Michael Breakspear

YNIMG Journal 2023 Journal Article

Functional re-organization of hippocampal-cortical gradients during naturalistic memory processes

• Léonie Borne
• Ye Tian
• Michelle K. Lupton
• Johan N. van der Meer
• Jayson Jeganathan
• Bryan Paton
• Nikitas Koussis
• Christine C. Guo

The functional organization of the hippocampus mirrors that of the cortex, changing smoothly along connectivity gradients and abruptly at inter-areal boundaries. Hippocampal-dependent cognitive processes require flexible integration of these hippocampal gradients into functionally related cortical networks. To understand the cognitive relevance of this functional embedding, we acquired fMRI data while participants viewed brief news clips, either containing or lacking recently familiarized cues. Participants were 188 healthy mid-life adults and 31 adults with mild cognitive impairment (MCI) or Alzheimer's disease (AD). We employed a recently developed technique - connectivity gradientography - to study gradually changing patterns of voxel to whole brain functional connectivity and their sudden transitions. We observed that functional connectivity gradients of the anterior hippocampus map onto connectivity gradients across the default mode network during these naturalistic stimuli. The presence of familiar cues in the news clips accentuates a stepwise transition across the boundary from the anterior to the posterior hippocampus. This functional transition is shifted in the posterior direction in the left hippocampus of individuals with MCI or AD. These findings shed new light on the functional integration of hippocampal connectivity gradients into large-scale cortical networks, how these adapt with memory context and how these change in the presence of neurodegenerative disease.

YNICL Journal 2021 Journal Article

A prospective cohort study of prodromal Alzheimer’s disease: Prospective Imaging Study of Ageing: Genes, Brain and Behaviour (PISA)

• Michelle K. Lupton
• Gail A. Robinson
• Robert J. Adam
• Stephen Rose
• Gerard J. Byrne
• Olivier Salvado
• Nancy A. Pachana
• Osvaldo P. Almeida

This prospective cohort study, "Prospective Imaging Study of Ageing: Genes, Brain and Behaviour" (PISA) seeks to characterise the phenotype and natural history of healthy adult Australians at high future risk of Alzheimer's disease (AD). In particular, we are recruiting midlife and older Australians with high and low genetic risk of dementia to discover biological markers of early neuropathology, identify modifiable risk factors, and establish the very earliest phenotypic and neuronal signs of disease onset. PISA utilises genetic prediction to recruit and enrich a prospective cohort and follow them longitudinally. Online surveys and cognitive testing are used to characterise an Australia-wide sample currently totalling over 3800 participants. Participants from a defined at-risk cohort and positive controls (clinical cohort of patients with mild cognitive impairment or early AD) are invited for onsite visits for detailed functional, structural and molecular neuroimaging, lifestyle monitoring, detailed neurocognitive testing, plus blood sample donation. This paper describes recruitment of the PISA cohort, study methodology and baseline demographics.

YNIMG Journal 2020 Journal Article

Manipulating the structure of natural scenes using wavelets to study the functional architecture of perceptual hierarchies in the brain

• Alexander M. Puckett
• Mark M. Schira
• Zoey J. Isherwood
• Jonathan D. Victor
• James A. Roberts
• Michael Breakspear

Functional neuroimaging experiments that employ naturalistic stimuli (natural scenes, films, spoken narratives) provide insights into cognitive function “in the wild”. Natural stimuli typically possess crowded, spectrally dense, dynamic, and multimodal properties within a rich multiscale structure. However, when using natural stimuli, various challenges exist for creating parametric manipulations with tight experimental control. Here, we revisit the typical spectral composition and statistical dependences of natural scenes, which distinguish them from abstract stimuli. We then demonstrate how to selectively degrade subtle statistical dependences within specific spatial scales using the wavelet transform. Such manipulations leave basic features of the stimuli, such as luminance and contrast, intact. Using functional neuroimaging of human participants viewing degraded natural images, we demonstrate that cortical responses at different levels of the visual hierarchy are differentially sensitive to subtle statistical dependences in natural images. This demonstration supports the notion that perceptual systems in the brain are optimally tuned to the complex statistical properties of the natural world. The code to undertake these stimulus manipulations, and their natural extension to dynamic natural scenes (films), is freely available.

YNIMG Journal 2020 Journal Article

Prediction-error signals to violated expectations about person identity and head orientation are doubly-dissociated across dorsal and ventral visual stream regions

• Jonathan E. Robinson
• Will Woods
• Sumie Leung
• Jordy Kaufman
• Michael Breakspear
• Andrew W. Young
• Patrick J. Johnston

Predictive coding theories of perception highlight the importance of constantly updated internal models of the world to predict future sensory inputs. Importantly, such theories suggest that prediction-error signalling should be specific to the violation of predictions concerning distinct attributes of the same stimulus. To interrogate this as yet untested prediction, we focused on two different aspects of face perception (identity and orientation) and investigated whether cortical regions which process particular stimulus attributes also signal prediction violations with respect to those same stimulus attributes. We employed a paradigm using sequential trajectories of images to create perceptual expectations about face orientation and identity, and then parametrically violated each attribute. Using MEG data, we identified double dissociations of expectancy violations in the dorsal and ventral visual streams, such that the right fusiform gyrus showed greater prediction-error signals to identity violations than to orientation violations, whereas the left angular gyrus showed the converse pattern of results. Our results suggest that perceptual prediction-error signalling is directly linked to regions associated with the processing of different stimulus properties.

