Quantitative Susceptibility Mapping MRI in Deep-Brain Nuclei in First-Episode Psychosis.

BACKGROUND: Psychosis is related to neurochemical changes in deep-brain nuclei, particularly suggesting dopamine dysfunctions. We used an magnetic resonance imaging-based technique called quantitative susceptibility mapping (QSM) to study these regions in psychosis. QSM quantifies magnetic susceptibility in the brain, which is associated with iron concentrations. Since iron is a cofactor in dopamine pathways and co-localizes with inhibitory neurons, differences in QSM could reflect changes in these processes. METHODS: We scanned 83 patients with first-episode psychosis and 64 healthy subjects. We reassessed 22 patients and 21 control subjects after 3 months. Mean susceptibility was measured in 6 deep-brain nuclei. Using linear mixed models, we analyzed the effect of case-control differences, region, age, gender, volume, framewise displacement (FD), treatment duration, dose, laterality, session, and psychotic symptoms on QSM. RESULTS: Patients showed a significant susceptibility reduction in the putamen and globus pallidus externa (GPe). Patients also showed a significant R2* reduction in GPe. Age, gender, FD, session, group, and region are significant predictor variables for QSM. Dose, treatment duration, and volume were not predictor variables of QSM. CONCLUSIONS: Reduction in QSM and R2* suggests a decreased iron concentration in the GPe of patients. Susceptibility reduction in putamen cannot be associated with iron changes. Since changes observed in putamen and GPe were not associated with symptoms, dose, and treatment duration, we hypothesize that susceptibility may be a trait marker rather than a state marker, but this must be verified with long-term studies.

Intra and inter-individual variability in functional connectomes of patients with First Episode of Psychosis.

Patients with Schizophrenia may show different clinical presentations, not only regarding inter-individual comparisons but also in one specific subject over time. In fMRI studies, functional connectomes have been shown to carry valuable individual level information, which can be associated with cognitive and behavioral variables. Moreover, functional connectomes have been used to identify subjects within a group, as if they were fingerprints. For the particular case of Schizophrenia, it has been shown that there is reduced connectome stability as well as higher inter-individual variability. Here, we studied inter and intra-individual heterogeneity by exploring functional connectomes' variability and related it with clinical variables (PANSS Total scores and antipsychotic's doses). Our sample consisted of 30 patients with First Episode of Psychosis and 32 Healthy Controls, with a test-retest approach of two resting-state fMRI scanning sessions. In our patients' group, we found increased deviation from healthy functional connectomes and increased intragroup inter-subject variability, which was positively correlated to symptoms' levels in six subnetworks (visual, somatomotor, dorsal attention, ventral attention, frontoparietal and DMN). Moreover, changes in symptom severity were positively related to changes in deviation from healthy functional connectomes. Regarding intra-subject variability, we were unable to replicate previous findings of reduced connectome stability (i.e., increased intra-subject variability), but we found a trend suggesting that result. Our findings highlight the relevance of variability characterization in Schizophrenia, and they can be related to evidence of Schizophrenia patients having a noisy functional connectome.

Dysconnectivity in Schizophrenia Revisited: Abnormal Temporal Organization of Dynamic Functional Connectivity in Patients With a First Episode of Psychosis.

BACKGROUND AND HYPOTHESIS: Abnormal functional connectivity between brain regions is a consistent finding in schizophrenia, including functional magnetic resonance imaging (fMRI) studies. Recent studies have highlighted that connectivity changes in time in healthy subjects. We here examined the temporal changes in functional connectivity in patients with a first episode of psychosis (FEP). Specifically, we analyzed the temporal order in which whole-brain organization states were visited. STUDY DESIGN: Two case-control studies, including in each sample a subgroup scanned a second time after treatment. Chilean sample included 79 patients with a FEP and 83 healthy controls. Mexican sample included 21 antipsychotic-naïve FEP patients and 15 healthy controls. Characteristics of the temporal trajectories between whole-brain functional connectivity meta-states were examined via resting-state functional MRI using elements of network science. We compared the cohorts of cases and controls and explored their differences as well as potential associations with symptoms, cognition, and antipsychotic medication doses. STUDY RESULTS: We found that the temporal sequence in which patients' brain dynamics visited the different states was more redundant and segregated. Patients were less flexible than controls in changing their network in time from different configurations, and explored the whole landscape of possible states in a less efficient way. These changes were related to the dose of antipsychotics the patients were receiving. We replicated the relationship with antipsychotic medication in the antipsychotic-naïve FEP sample scanned before and after treatment. CONCLUSIONS: We conclude that psychosis is related to a temporal disorganization of the brain's dynamic functional connectivity, and this is associated with antipsychotic medication use.

The enduring gap in educational attainment in schizophrenia according to the past 50 years of published research: a systematic review and meta-analysis.

BACKGROUND: Educational attainment is associated with wellbeing and health, but patients with schizophrenia achieve lower levels of education than people without. Several effective interventions can ameliorate this situation. However, the magnitude of the education gap in schizophrenia and its change over time are unclear. We aimed to reconstruct the trajectories of educational attainment in patients with schizophrenia and, if reported, their healthy comparator controls. METHODS: We did a systematic review and meta-analysis including all studies reporting on patients with schizophrenia (of mean age ≥18 years) and describing the number of years of education of the participants, with or without healthy controls. There were no other design constraints on studies. We excluded studies that included only patients with other schizophrenia spectrum disorders and studies that did not specify the number of years of education of the participants. 22 reviewers participated in retrieving data from a search in PubMed and PsycINFO (Jan 1, 1970, to Nov 24, 2020). We estimated the birth date of participants from their mean age and publication date, and meta-analysed these data using random-effects models, focusing on educational attainment, the education gap, and changes over time. The primary outcome was years of education. The protocol was registered on PROSPERO (CRD42020220546). FINDINGS: From 32 593 initial references, we included 3321 studies reporting on 318 632 patients alongside 138 675 healthy controls (170 941 women and 275 821 men from studies describing sex or gender; data on ethnicity were not collected). Patients' educational attainment increased over time, mirroring that of controls. However, patients with schizophrenia in high-income countries had 19 months less education than controls (-1·59 years, 95% CI -1·66 to -1·53; p<0·0001), which is equivalent to a Cohen's d of -0·56 (95% CI -0·58 to -0·54) and implies an odds ratio of 2·58 for not completing 12 years of education (ie, not completing secondary education) for patients compared with controls. This gap remained stable throughout the decades; the rate of change in number of total years of education in time was not significant (annual change: 0·0047 years, 95% CI -0·0005 to 0·0099; p=0·078). For patients in low-income and middle-income countries, the education gap was significantly smaller than in high-income countries (smaller by 0·72 years, 0·85 to 0·59; p<0·0001), yet there was evidence that this gap was widening over the years, approaching that of high-income countries (annual change: -0·024 years, -0·037 to -0·011; p=0·0002). INTERPRETATION: Patients with schizophrenia have faced persistent inequality in educational attainment in the last century, despite advances in psychosocial and pharmacological treatment. Reducing this gap should become a priority to improve their functional outcomes. FUNDING: Ciencia y Tecnología para el Desarrollo (CYTED) to the Latin American Network for the Study of Early Psychosis (ANDES).

Gender, age and geographical representation over the past 50 years of schizophrenia research.

Previous studies have suggested that subjects participating in schizophrenia research are not representative of the demographics of the global population of people with schizophrenia, particularly in terms of gender and geographical location. We here explored if this has evolved throughout the decades, examining changes in geographical location, gender and age of participants in studies of schizophrenia published in the last 50 years. We examined this using a meta-analytical approach on an existing database including over 3,000 studies collated for another project. We found that the proportion of studies and participants from low-and-middle income countries has significantly increased over time, with considerable input from studies from China. However, it is still low when compared to the global population they represent. Women have been historically under-represented in studies, and still are in high-income countries. However, a significantly higher proportion of female participants have been included in studies over time. The age of participants included has not changed significantly over time. Overall, there have been improvements in the geographical and gender representation of people with schizophrenia. However, there is still a long way to go so research can be representative of the global population of people with schizophrenia, particularly in geographical terms.

Functional Dysconnectivity in Ventral Striatocortical Systems in 22q11.2 Deletion Syndrome.

22q11.2 deletion syndrome (22q11.2DS) is a genetic neurodevelopmental disorder that represents one of the greatest known risk factors for psychosis. Previous studies in psychotic subjects without the deletion have identified a dopaminergic dysfunction in striatal regions, and dysconnectivity of striatocortical systems, as an important mechanism in the emergence of psychosis. Here, we used resting-state functional MRI to examine striatocortical functional connectivity in 22q11.2DS patients. We used a 2 × 2 factorial design including 125 subjects (55 healthy controls, 28 22q11.2DS patients without a history of psychosis, 10 22q11.2DS patients with a history of psychosis, and 32 subjects with a history of psychosis without the deletion), allowing us to identify network effects related to the deletion and to the presence of psychosis. In line with previous results from psychotic patients without 22q11.2DS, we found that there was a dorsal to ventral gradient of hypo- to hyperstriatocortical connectivity related to psychosis across both patient groups. The 22q11.2DS was additionally associated with abnormal functional connectivity in ventral striatocortical networks, with no significant differences identified in the dorsal system. Abnormalities in the ventral striatocortical system observed in these individuals with high genetic risk to psychosis may thus reflect a marker of illness risk.

Abnormal nodal and global network organization in resting state functional MRI from subjects with the 22q11 deletion syndrome.

The 22q11 deletion syndrome is a genetic disorder associated with a high risk of developing psychosis, and is therefore considered a neurodevelopmental model for studying the pathogenesis of schizophrenia. Studies have shown that localized abnormal functional brain connectivity is present in 22q11 deletion syndrome like in schizophrenia. However, it is less clear whether these abnormal cortical interactions lead to global or regional network disorganization as seen in schizophrenia. We analyzed from a graph-theory perspective fMRI data from 40 22q11 deletion syndrome patients and 67 healthy controls, and reconstructed functional networks from 105 brain regions. Between-group differences were examined by evaluating edge-wise strength and graph theoretical metrics of local (weighted degree, nodal efficiency, nodal local efficiency) and global topological properties (modularity, local and global efficiency). Connectivity strength was globally reduced in patients, driven by a large network comprising 147 reduced connections. The 22q11 deletion syndrome network presented with abnormal local topological properties, with decreased local efficiency and reductions in weighted degree particularly in hub nodes. We found evidence for abnormal integration but intact segregation of the 22q11 deletion syndrome network. Results suggest that 22q11 deletion syndrome patients present with similar aberrant local network organization as seen in schizophrenia, and this network configuration might represent a vulnerability factor to psychosis.

Transitions between human functional brain networks reveal complex, cost-efficient and behaviorally-relevant temporal paths.

Resting-state functional MRI activity is organized as a complex network. However, this coordinated brain activity changes with time, raising questions about its evolving temporal arrangement. Does the brain visit different configurations through time in a random or ordered way? Advances in this area depend on developing novel paradigms that would allow us to shed light on these issues. We here propose to study the temporal changes in the functional connectome by looking at transition graphs of network activity. Nodes of these graphs correspond to brief whole-brain connectivity patterns (or meta-states), and directed links to the temporal transition between consecutive meta-states. We applied this method to two datasets of healthy subjects (160 subjects and a replication sample of 54), and found that transition networks had several non-trivial properties, such as a heavy-tailed degree distribution, high clustering, and a modular organization. This organization was implemented at a low biological cost with a high cost-efficiency of the dynamics. Furthermore, characteristics of the subjects' transition graphs, including global efficiency, local efficiency and their transition cost, were correlated with cognition and motor functioning. All these results were replicated in both datasets. We conclude that time-varying functional connectivity patterns of the brain in health progress in time in a highly organized and complex order, which is related to behavior.

Selection of the Optimal Algorithm for Real-Time Estimation of Beta Band Power during DBS Surgeries in Patients with Parkinson's Disease.

Deep Brain Stimulation (DBS) is a surgical procedure for the treatment of motor disorders in patients with Parkinson's Disease (PD). DBS involves the application of controlled electrical stimuli to a given brain structure. The implantation of the electrodes for DBS is performed by a minimally invasive stereotactic surgery where neuroimaging and microelectrode recordings (MER) are used to locate the target brain structure. The Subthalamic Nucleus (STN) is often chosen for the implantation of stimulation electrodes in DBS therapy. During the surgery, an intraoperative validation is performed to locate the dorsolateral region of STN. Patients with PD reveal a high power in the ß band (frequencies between 13 Hz and 35 Hz) in MER signal, mainly in the dorsolateral region of STN. In this work, different power spectrum density methods were analyzed with the aim of selecting one that minimizes the calculation time to be used in real time during DBS surgery. In particular, the results of three nonparametric and one parametric methods were compared, each with different sets of parameters. It was concluded that the optimum method to perform the real-time spectral estimation of beta band from MER signal is Welch with Hamming windows of 1.5 seconds and 50% overlap.