Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks

Zhao, Feng; Chen, Zhiyuan; Rekik, Islem; Lee, Seong Whan; Shen, Dinggang

doi:10.3389/fnins.2020.00258

Cited by 57 publications

(44 citation statements)

References 53 publications

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“…In addition to the indicators above, we also display the improvement of fusion results compared to the single D-FCN in classification accuracy with the Promote(Lo/Ho) indicator (e.g., 2.4/2.0 represents that the fusion network improved 2.4 compared to Lo-D-FCN and 2.0 compared to Ho-D-FCN in the classification accuracy). Based on Table 3, we derive the following conclusions: (1) the feature fusion result consistently produces better results than features derived from a single network, i.e., the promote indicators are all greater than 0, which is similar to the conclusions of previous studies (Zhao et al, 2020); (2) our UPFFS strategy has better results and greater improvement compared with other strategies, which indicates that our feature selection framework can select more complementary discriminative features from Lo-D-FCN and Ho-D-FCN, thus the SVM classifier has a better learning and generalizability to unseen samples.…”

Section: Classification Performance Based On the Fusion Of Lo-d-fcn Asupporting

confidence: 83%

“…Feature Extraction. In this experiment, we utilized seven center-distance feature extraction methods for extracting the dynamic variation of FC among multiple ROIs along the scanning time ( Zhao et al, 2020 ). In particular, for Lo-D-FCN, the FC sequence of the i -th and the j -th ROI are defined as ρ i , j = [ρ i , j (1),ρ i , j (2),…,ρ i , j ( K )].…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Classification Results. For Ho-D-FCN construction, we select W = 60, S = 10 as the construction parameters according to the previous research ( Zhao et al, 2020 ). Since feature extraction and classifier learning are consistent with those above, we will not repeat them here.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Table 1 reports the best results of ASD identification with SVFS, CFS, and our UPFFS strategy in Lo-D-FCN where SVFS Lo denotes the classification model using Lo-D-FCN and SVFS 1 https://en.wikipedia.org/wiki/Sensitivity_and_specificity strategy. Specifically, for Lo-D-FCN construction, we select W = 60, S = 2 (e.g., the sliding-window length W equals to 60 and step size S equals to 2) as the optimal construction parameters according to the previous study (Zhao et al, 2020). The best results are highlighted in bold.…”

Section: Comparison Of Feature Selection Strategies Based On Lo-d-fcnmentioning

confidence: 99%

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A Novel Unit-Based Personalized Fingerprint Feature Selection Strategy for Dynamic Functional Connectivity Networks

et al. 2021

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The sliding-window-based dynamic functional connectivity networks (SW-D-FCN) derive from resting-state functional Magnetic Resonance Imaging has become an increasingly useful tool in the diagnosis of various neurodegenerative diseases. However, it is still challenging to learn how to extract and select the most discriminative features from SW-D-FCN. Conventionally, existing methods opt to select a single discriminative feature set or concatenate a few more from the SW-D-FCN. However, such reductionist strategies may fail to fully capture the personalized discriminative characteristics contained in each functional connectivity (FC) sequence of the SW-D-FCN. To address this issue, we propose a unit-based personalized fingerprint feature selection (UPFFS) strategy to better capture the most discriminative feature associated with a target disease for each unit. Specifically, we regard the FC sequence between any pair of brain regions of interest (ROIs) is regarded as a unit. For each unit, the most discriminative feature is identified by a specific feature evaluation method and all the most discriminative features are then concatenated together as a feature set for the subsequent classification task. In such a way, the personalized fingerprint feature derived from each FC sequence can be fully mined and utilized in classification decision. To illustrate the effectiveness of the proposed strategy, we conduct experiments to distinguish subjects diagnosed with autism spectrum disorder from normal controls. Experimental results show that the proposed strategy can select relevant discriminative features and achieve superior performance to benchmark methods.

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Section: Classification Performance Based On the Fusion Of Lo-d-fcn Asupporting

confidence: 83%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Comparison Of Feature Selection Strategies Based On Lo-d-fcnmentioning

confidence: 99%

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A Novel Unit-Based Personalized Fingerprint Feature Selection Strategy for Dynamic Functional Connectivity Networks

et al. 2021

Self Cite

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“…A generative method refers to describing an object to be tracked in a video through an object-representation method in computer vision and then extracting a corresponding object feature from a current frame containing the object to establish an object template [8][9]. The method then searches the subsequent frames for the area most similar to the object template and gradually iterates to finally achieve the positioning and tracking of the object in the subsequent frames [10][11][12][13][14][15][16][17]. A discriminant method refers to applying both the object template and background information to the tracking system.…”

Section: Introductionmentioning

confidence: 99%

A Novel Incremental Multi-Template Update Strategy for Robust Object Tracking

Xie

Liu

et al. 2020

IEEE Access

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In the field of correlation filter object tracking, the traditional template-update method easily causes template drift, so it performs poorly in complex scenes. To enhance the robustness of the template, a novel incremental multi-template update strategy is proposed in this paper. We find that reliability varies among all historical filters and that highly reliable filters are key to achieving accurate tracking. The incremental multi-template update strategy combines the local maximum-reliability filter template with the historical filter template incrementally, which is obviously different from the traditional update method. We apply this strategy to two trackers with superior performance. The experimental results of three test benchmarks, including the VOT2016, OTB100 and UAV123 datasets, show that the performance of our trackers is superior to that of the state-of-the-art trackers.

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Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study

Cao

et al. 2022

Human Brain Mapping

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Previous studies have explored resting‐state functional connectivity (rs‐FC) of the amygdala in patients with autism spectrum disorder (ASD). However, it remains unclear whether there are frequency‐specific FC alterations of the amygdala in ASD and whether FC in specific frequency bands can be used to distinguish patients with ASD from typical controls (TCs). Data from 306 patients with ASD and 314 age‐matched and sex‐matched TCs were collected from 28 sites in the Autism Brain Imaging Data Exchange database. The bilateral amygdala, defined as the seed regions, was used to perform seed‐based FC analyses in the conventional, slow‐5, and slow‐4 frequency bands at each site. Image‐based meta‐analyses were used to obtain consistent brain regions across 28 sites in the three frequency bands. By combining generative adversarial networks and deep neural networks, a deep learning approach was applied to distinguish patients with ASD from TCs. The meta‐analysis results showed frequency band specificity of FC in ASD, which was reflected in the slow‐5 frequency band instead of the conventional and slow‐4 frequency bands. The deep learning results showed that, compared with the conventional and slow‐4 frequency bands, the slow‐5 frequency band exhibited a higher accuracy of 74.73%, precision of 74.58%, recall of 75.05%, and area under the curve of 0.811 to distinguish patients with ASD from TCs. These findings may help us to understand the pathological mechanisms of ASD and provide preliminary guidance for the clinical diagnosis of ASD.

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Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks

Cited by 57 publications

References 53 publications

A Novel Unit-Based Personalized Fingerprint Feature Selection Strategy for Dynamic Functional Connectivity Networks

A Novel Unit-Based Personalized Fingerprint Feature Selection Strategy for Dynamic Functional Connectivity Networks

A Novel Incremental Multi-Template Update Strategy for Robust Object Tracking

Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study

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