The impact of substance use on brain structure in people at high risk of developing schizophrenia

Ventricular enlargement and reduced prefrontal volume are consistent findings in schizophrenia. Both are present in first episode subjects and may be detectable before the onset of clinical disorder. Substance misuse is more common in people with schizophrenia and is associated with similar brain abnormalities. We employ a prospective cohort study with nested case control comparison design to investigate the association between substance misuse, brain abnormality, and subsequent schizophrenia. Substance misuse history, imaging data, and clinical information were collected on 147 subjects at high risk of schizophrenia and 36 controls. Regions exhibiting a significant relationship between level of use of alcohol, cannabis or tobacco, and structure volume were identified. Multivariate regression then elucidated the relationship between level of substance use and structure volumes while accounting for correlations between these variables and correcting for potential confounders. Finally, we established whether substance misuse was associated with later risk of schizophrenia. Increased ventricular volume was associated with alcohol and cannabis use in a dose-dependent manner. Alcohol consumption was associated with reduced frontal lobe volume. Multiple regression analyses found both alcohol and cannabis were significant predictors of these abnormalities when simultaneously entered into the statistical model. Alcohol and cannabis misuse were associated with an increased subsequent risk of schizophrenia. We provide prospective evidence that use of cannabis or alcohol by people at high genetic risk of schizophrenia is associated with brain abnormalities and later risk of psychosis. A family history of schizophrenia may render the brain particularly sensitive to the risk-modifying effects of these substances

Meeting Curation Challenges in a Neuroimaging Group

The SCARP project is a series of short studies with two aims; firstly to discover more about disciplinary approaches and attitudes to digital curation through ‘immersion’ in selected cases; secondly to apply known good practice, and where possible, identify new lessons from practice in the selected discipline areas. The study summarised here is of the Neuroimaging Group in the University of Edinburgh’s Division of Psychiatry, which plays a leading role in eScience collaborations to improve the infrastructure for neuroimaging data integration and reuse. The Group also aims to address growing data storage and curation needs, given the capabilities afforded by new infrastructure. The study briefly reviews the policy context and current challenges to data integration and sharing in the neuroimaging field. It then describes how curation and preservation risks and opportunities for change were identified throughout the curation lifecycle; and their context appreciated through field study in the research site. The results are consistent with studies of neuroimaging eInfrastructure that emphasise the role of local data sharing and reuse practices. These sustain mutual awareness of datasets and experimental protocols through sharing peer to peer, and among senior researchers and students, enabling continuity in research and flexibility in project work. This “human infrastructure” is taken into account in considering next steps for curation and preservation of the Group’s datasets and a phased approach to supporting data documentation

Severe anemia in Malawian children

Background Severe anemia is a major cause of sickness and death in African children, yet the causes of anemia in this population have been inadequately studied. Methods We conducted a case-control study of 381 preschool children with severe anemia (hemoglobin concentration, <5.0 g per deciliter) and 757 preschool children without severe anemia in urban and rural settings in Malawi. Causal factors previously associated with severe anemia were studied. The data were examined by multivariate analysis and structural equation modeling. Results Bacteremia (adjusted odds ratio, 5.3; 95% confidence interval [CI], 2.6 to 10.9), malaria (adjusted odds ratio, 2.3; 95% CI, 1.6 to 3.3), hookworm (adjusted odds ratio, 4.8; 95% CI, 2.0 to 11.8), human immunodeficiency virus infection (adjusted odds ratio, 2.0; 95% CI, 1.0 to 3.8), the G6PD(sup -202/-376) genetic disorder (adjusted odds ratio, 2.4; 95% CI, 1.3 to 4.4), vitamin A deficiency (adjusted odds ratio, 2.8; 95% CI, 1.3 to 5.8), and vitamin B(sub 12) deficiency (adjusted odds ratio, 2.2; 95% CI, 1.4 to 3.6) were associated with severe anemia. Folate deficiency, sickle cell disease, and laboratory signs of an abnormal inflammatory response were uncommon. Iron deficiency was not prevalent in case patients (adjusted odds ratio, 0.37; 95% CI, 0.22 to 0.60) and was negatively associated with bacteremia. Malaria was associated with severe anemia in the urban site (with seasonal transmission) but not in the rural site (where malaria was holoendemic). Seventy-six percent of hookworm infections were found in children under 2 years of age. Conclusions There are multiple causes of severe anemia in Malawian preschool children, but folate and iron deficiencies are not prominent among them. Even in the presence of malaria parasites, additional or alternative causes of severe anemia should be considere

Data integration in eHealth: a domain/disease specific roadmap

The paper documents a series of data integration workshops held in 2006 at the UK National e-Science Centre, summarizing a range of the problem/solution scenarios in multi-site and multi-scale data integration with six HealthGrid projects using schizophrenia as a domain-specific test case. It outlines emerging strategies, recommendations and objectives for collaboration on shared ontology-building and harmonization of data for multi-site trials in this domain

Brain Imaging of Normal Subjects (BRAINS) age-specific MRI atlases from young adults to the very elderly (v1.0)

