by the European Union’s Horizon 2020 Research and Innovation Programme under Grants 720270 [Human Brain Project Specific Grant Agreement 1 (HBP SGA1)] and 785907 (HBP SGA2) (to G.D.), and by the Catalan Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) Programme 2017 Suport als Grups de Recerca (SGR) 1545 (to G.D.). Y.V. was supported by a Pew Latin American Fellowship and a Charles H. Revson Biomedical Science Fellowship.

We report a patient presenting drug-resistant, non-dominant temporal lobe epilepsy with ictal spitting and prosopometamorphopsia, both extremely rare semiologies. Second-phase pre-surgical monitoring was performed using SEEG due to lesion-negative imaging and the rare semiology. The seizure onset zone was delimited to the right anterior hippocampus and the temporobasal cortex, with the propagation zone within the entorhinal cortex. Interestingly, direct electrical stimulation to the entorhinal cortex, which was reproduced in a number of trials, evoked spitting without leading to seizures or post-discharges. After the resection of the epileptogenic zone, the patient remained seizure-free without AEDs for a follow-up period of five years (Engel Class 1a). The neuropathology revealed a focal cortical dysplasia type FCD-Ia. Spectral analysis of intracranial ictal EEG (iEEG) data suggested a possible role of the basal temporal and entorhinal cortex as a necessary node in ictal spitting. [Published with video sequences on www.epilepticdisorders.com].

Cardiac arrhythmias are a frequent complication related to seizures [1]. Most are benign although some, such as high-grade atrioventricular block, asystole, atrial fibrillation (AF), and ventricular fibrillation are potentially harmful. Indeed, several studies suggest that cardiac dysfunction may be the primary underlying cause of sudden unexpected death in epilepsy (SUDEP). Post-ictal atrial fibrillation (PIAF) is a very rare cardiac arrhythmia associated with seizures, and its relevance is currently unknown. AF characteristics include: 1) irregular R-R intervals, 2) absence of distinct repeating P waves, and 3) erratic atrial activity [2]. Atrial flutter is differentiated from AF because of the characteristic “saw tooth” waves on the ECG instead of the chaotic atrial rhythm of the AF. In fact, there are only four reported cases of PIAF diagnosed during video-EEG monitoring (VEM) [1], [3], [4]. We report a case of PIAF identified during long-term VEM, with recurrent episodes corroborated by Holter-ECG monitoring

The spatial mapping of localized events in brain activity critically depends on the correct identification of the pattern signatures associated with those events. For instance, in the context of epilepsy research, a number of different electrophysiological patterns have been associated with epileptogenic activity. Motivated by the need to define automated seizure focus detectors, we propose a novel data-driven algorithm for the spatial identification of localized events that is based on the following rationale: the distribution of emerging oscillations during confined events across all recording sites is highly non-uniform and can be mapped using a spatial entropy function. By applying this principle to EEG recording obtained from 67 distinct seizure epochs, our method successfully identified the seizure focus on a group of ten drug-resistant temporal lobe epilepsy patients (average sensitivity: 0.94, average specificity: 0.90) together with its characteristic electrophysiological pattern signature. Cross-validation of the method outputs with postresective information revealed the consistency of our findings in long follow-up seizure-free patients. Overall, our methodology provides a reliable computational procedure that might be used as in both experimental and clinical domains to identify the neural populations undergoing an emerging functional or pathological transition.Conflict of interestsThe authors declare no competing financial interests.

Atypical antipsychotic drugs (APDs) used to treat positive and negative symptoms in schizophrenia block serotonin receptors 5-HT2AR and dopamine receptors D2R and stimulate 5-HT1AR 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-HT1AR, 5-HT2AR and D2R 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-HT1AR antagonist WAY-100635, risperidone + 5-HT2A/2CR agonist DOI, risperidone + D2R agonist quinpirole). Risperidone, 5-HT1AR agonism with 8-OH-DPAT, 5-HT2AR antagonism with M100907, and D2R 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-HT2AR 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-HT1AR agonism and 5-HT2AR antagonism in risperidone-induced alterations of delta, beta and gamma oscillations, while D2R 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. This work was supported by the Brain and Behavior Research Foundation (NARSAD Young Investigator Award 23014), SAF2013-49129-C2-2-R and SAF2016-80726-R (AEI / FEDER, UE) by the Spanish Ministry of Economy and Competitiveness (MINECO; to MVP). M.V. Puig is a Ramon y Cajal Investigator (RyC-2012-10042), M. Alemany a FPI predoctoral fellow (BES-2014-070429) from MINECO, and C. Delgado a FI predoctoral fellow (2018FI_B_00112)from the Generalitat de Catalunya (AGAUR)

