Publications

Tobacco use is the top preventable cause of early mortality in schizophrenia. Over 60% of people with schizophrenia smoke, three times the general prevalence. The biological basis of this increased risk is not understood, and existing interventions do not target schizophrenia-specific pathology. We therefore used a connectome-wide analysis to identify schizophrenia-specific circuits of nicotine addiction. We reanalyzed data from two studies: In Cohort 1, 35 smokers (18 schizophrenia, 17 control) underwent resting-state fMRI and clinical characterization. A multivariate pattern analysis of whole-connectome data was used to identify the strongest links between cigarette use and functional connectivity. In Cohort 2, 12 schizophrenia participants and 12 controls were enrolled in a randomized, controlled crossover study of nicotine patch with resting-state fMRI. We correlated change in network functional connectivity with nicotine dose. In Cohort 1, the strongest (p < 0.001) correlate between connectivity and cigarette use was driven by individual variation in default mode network (DMN) topography. In individuals with greater daily cigarette consumption, we observed a pathological expansion of the DMN territory into the identified parieto-occipital region, while in individuals with lower daily cigarette consumption, this region was external to the DMN. This effect was entirely driven by schizophrenia participants. Given the relationship between DMN topography and nicotine use we observed in Cohort 1, we sought to directly test the impact of nicotine on this network using an independent second cohort. In Cohort 2, nicotine reduced DMN connectivity in a dose-dependent manner (R = −0.50; 95% CI −0.75 to −0.12, p < 0.05). In the placebo condition, schizophrenia subjects had hyperconnectivity compared to controls (p < 0.05). Nicotine administration normalized DMN hyperconnectivity in schizophrenia. We here provide direct evidence that the biological basis of nicotine dependence is different in schizophrenia and in non-schizophrenia populations. Our results suggest the high prevalence of nicotine use in schizophrenia may be an attempt to correct a network deficit known to interfere with cognition.

The study of clinical high risk (CHR) states for psychosis is at a crossroads. After decades of largely observational studies, the field is preparing to develop targeted interventions for prevention or amelioration of psychosis risk. This is timely given the large scale, international Accelerating Medicines Partnership® Program in Schizophrenia (AMP® SCZ) initiative that began in 2020 to identify biomarkers of psychosis risk that may serve as treatment targets in forthcoming clinical trials. This initiative reflects a recent view that while our knowledge of CHR states remains incomplete (Mittal and Addington, 2021), we cannot afford to wait longer to develop therapeutic interventions (McGorry et al., 2008; McGorry et al., 2021; Woods et al., 2021).

The Shanghai-at-Risk-for-Psychosis (SHARP) program has also emphasized observational studies of CHR for over the last decade (Zhang et al., 2018). Like AMP® SCZ, our focus in SHARP is to transition towards intervention. Based on our previous CHR and schizophrenia studies, we developed a mechanistic approach to identify and to manipulate neural networks involved in schizophrenia. This novel approach utilizes brain responses to targeted manipulations of specific neural networks as putative biomarkers that will provide foundations for intervention-based models. Specifically, we suggest that these targeted brain manipulations may reduce positive and negative symptoms in CHR. We suggest further that dynamic neural responses to such manipulations may reflect neuroplasticity, provide mechanistic understanding of psychosis risk, and predict the efficacy of future treatments for these core clinical problems. Below, we present a theoretical framework for the work that we propose. The model is complementary to AMP® SCZ and offers a way to identify and to modulate relevant neural pathways and networks. Our working model and its conceptual and empirical bases are described below.

Background: Neurovascular abnormalities are relevant to the pathophysiology of bipolar disorder (BD), which can be assessed using cerebral blood flow (CBF) imaging. CBF alterations have been identified in BD, but studies to date have been small and inconclusive. We aimed to determine cortical gray matter CBF (GM-CBF) differences between BD and healthy controls (HC) and to identify relationships between CBF and clinical or cognitive measures.

Methods: Cortical GM-CBF maps were generated using Pseudo-Continuous Arterial Spin Labeling (pCASL) for 109 participants (BD, n = 61; HC, n = 48). We used SnPM13 to perform non-parametric voxel-wise two-sample t-tests comparing CBF between groups. We performed multiple linear regression to relate GM-CBF with clinical and cognitive measures. Analysis was adjusted for multiple comparisons with 10,000 permutations. Significance was set at a voxel level threshold of P < .001 followed by AlphaSim cluster-wise correction of P < .05.

Results: Compared to HCs, BD patients had greater GM-CBF in the left lateral occipital cortex, superior division and lower CBF in the right lateral occipital, angular and middle temporal gyrus. Greater GM-CBF in the left lateral occipital cortex correlated with worse working memory, verbal memory, attention and speed of processing. We found using voxel-wise regression that decreased gray matter CBF in the bilateral thalamus and cerebellum, and increased right fronto-limbic CBF were associated with worse working memory. No clusters were associated with clinical variables after FDR correction.

Conclusions: Cortical GM-CBF alterations are seen in BD and may be related to cognitive function, which suggest neurovascular unit dysfunction as a possible pathophysiologic mechanism.

Psychiatric neuroimaging has had limited impact on the clinical care of psychiatric disorders. Despite decades of discoveries of abnormalities in brain circuits, neuroimaging findings have not translated into clinical application. Some have proposed the solution to this problem is larger samples and increasing amounts of imaging data per individual. We believe that these proposals are unlikely to close the translational gap between the bench and the bedside because they continue to rely on purely observational correlations between biology and phenotype. Because these studies never test the causality of these correlations, these approaches are unlikely to lead to a clinically-actionable result. We propose that observed imaging findings should be probed directly to determine if they are causally linked to clinical phenotypes and therefore targets for therapeutic engagement. In this Disruptive Innovation, we provide an example of how perturbing circuit-phenotype relationships can identify and validate circuit targets for both clinical intervention as well as generating models of pathophysiology. This approach can be readily implemented, as these technologies are mature and widely available, and there exist empirically-derived targets that can be tested. All that is needed is to change our mindset to test the validity of identified brain signals rather than generating ever more of them.

Background

Auditory hallucinations (AH) are typically associated with schizophrenia (SZ), but they are also prevalent in bipolar disorder (BD). Despite the large body of research on the neural correlates of AH in SZ, the pathophysiology underlying AH remains unclear. Few studies have examined the neural substrates associated with propensity for AH in BD. Investigating AH across the psychosis spectrum has the potential to inform about the neural signature associated with the trait of AH, irrespective of psychiatric diagnosis.

Methods

We compared resting state functional magnetic resonance imaging data in psychosis patients with (n = 90 AH; 68 SZ, 22 BD) and without (n = 55 NAH; 16 SZ, 39 BD) lifetime AH. We performed region of interest (ROI)-to-ROI functional connectivity (FC) analysis using 91 cortical, 15 subcortical, and 26 cerebellar atlas-defined regions. The primary aim was to identify FC differences between patients with and without lifetime AH. We secondarily examined differences between AH and NAH within each diagnosis.

Results

Compared to the NAH group, patients with AH showed higher FC between cerebellum and frontal (left precentral gyrus), temporal [right middle temporal gyrus (MTG), left inferior temporal gyrus (ITG), left temporal fusiform gyrus)], parietal (bilateral superior parietal lobules), and subcortical (left accumbens, left palldium) brain areas. AH also showed lower FC between temporal lobe regions (between right ITG and right MTG and bilateral superior temporal gyri) relative to NAH.

Conclusions

Our findings suggest that dysconnectivity involving the cerebellum and temporal lobe regions may be common neurofunctional elements associated with AH propensity across the psychosis spectrum. We also found dysconnectivity patterns that were unique to lifetime AH within SZ or bipolar psychosis, suggesting both common and distinct mechanisms underlying AH pathophysiology in these disorders.

The early stage of psychosis (ESP) is a critical period where effective intervention has the most favorable impact on outcomes. Thalamic connectivity abnormalities have been consistently found in psychosis, and are associated with clinical symptoms and cognitive deficits. However, most studies consider ESP patients as a homogeneous population and fail to take the duration of illness into account. In this study, we aimed to capture the progression of thalamic connectivity changes over the first five years of psychosis. Resting-state functional MRI scans were collected from 156 ESP patients (44 with longitudinal data) and 82 healthy controls (24 with longitudinal data). We first performed a case-control analysis comparing thalamic connectivity with 13 networks in the cortex and cerebellum. Next, we modelled the shape (flat, linear, curvilinear) of thalamic connectivity trajectories by comparing flexible non-linear versus linear models. We then tested the significance of the duration of illness and diagnosis in trajectories that changed over time. Connectivity changed over the ESP period between the thalamus and default mode network (DMN) and fronto-parietal network (FPN) nodes in both the cortex and cerebellum. Three models followed a curvilinear trajectory (early increase followed by a subsequent decrease), while thalamo-cerebellar FPN connectivity followed a linear trajectory of steady reductions over time, indicating different rates of change. Finally, diagnosis significantly predicted thalamic connectivity. Thalamo-cortical and thalamo-cerebellar connectivity change in a dynamic fashion during the ESP period. A better understanding of these changes may provide insights into the compensatory and progressive changes in functional connectivity in the early stages of illness.

