| Academic Year |
2025Year |
School/Graduate School |
Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Informatics and Data Science Program |
| Lecture Code |
WSN22601 |
Subject Classification |
Specialized Education |
| Subject Name |
脳情報科学特論 |
Subject Name
（Katakana） |
ノウジョウホウカガクトクロン |
Subject Name in
English |
Advanced Computational Neuroscience |
| Instructor |
FUKUSHIMA MAKOTO |
Instructor
(Katakana) |
フクシマ マコト |
| Campus |
Higashi-Hiroshima |
Semester/Term |
1st-Year, First Semester, 2Term |
| Days, Periods, and Classrooms |
(2T) Weds5-8 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Online (on-demand) |
| |
| Credits |
2.0 |
Class Hours/Week |
4 |
Language of Instruction |
B
:
Japanese／English |
| Course Level |
6
:
Graduate Advanced
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
|
| Keywords |
Brain Networks, Complex Networks, Graph Theory, Network Analysis, Network Neuroscience, Network Science |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Class Objectives
/Class Outline |
In this course, participants will learn the basics of network analysis and their application to brain networks. The objective of this course is to acquire the knowledge to effectively apply network analysis methods to brain connectivity data. |
| Class Schedule |
Lesson 1: Class Orientation
Lesson 2: An Introduction to Brain Networks
Lesson 3: Nodes and Edges
Lesson 4: Connectivity Matrices and Brain Graphs
Lesson 5: Node Degree and Strength
Lesson 6: Centrality and Hubs
Lesson 7: Components, Cores, and Clubs
Lesson 8: Summary of Lessons 3-7
Lesson 9: Paths, Diffusion, and Navigation
Lesson 10: Motifs, Small Worlds, and Network Economy
Lesson 11: Modularity
Lesson 12: Null Models
Lesson 13: Statistical Connectomics
Lesson 14: Summary of Lessons 9-13
Lesson 15: Advanced Topics
Assignments in Lessons 3-7 and 9-13 |
Text/Reference
Books,etc. |
[Textbook] Alex Fornito, Andrew Zalesky, Edward Bullmore. Fundamentals of Brain Network Analysis. Academic Press, 2016. |
PC or AV used in
Class,etc. |
Microsoft Teams, Other (see [More Details]) |
| (More Details) |
Lecture slides and videos. Information on how to access these materials will be posted on the class bulletin board. |
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Careful reading of the textbook will help participants understand the lecture content more deeply. |
| Requirements |
|
| Grading Method |
Evaluation is based on the grades of all 10 assignments. |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
[Details of Languages Used] Lecture slides and videos are in English. Japanese is used only for supplemental purposes. |
| Other |
|
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |