Towards a neurodynamical understanding of the prodrome in schizophrenia

The identification of biomarkers for the early diagnosis of schizophrenia that could inform novel treatment developments is an important objective of current research. This paper will summarize recent work that has investigated changes in oscillatory activity and event-related potentials with Electro/Magnetoencephalography (EEG/MEG) in participants at high-risk for the development of schizophrenia, highlighting disruptions in sensory and cognitive operations prior to the onset of the syndrome. Changes in EEG/MEG-data are consistent with evidence for alterations in Glutamatergic and GABAergic neurotransmission as disclosed by Magnetic Resonance Spectroscopy and brain stimulation, indicating changes in Excitation/Inhibition Parameters prior to the onset of psychosis. Together these data emphasize the importance of research into neuronal dynamics as a crucial approach to establish functional relationships between impairments in neural circuits and emerging psychopathology that together could be fundamental for early intervention and the identification of novel treatments for emerging psychosis

A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder

Improving our understanding of the in vivo modelling of psychotic disorders: a systematic review and meta-analysis

Psychotic disorders represent a severe category of mental disorders affecting about one percent of the population. Individuals experience a loss or distortion of contact with reality alongside other symptoms, many of which are still not adequately managed using existing treatments. While animal models of these disorders could offer insights into these disorders and potential new treatments, translation of this knowledge has so far been poor in terms of informing clinical trials and practice. The aim of this project was to improve our understanding of these pre-clinical studies and identify potential weaknesses underlying translational failure. I carried out a systematic search of the literature to provide an unbiased summary of publications reporting animal models of schizophrenia and other psychotic disorders. From these publications, data were extracted to quantify aspects of the field including reported quality of studies, study characteristics and behavioural outcome data. The latter of these data were then used to calculate estimates of efficacy using random-effects meta-analysis. Having identified 3847 publications of relevance, including 852 different methods used to induce the model, over 359 different outcomes tested in them and almost 946 different treatments reported to be administered. I show that a large proportion of studies use simple pharmacological interventions to induce their models of these disorders, despite the availability of models using other interventions that are arguably of higher translational relevance. I also show that the reported quality of these studies is low, and only 22% of studies report taking measures to reduce the risk of biases such as randomisation and blinding, which has been shown to affect the reliability of results drawn. Through this work it becomes apparent that the literature is incredibly vast for studies looking at animal models of psychotic disorders and that some of the relevant work potentially overlaps with studies describing other conditions. This means that drawing reliable conclusions from these data is affected by what is made available in the literature, how it is reported and identified in a search and the time that it takes to reach these conclusions. I introduce the idea of using computer-assisted tools to overcome one of these problems in the long term. Translation of results from studies looking at animals modelling uniquely-human psychotic disorders to clinical successes might be improved by better reporting of studies including publishing of all work carried out, labelling of studies more uniformly so that it is identifiable, better reporting of study design including improving on reporting of measures taken to reduce the risk of bias and focusing on models with greater validity to the human condition

Auditory neural oscillations and excitation/inhibition balance in emerging psychosis

Chronic schizophrenia (ScZ) is associated with impaired gamma oscillations, reflected by robust alterations in 40 Hz ASSR. Oscillatory deficits may arise from changes in the cortical E/I-balance. However, it is unclear whether aberrant oscillations and potential underlying mechanisms are present also in early and clinical high risk (CHR) stages of psychosis. In this thesis, data from a multimodal CHR study were used to explore auditory oscillatory alterations in CHR individuals, assessed using MEG-recorded 40 Hz Auditory Steady State Response (ASSR) measures, with the aim to establish how deficits may account for early alterations in neural circuits in emerging psychosis. To further map such changes, a group of first episode of psychosis (FEP) participants were also studied, and oscillatory measures were compared with H1-MRS measures of neurotransmitter levels as well as with clinical measures. The thesis first presents a meta-analysis of ASSR findings in ScZ so far, showing that the response is impaired in chronic patients. Each of the following four chapters respectively present separate data analyses, focusing on baseline ASSR data, connectivity analyses, proton magnetic resonance spectroscopy (1H-MRS) analyses, and data assessing longitudinal outcomes. Through these investigations, the thesis demonstrates impairments in RSMG 40 Hz spectral power and ITPC in CHR and FEP, with bidirectional connectivity impairments present between RSMG and primary auditory cortex in CHR participants. In addition, strong beta frequency reductions in power were observed in CHR and FEP participants relative to controls. No clear impairments were detected in 1H-MRS data, but a trend deficit in right auditory GABA levels was seen in FEP patients. Finally, investigations of longitudinal parameters revealed that RSMG oscillatory impairments are related to functioning at the time of scanning, but not to functioning at the one-year follow-up. Moreover, beta frequency power was found to be selectively impaired in individuals with sustained CHR symptoms and low GAF scores (at both baseline and 12 months). Combined, the results of this thesis provide evidence for complex, subtle neural circuit alterations in emerging psychosis, which can be captured non-invasively using the 40 Hz ASSR paradigm

Clinical features presented to primary care prior to diagnosis of giant cell arteritis: an electronic health records study

Background: Giant cell arteritis (GCA) is the most common form of medium and large vessel vasculitis. This condition is associated with serious complications, such as blindness if left untreated, and is therefore considered a medical emergency. However, GCA remains difficult to diagnose, in part, due to the wide variation of presenting symptoms, resulting in some patients facing significant diagnostic delay. Aim: To evaluate clinical features experienced by patients prior to a diagnosis of GCA. Methods: Four studies were undertaken. Firstly, a systematic review and meta-analysis of clinical features previously associated with a diagnosis of GCA. Subsequently using the Clinical Practice Research Datalink (CPRD), the remaining three studies investigated; the trends in incidence of GCA; the association of individual clinical features on the subsequent diagnosis of GCA; and finally, combinations of presenting clinical features prior to a GCA diagnosis. Results: The systematic review found 30 distinct clinical features, with the strongest pooled association for jaw claudication and elevated ESR. A total of 9205 GCA cases were identified from 1990-2017 in CPRD. Consultation incidence of GCA was 1.46 per 10,000 person-years in 2017. In the CPRD analysis, individual features most strongly associated with GCA prior to diagnosis were headache, hypertension, and visual impairment. Application of latent class analysis (LCA) suggested five distinct patterns of presenting features; polymyalgia rheumatica (PMR), hypertension and multiple other features, single or no feature, hypertension, and elevated ESR. Conclusion: GCA remains a difficult condition to recognise in primary care. Clinical features, such as headache, PMR, elevated ESR, and hypertension were consistently identified as important clinical features experienced prior to GCA diagnosis. This thesis has highlighted the need to research patterns of clinical features rather than individual features that occur prior to a GCA diagnosis