June 16, 2024

Most Brain Studies Have Too Few Participants To Yield Reliable Findings

The promise has yet to turn into truth, and a new study describes why: The results of most studies are undependable since they included too few participants.
Researchers rely on brainwide association studies to determine brain structure and function– using brain scans– and connect them to mental disease and other intricate behaviors. A research study by researchers at Washington University School of Medicine in St. Louis and the University of Minnesota, released March 16 in Nature, reveals that the majority of released brainwide association studies are performed with too few participants to yield dependable findings. To identify problems with brain-wide association studies, the research team– including Dosenbach, Marek, Tervo-Clemmens, co-senior author Damien A. Fair, PhD, director of the Masonic Institute for the Developing Brain at the University of Minnesota, and others– began by accessing the 3 largest neuroimaging datasets: the Adolescent Brain Cognitive Development Study (11,874 individuals), the Human Connectome Project (1,200 participants) and the UK Biobank (35,375 individuals). Published papers on brain-wide association studies regularly report effect sizes of 0.2 or more.

Findings will encourage more information sharing, collaboration amongst researchers.
As brain scans have become more helpful and in-depth in recent decades, neuroimaging has seemed to guarantee a way for physicians and researchers to “see” whats going wrong inside the brains of individuals with mental disorders or neurological conditions. Such imaging has exposed connections between brain anatomy or function and illness, suggesting prospective brand-new methods to detect and treat psychiatric, mental, and neurological conditions. But the pledge has yet to turn into reality, and a new research study discusses why: The outcomes of a lot of studies are unreliable since they involved too few participants.
Scientists depend on brainwide association studies to measure brain structure and function– utilizing MRI brain scans– and connect them to complex attributes such as personality, habits, cognition, neurological conditions, and mental illness. But a research study by researchers at Washington University School of Medicine in St. Louis and the University of Minnesota, released on March 16, 2022, in Nature, shows that the majority of released brainwide association research studies are performed with too couple of individuals to yield reliable findings.

Using publicly readily available information sets– including a total of nearly 50,000 participants– the researchers examined a series of sample sizes and discovered that brainwide association studies need thousands of individuals to attain greater reproducibility. Normal brainwide association studies enroll simply a couple lots individuals.
Researchers rely on brainwide association research studies to measure brain structure and function– using brain scans– and connect them to mental disorder and other complex behaviors. A research study by scientists at Washington University School of Medicine in St. Louis and the University of Minnesota, published March 16 in Nature, shows that many released brainwide association research studies are carried out with too few individuals to yield trustworthy findings. Credit: Alex Berdis
Such so-called underpowered studies are vulnerable to discovering spurious however strong associations by chance while missing out on genuine but weaker associations. Routinely underpowered brainwide association research studies result in a glut of astonishingly strong yet irreproducible findings that sluggish progress toward understanding how the brain works, the scientists said.
“Its not a problem with any individual scientist or research study. The field of genomics discovered a similar problem about a years earlier with genomic information and took steps to resolve it. The NIH (National Institutes of Health) began moneying bigger data-collection efforts and mandating that data must be shared publicly, which minimizes predisposition and as an outcome, genome science has gotten much better.
Author Scott Marek, PhD, an instructor in psychiatry at Washington University, and co-first author Brenden Tervo-Clemmens, PhD, a postdoctoral scientist at Massachusetts General Hospital/Harvard Medical School, recognized something was wrong with how brainwide association studies generally are carried out when they might not reproduce the outcomes of their own study.
” We were interested in finding out how cognitive ability is represented in the brain,” Marek said. It simply blew me away because a sample of a thousand should have been plenty huge enough. We were scratching our heads, questioning what was going on.”
To recognize issues with brain-wide association research studies, the research study group– consisting of Dosenbach, Marek, Tervo-Clemmens, co-senior author Damien A. Fair, PhD, director of the Masonic Institute for the Developing Brain at the University of Minnesota, and others– started by accessing the 3 largest neuroimaging datasets: the Adolescent Brain Cognitive Development Study (11,874 individuals), the Human Connectome Project (1,200 individuals) and the UK Biobank (35,375 individuals). They examined the datasets for connections between brain features and a range of market, cognitive, mental health and behavioral procedures, utilizing subsets of numerous sizes.
The researchers discovered that brain-behavior connections recognized using a sample size of 25– the average sample size in published documents– normally stopped working to reproduce in a separate sample. As the sample size grew into the thousands, correlations ended up being most likely to be reproduced.
Effect sizes are scaled from 0 to 1, with 0 being no connection and 1 being perfect connection. As sample sizes increased and correlations became more reproducible, the impact sizes decreased. Published documents on brain-wide association studies routinely report impact sizes of 0.2 or more.
In retrospect, it ought to have been apparent that the documented impact sizes were too high, Marek said.
” You can find effect sizes of 0.8 in the literature, but nothing in nature has an effect size of 0.8,” Marek stated. “The correlation in between height and weight is 0.4. The connection in between elevation and day-to-day temperature level is 0.3. Those are strong, apparent, quickly measured connections, and theyre nowhere near 0.8. So why did we ever think that the connection between two very intricate things, like brain function and depression, would be 0.8? That doesnt pass the sniff test.”
No individual private investigator has the time or money to scan thousands of participants for each study. If all of the data from numerous little research studies were pooled and examined together, consisting of statistically insignificant outcomes and small effect sizes, the outcome most likely would approximate the proper answer, Dosenbach stated.
” The future of the field is now bright and rests in open science, information sharing, and resource sharing throughout organizations in order to make big datasets readily available to any scientist who wishes to utilize them,” Fair said. “This very paper is an incredible example of that.”
Dosenbach, likewise an associate professor of biomedical engineering, of occupational treatment, of pediatrics and of radiology, added: “Theres a great deal of pledge to this sort of work in terms of finding services for mental diseases and simply understanding how the mind works. The terrific news is that weve identified a main reason why brain imaging has yet to deliver on its promise to transform mental health care. The work represents a significant juncture for connecting brain activity and behavior, by clearly defining not just the prior roadblocks, but also the appealing brand-new courses forward.”
Referral: “Reproducible brain-wide association studies require countless people” by Scott Marek, Brenden Tervo-Clemmens, Finnegan J. Calabro, David F. Montez, Benjamin P. Kay, Alexander S. Hatoum, Meghan Rose Donohue, William Foran, Ryland L. Miller, Timothy J. Hendrickson, Stephen M. Malone, Sridhar Kandala, Eric Feczko, Oscar Miranda-Dominguez, Alice M. Graham, Eric A. Earl, Anders J. Perrone, Michaela Cordova, Olivia Doyle, Lucille A. Moore, Gregory M. Conan, Johnny Uriarte, Kathy Snider, Benjamin J. Lynch, James C. Wilgenbusch, Thomas Pengo, Angela Tam, Jianzhong Chen, Dillan J. Newbold, Annie Zheng, Nicole A. Seider, Andrew N. Van, Athanasia Metoki, Roselyne J. Chauvin, Timothy O. Laumann, Deanna J. Greene, Steven E. Petersen, Hugh Garavan, Wesley K. Thompson, Thomas E. Nichols, B. T. Thomas Yeo, Deanna M. Barch, Beatriz Luna, Damien A. Fair and Nico U. F. Dosenbach, 16 March 2022, Nature.DOI: 10.1038/ s41586-022-04492-9.