Cerebellar dysconnectivity in schizophrenia and bipolar disorder is associated with cognitive and clinical variables

Background: Abnormal cerebellar functional connectivity (FC) has been implicated in the pathophysiology of schizophrenia (SCZ) and bipolar disorder (BD). However, the patterns of cerebellar dysconnectivity in these two disorders and their association with cognitive functioning and clinical symptoms have not been fully clarified. In this study, we examined cerebellar FC alterations in SCZ and BD-I and their association with cognition and psychotic symptoms. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) data of 39 SCZ, 43 BD-I, and 61 healthy controls from the Consortium for Neuropsychiatric Phenomics dataset were examined. The cerebellum was parcellated into ten functional networks, and seed-based FC was calculated for each cerebellar system. Principal component analyses were used to reduce the dimensionality of the diagnosis-related FC and cognitive variables. Multiple regression analyses were used to assess the relationship between FC and cognitive and clinical data. Results: We observed decreased cerebellar FC with the frontal, temporal, occipital, and thalamic areas in individuals with SCZ, and a more widespread decrease in cerebellar FC in individuals with BD-I, involving the frontal, cingulate, parietal, temporal, occipital, and thalamic regions. SCZ had increased within-cerebellum and cerebellar frontal FC compared to BD-I. In BD-I, memory and verbal learning performances, which were higher compared to SCZ, showed a greater interaction with cerebellar FC patterns. Additionally, patterns of increased cortico-cerebellar FC were marginally associated with positive symptoms in patients. Conclusions: Our findings suggest that shared and distinct patterns of cortico-cerebellar dysconnectivity in SCZ and BD-I could underlie cognitive impairments and psychotic symptoms in these disorders.


Introduction
The cerebellum has traditionally been implicated in motor functions, including planning, execution, control, and learning (Manto et al., 2012).However, in recent years, a growing number of studies have also demonstrated its role in cognitive and emotional processing (Habas, 2021).Indeed, cerebellar lesions have been associated with the "cerebellar cognitive affective syndrome", a syndrome characterized by deficits in executive function, linguistic processing, spatial cognition, and affect regulation (Schmahmann and Sherman, 1998).Importantly, the cerebellum is connected to several cortical areas by a cortico-cerebellarthalamic-cortical circuit, and a growing body of evidence from animal and human studies suggests that cerebellar activity could modulate cerebral cortical function (Heck et al., 2023;McAfee et al., 2022;Middleton and Strick, 2001).
In recent years, the "disconnection hypothesis" has been postulated for the pathogenesis of schizophrenia (SCZ).According to this theory, SCZ is associated with aberrant connectivity in widely distributed brain networks (Stephan et al., 2009).In addition to cortical and subcortical dysconnectivity, abnormal connectivity between the cerebellum and the cerebral cortex seems to play a crucial role in the pathogenesis of SCZ, theorizing the so-called "cognitive dysmetria" hypothesis (Andreasen et al., 1999(Andreasen et al., , 1998)).According to this theory, SCZ is the result of a disruption in the fluid coordination of mental activities based on the cerebellum that leads to normal cognitive processes (Andreasen et al., 1999;Andreasen and Pierson, 2008).Neuroimaging studies have contributed to this knowledge by consistently showing abnormalities in the cortico-cerebellar FC not only in SCZ (Cao and Cannon, 2019;Kim et al., 2020), but also in patients with first episode psychosis (Choi et al., 2023;Feng et al., 2022), and in individuals at genetic or clinical risk for the disorder (Bernard et al., 2017;Cao et al., 2019).Importantly, cerebellar FC abnormalities, involving regions of the cortico-cerebellarstriatal-thalamic circuit, have been proposed to underlie generalized cognitive deficits in SCZ (Sheffield and Barch, 2016).
Cerebellar functional dysconnectivity has also been observed in bipolar disorder (BD), regardless of the thymic state (Olivito et al., 2022;Saleem et al., 2023).In particular, cortico-cerebellar FC alterations have been associated with abnormalities in cognitive processing and mood regulation in BD (Bersani et al., 2017;Frazier et al., 2022;Martin et al., 2015;Olivito et al., 2022).Importantly, recent genetic studies have demonstrated extensive genetic overlap between SCZ and BD, with a genetic correlation from common variation near 0.6-0.7 (Lee et al., 2013;Prata et al., 2019).In addition, the two disorders present numerous similarities in their clinical presentation, including irritability, cognitive deficits, and psychotic symptoms (Mancuso et al., 2015).Crucially, aberrant cerebellar FC has also been described in psychotic BD (Shinn et al., 2017), suggesting that cerebellar functional dysconnectivity might underlie the susceptibility to psychotic symptoms across the psychosis spectrum.Although evidence indicates a role for corticocerebellar FC in SCZ and BD, few investigations have studied cerebellar-cortical FC in each diagnostic group separately (Cao et al., 2023;Shinn et al., 2017), and none have compared cerebellar FC patterns between the two disorders.
To note, although the cerebellum is a complex brain region composed of multiple subunits with distinct connectivity patterns (Küper et al., 2012), most studies have focused on preselected cerebellar regions of interest (ROIs) or have examined FC alterations with a wholebrain approach, and few investigations have explored the FC between the cerebellum and cortical areas in SCZ and BD.To allow the study of FC of different cerebellar subunits, Buckner et al. (2011) developed a parcellation of the human cerebellum, assigning every cerebellar voxel to its most strongly associated cortical network.They found that the human cerebellum presents a roughly homotopic map of the brain cortex and that most of the cerebellar cortex is connected to cerebral association networks (Buckner et al., 2011).More recently, Ji and colleagues created an atlas using the Human Connectome Project data, which identifies ten cerebellar systems that correspond to putative networks defined for the cerebral cortex (i.e., primary and secondary visual, somatomotor, cingulo-opercular, dorsal attention, language, fronto-parietal, auditory, default-mode, and multimodal systems) (Ji et al., 2019).Here, to comprehensively examine cerebellar dysconnectivity in SCZ and BD-I, we used the state-of-the-art functional atlas of the cerebellum created by Ji and colleagues (Ji et al., 2019).In particular, we aimed to clarify whether: a) different cerebellar systems were associated with similar or distinct dysconnectivity patterns in SCZ and BD-I; b) cerebellar FC changes were related to cognitive deficits in SCZ and BD-I; and c) cerebellar dysconnectivity was associated to psychotic symptoms in SCZ and BD-I.Based on the available literature, we expected to find altered patterns of cortico-cerebellar FC in individuals with SCZ and BD-I, mainly involving the cortico-cerebellar-striatalthalamic loop.We anticipated to observe decreased cerebellar FC in both disorders, as well as an increase in cerebellar FC patterns in SCZ, in light of the dysconnectivity hypothesis of SCZ.In addition, we hypothesized that these changes would be associated with worse cognitive functioning in SCZ and BD-I compared to HC.Also, we hypothesized that abnormal cortico-cerebellar FC would correlate with psychotic symptoms.

