Postsynaptic and spiking activity of pyramidal cells,the principal neurons in the rat hippocampal CA1 region,does not control the resultant BOLD response: a combined electrophysiologic and fMRI approach

The specific role of postsynaptic activity for the generation of a functional magnetic resonance imaging (fMRI) response was determined by a simultaneous measurement of generated field excitatory postsynaptic potentials (fEPSPs) and blood oxygen level-dependent (BOLD) response in the rat hippocampal CA1 region during electrical stimulation of the contralateral CA3 region. The stimulation electrode was placed either in the left CA3a/b or CA3c, causing the preferentially basal or apical dendrites of the pyramidal cells in the right CA1 to be activated. Consecutive stimulations with low-intensity stimulation trains (i.e., 16 pulses for 8 seconds) resulted in clear postsynaptic responses of CA1 pyramidal cells, but in no significant BOLD responses. In contrast, consecutive high-intensity stimulation trains resulted in stronger postsynaptic responses that came along with minor (during stimulation of the left CA3a/b) or substantial (during stimulation of the left CA3c) spiking activity of the CA1 pyramidal cells, and resulted in the generation of significant BOLD responses in the left and right hippocampus. Correlating the electrophysiologic parameters of CA1 pyramidal cell activity (fEPSP and spiking activity) with the resultant BOLD response revealed no positive correlation. Consequently, postsynaptic activity of pyramidal cells, the most abundant neurons in the CA1, is not directly linked to the measured BOLD response.     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.