Enhanced Myocardial Adenylyl Cyclase Activity Alters Heart-Brain Communication.

BACKGROUND: The central nervous system's influence on cardiac function is well described; however, direct evidence for signaling from heart to brain remains sparse. Mice with cardiac-selective overexpression of adenylyl cyclase type 8 (TGAC8) display elevated heart rate/contractility and altered neuroautonomic surveillance. OBJECTIVES: In this study the authors tested whether elevated adenylyl cyclase type 8-dependent signaling at the cardiac cell level affects brain activity and behavior. METHODS: A telemetry system was used to record electrocardiogram (ECG) and electroencephalogram (EEG) in TGAC8 and wild-type mice simultaneously. The Granger causality statistical approach evaluated variations in the ECG/EEG relationship. Mouse behavior was assessed via elevated plus maze, open field, light-dark box, and fear conditioning tests. Transcriptomic and proteomic analyses were performed on brain tissue lysates. RESULTS: Behavioral testing revealed increased locomotor activity in TGAC8 that included a greater total distance traveled (+43%; P < 0.01), a higher average speed (+38%; P < 0.01), and a reduced freezing time (-45%; P < 0.01). Dual-lead telemetry recording confirmed a persistent heart rate elevation with a corresponding reduction in ECG-R-waves interval variability and revealed increased EEG-gamma activity in TGAC8 vs wild-type. Bioinformatic assessment of hippocampal tissue indicated upregulation of dopamine 5, gamma-aminobutyric acid A, and metabotropic glutamate 1/5 receptors, major players in gamma activity generation. Granger causality analyses of ECG and EEG recordings showed a marked increase in informational flow between the TGAC8 heart and brain. CONCLUSIONS: Perturbed signals arising from the heart cause changes in brain activity, altering mouse behavior. More specifically, the brain interprets augmented myocardial humoral/functional output as a "sustained exercise-like" situation and responds by activating central nervous system output controlling locomotion.

A dynamical method to objectively assess infantile nystagmus based on eye tracking. A pilot study

Purpose The purpose of this research is to propose a new method for the easy, inexpensive and objective quantification of nystagmus using eye-tracking records collected during a simple reading task that could be implantable in clinical practice to assess patients with nystagmus. Methods This is a prospective, observational pilot study. Eye movements of 4 nystagmus patients and 9 healthy children during a reading task (a paragraph with 82 words) on a 15′’ monitor were collected and compared. Data are time series indicating the gaze position on the screen. Two quantifiers were proposed: IndS (based on the speed of movements) and IndF (based on the variation of the gaze trajectory). Results The indices proposed reflect differences in the behavior of eye movements between the two groups. Nystagmus patients present higher values of IndS - indicating smaller number of slow movements (16% of movements with speeds <0.33 1/s for nystagmus and 85% for the control group, with p = 0.01) - and higher values of IndF - indicating higher gaze fluctuation (p = 0.01). Differences were not related with reading speed as show the mean and standard deviation: the nystagmus group required 115±45 s to complete the task and the control group 151±85 s; p = 0.73. Conclusions The proposed indices provide a new method that allows an objective assessment of nystagmus, with potential use in clinical and research practice to improve the follow-up of patients by monitoring the nystagmus over time or treatment. (AU)

A Ventromedial Prefrontal-to-Lateral Entorhinal Cortex Pathway Modulates the Gain of Behavioral Responding During Threat.

BACKGROUND: The ability to correctly associate cues and contexts with threat is critical for survival, and the inability to do so can result in threat-related disorders such as posttraumatic stress disorder. The prefrontal cortex (PFC) and hippocampus are well known to play critical roles in cued and contextual threat memory processing. However, the circuits that mediate prefrontal-hippocampal modulation of context discrimination during cued threat processing are less understood. Here, we demonstrate the role of a previously unexplored projection from the ventromedial region of PFC (vmPFC) to the lateral entorhinal cortex (LEC) in modulating the gain of behavior in response to contextual information during threat retrieval and encoding. METHODS: We used optogenetics followed by in vivo calcium imaging in male C57/B6J mice to manipulate and monitor vmPFC-LEC activity in response to threat-associated cues in different contexts. We then investigated the inputs to, and outputs from, vmPFC-LEC cells using Rabies tracing and channelrhodopsin-assisted electrophysiology. RESULTS: vmPFC-LEC cells flexibly and bidirectionally shaped behavior during threat expression, shaping sensitivity to contextual information to increase or decrease the gain of behavioral output in response to a threatening or neutral context, respectively. CONCLUSIONS: Glutamatergic vmPFC-LEC cells are key players in behavioral gain control in response to contextual information during threat processing and may provide a future target for intervention in threat-based disorders.

