This study investigated the topology of the frontostriatal salience network in individuals with depression using precision functional mapping and longitudinal fMRI data. The researchers found that the salience network is significantly expanded in the cortex of most individuals with depression, a finding replicated across multiple independent samples and datasets. This expansion appears to be a stable, trait-like feature, detectable even before depression onset in children, and may be related to specific patterns of encroachment on neighboring brain networks.
Description: Illustrates the significant expansion of the salience network in the cortex of individuals with depression compared to healthy controls, providing visual and quantitative evidence of the effect size and replicability across samples.
Relevance: Provides the core visual evidence for the primary finding of the study.
Description: Demonstrates the heterogeneity of salience network expansion by identifying three distinct encroachment profiles affecting neighboring functional systems, highlighting individual variability in how the network is altered in depression.
Relevance: Expands upon the primary finding by revealing distinct patterns of network reorganization.
This study provides compelling evidence for a robust and reproducible expansion of the frontostriatal salience network in individuals with depression. The findings suggest that this expansion may be a stable, trait-like marker for depression risk, potentially detectable before symptom onset. Further research is needed to explore the causal relationships, functional consequences, and specificity of this finding, but it holds promise for advancing our understanding of depression and developing new strategies for early detection and personalized interventions.
This abstract presents a study that used precision functional mapping to investigate brain network topology in individuals with depression. The study found that the frontostriatal salience network is significantly expanded in the cortex of most individuals with depression, a finding that was replicated across multiple samples. This expansion appears to be a stable, trait-like feature present even before the onset of depression in children. The study also found that changes in connectivity within the salience network, particularly between the striatum and anterior cingulate cortex, tracked fluctuations in anhedonia symptoms and predicted future anhedonia.
The abstract effectively summarizes the key findings of the study in a clear and concise manner. The language is accessible, and the main points are easy to understand.
The abstract highlights the strength of the evidence supporting the findings, emphasizing replication across multiple samples and large datasets.
The abstract effectively conveys the novelty of the findings, particularly the identification of salience network expansion as a potential trait-like marker for depression risk.
While the abstract mentions potential mechanisms underlying salience network expansion, it could benefit from a brief elaboration on the possible interplay between activity-dependent and genetic factors.
Rationale: Providing a glimpse into the potential mechanisms would enhance the abstract's scientific depth and pique reader interest.
Implementation: Add a sentence briefly discussing the potential roles of both activity-dependent plasticity and genetic influences in shaping network topology.
The abstract could strengthen its conclusion by explicitly stating the potential clinical implications of the findings, particularly for early identification and personalized treatment of depression.
Rationale: Highlighting the translational potential of the research would underscore its significance and broader impact.
Implementation: Conclude with a sentence emphasizing the potential of these findings for developing new strategies for early detection and personalized interventions for depression.
The abstract could benefit from addressing the specificity of salience network expansion to depression. While a preliminary analysis is mentioned, a brief statement about the need for further investigation in other psychiatric conditions would be valuable.
Rationale: Clarifying the specificity of the findings to depression would strengthen the study's contribution to understanding the neurobiology of this disorder.
Implementation: Include a sentence acknowledging the need for further research to determine the specificity of salience network expansion to depression and its potential relevance to other psychiatric disorders.
This introduction section establishes the context for a study investigating the topology of functional brain networks in individuals with depression, particularly focusing on the salience network. It highlights the limitations of previous neuroimaging studies in depression, which have primarily relied on cross-sectional designs and group-average parcellations, resulting in modest effect sizes and limited understanding of mood-state transitions. The section introduces the concept of precision functional mapping as a novel approach to delineate individual-specific network topology and its potential to reveal more meaningful differences in brain organization related to depression.
The introduction effectively establishes the need for the study by clearly articulating the limitations of previous research in understanding the neurobiology of depression.
The introduction provides a strong rationale for using precision functional mapping by highlighting its potential to reveal individual-specific network topology and overcome the limitations of group-average approaches.
The introduction convincingly argues for the importance of longitudinal data in capturing the dynamic nature of depression and understanding mood-state transitions.
While the introduction mentions the salience network, it could benefit from a more detailed explanation of its functions and relevance to depression.
