Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture

Meszlényi, Regina; Búza, Krisztián; Vidnyánszky, Zoltán

doi:10.3389/fninf.2017.00061

Cited by 113 publications

(100 citation statements)

References 53 publications

(93 reference statements)

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“…These important features allow researchers to efficiently model complex systems without the burden of model/prior knowledge selection, especially in cases where too many features exist, as when analyzing medical images (Shen et al, ). Thus, DNNs are widely used by researchers for medical image analysis, such as brain image segmentation (Havaei et al, ; Wachinger et al, ; Zhang et al, ), neurology and psychiatric diagnostics (Hosseini‐Asl, Keynton, & El‐Baz, ; Meszlenyi, Buza, & Vidnyanszky, ; Plis et al, ; Vieira et al, ), brain state decoding (Jang et al, ), and brain computer interfaces (Schirrmeister et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…A variety of deep methods have been applied to fMRI data, such as the autoencoder (Kim, Calhoun, Shim, & Lee, 2016), deep belief network (DBN; Jang et al, 2017;Plis et al, 2014), long short-term memory (LSTM) recurrent neural network (RNN; Li & Fan, 2019), and 2D CNN (Meszlenyi et al, 2017). Although the autoencoder is known to be efficient, especially when the dataset is small, it over- (c) Accuracy of fivefold cross-validation classification on the motor task on a small dataset.…”

Section: Deep Learning As a Research Toolmentioning

confidence: 99%

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Decoding and mapping task states of the human brain via deep learning

Wang

Liang

Jiang

et al. 2019

Human Brain Mapping

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Support vector machine (SVM)‐based multivariate pattern analysis (MVPA) has delivered promising performance in decoding specific task states based on functional magnetic resonance imaging (fMRI) of the human brain. Conventionally, the SVM‐MVPA requires careful feature selection/extraction according to expert knowledge. In this study, we propose a deep neural network (DNN) for directly decoding multiple brain task states from fMRI signals of the brain without any burden for feature handcrafts. We trained and tested the DNN classifier using task fMRI data from the Human Connectome Project's S1200 dataset (N = 1,034). In tests to verify its performance, the proposed classification method identified seven tasks with an average accuracy of 93.7%. We also showed the general applicability of the DNN for transfer learning to small datasets (N = 43), a situation encountered in typical neuroscience research. The proposed method achieved an average accuracy of 89.0 and 94.7% on a working memory task and a motor classification task, respectively, higher than the accuracy of 69.2 and 68.6% obtained by the SVM‐MVPA. A network visualization analysis showed that the DNN automatically detected features from areas of the brain related to each task. Without incurring the burden of handcrafting the features, the proposed deep decoding method can classify brain task states highly accurately, and is a powerful tool for fMRI researchers.

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Section: Discussionmentioning

confidence: 99%

Section: Deep Learning As a Research Toolmentioning

confidence: 99%

Decoding and mapping task states of the human brain via deep learning

Wang

Liang

Jiang

et al. 2019

Human Brain Mapping

View full text Add to dashboard Cite

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“…To reduce the number of parameters we included only a single edge-to-edge layer (Meszlényi et al, 2017). The input to the CNN is the set of 14×14 interaction matrices that capture the metastability/synchrony between the 13 resting state networks (plus the thalamus) defined by the Gordon atlas.…”

Section: Classification Of Task and Rest Datamentioning

confidence: 99%

Metastable neural dynamics underlies cognitive performance across multiple behavioural paradigms

Alderson

Bokde

Kelso

et al. 2019

Preprint

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AbstractDespite resting state networks being associated with a variety of cognitive abilities, it remains unclear how these local areas act in concert to express particular cognitive operations. Theoretical and empirical accounts indicate that large-scale resting state networks reconcile dual tendencies toward integration and segregation by operating in a metastable regime of their coordination dynamics. One proposal is that metastability confers important behavioural qualities by dynamically binding distributed local areas into large-scale neurocognitive entities. We tested this hypothesis by analysing fMRI data in a large cohort of healthy individuals (N=566) and comparing the metastability of the brain’s large-scale resting network architecture at rest and during the performance of several tasks. Task-based reasoning was principally characterised by high metastability in cognitive control networks and low metastability in sensory processing areas. Although metastability between resting state networks increased during task performance, cognitive ability was more closely linked to spontaneous activity. High metastability in the intrinsic connectivity of cognitive control networks was linked to novel problem solving (or fluid intelligence) but was less important in tasks relying on prior experience (or crystallised intelligence). Crucially, subjects with resting architectures similar or ‘pre-configured’ to a task-general arrangement demonstrated superior cognitive performance. Taken together, our findings support a critical linkage between the spontaneous metastability of the large-scale networks of the cerebral cortex and cognition.

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“…The minor difference in CN/AD results can be due to the different dataset used in Guo H. et al [7]. [11] 72.9% --Yu. et al (ADNI) [16] 84.8% --…”

Section: Classifier Performancementioning

confidence: 99%

“…We compared the performance of our classifier with several studies performed for classification of AD and its prodromal stage, MCI, using functional connectivity features [1,17,2,7,11,16]. By evaluating and selecting features leading to classification, we made the following novel contributions:…”

Section: Introductionmentioning

confidence: 99%

Decoding brain functional connectivity implicated in AD and MCI

Gupta

Chan

Rajapakse

2019

Preprint

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Deep neural networks have been demonstrated to extract high level features from neuroimaging data when classifying brain states. Identifying salient features characterizing brain states further refines the focus of clinicians and allows design of better diagnostic systems. We demonstrate this while performing classification of resting-state functional magnetic resonance imaging (fMRI) scans of patients suffering from Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We use a 5-layer feed-forward deep neural network (DNN) to derive relevance scores of input features and show that an empirically selected subset of features improves accuracy scores for patient classification. The common distinctive salient brain regions were in the uncus and medial temporal lobe which closely correspond with previous studies. The proposed methods have cross-modal applications with several neuropsychiatric disorders.Keywords: ADNI · Alzheimer's disease · decoding brain activations · deep neural networks · functional MRI · mild cognitive impairment

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Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture

Cited by 113 publications

References 53 publications

Decoding and mapping task states of the human brain via deep learning

Decoding and mapping task states of the human brain via deep learning

Metastable neural dynamics underlies cognitive performance across multiple behavioural paradigms

Decoding brain functional connectivity implicated in AD and MCI

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