Faculty Bibliography

To examine relations between infant social withdrawal behavior and maternal major depression (MDD), 155 mother-infant dyads were evaluated at the 6-month primary care visit. Maternal depression was determined based on a psychiatric interview. Infant social withdrawal behavior was assessed with the Alarm Distress Baby Scale (ADBB; A. Guedeney & J. Fermanian, 2001) based on videotaped mother-infant interactions. Of the sample, 18.7% of mothers were diagnosed with MDD, and 39.4% of infants scored above the clinical ADBB cutoff. Infants of depressed mothers were more likely to score positive on the ADBB (75.8 vs. 31.0%, p < .001) and showed distinct patterns of withdrawal behavior. Within the group of withdrawn infants, however, no differential patterns of behavior could be identified for infants of depressed mothers as compared to infants of mothers with no depression. These findings confirm the validity of the ADBB for detection of infant social withdrawal in the context of MDD. At the same time, they support evidence that the ADBB identifies nonspecific infant distress behaviors. Future studies will need to determine if and how positive ADBB screening results in the absence of maternal MDD might be associated with other maternal psychiatric disorders such as anxiety or borderline personality disorder. These results have important implications for screening guidelines in primary care.

In this pilot study, amygdala connectivity related to trauma symptoms was explored using resting-state functional magnetic resonance imaging (R-fMRI) in 23 healthy adolescents ages 13-17 years with no psychiatric diagnoses. Adolescents completed a self-report trauma symptom checklist and a R-fMRI scan. We examined the relationship of trauma symptoms to resting-state functional connectivity of the amygdala. Increasing self-report of trauma symptoms by adolescents was associated with increasing functional connectivity with the right amygdala and a local limbic cluster and decreasing functional connectivity with the amygdala and a long-range frontoparietal cluster to the left amygdala, which can be a hallmark of immaturity. These pilot findings in adolescents provide preliminary evidence that even mild trauma symptoms can be linked to the configuration of brain networks associated with the amygdala.

Over a decade ago, the fMRI Data Center (fMRIDC) pioneered open-access data sharing in the task-based functional neuroimaging community. Well ahead of its time, the fMRIDC effort encountered logistical, sociocultural and funding barriers that impeded the field-wise instantiation of open-access data sharing. In 2009, ambitions for open-access data sharing were revived in the resting state functional MRI community in the form of two grassroots initiatives: the 1000 Functional Connectomes Project (FCP) and its successor, the International Neuroimaging Datasharing Initiative (INDI). Beyond providing open access to thousands of clinical and non-clinical imaging datasets, the FCP and INDI have demonstrated the feasibility of large-scale data aggregation for hypothesis generation and testing. Yet, the success of the FCP and INDI should not be confused with widespread embracement of open-access data sharing. Reminiscent of the challenges faced by fMRIDC, key controversies persist and include participant privacy, the role of informatics, and the logistical and cultural challenges of establishing an open science ethos. We discuss the FCP and INDI in the context of these challenges, highlighting the promise of current initiatives and suggesting solutions for possible pitfalls.

Attention-deficit/hyperactivity disorder (ADHD) and Asperger's Syndrome (AS) share a heterogeneous cognitive profile. Studies assessing executive functions (EF) and social cognition in both groups have found preserved and impaired performances. These inconsistent findings would be partially explained by the cognitive variability reported in these disorders. First, the present study explored the inter-individual variability in EF and social cognition in both patient groups. Second, we compared differential characteristics and commonalities in the cognitive profiles of EF and social cognition between ADHD, AS and control adults. We assessed 22 patients with ADHD, 23 adults with AS and 21 matched typically developing subjects using different measures of EF (working memory, cognitive flexibility and multitasking) and social cognition (theory of mind and decision-making). Group comparisons and multiple case series analyses (MCSA) were conducted. The between-group comparisons showed an EF deficit in working memory in ADHD and a theory of mind (ToM) impairment in AS. The MCSA evidenced that, compared to controls, ADHD patients had a higher inter-individual variability in EF, while individuals with AS had a more heterogeneous profile in social cognition tasks compared to both groups. Finally, the AS and ADHD groups presented higher task-related variability compared to controls and shared a common heterogeneous profile in EF. This is the first study to compare variability in EF and social cognition profiles of ADHD and AS. We propose that heterogeneity in EF performance is a link between ADHD and AS which may explain the overlap of symptomatology between both diagnoses. In addition, patients with AS seem to show a unique heterogeneous profile in ToM which may explain the low probability of finding AS symptoms in patients with ADHD.

Central to the development of clinical applications of functional connectomics for neurology and psychiatry is the discovery and validation of biomarkers. Resting state fMRI (R-fMRI) is emerging as a mainstream approach for imaging-based biomarker identification, detecting variations in the functional connectome that can be attributed to clinical variables (e.g., diagnostic status). Despite growing enthusiasm, many challenges remain. Here, we assess evidence of the readiness of R-fMRI based functional connectomics to lead to clinically meaningful biomarker identification through the lens of the criteria used to evaluate clinical tests (i.e., validity, reliability, sensitivity, specificity, and applicability). We focus on current R-fMRI-based prediction efforts, and survey R-fMRI used for neurosurgical planning. We identify gaps and needs for R-fMRI-based biomarker identification, highlighting the potential of emerging conceptual, analytical and cultural innovations (e.g., the Research Domain Criteria Project (RDoC), open science initiatives, and Big Data) to address them. Additionally, we note the need to expand future efforts beyond identification of biomarkers for disease status alone to include clinical variables related to risk, expected treatment response and prognosis.