Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach.

Functional magnetic resonance imaging (fMRI) is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a general linear model (GLM) approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power, and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making

Disentangling disorders of consciousness: Insights from diffusion tensor imaging and machine learning

Previous studies have suggested that disorders of consciousness (DOC) after severe brain injury may result from disconnections of the thalamo-cortical system. However, thalamo-cortical connectivity differences between vegetative state (VS), minimally conscious state minus (MCS−, i.e., low-level behavior such as visual pursuit), and minimally conscious state plus (MCS+, i.e., high-level behavior such as language processing) remain unclear. Probabilistic tractography in a sample of 25 DOC patients was employed to assess whether structural connectivity in various thalamo-cortical circuits could differentiate between VS, MCS−, and MCS+ patients. First, the thalamus was individually segmented into seven clusters based on patterns of cortical connectivity and tested for univariate differences across groups. Second, reconstructed whole-brain thalamic tracks were used as features in a multivariate searchlight analysis to identify regions along the tracks that were most informative in distinguishing among groups. At the univariate level, it was found that VS patients displayed reduced connectivity in most thalamo-cortical circuits of interest, including frontal, temporal, and sensorimotor connections, as compared with MCS+, but showed more pulvinar-occipital connections when compared with MCS−. Moreover, MCS− exhibited significantly less thalamo-premotor and thalamo-temporal connectivity than MCS+. At the multivariate level, it was found that thalamic tracks reaching frontal, parietal, and sensorimotor regions, could discriminate, up to 100% accuracy, across each pairwise group comparison. Together, these findings highlight the role of thalamo-cortical connections in patients\u27 behavioral profile and level of consciousness. Diffusion tensor imaging combined with machine learning algorithms could thus potentially facilitate diagnostic distinctions in DOC and shed light on the neural correlates of consciousness. Hum Brain Mapp 38:431–443, 2017. © 2016 Wiley Periodicals, Inc

Thalamo-frontal connectivity mediates top-down cognitive functions in disorders of consciousness

Objective: We employed functional MRI (fMRI) to assess whether (1) patients with disorders of consciousness (DOC) retain the ability to willfully engage in top-down processing and (2) what neurophysiologic factors distinguish patients who can demonstrate this ability from patients who cannot. Methods: Sixteen volunteers, 8 patients in vegetative state (VS), 16 minimally conscious patients (MCS), and 4 exit from MCS (eMCS) patients were enrolled in a prospective cross-sectional fMRI study. Participants performed a target detection task in which they counted the number of times a (changing) target word was presented amidst a set of distractors. Results: Three of 8 patients diagnosed as being in a VS exhibited significant activations in response to the task, thereby demonstrating a state of consciousness. Differential activations across tasks were also observed in 6 MCS patients and 1 eMCS patient. A psycho-physiologic interaction analysis revealed that the main factor distinguishing patients who responded to the task from those who did not was a greater connectivity between the anterior section of thalamus and prefrontal cortex. Conclusions: In our sample of patients, the dissociation between overt behavior observable in clinical assessments and residual cognitive faculties is prevalent among DOC patients (37%). A substantial number of patients, including some diagnosed with VS, can demonstrate willful engagement in top-down cognition. While neuroimaging data are not the same as observable behavior, this suggests that the mental status of some VS patients exceeds what can be appreciated clinically. Furthermore, thalamo-frontal circuits might be crucial to sustaining top-down functions

Motivation to change alcohol use and treatment engagement in incarcerated youth

Adolescents have been reported to be less motivated to engage and remain in substance abuse treatment than adults. When they appear motivated, it is often due to external motivators such as family pressure or court mandated treatment. The purpose of this study was to determine if adolescents\u27 motivation to change alcohol use was related to treatment engagement while incarcerated and alcohol use after release. Participants (N = 114) were youth in a state correctional facility in the Northeast and included adolescents who engaged in at least monthly drinking. Motivation to change alcohol use was measured by the Alcohol Ladder (AL), and treatment engagement was measured by the Treatment Participation Questionnaire (comprised of positive and negative treatment engagement). Measures were administered at baseline, 2 months in facility follow up, and 3 months post release follow up. Analysis indicated acceptable test-retest stability (r = .388, p ≤ .001). The AL at 3 months post release significantly predicted quantity and frequency of alcohol use after release. The AL at baseline also significantly predicted positive and negative treatment engagement at 2 months into incarceration (i.e., 2 months in facility follow up) indicating predictive validity. These results suggest that the AL is a reliable, valid, and useful instrument for incarcerated youth

