Common modulation of limbic network activation underlies musical emotions as they unfold.

Music is a powerful means for communicating emotions among individuals. Here we reveal that this continuous stream of affective information is commonly represented in the brains of different listeners and that particular musical attributes mediate this link. We examined participants' brain responses to two naturalistic musical pieces using functional Magnetic Resonance imaging (fMRI). Following scanning, as participants listened to the musical pieces for a second time, they continuously indicated their emotional experience on scales of valence and arousal. These continuous reports were used along with a detailed annotation of the musical features, to predict a novel index of Dynamic Common Activation (DCA) derived from ten large-scale data-driven functional networks. We found an association between the unfolding music-induced emotionality and the DCA modulation within a vast network of limbic regions. The limbic-DCA modulation further corresponded with continuous changes in two temporal musical features: beat-strength and tempo. Remarkably, this "collective limbic sensitivity" to temporal features was found to mediate the link between limbic-DCA and the reported emotionality. An additional association with the emotional experience was found in a left fronto-parietal network, but only among a sub-group of participants with a high level of musical experience (>5years). These findings may indicate two processing-levels underlying the unfolding of common music emotionality; (1) a widely shared core-affective process that is confined to a limbic network and mediated by temporal regularities in music and (2) an experience based process that is rooted in a left fronto-parietal network that may involve functioning of the 'mirror-neuron system'.

Psychophysiological whole-brain network clustering based on connectivity dynamics analysis in naturalistic conditions.

We introduce a novel method for delineating context-dependent functional brain networks whose connectivity dynamics are synchronized with the occurrence of a specific psychophysiological process of interest. In this method of context-related network dynamics analysis (CRNDA), a continuous psychophysiological index serves as a reference for clustering the whole-brain into functional networks. We applied CRNDA to fMRI data recorded during the viewing of a sadness-inducing film clip. The method reliably demarcated networks in which temporal patterns of connectivity related to the time series of reported emotional intensity. Our work successfully replicated the link between network connectivity and emotion rating in an independent sample group for seven of the networks. The demarcated networks have clear common functional denominators. Three of these networks overlap with distinct empathy-related networks, previously identified in distinct sets of studies. The other networks are related to sensorimotor processing, language, attention, and working memory. The results indicate that CRNDA, a data-driven method for network clustering that is sensitive to transient connectivity patterns, can productively and reliably demarcate networks that follow psychologically meaningful processes. Hum Brain Mapp 37:4654-4672, 2016. © 2016 Wiley Periodicals, Inc.

Decoding human spontaneous spiking activity in medial temporal lobe from scalp EEG.

Linking scalp electroencephalography (EEG) signals and spontaneous firing activity from deep nuclei in humans is not trivial. To examine this, we analyzed simultaneous recordings of scalp EEG and unit activity in deeply located sites recorded overnight from patients undergoing pre-surgical invasive monitoring. We focused on modeling the within-subject average unit activity of two medial temporal lobe areas: amygdala and hippocampus. Linear regression model correlates the units' average firing activity to spectral features extracted from the EEG during wakefulness or non-REM sleep. We show that changes in mean firing activity in both areas and states can be estimated from EEG (Pearson r > 0.2, p≪0.001). Region specificity was shown with respect to other areas. Both short- and long-term fluctuations in firing rates contributed to the model accuracy. This demonstrates that scalp EEG frequency modulations can predict changes in neuronal firing rates, opening a new horizon for non-invasive neurological and psychiatric interventions.

Delimiting subterritories of the human subthalamic nucleus by means of microelectrode recordings and a Hidden Markov Model.

Positive therapeutic response without adverse side effects to subthalamic nucleus deep brain stimulation (STN DBS) for Parkinson's disease (PD) depends to a large extent on electrode location within the STN. The sensorimotor region of the STN (seemingly the preferred location for STN DBS) lies dorsolaterally, in a region also marked by distinct beta (13-30 Hz) oscillations in the parkinsonian state. In this study, we present a real-time method to accurately demarcate subterritories of the STN during surgery, based on microelectrode recordings (MERs) and a Hidden Markov Model (HMM). Fifty-six MER trajectories were used, obtained from 21 PD patients who underwent bilateral STN DBS implantation surgery. Root mean square (RMS) and power spectral density (PSD) of the MERs were used to train and test an HMM in identifying the dorsolateral oscillatory region (DLOR) and nonoscillatory subterritories within the STN. The HMM demarcations were compared to the decisions of a human expert. The HMM identified STN-entry, the ventral boundary of the DLOR, and STN-exit with an error of -0.09 +/- 0.35, -0.27 +/- 0.58, and -0.20 +/- 0.33 mm, respectively (mean +/- standard deviation), and with detection reliability (error < 1 mm) of 95, 86, and 91%, respectively. The HMM was successful despite a very coarse clustering method and was robust to parameter variation. Thus, using an HMM in conjunction with RMS and PSD measures of intraoperative MER can provide improved refinement of STN entry and exit in comparison with previously reported automatic methods, and introduces a novel (intra-STN) detection of a distinct DLOR-ventral boundary.

Epidemiological Models Lacking Process Noise Can Be Overconfident.

Mathematic models of epidemics are the key tool for predicting future course of disease in a population and analyzing the effects of possible intervention policies. Typically, models that produce deterministic are applied for making predictions and reaching decisions. Stochastic modeling methods present an alternative. Here, we demonstrate by example why it is important that stochastic modeling be used in population health decision support systems.

A Data-Driven Decision-Support Tool for Population Health Policies.

Epidemiological models are key tools in assessing intervention policies for population health management. Statistical models, fitted with survey or health system data, can be combined with lab and field studies to provide reliable predictions of future population-level disease dynamics distributions and the effects of interventions. All too often, however, the end result of epidemiological modeling and cost-effectiveness studies is in the form of a report or journal paper. These are inherently limited in their coverage of locations, policy options, and derived outcome measures. Here, we describe a tool to support population health policy planning. The tool allows users to explore simulations of various policies, to view and compare interventions spanning multiple variables, time points, and locations. The design's modular architecture, and data representation separate the modeling methods, the outcome measures calculations, and the visualizations, making each component easily replaceable. These advantages make it extremely versatile and suitable for multiple uses.

Estimating the impact of prevention action: a simulation model of cervical cancer progression.

Cervical cancer is one of the highest occurring cancers for women in East Africa. Many studies have shown that disease occurrences and particularly the number of deaths due to the disease can be reduced significantly by screening and vaccination. East Africa and Kenya in particular are undergoing change and taking actions to reduce disease levels. However, up until today disease level in the different districts in Kenya is not known nor what be the prevalence of disease when prevention actions take place. In this paper we propose a novel Bayesian model for estimating disease levels based on available partial reports and demographic information. The result is a simulation engine that provides estimations of the impact of various potential prevention actions.

Spikernels: predicting arm movements by embedding population spike rate patterns in inner-product spaces.

Inner-product operators, often referred to as kernels in statistical learning, define a mapping from some input space into a feature space. The focus of this letter is the construction of biologically motivated kernels for cortical activities. The kernels we derive, termed Spikernels, map spike count sequences into an abstract vector space in which we can perform various prediction tasks. We discuss in detail the derivation of Spikernels and describe an efficient algorithm for computing their value on any two sequences of neural population spike counts. We demonstrate the merits of our modeling approach by comparing the Spikernel to various standard kernels in the task of predicting hand movement velocities from cortical recordings. All of the kernels that we tested in our experiments outperform the standard scalar product used in linear regression, with the Spikernel consistently achieving the best performance.