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Graeme D. Jackson

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21 papers

1 author row

AIIM Journal 2025 Journal Article

Toward responsible artificial intelligence in medicine: Reflections from the Australian epilepsy project

Mangor Pedersen
Heath R. Pardoe
Anton de Weger
Donna Hutchison
David F. Abbott
Karin Verspoor
Graeme D. Jackson

Artificial intelligence (AI) is a multidisciplinary scientific field that uses machines to solve real-world problems and predict outcomes. Despite the current enthusiasm about AI's potential as a clinical support tool, there is also a growing awareness and concern about the potentially harmful effects of AI. Because AI will likely impact expert-based decision-making in medicine, it is critical to consider the issues that AI raises in medical research. This paper outlines the AI guidelines of the Australian Epilepsy Project. This large-scale platform aims to democratise specialist care in epilepsy and use AI for clinical decision support based on prospective multimodal datasets (MRI, genetic, clinical, and cognitive data) from thousands of people with epilepsy. As AI develops rapidly, we focus on key areas of medical AI identified in the literature, including Trust, Responsibility and Safety. We believe AI is changing medicine, and we believe it is imperative to advance and update our AI guidelines adaptably while preparing for an era of augmented-intelligence-based medicine.

YNIMG Journal 2021 Journal Article

Temporal complexity of fMRI is reproducible and correlates with higher order cognition

Amir Omidvarnia
Andrew Zalesky
Sina Mansour L
Dimitri Van De Ville
Graeme D. Jackson
Mangor Pedersen

It has been hypothesized that resting state networks (RSNs), extracted from resting state functional magnetic resonance imaging (rsfMRI), likely display unique temporal complexity fingerprints, quantified by their multiscale entropy patterns (McDonough and Nashiro, 2014). This is a hypothesis with a potential capacity for developing digital biomarkers of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of McDonough and Nashiro (2014) was that rsfMRI data from only 20 healthy individuals was used for the analysis. To validate this hypothesis in a larger cohort, we used rsfMRI datasets of 987 healthy young adults from the Human Connectome Project (HCP), aged 22-35, each with four 14. 4-min rsfMRI recordings and parcellated into 379 brain regions. We quantified multiscale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We performed effect-size analysis on the data in 8 RSNs. Given that the morphology of multiscale entropy is affected by the choice of its tolerance parameter ( r ) and embedding dimension ( m ), we repeated the analyses at multiple values of r and m including the values used in McDonough and Nashiro (2014). Our results reinforced high temporal complexity in the default mode and frontoparietal networks. Lowest temporal complexity was observed in the subcortical areas and limbic system. We investigated the effect of temporal resolution (determined by the repetition time T R ) after downsampling of rsfMRI time series at two rates. At a low temporal resolution, we observed increased entropy and variance across datasets. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially when the tolerance parameter r is equal to 0. 5. The results confirmed that the relationship between functional brain connectivity strengths and rsfMRI temporal complexity changes over time scales. Finally, a non-random correlation was observed between temporal complexity of RSNs and fluid intelligence suggesting that complex dynamics of the human brain is an important attribute of high-level brain function.

YNIMG Journal 2018 Journal Article

On the relationship between instantaneous phase synchrony and correlation-based sliding windows for time-resolved fMRI connectivity analysis

