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ObjectiveManic and depressive phases of bipolar disorder (BD) show opposite symptoms in psychomotor, thought, and affective dimensions. Neuronally, these may depend on distinct patterns of alterations in the functional architecture of brain intrinsic activity. Therefore, the study aimed to characterize the spatial and temporal changes of resting-state activity in mania and depression, by investigating the regional homogeneity (ReHo) and degree of centrality (DC), in different frequency bands. MethodsUsing resting-state functional magnetic resonance imaging (fMRI), voxel-wise ReHo and DC were calculated—in the standard frequency band (SFB: 0.01–0.10 Hz), as well as in Slow5 (0.01–0.027 Hz) and Slow4 (0.027–0.073 Hz)—and compared between manic (n = 36), depressed (n = 43), euthymic (n = 29) patients, and healthy controls (n = 112). Finally, clinical correlations were investigated. ResultsMania was mainly characterized by decreased ReHo and DC in Slow4 in the medial prefrontal cortex (as part of the default-mode network [DMN]), which in turn correlated with manic symptomatology. Conversely, depression was mainly characterized by decreased ReHo in SFB in the primary sensory-motor cortex (as part of the sensorimotor network [SMN]), which in turn correlated with depressive symptomatology. ConclusionsOur data show a functional reconfiguration of the spatiotemporal structure of intrinsic brain activity to occur in BD. Mania might be characterized by a predominance of sensorimotor over associative networks, possibly driven by a deficit of the DMN (reflecting in internal thought deficit). Conversely, depression might be characterized by a predominance of associative over sensorimotor networks, possibly driven by a deficit of the SMN (reflecting in psychomotor inhibition).

BACKGROUNDAlthough mania is characteristic of bipolar disorder, it can also occur following focal brain damage. Such cases may provide unique insight into brain regions responsible for mania symptoms and identify therapeutic targets.METHODSLesion locations associated with mania were identified using a systematic literature search (n = 41) and mapped onto a common brain atlas. The network of brain regions functionally connected to each lesion location was computed using normative human connectome data (resting-state functional MRI, n = 1000) and contrasted with those obtained from lesion locations not associated with mania (n = 79). Reproducibility was assessed using independent cohorts of mania lesions derived from clinical chart review (n = 15) and of control lesions (n = 490). Results were compared with brain stimulation sites previously reported to induce or relieve mania symptoms.RESULTSLesion locations associated with mania were heterogeneous and no single brain region was lesioned in all, or even most, cases. However, these lesion locations showed a unique pattern of functional connectivity to the right orbitofrontal cortex, right inferior temporal gyrus, and right frontal pole. This connectivity profile was reproducible across independent lesion cohorts and aligned with the effects of therapeutic brain stimulation on mania symptoms.CONCLUSIONBrain lesions associated with mania are characterized by a specific pattern of brain connectivity that lends insight into localization of mania symptoms and potential therapeutic targets.FUNDINGFundação para a Ciência e Tecnologia (FCT), Harvard Medical School DuPont-Warren Fellowship, Portuguese national funds from FCT and Fundo Europeu de Desenvolvimento Regional, Child Neurology Foundation Shields Research, Sidney R. Baer, Jr. Foundation, Nancy Lurie Marks Foundation, Mather's Foundation, and the NIH.

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.

Background Bipolar disorder may begin as depression or mania, which can affect the treatment and prognosis. The physiological and pathological differences among pediatric bipolar disorder (PBD) patients with different onset symptoms are not clear. The aims of the present study were to investigate subcortical structural alterations in PBD patients with first-episode depressive (PBD-FED) and first-episode manic (PBD-FEM). Methods A total of 59 individuals including 28 PBD-FED, 13 PBD-FEM, and 18 healthy controls (HCs) underwent high-resolution structural magnetic resonance scans. FreeSurfer 7.2 was used to detect changes in subcortical volumes. Simultaneously, thalamic, hippocampal, and amygdala subregion volumes were compared between the three groups. Results Analysis of covariance controlling for age, sex, education, and estimated intracranial volume shows third and fourth ventricle enlargement in patients with PBD. Compared with the PBD-FED and HCs, the PBD-FEM group had reduced gray matter volume in the left thalamus, bilateral hippocampus, and right amygdala. Subsequent subregion analyses showed right cortico-amygdaloid transient, bilateral accessory-basal nucleus, left hippocampal tail, right hippocampal head, and body volume reduction in the PBD-FEM group. Conclusions The present findings provided evidence of decreased subcortical structure in PBD-FEM patients, which might present its trait feature.

