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Small vessel disease (SVD) accounts for approximately 25% of all strokes and 45% of all dementias. Although the small vessels cannot be visualised with conventional neuroimaging, the pathological changes in the cerebral white and deep grey matter secondary to SVD has been adopted as markers of SVD. These are best appreciated with magnetic resonance imaging (MRI) and includes recent small subcortical infarcts, white matter hyperintensity (WMH), lacunes, cerebral microbleeds and enlarged perivascular spaces (PVSs). There are however a number of outstanding questions regarding these surrogate neuroimaging markers of SVD and how these markers may influence clinical management. First, although a high burden of microbleeds have been associated with an increased risk of intracerebral haemorrhage (ICH) and possibly recurrent ischaemic stroke in patients with TIA or ischaemic stroke, how microbleeds should influence antithrombotic treatment use after TIA or ischaemic stroke remains uncertain. Second, the long-term prognostic implications of enlarged PVSs in patients with TIA or ischaemic stroke have not been studied. Third, although previous studies have shown possible ethnic differences in prevalence of microbleeds, whether there are any ethnic differences in prevalence of other neuroimaging markers of SVD remains unclear. Fourth, although a Total SVD Score was recently proposed to measure the global SVD burden, the prognostic value of this score in patients with TIA or ischaemic stroke has yet to be studied. Fifth, the relationships of long-term premorbid blood pressure with global SVD burden is unknown. Finally, the age and sex specific associations between renal impairment, carotid and cerebral pulsatility with burden of SVD has yet to be studied. The aim of my thesis was therefore to determine the clinical correlates, ethnic differences and long-term prognostic implications of a range of neuroimaging markers and global burden of SVD. I also aimed to determine the relationships of global SVD burden with long-term mean premorbid blood pressure, renal impairment and carotid pulsatiltiy. I have collected, collated and analysed clinical and neuroimaging data from two independent cohorts - the Oxford Vascular Study (OXVASC) and The University of Hong Kong (HKU). In particular I worked as one of the Clinical Research Fellows at OXVASC and was involved in regular recruitment, assessment and follow up of study patients. In OXVASC, 1080 predominantly Caucasians with TIA or ischaemic stroke who had a cerebral MRI performed at baseline was recruited during 2004 to 2014. I interpreted all these MRIs, specifically coding the burden of microbleeds, enlarged perivascular spaces and lacunes. I was involved in obtaining funding and developing the HKU cohort, which includes 1003 predominantly Chinese with ischaemic stroke recruited during 2008-2014 who had a cerebral MRI performed at baseline. I saw about 25% of the patients in the cohort and was involved in interpreting all of the MRIs of the cohort. All patients from both cohorts were followed-up regularly and adverse events including recurrent ischaemic stroke and ICH was determined. Presence and burden of periventricular and subcortical WMH, lacunes, microbleeds, basal ganglia and centrum semiovale PVSs was determined for all patients and the global burden of SVD estimated according to the Total SVD Score. There are several clinically relevant findings in this thesis. First, I have shown that in Caucasians and Chinese with ≥5 microbleeds, withholding antiplatelet drugs during the first year after TIA or ischaemic stroke may be inappropriate, especially early after TIA. However, the risk of ICH is likely to outweigh any benefit thereafter. Second, I have shown that TIA or ischaemic stroke patients with microbleeds on warfarin had an increased risk of subsequent ICH. However, this risk was not different from that of antiplatelet users with microbleeds. Third, I have shown that a high burden of MRI-visible basal ganglia PVSs is independently associated with an increased risk of recurrent ischaemic stroke, but not ICH. However, the prognostic value of MRI-visible centrum semiovale PVSs in the TIA or ischaemic stroke population is limited. Fourth, I demonstrated significant ethnic differences in underlying prevalence and burden of neuroimaging markers of SVD - Chinese had a greater prevalence of microbleeds, lacunes and subcortical WMH, whilst Caucasians had a greater prevalence of periventricular WMH and PVSs. Fifth, I validated the Total SVD Score and showed that the SVD Score is able to predict risk of recurrent ischaemic stroke and ICH in Caucasians and Chinese, but is unable to identify patients at high risk of ICH from those at high risk of recurrent ischaemic stroke. Sixth, I showed that mean premorbid blood pressure, especially diastolic blood pressure measurements taken 10-20 years prior to TIA or ischaemic stroke was most strongly associated with global SVD burden suggesting a latency effect of hypertension on the pathogenesis of SVD. Finally, I demonstrated age-specific associations between renal impairment, internal carotid artery pulsatility index and SVD burden.

