The Black Dog Institute

Sydney, Australia

The Black Dog Institute

Sydney, Australia
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Heitmann S.,University of New South Wales | Heitmann S.,The Black Dog Institute | Gong P.,University of Sydney | Breakspear M.,University of New South Wales | And 3 more authors.
Frontiers in Computational Neuroscience | Year: 2012

Adaptive changes in behavior require rapid changes in brain states yet the brain must also remain stable. We investigated two neural mechanisms for evoking rapid transitions between spatiotemporal synchronization patterns of beta oscillations (13-30 Hz) in motor cortex. Cortex was modeled as a sheet of neural oscillators that were spatially coupled using a center-surround connection topology. Manipulating the inhibitory surround was found to evoke reliable transitions between synchronous oscillation patterns and traveling waves. These transitions modulated the simulated local field potential in agreement with physiological observations in humans. Intermediate levels of surround inhibition were also found to produce bistable coupling topologies that supported both waves and synchrony. State-dependent perturbation between bistable states produced very rapid transitions but were less reliable. We surmise that motor cortex may thus employ state-dependent computation to achieve very rapid changes between bistable motor states when the demand for speed exceeds the demand for accuracy. © 2012 Heitmann, Gong and Breakspear.


McCraw S.,University of New South Wales | McCraw S.,The Black Dog Institute | Parker G.,University of New South Wales | Parker G.,The Black Dog Institute
Psychiatry Research | Year: 2017

This study examined the profile of stressful life experiences in patients with unipolar depression (UP) compared to bipolar disorder (BP), including their subsequent psychological impact and affirmation of post-traumatic stress symptoms. We studied 747 tertiary patients diagnosed with either a UP (n = 413) or BP condition (n = 334) according to a structured research interview and psychiatrist assessment. An online assessment collected additional study variables. Results showed that despite being younger in age, the BP group were more likely to have experienced 8 of the stressful life events, including childhood and adulthood abuse. However, both groups judged the consequential ‘impact’ of those events similarly. The BP group was significantly more likely than the UP group (45% vs. 36%) to affirm exposure to an extremely stressful event across their lifetime, resulting in 26.3% of the BP and 14.5% of the UP group affirming DSM-IV criteria for lifetime PTSD. The onset of DSM-IV-defined post-traumatic symptoms tended to occur in adulthood for both groups, with trauma often following the onset of the mood disorder. Thus, BP patients displayed greater vulnerability toward traumatic experiences and anxiety disorders, but reported a similar psychological impact in response to each stressor, compared with the UP group. © 2017


Aquino K.M.,University of New South Wales | Aquino K.M.,Queensland Institute of Medical Research | Aquino K.M.,University of Western Sydney | Robinson P.A.,University of New South Wales | And 11 more authors.
NeuroImage | Year: 2014

Functional magnetic resonance imaging (fMRI) is a powerful and broadly used means of non-invasively mapping human brain activity. However fMRI is an indirect measure that rests upon a mapping from neuronal activity to the blood oxygen level dependent (BOLD) signal via hemodynamic effects. The quality of estimated neuronal activity hinges on the validity of the hemodynamic model employed. Recent work has demonstrated that the hemodynamic response has non-separable spatiotemporal dynamics, a key property that is not implemented in existing fMRI analysis frameworks. Here both simulated and empirical data are used to demonstrate that using a physiologically based model of the spatiotemporal hemodynamic response function (stHRF) results in a quantitative improvement of the estimated neuronal response relative to unphysical space-time separable forms. To achieve this, an integrated spatial and temporal deconvolution is established using a recently developed stHRF. Simulated data allows the variation of key parameters such as noise and the spatial complexity of the neuronal drive, while knowing the neuronal input. The results demonstrate that the use of a spatiotemporally integrated HRF can avoid "ghost" neuronal responses that can otherwise be falsely inferred. Applying the spatiotemporal deconvolution to high resolution fMRI data allows the recovery of neuronal responses that are consistent with independent electrophysiological measures. © 2014 Elsevier Inc.


Heitmann S.,University of New South Wales | Heitmann S.,The Black Dog Institute | Boonstra T.,University of New South Wales | Boonstra T.,The Black Dog Institute | And 5 more authors.
PLoS Computational Biology | Year: 2013

Traveling waves of neuronal oscillations have been observed in many cortical regions, including the motor and sensory cortex. Such waves are often modulated in a task-dependent fashion although their precise functional role remains a matter of debate. Here we conjecture that the cortex can utilize the direction and wavelength of traveling waves to encode information. We present a novel neural mechanism by which such information may be decoded by the spatial arrangement of receptors within the dendritic receptor field. In particular, we show how the density distributions of excitatory and inhibitory receptors can combine to act as a spatial filter of wave patterns. The proposed dendritic mechanism ensures that the neuron selectively responds to specific wave patterns, thus constituting a neural basis of pattern decoding. We validate this proposal in the descending motor system, where we model the large receptor fields of the pyramidal tract neurons - the principle outputs of the motor cortex - decoding motor commands encoded in the direction of traveling wave patterns in motor cortex. We use an existing model of field oscillations in motor cortex to investigate how the topology of the pyramidal cell receptor field acts to tune the cells responses to specific oscillatory wave patterns, even when those patterns are highly degraded. The model replicates key findings of the descending motor system during simple motor tasks, including variable interspike intervals and weak corticospinal coherence. By additionally showing how the nature of the wave patterns can be controlled by modulating the topology of local intra-cortical connections, we hence propose a novel integrated neuronal model of encoding and decoding motor commands. © 2013 Heitmann et al.


Karim M.,University of New South Wales | Karim M.,The Black Dog Institute | Harris J.A.,University of Sydney | Morley J.W.,University of Western Sydney | And 4 more authors.
PLoS ONE | Year: 2012

Background: Vibrotactile discrimination tasks have been used to examine decision making processes in the presence of perceptual uncertainty, induced by barely discernible frequency differences between paired stimuli or by the presence of embedded noise. One lesser known property of such tasks is that decisions made on a single trial may be biased by information from prior trials. An example is the time-order effect whereby the presentation order of paired stimuli may introduce differences in accuracy. Subjects perform better when the first stimulus lies between the second stimulus and the global mean of all stimuli on the judged dimension ("preferred" time-orders) compared to the alternative presentation order ("nonpreferred" time-orders). This has been conceptualised as a "drift" of the first stimulus representation towards the global mean of the stimulus-set (an internal standard). We describe the influence of prior information in relation to the more traditionally studied factors of interest in a classic discrimination task. Methodology: Sixty subjects performed a vibrotactile discrimination task with different levels of uncertainty parametrically induced by increasing task difficulty, aperiodic stimulus noise, and changing the task instructions whilst maintaining identical stimulus properties (the "context"). Principal Findings: The time-order effect had a greater influence on task performance than two of the explicit factors-task difficulty and noise-but not context. The influence of prior information increased with the distance of the first stimulus from the global mean, suggesting that the "drift" velocity of the first stimulus towards the global mean representation was greater for these trials. Conclusions/Significance: Awareness of the time-order effect and prior information in general is essential when studying perceptual decision making tasks. Implicit mechanisms may have a greater influence than the explicit factors under study. It also affords valuable insights into basic mechanisms of information accumulation, storage, sensory weighting, and processing in neural circuits. © 2012 Karim et al.


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 7 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.


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 3 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.


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 | And 2 more authors.
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.


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 7 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.


PubMed | The Black Dog Institute, Ruhr University Bochum and Medical Research Council Cognition and Brain science Unit
Type: | Journal: Clinical psychology review | Year: 2017

We review evidence for training programmes that manipulate autobiographical processing in order to treat mood, anxiety, and stress-related disorders, using the GRADE criteria to judge evidence quality. We also position the current status of this research within the UK Medical Research Councils (2000, 2008) framework for the development of novel interventions. A literature search according to PRISMA guidelines identified 15 studies that compared an autobiographical episodic memory-based training (AET) programme to a control condition, in samples with a clinician-derived diagnosis. Identified AET programmes included Memory Specificity Training (Raes, Williams, & Hermans, 2009), concreteness training (Watkins, Baeyens, & Read, 2009), Competitive Memory Training (Korrelboom, van der Weele, Gjaltema, & Hoogstraten, 2009), imagery-based training of future autobiographical episodes (Blackwell & Holmes, 2010), and life review/reminiscence therapy (Arean et al., 1993). Cohens d was calculated for between-group differences in symptom change from pre- to post-intervention and to follow-up. We also completed meta-analyses for programmes evaluated across multiple studies, and for the overall effect of AET as a treatment approach. Results demonstrated promising evidence for AET in the treatment of depression (d=0.32), however effect sizes varied substantially (from -0.18 to 1.91) across the different training protocols. Currently, research on AET for the treatment of anxiety and stress-related disorders is not yet at a stage to draw firm conclusions regarding efficacy as there were only a very small number of studies which met inclusion criteria. AET offers a potential avenue through which low-intensity treatment for affective disturbance might be offered.

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