Brain-based graph-theoretical predictive modeling to map the trajectory of anhedonia, impulsivity, and hypomania from the human functional connectome

Abstract

Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM to a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders. Importantly, the generalizability of cross-sectional models was demonstrated in an external validation sample. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level.

Introduction

A major challenge in the treatment of mood disorders is to distinguish between unipolar and bipolar depression and, specifically, to predict future bipolar symptoms. Current assessments often fail to recognize the risk of developing manic symptoms in individuals seeking treatment during a depressive episode [1, 2]. It has been documented that up to 25% of individuals with major depressive disorder (MDD) might actually have an undiagnosed bipolar disorder (BD) [3], with rates reaching 50% in treatment-resistant depression [4]. This misdiagnosis can be dangerous, as standard pharmacotherapy (i.e., SSRIs) for unipolar depression can trigger or exacerbate manic symptoms [5]. Given that manic episodes can result in devastating financial, legal, and professional consequences as well as poor prognosis [1], there is a crucial need to identify pathophysiological mechanisms that predict the development of bipolar symptoms.

Predictive modeling, i.e., data-driven machine-learning models, can provide a powerful approach for predicting clinical symptoms at the individual level. The major advantage of predictive modeling over standard correlational/regression analyses is that they utilize cross-validation, where the model is built on a subsample of the data (training set), and prediction is done on a separate subsample (testing set). This method is crucial for increasing generalizability and reducing overfitting [6]. For psychiatric research especially, the generalization of findings to unseen patients is essential for clinical translation, e.g., for developing robust biomarkers. Brain-based biomarkers can be tailored based on circuit-level functional measures and used as targets for therapeutic interventions, including transcranial magnetic stimulation and neurofeedback [7, 8].

The connectome-based predictive modeling (CPM) [9] is a cross-validation model that maps phenotype measures (e.g., behavior, cognition) to whole-brain patterns of functional connections (hereafter “functional connectomes”). The CPM utilizes a unique method for feature selection, where the functional connections that are most strongly correlated with the phenotype are summed together to one brain metric. The CPM was primarily used in healthy populations [10–12] but has been increasingly shown to successfully model clinical conditions, including autism [13], childhood aggression [14], and cocaine abstinence [15].

Here, we propose a new brain-based predictive modeling approach and apply it to prospectively predict the trajectory of symptoms of anhedonia, impulsivity, and (hypo)mania among treatment-seeking patients with mood disorders. By employing graph theory to define brain predictors, our brain-based graph-theoretical predictive modeling (GPM) critically extends the CPM. Graph theory is a mathematical framework that enables characterization of complex networks (here, the organization of the brain) by quantifying both integrative processes and local specialization of communities (here, of brain regions) [16]. Notably, graph-theoretical metrics have been shown to track key clinical features of mood disorders, including depression severity [17, 18], rumination [19], suicidal ideation [20], and treatment response [21, 22]. Furthermore, graph-theoretical metrics have been found to differentiate between depressed individuals with BD and MDD [23, 24]. However, to date, most prior research adopted a classification approach and did not attempt to predict symptom development in individuals.

The rationale for developing the GPM comes from the observation that complex network measures, which are derived using theoretically-based mathematical definitions, inherently encompass summations of connections [25]. For example, global efficiency is a fundamental graph-theoretical metric that captures the level of integration across the network and is based on a summation of distances between regions. By using graph-theoretical metrics as brain predictors, the feature selection step of the CPM (which sums connections) can be replaced with a theoretically driven and biologically meaningful summation of functional connections. Importantly, the specific graph-theoretical metric which is identified to have predictive utility from a diverse set of possible measures can further inform the type of brain network dysfunction that is associated with a given symptom.

Our study goals included the following. First, using the GPM, we aimed to predict reward-related symptoms in individuals with mood disorders, cross-sectionally and prospectively, from baseline neuroimaging functional connectomes. Our a priori focus was on transdiagnostic symptoms of anhedonia, impulsivity, and (hypo)mania. Notably, most neuroimaging predictive studies are cross-sectional, where symptoms and neuroimaging data are both collected at baseline. However, to have clinical utility, it is essential for a model to be able to predict the development of future symptoms (while accounting for baseline). To address this gap, a longitudinal clinical evaluation was conducted.