ABSTRACT Introduction Prodromal symptoms of mania in children are predictive of the later development of bipolar disorder; yet, the neurostructural correlates of these early symptoms remain poorly understood. This study aimed to investigate the association between prodromal mania symptoms and brain structure in a large cohort of children. Methods We analyzed data from 10,662 nine‐ to 10‐year‐old children from the Adolescent Brain Cognitive Development (ABCD) Study, employing structural equation modeling to examine the concurrent and longitudinal associations between prodromal mania symptoms and cortical and subcortical gray matter volume. Results After adjusting for multiple comparisons and controlling for age, sex, scanner model, socioeconomic status, and medication use, we found that baseline mania symptoms were associated with reduced gray matter volume across both cortical and subcortical areas, suggesting a global effect. These findings were further supported by the loss of these effects when total intracranial volume was included as an additional covariate, suggesting that smaller overall brain size, rather than specific regional effects, is related to prodromal mania symptoms. Lastly, longitudinal analyses revealed that brain volume at baseline did not predict prodromal mania symptoms at the second‐year follow‐up. Conclusion Our results support the structural differences observed in adults with bipolar disorder in prior work and refine our understanding of the neurostructural correlates of prodromal mania symptoms in children. These findings could enhance early identification and intervention efforts for youth at risk of developing bipolar disorder. The current study examined the relationship between prodromal mania symptoms and brain structure in a large sample of children. We found that baseline mania symptoms were associated with smaller brain volumes in all cortical and subcortical regions, suggesting a global effect that is also supported by the loss of these effects when total intracranial volume was included as an additional covariate. These results help to refine our understanding of the neurostructural correlates of mania symptoms during the prodromal stage, which could be leveraged for improved identification and intervention.

Exploring the neural basis related to different mood states is a critical issue for understanding the pathophysiology underlying mood switching in bipolar disorder (BD), but research has been scarce and inconsistent. Resting-state functional magnetic resonance imaging data were acquired from 162 patients with BD: 33 (hypo)manic, 64 euthymic, and 65 depressive, and 80 healthy controls (HCs). The differences of large-scale brain network functional connectivity (FC) between the four groups were compared and correlated with clinical characteristics. To validate the generalizability of our findings, we recruited a small longitudinal independent sample of BD patients (n = 11). In addition, we examined topological nodal properties across four groups as exploratory analysis. A specific strengthened pattern of network FC, predominantly involving the default mode network (DMN), was observed in (hypo)manic patients when compared with HCs and bipolar patients in other mood states. Longitudinal observation revealed an increase in several network FCs in patients during (hypo)manic episode. Both samples evidenced an increase in the FC between the DMN and ventral attention network, and between the DMN and limbic network (LN) related to (hypo)mania. The altered network connections were correlated with mania severity and positive affect. Bipolar depressive patients exhibited decreased FC within the LN compared with HCs. The exploratory analysis also revealed an increase in degree in (hypo)manic patients. Our findings identify a distributed pattern of large-scale network disturbances in the unique context of (hypo)mania and thus provide new evidence for our understanding of the neural mechanism of BD.

Neuroimaging studies of psychotic disorders have demonstrated abnormalities in structural and functional connectivity involving widespread brain networks. However, these group-level observations have failed to yield any biomarkers that can provide confirmatory evidence of a patient’s current symptoms, predict future symptoms, or predict a treatment response. Lack of precision in both neuroanatomical and clinical boundaries have likely contributed to the inability of even well-powered studies to resolve these key relationships. Here, we employed a novel approach to defining individual-specific functional connectivity in 158 patients diagnosed with schizophrenia (n = 49), schizoaffective disorder (n = 37), or bipolar disorder with psychosis (n = 72), and identified neuroimaging features that track psychotic symptoms in a dimension- or disorder-specific fashion. Using individually specified functional connectivity, we were able to estimate positive, negative, and manic symptoms that showed correlations ranging from r = 0.35 to r = 0.51 with the observed symptom scores. Comparing optimized estimation models among schizophrenia spectrum patients, positive and negative symptoms were associated with largely non-overlapping sets of cortical connections. Comparing between schizophrenia spectrum and bipolar disorder patients, the models for positive symptoms were largely non-overlapping between the two disorder classes. Finally, models derived using conventional region definition strategies performed at chance levels for most symptom domains. Individual-specific functional connectivity analyses revealed important new distinctions among cortical circuits responsible for the positive and negative symptoms, as well as key new information about how circuits underlying symptom expressions may vary depending on the underlying etiology and illness syndrome from which they manifest.

