Biology subjects

Taylor, P. N.

Publications and source records attributed to Taylor, P. N..

7 recordsLinked to original sources

bioRxiv2025

Brain structure-function coupling - relationship with language lateralisation

Language is one of the most extensively studied lateralised cognitive functions in the human brain, predominantly relying on the left hemisphere in most individuals. However, the mechanisms by which a stable white matter architecture underpins individual language functions remain unclear. Previous studies have employed structural connectivity (SC) and functional connectivity (FC) coupling for individual fingerprinting and task decoding, suggesting that variability in brain entropy may serve as a distinguishing characteristic for language lateralisation. We examined a large cohort of healthy adults (n = 285) to investigate SC-FC coupling and identify markers distinguishing different language laterality groups. Functional connectivity was measured using resting-state fMRI (rsfMRI) time-series data, while structural connectivity was determined via probabilistic fibre tractography. SC-FC coupling was investigated using the SENSAAS language atlas and defined as the Pearson correlation between non-zero elements of regional structural and functional connectivity profiles. Group differences were assessed using the PALM toolbox in FSL. Our findings revealed that increased SC-FC coupling in the left precentral sulcus was associated with typical language lateralisation, while increased coupling in the right middle temporal gyrus and left anterior insula was observed in individuals with atypical language lateralisation (pFDR < 0.05). Non-lateralised individuals exhibited increased coupling in the left anterior insula compared to lateralised (pFDR<0.05). SC-FC coupling offers a promising framework to uncover functional and anatomical differences among individuals with varying language lateralisation. This regional specificity indicates that typical, atypical, and non-lateralised profiles rely on different structural-functional alignments, likely reflecting the recruitment of alternative pathways for language processing.

neuroscience↗

bioRxiv2024

The relationship between white matter architecture and language lateralisation in the healthy brain

Interhemispheric anatomical asymmetries have long been thought to be related to language lateralisation. Previous studies have explored whether asymmetries in the diffusion characteristics of white matter language tracts are consistent with language lateralisation. These studies, typically with smaller cohorts, yielded mixed results. This study investigated whether connectomic analysis of quantitative anisotropy (QA) and shape features of white matter tracts across the whole brain are associated with language lateralisation. We analysed 1040 healthy individuals from the Human Connectome Project database. Hemispheric language dominance for each participant was quantified using a laterality quotient (LQ) derived from fMRI activation in regions of interest (ROIs) associated with a language comprehension task compared against a math task. A linear regression model was used to examine the relationship between structural asymmetry and functional lateralisation. Connectometry revealed that LQs were significantly negatively correlated with QA of corpus callosum tracts, including forceps minor, body, tapetum, and forceps major, indicating that reduced language dominance (more bilateral language representation) is associated with increased QA in these regions. The QA of the left arcuate fasciculus, cingulum, and right cerebellar tracts was positively associated with LQ, suggesting that stronger structural asymmetry in these tracts may identify left language dominance. Language lateralisation was not significantly associated with the shape metrics (including length, span, curl, elongation, diameter, volume, and surface area) of all white matter tracts. These results suggest that diffusion measures of microstructural architecture, and not the geometric features of reconstructed white matter tracts, are associated with lateralisation of language comprehension functions. People with increased dependence on both cerebral hemispheres for language processing may have more developed commissural fibres, which may support more efficient interhemispheric communication.

neuroscience↗

bioRxiv2022

Immediate neural network impact after the loss of a semantic hub

The human brain extracts meaning from the world using an extensive neural system for semantic knowledge. Whether such broadly distributed systems1-3 crucially depend on or can compensate for the loss of one of their highly interconnected hubs4-6 is controversial4. The strongest level of causal evidence for the role of a brain hub is to evaluate its acute network-level impact following disconnection and any rapid functional compensation that ensues. We report rare neurophysiological data from two patients who underwent awake intracranial recordings during a speech prediction task immediately before and after neurosurgical treatment that required disconnection of the left anterior temporal lobe (ATL), a crucial hub for semantic knowledge4-6. Informed by a predictive coding framework, we tested three sets of hypotheses including diaschisis causing disruption in interconnected sites7 and incomplete or complete compensation by other language-critical and speech processing sites8-10. Immediately after ATL disconnection, we observed highly specific neurophysiological alterations in the recorded fronto-temporal network, including abnormally magnified high gamma responses to the speech sounds in auditory cortex. We also observed evidence for rapid compensation, seen as focal increases in effective connectivity involving language-critical sites in the inferior frontal gyrus and speech processing sites in auditory cortex. However, compensation was incomplete, in part because after ATL disconnection speech prediction signals were depleted in auditory cortex. This study provides direct causal evidence for a semantic hub in the human brain and shows striking neural impact and a rapid attempt at compensation in a neural network after the loss of one of its hubs.

neuroscience↗

bioRxiv2019

Node abnormality predicts seizure outcome and relates to long-term relapse after epilepsy surgery

