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Individualized Functional Connectome Identified Replicable Biomarkers for Dysphoric Symptoms in First-Episode Medication-Naïve Patients With Major Depressive Disorder
Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, ChinaFunctional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, ChinaResearch Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts
Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, ChinaFunctional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, ChinaResearch Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts
Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, ChinaFunctional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, ChinaResearch Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts
Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, ChinaDepartment of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MassachusettsDepartment of Neuroscience, Medical University of South Carolina, Charleston, South Carolina
Major depressive disorder (MDD) is a heterogeneous syndrome and can be conceptualized as a mixture of dimensional abnormalities across several specific brain circuits. The neural underpinnings of different symptom dimensions in MDD are not well understood. We aimed to identify robust, generalizable, functional connectivity (FC)–based biomarkers for different symptom dimensions in MDD using individualized functional connectomes.
Methods
Patterns of FC associated with symptom severity were identified using a novel, individualized, functional network parcellation analysis in conjunction with hierarchical clustering. Dimension-specific prediction models were trained to estimate symptom severity in first-episode medication-naïve patients (discovery dataset, n = 95) and replicated in an independent validation dataset (n = 94). The correlation between FC changes and symptom changes was further explored in a treatment dataset (n = 55).
Results
Two distinct symptom clusters previously identified in patients with MDD, namely dysphoric and anxiosomatic clusters, were robustly replicated in our data. A connectivity biomarker associated with dysphoric symptoms was identified, which mainly involved the default, dorsal attention, and limbic networks. Critically, this brain-symptom association was confirmed in the validation dataset. Moreover, the marker also tracked dysphoric symptom improvement following a 2-week antidepressant treatment. For comparison, we repeated our analyses using a nonindividualized approach and failed to identify replicable brain-symptom biomarkers. Further quantitative analysis indicated that the generalizability of the connectivity-symptom association was hampered when functional regions were not localized in individuals.
Conclusions
This work reveals robust, replicable FC biomarkers for dysphoric symptoms in MDD, demonstrates the advantage of individual-oriented approach, and emphasizes the importance of independent validation in psychiatric neuroimaging analysis.
). Despite the impressive growth of neuroimaging studies, the underlying neural mechanisms of MDD remain obscure. The National Institute of Mental Health Research Domain Criteria project has developed a framework for psychiatric disorder research based on behavioral dimensions and neurobiological measures (
). Accordingly, the symptoms of patients with MDD can be considered as a mixture of dimensional abnormalities across several specific brain circuits. This is in keeping with a recent transcranial magnetic stimulation study, which found that dysphoric and anxiosomatic symptoms of patients with MDD were alleviated following stimulation of two distinct neuroanatomical treatment targets (
Neuroimaging studies typically make group-level inferences, rendering it difficult to assess individual patients or develop personalized treatment plans. Thus, studies using machine learning methods have begun with a broad search for predictive models to make inferences at the individual level (
Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illness [published correction appears in Mol Psychiatry 2020; 25:2200].
). However, most models are trained and tested on a single dataset, which can lead to an overestimation of performance due to overfitting, resulting in poor generalizability (
). Ideally, studies should seek to train models with larger sample sizes and assess the generalizability of the findings using previously unseen datasets.
In addition, most neuroimaging studies have relied on applying group-level templates when assessing the functional organization of an individual’s brain. Accumulating evidence demonstrates that individuals vary in the layout of their brain’s functional networks, especially in the association cortices (
Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion [published correction appears in Cereb Cortex 2021; 31:3974].
Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion [published correction appears in Cereb Cortex 2021; 31:3974].
). Moreover, brain-behavior relationships are stronger when brain networks are defined using individual features than when solely defined based on group-level templates (
Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion [published correction appears in Cereb Cortex 2021; 31:3974].
