Modeling startle eyeblink electromyogram to assess fear learning

Pavlovian fear conditioning is widely used as a laboratory model of associative learning in human and nonhuman species. In this model, an organism is trained to predict an aversive unconditioned stimulus from initially neutral events (conditioned stimuli, CS). In humans, fear memory is typically measured via conditioned autonomic responses or fear-potentiated startle. For the latter, various analysis approaches have been developed, but a systematic comparison of competing methodologies is lacking. Here, we investigate the suitability of a model-based approach to startle eyeblink analysis for assessment of fear memory, and compare this to extant analysis strategies. First, we build a psychophysiological model (PsPM) on a generic startle response. Then, we optimize and validate this PsPM on three independent fear-conditioning data sets. We demonstrate that our model can robustly distinguish aversive (CS+) from nonaversive stimuli (CS-, i.e., has high predictive validity). Importantly, our model-based approach captures fear-potentiated startle during fear retention as well as fear acquisition. Our results establish a PsPM-based approach to assessment of fear-potentiated startle, and qualify previous peak-scoring methods. Our proposed model represents a generic startle response and can potentially be used beyond fear conditioning, for example, to quantify affective startle modulation or prepulse inhibition of the acoustic startle response

A pupil size response model to assess fear learning

During fear conditioning, pupil size responses dissociate between conditioned stimuli that are contingently paired (CS+) with an aversive unconditioned stimulus, and those that are unpaired (CS-). Current approaches to assess fear learning from pupil responses rely on ad hoc specifications. Here, we sought to develop a psychophysiological model (PsPM) in which pupil responses are characterized by response functions within the framework of a linear time-invariant system. This PsPM can be written as a general linear model, which is inverted to yield amplitude estimates of the eliciting process in the central nervous system. We first characterized fear-conditioned pupil size responses based on an experiment with auditory CS. PsPM-based parameter estimates distinguished CS+/CS- better than, or on par with, two commonly used methods (peak scoring, area under the curve). We validated this PsPM in four independent experiments with auditory, visual, and somatosensory CS, as well as short (3.5 s) and medium (6 s) CS/US intervals. Overall, the new PsPM provided equal or decisively better differentiation of CS+/CS- than the two alternative methods and was never decisively worse. We further compared pupil responses with concurrently measured skin conductance and heart period responses. Finally, we used our previously developed luminance-related pupil responses to infer the timing of the likely neural input into the pupillary system. Overall, we establish a new PsPM to assess fear conditioning based on pupil responses. The model has a potential to provide higher statistical sensitivity, can be applied to other conditioning paradigms in humans, and may be easily extended to nonhuman mammals

Asymmetric representation of aversive prediction errors in Pavlovian threat conditioning

Learning to predict threat is important for survival. Such learning may be driven by differences between expected and encountered outcomes, termed prediction errors (PEs). While PEs are crucial for reward learning, the role of putative PE signals in aversive learning is less clear. Here, we used functional magnetic resonance imaging in humans to investigate neural PE signals. Four cues, each with a different probability of being followed by an aversive outcome, were presented multiple times. We found that neural activity only at omission - but not at occurrence - of predicted threat related to PEs in the medial prefrontal cortex. More expected omission was associated with higher neural activity. In no brain region did neural activity fulfill necessary computational criteria for full signed PE representation. Our result suggests that, different from reward learning, aversive learning may not be primarily driven by PE signals in one single brain region