YNIMG Journal 2020 Journal Article

Subthalamic deep brain stimulation identifies frontal networks supporting initiation, inhibition and strategy use in Parkinson's disease

• Philip E. Mosley
• Katherine Robinson
• Terry Coyne
• Peter Silburn
• Megan S. Barker
• Michael Breakspear
• Gail A. Robinson
• Alistair Perry

Initiation and inhibition are executive functions whose disruption in Parkinson's disease impacts substantially on everyday activities. Management of Parkinson's disease with subthalamic deep brain stimulation (DBS) modifies initiation and inhibition, with prior work suggesting that these effects may be mediated via the connectivity of the subthalamic nucleus (STN) with the frontal cortex. Here, we employed high-resolution structural neuroimaging to investigate the variability in initiation, inhibition and strategy use in a cohort of twenty-five (ten females, mean age 62. 5, mean Hoehn and Yahr stage 2. 5) participants undertaking subthalamic DBS for Parkinson's disease. Neuropsychological assessment of initiation and inhibition was performed preoperatively and at six months postoperatively. We first reconstructed the preoperative connectivity of the STN with a frontal network of anterior and superior medial cortical regions. We then modelled the postoperative site of subthalamic stimulation and reconstructed the connectivity of the stimulation field within this same network. We found that, at both pre- and postoperative intervals, inter-individual variability in inhibition and initiation were strongly associated with structural network connectivity. Measures of subcortical atrophy and local stimulation effects did not play a significant role. Preoperatively, we replicated prior work, including a role for the right inferior frontal gyrus in inhibition and strategy use, as well as the left inferior frontal gyrus in tasks requiring selection under conditions of maintained inhibition. Postoperatively, greater connectivity of the stimulation field with right anterior cortical regions was associated with greater rule violations and suppression errors, supporting prior work implicating right-hemispheric STN stimulation in disinhibition. Our findings suggest that, in Parkinson's disease, connectivity of the frontal cortex with the STN is an important mediator of individual variability in initiation and inhibition, . Personalised information on brain network architecture could guide individualised brain circuit manipulation to minimise neuropsychological disruption after STN-DBS.

YNICL Journal 2018 Journal Article

Fronto-limbic dysconnectivity leads to impaired brain network controllability in young people with bipolar disorder and those at high genetic risk

• Jayson Jeganathan
• Alistair Perry
• Danielle S. Bassett
• Gloria Roberts
• Philip B. Mitchell
• Michael Breakspear

Recent investigations have used diffusion-weighted imaging to reveal disturbances in the neurocircuitry that underlie cognitive-emotional control in bipolar disorder (BD) and in unaffected siblings or children at high genetic risk (HR). It has been difficult to quantify the mechanism by which structural changes disrupt the superimposed brain dynamics, leading to the emotional lability that is characteristic of BD. Average controllability is a concept from network control theory that extends structural connectivity data to estimate the manner in which local neuronal fluctuations spread from a node or subnetwork to alter the state of the rest of the brain. We used this theory to ask whether structural connectivity deficits previously observed in HR individuals (n = 84, mean age 22. 4), patients with BD (n = 38, mean age 23. 9), and age- and gender-matched controls (n = 96, mean age 22. 6) translate to differences in the ability of brain systems to be manipulated between states. Localized impairments in network controllability were seen in the left parahippocampal, left middle occipital, left superior frontal, right inferior frontal, and right precentral gyri in BD and HR groups. Subjects with BD had distributed deficits in a subnetwork containing the left superior and inferior frontal gyri, postcentral gyrus, and insula (p = 0. 004). HR participants had controllability deficits in a right-lateralized subnetwork involving connections between the dorsomedial and ventrolateral prefrontal cortex, the superior temporal pole, putamen, and caudate nucleus (p = 0. 008). Between-group controllability differences were attenuated after removal of topological factors by network randomization. Some previously reported differences in network connectivity were not associated with controllability-differences, likely reflecting the contribution of more complex brain network properties. These analyses highlight the potential functional consequences of altered brain networks in BD, and may guide future clinical interventions.

