Network homeostasis: functional brain network alterations and relapse in remitted late-life depression

doi:10.21203/rs.3.rs-5005391/v1

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Network homeostasis: functional brain network alterations and relapse in remitted late-life depression

https://doi.org/10.21203/rs.3.rs-5005391/v1

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In this study, we aim to identify neurobiological factors that predict relapse risk in late-life depression (LLD). We recruited 145 older adults (age ≥ 60): 102 recently remitted LLD participants and 43 healthy comparisons. Participants underwent baseline MRI and evaluation of depression symptoms/status for up to 2 years. We evaluated intrinsic network connectivity for 111 participants (39 healthy comparisons, 47 stable remitted, 25 relapsed). LLD participants had lower connectivity primarily within and between the default mode (DMN), somatomotor, and visual networks and higher connectivity between the DMN and executive control network. Lower connectivity of DMN to somatomotor and salience networks was associated with relapse. Notably, the network structure of relapsed participants was more similar to healthy comparisons than stable remitted. These findings indicate that remission is associated with persistent functional network alterations while vulnerability to relapse may be associated with a failure to establish a new stable homeostatic functional network structure.

Health sciences/Diseases/Psychiatric disorders/Depression

Biological sciences/Neuroscience/Emotion

Late-life depression (LLD) is a highly recurrent illness^1,2 associated with disability and increased mortality³. Even with successful treatment, over half of patients will re-experience depression within four years⁴, mostly within the first two years⁵. Continued exposure to depression increases the risk of metabolic disease, cognitive decline, and death^6–8.

We previously conceptualized recurrence of LLD through a model of neural network homeostatic dysregulation⁹ (Fig. 1). Dynamic neural circuitry copes with stressors through allostatic responses encompassing both behavioral and physiologic adaptations¹⁰. While remission of depressive symptoms may reestablish homeostasis, remitted individuals may show long-term state-independent, altered brain network configurations¹¹. The homeostatic equilibrium of older remitted depressed individuals may be tenuous and further challenged by the degradation of appropriate allostatic responses in older age. This may render older adults particularly vulnerable to recurrence of depression. Our homeostatic disequilibrium hypothesis of LLD recurrence proposes that vulnerability to new depressive episodes stems from chronic fragility in neural network homeostasis persisting in remission. Stressors evoke aberrant neural responses that disrupt homeostasis, alter network function, and subsequently result in maladaptive cognitive and behavioral activity^2,9. This leads to a cycle of further disruption in neural function and, eventually, precipitation of another major depressive episode.

This model is consistent with recent dynamical systems views of psychiatric disorders^12,13, which postulates depression and health as attractor states. The attractor landscape defines the likelihood of relapse (conceptualized as critical transition), though the landscape is defined in abstract terms. Depression has been conceptualized as a disruption of large-scale brain networks, particularly the default mode network (DMN), executive control network (ECN), and salience network (SN)^9,14,15. These networks are altered in both aging and depression². More recently, the somatomotor network (SMN) and visual network have also been implicated in depression and specifically LLD^16,17. These networks are typically characterized with resting state functional magnetic resonance imaging (fMRI) studies. In addition to evidence for alterations in depression, we and others have demonstrated that, while resting state functional connectivity (FC) is affected by antidepressant treatment¹⁸, alterations persist in remission compared to healthy comparison older adults¹⁹. Thus, FC within and between large scale intrinsic brain networks may comprise a meaningful characterization of the neural landscape that define susceptibility or resilience to critical transitions leading to relapse.

In this study, we aimed to identify neurobiological factors associated with LLD recurrence risk by conducting a two-year longitudinal study of remitted LLD participants and health comparison older adults. This is one of the first studies to prospectively assess neural markers of relapse and recurrence in depression²⁰ and, to our knowledge, the first to do so in LLD. For this analysis, we compared resting state FC of seven canonical intrinsic brain networks in older healthy comparisons and participants recently remitted form an acute depressive episode. While our homeostatic disequilibrium model is primarily concerned with dynamic evolution of depressive symptoms and neural architecture, we can use the model to make specific predictions about how stable remission and relapse may differ from each other and healthy comparisons. Specifically, we would expect healthy comparisons to model a stable landscape. Stable remission should present as a similarly stable landscape, while the landscape of eventual relapse should present with greater differences that render it unstable. However, we also expect differences between the remitted landscape, regardless of eventual relapse status, and healthy comparisons. We hypothesized that: 1) within and between network FC, which we define as the neural landscape, will differ between healthy comparisons and remitted participants (regardless of eventual relapse status); 2) remitted participants who relapse will exhibit a different neural landscape compared to participants with stable remission, reflecting unstable vs. stable homeostatic setpoints; 3) the neural landscape in stable remission will be more similar to healthy comparisons than relapse will; and 4) these differences will largely lie in the DMN, ECN, and SN.

