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.

Simultaneous Parkinsonism and Dementia as Initial Presentation of Intracranial Dural Arteriovenous Fistulas: A Systematic Review.

BACKGROUND: Intracranial dural arteriovenous fistulas (IDAVFs) are abnormal vascular connections between dural arteries and various venous structures within the brain. IDAVFs, rarely present with parkinsonism and dementia concurrently, making this a unique and underexplored clinical scenario. To the best of our knowledge, this is the first systematic review to comprehensively analyze cases of IDAVFs manifesting as both parkinsonism and dementia. METHODS: We assessed databases from inception to September 18, 2023. We identified studies describing patients with IDAVFs initially presenting with dementia or parkinsonism. Inclusion criteria encompassed case reports and case series, while excluding review articles, guidelines, technical notes, comments, conference abstracts, and editorials. RESULTS: The systematic search resulted in the initial screening of 383 studies, with 33 articles meeting the inclusion criteria. Among these, 29 were case reports, often describing 3 or fewer patients. From the remaining 4 case series, data pertinent to patients presenting both parkinsonism and dementia were selectively extracted, yielding a total study population of 43 patients. The anatomical distribution of IDAVFs within this cohort was diverse, with the transverse and sigmoid sinuses being the most common locations. Although most of these patients received endovascular therapy, a few underwent microsurgical occlusion or combined surgical and endovascular treatment. CONCLUSIONS: IDAVFs presenting with both parkinsonism and dementia represent a rare clinical entity. This systematic review provides valuable insights into the clinical characteristics, treatment options, and outcomes for such cases. However, additional research involving larger cohorts is essential to better comprehend the underlying mechanisms and establish standardized therapeutic guidelines.

Identifying gaps on health impacts, exposures, and vulnerabilities to climate change on human health and wellbeing in South America: a scoping review.

There is an important gap in regional information on climate change and health, limiting the development of science-based climate policies in South American countries. This study aims to identify the main gaps in the existing scientific literature on the impacts, exposure, and vulnerabilities of climate change on population health. A scoping review was performed guided by four sub-questions focused on the impacts of climate change on physical and mental health, exposure and vulnerability factors of population to climate hazards. The main findings showed that physical impacts mainly included infectious diseases, while mental health impacts included trauma, depression, and anxiety. Evidence on population exposure to climate hazards is limited, and social determinants of health and individual factors were identified as vulnerability factors. Overall, evidence on the intersection between climate change and health is limited in South America and has been generated in silos, with limited transdisciplinary research. More formal and systematic information should be generated to inform public policy. Funding: None.

Can heart rate variability identify a high-risk state of upcoming seizure?

Heart rate variability (HRV) is an accessible and convenient means to assess the sympathetic/parasympathetic balance. Autonomic dysfunctions may reflect a pro-ictal state and occur before the seizure onset. Previous studies have reported HRV-based models to identify preictal states in continuous electrocardiogram (EKG) monitoring. Here, we evaluated the ability of HRV metrics extracted from daily single resting-state periods to estimate the risk of upcoming seizure(s) using probabilistic forecasts. Daily standardized 10-min vigilance-controlled EKG periods were recorded in 15 patients with drug-resistant focal epilepsy who underwent intracerebral electroencephalography (EEG). Analyses of a total of 156 periods, based on machine learning approaches, suggested that HRV features can identify preictal states with a median AUC of 0.75 [0.68;0.99]. Pseudoprospective daily forecasts yielded a median Brier score of 0.3 [0.18;0.48]. About 60% of preictal days were correctly forecasted, while false positive predictions were noticed in 24% of interictal days. Daily resting HRV seems to capture information on autonomic variations that may reflect a pro-ictal state. The method could be embedded in an ambulatory clinical seizure prediction device, but additional modalities (prodromes, EEG-based features, etc.) should be associated to improve its performance.

Disentangling local and global climate drivers in the population dynamics of mosquito-borne infections.

