inference network
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2022 ◽  
Vol 27 (01) ◽  
Author(s):  
Kevin Chew Figueroa ◽  
Bofan Song ◽  
Sumsum Sunny ◽  
Shaobai Li ◽  
Keerthi Gurushanth ◽  
...  

2021 ◽  
Author(s):  
Stephanie Noble ◽  
Mandy Mejia ◽  
Andrew Zalesky ◽  
Dustin Scheinost

Inference in neuroimaging commonly occurs at the level of "clusters" of neighboring voxels or connections, thought to reflect functionally specific brain areas. Yet increasingly large studies reveal effects that are shared throughout the brain, suggesting that reported clusters may only reflect the "tip of the iceberg" of underlying effects. Here, we empirically compare power of traditional levels of inference (edge and cluster) with broader levels of inference (network and whole-brain) by resampling functional connectivity data from the Human Connectome Project (n=40, 80, 120). Only network- and whole brain-level inference attained or surpassed "adequate" power (β =80%) to detect an average effect, with almost double the power for network- compared with cluster-level procedures at more typical sample sizes. Likewise, effects tended to be widespread, and more widespread pooling resulted in stronger magnitude effects. Power also substantially increased when controlling FDR rather than FWER. Importantly, there may be similar implications for task-based activation analyses where effects are also increasingly understood to be widespread. However, increased power with broader levels of inference may diminish the specificity to localize effects, especially for non-task contexts. These findings underscore the benefit of shifting the scale of inference to better capture the underlying signal, which may unlock opportunities for discovery in human neuroimaging.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Long Huang ◽  
Shaohua Xu ◽  
Kun Liu ◽  
Ruiping Yang ◽  
Lu Wu

A fuzzy radial basis adaptive inference network (FRBAIN) is proposed for multichannel time-varying signal fusion analysis and feature knowledge embedding. The model which combines the prior signal feature embedding mechanism of the radial basis kernel function with the rule-based logic inference ability of fuzzy system is composed of a multichannel time-varying signal input layer, a radial basis fuzzification layer, a rule layer, a regularization layer, and a T-S fuzzy classifier layer. The dynamic fuzzy clustering algorithm was used to divide the sample set pattern class into several subclasses with similar features. The fuzzy radial basis neurons (FRBNs) were defined and used as parameterized membership functions, and typical feature samples of each pattern subclass were used as kernel centers of the FRBN to realize the embedding of the diverse prior feature knowledge and the fuzzification of the input signals. According to the signal categories of FRBN kernel centers, nodes in the rule layer were selectively connected with nodes in the FRBN layer. A fuzzy multiplication operation was used to achieve synthesis of pattern class membership information and establishment of fuzzy inference rules. The excitation intensity of each rule was used as the input of T-S fuzzy classifier to classify the input signals. The FRBAIN can adaptively establish fuzzy set membership functions, fuzzy inference, and classification rules based on the learning of sample set, realize structural and data constraints of the model, and improve the modeling properties of imbalanced datasets. In this paper, the properties of FRBAIN were analyzed and a comprehensive learning algorithm was established. Experimental validation was performed with classification diagnoses from four complex cardiovascular diseases based on 12-lead ECG signals. Results demonstrated that, in the case of small-scale imbalanced datasets, the proposed method significantly improved both classification accuracy and generalizability comparing with other methods in the experiment.


Author(s):  
Jingwen Duan ◽  
Weidong Min ◽  
Deyu Lin ◽  
Jianfeng Xu ◽  
Xin Xiong

2021 ◽  
Vol 426 ◽  
pp. 47-57
Author(s):  
Chunlei Wu ◽  
Jie Wu ◽  
Haiwen Cao ◽  
Yiwei Wei ◽  
Leiquan Wang

Author(s):  
Yiyi Zhou ◽  
Rongrong Ji ◽  
Gen Luo ◽  
Xiaoshuai Sun ◽  
Jinsong Su ◽  
...  

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