Using insurance claims to predict and improve hospitalizations and biologics use in members with inflammatory bowel diseases.

OBJECTIVE: Inflammatory Bowel Disease (IBD) is an inflammatory disorder of the gastrointestinal tract that can necessitate hospitalization and the use of expensive biologics. Models predicting these interventions may improve patient quality of life and reduce expenditures. MATERIALS AND METHODS: We used insurance claims from 2011 to 2013 to predict IBD-related hospitalizations and the initiation of biologics. We derived and optimized our model from a 2011 training set of 7771 members, predicting their outcomes the following year. The best-performing model was then applied to a 2012 validation set of 7450 members to predict their outcomes in 2013. RESULTS: Our models predicted both IBD-related hospitalizations and the initiation of biologics, with average positive predictive values of 17% and 11%, respectively - each a 200% improvement over chance. Further, when we used topic modeling to identify four member subpopulations, the positive predictive value of predicting hospitalization increased to 20%. DISCUSSION: We show that our hospitalization model, in concert with a mildly-effective interventional treatment plan for members identified as high-risk, may both improve patient outcomes and reduce insurance expenditures. CONCLUSION: The success of our approach provides a roadmap for how claims data can complement traditional medical decision making with personalized, data-driven predictive medicine.

The Defensive Activation Theory: REM Sleep as a Mechanism to Prevent Takeover of the Visual Cortex.

Regions of the brain maintain their territory with continuous activity: if activity slows or stops (e.g., because of blindness), the territory tends to be taken over by its neighbors. A surprise in recent years has been the speed of takeover, which is measurable within an hour. These findings lead us to a new hypothesis on the origin of REM sleep. We hypothesize that the circuitry underlying REM sleep serves to amplify the visual system's activity periodically throughout the night, allowing it to defend its territory against takeover from other senses. We find that measures of plasticity across 25 species of primates correlate positively with the proportion of rapid eye movement (REM) sleep. We further find that plasticity and REM sleep increase in lockstep with evolutionary recency to humans. Finally, our hypothesis is consistent with the decrease in REM sleep and parallel decrease in neuroplasticity with aging.

Modulation of attention and stress with arousal: The mental and physical effects of riding a motorcycle.

Existing theories suggest that moderate arousal improves selective attention, as would be expected in the context of competitive sports or sensation-seeking activities. Here we investigated how riding a motorcycle, an attention-demanding physical activity, affects sensory processing. To do so, we implemented the passive auditory oddball paradigm and measured the EEG response of participants as they rode a motorcycle, drove a car, and sat at rest. Specifically, we measured the N1 and mismatch negativity to auditory tones, as well as alpha power during periods of no tones. We investigated whether riding and driving modulated non-CNS metrics including heart rate and concentrations of the hormones epinephrine, cortisol, DHEA-S, and testosterone. While participants were riding, we found a decrease in N1 amplitude, increase in mismatch negativity, and decrease in relative alpha power, together suggesting enhancement of sensory processing and visual attention. Riding increased epinephrine levels, increased heart rate, and decreased the ratio of cortisol to DHEA-S. Together, these results suggest that riding increases focus, heightens the brain's passive monitoring of changes in the sensory environment, and alters HPA axis response. More generally, our findings suggest that selective attention and sensory monitoring seem to be separable neural processes.

Differentiating weight-restored anorexia nervosa and body dysmorphic disorder using neuroimaging and psychometric markers.

Anorexia nervosa (AN) and body dysmorphic disorder (BDD) are potentially life-threatening conditions whose partially overlapping phenomenology-distorted perception of appearance, obsessions/compulsions, and limited insight-can make diagnostic distinction difficult in some cases. Accurate diagnosis is crucial, as the effective treatments for AN and BDD differ. To improve diagnostic accuracy and clarify the contributions of each of the multiple underlying factors, we developed a two-stage machine learning model that uses multimodal, neurobiology-based, and symptom-based quantitative data as features: task-based functional magnetic resonance imaging data using body visual stimuli, graph theory metrics of white matter connectivity from diffusor tensor imaging, and anxiety, depression, and insight psychometric scores. In a sample of unmedicated adults with BDD (n = 29), unmedicated adults with weight-restored AN (n = 24), and healthy controls (n = 31), the resulting model labeled individuals with an accuracy of 76%, significantly better than the chance accuracy of 35% ([Formula: see text]). In the multivariate model, reduced white matter global efficiency and better insight were associated more with AN than with BDD. These results improve our understanding of the relative contributions of the neurobiological characteristics and symptoms of these disorders. Moreover, this approach has the potential to aid clinicians in diagnosis, thereby leading to more tailored therapy.

Empathic Neural Responses Predict Group Allegiance.

