2026 ECNP Congress

PS02-2099

Phase-specific interhemispheric connectivity alterations in bipolar disorder

Mario Crespi — M. Paolini, L. Raffaelli, T. Cazzella, C. Milano, H.K. Shin, R. Zanardi, C. Colombo, B. Vai, F. Benedetti · IRCSS San Raffaele Hospital, Psychiatry and Clinical Psychobiology Unit - Division of Neuroscience, Milan, Italy

2026 ECNP Congress

Background

Bipolar disorder is a severe and chronic condition, characterized by the alternation of hypomanic or manic and depressive episodes, with fluctuations in mood and behavior [1]. Neuroimaging research has consistently documented widespread brain abnormalities in BD, with few studies investigating the extent to which these alterations are specific to distinct mood phases [2]. Existing evidence suggests a hemispheric imbalance in functional brain connectivity of BD patients, with phase specific alterations possibly contributing to the distinct clinical profiles observed in this condition [3]. Therefore, this study aimed to investigate phase specific neurobiological alterations using resting-state functional MRI interhemispheric correlation, and its predictive value through a machine learning approach.

Methods

Resting state data were acquired from 75 individuals with bipolar disorder (25 during a manic episode and 50 during a depressive episode), along with 50 healthy controls (HC). Interhemispheric correlation (IHC) represents a voxel-wise measure of the functional coupling between each region and its mirrored homologous region in the contralateral hemisphere [4]. Analyses were performed using the CONN toolbox, within a general linear model (GLM) framework and with pairwise group comparisons. Moreover, a machine learning approach was implemented to assess the discriminative value of IHC patterns. Individual voxel-wise IHC maps were entered in the Pattern Recognition for Neuroimaging Toolbox [5], and a support vector machine was used to perform binary classification models between groups (BD-M vs BD-D; BD-M vs HC; BD-D vs HC). Performance was evaluated with a 5-fold nested cross-validation procedure, and statistical significance was assessed through a 5000 permutation test for each model. Age, sex and handedness dominance were entered as nuisance covariates in both analyses.

Results

Group comparisons revealed that manic patients (BD-M) exhibited significantly higher interhemispheric correlation than depressive patients (BD-D) in the cerebellar crus II (pFDR = 0.015, k = 108), and in the amygdala and hippocampus (pFDR = 0.015, k = 98). Moreover, compared with HC, BD-M showed higher interhemispheric correlation in the parietal region (pFDR = 0.010, k = 92), the hippocampus (pFDR = 0.010, k = 89), the cerebellar crus II (pFDR = 0.029, k = 61), and the amygdala and putamen (pFDR = 0.029, k = 58). By contrast, BD-D exhibited reduced interhemispheric correlation relative to HC in the precentral and postcentral gyri (pFDR = 0.001, k = 144), as well as in the insular cortex, central opercular cortex and Heschl’s gyrus (pFDR = 0.042, k = 66). Concerning the machine learning approach, the model distinguishing BD-M and BD-D patients exhibited a balanced accuracy of 68% (p=0.013) and area under the curve of 0.75 (p=0.010). The models comparing patients and HC achieved balanced accuracies of 64% and 71% (BD-M vs HC: p=0.044; BD-D vs HC: p<0.001), with corresponding AUC values of 0.70 and 0.78 (BD-M vs HC: p=0.046; BD-D vs HC: p<0.001).

Conclusions

These findings highlight that BD is characterized by state and phase specific alterations in the connectivity of homotopic regions. Such alterations may be relevant as biomarkers for predictive purposes.

References

References

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