UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000057333 |
|---|---|
| Receipt number | R000065532 |
| Scientific Title | Development of Mood Variation Biomarkers and Machine Learning Models Utilizing Intracranial EEG in Resting and Task States |
| Date of disclosure of the study information | 2025/04/01 |
| Last modified on | 2025/03/18 18:32:28 |
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Basic information
Public title
Intracranial EEG Mood State Decoding Study
Acronym
Intracranial EEG-based Mood Decoding
Scientific Title
Development of Mood Variation Biomarkers and Machine Learning Models Utilizing Intracranial EEG in Resting and Task States
Scientific Title:Acronym
SEEG-based Mood Decoding (SEEG-MD)
Region
| Japan |
Condition
Condition
epilepsy
Classification by specialty
| Neurosurgery |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
We collect intracranial EEG data from epilepsy patients during rest and while presenting emotionally evocative images, using machine learning to identify neural activity patterns associated with mood fluctuations. Specifically, by comparing deep learning models with traditional machine learning models, we aim to explore the feasibility of achieving more accurate and personalized mood fluctuation biomarkers.
Basic objectives2
Others
Basic objectives -Others
The study aims to collect intracranial EEG data from epilepsy patients during both resting states and while viewing emotionally evocative images. The goal is to analyze these signals using machine learning to identify neural activity patterns associated with mood fluctuations. To achieve this, deep learning models will be implemented and compared with traditional machine learning approaches to assess their effectiveness. By evaluating these models, the study seeks to determine the feasibility of developing highly accurate and personalized biomarkers for mood fluctuations. Additionally, the potential clinical applications of these biomarkers for personalized treatment and mood regulation in epilepsy patients will be explored.
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
The accuracy of machine learning algorithms for predicting patients' mood state
Key secondary outcomes
1.Identification of the most contributing machine learning algorithms, brain regions, frequency bands, and biomarkers for predicting mood fluctuations
2.Verification of intersubject differences in key evaluation metrics
Base
Study type
Interventional
Study design
Basic design
Single arm
Randomization
Non-randomized
Randomization unit
Blinding
Open -no one is blinded
Control
Uncontrolled
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment
Intervention
No. of arms
1
Purpose of intervention
Prevention
Type of intervention
| Other |
Interventions/Control_1
Record neural activity while presenting emotion-evoking images. The procedure is as follows:
- Each participant is shown 25 images from each of the three categories positive negative and neutral selected from the OASIS dataset forming one session of 25 x 3 images
- Each image is presented for 5 seconds After the presentation of each image a fixation point such as a small dot or cross is displayed at the center of the screen for 400 to 600 milliseconds before the next image appears
Interventions/Control_2
Interventions/Control_3
Interventions/Control_4
Interventions/Control_5
Interventions/Control_6
Interventions/Control_7
Interventions/Control_8
Interventions/Control_9
Interventions/Control_10
Eligibility
Age-lower limit
| 18 | years-old | <= |
Age-upper limit
| 80 | years-old | >= |
Gender
Male and Female
Key inclusion criteria
Patients who are scheduled for or have undergone intracranial electrode implantation to identify the distribution of epileptic foci for surgical treatment of refractory epilepsy and have consented to participate in this study.
Key exclusion criteria
Patients meeting any of the following criteria:
- Severe intellectual disability making task execution difficult
- Expected difficulty in task execution or study participation due to complications or physical decline associated with electrode implantation
- Deemed unsuitable as study subjects by the principal investigator
Target sample size
20
Research contact person
Name of lead principal investigator
| 1st name | Takashi |
| Middle name | |
| Last name | Morishita |
Organization
Fukuoka university
Division name
Department of Neurosurgery, Faculty of Medicine
Zip code
814-0180
Address
7-45-1 Nanakuma, Jonan-ku, Fukuoka City
TEL
092-801-1011
tmorishita@fukuoka-u.ac.jp
Public contact
Name of contact person
| 1st name | Takashi |
| Middle name | |
| Last name | Morishita |
Organization
Fukuoka university
Division name
Department of Neurosurgery, Faculty of Medicine
Zip code
814-0180
Address
7-45-1 Nanakuma, Jonan-ku, Fukuoka City
TEL
092-801-1011
Homepage URL
tmorishita@fukuoka-u.ac.jp
Sponsor or person
Institute
Fukuoka University
Institute
Department
Personal name
Funding Source
Organization
Neuroad, Inc.
Organization
Division
Category of Funding Organization
Profit organization
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
Fukuoka university
Address
7-45-1 Nanakuma, Jonan-ku, Fukuoka City
Tel
092-801-1011
tmorishita@fukuoka-u.ac.jp
Secondary IDs
Secondary IDs
NO
Study ID_1
Org. issuing International ID_1
Study ID_2
Org. issuing International ID_2
IND to MHLW
Institutions
Institutions
Other administrative information
Date of disclosure of the study information
| 2025 | Year | 04 | Month | 01 | Day |
Related information
URL releasing protocol
Publication of results
Unpublished
Result
URL related to results and publications
Number of participants that the trial has enrolled
Results
Results date posted
Results Delayed
Results Delay Reason
Date of the first journal publication of results
Baseline Characteristics
Participant flow
Adverse events
Outcome measures
Plan to share IPD
IPD sharing Plan description
Progress
Recruitment status
Open public recruiting
Date of protocol fixation
| 2025 | Year | 03 | Month | 18 | Day |
Date of IRB
| 2025 | Year | 03 | Month | 10 | Day |
Anticipated trial start date
| 2025 | Year | 03 | Month | 18 | Day |
Last follow-up date
| 2027 | Year | 03 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
Management information
Registered date
| 2025 | Year | 03 | Month | 19 | Day |
Last modified on
| 2025 | Year | 03 | Month | 18 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000065532
