A framework for quantifying the coupling between brain connectivity and heartbeat dynamics: Insights into the disrupted network physiology in Parkinson's disease.

Parkinson's disease (PD) often shows disrupted brain connectivity and autonomic dysfunctions, progressing alongside with motor and cognitive decline. Recently, PD has been linked to a reduced sensitivity to cardiac inputs, that is, cardiac interoception. Altogether, those signs suggest that PD causes an altered brain-heart connection whose mechanisms remain unclear. Our study aimed to explore the large-scale network disruptions and the neurophysiology of disrupted interoceptive mechanisms in PD. We focused on examining the alterations in brain-heart coupling in PD and their potential connection to motor symptoms. We developed a proof-of-concept method to quantify relationships between the co-fluctuations of brain connectivity and cardiac sympathetic and parasympathetic activities. We quantified the brain-heart couplings from electroencephalogram and electrocardiogram recordings from PD patients on and off dopaminergic medication, as well as in healthy individuals at rest. Our results show that the couplings of fluctuating alpha and gamma connectivity with cardiac sympathetic dynamics are reduced in PD patients, as compared to healthy individuals. Furthermore, we show that PD patients under dopamine medication recover part of the brain-heart coupling, in proportion with the reduced motor symptoms. Our proposal offers a promising approach to unveil the physiopathology of PD and promoting the development of new evaluation methods for the early stages of the disease.

Daily resting-state intracranial EEG connectivity for seizure risk forecasts.

Forecasting seizure risk aims to detect proictal states in which seizures would be more likely to occur. Classical seizure prediction models are trained over long-term electroencephalographic (EEG) recordings to detect specific preictal changes for each seizure, independently of those induced by shifts in states of vigilance. A daily single measure-during a vigilance-controlled period-to estimate the risk of upcoming seizure(s) would be more convenient. Here, we evaluated whether intracranial EEG connectivity (phase-locking value), estimated from daily vigilance-controlled resting-state recordings, could allow distinguishing interictal (no seizure) from preictal (seizure within the next 24 h) states. We also assessed its relevance for daily forecasts of seizure risk using machine learning models. Connectivity in the theta band was found to provide the best prediction performances (area under the curve ≥ .7 in 80% of patients), with accurate daily and prospective probabilistic forecasts (mean Brier score and Brier skill score of .13 and .72, respectively). More efficient ambulatory clinical application could be considered using mobile EEG or chronic implanted devices.

Innovative participatory bilingual data analysis with Latinx/@ immigrants: Language, power, and transformation.

OBJECTIVE: The insights of Latinx/@ immigrants are essential to developing interventions that better address complex multilevel phenomena impacting mental health. Despite important advances in methods that genuinely embody participatory research practices, attention to collaborative data collection, analysis, and dissemination are limited. Our aim is to describe the development and implementation of research practices to address these gaps through an emphasis on and understanding of the centrality of language in collaborative research processes. METHOD: Guided from the outset by community-based participatory research principles, our community-academic research partnership recognized the importance of developing and intentionally studying our collaborative processes. As part of an ethnographic interview study with 24 Latinx/@ immigrants, a community-university research team developed innovative methods, including practices related to research team meetings, data collection, analysis, and dissemination, which we documented through ongoing discussion and reflection. RESULTS: The resulting participatory research processes were grounded in a theoretical framework of praxis and language and included six innovative and iterative stages: (a) Establishing the research team, (b) planning the interview process/data collection, (c) developing the data analysis methodology, (d) interpreting findings to adapt the intervention, (e) integrating results of the participatory process into the analysis, and (f) data analysis for dissemination. CONCLUSIONS: A focus on praxis and language revealed how the language of research structures' power, meaning, feeling, collaboration, analysis, and transformation. We also found that bilingual participatory analytic processes have important implications with respect to achieving genuine inclusion in rigorous research that moves toward equity for Latinx/@ immigrants and other populations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Neuronal excitability and sensory responsiveness in the thalamo-cortical network in a novel rat model of isoelectric brain state.

