News and Commentary Archive

Explore recent scientific discoveries and news as well as CLBB events, commentary, and press.


The Center for Law, Brain & Behavior puts the most accurate and actionable neuroscience in the hands of judges, lawyers, policymakers and journalists—people who shape the standards and practices of our legal system and affect its impact on people’s lives. We work to make the legal system more effective and more just for all those affected by the law.

3 Hours of Exercise a Week May Lower Your Depression Risk

The New York Times reported on a recently published study from CLBB affiliated faculty member Dr. Jordan Smoller’s lab, which found observational evidence to suggest that physical activity is related to mental health, regardless of genetic vulnerability. The study was featured prominently in the article:

“So, for the new study, which was published this month in Depression and Anxiety, researchers at Harvard University and other institutions decided to look into those issues. They began by turning to a trove of health data gathered for the ongoing Partners Biobank study. It contains records for thousands of men and women in the greater Boston area who have provided DNA samples and opened their electronic health records to investigators.

The researchers pulled the records of almost 8,000 of these men and women who had filled out a questionnaire about exercise habits. It asked them to recall how much time each week during the past year they had spent in a variety of activities. Those activities included walking, whether for exercise or transportation, running, biking, using exercise machines, or attending dance or yoga classes.

The researchers then examined the men’s and women’s DNA, looking for genetic variations believed to increase the risk for depression, and scored their volunteers as being at high, moderate or low inherited risk for depression.

They also checked each person’s medical records for codes indicating a diagnosis of depression, either before they joined the biobank or for two years afterward.

Then the researchers crosschecked all of this data and soon noted several interesting and consistent patterns. Perhaps least surprising, those men and women harboring a high genetic risk for depression were more likely, in general, to develop depression than volunteers with low risk scores.

At the same time, physically active people had less risk than people who rarely moved, and the type of exercise barely mattered. If someone spent at least three hours a week participating in any activity, whether it was vigorous, such as running, or gentler, like yoga or walking, he or she was less likely to become depressed than sedentary volunteers, and the risk fell another 17 percent with each additional 30 minutes or so of daily activity.

This link between movement and improved mental health held true for people who had experienced depression in the past. If they reported exercising now, their risk for a subsequent episode of depression fell, compared to the risks for inactive people with a history of depression.

Exercise also substantially altered the risk calculus for people whose DNA predisposed them to depression. If they carried multiple worrisome gene snippets but often exercised, they were no more likely to develop depression than inactive people with little genetic risk.

In effect, physical activity “neutralized” much of the added risk for people born with a propensity for depression, says Karmel Choi, a clinical and research fellow at Massachusetts General Hospital and Harvard’s T.H. Chan School of Public Health, who led the new study.

Exercise did not erase the risk of depression for everyone, she continues. Some active people developed depression. But exercise buffered the risks, even for people born with a predilection for the condition.

This kind of observational study cannot show us, though, if being physically active directly causes people to remain mentally healthy, only that exercise and mental health are linked. It also relied on people’s memories of how much they had exercised recently, which can be notoriously unreliable. In addition, it looked at preventing depression, not treating it.

Despite those caveats, the results suggest that “physical activity of many kinds seems to have beneficial effects” for mental health, says Dr. Jordan Smoller, the study’s senior author and a professor of psychiatry at Harvard Medical School.”

Read the full article, “3 Hours of Exercise a Week May Lower Your Depression Risk”, published by the New York Times on November 20, 2019.

COMT Val158Met Genotype is Associated with Reward Learning: A Replication Study and Meta-Analysis

By N. S. Corral-Frías, D. A. Pizzagalli, J. M. Carré, L. J. Michalski, Y. S. Nikolova, R. H. Perlis, J. Fagerness, M. R. Lee, E. Drabant Conley, T. M. Lancaster, S. Haddad, A. Wolf, J. W. Smoller, A. R. Hariri, and R. Bogdan | Genes, Brain, and Behavior | June 1, 2016


