Psychiatry - Research Publications
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ItemRich club and reward network connectivity as endophenotypes for alcohol dependence: a diffusion tensor imaging study(WILEY, 2019-03)We aimed to examine the whole-brain white matter connectivity and local topology of reward system nodes in patients with alcohol use disorder (AUD) and unaffected siblings, relative to healthy comparison individuals. Diffusion-weighted magnetic resonance imaging scans were acquired from 18 patients with AUD, 15 unaffected siblings of AUD patients and 15 healthy controls. Structural networks were examined using network-based statistic and connectomic analysis. Connectomic analysis showed a significant ordered difference in normalized rich club organization (AUD < Siblings < Controls). We also found rank ordered differences (Control > Sibling > AUD) for both nodal clustering coefficient and nodal local efficiency in reward system nodes, particularly left caudate, right putamen and left hippocampus. Network-based statistic analyses showed that AUD group had significantly weaker connectivity than controls in the right hemisphere, mostly in the edges connecting putamen and hippocampus with other brain regions. Our results suggest that reward system network abnormalities, especially in subcortical structures, and impairments in rich-club organization might be related to the familial predisposition for AUD.
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ItemMapping connectomes with diffusion MRI: deterministic or probabilistic tractography?(WILEY, 2019-02)PURPOSE: Human connectomics necessitates high-throughput, whole-brain reconstruction of multiple white matter fiber bundles. Scaling up tractography to meet these high-throughput demands yields new fiber tracking challenges, such as minimizing spurious connections and controlling for gyral biases. The aim of this study is to determine which of the two broadest classes of tractography algorithms-deterministic or probabilistic-is most suited to mapping connectomes. METHODS: This study develops numerical connectome phantoms that feature realistic network topologies and that are matched to the fiber complexity of in vivo diffusion MRI (dMRI) data. The phantoms are utilized to evaluate the performance of tensor-based and multi-fiber implementations of deterministic and probabilistic tractography. RESULTS: For connectome phantoms that are representative of the fiber complexity of in vivo dMRI, multi-fiber deterministic tractography yields the most accurate connectome reconstructions (F-measure = 0.35). Probabilistic algorithms are hampered by an abundance of false-positive connections, leading to lower specificity (F = 0.19). While omitting connections with the fewest number of streamlines (thresholding) improves the performance of probabilistic algorithms (F = 0.38), multi-fiber deterministic tractography remains optimal when it benefits from thresholding (F = 0.42). CONCLUSIONS: Multi-fiber deterministic tractography is well suited to connectome mapping, while connectome thresholding is essential when using probabilistic algorithms.
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ItemEstimating the impact of structural directionality: How reliable are undirected connectomes?(MIT PRESS, 2018)Directionality is a fundamental feature of network connections. Most structural brain networks are intrinsically directed because of the nature of chemical synapses, which comprise most neuronal connections. Because of the limitations of noninvasive imaging techniques, the directionality of connections between structurally connected regions of the human brain cannot be confirmed. Hence, connections are represented as undirected, and it is still unknown how this lack of directionality affects brain network topology. Using six directed brain networks from different species and parcellations (cat, mouse, C. elegans, and three macaque networks), we estimate the inaccuracies in network measures (degree, betweenness, clustering coefficient, path length, global efficiency, participation index, and small-worldness) associated with the removal of the directionality of connections. We employ three different methods to render directed brain networks undirected: (a) remove unidirectional connections, (b) add reciprocal connections, and (c) combine equal numbers of removed and added unidirectional connections. We quantify the extent of inaccuracy in network measures introduced through neglecting connection directionality for individual nodes and across the network. We find that the coarse division between core and peripheral nodes remains accurate for undirected networks. However, hub nodes differ considerably when directionality is neglected. Comparing the different methods to generate undirected networks from directed ones, we generally find that the addition of reciprocal connections (false positives) causes larger errors in graph-theoretic measures than the removal of the same number of directed connections (false negatives). These findings suggest that directionality plays an essential role in shaping brain networks and highlight some limitations of undirected connectomes.