Second, in light of the difficulty to separate between unipolar and bipolar depression, we utilized a Research Domain Criteria (RDoC) [26] approach. In the RDoC framework, psychiatric illness is conceptualized as a continuum across behavioral, psychological, and biological measurements. Thus, DSM diagnoses were not considered in our predictive models and treatment-seeking individuals were enrolled based on their performance on a reward-learning task, to allow high variability in reward-related phenotypes. Third, we aimed to examine the influence of the cognitive state on symptom prediction. Since it has been suggested that functional connectomes acquired during tasks can improve prediction performance [27], we hypothesized that a reinforcement-learning (RL) task would increase the success of predicting reward-related symptoms. Last, we hypothesized that network measures of the reward circuit would better predict reward-related symptoms compared to whole-brain global measures.

Materials and Methods

Participants

A sample of 80 individuals with mood disorders was recruited. The sample included 58 individuals with unipolar mood pathology (MDD, dysthymia, MDD in partial remission) and 22 individuals with bipolar mood pathology (BD type I or II, depressed, mixed, or hypomanic). Participants were treatment-seeking, although none were acutely manic or suicidal. A demographically matched sample of 32 healthy controls was also recruited to this study but were not included in analyses in light of the study’s goal of modeling and predicting symptoms among treatment-seeking patients. Participants were enrolled according to their reward-learning performance as characterized by the probabilistic reward task [28], such that each quantile of the normative distribution of reward-learning was equally represented (see [29] for details). Clinical diagnoses and eligibility were evaluated using the Structured Clinical Interview for DSM-IV [30] (Supplementary Methods). Stable antidepressants or mood stabilizing medication were allowed. The study was approved by the Partners Human Research Committee and participants provided written informed consent.

Study design and evaluation of reward-related symptoms

Participants underwent an fMRI scan, including (i) resting-state and (ii) an RL task [31]. The RL dataset was previously presented [29, 32], however, resting-state datasets were not analyzed or published before. Symptoms of anhedonia, impulsivity, and (hypo)mania were measured at baseline in a separate visit before the fMRI scan and at 3- and 6-month follow-ups. Anhedonia was assessed using the Anhedonic Depression subscale of the 62-item Mood and Anxiety Symptom Questionnaire (MASQ-AD) [33], impulsivity was assessed using the Barratt Impulsiveness Scale (BIS) [34], and (hypo)mania was assessed using the Mania subscale of the Bipolar Inventory of Symptoms Scale (BISS-mania) [35].

MRI data acquisition and preprocessing

MRI data were collected at McLean Imaging Center on a 3 T Siemens Tim Trio using a 32-channel head coil. Preprocessing of fMRI data was done using fMRIPrep [36] and CONN [37] (Supplementary Methods).

Whole-brain functional connectomes

A whole-brain functional connectome was computed for each individual and paradigm (RL task, rest). Note that the three runs of the RL task were conjugated to compute one connectome. The cortex was parcellated using the Schaefer 200-node atlas [38] and the subcortex was parcellated using the Harvard-Oxford (HO) atlas [39]. Pearson’s correlations were computed between the average BOLD time series from all pairs of nodes and Fisher’s transforms were applied.

Graph-theoretical analysis

Graph theoretical analyses were carried out on the weighted positive functional connectome matrices using the Brain Connectivity Toolbox [25]. Negative functional connections were not included since most graph measures can be calculated only for positive weights. For each individual and state, global measures were computed across the entire network, in addition to local measures which were calculated for each of the reward circuit’s regions: anterior cingulate cortex (ACC), caudate, putamen, nucleus accumbens (NAc), lateral and medial orbitofrontal cortex (OFC). (i) Global measures: characteristic path length, global efficiency, mean clustering coefficient, mean local efficiency, mean betweenness centrality. (ii) Local measures: clustering coefficient, local efficiency, betweenness centrality (Supplementary Methods).