Mangor Pedersen
Amir Omidvarnia
Andrew Zalesky
Graeme D. Jackson

Correlation-based sliding window analysis (CSWA) is the most commonly used method to estimate time-resolved functional MRI (fMRI) connectivity. However, instantaneous phase synchrony analysis (IPSA) is gaining popularity mainly because it offers single time-point resolution of time-resolved fMRI connectivity. We aim to provide a systematic comparison between these two approaches, on temporal, topological and anatomical levels. For this purpose, we used resting-state fMRI data from two separate cohorts with different temporal resolutions (45 healthy subjects from Human Connectome Project fMRI data with repetition time of 0. 72 s and 25 healthy subjects from a separate validation fMRI dataset with a repetition time of 3 s). For time-resolved functional connectivity analysis, we calculated tapered CSWA over a wide range of different window lengths that were compared to IPSA. We found a strong association in connectivity dynamics between IPSA and CSWA when considering the absolute values of CSWA. The association between CSWA and IPSA was stronger for a window length of ∼20 s (shorter than filtered fMRI wavelength) than ∼100 s (longer than filtered fMRI wavelength), irrespective of the sampling rate of the underlying fMRI data. Narrow-band filtering of fMRI data (0. 03–0. 07 Hz) yielded a stronger relationship between IPSA and CSWA than wider-band (0. 01–0. 1 Hz). On a topological level, time-averaged IPSA and CSWA nodes were non-linearly correlated for both short (∼20 s) and long (∼100 s) windows, mainly because nodes with strong negative correlations (CSWA) displayed high phase synchrony (IPSA). IPSA and CSWA were anatomically similar in the default mode network, sensory cortex, insula and cerebellum. Our results suggest that IPSA and CSWA provide comparable characterizations of time-resolved fMRI connectivity for appropriately chosen window lengths. Although IPSA requires narrow-band fMRI filtering, it does not mandate a (semi-)arbitrary choice of window length and window overlap. A code for calculating IPSA is provided.

YNICL Journal 2017 Journal Article

Hierarchical disruption in the Bayesian brain: Focal epilepsy and brain networks

Amir Omidvarnia
Mangor Pedersen
Richard E. Rosch
Karl J. Friston
Graeme D. Jackson

In this opinion paper, we describe a combined view of functional and effective brain connectivity along with the free-energy principle for investigating persistent disruptions in brain networks of patients with focal epilepsy. These changes are likely reflected in effective connectivity along the cortical hierarchy and construct the basis of increased local functional connectivity in focal epilepsy. We propose a testable framework based on dynamic causal modelling and functional connectivity analysis with the capacity of explaining commonly observed connectivity changes during interictal periods. We then hypothesise their possible relation with disrupted free-energy minimisation in the Bayesian brain. This may offer a new approach for neuroimaging to specifically develop and address hypotheses regarding the network pathomechanisms underlying epileptic phenotypes.

YNICL Journal 2017 Journal Article

The diminishing dominance of the dominant hemisphere: Language fMRI in focal epilepsy

Chris Tailby
David F. Abbott
Graeme D. Jackson

"Which is the dominant hemisphere?" is a question that arises frequently in patients considered for neurosurgery. The concept of the dominant hemisphere implies uniformity of language lateralisation throughout the brain. It is increasingly recognised that this is not the case in the healthy control brain, and it is especially not so in neurological diseases such as epilepsy. In the present work we adapt our published objective lateralisation method (based on the construction of laterality curves) for use with sub-lobar cortical, subcortical and cerebellar regions of interest (ROIs). We apply this method to investigate regional lateralisation of language activation in 12 healthy controls and 18 focal epilepsy patients, using three different block design language fMRI paradigms, each tapping different aspects of language processing. We compared lateralisation within each ROI across tasks, and investigated how the quantity of data collected affected the ability to robustly estimate laterality across ROIs. In controls, lateralisation was stronger, and the variance across individuals smaller, in cortical ROIs, particularly in the Inferior Frontal (Broca) region. Lateralisation within temporal ROIs was dependent on the nature of the language task employed. One of the healthy controls was left lateralised anteriorly and right lateralised posteriorly. Consistent with previous work, departures from normality occurred in ~ 15-50% of focal epilepsy patients across the different ROIs, with atypicality most common in the Lateral Temporal (Wernicke) region. Across tasks and ROIs the absolute magnitude of the laterality estimate increased and its across participant variance decreased as more cycles of task and rest were included, stabilising at ~ 4 cycles (~ 4 min of data collection). Our data highlight the importance of considering language as a complex task where lateralisation varies at the subhemispheric scale. This is especially important for presurgical planning for focal resections where the concept of 'hemispheric dominance' may be misleading. This is a precision medicine approach that enables objective evaluation of language dominance within specific brain regions and can reveal surprising and unexpected anomalies that may be clinically important for individual cases.