KEY POINTS: The neuronal and network properties that persist during an isoelectric coma remain largely unknown.  We developed a new in vivo rat model to assess cell excitability and sensory responsiveness in the thalamo-cortical pathway during an isoflurane-induced isoelectric brain state.  The isoelectric electrocorticogram reflected a complete interruption of spontaneous synaptic and firing activities in cortical and thalamic neurons.  Cell excitability and sensory responses in the thalamo-cortical network persisted at a reduced level in the isoelectric condition and returned to control values after resumption of background brain activity.  These findings could lead to a reassessment of the functional status of the drug-induced isoelectric state: a latent state in which individual neurons and networks retain to some extent the ability of being activated by external inputs. ABSTRACT: The neuronal and network properties that persist in an isoelectric brain completely deprived of spontaneous electrical activity remain largely unexplored. Here, we developed a new in vivo rat model to examine cell excitability and sensory responsiveness in somatosensory thalamo-cortical networks during the interruption of endogenous brain activity induced by high doses of isoflurane. Electrocorticograms (ECoGs) from the barrel cortex were captured simultaneously with either intracellular recordings of subjacent cortical pyramidal neurons or extracellular records of the related thalamo-cortical neurons. Isoelectric ECoG periods reflected the disappearance of spontaneous synaptic and firing activities in cortical and thalamic neurons. This was associated with a sustained membrane hyperpolarization and a reduced intrinsic excitability in deep-layer cortical neurons, without significant changes in their membrane input resistance. Concomitantly, we found that whisker-evoked potentials in the ECoG and synaptic responses in cortical neurons were attenuated in amplitude and increased in latency. Impaired responsiveness in the barrel cortex paralleled with a lowering of the sensory-induced firing in thalamic cells. The return of endogenous brain electrical activities, after reinstatement of a control isoflurane concentration, led to the recovery of cortical neurons excitability and sensory responsiveness. These findings demonstrate the persistence of a certain level of cell excitability and sensory integration in the isoelectric state and the full recovery of cortico-thalamic functions after restoration of internal cerebral activities.

Preictal state detection using prodromal symptoms: A machine learning approach.

A reliable identification of a high-risk state for upcoming seizures may allow for preemptive treatment and improve the quality of patients' lives. We evaluated the ability of prodromal symptoms to predict preictal states using a machine learning (ML) approach. Twenty-four patients with drug-resistant epilepsy were admitted for continuous video-electroencephalographic monitoring and filled out a daily four-point questionnaire on prodromal symptoms. Data were then classified into (1) a preictal group for questionnaires completed in a 24-h period prior to at least one seizure (n1  = 58) and (2) an interictal group for questionnaires completed in a 24-h period without seizures (n2  = 190). Our prediction model was based on a support vector machine classifier and compared to a Fisher's linear classifier. The combination of all the prodromal symptoms yielded a good prediction performance (area under the curve [AUC] = .72, 95% confidence interval [CI] = .61-.81). This performance was significantly enhanced by selecting a subset of the most relevant symptoms (AUC = .80, 95% CI = .69-.88). In comparison, the linear classifier systematically failed (AUCs < .6). Our findings indicate that the ML analysis of prodromal symptoms is a promising approach to identifying preictal states prior to seizures. This could pave the way for development of clinical strategies in seizure prevention and even a noninvasive alarm system.

Abnormal Activity of Neck Inspiratory Muscles during Sleep as a Prognostic Indicator in Chronic Obstructive Pulmonary Disease.

Rationale: In patients with chronic obstructive pulmonary disease (COPD), increased activity of neck inspiratory muscles has been reported as a compensatory response to hyperinflation-related diaphragmatic dysfunction. The persistence of this activity during sleep could attenuate sleep-related hypoventilation and also negatively impact sleep and clinical outcomes.Objectives: To assess the persistence of neck-muscle activity during sleep in patients with COPD recovering from severe exacerbations (i.e., requiring hospitalization) and its impact on sleep quality and recurrence of exacerbations.Methods: Video polysomnography with neck-muscle EMG was performed in patients with COPD who were recovering from a severe exacerbation. The follow-up period lasted 6 months to record the next severe exacerbation.Measurements and Main Results: Twenty-nine patients were included in the study (median [25th-75th percentile] age, 71 [64-72] yr; 55% male; body mass index, 24 [21-29]; FEV1% predicted, 37 [29-45]; and BODE [body mass index, airflow obstruction, dyspnea, and exercise] index, 6 [5-7]). Twenty-six of these patients exhibited sleep-related neck-muscle activity, which was intermittent (limited to stage 3 sleep) in 17 and permanent throughout sleep in 9. α-Delta EEG activity during stage 3 sleep was observed in 87% of the patients. Compared with patients with no or intermittent neck-muscle activity, those with permanent neck-muscle activity showed more disrupted sleep, had experienced more exacerbations in the previous year, and suffered their next severe exacerbation earlier.Conclusions: Sleep-related neck-muscle activity occurs frequently in patients with COPD who are recovering from a severe exacerbation and seems to negatively affect sleep quality and prognosis; therefore, identification of this activity might improve COPD management after a severe exacerbation.

