Neurobiological and Genomic Predictors of Relapse After Discontinuation of Antidepressant Treatment in Major Depressive Disorder: An Observational Prospective Study
Conditions: Healthy, Major Depressive Disorder (MDD)
Sponsor: Mehmet Kemal Arikan

CLINICAL TRIAL PROTOCOL 
Title: Multimodal Biomarker Predictors of Relapse in Major Depressive Disorder: A Hybrid 
Retrospective–Prospective Cohort Study 
 
NCT Number: Not yet available  
Document Date: 01.12.2025 

CLINICAL TRIAL PROTOCOL 
1. Brief Summary 
At the first step of treatment for Major Depressive Disorder, second-generation antidepressants 
(selective serotonin reuptake inhibitors and serotonin–norepinephrine reuptake inhibitors) and/or 
psychotherapies with evidence-based efficacy are recommended (American Psychological 
Association, 2019). 
It is clear and well documented that antidepressants prevent relapse in depression; however, it 
should also be remembered that 20% of individuals who continue taking medication still relapse 
within the first year, and 40% of those who discontinue the medication relapse (Kato et al., 
2021). Therefore, there is no clear consensus regarding the optimal duration of treatment, and it 
is often suggested that, if possible, treatment should be continued lifelong. Although the long-
term use of antidepressants is associated with relatively low side-effect profiles, half of patients 
report these effects as bothersome (Cascade et al., 2009). In our meta-analysis, clinical and 
demographic variables such as age, sex, and treatment resistance were examined as potential 
predictors of relapse in depression, but no factor that reliably predicted relapse could be 
identified (Arıkan et al., 2023). 
Considering that the therapeutic effect of antidepressants can also be observed through the 
normalization of physiological parameters disturbed by depression, it is assumed that predictors 
of relapse may not be clinical or demographic but rather biological, electrophysiological, and 
genetic markers. 
Previous studies have reported that electrophysiological markers such as P300 amplitude and 
latency, REM sleep latency, or frontal alpha asymmetry differ between healthy individuals and 
patients with MDD (Arıkan et al., 2023b; Arıkan et al., 2024). Therefore, these markers are 
considered potential indicators that could be used to guide the discontinuation of antidepressant 
treatment in patients. In the genetic field, the majority of research consists of RNA and small 
RNA studies (Wang et al., 2022; Ding et al., 2023). However, to date there has been no study in 
which an untreated patient group was followed to show the acute and chronic effects of 
medication use, and thus no biomarker has been defined to determine when an antidepressant can 
safely be discontinued. 
 
This hybrid clinical study aims to identify biological, electrophysiological, sleep-related, and 
genetic biomarkers that can predict relapse following antidepressant discontinuation in patients 
with Major Depressive Disorder (MDD). The project integrates (1) a retrospective cohort with 
known recurrence outcomes, (2) a prospective cohort followed from treatment initiation through 
remission and medication discontinuation, and (3) a healthy control group providing normative 
biomarker values. 

We hypothesize that relapse risk is associated with failure of biological normalization, even 
when clinical remission is achieved. 
2. Detailed Description of Study Design 
2.1. Participants 
The study is conducted as a single-center investigation at the Kemal Arıkan Psychiatry Clinic in 
Istanbul, Turkey. All participants will be recruited from patients who present to the clinic and 
receive a DSM-5 diagnosis of Major Depressive Disorder (MDD).  
Study Type 
Observational / Prospective & Retrospective Hybrid Cohort 
(Non-interventional; naturalistic antidepressant treatment continuation according to guidelines.) 
Study Groups 
Group A – Retrospective Cohort (Known Recurrence Status) 
• 
Patients previously diagnosed with MDD 
• 
Known: Recurrent vs. Non-recurrent phenotype 
• 
Single timepoint data available (T0) 
• 
Biomarkers obtained: qEEG, P300, REM latency, MRI, Genotype, RNA/miRNA 
• 
Purpose: Identify trait-level baseline differences between recurrent and non-recurrent 
groups. 
Group B – Prospective Cohort (Unknown Recurrence Status) 
Participants newly entering treatment and followed for ~18 months: 
Measurement Timeline: 
• 
T0 – Baseline: qEEG, P300, REM, full RNA, miRNA, methylation, length of DNA, 
Genotype, HDRS-17, HARS, MMSE, WHOQOL-BREF, DASS-21. For a subgroup 
MRI-DTI will be received. 
• 
Treatment: Antidepressant per clinical guidelines 
• 
T1 (4–8 weeks): Early change: qEEG + P300, HDRS-17, DASS-21 
• 
T2 (≥6 months of remission): Before discontinuation → Full biomarker battery 
• 
T3 (6 months medication-free): Full biomarker battery; relapse vs. no relapse 
determined 
Group C – Healthy Control Group 