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., Author summary Understanding and predicting the generation of seizures in epileptic patients is fundamental to improving the quality of life of the more than 1% of the world population who suffer from this illness. Although seizure prediction has made important advances over the last decade, there is a lack of understanding of the common principles explaining the transitions that brain activity undergoes before a seizure. In this study, we characterized this transition from a novel perspective grounded on the mathematical analysis of continuous recordings inside the brains of epileptic patients over several days using depth electrodes. We show that the critical period preceding a seizure unfolds in a two-stage process. It begins with a phase of several hours when the highly correlated activity in the preceding days is altered, and it proceeds with a second, shorter phase of decrease in global connectivity before the seizure onset. Furthermore, our analysis reveals that these global alterations are more locally manifested in areas that are selected for surgical treatment. Our study suggests that preseizure activity might follow global stereotyped dynamics that could be targeted and modulated to prevent epileptic seizures.

Cognitive processing requires the ability to flexibly integrate and process information across large brain networks. How do brain networks dynamically reorganize to allow broad communication between many different brain regions in order to integrate information? We record neural activity from 12 epileptic patients using intracranial EEG while performing three cognitive tasks. We assess how the functional connectivity between different brain areas changes to facilitate communication across them. 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). We also found higher levels of global synchronization and functional connectivity during task execution, again particularly in the gamma band. More importantly, functional connectivity modulations were not caused by changes in the level of the underlying oscillations. Instead, these modulations were caused by a rearrangement of the mutual synchronization between the different nodes as proposed by the “Communication Through Coherence” Theory. The authors would like to thank all patients for their participation and the IMIM-Hospital del Mar Epilepsy Unit staff for their technical assistance in collecting the data. This work was supported by Gustavo Deco's ERC Advanced Grant: DYSTRUCTURE (n. 295129), from the European Union's Horizon 2020 research and innovation programme under grant agreement No. (HBP SGA1) and by the Spanish Research Project PSI2013-42091-P. M.L.K. is supported by the ERC Consolidator Grant CAREGIVING (n. 615539) and the Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117).

We investigated whether it is possible to study the network dynamics and the anatomical regions involved in the earliest moments of picture naming by using invasive electroencephalogram (EEG) traces to predict naming errors. Four right-handed participants with focal epilepsy explored with extensive stereotactic implant montages that recorded temporal, parietal and occipital regions -in two patients of both hemispheres-named a total of 228 black and white pictures in three different sessions recorded in different days. The subjects made errors that involved anomia and semantic dysphasia, which related to word frequency and not to visual complexity. Using different modalities of spectrum analysis and classification with a support vector machine (SVM) we could predict errors with rates that ranged from slightly above chance level to 100%, even in the preconscious phase, i.e., 100 msec after stimulus presentation. The highest rates were obtained using the gamma bands of all contact spectra without averaging, which implies a fine modulation of the neuronal activity at a network level. Despite no subset of nodes could match the whole set, rates close to the best prediction scores were obtained through the spectra of the temporal-parietal and temporal-occipital junction along with the temporal pole and hippocampus. When both hemispheres were explored nodes from the left side dominated in the best subsets. We argue that posterior temporal regions, especially of the dominant side, are involved very early, even in the preconscious phase (100 msec), in language production.

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., Comment: Article and Supplementary Information

OBJECTIVE: We introduce a method that quantifies the consistent involvement of intracranially monitored regions in recurrent focal seizures. METHODS: We evaluated the consistency of two ictal spectral activation patterns (mean power change and power change onset time) in intracranial recordings across focal seizures from seven patients with clinically marked seizure onset zone (SOZ). We examined SOZ discrimination using both patterns in different frequency bands and periods of interest. RESULTS: Activation patterns were proved to be consistent across more than 80% of recurrent ictal epochs. In all patients, whole-seizure mean activations were significantly higher for SOZ than non-SOZ regions (P