ObjectiveWe assessed pre-operative structural brain networks and clinical characteristics of patients with drug resistant temporal lobe epilepsy (TLE) to identify correlates of post-surgical seizure outcome at 1 year and seizure relapses up to 5 years.\n\nMethodsWe retrospectively examined data from 51 TLE patients who underwent anterior temporal lobe resection (ATLR) and 29 healthy controls. For each patient, using the pre-operative structural, diffusion, and post-operative structural MRI, we generated two networks: pre-surgery network and surgically-spared network. The pre-surgery network is the whole-brain network before surgery and the surgically-spared network is a subnetwork of the pre-surgery network which is expected to remain unaffected by surgery and hence present post-operatively. Standardising these networks with respect to controls, we determined the number of abnormal nodes before surgery and expected to remain after surgery. We incorporated these 2 abnormality measures and 13 commonly acquired clinical data from each patient in a robust machine learning framework to estimate patient-specific chances of seizures persisting after surgery.\n\nResultsPatients with more abnormal nodes had lower chance of seizure freedom at 1 year and even if seizure free at 1 year, were more likely to relapse within five years. In the surgically-spared networks of poor outcome patients, the number of abnormal nodes was greater and their locations more widespread than in good outcome patients. We achieved 0.84 {+/-} 0.06 AUC and 0.89 {+/-} 0.09 specificity in detecting unsuccessful seizure outcomes at 1-year. Moreover, the model-predicted likelihood of seizure relapse was significantly correlated with the grade of surgical outcome at year-one and associated with relapses up-to five years post-surgery.\n\nConclusionNode abnormality offers a personalised non-invasive marker, that can be combined with clinical data, to better estimate the chances of seizure freedom at 1 year, and subsequent relapse up to 5 years after ATLR.

neuroscience↗

bioRxiv2019

Slow changes in seizure pathways in individual patients with focal epilepsy

Personalised medicine requires that treatments adapt to not only the patient, but changing factors within each individual. Although epilepsy is a dynamic disorder that is characterised by pathological fluctuations in brain state, surprisingly little is known about whether and how seizures vary in the same patient. We quantitatively compared within-patient seizure network dynamics using intracranial recordings of over 500 seizures from 31 patients with focal epilepsy (mean 16.5 seizures/patient). In all patients, we found variability in seizure paths through the space of possible network dynamics, producing either a spectrum or clusters of different dynamics. Seizures with similar pathways tended to occur closer together in time, and a simple model suggested that seizure pathways change on circadian and/or slower timescales in the majority of patients. These temporal relationships occurred independent of whether the patient underwent antiepileptic medication reduction. Our results suggest that various modulatory processes, operating at different timescales, shape within-patient seizure dynamics, leading to variable seizure pathways that may require tailored treatment approaches.

neuroscience↗

bioRxiv2018

White matter microstructural properties in bipolar disorder and its relationship to the spatial distribution of lithium in the brain

BackgroundLithium treatment is associated with an increase in magnetic resonance imaging derived measures of white matter integrity, but the relationship between the spatial distribution of brain lithium and white matter integrity is unknown.\n\nMethodsEuthymic patients with bipolar disorder receiving lithium treatment (n=12) and those on other medications but naive to lithium (n=17) underwent diffusion imaging alongside matched healthy controls (n=16). Generalised fractional anisotropy (gFA) within white matter was compared between groups using a standard space white matter atlas. Lithium-treated patients also underwent novel multinuclear 3D lithium magnetic resonance imaging (7Li-MRI) to determine relative lithium concentration across the brain. The relationship between 7Li-MRI signal intensity and gFA was investigated at the resolution of the 7Li-MRI sequence in native space.\n\nResultsThe lithium-treated bipolar disorder and healthy control groups had higher mean gFA in white matter than the bipolar disorder group treated with other medications but naive to lithium (t = 2.5, p < 0.05; t = 2.7, p < 0.03, respectively). No differences in gFA were found between patients taking lithium and healthy controls (t = 0.02, p = 1). These effects were seen consistently across most regions in the white matter atlas. In the lithium-treated group, a significant effect of the 7Li-MRI signal in predicting the gFA (p < 0.01) was identified in voxels containing over 50% white matter.\n\nConclusionsLithium treatment of bipolar disorder is associated with higher gFA throughout brain white matter, and the spatial distribution of lithium is also positively associated with white matter gFA.

neuroscience↗

bioRxiv2018

Intracranial interictal functional networks predict the effect of subacute cortical stimulation in focal epilepsy

BackgroundElectric neurostimulation is being developed as an alternative treatment for drug-resistant epilepsy patients. A major challenge is to identify, among all possible combinations of stimulation parameters, the most effective stimulation protocol to achieve seizure control for each patient.\n\nObjectiveTo estimate the value of interictal intracranial EEG recordings in identifying the most effective stimulation protocol in order to decrease number of seizures during intracranial subacute cortical stimulation (SCS).\n\nMethodsFive patients undergoing SCS were included in this retrospective study. Bivariate correlation measures were applied to baseline interictal intracranial EEG recordings to infer functional correlation networks. The node strength of these networks was calculated for each recording electrode (indicating the overall correlation between activity from one electrode with the activity from all other electrodes). The relationship between node strength and the change in seizure rate associated with SCS was studied for the electrodes used for stimulation.\n\nResultsAcross all five patients, node strength was negatively correlated with seizure rate ratio (ratio of seizure rate during SCS relative to during baseline) for all frequency bands tested (delta, theta, alpha, beta, gamma, and high gamma), and both the common reference and the common average montage.\n\nConclusionInterictal functional correlation networks contain information that correlates with the effect of SCS on the rate of epileptic seizures, suggesting that stimulation of those regions with higher node strength (derived from correlation networks) is more effective in reducing number of seizures.\n\nHIGHLIGHTSO_LIIn focal epilepsy, effective brain stimulation targets are lacking\nC_LIO_LIWe use baseline interictal functional networks to outline stimulation targets\nC_LIO_LIStimulation of regions with higher node strength is most effective in our study\nC_LI

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