• 
Single-timepoint biomarker acquisition: qEEG, P300, REM, full RNA, miRNA, 
methylation, length of DNA. For a subgroup MRI-DTI will be received. 
• 
Normative reference for defining “biological normalization” 
 
3. Objectives 
Primary Objective 
To determine whether post-treatment normalization of neurophysiological (EEG/P300, REM 
sleep), genetic (genome, transcriptome, small RNA), and neuroimaging (MRI) biomarkers 
predicts relapse following antidepressant discontinuation. 
Secondary Objectives 
1. Compare baseline (trait) biomarkers between: 
o Recurrent MDD 
o One-episode/non-recurrent MDD 
o Healthy controls 
2. Evaluate whether early changes (T0 → T1) in EEG/P300 predict long-term relapse. 
3. Examine whether persistent deviations from healthy biomarker ranges at T2 are 
associated with recurrence at T3. 
4. Build a Clinical Decision Algorithm for “When to Safely Stop Antidepressants” based 
on multimodal biomarker normalization. 
4. Hypotheses 
H1 (Trait Differences) 
At baseline, patients with a recurrent course show: 
• 
Abnormal qEEG patterns 
• 
Prolonged P300 latency / reduced P300 amplitude 
• 
Shortened REM latency 
• 
MRI structural/functional deviations 
• 
Distinct RNA/miRNA signatures 
Compared to non-recurrent patients and healthy controls. 
H2 (State Normalization Differences) 

Prospective patients who do not relapse will show: 
• 
Biomarker normalization at T2 (prior to discontinuation) to levels not different from 
healthy controls. 
Relapsing patients will show: 
• 
Persistent abnormalities at T2 despite clinical remission. 
H3 (Early Change Predicts Outcome) 
T0 → T1 improvements in qEEG spectral power and P300 parameters predict remission stability 
at T3. 
5. Outcome Measures 
Primary Outcome 
Relapse Status at 6-month drug-free follow-up (Yes/No), defined via structured clinical 
interview and HAM-D score higher than 7. 
Primary Biomarker Predictors 
• 
qEEG spectral power (delta, theta, alpha, beta, gamma) 
• 
P300 latency/amplitude 
• 
REM latency (Sleep Profiler) 
• 
Whole-genome SNV/CNV structural markers 
• 
Transcriptome & small RNA signatures 
• 
Long-read structural variant resolution: large insertions, deletions, repeats, fusions) 
• 
Allele-specific methylation patterns (5mC) 
Secondary Outcomes 
• 
Early treatment response (T0 → T1 EEG/P300 change) 
• 
Clinical scales: HAM-D, HAM-A, CGI, BPRS, WHOQOL-BREF, PSS, DASS-21 
• 
MRI volumes/connectivity 
 
6. Eligibility Criteria 
Inclusion 
• 
DSM-5 Major Depressive Disorder diagnosis 

• 
Age 18+ 
• 
No neurological or major psychiatric comorbidity except anxiety 
 
Exclusion 
• 
Hearing impairment (P300 auditory oddball paradigm) 
• 
Diabetes mellitus (EEG artifact risk) 
• 
Medications affecting REM sleep 
 