YNICL Journal 2018 Journal Article

The site of stimulation moderates neuropsychiatric symptoms after subthalamic deep brain stimulation for Parkinson's disease

• Philip E. Mosley
• David Smith
• Terry Coyne
• Peter Silburn
• Michael Breakspear
• Alistair Perry

Deep brain stimulation of the subthalamic nucleus for Parkinson's disease is an established advanced therapy that addresses motor symptoms and improves quality of life. However, it has also been associated with neuropsychiatric symptoms such as impulsivity and hypomania. When significant, these symptoms can be distressing, necessitating psychiatric intervention. However, a comprehensive analysis of neurocognitive and neuropsychiatric outcomes with reference to the site of subthalamic stimulation has not been undertaken. We examined this matter in a consecutive sample of 64 persons with Parkinson's disease undertaking subthalamic deep brain stimulation. Participants were assessed with a battery of neuropsychiatric instruments at baseline and at repeated postoperative intervals. A psychiatrist identified patients with emergent, clinically-significant symptoms due to stimulation. The site of the active electrode contact and a simulated volume of activated tissue were evaluated with reference to putative limbic, associative and motor subregions of the subthalamic nucleus. We studied anatomical correlates of longitudinal neuropsychiatric change and delineated specific subthalamic regions associated with neuropsychiatric impairment. We tested the ability of these data to predict clinically-significant symptoms. Subthalamic stimulation within the right associative subregion was associated with inhibitory errors on the Excluded Letter Fluency task at 6-weeks (p = 0. 023) and 13-weeks postoperatively (p = 0. 0017). A cluster of subthalamic voxels associated with inhibitory errors was identified in the right associative and motor subregions. At 6-weeks, clinically-significant neuropsychiatric symptoms were associated with the distance of the active contact to the right associative subregion (p = 0. 0026) and stimulation within the right associative subregion (p = 0. 0009). At 13-weeks, clinically-significant symptoms were associated with the distance to the right (p = 0. 0027) and left (p = 0. 0084) associative subregions and stimulation within the right associative subregion (p = 0. 0026). Discrete clusters of subthalamic voxels associated with high and low likelihood of postoperative neuropsychiatric symptoms were identified in ventromedial and dorsolateral zones, respectively. When a classifier was trained on these data, clinically-significant symptoms were predicted with an accuracy of 79%. These data underscore the importance of accurate electrode targeting, contact selection and device programming to reduce postoperative neuropsychiatric impairment. The ability to predict neuropsychiatric symptoms based on subthalamic data may permit anticipation and prevention of these occurrences, improving safety and tolerability.

YNICL Journal 2018 Journal Article

Transcranial magnetic stimulation in obsessive-compulsive disorder: A focus on network mechanisms and state dependence

• Luca Cocchi
• Andrew Zalesky
• Zoie Nott
• Geneviève Whybird
• Paul B. Fitzgerald
• Michael Breakspear

Background: Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique that has shown promise as an adjunct treatment for the symptoms of Obsessive-Compulsive Disorder (OCD). Establishing a clear clinical role for TMS in the treatment of OCD is contingent upon evidence of significant efficacy and reliability in reducing symptoms. Objectives: We present the basic principles supporting the effects of TMS on brain activity with a focus on network-based theories of brain function. We discuss the promises and pitfalls of this technique as a means of modulating brain activity and reducing OCD symptoms. Methods: Synthesis of trends and critical perspective on the potential benefits and limitations of TMS interventions in OCD. Findings: Our critical synthesis suggests the need to better quantify the role of TMS in a clinical setting. The context in which the stimulation is performed, the neural principles supporting the effects of local stimulation on brain networks, and the heterogeneity of neuroanatomy are often overlooked in the clinical application of TMS. The lack of consideration of these factors may partly explain the variable efficacy of TMS interventions for OCD symptoms. Conclusions: Results from existing clinical studies and emerging knowledge about the effects of TMS on brain networks are encouraging but also highlight the need for further research into the use of TMS as a means of selectively normalising OCD brain network dynamics and reducing related symptoms. The combination of neuroimaging, computational modelling, and behavioural protocols known to engage brain networks affected by OCD has the potential to improve the precision and therapeutic efficacy of TMS interventions. The efficacy of this multimodal approach remains, however, to be established and its effective translation in clinical contexts presents technical and implementation challenges. Addressing these practical, scientific and technical issues is required to assess whether OCD can take its place alongside major depressive disorder as an indication for the use of TMS.

YNIMG Journal 2017 Journal Article

Consistency-based thresholding of the human connectome

• James A. Roberts
• Alistair Perry
• Gloria Roberts
• Philip B. Mitchell
• Michael Breakspear

Densely seeded probabilistic tractography yields weighted networks that are nearly fully connected, hence containing many spurious fibers. It is thus necessary to prune spurious connections from probabilistically-derived networks to obtain a more reliable overall estimate of the connectivity. A standard method is to threshold by weight, keeping only the strongest edges. Here, by measuring the consistency of edge weights across subjects, we propose a new thresholding method that aims to reduce the rate of false-positives in group-averaged connectivity matrices. Close inspection of the relationship between consistency, weight, and distance suggests that the most consistent edges are in fact those that are strong for their length, rather than simply strong overall. Hence retaining the most consistent edges preserves more long-distance connections than traditional weight-based thresholding, which penalizes long connections for being weak regardless of anatomy. By comparing our thresholded networks to mouse and macaque tracer data, we also show that consistency-based thresholding exhibits the species-invariant exponential decay of connection weights with distance, while weight-based thresholding does not. We also show that consistency-based thresholding can be used to identify highly consistent and highly inconsistent subnetworks across subjects, enabling more nuanced analyses of group-level connectivity than just the mean connectivity.