Rationale: Providing a more comprehensive overview of the salience network would enhance the reader's understanding of its potential role in depression and why its topology is a focus of the study.
Implementation: Add a paragraph briefly describing the key functions of the salience network, its involvement in reward processing, interoception, and cognitive control, and its potential implications for depression based on previous research.
The introduction could be strengthened by stating more specific hypotheses regarding the expected differences in salience network topology in individuals with depression.
Rationale: Formulating specific, testable hypotheses would provide a clearer direction for the study and enhance the interpretation of the findings.
Implementation: Based on the rationale provided, propose specific hypotheses about the expected size, shape, or spatial location of the salience network in individuals with depression compared to healthy controls. For example, hypothesize that the salience network will be larger or that its boundaries will encroach on specific neighboring networks in individuals with depression.
While not explicitly mentioned, the introduction could briefly acknowledge the potential ethical implications of identifying brain-based markers for depression risk.
Rationale: Addressing ethical considerations, such as potential stigma or misuse of such markers, is crucial for responsible scientific conduct.
Implementation: Add a sentence acknowledging the importance of considering the ethical implications of identifying brain-based markers for depression risk and the need for responsible use and interpretation of such findings.
This Results section presents the findings of a study investigating the topology of the frontostriatal salience network in individuals with depression. The key finding is a significant expansion of this network in the cortex of depressed individuals, a phenomenon replicated across multiple samples and datasets. The section further explores the nature of this expansion, identifying three distinct modes of encroachment on neighboring networks, and investigates its stability over time, relationship to symptom severity, and presence before symptom onset in children. Finally, the section examines the dynamic changes in functional connectivity within the salience network, particularly between the striatum and anterior cingulate cortex, and their relationship to fluctuations in anhedonia symptoms.
The section presents compelling evidence for the salience network expansion in depression, with multiple replications across different samples and datasets, including both individual-level and group-average data. This strengthens the reliability and generalizability of the findings.
The section goes beyond simply reporting the expansion and provides a detailed characterization of its nature, including the identification of three distinct encroachment modes and their relationship to specific neighboring networks. This adds depth to the understanding of how the salience network is altered in depression.
The use of longitudinal data allows the authors to investigate the stability of the salience network expansion over time and its relationship to symptom fluctuations. This provides valuable insights into the trait-like nature of the expansion and its potential as a risk marker for depression.
While the section thoroughly describes the topological changes in the salience network, it could benefit from a more in-depth discussion of the functional significance of these alterations. How might the expansion and encroachment on other networks affect the salience network's role in reward processing, interoception, and cognitive control in individuals with depression?
Rationale: Exploring the functional implications of the findings would bridge the gap between structural changes and their potential behavioral and clinical manifestations.
Implementation: Add a paragraph discussing the potential functional consequences of salience network expansion, drawing on existing literature on the salience network's functions and its relevance to depression. Consider how the encroachment on specific neighboring networks might contribute to specific symptom domains or cognitive deficits observed in depression.
The section mentions a preliminary analysis suggesting that the salience network is also expanded in individuals with bipolar II disorder. This raises the question of the specificity of this finding to depression. Further investigation into other psychiatric conditions with overlapping symptoms, such as anxiety disorders or trauma-related disorders, is warranted.
Rationale: Determining the specificity of salience network expansion to depression or its presence in other disorders would clarify its potential as a diagnostic or transdiagnostic marker.
Implementation: Expand the preliminary analysis to include a larger sample of individuals with bipolar disorder and other relevant psychiatric conditions. Compare the patterns of salience network expansion across disorders to identify potential similarities or differences. Discuss the implications of these findings for understanding the shared and distinct neurobiological mechanisms underlying different psychiatric conditions.
The section primarily focuses on correlational relationships between salience network topology and symptoms. While the longitudinal data provide some insights into temporal associations, further research is needed to establish causal relationships. Does salience network expansion contribute to the development of depression, or is it a consequence of the disorder?
Rationale: Understanding the causal direction of the relationship between salience network expansion and depression is crucial for developing targeted interventions.
Implementation: Consider longitudinal studies with larger samples and longer follow-up periods to assess whether salience network expansion precedes the onset of depression. Explore potential genetic or environmental factors that might contribute to both salience network expansion and depression risk. Investigate the effects of interventions, such as psychotherapy or neuromodulation, on both salience network topology and symptom severity to assess whether changes in network structure are associated with clinical improvement.
