Improved Diagnostic Accuracy of Alzheimer's Disease by Combining Regional Cortical Thickness and Default Mode Network Functional Connectivity: Validated in the Alzheimer's Disease Neuroimaging Initiative Set

Park, Ji Eun; Park, Bumwoo; Kim, Sang Joon; Kim, Ho Sung; Choi, Choong Gon; Jung, Sung Eun; Oh, Joo Young; Lee, Jae‐Hong; Roh, Jee Hoon; Shim, Woo Hyun; Initiative, Alzheimer’s Disease Neuroimaging

doi:10.3348/kjr.2017.18.6.983

Cited by 17 publications

(31 citation statements)

References 34 publications

(44 reference statements)

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“…In addition, we also compared our results with age‐related disorders. For example, compared with the controls, Park et al revealed significant differences in the left superior temporal and left supramarginal regions in patients with Alzheimer's disease. In our study, we also found obvious significance in two regions.…”

Section: Discussionmentioning

confidence: 99%

“…Unhealthy aging is a risk factor that can contribute to the formation of brain diseases, and as the central processor of human life activity, the cerebral structure changes in many ways with increasing age . Cortical thickness changes during normal development and aging as well as with neuropsychiatric and other disorders . Additionally, some references reported using the local gyrification index (LGI) to identify the abnormal cortical complexity of psychosis and neuropathy .…”

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confidence: 99%

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Age‐related changes in cortical and subcortical structures of healthy adult brains: A surface‐based morphometry study

Zheng

Liu

Yuan

et al. 2018

Magnetic Resonance Imaging

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Age‐related changes in cortical and subcortical structures of healthy adult brains: A surface‐based morphometry study

Zheng

Liu

Yuan

et al. 2018

Magnetic Resonance Imaging

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“…In total, 50 KJR (9101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354565758), 492 ER, and 254 Radiology articles reported original research studies analyzing patient data. Of these, 44 (88%; 95% CI: 75.8–94.8%) (910121314151617181920212324262728293031323435363738394041424344474849505152535455565758), 359 (73%; 95% CI: 68.9–76.7%), and 211 (83.1%; 95% CI: 78–87.2%) articles, respectively, revealed the start and end of data collection to the level of calendar month.…”

Section: Resultsmentioning

confidence: 99%

“…Of these, 44 (88%; 95% CI: 75.8–94.8%) (910121314151617181920212324262728293031323435363738394041424344474849505152535455565758), 359 (73%; 95% CI: 68.9–76.7%), and 211 (83.1%; 95% CI: 78–87.2%) articles, respectively, revealed the start and end of data collection to the level of calendar month. The point estimate value of this proportion was slightly larger in KJR than in ER and Radiology, although the difference was not statistically significant.…”

Section: Resultsmentioning

confidence: 99%

Age of Data in Contemporary Research Articles Published in Representative General Radiology Journals

2018

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ObjectiveTo analyze and compare the age of data in contemporary research articles published in representative general radiology journals.Materials and MethodsWe searched for articles reporting original research studies analyzing patient data that were published in the print issues of the Korean Journal of Radiology (KJR), European Radiology (ER), and Radiology in 2017. Eligible articles were reviewed to extract data collection period (time from first patient recruitment to last patient follow-up) and age of data (time between data collection end and publication). The journals were compared in terms of the proportion of articles reporting the data collection period to the level of calendar month and regarding the age of data.ResultsThere were 50, 492, and 254 eligible articles in KJR, ER, and Radiology, respectively. Of these, 44 (88%; 95% confidence interval [CI]: 75.8–94.8%), 359 (73%; 95% CI: 68.9–76.7%), and 211 (83.1%; 95% CI: 78–87.2%) articles, respectively, provided enough details of data collection period, revealing a significant difference between ER and Radiology (p = 0.002). The age of data was significantly greater in KJR (median age: 826 days; range: 299–2843 days) than in ER (median age: 570 days; range: 56–4742 days; p < 0.001) and Radiology (median age: 618; range: 75–4271 days; p < 0.001).ConclusionKorean Journal of Radiology did not fall behind ER or Radiology in reporting of data collection period, but showed a significantly greater age of data than ER and Radiology, suggesting that KJR should take measures to improve the timeliness of its data.

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“…Similar ndings have been detected by other research groups. Using multimodality MRI, several studies have constructed SVM classi ers for the identi cation of ASD, Alzheimer's Disease, and schizophrenia [46,[52][53]. Beyond this, some researchers have combined multimodality MRI with other characteristics of these diseases, such as cerebral spinal uid, electroencephalography, and eye-tracking [54][55].…”

Section: Discussionmentioning

confidence: 99%

Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

Cui

Cao³

et al. 2020

Preprint

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Background: Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed for years. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.Methods: In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, voxel-based morphometry (VBM), and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed.Results: After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5%, sensitivity of 86.4%, and specificity of 88.9% in the test data (p=0.0022).Conclusions: A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

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Improved Diagnostic Accuracy of Alzheimer's Disease by Combining Regional Cortical Thickness and Default Mode Network Functional Connectivity: Validated in the Alzheimer's Disease Neuroimaging Initiative Set

Cited by 17 publications

References 34 publications

Age‐related changes in cortical and subcortical structures of healthy adult brains: A surface‐based morphometry study

Age‐related changes in cortical and subcortical structures of healthy adult brains: A surface‐based morphometry study

Age of Data in Contemporary Research Articles Published in Representative General Radiology Journals

Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

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