Muscle deficits with normal bone microarchitecture and geometry in young adults with well-controlled childhood-onset Crohn's disease.

BACKGROUND: Muscle-bone deficits are common in pediatric Crohn's disease; however, few studies have assessed long-term musculoskeletal outcomes in adults with childhood-onset Crohn's disease. This study assessed the prevalence of musculoskeletal deficits in young adults with childhood-onset Crohn's disease compared with healthy controls. METHODS: High-resolution MRI and MR spectroscopy were used to assess bone microarchitecture, cortical geometry and muscle area, and adiposity at distal femur and bone marrow adiposity (BMA) at lumbar spine. Muscle function and biomarkers of the muscle-bone unit were also assessed. RESULTS: Twenty-seven adults with Crohn's disease with median (range) age 23.2 years (18.0, 36.1) and 27 age and sex-matched controls were recruited. Trabecular microarchitecture, cortical geometry and BMA were not different between Crohn's disease and controls (P > 0.05 for all). Muscle area was lower (P = 0.01) and muscle fat fraction was higher (P = 0.04) at the distal femur in Crohn's disease compared to controls. Crohn's disease participants had lower grip strength [-4.3 kg (95% confidence interval (CI), -6.8 to -1.8), P = 0.001] and relative muscle power [-5.0 W/kg (95% CI, -8.8 to -1.2), P = 0.01]. Crohn's disease activity scores negatively associated with trabecular bone volume (r = -0.40, P = 0.04) and muscle area (r = -0.41, P = 0.03). CONCLUSION: Young adults with well-controlled Crohn's disease managed with contemporary therapies did not display abnormal bone microarchitecture or geometry at the distal femur but exhibited muscle deficits. The observed muscle deficits may predispose to musculoskeletal morbidity in future and interventions to improve muscle mass and function warrant investigation.

Development and validation of a full model of a four-headed neuroimaging single-photon emission computed tomography scanner.

OBJECTIVE: The Nucline X-Ring 4R is a four-headed gamma camera dedicated to neuroimaging. In this paper, we describe and validate a GATE (Geant4 Application for Tomographic Emission) model of the Nucline X-Ring 4R. MATERIALS AND METHODS: Images produced during model simulations were compared with those acquired experimentally to confirm the model was an accurate representation of the scanner. The most commonly reported measurements used to validate a GATE model include energy resolution, spatial resolution and sensitivity. In addition to the commonly reported static imaging measures, single-photon emission computed tomography (SPECT) spatial resolution was investigated to confirm that the model produces similar SPECT images to the experimental output. RESULTS: The experimental full-width at half-maximum was calculated to be 12.3 keV, which corresponds to an energy resolution of 8.8%. The simulated full-width at half-maximum was measured to be 12 keV, giving an energy resolution of 8.6%. The average spatial resolutions were found to be well matched (5.69 mm - simulated and 5.64 mm - experimental). However, the sensitivity was overestimated using the GATE model (47.8 and 54.3 cps/MBq) compared with the values obtained experimentally (42.7 and 44.3 cps/MBq). Finally, the simulated SPECT spatial resolution images were found to produce qualitatively comparable results. CONCLUSION: The model developed has been shown to produce similar results and images to those obtained experimentally. This model has the potential to simulate patient scans with the aim of improving patient care by optimizing scanner protocols.

Assessing ADHD symptoms in children and adults: evaluating the role of objective measures.

BACKGROUND: Diagnostic guidelines recommend using a variety of methods to assess and diagnose ADHD. Applying subjective measures always incorporates risks such as informant biases or large differences between ratings obtained from diverse sources. Furthermore, it has been demonstrated that ratings and tests seem to assess somewhat different constructs. The use of objective measures might thus yield valuable information for diagnosing ADHD. This study aims at evaluating the role of objective measures when trying to distinguish between individuals with ADHD and controls. Our sample consisted of children (n = 60) and adults (n = 76) diagnosed with ADHD and matched controls who completed self- and observer ratings as well as objective tasks. Diagnosis was primarily based on clinical interviews. A popular pattern recognition approach, support vector machines, was used to predict the diagnosis. RESULTS: We observed relatively high accuracy of 79% (adults) and 78% (children) applying solely objective measures. Predicting an ADHD diagnosis using both subjective and objective measures exceeded the accuracy of objective measures for both adults (89.5%) and children (86.7%), with the subjective variables proving to be the most relevant. CONCLUSIONS: We argue that objective measures are more robust against rater bias and errors inherent in subjective measures and may be more replicable. Considering the high accuracy of objective measures only, we found in our study, we think that they should be incorporated in diagnostic procedures for assessing ADHD.

Failure of hippocampal deactivation during loss events in treatment-resistant depression.

