Explore the vast range of titles available.

How spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods to Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture.

Ambiguities in the source reconstruction of magnetoencephalographic (MEG) measurements can cause spurious correlations between estimated source time-courses. In this paper, we propose a symmetric orthogonalisation method to correct for these artificial correlations between a set of multiple regions of interest (ROIs). This process enables the straightforward application of network modelling methods, including partial correlation or multivariate autoregressive modelling, to infer connectomes, or functional networks, from the corrected ROIs. Here, we apply the correction to simulated MEG recordings of simple networks and to a resting-state dataset collected from eight subjects, before computing the partial correlations between power envelopes of the corrected ROItime-courses. We show accurate reconstruction of our simulated networks, and in the analysis of real MEGresting-state connectivity, we find dense bilateral connections within the motor and visual networks, together with longer-range direct fronto-parietal connections. •A method for removing source leakage from multivariate network analyses in MEG.•Network inference performed using regularised partial correlations between ROIs.•Artificial correlations are removed using a symmetric orthogonalisation step.•Simulations show accurate false-positive rates for network edge detection.•Resting-state networks show increased bilateral connectivity after correction.

The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.

This review seeks to provide a current overview of musculoskeletal pain in overweight and obese children. Databases searched were Academic Search Complete, CINAHL, Medline, Proquest Health and Medical Complete, Scopus, Google Scholar, SPORTDiscuss and Trove for studies published between 1 January 2000 and 30 December 2012. We used a broad definition of children within a 3- to 18-year age range. The search strategy included the following terms: obesity, morbid obesity, overweight, pain, musculoskeletal pain, child, adolescent, chronic pain, back pain, lower back pain, knee pain, hip pain, foot pain and pelvic pain. Two authors independently assessed each record, and any disagreement was resolved by the third author. Data were analysed using a narrative thematic approach owing to the heterogeneity of reported outcome measures. Ninety-seven records were initially identified using a variety of terms associated with children, obesity and musculoskeletal pain. Ten studies were included for thematic analysis when predetermined inclusion criteria were applied. Bone deformity and dysfunction, pain reporting and the impact of children being overweight or obese on physical activity, exercise and quality of life were the three themes identified from the literature. Chronic pain, obesity and a reduction in physical functioning and activity may contribute to a cycle of weight gain that affects a child’s quality of life. Future studies are required to examine the sequela of overweight and obese children experiencing chronic musculoskeletal pain.

Functional magnetic resonance imaging (fMRI) studies of the human brain have suggested that low-frequency fluctuations in resting fMRI data collected using blood oxygen level dependent (BOLD) contrast correspond to functionally relevant resting state networks (RSNs). Whether the fluctuations of resting fMRI signal in RSNs are a direct consequence of neocortical neuronal activity or are low-frequency artifacts due to other physiological processes (e.g., autonomically driven fluctuations in cerebral blood flow) is uncertain. In order to investigate further these fluctuations, we have characterized their spatial and temporal properties using probabilistic independent component analysis (PICA), a robust approach to RSN identification. Here, we provide evidence that: i. RSNs are not caused by signal artifacts due to low sampling rate (aliasing); ii. they are localized primarily to the cerebral cortex; iii. similar RSNs also can be identified in perfusion fMRI data; and iv. at least 5 distinct RSN patterns are reproducible across different subjects. The RSNs appear to reflect “default” interactions related to functional networks related to those recruited by specific types of cognitive processes. RSNs are a major source of non-modeled signal in BOLD fMRI data, so a full understanding of their dynamics will improve the interpretation of functional brain imaging studies more generally. Because RSNs reflect interactions in cognitively relevant functional networks, they offer a new approach to the characterization of state changes with pathology and the effects of drugs.