Functional magnetic resonance imaging (fMRI) was used to examine functional brain activity in two demographically matched depressed women following their participation in a Behavioral Activation Treatment for Depression (BATD; Hopko & Lejuez, 2007) or Pragmatic Psychodynamic Psychotherapy (PPP; Summers & Barber, 2010). A reward responsiveness pleasurable music listening scanner paradigm was employed during brain scanning to assess reward responsivity prior to and following treatment. Both women responded positively to treatment, evidenced reductions in depression, and exhibited changes in their blood oxygenation level dependence (BOLD) response as measured by fMRI following treatment. BOLD response changes were not observed in either patient in subcortical regions implicated in reward responsiveness following treatment. However, BOLD response changes were observed for both patients in regions of the dorsolateral and medial orbital prefrontal cortex and subgenual cingulate following treatment, with each treatment affecting these areas. These findings support the notion that when BATD and PPP are implemented effectively they are associated with functional brain changes in areas implicated in the pathophysiology of depression.

I investigate the use of vertical cavity surface emitting lasers (VCSELs) for portable optical neural imaging. In particular, I attempt to use one laser source to produce the different illumination needed for three techniques, imaging flow, oxygenation, and fluorescence. Our group's goal is to combine these techniques using a single source in a simple imaging format, which can be modified for portable use in awake animals. We exploit tuning properties of VCSELs to implement two distinct operation schemes with different illumination characteristics. Single mode operation provides high speckle contrasts, used to map flow speeds in laser speckle contrast imaging (LSCI). Sweep operation provides low noise, allowing us to image small signal changes with intrinsic optical signal imaging (IOSI), which relate to blood oxygenation. Finally, we use our VCSEL devices for on-chip integrated fluorescence sensing. Future work aims to develop a fully portable format, using CMOS detectors and integrated circuit control.

I investigate the use of vertical cavity surface emitting lasers (VCSELs) for portable optical neural imaging. In particular, I attempt to use one laser source to produce the different illumination needed for three techniques, imaging flow, oxygenation, and fluorescence. Our group's goal is to combine these techniques using a single source in a simple imaging format, which can be modified for portable use in awake animals. We exploit tuning properties of VCSELs to implement two distinct operation schemes with different illumination characteristics. Single mode operation provides high speckle contrasts, used to map flow speeds in laser speckle contrast imaging (LSCI). Sweep operation provides low noise, allowing us to image small signal changes with intrinsic optical signal imaging (IOSI), which relate to blood oxygenation. Finally, we use our VCSEL devices for on-chip integrated fluorescence sensing. Future work aims to develop a fully portable format, using CMOS detectors and integrated circuit control.

Cognitive emotion regulation strategies can be used to counter the negative effects of life stress. In neuroimaging paradigms, many different types of reappraisal strategies have been used to promote cognitive coping with impersonal, emotion-evoking stimuli, but limited research has been done utilizing specific reappraisal strategies with real-life events. Big picture reappraisal is a specific emotion regulation strategy that offers a way of managing distress aiming to promote acceptance and cognitive coping. Big picture reappraisal instructions (experimental condition) were compared to distraction and rumination instructions (control conditions) resulting in activation in areas associated with cognitive control (orbital frontal cortex, superior parietal lobe, cerebellum lobule VI). Mood ratings collected after each of several condition prompts were significantly more positive in the distraction compared to the big picture reappraisal condition during the first third of the induction, but as the task progressed the effectiveness of distraction declined considerably. There were no significant condition differences in mood during the second and third segments of the induction. / text