Subjects
Participants were derived from the Consortium for Neuropsychiatric Phenomics dataset (https://openneuro.org/datasets/ds000030/versions/00016).Briefly, this dataset contains data from 229 individuals with SCZ, BD-I, and healthy controls (HC), aged 21-50, who completed a minimum of eight years of formal education.From the original database, we excluded subjects with head injuries that resulted in loss of consciousness, current medical illness, past or current substance abuse and/ or dependence, major depressive disorder, anxiety disorders, and attention hyperactivity disorder.Furthermore, HC were excluded if they had any past or current diagnosis of psychiatric disorders.This resulted in the exclusion of 74 individuals.Of the remaining subjects, three did not complete the resting-state fMRI acquisition and nine were excluded for excessive head-motion (see below).The final sample included 39 individuals with SCZ, 43 individuals with BD-I in partial or full remission, and 61 HC.

Data preprocessing
The preprocessing steps of structural and functional MRI data were performed using Data Processing & Analysis for Brain Imaging (DPABI, http://rfmri.org/dpabi)(Yan et al., 2016) running under the MATLAB R2022a (The Mathworks, Sherborn, MA, USA).First, all images were inspected for anatomical abnormalities or artefacts by an expert neuroimager (GC).Then, images were reoriented and realigned for head motion correction.After this, functional and structural images were coregistered.T1-weighted images were then segmented into grey matter, white matter, and cerebrospinal fluid.Functional images were normalized to the Montreal Neurological Institute (MNI) space using DARTEL registration with a resulting isotropic voxel size of 3 × 3 × 3 mm 3 .The images were then smoothed at 4-mm FWHM.Linear regression was applied to remove spurious covariates, including head motion parameters and the signal from white matter and cerebrospinal fluid.Global signal was not regressed out to avoid artefactual negative results and the possible loss of important neuronal components from the resting-state FC data (Hahamy et al., 2014).After realigning and co-registration, frame-wise displacement (FD) was calculated for all resting state volumes.Subjects with an average FD > 0.3 mm were excluded to mitigate the effects of head motion on functional connectivity measures (Smith et al., 2022).This led to the exclusion of eight patients and one control.