YNICL Journal 2017 Journal Article

The dynamics of functional connectivity in neocortical focal epilepsy

Mangor Pedersen
Amir Omidvarnia
Evan K. Curwood
Jennifer M. Walz
Genevieve Rayner
Graeme D. Jackson

Focal epilepsy is characterised by paroxysmal events, reflecting changes in underlying local brain networks. To capture brain network activity at the maximal temporal resolution of the acquired functional magnetic resonance imaging (fMRI) data, we have previously developed a novel analysis framework called Dynamic Regional Phase Synchrony (DRePS). DRePS measures instantaneous mean phase coherence within neighbourhoods of brain voxels. We use it here to examine how the dynamics of the functional connections of regional brain networks are altered in neocortical focal epilepsy. Using task-free fMRI data from 21 subjects with focal epilepsy and 21 healthy controls, we calculated the power spectral density of DRePS, which is a measure of signal variability in local connectivity estimates. Whole-brain averaged power spectral density of DRePS, or signal variability of local connectivity, was significantly higher in epilepsy subjects compared to healthy controls. Maximal increase in DRePS spectral power was seen in bilateral inferior frontal cortices, ipsilateral mid-cingulate gyrus, superior temporal gyrus, caudate head, and contralateral cerebellum. Our results provide further evidence of common brain abnormalities across people with focal epilepsy. We postulate that dynamic changes in specific cortical brain areas may help maintain brain function in the presence of pathological epileptiform network activity in neocortical focal epilepsy.

YNICL Journal 2015 Journal Article

Increased segregation of brain networks in focal epilepsy: An fMRI graph theory finding

Mangor Pedersen
Amir H. Omidvarnia
Jennifer M. Walz
Graeme D. Jackson

Focal epilepsy is conceived of as activating local areas of the brain as well as engaging regional brain networks. Graph theory represents a powerful quantitative framework for investigation of brain networks. Here we investigate whether functional network changes are present in extratemporal focal epilepsy. Task-free functional magnetic resonance imaging data from 15 subjects with extratemporal epilepsy and 26 age and gender matched healthy controls were used for analysis. Local network properties were calculated using local efficiency, clustering coefficient and modularity metrics. Global network properties were assessed with global efficiency and betweenness centrality metrics. Cost-efficiency of the networks at both local and global levels was evaluated by estimating the physical distance between functionally connected nodes, in addition to the overall numbers of connections in the network. Clustering coefficient, local efficiency and modularity were significantly higher in individuals with focal epilepsy than healthy control subjects, while global efficiency and betweenness centrality were not significantly different between the two groups. Local network properties were also highly efficient, at low cost, in focal epilepsy subjects compared to healthy controls. Our results show that functional networks in focal epilepsy are altered in a way that the nodes of the network are more isolated. We postulate that network regularity, or segregation of the nodes of the networks, may be an adaptation that inhibits the conversion of the interictal state to seizures. It remains possible that this may be part of the epileptogenic process or an effect of medications.

YNIMG Journal 2015 Journal Article

Resting state functional connectivity changes induced by prior brain state are not network specific

Chris Tailby
Richard A.J. Masterton
Jenny Y. Huang
Graeme D. Jackson
David F. Abbott

