Affective Computing, Artificial Intelligence, Bipolar Disorder, Manic, Multi- Modal Analysis, Neurocognition, Treatment Resistant Disorders
Conditions
Keywords
Bipolar and Related Disorders, Mood Disorders, Mental Disorders, Behavioral Symptoms, Cognitive functions, Simulation
Brief summary
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.
Detailed description
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.
Interventions
DRUGOngoing treatment for bipolar mania
Prescribed by the following doctor during hospitalization and after discharge
DIAGNOSTIC_TESTAudiovisual recording during guided presentation
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
Sponsors
Namik Kemal University
Bosphorus University
Istanbul Saglik Bilimleri University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 65 Years
Healthy volunteers
Yes
Inclusion criteria
* diagnosis of BD type I, manic episode according to DSM-5 \[10\] given by the following doctor, * being informed of the purpose of the study and having given signed consent before enrollment.
Exclusion criteria
* being younger than 18 years or older than 60 years, * showing low mental capacity during the interview * expression of hallucinations and disruptive behaviors during the interview, * presence of severe organic disease, * presence of any organic disease that may affect cognition * having less than five years of public education * diagnosis of substance or alcohol abuse in the last three months (except nicotine and caffeine) * presence of cerebrovascular disorder, head trauma with longer duration of loss of consciousness, severe hemorrhage and dementia, * having electroconvulsive therapy in the last one year. For the healthy control group, the following additional criteria were considered for exclusion * presence of family history of mood or psychotic disorder, * presence of psychiatric disorder during interview or in the past.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Treatment response | from baseline until 3rd month | 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 1. Remission: Yt \<= 7 2. Hypomania: 7 \< Yt \< 20 3. Mania: Yt \>= 20. |
| Changes in visual features | Baseline and 3rd month | 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 |
| Changes in audio features | Baseline and 3rd month | 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. |
| in Stop Signal Test | Baseline and 3rd month | (milisecond) SST- Succesful Stop Ratio SST- go- Reaction Time SST- Stop Signal Delay SST- Stop Signal Reaction Time SST- Total Correct |
| Changes in Rapid Visual Processing | Baseline and 3rd month | 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) |
| in Cambridge Gambling Task | Baseline and 3rd month | (milisecond) CGT Quality of decision making CGT Deliberation time CGT Delay aversion CGT Overall proportion bet |
| Changes in Emotion Recognition Test | Baseline and 3rd month | (rate of emotion prediction) Percent and numbers correct/incorrect prediction |
Countries
Turkey (Türkiye)
Outcome results
None listed