Demographic and clinical characteristics are provided in Table 1. Three participants did not have MRI data, and 31 participants had excessive motion (greater than 20% of volumes had root mean square motion > 0.5 mm) during the resting state scans, yielding a sample of 111 participants (39 healthy comparison, 47 stable remitted, 25 relapsed) for analysis. The group composition differed by site, with significantly more LLD participants recruited at VUMC due to differing COVID-19 restrictions at Pitt and UIC early in the study. Sex and education differed between groups and was controlled for in all models along with age, race, and site.

Table 1

Participant summary. ITP – initial treatment phase; LLD – late-life depression; MADRS – Montgomery Asberg Depression Rating Scale; SD – standard deviation.
	Overall (n = 145)	Healthy comparison (n = 43)	LLD (n = 102)	Difference, p value
Age (mean, SD)	66.9 (4.8)	66.6 (5.4)	67.0 (4.6)	\(\:{t}_{68}=-0.35,\:p=0.723\)
Sex (female, %)	95 (65.5%)	21 (48.8%)	74 (72.5%)	\(\:{\chi\:}_{1}^{2}=5.31,\:p=0.021\)
Race² White Black Asian Native Am. Multiracial Other (7)	119 (82.1%) 15 (10.3%) 4 (2.8%) 1 (0.7%) 5 (3.4%) 1 (0.7%)	31 (72.1%) 6 (14.0%) 4 (9.3%) 1 (2.3%) 1 (2.3%) 0 (0.0%)	88 (86.3%) 9 (8.8%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 1 (1.0%)	\(\:{\chi\:}_{1}^{2}=3.23,\:p=0.072\)
Education (mean, SD)	16.1 (2.3)	16.7 (2.0)	15.8 (2.4)	\(\:{t}_{94}=2.19,\:p=0.031\)
Site VUMC Pitt UIC	77 (53.1%) 34 (23.4%) 34 (23.4%)	14 (32.6%) 14 (32.6%) 15 (34.9%)	63 (61.8%) 20 (19.6%) 19 (18.6%)	\(\:{\chi\:}_{2}^{2}=10.43,\:p=0.005\)
Baseline MADRS (mean, SD)	4.1 (3.6)	0.9 (1.5)	5.5 (3.3)	\(\:{t}_{139}=11.35,\:p<0.001\)
Treatment type (ITP, %)	-	-	58 (57%)	-
	Healthy comparison (n = 43)	Stable Remitted (n = 59)	Relapsed (n = 43)	Difference, p value
Age (mean, SD)	66.6 (5.4)	66.9 (4.7)	67.0 (4.4)	\(\:{F}_{2}=0.07,\:p=0.930\)
Sex^a (female, %)	21 (48.8%)	41 (69.5%)	33 (76.7%)	\(\:{\chi\:}_{2}^{2}=8.11,\:p=0.017\)
Race^b White Black Asian Native Am. Multiracial Other (7)	31 (72.1%) 6 (14.0%) 4 (9.3%) 1 (2.3%) 1 (2.3%) 0 (0.0%)	51 (86.4%) 5 (4.9%) 0 (0.0%) 0 (0.0%) 2 (3.4%) 1 (1.7%)	37 (86.0%) 4 (9.3%) 0 (0.0%) 0 (0.0%) 2 (4.7%) 0 (0.0%)	\(\:{\chi\:}_{2}^{2}=4.14,\:p=0.126\)
Education (mean, SD)	16.7 (2.0)	15.9 (2.1)	15.6 (2.7)	\(\:{F}_{2}=2.32,\:p=0.102\)
Site VUMC Pitt UIC	14 (32.6%) 14 (32.6%) 15 (34.9%)	31 (52.5%) 14 (23.7%) 14 (23.7%)	32 (74.4%) 6 (14.0%) 5 (11.6%)
Baseline MADRS (mean, SD)		0.9 (1.5)	5.4 (3.6)	5.7 (3.0)	\(\:{F}_{2}=38.01,\:p<0.001\)
Treatment type (ITP, %)	-	30 (50.8%)	28 (65.1%)	\(\:{\chi\:}_{1}^{2}=4.17,\:p=0.041\)
a. Self-reported
b. Race coded as binary variable for analysis with groups of black/Asian/Native American/Multiracial/Other and white.