Identifying mechanisms through which individual differences in reward learning emerge offers an opportunity to understand both a fundamental form of adaptive responding as well as etiological pathways through which aberrant reward learning may contribute to maladaptive behaviors and psychopathology. One candidate mechanism through which individual differences in reward learning may emerge is variability in dopaminergic reinforcement signaling. A common functional polymorphism within the catechol-O-methyl transferase gene (COMT; rs4680, Val158Met) has been linked to reward learning, where homozygosity for the Met allele (linked to heightened prefrontal dopamine function and decreased dopamine synthesis in the midbrain) has been associated with relatively increased reward learning. Here, we used a probabilistic reward learning task to asses response bias, a behavioral form of reward learning, across three separate samples that were combined for analyses (age: 21.80 ± 3.95; n = 392; 268 female; European-American: n = 208). We replicate prior reports that COMTrs4680 Met allele homozygosity is associated with increased reward learning in European-American participants (β = 0.20, t = 2.75, P < 0.01; ΔR2 = 0.04). Moreover, a meta-analysis of 4 studies, including the current one, confirmed the association between COMT rs4680 genotype and reward learning (95% CI −0.11 to −0.03; z = 3.2; P < 0.01). These results suggest that variability in dopamine signaling associated withCOMT rs4680 influences individual differences in reward which may potentially contribute to psychopathology characterized by reward dysfunction.

Read the full article here.

Genome-Wide Association Studies of Posttraumatic Stress Disorder in 2 Cohorts of US Army Soldiers

By Murray B. Stein, Chia-Yen Chen, Robert J. Ursano, Tianxi Cai, Joel Gelernter, Steven G. Heeringa, Sonia Jain, Kevin P. Jensen, Adam X. Maihofer, Colter Mitchell, Caroline M. Nievergelt, Matthew K. Nock, Benjamin M. Neale, Renato Polimanti, Stephan Ripke, Xiaoying Sun, Michael L. Thomas, Qian Wang, Erin B. Ware, Susan Borja, Ronald C. Kessler, and Jordan W. Smoller | JAMA Psychiatry | May 11, 2016


Importance —  Posttraumatic stress disorder (PTSD) is a prevalent, serious public health concern, particularly in the military. The identification of genetic risk factors for PTSD may provide important insights into the biological foundation of vulnerability and comorbidity.

Objective —  To discover genetic loci associated with the lifetime risk for PTSD in 2 cohorts from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

Design, Setting, and Participants —  Two coordinated genome-wide association studies of mental health in the US military contributed participants. The New Soldier Study (NSS) included 3167 unique patients with PTSD and 4607 trauma-exposed control individuals; the Pre/Post Deployment Study (PPDS) included 947 unique patients with PTSD and 4969 trauma-exposed controls. The NSS data were collected from February 1, 2011, to November 30, 2012; the PDDS data, from January 9 to April 30, 2012. The primary analysis compared lifetime DSM-IV PTSD cases with trauma-exposed controls without lifetime PTSD. Data were analyzed from March 18 to December 27, 2015.

Main Outcomes and Measures —  Association analyses for PTSD used logistic regression models within each of 3 ancestral groups (European, African, and Latino American) by study, followed by meta-analysis. Heritability and genetic correlation and pleiotropy with other psychiatric and immune-related disorders were estimated.

Results —  The NSS population was 80.7% male (6277 of 7774 participants; mean [SD] age, 20.9 [3.3] years); the PPDS population, 94.4% male (5583 of 5916 participants; mean [SD] age, 26.5 [6.0] years). A genome-wide significant locus was found in ANKRD55 on chromosome 5 (rs159572; odds ratio [OR], 1.62; 95% CI, 1.37-1.92; P = 2.34 × 10−8) and persisted after adjustment for cumulative trauma exposure (adjusted OR, 1.64; 95% CI, 1.39-1.95; P = 1.18 × 10−8) in the African American samples from the NSS. A genome-wide significant locus was also found in or near ZNF626 on chromosome 19 (rs11085374; OR, 0.77; 95% CI, 0.70-0.85; P = 4.59 × 10−8) in the European American samples from the NSS. Similar results were not found for either single-nucleotide polymorphism in the corresponding ancestry group from the PPDS sample, in other ancestral groups, or in transancestral meta-analyses. Single-nucleotide polymorphism–based heritability was nonsignificant, and no significant genetic correlations were observed between PTSD and 6 mental disorders or 9 immune-related disorders. Significant evidence of pleiotropy was observed between PTSD and rheumatoid arthritis and, to a lesser extent, psoriasis.

