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There is considerable trial-to-trial variability in single cortical neurons when performing the same action repeatedly. One possibility is that neural populations are more stable in representing actions; alternatively, they too may be distinctly engaged from trial-to-trial. To address the nature of variability in large neural populations, we captured the EEG signals time-locked to repeated voluntary thumb flexion movements (∼500 repetitions, 23 subjects). By using non-negative matrix factorization on the low-frequency sensorimotor cortical signals, we quantified the trial-to-trial motor-related potentials (MRPs) in terms of prototypical signals (meta-MRPs) and their corresponding prominence at each trial (meta-trials). Clustering the meta-MRPs across the sampled population revealed 5 distinct signal patterns. There were brain-wide correlates of these meta-MRP clusters. Cortical hemispheres were distinctly recruited from trial-to-trial as certain clusters were accompanied by bilateral motor negativity while others were characterized by ipsilateral motor negativity. The sensory feedback too was distinctly processed from trial-to-trial as the central post-movement positivity was present only for certain clusters. A poorly understood pre-motor positivity accompanied all clusters albeit varying in their timing from trial-to-trial. These patterns – including the time-varying positivity preceding the movement – were rendered invisible by the traditional averaging of the signals. We suggest that the variability in EEG signals is not just noise but a consequence of distinct activation patterns deployed by the cortex. We support the idea that the cortex flexibly switches between distinct forms of neural processing to achieve the same behavioral goals.

Smartphones offer unique opportunities to trace the convoluted behavioral patterns accompanying healthy aging. Here we captured smartphone touchscreen interactions from a healthy population (N = 684, ∼309 million interactions) spanning 16 to 86 years of age and trained a decision tree regression model to estimate chronological age based on the interactions. The interactions were clustered according to their next interval dynamics to quantify diverse smartphone behaviors. The regression model well-estimated the chronological age in health (mean absolute error = 6 years, R2 = 0.8). We next deployed this model on a population of stroke survivors (N = 41) to find larger prediction errors such that the estimated age was advanced by 6 years. A similar pattern was observed in people with epilepsy (N = 51), with prediction errors advanced by 10 years. The smartphone behavioral model trained in health can be used to study altered aging in neurological diseases.

Nonlinear dependencies among highly correlated variables of a multifaceted process system pose significant challenges for process safety assessment. The copula function is a flexible statistical tool to capture complex dependencies and interactions among process variables in the causation of process faults. An integration of the copula function with the Bayesian network provides a framework to deal with such complex dependence. This study attempts to compare the performance of the copula-based Bayesian network with that of the traditional Bayesian network in predicting failure of a multivariate time dependent process system. Normal and abnormal process data from a small-scale pilot unit were collected to test and verify performances of failure models. Results from analysis of the collected data establish that the performance of copula-based Bayesian network is robust and superior to the performance of traditional Bayesian network. The structural flexibility, consideration of non-linear dependence among variables, uncertainty and stochastic nature of the process model provide the copula-based Bayesian network distinct advantages. This approach can be further tested and implemented as an online process monitoring and risk management tool.

Summary A range of abnormal electrical activity patterns termed epileptiform discharges can occur in the brains of persons with epilepsy. These epileptiform discharges can be monitored and recorded with implanted devices that deliver therapeutic neurostimulation. These continuous recordings provide an opportunity to study the behavioral correlates of epileptiform discharges as the patients go about their daily lives. Here, we captured the smartphone touchscreen interactions in eight patients in conjunction with electrographic recordings (accumulating 35,714 h) and by using an artificial neural network model addressed if the behavior reflected the epileptiform discharges. The personalized model outputs based on smartphone behavioral inputs corresponded well with the observed electrographic data (R: 0.2–0.6, median 0.4). The realistic reconstructions of epileptiform activity based on smartphone use demonstrate how day-to-day digital behavior may be converted to personalized markers of disease activity in epilepsy, Graphical abstract, Highlights • Brain implants can continuously log abnormal activity patterns in epilepsy. • Real-world behavioral implications of the abnormal activity are not clear. • An artificial neural network inferred brain activity based on smartphone behavior. • Diurnal and other behavioral patterns were leveraged by the network., Behavioral neuroscience; Bioengineering; Neural networks

The complexities of process plants are increasing because of process integration and plant-wide optimization. Failure models of a process system (henceforth referred to as process-accident models) ...

