Combinations of Multiple Neuroimaging Markers using Logistic Regression for Auxiliary Diagnosis of Alzheimer Disease and Mild Cognitive Impairment

Mao, Nini; Liu, Yunting; Chen, Kewei; Yao, Li; Wu, Xia

doi:10.1159/000487801

Cited by 3 publications

(6 citation statements)

References 36 publications

(37 reference statements)

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“…The AUC value tended to be higher in the model with FDG‐PET and the MMSE score than in the model with the MMSE score alone. This was consistent with findings of a previous study reporting that FDG‐PET might be more effective than amyloid PET and MRI in discriminating individuals with MCI from controls . Furthermore, FDG‐PET can detect cognitive decline‐associated early changes in AD with high sensitivity compared with MRI .…”

Section: Discussionsupporting

confidence: 91%

“…Although the AUC value was significantly higher in the model with all three modalities than in the model with the MMSE score alone or in the model with MRI in addition to the MMSE score, the combination of all three modalities achieved the highest AUC for discrimination accuracy. Few studies have investigated the utility of combining MRI, FDG‐PET and amyloid PET for the diagnosis of amnestic MCI . One study compared the accuracy of identifying MCI or AD among combinations of these three imaging methods using logistic regression analysis .…”

Section: Discussionmentioning

confidence: 99%

“…Few studies have investigated the utility of combining MRI, FDG‐PET and amyloid PET for the diagnosis of amnestic MCI . One study compared the accuracy of identifying MCI or AD among combinations of these three imaging methods using logistic regression analysis . The results showed that the combination of the three methods could discriminate individuals with MCI from controls with an AUC of 0.840, and could improve the discrimination accuracy compared with the use of MRI (AUC = 0.801), FDG‐PET (AUC = 0.74), or florbetapir‐PET (AUC = 0.681) alone.…”

Section: Discussionmentioning

confidence: 99%

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Diagnosis of mild cognitive impairment using multiple neuroimaging modalities in addition to the Mini‐Mental State Examination

Kaneko

Kimura

Nojima

et al. 2019

Geriatrics Gerontology Int

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Aim: This prospective study investigated the additional utility of combining multiple imaging modalities with the Mini-Mental State Examination (MMSE) in discriminating individuals with mild cognitive impairment (MCI) from cognitively normal individuals.Methods: A total of 103 individuals with amnestic MCI and normal cognitive function were recruited from outpatients; they underwent examination using the MMSE, magnetic resonance imaging, 18 F-fluorodeoxyglucose positron emission tomography and 11 C-Pittsburgh compound B positron emission tomography. Binary logistic regression with receiver operator characteristic analysis was used to assess the combination of the abovementioned single or multiple imaging modalities with the MMSE. Eight models were constructed, and their area under the curve values and misclassification rates in discriminating individuals with amnestic MCI from cognitively normal individuals were evaluated.Results: The addition of each of the imaging modalities improved the discrimination accuracy over that of the MMSE alone; the accuracy obtained with the addition of 18 Ffluorodeoxyglucose positron emission tomography was the highest. The best model with the highest accuracy included a combination of all the imaging modalities in addition to the MMSE (area under the curve value 0.902). Conclusions:The combination of all the imaging modalities in addition to the MMSE could facilitate more accurate diagnosis of amnestic MCI. Of the individual imaging modalities, the highest accuracy resulted from the addition of 18 F-fluorodeoxyglucose positron emission tomography to the MMSE.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Diagnosis of mild cognitive impairment using multiple neuroimaging modalities in addition to the Mini‐Mental State Examination

Kaneko

Kimura

Nojima

et al. 2019

Geriatrics Gerontology Int

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“…The results showed that their applied classification algorithm (support vector machine, SVM) could better diagnose patients with mood disorders and accurately predict the drug response of complex patients. Mao et al 12 used a logistic regression method and combined multiple neuroimaging data for the diagnosis of Alzheimer’s disease and mild cognitive impairment. Their results suggested that the use of multiple neuroimaging markers can improve the diseases diagnosis performance.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic identification of chronic insomnia using ALFF and FC features of resting-state functional MRI and logistic regression approach