Functional Segregation of Human Brain Networks Across the Lifespan: An Exploratory Analysis of Static and Dynamic Resting-State Functional Connectivity

Prior research has shown that during development, there is increased segregation between, and increased integration within, prototypical resting-state functional brain networks. Functional networks are typically defined by static functional connectivity over extended periods of rest. However, little is known about how time-varying properties of functional networks change with age. Likewise, a comparison of standard approaches to functional connectivity may provide a nuanced view of how network integration and segregation are reflected across the lifespan. Therefore, this exploratory study evaluated common approaches to static and dynamic functional network connectivity in a publicly available dataset of subjects ranging from 8 to 75 years of age. Analyses evaluated relationships between age and static resting-state functional connectivity, variability (standard deviation) of connectivity, and mean dwell time of functional network states defined by recurring patterns of whole-brain connectivity. Results showed that older age was associated with decreased static connectivity between nodes of different canonical networks, particularly between the visual system and nodes in other networks. Age was not significantly related to variability of connectivity. Mean dwell time of a network state reflecting high connectivity between visual regions decreased with age, but older age was also associated with increased mean dwell time of a network state reflecting high connectivity within and between canonical sensorimotor and visual networks. Results support a model of increased network segregation over the lifespan and also highlight potential pathways of top-down regulation among networks.</p

The ectomycorrhizal fungus Pisolithus microcarpusencodes a microRNA involved in cross-kingdom gene silencing during symbiosis

Small RNAs (sRNAs) are known to regulate pathogenic plant-microbe interactions. Emerging evidence from the study of these model systems suggests that microRNAs (miRNAs) can be translocated between microbes and plants to facilitate symbiosis. The roles of sRNAs in mutualistic mycorrhizal fungal interactions, however, are largely unknown. In this study, we characterized miRNAs encoded by the ectomycorrhizal fungus Pisolithus microcarpus and investigated their expression during mutualistic interaction with Eucalyptus grandis. Using sRNA sequencing data and in situ miRNA detection, a novel fungal miRNA, Pmic_miR-8, was found to be transported into E. grandis roots after interaction with P. microcarpus. Further characterization experiments demonstrate that inhibition of Pmic_miR-8 negatively impacts the maintenance of mycorrhizal roots in E. grandis, while supplementation of Pmic_miR-8 led to deeper integration of the fungus into plant tissues. Target prediction and experimental testing suggest that Pmic_miR-8 may target the host NB-ARC domain containing transcripts, suggesting a potential role for this miRNA in subverting host signaling to stabilize the symbiotic interaction. Altogether, we provide evidence of previously undescribed cross-kingdom sRNA transfer from ectomycorrhizal fungi to plant roots, shedding light onto the involvement of miRNAs during the developmental process of mutualistic symbioses

Spectral signatures of reorganised brain networks in disorders of consciousness.

