A Brain Network Constructed on an L1-Norm Regression Model Is More Sensitive in Detecting Small World Network Changes in Early AD
Most previous imaging studies have used traditional Pearson correlation analysis to construct brain networks. This approach fails to adequately and completely account for the interaction between adjacent brain regions. In this study, we used the L1-norm linear regression model to test the small-worl...
Saved in:
| Main Authors: | Hao Liu, Haimeng Hu, Huiying Wang, Jiahui Han, Yunfei Li, Huihui Qi, Meimei Wang, Sisi Zhang, Huijin He, Xiaohu Zhao |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Neural Plasticity |
| Online Access: | http://dx.doi.org/10.1155/2020/9436406 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Erratum to “More Benefits of Adding Sparse Random Links to Wireless Networks:
Yet Another Case for Hybrid Networks”
by: Gunes Ercal
Published: (2012-11-01) -
Young children conform more to norms than to preferences.
by: Leon Li, et al.
Published: (2021-01-01) -
Constructive Analysis for Least Squares Regression with Generalized K-Norm Regularization
by: Cheng Wang, et al.
Published: (2014-01-01) -
Ad hoc network
by: Kelsie Nabben, et al.
Published: (2022-04-01) -
On the computable cross norm in tensor networks and holography
by: Alexey Milekhin, et al.
Published: (2025-02-01)