YNIMG Journal 2016 Journal Article

Connectome sensitivity or specificity: which is more important?

• Andrew Zalesky
• Alex Fornito
• Luca Cocchi
• Leonardo L. Gollo
• Martijn P. van den Heuvel
• Michael Breakspear

Connectomes with high sensitivity and high specificity are unattainable with current axonal fiber reconstruction methods, particularly at the macro-scale afforded by magnetic resonance imaging. Tensor-guided deterministic tractography yields sparse connectomes that are incomplete and contain false negatives (FNs), whereas probabilistic methods steered by crossing-fiber models yield dense connectomes, often with low specificity due to false positives (FPs). Densely reconstructed probabilistic connectomes are typically thresholded to improve specificity at the cost of a reduction in sensitivity. What is the optimal tradeoff between connectome sensitivity and specificity? We show empirically and theoretically that specificity is paramount. Our evaluations of the impact of FPs and FNs on empirical connectomes indicate that specificity is at least twice as important as sensitivity when estimating key properties of brain networks, including topological measures of network clustering, network efficiency and network modularity. Our asymptotic analysis of small-world networks with idealized modular structure reveals that as the number of nodes grows, specificity becomes exactly twice as important as sensitivity to the estimation of the clustering coefficient. For the estimation of network efficiency, the relative importance of specificity grows linearly with the number of nodes. The greater importance of specificity is due to FPs occurring more prevalently between network modules rather than within them. These spurious inter-modular connections have a dramatic impact on network topology. We argue that efforts to maximize the sensitivity of connectome reconstruction should be realigned with the need to map brain networks with high specificity.

YNIMG Journal 2016 Journal Article

The contribution of geometry to the human connectome

• James A. Roberts
• Alistair Perry
• Anton R. Lord
• Gloria Roberts
• Philip B. Mitchell
• Robert E. Smith
• Fernando Calamante
• Michael Breakspear

The human connectome is a topologically complex, spatially embedded network. While its topological properties have been richly characterized, the constraints imposed by its spatial embedding are poorly understood. By applying a novel resampling method to tractography data, we show that the brain's spatial embedding makes a major, but not definitive, contribution to the topology of the human connectome. We first identify where the brain's structural hubs would likely be located if geometry was the sole determinant of brain topology. Empirical networks show a widespread shift away from this geometric center toward more peripheral interconnected skeletons in each hemisphere, with discrete clusters around the anterior insula, and the anterior and posterior midline regions of the cortex. A relatively small number of strong inter-hemispheric connections assimilate these intra-hemispheric structures into a rich club, whose connections are locally more clustered but globally longer than predicted by geometry. We also quantify the extent to which the segregation, integration, and modularity of the human brain are passively inherited from its geometry. These analyses reveal novel insights into the influence of spatial geometry on the human connectome, highlighting specific topological features that likely confer functional advantages but carry an additional metabolic cost.

YNIMG Journal 2016 Journal Article

The integration of the internal and external milieu in the insula during dynamic emotional experiences

• Vinh Thai Nguyen
• Michael Breakspear
• Xintao Hu
• Christine Cong Guo

Whilst external events trigger emotional responses, interoception (the perception of internal physiological states) is fundamental to core emotional experience. By combining high resolution functional neuroimaging with concurrent physiological recordings, we investigated the neural mechanisms of interoceptive integration during free listening to an emotionally salient audio film. We found that cardiac activity, a key interoceptive signal, was robustly synchronised across participants and centrally represented in the posterior insula. Effective connectivity analysis revealed that the anterior insula, specifically tuned to the emotionally salient moments of the audio stream, serves as an integration hub of interoceptive processing: interoceptive states represented in the posterior insula are integrated with exteroceptive representations by the anterior insula to highlight these emotionally salient moments. Our study for the first time demonstrates the insular hierarchy for interoceptive processing during natural emotional experience. These findings provide an ecologically-valid framework for elucidating the neural underpinnings of emotional deficits in neuropsychiatric disorders.

