Biology subjects

Pardoe, H. R.

Publications and source records attributed to Pardoe, H. R..

6 recordsLinked to original sources

bioRxiv2026

SYNGAP1 haploinsufficiency disrupts early neurodevelopment and accelerates intrinsic neuronal maturation in human patient-derived models

SYNGAP1 developmental and epileptic encephalopathy (DEE) is a severe neurodevelopmental disorder characterised by intellectual disability, developmental delay, and refractory epilepsy caused by heterozygous variants in SYNGAP1, which encodes Synaptic Ras GTPase-activating protein 1. While SYNGAP1 is best known for its role at the postsynaptic density, increasing evidence indicates that haploinsufficiency also disrupts early neurodevelopment. Here, we used patient-derived induced pluripotent stem cell (iPSC) models to investigate early neurodevelopmental and neuronal phenotypes associated with SYNGAP1 haploinsufficiency. iPSCs derived from a female patient carrying the frameshift variant p.Leu150Valfs*6 were differentiated into two complementary models: micropatterned neural rosettes representing early neuroepithelial organisation and NGN2-induced excitatory neurons representing postmitotic functional development. Patient-derived neural rosettes displayed enlarged, dysmorphic lumens, indicating disrupted neuroepithelial organisation at the earliest stages of brain development. Transcriptomic profiling revealed widespread dysregulation of genes involved in neurodevelopment, cell adhesion and ion channel regulation, including coordinated downregulation of protocadherin family members. Whole-cell patch-clamp electrophysiology demonstrated reduced input resistance, larger action potential amplitudes, and increased inward and outward current densities, consistent with accelerated intrinsic neuronal maturation rather than generalized hyperexcitability. Together, these complementary findings demonstrate that SYNGAP1 haploinsufficiency disrupts early human brain development and accelerates intrinsic neuronal maturation, with pathogenic mechanisms emerging before synaptogenesis and extending beyond SYNGAP1s established synaptic role.

neuroscience↗

bioRxiv2026

Multilayer Network Modelling of the Human Reading System

Reading is supported by rapid and flexible coordination of neural activity across distributed brain regions. We have previously shown that left fusiform gyrus (FusG) provides a bridge flexibly linking the visual form analysis required for reading with the language system. Here, we investigate the dynamic organisation of an extending reading network encompassing classical perisylvian language areas and FusG. We do so by applying multilayer network modelling to language fMRI data acquired through the Australian Epilepsy Project, using a paradigm that contrasts reading with visuospatial judgements. The dataset included 201 participants with left dominant language, both with and without seizure disorders. We hypothesised that the relative strength of dynamic inter-actions within this extended language network is associated with reading ability. Time resolved functional connectivity was estimated using a sliding window Pearsons correlation approach, and the resulting connectivity matrices were entered into a multilayer community detection algorithm to quantify spatiotemporal community structure within the reading network. We concentrate our analyses on allegiance, the probability that a pair of regions is assigned to the same community over time. Our results show that community structure within the reading network is characterised by a preference for within hemisphere assignment over cross hemisphere assignment, as well as higher nodal allegiance among left language regions compared with their right hemisphere homologues. As anticipated, within versus between network allegiance followed a similar gradient in both language and attention networks: lowest between left language and right attentional regions, intermediate between the left FusG and each respective network, and highest within-network (left language or right attention). Importantly, as hypothesised, reading ability was associated with FusG-inferior frontal gyrus (IFG) interactions: higher left FusG-left IFG allegiance correlated with better reading performance, whereas increased right FusG-left IFG allegiance correlated with poorer reading. These findings highlight hemispheric asymmetries in the dynamic organisation of the reading system and provide novel evidence linking individual differences in reading ability to network level dynamics. Our findings align with a developmental literature suggesting that as reading proficiency improves, there is a shift from bilateral to unilateral left occipitotemporal engagement.

neuroscience↗

bioRxiv2025

Common pitfalls during model specification in psychophysiological interaction analysis

Psychophysiological interaction (PPI) analysis is a widely used regression method in functional neuroimaging for capturing task-dependent changes in connectivity from a seed region. The present work identifies, and provides corrections for, common methodological pitfalls in PPI analysis that compromise model validity. Firstly, if the seed time series is extracted with prewhitening, the temporal structure of the signal is altered and subsequent deconvolution of prewhitened data becomes suboptimal. Furthermore, prewhitening again during model fitting results in double prewhitening of the seed regressor. Secondly, a failure to mean-centre the task regressor when calculating the interaction term can also lead to model misspecification and potentially spurious inferences. By using simulations and empirical language fMRI data from the Australian Epilepsy Project, we demonstrate the adverse effects of these issues, and how they are resolved when corrected. A systematic review of current practices revealed widespread model misspecification, and underreporting of methods, in published PPI studies. We provide clearer reporting guidelines, and advocate for appropriate methods for handling of prewhitening and mean-centring to ensure the validity of PPI analyses.

