Integration of structural and functional magnetic resonance imaging improves mild cognitive impairment detection

Kim, JungHoe; Lee, Jong Hwan

doi:10.1016/j.mri.2012.11.009

Cited by 19 publications

(14 citation statements)

References 58 publications

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“…Despite the protection toward bias offered by a datadriven approach and a sample of comparable or larger size than that of other studies, [16][17][18][19][20] the outcome is still the result of feature and algorithm definition. Although we selected "standard" cognitive tests and segmentation/parcellation atlases, and 2 basic machine-learning algorithms, we cannot rule out the possibility that other methodological choices might have yielded slightly different patterns of findings.…”

Section: Limitationsmentioning

confidence: 97%

“…A number of recent studies have implemented these classificatory techniques to identify MCI patients using RS-fMRI as a single source of diagnostic information, [15][16][17][18] or in combination with sMRI. [19][20][21] In this study we used machine-learning methods to carry out classifications of participants with a diagnosis of MCI based on features extracted from cognitive performance, sMRI, and RS-fMRI, with a series of single-type and mixed classifiers. No specific hypothesis was formulated in association with cognitive classifiers as the diagnostic status was heavily dependent on cognitive performance.…”

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confidence: 99%

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Machine-learning Support to Individual Diagnosis of Mild Cognitive Impairment Using Multimodal MRI and Cognitive Assessments

Marco¹,

Beltrachini

Biancardi

et al. 2017

Alzheimer Disease &Amp; Associated Disorders

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ReuseThis article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: https://creativecommons.org/licenses/ Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request.

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Section: Limitationsmentioning

confidence: 97%

mentioning

confidence: 99%

Machine-learning Support to Individual Diagnosis of Mild Cognitive Impairment Using Multimodal MRI and Cognitive Assessments

Marco¹,

Beltrachini

Biancardi

et al. 2017

Alzheimer Disease &Amp; Associated Disorders

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“…Several studies have shown that combined multivariate or multimodal data – structural MRI including cortical thickness, volume, and tensor-based morphemotry, functional MRI, FDG-PET, and non-imaging data including CSF biomarker and neurocognition – could improve diagnostic power (Desikan et al, 2009; Fan et al, 2008; Hinrichs et al, 2011; Kim and Lee, 2012; Park et al, 2012; Zhang et al, 2011), and combining cortical gray matter thickness and white matter surface measures could increase prediction accuracy in autism (Ecker et al, 2010). In this paper, we present the first study to integrate cortical white matter surface geometric and cortical thickness measures on the cortical surface vertices to discriminate very mild AD from cognitively normal controls.…”

Section: Introductionmentioning

confidence: 99%

Integrated cortical structural marker for Alzheimer's disease

Ming

Harms

Morris³

et al. 2015

Neurobiology of Aging

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In this paper we propose an approach to integrate cortical morphology measures for improving the discrimination of individuals with and without very mild AD. FreeSurfer was applied to scans collected from 83 participants with very mild AD and 124 cognitively normal individuals. We generated cortex thickness, white matter convexity (aka “sulcal depth”) and white matter surface metric distortion measures on a normalized surface atlas in this first study to integrate high resolution gray matter thickness and white matter surface geometric measures in identifying very mild AD. Principal component analysis (PCA) was applied to each individual structural measure to generate eigenvectors. Discrimination power based on individual and combined measures are compared, based on stepwise logistic regression and 10-fold cross-validation. Global AD likelihood index and surface-based likelihood maps were also generated. Our results show complementary patterns on the cortical surface between thickness, which reflects gray matter atrophy, convexity, which reflects white matter sulcal depth changes; and metric distortion, which reflects white matter surface area changes. The classifier integrating all three types of surface measures significantly improved classification performance compared to classification based on single measures. The PCA-based approach provides a framework for achieving high discrimination power by integrating high-dimensional data, and this method could be very powerful in future studies for early diagnosis of diseases that are known to be associated with abnormal gyral and sulcal patterns.

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“…Then, the in-brain mask that overlapped across all fMRI volumes and all subjects was used to define the final set of input voxels (Kim et al, 2013; Kim and Lee, 2013), resulting in a total of 74,484 in-brain voxels. The BOLD intensities of the fMRI volumes within a task-related period (i.e., three blocks of 20-s task periods after a 6-s delay from the task onset for each fMRI run) were normalized to percent signal change relative to the average BOLD signal with the 80-s baseline period.…”

Section: Methodsmentioning

confidence: 99%

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

et al. 2017

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Feedforward deep neural networks (DNN), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean ± standard deviation; %) of 6.9 (± 3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4 ± 4.6) and the two-layer network (7.4 ± 4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the extraction of hidden representations of fMRI volumes associated with tasks across multiple hidden layers. Our study may be beneficial to the automatic classification/diagnosis of neuropsychiatric and neurological diseases and prediction of disease severity and recovery in (pre-) clinical settings using fMRI volumes without requiring an estimation of activation patterns or ad hoc statistical evaluation.

show abstract

Integration of structural and functional magnetic resonance imaging improves mild cognitive impairment detection

Cited by 19 publications

References 58 publications

Machine-learning Support to Individual Diagnosis of Mild Cognitive Impairment Using Multimodal MRI and Cognitive Assessments

Machine-learning Support to Individual Diagnosis of Mild Cognitive Impairment Using Multimodal MRI and Cognitive Assessments

Integrated cortical structural marker for Alzheimer's disease

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

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