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ItemSpontaneous brain network activity: Analysis of its temporal complexity(MIT PRESS, 2017)The brain operates in a complex way. The temporal complexity underlying macroscopic and spontaneous brain network activity is still to be understood. In this study, we explored the brain's complexity by combining functional connectivity, graph theory, and entropy analyses in 25 healthy people using task-free functional magnetic resonance imaging. We calculated the pairwise instantaneous phase synchrony between 8,192 brain nodes for a total of 200 time points. This resulted in graphs for which time series of clustering coefficients (the "cliquiness" of a node) and participation coefficients (the between-module connectivity of a node) were estimated. For these two network metrics, sample entropy was calculated. The procedure produced a number of results: (1) Entropy is higher for the participation coefficient than for the clustering coefficient. (2) The average clustering coefficient is negatively related to its associated entropy, whereas the average participation coefficient is positively related to its associated entropy. (3) The level of entropy is network-specific to the participation coefficient, but not to the clustering coefficient. High entropy for the participation coefficient was observed in the default-mode, visual, and motor networks. These results were further validated using an independent replication dataset. Our work confirms that brain networks are temporally complex. Entropy is a good candidate metric to explore temporal network alterations in diseases with paroxysmal brain disruptions, including schizophrenia and epilepsy.
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ItemHippocampal harms, protection and recovery following regular cannabis use(NATURE PUBLISHING GROUP, 2016-01-12)Shifting policies towards legalisation of cannabis for therapeutic and recreational use raise significant ethical issues for health-care providers seeking evidence-based recommendations. We investigated whether heavy cannabis use is associated with persistent harms to the hippocampus, if exposure to cannabidiol offers protection, and whether recovery occurs with abstinence. To do this, we assessed 111 participants: 74 long-term regular cannabis users (with an average of 15.4 years of use) and 37 non-user healthy controls. Cannabis users included subgroups of participants who were either exposed to Δ9-tetrahydrocannabinol (THC) but not to cannabidiol (CBD) or exposed to both, and former users with sustained abstinence. Participants underwent magnetic resonance imaging from which three measures of hippocampal integrity were assessed: (i) volume; (ii) fractional anisotropy; and (iii) N-acetylaspartate (NAA). Three curve-fitting models across the entire sample were tested for each measure to examine whether cannabis-related hippocampal harms are persistent, can be minimised (protected) by exposure to CBD or recovered through long-term abstinence. These analyses supported a protection and recovery model for hippocampal volume (P=0.003) and NAA (P=0.001). Further pairwise analyses showed that cannabis users had smaller hippocampal volumes relative to controls. Users not exposed to CBD had 11% reduced volumes and 15% lower NAA concentrations. Users exposed to CBD and former users did not differ from controls on any measure. Ongoing cannabis use is associated with harms to brain health, underpinned by chronic exposure to THC. However, such harms are minimised by CBD, and can be recovered with extended periods of abstinence.
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ItemScene unseen: Disrupted neuronal adaptation in melancholia during emotional film viewing(ELSEVIER SCI LTD, 2015)Impairments in attention and concentration are distinctive features of melancholic depression, and may diminish the ability to shift focus away from internal dysphoric states. Disrupted brain networks may underlie the inability to effectively disengage from interoceptive signals in this disorder. This study investigates changes in effective connectivity between cortical systems supporting attention, interoception, and perception in those with melancholic depression when shifting attention from rest to viewing dynamic film stimuli. We hypothesised that those with melancholia would show impaired attentional shifting from rest to emotional film viewing, captured in neuronal states that differed little across conditions. Functional magnetic resonance imaging (fMRI) data were acquired from 48 participants (16 melancholic depressed, 16 non-melancholic depressed, and 16 healthy controls) at rest and whilst viewing emotionally salient movies. Using independent component analysis, we identified 8 cortical modes (default mode, executive control, left/right frontoparietal attention, left/right insula, visual and auditory) and studied their dynamics using dynamic causal modelling. Engagement with dynamic emotional material diminished in melancholia and was associated with network-wide increases in effective connectivity. Melancholia was also characterised by an increase in effective connectivity amongst cortical regions involved in attention and interoception when shifting from rest to negative film viewing, with the converse pattern in control participants. The observed involvement of attention- and insula-based cortical systems highlights a potential neurobiological mechanism for disrupted attentional resource allocation, particularly in switching between interoceptive and exteroceptive signals, in melancholia.