Brain-based graph-theoretical predictive modeling (GPM)

The GPM is illustrated in Fig. 1 and includes the following steps for symptom prediction: (i) calculation of whole-brain functional connectomes; (ii) computation of graph-theoretical metrics from functional connectomes (measures of integration, segregation, centrality); (iii) feature selection: choosing the graph metric (only one) which is most strongly associated with the clinical symptom. Note that this can be either a global metric, e.g., global efficiency, or a metric of a specific region, e.g., centrality of the left caudate; (iv) model building: mapping between graph metric and clinical symptom; (v) model prediction: applying the model to previously unseen data. The GPM utilized leave-one-out cross-validation (LOOCV). In LOOCV, the data are divided in each iteration into a training set (N-1 subjects, where N is the sample size) and a testing set (remaining subject). The model is built on the training set (steps i-iv) and prediction is done on the testing set (step v). This procedure is repeated N times to obtain a predicted score for all participants. Note that similar results were obtained with 10-fold cross-validation (Supplementary Results).

Fig. 1. Illustration of the brain-based graph-theoretical predictive modeling (GPM).

The GPM utilizes cross-validation and includes the following steps: (i) calculation of whole-brain functional connectomes for each individual; (ii) computation of graph-theoretical metrics from brain connectomes; (iii) feature selection: choosing the graph metric most strongly associated with the clinical symptom; (iv) model building: mapping between graph metric and clinical symptom; (v) model prediction: applying the model to previously unseen data. The model is built on the training set (steps i-iv) and prediction is done on the testing set (step v).

Model building was accomplished using multiple regression while controlling for all other baseline symptoms and psychotropic medication load [40]. For example, when predicting anhedonia at baseline, (hypo)mania and impulsivity at baseline were covaried. For predicting symptom severity at follow-up, symptom severity (for all symptom scales) at baseline was covaried. For the prediction of (hypo)manic symptoms, due to the skewed distribution of scores, a log (x + 1) transform was used. DSM categories (MDD, BP) were not considered in the model.

The model’s predictive performance (the correspondence between predicted and observed scores) was evaluated by the mean squared error (MSE) and compared to a null model. The null model was defined as the same model without the graph-theoretical brain predictor, including only baseline symptoms and psychotropic medication load. To formally compare models, the corrected repeated k-fold cv test was used [41]. Pearson’s correlation was computed, and p values were obtained using permutation testing with 10,000 repetitions (Supplementary Methods).

The GPM’s feature-selection step implements a “winner-take-all” approach, i.e., only the best graph-theoretical metric is included in the predictive model. As an alternative approach, we tested the inclusion of several graph-theoretical metrics by utilizing an elastic-net algorithm to generate the predictive model [42, 43] (Supplementary Methods). Finally, to further evaluate if predictions are transdiagnostic and not driven by one patient group over the other, we split the sample to unipolar and bipolar disorders, trained the GPM on the unipolar disorders group and tested on the bipolar disorders group. Due to smaller sample sizes for follow-ups, this analysis was conducted for baseline symptoms.

External validation analysis

The generalizability of our models was tested on an independent sample of 96 individuals (ages: 18-48; 77 female), including 44 MDD and 52 HC [44, 45]. Impulsivity scores were available for a subset of 53 individuals (25 MDD, 28 HC). Models were built on the primary sample and prediction was tested on the external sample. Note that for this sample, data were collected during the Monetary Incentive Delay task (MID) [46] instead of the RL task [31]. Thus, for anhedonia, the model was built on the RL task and prediction was tested on the MID. Anhedonia was predicted for individuals with MDD and impulsivity was predicted across MDD and HC. Full details are provided in the Supplementary Methods.