Resting state functional connectivity (rFC) is used to identify functionally related brain areas without requiring subjects to perform specific tasks. Previous work suggests that prior brain state, as determined by the activity engaged in immediately prior to collection of resting state data, can influence the networks recovered by rFC analyses. We determined the prevalence and network specificity of rFC changes induced by manipulations of prior state (including an unstructured (unconstrained) state, and language and motor tasks). Three blocks of rest data (one after each of the specified prior states) were acquired on each of 25 subjects. We hypothesised that prior state induced changes in rFC would be greatest within the networks most actively recruited by that prior state. Changes in rFC were greatest following the motor task and, contrary to our hypothesis, were not network specific. This was demonstrated by comparing (1) the timecourses within a set of ROIs selected on the basis of task-related de/activation, and (2) seed-based whole brain voxel-wise connectivity maps, seeded from local maxima in the task-related de/activation maps. Changes in connectivity strength tended to manifest as increases in rFC relative to that in the unstructured rest state, with change maps resembling partially complete maps of the primary sensory cortices and the cognitive control network. The majority of rFC changes occurred in areas moderately (but not weakly) connected to the seeds. Constrained prior states were associated with lower across-participant variance in rFC. This systematic investigation of the effect of prior brain state on rFC indicates that the rFC changes induced by prior brain state occur both in brain networks related to that brain activity and in networks nominally unrelated to that brain activity.

YNIMG Journal 2013 Journal Article

Mapping brain activity using event-related independent components analysis (eICA): Specific advantages for EEG-fMRI

Richard A.J. Masterton
Graeme D. Jackson
David F. Abbott

Event-related analyses of functional MRI (fMRI) typically assume that the onset and offset of neuronal activity match stimuli onset and offset, and that evoked fMRI signal changes follow the canonical haemodynamic response function (HRF). Some event types, however, may be unsuited to this approach: brief stimuli might elicit an extended neuronal response; anticipatory effects might result in activity preceding the event; or altered neurovascular coupling may result in a non-canonical HRF. An example is interictal epileptiform discharges (IEDs), which may show a non-canonical HRF and fMRI signal changes preceding their onset as detected on EEG. In such cases, less constrained analyses – capable of detecting early, non-canonical responses – may be necessary. A consequence of less constrained analyses, however, is that artefactual sources of signal change – motion or physiological noise for example – may also be detected and mixed with the neuronally-generated signals. In this paper, to address this issue, we describe an event-related independent components analysis (eICA) that identifies different sources of event-related signal change that can then be separately assessed to identify likely artefacts and separate primary from propagated activity. We also describe a group analysis that identifies eICA components that are spatially and temporally consistent across subjects and provides an objective approach for selecting group-specific components likely to be of neural origin. We apply eICA to patients with rolandic epilepsy – with stereotypical IEDs arising from a focus in the rolandic fissure – and demonstrate that a single event-related component, concordant with this source location, is detected.

YNIMG Journal 2012 Journal Article

Selecting appropriate voxel-based methods for neuroimaging studies

David F. Abbott
Gaby S. Pell
Heath R. Pardoe
Graeme D. Jackson

We highlight a fundamental difference between voxel-based methods that interrogate signal intensity directly and those that interrogate morphometric features; we discuss how signal intensity changes might erroneously affect morphometric measures, and we provide some guidance for selection of appropriate methods to address particular hyphotheses. Our discussion is motivated by a recent application of voxel-based morphometry methods to T2-weighted images (T2-Voxel Based Morphometry; T2-VBM). In this context we discuss alternative approaches including Voxel-Based T2-Relaxometry (VBR) and Voxel Based Iterative Sensitivity analysis of T2-Weighted Images (VBIS-T2).

YNIMG Journal 2011 Journal Article

Track density imaging (TDI): Validation of super resolution property

Fernando Calamante
Jacques-Donald Tournier
Robin M. Heidemann
Alfred Anwander
Graeme D. Jackson
Alan Connelly

We have recently introduced a novel MRI methodology, so-called super resolution track-density imaging (TDI), which produces high-quality white matter images, with high spatial resolution and exquisite anatomical contrast not available from other MRI modalities. This method achieves super resolution by utilising the long-range information contained in the diffusion MRI fibre tracks. In this study, we validate the super resolution property of the TDI method by using in vivo diffusion MRI data acquired at ultra-high magnetic field strength (7T), and in silico diffusion MRI data from a well-characterised numerical phantom. Furthermore, an alternative version of the TDI technique is described, which mitigates the track length weighting of the TDI map intensity. For the in vivo data, high-resolution diffusion images were down-sampled to simulate low-resolution data, for which the high-resolution images serve as a gold standard. For the in silico data, the gold standard is given by the known simulated structures of the numerical phantom. Both the in vivo and in silico data show that the structures that could be identified in the TDI maps only after using super resolution were consistent with the corresponding structures identified in the reference maps. This supports the claim that the structures identified by the super resolution step are accurate, thus providing further evidence for the important potential role of the super resolution TDI methodology in neuroscience.

YNIMG Journal 2010 Journal Article

fMRI assessment of language lateralization: An objective approach

David F. Abbott
Anthony B. Waites
Leasha M. Lillywhite
Graeme D. Jackson

Language lateralization based on functional magnetic resonance imaging (fMRI) is often used in clinical neurological settings. Currently, interpretation of the distribution, pattern and extent of language activation can be heavily dependent on the chosen statistical threshold. The aim of the present study was to 1) test the robustness of adaptive thresholding of fMRI data to yield a fixed number of active voxels, and to 2) develop a largely threshold-independent method of assessing when individual patients have statistically atypical language lateralization. Simulated data and real fMRI data in 34 healthy controls and 4 selected epilepsy patients performing a verbal fluency language fMRI task were used. Dependence of laterality on the thresholding method is demonstrated for simulated and real data. Simulated data were used to test the hypothesis that thresholding based upon a fixed number of active voxels would yield a laterality index that was more stable across a range of signal strengths (study power) compared to thresholding at a fixed p value. This stability allowed development of a method comparing an individual to a group of controls across a wide range of thresholds, providing a robust indication of atypical lateralization that is more objective than conventional methods. Thirty healthy controls were used as normative data for the threshold-independent method, and the remaining subjects were used as illustrative examples. The method could also be used more generally to assess relative regional distribution of activity in other neuroimaging paradigms (for example, one could apply it to the assessment of lateralization of activation in a memory task, or to the assessment of anterior–posterior distribution rather than laterality).

YNIMG Journal 2010 Journal Article

Focal epileptiform spikes do not show a canonical BOLD response in patients with benign rolandic epilepsy (BECTS)

Richard A.J. Masterton
A. Simon Harvey
John S. Archer
Leasha M. Lillywhite
David F. Abbott
Ingrid E. Scheffer
Graeme D. Jackson

Simultaneous EEG and functional MRI (EEG-fMRI) studies of focal epileptiform spikes commonly use the canonical haemodynamic response function (HRF) to model the blood-oxygenation-level-dependent (BOLD) response to these events. Support for the use of the canonical HRF has come from large studies that contain mixed cohorts of epilepsy syndromes and discharge types, and has demonstrated plausible epileptic localisation results in the majority of patients. Other studies, however, have reported that some patients show a BOLD response that differs markedly from a canonical HRF. Our aim in this study was to see if the BOLD response is well modelled by a canonical HRF in a homogeneous cohort of patients with benign epilepsy with centrotemporal spikes (BECTS), an idiopathic partial epilepsy with stereotypical centrotemporal spikes on the EEG. We studied eight well-characterised and typical BECTS patients and found that the shape of the average BOLD response was different to the canonical HRF. Furthermore, a localisation analysis using the group-average response provided increased sensitivity and specificity compared to the canonical HRF. Our findings suggest that the canonical HRF may not provide the best model for the BOLD response in some epilepsy syndromes or spike-types. In studies of homogeneous patient groups, therefore, localisation results may be improved by using a group-specific BOLD response.