2.1. All remitted LLD participants vs. healthy comparisons

Results are summarized in Fig. 2 and full detail is provided in supplemental Table 1. LLD participants exhibited lower FC than healthy comparisons within the DMN (\(\:HC=7.94,\:p=0.0008\)), SN (\(\:HC=10.4,\:p=0.0001\)), visual (\(\:HC=7.18,p=0.002\)), and SMN (\(\:HC=20.0,\:p<0.0001\)), as well as between the visual network and DMN (\(\:HC=9.16,p=0.0002\)), limbic (\(\:HC=7.86,p=0.0008\)), and SMN (\(\:HC=18.9,p<0.0001\)). FC between the DMN and ECN was higher in LLD participants than healthy comparisons (\(\:HC=12.8,p<0.0001\)).

2.2. Stable remitted vs. relapse vs. healthy comparisons

Results are summarized in Fig. 3 and full detail is provided in supplemental Table 2. For the three group comparison, the DMN exhibited significant FC differences between all 7 networks: ECN (\(\:HC=10.2,p=0.0001\)), SN (\(\:HC=9.43,p=0.0001\)), dorsal attention network (DAN) (\(\:HC=12.6,p<0.0001\)), limbic (\(\:HC=7.93,p=0.0008\)), visual (\(\:HC=11.5,p<0.0001\)), SMN (\(\:HC=9.24,p=0.0002\)) and within DMN (\(\:HC=10.4,p=0.0001\)). Additional differences were observed between the ECN and visual (\(\:HC=10.7,p<0.0001\)) and SMN (\(\:HC=8.00,p=0.0007\)), between the DAN and SMN (\(\:HC=7.23,p=0.0017\)), and between visual and SMN (\(\:HC=7.43,p=0.0015\)). Only these network pairs were tested for pairwise group differences to determine which groups exhibited differences and directionality of effects.

2.2.1. Stable remitted vs. relapse participants

Results are summarized in Fig. 4A and full detail is provided in supplemental Table 3. LLD participants who went on to relapse exhibited lower FC than stable remitted participants between the DMN and SN (\(\:HC=12.9,p<0.0001\)), DMN and SMN (\(\:HC=17.0,p<0.0001\)), and ECN and SMN (\(\:HC=7.67,p=0.0014\)). The DMN-SN (\(\:HC=13.6,p<0.0001)\) and DMN-SMN (\(\:HC=19.3,p<0.0001\)) connectivities were also indicative of time to relapse in the survival analysis.

2.2.2. Stable remitted and relapse vs. healthy comparisons

Results are summarized in Fig. 4B and 4C and full detail is provided in supplemental Tables 4 and 5. Stable remitted participants exhibited widespread differences from healthy comparisons, including greater FC between DMN and ECN (\(\:HC=15.7,p<0.0001\)), SN(\(\:HC=9.18,p=0.0002\)), and SMN (\(\:HC=10.3,p=0.0001\)) and lower FC within the DMN (\(\:HC=7.21,p=0.0017\)), between DMN and visual (\(\:HC=12.2,p<0.0001\)), and between visual and SMN (\(\:HC=19.9,p<0.0001\)). By comparison, participants who go on to relapse have far fewer differences from healthy comparisons: greater FC between DMN and ECN (\(\:HC=9.64,p=0.0001\)) and lower FC between SMN and DMN (\(\:HC=8.87,p=0.0003\)) and visual (\(\:HC=17.2,p<0.0001\)).

To test this further, we computed participant-wise similarity matrices for the functional connectomes and compared the similarities between healthy comparison and stable remitted to the similarities between healthy comparison and relapse. Overall, the neural landscape of the relapse group was more similar to healthy comparisons than the neural landscape of the stable remission group was (\(\:t=-2.51,p=0.0123\)).