Comparison with the CPM

The CPM was applied for symptom prediction using available code [47]. The CPM was run on the functional connectome including negative weights, as originally done [9], and on the positive-only connectome. A threshold of p = 0.01 was applied for edge selection. Apart from inherent differences between the GPM and CPM, all methodological choices were kept identical (Supplementary Methods).

Results

Clinical characteristics and symptom trajectories

The clinical and demographic characteristics of the sample are presented in Table 1. Eight and 13 participants, respectively, were lost to follow-up at 3- and 6-month. BIS scores at baseline were missing from 6 participants. In addition, BISS-mania scores at 3- and 6-month were missing from 1 participant; BIS and MASQ-AD scores were missing from 1 participant at 3-month follow-up and from 2 participants at 6-month follow-up. The distributions of anhedonia, impulsivity, and (hypo)mania symptoms across baseline, 3- and 6-month follow-ups are shown in Fig. 2. Supplementary Table 1 includes the list of medications used by participants and Supplementary Table 2 presents the cross-correlations among symptoms over time.

Table 1.

Clinical and demographic characteristics of the sample.

	Unipolar disorders (n = 58)	Bipolar disorders (n = 22)
Demographics
Age, years, mean ± SD (range)	28.0 ± 8.6 (18-60)	31.7 ± 13.2 (18-57)
Female, n (%)	41 (71.7)	12 (54.5)
Education, years, mean ± SD (range)	16.0 ± 2.8 (10-25)	16.0 ± 3.0 (10-24)
White, n (%)	40 (69.0)	19 (86.4)
Hispanic, n (%)	6 (10.3)	1 (4.5)
Clinical diagnoses, n (%)
Current MDD	49 (84.5)	–
Current dysthymia	1 (1.7)	–
MDD in partial remission	8 (13.8)	–
BD-I depressed	–	7 (31.8)
BD-I mixed	–	0 (0.0)
BD-I hypomanic	–	2 (9.1)
BD-II depressed	–	9 (40.9)
BD-II mixed	–	1 (4.6)
BD-II hypomanic	–	3 (13.6)
Medication, n (%)
Antidepressants	19 (32.8)	3 (13.6)
Mood stabilizers or anticonvulsants	1 (1.7)	9 (40.9)
Reward-related symptoms, mean ± SD (range)
Anhedonia (MASQ-AD)
Baseline	81.5 ± 11.9 (49–101)	74.3 ± 12.9 (39–98)
3-month follow-up	76.1 ± 16.0 (37–102)	65.3 ± 14.7 (29–87)
6-month follow-up	73.1 ± 14.1 (38–104)	64.4 ± 14.5 (39–90)
Impulsivity (BIS)
Baseline	67.5 ± 10.6 (45–90)	68.0 ± 13.2 (44–94)
3-month follow-up	67.7 ± 11.5 (48–93)	69.3 ± 14.8 (41–95)
6-month follow-up	67.2 ± 11.7 (44–93)	66.9 ± 13.7 (46–91)
Mania (BISS-mania)
Baseline	5.1 ± 3.9 (0–19)	13.6 ± 10.1 (0–37)
3-month follow-up	4.5 ± 3.8 (0–18)	6.8 ± 9.6 (0–38)
6-month follow-up	3.7 ± 3.5 (0–16)	7.9 ± 11.2 (0–40)

BD-I/II bipolar disorder type I/II, BIS Barratt Impulsiveness Scale, BISS-mania Bipolar Inventory of Symptoms Scale Mania subscale, MASQ-AD Anhedonic Depression subscale of the 62-item Mood and Anxiety Symptom Questionnaire, MDD major depressive disorder.

Fig. 2. Trajectories of anhedonia, impulsivity, and (hypo)mania symptoms.

The distributions (using violin plots), scatter plots, and boxplots of A anhedonia, B impulsivity, and C (hypo)mania symptoms are presented for baseline, 3-month (3M) and 6-month (6M) follow-ups. A demographically matched group of 32 healthy controls (HC) (age = 28.40 ± 7.72, 17 female) is included for comparison.

