GP Kernels for Cross-Spectrum Analysis

Abstract Multi-output Gaussian processes provide a convenient framework for multi-task problems. An illustrative and motivating example of a multi-task problem is multi-region electrophysiological time-series data, where experimentalists are interested in both power and phase coherence between channels. Recently, Wilson and Adams (2013) proposed the spectral mixture (SM) kernel to model the spectral density of a single task in a Gaussian process framework. In this paper, we develop a novel covariance kernel for multiple outputs, called the cross-spectral mixture (CSM) kernel. This new, flexible kernel represents both the power and phase relationship between multiple observation channels. We demonstrate the expressive capabilities of the CSM kernel through implementation of a Bayesian hidden Markov model, where the emission distribution is a multi-output Gaussian process with a CSM covariance kernel. Results are presented for measured multi-region electrophysiological data

Altered mGluR5-Homer scaffolds and corticostriatal connectivity in a Shank3 complete knockout model of autism

Human neuroimaging studies suggest that aberrant neural connectivity underlies behavioural deficits in autism spectrum disorders (ASDs), but the molecular and neural circuit mechanisms underlying ASDs remain elusive. Here, we describe a complete knockout mouse model of the autism-associated Shank3 gene, with a deletion of exons 4–22 (Δe4–22). Both mGluR5-Homer scaffolds and mGluR5-mediated signalling are selectively altered in striatal neurons. These changes are associated with perturbed function at striatal synapses, abnormal brain morphology, aberrant structural connectivity and ASD-like behaviour. In vivo recording reveals that the cortico-striatal-thalamic circuit is tonically hyperactive in mutants, but becomes hypoactive during social behaviour. Manipulation of mGluR5 activity attenuates excessive grooming and instrumental learning differentially, and rescues impaired striatal synaptic plasticity in Δe4–22−/− mice. These findings show that deficiency of Shank3 can impair mGluR5-Homer scaffolding, resulting in cortico-striatal circuit abnormalities that underlie deficits in learning and ASD-like behaviours. These data suggest causal links between genetic, molecular, and circuit mechanisms underlying the pathophysiology of ASDs

Persistent Hyperdopaminergia Decreases the Peak Frequency of Hippocampal Theta Oscillations during Quiet Waking and REM Sleep

Long-term changes in dopaminergic signaling are thought to underlie the pathophysiology of a number of psychiatric disorders. Several conditions are associated with cognitive deficits such as disturbances in attention processes and learning and memory, suggesting that persistent changes in dopaminergic signaling may alter neural mechanisms underlying these processes. Dopamine transporter knockout (DAT-KO) mice exhibit a persistent five-fold increase in extracellular dopamine levels. Here, we demonstrate that DAT-KO mice display lower hippocampal theta oscillation frequencies during baseline periods of waking and rapid-eye movement sleep. These altered theta oscillations are not reversed via treatment with the antidopaminergic agent haloperidol. Thus, we propose that persistent hyperdopaminergia, together with secondary alterations in other neuromodulatory systems, results in lower frequency activity in neural systems responsible for various cognitive processes

Incubating the Research Independence of a Medical Scientist Training Program Graduate

ProblemPhysician-scientists play a critical role in discovering new biological knowledge and translating findings into medical practices that can improve clinical outcomes. Collectively, the National Institutes of Health (NIH) and its affiliated Medical Scientist Training Programs (MSTPs) invest upwards of $500,000 to fully train each of the 900+ MD/PhD students enrolled in these programs. Nevertheless, graduates face the challenges of navigating fragmented intervals of clinical training and research engagement, reinitiating research upon completing their residencies, managing financial pressures, and competing for funding following what is typically four or more years of research inactivity. Together, these barriers contribute to the high attrition rate of MSTP graduates from research careers.ApproachThe authors designed and implemented (2009-2014), for a single trainee, an alternative postgraduate training model characterized by early research engagement, strategic mentoring, unyoked clinical and research milestones, and dedicated financial support.OutcomesThe pilot training experiment was so successful that the trainee secured an NIH project grant and completed his transition to research independence 3.5 years after starting the experimental training schedule-nearly 9 years earlier (based on age) than is typical for MD/PhDs transitioning from mentored to independent research. This success has demonstrated that unyoking research engagement from conventional calendar-based clinical training milestones is a feasible, effective means of incubating research independence in MSTP graduates.Next stepsThe authors encourage the design and application of similar unconventional approaches that interweave residency training with ongoing research activity for appropriate candidates, especially in subspecialties with increased MSTP graduate enrollment

Incubating the Research Independence of a Medical Scientist Training Program Graduate

ProblemPhysician-scientists play a critical role in discovering new biological knowledge and translating findings into medical practices that can improve clinical outcomes. Collectively, the National Institutes of Health (NIH) and its affiliated Medical Scientist Training Programs (MSTPs) invest upwards of $500,000 to fully train each of the 900+ MD/PhD students enrolled in these programs. Nevertheless, graduates face the challenges of navigating fragmented intervals of clinical training and research engagement, reinitiating research upon completing their residencies, managing financial pressures, and competing for funding following what is typically four or more years of research inactivity. Together, these barriers contribute to the high attrition rate of MSTP graduates from research careers.ApproachThe authors designed and implemented (2009-2014), for a single trainee, an alternative postgraduate training model characterized by early research engagement, strategic mentoring, unyoked clinical and research milestones, and dedicated financial support.OutcomesThe pilot training experiment was so successful that the trainee secured an NIH project grant and completed his transition to research independence 3.5 years after starting the experimental training schedule-nearly 9 years earlier (based on age) than is typical for MD/PhDs transitioning from mentored to independent research. This success has demonstrated that unyoking research engagement from conventional calendar-based clinical training milestones is a feasible, effective means of incubating research independence in MSTP graduates.Next stepsThe authors encourage the design and application of similar unconventional approaches that interweave residency training with ongoing research activity for appropriate candidates, especially in subspecialties with increased MSTP graduate enrollment

Incubating the research independence of a medical scientist training program graduate: A case study

10.1097/ACM.0000000000000568Academic Medicine902176-17

GP Kernels for Cross-Spectrum Analysis

Abstract Multi-output Gaussian processes provide a convenient framework for multi-task problems. An illustrative and motivating example of a multi-task problem is multi-region electrophysiological time-series data, where experimentalists are interested in both power and phase coherence between channels. Recently, Wilson and Adams (2013) proposed the spectral mixture (SM) kernel to model the spectral density of a single task in a Gaussian process framework. In this paper, we develop a novel covariance kernel for multiple outputs, called the cross-spectral mixture (CSM) kernel. This new, flexible kernel represents both the power and phase relationship between multiple observation channels. We demonstrate the expressive capabilities of the CSM kernel through implementation of a Bayesian hidden Markov model, where the emission distribution is a multi-output Gaussian process with a CSM covariance kernel. Results are presented for measured multi-region electrophysiological data