Summary Striatal dopamine and smartphone behavior have both been linked with behavioral variability. Here, we leverage day-to-day logs of natural, unconstrained smartphone behavior and establish a correlation between a measure of smartphone social activity previously linked with behavioral variability and a measure of striatal dopamine synthesis capacity using [18F]-DOPA PET in (N = 22) healthy adult humans. Specifically, we find that a higher proportion of social app interactions correlates with lower dopamine synthesis capacity in the bilateral putamen. Permutation tests and penalized regressions provide evidence that this link between dopamine synthesis capacity and social versus non-social smartphone interactions is specific. These observations provide a key empirical grounding for current speculations about dopamine's role in digital social behavior., Graphical abstract, Highlights • Putamen dopamine synthesis capacity correlates with smartphone social app use. • The correlation parallels a prior link between social app use and motor variability. • It is selective to social app use, controlling for multiple smartphone use factors., Behavioral neuroscience; Biological sciences; Neuroscience

Brain areas representing the body can change in response to behavioral alterations. This idea is firmly established for the adult cortex in response to extraordinary alterations such as traumatic amputation or casting of the limb. Here we address how adult cortical sensorimotor processing alters in response to a subtle perturbation in the form of smartphone deprivation lasting for ~7 days. We quantified the sensorimotor processes associated with the fingertips before and after the deprivation in right-handed smartphone users. The measurements were contrasted with those of a control group with unperturbed smartphone behavior. First, smartphone tapping speed in daily life became slower after the deprivation. Second, according to reaction time tests conducted in the laboratory the asymmetrically superior performance of the right vs. left thumb was eroded by the deprivation. Third, according to EEG measurements at physical rest, tactile stimulation at the right thumb tip resulted in smaller signal amplitudes after the deprivation. Moreover, the EEG measurements during smartphone use revealed larger signal amplitudes for tactile stimulation at the right little fingertip after the deprivation. We show that cortical plasticity can occur by merely disengaging from a common day-to-day behavior. We suggest that in daily life the adult brain continuously and selectively updates its sensorimotor processing according to recent experience.

Clinical observations suggest dynamic alterations in behavior after brain surgery. While some alterations reportedly occur within days others gradually develop over several months. These alterations can be attributed to the pre-surgical impact of the diseased tissue, neuronal damage caused by the surgery, and subsequent plasticity. A key step towards a systems-level understanding of the brain-behavior relationships is to capture the dynamics of the cognitive alterations. Here, we first established in 38 healthy individuals that the day-to-day smartphone interactions can be used to inform on cognitive processing speed. Next, we analyzed the smartphone interactions in 12 patients undergoing intracranial tumor surgery, with postsurgical follow-up of up to a year. In healthy individuals, the speed of the touchscreen interactions was highly correlated to choice reaction times (R2 = 0.71) but less so to simple reaction times (R2 = 0.15) on Deary-Liewald tests. Touchscreen interactions slowed immediately after surgery but the post-surgical changes varied between patients. Data-driven models revealed the time-constant of the short-term postsurgical changes and the time taken to stabilize after the surgery. Furthermore, by using conceptually distinct types of touchscreen interaction speeds – i.e. unlocking time and app locating speed – we established that the post-surgical changes are domain-specific. Interestingly, in this small sample, the pre-surgical smartphone speeds were highly related to the speeds post-stabilization (R2 = 0.75 to 0.95). The proxy measures of cognition seamlessly captured on the smartphone can reveal postsurgical dynamics inaccessible to conventional testing. We propose that the transient cognitive alterations indicate the time-constrained influence of distinct neuronal mechanisms triggered by the surgery.

The distribution of intervals between human actions such as email posts or keyboard strokes demonstrates distinct properties at short vs long time scales. For instance, at long time scales, which are presumably controlled by complex process such as planning and decision making, it has been shown that those inter-event intervals follow a scale-invariant (or power-law) distribution. In contrast, at shorter time-scales - which are governed by different process such as sensorimotor skill - they do not follow the same distribution and little do we know how they relate to the scale-invariant pattern. Here, we analyzed 9 millions intervals between smartphone screen touches of 84 individuals which span several orders of magnitudes (from milliseconds to hours). To capture these intervals, we extend a priority-based generative model to smartphone touching events. At short-time scale, the model is governed by refractory effects, while at longer time scales, the inter-touch intervals are governed by the priority difference between smartphone tasks and other tasks. The flexibility of the model allows to capture inter-individual variations at short and long time scales while its tractability enables efficient model fitting. According to our model, each individual has a specific power-low exponent which is tightly related to the effective refractory time constant suggesting that motor processes which influence the fast actions are related to the higher cognitive processes governing the longer inter-event intervals., Comment: 11 pages, 6 figures, 1 table