GPM symptom prediction at baseline

At baseline, the GPM predicted anhedonia from the global efficiency of the functional connectome during the RL task (Fig. 3A) [N = 61; GPM: r = 0.317, p = 0.028 permutation testing, MSE = 152.60; null model: r = 0.070, p = 0.161 permutation testing, MSE = 172.15]. Decreased global efficiency was associated with greater anhedonia. The GPM outperformed the null model (t = 4.02, p < 10⁻⁴ corrected repeated k-fold CV test) and had an 11.35% lower MSE relative to the null model.

Fig. 3. The GPM predicted anhedonia and impulsivity at baseline.

Predicted clinical scores (y axis) are presented as a function of the observed clinical scores (x axis). For each symptom, the predictions of the GPM are presented on the left panel and the predictions of the corresponding null model (without the brain predictor) are presented on the right panel. A Anhedonia was predicted by the GPM from the global efficiency of the functional connectome during the reinforcement-learning task. B Impulsivity was predicted by the GPM from the centrality of the left anterior cingulate cortex during resting-state. All p values were computed using permutation testing. Prediction was done while controlling for other baseline symptoms and medication load.

The GPM predicted impulsivity from the centrality of the left ACC during resting-state (Fig. 3B) [N = 73; GPM: r = 0.310, p = 0.020 permutation testing, MSE = 119.52; null model: r = 0.025, p = 0.226 permutation testing, MSE = 136.78]. Increased ACC centrality was associated with greater impulsivity. The GPM outperformed the null model (t = 5.03, p < 10⁻⁴ corrected repeated k-fold CV test) and had a 12.62% lower MSE relative to the null model. In a post-hoc analysis, we tested whether specific subtypes of impulsivity (as measured by the six subscales of the BIS, using a Bonferroni corrected threshold of p < 0.008) could be predicted from the centrality of the left ACC. The GPM predicted the non-planning/self-control subscale of the BIS [N = 73; GPM: r = 0.406, p = 3·10⁻⁴ permutation testing, MSE = 11.72; null model: r = 0.170, p = 0.052 permutation testing, MSE = 13.82]. The GPM outperformed the null model (t = 25.30, p < 10⁻⁴ corrected repeated k-fold CV test) and had a 15.17% lower MSE relative to the null model.

GPM symptom prediction at 6-month follow-up

At 6-month follow-up, the GPM predicted (hypo)mania from the local efficiency of the left NAc during the RL task (Fig. 4A) [N = 51; GPM: r = 0.487, p = 0.002 permutation testing, MSE = 0.708; null model: r = 0.400, p = 0.003 permutation testing, MSE = 0.785]. Increased NAc efficiency was associated with greater (hypo)mania. The GPM outperformed the null model (t = 6.36, p < 10⁻⁴ corrected repeated k-fold CV test) and had a 9.82% lower MSE relative to the null model. The GPM predicted anhedonia at 6-month follow-up from the centrality of the left caudate during resting-state (Fig. 4B) [N = 64; GPM: r = 0.523, p = 2.1·10⁻⁴ permutation testing, MSE = 164.23; null model: r = 0.418, p = 7.4·10⁻⁴ permutation testing, MSE = 187.16]. Increased centrality of the caudate was associated with greater anhedonia. The GPM outperformed the null model (t = 4.75, p < 10⁻⁴ corrected repeated k-fold CV test) and had a 12.25% lower MSE relative to the null model.

Fig. 4. The GPM predicted (hypo)mania and anhedonia at 6-month follow-up.

Predicted clinical scores (y axis) are presented as a function of the observed clinical scores (x axis). A (Hypo)mania at 6-month (6M) follow-up was predicted by the GPM from the local efficiency of the left nucleus accumbens during the reinforcement-learning task. B Anhedonia at 6-month follow-up was predicted by the GPM from the centrality of the left caudate during resting-state. All p values were computed using permutation testing. Prediction was done while controlling for all baseline symptoms and medication load.

