Symptom patterns in women with premenstrual syndrome complaints: a prospective assessment using a marker for ovulation and screening criteria for adequate ovarian function

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72929/1/j.1365-2648.1991.tb01727.x.pd

fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience

As data analysis pipelines grow more complex in brain imaging research, understanding how methodological choices affect results is essential for ensuring reproducibility and transparency. This is especially relevant for functional Near-Infrared Spectroscopy (fNIRS), a rapidly growing technique for assessing brain function in naturalistic settings and across the lifespan, yet one that still lacks standardized analysis approaches. In the fNIRS Reproducibility Study Hub (FRESH) initiative, we asked 38 research teams worldwide to independently analyze the same two fNIRS datasets. Despite using different pipelines, nearly 80% of teams agreed on group-level results, particularly when hypotheses were strongly supported by literature. Teams with higher self-reported analysis confidence, which correlated with years of fNIRS experience, showed greater agreement. At the individual level, agreement was lower but improved with better data quality. The main sources of variability were related to how poor-quality data were handled, how responses were modeled, and how statistical analyses were conducted. These findings suggest that while flexible analytical tools are valuable, clearer methodological and reporting standards could greatly enhance reproducibility. By identifying key drivers of variability, this study highlights current challenges and offers direction for improving transparency and reliability in fNIRS research

Current benzodiazepine issues

This article deals with some of the recent evidence bearing on the issues of the liability of benzodiazepines to lead to abuse, dependence, and adverse behavioral effects. Reviews of epidemiological, clinical and experimental literature indicated that the previous conclusion about abuse of these drugs still holds: the vast majority of the use of benzodiazepines is appropriate. Problems of nonmedical use arise nearly exclusively among people who abuse other drugs. Nevertheless, there are reasons for concern about patients who take benzodiazepines regularly for long periods of time. These drugs can produce physiological dependence when taken chronicaly, and although this does not appear to result in dose escalation or other evidence of “psychological dependence,” physiological dependence can result in patient discomfort if drug use is abruptly discontiniued. Also, physicians are currently prescribing shorter-acting benzodiazepines in preference to longer-acting benzodiazepines. The shorter-acting drugs can produce a more intense withdrawal syndrome following chronic administration. Furthermore, rates of use of benzodiazepines increase with age, and elderly patients are more likely than younger ones to take the drug chronically. The clearest adverse effect of benzodiazepines is impairment of memory. This, too, may be particular concern in older patients whose recall in the absence of drug is typically impaired relative to younger individuals, and who are more compromised following drug administration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46347/1/213_2005_Article_BF02245824.pd

fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience

As data analysis pipelines grow more complex in brain imaging research, understanding how methodological choices affect results is essential for ensuring reproducibility and transparency. This is especially relevant for functional Near-Infrared Spectroscopy (fNIRS), a rapidly growing technique for assessing brain function in naturalistic settings and across the lifespan, yet one that still lacks standardized analysis approaches. In the fNIRS Reproducibility Study Hub (FRESH) initiative, we asked 38 research teams worldwide to independently analyze the same two fNIRS datasets. Despite using different pipelines, nearly 80% of teams agreed on group-level results, particularly when hypotheses were strongly supported by literature. Teams with higher self-reported analysis confidence, which correlated with years of fNIRS experience, showed greater agreement. At the individual level, agreement was lower but improved with better data quality. The main sources of variability were related to how poor-quality data were handled, how responses were modeled, and how statistical analyses were conducted. These findings suggest that while flexible analytical tools are valuable, clearer methodological and reporting standards could greatly enhance reproducibility. By identifying key drivers of variability, this study highlights current challenges and offers direction for improving transparency and reliability in fNIRS research