Population coding in area V4 during rapid shape detections.

While previous studies have suggested that neuronal correlations are common in visual cortex over a range of timescales, the effect of correlations on rapid visually based decisions has received little attention. We trained Macaca mulatta to saccade to a peripherally presented shape embedded in dynamic noise as soon as the shape appeared. While the monkeys performed the task, we recorded from neuronal populations (5-29 cells) using a microelectrode array implanted in area V4, a visual area thought to be involved in form perception. While modest correlations were present between cells during visual stimulation, their magnitude did not change significantly subsequent to the appearance of a shape. We quantified the reliability and temporal precision with which neuronal populations signaled the appearance of the shape and predicted the animals' choices using mutual information analyses. To study the impact of correlations, we shuffled the activity from each cell across observations while retaining stimulus-dependent modulations in firing rate. We found that removing correlations by shuffling across trials minimally affected the reliability or timing with which pairs, or larger groups of cells, signaled the presence of a shape. To assess the downstream impact of correlations, we also studied how shuffling affected the ability of V4 populations to predict behavioral choices. Surprisingly, shuffling created a modest increase in the accuracy of such predictions, suggesting that the reliability of downstream neurons is slightly compromised by activity correlations. Our findings are consistent with neuronal correlations having a minimal effect on the reliability and timing of rapid perceptual decisions.

Inattention blindness to motion in middle temporal area.

Subjects naturally form and use expectations to solve familiar tasks, but the accuracy of these expectations and the neuronal mechanisms by which these expectations enhance behavior are unclear. We trained animals (Macaca mulatta) in a challenging perceptual task in which the likelihood of a very brief pulse of motion was consistently modulated over time and space. Pulse likelihood had dramatic effects on behavior: unexpected pulses were nearly invisible to the animals. To examine the neuronal basis of such inattention blindness, we recorded from single neurons in the middle temporal (MT) area, an area related to motion perception. Fluctuations in how reliably MT neurons both signaled stimulus events and predicted behavioral choices were highly correlated with changes in performance over the course of individual trials. A simple neuronal pooling model reveals that the dramatic behavioral effects of attention in this task can be completely explained by changes in the reliability of a small number of MT neurons.

Applying computable phenotypes within a common data model to identify heart failure patients for an implantable cardiac device registry.

BACKGROUND: Use of existing data in electronic health records (EHRs) could be used more extensively to better leverage real world data for clinical studies, but only if standard, reliable processes are developed. Numerous computable phenotypes have been validated against manual chart review, and common data models (CDMs) exist to aid implementation of such phenotypes across platforms and sites. Our objective was to measure consistency between data that had previously been manually collected for an implantable cardiac device registry and CDM-based phenotypes for the condition of heart failure (HF). METHODS: Patients enrolled in an implantable cardiac device registry at two hospitals from 2013 to 2018 contributed to this analysis wherein registry data were compared to PCORnet CDM-formatted EHR data. Seven different phenotype algorithms were used to search for the presence of HF and compare the results with the registry. Sensitivity, specificity, predictive value and congruence were calculated for each phenotype. RESULTS: In the registry, 176 of 319 (55%) patients had history of HF, compared with different phenotypes estimating between 96 (30%) and 188 (59%). The least-restrictive phenotypes (any diagnosis) had high sensitivity and specificity (90%/80%), but more restrictive phenotypes had higher specificity (e.g., code present in problem list, 94%). Differences were observed using time-based criteria (e.g., days between visit diagnoses) and between participating hospitals. CONCLUSIONS: Consistency between manually-collected registry data and CDM-based phenotypes for history of HF was high overall, but use of different phenotypes impacted sensitivity and specificity, and results may differ depending on the medical condition of interest.

Rapid shape detection signals in area V4.

Vision in foveate animals is an active process that requires rapid and constant decision-making. For example, when a new object appears in the visual field, we can quickly decide to inspect it by directing our eyes to the object's location. We studied the contribution of primate area V4 to these types of rapid foveation decisions. Animals performed a reaction time task that required them to report when any shape appeared within a peripherally-located noisy stimulus by making a saccade to the stimulus location. We found that about half of the randomly sampled V4 neurons not only rapidly and precisely represented the appearance of this shape, but they were also predictive of the animal's saccades. A neuron's ability to predict the animal's saccades was not related to the specificity with which the cell represented a single type of shape but rather to its ability to signal whether any shape was present. This relationship between sensory sensitivity and behavioral predictiveness was not due to global effects such as alertness, as it was equally likely to be observed for cells with increases and decreases in firing rate. Careful analysis of the timescales of reliability in these neurons implies that they reflect both feedforward and feedback shape detecting processes. In approximately 7% of our recorded sample, individual neurons were able to predict both the delay and precision of the animal's shape detection performance. This suggests that a subset of V4 neurons may have been directly and causally contributing to task performance and that area V4 likely plays a critical role in guiding rapid, form-based foveation decisions.