Pattern Analysis
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2021 ◽  
pp. 095679762110218
Ryuhei Ueda ◽  
Nobuhito Abe

Having an intimate romantic relationship is an important aspect of life. Dopamine-rich reward regions, including the nucleus accumbens (NAcc), have been identified as neural correlates for both emotional bonding with the partner and interest in unfamiliar attractive nonpartners. Here, we aimed to disentangle the overlapping functions of the NAcc using multivoxel pattern analysis, which can decode the cognitive processes encoded in particular neural activity. During functional MRI scanning, 46 romantically involved men performed the social-incentive-delay task, in which a successful response resulted in the presentation of a dynamic and positive facial expression from their partner and unfamiliar women. Multivoxel pattern analysis revealed that the spatial patterns of NAcc activity could successfully discriminate between romantic partners and unfamiliar women during the period in which participants anticipated the target presentation. We speculate that neural activity patterns within the NAcc represent the relationship partner, which might be a key neural mechanism for committed romantic relationships.

Ecology ◽  
2021 ◽  
Luke A. Yates ◽  
Barry W. Brook ◽  
Jessie C. Buettel

2021 ◽  
Vol 11 (1) ◽  
Hosu Kim ◽  
Jaehoon Jung ◽  
Young-Seok Cho ◽  
Joon Young Choi ◽  
Hyunju Park ◽  

AbstractSerum thyrotropin (TSH) level after thyroid surgery affects the prognosis of differentiated thyroid cancer (DTC). However, the effects of preoperative serum TSH levels on the prognosis of DTC remain contradictory. In this study, to better understand the relationship between preoperative TSH levels and the prognosis of DTC, we performed pattern analysis of prognostic factors of DTC according to preoperative serum TSH levels. We retrospectively reviewed the clinical records of patients who were diagnosed and treated for DTC at the Samsung Medical Center, between 1994 and 2016. We reviewed preoperative serum TSH levels and performed a pattern analysis with prognostic risk factors for DTC. For pattern analysis, TSH was divided into 10 groups of equal fractions (TSH decile). We found a linear association between preoperative TSH levels and extra-thyroidal extension and lymph node metastasis. However, primary tumor size and initial distant metastasis showed a bimodal peak, which was similar to the pattern of overall and disease-specific death. We found that preoperative TSH range which showed the lowest mortality rate was about 0.8 to 1.59 mIU/L, which are slightly lower normal TSH levels. Although there was no linear trend, the primary tumor size, initial distant metastasis, and mortality of DTC were closely related with preoperative TSH decile and they showed a bimodal pattern. The results obtained in this study provide additional information for understanding the association between preoperative TSH levels and DTC prognosis.

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Xin Xu

Purpose The purpose of this study is to address the limitations of existing target group distribution pattern analysis methods and identify subtle distribution differences within and between the groups with no pre-specified distribution features. Classical work generally concentrates on either the group distribution tendency or shape as a whole and simply ignores the subtle distribution differences within the group. Other work is constrained to pre-defined spatial distribution features. Design/methodology/approach This study proposes a novel algorithm for target group distribution pattern analysis. This study first transforms the group distribution data with uncertain measurements into a distributional image. Upon that, a bagged convolutional neural network model is constructed to discriminate the delicate group distribution patterns. Findings Experimental results indicate that our method is robust to target missing and location variance and scalable with dataset size. Our method has outperformed the benchmark machine learning methods significantly in pattern identification accuracy. Originality/value Our method is applicable for complex unmanned aerial vehicle distribution pattern identification.

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