Cerebellar connectivity analysis
Based on Ji et al. (2019), we defined ten functional cerebellar subunits, including the primary and secondary visual, somatomotor, cingulo-opercular, dorsal attention, language, fronto-parietal, auditory, default-mode, and multimodal systems (Fig. 1) (Ji et al., 2019).For each subject, each of the ten functional cerebellar subunits was employed as a seed.A Pearson correlation was calculated between the preprocessed time series of the seed region and all brain voxels to estimate the FC for G. Cattarinussi et al. each seed.After a z-transformation, the derived seed-based connectivity maps entered second-level analyses.

Clinical and cognitive assessment
Current and lifetime psychiatric diagnoses were assessed with the Structural Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (SCID) (DSM-IV).Cognitive abilities were investigated with the Wechsler memory scale, the California Verbal Learning test, the Stroop test, the Attentional network task, the Continuous performance test, the Task Switch task, and the Stop Signal task.Psychopathological evaluation was carried out using the Scale for the Assessment of Negative Symptoms (SANS) (Andersen, 1989), and the Scale for the Assessment of Positive Symptoms (SAPS) (Andersen, 1984).

Statistical analyses
Demographic and clinical data were compared using two-sample ttests, ANOVAs with Tukey post-hoc tests, Kruskal-Wallis tests, and χ 2 tests as appropriate.The normality of the data was tested with the Shapiro-Wilk test.An analysis of covariance (ANCOVA) was performed using a flexible factorial model on all functional connectivity maps (within-subject factor) with diagnosis and seed as predictors, controlling for age, sex, and FD using Statistical Parametrical Mapping (SPM12).First, we explored the main effect of diagnosis in all cerebellar seeds to compare cerebellar-based FC patterns across diagnostic groups.Then, to ascertain a diagnosis-dependent effect on specific cortico-cerebellar networks, we tested diagnosis by seed interactions.Lastly, diagnostic effects for each seed were examined separately to elucidate dysconnectivity patterns for each cerebellar seed.This was done by estimating the interaction effect of the group by seed in the ANCOVA model.The voxel-wise threshold was set at p < 0.001 with FWE cluster-level correction α = 0.05.To reduce the dimensionality of FC dysconnectivity, we performed a principal component analysis (PCA) using parallel analysis with an Oblimin rotation on the mean signal of the clusters that showed a significant effect of diagnosis to estimate functional principal components (fPC).A PCA was also conducted on cognitive scores, using a parallel analysis and Varimax rotation to summarize the cognitive principal components (cPC).Each fPC and cPC was compared among diagnoses using ANOVA with Tukey post-hoc test or Kruskal-Wallis test.
Statistical significance was assessed using an FDR α = 0.05.For the cPCs that showed a significant effect of diagnosis, a multiple regression on the overall sample was conducted to predict cognitive performance using fPCs, diagnosis, and their linear interactions as predictors and age, sex, and FD as nuisance variables.Similarly, in patients, a multiple regression was conducted to predict SANS and SAPS scores using fPCs, diagnosis, and their linear interactions as predictors and age, sex, and FD as nuisance variables.At an exploratory level, we tested the correlations between cortico-cerebellar dysconnectivity for each cluster of significant group differences and clinical and cognitive data.Statistical analyses were conducted with Jamovi (2.0.0.0).

Clinical data
As expected, SCZ presented higher SANS and SAPS scores compared to BD-I (p < 0.001) (Table 1).SAPS and SANS scores indicated mild psychotic symptoms in both groups (Andreasen et al., 2005;Levine and Leucht, 2013).

Main effects of diagnosis across all seeds
The seed-based FC analysis showed a consistent pattern of hypoconnectivity in SCZ and BD-I compared to HC (Fig. 2).Specifically, decreased FC was observed in SCZ between all cerebellar seeds and the left middle frontal gyrus (MFG), the right lingual gyrus, the right inferior temporal gyrus (ITG), and the right thalamus compared to HC.
In BD-I, we found a reduction in FC between all cerebellar seeds and the right thalamus, the right inferior occipital gyrus (IOG), the left inferior frontal gyrus (IFG), the right middle temporal pole, the left posterior cingulate cortex (PCC), the right gyrus rectus, the right and left postcentral gyrus relative to HC.
SCZ had a higher FC between all cerebellar seeds and the left MFG and the left cerebellum compared to BD-I (Table 2).

