Clinical trial NCT03670823: Prediction of SSRI Treatment in Major Depression.
Official title: A Combination of Innovative Technologies: EEG, Eye Tracking Device and fMRI in Order to Predict the Success of SSRI Treatment in Patients With Major Depression.
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Major Depression
Keywords: Major Depression
EEG
Eye Tracker
fMRI
MRI
Resting State Connectivity
Cognition
Lead sponsor: ElMindA Ltd
Sponsor class: INDUSTRY
Collaborators: name: Sheba Medical Center
name: Hebrew University of Jerusalem
First posted: 2018-09-14
Enrollment: 100
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Study population will include 2 groups of subjects: healthy subjects and patients with major depression

Brief summary: This project will combine the data collected from EEG, Eye tracking, structural and functional MRI scans and neuropsychological performance from patients with major depression receiving SSRI treatment. The purpose of this research is to predict the success of the SSRI treatment and to categorize patients into sub-groups according to similar patterns of brain activation to personalize treatment.

Detailed description: Major depression is a mood disorder affecting 350 million people worldwide. The disorder is characterized by depressed mood, anhedonia, decreased quality of life, deficits in cognitive functions and even suicide thoughts. Treatment of depression is often a long process and includes taking different types and quantities of medications. Therefore, there is a need to predict the success of the SSRI treatment. Our research will examine the outcomes of the combined technologies: BNA (EEG), Eye-tracker, structural and functional MRI scans and neuropsychology tasks in patients with depression while receiving SSRI treatment. The purpose of the research is to track biomarkers and other measures, which will allow predicting the SSRI treatment's success within 4 weeks instead of 8 weeks. In addition, the investigators will attempt to categorize patients into different subgroups according to their brain activation and eye movements. This division into subgroups may contribute to the understanding of the mechanisms that account for the responsiveness to SSRI treatment and to the possibility of targeting patients with depression towards a particular treatment. From this research, the investigators aim to personalize the treatment of depression, make it more efficient and reduce the amount of time for the patient to reach an optimal responsiveness.

Eligibility criteria: Inclusion Criteria:

1. Patients 18-65 years old
2. Male and female
3. Ability to comprehend and sign informed consent
4. DSM-5 diagnosis with MINI 7.0.2 (healthy subjects need to be ruled out)

Inclusion Criteria for patients with depression:

1. DSM-5 diagnosis
2. 0-2 failed treatments
3. Patients which will start SSRI treatment

Exclusion Criteria:

1. unconsciousness
2. Pregnancy or nursing
3. Cardiovascular instability
4. Metabolic instability (water, electrolytes, sugar)
5. Fever or evidence of microbiological pollutant
6. Deafness or blindness
7. Schizophrenia
8. Addiction disorders
9. Eating disorders
10. Bi-polar disorder
11. Cognitive deficits
12. Start a new psychotherapy during the research
13. Unable to enter the MRI scanner

Primary outcomes:
measure: EEG responses to cognitive tasks in combination with the Eye-tracker Device.
timeFrame: 2 years
Secondary outcomes:
measure: Resting state connectivity analysis
timeFrame: 2 years
measure: Examine correlations between the different methods
timeFrame: 2 years
measure: EEG brain activation to cognitive tasks
timeFrame: 2 years
measure: Eye-tracking tasks
timeFrame: 2 years
measure: Examine MRI structural changes
timeFrame: 2 years
measure: Cognitive scores on CANTAB (computerized cognitive assessments)
timeFrame: 2 years
Other outcomes:
Locations:
city: Ramat Gan
country: Israel
facility: Sheba Medical CENTER
Study officials:
References:
citation: Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, Fetcho RN, Zebley B, Oathes DJ, Etkin A, Schatzberg AF, Sudheimer K, Keller J, Mayberg HS, Gunning FM, Alexopoulos GS, Fox MD, Pascual-Leone A, Voss HU, Casey BJ, Dubin MJ, Liston C. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med. 2017 Jan;23(1):28-38. doi: 10.1038/nm.4246. Epub 2016 Dec 5.
citation: Ogawa T, Sekino H, Uzura M, Sakamoto T, Taguchi Y, Yamaguchi Y, Hayashi T, Yamanaka I, Oohama N, Imaki S. Comparative study of magnetic resonance and CT scan imaging in cases of severe head injury. Acta Neurochir Suppl (Wien). 1992;55:8-10. doi: 10.1007/978-3-7091-9233-7_3.
citation: Raichle ME, Snyder AZ. A default mode of brain function: a brief history of an evolving idea. Neuroimage. 2007 Oct 1;37(4):1083-90; discussion 1097-9. doi: 10.1016/j.neuroimage.2007.02.041. Epub 2007 Mar 6.
citation: Rohden AI, Benchaya MC, Camargo RS, Moreira TC, Barros HMT, Ferigolo M. Dropout Prevalence and Associated Factors in Randomized Clinical Trials of Adolescents Treated for Depression: Systematic Review and Meta-analysis. Clin Ther. 2017 May;39(5):971-992.e4. doi: 10.1016/j.clinthera.2017.03.017. Epub 2017 May 2.