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Convention 2020
EMPLOYING NEUROSCIENCE IN YOUR PRACTICE

Talking points for organizations wishing to promote neuroscience for clinical practice

Neuroscience provides formulations which help frame how mental health care providers talk about psychiatric conditions and interventions with patients. Neuroscience augments our understanding of patients' experiences with explanations for affect, cognition, and behaviors. Conveying information about biology can help to reduce patient stigma and shame (Lebowitz & Ahn, 2012; Cooper, Korb, & Akil, 2019), decrease patients' self-blame for their conditions (Lee et al., 2016), and may increase their willingness to engage with psychological treatments when their own biology is emphasized (Illes et al., 2008). Providers may promote patient agency by introducing the potential for one's behaviors and environment to influence structural and neural changes within the brain (Lebowitz & Ahn, 2015). This lays the framework for introducing interventions as one such catalyst of therapeutic neural change, and providers may reference prior research demonstrating this across interventions and psychiatric conditions (Barsaglini et al., 2014; Young et al., 2018).

Neuroscience provides strategies for optimizing behavioral interventions. The clinical translation of research on inhibitory learning has resulted in recommendations for improving exposure-based treatments for anxiety (Craske et al., 2014), such as maximizing expectancy violations (discrepancy between predicted and actual aversive exposure outcomes), increasing variability of feared stimuli and contexts, and limiting safety signals. Neuroscience has also indicated potential neuroenhancers for improving therapeutic outcomes of exposures such as augmenting exposure therapy with NMDA partial agonists (D-cycloserine; Hofmann, Mundy, & Curtiss, 2015). Thus, neuroscience can help to refine mechanistic targets of conventional interventions.

Neuroscience provides targeted interventions. Neurofeedback is one such targeted intervention in which a patient learns to regulate neural functions by receiving real-time information about their neural responses via signal obtained from EEG or fMRI. There is empirical support for the clinical use of neurofeedback across diverse psychiatric conditions (Marzbani, Marateb, & Mansourian, 2016). Repetitive transcranial magnetic stimulation, another non-invasive targeted intervention, involves the delivery of repetitive magnetic pulse to modify neural function, and has shown particular promise in the treatment of depression (Guo, Li, & Wang, 2017). There is empirical support for more invasive targeted interventions such as deep-brain stimulation and vagus nerve stimulation in the treatment of mood disorders (Cimpianu et al., 2017; Dandekar et al., 2018). Computer administered interventions have been helpful in targeting information processing biases and cognitive deficits associated with psychopathology (Siegle, Ghinassi, & Thase, 2007; Bar-Haim, 2010). These interventions may allow us to intervene directly on circuitry necessary to take advantage of conventional psychological approaches increasing the chances of recovery (Kernel Networks Inc., 2019). This approach has the potential to turn likely non-responders into likely responders to an intervention increasing overall efficacy rates.

Neuroscience provides the promise of continued advancements toward precision medicine and mechanistic assessments. Precision medicine is a particularly promising area of continued research in psychiatry, given the large societal costs of psychiatric conditions, substantial variability in treatment response, and the significant length of time required before observable improvement from a given treatment. There is growing empirical support for neural (functional (e.g., activity) and structural (e.g., gray matter volume)) predictors of treatment response (Lener & Losifescu, 2015), often in regions believed to be associated with emotion regulation. However, additional work is necessary before predictors can be used in routine clinics, such as identifying where neural predictors fit with other clinical predictors and understanding how they might be used to guide treatment decisions (e.g., neurally targeted pre-treatments in combination with conventional interventions rather than denying treatment based on them), as well as practical considerations. In addition to prognostic measures, interventions have also been shown to result in observable neural changes in related emotion regulation regions (Brooks & Stein, 2015). Further research is required before these findings may be readily translated.