Identifying climate drivers is essential to understand and predict epidemics of mosquito-borne infections whose population dynamics typically exhibit seasonality and multiannual cycles. Which climate covariates to consider varies across studies, from local factors such as temperature to remote drivers such as the El Niño-Southern Oscillation. With partial wavelet coherence, we present a systematic investigation of nonstationary associations between mosquito-borne disease incidence and a given climate factor while controlling for another. Analysis of almost 200 time series of dengue and malaria around the globe at different geographical scales shows a systematic effect of global climate drivers on interannual variability and of local ones on seasonality. This clear separation of time scales of action enhances detection of climate drivers and indicates those best suited for building early-warning systems.

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.

Combined head accelerometry and EEG improves the detection of respiratory-related cortical activity during inspiratory loading in healthy participants.

Mechanical ventilation is a highly utilized life-saving tool, particularly in the current era. The use of EEG in a brain-ventilator interface (BVI) to detect respiratory discomfort (due to sub-optimal ventilator settings) would improve treatment in mechanically ventilated patients. This concept has been realized via development of an EEG covariance-based classifier that detects respiratory-related cortical activity associated with respiratory discomfort. The aim of this study was to determine if head movement, detected by an accelerometer, can detect and/or improve the detection of respiratory-related cortical activity compared to EEG alone. In 25 healthy participants, EEG and acceleration of the head were recorded during loaded and quiet breathing in the seated and lying postures. Detection of respiratory-related cortical activity using an EEG covariance-based classifier was improved by inclusion of data from an Accelerometer-based classifier, i.e. classifier 'Fusion'. In addition, 'smoothed' data over 50s, rather than one 5 s window of EEG/Accelerometer signals, improved detection. Waveform averages of EEG and head acceleration showed the incidence of pre-inspiratory potentials did not differ between loaded and quiet breathing, but head movement was greater in loaded breathing. This study confirms that compared to event-related analysis with >5 min of signal acquisition, an EEG-based classifier is a clinically valuable tool with rapid processing, detection times, and accuracy. Data smoothing would introduce a small delay (<1 min) but improves detection results. As head acceleration improved detection compared to EEG alone, the number of EEG signals required to detect respiratory discomfort with future BVIs could be reduced if head acceleration is included.

Clinico-biological markers for the prognosis of status epilepticus in adults.

Prediction of mortality, functional outcome and recovery after status epilepticus (SE) is a challenge. Biological and clinical markers have been proposed to reflect the brain injury or to monitor critical ill patients' severity. The aim of this study was to characterize short-term and long-term prognostic factors for SE patients hospitalized in intensive care unit. Patient's outcome was assessed using the modified Rankin Scale at discharge and after 6-12 months. We first assessed the univariate prognosis significance of 51 clinical, demographic or biochemical markers. Next, we built multivariate clinico-biological models by combining most important factors. Statistical models' performances were compared to those of two previous published scales STESS and mSTESS. Eighty-one patients were enrolled. Thirty-five patients showed a steady state while 46 patients clinically worsened at discharge: 14 died, 14 had persistent disability at 6-12 months and 18 recovered. Logistic regression analysis revealed that clinical markers (SE refractoriness, SE duration, de novo SE) were significant independent predictors of worsening while lipids markers and progranulin better predicted mortality. The association of clinico-biological variables allowed to accurately predict worsening at discharge (AUC > 0.72), mortality at discharge (AUC 0.83) and recovery at long-term (AUC 0.89). Previous scales provided lower prediction for worsening (AUC 0.63, STESS; 0.53, mSTESS) and mortality (AUC 0.56, STESS; 0.62, mSTESS) (p < 0.001). We proposed new clinico-biological models with a strong discrimination power for prediction of short- and long-term outcome of hospitalized status epilepticus patients. Their implementation in electronic devices may enhance their clinical liability.

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).

