Jason et al qEEG/LORETA in Assessment of Neurocognitive Impairment in a Patient with CFS

mango

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qEEG / LORETA in Assessment of Neurocognitive Impairment in a Patient with Chronic Fatigue Syndrome: A Case Report

Marcie L Zinn*, Mark A Zinn, Leonard A Jason
DePaul University, Center for Community Research, Chicago, IL


Abstract
Importance:
Chronic Fatigue Syndrome (CFS) is a chronic disease resulting in considerable and widespread cognitive deficits. Accurate and accessible measurement of the extent and nature of these deficits can aid healthcare providers and researchers in the diagnosis of this condition, choosing interventions and tracking treatment effects. Here, we present a case of a middle-aged man diagnosed with CFS which began following a typical viral illness.

Observations: LORETA source density measures of surface EEG connectivity at baseline were performed on 3 minutes of eyes closed deartifacted19-channel qEEG. The techniques used to analyze the data are described along with the hypothesized effects of the deregulation found in this data set. Nearly all (>90%) patients with CFS complain of cognitive deficits such as slow thinking, difficulty in reading comprehension, reduced learning and memory abilities and an overall feeling of being in a “fog.”Therefore, impairment may be seen in deregulated connections with other regions (functional connectivity); this functional impairment may serve as one cause of the cognitive decline in CFS. Here, the functional connectivity networks of this patient were sufficiently deregulated to cause the symptoms listed above.

Conclusions and significance: This case report increased our understanding of CFS from the perspective of brain functional networks by offering some possible explanations for cognitive deficits in patients with CFS. There are only a few reports of using source density analysis or qEEG connectivity analysis for cognitive deficits in CFS. While no absolute threshold exists to advise the physician as to when to conduct such analyses, the basis of his or her decision whether or not to use these tools should be a function of clinical judgment and experience. These analyses may potentially aid in clinical diagnosis, symptom management, treatment response and can alert the physician as to when intervention may be warranted.

Keywords
qEEG; LORETA; Source analysis; Chronic fatigue syndrome; Phase lag; Phase shift; Phase reset; Phase; Coherence; Cognitive impairment

https://sciforschenonline.org/journals/clinical-research/CLROA-2-110.php

Open Access
 

Daisymay

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Thanks for posting this Mango, very interesting indeed, and if it can be replicated could this not be used, perhaps in conjunction with other tests, for diagnosis? And for disability claims?

I wonder how expensive such testing is and it it is something which would be widely available to be used as part of diagnosis?
 

Daisymay

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And wouldn't it be interesting if they tried to run this test at baseline and then at various times during and after physical (and mental) exertion to see if it could give concrete evidence of PEM?
 

medfeb

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The Zinns presented some other qEEG work at the 2014 IACFS/ME conference. Of that work, Komaroff said the changes they saw demonstrate brain dysregulation seen “in a whole host of well-documented neurologic diseases.” If I remember, the findings correlated with severity.

http://iacfsme.org/PDFS/2014Syllabus25.aspx - page 42.

EEG peak alpha frequency is associated with chronic fatigue syndrome: a case-control observational study Marcie Zinn, Ph.D., Mark Zinn, MM, Jose Maldonado, MD, FAPM, Jane Norris, PA-C, Ian Valencia, BS, Jose G. Montoya, MD

Abstract Objectives: The two cardinal symptoms of chronic fatigue syndrome (CFS) are ubiquitous, severe, disabling fatigue and cognitive impairment known as ‘brain fog.’ Remarkably, neuroimaging and neuropsychological studies have mixed results in finding structural changes commensurate with these two cardinal symptoms of CFS. The objectives of this pilot study were to evaluate the relationship between electroencephalogram (EEG) peak alpha frequency (PAF) in CFS as compared to age- and sex-matched controls and to develop a diagnostic criteria using qEEG.

Methods:
A 19- channel quantitative EEG and two fatigue measures were obtained on 50 CFS patients and 50 healthy control participants in a 3-minute eyes closed condition using a resting-state only case-control design.

Results:
Mixed ANOVA results found decreased PAF over 58% of the entire cortex in CFS patients when compared to controls, Wilks’ L = .66, (F(18,80) = 2.424, p = .006, partial h2 =.31); bonferroni-corrected followup indicated significant differences in PAF at the following electrode sites: C3, C4, Cz, F3, F4, FP1, FP2, Fz, P3, Pz and T3 (p<.05). Two hierarchical multiple regression models found the best linear combination of predictors to predict fatigue: analysis 1 used the MFI-20 as the criterion variable,[ R2 = .897, F(5,1894) = 3287.76, p =.000], analysis 2 used the Fatigue Severity Scale as the criterion, [R2 = .887, R2 change = .865, F(5, 1894) = 3058.93, p=.000]. To assess fatigue levels between groups, we used the Mann-Whitney U Test, first with MFI-20 (z = -37.474, p < .000) then the FSS (z = -37.757, p < .000).