7. Study Procedures  
The primary measurements will include resting-state brain activity recorded with quantitative 
EEG, P300 amplitude obtained from event-related potentials using an auditory paradigm, REM 
sleep latency measured with a portable EEG device, and depression severity assessed with the 
Hamilton Depression Rating Scale. 
Genetic measures will be obtained by analyzing total RNA (transcriptome) and small RNA 
(miRNA) from blood samples collected before medication use in healthy individuals and in 
patients with Major Depressive Disorder, and by determining changes in these RNAs after 
medication use. Following conventional discontinuation of the medication, the expression levels 
of these RNAs will be reassessed either at one year or at the time of relapse, whichever occurs 
first. In addition, genomic data obtained from patients will be used for secondary analyses of 
different genomic variations and the associated genes. The genomic data will be analyzed in 
terms of disease etiology and medication choice (pharmacogenetics). 
7.1. Parameters 
Resting-State Quantitative EEG 
Quantitative EEG (qEEG) refers to the numerical analysis of EEG data. When EEG signals are 
subjected to spectral analysis, we speak of quantitative EEG. Spectral analysis transforms any 
time series into the frequency domain. After raw EEG data are subjected to spectral analysis, 
they must be compared with a control group or a normative database; this is termed normative 
EEG. In our study, qEEG data obtained from the healthy control group will constitute the 
normative database and will be used to compare the current state of patients with depression. 
P300 Wave Amplitude 
The P300 wave is an electrophysiological parameter that provides information about the status of 
an individual’s cognitive functions. It is measured with EEG. The P300 wave reflects the brain’s 
discrimination response when a person hears or sees an infrequent, unfamiliar stimulus. A 
prominent P300 wave (higher P300 amplitude) indicates that cognitive functions such as 

attention and reasoning are in good condition. Our meta-analysis showed that P300 amplitude is 
lower in patients with depression than in healthy controls (Arıkan et al., 2024). Other studies 
have reported that this abnormality improves with treatment (Zhong et al., 2019). 
REM Latency 
REM (rapid eye movement) sleep is the stage in which we dream. It is measured with sleep 
EEG. Our systematic review and meta-analysis demonstrated that the time to enter REM sleep 
(REM latency) is shortened in patients with depression (Arıkan et al., 2023). Some studies have 
shown that REM latency normalizes in patients whose depression is treated (Pillai et al., 2011). 
Genetic Parameters 
Multiple factors contribute to the development of depression, one of which is gene–environment 
interaction. Life events and stress can increase the expression of certain genes while silencing 
others. MicroRNAs (miRNAs) play a key role in regulating gene expression. In the literature, 
analyses of blood samples have reported that several miRNAs are differentially expressed in 
individuals with depression compared with controls (Maffioletti et al., 2016). Certain microRNA 
polymorphisms have also been associated both with depression and with the P300 wave (Xu et 
al., 2010). Finally, studies in patients receiving antidepressants have reported that expression 
levels of miR-124 and miR-132, which were previously high, decrease with treatment, whereas 
the low expression of miR-16 increases (Ahmadimanesh et al., 2023). 
Techniques to Be Used 
1) Clinical Interview 
Psychiatric assessment will be conducted in accordance with the Structured Clinical Interview 
for DSM-5 Disorders (SCID-5; First et al., 2016). Depression severity will be assessed before 
and after treatment using clinical scales that are routinely administered in the clinic. The scales to 
be used are described below. 
2) Hamilton Depression Rating Scale (HAM-D) 
The HAM-D was developed by Hamilton (1960) to assess depression severity. In a semi-
structured interview, the clinician inquires about depressive symptoms experienced during the 
previous week. Among the different versions, the 17-item form will be used in the present study. 
Some items are scored from 0 to 4, some from 0 to 2, and some from 0 to 3. The maximum total 
score is 53; scores ≥29 indicate severe depression, 16–28 moderate, 8–15 mild, and 0–7 no 
depression (Williams, 1988). The Turkish adaptation and reliability–validity study were 
conducted by Akdemir et al. (1996), yielding an internal consistency coefficient of .75. A ≥50% 
reduction in HAM-D score will be used as the criterion for treatment response (Cusin et al., 
2010). 
4) Hamilton Anxiety Rating Scale (HAM-A / HARS) 