Insuficiencia renal y hemodiálisis en pacientes hospitalizados con COVID-19 durante la primera ola en Lima, Perú / Renal failure and hemodialysis in hospitalized patients with COVID-19 during the first wave in Lima, Peru

RESUMEN Antecedentes y objetivo: La insuficiencia renal es una de las complicaciones extrapulmonares más frecuente en pacientes hospitalizados con COVID-19 condicionando peores desenlaces. Sin embargo, estudios comparan pacientes con insuficiencia renal aguda (IRA) o crónica (ERC) con pacientes sanos. Determinar características clínicas de pacientes con COVID-19 e insuficiencia renal hospitalizados y evaluar el efecto del tipo de insuficiencia renal y el recibir hemodiálisis en los desenlaces clínicos negativos. Métodos: Cohorte descriptiva que incluyó pacientes con algún tipo de insuficiencia renal y COVID-19 hospitalizados durante marzo y julio del 2020, que tuvieron una interconsulta con nefrología. La insuficiencia renal se clasificó como aguda, crónica, y crónica en estadio V con hemodiálisis crónica. Se recolectó información sobre mortalidad, uso de inotrópicos, ventilación mecánica y recibir hemodiálisis aguda. Resultados: Se analizó a 279 pacientes, 22.6 % tenían IRA, 33.3 % tenían ERC, y 44.1 % tenían ERC V. Se describe una mortalidad general de 32.9 %. Entre los pacientes con IRA y ERC el 12.9 % recibió hemodiálisis por primera vez. El desarrollo de IRA se asoció a ventilación mecánica (RPa: 6.46), uso de inotrópicos (RPa: 7.02) y fallecer (RPa: 2.41), en comparación con los que tenían sólo ERC. Entre quienes tenían IRA o ERC, aquellos que recibieron hemodiálisis por primera vez tienen mayor prevalencia de fallecer (RPa: 2.95; IC95%:2.20 a 3.94) en comparación con los que no recibieron hemodiálisis. La hemodiálisis aguda podría ser un modificador de efecto de la asociación entre tipo de insuficiencia renal (IRA o ERC) y desenlaces clínicos negativos (p<0.001). Conclusión: Es importante identificar a pacientes hospitalizados por COVID-19 que desarrollan IRA y/o necesitan hemodiálisis aguda pues se encuentran en alto riesgo de tener una mala evolución clínica.

ABSTRACT Introduction: Kidney failure is one of the most frequent extrapulmonary complications in patients hospitalized with COVID-19, leading to poorer outcomes, and this may have serious consequences for the Peruvian health system. Nonetheless, there are studies comparing patients with acute kidney failure (AKF) and chronic kidney failure (CKF) against healthy subjects. Objective: To determine the clinical characteristics of hospitalized patients with COVID-19 and kidney failure, and to assess the effect of the type of kidney failure and undergo hemodialysis with respect to negative clinical outcomes. Methods: This is a descriptive cohort study that included patients with some kind of kidney failure and COVID-19 who were hospitalized between March and June 2020, and who had a consultation with the nephrology service. Kidney failure was classified as acute, chronic, and stage V chronic undergoing chronic hemodialysis. Data with respect to mortality, inotrope use, mechanical ventilation, and acute hemodialysis was collected. Results: Two-hundred and seventy-nine patients were included, 22.6% had acute kidney failure, 33.3% had chronic kidney failure, and 44.1 had stage V chronic kidney failure. General mortality rate was 32.0%, and 27% received inotrope agents and underwent mechanical ventilation. Amongst patients with AKF and CKF, 12.9% underwent hemodialysis for the first time. Studied adult subjects with CKD and stage V CKD undergoing hemodialysis had lower frequency of diabetes mellitus (23.7% and 43.9%, respectively) and high blood pressure (31.2% and 59.4%, respectively) compared with adult subjects with AKF (81.0 and 73%, respectively) (p<0.001). The occurrence of AKF was associated with mechanical ventilation (RPa: 6.46), inotrope use (RPa: 7.02), and death (RPa: 2.41), compared with those who had CKF. Amongst those subjects who had AKF or CKF, those who underwent dialysis for the first time were more likely to die (RPa: 2.95; 95% CI: 2.20-3.94) compared with those who did not undergo hemodialysis. Acute hemodialysis may be an effect modifier for the association between the type of kidney failure (AKF or CKF) and negative clinical outcomes (p<0.001). Conclusion: It is important to identify hospitalized patients with COVID-19 that may develop AKF and/or who may need acute hemodialysis, since they are at high risk for a poor clinical outcome.