Conclusions: These findings are consistent with reduced efficiency of thalamo-cortical connections in CFS participants. EEG PAF measurement of cognitive fog and fatigue in CFS may have prognostic value and facilitate the evaluation of CFS as part of a diagnostic regimen.

The IACFS/ME abstract also said
"qEEG measurement provides a quick, inexpensive and reliable diagnostic tool for most types of cortical issues. qEEG does not provide a stand-alone diagnostic, but integrates well into a clinical diagnostic regimen."
 

*GG*

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And wouldn't it be interesting if they tried to run this test at baseline and then at various times during and after physical (and mental) exertion to see if it could give concrete evidence of PEM?
Guess I am not understanding what this study really determined?

GG
 

viggster

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Hi - This was my brain. The Zinns were so excited my brain patterns were typically atypical. I included the test results in my disability case against Prudential (Prudential ignored it), and in my SSDI application. (No word from them yet.) I know another patient who got an unusually swift, positive ruling on her SSDI app in part because of a qEEG study from the Zinns.

I don't know how it differs from an EEG a neurologist would use for epilepsy. It's easy and quick. You sit in a chair, they put the electrode cap on your head, you close your eyes for 3 minutes, and you're done. I think it would be very inexpensive if offered clinically. The Zinns think this test has potential to aid in diagnosis, but the abnormalities are not specific enough to ME/CFS to make a diagnosis using only this test.

What is qEEG? It's a regular EEG - which measures electrical activity at the surface of the brain - that has a bunch of fancy math applied to it to impute activity far deeper in the brain. So it's a 3D map of brain activity millisecond-by-millisecond. The resolution is about 1 cubic centimeter, which is about the same as an fMRI machine - except the time resolution is much better.

As an aside, the Zinns told me qEEG and the analytic technique they use was developed by a neurologist at NIH (whose name I've forgotten).

BV
 

Pyrrhus

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qEEG / LORETA in Assessment of Neurocognitive Impairment in a Patient with Chronic Fatigue Syndrome: A Case Report

Marcie L Zinn*, Mark A Zinn, Leonard A Jason
DePaul University, Center for Community Research, Chicago, IL

Excerpts from this paper:
Zinn et al 2016 said:
Measures which address neural dynamics with a high time resolution can be co-registered to the MRI and PET and SPECT images, providing a millisecond analysis of brain neural activity, which can be easily compared to studies using other modalities. qEEG/LORETA measures are the only such modalities which capture this millisecond time scale activity [11-13].

A much more thorough understanding of cognitive deficits in neurocognitive disorders will require the depiction of the rapid coupling that takes place in neural oscillations on a millisecond timescale [14,15]. Using LORETA (source analysis of the qEEG signal) [16], the spatial resolution is about 1-3 cm and in qEEG the spatial resolution is about 1 cubic centimeter [17]. The maximum spatial resolution of fMRI is a little less than 1 cm, which is only slightly higher than qEEG or LORETA [16,18,19].

The advantage of using qEEG or LORETA is the greatly decreased cost and the considerably superior temporal resolution [11,18,20]. qEEG measures for connectivity analysis lack the spatial resolution of LORETA source analysis, but have the same temporal resolution, providing inexpensive and easy to interpret brain function [20].
[...]
Research [22] has found surface qEEG effects of peak alpha frequency (PAF), computed within the 8-12 Hz frequency band based on each participant’s EEG indicating significantly decreased PAF over 58% of the entire cortex in patients with CFS when compared to controls (11 electrode sites, p < 0.05).

These findings are consistent with previous reports of reduced efficiency of thalamocortical connections in cognitive impairment [25-30] and suggest that EEG PAF measurement may have both diagnostic and prognostic value in patients [31,32].
[...]
Attention and arousal are produced when a novel event occurs followed by excitation of the reticular formation which then promotes excitatory activity in the cortex [35,36]. During attention, the brain first filters out irrelevant information, then continues to process the relevant information [37-39].
[...]
Using qEEG/ LORETA methods may provide a vehicle whereby the patients’ symptoms and complaints can be validated by analyzing both surface and deeper electric current sources occurring within the brain in 3 dimensions [56- 58]. This study involved only one patient, so until it is replicated with larger samples, the results need to be considered preliminary.
(spacing added for readability)