The Hamilton Anxiety Rating Scale was developed by Max Hamilton (1959) to assess anxiety 
severity. In a semi-structured interview, the clinician inquires about anxiety symptoms 
experienced in the previous 72 hours. The scale consists of 14 items, each scored from 0 to 4. 
The maximum total score is 56; scores ≥31 indicate severe anxiety, 25–30 moderate to severe, 
18–24 mild to moderate, and ≤17 mild anxiety (Maier et al., 1988). The Turkish adaptation and 
reliability–validity study, with an internal consistency coefficient of .72, was performed by 
Yazıcı et al. (1998). 
5) Brief Psychiatric Rating Scale (BPRS) 
The BPRS is an 18-item scale developed to assess symptom severity in psychiatric disorders 
(Overall & Gorham, 1962). It is completed by a trained interviewer. It is used to evaluate the 
severity of psychiatric disorders and to monitor the course of illness. Each item is scored from 0 
to 6 according to symptom severity. Total scores range from 0 to 108, with higher scores 
indicating more severe psychopathology. The Turkish adaptation was carried out by Soykan 
(1990). 
6) Clinical Global Impression (CGI) 
The Clinical Global Impression scale is a clinician-rated instrument used to evaluate illness 
severity, degree of improvement, and response to treatment. It was developed by Guy in 1976 
(Guy, 1976). It was initially designed for psychiatric disorders but is now widely used in other 
medical fields. The CGI typically consists of three main components: 
• 
CGI-Severity (CGI-S): Rates the current severity of the illness from 1 (normal, not at all 
ill) to 7 (among the most extremely ill patients). 
• 
CGI-Improvement (CGI-I): Rates change over the course of treatment from 1 (very 
much improved) to 7 (very much worse). 
• 
CGI-Efficacy Index (CGI-E): Evaluates the overall response to treatment relative to 
side effects, rated from 1 (very favorable) to 4 (poor). 
7) World Health Organization Quality of Life – Short Form (WHOQOL-BREF) 
The WHOQOL-BREF is a comprehensive scale developed by the World Health Organization to 
assess well-being and enable cross-cultural comparisons. Following pilot studies conducted at 15 
centers worldwide, the 100-item WHOQOL-100 and the 26-item WHOQOL-BREF were 
developed (WHOQOL Group, 1998). 
The WHOQOL-BREF consists of 26 items, including two items on overall perceived quality of 
life and perceived health, and four domains: physical, psychological, social relationships, and 
environment. Each domain yields scores on a 20- or 100-point scale. The Turkish adaptation was 
performed by Eser et al. (1999). 
8) Mini Mental State Examination (MMSE) 

The Mini Mental State Examination was first published by Folstein and colleagues in 1975. The 
Turkish reliability and validity study was conducted by Güngen et al. (2002). The MMSE is a 
brief, widely used test to assess cognitive functioning. It is commonly used by clinicians for 
mental status examination and consists of five main components: 
1. Orientation in time and place: The person is asked to provide the date, day, month, 
year, and season, as well as the name of the place, country, city, and building. 
2. Registration and short-term memory: The examiner states three words and asks the 
person to repeat them; recall is assessed a few minutes later. 
3. Attention and calculation: The person is asked to subtract serial sevens from 100 (e.g., 
100, 93, 86, ...) or to spell a word backwards. 
4. Language and naming: The person is asked to name several objects, to follow a simple 
command (e.g., “Take the paper and fold it in half”), and to read and understand a written 
sentence. 
5. Visuospatial skills: The person is asked to copy a specific design or produce a simple 
drawing. 
The MMSE is scored out of 30 points and is generally interpreted as follows: 24–30 = normal 
cognitive functioning; 19–23 = mild cognitive impairment; 10–18 = moderate impairment; 0–9 = 
severe impairment. 
9) qEEG Recording 
Resting-state qEEG recordings will be obtained in a quiet, dimly lit, air-conditioned room. A 19-
channel qEEG cap (FP1, F7, T3, T5, F3, C3, P3, O1, Fz, Cz, Pz, F4, C4, P4, O2, FP2, F8, T4, 
T6) placed according to the international 10–20 system will be used. A transparent electrode gel 
will be injected into scalp electrodes to increase conductivity. The ground electrode will be 
placed at FPz. Mastoid electrodes will be attached to both earlobes as reference electrodes. 
Electrode impedance will be kept below 5 kΩ for all electrodes. Patients will sit comfortably 
with eyes closed in a resting state during recording. A Neuron-Spectrum-4/P device (Neurosoft, 
2023) will be used. Total recording time will be approximately 7 minutes, consisting of 3 
minutes of baseline recording, 30 seconds of eyes-open condition, and 3.5 minutes of eyes-
closed condition. Data will be sampled at 500 Hz; the low-frequency filter will be set at 0.15 Hz, 
the high-frequency filter at 70 Hz, and a 50-Hz notch filter will be applied. 
10) qEEG Analysis 
Raw qEEG recordings will be saved in European Data Format (EDF). Offline muscle artifacts 
will be manually removed in NeuroGuide (Applied Neuroscience Inc., version 3.8.2) by an 
experienced qEEG reader. A 3-minute artifact-free segment will then be obtained. For each 
electrode, mean activity across the recording period will be calculated, and Fast Fourier 