Figure 1, titled 'Frontostriatal salience network is expanded nearly twofold in the cortex of highly sampled individuals with depression,' illustrates the expansion of the salience network in individuals with depression compared to healthy controls. The figure includes brain images showing the location and size of the salience network, scatter plots comparing the percentage of cortex occupied by the network in different groups (73% larger in the SIMD dataset), density maps highlighting the expansion in cortical zones, and a confusion matrix demonstrating the accuracy (78.4%) of an SVM classifier in distinguishing individuals with depression from healthy controls based on functional network sizes. Key numeric data includes: 5.49% ± 0.76% of cortex occupied by the salience network in the SIMD dataset, 3.17% ± 0.85% in healthy controls, Cohen's d of 1.99 for the group difference, and classifier accuracy of 78.4%.
Text: "It was immediately apparent on visual inspection that the salience network, which is involved in reward processing and conscious integration of autonomic feedback and responses with internal goals and environmental demands30,40,41, was markedly larger in these individuals with depression (Fig. 1a,b)."
Context: This sentence appears in the third paragraph of the 'Salience network expansion in depression' section on page 3. It introduces the observation that the salience network is visibly larger in individuals with depression, as depicted in Figure 1.
Relevance: This figure is central to the section's main point about the expanded salience network in depression. It provides visual and quantitative evidence supporting this claim, demonstrating the effect's magnitude and replicability across different samples and methodologies.
Figure 2, titled 'Three modes of salience network expansion in depression,' explores the different patterns of salience network expansion in individuals with depression. It shows a central tendency functional network map for healthy controls, identifies encroaching parts of the salience network in individuals with depression, and classifies them as ectopic intrusions or border shifts. The figure also presents bar graphs quantifying the frequency of border shifts and ectopic intrusions, the networks most often displaced by salience network expansion (default mode, frontoparietal, and cingulo-opercular networks), and the network displacement in different cortical zones. A heatmap reveals three distinct modes of encroachment across individuals.
Text: "To this end, we first generated a central tendency functional network map for the 37 healthy controls (Fig. 2a)."
Context: This sentence, found in the second paragraph of the 'Three salience network expansion modes' section on page 4, introduces the first step in analyzing the different modes of salience network expansion, as depicted in Figure 2a.
Relevance: Figure 2 builds upon the findings of Figure 1 by delving into the heterogeneity of salience network expansion. It demonstrates that this expansion is not random but follows specific patterns, primarily involving border shifts affecting neighboring higher-order functional systems.
Figure 3, titled 'Salience network expansion is stable over time and present before symptom onset,' investigates the stability of salience network expansion and its presence before the onset of depression. It includes line graphs showing the stability of salience network size across multiple study visits in both healthy controls and individuals with depression, scatter plots demonstrating no significant correlation between salience network size and depression severity (HDRS6), symptom severity, or chronicity, and a bar graph indicating no change in salience network size after rTMS treatment. A key finding is that the salience network is larger in children who later developed depression compared to those who did not, suggesting its presence before symptom onset.
Text: "Furthermore, within-subject analyses showed no significant correlation between fluctuations in depression symptoms (HDRS6, a more sensitive measure of changes on shorter timescales) and changes in salience network size over time in any of the densely sampled individuals in our SIMD dataset (Fig. 3b)."
Context: This sentence, located in the first paragraph of the 'Salience network topology is trait-like' section on page 4, highlights the lack of correlation between symptom fluctuations and salience network size, as illustrated in Figure 3b.
Relevance: Figure 3 addresses a crucial question about the nature of salience network expansion: is it a state-dependent marker fluctuating with mood or a stable trait-like feature? The figure's data supports the latter, showing the expansion's stability over time and its presence even before symptom onset, suggesting a potential role in depression risk.
Figure 4, titled 'Frontostriatal salience network connectivity predicts fluctuations in anhedonia and anxiety symptoms in deeply sampled individuals with depression over time,' examines the relationship between salience network connectivity and symptom fluctuations. It includes a heatmap showing variations in anhedonia-related symptoms over time, brain images highlighting the relevant network nodes, correlation matrices depicting the association between connectivity strength and symptoms, and scatter plots showing the predictive relationship between connectivity and future symptoms. Key findings include: connectivity between the nucleus accumbens and anterior cingulate cortex tracks anhedonia fluctuations and predicts future anhedonia in one individual, while connectivity between the nucleus accumbens and anterior insula correlates with anxiety fluctuations.