YNIMG Journal 2016 Journal Article

The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex

• Alexander M. Puckett
• Kevin M. Aquino
• P.A. Robinson
• Michael Breakspear
• Mark M. Schira

The gray matter of human cortex is characterized by depth-dependent differences in neuronal activity and connections (Shipp, 2007) as well as in the associated vasculature (Duvernoy et al. , 1981). The resolution limit of functional magnetic resonance imaging (fMRI) measurements is now below a millimeter, promising the non-invasive measurement of these properties in awake and behaving humans (Muckli et al. , 2015; Olman et al. , 2012; Ress et al. , 2007). To advance this endeavor, we present a detailed spatiotemporal hemodynamic response function (HRF) reconstructed through the use of high-resolution, submillimeter fMRI. We decomposed the HRF into directions tangential and perpendicular to the cortical surface and found that key spatial properties of the HRF change significantly with depth from the cortical surface. Notably, we found that the spatial spread of the HRF increases linearly from 4. 8mm at the gray/white matter boundary to 6. 6mm near the cortical surface. Using a hemodynamic model, we posit that this effect can be explained by the depth profile of the cortical vasculature, and as such, must be taken into account to properly estimate the underlying neuronal responses at different cortical depths.

YNIMG Journal 2016 Journal Article

Upregulation of cortico-cerebellar functional connectivity after motor learning

• Saeid Mehrkanoon
• Tjeerd W. Boonstra
• Michael Breakspear
• Mark Hinder
• Jeffery J. Summers

Interactions between the cerebellum and primary motor cortex are crucial for the acquisition of new motor skills. Recent neuroimaging studies indicate that learning motor skills is associated with subsequent modulation of resting-state functional connectivity in the cerebellar and cerebral cortices. The neuronal processes underlying the motor-learning-induced plasticity are not well understood. Here, we investigate changes in functional connectivity in source-reconstructed electroencephalography (EEG) following the performance of a single session of a dynamic force task in twenty young adults. Source activity was reconstructed in 112 regions of interest (ROIs) and the functional connectivity between all ROIs was estimated using the imaginary part of coherence. Significant changes in resting-state connectivity were assessed using partial least squares (PLS). We found that subjects adapted their motor performance during the training session and showed improved accuracy but with slower movement times. A number of connections were significantly upregulated after motor training, principally involving connections within the cerebellum and between the cerebellum and motor cortex. Increased connectivity was confined to specific frequency ranges in the mu- and beta-bands. Post hoc analysis of the phase spectra of these cerebellar and cortico-cerebellar connections revealed an increased phase lag between motor cortical and cerebellar activity following motor practice. These findings show a reorganization of intrinsic cortico-cerebellar connectivity related to motor adaptation and demonstrate the potential of EEG connectivity analysis in source space to reveal the neuronal processes that underpin neural plasticity.

YNICL Journal 2016 Journal Article

Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy

• Jonathan Wirsich
• Alistair Perry
• Ben Ridley
• Timothée Proix
• Mathieu Golos
• Christian Bénar
• Jean-Philippe Ranjeva
• Fabrice Bartolomei

The in vivo structure-function relationship is key to understanding brain network reorganization due to pathologies. This relationship is likely to be particularly complex in brain network diseases such as temporal lobe epilepsy, in which disturbed large-scale systems are involved in both transient electrical events and long-lasting functional and structural impairments. Herein, we estimated this relationship by analyzing the correlation between structural connectivity and functional connectivity in terms of analytical network communication parameters. As such, we targeted the gradual topological structure-function reorganization caused by the pathology not only at the whole brain scale but also both in core and peripheral regions of the brain. We acquired diffusion (dMRI) and resting-state fMRI (rsfMRI) data in seven right-lateralized TLE (rTLE) patients and fourteen healthy controls and analyzed the structure-function relationship by using analytical network communication metrics derived from the structural connectome. In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information) in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures.

YNIMG Journal 2015 Journal Article

Effective connectivity within the frontoparietal control network differentiates cognitive control and working memory

• Ian H. Harding
• Murat Yücel
• Ben J. Harrison
• Christos Pantelis
• Michael Breakspear

Cognitive control and working memory rely upon a common fronto-parietal network that includes the inferior frontal junction (IFJ), dorsolateral prefrontal cortex (dlPFC), pre-supplementary motor area/dorsal anterior cingulate cortex (pSMA/dACC), and intraparietal sulcus (IPS). This network is able to flexibly adapt its function in response to changing behavioral goals, mediating a wide range of cognitive demands. Here we apply dynamic causal modeling to functional magnetic resonance imaging data to characterize task-related alterations in the strength of network interactions across distinct cognitive processes. Evidence in favor of task-related connectivity dynamics was accrued across a very large space of possible network structures. Cognitive control and working memory demands were manipulated using a factorial combination of the multi-source interference task and a verbal 2-back working memory task, respectively. Both were found to alter the sensitivity of the IFJ to perceptual information, and to increase IFJ-to-pSMA/dACC connectivity. In contrast, increased connectivity from the pSMA/dACC to the IPS, as well as from the dlPFC to the IFJ, was uniquely driven by cognitive control demands; a task-induced negative influence of the dlPFC on the pSMA/dACC was specific to working memory demands. These results reflect a system of both shared and unique context-dependent dynamics within the fronto-parietal network. Mechanisms supporting cognitive engagement, response selection, and action evaluation may be shared across cognitive domains, while dynamic updating of task and context representations within this network are potentially specific to changing demands on cognitive control.