Ultrasound neuromodulation of the deep brain.

Noninvasive, reversible stimulation of neural circuits can regulate behavior.

Slow fluctuations in ongoing brain activity decrease in amplitude with ageing yet their impact on task-related evoked responses is dissociable from behavior.

In humans, ageing is characterized by decreased brain signal variability and increased behavioral variability. To understand how reduced brain variability segregates with increased behavioral variability, we investigated the association between reaction time variability, evoked brain responses and ongoing brain signal dynamics, in young (N=36) and older adults (N=39). We studied the electroencephalogram (EEG) and pupil size fluctuations to characterize the cortical and arousal responses elicited by a cued go/no-go task. Evoked responses were strongly modulated by slow (<2 Hz) fluctuations of the ongoing signals, which presented reduced power in the older participants. Although variability of the evoked responses was lower in the older participants, once we adjusted for the effect of the ongoing signal fluctuations, evoked responses were equally variable in both groups. Moreover, the modulation of the evoked responses caused by the ongoing signal fluctuations had no impact on reaction time, thereby explaining why although ongoing brain signal variability is decreased in older individuals, behavioral variability is not. Finally, we showed that adjusting for the effect of the ongoing signal was critical to unmask the link between neural responses and behavior as well as the link between task-related evoked EEG and pupil responses.

Three legs of the missing heritability problem.

The so-called 'missing heritability problem' is often characterized by behavior geneticists as a numerical discrepancy between alternative kinds of heritability. For example, while 'traditional heritability' derived from twin and family studies indicates that approximately ∼50% of variation in intelligence is attributable to genetics, 'SNP heritability' derived from genome-wide association studies indicates that only ∼10% of variation in intelligence is attributable to genetics. This 40% gap in variance accounted for by alternative kinds of heritability is frequently referred to as what's "missing." Philosophers have picked up on this reading, suggesting that "dissolving" the missing heritability problem is merely a matter of closing the numerical gap between traditional and molecular kinds of heritability. We argue that this framing of the problem undervalues the severity of the many challenges to scientific understanding of the "heritability" of human behavior. On our view, resolving the numerical discrepancies between alternative kinds of heritability will do little to advance scientific explanation and understanding of behavior genetics. Thus, we propose a new conceptual framework of the missing heritability problem that comprises three independent methodological and explanatory challenges: the numerical gap, the prediction gap, and the mechanism gap.

Demarcating the boundary conditions of memory reconsolidation: An unsuccessful replication.

Disrupting memory reconsolidation provides an opportunity to abruptly reduce the behavioural expression of fear memories with long-lasting effects. The success of a reconsolidation intervention is, however, not guaranteed as it strongly depends on the destabilization of the memory. Identifying the necessary conditions to trigger destabilization remains one of the critical challenges in the field. We aimed to replicate a study from our lab, showing that the occurrence of a prediction error (PE) during reactivation is necessary but not sufficient for destabilization. We tested the effectiveness of a reactivation procedure consisting of a single PE, compared to two control groups receiving no or multiple PEs. All participants received propranolol immediately after reactivation and were tested for fear retention 24 h later. In contrast to the original results, we found no evidence for a reconsolidation effect in the single PE group, but a straightforward interpretation of these results is complicated by the lack of differential fear retention in the control groups. Our results corroborate other failed reconsolidation studies and exemplify the complexity of experimentally investigating this process in humans. Thorough investigation of the interaction between learning and memory reactivation is essential to understand the inconsistencies in the literature and to improve reconsolidation interventions.

Bodily ownership of an independent supernumerary limb: an exploratory study.