Transform (FFT) will be applied to compute spectral power in five main frequency bands: 
gamma (>35 Hz), beta (12–35 Hz), alpha (8–12 Hz), theta (4–8 Hz), and delta (0–4 Hz), 
including the following sub-bands: alpha1 (8–10 Hz), alpha2 (10–12 Hz), beta1 (12–15 Hz), 
beta2 (15–18 Hz), beta3 (18–25 Hz), gamma1 (30–35 Hz), and gamma2 (35–40 Hz). The 
calculated data will be saved as TDT files and exported to SPSS. 
11) Event-Related Potential Measurement 
Parameters of the P300 event-related potential will be assessed using an auditory oddball 
paradigm recorded with an EEG system (NeuronSpectrum-5, 2024). Recordings will take place 
in a sound-attenuated, semi-dark, well-ventilated room. A series of non-verbal auditory stimuli 
will be presented in random order, and participants will be instructed to press a button when they 
hear the target stimulus through headphones. 
Stimuli will be delivered binaurally with a duration of 50 ms, intensity of 80 dB, and inter-
stimulus interval of 1 s, at frequencies of 2000 Hz (target stimulus) and 1000 Hz (non-target 
stimulus). The number of target trials is expected to be approximately 25–30. Response 
amplitude and latency will be calculated as the peak-to-peak amplitude (µV) of the N250–P300 
complex and the P300 latency (ms), respectively. P300 amplitude and latency will be measured 
using an EEG cap placed according to the 10–20 system with monopolar mastoid reference 
electrodes. 
 
12) Measurement of REM Latency in Sleep EEG 
Sleep Profiler (SP) recordings will be obtained from EEG sensor regions AF7–AF8, AF7–Fpz, 
and AF8–Fpz (Advanced Brain Monitoring, Carlsbad, CA, USA) applied by participants 
themselves at home. SP recordings will be automatically staged using machine-learning 
algorithms previously described in the literature, designed to comply with the standard visual 
scoring rules of the American Academy of Sleep Medicine. These AI-based staging algorithms 
combine epoch-level temporal power spectral features, automatically detected individual slow 
waves, sleep spindles, cortical arousals, EMG arousals, and epoch-level phasic correlations 
between two signals that contain electro-oculographic activity (AF7–Fpz versus AF8–Fpz). 
The accuracy of automatic staging, with and without technical review, has been validated against 
visually scored PSG recordings, including in subjects referred for evaluation of sleep-disordered 
breathing. Sleep staging accuracy has also been validated against simultaneously acquired PSG 
recordings (Lewendowski et al., 2023). 
 
13) Whole-Genome Sequencing 

Whole-genome sequencing will be performed on genomic DNA isolated from peripheral blood 
samples. This approach yields the most comprehensive genetic data currently available. 
Sequencing allows the detection of single nucleotide variations (SNVs), copy number variations 
(CNVs), insertions and deletions, haplotypes, gene fusions, and structural rearrangements. 
Whole-genome sequencing generates large amounts of data, which are analyzed using complex 
bioinformatic approaches. Common applications include undiagnosed diseases, population 
studies, pharmacogenetic research, and cancer research. 
As technology advances, data quality increases, costs decrease, and bioinformatic analyses 
improve, leading to growing use worldwide. In our setting, genomic studies have been conducted 
using exome sequencing, which examines only protein-coding exons, to diagnose patients and to 
perform pharmacogenetic analyses of drug metabolism. Third-generation long-read sequencing, 
considered a new-generation technology, reveals all structural rearrangements and will allow us 
to perform both tissue compatibility analyses, which are very important in organ transplantation, 
and haplotype analyses for drug metabolism at lower cost and higher quality. Globally, 
technology is shifting in this direction. 
14) Transcriptome Sequencing (RNA-Seq) 
Transcriptome sequencing aims to identify all RNA molecules expressed in a given cell 
population at a specific time. Unlike DNA sequencing, RNA-Seq provides information about the 
dynamic properties of target cell populations. 
RNA-Seq enables the examination of read counts for all expressed RNAs in the target cells, 
single nucleotide changes in expressed regions, insertions/deletions, fusion genes, and functional 
outcomes such as alternative splicing events caused by certain variants. 
It is known that RNA profiles change in conditions such as disease, medication use, and 
infection. Therefore, RNA-Seq approaches are used to elucidate disease etiology, monitor 
treatment efficacy, and discover biomarkers. 
15) Small RNA Sequencing 
In addition to coding RNAs, the human genome contains short non-coding RNA molecules, 
including siRNA (small interfering RNA), miRNA (microRNA), snoRNA (small nucleolar 
RNA), and sncRNA (small non-coding RNA). These RNAs regulate various physiological and 
pathological processes within the cell. They control gene expression levels by degrading their 
target RNAs or by suppressing protein synthesis. Recent research has shown that small RNAs 
have great potential as biomarkers for the diagnosis and monitoring of diseases. 
Small RNA studies, like transcriptome sequencing, rely on comparisons between groups and 
subsequent statistical analyses. 
16) Long-Read Sequencing With PacBio: Methylation and Genotyping 