). To meet the challenge of advancing precision medicine, our group has developed individual-oriented approaches in resting-state functional connectivity (FC) to investigate functional network organization at the individual level, which holds great promise in clinical research (
Although there are a considerable number of neuroimaging studies that have aimed to find biomarkers that reflect the severity of clinical symptoms or cognitive decline in patients, the most important problem in this field is the substantial heterogeneity across findings and, relatedly, the lack of replicability of these neuroimaging results. Hence, the aim of our study was to find reliable biomarkers for distinct symptom dimensions in a relatively large sample of medication-naïve patients with first-episode MDD. We first mapped the fine-grained functional regions in each participant and constructed individualized functional connectomes to represent the participants’ network organization. Next, in a discovery dataset (n = 95), 28 MDD symptom measures were clustered according to their relationship with brain connectivity, i.e., symptoms that appeared to have shared connectivity underpinnings were grouped together, yielding several symptom clusters. We then trained machine learning models to predict the symptom cluster scores in the discovery dataset and tested the model in a previously unseen dataset (n = 94) to assess its generalizability. We further explored the relationship between FC changes and symptom changes following a 2-week antidepressant treatment. Finally, we performed parallel analyses using a group-level template as a benchmark to assess whether the individualized approach facilitated the discovery of neural biomarkers.
) were recruited from the Mental Health Centre of West China Hospital from November 2010 to November 2018. Inclusion criteria were 1) age 18 to approximately 65 years, 2) first episode, 3) did not receive psychiatric medication treatment earlier, 4) total Hamilton Depression Rating Scale scores >7, and 5) did not exhibit psychiatric comorbidities, such as anxiety disorders. Data from 189 participants were retained after quality control of the magnetic resonance imaging (MRI) data and divided into two matched groups: a discovery dataset (n = 95) and a validation dataset (n = 94). In addition, we assessed a group of patients (n = 55) before and after 2 weeks of antidepressant treatment (see Supplemental Methods for more details on participant information). Of the 55 patients, 41 patients were from the first-episode medication-naïve group, and the remaining 14 patients were recruited additionally (see Table 1 for demographic and clinical characteristics and Table S1 for details on psychopharmacology). In this experiment, we evaluated brain changes after a treatment of only 2 weeks. There is evidence that early response could be a clinical turning point in antidepressant treatment. A meta-analysis of 41 single- or double-blind clinical trials showed that improvement in the first 2 weeks of treatment is a predictor of stable response and remission in patients with MDD (
Early improvement in the first 2 weeks as a predictor of treatment outcome in patients with major depressive disorder: A meta-analysis including 6562 patients.
High-resolution T1 and resting-state functional MRI (fMRI) data were acquired from each participant using a 3.0T MR scanner (Siemens Trio). MRI data were processed using procedures previously described (
), including discarding the first four volumes, slice timing correction, motion correction, bandpass filtering (0.01–0.1 Hz), motion regression, whole-brain signal regression, and ventricular and white matter regression (see Supplemental Methods).
) (see Supplemental Methods). Second, individual-level parcellations of each lobe (prefrontal, temporal, parietal, occipital, and sensorimotor cortices) were performed by applying an iterative clustering approach similar to our previous whole-brain parcellation strategy (
Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illness [published correction appears in Mol Psychiatry 2020; 25:2200].
). Briefly, the group-level functional atlas was projected onto each individual’s cerebral cortex, after which the boundaries of the functional clusters were gradually refined using an iterative approach with a weighting strategy to minimize the impact of the group-level functional atlas and maximize the usage of the individuals’ data. Finally, an FC matrix (92 × 92) was constructed for each individual by calculating connectivity among the 92 functional regions.
Clustering of Symptoms
Correlations were computed between 28 symptom items (see Supplemental Methods) and the individualized FC matrices across subjects after controlling for age, sex, education, and head motion. Then, the 28 FC-symptom correlation matrices were converted to a normal distribution using Fisher’s r to z transform. We adopted the method by Siddiqi et al. (
), i.e., symptoms that had shared connectivity substrates were clustered together. The optimal cluster solution was determined according to reliability, as measured by the Dice coefficient (see Supplemental Methods).
Estimating Symptom Scores Using Connectivity
We constructed symptom prediction models using data from the discovery dataset. To determine the model parameters, we performed the following analyses:
1.
For each symptom cluster that included multiple symptom measures, FC-symptom correlation matrices were averaged across all symptom items, yielding a mean FC-symptom matrix.
2.
We then performed nonparametric permutation tests to identify connections that were reliably associated with the symptom cluster. Specifically, if the symptom cluster involved n symptom items, we then randomly selected n FC-symptom matrices from the 28 FC-symptom matrices and calculated the mean matrix. This randomization was repeated 1000 times. For each functional connection, a permutation p value was then calculated as the percentage of permutation tests that yielded a higher absolute FC-symptom correlation value than the value obtained in step 1. Connections with p < .001 were kept as reliable features.