YNIMG Journal 2015 Journal Article

Heritability of the network architecture of intrinsic brain functional connectivity

• Benjamin Sinclair
• Narelle K. Hansell
• Gabriëlla A.M. Blokland
• Nicholas G. Martin
• Paul M. Thompson
• Michael Breakspear
• Greig I. de Zubicaray
• Margaret J. Wright

The brain's functional network exhibits many features facilitating functional specialization, integration, and robustness to attack. Using graph theory to characterize brain networks, studies demonstrate their small-world, modular, and “rich-club” properties, with deviations reported in many common neuropathological conditions. Here we estimate the heritability of five widely used graph theoretical metrics (mean clustering coefficient (γ), modularity (Q), rich-club coefficient (ϕnorm), global efficiency (λ), small-worldness (σ)) over a range of connection densities (k=5–25%) in a large cohort of twins (N=592, 84 MZ and 89 DZ twin pairs, 246 single twins, age 23±2. 5). We also considered the effects of global signal regression (GSR). We found that the graph metrics were moderately influenced by genetic factors h2 (γ=47–59%, Q=38–59%, ϕnorm =0–29%, λ=52–64%, σ=51–59%) at lower connection densities (≤15%), and when global signal regression was implemented, heritability estimates decreased substantially h2 (γ=0–26%, Q=0–28%, ϕnorm =0%, λ=23–30%, σ=0–27%). Distinct network features were phenotypically correlated (|r|=0. 15–0. 81), and γ, Q, and λ were found to be influenced by overlapping genetic factors. Our findings suggest that these metrics may be potential endophenotypes for psychiatric disease and suitable for genetic association studies, but that genetic effects must be interpreted with respect to methodological choices.

YNICL Journal 2015 Journal Article

Scene unseen: Disrupted neuronal adaptation in melancholia during emotional film viewing

• Matthew P. Hyett
• Gordon B. Parker
• Christine C. Guo
• Andrew Zalesky
• Vinh T. Nguyen
• Tamara Yuen
• Michael Breakspear

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.

YNIMG Journal 2015 Journal Article

The organisation of the elderly connectome

• Alistair Perry
• Wei Wen
• Anton Lord
• Anbupalam Thalamuthu
• Gloria Roberts
• Philip B. Mitchell
• Perminder S. Sachdev
• Michael Breakspear

Investigations of the human connectome have elucidated core features of adult structural networks, particularly the crucial role of hub-regions. However, little is known regarding network organisation of the healthy elderly connectome, a crucial prelude to the systematic study of neurodegenerative disorders. Here, whole-brain probabilistic tractography was performed on high-angular diffusion-weighted images acquired from 115 healthy elderly subjects (age 76–94years; 65 females). Structural networks were reconstructed between 512 cortical and subcortical brain regions. We sought to investigate the architectural features of hub-regions, as well as left–right asymmetries, and sexual dimorphisms. We observed that the topology of hub-regions is consistent with a young adult population, and previously published adult connectomic data. More importantly, the architectural features of hub connections reflect their ongoing vital role in network communication. We also found substantial sexual dimorphisms, with females exhibiting stronger inter-hemispheric connections between cingulate and prefrontal cortices. Lastly, we demonstrate intriguing left-lateralized subnetworks consistent with the neural circuitry specialised for language and executive functions, whilst rightward subnetworks were dominant in visual and visuospatial streams. These findings provide insights into healthy brain ageing and provide a benchmark for the study of neurodegenerative disorders such as Alzheimer's disease (AD) and frontotemporal dementia (FTD).

YNIMG Journal 2015 Journal Article

Towards a statistical test for functional connectivity dynamics

• Andrew Zalesky
• Michael Breakspear

Sliding-window correlation is an emerging method for mapping time-resolved, resting-state functional connectivity. To avoid mapping spurious connectivity fluctuations (false positives), Leonardi and Van De Ville recently recommended choosing a window length exceeding the longest wavelength composing the BOLD signal, usually assumed to be ~100s. Here, we provide further statistical support for this rule of thumb. However, we demonstrate that non-stationary fluctuations in functional connectivity can in theory be detected with much shorter window lengths (e. g. 40s), while maintaining nominal control of false positives. We find that statistical power is near-maximal for window lengths chosen according to Leonardi and Van De Ville's rule of thumb. Furthermore, we lay some foundations for a parametric test to identify non-stationary fluctuations in functional connectivity, also noting limitations of the sinusoidal model upon which our work, and the work of Leonardi and Van De Ville, is based. Most notably, our analytical results pertain to covariances, as does our statistical test, whereas functional connectivity is more commonly measured using correlations.

YNIMG Journal 2014 Journal Article

Fusing concurrent EEG–fMRI with dynamic causal modeling: Application to effective connectivity during face perception

• Vinh T. Nguyen
• Michael Breakspear
• Ross Cunnington

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 170ms, 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.