Group by seed interaction and effects for each seed
We found a significant group by seed interactions in all cerebellar seeds (Fig. 3).Overall, three FC patterns emerged for all corticocerebellar seeds: BD-I specific hypoconnectivity (relative to HC), patient-shared hypoconnectivity (BD-I vs. HC and SCZ vs. HC) and SCZ-  specific hyperconnectivity (relative to HC and BD-I).

Associations between cerebellar dysconnectivity and cognitive and clinical data
Multiple regression on cPC 1 determined that 18 % of the variance was explained by the predictors of the model [R 2 = 0.180, F(10,128) = 2.81, p = 0.004].Diagnosis (SCZ-BD-I, p = 0.001), fPC 1 * fPC 2 interaction (p = 0.024), and fPC 1 * fPC 2 * diagnosis interaction (SCZ-BD-I, p = 0.048) were the only significant predictors.In particular, a higher fPC 1 strength was associated with better cPC 1 loadings in BD-I when the fPC 2 strength was lower relative to SCZ.Conversely, the reduced strength of fPC1 was associated with greater cPC 1 loadings in BD-I when the strength of fPC 2 was greater compared to SCZ.No effect was present for fPC 2 strength (Fig. 4).Multiple regression on cPC 2 determined that 27.8 % of the variance was explained by the predictors in the model [R 2 = 0.278, F(10,128) = 4.93, p < 0.001].Diagnosis (HC-BD-I HC-SCZ, p = 0.037) and age (p = 0.008) were the only significant predictors.The multiple regression model for cPC4 was not significant.
In addition, multiple regression on SAPS scores determined that 42.5 % of the variance was explained by the predictors in the model [R 2 = 0.425, F(8,73) = 6.76, p < 0.001].The diagnosis (SCZ-BD-I) was significantly associated with SAPS scores (p = 0.003), while fPC2 showed a trend towards a correlation with SAPS scores (p = 0.071).No associations with SANS scores were found.
Correlations between cortico-cerebellar dysconnectivity for each cluster of significant group differences and clinical and cognitive data are reported in the Supplementary Material.

