Background The greater presence of neurodevelopmental antecedants may differentiate schizophrenia from bipolar disorders (BD). Machine learning/pattern recognition allows us to estimate the biological age of the brain from structural magnetic resonance imaging scans (MRI). The discrepancy between brain and chronological age could contribute to early detection and differentiation of BD and schizophrenia. Methods We estimated brain age in 2 studies focusing on early stages of schizophrenia or BD. In the first study, we recruited 43 participants with first episode of schizophrenia-spectrum disorders (FES) and 43 controls. In the second study, we included 96 offspring of bipolar parents (48 unaffected, 48 affected) and 60 controls. We used relevance vector regression trained on an independent sample of 504 controls to estimate the brain age of study participants from structural MRI. We calculated the brain-age gap estimate (BrainAGE) score by subtracting the chronological age from the brain age. Results Participants with FES had higher BrainAGE scores than controls (F(1, 83) = 8.79, corrected P = .008, Cohen’s d = 0.64). Their brain age was on average 2.64 ± 4.15 years greater than their chronological age (matched t(42) = 4.36, P < .001). In contrast, participants at risk or in the early stages of BD showed comparable BrainAGE scores to controls (F(2,149) = 1.04, corrected P = .70, η2 = 0.01) and comparable brain and chronological age. Conclusions Early stages of schizophrenia, but not early stages of BD, were associated with advanced BrainAGE scores. Participants with FES showed neurostructural alterations, which made their brains appear 2.64 years older than their chronological age. BrainAGE scores could aid in early differential diagnosis between BD and schizophrenia.

Background-To translate our knowledge about neuroanatomy of bipolar disorder (BD) into a diagnostic tool, it is necessary to identify the neural signature of predisposition for BD and separate it from effects of long-standing illness and treatment. Thus, we examined the associations among genetic risk, illness burden, lithium treatment, and brain structure in BD.

The aim of the study was to investigate the potential association of epilepsy and EEG abnormalities with autistic regression and mental retardation. We examined a group of 77 autistic children (61 boys, 16 girls) with an average age of 9.1 +/- 5.3 years. Clinical interview, neurological examination focused on the evaluation of epilepsy, IQ testing, and 21-channel EEG (including night sleep EEG recording) were performed. Normal EEGs were observed in 44.4% of the patients, non-epileptiform abnormal EEGs in 17.5%, and abnormal EEGs with epileptiform discharges in 38.1% of the patients. Epilepsy was found in 22.1% of the subjects. A history of regression was reported in 25.8% of the patients, 54.8% of the sample had abnormal development during the first year of life, and 79.7% of the patients were mentally retarded. Autistic regression was significantly more frequent in patients with epilepsy than in non-epileptic patients (p = 0.003). Abnormal development during the first year of life was significantly associated with epileptiform EEG abnormalities (p = 0.014). Epilepsy correlated significantly with mental retardation (p = 0.001). Although the biological basis and possible causal relationships of these associations remain to be explained, they may point to different subgroups of patients with autistic spectrum disorders.

BackgroundSevere mental illness (SMI), including schizophrenia, bipolar disorder and severe depression, is responsible for a substantial proportion of disability in the population. This article describes the aims and design of a research study that takes a novel approach to targeted prevention of SMI. It is based on the rationale that early developmental antecedents to SMI are likely to be more malleable than fully developed mood or psychotic disorders and that low-risk interventions targeting antecedents may reduce the risk of SMI.Methods/DesignFamilies Overcoming Risks and Building Opportunities for Well-being (FORBOW) is an accelerated cohort study that includes a large proportion of offspring of parents with SMI and embeds intervention trials in a cohort multiple randomized controlled trial (cmRCT) design. Antecedents are conditions of the individual that are distressing but not severely impairing, predict SMI with moderate-to-large effect sizes and precede the onset of SMI by at least several years. FORBOW focuses on the following antecedents: affective lability, anxiety, psychotic-like experiences, basic symptoms, sleep problems, somatic symptoms, cannabis use and cognitive delay. Enrolment of offspring over a broad age range (0 to 21 years) will allow researchers to draw conclusions on a longer developmental period from a study of shorter duration. Annual assessments cover a full range of psychopathology, cognitive abilities, eligibility criteria for interventions and outcomes. Pre-emptive early interventions (PEI) will include skill training for parents of younger children and courses in emotional well-being skills based on cognitive behavioural therapy for older children and youth. A sample enriched for familial risk of SMI will enhance statistical power for testing the efficacy of PEI.DiscussionFORBOW offers a platform for efficient and unbiased testing of interventions selected according to best available evidence. Since few differences exist between familial and ’sporadic’ SMI, the same interventions are likely to be effective in the general population. Comparison of short-term efficacy of PEI on antecedents and the long term efficacy for preventing the onset of SMI will provide an experimental test of the etiological role of antecedents in the development of SMI.Electronic supplementary materialThe online version of this article (doi:10.1186/s12888-014-0344-2) contains supplementary material, which is available to authorized users.