Increased alpha suppression with age during involuntary memory retrieval

Recent work suggests that while voluntary episodic memory declines with age, involuntary episodic memory, which comes to mind spontaneously without intention, remains relatively intact. However, the neurophysiology underlying these differences has yet to be established. The current study used electroencephalography (EEG) to investigate voluntary and involuntary retrieval in older and younger adults. Participants first encoded sounds, half of which were paired with pictures, the other half unpaired. EEG was then recorded as they listened to the sounds, with participants in the involuntary group performing a sound localization cover task, and those in the voluntary group additionally attempting to recall the associated pictures. Participants later reported which sounds brought the paired picture to mind during the localization task. Reaction times on the localization task were slower for voluntary than involuntary retrieval and for paired than unpaired sounds, possibly reflecting increased attentional demands of voluntary retrieval and interference from reactivation of the associated pictures respectively. For the EEG analyses, young adults showed greater alpha event-related desynchronization (ERD) during voluntary than involuntary retrieval at frontal and occipital sites, while older adults showed pronounced alpha ERD regardless of intention. Additionally, older adults showed greater ERD for paired than unpaired sounds at occipital sites, likely reflecting visual reactivation of the associated pictures. Young adults did not show this alpha ERD memory effect. Taken together, these data suggest that involuntary memory is largely preserved with age, but this may be due to older adults' greater recruitment of top-down control even when demand for such control is limited.     

Bone mineral density changes after parathyroidectomy are dependent on biochemical profile

Background

Bone mineral density (BMD) has been found to improve after parathyroidectomy (PTX) in patients with primary hyperparathyroidism. There are few data on the effect of PTX on BMD in normocalcemic and normohormonal primary hyperparathyroidism.

Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis

A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross‐covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity.     

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.