Granger causality is a method for identifying directed functional connectivity based on time series analysis of precedence and predictability. The method has been applied widely in neuroscience, ...however its application to functional MRI data has been particularly controversial, largely because of the suspicion that Granger causal inferences might be easily confounded by inter-regional differences in the hemodynamic response function. Here, we show both theoretically and in a range of simulations, that Granger causal inferences are in fact robust to a wide variety of changes in hemodynamic response properties, including notably their time-to-peak. However, when these changes are accompanied by severe downsampling, and/or excessive measurement noise, as is typical for current fMRI data, incorrect inferences can still be drawn. Our results have important implications for the ongoing debate about lag-based analyses of functional connectivity. Our methods, which include detailed spiking neuronal models coupled to biophysically realistic hemodynamic observation models, provide an important ‘analysis-agnostic’ platform for evaluating functional and effective connectivity methods.
► GC is invariant to confounding times-to-peak in hemodynamic responses applied to fMRI. ► We integrate theoretical analysis, simple simulations, and detailed spiking models. ► Our spiking/balloon model provides a ‘analysis-agnostic’ simulation test-bed. ► GC can't be applied naively to fMRI since downsampling and noise affect inference. ► We establish constraints & principles for functional connectivity analysis of fMRI.
The architectonic subdivisions of the brain are believed to be functional modules, each processing parts of global functions. Previously, we showed that neurons in different regions operate in ...different firing regimes in monkeys. It is possible that firing regimes reflect differences in underlying information processing, and consequently the firing regimes in homologous regions across animal species might be similar. We analyzed neuronal spike trains recorded from behaving mice, rats, cats, and monkeys. The firing regularity differed systematically, with differences across regions in one species being greater than the differences in similar areas across species. Neuronal firing was consistently most regular in motor areas, nearly random in visual and prefrontal/medial prefrontal cortical areas, and bursting in the hippocampus in all animals examined. This suggests that firing regularity (or irregularity) plays a key role in neural computation in each functional subdivision, depending on the types of information being carried.
By analyzing neuronal spike trains recorded from mice, rats, cats, and monkeys, we found that different brain regions have intrinsically different firing regimes that are more similar in homologous areas across species than across areas in one species. Because different regions in the brain are specialized for different functions, the present finding suggests that the different activity regimes of neurons are important for supporting different functions, so that appropriate neuronal codes can be used for different modalities.
Dopaminergic neurons in the mammalian substantia nigra display characteristic phasic responses to stimuli which reliably predict the receipt of primary rewards. These responses have been suggested to ...encode reward prediction-errors similar to those used in reinforcement learning. Here, we propose a model of dopaminergic activity in which prediction-error signals are generated by the joint action of short-latency excitation and long-latency inhibition, in a network undergoing dopaminergic neuromodulation of both spike-timing dependent synaptic plasticity and neuronal excitability. In contrast to previous models, sensitivity to recent events is maintained by the selective modification of specific striatal synapses, efferent to cortical neurons exhibiting stimulus-specific, temporally extended activity patterns. Our model shows, in the presence of significant background activity, (i) a shift in dopaminergic response from reward to reward-predicting stimuli, (ii) preservation of a response to unexpected rewards, and (iii) a precisely timed below-baseline dip in activity observed when expected rewards are omitted.
In this thesis I explore functions of the neuromodulator dopamine in the context of autonomous learning and behaviour. I first investigate dopaminergic influence within a simulated agent-based model, ...demonstrating how modulation of synaptic plasticity can enable reward-mediated learning that is both adaptive and self-limiting. I describe how this mechanism is driven by the dynamics of agentenvironment interaction and consequently suggest roles for both complex spontaneous neuronal activity and specific neuroanatomy in the expression of early, exploratory behaviour. I then show how the observed response of dopamine neurons in the mammalian basal ganglia may also be modelled by similar processes involving dopaminergic neuromodulation and cortical spike-pattern representation within an architecture of counteracting excitatory and inhibitory neural pathways, reflecting gross mammalian neuroanatomy. Significantly, I demonstrate how combined modulation of synaptic plasticity and neuronal excitability enables specific (timely) spike-patterns to be recognised and selectively responded to by efferent neural populations, therefore providing a novel spike-timing based implementation of the hypothetical ‘serial-compound' representation suggested by temporal difference learning. I subsequently discuss more recent work, focused upon modelling those complex spike-patterns observed in cortex. Here, I describe neural features likely to contribute to the expression of such activity and subsequently present novel simulation software allowing for interactive exploration of these factors, in a more comprehensive neural model that implements both dynamical synapses and dopaminergic neuromodulation. I conclude by describing how the work presented ultimately suggests an integrated theory of autonomous learning, in which direct coupling of agent and environment supports a predictive coding mechanism, bootstrapped in early development by a more fundamental process of trial-and-error learning.
