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

Automatic covariate selection in logistic models for chest pain diagnosis: A new approach

A newly established method for optimizing logistic models via a minorization-majorization procedure is applied to the problem of diagnosing acute coronary syndromes (ACS). The method provides a principled approach to the selection of covariates which would otherwise require the use of a suboptimal method owing to the size of the covariate set. A strategy for building models is proposed and two models optimized for performance and for simplicity are derived via ten-fold cross-validation. These models confirm that a relatively small set of covariates including clinical and electrocardiographic features can be used successfully in this task. The performance of the models is comparable with previously published models using less principled selection methods. The models prove to be portable when tested on data gathered from three other sites. Whilst diagnostic accuracy and calibration diminishes slightly for these new settings, it remains satisfactory overall. The prospect of building predictive models that are as simple as possible for a required level of performance is valuable if data-driven decision aids are to gain wide acceptance in the clinical situation owing to the need to minimize the time taken to gather and enter data at the bedside

Disrupted working memory circuitry and psychotic symptoms in 22q11.2 deletion syndrome.

22q11.2 deletion syndrome (22q11DS) is a recurrent genetic mutation that is highly penetrant for psychosis. Behavioral research suggests that 22q11DS patients exhibit a characteristic neurocognitive phenotype that includes differential impairment in spatial working memory (WM). Notably, spatial WM has also been proposed as an endophenotype for idiopathic psychotic disorder, yet little is known about the neurobiological substrates of WM in 22q11DS. In order to investigate the neural systems engaged during spatial WM in 22q11DS patients, we collected functional magnetic resonance imaging (fMRI) data while 41 participants (16 22q11DS patients, 25 demographically matched controls) performed a spatial capacity WM task that included manipulations of delay length and load level. Relative to controls, 22q11DS patients showed reduced neural activation during task performance in the intraparietal sulcus (IPS) and superior frontal sulcus (SFS). In addition, the typical increases in neural activity within spatial WM-relevant regions with greater memory load were not observed in 22q11DS. We further investigated whether neural dysfunction during WM was associated with behavioral WM performance, assessed via the University of Maryland letter-number sequencing (LNS) task, and positive psychotic symptoms, assessed via the Structured Interview for Prodromal Syndromes (SIPS), in 22q11DS patients. WM load activity within IPS and SFS was positively correlated with LNS task performance; moreover, WM load activity within IPS was inversely correlated with the severity of unusual thought content and delusional ideas, indicating that decreased recruitment of working memory-associated neural circuitry is associated with more severe positive symptoms. These results suggest that 22q11DS patients show reduced neural recruitment of brain regions critical for spatial WM function, which may be related to characteristic behavioral manifestations of the disorder