YNIMG Journal 2014 Journal Article

The reorganization of corticomuscular coherence during a transition between sensorimotor states

• Saeid Mehrkanoon
• Michael Breakspear
• Tjeerd W. Boonstra

Recent research suggests that neural oscillations in different frequency bands support distinct and sometimes parallel processing streams in neural circuits. Studies of the neural dynamics of human motor control have primarily focused on oscillations in the beta band (15–30Hz). During sustained muscle contractions, corticomuscular coherence is mainly present in the beta band, while coherence in the alpha (8–12Hz) and gamma (30–80Hz) bands has not been consistently found. Here we test the hypothesis that the frequency of corticomuscular coherence changes during transitions between sensorimotor states. Corticomuscular coherence was investigated in twelve participants making rapid transitions in force output between two targets. Corticomuscular coherence was present in the beta band during sustained contractions but vanished before movement onset, being replaced by transient synchronization in the alpha and gamma bands during dynamic force output. Analysis of the phase spectra suggested a time delay from muscle to cortex for alpha-band coherence, by contrast to a time delay from cortex to muscle for gamma-band coherence, indicating afferent and efferent corticospinal interactions respectively. Moreover, alpha and gamma-band coherence revealed distinct spatial topologies, suggesting different generative mechanisms. Coherence in the alpha and gamma bands was almost exclusively confined to trials showing a movement overshoot, suggesting a functional role related to error correction. We interpret the dual-band synchronization in the alpha and gamma bands as parallel streams of corticospinal processing involved in parsing prediction errors and generating new motor predictions.

YNIMG Journal 2013 Journal Article

Dynamic and stochastic models of neuroimaging data: A comment on Lohmann et al.

• Michael Breakspear

DCM is a platform for inferring the architecture of dynamical systems, combining a user-dependent model specification step with a Bayesian model selection scheme. In their critique of the model selection procedure, Lohmann et al confine themselves to models generated from the classic bilinear deterministic DCM. Although brief reference is made to recent modeling advances, such as stochastic DCM and nonlinear DCM, these are negatively cast as guilty of further exploding the combinatorial problem that is proposed to plague model selection. Yet this is only a problem if a naïve approach is adopted towards the model generation process. Where the user draws on prior knowledge of the system being modeled and the statistical properties of the particular data set, these advances can be employed precisely to address the type of concerns Lohmann et al raise in their exemplar analysis (Fig. 6). This note provides a putative generative model for their data by adding stochastic effects, using independent evidence to increase its biological plausibility and challenging the notion that model fit should be assessed using simple linear correlations. Rather than encouraging reliance on future developments in imaging hardware and data-driven multivariate algorithms, informed engagement with causal models of neuronal dynamics allows imaging researchers develop detailed theories of brain function across a broad range of data sets and cognitive phenomena.

YNIMG Journal 2013 Journal Article

Graph analysis of the human connectome: Promise, progress, and pitfalls

• Alex Fornito
• Andrew Zalesky
• Michael Breakspear

The human brain is a complex, interconnected network par excellence. Accurate and informative mapping of this human connectome has become a central goal of neuroscience. At the heart of this endeavor is the notion that brain connectivity can be abstracted to a graph of nodes, representing neural elements (e. g. , neurons, brain regions), linked by edges, representing some measure of structural, functional or causal interaction between nodes. Such a representation brings connectomic data into the realm of graph theory, affording a rich repertoire of mathematical tools and concepts that can be used to characterize diverse anatomical and dynamical properties of brain networks. Although this approach has tremendous potential — and has seen rapid uptake in the neuroimaging community — it also has a number of pitfalls and unresolved challenges which can, if not approached with due caution, undermine the explanatory potential of the endeavor. We review these pitfalls, the prevailing solutions to overcome them, and the challenges at the forefront of the field.

YNIMG Journal 2011 Journal Article

Networks, noise and models: Reconceptualizing the brain as a complex, distributed system

• Michael Breakspear
• Anthony R. McIntosh

YNIMG Journal 2010 Journal Article

Computational models of the brain: From structure to function

• Michael Breakspear
• Viktor Jirsa
• Gustavo Deco

YNIMG Journal 2008 Journal Article

Nonlinear dynamic causal models for fMRI

• Klaas Enno Stephan
• Lars Kasper
• Lee M. Harrison
• Jean Daunizeau
• Hanneke E.M. den Ouden
• Michael Breakspear
• Karl J. Friston