In this thesis I explore functions of the neuromodulator dopamine in the context of autonomous learning and behaviour. I first investigate dopaminergic influence within a simulated agent-based model, ...demonstrating how modulation of synaptic plasticity can enable reward-mediated learning that is both adaptive and self-limiting. I describe how this mechanism is driven by the dynamics of agentenvironment interaction and consequently suggest roles for both complex spontaneous neuronal activity and specific neuroanatomy in the expression of early, exploratory behaviour. I then show how the observed response of dopamine neurons in the mammalian basal ganglia may also be modelled by similar processes involving dopaminergic neuromodulation and cortical spike-pattern representation within an architecture of counteracting excitatory and inhibitory neural pathways, reflecting gross mammalian neuroanatomy. Significantly, I demonstrate how combined modulation of synaptic plasticity and neuronal excitability enables specific (timely) spike-patterns to be recognised and selectively responded to by efferent neural populations, therefore providing a novel spike-timing based implementation of the hypothetical ‘serial-compound' representation suggested by temporal difference learning. I subsequently discuss more recent work, focused upon modelling those complex spike-patterns observed in cortex. Here, I describe neural features likely to contribute to the expression of such activity and subsequently present novel simulation software allowing for interactive exploration of these factors, in a more comprehensive neural model that implements both dynamical synapses and dopaminergic neuromodulation. I conclude by describing how the work presented ultimately suggests an integrated theory of autonomous learning, in which direct coupling of agent and environment supports a predictive coding mechanism, bootstrapped in early development by a more fundamental process of trial-and-error learning.
A novel series of small-molecule inhibitors has been developed to target the double mutant form of the epidermal growth factor receptor (EGFR) tyrosine kinase, which is resistant to treatment with ...gefitinib and erlotinib. Our reported compounds also show selectivity over wild-type EGFR. Guided by molecular modeling, this series was evolved to target a cysteine residue in the ATP binding site via covalent bond formation and demonstrates high levels of activity in cellular models of the double mutant form of EGFR. In addition, these compounds show significant activity against the activating mutations, which gefitinib and erlotinib target and inhibition of which gives rise to their observed clinical efficacy. A glutathione (GSH)-based assay was used to measure thiol reactivity toward the electrophilic functionality of the inhibitor series, enabling both the identification of a suitable reactivity window for their potency and the development of a reactivity quantitative structure-property relationship (QSPR) to support design.
As efforts to mitigate the effects of climate change grow, reliable and thorough reporting of greenhouse gas emissions are essential for measuring progress towards international and domestic ...emissions reductions targets. New Zealand’s national emissions inventories are currently reported between 15 to 27 months out-of-date. We present a machine learning approach to nowcast (dynamically estimate) national greenhouse gas emissions in New Zealand in advance of the national emissions inventory’s release, with just a two month latency due to current data availability. Key findings include an estimated 0.2% decrease in national gross emissions since 2020 (as at July 2022). Our study highlights the predictive power of a dynamic view of emissions intensive activities. This methodology is a proof of concept that a machine learning approach can make sub-annual estimates of national greenhouse gas emissions by sector with a relatively low error that could be of value for policy makers.
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•Daily and low latency predictions of the New Zealand Greenhouse Gas Inventory.•Cross validation ensures accuracy, stability, low over-fitting and explainability.•Daily predictions contrast emissions during lockdowns with global financial crisis.
Epidermal growth factor receptor (EGFR) inhibitors have been used clinically in the treatment of non-small-cell lung cancer (NSCLC) patients harboring sensitizing (or activating) mutations for a ...number of years. Despite encouraging clinical efficacy with these agents, in many patients resistance develops leading to disease progression. In most cases, this resistance is in the form of the T790M mutation. In addition, EGFR wild type receptor inhibition inherent with these agents can lead to dose limiting toxicities of rash and diarrhea. We describe herein the evolution of an early, mutant selective lead to the clinical candidate AZD9291, an irreversible inhibitor of both EGFR sensitizing (EGFRm+) and T790M resistance mutations with selectivity over the wild type form of the receptor. Following observations of significant tumor inhibition in preclinical models, the clinical candidate was administered clinically to patients with T790M positive EGFR-TKI resistant NSCLC and early efficacy has been observed, accompanied by an encouraging safety profile.
Breast cancer is the most common cancer diagnosed in women, both in the UK and worldwide. A small proportion of women are at very high risk of breast cancer, having a particularly strong family ...history. The National Institute for Health and Clinical Excellence (NICE) has advised that practitioners should not, in most instances, actively seek to identify women with a family history of breast cancer. An audit was undertaken at an urban primary care practice of 15,000 patients, using a paper-based, self-administered questionnaire sent to patients identified with a personal history of breast cancer. The aim of this audit was to determine whether using targeted screening of relatives of patients with breast cancer to identify familial cancer risk is worthwhile in primary care. Since these patients might already expected to have been risk assessed following their initial diagnosis, this audit acts as a quality improvement exercise. The audit used a validated family history questionnaire and risk assessment tool as a screening approach for identifying and grading familial risk in line with the NICE guidelines, to guide referral to the familial cancer screening service. The response rate to family history questionnaires was 54 % and the majority of patients responded positively to their practitioner seeking to identify familial cancer risks in their family. Of the 57 returned questionnaires, over a half (54 %) contained pedigrees with individuals eligible for referral. Patients and their relatives who are often registered with the practice welcome the discussion. An appropriate referral can therefore be made. The findings suggest a role for primary care practitioners in the identification of those at higher familial risk. However integrated systems and processes need designing to facilitate this work.
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