Text: "During a period spanning 1.5 years, we observed significant fluctuations in ten anhedonia-related measures (Fig. 4a), which were derived from five standardized depressive symptom scales and identified by a consensus clinical decision by three study co-authors (Supplementary Fig. 9), ranging from mild or negligible to severe."
Context: This sentence, found in the first paragraph of the 'Connectivity state predicts anhedonia' section on page 6, describes the observed fluctuations in anhedonia-related measures in one individual (SIMD-4), as depicted in Figure 4a.
Relevance: Figure 4 shifts the focus from network topology to connectivity, demonstrating that while the salience network's size is stable, its connectivity fluctuates with mood state. This finding suggests that connectivity changes within the salience network, particularly in frontostriatal circuits, might contribute to the dynamic nature of depressive symptoms.
Extended Data Figure 1 provides a visual overview of the Serial Imaging of Major Depression (SIMD) project. It includes a timeline illustrating the study procedures, which involved repeated multi-echo resting-state fMRI scans and clinical assessments. The figure also depicts brain surface renderings showcasing functional network parcellations and surface area representations. Additionally, it presents a stacked bar chart comparing network sizes between depression and control groups, a line graph visualizing symptom severity fluctuations over time, and a bar chart displaying network size calculations for different brain regions.
Text: "The same precision mapping procedures were applied to 37 highly sampled healthy controls with an average of 327.49 min of fMRI data per subject (range 43.36–841.2 min) across 12 sessions (range 2–84 sessions)."
Context: This sentence, appearing early in the 'Salience network expansion in depression' section, introduces the healthy control sample used for comparison and mentions that the same precision mapping procedures were applied to them, as detailed in Extended Data Figure 1.
Relevance: This figure is relevant because it visually outlines the study design and methods of the SIMD project, providing context for the subsequent findings on salience network expansion in individuals with depression. It helps the reader understand the data acquisition process and the approach to analyzing functional network topology.
Extended Data Figure 2 presents six scatter plots demonstrating that the observed salience network expansion in individuals with depression remains statistically significant even after controlling for potential confounding variables. The plots compare the percentage of cortex occupied by the salience network in healthy controls and individuals with depression, accounting for sex ratio imbalance, head motion, and age. The figure shows that the salience network is consistently larger in individuals with depression across all three replication samples and the discovery sample, regardless of the covariates included.
Text: "This effect was replicated thrice (Fig. 1c, right), again with medium to large effect sizes (Cohen’s d = 0.77–0.84), remained statistically significant when controlling for the sex ratio imbalance in our samples (56.7% of individuals with depression were female, versus 31% of the healthy controls; Supplementary Fig. 4 and Extended Data Fig. 2) and with or without correction for potential site- or scanner-induced biases (Supplementary Fig. 5)."
Context: This sentence, following the initial description of salience network expansion in individuals with depression, emphasizes the replication of the finding and mentions that the effect remained statistically significant even after controlling for sex ratio imbalance, as detailed in Extended Data Figure 2.
Relevance: This figure is crucial in addressing potential concerns about confounding variables influencing the observed salience network expansion. It strengthens the study's findings by demonstrating that the effect is not solely driven by differences in sex, head motion, or age between the groups.
Extended Data Figure 3 explores the relationship between salience network expansion and the size of neighboring functional systems. It presents brain images depicting the salience network, default mode network, frontoparietal network, and cingulo-opercular network in both a group-average map of healthy controls and three representative individuals with depression. The figure also includes scatter plots comparing the size of these networks between healthy controls and individuals with depression. Notably, the figure suggests that while salience network expansion is consistently observed, the contraction of other networks, particularly the cingulo-opercular network, is less consistent across samples.
Text: "Accordingly, expansion of the salience network in cortex was accompanied by contraction of neighbouring functional systems in the SIMD sample (Extended Data Fig. 3)."
