DE-NNs：基于动态证据神经网络的脑网络分析算法.pdfVIP

计算机研究与发展

Journal

of

Computer

Research

and

Development62（4）：888−904，2025

DE-NNs：基于动态证据神经网络的脑网络分析算法

1,211,21

侯涛丁卫平黄嘉爽鞠恒荣

1

（南通大学人工智能与计算机学院江苏南通226019）

2

（模式分析与机器智能工业和信息化部重点实验室（南京航空航天大学）南京211106）

（houtao_30@163.com）

DE-NNs:BrainNetworkAnalysisAlgorithmBasedonDynamicEvidenceNeural

Networks

1,2

1

1,2

1

Hou

Tao,

Ding

Weiping,

Huang

Jiashuang,

and

Ju

Hengrong

1

（SchoolofArtificialIntelligenceandComputerScience,NantongUniversity,Nantong,Jiangsu226019）

2

（MIITKeyLaboratoryofPatternAnalysisandMachineIntelligence(NanjingUniversityofAeronauticsandAstronautics),Nanjing211106）

AbstractDynamic

functional

connections

(dFCs)

can

be

regarded

as

a

process

of

dynamic

changes

in

multiple

time

windows

to

explore

the

changes

in

functional

connections

of

the

brain

in

different

time

periods.

It

has

been

widely

used

in

resting

state

functional

magnetic

resonance

imaging

(rs-fMRI)

analysis,

providing

a

new

perspective

and

strategy

for

the

diagnosis

of

brain

diseases.

However,

the

common

dynamic

brain

network

analysis

methods

can

not

effectively

use

the

potential

correlation

and

timing

between

dynamic

data,

and

ignore

the

uncertainty

factors

caused

by

the

inconsistent

data

quality

of

each

window.

Therefore,

we

propose

a

brain

network

analysis

algorithm

based

on

dynamic

evidence

neural

networks

(DE-NNs).

This

algorithm

designs

a

multi-view

evidence

acquisition

module

of

dynamic

brain

network,

which

treats

each

time

window

of

dynamic

brain

network

as

a

view.

Three

different

convolu