YNIMG Journal 2010 Journal Article

Reduced variance in monozygous twins for multiple MR parameters: Implications for disease studies and the genetic basis of brain structure

Gaby S. Pell
Regula S. Briellmann
Kate M. Lawrence
Deborah Glencross
R. Mark Wellard
Samuel F. Berkovic
Graeme D. Jackson

Twin studies offer the opportunity to determine the relative contribution of genes versus environment in traits of interest. Here, we investigate the extent to which variance in brain structure is reduced in monozygous twins with identical genetic make-up. We investigate whether using twins as compared to a control population reduces variability in a number of common magnetic resonance (MR) structural measures, and we investigate the location of areas under major genetic influences. This is fundamental to understanding the benefit of using twins in studies where structure is the phenotype of interest. Twenty-three pairs of healthy MZ twins were compared to matched control pairs. Volume, T2 and diffusion MR imaging were performed as well as spectroscopy (MRS). Images were compared using (i) global measures of standard deviation and effect size, (ii) voxel-based analysis of similarity and (iii) intra-pair correlation. Global measures indicated a consistent increase in structural similarity in twins. The voxel-based and correlation analyses indicated a widespread pattern of increased similarity in twin pairs, particularly in frontal and temporal regions. The areas of increased similarity were most widespread for the diffusion trace and least widespread for T2. MRS showed consistent reduction in metabolite variation that was significant in the temporal lobe N-acetylaspartate (NAA). This study has shown the distribution and magnitude of reduced variability in brain volume, diffusion, T2 and metabolites in twins. The data suggest that evaluation of twins discordant for disease is indeed a valid way to attribute genetic or environmental influences to observed abnormalities in patients since evidence is provided for the underlying assumption of decreased variability in twins.

YNIMG Journal 2010 Journal Article

Track-density imaging (TDI): Super-resolution white matter imaging using whole-brain track-density mapping

Fernando Calamante
Jacques-Donald Tournier
Graeme D. Jackson
Alan Connelly

Neuroimaging advances have given rise to major progress in neurosciences and neurology, as ever more subtle and specific imaging methods reveal new aspects of the brain. One major limitation of current methods is the spatial scale of the information available. We present an approach to gain spatial resolution using post-processing methods based on diffusion MRI fiber-tracking, to reveal structures beyond the resolution of the acquired imaging voxel; we term such a method as super-resolution track-density imaging (TDI). A major unmet challenge in imaging is the identification of abnormalities in white matter as a cause of illness; super-resolution TDI is shown to produce high-quality white matter images, with high spatial resolution and outstanding anatomical contrast. A unique property of these maps is demonstrated: their spatial resolution and signal-to-noise ratio can be tailored depending on the chosen image resolution and total number of fiber-tracks generated. Super-resolution TDI should greatly enhance the study of white matter in disorders of the brain and mind.

YNIMG Journal 2009 Journal Article

Voxel-Based Iterative Sensitivity (VBIS) analysis: Methods and a validation of intensity scaling for T2-weighted imaging of hippocampal sclerosis

David F. Abbott
Gaby S. Pell
Heath Pardoe
Graeme D. Jackson

Abnormalities in the brain generally manifest on MRI as changes in shape (morphometry) or changes in the nature of the tissue (signal intensity). Voxel Based Morphometry (VBM) is a whole brain quantitative way of assessing morphometric changes. Voxel Based Relaxometry (VBR) directly assesses signal intensity changes in quantitative maps of T2 relaxation time, but this requires specialised multiple-echo acquisition sequences that are not usually available at clinical sites. This paper introduces and assesses an objective voxel-based statistical method for evaluation of signal intensity in groups of routinely acquired qualitative images. We call the method Voxel-Based Iterative Sensitivity (VBIS) analysis. It adaptively optimises the relative global scaling of images to maximise sensitivity to regional effects. We apply and validate the method of analysis for T2-weighted images of the human brain. To validate the method, it was directly compared with VBR by extracting T2-weighted images of a single echo from multi-echo T2 relaxometry acquisitions from a group of 24 patients with left hemisphere hippocampal sclerosis and 97 healthy controls. Expected signal abnormalities in the patients were detectable with VBIS-T2, confirming the feasibility of the technique. This opens the door to the use of a voxel-based analysis approach on the vast amount of T2-weighted image data that has been and is being acquired on MRI scanners. When a quantitative modality is not available, VBIS can be an effective way to quantify differences between groups. We expect the method could also assist quantitative analysis of other qualitative modalities such as T1-weighted MRI, SPECT and CT.