Striatal dopamine has been implicated in social behavior across humans, rodents, and non-human primates in artificial laboratory settings with highly-practiced tasks and fixed reward contingencies. Whether striatal dopamine drives naturalistic, spontaneous social behavior remains unclear. Here, we leverage day-to-day logs of unconstrained smartphone behavior and establish a novel link between smartphone social activity and individual differences in striatal dopamine synthesis capacity using [18F]-DOPA PET in (N=22) healthy adult humans. We find a strong relationship such that a higher proportion of social app interactions correlates with lower dopamine synthesis capacity in the bi-lateral putamen. Permutation tests and penalized regressions provide evidence that this link between dopamine synthesis capacity and social versus non-social smartphone taps is specific. These observations provide a key empirical grounding for current speculations about dopamine’s role in digital social behavior.

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Summary Dietary interventions have not been effective in the treatment of multiple sclerosis (MS). Here, we show that periodic 3-day cycles of a fasting mimicking diet (FMD) are effective in ameliorating demyelination and symptoms in a murine experimental autoimmune encephalomyelitis (EAE) model. The FMD reduced clinical severity in all mice and completely reversed symptoms in 20% of animals. These improvements were associated with increased corticosterone levels and regulatory T (Treg) cell numbers and reduced levels of pro- inflammatory cytokines, TH1 and TH17 cells, and antigen-presenting cells (APCs). Moreover, the FMD promoted oligodendrocyte precursor cell regeneration and remyelination in axons in both EAE and cuprizone MS models, supporting its effects on both suppression of autoimmunity and remyelination. We also report preliminary data suggesting that an FMD or a chronic ketogenic diet are safe, feasible, and potentially effective in the treatment of relapsing-remitting multiple sclerosis (RRMS) patients (NCT01538355).

Body movements drop with sleep, and this behavioural signature is widely exploited to infer sleep duration. However, a reduction in body movements may also occur in periods of intense cognitive activity, and the ubiquitous use of smartphones may capture these wakeful periods otherwise hidden in the standard measures of sleep. Here, we continuously captured the gross body movements using standard wrist-worn accelerometers to quantify sleep (actigraphy) and logged the timing of the day-to-day touchscreen events (‘tappigraphy’). Using these measures, we addressed how the gross body movements overlap with the cognitively engaging digital behaviour (from n = 79 individuals, accumulating ~1400 nights). We find that smartphone use was distributed across a broad spectrum of physical activity levels, but consistently peaked at rest. We estimated the putative sleep onset and wake-up times from the actigraphy data to find that these times were well correlated to the estimates from tappigraphy (R2 = 0.9 for sleep-onset time and wake-up time). However, actigraphy overestimated sleep as virtually all of the users used their phones during the putative sleep period. Interestingly, the probability of touches remained greater than zero for ~2 h after the putative sleep onset, and ~2 h before the putative wake-up time. Our findings suggest that touchscreen interactions are widely integrated into modern sleeping habits—surrounding both sleep onset and waking-up periods—yielding a new approach to measuring sleep. Smartphone interactions can be leveraged to update the behavioural signatures of sleep with these peculiarities of modern digital behaviour.

In this paper, seven critical parameters have been identified contributing to generation shortages, and affecting the peak load demand forecasting for power system generation planning. Due to the difficulty to predict the occurrence of each parameter effectively, the time series regression models of these parameters and the peak load demand are developed using the curve fitting tool box in MATLAB. The accuracy of the developed models is evaluated through the residual, goodness of fit, and percentage errors calculated between actual and calculated data.

