Summary: For years, neuroscientists have focused on the strongest 10% of brain signals, dismissing the rest as \u201cnoise.\u201d However, a new study reveals that the other 90% of brain connections, the parts usually thrown away, can predict behavior with equal or even greater accuracy.<\/p>\n
The research suggests that predictive information is widely distributed across the brain, meaning there isn\u2019t just one \u201ccorrect\u201d network for a specific behavior, but many.<\/p>\n
Key Findings<\/p>\n
Multiple Pathways: The study proves that there are multiple, non-overlapping networks capable of predicting the same behavior. This suggests the brain has significant redundancy and \u201cfunctional flexibility.\u201dPsychiatric Implications: For conditions like depression, different individuals may rely on entirely different neural pathways to arrive at the same behavioral outcome.Therapeutic Targets: If several circuits can predict an illness, treatment shouldn\u2019t be limited to the \u201ctop\u201d networks. Targeting these overlooked circuits could provide a breakthrough for patients who are \u201ctreatment-resistant\u201d to current therapies.The Accuracy Myth: High statistical strength does not necessarily mean higher biological relevance. The \u201cnoise\u201d of today could be the precision medicine of tomorrow.<\/p>\n
Source: Yale<\/p>\n
Scientists who use imaging to understand the brain\u2019s complexity often focus on the strongest signals and ignore the rest. But this strategy, researchers warn, may reveal only the tip of the iceberg.<\/p>\n
A new study published in Nature Human Behavior reveals that connections routinely overlooked as \u201cnoise\u201d during neuroimaging data analysis can predict behavior with remarkable accuracy\u2014and implicate entirely different brain networks.<\/p>\n
The finding could open many new targets for treating psychiatric illness, the researchers say.<\/p>\n
\u201cMany studies that rely on techniques like feature selection\u2014which simplifies the brain down to a narrow slice\u2014might only uncover a small part of the true neurobiology that underlies a given behavior,\u201d says lead author Brendan Adkinson, PhD, an MD-PhD student at Yale School of Medicine.<\/p>\n
\u201cOur study suggests that there may be multiple, non-overlapping networks capable of predicting a given behavior just as well.\u201d<\/p>\n
Overlooked brain connections<\/p>\n
One goal of human neuroimaging is to illuminate the brain mechanisms that drive cognition and mental health. But the complexity of brain connectivity makes data interpretation challenging. To address this, researchers often use feature selection, which focuses on the strongest 10% of brain connections to make the data easier to interpret.<\/p>\n
For the study, researchers investigated whether signals discarded by feature selection could reveal meaningful insights about brain and behavior. The team examined brain imaging and behavioral data from more than 12,000 participants across four major U.S. datasets. For every participant, the team calculated the strength of association between brain connections and the outcome they wanted to predict.<\/p>\n
All the connections were then ranked from the strongest to weakest associated and divided into 10 non-overlapping groups. Group one contained the top 10% of connections, those that scientists usually select, while groups two through 10 held the remaining 90% of connections\u2014the connections often dismissed as noise. The team then built 10 prediction models, one for each group.<\/p>\n
The team found that lower-ranked connections\u2014groups two through nine\u2014consistently achieved prediction accuracy similar to the top 10% of connections. In some cases, models built on lower groups of connections performed better than those trained on the top group. The authors suggest this might be because predictive information is widely distributed throughout brain connections and not just concentrated within the strongest ones.<\/p>\n
\u201cTo our surprise, even when we completely excluded the networks people usually rely on to predict behavior, we still achieved nearly the same level of accuracy using everything that\u2019s typically left behind,\u201d says Adkinson, who works in the lab of senior author Dustin Scheinost, PhD, associate professor of radiology and biomedical imaging at YSM and associate director of biomedical imaging technology at the Yale Biomedical Imaging Institute.<\/p>\n
Individual differences in mental health<\/p>\n
The results indicate that by narrowing their focus, scientists risk oversimplifying the brain\u2019s complexity, especially when dealing with brain disorders. For psychiatric disorders such as depression, individuals may rely on different neural pathways for the same behavior. And if several brain circuits can achieve similar prediction accuracy, it also suggests that therapeutic targets shouldn\u2019t be limited to only the top networks.<\/p>\n
\u201cWhile the networks traditionally targeted by interventions may work for most patients, these overlooked networks might hold more utility for certain subsets of individuals,\u201d says Adkinson.<\/p>\n
\u201cThis could help explain why some people don\u2019t currently respond to treatments that work for others.\u201d<\/p>\n
With these results, the team hopes to increase the clinical efficacy of brain-based biomarkers by better reflecting the brain\u2019s complexity and individual variability.<\/p>\n
Key Questions Answered:Q: If \u201cweak\u201d signals are so important, why did we ignore them for so long?<\/p>\n
A: It\u2019s a matter of data management. The brain has billions of connections. To make sense of the math, scientists \u201csimplify\u201d the brain by looking for the loudest voices in the room. This study shows that the \u201cwhispers\u201d in the background are actually telling the same story, just in a different way.<\/p>\n
Q: Does this mean current brain-based treatments are wrong?<\/p>\n
A: Not wrong, just incomplete. Current treatments (like TMS or certain meds) target the \u201cloudest\u201d networks. This study explains why those treatments work for some but not others, some people\u2019s brains might be using one of the \u201coverlooked\u201d networks instead.<\/p>\n
Q: Can this help diagnose mental illness more accurately?<\/p>\n
A: Yes. By including more of the brain\u2019s complexity in our models, we can create better \u201cbiomarkers.\u201d Instead of looking for one single \u201cdepression signal,\u201d we can look at the whole \u201ciceberg\u201d to see which specific pathway is causing an issue for a specific person.<\/p>\n
Editorial Notes:This article was edited by a Neuroscience News editor.Journal paper reviewed in full.Additional context added by our staff.About this mental health and neuroscience research news<\/p>\n
Author:\u00a0Colleen Moriarty<\/a>
Source:\u00a0Yale<\/a>
Contact:\u00a0Colleen Moriarty \u2013 Yale
Image:\u00a0The image is credited to Neuroscience News<\/p>\n