Functional disconnection of associative cortical areas predicts performance during BCI training.

Brain-computer interfaces (BCIs) have been largely developed to allow communication, control, and neurofeedback in human beings. Despite their great potential, BCIs perform inconsistently across individuals and the neural processes that enable humans to achieve good control remain poorly understood. To address this question, we performed simultaneous high-density electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings in a motor imagery-based BCI training involving a group of healthy subjects. After reconstructing the signals at the cortical level, we showed that the reinforcement of motor-related activity during the BCI skill acquisition is paralleled by a progressive disconnection of associative areas which were not directly targeted during the experiments. Notably, these network connectivity changes reflected growing automaticity associated with BCI performance and predicted future learning rate. Altogether, our findings provide new insights into the large-scale cortical organizational mechanisms underlying BCI learning, which have implications for the improvement of this technology in a broad range of real-life applications.

Quality Assessment of Single-Channel EEG for Wearable Devices.

The recent embedding of electroencephalographic (EEG) electrodes in wearable devices raises the problem of the quality of the data recorded in such uncontrolled environments. These recordings are often obtained with dry single-channel EEG devices, and may be contaminated by many sources of noise which can compromise the detection and characterization of the brain state studied. In this paper, we propose a classification-based approach to effectively quantify artefact contamination in EEG segments, and discriminate muscular artefacts. The performance of our method were assessed on different databases containing either artificially contaminated or real artefacts recorded with different type of sensors, including wet and dry EEG electrodes. Furthermore, the quality of unlabelled databases was evaluated. For all the studied databases, the proposed method is able to rapidly assess the quality of the EEG signals with an accuracy higher than 90%. The obtained performance suggests that our approach provide an efficient, fast and automated quality assessment of EEG signals from low-cost wearable devices typically composed of a dry single EEG channel.

A Topological Criterion for Filtering Information in Complex Brain Networks.

In many biological systems, the network of interactions between the elements can only be inferred from experimental measurements. In neuroscience, non-invasive imaging tools are extensively used to derive either structural or functional brain networks in-vivo. As a result of the inference process, we obtain a matrix of values corresponding to a fully connected and weighted network. To turn this into a useful sparse network, thresholding is typically adopted to cancel a percentage of the weakest connections. The structural properties of the resulting network depend on how much of the inferred connectivity is eventually retained. However, how to objectively fix this threshold is still an open issue. We introduce a criterion, the efficiency cost optimization (ECO), to select a threshold based on the optimization of the trade-off between the efficiency of a network and its wiring cost. We prove analytically and we confirm through numerical simulations that the connection density maximizing this trade-off emphasizes the intrinsic properties of a given network, while preserving its sparsity. Moreover, this density threshold can be determined a-priori, since the number of connections to filter only depends on the network size according to a power-law. We validate this result on several brain networks, from micro- to macro-scales, obtained with different imaging modalities. Finally, we test the potential of ECO in discriminating brain states with respect to alternative filtering methods. ECO advances our ability to analyze and compare biological networks, inferred from experimental data, in a fast and principled way.

Cortical neurons and networks are dormant but fully responsive during isoelectric brain state.