Can our brain perceive a sense of ownership towards an independent supernumerary limb; one that can be moved independently of any other limb and provides its own independent movement feedback? Following the rubber-hand illusion experiment, a plethora of studies have shown that the human representation of "self" is very plastic. But previous studies have almost exclusively investigated ownership towards "substitute" artificial limbs, which are controlled by the movements of a real limb and/or limbs from which non-visual sensory feedback is provided on an existing limb. Here, to investigate whether the human brain can own an independent artificial limb, we first developed a novel independent robotic "sixth finger." We allowed participants to train using the finger and examined whether it induced changes in the body representation using behavioral as well as cognitive measures. Our results suggest that unlike a substitute artificial limb (like in the rubber hand experiment), it is more difficult for humans to perceive a sense of ownership towards an independent limb. However, ownership does seem possible, as we observed clear tendencies of changes in the body representation that correlated with the cognitive reports of the sense of ownership. Our results provide the first evidence to show that an independent supernumerary limb can be embodied by humans.

Scalp recorded theta activity is modulated by reward, direction, and speed during virtual navigation in freely moving humans.

Theta oscillations (~ 4-12 Hz) are dynamically modulated by speed and direction in freely moving animals. However, due to the paucity of electrophysiological recordings of freely moving humans, this mechanism remains poorly understood. Here, we combined mobile-EEG with fully immersive virtual-reality to investigate theta dynamics in 22 healthy adults (aged 18-29 years old) freely navigating a T-maze to find rewards. Our results revealed three dynamic periods of theta modulation: (1) theta power increases coincided with the participants' decision-making period; (2) theta power increased for fast and leftward trials as subjects approached the goal location; and (3) feedback onset evoked two phase-locked theta bursts over the right temporal and frontal-midline channels. These results suggest that recording scalp EEG in freely moving humans navigating a simple virtual T-maze can be utilized as a powerful translational model by which to map theta dynamics during "real-life" goal-directed behavior in both health and disease.

Event boundaries shape temporal organization of memory by resetting temporal context.

In memory, our continuous experiences are broken up into discrete events. Boundaries between events are known to influence the temporal organization of memory. However, how and through which mechanism event boundaries shape temporal order memory (TOM) remains unknown. Across four experiments, we show that event boundaries exert a dual role: improving TOM for items within an event and impairing TOM for items across events. Decreasing event length in a list enhances TOM, but only for items at earlier local event positions, an effect we term the local primacy effect. A computational model, in which items are associated to a temporal context signal that drifts over time but resets at boundaries captures all behavioural results. Our findings provide a unified algorithmic mechanism for understanding how and why event boundaries affect TOM, reconciling a long-standing paradox of why both contextual similarity and dissimilarity promote TOM.

Inflammation in the brain and periphery found in animal models of depression and its behavioral relevance.

Currently used antidepressant drugs target and facilitate the action of monoamine neurotransmission. However, approximately 30% of patients do not respond to these drugs. Therefore, there is an urgent need to develop novel therapeutic targets. Several clinical studies have reported that inflammatory cytokines and neutrophils are increased in the blood of patients with major depression. Since social and environmental stress is a risk factor for mental illnesses such as major depression, many research groups have employed chronic stress models in which mice are repeatedly exposed to stressful events. Chronic stress induces neuroinflammation originating from microglia in the medial prefrontal cortex, leading to depressive-like behavior. Moreover, chronic stress influences peripheral immune cells by activating the sympathetic nervous system and the hypothalamus-pituitary-adrenal gland axis. The infiltration of monocytes expressing interleukin (IL)-1ß into the brain is involved in chronic stress-induced elevated anxiety. The penetration of IL-6 derived from monocytes into the nucleus accumbens is involved in chronic stress-induced depression-like behavior. Furthermore, cell-cell and peripheral brain interactions and their molecular basis have been discovered. These findings may pave the way for the development of biological markers and therapeutic drugs.

Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships.