Objective

Negative symptoms of schizophrenia are substantially disabling and treatment resistant. Novel treatments like repetitive transcranial magnetic stimulation (TMS) need to be examined for the same using the experimental medicine approach that incorporates tests of mechanism of action in addition to clinical efficacy in trials.

Methods

Study was a double-blind, parallel, randomized, sham-controlled trial recruiting schizophrenia with at least a moderate severity of negative symptoms. Participants were randomized to real or sham intermittent theta burst stimulation (iTBS) under MRI-guided neuro-navigation, targeting the cerebellar vermis area VII-B, at a stimulus intensity of 100% active motor threshold, two sessions/day for five days (total = 6000 pulses). Assessments were conducted at baseline (T0), day-6 (T1) and week-6 (T2) after initiation of intervention. Main outcomes were, a) Scale for the Assessment of Negative Symptoms (SANS) score (T0, T1, T2), b) fronto-cerebellar resting state functional connectivity (RSFC) (T0, T1).

Results

Thirty participants were recruited in each arm. Negative symptoms improved in both arms (p < 0.001) but was not significantly different between the two arms (p = 0.602). RSFC significantly increased between the cerebellar vermis and the right inferior frontal gyrus (p_cluster-FWER = 0.033), right pallidum (p_cluster-FWER = 0.042) and right frontal pole (p_cluster-FWER = 0.047) in the real arm with no change in the sham arm.

Conclusion

Cerebellar vermal iTBS engaged a target belonging to the class of cerebello-subcortical-cortical networks, implicated in negative symptoms of schizophrenia. However, this did not translate to a superior clinical efficacy. Future trials should employ enhanced midline cerebellar TMS stimulation parameters for longer durations that can potentiate and translate biological changes into clinical effects.

The brain is the seat of body weight homeostasis. However, our inability to control the increasing prevalence of obesity highlights a need to look beyond canonical feeding pathways to broaden our understanding of body weight control^1,2,3. Here we used a reverse-translational approach to identify and anatomically, molecularly and functionally characterize a neural ensemble that promotes satiation. Unbiased, task-based functional magnetic resonance imaging revealed marked differences in cerebellar responses to food in people with a genetic disorder characterized by insatiable appetite. Transcriptomic analyses in mice revealed molecularly and topographically -distinct neurons in the anterior deep cerebellar nuclei (aDCN) that are activated by feeding or nutrient infusion in the gut. Selective activation of aDCN neurons substantially decreased food intake by reducing meal size without compensatory changes to metabolic rate. We found that aDCN activity terminates food intake by increasing striatal dopamine levels and attenuating the phasic dopamine response to subsequent food consumption. Our study defines a conserved satiation centre that may represent a novel therapeutic target for the management of excessive eating, and underscores the utility of a ‘bedside-to-bench’ approach for the identification of neural circuits that influence behaviour.

Subtypes of schizophrenia, constructed using clinical phenomenology to resolve illness heterogeneity, have faced criticism due to overlapping symptomatology and longitudinal instability; they were therefore dropped from the Diagnostic Statistical Manual-5. Cognitive and imaging findings comparing paranoid (P-SZ) and non-paranoid (disorganized, residual and undifferentiated; NP-SZ) schizophrenia have been limited due to small sample sizes. We assessed P-SZ and NP-SZ using symptomatology, cognition and brain structure and predicted that there would be few neurobiological differences. P-SZ (n = 237), NP-SZ (n = 127) and controls (n = 430) were included from a multi-site study. In a subset of this sample, structural imaging measures (P-SZ, n = 133; NP-SZ, n = 67; controls, n = 310) were calculated using Freesurfer 6.0. Group contrasts were run using analysis of covariance, controlling for age, sex, race and site, p-values were corrected using False Discovery Rate (FDR) and were repeated excluding the residual subtype. Compared to NP-SZ (with and without the residual subtype), P-SZ displayed fewer negative symptoms, faster speed of processing, larger bilateral hippocampus, right amygdala and their subfield volumes. Additionally, NP-SZ (with residual subtype) displayed fewer depressive symptoms and higher left transverse temporal cortical thickness (CT) but NP-SZ without residual subtype showed lower GAF scores and worse digit sequencing compared to P-SZ. No differences in positive symptoms and functioning (global or social) were detected. Subtle but significant differences were seen in cognition, symptoms, CT and subcortical volumes between P-SZ and NP-SZ. While the magnitude of these differences is not large enough to justify them as distinct categories, the paranoid- nonparanoid distinction in schizophrenia merits further investigation.