Context: This sentence, appearing after the description of salience network expansion in individuals with depression, introduces the observation that this expansion is accompanied by a contraction of neighboring functional systems, as illustrated in Extended Data Figure 3.
Relevance: This figure is relevant because it investigates the potential consequences of salience network expansion on the organization of other functional brain networks. It suggests that the brain may compensate for the increased salience network size by reducing the size of neighboring networks, although this effect appears to be variable across datasets.
Extended Data Figure 4 provides further evidence for salience network expansion in individuals with depression by analyzing large group-average datasets. It presents brain images depicting the salience network mapped using data from previous studies of both healthy controls and individuals with depression. The figure shows that the salience network occupies a significantly larger percentage of cortex in the depression samples compared to the healthy control samples, supporting the findings from the individual-level analyses.
Text: "To better understand whether this effect was also detectable in large, previously published samples involving conventional single-echo fMRI data, we identified the salience network in group-average functional connectivity data from two large datasets involving n = 812 (ref. 47) and n = 120 (ref. 45) healthy controls, respectively, and in a third dataset involving n = 299 individuals with treatment-resistant depression scanned in association with a neuromodulation intervention study48."
Context: This sentence, appearing towards the end of the 'Salience network expansion in depression' section, introduces the analysis of large group-average datasets to further validate the finding of salience network expansion in individuals with depression, as detailed in Extended Data Figure 4.
Relevance: This figure is important because it demonstrates that the observed salience network expansion is not limited to the specific samples analyzed in the study but is also detectable in large, independent datasets. This strengthens the generalizability of the findings and suggests that salience network expansion may be a robust feature of depression.
Extended Data Figure 5 assesses the within-person stability of salience network topology and connectivity using split-half reliability testing. It presents brain images depicting the salience network in two individuals with depression (SIMD-2 and SIMD-4), comparing the network maps derived from the first and second halves of their fMRI data. The figure shows highly similar patterns of salience network topology and functional connectivity in both individuals, indicating that the observed expansion is a stable and reproducible feature within individuals.
Text: "Furthermore, highly similar patterns of salience network topology and functional connectivity were produced in split-half analyses of each SIMD dataset (Extended Data Fig. 5), indicating that salience network expansion was a robust and reproducible feature of the brains of these highly sampled individuals."
Context: This sentence, appearing at the end of the 'Salience network expansion in depression' section, highlights the stability and reproducibility of salience network expansion within individuals, as demonstrated by split-half analyses detailed in Extended Data Figure 5.
Relevance: This figure is relevant because it addresses the reliability of the observed salience network expansion. By demonstrating high split-half reliability, the figure supports the notion that the expansion is not due to random fluctuations in the data but reflects a stable characteristic of brain organization in individuals with depression.
Extended Data Fig. 6 explores the relationship between salience network expansion and other brain characteristics. Panel (a) is a bar chart showing the percentage enrichment of different brain networks affected by salience network expansion. Panel (b) includes a dot plot depicting the similarity of the average salience network encroachment map to 73 canonical brain maps from the neuromaps toolbox, along with brain images visualizing some of these maps. The dot plot highlights significant associations with gradients of functional connectivity and gene expression, neurotransmitter receptor distribution, intracortical myelin, and individual variability in functional connectivity.
Text: "Comparison to 73 independent molecular, microstructural, electrophysiological, developmental and functional brain maps from neuromaps toolbox53 showed that salience network expansion frequently occurred in brain regions with less intracortical myelin and thus greater capacity for synaptic plasticity54 and for which individual differences in functional connectivity55 and the concentration of particular neurotransmitter receptors (μ-opioid56 and histamine H3 receptors57) are most pronounced (Extended Data Fig. 6)."
Context: This sentence, located in the 'Three salience network expansion modes' section on page 4, describes the analysis comparing the salience network encroachment map to various brain characteristics using the neuromaps toolbox. It highlights the finding that salience network expansion is associated with specific brain regions characterized by lower intracortical myelin, greater plasticity, and distinct patterns of functional connectivity and neurotransmitter receptor distribution.
Relevance: This figure provides insights into the potential biological mechanisms underlying salience network expansion in depression. It suggests that this expansion is not random but rather targets specific brain regions with distinct molecular, microstructural, and functional properties, potentially reflecting altered plasticity and information processing priorities.