In this study of relapse and recurrence in late-life depression, we found robust differences in network connectivity between relapse, stable remission, and heathy comparisons, differences that centered heavily on the DMN. The remitted LLD group as a whole exhibited lower within and between network connectivity of the DMN, visual, and SMN than healthy comparisons, with the exception of greater connectivity between DMN and ECN. Stable remission and relapse differed primarily by lower connectivity between DMN and SN and SMN in the relapse group. Overall, the connectivity of participants that went on to relapse was more similar to healthy comparisons than was the connectivity of the stable remitted participants.

Consistent with our first hypothesis, the remitted LLD group as a whole showed significant differences in network connectivity from healthy comparisons, reflecting a new homeostatic setpoint associated with remission. The DMN featured prominently in these differences, consistent with previous working implicating aberrant DMN connectivity in both adult and geriatric depression^2,21. The finding of greater connectivity between the DMN and ECN in the remitted LLD group partially replicates findings in younger adults that connectivity between specific DMN-ECN regions was elevated in relapsing participants (though not in stable remitted participants)^22,23. There is also evidence that DMN-ECN connectivity is higher in acutely depressed individuals²¹, while our previous review identified a positive association between DMN-ECN connectivity and antidepressant treatment response²⁴. The coherence of the DMN and ECN appears to be a key feature of depression, though the precise role remains unclear. The SMN and visual network also showed lower within-network connectivity in LLD, which is consistent with findings from a large Chinese consortium^16,17, though at least one small study has reported the opposite effect in the visual network²⁵. Overall, remitted LLD participants show robust differences from healthy comparisons, supporting that stable remission from depression is not simply a “return to normal,” but is associated with a new configuration of the neural landscape.

The neural landscape also differed between stabled remitted and relapsed participants. DMN-SN connectivity was lower in relapsed participants than in stable remitted, and was also associated with time to relapse. This is consistent with a recent study in midlife depression that reported relapse was associated with lower connectivity between the DMN and portions of the SN²⁶, but stands in contrast to another a recent finding (albeit in only 9 relapsed participants) showing higher FC between the right anterior insula (a SN hub) and the subcallosal cingulate (a DMN hub) predicted depression recurrence²⁰. Interestingly, lower DMN-SN connectivity has also been reported in acutely depessed individuals²⁷. Lower SMN connectivity to the DMN and ECN was also associated with relapse which is the first finding to our knowledge relating SMN connectivity to relapse and requires further exploration.

Contrary to our third hypothesis, participants who would go on to relapse displayed a functional connectivity profile more similar to healthy comparisons than the stable remitted participants did. This finding may indicate that significant reconfigurations of the neural landscape in context of successful antidepressant treatment are required to result in stable remission. There is wide-spread evidence for this reconfiguration across the DMN, ECN, and SN^28–32. This type of dynamic network reconfiguration (as opposed to a return to baseline) is consistent with work in learning reversal, showing that new circuits that will override the learned behavior, rather than a reversal of the learned circuitry³³. As a corollary, a partial return to baseline (i.e., failure to establish a new stable setpoint) may represent an unstable equilibrium and thus susceptibility to relapse, especially in the presence of other persistent alterations. This may be evidenced in our study by relapsed participants showing greater similarity of functional network organization to healthy comparisons than stable remitted participants.

Our final hypothesis—that the key networks of the LLD neural landscape are the DMN, ECN, and SN—appears to be mostly true. The DMN showed widespread and persistent differences between the groups, underscoring its crucial role in the neural underpinnings of late-life depression. Importantly, these differences did not lie just within the DMN, but often in the interaction between the DMN and other networks. The ECN and SN did not differ as robustly as the DMN, though their connectivity with the DMN served as important differentiators between healthy comparisons and LLD, and between stable remission and relapse, respectively. Connectivity of the SMN and visual networks also differed frequently between groups, adding to a growing body of evidence that these unimodal networks are also involved in depression¹⁷.