Discussion
This study investigated cerebellar dysconnectivity patterns in individuals with SCZ and BD-I, providing evidence of common and distinct FC alterations in the two disorders.In particular, we observed decreased cerebellar FC with the prefrontal, temporal, occipital, and thalamic areas in SCZ, and a more widespread decrease in cerebellar FC in BD-I, involving the prefrontal, cingulate, parietal, temporal, occipital, and thalamic regions.Direct comparison of cerebellar FC patterns between SCZ and BD-I demonstrated an increase of within-cerebellum-FC and cerebellar-prefrontal FC in SCZ compared to BD-I.With regards to the association with cognitive measures, we observed that memory and verbal learning performances in BD-I showed a larger modulatory effect of cortico-cerebellar FC compared to SCZ, suggesting a greater involvement of cortico-cerebellar FC in cognitive processing in BD.Additionally, the patterns of increased cerebellar FC were weakly associated with positive symptoms in SCZ and BD-I patients.
Both SCZ and BD-I presented a decrease in FC between the cerebellum and the thalamus.The thalamus is connected to the cerebellum via glutamatergic projection neurons, and it plays a crucial role in the integration of sensory and motor information (Gornati et al., 2018).In SCZ and BD-I, several changes have been found in this region, and thalamic abnormalities have been implicated in the risk of both SCZ and BD (Cattarinussi et al., 2023(Cattarinussi et al., , 2022)).Interestingly, reduced thalamocerebellar FC has been described in SCZ (Ferri et al., 2018), BD (Zeng et al., 2023), and in the psychosis spectrum (Ramsay et al., 2022).Notably, these alterations seem to contribute to clinical symptomatology not only in subjects with psychosis but also in individuals at familial risk for psychosis (Ramsay et al., 2022), indicating a possible role of thalamo-cerebellar dysconnectivity as a phenotype of risk for the two disorders.Furthermore, we found that both groups of patients showed altered cerebellar-prefrontal FC, with selective changes in MFG in SCZ, and alterations in the ventral part of the prefrontal cortex in BD-I.Crucially, abnormalities in cerebellar-prefrontal FC have also been reported in drug-naïve first-episode patients with SCZ, where an increase in FC has been described (Guo et al., 2015), suggesting that changes in cerebellar-prefrontal FC might underlie the pathophysiology of SCZ, with a progressive reduction in FC as the disease progresses.Dysfunctional prefrontal-thalamic-cerebellar circuitry in SCZ appears to be the pathophysiological mechanism underlying "cognitive dysmetria", defined as the difficulty in coordinating the process of retrieving, receiving, processing, and expressing information (Andreasen et al., 1996;Ha et al., 2023).Furthermore, cerebellar and cerebellar-prefrontal abnormalities have been implicated in deficits in emotional processing and affect regulation (Frazier et al., 2022;Lupo et al., 2019).In BD, and in particular in BD with psychotic symptoms (BD-I), it has been hypothesized that cortico-cerebellar hypoconnectivity may underlie deficits in timing, integration, coordination, and signal processing, leading to cognitive, affective, and behavioral symptoms of the disorder (Shinn et al., 2017).In line with our results, previous meta-analytic evidence displayed the distinct involvement of different parts of the prefrontal cortex in the risk for SCZ and BD (Cattarinussi et al., 2022).Taken together, these data suggest that alterations in the prefrontal-thalamiccerebellar FC could represent a transdiagnostic marker of the SCZ -BD spectrum, with specific involvement of different parts of the prefrontal cortex associated with the distinct clinical manifestations, affective symptoms, and cognitive deficits observed in the two disorders.
In addition to increased prefrontal-cerebellar FC, SCZ also presented higher within-cerebellum FC compared to BD-I.Although we did not detect changes in within-cerebellum FC in SCZ compared to HC, previous meta-analytic evidence demonstrated that first-episode drug-naïve SCZ presented reduced FC strength in the left cerebellar lobule IV/V, as well as greater FC strength in the left cerebellar Crus I and II compared with HC (Ding et al., 2019).In addition, previous studies have reported altered cerebellar activity (fMRI) in SCZ relative to HC in cognitive, emotive, and motor tasks (Bernard and Mittal, 2015), leading to the speculation that abnormal FC within the cerebellum may reflect loss of hierarchical functional specialization in the prefrontal-cerebellar circuit, which may eventually fail to maintain cognitive performance (Brady et al., 2019;Ha et al., 2023).The findings of SCZ-specific hyperconnectivity highlight the potential role of cerebellar alterations in the pathophysiology of SCZ and could explain some of the differences in the clinical presentation of the two disorders.
To note, individuals with BD-I showed a widespread pattern of reduced cerebellar FC involving the parietal, cingulate, temporal, and occipital areas compared to HC.Our results are in line with recent evidence that indicates a central role of cerebellar FC abnormalities in the pathophysiology of BD.In particular, a recent investigation has reported a higher FC in BD-I compared to HC between the vermis of the cerebellum and regions involved in motor control and emotion, as well as a reduced FC with regions associated with language production (Saleem et al., 2023).Furthermore, another study that employed the dentate nucleus as a seed showed aberrant FC with the temporal cortex, PCC, and limbic regions in remitted patients with BD-I and BD-II (Olivito et al., 2022).Similarly, disruptions in the FC of the cerebellum with the superior temporal gyrus, insula, rolandic operculum, putamen, and precentral gyrus were reported in BD-I patients in different affective states (i.e., manic, hypomanic, depressed, mixed, and euthymic) (Cui et al., 2022).Therefore, aberrant FC of the cerebellum with cortical areas subjacent cognitive and emotional processing seems to be a consistent finding in BD, regardless of mood phase, supporting the hypothesis that cerebellum-cortical FC changes reflect the neural correlate of trait-based pathophysiology.Subjects with BD-I had a more widespread pattern of cerebellar FC abnormalities compared to SCZ.An accumulating body of evidence suggests that the pathogenesis of BD is associated with distributed cerebellar-cerebral FC abnormalities (Wang et al., 2017).Interestingly, recent investigations have suggested that several factors may contribute to the heterogeneity of cerebellar alterations in BD, including age, mood state, number of mood episodes, treatment, genetic risk load, and adverse childhood experiences (Brambilla et al., 2001;Harmata et al., 2023;Houenou et al., 2011;Moorhead et al., 2007).Future large studies that can allow for a stratification of these variables are warranted to ascertain their role in cerebellar neurobiology in BD.
