Neuroscience research: how to ensure reliability for neuroimaging datasets

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According to the paper published in Nature, large scale datasets are needed for neuroscience research to study individual differences. The paper focuses on how to ensure reliability for neuroimaging datasets.

The authors describe the concern of neuroimaging datasets for neuroscience research: “Our concern is simple: researchers are working hard to amass large-scale datasets, whether through data sharing or coordinated data generation initiatives, but failing to optimize their data collections for relevant reliabilities.”

“They may be collecting larger amounts of suboptimal data, rather than smaller amounts of higherquality data, a trade-off that does not bode well for the field, particularly when it comes to making inferences and predictions at the individual level. We believe that this misstep can be avoided by critical assessments of reliability upfront.”

The problem of false negatives in neuroscience research

The authors add: “From a statistical perspective, the risks of underpowered samples yielding increased false negatives and artificially inflated effect sizes (i.e., the ‘winner’s curse’ bias) are well known.”

“All these phenomena reduce the reproducibility of findings across studies, a challenge that other fields (for example, genetics) have long worked to overcome. In the context of neuroimaging or human brain mapping, an additional concern is that we may be biased to overvalue those brain areas for which measurement reliability is greater.”

Solutions for reliable neuroimaging datasets

The authors’ goal is to draw attention to the necessity of assessment and optimisation of reliability, writing that this is “typically underappreciated in neuroscience research.”

They conclude: “As neuroscientists make strides in our efforts to deliver clinically useful tools, it is essential that assessments and optimisations for reliability become common practice. This will require improved research practices among investigators, as well as support from funding agencies in the generation of open community resources upon which these essential properties can be quantified.”

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