Human Pavlovian fear conditioning conforms to probabilistic learning

Learning to predict threat from environmental cues is a fundamental skill in changing environments. This aversive learning process is exemplified by Pavlovian threat conditioning. Despite a plethora of studies on the neural mechanisms supporting the formation of associations between neutral and aversive events, our computational understanding of this process is fragmented. Importantly, different computational models give rise to different and partly opposing predictions for the trial-by-trial dynamics of learning, for example expressed in the activity of the autonomic nervous system (ANS). Here, we investigate human ANS responses to conditioned stimuli during Pavlovian fear conditioning. To obtain precise, trial-by-trial, single-subject estimates of ANS responses, we build on a statistical framework for psychophysiological modelling. We then consider previously proposed non-probabilistic models, a simple probabilistic model, and non-learning models, as well as different observation functions to link learning models with ANS activity. Across three experiments, and both for skin conductance (SCR) and pupil size responses (PSR), a probabilistic learning model best explains ANS responses. Notably, SCR and PSR reflect different quantities of the same model: SCR track a mixture of expected outcome and uncertainty, while PSR track expected outcome alone. In summary, by combining psychophysiological modelling with computational learning theory, we provide systematic evidence that the formation and maintenance of Pavlovian threat predictions in humans may rely on probabilistic inference and includes estimation of uncertainty. This could inform theories of neural implementation of aversive learnin

Prediction of cognitive outcome based on the progression of auditory discrimination during coma.

To date, no clinical test is able to predict cognitive and functional outcome of cardiac arrest survivors. Improvement of auditory discrimination in acute coma indicates survival with high specificity. Whether the degree of this improvement is indicative of recovery remains unknown. Here we investigated if progression of auditory discrimination can predict cognitive and functional outcome. We prospectively recorded electroencephalography responses to auditory stimuli of post-anoxic comatose patients on the first and second day after admission. For each recording, auditory discrimination was quantified and its evolution over the two recordings was used to classify survivors as "predicted" when it increased vs. "other" if not. Cognitive functions were tested on awakening and functional outcome was assessed at 3 months using the Cerebral Performance Categories (CPC) scale. Thirty-two patients were included, 14 "predicted survivors" and 18 "other survivors". "Predicted survivors" were more likely to recover basic cognitive functions shortly after awakening (ability to follow a standardized neuropsychological battery: 86% vs. 44%; p=0.03 (Fisher)) and to show a very good functional outcome at 3 months (CPC 1: 86% vs. 33%; p=0.004 (Fisher)). Moreover, progression of auditory discrimination during coma was strongly correlated with cognitive performance on awakening (phonemic verbal fluency: rs=0.48; p=0.009 (Spearman)). Progression of auditory discrimination during coma provides early indication of future recovery of cognitive functions. The degree of improvement is informative of the degree of functional impairment. If confirmed in a larger cohort, this test would be the first to predict detailed outcome at the single-patient level

Experience-based Auditory Predictions Modulate Brain Activity to Silence as do Real Sounds.

Interactions between stimuli's acoustic features and experience-based internal models of the environment enable listeners to compensate for the disruptions in auditory streams that are regularly encountered in noisy environments. However, whether auditory gaps are filled in predictively or restored a posteriori remains unclear. The current lack of positive statistical evidence that internal models can actually shape brain activity as would real sounds precludes accepting predictive accounts of filling-in phenomenon. We investigated the neurophysiological effects of internal models by testing whether single-trial electrophysiological responses to omitted sounds in a rule-based sequence of tones with varying pitch could be decoded from the responses to real sounds and by analyzing the ERPs to the omissions with data-driven electrical neuroimaging methods. The decoding of the brain responses to different expected, but omitted, tones in both passive and active listening conditions was above chance based on the responses to the real sound in active listening conditions. Topographic ERP analyses and electrical source estimations revealed that, in the absence of any stimulation, experience-based internal models elicit an electrophysiological activity different from noise and that the temporal dynamics of this activity depend on attention. We further found that the expected change in pitch direction of omitted tones modulated the activity of left posterior temporal areas 140-200 msec after the onset of omissions. Collectively, our results indicate that, even in the absence of any stimulation, internal models modulate brain activity as do real sounds, indicating that auditory filling in can be accounted for by predictive activity

Evidence of trace conditioning in comatose patients revealed by the reactivation of EEG responses to alerting sounds.