The prospective design and 4 month inclusion cutoff following remission are significant strengths of our study, providing ecological validity as evidenced by the excellent agreement with previously observed relapse rates of 43% within two years³⁴. This study has several limitations. Our ability to assess the temporal stability of neural networks is hampered by analyzing only baseline neuroimaging. We do not have pretreatment imaging data in the LLD group. Sample size is moderate, although above average for neuroimaging of clinical populations and sufficiently powered for our analytic method that leverages omnibus testing to reduce dimensionality. Remitted participants did not receive uniform antidepressant treatment; while a general treatment algorithm was followed, medications were individually tailored. However, this resulted in a high rate of remission (73% across all three sites) and provides better generalization/clinical translation. The delineation between the stable remitted and relapsed groups was subject to right-censoring; while all participants had ≥ 8 months of follow up at the time of analysis, most participants had a longer duration (up to 2 years). We were insufficiently powered to analyze a uniform cutoff of relapse within 8 months, which would result in only 13 relapsed participants. Since relapse tends to occur sooner rather than later (e.g., only 29% of relapsed participants with two years of data relapsed after one year) and 85% of the participants had ≥ 1 year of follow up, we believe our inclusive approach represents a better approximation to the “true” relapse group that we would observe with 2 years of longitudinal data for all participants. In this study we chose to define the neural landscape in terms of within and between network connectivity using the canonical seven Yeo networks. While this choice is well-justified, there are models/evidence that support other meaningful organizational levels of inquiry (e.g., subnetworks or specific regions, especially subcortical regions). Restricting our analysis to 7 networks allowed for cortex-wide coverage while also minimizing the number of comparisons. This investigation is focused on resting state fMRI; future investigations will tests the effect of task-based fMRI or structural connectivity (e.g., white matter hyperintensities, diffusion imagining measures).

In conclusion, we identified robust differences in the functional connectome between healthy comparison and remitted participants in late-life, as well as differences between participants who relapsed and those who remained depression-free. These differences were apparent at the network level and were robust to parcellation scheme. These findings, consistent with our proposed disruption of neural homeostasis model of recurrence and relapse in late-depression, may be used to identify depressed older adults at higher risk of relapse and to adequately tailor preventative interventions to prevent the burden of additional depressive episodes.

4.1. Overview

Participants were enrolled in the multisite REMBRANDT Study (Recurrence markers, cognitive burden, and neurobiological homeostasis in late-life depression) at Vanderbilt University Medical Center (VUMC), University of Pittsburgh (Pitt.), and University of Illinois – Chicago (UIC). A full description of the study design and rationale has been published previously³⁵. Briefly, LLD participants enter through an Initial Treatment Phase (ITP) if currently depressed, or are recruited directly into the longitudinal phase if they recently (< 4 months) remitted to clinical treatment. LLD participants were treated for up to 20 weeks with an algorithm informed by STAR*D³⁶ and the Duke Neurocognitive Outcomes of Depression in the Elderly studies³⁷. Individuals who did not remit were referred for clinical care. Remission was defined as Montgomery-Åsberg Depression Rating Scale³⁸ (MADRS) score of ≤ 10 concluding the ITP and longitudinal baseline visit, which must occur within 4 months (but no sooner than 1 month) following remission. Remitted LLD and healthy comparison participants entered the two-year longitudinal phase involving scheduled contact every 2 months. This study was approved by the institutional review boards at all three sites. All participants provided informed consent. A CONSORT diagram for the study is shown in Supplemental Fig. 1.

4.2. Participants

We enrolled 145 participants, including 102 remitted LLD participants and 43 healthy comparison participants who completed baseline neuroimaging and at least 8 months of clinical follow-up. Inclusion criteria for all participants were: age ≥ 60, fluent in English, Montreal Cognitive Assessment (MoCA) ≥ 24 or MoCA-BLIND ≥ 18. Further inclusion criteria for LLD: current diagnosis of recurrent major depressive disorder (MDD) based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and MADRS ≥ 15 for depressed participants entering the ITP; remission from current MDD episode and MADRS ≤ 10 for LLD participants entering the longitudinal phase. Exclusion criteria for all participants: current or past Axis I diagnosis except generalized anxiety disorder, panic disorder, or simple phobias; history of substance dependence/abuse in the past year; acute grief or suicidality; current or past psychosis; neurological disorders, including dementia; unstable medical illness requiring treatment; MRI contraindications, electroconvulsive therapy in the past 6 months; current brain stimulation or ketamine/esketamine treatment. Healthy comparison participants were excluded for MADRS scores > 8, current or past depression diagnosis or use of psychotropic medication for psychiatric symptoms. Past brief therapy for specific challenges or losses was allowable.

