UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000055336 |
|---|---|
| Receipt number | R000063239 |
| Scientific Title | Development of Machine Learning Algorithms Using Intracranial Electroencephalography and Exploration of Biomarkers for Mood Variability |
| Date of disclosure of the study information | 2024/10/01 |
| Last modified on | 2025/08/27 15:44:35 |
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Basic information
Public title
Development of Machine Learning Algorithms Using Intracranial Electroencephalography and Exploration of Biomarkers for Mood Variability
Acronym
NeuroAI for Mood Decoding
Scientific Title
Development of Machine Learning Algorithms Using Intracranial Electroencephalography and Exploration of Biomarkers for Mood Variability
Scientific Title:Acronym
NeuroAI for Mood Decoding
Region
| Japan |
Condition
Condition
Treatment-resistant epilepsy
Classification by specialty
| Neurology | Neurosurgery |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
In this study, we aim to identify biomarkers of mood variability using intracranial electrodes implanted during craniotomy or burr hole surgery, which are performed as part of clinical assessments in patients with refractory epilepsy who require surgical treatment. Specifically, brain potentials will be recorded at rest and during task performance using these intracranial electrodes. We will then develop a novel machine learning (artificial intelligence) algorithm to explore distinctive patterns and responses (biomarkers) associated with mood variability. The performance of the developed machine learning (AI) algorithm will be evaluated based on its accuracy in predicting the mood variability of patients. Through this approach, we aim to elucidate the biological underpinnings of mood variability and provide objective indicators and new approaches for the assessment and treatment of mood fluctuations.
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Accuracy of machine learning algorithms for predicting patient mood fluctuations
Key secondary outcomes
1. Identification of the machine learning algorithms, brain regions, frequency bands, and biomarkers that most contribute to predicting mood fluctuations in patients.
2. Verification of the differences found in item 1 across different subjects.
Base
Study type
Interventional
Study design
Basic design
Single arm
Randomization
Non-randomized
Randomization unit
Blinding
Open -no one is blinded
Control
Self control
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment
Intervention
No. of arms
1
Purpose of intervention
Prevention
Type of intervention
| Behavior,custom |
Interventions/Control_1
While recording intracranial EEG, patients are presented with images for a total of 6 minutes, followed by a few minutes to complete questionnaires before and after the presentation. This process is repeated once a day (totaling no more than 30 minutes per day) for up to 14 days. The images presented are from the OASIS (Open Affective Standardized Image Set) dataset. The OASIS dataset contains 900 color images across a wide range of themes, including humans, animals, objects, and landscapes. It is widely used in emotional research as stimuli to evoke different emotional states.
The two types of questionnaires used are:
1. IMS-12: A questionnaire commonly used in previous studies to assess the degree of depressive symptoms at the moment.
2. PHQ-9: A widely used questionnaire to evaluate the degree of depressive symptoms over the past week.
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
| 20 | years-old | <= |
Age-upper limit
| 66 | years-old | > |
Gender
Male and Female
Key inclusion criteria
Patients who are scheduled to have, or have already had, intracranial electrodes implanted with the purpose of identifying the distribution of epileptic foci for surgical treatment of refractory epilepsy, and who have consented to participate in this study.
Key exclusion criteria
1. Patients under 20 years old or over 66 years old.
2. Patients with severe intellectual disabilities who find it difficult to perform tasks.
3. Patients who are expected to have difficulty performing tasks or cooperating with the study due to complications or reduced physical strength following electrode placement surgery.
4. Other patients deemed unsuitable as study subjects by the principal investigator.
Target sample size
50
Research contact person
Name of lead principal investigator
| 1st name | Yuichi |
| Middle name | |
| Last name | Kubota |
Organization
TOKYO WOMEN'S MEDICAL UNIVERSITY, ADACHI MEDICAL CENTER
Division name
Department of Neurosurgery
Zip code
123-8558
Address
4-33-1 Kohoku, Adachi-ku, Tokyo
TEL
03-6807-1650
kubota.yuichi@twmu.ac.jp
Public contact
Name of contact person
| 1st name | Yuichi |
| Middle name | |
| Last name | Kubota |
Organization
TOKYO WOMEN'S MEDICAL UNIVERSITY, ADACHI MEDICAL CENTER
Division name
Department of Neurosurgery
Zip code
123-8558
Address
4-33-1 Kohoku, Adachi-ku, Tokyo
TEL
03-6807-1650
Homepage URL
kubota.yuichi@twmu.ac.jp
Sponsor or person
Institute
TOKYO WOMEN'S MEDICAL UNIVERSITY, ADACHI MEDICAL CENTER
Institute
Department
Personal name
Funding Source
Organization
None
Organization
Division
Category of Funding Organization
Other
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
Ethics Committee of Tokyo Women's Medical University
Address
8-1 Kawada-cho, Shinjuku-ku, Tokyo
Tel
8-1 Kawada-cho, Shinjuku-ku, Tokyo
8-1 Kawada-cho, Shinjuku-ku, Tokyo
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
| 2024 | Year | 10 | 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
| 2024 | Year | 09 | Month | 01 | Day |
Date of IRB
| 2024 | Year | 09 | Month | 05 | Day |
Anticipated trial start date
| 2024 | Year | 09 | Month | 01 | Day |
Last follow-up date
| 2028 | 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
| 2024 | Year | 08 | Month | 26 | Day |
Last modified on
| 2025 | Year | 08 | Month | 27 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000063239