Theoretical advances in the science of consciousness have proposed that it is concomitant with balanced cortical integration and differentiation, enabled by efficient networks of information transfer across multiple scales. Here, we apply graph theory to compare key signatures of such networks in high-density electroencephalographic data from 32 patients with chronic disorders of consciousness, against normative data from healthy controls. Based on connectivity within canonical frequency bands, we found that patient networks had reduced local and global efficiency, and fewer hubs in the alpha band. We devised a novel topographical metric, termed modular span, which showed that the alpha network modules in patients were also spatially circumscribed, lacking the structured long-distance interactions commonly observed in the healthy controls. Importantly however, these differences between graph-theoretic metrics were partially reversed in delta and theta band networks, which were also significantly more similar to each other in patients than controls. Going further, we found that metrics of alpha network efficiency also correlated with the degree of behavioural awareness. Intriguingly, some patients in behaviourally unresponsive vegetative states who demonstrated evidence of covert awareness with functional neuroimaging stood out from this trend: they had alpha networks that were remarkably well preserved and similar to those observed in the controls. Taken together, our findings inform current understanding of disorders of consciousness by highlighting the distinctive brain networks that characterise them. In the significant minority of vegetative patients who follow commands in neuroimaging tests, they point to putative network mechanisms that could support cognitive function and consciousness despite profound behavioural impairment.This work was supported by grants from the Wellcome Trust [WT093811MA to T.B.]; the James S. McDonnell Foundation [to A.M.O. and J.D.P.]; the UK Medical Research Council [U.1055.01.002.00001.01 to A.M.O. and J.D.P.]; the Canada Excellence Research Chairs program [to A.M.O.]; the National Institute for Health Research Cambridge Biomedical Research Centre [to J.D.P.]; and the National Institute for Health Research Senior Investigator and Healthcare Technology Cooperative awards [to J.D.P.].This is the final version of the article. It first appeared from PLOS via http://dx.doi.org

Mechanisms underlying disorders of consciousness: Bridging gaps to move toward an integrated translational science

AIM: In order to successfully detect, classify, prognosticate, and develop targeted therapies for patients with disorders of consciousness (DOC), it is crucial to improve our mechanistic understanding of how severe brain injuries result in these disorders. METHODS: To address this need, the Curing Coma Campaign convened a Mechanisms Sub-Group of the Coma Science Work Group (CSWG), aiming to identify the most pressing knowledge gaps and the most promising approaches to bridge them. RESULTS: We identified a key conceptual gap in the need to differentiate the neural mechanisms of consciousness per se, from those underpinning connectedness to the environment and behavioral responsiveness. Further, we characterised three fundamental gaps in DOC research: (1) a lack of mechanistic integration between structural brain damage and abnormal brain function in DOC; (2) a lack of translational bridges between micro- and macro-scale neural phenomena; and (3) an incomplete exploration of possible synergies between data-driven and theory-driven approaches. CONCLUSION: In this white paper, we discuss research priorities that would enable us to begin to close these knowledge gaps. We propose that a fundamental step towards this goal will be to combine translational, multi-scale, and multimodal data, with new biomarkers, theory-driven approaches, and computational models, to produce an integrated account of neural mechanisms in DOC. Importantly, we envision that reciprocal interaction between domains will establish a virtuous cycle, leading towards a critical vantage point of integrated knowledge that will enable the advancement of the scientific understanding of DOC and consequently, an improvement of clinical practice

Interaction of β-Sheet Folds with a Gold Surface

The adsorption of proteins on inorganic surfaces is of fundamental biological importance. Further, biomedical and nanotechnological applications increasingly use interfaces between inorganic material and polypeptides. Yet, the underlying adsorption mechanism of polypeptides on surfaces is not well understood and experimentally difficult to analyze. Therefore, we investigate here the interactions of polypeptides with a gold(111) surface using computational molecular dynamics (MD) simulations with a polarizable gold model in explicit water. Our focus in this paper is the investigation of the interaction of polypeptides with β-sheet folds. First, we concentrate on a β-sheet forming model peptide. Second, we investigate the interactions of two domains with high β-sheet content of the biologically important extracellular matrix protein fibronectin (FN). We find that adsorption occurs in a stepwise mechanism both for the model peptide and the protein. The positively charged amino acid Arg facilitates the initial contact formation between protein and gold surface. Our results suggest that an effective gold-binding surface patch is overall uncharged, but contains Arg for contact initiation. The polypeptides do not unfold on the gold surface within the simulation time. However, for the two FN domains, the relative domain-domain orientation changes. The observation of a very fast and strong adsorption indicates that in a biological matrix, no bare gold surfaces will be present. Hence, the bioactivity of gold surfaces (like bare gold nanoparticles) will critically depend on the history of particle administration and the proteins present during initial contact between gold and biological material. Further, gold particles may act as seeds for protein aggregation. Structural re-organization and protein aggregation are potentially of immunological importance