Extended Data Fig. 7, with its full caption on the following page, investigates the stability of atypical functional connectivity patterns associated with salience network expansion in depression. It presents two heatmaps (a and b) showing the functional connectivity strength between encroaching and non-encroaching vertices of the salience network relative to runner-up network assignments, using split halves of each individual's resting-state fMRI data. The accompanying bar graphs display the average functional connectivity strength for each network. The caption highlights that functional connectivity between encroaching salience network vertices and the rest of the salience network was significantly stronger than with the runner-up networks, indicating stable atypical connectivity patterns.
Text: "As expected, the functional connectivity of encroaching salience network nodes with the rest of the salience network was significantly stronger (mean Z(r) = 0.26) than with the displaced networks (all mean Z(r) < 0.12), consistent with weakened connectivity between encroaching nodes and the functional networks that typically occupy that space in healthy controls (Extended Data Fig. 7)."
Context: This sentence, found in the 'Three salience network expansion modes' section on page 4, describes the analysis comparing the functional connectivity strength of encroaching salience network nodes with the rest of the salience network versus the displaced networks. It highlights the finding that encroaching nodes show stronger connectivity with the salience network, supporting the idea of altered network allocation.
Relevance: This figure supports the finding that salience network expansion in depression is not merely a spatial phenomenon but also involves stable alterations in functional connectivity. It demonstrates that encroaching salience network nodes exhibit stronger connectivity with the salience network than with the networks they displace, suggesting a functional reallocation of these regions.
This figure, a continuation of Extended Data Fig. 7, delves deeper into the stability of atypical functional connectivity associated with salience network expansion. It uses split-half analyses of resting-state fMRI data to compare the functional connectivity strength of encroaching salience network vertices with the rest of the salience network versus the networks they typically displace in healthy controls. The figure demonstrates that encroaching vertices exhibit significantly stronger connectivity with the salience network, a pattern consistent across both halves of the data, indicating stability.
Text: "This analysis was performed using split halves of each individual’s resting-state fMRI dataset to assess the stability of the salience network assignment associated with the encroaching vertices relative to the runner-up assignments (most often either the default mode, frontoparietal or cingulo-opercular network)."
Context: This sentence, located in the 'Three salience network expansion modes' section on page 4, introduces the split-half analysis used to evaluate the stability of the salience network assignment for encroaching vertices. It emphasizes the comparison to runner-up network assignments, typically the default mode, frontoparietal, or cingulo-opercular networks.
Relevance: This figure reinforces the concept that salience network expansion in depression is accompanied by stable changes in functional connectivity. The split-half analyses provide strong evidence that encroaching salience network vertices are functionally more connected to the salience network than to the networks they displace, supporting the notion of a functional reallocation of cortical territory.
Extended Data Fig. 8 investigates the cortical representation of the salience network in adults with late-onset depression (LOD). It presents a bar graph comparing the percentage of cortex occupied by the salience network in five individuals with LOD and a control group of 37 healthy individuals. The graph shows that the salience network occupies a significantly larger portion of the cortex in individuals with LOD compared to healthy controls.
Text: "A similar effect was observed in adults with late-onset depression (Extended Data Fig. 8)."
Context: This sentence, located in the 'Salience network topology is trait-like' section on page 5, refers to the finding that salience network expansion is also present in adults with late-onset depression, similar to the observation in children who later developed depression.
Relevance: This figure extends the main finding of salience network expansion to a specific subgroup of individuals with depression, those with late-onset depression. It suggests that this atypical network topology is not limited to early-onset depression but may be a more general feature of the disorder, regardless of age of onset.
Extended Data Fig. 9 focuses on the relationship between depressive symptoms and functional connectivity in a second individual with depression (SIMD-6). Panel (a) presents a heatmap illustrating fluctuations in anhedonia-related symptoms over time, derived from various clinical scales. Panel (b) shows brain images depicting the functional connectivity of salience network voxels in the nucleus accumbens during periods of low and high anhedonia. Panel (c) displays histograms illustrating the stability of the correlation between nucleus accumbens-anterior cingulate functional connectivity and anhedonia, assessed using bootstrap resampling.
Text: "An identical analysis in SIMD-6, involving 39 study visits with clinical and fMRI data over 8 months, replicated this effect (Fig. 4c,d and Extended Data Fig. 9a,b)."
