Researchers have used functional brain imaging data to discover biomarkers that have the potential to predict early-onset depression in pre-adolescent youths with a family history of depression.

Dylan G. Gee, Ph.D., a 2015 BBRF Young Investigator at Yale University, led a team that made use of data collected by an important NIMH-funded study called the Adolescent Brain Cognitive Development (ABCD) Study. Between 2016 and 2018, a diverse cohort of 11,878 young people from around the nation were recruited for ABCD, which was set up to track their developing brains from pre-adolescence through the end of adolescence. The brain is extremely plastic during these years, in which both neurobiological and social changes are associated with increased vulnerability to developing psychiatric problems.

Taylor J. Keding, Ph.D., a 2023 BBRF Young Investigator, and Jutta Joormann, Ph.D., a 2006 BBRF Young Investigator, were members of the research team in the new study. Bailey Holt-Gosselin was first author of the team’s paper appearing in the journal Developmental Cognitive Neuroscience.

Dr. Gee and colleagues used a subset of the vast—and still growing—multi-dimensional ABCD database that included 559 children who had no psychiatric symptoms or history at the time of their enrollment, but did have at least one parent with a history of major depressive disorder. Resting-state functional brain imaging scans of these children, enrolled at ages 9 to 10, were compared with scans made of 1,203 children enrolled at the same age who had no psychiatric history and no parental history of depression. The children in the first group were considered, for purposes of the study, at high familial risk for depression, while those in the second group were considered at low familial risk.

Youths who have a parent with depression have a 3- to 5-fold increased risk for developing depression themselves, the research team noted. This, along with the increased vulnerability experienced by all young people for development of psychopathology, “highlights the pressing need to identify predictive neural markers for development of depression prior to the onset of adolescence, especially among children already at high familial risk,” the team said.

Evidence from past studies suggests that familial depression risk manifests itself via atypical development of neural circuits implicated in reward and emotion processing, even in children with no history of depression symptoms in pre-adolescence. But the evidence for children is relatively sparse and it is not clear how atypical circuitry emerges over time or where precisely in key brain regions it appears.

Past studies based on resting-state functional brain scans of high familial risk youths prior to the beginning of adolescence have often lacked comparison data from children with low familial risk. Also, the age range of participants in prior studies has been comparatively large, e.g., ages 8-14 or 8-17 years. Dr. Gee and colleagues made certain to limit the age range in their study to 9-10, with the aim of searching for biomarkers that predicted onset of depression symptoms just 2 years later, at ages 11-12. This near-term outcome data was already in the ABCD database.

“Pre-adolescence is a particularly useful window to identify pre-existing neural vulnerability markers,” the team said. Vulnerability markers that may be present prior to the emergence of depression “are valuable because this knowledge can lead to early identification of vulnerable youth,” who, if treated promptly, stand to have better outcomes.

Each of the 1,762 youths whose data was examined in the study, like all children enrolled in the ABCD study, completed four 5-minute resting-state fMRI scans when they entered the study. Resting-state fMRI measures connectivity in brain circuits while an individual is not focused on a particular mental task. Dr. Gee and colleagues used a variety of methods to spot functional connectivity patterns involving areas involved in processing reward and emotions: the amygdala, putamen, nucleus accumbens, and caudate regions of the brain.

They were able to identify a number of potentially important patterns in the scans. Specific functional connectivity patterns between the amygdala and striatal regions and visual and sensory-sensorimotor networks were found to be predictive of depression 2 years later.

These patterns—again, seen in scans of preadolescents without a diagnosis of a psychiatric disorder at ages 9 and 10—appeared to the team to be potential biomarkers for depression onset by ages 11-12, particularly for youth with a family history of depression.

“The majority of depression-predictive functional connectivity patterns involved regions within visual and sensory/sensorimotor networks which typically mature earlier in development,” the researchers pointed out. “Association networks are still undergoing significant development within the age range of our sample. By contrast, sensorimotor networks may have reached greater relative maturity by this age, and therefore may be more likely to differentially shape future [depression] symptom development as a function of familial risk status.” It is possible, they speculated, that as neurodevelopment progresses within association networks, these networks “may begin to be more predictive of future symptoms at older ages (e.g., ages 14-18).”

For this reason, the team suggests future studies with the ABCD data investigate later-developing functional connectivity patterns and their possible relation to development of depression symptoms at older ages. They said that being able to predict symptoms at later time points following the pre-adolescent scan (5 or 6 years after) is an important goal for future research, as is consideration of how environmental factors such as early-life adversity affect risk profiles for the emergence of depression.

In identifying vulnerability factors that can be discerned in children before adolescence begins, the current study is a step toward early treatment and perhaps one day prevention of depressive disorders in vulnerable youth.