We collected demographic, clinical, neuropsychological and MRI data from all participants. While data collection is currently ongoing, we have at least 8 months of longitudinal clinical data on all participants. Based on the available longitudinal data, we further stratified the LLD group into 59 stable remitted participants and 43 relapsed participants (see Assessments for definition).

4.3. Assessments

Depression severity was assessed at baseline and every two months throughout the study. Remitted LLD participants who maintained MADRS ≤ 15 over the duration of the study were classified as stable remitted. Initially remitted participants who experienced a MADRS > 15 plus DSM-5 MDD criteria for at least two weeks were classified as relapsed. In this study, we do not differentiate between relapse (reoccurrence of symptoms from previous episode) and recurrence (occurrence of a new depressive episode) since this delineation is often unclear or relies on arbitray time cutoffs.

4.4. MRI Acquisition

MRI were acquired 1–4 months after remission from an acute depressive episode at 3T on Philips Elition (VUMC), Siemens Prisma (Pitt) and GE Discovery MR750 System (UIC) scanners using 32-channel head coils following the Adolescent Brain Cognitive Development (ABCD) Study MRI Protocol³⁹ to harmonize across sites. We collected high resolution structural images with a sagittal T1-weighted Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequence, an axial T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) sequence, resting state with an axial T2* blood oxygen level dependent (BOLD) gradient-echo echoplanar imaging sequence, and a reverse encoded top-up image for correcting susceptibility artifacts matched to the resting sate acquisition parameters. Specific acquisition parameters are provided in Supplemental Tables 4 and 5 of the study protocol³⁵. Two separate 5 minute resting state scans (188 volumes per scan) were acquired back-to-back, which has been shown to improve reliability of measures⁴⁰. We assessed scanner variability across the sites using structural⁴¹ and functional phantoms⁴². All sites showed acceptable levels of signal-to-noise, though there were differences by site; therefore, we control for site in all analyses.

4.5. MRI Processing

MRI were processed at VUMC using Docker containers in XNAT to ensure reproducibility with in-house scripts utilizing the Statistical Parametric Mapping (SPM12) toolbox⁴³ except where noted. The FLAIR image was coregistered to the MPRAGE and both images were used in multispectral segmentation into six canonical tissue types, which produces a deformation field for normalizing images into Montreal Neurological Institute (MNI) space. Two were Gaussians used for white matter to account for white matter hyperintensities common in older adults⁴⁴. The resulting grey matter, white matter, and cerebrospinal fluid (CSF) tissue maps were thresholded at 0.1, filled, and smoothed to create an intracranial volume mask used to skull-strip the structural images.

Resting state images were slice-time corrected, corrected for susceptibility induced distortions using topup in FSL⁴⁵, realigned to the mean volume using rigid body transformations, skull-stripped using the brain extraction tool in FSL⁴⁶, coregistered to the MPRAGE, normalized to MNI space (2mm³ isotropic resolution) using the structural deformation field, and smoothed with a Gaussian kernel with 8mm full width at half maximum. The first five principal components of the white matter and CSF, six motion parameters, and sinusoids for bandpass filtering in the 0.008–0.15 Hz frequency range were regressed out of the image.

4.6. Connectome Calculation

We calculated FC matrices by extracting the first principal component of processed image time series in predefined parcellation regions and quantifying region-to-region FC as Pearson correlation of the time series. To ensure results are robust to parcellation scheme, we employed three different functionally-defined parcellations: Shen268⁴⁷, Schaefer200, and Schaefer400⁴⁸, resulting in three FC matrices of size 268x268, 200x200, and 400x400 per scan, with each entry referred to as an edge in the connectome. We overlayed the Yeo 7 Network definitions⁴⁹ (DMN, ECN, SN, DAN, limbic network, visual network, and SMN) on these atlases and assigned each node to a canonical intrinsic network with a winner-take-all approach. We restrict our network analysis to within and between network connectivity of these 7 networks for a total of 28 “features” of the neural landscape.