Conclusions and Relevance —  In the largest genome-wide association study of PTSD to date, involving a US military sample, limited evidence of association for specific loci was found. Further efforts are needed to replicate the genome-wide significant association with ANKRD55—associated in prior research with several autoimmune and inflammatory disorders—and to clarify the nature of the genetic overlap observed between PTSD and rheumatoid arthritis and psoriasis.

Read the full article here.

Common Measures for National Institute of Mental Health Funded Research

By Deanna M. Barch, Ian H. Gotlib, Robert M. Bilder, Daniel S. Pine, Jordan W. Smoller, C. Hendricks Brown, Wayne Huggins, Carol Hamilton, Adam Haim, and Gregory K. Farber | Biological Psychiatry | February 19, 2016


One of the most encouraging, but also the most challenging, aspects of current research on psychopathology is the diversity of measures used to assess constructs across research studies and programs. Clearly, this diversity reflects the creativity and generativity of our field and the continual growth of our science. At the same time, however, this diversity also makes data harmonization across studies difficult, if not sometimes impossible. The National Human Genome Research Institute recognized this conundrum in the field of genetics and started an initiative referred to as consensus measures for Phenotypes and eXposures (PhenX) to identify and recommend a small number of measures for each of 21 broad research domains that could be used as common assessments to facilitate integration across genome-wide association studies (1, 2, 3 and 4). These measures are made available to the scientific community, at no cost, in the PhenX Toolkit ( Subsequently, the PhenX consensus process was used to identify measures in support of substance abuse and addiction (SAA) research, adding depth to the toolkit in this area. This project was funded by the National Institute on Drug Abuse (NIDA) with the participation of the National Institute on Alcohol Abuse and Alcoholism. Perhaps due to a growing awareness of the need to share data across studies to increase statistical power and study impact, a number of other common data element programs have been underway, including the Patient-Reported Outcomes Measurement Information System (5), the National Institutes of Health (NIH) Toolbox (6), the Neurological Quality of Life (7), the National Institute of Neurological Disorders and Stroke Common Data Elements program (8 and 9), and the NIH Common Data Elements program ( The program staff at the National Institute of Mental Health (NIMH), as well as its funded researchers, have also recognized the challenges posed by a lack of common measures across studies. The NIMH has taken note of this recent emphasis on larger scale studies to address core questions about the mechanisms of psychopathology and recent attempts at data harmonization across studies of psychopathology that address similar issues. Accordingly, the NIMH felt that it was time to identify brief, low-burden measures that NIMH-funded researchers could include in their studies to increase cross-study data compatibility. The goal of the current report is to briefly describe the genesis and development of the PhenX project, to outline the process that the Mental Health Research Panel used to select a set of common measures, to describe the measures themselves, and to outline the goals associated with including these measures in future studies.

Continue reading the full report here.

Genetic Influences on Schizophrenia and Subcortical Brain Volumes: Large-Scale Proof of Concept

By Barbara FrankeJason L. SteinStephan RipkeVerneri AnttilaDerrek P. HibarKimm J. E. van HulzenAlejandro Arias-VasquezJordan W. SmollerThomas E. NicholsMichael C. NealeAndrew M. McIntoshPhil LeeFrancis J. McMahonAndreas Meyer-LindenbergManuel MattheisenOle A. AndreassenOliver GruberPerminder S. SachdevRoberto Roiz-SantiañezAndrew J. SaykinStefan EhrlichKaren A. MatherJessica A. TurnerEmanuel SchwarzAnbupalam Thalamuthu, et al. | Nature Neuroscience | February 1, 2016


Schizophrenia is a devastating psychiatric illness with high heritability. Brain structure and function differ, on average, between people with schizophrenia and healthy individuals. As common genetic associations are emerging for both schizophrenia and brain imaging phenotypes, we can now use genome-wide data to investigate genetic overlap. Here we integrated results from common variant studies of schizophrenia (33,636 cases, 43,008 controls) and volumes of several (mainly subcortical) brain structures (11,840 subjects). We did not find evidence of genetic overlap between schizophrenia risk and subcortical volume measures either at the level of common variant genetic architecture or for single genetic markers. These results provide a proof of concept (albeit based on a limited set of structural brain measures) and define a roadmap for future studies investigating the genetic covariance between structural or functional brain phenotypes and risk for psychiatric disorders.

Read the full paper here.