Watching another person in pain activates brain areas involved in the sensation of our own pain. Importantly, this neural mirroring is not constant; rather, it is modulated by our beliefs about their intentions, circumstances, and group allegiances. We investigated if the neural empathic response is modulated by minimally-differentiating information (e.g., a simple text label indicating another's religious belief), and if neural activity changes predict ingroups and outgroups across independent paradigms. We found that the empathic response was larger when participants viewed a painful event occurring to a hand labeled with their own religion (ingroup) than to a hand labeled with a different religion (outgroup). Counterintuitively, the magnitude of this bias correlated positively with the magnitude of participants' self-reported empathy. A multivariate classifier, using mean activity in empathy-related brain regions as features, discriminated ingroup from outgroup with 72% accuracy; the classifier's confidence correlated with belief certainty. This classifier generalized successfully to validation experiments in which the ingroup condition was based on an arbitrary group assignment. Empathy networks thus allow for the classification of long-held, newly-modified and arbitrarily-formed ingroups and outgroups. This is the first report of a single machine learning model on neural activation that generalizes to multiple representations of ingroup and outgroup. The current findings may prove useful as an objective diagnostic tool to measure the magnitude of one's group affiliations, and the effectiveness of interventions to reduce ingroup biases.

Do Complexity Measures of Frontal EEG Distinguish Loss of Consciousness in Geriatric Patients Under Anesthesia?

While geriatric patients have a high likelihood of requiring anesthesia, they carry an increased risk for adverse cognitive outcomes from its use. Previous work suggests this could be mitigated by better intraoperative monitoring using indexes defined by several processed electroencephalogram (EEG) measures. Unfortunately, inconsistencies between patients and anesthetic agents in current analysis techniques have limited the adoption of EEG as standard of care. In attempts to identify new analyses that discriminate clinically-relevant anesthesia timepoints, we tested 1/f frequency scaling as well as measures of complexity from nonlinear dynamics. Specifically, we tested whether analyses that characterize time-delayed embeddings, correlation dimension (CD), phase-space geometric analysis, and multiscale entropy (MSE) capture loss-of-consciousness changes in EEG activity. We performed these analyses on EEG activity collected from a traditionally hard-to-monitor patient population: geriatric patients on beta-adrenergic blockade who were anesthetized using a combination of fentanyl and propofol. We compared these analyses to traditional frequency-derived measures to test how well they discriminated EEG states before and after loss of response to verbal stimuli. We found spectral changes similar to those reported previously during loss of response. We also found significant changes in 1/f frequency scaling. Additionally, we found that our phase-space geometric characterization of time-delayed embeddings showed significant differences before and after loss of response, as did measures of MSE. Our results suggest that our new spectral and complexity measures are capable of capturing subtle differences in EEG activity with anesthesia administration-differences which future work may reveal to improve geriatric patient monitoring.

Endothelial Regeneration of Large Vessels Is a Biphasic Process Driven by Local Cells with Distinct Proliferative Capacities.

The cellular and mechanistic bases underlying endothelial regeneration of adult large vessels have proven challenging to study. Using a reproducible in vivo aortic endothelial injury model, we characterized cellular dynamics underlying the regenerative process through a combination of multi-color lineage tracing, parabiosis, and single-cell transcriptomics. We found that regeneration is a biphasic process driven by distinct populations arising from differentiated endothelial cells. The majority of cells immediately adjacent to the injury site re-enter the cell cycle during the initial damage response, with a second phase driven by a highly proliferative subpopulation. Endothelial regeneration requires activation of stress response genes including Atf3, and aged aortas compromised in their reparative capacity express less Atf3. Deletion of Atf3 reduced endothelial proliferation and compromised the regeneration. These findings provide important insights into cellular dynamics and mechanisms that drive responses to large vessel injury.

Spatial warping by oriented line detectors can counteract neural delays.

The slow speed of neural transmission necessitates that cortical visual information from dynamic scenes will lag reality. The "perceiving the present" (PTP) hypothesis suggests that the visual system can mitigate the effect of such delays by spatially warping scenes to look as they will in ~100 ms from now (Changizi, 2001). We here show that the Hering illusion, in which straight lines appear bowed, can be induced by a background of optic flow, consistent with the PTP hypothesis. However, importantly, the bowing direction is the same whether the flow is inward or outward. This suggests that if the warping is meant to counteract latencies, it is accomplished by a simple strategy that is insensitive to motion direction, and that works only under typical (forward-moving) circumstances. We also find that the illusion strengthens with longer pulses of optic flow, demonstrating motion integration over ~80 ms. The illusion is identical whether optic flow precedes or follows the flashing of bars, exposing the spatial warping to be equally postdictive and predictive, i.e., peri-dictive. Additionally, the illusion is diminished by cues which suggest the bars are independent of the background movement. Collectively, our findings are consistent with a role for networks of visual orientation-tuned neurons (e.g., simple cells in primary visual cortex) in spatial warping. We conclude that under the common condition of forward ego-motion, spatial warping counteracts the disadvantage of neural latencies. It is not possible to prove that this is the purpose of spatial warping, but our findings at minimum place constraints on the PTP hypothesis, demonstrating that any spatial warping for the purpose of counteracting neural delays is not a precise, on-the-fly computation, but instead a heuristic achieved by a simple mechanism that succeeds under normal circumstances.