Do Epilepsy Patients with Cognitive Impairment Have Alzheimer's Disease-like Brain Metabolism?

被引:3
|
作者
He, Michael [1 ]
Kolesar, Tiffany A. [2 ,3 ]
Goertzen, Andrew L. [4 ,5 ]
Ng, Marcus C. [5 ,6 ]
Ko, Ji Hyun [2 ,3 ,5 ]
机构
[1] Univ Manitoba, Rady Fac Hlth Sci, Undergraduate Med Educ, Winnipeg, MB R3E 0T6, Canada
[2] Univ Manitoba, Rady Fac Hlth Sci, Dept Human Anat & Cell Sci, Winnipeg, MB R3E 0J9, Canada
[3] Kleysen Inst Adv Med, Hlth Sci Ctr, Neurosci Res Program, Winnipeg, MB R3E 3J7, Canada
[4] Univ Manitoba, Rady Fac Hlth Sci, Dept Radiol, Sect Nucl Med, Winnipeg, MB R3T 2N2, Canada
[5] Univ Manitoba, Price Fac Engn, Grad Program Biomed Engn, Winnipeg, MB R3T 5V6, Canada
[6] Univ Manitoba, Rady Fac Hlth Sci, Dept Internal Med, Sect Neurol, Winnipeg, MB R3E 0W2, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
epilepsy; Alzheimer's disease; FDG-PET; machine learning; support vector machine; metabolic classification; neurodegenerative disease; biomarker; TEMPORAL-LOBE EPILEPSY; DEMENTIA; F-18-FDG-PET; DIAGNOSIS; SEIZURES; DEFICITS; PET;
D O I
10.3390/biomedicines11041108
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Although not classically considered together, there is emerging evidence that Alzheimer's disease (AD) and epilepsy share a number of features and that each disease predisposes patients to developing the other. Using machine learning, we have previously developed an automated fluorodeoxyglucose positron emission tomography (FDG-PET) reading program (i.e., MAD), and demonstrated good sensitivity (84%) and specificity (95%) for differentiating AD patients versus healthy controls. In this retrospective chart review study, we investigated if epilepsy patients with/without mild cognitive symptoms also show AD-like metabolic patterns determined by the MAD algorithm. Scans from a total of 20 patients with epilepsy were included in this study. Because AD diagnoses are made late in life, only patients aged >= 40 years were considered. For the cognitively impaired patients, four of six were identified as MAD+ (i.e., the FDG-PET image is classified as AD-like by the MAD algorithm), while none of the five cognitively normal patients was identified as MAD+ (chi(2) = 8.148, p = 0.017). These results potentially suggest the usability of FDG-PET in prognosticating later dementia development in non-demented epilepsy patients, especially when combined with machine learning algorithms. A longitudinal follow-up study is warranted to assess the effectiveness of this approach.
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页数:10
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