3.
Correlations between the feature connections were identified in step 2 and symptom cluster scores were used as weights. Feature connections and their weights were extracted, yielding a prediction model for the symptom.
We tested the prediction models on the validation dataset. Specifically, for each subject, we first obtained the individualized functional connectome and then calculated the weighted sum of FC values according to the symptom prediction model defined in the discovery dataset. The weighted sum was considered the predicted symptom score of the subject. Finally, correlations between the predicted and the actual symptom cluster score were calculated, with age, sex, education, and head motion as covariates. These tests were performed without prespecified hypotheses; thus, Bonferroni adjustments were applied.
To quantify the contributions of different functional networks to symptom estimation, the 92 functional regions were grouped into the seven canonical large-scale functional networks: the visual network, sensorimotor network, dorsal attention network (DAN), salience network, limbic (LMB) network, frontoparietal control network, and default network (DN) (
). For each network, within-network and between-network weights were estimated (see Supplemental Methods). We further calculated the correlation between the average FC changes and cluster symptom changes following the 2-week antidepressant treatment, with age, sex, education, baseline head motion, and follow-up head motion as covariates.
Control Analyses
As a comparison, we generated FC matrices of 92 functional regions as defined by the group-level network atlas and repeated the prediction analyses. Correlations between feature connections and measured symptom cluster scores based on individualized versus atlas-based data were compared (two-sample t test, p < .05).
To examine the impact of anatomical characteristics on the symptom biomarkers, we calculated cortical thickness, sulcus depth, cortical volume, cortical area, and curvature values and used these as covariates in the prediction model (see Supplemental Methods). Finally, to determine whether our results were affected by head motion, correlations between head motion and measured and estimated symptom scores were also calculated.
Results
Two Symptom Clusters, Dysphoric and Anxiosomatic, Can Be Reliably Identified
To parse neural mechanisms underlying distinct symptom dimensions, 28 symptom measures were clustered according to their relationship with brain connectivity among 92 subject-specific brain regions (
). A two-cluster solution exhibited the highest test-retest reliability, as measured by the Dice coefficient (Figure 1; Figure S1 and Table S2). One cluster involved nine symptoms, including depressed mood, decreased interest, and suicidality; the other cluster involved 19 symptoms, including somatic anxiety, somatic general, anxious mood, and insomnia. These two clusters were largely consistent with previous findings by Siddiqi et al. (
) and were referred to as dysphoric and anxiosomatic symptom clusters, respectively.
Figure 1Hierarchical clustering of functional connectivity (FC)–symptom matrices. For each subject, an individualized FC matrix was created by calculating the correlation between the time series of 92 individualized functional regions generated using a fine-grained functional parcellation. For all subjects, 28 FC-symptom matrices were created by correlating their individualized FC matrices with their scores on 28 items assessing various major depressive disorder symptoms. The distance matrix between each pair of FC-symptom matrices was calculated and used for Ward’s hierarchical clustering analysis. In the clustering procedure, FC-symptom matrices were grouped according to similarity between pairs of matrices (shorter line length in the dendrogram indicates greater similarity). The names of symptom items are colored according to their cluster assignment. HAMA, Hamilton Anxiety Rating Scale; HAMD, Hamilton Depression Rating Scale.
To determine whether the individually specified FC tracked symptom severity, we trained the models to estimate the symptom cluster scores in the discovery dataset (n = 95). We found that dysphoric and anxiosomatic symptoms were associated with a set of functional connections, with significant correlations between estimated and observed scores (dysphoric symptoms: r = 0.559, p < .001; anxiosomatic symptoms: r = 0.621, p < .001) (Figure S2A, B). We then examined these associations in the validation dataset (n = 94) and found a significant correlation between the estimated and observed scores for dysphoric symptoms (r = 0.345, p < .001) (Figure 2A and Figure S2C) but not for anxiosomatic symptoms (r = −0.040, p = .708) (Figure S2D).
