Combining SPECT and Quantitative EEG Analysis for the Automated Differential Diagnosis of Disorders with Amnestic Symptoms

Frontiers in Aging Neuroscience, 2017 · DOI: 10.3389/fnagi.2017.00290 · Published: September 7, 2017

Simple Explanation

This study aimed to improve the diagnosis of memory disorders by combining two brain scanning methods: SPECT and EEG. SPECT measures blood flow in the brain, while EEG measures electrical activity. The researchers hypothesized that combining SPECT with EEG connectivity measures would provide a more accurate diagnosis than using either method alone. They looked at patients with Alzheimer's disease, mild cognitive impairment, subjective cognitive complaints and depression related cognitive issues. By combining the two techniques, the study was able to better differentiate between the different conditions causing memory loss, leading to the conclusion that combining EEG with imaging methods such as SPECT could support differentiating AD, aMCI, aSCC, and DCC.

Study Duration

June 2007 and March 2011

Participants

220 patients: 39 with Alzheimer’s dementia (AD), 69 with depressive cognitive impairment (DCI), 71 with amnestic mild cognitive impairment (aMCI), and 41 with amnestic subjective cognitive complaints (aSCC)

Evidence Level

Not specified

Key Findings

1
Combining SPECT with EEG biomarkers improved classification accuracy for aSCC vs. AD (90%), aMCI vs. AD (70%), and AD vs. DCI (100%).
2
EEG measures alone performed best when classifying aSCC vs. aMCI (82%) and aMCI vs. DCI (90%).
3
Measures of interaction (connectivity measures) were generally more accurate than graph-theoretical measures.

Research Summary

The study investigated the use of combined SPECT and EEG data to improve the differential diagnosis of amnestic disorders. The researchers aimed to see if combining cerebral perfusion data with EEG connectivity analysis would increase diagnostic accuracy. The results indicated that combining SPECT with EEG measures outperformed the use of either method alone in certain classifications, particularly those involving Alzheimer's disease. EEG measures alone performed best when classifying aSCC vs. aMCI and aMCI vs. DCI. The study suggests that quantitative EEG analysis and machine learning techniques can be beneficial for differentiating between AD, aMCI, aSCC, and DCI, especially when combined with imaging methods like SPECT. Quantitative analysis of EEG connectivity could become an integral part for early differential diagnosis of cognitive impairment.

Practical Implications

Improved Diagnostic Accuracy

Combining SPECT and EEG can improve the accuracy of diagnosing different types of amnestic disorders, especially AD.

Early Detection

Quantitative analysis of EEG connectivity has the potential to become an integral part of early differential diagnosis of cognitive impairment.

Personalized Treatment

More accurate differential diagnosis can lead to more targeted and effective interventions, potentially improving the quality of life for patients with memory disorders.

Combining SPECT and Quantitative EEG Analysis for the Automated Differential Diagnosis of Disorders with Amnestic Symptoms

Simple Explanation

Key Findings

Research Summary

Practical Implications

Improved Diagnostic Accuracy

Early Detection

Personalized Treatment

Study Limitations

Your Feedback

Was this summary helpful?

Combining SPECT and Quantitative EEG Analysis for the Automated Differential Diagnosis of Disorders with Amnestic Symptoms

Simple Explanation

Key Findings

Research Summary

Practical Implications

Improved Diagnostic Accuracy

Early Detection

Personalized Treatment

Study Limitations

Your Feedback

Was this summary helpful?