Alterations in functional network dynamics following ketamine treatment in major depression
Presented During:
Wednesday, June 26, 2024: 11:30 AM - 12:45 PM
COEX
Room:
Grand Ballroom 101-102
Poster No:
483
Submission Type:
Abstract Submission
Authors:
Brandon Taraku1, Jason Nomi2, Artemis Zavaliangos-Petropulu2, Noor Al-Sharif2, Paloma Pfeiffer2, Randall Espinoza2, Katherine Narr3
Institutions:
1UCLA, Los Angeles, CA, 2University of California, Los Angeles, Los Angeles, CA, 3University of California Los Angeles, Los Angeles, CA
First Author:
Co-Author(s):
Introduction:
Ketamine, an N-methyl-D-aspartate receptor (NMDAR) antagonist, produces rapid antidepressant effects in major depressive disorder (MDD). Recent investigations show that patients with MDD exhibit altered resting brain network dynamics that manifest as increased transitions between the central executive (CEN) and default mode (DMN) networks(Belleau et al., 2022), and increased dwell time in fronto-insular brain networks, which associate with rumination(Kaiser et al., 2019). We thus used Co-Activation Pattern (CAP) analysis(Liu et al., 2013) to determine how ketamine affects the dynamic properties of resting brain networks and the relationships with therapeutic effects in patients with treatment resistant depression (TRD).
Methods:
Participants included 58 TRD patients (mean age=40.7 years, female=28) and 56 healthy controls (HC) (mean age=32.8 years, female=32). TRD patients received 4 serial ketamine infusions (SKI) (0.5 mg/kg) over 2 weeks. MRI scans included structural (T1/T2w) and resting state fMRI(acquisition time:13min, TR:0.8s, VS:2mm isotropic), and clinical scales included the Hamilton Depression Rating scale (HDRS) and Rumination and Reflection Scale (RRS). MRI and clinical data were collected at baseline for all participants, and 24hrs after the last ketamine infusion for TRD patients. MRI preprocessing included the HCP minimal preprocessing pipeline(Glasser et al., 2013), followed by global signal regression and band-pass filtering [0.01 – 0.1 Hz]. fMRI data, parcellated using the Schaefer 400 atlas(Schaefer et al., 2018) plus 54 subcortical regions(Tian et al., 2020), were z-scored to normalize BOLD activity. CAP analysis included k-means clustering in MATLAB across a range of clustering solutions to find the optimal cluster number. Here, the cluster validity index was computed(Allen et al., 2014), and an L-curve was fitted to the range of clustering scores using least squares regression to determine the 'elbow' point. For the optimal cluster number, CAP metrics were computed including 1) the fraction of time spent in a CAP (F), and 2) the transition probability from one CAP to another (T). Statistical analyses addressed changes in CAP metrics over time in TRD patients using paired t-tests, and associations with mood scores using Pearson correlations. CAP metrics showing significant SKI effects were compared between TRD and HC at baseline with independent sample t-tests controlling for age and sex. Bonferroni correction addressed multiple comparisons.
Results:
Six CAP clusters were revealed as optimal (Fig 1). Significant decreases in F for visual (VN) CAP (t=-3.57, p=7.37E-04) and increases in F for CEN CAP (t=3.26, p=1.90E-03) occurred post SKI. Follow-up analyses for CEN and VN CAPs found significant increases in T from salience (SN) to CEN (t=3.65, p=5.79E-04), and decreases in T from SN to VN (t=-3.04, p=3.60E-03). Decreases in F for SN CAP were significantly correlated with improvements in RRS (r=-0.402, p=1.90E-03). Baseline analyses revealed significantly lower F for CEN CAP (t=-3.55, p=5.70E-04) and lower T from SN to CEN (t=-2.44, p=0.016) and higher T from SN to VN (t=3.08, p=2.60E-03) for TRD compared to HCs (Fig 2).
Conclusions:
Results suggest that SKI primarily affects network dynamics between VN, CEN and SN in TRD, and that SN transitions account for these dynamics. These findings support the hypothesis that the SN acts as mediator by switching between large scale brain networks(Menon & Uddin, 2010), and suggest this pattern is modulated by ketamine. Cross sectional results suggest that ketamine normalizes baseline differences between patients and HCs including decreased occurrences of CEN and SN to CEN transitions, and increased SN to VN transitions. Findings align with prior research suggesting decreased CEN activity in MDD is linked with emotion regulation(Brakowski et al., 2017) and CAP findings that showed increased occurrences of states involving the insula, a key hub of the SN, associate with rumination(Kaiser et al., 2019).
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
fMRI Connectivity and Network Modeling 2
Keywords:
Affective Disorders
FUNCTIONAL MRI
Psychiatric Disorders
Treatment
Other - depression;dynamic functional connectivity;co-activation patterns;ketamine
1|2Indicates the priority used for review
Provide references using author date format
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