We have developed seven age-specific atlases of T1 brain MRI from 25 to 92 years../derivatives/group00001/anatomy/group00001_T1w_CSF_probability.nii ./derivatives/group00001/anatomy/group00001_T1w_CSF_probability_thresholded.1.nii ./derivatives/group00001/anatomy/group00001_T1w_GM_probability.nii ./derivatives/group00001/anatomy/group00001_T1w_GM_probability_thresholded.1.nii ./derivatives/group00001/anatomy/group00001_T1w_mean_icv_mask.nii ./derivatives/group00001/anatomy/group00001_T1w_mean.nii ./derivatives/group00001/anatomy/group00001_T1w_WM_probability.nii ./derivatives/group00001/anatomy/group00001_T1w_WM_probability_thresholded.1.nii ./derivatives/group00002/anatomy/group00002_T1w_CSF_probability.nii ./derivatives/group00002/anatomy/group00002__T1w_CSF_probability_thresholded.1.nii ./derivatives/group00002/anatomy/group00002_T1w_GM_probability.nii ./derivatives/group00002/anatomy/group00002__T1w_GM_probability_thresholded.1.nii ./derivatives/group00002/anatomy/group00002_T1w_mean_icv_mask.nii ./derivatives/group00002/anatomy/group00002_T1w_mean.nii ./derivatives/group00002/anatomy/group00002_T1w_WM_probability.nii ./derivatives/group00002/anatomy/group00002__T1w_WM_probability_thresholded.1.nii ./derivatives/group00003/anatomy/group00003_T1w_CSF_probability.nii ./derivatives/group00003/anatomy/group00003__T1w_CSF_probability_thresholded.1.nii ./derivatives/group00003/anatomy/group00003_T1w_GM_probability.nii ./derivatives/group00003/anatomy/group00003__T1w_GM_probability_thresholded.1.nii ./derivatives/group00003/anatomy/group00003_T1w_mean_icv_mask.nii ./derivatives/group00003/anatomy/group00003_T1w_mean.nii ./derivatives/group00003/anatomy/group00003_T1w_WM_probability.nii ./derivatives/group00003/anatomy/group00003__T1w_WM_probability_thresholded.1.nii ./derivatives/group00004/anatomy/group00004_T1w_CSF_probability.nii ./derivatives/group00004/anatomy/group00004__T1w_CSF_probability_thresholded.1.nii ./derivatives/group00004/anatomy/group00004_T1w_GM_probability.nii ./derivatives/group00004/anatomy/group00004__T1w_GM_probability_thresholded.1.nii ./derivatives/group00004/anatomy/group00004_T1w_mean_icv_mask.nii ./derivatives/group00004/anatomy/group00004_T1w_mean.nii ./derivatives/group00004/anatomy/group00004_T1w_WM_probability.nii ./derivatives/group00004/anatomy/group00004__T1w_WM_probability_thresholded.1.nii ./derivatives/group00005/anatomy/group00005_T1w_CSF_probability.nii ./derivatives/group00005/anatomy/group00005__T1w_CSF_probability_thresholded.1.nii ./derivatives/group00005/anatomy/group00005_T1w_GM_probability.nii ./derivatives/group00005/anatomy/group00005__T1w_GM_probability_thresholded.1.nii ./derivatives/group00005/anatomy/group00005_T1w_mean_icv_mask.nii ./derivatives/group00005/anatomy/group00005_T1w_mean.nii ./derivatives/group00005/anatomy/group00005_T1w_WM_probability.nii ./derivatives/group00005/anatomy/group00005__T1w_WM_probability_thresholded.1.nii ./derivatives/group00006/anatomy/group00006_T1w_CSF_probability.nii ./derivatives/group00006/anatomy/group00006__T1w_CSF_probability_thresholded.1.nii ./derivatives/group00006/anatomy/group00006_T1w_GM_probability.nii ./derivatives/group00006/anatomy/group00006__T1w_GM_probability_thresholded.1.nii ./derivatives/group00006/anatomy/group00006_T1w_mean_icv_mask.nii ./derivatives/group00006/anatomy/group00006_T1w_mean.nii ./derivatives/group00006/anatomy/group00006_T1w_WM_probability.nii ./derivatives/group00006/anatomy/group00006__T1w_WM_probability_thresholded.1.nii ./derivatives/group00007/anatomy/group00007_T1w_CSF_probability.nii ./derivatives/group00007/anatomy/group00007__T1w_CSF_probability_thresholded.1.nii ./derivatives/group00007/anatomy/group00007_T1w_GM_probability.nii ./derivatives/group00007/anatomy/group00007__T1w_GM_probability_thresholded.1.nii ./derivatives/group00007/anatomy/group00007_T1w_mean_icv_mask.nii ./derivatives/group00007/anatomy/group00007_T1w_mean.nii ./derivatives/group00007/anatomy/group00007_T1w_WM_probability.nii ./derivatives/group00007/anatomy/group00007__T1w_WM_probability_thresholded.1.nii ./experiment_description.doc

Grey matter changes can improve the prediction of schizophrenia in subjects at high risk

BACKGROUND: We hypothesised that subjects at familial high risk of developing schizophrenia would have a reduction over time in grey matter, particularly in the temporal lobes, and that this reduction may predict schizophrenia better than clinical measurements. METHODS: We analysed magnetic resonance images of 65 high-risk subjects from the Edinburgh High Risk Study sample who had two scans a mean of 1.52 years apart. Eight of these 65 subjects went on to develop schizophrenia an average of 2.3 years after their first scan. RESULTS: Changes over time in the inferior temporal gyrus gave a 60% positive predictive value (likelihood ratio >10) of developing schizophrenia compared to the overall 13% risk in the cohort as a whole. CONCLUSION: Changes in grey matter could be used as part of a predictive test for schizophrenia in people at enhanced risk for familial reasons, particularly for positive predictive power, in combination with other clinical and cognitive predictive measures, several of which are strong negative predictors. However, because of the limited number of subjects, this test requires independent replication to confirm its validity