A continuous isoelectric electroencephalogram reflects an interruption of endogenously-generated activity in cortical networks and systematically results in a complete dissolution of conscious processes. This electro-cerebral inactivity occurs during various brain disorders, including hypothermia, drug intoxication, long-lasting anoxia and brain trauma. It can also be induced in a therapeutic context, following the administration of high doses of barbiturate-derived compounds, to interrupt a hyper-refractory status epilepticus. Although altered sensory responses can be occasionally observed on an isoelectric electroencephalogram, the electrical membrane properties and synaptic responses of individual neurons during this cerebral state remain largely unknown. The aim of the present study was to characterize the intracellular correlates of a barbiturate-induced isoelectric electroencephalogram and to analyse the sensory-evoked synaptic responses that can emerge from a brain deprived of spontaneous electrical activity. We first examined the sensory responsiveness from patients suffering from intractable status epilepticus and treated by administration of thiopental. Multimodal sensory responses could be evoked on the flat electroencephalogram, including visually-evoked potentials that were significantly amplified and delayed, with a high trial-to-trial reproducibility compared to awake healthy subjects. Using an analogous pharmacological procedure to induce prolonged electro-cerebral inactivity in the rat, we could describe its cortical and subcortical intracellular counterparts. Neocortical, hippocampal and thalamo-cortical neurons were all silent during the isoelectric state and displayed a flat membrane potential significantly hyperpolarized compared with spontaneously active control states. Nonetheless, all recorded neurons could fire action potentials in response to intracellularly injected depolarizing current pulses and their specific intrinsic electrophysiological features were preserved. Manipulations of the membrane potential and intracellular injection of chloride in neocortical neurons failed to reveal an augmented synaptic inhibition during the isoelectric condition. Consistent with the sensory responses recorded from comatose patients, large and highly reproducible somatosensory-evoked potentials could be generated on the inactive electrocorticogram in rats. Intracellular recordings revealed that the underlying neocortical pyramidal cells responded to sensory stimuli by complex synaptic potentials able to trigger action potentials. As in patients, sensory responses in the isoelectric state were delayed compared to control responses and exhibited an elevated reliability during repeated stimuli. Our findings demonstrate that during prolonged isoelectric brain state neurons and synaptic networks are dormant rather than excessively inhibited, conserving their intrinsic properties and their ability to integrate and propagate environmental stimuli.

Neurophysiological Evidence for a Cortical Contribution to the Wakefulness-Related Drive to Breathe Explaining Hypocapnia-Resistant Ventilation in Humans.

Spontaneous ventilation in mammals is driven by automatic brainstem networks that generate the respiratory rhythm and increase ventilation in the presence of increased carbon dioxide production. Hypocapnia decreases the drive to breathe and induces apnea. In humans, this occurs during sleep but not during wakefulness. We hypothesized that hypocapnic breathing would be associated with respiratory-related cortical activity similar to that observed during volitional breathing, inspiratory constraints, or in patients with defective automatic breathing (preinspiratory potentials). Nineteen healthy subjects were studied under passive (mechanical ventilation, n = 10) or active (voluntary hyperventilation, n = 9) profound hypocapnia. Ventilatory and electroencephalographic recordings were performed during voluntary sniff maneuvers, normocapnic breathing, hypocapnia, and after return to normocapnia. EEG recordings were analyzed with respect to the ventilatory flow signal to detect preinspiratory potentials in frontocentral electrodes and to construct time-frequency maps. After passive hyperventilation, hypocapnia was associated with apnea in 3 cases and ventilation persisted in 7 cases (3 and 6 after active hyperventilation, respectively). No respiratory-related EEG activity was observed in subjects with hypocapnia-related apneas. In contrast, preinspiratory potentials were present at vertex recording sites in 12 of the remaining 13 subjects (p < 0.001). This was corroborated by time-frequency maps. This study provides direct evidence of a cortical substrate to hypocapnic breathing in awake humans and fuels the notion of corticosubcortical cooperation to preserve human ventilation in a variety of situations. Of note, maintaining ventilatory activity at low carbon dioxide levels is among the prerequisites to speech production insofar as speech often induces hypocapnia. SIGNIFICANCE STATEMENT: Human ventilatory activity persists, during wakefulness, even when hypocapnia makes it unnecessary. This peculiarity of human breathing control is important to speech and speech-breathing insofar as speech induces hypocapnia. This study evidences a specific respiratory-related cortical activity. This suggests that human hypocapnic breathing is driven, at least in part, by cortical mechanisms similar to those involved in volitional breathing, in breathing against mechanical constraints or with weak inspiratory muscle, and in patients with defective medullary breathing pattern generators. This fuels the notion that the human ventilatory drive during wakefulness often results from a corticosubcortical cooperation, and opens new avenues to study certain ventilatory and speech disorders.