In recent decades, several theories have been postulated behind the disruptions in brain functional integration.Among these, the disconnection hypothesis proposed by Friston and colleagues suggests that psychosis is the result of aberrant neuromodulation of synaptic efficacy, leading to abnormal functional integration of neural systems (Friston and Frith, 1995).Importantly, this hypothesis has been confirmed by genetic studies showing that genes linked to psychosis are also involved in establishing long-range connections during neurodevelopment and in regulating synaptic plasticity (Stephan et al., 2006).More recently, Northoff and colleagues, based on evidence derived from electroencephalography studies, have proposed the phase-based temporal imprecision model of psychosis.According to this concept, individuals with psychosis are characterized by temporal imprecision in the millisecond range, leading to reduced neural synchronization of the timing of external stimuli (Lechner and Northoff, 2023;Wolff and Northoff, 2024).Importantly, temporal imprecision at the neural level seems to be associated with temporal imprecision at the behavioral level (Wolff et al., 2022).Therefore, our results of abnormal cerebellar FC could be interpreted both in terms of altered synaptic efficacy resulting in neurodevelopmental changes, or as the result of aberrant temporal synchronization in SCZ and BD patients.
Consistent with a large body of literature, SCZ presented worse cognitive performances compared to both BD-I and HC (Bogie et al., 2023;Cattarinussi et al., 2023;Orellana and Slachevsky, 2013).Our study also showed an interaction between FC patterns and memory and verbal learning performances in BD-I, but not in SCZ, suggesting a role for cerebellar FC in cognition in BD-I.These results align with the "cerebellar cognitive affective syndrome" defined by Schmahmann et al. (Schmahmann and Sherman, 1998), and highlight the central role of the cerebellum in cognitive processing in BD-I.Although we did not observe a direct association between cerebellar FC and cognitive functions in SCZ, patients with SCZ presented worse cognitive performances compared to the other groups, indicating that the increase in prefrontalcerebellar and within-cerebellum FC might represent an ineffective compensatory mechanism that fails to maintain cognitive performance in SCZ.Future research focused on clarifying the interplay between neurodevelopment, cerebellar FC, and cognition is warranted.Moreover, the field will benefit from a clearer understanding of how specific cerebellar FC alterations contribute to different cognitive profiles in SCZ.
Furthermore, consistent with previous studies showing a correlation between cortical-cerebellar and thalamic-cerebellar FC and hallucinations, delusions, and bizarre behavior (Ferri et al., 2018;Jia et al., 2023;Zhuo et al., 2018), we observed a trend for an association between increased cerebellar FC and positive symptoms in both patient groups.Large evidence supports the hypothesis that positive symptoms may be related to dysconnectivity between the cerebellum and cortical regions (Andreasen and Pierson, 2008).Indeed, altered prefrontal-thalamiccerebellar FC could result in abnormal coordination and modulation of cortical functions in a range of higher-order tasks (Heck et al., 2023;McAfee et al., 2021), ultimately leading to deficits in the perception and processing of external stimuli.These results were confirmed by recent investigations showing the clinical efficacy of transcranial magnetic stimulation of the cerebellum in the treatment of verbal hallucinations (Xie et al., 2022).However, it is noteworthy that our results did not reach statistical significance and this may be partly due to the mild severity of psychotic symptoms in our sample.Therefore, we cannot draw any general conclusions on the association between psychotic symptoms and cerebellar FC.
Lastly, similarities in cerebellar FC patterns between SCZ and BD-I may also explain the similar profile between the two disorders of neurological and cerebellar soft signs, defined as nonspecific subtle disturbances in motor and sensory performance (Chrobak et al., 2016).In particular, based on previous findings in psychosis (Cai et al., 2021;Mittal et al., 2014;Viher et al., 2022), we speculate that abnormal prefrontal-thalamic-cerebellar FC in SCZ and BD could underlie the expression of neurological and cerebellar soft signs throughout the psychosis spectrum.Future studies exploring the association between neurological and cerebellar soft signs, cognition, and cerebellar FC could clarify the common and distinct neuropathological mechanisms underlying motor and cognitive deficits in psychosis.
Our study presents some limitations.First, the relatively small sample size and the lack of independent replication in an external dataset limit the generalizability of our findings.Second, although we employed a state-of-the-art functional atlas to study cerebellar FC patterns in SCZ and BD-I, we cannot exclude that our results were at least partially affected by the parcellation accuracy of this novel atlas.Third, most of the included patients were taking medications (atypical antipsychotics, mood stabilizers, antidepressants, sedatives, and psychostimulants) at the time of the scan, which could have influenced cerebellar FC patterns.However, previous evidence demonstrated that prefrontal-thalamic and motor/somatosensory-thalamic dysconnectivity, which are core components of the cortico-cerebellar circuit, were not related to antipsychotic dose in SCZ (Woodward et al., 2012).In addition, drug-naïve SCZ patients presented similar FC abnormalities within the cortico-cerebellar-thalamo-cortical circuit compared to medicated SCZ patients (Martino et al., 2018), suggesting that antipsychotic medications have no effect on cerebellar FC patterns.Lastly, we did not conduct global signal regression, which may have resulted in the loss of physiologically meaningful information on the source of local withinnetwork changes and artefactual negative correlations (Scalabrini et al., 2020).Future studies aimed at clarifying the spatiotemporal structure and organization of brain intrinsic activity and FC performed with global signal regression might help.
In conclusion, employing an updated functional parcellation of the cerebellum, we observed widespread hypoconnectivity across all cerebellar systems in SCZ and BD-I, mainly involving the cortical-thalamiccerebellar circuit.These findings expand our knowledge of shared and distinct patterns of cerebellar dysconnectivity across the SCZ -BD spectrum and highlight the potential role of cerebellar stimulation as a promising intervention for individuals with SCZ and BD-I.