One of the main goals of current systems neuroscience is to understand how neuronal populations integrate sensory information to inform behavior. However, estimating stimulus or behavioral information that is encoded in high-dimensional neuronal populations is challenging. We propose a method based on parametric copulas which allows modeling joint distributions of neuronal and behavioral variables characterized by different statistics and timescales. To account for temporal or spatial changes in dependencies between variables, we model varying copula parameters by means of Gaussian Processes (GP). We validate the resulting Copula-GP framework on synthetic data and on neuronal and behavioral recordings obtained in awake mice. We show that the use of a parametric description of the high-dimensional dependence structure in our method provides better accuracy in mutual information estimation in higher dimensions compared to other non-parametric methods. Moreover, by quantifying the redundancy between neuronal and behavioral variables, our model exposed the location of the reward zone in an unsupervised manner (i.e., without using any explicit cues about the task structure). These results demonstrate that the Copula-GP framework is particularly useful for the analysis of complex multidimensional relationships between neuronal, sensory and behavioral variables.

Multivariate patterns of brain-behavior associations across the adult lifespan.

The nature of brain-behavior covariations with increasing age is poorly understood. In the current study, we used a multivariate approach to investigate the covariation between behavioral-health variables and brain features across adulthood. We recruited healthy adults aged 20-73 years-old (29 younger, mean age = 25.6 years; 30 older, mean age = 62.5 years), and collected structural and functional MRI (s/fMRI) during a resting-state and three tasks. From the sMRI, we extracted cortical thickness and subcortical volumes; from the fMRI, we extracted activation peaks and functional network connectivity (FNC) for each task. We conducted canonical correlation analyses between behavioral-health variables and the sMRI, or the fMRI variables, across all participants. We found significant covariations for both types of neuroimaging phenotypes (ps = 0.0004) across all individuals, with cognitive capacity and age being the largest opposite contributors. We further identified different variables contributing to the models across phenotypes and age groups. Particularly, we found behavior was associated with different neuroimaging patterns between the younger and older groups. Higher cognitive capacity was supported by activation and FNC within the executive networks in the younger adults, while it was supported by the visual networks' FNC in the older adults. This study highlights how the brain-behavior covariations vary across adulthood and provides further support that cognitive performance relies on regional recruitment that differs between older and younger individuals.

Feasibility study of immersive virtual prism adaptation therapy with depth-sensing camera using functional near-infrared spectroscopy in healthy adults.

Prism Adaptation (PA) is used to alleviate spatial neglect. We combined immersive virtual reality with a depth-sensing camera to develop virtual prism adaptation therapy (VPAT), which block external visual cues and easily quantify and monitor errors than conventional PA. We conducted a feasibility study to investigate whether VPAT can induce behavioral adaptations by measuring after-effect and identifying which cortical areas were most significantly activated during VPAT using functional near-infrared spectroscopy (fNIRS). Fourteen healthy subjects participated in this study. The experiment consisted of four sequential phases (pre-VPAT, VPAT-10°, VPAT-20°, and post-VPAT). To compare the most significantly activated cortical areas during pointing in different phases against pointing during the pre-VPAT phase, we analyzed changes in oxyhemoglobin concentration using fNIRS during pointing. The pointing errors of the virtual hand deviated to the right-side during early pointing blocks in the VPAT-10° and VPAT-20° phases. There was a left-side deviation of the real hand to the target in the post-VPAT phase, demonstrating after-effect. The most significantly activated channels during pointing tasks were located in the right hemisphere, and possible corresponding cortical areas included the dorsolateral prefrontal cortex and frontal eye field. In conclusion, VPAT may induce behavioral adaptation with modulation of the dorsal attentional network.

A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets.

Canonical Correlation Analysis (CCA) and its regularised versions have been widely used in the neuroimaging community to uncover multivariate associations between two data modalities (e.g., brain imaging and behaviour). However, these methods have inherent limitations: (1) statistical inferences about the associations are often not robust; (2) the associations within each data modality are not modelled; (3) missing values need to be imputed or removed. Group Factor Analysis (GFA) is a hierarchical model that addresses the first two limitations by providing Bayesian inference and modelling modality-specific associations. Here, we propose an extension of GFA that handles missing data, and highlight that GFA can be used as a predictive model. We applied GFA to synthetic and real data consisting of brain connectivity and non-imaging measures from the Human Connectome Project (HCP). In synthetic data, GFA uncovered the underlying shared and specific factors and predicted correctly the non-observed data modalities in complete and incomplete data sets. In the HCP data, we identified four relevant shared factors, capturing associations between mood, alcohol and drug use, cognition, demographics and psychopathological measures and the default mode, frontoparietal control, dorsal and ventral networks and insula, as well as two factors describing associations within brain connectivity. In addition, GFA predicted a set of non-imaging measures from brain connectivity. These findings were consistent in complete and incomplete data sets, and replicated previous findings in the literature. GFA is a promising tool that can be used to uncover associations between and within multiple data modalities in benchmark datasets (such as, HCP), and easily extended to more complex models to solve more challenging tasks.

