Detecting rhythmic spiking through the power spectra of point process model residuals.

Objective: Oscillations figure prominently as neurological disease hallmarks and neuromodulation targets. To detect oscillations in a neuron's spiking, one might attempt to seek peaks in the spike train's power spectral density (PSD) which exceed a flat baseline. Yet for a non-oscillating neuron, the PSD is not flat: The recovery period ("RP", the post-spike drop in spike probability, starting with the refractory period) introduces global spectral distortion. An established "shuffling" procedure corrects for RP distortion by removing the spectral component explained by the inter-spike interval (ISI) distribution. However, this procedure sacrifices oscillation-related information present in the ISIs, and therefore in the PSD. We asked whether point process models (PPMs) might achieve more selective RP distortion removal, thereby enabling improved oscillation detection. Approach: In a novel "residuals" method, we first estimate the RP duration (nr) from the ISI distribution. We then fit the spike train with a PPM that predicts spike likelihood based on the time elapsed since the most recent of any spikes falling within the preceding nr milliseconds. Finally, we compute the PSD of the model's residuals. Main results: We compared the residuals and shuffling methods' ability to enable accurate oscillation detection with flat baseline-assuming tests. Over synthetic data, the residuals method generally outperformed the shuffling method in classification of true- versus false-positive oscillatory power, principally due to enhanced sensitivity in sparse spike trains. In single-unit data from the internal globus pallidus (GPi) and ventrolateral anterior thalamus (VLa) of a parkinsonian monkey -- in which alpha-beta oscillations (8-30 Hz) were anticipated -- the residuals method reported the greatest incidence of significant alpha-beta power, with low firing rates predicting residuals-selective oscillation detection. Significance: These results encourage continued development of the residuals approach, to support more accurate oscillation detection. Improved identification of oscillations could promote improved disease models and therapeutic technologies.

Abstract inference of unchosen option values.

Reinforcement learning research has pursued a persistent question: Does reward feedback prompt inferences that transcend simple associations? Reversal learning data suggest an affirmative answer: When the positive stimulus (S+) becomes the negative stimulus (S-), trained humans rapidly switch to choosing the former S-. The operations supporting such inferences remain ambiguous. Do participants identify transitions between stimulus-specific contexts (i.e., A+B- and A-B+), or deduce values by learning the abstract contingency structure? Across two experiments, we probed humans' use of abstract rules to infer the values of unchosen alternatives. In Experiment 1, 37 participants attempted a task that originally demonstrated monkeys' difficulty with this form of inference. We presented modified discrimination problems in which the initially chosen stimulus (abstract inference group) or unchosen stimulus (control group) was replaced with a novel stimulus of identical status on Trial 2. In the abstract inference condition, accurate performance can be achieved by applying the consistent contingency structure (but not memory of stimulus-specific reward associations) to infer to the unchosen stimulus' value. The abstract inference group learned to make accurate choices, but only after committing substantially more errors than were observed among control participants-suggesting that unchosen value inferences are infrequently drawn in standard discrimination scenarios. In Experiment 2, 17 participants completed abstract inference problems that had been modified to be suitable for fMRI investigations. Behavioral results both corroborated the Experiment 1 trends and further revealed marked individual differences in explicit awareness of the novel stimulus values.

BOLD subjective value signals exhibit robust range adaptation.

Many theories of decision making assume that choice options are assessed along a common subjective value (SV) scale. The neural correlates of SV are widespread and reliable, despite the wide variation in the range of values over which decisions are made (e.g., between goods worth a few dollars, in some cases, or hundreds of dollars, in others). According to adaptive coding theories (Barlow, 1961), an efficient value signal should exhibit range adaptation, such that neural activity maintains a fixed dynamic range, and the slope of the value response varies inversely with the range of values within the local context. Although monkey data have demonstrated range adaptation in single-unit correlates of value (Padoa-Schioppa, 2009; Kobayashi et al., 2010), whether BOLD value signals exhibit similar range adaptation is unknown. To test for this possibility, we presented human participants with choices between a fixed immediate and variable delayed payment options. Across two conditions, the delayed options' SVs spanned either a narrow or wide range. SV-tracking activity emerged in the posterior cingulate, ventral striatum, anterior cingulate, and ventromedial prefrontal cortex. Throughout this network, we observed evidence consistent with the predictions of range adaptation: the SV response slope increased in the narrow versus wide range, with statistically significant slope changes confirmed for the posterior cingulate and ventral striatum. No regions exhibited a reliably increased BOLD activity range in the wide versus narrow condition. Our observations of range adaptation present implications for the interpretation of BOLD SV responses that are measured across different contexts or individuals.

Striatal outcome processing in healthy aging.

Functional MRI of young adults has implicated the striatum in the processing of rewarding and punishing events. To date, only two published experiments (Samanez-Larkin et al., 2007; Schott et al., 2007) have explored similar phenomena in older adults, with both studies emphasizing the anticipation of monetary outcomes. To better understand older participants' striatal responses to delivered outcomes, we engaged 20 older adults and 13 younger adults in a card-guessing task that rewarded correct guesses with monetary gain and punished incorrect guesses with monetary loss. Overall, the older adults retained most of the typical features of the striatal response, so that activity in the caudate head showed reliable differentiation between rewards and punishments during the 6- to 9-sec postoutcome window. Comparison of the older and younger adults also pointed to some potential aging effects on outcome activity, including reductions in the magnitude and extent of striatal activation, and a trend for the older adults to show a decreased early punishment response. In sum, our data suggest that the signaling of outcome valence remains relatively stable into late adulthood, although more research is needed to understand some subtle changes that might occur across the life span.