Figure 2The prediction model derived from the discovery dataset can predict the dysphoric symptom severity in the validation dataset. (A) Correlation (r = 0.345, p < .001) between the dysphoric symptom scores that were estimated by individualized functional connections and the scores that were actually observed in the 94 patients of the validation dataset. (B) The top 20 connections that contributed the most to estimation of dysphoric symptom scores are shown. (C) A total of 26 functional regions that are involved in these top 20 connections are shown. These nodes are color-coded according to the seven canonical networks. DAN, dorsal attention network; DN, default network; FPN, frontoparietal control network; LMB, limbic network; MOT, sensorimotor network; SAL, salience network; VIS, visual network.
Thus, only the dysphoric symptom model, which involved 80 functional connections, was robust in the previously unseen data. The connections contributing to dysphoric symptom estimation were mainly between-network connections involving the DN, DAN, and LMB networks (Figure S3). The top 20 connections that contributed to the estimation involved a set of brain regions, including the orbitofrontal cortex (OFC), parahippocampal cortex, dorsomedial prefrontal cortex, anterior cingulate cortex (ACC), posterior cingulate cortex, premotor cortex, and superior parietal lobule (SPL) (Figure 2B, C).
FC Changes Track With Dysphoric Symptom Changes Following Treatment
After a 2-week antidepressant treatment, both the dysphoric and anxiosomatic symptoms were alleviated (Table S3). Focusing on the connections that were selected as features in the prediction model, we sought to determine whether changes in these connections following treatment could track the alleviation in dysphoric symptoms. We found a significant correlation between FC changes and dysphoric symptom changes (r = 0.301, p = .033) (Figure 3), indicating the relevance of these connections in symptom alleviation. However, there was no significant correlation between FC changes and anxiosomatic symptom changes (r = 0.210, p = .144) (Figure S4).
Figure 3Functional connectivity (FC) changes among individually specified functional clusters track with dysphoric symptom alleviation after a 2-week antidepressant treatment (r = 0.301, p = .033).
An Atlas-Based Functional Connectome Model Failed to Predict Symptom Severity
Our prediction model was based on FC measured among subject-specific functional regions. As a comparison, we repeated our analyses using the 92 functional clusters extracted from the group-level functional network atlas. As in our initial analysis, the 28 symptoms could be clustered into dysphoric and anxiosomatic symptoms according to symptom-FC correlations (Table S2). The prediction models constructed from atlas-based FC were able to estimate dysphoric symptoms (r = 0.556, p < .001) and anxiosomatic symptoms (r = 0.575, p < .001) in the discovery dataset. However, the models showed weak generalizability and could not be confirmed in the validation dataset after Bonferroni correction (dysphoric symptoms: r = 0.233, p = .027; anxiosomatic symptoms: r = −0.090, p = .399) (Figure 4A, B).
Figure 4The prediction models based on atlas-based functional connectomes were unable to estimate (A) dysphoric and (B) anxiosomatic symptom scores in the validation dataset. (C) In the discovery dataset, the individualized and atlas-based connections showed no significant difference in their correlations with dysphoric symptom scores (t = 0.212, p = .833, Hedges’ g = 0.033). However, in the validation dataset, the atlas-based connections showed weaker correlations with dysphoric symptoms when compared with individualized connections (t = 2.132, p = .035, Hedges’ g = 0.330). Error bars denote standard deviations. ∗ indicates a statistically significant difference (p < .05).
In the discovery dataset, the individualized and atlas-based connections showed no significant differences in the correlations between feature connections and measured dysphoric symptoms (t164 = 0.212, p = .833, Hedges’ g = 0.033) (Figure 4C). However, in the validation dataset, these individualized connections showed significantly higher correlations to dysphoric symptoms when compared with atlas-based connections (t164 = 2.132, p = .035, Hedges’ g = 0.330) (Figure 4C), indicating that brain-symptom associations identified in a dataset cannot be easily generalized to an independent dataset if the functional features are not individually specified.
To ensure that various confounding factors did not significantly affect our results, we repeated our analyses with nine factors as covariates: age, sex, education, head motion, and five anatomical measurements (cortical thickness, sulcus depth, cortical volume, cortical area, and curvature). We also calculated the correlations between head motion and symptom scores. The prediction results did not change after adding these covariates (Table S4), and both the measured symptom scores and individualized biomarker scores were uncorrelated with head motion (p > .1) in both the discovery and validation datasets (Figures S5 and S6).