Long-read whole-genome sequencing will be performed using Pacific Biosciences (PacBio) 
long-read technology. This platform generates high-fidelity (HiFi) long reads that enable 
comprehensive characterization of genomic variation and epigenetic regulation in a single 
experiment.  PacBio long-read sequencing will be used to perform genome-wide genotyping, 
including single nucleotide variants (SNVs), insertions/deletions (indels), copy number 
variations (CNVs), and structural rearrangements, resolve haplotypes across clinically relevant 
loci, provide allele-specific information that is important for pharmacogenetic analyses and 
depression-related risk loci, simultaneously measure DNA methylation at single-base resolution. 
Because PacBio sequencing measures the kinetics of DNA polymerase during sequencing, it can 
directly detect base modifications, including 5-methylcytosine (5mC), without the need for 
bisulfite conversion. In this study, we will use these data to obtain:  
• 
Genome-wide DNA methylation profiles, 
• 
CpG methylation patterns in genes and pathways previously associated with Major 
Depressive Disorder, stress response, and neuroplasticity, 
• 
Allele-specific methylation in candidate regions that may influence treatment response 
and relapse risk. 
 
Timepoint 
Clinical EEG P300 REM MRI RNA/Genome 
T0 
✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
T1 (4–8 wks) 
✓ 
✓ 
✓ 
– 
– 
– 
T2 (≥6 months remission) ✓ 
✓ 
✓ 
✓ 
– 
✓ 
T3 (6 months drug-free) ✓ 
✓ 
✓ 
✓ 
✓ 
✓ 
Healthy controls receive one full measurement set. 
 
8. Sample Size 
The sample size calculation was performed using the OpenEpi web-based platform (OpenEpi – 
Sample Size for Unmatched Case-Control Studies, n.d.). OpenEpi is a free and open-source 
software widely used for epidemiological statistics. In the application, the module for 
unmatched case–control studies was selected. 

Within this module, the desired confidence level (95% CI), statistical power (80%), an assumed 
exposure prevalence among controls (25%), and either an odds ratio of 1 or an assumed exposure 
prevalence among cases (50%) were entered. 
Based on these parameters, the required sample size was calculated as 170 participants using 
the Fleiss test with continuity correction. 
An additional 20% dropout rate, as reported in the literature (Pradier et al., 2020), was then 
added. Accordingly, the final required sample size was determined to be 204 participants. 
9. Ethical Considerations  
• 
Study approved by Üsküdar University Non-interventional Ethics Committee 
(61351342/020-684-December 2024-33) 
• 
Informed consent obtained. 
• 
Non-interventional, observational design; medication decisions follow clinical guidelines. 
10. Expected Results 
1. Relapsers vs Non-relapsers: 
Significant differences in EEG, REM, P300, RNA/miRNA, MRI parameters. 
2. Healthy Comparison: 
Relapsers will differ significantly from healthy at T2; 
Non-relapsers will not differ from healthy at T2. 
3. Clinical Utility: 
Biomarkers that normalize → safe antidepressant discontinuation 
Biomarkers that do NOT normalize → high relapse risk, continue medication. 
11. Impact and Significance 
This study will provide the first multimodal, longitudinally validated biological algorithm for 
determining when antidepressant medication can be safely discontinued in MDD. 
It proposes a shift from symptom-based to biologically informed treatment duration decisions. 
 
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