May the power be with you: are there highly powered studies in neuroscience, and how can we get more of them?

Algermissen, Johannes; Mehler, David Marc Anton

doi:10.1152/jn.00765.2017

Cited by 54 publications

(58 citation statements)

References 12 publications

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“…Furthermore, small sample sizes imply higher variability around effect size estimates. In combination with publication bias (i.e., the tendency to publish mainly significant findings), reported effects thus tend to be overestimated (Algermissen and Mehler, 2018), rendering the scientific literature in psychology and neuroscience an unreliable basis for conducting power analyses for future studies (Allen and Mehler, 2019;Schäfer and Schwarz, 2019;Szucs and Ioannidis, 2017).…”

Section: Statistical Power/sensitivitymentioning

confidence: 99%

The potential of functional near-infrared spectroscopy-based neurofeedback – a systematic review and recommendations for best practice

Mehler¹,

Lührs²,

Thibault³

et al. 2019

Preprint

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Background: The effects of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI)-neurofeedback on brain activation and behaviors have been studied extensively in the past. More recently, researchers have begun to investigate the effects of functional near-infrared spectroscopy-based neurofeedback (fNIRS-neurofeedback). FNIRS is a functional neuroimaging technique based on brain hemodynamics, which is easy to use, portable, inexpensive, and has reduced sensitivity to movement artifacts.Method: We provide the first systematic review and database of fNIRS-neurofeedback studies, synthesizing findings from 22 peer-reviewed studies (including a total of N=441 participants; 337 healthy, 104 patients). We (1) give a comprehensive overview of how fNIRS-neurofeedback training protocols were implemented, (2) review the online signal-processing methods used, (3) evaluate the quality of studies using pre-set methodological and reporting quality criteria and also present statistical sensitivity/power analyses, (4) investigate the effectiveness of fNIRS-neurofeedback in modulating brain activation, and (5) review its effectiveness in changing behavior in healthy and pathological populations. Results and discussion: (1-2) Published studies are heterogeneous (e.g., neurofeedback targets, investigated populations, applied training protocols, and methods). (3) Large randomized controlled trials are still lacking. In view of the novelty of the field, the quality of the published studies is moderate. We identified room for improvement in reporting important information and statistical power to detect realistic effects. (4) Several studies show that people can regulate hemodynamic signals from cortical brain regions with fNIRS-neurofeedback and (5) these studies indicate the feasibility of modulating motor control and prefrontal brain functioning in healthy participants and ameliorating symptoms in clinical populations (stroke, ADHD, autism, and social anxiety). However, valid conclusions about specificity or potential clinical utility are premature. Conclusion: Due to the advantages of practicability and relatively low cost, fNIRS-neurofeedback might provide a suitable and powerful alternative to EEG and fMRI neurofeedback and has great potential for clinical translation of neurofeedback. Together with more rigorous research and reporting practices, further methodological improvements may lead to a more solid understanding of fNIRS-neurofeedback. Future research will benefit from exploiting the advantages of fNIRS, which offers unique opportunities for neurofeedback research.

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Section: Statistical Power/sensitivitymentioning

confidence: 99%

The potential of functional near-infrared spectroscopy-based neurofeedback – a systematic review and recommendations for best practice

Mehler¹,

Lührs²,

Thibault³

et al. 2019

Preprint

Self Cite

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“…To mitigate this and enhance the reliability of our findings, we used multiple resampling techniques. With the field progressing towards larger sample sizes with more power (56), we encourage future studies to keep moving in this direction. Third, the implications of our findings are limited by the original study design.…”

Section: Discussionmentioning

confidence: 99%

Predicting future drinking among young adults: using ensemble machine-learning to combine MRI with psychometrics and behaviour

Groefsema

Luijten

Engels

et al. 2020

Preprint

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Background While most research into predictors of problematic alcohol use has focused on adolescence, young adults are also at elevated risk, and differ from adolescents and adults in terms of exposure to alcohol and neurodevelopment. Here we examined predictors of alcohol use among young adults at a 1-year follow-up using a broad predictive modelling approach. Methods Data in four modalities were included from 128 men aged between 18 and 25 years; functional MRI regions-ofinterest from 1) a beer-incentive delay task, and 2) a social alcohol cue-exposure task, 3) grey matter data, and 4) non-neuroimaging data (i.e. psychometric and behavioural). These modalities were combined into an ensemble model to predict follow-up Alcohol Use Disorder Identification (AUDIT) scores, and were tested separately for their contribution. To reveal specificity for the prediction of future AUDIT scores, the same analyses were carried out for current AUDIT score. Results The ensemble resulted in a more accurate estimation of follow-up AUDIT score than any single modality. Only removal of the social alcohol cue-exposure task and of the non-neuroimaging data significantly worsened predictions. Reporting to need a drink in the morning to start the day was the strongest unique predictor of future drinking along with anterior cingulate cortex and cerebellar activity. Conclusions Alcohol-related task fMRI activity is a valuable predictor for future drinking among young adults alongside non-neuroimaging variables. Multi-modal prediction models best predict future drinking among young adults and may play an important part in the move towards individualized treatment and prevention efforts.

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“…Considered as two key means to improve the reliability of CSE assessment [5], we reasoned this would prevent post-hoc arbitrary data trimming, which can lead to circular analyses and double-dipping [6]. Namely, doing so can erroneously reduce data variability and inflate the effect size, thus giving the impression of increased statistical power [7]. In order to increase the reliability of conclusions in neuroscience, it is best advised to discourage such practice [6,8].…”

Section: Dear Editormentioning

confidence: 99%

“…Similarly, for the one-sample ttest conducted on the median values of all 6 post-measurements (normalized MEP data), the achieved power was 51.2% (t (17) ¼ 2.181, p ¼ 0.0435, Cohen's dz ¼ 0.514). Given the current replication crisis in neuroscience [7,9], efforts must be promptly devoted to incorporate such considerations when interpreting results in order to increase the validity and reliability of scientific conclusions.…”

Section: Dear Editormentioning

confidence: 99%

“…To conclude, we believe that our initial conclusion that tSMS did neither yield a significant nor meaningful depressing effect on CSE is adequately supported by our data. As small studies (n 10) are more likely to report inflated effect sizes [7,9], and that post-hoc and flexible statistical analysis bears an important risk of unwarranted conclusion [6,8], studies with larger sample size (n 34; if a Cohen's dz of 0.5 is considered as the smallest effect size of interest [10]; power ¼ 80%, paired t-tests) will be needed to ascertain that tSMS effectively depresses CSE, and this, to a magnitude relevant to the field.…”

Section: Dear Editormentioning

confidence: 99%

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Response to significant influence of static magnetic stimulation applied for 30 minutes over the human M1 on corticospinal excitability

et al. 2020

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May the power be with you: are there highly powered studies in neuroscience, and how can we get more of them?

Cited by 54 publications

References 12 publications

The potential of functional near-infrared spectroscopy-based neurofeedback – a systematic review and recommendations for best practice

The potential of functional near-infrared spectroscopy-based neurofeedback – a systematic review and recommendations for best practice

Predicting future drinking among young adults: using ensemble machine-learning to combine MRI with psychometrics and behaviour

Response to significant influence of static magnetic stimulation applied for 30 minutes over the human M1 on corticospinal excitability

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