SBU Erenkoy Mental State Hospital
Istanbul, 34736, Turkey (Türkiye)
NCT Number: NCT03857438
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.
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Notify Me18 year–65 year
All sexes
Observational
Istanbul, 34736, Turkey (Türkiye)
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.
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
For the healthy control group, the following additional criteria were considered for exclusion
Prescribed by the following doctor during hospitalization and after discharge
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
Time frame: 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
Time frame: 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
Time frame: 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.
Time frame: 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
Time frame: 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)
Time frame: Baseline and 3rd month
(milisecond) CGT Quality of decision making CGT Deliberation time CGT Delay aversion CGT Overall proportion bet
Time frame: Baseline and 3rd month
(rate of emotion prediction) Percent and numbers correct/incorrect prediction
Istanbul Saglik Bilimleri University
Other
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