YNIMG Journal 2008 Journal Article

Composite voxel-based analysis of volume and T2 relaxometry in temporal lobe epilepsy

Gaby S. Pell
Regula S. Briellmann
Heath Pardoe
David F. Abbott
Graeme D. Jackson

Voxel-based analyses of tissue characteristics such as volume and T2 are usually carried out in isolation. However, as the images are analysed in a common voxel-based framework, it is possible to directly assess the spatial relationships of abnormalities detected by each technique. We utilize this approach in well-characterized patients with unilateral temporal lobe epilepsy (TLE) with hippocampal sclerosis (HS). TLE is associated with potentially widespread volume and T2 signal abnormalities in MRI images but the relationship between these two aspects of tissue abnormality is not well understood. Here we use a novel approach of combined univariate and multivariate voxel-wise analysis to investigate the spatial relationship of these abnormalities. We studied 19 TLE patients and compared them to 115 control subjects. Grey matter (GM) and white matter (WM) volume changes were assessed with voxel-based morphometry (VBM), and changes in T2 relaxation times were evaluated with voxel-based relaxometry (VBR). The volume and T2 changes obtained using the combined univariate approach were found in an extensive area, prominently in the ipsilateral hippocampus and amygdala (overlap of GM–VBM and VBR), and in the remaining temporal lobe (overlap of WM–VBR and VBR). Other cortical and subcortical areas showed isolated volume or T2 changes. The multivariate analysis based on the Hotelling T 2 statistic, indicated a similar pattern of distributed changes across the brain but with a greater degree of statistical significance in certain areas. The composite analyses appear to identify a network of affected areas not as easily appreciated by the individual analysis of volume or T2 changes.

YNIMG Journal 2008 Journal Article

Multi-site voxel-based morphometry: Methods and a feasibility demonstration with childhood absence epilepsy

Heath Pardoe
Gaby S. Pell
David F. Abbott
Anne T. Berg
Graeme D. Jackson

Aim: Voxel-based morphometry analysis of neurological disorders would benefit if it could use data acquired from different scanners, but scanner based contrast variation could interfere with the detection of disease-specific structural abnormalities. In this study we examine MRI data from three different sites to investigate structural differences between childhood absence epilepsy (CAE) subjects and controls. Methods: T1-weighted structural MRI scans were acquired from: Site A. 10 CAE, 213 controls; Site B. 15 CAE, 33 controls; and Site C. 19 CAE, 11 controls. The images were processed using the optimised VBM protocol. Three statistical analyses were undertaken: (1) Comparisons of CAE subjects and controls stratified by site. (2) Between-site comparison of controls from each site. (3) Factorial analysis of all data with site and disease status as factors. Results: Consistent regions of structural change, located in the thalamic nuclei, were observed in the within-site analysis of CAE vs controls. Analysis of control scans, however, indicated site-specific differences between controls, which required that we adjust for site in combined analyses. Analysis of all data with adjustment for site confirmed the finding of thalamic atrophy in CAE cases. Conclusion: Combined VBM analysis of structural MRI scans acquired from different sites yield consistent patterns of structural change in CAE when site is included as a factor in the statistical analysis of the processed images. In MRI studies of diseases where only a limited number of subjects can be imaged at each site, our study supports the possibility of effective multi-site studies as long as both disease subjects and healthy controls are acquired from each site.

