Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| Namik Kemal University | OTHER |
| Bosphorus University | UNKNOWN |
Not provided
Not provided
Not provided
Not provided
The aim of this study is to show the physiological changes during manic episode in bipolar mania how much they differentiate from remission and healthy control. Relation of audio-visual features as physiological changes and cognitive functions and clinical variables will be searched. The aim is to find biologic markers for predictors of treatment response via machine learning techniques to be able to reduce treatment resistance and give an idea for personalized treatment of bipolar patients.
The objective of this research protocol is to find audio-visual features which differentiates bipolar mani/ remission/ health/ simulation and predicts treatment response earlier and detect neurocognitive changes during mania/ remission and difference from the healthy control. During hospitalization in every follow up day (0th- 3rd- 7th- 14th- 28th day) and after discharge on the 3rd month, presence of depressive and manic features for patients was evaluated using Young Mania Rating Scale(YMRS) and Montgamery- Asberg Depresyon Scale (MADRS). Audiovisual recording is done by a video camera in every follow up day for patients and for healthy controls which includes also depression and mania simulation. Cambridge Neurophysiological Assessment Battery (CANTAB) were administered to both groups( for patients both in the manic phase and in the remission) to assess neurocognitive functions.
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Bipolar Mania | Diagnosis of BD type I, manic episode according to DSM-5 given by the following doctor |
| |
| Healthy Control | showing normal mental capacity during interview, have more than five years of public education, no diagnosis of substance or alcohol abuse in the last three months (except nicotine and caffeine, no presence of family history of mood or psychotic disorder, and no presence of psychiatric disorder during interview or in the past, no presence of severe organic disease. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Ongoing treatment for bipolar mania | Drug | Prescribed by the following doctor during hospitalization and after discharge |
|
| Measure | Description | Time Frame |
|---|---|---|
| Treatment response | The proportion of Young Mania Rating Scale(YMRS) score ( at baseline to 3rd- 7th- 14th- 28th day and 3rd month ( Baseline scale/ Follow-up day scale) YMRS score utilized rating scales to assess manic symptoms ranged between 0-76
| from baseline until 3rd month |
| Changes in visual features | Functionals of appearance descriptors extracted from fine-tuned Deep Convolutional Neural Networks (DCNN), geometric features obtained using tracked facial landmarks (Unweighted Average Recall) Geometric frame level 23 geometric features and apperance descriptors 4096 dimensional features from the last convolutional layer of the FER fine-tuned CNN which are summarized via mean and range functionals over sub-clips and the decisions are voted at video level, an UAR performance is obtained. Feature vectors extracted from video is modelled using Partial Least Squares (PLS) regression and Extreme Learning Machines classifiers Unweighted Average Recall (UAR), which is mean of class-wise recall scores, is commonly used as performance measure, instead of accuracy, which can be misleading in the case of class-imbalance | Baseline and 3rd month |
| Changes in audio features | Functionals of acoustic features extracted via openSMILE tool (Unweighted Average Recall) Acoustic low level descriptors including prosody (energy, Fundamental Frequency - F0), voice quality features (jitter and shimmer), Mel Frequency Cepstral Coefficients, which are commonly used in many speech technologies from audio, we use the 76-dimensional standard feature set used in the INTERSPEECH 2010 paralinguistic challenge as baseline. The second is our proposed set of 10 functionals, Mean, standard deviation, curvature coefficient , slope and offset , minimum value and its relative position, maximum value and its relative position, and the range Feature vectors extracted from audio is modelled using Partial Least Squares (PLS) regression and Extreme Learning Machines classifiers. | Baseline and 3rd month |
| in Stop Signal Test |
Not provided
Not provided
Inclusion Criteria:
Exclusion Criteria:
For the healthy control group, the following additional criteria were considered for exclusion
Not provided
Not provided
Bipolar mania patients from the inpatient service Healthy controls from the community around hospital
Not provided
| Name | Affiliation | Role |
|---|---|---|
| Heysem Kaya, Ass Prof | Namik Kemal University | Principal Investigator |
| Ali A Salah, Ass Prof | Bogazici University | Study Chair |
| Hüseyin Gülec, Ass Prof | İstanbul Saglık Bilimleri University | Study Chair |
| Elvan Ciftci, MD PhD | İstanbul Saglık Bilimleri University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| SBU Erenkoy Mental State Hospital | Istanbul | 34736 | Turkey (Türkiye) |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| Background | Ciftci E, Kaya H, Gulec H, Salah AA (2018) The Turkish Audio-Visual Bipolar Disorder Corpus. In: 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia), pp 1-6. IEEE. Available at: https://ieeexplore.ieee.org/document/8470362/ | ||
| Background | Çiftçi E, Kaya H, Güleç H and Salah AA Potential audio treatment predictors for bipolar mania Psychiatry and Clinical Psychopharmacology Supplementary |
Not provided
Not provided
The database will be introduced in AVEC 2018 competition and shared with participants. After the competition the databese will be shared under the special EULA permission.
5 years
EULA permission
Not provided
| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot | Yes | No | No | Study Protocol | Aug 1, 2017 | Oct 29, 2018 |
Not provided
Not provided
Not provided
Not provided
| Audiovisual recording during guided presentation | Diagnostic Test | Seven tasks such as explaining the reason to come to hospital/participate in the activity, describing happy and sad memories, counting up to thirty, explaining two emotion eliciting pictures |
|
(milisecond) SST- Succesful Stop Ratio SST- go- Reaction Time SST- Stop Signal Delay SST- Stop Signal Reaction Time SST- Total Correct |
| Baseline and 3rd month |
| Changes in Rapid Visual Processing | RVP A' (A prime) is the signal detection measure of sensitivity to the target, regardless of response tendency (range 0.00 to 1.00; bad to good). RVP B'' (B double prime) is the signal detection measure of the strength of trace required to elicit a response (range -1.00 to +1.00) | Baseline and 3rd month |
| in Cambridge Gambling Task | (milisecond) CGT Quality of decision making CGT Deliberation time CGT Delay aversion CGT Overall proportion bet | Baseline and 3rd month |
| Changes in Emotion Recognition Test | (rate of emotion prediction) Percent and numbers correct/incorrect prediction | Baseline and 3rd month |
| Prot_000.pdf |
| SAP | No | Yes | No | Statistical Analysis Plan | Aug 1, 2017 | Oct 29, 2018 | SAP_001.pdf |
| ID | Term |
|---|---|
| D001714 | Bipolar Disorder |
| D000068105 | Bipolar and Related Disorders |
| D019964 | Mood Disorders |
| D001523 | Mental Disorders |
| D001526 | Behavioral Symptoms |
| ID | Term |
|---|---|
| D001519 | Behavior |
Not provided
Not provided