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ItemThe impact of premorbid and current intellect in schizophrenia: cognitive, symptom, and functional outcomes(SPRINGERNATURE, 2015)BACKGROUND: Cognitive heterogeneity among people with schizophrenia has been defined on the basis of premorbid and current intelligence quotient (IQ) estimates. In a relatively large, community cohort, we aimed to independently replicate and extend cognitive subtyping work by determining the extent of symptom severity and functional deficits in each group. METHODS: A total of 635 healthy controls and 534 patients with a diagnosis of schizophrenia or schizoaffective disorder were recruited through the Australian Schizophrenia Research Bank. Patients were classified into cognitive subgroups on the basis of the Wechsler Test of Adult Reading (a premorbid IQ estimate) and current overall cognitive abilities into preserved, deteriorated, and compromised groups using both clinical and empirical (k-means clustering) methods. Additional cognitive, functional, and symptom outcomes were compared among the resulting groups. RESULTS: A total of 157 patients (29%) classified as 'preserved' performed within one s.d. of control means in all cognitive domains. Patients classified as 'deteriorated' (n=239, 44%) performed more than one s.d. below control means in all cognitive domains except estimated premorbid IQ and current visuospatial abilities. A separate 138 patients (26%), classified as 'compromised,' performed more than one s.d. below control means in all cognitive domains and displayed greater impairment than other groups on symptom and functional measures. CONCLUSIONS: In the present study, we independently replicated our previous cognitive classifications of people with schizophrenia. In addition, we extended previous work by demonstrating worse functional outcomes and symptom severity in the compromised group.
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ItemA hierarchy of timescales explains distinct effects of local inhibition of primary visxc ual cortex and frontal eye fields(ELIFE SCIENCES PUBLICATIONS LTD, 2016-09-06)Within the primate visual system, areas at lower levels of the cortical hierarchy process basic visual features, whereas those at higher levels, such as the frontal eye fields (FEF), are thought to modulate sensory processes via feedback connections. Despite these functional exchanges during perception, there is little shared activity between early and late visual regions at rest. How interactions emerge between regions encompassing distinct levels of the visual hierarchy remains unknown. Here we combined neuroimaging, non-invasive cortical stimulation and computational modelling to characterize changes in functional interactions across widespread neural networks before and after local inhibition of primary visual cortex or FEF. We found that stimulation of early visual cortex selectively increased feedforward interactions with FEF and extrastriate visual areas, whereas identical stimulation of the FEF decreased feedback interactions with early visual areas. Computational modelling suggests that these opposing effects reflect a fast-slow timescale hierarchy from sensory to association areas.
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ItemBuilding connectomes using diffusion MRI: why, how and but(WILEY, 2019-04)Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments.
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ItemConnectome analysis with diffusion MRI in idiopathic Parkinson's disease: Evaluation using multi-shell, multi-tissue, constrained spherical deconvolution(ELSEVIER SCI LTD, 2018)Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects extensive regions of the central nervous system. In this work, we evaluated the structural connectome of patients with PD, as mapped by diffusion-weighted MRI tractography and a multi-shell, multi-tissue (MSMT) constrained spherical deconvolution (CSD) method to increase the precision of tractography at tissue interfaces. The connectome was mapped with probabilistic MSMT-CSD in 21 patients with PD and in 21 age- and gender-matched controls. Mapping was also performed by deterministic single-shell, single tissue (SSST)-CSD tracking and probabilistic SSST-CSD tracking for comparison. A support vector machine was trained to predict diagnosis based on a linear combination of graph metrics. We showed that probabilistic MSMT-CSD could detect significantly reduced global strength, efficiency, clustering, and small-worldness, and increased global path length in patients with PD relative to healthy controls; by contrast, probabilistic SSST-CSD only detected the difference in global strength and small-worldness. In patients with PD, probabilistic MSMT-CSD also detected a significant reduction in local efficiency and detected clustering in the motor, frontal temporoparietal associative, limbic, basal ganglia, and thalamic areas. The network-based statistic identified a subnetwork of reduced connectivity by MSMT-CSD and probabilistic SSST-CSD in patients with PD, involving key components of the cortico-basal ganglia-thalamocortical network. Finally, probabilistic MSMT-CSD had superior diagnostic accuracy compared with conventional probabilistic SSST-CSD and deterministic SSST-CSD tracking. In conclusion, probabilistic MSMT-CSD detected a greater extent of connectome pathology in patients with PD, including those with cortico-basal ganglia-thalamocortical network disruptions. Connectome analysis based on probabilistic MSMT-CSD may be useful when evaluating the extent of white matter connectivity disruptions in PD.