Major depressive disorder is characterized by anhedonia, cognitive biases, ruminations, hopelessness and increased anxiety. Blunted responses to rewards have been reported in a number of recent neuroimaging and behavioural studies of major depressive disorder. In contrast, neural responses to aversive events remain an under-studied area. While selective serotonergic reuptake inhibitors are often effective in treating major depressive disorder, their mechanism of action remains unclear. Following a series of animal model investigations of depressive illness and serotonergic function, Deakin and Graeff predicted that brain activity in patients with major depressive disorder is associated with an overactive dorsal raphe nucleus with overactive projections to the amygdala, periaqueductal grey and striatum, and an underactive median raphe nucleus with underactive projections to the hippocampus. Here we describe an instrumental loss-avoidance and win-gain reinforcement learning functional magnetic resonance imaging study with 40 patients with highly treatment-resistant major depressive disorder and never-depressed controls. The dorsal raphe nucleus/ periaqueductal grey region of the midbrain and hippocampus were found to be overactive in major depressive disorder during unsuccessful loss-avoidance although the median raphe nucleus was not found to be underactive. Hippocampal overactivity was due to a failure to deactivate during loss events in comparison to controls, and hippocampal over-activity correlated with depression severity, self-report 'hopelessness' and anxiety. Deakin and Graeff argued that the median raphe nucleus normally acts to inhibit consolidation of aversive memories via the hippocampus and this system is underactive in major depressive disorder, facilitating the development of ruminations, while the dorsal raphe nucleus system is engaged by distal cues predictive of threats and is overactive in major depressive disorder. During win events the striatum was underactive in major depressive disorder. We tested individual patient consistency of these findings using within-study replication. Abnormal hippocampal activity correctly predicted individual patient diagnostic status in 97% (sensitivity 95%, specificity 100%) of subjects, and abnormal striatal activity predicted diagnostic status in 84% (sensitivity 79%, specificity 89%) of subjects. We conclude that the neuroimaging findings were largely consistent with Deaken and Graeff's predictions, abnormally increased hippocampal activity during loss events was an especially consistent abnormality, and brainstem serotonergic nuclei merit further study in depressive illness.

Structural MRI-Based Predictions in Patients with Treatment-Refractory Depression (TRD).

The application of machine learning techniques to psychiatric neuroimaging offers the possibility to identify robust, reliable and objective disease biomarkers both within and between contemporary syndromal diagnoses that could guide routine clinical practice. The use of quantitative methods to identify psychiatric biomarkers is consequently important, particularly with a view to making predictions relevant to individual patients, rather than at a group-level. Here, we describe predictions of treatment-refractory depression (TRD) diagnosis using structural T1-weighted brain scans obtained from twenty adult participants with TRD and 21 never depressed controls. We report 85% accuracy of individual subject diagnostic prediction. Using an automated feature selection method, the major brain regions supporting this significant classification were in the caudate, insula, habenula and periventricular grey matter. It was not, however, possible to predict the degree of 'treatment resistance' in individual patients, at least as quantified by the Massachusetts General Hospital (MGH-S) clinical staging method; but the insula was again identified as a region of interest. Structural brain imaging data alone can be used to predict diagnostic status, but not MGH-S staging, with a high degree of accuracy in patients with TRD.

Predicting methylphenidate response in attention deficit hyperactivity disorder: a preliminary study.

Methylphenidate (MPH) is established as the main pharmacological treatment for patients with attention deficit hyperactivity disorder (ADHD). Whilst MPH is generally a highly effective treatment, not all patients respond, and some experience adverse reactions. Currently, there is no reliable method to predict how patients will respond, other than by exposure to a trial of medication. In this preliminary study, we sought to investigate whether an accurate predictor of clinical response to methylphenidate could be developed for individual patients, using sociodemographic, clinical and neuropsychological measures. Of the 43 boys with ADHD included in this proof-of-concept study, 30 were classed as responders and 13 as non-responders to MPH, with no significant differences in age nor verbal intelligence quotient (IQ) between the groups. Here we report the application of a multivariate analysis approach to the prediction of clinical response to MPH, which achieved an accuracy of 77% (p = 0.005). The most important variables to the classifier were performance on a 'go/no go' task and comorbid conduct disorder. This preliminary study suggested that further investigation is merited. Achieving a highly significant accuracy of 77% for the prediction of MPH response is an encouraging step towards finding a reliable and clinically useful method that could minimise the number of children needlessly being exposed to MPH.

Brainstem abnormalities in attention deficit hyperactivity disorder support high accuracy individual diagnostic classification.

Despite extensive research, psychiatry remains an essentially clinical and, therefore, subjective clinical discipline, with no objective biomarkers to guide clinical practice and research. Development of psychiatric biomarkers is consequently important. A promising approach involves the use of machine learning with neuroimaging, to make predictions of diagnosis and treatment response for individual patients. Herein, we describe predictions of attention deficit hyperactivity disorder (ADHD) diagnosis using structural T(1) weighted brain scans obtained from 34 young males with ADHD and 34 controls and a support vector machine. We report 93% accuracy of individual subject diagnostic prediction. Importantly, automated selection of brain regions supporting prediction was used. High accuracy prediction was supported by a region of reduced white matter in the brainstem, associated with a pons volumetric reduction in ADHD, adjacent to the noradrenergic locus coeruleus and dopaminergic ventral tegmental area nuclei. Medications used to treat ADHD modify dopaminergic and noradrenergic function. The white matter brainstem finding raises the possibility of "catecholamine disconnection or dysregulation" contributing to the ADHD syndrome, ameliorated by medication.