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Screening & Managing MCI (Mild Cognitive Impairment)

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Screening & Managing MCI (Mild Cognitive Impairment)

Mild cognitive impairment (MCI) defines the intermediate state between healthy cognition and early dementia​1​ and is not typical for normal brain aging​2​. In 2020 the estimated prevalence of MCI in the elderly population was 22.7%, and it is projected that the number of people in the US with MCI will almost double by 2060​3​. The associated increase in the rate of dementia underlines the importance of early detection of its forerunners and MCI, as this will enable better management of dementia and its consequences. However, the heterogeneity in cognitive deficits, the long pre-symptomatic phase (See Figure 1), and the fact that memory problems may not be the only symptom in every case ​4​, make the identification and monitoring of MCI particularly challenging. Current routine examinations like the Mini Mental State Exam (MMSE) have been shown to be insufficient as stand-alone tools in managing MCI and dementia​5​. Thus, there is a pressing need for new clinical examinations and tools that can track subtle cognitive changes, detect the onset of MCI, and register the progression to dementia. 

Normal - MCI - Dementia graph
Figure 1: Transition from healthy aging to dementia. The clinical and pathological time course of AD dementia emphasizes the long presymptomatic phase of the illness when pathology (red line) is accruing in the absence of clinical symptoms (green line). From Decarli, C. (2003). Mild cognitive impairment: prevalence, prognosis, aetiology, and treatment. THE LANCET Neurology, 2, 15–21. 


The MCI Case 

A 67-year-old male with suspected MCI enrolled in a study.  


BNA was recorded twice in the first week and again after five and a half months. The completed BNA tests included the visual go-no-go task to acquire event-related potentials (ERPs) and the simple resting task with eyes closed for the quantitative electroencephalography analysis (QEEG). The MMSE score was calculated at each visit, showing the highest (normal) possible score at all three visits (See Figure 2). 

MMSE Score by Visit Chart
Figure 2: Development of the patient’s MMSE scores across all three visits. Despite the suspected MCI, the MMSE score indicates normal cognition.

BNA Results & Physician Interpretation 

The research physician interpreted the BNA results as follows:  

BNA Visit 1: On the first visit, the most remarkable result was the low neural consistency (Z-score -2.1). Low neural consistency indicates little reliability in how the brain reacts to the same type of stimulus. Internal Firefly Neuroscience studies could correlate such patterns with impacted cognitive functions. In line with this, all three assessed ERP components (P200, N200, P3a) showed increased amplitudes (+1.2, +1.1, and +1.3, respectively). The P200, N200, and P3a recorded have commonly been associated with attentional and inhibitory processes.​6–8​ The physician interpreted the increase in amplitude in these ERP components as increased neural recruitment to compensate for increasingly impaired attentional and inhibitory functions. Such compensatory mechanisms can appear early in degenerative diseases before the amplitudes commonly decline as the disease progresses.​9​ Behaviorally, the patient presented a poor ability to inhibit his responses to the No-Go stimuli (51% correct). No other behavioral deviances were observed. 

tracking MCI with visual go no-go task
Summary Report of the Visual Go No-Go Task 

The QEEG report indicated a slightly increased absolute theta and strongly increased absolute power in the delta range. An increase of slow-range frequency bands, such as theta and delta, is commonly observed in diseases associated with a cognitive decline 10–12​ . These results further support the suspected MCI diagnosis, despite the absence of deviations in the MMSE score.  

BNA Visit 2: At the second visit, conducted one week later, the neural consistency remained low (Z-score -1.8), suggesting a continuous neural impact. The amplitudes for the N200 (Impulse Control) and the P3a (Response Inhibition) dropped in amplitude (both Z-scores at -0.5). The patient’s accuracy improved. However, the improved accuracy was likely due to a switch in strategy, favoring accuracy over response time, with the latter slowing strongly (Z-score +1.2). Noteworthy, the Response Variability became highly variable (Z-score +1.7), indicating a potential difficulty for the patient to remain focused. The physician interpreted these fluctuations in the ERP components and behavioral results within only one week as typical for MCI patients in which “good” and “bad days” alternate and cognitive functions may be differently affected daily​13​.  

The QEEG results indicated a stable high absolute power in the delta band and an increase in power in the theta band compared to the first visit, which is aligned with a progressing degenerative disease.  

BNA Visit 3: The last BNA visit occurred five and a half months later. The neural consistency remained low, despite a slight increase (Z-score -1.6), solidifying the image of impaired neural networks. The amplitudes of the N200 (Impulse Control) and P3a (Response Inhibition) continued to decrease, and notably, the P3a reached highly deviant levels with a Z-score at -2.3. The physician interpreted this drop in amplitudes as increased difficulty in compensating for the neural degeneration. This aligns with the behavioral decline towards less accurate responses while becoming slower and more variable in his responses (Z-score +1.4 and +2.0, respectively).  

The QEEG results showed a continuation of increased slowing, as indicated by the high absolute values in the theta and beta band. The later increase in delta, as opposed to the earlier increase in theta, has been found in the literature to potentially indicate a progression to more severe stages of cognitive impairment within the framework of dementia.  

tracking mci with quantitative EEG report
Average Absolute Delta & Theta Power as shown in the Quantitative EEG Report 

In sum, the ERP, QEEG, and behavioral results support a disease progression to more severe stages. These BNA findings are remarkable, given that the MMSE assessment still appears insensitive to the patient’s advancing cognitive functional loss. 

Benefits of BNA for MCI Detection and Monitoring 

The unique BNA combination of ERP, behavioral and QEEG results gave comprehensive insights into the clinical picture and generated trust in the diagnosis. Despite the MMSE score remaining on a normal level, the BNA scores indicated from the first visit substantial deviations from the healthy age-matched norm in the attentional (P200) and inhibitory domains (N200 & P3a) as well as in the theta power. The physician interpreted these BNA results as indicators of compromised cognitive networks that the MMSE was not sensitive enough to detect at such early stages. Using BNA in primary care settings could sensitize standard screenings to unveil subtle cognitive changes that may otherwise go unnoticed. The monitoring of results across the three visits further identified variations in neurophysiology that could indicate the first signs of a progression to dementia, making BNA a valuable tool for both the screening and monitoring phase in MCI and dementia. 


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​9. Lopez-Gongora M, Escartin A, Martinez-Horta S, et al. Neurophysiological evidence of compensatory brain mechanisms in early-stage multiple sclerosis. PLoS One. 2015;10(8):1-15. doi:10.1371/journal.pone.0136786 

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