Saturday, 5 October 2019

Why EEG Test for Mental Health and Suicide Prevention

“I was born not knowing and have had only a little time to change that here and there” ~ RF~

I like Feynman’s quote — it reflects the essence of science and the difficulty to make real life changes based on science.
I’m trying to tell you that EEG is highly valuable especially in psychiatry

Only a few know that EEG was invented a century ago by a psychiatrist. He did it for good reasons since most psychiatric problems involve dysfunctions of frontal cortical areas Today EEG is almost forgotten and many psychiatrists do not see it “as clinically useful”. Well, they are completely wrong, as Feynman would say -they were “born not knowing”.
There are over 25 drugs, from SSRI, SNRI, to NDRI, .. . see hard to select one that works for you. Many patients respond differently to the same prescribed drug. The side effects of such drugs should be a matter of concern — don’t try too many!
Based on EEG data anyone can “see” deep inside your brain, what areas are targeted by drug therapy, what kind of effects has in your case. One can even “see” if there is high risk of suicide and the progress of therapy.
The “response to situational distress” in suicidal prevention is the same as the “beauty of a flower” — see the “Ode to a Flower” Feynman’s Famous Monologue   

“ I can appreciate the beauty of a flower “ but the logic tells us that “ insects can see the color”.
Science helps us to see much more about the flower.

The same logic tells us that there is a relationship between depression, the lack of sleep and suicide. In fact we already know which brain regions trigger suicidal thoughts and lead to suicide. Importantly, humans are good and kind by nature, they can badly hurt themselves and others only if their brain is injured 

Every brain is different, and it would be a poor choice to let anyone blindly prescribe therapy solely based on your symptoms. I know we can treat you better! Do you know that you can get better therapy?  

Over  10% of people take antidepressants many of them based on a very poor diagnostic, and a percent of them commit suicide after taking antipsychotics. 

With our novel brain maps we can see much more about depression, suicide, or “situational distress” than anyone else. Importantly, we provide these brain maps online based on your raw EEG data.
If you feel depressed, have mood swings or trouble sleeping, do the right thing –ask for an EEG test!

AddBrain Inc. provides these new types of brain maps online based on recorded EEG data and you can see clearly where brain dysfunctions are located and what type of therapy is recommended

Tuesday, 12 July 2011

The Myth of Temporal Coding

NeuroElectroDynamics or NED is the study of  the dynamics and interaction of electrical charges in the brain [1]. The word neuroelectrodynamics is derived from neuro- meaning neurons, electro- electric field and -dynamics meaning movement.
The main idea of NED is that under the influence of electric fields, charges that interact perform computations and are capable to read, write and store information in their spatial distribution at molecular level within active neurons. The universal physical laws from classical mechanics, thermodynamics to quantum theory can be applied to generate a consistent mathematical model of brain computation.
The fundamental claim of NED is that temporal observables associated with neural coding ( temporal coding, spike timing occurrence, spike-timing-dependent plasticity, ) are epiphenomena determined by the dynamics and interaction of electric charges modulated by molecular changes in neurotransmitters levels, regulatory mechanisms of gene expression from DNA to proteins synthesis.
NED  highlights a specific form of computation by interaction which is a general physical model of computation extensively present in nature [6].
A spontaneous generation of action potentials and synaptic activities is needed to maintain physical interaction. Meaningful information encoded (written) within neurons and synapses at a molecular level  can be   transmitted synaptically and non-synaptically  during action potential propagation [1][5].

During these events, the required information is exchanged between molecular structures (proteins), which store fragments of information, and the generated electric flux, which carries and integrates meaningful information in the brain [5].


Early work started with a fundamental electrophysiological observation. Contrary to common belief, action potentials generated by the same neuron are not alike, they display changes in electrical patterns not just temporal variability.
Every recorded action potential can be characterized by a new measure, spike directivity that describes electrical activity in a biological neuron [2]. Significant changes in spike directivity are correlated with changes in behavior [3]. Since information is carried by electric charges [4], then their dynamics and interaction characterize complex computational processes in the brain.


[1] Aur D., Jog, MS., 2010 Neuroelectrodynamics: Understanding the brain language, IOS Press, 2010.
[2] Aur D., Connolly C.I., and Jog M.S., 2005 Computing spike directivity with tetrodes. J. Neurosci. Vol. 149, Issue 1, pp. 57–63.
[3] Aur D., Jog, M.S., 2007 Reading the Neural Code: What do Spikes Mean for Behavior? Nature Precedings,
[4] Aur D., Connolly C.I. and Jog M.S., 2006 Computing Information in Neuronal Spikes, Neural Processing Letters, 23:183-199.
[5] Aur, D. 2012,  A comparative analysis of integrating visual information in local neuronal ensembles. Journal of neuroscience methods, 207(1), 23- 30.
[6] Aur D, Jog MS, Poznanski, R, Computing by physical interaction in neurons, Journal of integrative Neuroscience, vol. 10, Issue: 4, 2011, pp. 413-422

Starting with Adrian's recordings the idea was that the neural code was embedded in  temporal patterns. Therefore,  in the last sixty years everything was felt to revolve around temporal patterns.
However, the main hypothesis of computational  neuroscience ( digital-like uniformity of action potentials) is not validated by recent experimental data [1],[2].
 From a false hypothesis (stereotypical spike) one can reach  false conclusions following  a correct analysis of data. The unrealistic hypothesis  impacted model validity, created a strong debate and generated unrealistic predictions  in behavioral studies, visual object recognition (e.g. grandmother cell).

The power of  counterexamples

We completely understand the frustration of neuroscientists regarding temporal coding. For more than 60 years they have tried to prove that temporal coding is the right model of  information processing that reflects the subtle nature of neural code. The paradox is that the same experiment used to show the organization of temporal patterns proves the fallacy of temporal coding. The procedural T-maze learning task became the best counterexample for temporal coding theory Currently, we can provide other  examples, however there is no need to generate new evidence. That’s the beauty of counterexamples and experimental design. You only need one single counterexample to throw down a "solid"theoretical construct. Quoting Feynman "It doesn't matter how beautiful your theory is, it doesn't matter how smart you are or how many years you can keep a theory alive ! If it doesn't agree with experiment, it's wrong."
With a  digital (stereotype) spike, the temporal coding theory was born dead.

All issues in interpretation are generated by this  FALSE hypothesis of 
a  digital (stereotype) spike, see the analogy:
a. 3=-3                   -------                    (3)2=(-3)2              -----                        9=9
b. (FALSE hypothesis) ------     (operation/transformation) ---- (CORRECT/ FALSE ?)
A.Stereotype spike    --------  adding spikes/firing rate   -------CONTROVERSIES
B. (FALSE hypothesis) ------- (transformation, STATISTICS )  -- CORRECT /FALSE ?

Therefore,  Bayes theory, nonlinear dynamics  ... have no real value if they are attached to a false hypothesis (digital spike). One can learn mathematics, solve complex problems without pretending to apply the mathematical theory to understand  ‘neural code’.  In fact  many controversies in the field were generated by keeping alive the reductionist  temporal coding paradigm, they lead to  inconsistent and naive interpretations regarding place cells, concept cells or mirror neurons.


1. Dorian Aur , Christopher I.  Connolly, Mandar S. Jog, 2005, Computing spike directivity with tetrodes, Journal of Neuroscience Methods, 149 (1), pp. 57-63.
2. Takuya Sasaki, Norio Matsuki, Yuji Ikegaya, 2011, Action-potential modulation during axonal conduction Science 331 (6017), pp. 599-601