Emerging brain-based interventions for children and adolescents: overview and clinical perspective.

The NeuroDevelopment Center, 260 West Exchange Street, Suite 302, Providence, RI 02903, USA.
Child and Adolescent Psychiatric Clinics of North America (Impact Factor: 2.6). 02/2005; 14(1):1-19, v. DOI: 10.1016/j.chc.2004.07.011
Source: PubMed

ABSTRACT Electroencephalogram biofeedback (EBF), repetitive transcranial magnetic stimulation (rTMS), and vagal nerve stimulation (VNS) are emerging interventions that attempt to directly impact brain function through neurostimulation and neurofeedback mechanisms. This article provides a brief overview of each of these techniques, summarizes the relevant research findings, and examines the implications of this research for practice standards based on the guidelines for recommending evidence based treatments as developed by the American Academy of Child and Adolescent Psychiatry for attention deficit hyperactivity disorder (ADHD). EBF meets the "Clinical Guidelines" standard for ADHD, seizure disorders, anxiety, depression, and traumatic brain injury. VNS meets this same standard for treatment of refractory epilepsy and meets the lower "Options" standard for several other disorders. rTMS meets the standard for "Clinical Guidelines" for bipolar disorder, unipolar disorder, and schizophrenia. Several conditions are discussed regarding the use of evidence based thinking related to these emerging interventions and future directions.


Available from: Jean Frazier, May 27, 2015
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: While reducing the burden of brain disorders remains a top priority of organizations like the World Health Organization and National Institutes of Health, the development of novel, safe and effective treatments for brain disorders has been slow. In this paper, we describe the state of the science for an emerging technology, real time functional magnetic resonance imaging (rtfMRI) neurofeedback, in clinical neurotherapeutics. We review the scientific potential of rtfMRI and outline research strategies to optimize the development and application of rtfMRI neurofeedback as a next generation therapeutic tool. We propose that rtfMRI can be used to address a broad range of clinical problems by improving our understanding of brain–behavior relationships in order to develop more specific and effective interventions for individuals with brain disorders. We focus on the use of rtfMRI neurofeedback as a clinical neurotherapeutic tool to drive plasticity in brain function, cognition, and behavior. Our overall goal is for rtfMRI to advance personalized assessment and intervention approaches to enhance resilience and reduce morbidity by correcting maladaptive patterns of brain function in those with brain disorders.
    01/2014; 5. DOI:10.1016/j.nicl.2014.07.002
  • [Show abstract] [Hide abstract]
    ABSTRACT: Accounting for a patient's emotional state is integral in medical care. Positive emotions have a significant influence on mental and physical health. Much research has been carried out on the impact of emotion on the development and course of different illnesses. Emotion and significant contexts have an important role in helping people cope with depression, but lack of support undermines coping. In this paper, we present an emotion-focused e-health monitoring support system in assisting them to cope with their illness. In the system proposed, a user could monitor his emotional states in the absence of a doctor and regulate his emotions through the system. To provide personalized health care services to the user anywhere and anytime, the system should convert low-level multimodal context (including physiological signal, user profile and environment information) to high-level context. The objective of this research is establishing an emotion-focused e-health system for health care services.
    Complex, Intelligent, and Software Intensive Systems (CISIS), 2013 Seventh International Conference on; 01/2013
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we propose sensibility recognition model that recognize user's sensibility using brain waves. Method to acquire quantitative data of brain waves including priority living body data or sensitivity data to recognize user's sensitivity need and pattern recognition techniques to examine closely present user's sensitivity state through next acquired brain waves becomes problem that is important. In this paper, we used pattern recognition techniques to use Multi Layer Perceptron (MLP) that is pattern recognition techniques that recognize user's sensibility state through brain waves. We measures several subject's emotion brain waves in specification space for an experiment of sensibility recognition model's which propose in this paper and we made a emotion DB by the meaning data that made of concentration or stability by the brain waves measured. The model recognizes new user's sensibility by the user's brain waves after study by sensibility recognition model which propose in this paper to emotion DB. Finally, we estimates the performance of sensibility recognition model which used brain waves as that measure the change of recognition rate by the number of subjects and a number of hidden nodes.
    06/2006; 16(3). DOI:10.5391/JKIIS.2006.16.3.372