Integrating Social Neuroscience and Social Work: Innovations for Advancing Practice-Based Research

By Matto, Holly C.; Strolin-Goltzman, Jessica | Social Work, April 2010 | Go to article overview
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Integrating Social Neuroscience and Social Work: Innovations for Advancing Practice-Based Research


Matto, Holly C., Strolin-Goltzman, Jessica, Social Work


Social neuroscience, an interdisciplinary field of study examining the relations between biological systems and social processes, has moved forward in exciting new and integrative ways, resulting, in part, from the accelerated pace of brain imaging technological developments and the subsequent proliferation of scientific studies. In recent years, the National Institutes of Health (NIH) have spearheaded initiatives to investigate larger social processes and their impact on lifelong biological and neurological functioning, and they continue to actively promote the integration of sociobehavioral and biomedical research. Social neuroscience, as a field of scientific study, developed in response to the traditional genetic determinism of early biologists and the lack of neural substrate understanding of social scientists, seeking to move understanding of human behavior and development to a multilevel integrative approach that examines complex questions of gene--environment interactions (see, for example, Cacioppo, Berntson, Sheridan, & McClintock, 2002). For example, recent research shows that gene variants influence neurotransmitter effects on the central nervous system and, in interaction with specific environmental influences, increase the risk of development of substance use and mental health disorders (Enoch, 2006). Indeed, research agendas that examine biology, behavior, and environmental transactions have garnered much attention from the scientific community in the past decade--attention that will likely continue for decades to come.

The integration of social neuroscience research into social work's explanatory and clinical change research methods, and the concomitant interdisciplinary partnerships forged as a result, is one area where a blended research trajectory (explanatory and clinical change-intervention models) has been operationalized. The purpose of this article is to describe a blended research trajectory, review the neuroscience literature as related to psychosocial interventions, and provide a specific example of how a blended methodological approach was used to develop a psychosocial intervention for social work practice.

BLENDED RESEARCH

Fraser (2003) discussed the distinction between explanatory and control (or intervention) research, suggesting that explanatory research seeks to identify the correlates of causal risk mechanisms related to specific problems, whereas intervention research (or "clinical change models") seeks to examine change strategies that alter risk sequences, develop protective factors, or otherwise modify an identified problem. Although explanatory research and intervention research have been used as distinct approaches, with their own sets of questions and methodologies, they may be more powerful in answering complex social work practice questions when integrated. Videka (2003) made a recommendation along these lines by advocating for a blended methodological approach that includes information flow from both epidemiological and basic sciences (explanatory) and efficacy (intervention) research approaches: "What is needed now is a new generation of studies that examine the moderating effects of epidemiological risk and protective factors on processes with the outcomes of interventions" (p. 181).A blended approach holds significant promise for the development of psychosocial interventions, allowing for thoughtful examination of individual and contextual moderators that explain variation in treatment response (Videka, 2003).

Specifically, social work researchers can use neuroscientific knowledge about the mechanisms of risk related to psychosocial problems to advance the development of new treatment interventions, and they can use brain-imaging technology to measure outcomes at the neural level. For example, studies show that neurobiological functioning, such as strength of brain circuitry in the prefrontal cortex, may affect treatment response and explain variation in effectiveness of evidence-based interventions like cognitive--behavioral therapy (CBT) for adolescent drug abuse prevention (Fishbein, Hyde, Coe, & Paschall, 2004), suggesting a need to tailor CBT interventions accordingly.

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