At the heart of the interoception network: Influence of the parasubthalamic nucleus on autonomic functions and motivated behaviors.

The parasubthalamic nucleus (PSTN), a small nucleus located on the lateral edge of the posterior hypothalamus, has emerged in recent years as a highly interconnected node within the network of brain regions sensing and regulating autonomic function and homeostatic needs. Furthermore, the strong integration of the PSTN with extended amygdala circuits makes it ideally positioned to serve as an interface between interoception and emotions. While PSTN neurons are mostly glutamatergic, some of them also express neuropeptides that have been associated with stress-related affective and motivational dysfunction, including substance P, corticotropin-releasing factor, and pituitary adenylate-cyclase activating polypeptide. PSTN neurons respond to food ingestion and anorectic signals, as well as to arousing and distressing stimuli. Functional manipulation of defined pathways demonstrated that the PSTN serves as a central hub in multiple physiologically relevant networks and is notably implicated in appetite suppression, conditioned taste aversion, place avoidance, impulsive action, and fear-induced thermoregulation. We also discuss the putative role of the PSTN in interoceptive dysfunction and negative urgency. This review aims to synthesize the burgeoning preclinical literature dedicated to the PSTN and to stimulate interest in further investigating its influence on physiology and behavior.

Building memories on prior knowledge: behavioral and fMRI evidence of impairment in early Alzheimer's disease.

Impaired memory is a hallmark of prodromal Alzheimer's disease (AD). Prior knowledge associated with the memoranda improves memory in healthy individuals, but we ignore whether the same occurs in early AD. We used functional MRI to investigate whether prior knowledge enhances memory encoding in early AD, and whether the nature of this prior knowledge matters. Patients with early AD and Controls underwent a task-based fMRI experiment where they learned face-scene associations. Famous faces carried pre-experimental knowledge (PEK), while unknown faces with which participants were familiarized prior to learning carried experimental knowledge (EK). Surprisingly, PEK strongly enhanced subsequent memory in healthy controls, but importantly not in patients. Partly nonoverlapping brain networks supported PEK vs. EK associative encoding in healthy controls. No such networks were identified in patients. In addition, patients displayed impaired activation in a right sub hippocampal region where activity predicted successful associative memory formation for PEK stimuli. Despite the limited sample sizes of this study, these findings suggest that the role prior knowledge in new learning might have been so far overlooked and underestimated in AD patients. Prior knowledge may drive critical differences in the way healthy elderly and early AD patients learn novel associations.

Predicting behavior through dynamic modes in resting-state fMRI data.

Dynamic properties of resting-state functional connectivity (FC) provide rich information on brain-behavior relationships. Dynamic mode decomposition (DMD) has been used as a method to characterize FC dynamics. However, it remains unclear whether dynamic modes (DMs), spatial-temporal coherent patterns computed by DMD, provide information about individual behavioral differences. This study established a methodological approach to predict individual differences in behavior using DMs. Furthermore, we investigated the contribution of DMs within each of seven specific frequency bands (0-0.1,...,0.6-0.7 Hz) for prediction. To validate our approach, we tested whether each of 59 behavioral measures could be predicted by performing multivariate pattern analysis on a Gram matrix, which was created using subject-specific DMs computed from resting-state functional magnetic resonance imaging (rs-fMRI) data of individuals. DMD successfully predicted behavior and outperformed temporal and spatial independent component analysis, which is the conventional data decomposition method for extracting spatial activity patterns. Most of the behavioral measures that were predicted with significant accuracy in a permutation test were related to cognition. We found that DMs within frequency bands <0.2 Hz primarily contributed to prediction and had spatial structures similar to several common resting-state networks. Our results indicate that DMD is efficient in extracting spatiotemporal features from rs-fMRI data.