GPM prediction using elastic-net

The inclusion of several graph-theoretical predictors by utilizing elastic-net did not result in an overall better predictive performance than the “winner-take-all” approach (Supplementary Results).

GPM using data-splitting: training on unipolar disorders and predicting for bipolar disorders

The GPM predicted anhedonia at baseline from the global efficiency of the functional connectome during the RL task [train (unipolar): N = 45; test (bipolar): N = 16; GPM: MSE = 172.59; null model: MSE = 247.90]. The GPM had a 30.38% lower MSE relative to the null model. The GPM predicted impulsivity at baseline from the centrality of the right ACC during resting-state [train (unipolar): N = 53; test (bipolar): N = 20; GPM: MSE = 177.53; null model: MSE = 202.27]. The GPM had a 12.23% lower MSE relative to the null model. Notably, these results are similar to the ones obtained on the whole transdiagnostic sample using cross-validation.

GPM external validation

For anhedonia at baseline, the GPM model defined on the primary sample’s RL task data (using the global efficiency) predicted anhedonia scores for the validation sample during the MID task (Supplementary Fig. 1A) [N = 37; r = 0.435 p = 0.007]. For impulsivity at baseline, GPM model defined on the primary sample’s resting-state (using the centrality of the left ACC) predicted impulsivity scores for the validation sample during resting-state (Supplementary Fig. 1B) [N = 51; r = 0.348 p = 0.012].

CPM symptom prediction

Baseline anhedonia was predicted from the CPM’s negative-feature set during the RL task [N = 61; CPM: r = 0.281, p = 0.020 permutation testing, MSE = 164.34; null model: r = 0.070, p = 0.161 permutation testing, MSE = 172.15]. The CPM outperformed the null model (t = 3.66, p = 1.3·10⁻⁴ corrected repeated k-fold CV test) with a 4.54% lower MSE relative to the null model. Compared to the GPM, the CPM had a 7.69% higher MSE suggesting decreased performance, however, the difference between the models was not significant (t = 1.19, p = 0.116 corrected repeated k-fold CV test). Similar results were obtained with the positive-only weighted connectome (Supplementary Results). The CPM did not predict any other symptoms at baseline or follow-up.

Discussion

Here, we developed and optimized a new brain-based model – the GPM – to predict symptoms in treatment-seeking individuals with mood disorders. Within the GPM, prediction was based on graph-theoretical measures of brain functional organization. We found that the efficiency and centrality of the reward circuit, specifically the ACC, caudate, and NAc, predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. Importantly, cross-sectional predictive models generalized to an external validation sample, across different reward tasks, imaging protocols, and clinical characteristics.

Anhedonia at baseline was predicted by the GPM from the global efficiency of the functional connectome during the RL task. Greater anhedonia was associated with decreased global efficiency across the brain. Notably, a data-splitting analysis indicated that the prediction was cross-diagnostic: anhedonia in individuals with bipolar disorders was predicted after training the GPM on individuals with unipolar disorders. Anhedonia is one of the two cardinal symptoms of depression and is defined as a loss of pleasure, motivational drive, or lack of reactivity to pleasurable stimuli [48]. Global efficiency is a core network measure of integration and reflects the network’s ability to pass information through short paths, which are considered important for the flow of signals and communication [49]. To the best of our knowledge, this is the first demonstrated link between anhedonia and global efficiency or integration. In agreement with our results, decreased global integration was reported in MDD [50].

Anhedonia at 6-month follow-up was predicted by the GPM from the centrality of the left caudate during resting-state. Greater anhedonia was associated with increased centrality of the caudate. Measures of centrality characterize the contribution of a brain region to the cohesiveness of the network [51]. There is emerging evidence associating anhedonia with increased centrality of the caudate. Specifically, a recent study found that centrality of the ventral striatum was positively associated with anhedonia at baseline, 2- and 4-year follow-ups [52].