Interhemispheric Connectivity Characterizes Cortical Reorganization in Motor-Related Networks After Cerebellar Lesions.

Although cerebellar-cortical interactions have been studied extensively in animal models and humans using modern neuroimaging techniques, the effects of cerebellar stroke and focal lesions on cerebral cortical processing remain unknown. In the present study, we analyzed the large-scale functional connectivity at the cortical level by combining high-density electroencephalography (EEG) and source imaging techniques to evaluate and quantify the compensatory reorganization of brain networks after cerebellar damage. The experimental protocol comprised a repetitive finger extension task by 10 patients with unilateral focal cerebellar lesions and 10 matched healthy controls. A graph theoretical approach was used to investigate the functional reorganization of cortical networks. Our patients, compared with controls, exhibited significant differences at global and local topological level of their brain networks. An abnormal rise in small-world network efficiency was observed in the gamma band (30-40 Hz) during execution of the task, paralleled by increased long-range connectivity between cortical hemispheres. Our findings show that a pervasive reorganization of the brain network is associated with cerebellar focal damage and support the idea that the cerebellum boosts or refines cortical functions. Clinically, these results suggest that cortical changes after cerebellar damage are achieved through an increase in the interactions between remote cortical areas and that rehabilitation should aim to reshape functional activation patterns. Future studies should determine whether these hypotheses are limited to motor tasks or if they also apply to cerebro-cerebellar dysfunction in general.

Multilayer motif analysis of brain networks.

In the last decade, network science has shed new light both on the structural (anatomical) and on the functional (correlations in the activity) connectivity among the different areas of the human brain. The analysis of brain networks has made possible to detect the central areas of a neural system and to identify its building blocks by looking at overabundant small subgraphs, known as motifs. However, network analysis of the brain has so far mainly focused on anatomical and functional networks as separate entities. The recently developed mathematical framework of multi-layer networks allows us to perform an analysis of the human brain where the structural and functional layers are considered together. In this work, we describe how to classify the subgraphs of a multiplex network, and we extend the motif analysis to networks with an arbitrary number of layers. We then extract multi-layer motifs in brain networks of healthy subjects by considering networks with two layers, anatomical and functional, respectively, obtained from diffusion and functional magnetic resonance imaging. Results indicate that subgraphs in which the presence of a physical connection between brain areas (links at the structural layer) coexists with a non-trivial positive correlation in their activities are statistically overabundant. Finally, we investigate the existence of a reinforcement mechanism between the two layers by looking at how the probability to find a link in one layer depends on the intensity of the connection in the other one. Showing that functional connectivity is non-trivially constrained by the underlying anatomical network, our work contributes to a better understanding of the interplay between the structure and function in the human brain.

Integrative properties and transfer function of cortical neurons initiating absence seizures in a rat genetic model.

KEY POINTS: Absence seizures are accompanied by spike-and-wave discharges in cortical electroencephalograms. These complex paroxysmal activities, affecting the thalamocortical networks, profoundly alter cognitive performances and preclude conscious perception. Here, using a well-recognized genetic model of absence epilepsy, we investigated in vivo how information processing was impaired in the ictogenic neurons, i.e. the population of cortical neurons responsible for seizure initiation. In between seizures, ictogenic neurons were more prone to generate bursting activity and their firing response to weak depolarizing events was considerably facilitated compared to control neurons. In the course of seizures, information processing became unstable in ictogenic cells, alternating between an increased and a decreased responsiveness to excitatory inputs, depending on the spike and wave patterns. The state-dependent modulation in the excitability of ictogenic neurons affects their inter-seizure transfer function and their time-to-time responsiveness to incoming inputs during absences. ABSTRACT: Epileptic seizures result from aberrant cellular and/or synaptic properties that can alter the capacity of neurons to integrate and relay information. During absence seizures, spike-and-wave discharges (SWDs) interfere with incoming sensory inputs and preclude conscious experience. The Genetic Absence Epilepsy Rats from Strasbourg (GAERS), a well-established animal model of absence epilepsy, allows exploration of the cellular basis of this impaired information processing. Here, by combining in vivo electrocorticographic and intracellular recordings from GAERS and control animals, we investigated how the pro-ictogenic properties of seizure-initiating cortical neurons modify their integrative properties and input-output operation during inter-ictal periods and during the spike (S-) and wave (W-) cortical patterns alternating during seizures. In addition to a sustained depolarization and an excessive firing rate in between seizures, ictogenic neurons exhibited a pronounced hyperpolarization-activated depolarization compared to homotypic control neurons. Firing frequency versus injected current relations indicated an increased sensitivity of GAERS cells to weak excitatory inputs, without modifications in the trial-to-trial variability of current-induced firing. During SWDs, the W-component resulted in paradoxical effects in ictogenic neurons, associating an increased membrane input resistance with a reduction in the current-evoked firing responses. Conversely, the collapse of cell membrane resistance during the S-component was accompanied by an elevated current-evoked firing relative to W-sequences, which remained, however, lower compared to inter-ictal periods. These findings show a dynamic modulation of ictogenic neurons' intrinsic properties that may alter inter-seizure cortical function and participate in compromising information processing in cortical networks during absences.