Alpha activity neuromodulation induced by individual alpha-based neurofeedback learning in ecological context: a double-blind randomized study.

The neuromodulation induced by neurofeedback training (NFT) remains a matter of debate. Investigating the modulation of brain activity specifically associated with NF requires controlling for multiple factors, such as reward, performance, congruency between task and targeted brain activity. This can be achieved using sham feedback (FB) control condition, equating all aspects of the experiment but the link between brain activity and FB. We aimed at investigating the modulation of individual alpha EEG activity induced by NFT in a double-blind, randomized, sham-controlled study. Forty-eight healthy participants were assigned to either NF (n = 25) or control (n = 23) group and performed alpha upregulation training (over 12 weeks) with a wearable EEG device. Participants of the NF group received FB based on their individual alpha activity. The control group received the auditory FB of participants of the NF group. An increase of alpha activity across training sessions was observed in the NF group only (p < 0.001). This neuromodulation was selective in that there was no evidence for similar effects in the theta (4-8 Hz) and low beta (13-18 Hz) bands. While alpha upregulation was found in the NF group only, psychological outcome variables showed overall increased feeling of control, decreased anxiety level and increased relaxation feeling, without any significant difference between the NF and the control groups. This is interpreted in terms of learning context and placebo effects. Our results pave the way to self-learnt, NF-based neuromodulation with light-weighted, wearable EEG systems.

Outliers in clinical symptoms as preictal biomarkers.

Previous findings have suggested that a preictal state might precede the epileptic seizure onset, which is the basis for seizure prediction attempts. Preictal states can be apprehended as outliers that differ from an interictal baseline and display clinical changes. We collected daily clinical scores from patients with epilepsy who underwent continuous video-EEG and assessed the ability of several outlier detection methods to identify preictal states. Results from 24 patients suggested that outlying clinical features were suggestive of preictal states and can be identified by statistical methods: AUC = 0.71, 95 % CI = [0.63 - 0.79]; PPV = 0.77, 95 % CI = [0.70 - 0.84]; FPR = 0.31, 95 % CI = [0.21 - 0.44]); and F1 score = 0.74, 95 % CI = [0.64 - 0.81]. Such algorithms could be straightforwardly implemented in a mobile device (e.g., tablet or smartphone), which would allow a longer data collection that could improve prediction performances. Additional clinical - and even multimodal - parameters could identify more subtle physiological modifications.

Association of Clinical, Biological, and Brain Magnetic Resonance Imaging Findings With Electroencephalographic Findings for Patients With COVID-19.

Importance: There is evidence of central nervous system impairments associated with coronavirus disease 2019 (COVID-19) infection, including encephalopathy. Multimodal monitoring of patients with COVID-19 may delineate the specific features of COVID-19-related encephalopathy and guide clinical management. Objectives: To investigate clinical, biological, and brain magnetic resonance imaging (MRI) findings in association with electroencephalographic (EEG) features for patients with COVID-19, and to better refine the features of COVID-19-related encephalopathy. Design, Setting, and Participants: This retrospective cohort study conducted in Pitié-Salpêtrière Hospital, Paris, France, enrolled 78 hospitalized adults who received a diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-Cov2) and underwent EEG between March 30 and June 11, 2020. Exposures: Detection of SARS-CoV-2 from a nasopharyngeal specimen using a reverse transcription-polymerase chain reaction assay or, in the case of associated pneumonia, on a computed tomography scan of the chest. Main Outcomes and Measures: Data on the clinical and paraclinical features of the 78 patients with COVID-19 were retrieved from electronic patient records. Results: Of 644 patients who were hospitalized for COVID-19, 78 (57 men [73%]; mean [SD] age, 61 [12] years) underwent EEG. The main indications for EEG were delirium, seizure-like events, and delayed awakening in the intensive care unit after stopping treatment with sedatives. Sixty-nine patients showed pathologic EEG findings, including metabolic-toxic encephalopathy features, frontal abnormalities, periodic discharges, and epileptic activities. Of 57 patients who underwent brain MRI, 41 showed abnormalities, including perfusion abnormalities, acute ischemic lesions, multiple microhemorrhages, and white matter-enhancing lesions. Fifty-five patients showed biological abnormalities, including dysnatremia, kidney failure, and liver dysfunction, the same day as the EEG. The results of cerebrospinal fluid analysis were negative for SARS-Cov-2 for all tested patients. Nine patients who had no identifiable cause of brain injury outside COVID-19 were further isolated; their brain injury was defined as COVID-19-related encephalopathy. They represented 1% (9 of 644) of patients with COVID-19 requiring hospitalization. Six of these 9 patients had movement disorders, 7 had frontal syndrome, 4 had brainstem impairment, 4 had periodic EEG discharges, and 3 had MRI white matter-enhancing lesions. Conclusions and Relevance: The results from this cohort of patients hospitalized with COVID-19 suggest there are clinical, EEG, and MRI patterns that could delineate specific COVID-19-related encephalopathy and guide treatment strategy.