The objective of this study was to present nationally representative findings on sociodemographic and psychopathologic predictors of first incidence of Diagnostic and Statistical Manual of Mental Disorders , 4th edn (DSM-IV) substance, mood and anxiety disorders using the Wave 2 National Epidemiologic Survey on Alcohol and Related Conditions. One-year incidence rates of DSM-IV substance, mood and anxiety disorders were highest for alcohol abuse (1.02), alcohol dependence (1.70), major depressive disorder (MDD; 1.51) and generalized anxiety disorder (GAD; 1.12). Incidence rates were significantly greater ( P <0.01) among men for substance use disorders and greater among women for mood and anxiety disorders except bipolar disorders and social phobia. Age was inversely related to all disorders. Black individuals were at decreased risk of incident alcohol abuse and Hispanic individuals were at decreased risk of GAD. Anxiety disorders at baseline more often predicted incidence of other anxiety disorders than mood disorders. Reciprocal temporal relationships were found between alcohol abuse and dependence, MDD and GAD, and GAD and panic disorder. Borderline and schizotypal personality disorders predicted most incident disorders. Incidence rates of substance, mood and anxiety disorders were comparable to or greater than rates of lung cancer, stroke and cardiovascular disease. The greater incidence of all disorders in the youngest cohort underscores the need for increased vigilance in identifying and treating these disorders among young adults. Strong common factors and unique factors appear to underlie associations between alcohol abuse and dependence, MDD and GAD, and GAD and panic disorder. The major results of this study are discussed with regard to prevention and treatment implications.

Background This study aimed to identify and describe links between pain medication use and self-reported pain among people aged ≥ 50 years with osteoarthritis (OA) in an Irish population, and to examine the relationships between pain, medication usage and socioeconomic and clinical characteristics. Methods Secondary data analysis of wave 1 cross-sectional data from The Irish Longitudinal Study on Ageing (TILDA) was undertaken of 1042 people with self-reported doctor-diagnosed OA. We examined use of medications typically included in OA clinical guidelines, including non-opioid analgesics (e.g. paracetamol), topical and oral non-steroidal anti-inflammatory drugs (NSAIDs), opioids and nutraceuticals. Latent Class Analysis (LCA) was used to identify underlying clinical subgroups based on medication usage patterns, and self-reported pain severity. Multinomial logistic regression was used to explore sociodemographic and clinical characteristic links to latent class membership. Results A total of 358 (34.4%) of the 1042 people in this analysis were taking pain medications including oral NSAIDs (17.5%), analgesics (11.4%) and opioids (8.7%). Nutraceutical (glucosamine/chondroitin) use was reported by 8.6% and topical NSAID use reported by 1.4%. Three latent classes were identified: (1) Low medication use/no pain ( n  = 382, 37%), (2) low medication use/moderate pain ( n  = 523, 50%) and (3) moderate medication use/high pain ( n  = 137, 13%). Poorer self-rated health and greater sleep disturbance were associated with classes 2 and 3; depressive symptoms and female gender were associated with class 2, and retirement associated with class 3. Conclusions Whilst pain medication use varied with pain severity, different medication types reported broadly aligned with OA guidelines. The two subgroups exhibiting higher pain levels demonstrated poorer self-rated health and greater sleep disturbance. Significance Latent class analysis identified different subgroups based on pain severity and pain medication use, which may help to identify more targeted individualised pain management interventions.