Role of the funding source
This work was supported by the Italian Ministry of Education, University and Research (MUR), PRIN 2022 PNRR grant number P2022W28TX to F.S. The funding source had no role in this publication.

Declaration of competing interest
No funding source supported this project.None of the authors report biomedical financial interests or potential conflicts of interest.

Fig. 2 .
Fig. 2. Main Effects of Group Across All Seeds.A) Decreased FC in SCZ compared to HC between the cerebellum and left MFG, right lingual gyrus, right ITG and right thalamus.B) Decreased FC in SCZ compared to HC between the cerebellum and the right thalamus, right IOG, left IFG, right middle temporal pole, left PCC, right gyrus rectus, bilateral postcentral gyrus.C) Increased FC SCZ compared to BD between the cerebellum and the left MFG and the left cerebellum.Probability maps of intrinsic network loading differences are thresholded at p = 0.005 and corrected for multiple comparisons with alpha = 0.05 and overlaid on the Montreal Neurological Institute brain template.Blue and red denote increased and decreased cerebellar FC, respectively.R, right.(For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3 .
Fig. 3. Group by seed interactions.The bar graphs indicate the mean connectivity of the regions in Fig. 2 and the error bars indicate the standard error.BD: bipolar disorder; HC: healthy controls; SCZ: schizophrenia.* = significant in the comparison between HC and BD; ** = significant in the comparison between HC and SCZ; *** = significant in the comparison between SCZ and BD.

Fig. 4 .
Fig. 4. Interaction between FC, cognitive performance, and diagnosis.The cPC 1 loadings were lower in SCZ; They were predicted by the fPC1 * fPC2 interaction and by the fPC1 * fPC2 * diagnosis interaction.The marginal means and 95 % CI were calculated from a multiple regression model with age, sex, FD, diagnosis, fPC 1 and fPC2, their interaction and their interaction with diagnosis.All means are centered around 0, with a standard deviation of 1, and are expressed in arbitrary units (a.u.).Colors indicate fPC 1 values and the boxes indicate fPC 2 values.The x-axis indicates diagnostic groups and the y-axis indicates cPC 1 scores.BD: bipolar disorder; HC: healthy controls; PC: principal component; SCZ: schizophrenia; SD: standard deviation.

Table 1
Clinico-demographic characteristics of the sample.
BD: bipolar disorder; F: females; FD: framewise displacement; HC: healthy controls; m: mean; M: males; NA: not applicable: SANS: Scale for the Assessment of Negative Symptoms; SAPS: the Scale for the Assessment of Positive Symptoms; SCZ: schizophrenia; SD: standard deviation.aSignificant in the comparison with HC.

Table 2
Group main effects across all cerebellar seeds in schizophrenia and bipolar disorder.