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.

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.

Experience, circuit dynamics, and forebrain recruitment in larval zebrafish prey capture.

Experience influences behavior, but little is known about how experience is encoded in the brain, and how changes in neural activity are implemented at a network level to improve performance. Here we investigate how differences in experience impact brain circuitry and behavior in larval zebrafish prey capture. We find that experience of live prey compared to inert food increases capture success by boosting capture initiation. In response to live prey, animals with and without prior experience of live prey show activity in visual areas (pretectum and optic tectum) and motor areas (cerebellum and hindbrain), with similar visual area retinotopic maps of prey position. However, prey-experienced animals more readily initiate capture in response to visual area activity and have greater visually-evoked activity in two forebrain areas: the telencephalon and habenula. Consequently, disruption of habenular neurons reduces capture performance in prey-experienced fish. Together, our results suggest that experience of prey strengthens prey-associated visual drive to the forebrain, and that this lowers the threshold for prey-associated visual activity to trigger activity in motor areas, thereby improving capture performance.

Latinx/@ immigrant inclusion trajectories: Individual agency, structural constraints, and the role of community-based organizations in immigrant mobilities.

Immigration is at the forefront of national, state, and local policy struggles in the United States, and Latinx/@ immigrants have experienced increased deportations, detention, and individual threats. A mobilities perspective allows analysis to extend our view of migration beyond frameworks confined to pre- and postmigration, examining trajectories of social inclusion and exclusion that are influenced by multiple factors in the receiving country. The Immigrant Well-being Project, a community-based participatory research project involving university faculty, students, staff, and representatives from 4 community-based organizations (CBOs), was initiated in New Mexico in 2017 to better understand and promote Latinx/@ immigrant mental health and integration by creating change at multiple levels. We began these efforts by conducting an in-depth study of the mental health needs, stressors, current socioeconomic, legal, and political context, and local solutions as experienced by 24 Latinx/@ immigrants and their mixed status families. Five trajectories of immigrant integration emerged: continuous exclusion, simultaneous exclusion and inclusion, continuous inclusion, movement from exclusion to inclusion, and movement from inclusion to exclusion. These diverse mobilities were shaped by participants' social locations, agency, and experiences with CBOs, which played critical roles in creating, maintaining, and/or transforming immigrants' trajectories. However, CBOs could not completely buffer immigrants from the current hostile climate and related stressors that resulted in experiences of exclusion or movement from inclusion to exclusion. These findings add to understandings of immigrant mental health, complex ongoing mobility, and mechanisms of resilience and resistance within the United States and have important implications for policy and practice. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Using symbolic networks to analyse dynamical properties of disease outbreaks.

We introduce a new methodology, which is based on the construction of epidemic networks, to analyse the evolution of epidemic time series. First, we translate the time series into ordinal patterns containing information about local fluctuations in disease prevalence. Each pattern is associated with a node of a network, whose (directed) connections arise from consecutive appearances in the series. The analysis of the network structure and the role of each pattern allows them to be classified according to the enhancement of entropy/complexity along the series, giving a different point of view about the evolution of a given disease.

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.