Yang

Yuan

et al. 2023

Sci Rep

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This study investigated whether the amplitude of low-frequency fluctuation (ALFF) and functional connectivity (FC) features could be used as potentially neurological markers to identify chronic insomnia (CI) using resting-state functional MRI and machine learning method logistic regression (LR). This study included 49 CI patients and 47 healthy controls (HC). Voxel-wise features, including the amplitude of low-frequency fluctuations (ALFF) and functional connectivity (FC), were extracted from resting-state functional magnetic resonance brain images. Then, we divided the data into two independent cohorts for training (44 CI patients and 42 HC patients), and independent validation (5 CI patients and 5 HC patients) by using logistic regression. The model was evaluated using 20 rounds of fivefold cross‑validation for training. In particular, a two-sample t-test (GRF corrected, p-voxel < 0.001, p-cluster < 0.05) was used for feature selection during the model training. Finally, single‑shot testing of the final model was performed on the independent validation cohort. A correlation analysis (Bonferroni correction, p < 0.05/4) was also conducted to determine whether the features contributing to the prediction were correlated with clinical characteristics, including the Insomnia Severity Index (ISI), Pittsburgh sleep quality index (PSQI), self-rating anxiety scale (SAS), and self-rating depression scale (SDS). Results showed that resting-state features had a discrimination accuracy of 86.40%, with a sensitivity of 93.00% and specificity of 79.80%. The area under the curve (AUC) was 0.89 (all $${P}_{\mathrm{permutation}}$$ P permutation < 0.001). The ALFF and FC features showed significant differences between the CI patients and HC. The regions contributing to the prediction mainly included the anterior cingulate, prefrontal cortex, orbital part of the frontal lobe, angular gyrus, cingulate gyrus, praecuneus, parietal lobe, temporal gyrus, superior temporal gyrus, and middle temporal gyrus. Furthermore, some specific functional connectivity among related regions was positively correlated with the ISI, and also negatively related to the SDS in correlation analysis. Our current study suggested that ALFF and FC in the regions contributing to diagnostic identification might serve as potential neuromarkers for CI.

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“…In SVM, a hyperplane or set of hyperplanes is constructed in a large or infinite-dimensional space and is used for classification (Salvatore et al, 2016). Logistic regression (Mao et al, 2018) is based on a multinomial logistic regression model with a ridge estimator. Random forests are an ensemble learning method for classification based on a multitude of decision trees (Bi et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer’s disease: Machine learning approaches

et al. 2021

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Background: d-glutamate, which is involved in N-methyl-d-aspartate receptor modulation, may be associated with cognitive ageing. Aims: This study aimed to use peripheral plasma d-glutamate levels to differentiate patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) from healthy individuals and to evaluate its prediction ability using machine learning. Methods: Overall, 31 healthy controls, 21 patients with MCI and 133 patients with AD were recruited. Serum d-glutamate levels were measured using high-performance liquid chromatography (HPLC). Cognitive deficit severity was assessed using the Clinical Dementia Rating scale and the Mini-Mental Status Examination (MMSE). We employed four machine learning algorithms (support vector machine, logistic regression, random forest and naïve Bayes) to build an optimal predictive model to distinguish patients with MCI or AD from healthy controls. Results: The MCI and AD groups had lower plasma d-glutamate levels (1097.79 ± 283.99 and 785.10 ± 720.06 ng/mL, respectively) compared to healthy controls (1620.08 ± 548.80 ng/mL). The naïve Bayes model and random forest model appeared to be the best models for determining MCI and AD susceptibility, respectively (area under the receiver operating characteristic curve: 0.8207 and 0.7900; sensitivity: 0.8438 and 0.6997; and specificity: 0.8158 and 0.9188, respectively). The total MMSE score was positively correlated with d-glutamate levels ( r = 0.368, p < 0.001). Multivariate regression analysis indicated that d-glutamate levels were significantly associated with the total MMSE score ( B = 0.003, 95% confidence interval 0.002–0.005, p < 0.001). Conclusions: Peripheral plasma d-glutamate levels were associated with cognitive impairment and may therefore be a suitable peripheral biomarker for detecting MCI and AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing MCI and AD in outpatient clinics.

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Combinations of Multiple Neuroimaging Markers using Logistic Regression for Auxiliary Diagnosis of Alzheimer Disease and Mild Cognitive Impairment

Cited by 3 publications

References 36 publications

Diagnosis of mild cognitive impairment using multiple neuroimaging modalities in addition to the Mini‐Mental State Examination

Diagnosis of mild cognitive impairment using multiple neuroimaging modalities in addition to the Mini‐Mental State Examination

Diagnostic identification of chronic insomnia using ALFF and FC features of resting-state functional MRI and logistic regression approach

Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer’s disease: Machine learning approaches

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