Context: This sentence, located in the 'Connectivity state predicts anhedonia' section on page 8, refers to the replication of the finding that functional connectivity between salience network nodes is correlated with changes in anhedonia over time. It specifically mentions the analysis in SIMD-6, which is detailed in Extended Data Fig. 9.
Relevance: This figure provides further support for the link between functional connectivity within the salience network and fluctuations in anhedonia symptoms. It replicates the findings from SIMD-4 in a second individual with depression, strengthening the evidence for the role of this circuit in anhedonia.
Extended Data Figure 10 presents a visual representation of the long-term assessment of anhedonia and anxiety-related symptoms in two deeply-sampled individuals with major depression, SIMD-4 and SIMD-6. The figure features two panels, one for each individual, each containing a heatmap that illustrates fluctuations in the severity of 27 clinical symptoms related to anxiety over time. The heatmaps use a color gradient, with blue indicating lower severity and red indicating higher severity. The figure also includes line graphs depicting the fluctuations in anhedonia (blue) and anxiety (red) over time for each individual. The caption mentions that the first principal component (PC1) of the anhedonia and anxiety measures were modestly correlated with one another within each individual over time (Pearson correlation, MDD04: r = 0.41, P < 0.001; MDD06: r = 0.45, P < 0.001).
Text: "See Extended Data Fig. 10 for stacked anhedonia and anxiety symptom heatmaps."
Context: This sentence appears in the 'Connectivity state predicts anhedonia' section on page 8, within the context of discussing the dissociability of anhedonia and anxiety symptoms in depression.
Relevance: This figure is relevant to the section's discussion of the relationship between salience network connectivity and specific symptom domains in depression. It provides visual evidence that anhedonia and anxiety symptoms can fluctuate independently over time, supporting the notion that different neural circuits may be involved in these distinct symptom domains.
This Discussion section interprets the study's findings on salience network expansion in depression, highlighting its potential as a trait-like marker for depression risk and discussing possible underlying mechanisms. It also addresses the implications of the findings for understanding the interplay between trait and state effects in depression, the potential clinical utility of salience network expansion as a biomarker, and the need for further research to explore causal relationships and specificity to depression. The section concludes by emphasizing the value of precision functional mapping and longitudinal sampling in advancing our understanding of depression and developing personalized treatments.
The discussion provides a thorough and insightful interpretation of the study's findings, considering multiple perspectives and addressing key questions raised by the results.
The discussion goes beyond simply describing the findings and delves into potential underlying mechanisms, proposing two plausible hypotheses supported by evidence from the current study and previous research.
The discussion effectively highlights the potential clinical implications of the findings, particularly for early identification of individuals at risk for depression and for developing personalized treatment strategies.
While the discussion touches upon the potential functional significance of salience network expansion, it could benefit from a more in-depth exploration of how the altered network topology might affect specific cognitive processes and behavioral patterns relevant to depression.
Rationale: A more detailed discussion of the functional consequences would strengthen the link between the observed structural changes and their potential behavioral and clinical manifestations.
Implementation: Expand the discussion on functional significance by elaborating on how salience network expansion might affect specific cognitive functions, such as attention to internal states (interoception), reward sensitivity, emotional regulation, and cognitive control. Consider how these alterations might contribute to the core symptoms of depression, such as anhedonia, fatigue, and difficulty concentrating.
The discussion acknowledges the need for further research to assess the specificity of salience network expansion to depression. This could be strengthened by explicitly considering the possibility of this finding being a transdiagnostic marker, relevant to other psychiatric conditions characterized by similar disruptions in reward processing, emotional regulation, or cognitive control.
Rationale: Exploring the transdiagnostic potential of salience network expansion would broaden the scope of the findings and contribute to a more comprehensive understanding of the neurobiological underpinnings of various psychiatric disorders.
Implementation: Expand the discussion on specificity by explicitly addressing the possibility of salience network expansion being a transdiagnostic marker. Discuss the potential relevance of this finding to other disorders, such as anxiety disorders, trauma-related disorders, and substance use disorders, which share overlapping symptoms and neural circuitry with depression. Propose specific research questions and study designs to investigate the presence and clinical significance of salience network expansion in these other conditions.
