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Aquino K.M.,University of New South Wales | Schira M.M.,Neuroscience Research Australia | Schira M.M.,University of New South Wales | Robinson P.A.,University of New South Wales | And 6 more authors.
PLoS Computational Biology | Year: 2012

Functional MRI (fMRI) experiments rely on precise characterization of the blood oxygen level dependent (BOLD) signal. As the spatial resolution of fMRI reaches the sub-millimeter range, the need for quantitative modelling of spatiotemporal properties of this hemodynamic signal has become pressing. Here, we find that a detailed physiologically-based model of spatiotemporal BOLD responses predicts traveling waves with velocities and spatial ranges in empirically observable ranges. Two measurable parameters, related to physiology, characterize these waves: wave velocity and damping rate. To test these predictions, high-resolution fMRI data are acquired from subjects viewing discrete visual stimuli. Predictions and experiment show strong agreement, in particular confirming BOLD waves propagating for at least 5-10 mm across the cortical surface at speeds of 2-12 mm s-1. These observations enable fundamentally new approaches to fMRI analysis, crucial for fMRI data acquired at high spatial resolution. © 2012 Aquino et al. Source


Christl B.,University of New South Wales | Reilly N.,University of New South Wales | Yin C.,University of New South Wales | Austin M.-P.,University of New South Wales | Austin M.-P.,The Black Dog Institute
Archives of Women's Mental Health | Year: 2015

This study examines the clinical profile of women admitted to a psychiatric mother-baby unit as well as change in their clinical, parenting, attachment and quality of life outcomes. Data was collected from 191 mothers through self-report measures at admission and discharge. Change was analysed in terms of Edinburgh Postnatal Depression Scale (EPDS) score, parenting confidence, maternal attachment to the infant and overall functioning. Psychosocial factors impacting on symptom severity and recovery were examined. Most women (64.8 %) were admitted in the first 3 months after birth with an ICD-10 unipolar depressive episode (52.3 %) or anxiety disorder (25.7 %), and 47.6 % had comorbid diagnoses. Improvement from admission to discharge was seen with large effect sizes (≥one standard deviation, i.e. μ) in terms of clinical symptoms (EPDS, μ = 1.7), parenting confidence (Karitane Parenting Confidence Scale (KPCS), μ = 1.1) and attachment to their infant (Maternal Postpartum Attachment Scale (MPAS), μ = 0.9) as well as overall level of functioning (SF-14, μ = 1.9). The majority (73.3 %) recovered symptomatically, and this was associated with increasing maternal age (odds ratio (OR) = 1.129, p = 0.002) and lower levels of psychosocial risk at admission (OR = 0.963, p = 0.008). Improvement in parenting confidence was associated with increasing maternal age (OR = 1.17, p = 0.003). No predictive factors were found for improvement in maternal attachment after controlling for admission scores. In the short term, joint admission of mothers with their infants is highly beneficial in terms of clinical, functional and parenting outcomes, but follow up studies are needed to assess the longer term benefits for mother–infant dyads. The use of an observational tool to enhance our assessment of maternal–infant interaction and some measure of maternal emotional dysregulation—both important mediators of development of secure infant attachment—would also enhance our ability to tailor therapeutic interventions. © 2015, Springer-Verlag Wien. Source


Nguyen V.T.,University of Queensland | Nguyen V.T.,QIMR Berghofer Medical Research Institute | Breakspear M.,QIMR Berghofer Medical Research Institute | Breakspear M.,University of New South Wales | And 2 more authors.
Journal of Neuroscience | Year: 2014

