Prediction of SSRI Treatment in Major Depression.

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.

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.

Study Overview

• Status: Unknown
• Conditions: Major Depression
• Intervention / Treatment: Device: SIEMENS PRISMA MRI

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.

• Study Type: Observational
• Enrollment (Anticipated): 100

Contacts and Locations

Study Locations

• Israel
  • Ramat Gan, Israel, 52621
    • Recruiting
    • Sheba Medical Center
    • Contact: Sarel Shlomo; Phone Number: 03-5303075; Email: nivdakim.sheba@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 16 years to 63 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

Study population will include 2 groups of subjects: healthy subjects and patients with major depression

Description

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

Study Plan

How is the study designed?

Design Details

• Observational Models: Case-Control
• Time Perspectives: Prospective

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Healthy subjects; 50 healthy subjects for a control group	Device: SIEMENS PRISMA MRI; Collect data on brain activation from different methods; Other Names: BNA (EEG) Technology, Eyetracking device
Patients with Major Depression; 50 patients with major depression for a research group	Device: SIEMENS PRISMA MRI; Collect data on brain activation from different methods; Other Names: BNA (EEG) Technology, Eyetracking device

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
EEG responses to cognitive tasks in combination with the Eye-tracker Device.	Categorize patients into subgroups according to combined measures of EEG and Eye	2 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Resting state connectivity analysis	Examine the difference in resting state connectivity between the groups.	2 years
Examine correlations between the different methods	Examine correlations between the different methods EEG, Eye-tracking and fMRI	2 years
EEG brain activation to cognitive tasks	Categorize patients into subgroups according to similar brain activity	2 years
Eye-tracking tasks	Categorize patients into subgroups according to similar patterns of eye movements.	2 years
Examine MRI structural changes	Compare structural changes between the groups (patients with depression, healthy subjects).	2 years
Cognitive scores on CANTAB (computerized cognitive assessments)	Examine the difference in responses to different cognitive exams between the groups	2 years

Collaborators and Investigators

• Sponsor: ElMindA Ltd
• Collaborators: Sheba Medical Center; Hebrew University of Jerusalem

Publications and helpful links

General Publications

• 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. Erratum In: Nat Med. 2017 Feb 7;23 (2):264.
• 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.
• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (Actual): March 1, 2019
• Primary Completion (Anticipated): October 1, 2020
• Study Completion (Anticipated): October 1, 2020

Study Registration Dates

• First Submitted: September 12, 2018
• First Submitted That Met QC Criteria: September 13, 2018
• First Posted (Actual): September 14, 2018

Study Record Updates

• Last Update Posted (Actual): June 7, 2019
• Last Update Submitted That Met QC Criteria: June 6, 2019
• Last Verified: May 1, 2019

More Information

Terms related to this study

• Keywords: MRI; fMRI; Cognition; EEG; Major Depression; Eye Tracker; Resting State Connectivity
• Additional Relevant MeSH Terms: Behavioral Symptoms; Mental Disorders; Mood Disorders; Depression; Depressive Disorder; Depressive Disorder, Major
• Other Study ID Numbers: ELM-55 5323-18-SMC

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes