André Zugman

Cytokines in schizophrenia: possible role of anti-inflammatory medications in clinical and preclinical stages.

Aims:  In this paper, we review the literature on the efficacy of anti‐inflammatory agents as neuroprotectors in clinical and preclinical stages of schizophrenia.

Age effects on the default mode and control networks in typically developing children

BACKGROUND The investigation of neurodevelopment during late childhood and pre-adolescence has recently attracted a great deal of interest in the field of neuroimaging. One promising topic in this field is the formation of brain networks in healthy subjects. The integration between neural modules characterizes the ability of the network to process information globally. Although many fMRI-based neurodevelopment studies can be found in the literature, the analyses of very large samples (on the order of hundreds of subjects) that focus on the late childhood/pre-adolescence period and resting state fMRI are scarce, and most studies have focused solely on North American and European populations. AIMS In this study, we present a descriptive investigation of the developmental formation of the Default Mode Network and the Control Network based on a Brazilian, cross-sectional community sample of 447 typically developing subjects aged 7-15 years old. METHODS Resting state fMRI data were acquired using two MRI systems from the same manufacturer using the same acquisition parameters. We estimated the age effects on the strength of the links (between brain regions) and the network features (graph descriptors: degree and eigenvector centrality). RESULTS Our findings showed an increase in the antero-posterior connectivity in both studied networks during brain development. The graph analyses showed an increase in centrality with age for most regions in the Default Mode Network and the dorsal anterior and posterior cingulate, the right anterior insula and the left posterior temporal cortex in the Control Network. CONCLUSION We conclude that the period of 7-15 years of age is crucial for the development of both the Default Mode and Control networks, with integration between the posterior and anterior neuronal modules and an increase in the centrality measures of the hub regions.

Decreased centrality of subcortical regions during the transition to adolescence: A functional connectivity study

Investigations of brain maturation processes are a key step to understand the cognitive and emotional changes of adolescence. Although structural imaging findings have delineated clear brain developmental trajectories for typically developing individuals, less is known about the functional changes of this sensitive development period. Developmental changes, such as abstract thought, complex reasoning, and emotional and inhibitory control, have been associated with more prominent cortical control. The aim of this study is to assess brain networks connectivity changes in a large sample of 7- to 15-year-old subjects, testing the hypothesis that cortical regions will present an increasing relevance in commanding the global network. Functional magnetic resonance imaging (fMRI) data were collected in a sample of 447 typically developing children from a Brazilian community sample who were submitted to a resting state acquisition protocol. The fMRI data were used to build a functional weighted graph from which eigenvector centrality (EVC) was extracted. For each brain region (a node of the graph), the age-dependent effect on EVC was statistically tested and the developmental trajectories were estimated using polynomial functions. Our findings show that angular gyrus become more central during this maturation period, while the caudate; cerebellar tonsils, pyramis, thalamus; fusiform, parahippocampal and inferior semilunar lobe become less central. In conclusion, we report a novel finding of an increasing centrality of the angular gyrus during the transition to adolescence, with a decreasing centrality of many subcortical and cerebellar regions.

N-acetylcysteine as a potentially useful medication to prevent conversion to schizophrenia in at-risk individuals

Abstract Schizophrenia is a chronic and often severe psychotic disorder. Its causes include imbalances in mediators involved in neuroplasticity, apoptosis, cell resilience and dendritic arborization. Among these mediators, oxidative species are particularly relevant for the pathophysiology of the disease, and this is the rationale for experimental use of antioxidant medications, such as N-acetylcysteine (NAC). Onset of schizophrenia is usually preceded by a period of subtle and unspecific symptoms, the prodrome, in which preventive interventions could delay or even stop the progression to full-blown psychosis. In this article, we propose that NAC could be a useful medication to prevent evolution of schizophrenia in individuals at risk for psychosis.

Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia

Neuroimaging-based models contribute to increasing our understanding of schizophrenia pathophysiology and can reveal the underlying characteristics of this and other clinical conditions. However, the considerable variability in reported neuroimaging results mirrors the heterogeneity of the disorder. Machine learning methods capable of representing invariant features could circumvent this problem. In this structural MRI study, we trained a deep learning model known as deep belief network (DBN) to extract features from brain morphometry data and investigated its performance in discriminating between healthy controls (N = 83) and patients with schizophrenia (N = 143). We further analysed performance in classifying patients with a first-episode psychosis (N = 32). The DBN highlighted differences between classes, especially in the frontal, temporal, parietal, and insular cortices, and in some subcortical regions, including the corpus callosum, putamen, and cerebellum. The DBN was slightly more accurate as a classifier (accuracy = 73.6%) than the support vector machine (accuracy = 68.1%). Finally, the error rate of the DBN in classifying first-episode patients was 56.3%, indicating that the representations learned from patients with schizophrenia and healthy controls were not suitable to define these patients. Our data suggest that deep learning could improve our understanding of psychiatric disorders such as schizophrenia by improving neuromorphometric analyses.

Reduced dorso-lateral prefrontal cortex in treatment resistant schizophrenia

BACKGROUND Treatment resistance affects up to one third of patients with schizophrenia (SCZ). A better understanding of its biological underlying processes could improve treatment. The aim of this study was to compare cortical thickness between non-resistant SCZ (NR-SCZ), treatment-resistant SCZ (TR-SCZ) patients and healthy controls (HC). METHODOLOGY Structural MRI scans were obtained from 3 groups of individuals: 61 treatment resistant SCZ individuals, 67 non-resistant SCZ and 80 healthy controls. Images were analyzed using cortical surface modelling (implemented in freesurfer package) to identify group differences in cortical thickness. Statistical significant differences were identified using Monte-Carlo simulation method with a corrected p-cluster<0.01. RESULTS Patients in the TR-SCZ group showed a widespread reduction in cortical thickness in frontal, parietal, temporal and occipital regions bilaterally. NR-SCZ group had reduced cortex in two regions (left superior frontal cortex and left caudal middle frontal cortex). TR-SCZ group also showed decreased thickness in the left dorsolateral prefrontal cortex (DLPFC) when compared with patients from NR-SCZ group. CONCLUSIONS The reduction in cortical thickness in DLPFC indicates a more severe form of the disease or a specific finding for this group. Alterations in this region should be explored as a putative marker for treatment resistance. Prospective studies, with individuals being followed from first episode psychosis until refractoriness is diagnosed, are needed to clarify these hypotheses.

Can neuroimaging be used to predict the onset of psychosis

The onset of psychotic disorders is preceded by a high-risk phase characterised by attenuated or brief psychotic symptoms and a marked decline in functioning. About a third of individuals presenting with these features develop a psychotic disorder within 3 years. A fundamental challenge in the clinical management of this population is that it is not possible to predict whether an individual at high risk will go on to develop psychosis on the basis of their presenting features. Consequently, preventive interventions that might reduce the risk of progression to psychosis cannot be selectively offered to those patients for whom they would be most useful. However, neuroimaging investigation suggests that the structure, function, and chemistry of the brain in high-risk individuals who become psychotic differ from those in individuals who do not become psychotic. In this Personal View, we review these findings and discuss the main challenges for translating them into clinical practice. The development of techniques that allow clinicians to tailor interventions to the level of risk is a major translational goal for research in this field.

Diabetes Mellitus and Disturbances in Brain Connectivity: A Bidirectional Relationship?

Diabetes mellitus (DM) is associated with deficits across multiple cognitive domains. The observed impairments in cognitive function are hypothesized to be subserved by alterations in brain structure and function. Several lines of evidence indicate that alterations in glial integrity and function, as well as abnormal synchrony within brain circuits and associated networks, are observed in adults with DM. Microangiopathy and alterations in insulin homeostasis appear to be principal effector systems, although a unitary explanation subsuming the complex etiopathology of white matter in DM is unavailable. A contemporary model of disease pathophysiology for several mental disorders, including but not limited to mood disorders, posits abnormalities in the synchronization of cellular systems in circuits. The observation that similar abnormalities occur in diabetic populations provides the basis for hypothesizing the convergence of pathoetiological factors. Herein, we propose that abnormal structure, function and chemical composition as well as synchrony within and between circuits is an accompaniment of DM and is shared in common with several mental disorders.

Peripheral immuno-inflammatory abnormalities in ultra-high risk of developing psychosis

BACKGROUND Immuno-inflammatory imbalances have been documented in schizophrenia, but very little is known about the immunological changes prior to the onset of disease. OBJECTIVE This work aimed to compare serum levels of pro- and anti-inflammatory cytokines in young subjects at ultra-high risk (UHR) of developing psychosis with age- and sex-matched healthy controls. METHODS A total of 12 UHR and 16 age- and sex-matched healthy controls (HC) subjects were enrolled in this study. Clinical profile was assessed using the Comprehensive Assessment of At-Risk Mental States (CAARMS), Semi-Structured Clinical Interview for DSM-IV Axis-I (SCID-I) or Kiddie-SADS-Present and Lifetime Version (K-SADS-PL), and Global Assessment of Functioning (GAF) scale. Serum interleukin (IL)-2, IL-4, IL-6, IL-10, tumor necrosis factor (TNF)-α, IFN-γ, and IL-17 were measured by flow cytometry using the Th1/Th2/Th17 cytometric bead array. RESULTS Compared with the healthy control group, patients in UHR showed increased IL-6 levels (Z=-2.370, p=0.018) and decreased IL-17 levels in serum (Z=-1.959, p=0.050). Levels of IL-17 positively correlated to the values in GAF symptoms (rho=0.632, p=0.028). CONCLUSION Our results suggest that immunological imbalances could be present in the early stages of psychosis, including in at-risk stages. Future studies should replicate and expand these results.

Is there a role for curcumin in the treatment of bipolar disorder

Curcumin is a polyphenolic nonflavonoid compound extracted from the rhizome of turmeric (Curcuma longa), a plant commonly used in Indian and Chinese traditional medicine to treat rheumatism, cough, inflammation and wounds. Curcumin putative targets, known based on studies of diverse central nervous system disorders other than bipolar disorders (BD) include several proteins currently implicated in the pathophysiology of BD. These targets include, but are not limited to, transcription factors activated by environmental stressors and pro-inflammatory cytokines, protein kinases (PKA, PKC), enzymes, growth factors, inflammatory mediators, and anti-apoptotic proteins (Bcl-XL). Herein, we review previous studies on the anti-inflammatory and anti-oxidant properties of curcumin and discuss its therapeutic potential in BD.