Bipolar disorder I (BD-I) is defined by episodes of mania, depression and euthymic states. These episodes are among other symptoms characterized by altered reward processing and negative symptoms (NS), in particular apathy. However, the neural correlates of these deficits are not well understood. We first assessed the severity of NS in 25 euthymic BD-I patients compared with 25 healthy controls (HC) and 27 patients with schizophrenia (SZ). Then, we investigated ventral (VS) and dorsal striatal (DS) activation during reward anticipation in a Monetary Incentive Delayed Task and its association with NS. In BD-I patients NS were clearly present and the severity of apathy was comparable to SZ patients. Apathy scores in the BD-I group but not in the SZ group correlated with sub-syndromal depression scores. At the neural level, we found significant VS and DS activation in BD-I patients and no group differences with HC or SZ patients. In contrast to patients with SZ, apathy did not correlate with striatal activation during reward anticipation. Explorative whole-brain analyses revealed reduced extra-striatal activation in BD-I patients compared with HC and an association between reduced activation of the inferior frontal gyrus and apathy. This study found that in BD-I patients apathy is present to an extent comparable to SZ, but is more strongly related to sub-syndromal depressive symptoms. The findings support the view of different pathophysiological mechanisms underlying apathy in the two disorders and suggest that extra-striatal dysfunction may contribute to impaired reward processing and apathy in BD-I.

The study is aimed to identify brain functional connectomes predictive of depressed and elevated mood symptomatology in individuals with bipolar disorder (BD) using the machine learning approach Connectome-based Predictive Modeling (CPM). Functional magnetic resonance imaging data were obtained from 81 adults with BD while they performed an emotion processing task. CPM with 5000 permutations of leave-one-out cross-validation was applied to identify functional connectomes predictive of depressed and elevated mood symptom scores on the Hamilton Depression and Young Mania rating scales. The predictive ability of the identified connectomes was tested in an independent sample of 43 adults with BD. CPM predicted the severity of depressed [concordance between actual and predicted values ( = 0.23, = 0.031) and elevated ( = 0.27, = 0.01) mood. Functional connectivity of left dorsolateral prefrontal cortex and supplementary motor area nodes, with inter- and intra-hemispheric connections to other anterior and posterior cortical, limbic, motor, and cerebellar regions, predicted depressed mood severity. Connectivity of left fusiform and right visual association area nodes with inter- and intra-hemispheric connections to the motor, insular, limbic, and posterior cortices predicted elevated mood severity. These networks were predictive of mood symptomatology in the independent sample ( ⩾ 0.45, = 0.002). This study identified distributed functional connectomes predictive of depressed and elevated mood severity in BD. Connectomes subserving emotional, cognitive, and psychomotor control predicted depressed mood severity, while those subserving emotional and social perceptual functions predicted elevated mood severity. Identification of these connectome networks may help inform the development of targeted treatments for mood symptoms.

Amygdala functional dysconnectivity lies at the heart of the pathophysiology of bipolar disorder (BD). Recent preclinical studies suggest that the amygdala is a heterogeneous group of nuclei, whose specific connectivity could drive positive or negative emotional valence. We investigated functional connectivity (FC) changes within these circuits emerging from each amygdala’s subdivision in 127 patients with BD in different mood states and 131 healthy controls (HC), who underwent resting-state functional MRI. FC was evaluated between lateral and medial nuclei of amygdalae, and key subcortical regions of the emotion processing network: anterior and posterior parts of the hippocampus, and core and shell parts of the nucleus accumbens. FC was compared across groups, and subgroups of patients depending on their mood states, using linear mixed models. We also tested correlations between FC and depression (MADRS) and mania (YMRS) scores. We found no difference between the whole sample of BD patients vs. HC but a significant correlation between MADRS and right lateral amygdala /right anterior hippocampus, right lateral amygdala/right posterior hippocampus and right lateral amygdala/left anterior hippocampus FC ( r  = −0.44, r  = −0.32, r  = −0.27, respectively, all pFDR<0.05). Subgroup analysis revealed decreased right lateral amygdala/right anterior hippocampus and right lateral amygdala/right posterior hippocampus FC in depressed vs. non-depressed patients and increased left medial amygdala/shell part of the left nucleus accumbens FC in manic vs non-manic patients. These results demonstrate that acute mood states in BD concur with FC changes in individual nuclei of the amygdala implicated in distinct emotional valence processing. Overall, our data highlight the importance to consider the amygdala subnuclei separately when studying its FC patterns including patients in distinct homogeneous mood states.