Resources:

1. Clinical neuroscience training resources for mental health providers: https://www.nncionline.org/our-resources/

2. Relevant readings including neurotranslational article repository:
https://docs.google.com/document/d/1UrVqKplo-ZLq36h6Bq7h6y41WNQgnYNb8Qbm7Ai5mSg/edit
https://docs.google.com/document/d/1ym74NmoYt3qLwXZo9fJBI0l9vTutbN7PSug3V2P1AAM/edit

References:

  • Bar-Haim, Y. (2010) 'Research Review: attention bias modification (ABM): a novel treatment for anxiety disorders', Journal of child psychology and psychiatry, and allied disciplines. Wiley Online Library, 51(8), pp. 859-870.
  • Barsaglini, A. et al. (2014) 'The effects of psychotherapy on brain function: a systematic and critical review', Progress in neurobiology. Elsevier, 114, pp. 1-14.
  • Brooks, S. J. and Stein, D. J. (2015) 'A systematic review of the neural bases of psychotherapy for anxiety and related disorders', Dialogues in clinical neuroscience. ncbi.nlm.nih.gov, 17(3), pp. 261-279.
  • Cimpianu, C.-L. et al. (2017) 'Vagus nerve stimulation in psychiatry: a systematic review of the available evidence', Journal of neural transmission . Springer, 124(1), pp. 145-158.
  • Cooper, J. J., Korb, A. S. and Akil, M. (2019) 'Bringing Neuroscience to the Bedside', FOCUS. American Psychiatric Publishing, 17(1), pp. 2-7.
  • Craske, M. G. et al. (2014) 'Maximizing exposure therapy: an inhibitory learning approach', Behaviour research and therapy. Elsevier, 58, pp. 10-23.
  • Dandekar, M. P. et al. (2018) 'Deep brain stimulation for treatment-resistant depression: an integrative review of preclinical and clinical findings and translational implications', Molecular psychiatry. nature.com, 23(5), pp. 1094-1112.
  • Guo, Q., Li, C. and Wang, J. (2017) 'Updated Review on the Clinical Use of Repetitive Transcranial Magnetic Stimulation in Psychiatric Disorders', Neuroscience bulletin. Springer, 33(6), pp. 747-756.
  • Hofmann, S. G., Mundy, E. A. and Curtiss, J. (2015) 'Neuroenhancement of Exposure Therapy in Anxiety Disorders', AIMS neuroscience. ncbi.nlm.nih.gov, 2(3), pp. 123-138.
  • Illes, J. et al. (2008) 'In the mind's eye: provider and patient attitudes on functional brain imaging', Journal of psychiatric research, 43(2), pp. 107-114.
  • Kernel Networks Inc. (2019) 'Combined TMS and Brief Cognitive Behavioral Therapy to Reduce Suicide', Case Medical Research. doi: 10.31525/ct1-nct03952468.
  • Lebowitz, M. S. and Ahn, W.-K. (2012) 'Combining biomedical accounts of mental disorders with treatability information to reduce mental illness stigma', Psychiatric services . Am Psychiatric Assoc, 63(5), pp. 496-499.
  • Lebowitz, M. S. and Ahn, W.-K. (2015) 'Emphasizing Malleability in the biology of depression: Durable effects on perceived agency and prognostic pessimism', Behaviour research and therapy. Elsevier, 71, pp. 125-130.
  • Lee, A. A. et al. (2016) 'Comparing Treatment Relevant Etiological Explanations for Depression and Social Anxiety: Effects on Self-Stigmatizing Attitudes', Journal of Social and Clinical Psychology, pp. 571-588. doi: 10.1521/jscp.2016.35.7.571.
  • Lener, M. S. and Iosifescu, D. V. (2015) 'In pursuit of neuroimaging biomarkers to guide treatment selection in major depressive disorder: a review of the literature', Annals of the New York Academy of Sciences. Wiley Online Library, 1344, pp. 50-65.
  • Marzbani, H., Marateb, H. R. and Mansourian, M. (2016) 'Neurofeedback: A Comprehensive Review on System Design, Methodology and Clinical Applications', Basic and clinical neuroscience. ncbi.nlm.nih.gov, 7(2), pp. 143-158.
  • Siegle, G. J., Ghinassi, F. and Thase, M. E. (2007) 'Neurobehavioral Therapies in the 21st Century: Summary of an Emerging Field and an Extended Example of Cognitive Control Training for Depression', Cognitive therapy and research. Springer, 31(2), pp. 235-262.
  • Young, K. S. et al. (2018) 'The impact of mindfulness-based interventions on brain activity: A systematic review of functional magnetic resonance imaging studies', Neuroscience and biobehavioral reviews. Elsevier, 84, pp. 424-433.
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