Biology subjects

Halliday, D. M.

Publications and source records attributed to Halliday, D. M..

3 recordsLinked to original sources

bioRxiv2025

Biologically-informed machine learning identifies a new clinically-actionable bladder cancer subgroup characterised by NRF2 overactivity

Muscle-invasive bladder cancer is a diverse disease where subtyping is ambiguous. Gene expression profiling followed by unsupervised machine learning (ML) has broadened our understanding of tumour biology, but has failed to provide high-confidence clinically-actionable subgroups. To focus on tissue-specific urothelial biology, we generated co-expression networks from histologically normal bladder, including multiple differentiation states and prioritising transcription factors (TFs). This strategy revealed an emergent set of 98 TFs which we used to stratify The Cancer Genome Atlas bladder cancer cohort, revealing a subdivision of basal tumours characterised by the detoxification and glutaminolysis activity of NRF2, rendering them resistant to standard bladder cancer interventions. These 20 tumours (4.9%) expressed squamous markers, were highly aggressive (15% 2-year survival), and had signatures of active PI3K, MTOR and retinoic acid signalling. Intriguingly, only half of the subgroup had activating mutations in the NRF2/KEAP1 pathway, whilst half of putative driver NFE2L2 mutations were excluded. This highlighted the importance of expression-based classification, particularly as re-analysis of NFE2L2-mutated lung cancer trial data showed only mutations consistent with our classification strategy responded to NRF2 inhibition. Our approach provides the first direct evidence that unsupervised ML can be biologically-informative in identifying clinically-actionable subgroups.

cancer biology↗

bioRxiv2023

Dopamine D1-like receptors modulate synchronized oscillations in the hippocampal-prefrontal-amygdala circuit in contextual fear

Contextual fear conditioning (CFC) is mediated by a neural circuit that includes the hippocampus, prefrontal cortex, and amygdala, but the neurophysiological mechanisms underlying the regulation of CFC by neuromodulators remain unclear. Dopamine D1-like receptors (D1Rs) in this circuit regulate CFC and local synaptic plasticity, which is facilitated by synchronized oscillations between these areas. In rats, we determined the effects of systemic D1R blockade on CFC and oscillatory synchrony between dorsal hippocampus (DH), prelimbic (PL) cortex, basolateral amygdala (BLA), and ventral hippocampus (VH), which sends hippocampal projections to PL and BLA. D1R blockade altered DH-VH and reduced VH-PL and VH-BLA synchrony during CFC, as inferred from theta and gamma coherence and theta-gamma coupling. D1R blockade also impaired CFC, as indicated by decreased freezing at retrieval, which was characterized by altered DH-VH and reduced VH-PL, VH-BLA, and PL-BLA synchrony. This reduction in VH-PL-BLA synchrony was not fully accounted for by non-specific locomotor effects, as revealed by comparing between epochs of movement and freezing in the controls. These results suggest that D1Rs regulate CFC by modulating synchronized oscillations within the hippocampus-prefrontal-amygdala circuit. They also add to growing evidence indicating that this circuit synchrony at retrieval reflects a neural signature of learned fear.

neuroscience↗

bioRxiv2019

Measuring directed functional connectivity in neural data using Non-Parametric Directionality Analysis: validation and comparison with Non-Parametric Granger Causality

Background Non-parametric directionality (NPD) is a novel method for estimation of directed functional connectivity (dFC) in neural data. The method has previously been verified in its ability to recover causal interactions in simulated spiking networks in Halliday et al. (2015) MethodsThis work presents a validation of NPD in continuous neural recordings (e.g. local field potentials). Specifically, we use autoregressive model to simulate time delayed correlations between neural signals. We then test for the accurate recovery of networks in the face of several confounds typically encountered in empirical data. We examine the effects of NPD under varying: a) signal-to-noise ratios, b) asymmetries in signal strength, c) instantaneous mixing, d) common drive, e) and parallel/convergent signal routing. We also apply NPD to data from a patient who underwent simultaneous magnetoencephalography and deep brain recording. ResultsWe demonstrate that NPD can accurately recover directed functional connectivity from simulations with known patterns of connectivity. The performance of the NPD metric is compared with non-parametric Granger causality (NPG), a well-established methodology for model free estimation of dFC. A series of simulations investigating synthetically imposed confounds demonstrate that NPD provides estimates of connectivity that are equivalent to NPG. However, we provide evidence that: i) NPD is less sensitive than NPG to degradation by noise; ii) NPD is more robust to the generation of false positive identification of connectivity resulting from SNR asymmetries; iii) NPD is more robust to corruption via moderate degrees of instantaneous signal mixing. ConclusionsThe results in this paper highlight that to be practically applied to neural data, connectivity metrics should not only be accurate in their recovery of causal networks but also resistant to the confounding effects often encountered in experimental recordings of multimodal data. Taken together, these findings position NPD at the state-of-the-art with respect to the estimation of directed functional connectivity in neuroimaging. HighlightsO_LINon-parametric directionality (NPD) is a novel directed connectivity metric. C_LIO_LINPD estimates are equivalent to Granger causality but more robust to signal confounds. C_LIO_LIMultivariate extensions of NPD can correctly identify signal routing. C_LI Abbreviations

neuroscience↗