Unconstrained day-to-day activities are difficult to quantify and how the corresponding movements shape the brain remain unclear. Here, we recorded all touchscreen smartphone interactions at a sub-second precision and show that the unconstrained day-to-day behavior captured on the phone reflects in the simple sensorimotor computations measured in the laboratory. The behavioral diversity on the phone, the speed of interactions, the amount of social & non-social interactions, all uniquely influenced the trial-to-trial motor variability used to measure the amount of intrinsic neuronal noise. Surprisingly, both the motor performance and the early somatosensory cortical signals (assessed using EEG in passive conditions) became noisier with increased social interactions. Inter-individual differences in how people use the smartphone can help thus decompose the structure of low-level sensorimotor computations., npj Digital Medicine, 1 (1), ISSN:2398-6352

Explaining how the human brain operates in the real world remains far from the reach of modern systems and cognitive neuroscience. The brain is measured in highly controlled laboratory settings and most neuroscientific research is laser focused on explaining highly simplistic behaviours designed in the laboratory. The ubiquitous use of smartphones provides a fresh opportunity to radically reverse this trend by providing a quantitative insight into human actions in the real world. Addressing how this digital behaviour maps onto elementary neuronal measures is a powerful starting point towards appreciating the complexity of human actions.

As a common neuroscientific observation, the more a body part is used, the less variable the corresponding computations become. We here report a more complicated scenario concerning the fingertips of smartphone users. We sorted 21-days histories of touchscreen use of 57 volunteers into social and non-social categories. Sensorimotor variability was measured in a laboratory setting by simple button depressions and scalp electrodes (electroencephalogram, EEG). The ms range trial-to-trial variability in button depression was directly proportional to the number of social touches and inversely proportional to non-social touches. Variability of the early tactile somatosensory potentials was also proportional to the number of social touches, but not to non-social touches. The number of Apps and the speed of touchscreen use also reflected this variability. We suggest that smartphone use affects elementary computations even in tasks not involving a phone and that social activities uniquely reconfigure the thumb to touchscreen use.Impact StatementUnconstrained behavior on a smartphone is a powerful predictor of neuronal functions measured in the laboratory and the details of the smartphone-neuronal association challenges the established ideas of brain plasticity.

The capacity to inhibit actions is central to voluntary motor control. However, the control mechanisms and subjective experience involved in voluntarily stopping an involuntary movement remain poorly understood. Here we examined, in humans, the voluntary inhibition of the Kohnstamm phenomenon, in which sustained voluntary contraction of shoulder abductors is followed by involuntary arm raising. Participants were instructed to stop the involuntary movement, hold the arm in a constant position, and 'release' the inhibition after ∼2s. Participants achieved this by modulating agonist muscle activity, rather than by antagonist contraction. Specifically, agonist muscle activity plateaued during this voluntary inhibition, and resumed its previous increase thereafter. There was no discernible antagonist activation. Thus, some central signal appeared to temporarily counter the involuntary motor drive, without directly affecting the Kohnstamm generator itself. We hypothesise a form of "negative motor command" to account for this novel finding. We next tested the specificity of the negative motor command, by inducing bilateral Kohnstamm movements, and instructing voluntary inhibition for one arm only. The results suggested negative motor commands responsible for inhibition are initially broad, affecting both arms, and then become focused. Finally, a psychophysical investigation found that the perceived force of the aftercontraction was significantly overestimated, relative to voluntary contractions with similar EMG levels. This finding is consistent with the hypothesis that the Kohnstamm generator does not provide an efference copy signal. Our results shed new light on this interesting class of involuntary movement, and provide new information about voluntary inhibition of action.

The perceived distance between touches on the limbs is generally bigger for distances oriented across the width of the limb than for distances oriented along the length of the limb. The present study aimed to investigate the coherence of such distortions of tactile size perception across different skin surfaces. We investigated distortions of tactile size perception on the dorsal and palmar surfaces of both the left and right hands as well as the forehead. Participants judged which of two tactile distances felt larger. One distance was aligned with the proximodistal axis ( along the body), the other with the mediolateral axis ( across the body). Clear distortions were found on all five skin surfaces, with stimuli oriented across the width of the body being perceived as farther apart than those oriented along the length of the body. Consistent with previous results, distortions were smaller on the palmar than on the dorsal hand surface. Distortion on the forehead was intermediate between the dorsal and palmar surfaces. There were clear correlations between distortion on the left and right hands, for both the dorsal and palmar skin surfaces. In contrast, within each hand, there was no significant correlation between the two skin surfaces. Distortion on the forehead was not significantly correlated with that on any of the other skin surfaces. These results provide evidence for bilaterally symmetric representations underlying tactile size perception.