YNIMG Journal 2008 Journal Article

Selection of the control group for VBM analysis: Influence of covariates, matching and sample size

Gaby S. Pell
Regula S. Briellmann
Chow Huat (Patrick) Chan
Heath Pardoe
David F. Abbott
Graeme D. Jackson

Variability in the control group plays a crucial role in voxel-based morphometry (VBM) detection of structural abnormalities. Two common methods of minimising this variance are inclusion of covariates and matching of control and patient groups. We address two major questions: What are the optimal covariates in the VBM design? When a large pool of controls are available, is it better to choose a subset of matched control subjects at the expense of numbers, or include all available controls? We used regression analysis in a group of 176 controls to determine the contribution of gender, age, and total intracranial volume (TIV) to volume variation. We then used different matching and covariate strategies to determine the optimal design for VBM detection of abnormality in epilepsy patients with hippocampal sclerosis. In the regression analysis, focal gender effects disappeared with inclusion of TIV as an additional regressor. Age had a small but unique contribution to focal volume changes. In the VBM analysis of HS patients, detection of abnormalities was strongly influenced by choice of covariates. The optimal combination was different for grey and white matter (for grey matter: TIV; for temporal lobe white matter: TIV, age and gender). A control group size of 70–90 subjects allowed optimal detection of volume loss in the hippocampus and thalamus. At these group sizes, matched control groups did not consistently prove superior to deliberately “unmatched” groups of the same size. The optimal detection of volume loss was obtained with all available control subjects.

YNIMG Journal 2007 Journal Article

Measurement and reduction of motion and ballistocardiogram artefacts from simultaneous EEG and fMRI recordings

Richard A.J. Masterton
David F. Abbott
Steven W. Fleming
Graeme D. Jackson

Recording the electroencephalogram (EEG) during functional magnetic resonance imaging (fMRI) permits the identification of haemodynamic changes associated with EEG events. However, subject motion within the MR scanner can cause unpredictable and frustrating artefacts on the EEG that may appear focally, bilaterally or unilaterally and can sometimes be confused for epileptiform activity. Motion may arise from a number of sources: small involuntary cardiac-related body movements (ballistocardiogram); acoustic vibrations due to the scanner machinery; and voluntary subject movements. Here we describe a new real-time technique for removing ballistocardiogram (BCG) and movement artefact from EEG recordings in the MR scanner using a novel method for recording subject motion. We record the current induced in a number of wire loops, attached to a cap worn by the subject, due to motion in the static magnetic field of the scanner (Faraday's Law). This is the same process that leads to the motion artefacts on the EEG, and hence these signals are ideally suited to filtering these artefacts from the EEG. Our filter uses a linear adaptive technique based upon the Recursive Least Squares (RLS) algorithm. We demonstrate in both simulations and real EEG recordings from epilepsy patients that our filter significantly reduces the artefact power whilst preserving the underlying EEG signal.

YNIMG Journal 2005 Journal Article

How reliable are fMRI–EEG studies of epilepsy? A nonparametric approach to analysis validation and optimization

Anthony B. Waites
Marnie E. Shaw
Regula S. Briellmann
Angelo Labate
David F. Abbott
Graeme D. Jackson

Simultaneously acquired functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data hold great promise for localizing the spatial source of epileptiform events detected in the EEG trace. Despite a number of studies applying this method, there has been no independent and systematic validation of the approach. The present study uses a nonparametric method to show that interictal discharges lead to a blood oxygen level dependent (BOLD) response that is significantly different to that obtained by examining random ‘events’. We also use this approach to examine the optimization of analysis strategy for detecting these BOLD responses. Two patients with frequent epileptiform events and a healthy control were studied. The fMRI data for each patient were analyzed using a model derived from the timings of the epileptiform events detected on EEG during fMRI scanning. Twenty sets of random pseudoevents were used to generate a null distribution representing the level of chance correlation between the EEG events and fMRI data. The same pseudoevents were applied to control data. We demonstrate that it is possible to detect blood oxygen level-dependent (BOLD) changes related to interictal discharges with specific and independent knowledge about the reliability of this activation. Biologically generated events complicate the fMRI–EEG experiment. Our proposed validation examines whether identified events have an associated BOLD response beyond chance and allows optimization of analysis strategies. This is an important step beyond standard analysis. It informs clinical interpretation because it permits assessment of the reliability of the connection between interictal EEG events and the BOLD response to those events.