Causality analysis is an approach to time series analysis that is being used increasingly to investigate neuroimaging data. The reason for its popularity is the useful perspective it provides in describing the ordered operations of various brain regions using indirectly and passively measured neurophysiological signals. Although there are numerous frameworks with which causality analysis can be performed, one concept in particular – termed Granger causality (GC) – is receiving much of the attention because of its ease of implementation and interpretability. GC makes use of the fact that a predictive relationship between the history of one signal and the future of another signal provides evidence for there being a causal relationship between the two signals, and as a result, the physical events underlying those signals. If such a relationship can be established across neural time series, causal dependencies between neural pathways can be inferred and their contribution to brain function can be studied. Several analysis frameworks exist for applying GC to neurophysiological questions but many of these frameworks have deficiencies that impede their application to large and highly multivariate neuroimaging datasets. To address some of these concerns, this study develops the theory and methods for a novel neural time series classification procedure – referred to as GC classification – based on concepts in GC analysis. Validation of this method in neuroimaging research is provided by showing that it can be applied to heterogeneous datasets, that it makes use of many parallel sources of information about causal relationships, and that it can be adapted to different types of preprocessing steps to uncover causal relationships in multivariate neural time series data. Application of this analysis method to human behavioural MEG data revealed that, during a cued button-pressing task, distinct causal relationships exist between sensory cortices and their downstream targets preceding the initiation of actions that differ by whether or not they were the result of a decision being made. These results provide evidence that the GC classification procedure is a useful and robust technique for studying causal relationships in neurophysiological time series.

Research suggests there are neurological predictors of violence, such as brain function abnormalities most frequently displayed by violent offenders who may suffer from a psychological phenomenon termed “psychopathy.” Functional magnetic resonance imaging (fMRI) can detect signs of some of these brain abnormalities. Neurological markers of violence, evident in a convicted individual’s fMRI results, could speak to that offender’s tendency to act violently in the future. Can fMRI play a meaningful role in estimating recidivism rates and in sentencing? Even if fMRI evidence meets legal thresholds for use in sentencing, should it be employed in light of many concerns, such as reliability, as the implications of predicting an individual’s dangerousness based on fMRI evidence are substantial, especially in the context of defendant rights. Moreover, neurological indicators of violence may undermine a holistic approach to sentencing that considers the convicted individual’s particular story.

Causality analysis is an approach to time series analysis that is being used increasingly to investigate neuroimaging data. The reason for its popularity is the useful perspective it provides in describing the ordered operations of various brain regions using indirectly and passively measured neurophysiological signals. Although there are numerous frameworks with which causality analysis can be performed, one concept in particular – termed Granger causality (GC) – is receiving much of the attention because of its ease of implementation and interpretability. GC makes use of the fact that a predictive relationship between the history of one signal and the future of another signal provides evidence for there being a causal relationship between the two signals, and as a result, the physical events underlying those signals. If such a relationship can be established across neural time series, causal dependencies between neural pathways can be inferred and their contribution to brain function can be studied. Several analysis frameworks exist for applying GC to neurophysiological questions but many of these frameworks have deficiencies that impede their application to large and highly multivariate neuroimaging datasets. To address some of these concerns, this study develops the theory and methods for a novel neural time series classification procedure – referred to as GC classification – based on concepts in GC analysis. Validation of this method in neuroimaging research is provided by showing that it can be applied to heterogeneous datasets, that it makes use of many parallel sources of information about causal relationships, and that it can be adapted to different types of preprocessing steps to uncover causal relationships in multivariate neural time series data. Application of this analysis method to human behavioural MEG data revealed that, during a cued button-pressing task, distinct causal relationships exist between sensory cortices and their downstream targets preceding the initiation of actions that differ by whether or not they were the result of a decision being made. These results provide evidence that the GC classification procedure is a useful and robust technique for studying causal relationships in neurophysiological time series.

Research suggests there are neurological predictors of violence, such as brain function abnormalities most frequently displayed by violent offenders who may suffer from a psychological phenomenon termed “psychopathy.” Functional magnetic resonance imaging (fMRI) can detect signs of some of these brain abnormalities. Neurological markers of violence, evident in a convicted individual’s fMRI results, could speak to that offender’s tendency to act violently in the future. Can fMRI play a meaningful role in estimating recidivism rates and in sentencing? Even if fMRI evidence meets legal thresholds for use in sentencing, should it be employed in light of many concerns, such as reliability, as the implications of predicting an individual’s dangerousness based on fMRI evidence are substantial, especially in the context of defendant rights. Moreover, neurological indicators of violence may undermine a holistic approach to sentencing that considers the convicted individual’s particular story.