Electroencephalographic detection of respiratory-related cortical activity in humans: from event-related approaches to continuous connectivity evaluation.

The presence of a respiratory-related cortical activity during tidal breathing is abnormal and a hallmark of respiratory difficulties, but its detection requires superior discrimination and temporal resolution. The aim of this study was to validate a computational method using EEG covariance (or connectivity) matrices to detect a change in brain activity related to breathing. In 17 healthy subjects, EEG was recorded during resting unloaded breathing (RB), voluntary sniffs, and breathing against an inspiratory threshold load (ITL). EEG were analyzed by the specially developed covariance-based classifier, event-related potentials, and time-frequency (T-F) distributions. Nine subjects repeated the protocol. The classifier could accurately detect ITL and sniffs compared with the reference period of RB. For ITL, EEG-based detection was superior to airflow-based detection (P < 0.05). A coincident improvement in EEG-airflow correlation in ITL compared with RB (P < 0.05) confirmed that EEG detection relates to breathing. Premotor potential incidence was significantly higher before inspiration in sniffs and ITL compared with RB (P < 0.05), but T-F distributions revealed a significant difference between sniffs and RB only (P < 0.05). Intraclass correlation values ranged from poor (-0.2) to excellent (1.0). Thus, as for conventional event-related potential analysis, the covariance-based classifier can accurately predict a change in brain state related to a change in respiratory state, and given its capacity for near "real-time" detection, it is suitable to monitor the respiratory state in respiratory and critically ill patients in the development of a brain-ventilator interface.

An unusual type of biliar cyst: A case report.

 We present the case of a 56-yr-old woman with vague abdominal pain of approximately 5 months duration. An ultrasound study showed moderate dilation of the common bile duct. Magnetic resonance cholangiopancreatography confirmed a cystic dilatation of the right hepatic duct with intra and extra hepatic component. The patient underwent right hepatectomy and complete excision of the cyst. Microscopically, the cyst wall was formed by fibrous tissue with mild acute and chronic inflammatory infiltrate, the inner surface showed a single layer of columnar epithelium and extensive squamous metaplasia without atypia, wich expressed p63 and high molecular weight cytoqueratin (34BE12).

Excitability and responsiveness of rat barrel cortex neurons in the presence and absence of spontaneous synaptic activity in vivo.

The amplitude and temporal dynamics of spontaneous synaptic activity in the cerebral cortex vary as a function of brain states. To directly assess the impact of different ongoing synaptic activities on neocortical function, we performed in vivo intracellular recordings from barrel cortex neurons in rats under two pharmacological conditions generating either oscillatory or tonic synaptic drive. Cortical neurons membrane excitability and firing responses were compared, in the same neurons, before and after complete suppression of background synaptic drive following systemic injection of a high dose of anaesthetic. Compared to the oscillatory state, the tonic pattern resulted in a more depolarized and less fluctuating membrane potential (Vm), a lower input resistance (Rm) and steeper relations of firing frequency versus injected current (F-I). Whatever their temporal dynamics, suppression of background synaptic activities increased mean Vm, without affecting Rm, and induced a rightward shift of F-I curves. Both types of synaptic drive generated a high variability in current-induced firing rate and patterns in cortical neurons, which was much reduced after removal of spontaneous activity. These findings suggest that oscillatory and tonic synaptic patterns differentially facilitate the input-output function of cortical neurons but result in a similar moment-to-moment variability in spike responses to incoming depolarizing inputs.