Models of effective connectivity characterize the influence that neuronal populations exert over each other. Additionally, some approaches, for example Dynamic Causal Modelling (DCM) and variants of Structural Equation Modelling, describe how effective connectivity is modulated by experimental manipulations. Mathematically, both are based on bilinear equations, where the bilinear term models the effect of experimental manipulations on neuronal interactions. The bilinear framework, however, precludes an important aspect of neuronal interactions that has been established with invasive electrophysiological recording studies; i. e. , how the connection between two neuronal units is enabled or gated by activity in other units. These gating processes are critical for controlling the gain of neuronal populations and are mediated through interactions between synaptic inputs (e. g. by means of voltage-sensitive ion channels). They represent a key mechanism for various neurobiological processes, including top-down (e. g. attentional) modulation, learning and neuromodulation. This paper presents a nonlinear extension of DCM that models such processes (to second order) at the neuronal population level. In this way, the modulation of network interactions can be assigned to an explicit neuronal population. We present simulations and empirical results that demonstrate the validity and usefulness of this model. Analyses of synthetic data showed that nonlinear and bilinear mechanisms can be distinguished by our extended DCM. When applying the model to empirical fMRI data from a blocked attention to motion paradigm, we found that attention-induced increases in V5 responses could be best explained as a gating of the V1→V5 connection by activity in posterior parietal cortex. Furthermore, we analysed fMRI data from an event-related binocular rivalry paradigm and found that interactions amongst percept-selective visual areas were modulated by activity in the middle frontal gyrus. In both practical examples, Bayesian model selection favoured the nonlinear models over corresponding bilinear ones.

YNIMG Journal 2007 Journal Article

Multivariate time–frequency analysis of electromagnetic brain activity during bimanual motor learning

• Tjeerd W. Boonstra
• Andreas Daffertshofer
• Michael Breakspear
• Peter J. Beek

Although the relationship between brain activity and motor performance is reasonably well established, the manner in which this relationship changes with motor learning remains incompletely understood. This paper presents a study of cortical modulations of event-related beta activity when participants learned to perform a complex bimanual motor task: 151 channel MEG data were acquired from nine healthy adults whilst learning a bimanual 3: 5 polyrhythm. Sources of MEG activity were determined by means of synthetic aperture magnetometry that yielded locations and time courses of beta activities. The relationship between changes in performance and corresponding changes in event-related power were assessed using partial least squares. Behavioral data revealed that participants successfully learned to perform the 3: 5 polyrhythm and that performance improvement was mainly achieved through the proper timing of the finger producing the slow rhythm. We found event-related modulation of beta power in the contralateral motor cortex that was inversely related to force output. The degree of beta modulation increased during the experiment – although the force level remained constant – and was positively correlated with motor performance, in particular for the motor cortex contralateral to the slow hand. These electrophysiological findings support the view that activity in motor cortex co-varies closely with behavioral changes over the course of learning.

YNIMG Journal 2006 Journal Article

The multiscale character of evoked cortical activity

• Michael Breakspear
• Ed T. Bullmore
• Kevin Aquino
• Pritha Das
• Leanne M. Williams

Both the architecture and the dynamics of the brain have characteristic features at different spatial scales. However, the existence, nature and function of dynamical interdependencies between such scales have not been investigated. We studied the multiscale properties of functional magnetic resonance imaging (fMRI) data acquired while human subjects viewed a visual image. Traditional “region of interest” analysis of this data set revealed evoked activity in primary and extrastriate visual cortex. Wavelet transform in the spatial domain provides a multiscale representation of this evoked brain activity. Studying the correlation structure of this representation revealed strong and novel interdependencies in these data within and between different spatial scales. We found that such correlations are stronger than those evident in the original data and comparable in magnitude to those obtained after Gaussian smoothing. However, analysis of the data in the wavelet domain revealed additional structure such as positive correlations, strong anti-correlations and phase-lagged interdependencies. Statistical significance of these effects was inferred through nonparametric bootstrap techniques. We conclude that the spatial analysis of functional neuroimaging data in the wavelet domain provides novel information which may reflect complex spatiotemporal neuronal activity and information encoding. It also affords a quantitative means of testing hierarchical and multiscale models of cortical activity.

YNIMG Journal 2004 Journal Article

Wavelets and functional magnetic resonance imaging of the human brain

• Ed Bullmore
• Jalal Fadili
• Voichita Maxim
• Levent Şendur
• Brandon Whitcher
• John Suckling
• Michael Brammer,
• Michael Breakspear

The discrete wavelet transform (DWT) is widely used for multiresolution analysis and decorrelation or “whitening” of nonstationary time series and spatial processes. Wavelets are naturally appropriate for analysis of biological data, such as functional magnetic resonance images of the human brain, which often demonstrate scale invariant or fractal properties. We provide a brief formal introduction to key properties of the DWT and review the growing literature on its application to fMRI. We focus on three applications in particular: (i) wavelet coefficient resampling or “wavestrapping” of 1-D time series, 2- to 3-D spatial maps and 4-D spatiotemporal processes; (ii) wavelet-based estimators for signal and noise parameters of time series regression models assuming the errors are fractional Gaussian noise (fGn); and (iii) wavelet shrinkage in frequentist and Bayesian frameworks to support multiresolution hypothesis testing on spatially extended statistic maps. We conclude that the wavelet domain is a rich source of new concepts and techniques to enhance the power of statistical analysis of human fMRI data.