Trace conditioning refers to a learning process occurring after repeated presentation of a neutral conditioned stimulus (CS+) and a salient unconditioned stimulus (UCS) separated by a temporal gap. Recent studies have reported that trace conditioning can occur in humans in reduced levels of consciousness by showing a transfer of the unconditioned autonomic response to the CS+ in healthy sleeping individuals and in vegetative state patients. However, no previous studies have investigated the neural underpinning of trace conditioning in the absence of consciousness in humans. In the present study, we recorded the EEG activity of 29 post-anoxic comatose patients while presenting a trace conditioning paradigm using neutral tones as CS+ and alerting sounds as UCS. Most patients received therapeutic hypothermia and all were deeply unconscious according to standardized clinical scales. After repeated presentation of the CS+ and UCS couple, learning was assessed by measuring the EEG activity during the period where the UCS is omitted after CS+ presentation. Specifically we assessed the 'reactivation' of the neural response to UCS omission by applying a decoding algorithm derived from the statistical model of the EEG activity in response to the UCS presentation. The same procedure was used in a group of 12 awake healthy controls. We found a reactivation of the UCS response in absence of stimulation in eight patients (five under therapeutic hypothermia) and four healthy controls. Additionally, the reactivation effect was temporally specific within trials since it manifested primarily at the specific latency of UCS presentation and significantly less before or after this period. Our results show for the first time that trace conditioning may manifest as a reactivation of the EEG activity related to the UCS and even in the absence of consciousness

Prediction of awakening from hypothermic post anoxic coma based on auditory discrimination.

OBJECTIVE: Most of the available clinical tests for prognosis of post-anoxic coma are informative of poor outcome. Previous work has shown that an improvement in auditory discrimination over the first days of coma is predictive of awakening. Here, we aimed at evaluating this test on a large cohort of patients undergoing therapeutic hypothermia and at investigating its added value on existing clinical measures. METHODS: We recorded electroencephalography responses to auditory stimuli in 94 comatose patients, under hypothermia and after re-warming to normal temperature. Auditory discrimination was semi-automatically quantified by decoding electroencephalography responses to frequently repeated vs. rare sounds. Outcome prediction was based on the change of decoding performance from hypothermia to normothermia. RESULTS: An increase in auditory discrimination from hypothermia to normothermia was observed for 33 out of 94 patients. Among them, 27 awoke from coma, resulting in a positive predictive value of awakening of 82% (95% confidence interval: 0.65-0.93). Most non-survivors showing an improvement in auditory discrimination had incident status epilepticus. By excluding them, 27 out of 29 patients with improvement in auditory discrimination survived, resulting in a considerable improvement of the predictive value for awakening (93%, with 95% confidence interval: 0.77-0.99). Importantly, this test predicted the awakening of 13 out of 51 patients for which the outcome was uncertain based on current tests. INTERPRETATION: The progression of auditory discrimination from hypothermia to normothermia has a high predictive value for awakening. This quantitative measure provides an added value to existing clinical tests and encourages the maintenance of life support. This article is protected by copyright. All rights reserved

Neural detection of complex sound sequences in the absence of consciousness.

The neural response to a violation of sequences of identical sounds is a typical example of the brain's sensitivity to auditory regularities. Previous literature interprets this effect as a pre-attentive and unconscious processing of sensory stimuli. By contrast, a violation to auditory global regularities, i.e. based on repeating groups of sounds, is typically detectable when subjects can consciously perceive them. Here, we challenge the notion that global detection implies consciousness by testing the neural response to global violations in a group of 24 patients with post-anoxic coma (three females, age range 45-87 years), treated with mild therapeutic hypothermia and sedation. By applying a decoding analysis to electroencephalographic responses to standard versus deviant sound sequences, we found above-chance decoding performance in 10 of 24 patients (Wilcoxon signed-rank test, P < 0.001), despite five of them being mildly hypothermic, sedated and unarousable. Furthermore, consistently with previous findings based on the mismatch negativity the progression of this decoding performance was informative of patients' chances of awakening (78% predictive of awakening). Our results show for the first time that detection of global regularities at neural level exists despite a deeply unconscious state