Discussion
This study investigated the functional network underpinnings of different symptom dimensions in medication-naïve patients with first-episode MDD. To properly account for the interindividual variability in functional brain organization, we used a novel individual-oriented approach to examine functional connectomes. Consistent with a previous report, we were able to identify two distinct symptom clusters for MDD, i.e., dysphoric and anxiosomatic symptom clusters. We found a robust imaging biomarker for dysphoric symptoms, while anxiosomatic symptom estimation was unreliable. Our data suggest that MDD-related dysphoric symptoms are associated with altered connectivity among broad networks, mainly involving the DN, DAN, and LMB networks. In addition, FC changes in these networks tracked dysphoric symptom alleviation following a 2-week antidepressant treatment. Critically, a parallel analysis with atlas-based functional connectomes, which ignored interindividual variability in FC, failed to identify replicable biomarkers for either symptom dimension.
Cortical Connectivity Biomarkers of Dysphoric Symptoms
The dysphoric symptom cluster included depressed mood, decreased interest or pleasure (i.e., anhedonia), and suicidality, which are often referred to as core symptoms of depression (
). The most predictive connections for dysphoric symptoms involved several regions, including the OFC, parahippocampal cortex, dorsomedial prefrontal cortex, ACC, posterior cingulate cortex, premotor cortex, and SPL. The OFC and ACC are two critical regions in emotion regulation and cognition (
) and have shown a variety of alterations in gross morphology, neuronal structure, function, connectivity, and neurochemistry in MDD [for a review, see (
)]. Across a variety of reward processing task studies, anhedonia has been associated with hypoactivation of the OFC and ACC in patients with depression (
). In addition, the ACC, dorsomedial prefrontal cortex, posterior cingulate cortex, and parahippocampal cortex are implicated in self-referential functions (
). Taken together, dysfunctions within and between the structures mentioned above are associated with disturbances in emotional regulation and cognition in depression, lending credence to their relevance in the prediction of dysphoric symptoms in patients with MDD.
It is worth noting that the core regions involved in our dysphoric symptom model (Figure 2C) are not exactly the same as the dysphoric symptom circuit found by Siddiqi et al. (
). Approximately 70% of our core regions (i.e., DAN, salience network, sensorimotor network, visual network, and frontoparietal control network) are located in the positive areas in Siddiqi’s circuit, and the remaining 30% of regions (i.e., DN and LMB network) are in their negative areas. However, our results are not contradictory. Siddiqi’s circuit is a general pattern of symptom-specific treatment target (i.e., transcranial magnetic stimulation in the positive areas could alleviate the corresponding symptoms), but it does not pinpoint specific connection changes related to symptom alleviation. Symptom-related connections may involve regions outside Siddiqi’s circuit through anticorrelations. Our work aimed to identify specific connections that are reliably associated with the symptom cluster, and our dysphoric symptom model is based on 80 specific functional connections (Figure S7). In addition, our study and the study by Siddiqi et al. used different sample characteristics: for example, Siddiqi et al. included treatment-resistant patients with depression, while we only included medication-naïve individuals. Such discrepancies may also account for the differences in our results. In contrast, our anxiosomatic symptom model was relatively unreliable. There are at least two possible explanations for this. The first is that we used a high standard for reliability; we required that our models be replicated with a previously unseen dataset. The second potential explanation is that the patients with MDD in our study did not exhibit psychiatric comorbidities, such as anxiety disorders; therefore, they had an overrepresentation of dysphoric symptoms compared with anxiosomatic symptoms (t = 9.961, p < .001) (Figure S8). Further research using a transdiagnostic approach is warranted.
Abnormal Connectivity Between Functional Networks in MDD
From the network perspective, we found that connections among the DN, DAN, and LMB network (Figure S3) contributed heavily to the prediction of dysphoric symptom severity, suggesting that dysfunctional interactions between multiple higher-order association networks are associated with dysphoric symptoms. The DN is responsible for internal states (
Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks [published correction appears in Proc Natl Acad Sci U S A 2018; 115:E3068].
Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks [published correction appears in Proc Natl Acad Sci U S A 2018; 115:E3068].