4.7. Statistical Analysis

We assessed for differences between the healthy comparison and LLD participants (2 group comparison) and between the healthy comparison, stable remitted, and relapsed groups (3 group comparison) using the generalized linear model. Post-hoc tests for significant 3 group comparison results are performed to determine which groups show differences and the directionality of the effect. We also tested time-to-relapse in the LLD group using a Cox proportional hazards model. Age, sex, race, education, and site were controlled for in all models.

For this analysis, we employ mass univariate testing at the edge-level (each entry of the FC matrices) and perform inference at higher organizational levels (e.g., network) on subsets of these tests using higher criticism (HC). HC is omnibus test optimal for detecting rare and weak signals^50,51. This is accomplished by comparing the observed p-values to the theoretical null using a modified Kolmogrov-Smirnov statistic; hence HC only tests for difference among a collection of edges—it does NOT offer inference at the edge-level. This approach optimally balances power and specificity for connectome analysis and has recently been applied to neuroimaging^52–54.At the network level, HC is applied only to entries in the FC matrix corresponding to the specified network pairs (i.e., partitioned submatrices for between network connectivity and triangular blocks on the diagonal for within network connectivity). Thus, while we preform primary statistical tests on full connectome matrices with 200 to 400 nodes, we only perform inference on the 7-network connectome with the many region-to-region tests essentially functioning as multiple observations within each network pair. We apply the Benjamini-Hochberg procedure for controlling the False Discovery Rate at \(\:\alpha\:=0.05\) with \(\:m=28\). We calculate p values for the HC statistic by comparison to a Monte Carlo simulation of 10,000 null distributions for the given number of tests (edges or entries in the FC submatrix).

To compare the connectivity profiles of stable remitted and relapse participants to healthy comparisons, we computed a participant similarity matrix. Similarity was calculated as the inverse of the Euclidean distance between the FC matrix entries of two participants (inverse of the Frobenius norm of the difference FC matrix). Similarity between healthy comparisons and stable remitted was compared to similarity between healthy comparisons and relapse with t-tests. Note that age, sex, race, and education were not controlled for in this analysis since each similarity metric compares two participants.

We only report network results that were significant across at least two of the parcellations and note results that were significant across all three parcellations. For brevity, we only report median test statistics across the three parcellations in the text with results for all parcellations contained in the supplement.

Disclosures: Olusola Ajilore is a co-founder of Keywise AI, has served as a consultant for Sage Therapeutics and Otsuka, has received honoraria from Boehringer Ingelheim, and is on the advisory board for Blueprint Health and Embodied Labs. Antonija Kolobaric serves as a consultant for Radicle Science. No other authors have disclosures to report.

Acknowledgements: We’d like to thank the numerous staff at VUMC, Pitt, and UIC who helped administer the study protocol and process the data.

Previous presentation: This work was presented as a poster at conferences for the American Association for Geriatric Psychiatry (March 16, 2024, Atlanta, GA) and the Society for Biological Psychiatry (May 10, 2024, Austin, TX).

Data Availability: Deidentified data is available upon reasonable request. The analysis script is available at https://github.com/REMBRANDT-study/resting_state_network_analysis_baseline145.

Funding: This work was supported by National of Institute of Health grants R01 MH121619, R01 MH121620, R01 MH121384, K01 MH133913, R01 MH108509, K01 MH122741, T32 MH019986, and the National Center for Advancing Translational Sciences grants UL1 TR000445 and UL1 TR002243.

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Yes there is potential Competing Interest. Olusola Ajilore is a co-founder of Keywise AI, has served as a consultant for Sage Therapeutics and Otsuka, has received honoraria from Boehringer Ingelheim, and is on the advisory board for Blueprint Health and Embodied Labs. Antonija Kolobaric serves as a consultant for Radicle Science. No other authors have disclosures to report.

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Network homeostasis: functional brain network alterations and relapse in remitted late-life depression

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Abstract

Figures

1. Introduction

2. Results

2.1. All remitted LLD participants vs. healthy comparisons

2.2. Stable remitted vs. relapse vs. healthy comparisons

2.2.1. Stable remitted vs. relapse participants

2.2.2. Stable remitted and relapse vs. healthy comparisons

3. Discussion

4. Methods

4.1. Overview

4.2. Participants

4.3. Assessments

4.4. MRI Acquisition

4.5. MRI Processing

4.6. Connectome Calculation

4.7. Statistical Analysis

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References

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Supplementary Files

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