Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach

Progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and idiopathic Parkinson’s disease (IPD) can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs). An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i) a subcortical motor network; (ii) each of its component regions and (iii) the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process

Performance, Politics and Media: How the 2010 British General Election leadership debates generated ‘talk’ amongst the electorate.

During the British General Election 2010 a major innovation was introduced in part to improve engagement: a series of three live televised leadership debates took place where the leader of each of the three main parties, Labour, Liberal Democrat and Conservative, answered questions posed by members of the public and subsequently debated issues pertinent to the questions. In this study we consider these potentially ground breaking debates as the kind of event that was likely to generate discussion. We investigate various aspects of the ‘talk’ that emerged as a result of watching the debates. As an exploratory study concerned with situated accounts of the participants experiences we take an interpretive perspective. In this paper we outline the meta-narratives (of talk) associated with the viewing of the leadership debates that were identified, concluding our analysis by suggesting that putting a live debate on television and promoting and positioning it as a major innovation is likely to mean that is how the audience will make sense of it – as a media event

Multivariate decoding of brain images using ordinal regression.

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection

SCoRS - a method based on stability for feature selection and mapping in neuroimaging.

Feature selection (FS) methods play two important roles in the context of neuroimaging based classification: potentially increase classification accuracy by eliminating irrelevant features from the model and facilitate interpretation by identifying sets of meaningful features that best discriminate the classes. Although the development of FS techniques specifically tuned for neuroimaging data is an active area of research, up to date most of the studies have focused on finding a subset of features that maximizes accuracy. However, maximizing accuracy does not guarantee reliable interpretation as similar accuracies can be obtained from distinct sets of features. In the current paper we propose a new approach for selecting features: SCoRS (Survival Count on Random Subsamples) based on a recently proposed Stability Selection theory. SCoRS relies on the idea of choosing relevant features that are stable under data perturbation. Data are perturbed by iteratively subsampling both features (subspaces) and examples. We demonstrate the potential of the proposed method in a clinical application to classify depressed patients versus healthy individuals based on fMRI data acquired during visualization of happy faces

Sport, social change and the public intellectual

This article argues that the role of the public intellectual in sport is desperately needed. The research for the article draws upon key interviews and newspaper reports. The paper examines three questions: (i) What is the role of the public intellectual in sport? (ii) Do we wish to encourage the role of the public intellectual in sport? (iii) How does one balance the objective of challenging unseen silences in sport with its potential transformative capacity to produce change (or at least be a resource for hope) in many communities. The challenge is for today’s sociologists of sport and others not to accept the narrow job description of the academic but instead to ensure that the social study of sport is one of these very public, visible forms of activity and engagement

Examination of the predictive value of structural magnetic resonance scans in bipolar disorder:a pattern classification approach

Background - Bipolar disorder (BD) is one of the leading causes of disability worldwide. Patients are further disadvantaged by delays in accurate diagnosis ranging between 5 and 10 years. We applied Gaussian process classifiers (GPCs) to structural magnetic resonance imaging (sMRI) data to evaluate the feasibility of using pattern recognition techniques for the diagnostic classification of patients with BD. Method - GPCs were applied to gray (GM) and white matter (WM) sMRI data derived from two independent samples of patients with BD (cohort 1: n = 26; cohort 2: n = 14). Within each cohort patients were matched on age, sex and IQ to an equal number of healthy controls. Results - The diagnostic accuracy of the GPC for GM was 73% in cohort 1 and 72% in cohort 2; the sensitivity and specificity of the GM classification were respectively 69% and 77% in cohort 1 and 64% and 99% in cohort 2. The diagnostic accuracy of the GPC for WM was 69% in cohort 1 and 78% in cohort 2; the sensitivity and specificity of the WM classification were both 69% in cohort 1 and 71% and 86% respectively in cohort 2. In both samples, GM and WM clusters discriminating between patients and controls were localized within cortical and subcortical structures implicated in BD. Conclusions - Our results demonstrate the predictive value of neuroanatomical data in discriminating patients with BD from healthy individuals. The overlap between discriminative networks and regions implicated in the pathophysiology of BD supports the biological plausibility of the classifiers

Effects of urban living environments on mental health in adults

Urban-living individuals are exposed to many environmental factors that may combine and interact to influence mental health. While individual factors of an urban environment have been investigated in isolation, no attempt has been made to model how complex, real-life exposure to living in the city relates to brain and mental health, and how this is moderated by genetic factors. Using the data of 156,075 participants from the UK Biobank, we carried out sparse canonical correlation analyses to investigate the relationships between urban environments and psychiatric symptoms. We found an environmental profile of social deprivation, air pollution, street network and urban land-use density that was positively correlated with an affective symptom group (r = 0.22, P perm < 0.001), mediated by brain volume differences consistent with reward processing, and moderated by genes enriched for stress response, including CRHR1, explaining 2.01% of the variance in brain volume differences. Protective factors such as greenness and generous destination accessibility were negatively correlated with an anxiety symptom group (r = 0.10, P perm < 0.001), mediated by brain regions necessary for emotion regulation and moderated by EXD3, explaining 1.65% of the variance. The third urban environmental profile was correlated with an emotional instability symptom group (r = 0.03, P perm < 0.001). Our findings suggest that different environmental profiles of urban living may influence specific psychiatric symptom groups through distinct neurobiological pathways

Diluting education? An ethnographic study of change in an Australian Ministry of Education

This ethnographic study captures the processes that led to change in an Australian public education system. The changes were driven by strong neo-liberal discourses which resulted in a shift from a shared understanding about leading educational change in schools by knowledge transfer to managing educational change as a process, in other words, allowing the schools to decide how to change. Inside an Australian state education bureaucracy at a time when the organisation was restructured and services decentralised, this study helps show some of the disturbing trends resulting from the further entrenchment of neo-liberal strategies. Although control was re-centralised by legitimising performance mechanisms, in the form of national testing, there are indications that the focus on national tests may have alarming consequences for the content and context of education. I argue that the complexities of learning and fundamental pedagogies are being lost in preference for an over reliance on data systems that are based on a shallow and narrow set of standardised measures

The contested and contingent outcomes of Thatcherism in the UK

The death of Margaret Thatcher in April 2013 sparked a range of discussions and debates about the significance of her period in office and the political project to which she gave her name: Thatcherism. This article argues that Thatcherism is best understood as a symbolically important part of the emergence of first-phase neoliberalism. It engages with contemporary debates about Thatcherism among Marxist commentators and suggests that several apparently divergent positions can help us now reach a more useful analysis of Thatcherism’s short- and long-term outcomes for British political economy. The outcomes identified include: an initial crisis in the neoliberal project in the UK; the transformation of the party political system to be reflective of the politics of neoliberalism, rather than its contestation; long-term attempts at the inculcation of the neoliberal individual; de-industrialisation and financial sector dependence; and a fractured and partially unconscious working class. In all long-term outcomes, the contribution of Thatcherism is best understood as partial and largely negative, in that it cleared the way for a longer-term and more constructive attempt to embed neoliberal political economy. The paper concludes by suggesting that this analysis can inform current debates on the left of British politics about how to oppose and challenge the imposition of neoliberal discipline today