Voluntary action is one of the core functions of the human brain, and is accompanied by the well known readiness potential or Bereitschaftspotential. A network of cortical areas is responsible for the motor preparation process, including the anterior mid-cingulate cortex (aMCC) and the SMA. However, the relationship between activity in these regions during movement preparation and the readiness potential is poorly understood. We examined this relationship by integrating simultaneously acquired EEG and fMRI through computational modeling. We first observed that global field power of premovement neural activity showed a specific correlation with BOLD responses in the aMCC. We then used dynamic causal modeling to infer premovement interactions between these regions and their relationship to the premovement neural activity underlying the readiness potential. These analyses suggest that SMA and aMCC have strong reciprocal connections that act to sustain each other’s activity, and that this interaction is mediated during movement preparation according to the readiness potential amplitude, as reflected in global cortical field power. Our study suggests that the reciprocal connections between SMA and aMCC are important to maintain the sustained activity of the readiness potential before movement and lead to a weak system instability at movement onset. We suggest that the effective connectivity of this network underlies its functional role in the preparation of self-generated actions. ©2014 the authors. Source


Lord A.,Queensland Institute of Medical Research | Lord A.,University of Queensland | Horn D.,Leibnitz Institute for Neurobiology | Horn D.,Otto Von Guericke University of Magdeburg | And 6 more authors.
PLoS ONE | Year: 2012

Major depression is a prevalent disorder that imposes a significant burden on society, yet objective laboratory-style tests to assist in diagnosis are lacking. We employed network-based analyses of "resting state" functional neuroimaging data to ascertain group differences in the endogenous cortical activity between healthy and depressed subjects. We additionally sought to use machine learning techniques to explore the ability of these network-based measures of resting state activity to provide diagnostic information for depression. Resting state fMRI data were acquired from twenty two depressed outpatients and twenty two healthy subjects matched for age and gender. These data were anatomically parcellated and functional connectivity matrices were then derived using the linear correlations between the BOLD signal fluctuations of all pairs of cortical and subcortical regions. We characterised the hierarchical organization of these matrices using network-based matrics, with an emphasis on their mid-scale "modularity" arrangement. Whilst whole brain measures of organization did not differ between groups, a significant rearrangement of their community structure was observed. Furthermore we were able to classify individuals with a high level of accuracy using a support vector machine, primarily through the use of a modularity-based metric known as the participation index. In conclusion, the application of machine learning techniques to features of resting state fMRI network activity shows promising potential to assist in the diagnosis of major depression, now suggesting the need for validation in independent data sets. © 2012 Lord et al. Source


Nguyen V.T.,University of Queensland | Breakspear M.,Queensland Institute of Medical Research | Breakspear M.,University of New South Wales | Breakspear M.,The Black Dog Institute | Cunnington R.,University of Queensland
NeuroImage | Year: 2014

Despite the wealth of research on face perception, the interactions between core regions in the face-sensitive network of the visual cortex are not well understood. In particular, the link between neural activity in face-sensitive brain regions measured by fMRI and EEG markers of face-selective processing in the N170 component is not well established. In this study, we used dynamic causal modeling (DCM) as a data fusion approach to integrate concurrently acquired EEG and fMRI data during the perception of upright compared with inverted faces. Data features derived from single-trial EEG variability were used as contextual modulators on fMRI-derived estimates of effective connectivity between key regions of the face perception network. The overall construction of our model space was highly constrained by the effects of task and ERP parameters on our fMRI data. Bayesian model selection suggested that the occipital face area (OFA) acted as a central gatekeeper directing visual information to the superior temporal sulcus (STS), the fusiform face area (FFA), and to a medial region of the fusiform gyrus (mFG). The connection from the OFA to the STS was strengthened on trials in which N170 amplitudes to upright faces were large. In contrast, the connection from the OFA to the mFG, an area known to be involved in object processing, was enhanced for inverted faces particularly on trials in which N170 amplitudes were small. Our results suggest that trial-by-trial variation in neural activity at around 170. ms, reflected in the N170 component, reflects the relative engagement of the OFA to STS/FFA network over the OFA to mFG object processing network for face perception. Importantly, the DCMs predicted the observed data significantly better by including the modulators derived from the N170, highlighting the value of incorporating EEG-derived information to explain interactions between regions as a multi-modal data fusion method for combined EEG-fMRI. © 2013 Elsevier Inc. Source

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