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The future of Metal Health Diagnosis & Quantified EEG Analysis for trauma

Future of Mental Health Diagnostic Criteria & EEG

It is probable that DSM-5 will be the last of its kind. In 2013, the National Institute of Mental Health (NIMH) announced that it would be diverting research funds from the symptom-based “set of labels” of the DSM, to research that supports a new classification system, as “Patients with mental disorders deserve better.” 12

NIMH believe that “Mapping the cognitive, circuit, and genetic aspects of mental disorders will yield new and better targets for treatment”.

One effect of this has been a surge in research into the science of the electroencephalogram (EEG), or “brainwave” signals generated by neuronal activity that can be measured with sensors on the scalp:BrainTrainUK applies this increased academic focus on EEG neuroscience to mental health.

Quantified EEG Analysis

EEG analysis is an established medical technique for neurological investigations of suspected epilepsy conditions, for monitoring brain activity in Intensive Care Units (ICU) or during surgery, and supporting investigations into dementia and stroke13 .

Quantified EEG analysis also known as ‘brain mapping’ techniques have been available since the advent of personal computers.

Until recently these techniques have been focused on comparing a subject’s EEG patterns with those of a ‘normative’ database. The scientific validity of the concept of characterising an ‘average’ brain in this way is questionable14,15. We term these ‘Standard QEEG Brain Mapping’. They provide a lot of data (numbers) but lack information, analysis and insight.

More recently, techniques have evolved to identify patterns or markers within the EEG that correlate with specific traits.

In 2013 NEBA® Health16 obtained FDA approval17 of a device to assist with ADHD diagnosis by measuring the ratio of theta:beta brainwaves*.

Juri Kropotov18,19,20 in St Petersburg and David A Kaiser21,22 in Los Angeles have led the research efforts to establish these ‘neuromarkers’ for a range of traits and histories.

David A Kaiser, together Barry Sterman (see below) developed the Sterman Kaiser Imaging Labs (SKIL) software23 in the 2000s. Neuromarkers** have been identified for multiple traits through external and empirical research, including the analysis of ‘Death Row’ inmates EEG patterns. This has evolved into what we term an ‘Advanced QEEG Brain Mapping’ capability.

 

**Example Neuromarkers: Advanced QEEG Brain Mapping of Pre-frontal Cortex a young person with an ACE score of 8, showing markers for attachment issues and anxiety (highlighted in red)

Comparison of Standard and Advanced QEEG Brain Mapping outputs


Advanced QEEG Brain Mapping Data, Information, Analysis & Insight features

Key parameters identified by Advanced QEEG Brain Mapping are patterns of Corticolimbic Integration measures, which are analogous to Bruce Perry’s “Cortical Modulation Ratio” (CMR).

Perry describes CMR24 as a crude estimate of the “strength” of cognitive regulatory capacity relative to the “dysregulation” of lower networks in the brain. Corticolimbic Integration has the benefit of being a measure, based on David Kaiser’s Unity parameter25, rather than an estimate.

There are a number of users of SKIL in North and South America. BrainTrainUK are the only users of SKIL to provide Advanced QEEG Brain Mapping outside these territories.

→Continue reading about how Neurofeedback Therapy can help trauma recovery

* EEG activity frequency bands are named after Greek letters: Delta = 1-4Hz, Theta = 4-7Hz, Alpha = 8-15Hz, Beta = 16-31Hz, Gamma = 32-40Hz. A sub-type of ADHD has been correlated with a high ratio of Theta to Beta activity.

References

12httpss://www.nimh.nih.gov/about/directors/thomas-insel/blog/2013/transforming-diagnosis.shtml

13Nuwer, Marc. “Assessment of digital EEG, quantitative EEG, and EEG brain mapping: report of the American Academy of Neurology and the American Clinical Neurophysiology Society.” Neurology 49.1 (1997): 277-292.

14Rose, Todd. The end of average: How to succeed in a world that values sameness. Penguin UK, 2016, pp.19-22.

15Miller, Michael B., et al. “Extensive individual differences in brain activations associated with episodic retrieval are reliable over time.” Journal of Cognitive Neuroscience 14.8 (2002): 1200-1214.

16httpss://nebahealth.com/products/neba-adhd-brainwave-assessment-aid/

17httpss://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMN/denovo.cfm?ID=DEN110019

18Kropotov, J.D. et al. “Independent component approach to the analysis of EEG recordings at early stages of depressive disorders.” Clinical Neurophysiology, 121.3 (2010) pp.281-289.

19Kropotov, Juri D. Functional neuromarkers for psychiatry: Applications for diagnosis and treatment. Academic Press, 2016.

20Ogrim, G., Kropotov, J., Brunner, J.F., Candrian, G., Sandvik, L. and Hestad, K.A., 2014. Predicting the clinical outcome of stimulant medication in pediatric attention-deficit/hyperactivity disorder: data from quantitative electroencephalography, event-related potentials, and a go/no-go test. Neuropsychiatric disease and treatment, 10, p.231.

21Kaiser, David A. “Basic principles of quantitative EEG.” Journal of Adult Development 12.2-3 (2005): 99-104.

22Sterman, M. Barry, and David Kaiser. “Comodulation: A new QEEG analysis metric for assessment of structural and functional disorders of the central nervous system.” Journal of Neurotherapy 4.3 (2000): 73-83.

23Sterman, M. B., and D. A. Kaiser. “SKIL Topometric Software.” Los Angeles, CA: Sterman-Kaiser Imaging Laboratory (2005).

24Kristie Brandt, C. N. M., Perry, B. D., Stephen Seligman, D. M. H., & Tronick, E. (Eds.). (2013). Infant and early childhood mental health: Core concepts and clinical practice. American Psychiatric Pub, p.27.

25Kaiser, D. A. (2008). Functional connectivity and aging: Comodulation and coherence differences. Journal of Neurotherapy, 12(2-3), p 134.

 

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