Disentangling Spatial-Temporal Functional Brain Networks via Twin-Transformers

Yu, Xin; Zhang, Lu; Zhang, Lin; Lyu, Yanjun; Liu, Tianming; Zhu, Dajiang

doi:10.48550/arxiv.2204.09225

Cited by 2 publications

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“…They proved that the Transformer contributed to the early diagnosis of ASD, AD, depression, and other neurological diseases. Yu et al [184] proposed a twin-Transformer framework that built pairwise Transformers to analyze spatial and temporal information. The twin-Transformer framework deduced the functional network using self-supervised training, and was successfully applied in motor task.…”

Section: Fmri Modelingmentioning

confidence: 99%

Understanding the brain with attention: A survey of transformers in brain sciences

Chen,

Wang,

Chen

et al. 2023

Brain-X

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Owing to their superior capabilities and advanced achievements, Transformers have gradually attracted attention with regard to understanding complex brain processing mechanisms. This study aims to comprehensively review and discuss the applications of Transformers in brain sciences. First, we present a brief introduction of the critical architecture of Transformers. Then, we overview and analyze their most relevant applications in brain sciences, including brain disease diagnosis, brain age prediction, brain anomaly detection, semantic segmentation, multi‐modal registration, functional Magnetic Resonance Imaging (fMRI) modeling, Electroencephalogram (EEG) processing, and multi‐task collaboration. We organize the model details and open sources for reference and replication. In addition, we discuss the quantitative assessments, model complexity, and optimization of Transformers, which are topics of great concern in the field. Finally, we explore possible future challenges and opportunities, exploiting some concrete and recent cases to provoke discussion and innovation. We hope that this review will stimulate interest in further research on Transformers in the context of brain sciences.

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Section: Fmri Modelingmentioning

confidence: 99%

Understanding the brain with attention: A survey of transformers in brain sciences

Chen,

Wang,

Chen

et al. 2023

Brain-X

View full text Add to dashboard Cite

show abstract

“…These visual models are pre-trained on massive image datasets and possess the ability to understand the content of images and extract rich semantic information. Examples of pre-trained visual models include ViT [29], Swin Transformer [30], VideoMAE V2 [31] and others [32][33][34][35][36][37][38][39][40][41]. By learning representations and features from a large amount of data, these models enable computers to more effectively comprehend and analyze images for diverse downstream applications [42][43][44][45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

School Mathematics Textbooks in China

Wang¹

2015

East China Normal University Scientific Reports

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Let µ and ν be two probability measures on R d , where µ(dx) = e −V (x) dx R d e −V (x) dx for some V ∈ C 1 (R d ). Explicit sufficient conditions on V and ν are presented such that µ * ν satisfies the log-Sobolev, Poincaré and super Poincaré inequalities. In particular, if V (x) = λ|x| 2 for some λ > 0 and ν(e λθ|•| 2 ) < ∞ for some θ > 1, then µ * ν satisfies the log-Sobolev inequality. This improves and extends the recent results on the log-Sobolev inequality derived in [20] for convolutions of the Gaussian measure and compactly supported probability measures. On the other hand, it is well known that the log-Sobolev inequality for µ * ν implies ν(e ε|•| 2 ) < ∞ for some ε > 0. RésuméDes conditions explicites suffisantes sur V et ν sont présentées telles que µ * ν satisfait des inégalités de Sobolev logarithmique, de Poincaré et de super-Poincaré. En particulier, si V (x) = λ|x| 2 pour quelque λ > 0 et ν(e λθ|•| 2 ) < ∞ avec θ > 1, alors µ * ν satisfait l'inégalité de Sobolev logarithmique. Cela améliore et étend des résultats récents sur l'inégalité de Sobolev logarithmique obtenus dans [20] pour des convolutions de la mesure de Gauss et des mesures de probabilité à support compact. D'autre part, il est bien connu que l'inégalité de Sobolev logarithmique pour µ * ν implique ν(e ε|•| 2 ) < ∞ pour quelque ε > 0.

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Disentangling Spatial-Temporal Functional Brain Networks via Twin-Transformers

Cited by 2 publications

References 0 publications

Understanding the brain with attention: A survey of transformers in brain sciences

Understanding the brain with attention: A survey of transformers in brain sciences

School Mathematics Textbooks in China

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