While the discussion mentions the potential clinical utility of salience network expansion as a biomarker, it does not explicitly address the ethical implications of identifying individuals at risk for depression based on brain imaging data.
Rationale: Discussing the ethical considerations is crucial for ensuring responsible use and interpretation of such findings and mitigating potential harms, such as stigma, discrimination, or inappropriate interventions.
Implementation: Add a paragraph discussing the ethical implications of using salience network expansion as a biomarker for depression risk. Address potential concerns related to stigma, privacy, informed consent, and the potential for misuse of such information. Emphasize the importance of careful interpretation of brain imaging data in clinical settings and the need for clear guidelines and safeguards to protect individuals' rights and well-being.
This Methods section meticulously outlines the datasets, MRI acquisition parameters, preprocessing steps, and analysis techniques employed in the study investigating salience network topology in individuals with depression. It provides a comprehensive description of the participant groups, including the SIMD dataset of deeply sampled individuals with depression and various healthy control datasets. The section details the multi-echo and single-echo fMRI acquisition protocols, anatomical preprocessing, cortical surface generation, and denoising procedures for both multi-echo and single-echo data. It further explains the surface processing, CIFTI generation, precision mapping of functional brain networks using the InfoMap algorithm, calculation of network size and spatial locations, classification analysis, evaluation of network displacement, assessment of network stability over time, and longitudinal analyses relating connectivity changes to symptom severity.
The Methods section provides a highly detailed and comprehensive description of the datasets, MRI acquisition parameters, preprocessing steps, and analysis techniques. This level of detail is commendable and allows for thorough replication of the study.
The study employs state-of-the-art neuroimaging techniques, including multi-echo fMRI, advanced denoising methods (ME-ICA and ICA-AROMA), and precision functional mapping using the InfoMap algorithm. This ensures high data quality and allows for robust and reliable analysis of individual-specific network topology.
The Methods section emphasizes the importance of quality control throughout the data acquisition and analysis pipeline. This includes manual review of component classifications in ICA, careful motion censoring, and correction for potential biases. Such rigorous quality control procedures enhance the validity and reliability of the findings.
While the Methods section states that no statistical tests were used to predetermine sample sizes, it would be beneficial to provide a rationale for the chosen sample sizes, particularly for the SIMD dataset (n=6). Given the focus on individual-level analyses, a discussion of the statistical power and the feasibility of detecting meaningful effects with such a small sample size would be valuable.
Rationale: Addressing sample size considerations would enhance the transparency and rigor of the study, allowing readers to better assess the generalizability of the findings.
Implementation: Add a paragraph discussing the rationale for the chosen sample sizes, particularly for the SIMD dataset. Consider factors such as the expected effect size, the variability within the population, and the feasibility of recruiting and scanning deeply sampled individuals. If possible, provide power calculations or simulations to support the chosen sample sizes.
The Methods section mentions that free parameters for the InfoMap algorithm were fixed across subjects but does not specify the chosen parameter values. Providing a rationale for the selected parameters and exploring the potential impact of parameter choices on the resulting network parcellations would be beneficial.
Rationale: Transparency regarding parameter choices and their potential influence on the results is essential for reproducibility and for understanding the robustness of the findings.
Implementation: Specify the chosen parameter values for the InfoMap algorithm and provide a justification for these choices. Consider conducting sensitivity analyses to assess the impact of varying parameter values on the resulting network parcellations. Discuss the implications of these findings for the interpretation of the main results.
The Methods section describes the motion censoring procedure but does not explicitly address the potential impact of head motion on the results, particularly for the longitudinal analyses relating connectivity changes to symptom severity. Given the known influence of head motion on functional connectivity estimates, a discussion of how head motion was accounted for in these analyses and the potential limitations related to motion artifacts would be valuable.
Rationale: Addressing head motion considerations would strengthen the robustness of the longitudinal analyses and provide a more nuanced interpretation of the relationship between connectivity changes and symptom fluctuations.
Implementation: In the section describing the longitudinal analyses, add a paragraph discussing how head motion was accounted for. Consider including head motion parameters as covariates in the statistical models or using scrubbing techniques to remove motion-contaminated time points. Discuss the potential limitations of the chosen approach and the possible impact of residual motion artifacts on the results.