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Review of EEG microstate analysis shows high diagnostic accuracy for distinguishing neuropsychiatric symptoms from cognitive impairmentNew brain wave test spots mental health symptoms in dementia patients

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Key Takeaway
Consider EEG microstate analysis as a diagnostic tool with high sensitivity and specificity for distinguishing neuropsychiatric symptoms from cognitive impairment.

This publication is a review of a cross-sectional study involving 78 individuals with cognitive impairment and neuropsychiatric symptoms. The authors utilized EEG microstate analysis to distinguish patients with neuropsychiatric symptoms from those with cognitive impairment. The primary outcome measured diagnostic model performance using area under the curve, sensitivity, and specificity. No medications were evaluated in this analysis.

The diagnostic model for distinguishing neuropsychiatric patients from cognitive impairment patients achieved an area under the curve of 0.905. The 95% confidence interval for the AUC ranged from 0.784 to 1.000. Sensitivity was reported as 100.0% and specificity was 76.9%. These metrics indicate strong potential for diagnostic differentiation in this specific clinical context.

Safety data, including adverse events, serious adverse events, discontinuations, and tolerability, were not reported. Funding or conflicts of interest were not reported. The study limitations include the small sample size and the cross-sectional design. Practice relevance remains uncertain due to the lack of longitudinal data and generalizability concerns inherent in this study design.

Maria’s mother has dementia. She forgets names. She repeats questions. But lately, she’s also withdrawn, angry, and refuses to eat. Her doctor isn’t sure if it’s the dementia getting worse or something else.

Millions face this confusion. Over half of people with cognitive impairment develop neuropsychiatric symptoms like depression, anxiety, or agitation. These are hard to diagnose. Patients often can’t explain how they feel. Families struggle to tell what’s what.

Right now, doctors rely on observation and guesswork.

But brain activity might hold the answer.

A signal hiding in plain sight

For years, scientists have studied brain waves using EEG. These tests track electrical activity across the brain. Most focus on seizures or sleep. But a quiet signal called “microstates” has been overlooked.

Think of microstates as the brain’s default radio stations. They switch rapidly, like channels flicking every fraction of a second. Each one controls a different mental task. One handles attention. Another manages emotion.

In healthy brains, these stations switch smoothly. In dementia, the signal gets fuzzy. Some stations stay on too long. Others vanish.

Now, researchers have found four key microstate patterns that act like fingerprints for mental health symptoms in dementia.

Four brain wave clues

The study looked at 78 older adults with cognitive impairment. Some had mood or behavior symptoms. Others did not. All had EEGs.

Scientists analyzed 36 different microstate traits. Four stood out.

The most important was the duration of Class C microstate. It stayed active longer in patients with neuropsychiatric symptoms. The other three were transition patterns between brain states—how the brain jumps from one station to another.

When these four markers were combined, they formed a powerful signal.

The model worked almost perfectly

A computer model used these four traits to tell who had mental health symptoms.

It was right 90% of the time.

Even better, it never missed a case. Sensitivity was 100%. That means every patient with symptoms was correctly flagged. Specificity was 77%, meaning a few people without symptoms were flagged by mistake.

That’s far better than current tools, which often miss these issues or confuse them with dementia itself.

This doesn't mean this treatment is available yet.

But there’s a catch.

The study was small. Just 78 people. And it was a snapshot in time, not a long-term test. The results need to be confirmed in larger, more diverse groups.

Also, the model hasn’t been tested outside the lab. EEGs are common, but analyzing microstates requires special software and training. Most clinics don’t have that.

Still, experts say this opens a new path.

A new window into the mind

Right now, we treat the brain like a black box in dementia care. We see behavior and guess what’s inside.

This method lets us peek inside without scans or spinal taps.

It could help doctors tell if a patient is depressed or just more forgetful. That changes treatment. Depression can be managed with therapy or meds. Agitation might need different support.

Right now, many go untreated because symptoms are mistaken for dementia progression.

Not ready for your doctor’s office

You can’t get this test today. It’s still in research labs.

But the tools are close. EEG machines are already in many hospitals. The next step is building user-friendly software that can run this analysis automatically.

Researchers plan to test the model in bigger studies, including people with mild cognitive impairment and different types of dementia.

A step toward personalized care

Dementia is not one disease. It affects people differently. Some lose memory first. Others lose mood control.

This approach could help tailor care to the individual.

Instead of guessing, doctors could use brain wave data to guide treatment.

That day is still a few years away. But for families like Maria’s, it offers hope.

One day, a simple EEG might answer the question: Is it the dementia—or something else?

And that could change how we care for millions.

Study Details

Sample sizen = 78
EvidenceLevel 5
PublishedApr 2026
View Original Abstract ↓
Objectives: Neuropsychiatric symptoms (NPS) are prevalent in individuals of cognitive impairement (CI). However, the similarities and disparatenesses in whole-brain dynamics between individuals of CI and NPS are controversy. Electroencephalography (EEG) microstates reflect the whole-brain dynamics. This study aimed to investigate the differential EEG microstates parameters between CI and NPS and to construct related diagnostic model. Methods/design: This study was a cross-sectional study. Clinical and EEG data were collected, and an EEG microstate analysis were performed. The Least absolute shrinkage and selection operation (LASSO) regression model was used to identify significant differential EEG microstates parameters between CI and NPS and to construct a diagnostic model. The model performance was tested by the receiver operating characteristic curve (ROC). Results: This study enrolled 78 participants. A total of 36 EEG microstates parameters were identified and included in the differential analysis. In the LASSO regression model, 4 significant differential EEG microstates parameters were selected, including the duration of class C, TPAB, TPBA, and TPDC. The ROC results showed that the diagnostic model for distinguishing NPS patients from CI patients achieved an area under the curve (AUC) of 0.905(95% CI: 0.784-1.000) , with a sensitivity of 100.0% and a specificity of 76.9%. Conclusions: The diagnostic model based on EEG microstate parameters showed a good performance for differentiating NPS patients from CI patients.
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