Conversely, impulsivity was predicted at baseline by the GPM from the centrality of the left ACC during resting-state. Greater impulsivity was associated with increased ACC centrality. Critically, evidence of transdiagnostic generalization emerged: the GPM derived from individuals with unipolar disorders (centrality of the right ACC during resting-state) predicted baseline impulsivity among individuals with bipolar disorders. Impulsivity is a feature of several psychiatric disorders and can be defined as the tendency to act without adequate forethought or conscious judgment [53]. Several models have suggested that impulsivity is a heterogeneous construct composed of different dimensions. For example, the BIS divides impulsivity into three components: motor (acting without thinking), attentional (lack of focus on the task), and non-planning (orienting toward the present instead of the future).

The ACC has been suggested to play a key role in impulsivity, due to its involvement in diverse higher-order cognitive processes related to executive functioning [54]. In patients suffering from focal brain injuries, greater impulsivity was associated with lesions to the ACC [55]. Difficulties in perseverance, a subtype of impulsivity, were correlated with greater functional connectivity of the ACC with prefrontal regions [56]. In accordance with our findings, neuroimaging studies have supported the involvement of the ACC in the non-planning impulsivity subtype [57, 58].

(Hypo)mania at 6-month follow-up was predicted by the GPM from the local efficiency of the left NAc during the RL task, with greater (hypo)mania associated with increased NAc efficiency. Mania can be described as a distinct period of abnormally and persistently elevated, expansive, or irritable mood [30]. Notably, the NAc has been strongly implicated in manic symptoms. For example, transient (hypo)mania is the most commonly observed side effect after deep brain stimulation to the NAc [59–61]. Recently, preliminary evidence associated mania with increased structural connectivity [62] and functional connectivity [63] of the NAc.

For clinical translation, it is important to identify the cognitive state that maximizes the predictive accuracy of the GPM. Contrary to our hypothesis, the RL task did not amplify prediction, but rather a similar number of predictions was made from the RL task and resting-state. However, we note that the data for the two states were collected using different imaging sequences: single-band for the RL vs. multiband for the resting-state, and differed in scan duration and number of volumes. Thus, future investigations are needed to delineate more clearly the contribution of cognitive activities to the predictive accuracy of reward-related symptoms.

Most symptom predictions were based on regional network measures of the reward circuit, and not global ones, in accordance with our hypothesis. This finding resonates with numerous evidence for the pivotal role of the reward circuit in the pathophysiology of mood disorders [64] and extends previous literature by demonstrating the predictive utility of its network attributes. Importantly, different network measures were found to predict baseline symptoms and future ones. Since the ultimate goal of predictive modeling in psychiatry is to map future symptoms (and not baseline ones, which are generally known and can be assessed directly), our finding underscores the critical importance of longitudinal studies which monitor symptom trajectories. In addition, we note that the GPM outperformed the CPM for symptom prediction. This result was found when applying the CPM to either the functional connectome including negative weights, or the positive-only functional connectome.

Brain-based predictive modeling holds the cardinal promise to transcend the current paradigm of psychiatric assessments which are based solely on a patient’s subjective report. An accurate, reproducible, predictive model can support important clinical decision making, such as selecting among possible treatments, initiating preventive strategies, and risk monitoring. While our study took a step toward achieving that goal, clearly more large-scale neuroimaging studies in individuals with mood disorders, with greater sample sizes, are essential to establish the predictive utility of the GPM. Nevertheless, our findings highlight several brain metrics that can be further tested as potential biomarkers, particularly for identifying individuals with depression that are at risk for developing bipolar symptoms. Although taking the leap toward translational neuroscience is still rarely done, a few studies have directly investigated the clinical utility of neuroimaging biomarkers. For example, Kelley et al. assigned treatment for patients with MDD based on fluorodeoxyglucose positron emission tomography activity in the insula [65].