Increasing age and carrying an APOEε4 allele are well established risk factors for Alzheimer's disease (AD). The earlier age of onset of AD observed in ε4-carriers may reflect an accelerated aging process. We recently reported that APOE genotype modulates brain function decades before the appearance of any cognitive or clinical symptoms. Here we test the hypothesis that APOE influences brain aging by comparing healthy ε4-carriers and non-carriers, using the same imaging protocol in distinct groups of younger and older healthy volunteers. A cross-sectional factorial design was used to examine the effects of age and APOE genotype, and their interaction, on fMRI activation during an encoding memory task. The younger (N=36; age range 20–35; 18 ε4-carriers) and older (35 middle-age/elderly; age range 50–78years; 15 ε4-carriers) healthy volunteers taking part in the study were cognitively normal. We found a significant interaction between age and ε4-status in the hippocampi, frontal pole, subcortical nuclei, middle temporal gyri and cerebellum, such that aging was associated with decreased activity in e4-carriers and increased activity in non-carriers. Reduced cerebral blood flow was found in the older ε4-carriers relative to older non-carriers despite preserved grey matter volume. Overactivity of brain function in young ε4-carriers is disproportionately reduced with advancing age even before the onset of measurable memory impairment. The APOE genotype determines age-related changes in brain function that may reflect the increased vulnerability of ε4-carriers to late-life pathology or cognitive decline. ►APOE genotype has different consequences for brain function depending on age. ►Differential aging processes in ε4-carriers may underlie increased risk of old-age pathology. ►Functional imaging techniques are sensitive to the effects of APOE on brain physiology. ►Our results may explain inconsistencies in the literature on APOE and brain function.

Background/Objectives: Adults often misreport dietary intake; the magnitude varies by the methods used to assess diet and classify participants. The objective was to quantify the accuracy of the Goldberg method for categorizing misreporters on a food frequency questionnaire (FFQ) and two 24-h recalls (24HRs). Subjects/Methods: We compared the Goldberg method, which uses an equation to predict total energy expenditure (TEE), with a criterion method that uses doubly labeled water (DLW), in a study of 451 men and women. Underreporting was classified using recommended cut points and calculated values. Sensitivity and specificity, positive predictive value (PPV) and negative predictive value and the area under the receiver operating characteristic curve (AUC) were calculated. Predictive models of underreporting were contrasted for the Goldberg and DLW methods. Results: AUCs were 0.974 and 0.972 on the FFQ, and 0.961 and 0.938 on the 24HR for men and women, respectively. The sensitivity of the Goldberg method was higher for the FFQ (92%) than the 24HR (50%); specificity was higher for the 24HR (99%) than the FFQ (88%); PPV was high for the 24HR (92%) and FFQ (88%). Simulation studies indicate attenuation in odds ratio estimates and reduction of power in predictive models. Conclusions: Although use of the Goldberg method may lead to bias and reduction in power in predictive models of underreporting, the method has high predictive value for both the FFQ and the 24HR. Thus, in the absence of objective measures of TEE or physical activity, the Goldberg method is a reasonable approach to characterize underreporting.

Little is known about the impact of treatment-resistant hypertension (TRH) on health-related quality of life (HrQoL). We aimed to compare HrQoL measures in adults with apparent TRH (aTRH) and non-resistant hypertension among nationally representative US Medical Expenditure Panel Survey data pooled from 2000 to 2011. Cohorts compared were adults with aTRH (⩾2 unique fills from ⩾4 antihypertensive classes during a year) versus non-resistant hypertension (those with hypertension not meeting the aTRH definition). Key outcomes were cohort differences in SF-12v2 physical component summary (PCS) and mental component summary (MCS) scores and disease-state utility using the SF-6D. Of 57 150 adults with hypertension, 2501 (4.4%) met criteria for aTRH. Persons with aTRH, compared with non-resistant hypertension, were older (mean, 68 vs 61 years), had a higher BMI (30.9 vs 29.7 kg m − 2 ) and were more likely to be Black (20% vs 14%), but less likely to be female (46% vs 54%). Persons with aTRH, compared with non-resistant hypertension, had lower mean PCS scores (35.8 vs 43.2; P <0.0001), and utility (0.68 vs 0.74; P <0.0001), but similar MCS scores (49.1 vs 50.4). In multivariable-adjusted analyses, aTRH was associated with a 2.37 (95% CI 1.71 to 3.02) lower PCS score and 0.02 (95% CI 0.01 to 0.03) lower utility, compared with non-resistant hypertension. In conclusion, aTRH was associated with substantially lower HrQoL in physical functioning and health utility, but not in mental functioning, compared with non-resistant hypertension. The multivariable-adjusted reduction in physical functioning was similar in magnitude to previous observations comparing hypertension with no hypertension.