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For two decades, researchers have used neuroimaging technology to try to determine how a person’s brain structure and function are linked to a variety of mental health conditions, from anxiety and depression to suicidal tendencies.

However new paper, The research, published Wednesday in Nature, questions whether much of this research actually provides valid findings. The paper’s authors found that many such studies tend to include fewer than two dozen participants, far from the number needed to produce reliable results.

“You need thousands,” said Scott Marek, a psychiatry researcher at the Washington University School of Medicine in St. Louis and author of the paper. He described the finding as an “inside punch” for typical studies that use imaging to try to better understand mental health.

Studies using magnetic resonance imaging technology often soften their conclusions by a cautionary statement that draws attention to the small sample size. But neurologist at the Washington University School of Medicine and another author of the paper, Dr. Nico Dosenbach said recruiting participants can be time-consuming and expensive, ranging from $600 to $2,000 an hour. The median number of subjects in mental health studies using neuroimaging is around 23, he added.

But the Nature paper shows that data from just two dozen subjects is often insufficient to be reliable and can actually lead to “vastly inflated” findings,” said Dr. dosenbach

For their analysis, the researchers reviewed three of the largest studies that used neuroimaging technology to draw conclusions about brain structure and mental health. All three studies are ongoing: the Human Connectome Project with 1,200 participants; Adolescent Brain Cognitive Development or ABCD study with 12,000 participants; and the UK Biobank study with 35,700 participants.

The authors of the Nature paper looked at subsets of data from these three studies to determine whether the smaller slices were misleading or “reproducible,” meaning the findings could be considered scientifically valid.

For example, the ABCD study looks at, among other things, whether the thickness of the brain’s gray matter can be associated with mental health and problem-solving ability. The authors of the Nature paper looked at small subsets within the large study and found that the subsets produced unreliable results when compared to results from the full dataset.

On the other hand, the authors found that when results were generated from sample sizes of several thousand subjects, the findings were similar to those from the full dataset.

The authors performed millions of calculations using hundreds of brain regions and different sample sizes discovered in various large studies. Researchers have repeatedly discovered that subsets of data from less than a few thousand individuals do not produce results consistent with those of the full dataset.

Dr. Marek said the paper’s findings “definitely” hold true beyond sanity. He noted that other fields, such as genomics and cancer research, have their own reckoning with the limitations of small sample sizes and are trying to set the course right.

“My hunch is that it’s more about demographics than any of those fields,” he said.