Several limitations should be noted. First, our samples were moderate in size and thus the findings should be carefully validated with larger datasets (though we obtained validation in an independent, external dataset). Second, the range of manic symptoms was relatively narrow and included mainly hypomania symptoms. Third, symptoms at 3-month follow-up were not predicted from brain measures due to high similarity with baseline scores (i.e., variance at 3 months was captured mostly by baseline scores). Moreover, impulsivity was quite stable across the 3 time-points. This may be due to the BIS capturing predominantly trait impulsivity. We note that a follow-up period of 6 months was chosen to facilitate the retention of participants, however, symptoms might change more substantially with longer follow-ups. Fourth, we did not test the influence of preprocessing choices or the parcellation atlas on predictive performance. Finally, alternative approaches to estimating functional connectomes may yield complementary information, e.g., aggregating fMRI data across paradigms (task and rest) [66], but were outside the scope of this investigation and merit future study. The optimal neuroimaging setting for detecting individual differences that are clinically relevant remains an ongoing research topic and an important direction for future research in psychiatry.

Conclusions

The GPM is an innovative tool that can map between clinical symptoms and network neuroimaging markers at the individual level. Across mood disorders, the GPM successfully predicted symptoms of anhedonic depression, impulsivity, and (hypo)mania, cross-sectionally and prospectively, from network features of the reward circuit. Ultimately, these results may have implications for prognostic indicators of mood symptoms. The clinical need is acute since a timely and effective treatment for individuals suffering from mood disorders, especially bipolar mood pathology, is crucial to reduce morbidity and mortality [67].

Author contributions

RD: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Resources; Software; Visualization; Writing - Original draft; Writing – Review & Editing. AEW: Conceptualization; Investigation; Project administration; Methodology; Resources; Writing – Review & Editing. MTT: Investigation; Project administration; Resources; Writing – Review & Editing. AVR: Investigation; Project administration; Resources; Writing – Review & Editing. PK: Investigation; Project administration; Resources; Writing – Review & Editing. MLI: Investigation; Project administration; Resources; Writing – Review & Editing. RHK: Investigation; Project administration; Resources; Writing – Review & Editing. BR: Methodology; Writing – Review & Editing. DAP: Conceptualization; Funding acquisition; Methodology; Resources; Supervision; Writing – Review & Editing.

Funding

The study was supported by the National Institute of Mental Health (NIMH) (Grant Nos. R01 MH101521 and R37 MH068376 to DAP). RD is an Awardee of the Weizmann Institute of Science – Israel National Postdoctoral Award Program for Advancing Women in Science. AEW was supported by a National Health and Medical Research Council Investigator Grant (GNT 2017521). Biostatistical consultation was provided by Harvard Catalyst.

Data availability

Datasets will be shared on reasonable request to the corresponding author.

Code availability

The Brain Connectivity Toolbox was used for graph theoretical analyses and is freely available at https://sites.google.com/site/bctnet. The following functions were used: weight_conversion.m, clustering_coef_wu.m, efficiency_wei.m, distance_wei.m, charpath.m, betweenness_wei.m. Connectome-based predictive modeling (CPM) was done using MATLAB scripts available by Shen and colleagues [47].

Competing interests

Over the past 3 years, Dr. Pizzagalli has received consulting fees from Boehringer Ingelheim, Compass Pathways, Engrail Therapeutics, Neumora Therapeutics (formerly BlackThorn Therapeutics), Neurocrine Biosciences, Neuroscience Software, Otsuka, Sage Therapeutics, Sama Therapeutics, Sunovion, and Takeda; he has received honoraria from the American Psychological Association, Psychonomic Society and Springer (for editorial work) and from Alkermes; he has received research funding from the Bird Foundation, Brain and Behavior Research Foundation, Dana Foundation, Wellcome Leap, Millennium Pharmaceuticals, and NIMH; he has received stock options from Compass Pathways, Engrail Therapeutics, Neumora Therapeutics, and Neuroscience Software; he has a financial interest in Neumora Therapeutics, which has licensed the copyright to the human version of the probabilistic reward task through Harvard University. No funding from these entities was used to support the current work, and all views expressed are solely those of the authors. All other authors have no conflicts of interest or relevant disclosures.

Supplementary information

The online version contains supplementary material available at 10.1038/s41386-024-01842-1.