neuroscience↗

bioRxiv2021

In-scanner head motion and structural covariance networks

In-scanner head motion systematically reduces estimated regional gray matter volumes obtained from structural brain MRI. Here, we investigate how head motion affects structural covariance networks that are derived from regional gray matter volumetric estimates. We acquired motion-affected and motion-free whole brain T1-weighted MRI in 29 healthy adult subjects and estimated relative regional gray matter volumes using a voxel-based morphometry approach. Structural covariance network analyses were undertaken while systematically increasing the number of motion-affected scans included in the analysis. We demonstrate that the standard deviation in regional gray matter estimates increases as the number of motion-affected scans increases. This subsequently increases pair-wise correlations between regions, a key determinant for construction of structural covariance networks. We further demonstrate that head motion systematically alters graph theoretic metrics derived from these networks. Our findings suggest that in-scanner head motion is a source of error that violates the assumption that structural covariance networks reflect neuroanatomical connectivity between brain regions. Results of structural covariance studies should be interpreted with caution, particularly when subject groups are likely to move their heads in the scanner.

neuroscience↗

bioRxiv2021

Estimation of in-scanner head pose changes during structural MRI using a convolutional neural network trained on eye tracker video

IntroductionIn-scanner head motion is a common cause of reduced image quality in neuroimaging, and causes systematic brain-wide changes in cortical thickness and volumetric estimates derived from structural MRI scans. There are few widely available methods for measuring head motion during structural MRI. Here, we train a deep learning predictive model to estimate changes in head pose using video obtained from an in-scanner eye tracker during an EPI-BOLD acquisition with participants undertaking deliberate in-scanner head movements. The predictive model was used to estimate head pose changes during structural MRI scans, and correlated with cortical thickness and subcortical volume estimates. Methods21 healthy controls (age 32 {+/-} 13 years, 11 female) were studied. Participants carried out a series of stereotyped prompted in-scanner head motions during acquisition of an EPI-BOLD sequence with simultaneous recording of eye tracker video. Motion-affected and motion-free whole brain T1-weighted MRI were also obtained. Image coregistration was used to estimate changes in head pose over the duration of the EPI-BOLD scan, and used to train a predictive model to estimate head pose changes from the video data. Model performance was quantified by assessing the coefficient of determination (R2). We evaluated the utility of our technique by assessing the relationship between video-based head pose changes during structural MRI and (i) vertex-wise cortical thickness and (ii) subcortical volume estimates. ResultsVideo-based head pose estimates were significantly correlated with ground truth head pose changes estimated from EPI-BOLD imaging in a hold-out dataset. We observed a general brain-wide overall reduction in cortical thickness with increased head motion, with some isolated regions showing increased cortical thickness estimates with increased motion. Subcortical volumes were generally reduced in motion affected scans. ConclusionsWe trained a predictive model to estimate changes in head pose during structural MRI scans using in-scanner eye tracker video. The method is independent of individual image acquisition parameters and does not require markers to be to be fixed to the patient, suggesting it may be well suited to clinical imaging and research environments. Head pose changes estimated using our approach can be used as covariates for morphometric image analyses to improve the neurobiological validity of structural imaging studies of brain development and disease.

neuroscience↗

bioRxiv2017

NAPR: a cloud-based framework for neuroanatomical age prediction

This paper describes NAPR, a cloud-based framework for accessing age prediction models created using machine learning-based analysis of neuroimaging data. The NAPR service is provided at https://www.cloudneuro.org. The NAPR system allows external users to predict the age of individual subjects using their own MRI data. As a demonstration of the NAPR approach, age prediction models were trained using healthy control data from the ABIDE, CoRR, DLBS and NKI Rockland neuroimaging datasets (total N = 2367). MRI scans were processed using Freesurfer v5.3. Age prediction models were trained using relevance vector machines and Gaussian processes machine learning techniques. NAPR will allow for rigorous and transparent out-of-sample assessment of age prediction model performance, and may therefore assist in the translation of neuroimaging-based modelling techniques to the clinic.

neuroscience↗