). Neuroanatomical and functional alterations in the DAN have been shown to be related to some aspects of emotional, visceral, and autonomic dysregulation in patients with MDD (
). Focusing on the symptom-related connections, we found that average FC changes over 2 weeks of antidepressant treatment were positively correlated with alleviation of dysphoric symptoms. These results imply that these feature connections are reliably associated with dysphoric symptoms because they are associated with baseline dysphoria in addition to tracking the changes in symptom severity over time. Taken together, abnormal interactions among the DN, DAN, and LMB network could be associated with disruptions in the balance between internal states, external awareness, and emotion regulation, which may reflect biases toward ruminative thoughts at the cost of attending to the external world (
) and potentially cause dysphoric symptoms in MDD.
Interindividual Differences in the Functional Organization of the Brain Are Critical
To examine the generalizability of our findings on brain-symptom associations, we showed that FC defined using individually specified functional regions was predictive of dysphoric symptom severity in both the discovery and validation datasets. However, the prediction models using connectivity derived from a group-level atlas obscured the biological signals and failed to predict any dimension-specific symptom severity in previously unseen datasets (Figure 4A, B). Further analyses indicated that these atlas-based connections were less correlated with symptom scores when compared with individualized connections in previously unseen data (Figure 4C). Thus, applying a group-level atlas to define functional regions within individual subjects, which is the standard procedure in most fMRI studies, likely yields results that are unstable or that may not be replicated in independent datasets.
Limitations
First, our brain-symptom association analyses were based on rating scales that are inherently subjective. Our hypothesis is that there are some biological bases for certain symptom dimensions, although the symptom measures in these dimensions are subjective and coarse. This study identified a replicable FC marker for dysphoric symptoms, suggesting that there are some objective brain signatures associated with the subjective rating of symptoms. Should a more objective symptom measure be developed in the future, we will be able to improve the prediction model and identify biologically more meaningful markers. Second, a reliable technique for mapping subcortical functional networks in individual participants is not yet available, precluding our ability to investigate subcortical connectivity in this study. However, we plan to adapt our individual-level approach to examine corticostriatal connectivity in future studies, given the pivotal role of these circuits in psychiatric disorders (
). Third, our analyses did not include healthy control subjects because symptom scores have little to no variation within healthy subjects, and therefore including them may strongly bias the symptom estimation models. Fourth, to maximize the potential benefits to patients, we did not include a placebo control for the 2-week antidepressant treatment group; thus, these early effects may include a placebo component. Fifth, although our individualized parcellation has been partially validated by electrical cortical stimulation, the current gold standard for preoperative functional mapping, and task fMRI data, the accuracy of individualized parcellation would inevitably be influenced by the quality of resting-state fMRI data.
Conclusions
Using individualized functional connectomes, we identified replicable imaging biomarkers for dysphoric symptomatology in medication-naïve patients with first-episode MDD. Changes in these connections tracked with improvements in dysphoric symptom severity after treatment. Taken together, this study highlights the importance of accounting for interindividual variability in functional brain organization, which facilitates the discovery of robust, replicable neural biomarkers in comparison with typical atlas-based approaches.
Acknowledgments and Disclosures
This study was supported by the National Natural Science Foundation of China (Grant Nos. 81790652 [to HL], 81790650 [to HL], 81621003 [to QG], 81820108018 [to QG], 81761128023 [to QG], 82027808 [to QG], and 82001795 [to YZ]), the National Institutes of Health/National Institute of Mental Health (Grant Nos. 1R01DC017991 [to HL], R01MH045573 [to HL], P50DA046373 [to HL], 5K01MH111802 [to DW], and R01MH112189-01 [to QG]), China Postdoctoral Science Foundation (Grant No. 2020M673245 [to YZ]), the Post-Doctor Research Project of West China Hospital of Sichuan University (Grant No. 2021HXBH025 [to YZ]), and a Canadian Institutes of Health Research postdoctoral fellowship (Grant No. MFE-171291 [to LD]).
The authors report no biomedical financial interests or potential conflicts of interest.
Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illness [published correction appears in Mol Psychiatry 2020; 25:2200].
Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion [published correction appears in Cereb Cortex 2021; 31:3974].
Early improvement in the first 2 weeks as a predictor of treatment outcome in patients with major depressive disorder: A meta-analysis including 6562 patients.
Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks [published correction appears in Proc Natl Acad Sci U S A 2018; 115:E3068].
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