Abnormal functional connectivity between motor cortex and pedunculopontine nucleus following chronic dopamine depletion.

The activity of the basal ganglia is altered in Parkinson's disease (PD) as a consequence of the degeneration of dopamine neurons in the substantia nigra pars compacta. This results in aberrant discharge patterns and expression of exaggerated oscillatory activity across the basal ganglia circuit. Altered activity has also been reported in some of the targets of the basal ganglia, including the pedunculopontine nucleus (PPN), possibly due to its close interconnectivity with most regions of the basal ganglia. However, the nature of the involvement of the PPN in the pathophysiology of PD has not been fully elucidated. Here, we recorded local field potentials in the motor cortex and the PPN in the 6-hydroxydopamine (6-OHDA)-lesioned rat model of PD under urethane anesthesia. By means of linear and nonlinear statistics, we analyzed the synchrony between the motor cortex and the PPN and the delay in the interaction between these two structures. We observed the presence of coherent activity between the cortex and the PPN in low (5-15 Hz)- and high (25-35 Hz)-frequency bands during episodes of cortical activation. In each case, the cortex led the PPN. Dopamine depletion strengthened the interaction of the low-frequency activities by increasing the coherence specifically in the theta and alpha ranges and reduced the delay of the interaction in the gamma band. Our data show that cortical inputs play a determinant role in leading the coherent activity with the PPN and support the involvement of the PPN in the pathophysiology of PD.

Abdominal cerebrospinal fluid pseudocyst: a comparative analysis between children and adults.

PURPOSE: Abdominal cerebrospinal fluid (CSF) pseudocyst is a rare but important complication in patients with ventriculoperitoneal shunt (VPS). In addition to presenting our experience, we performed a comparative analysis between children and adults with this entity. To the author's knowledge, there are no studies in which this condition has been compared. METHODS: The PubMed database was searched for all relevant reports published from 1954 to 2012. The differences were statistically compared, especially regarding clinical investigations, etiology of the hydrocephalus, shunt revision, CSF infection, treatment, and recurrence. Chi-square test or Fisher's exact test was used to find associations among the variables. RESULTS: Compiled from literature, we found 393 cases of abdominal pseudocyst: 295 children, including our cases, and 55 adults, with age not informed in 43 cases. In children, 33 % of the patients have a positive culture on presentation, with higher incidence in children younger than 10 years. In contrast, only 15 % among adults were positive CSF culture. In total, 287 abdominal pseudocyst cases who underwent shunt revision have been reported; 78.4 % of children and 62.2 % of adults. The main occurrence of this complication according to the etiology of hydrocephalus in children was different from adults. The recurrence of pseudocyst occurred in 19.8 and 24.2 % of children and adults, respectively. CONCLUSIONS: The differences between children and adults might represent distinct trends on the etiology and treatment of this entity. Hence, additional well-designed cohort studies will be necessary to strengthen our findings.

Measures of the coupling between fluctuating brain network organization and heartbeat dynamics.

In recent years, there has been an increasing interest in studying brain-heart interactions. Methodological advancements have been proposed to investigate how the brain and the heart communicate, leading to new insights into some neural functions. However, most frameworks look at the interaction of only one brain region with heartbeat dynamics, overlooking that the brain has functional networks that change dynamically in response to internal and external demands. We propose a new framework for assessing the functional interplay between cortical networks and cardiac dynamics from noninvasive electrophysiological recordings. We focused on fluctuating network metrics obtained from connectivity matrices of EEG data. Specifically, we quantified the coupling between cardiac sympathetic-vagal activity and brain network metrics of clustering, efficiency, assortativity, and modularity. We validate our proposal using open-source datasets: one that involves emotion elicitation in healthy individuals, and another with resting-state data from patients with Parkinson's disease. Our results suggest that the connection between cortical network segregation and cardiac dynamics may offer valuable insights into the affective state of healthy participants, and alterations in the network physiology of Parkinson's disease. By considering multiple network properties, this framework may offer a more comprehensive understanding of brain-heart interactions. Our findings hold promise in the development of biomarkers for diagnostic and cognitive/motor function evaluation.