Biology subjects

Kwan, P.

Publications and source records attributed to Kwan, P..

9 recordsLinked to original sources

bioRxiv2026

Activating Ras-MAPK pathway variants drive hippocampal clonal competition in human epilepsy

Mesial (a.k.a., medial) temporal lobe epilepsy (MTLE) is the most common focal epilepsy1,2 and, in drug-resistant cases, is treated by surgical removal of the anterior temporal lobe, which often shows neuronal loss and gliosis consistent with hippocampal sclerosis (HS)2. MTLE with HS has minimal contribution from germline genetic variation3, and is associated with prior precipitating insults such as prolonged childhood seizures and head trauma4-6. Somatic variants in Ras-MAPK pathway genes were recently reported in a few MTLE surgical specimens7,8, but their prevalence, clinical relevance, and underlying biological mechanisms remain unknown. Targeted duplex sequencing of hippocampal DNA from 462 surgical resections revealed significant enrichment of deleterious somatic variants in MTLE versus controls, with >40% of MTLE specimens harboring activating Ras-MAPK variants in PTPN11, NF1, BRAF, KRAS, and twelve genes not previously associated with focal epilepsy. Eight Ras-MAPK genes showed positive clonal selection in MTLE. Increased somatic variant burden predicted worse surgical outcome. Somatic Ras-MAPK variants at ultra-low (<0.5%) variant allele fractions were associated with older seizure onset and HS pathology, supporting a late prenatal or postnatal origin. Ras-MAPK variants in MTLE were enriched in cells derived from hippocampal progenitors--neurons, astrocytes, oligodendrocytes--in line with the known neuronal hyperexcitability and seizures induced by Ras-MAPK overactivation9,10; in contrast, Alzheimer disease hippocampi exhibited microglial enrichment of Ras-MAPK variants, consistent with prior reports11. Single-nucleus RNA sequencing showed increased expression of Ras-MAPK genes in neurons and upregulation of pathways mediating neurogenesis and neural development in MTLE. Functional validation of novel, recurrent PTPN11 variants confirmed gain-of-function, while cellular modeling in induced pluripotent stem cells demonstrated proliferative/survival advantages for mutant cells in mosaic culture. Overall, our data suggest that somatic Ras-MAPK variants and acquired risk factors may converge on clonal competition in the hippocampus to modulate epilepsy risk.

genetics↗

bioRxiv2024

Automated segmentation of epilepsy surgical resection cavities: comparison of four methods to manual segmentation

Accurate resection cavity segmentation on MRI is important for neuroimaging research involving epilepsy surgical outcomes. Manual segmentation, the gold standard, is highly labour intensive. Automated pipelines are an efficient potential solution; however, most have been developed for use following temporal epilepsy surgery. Our aim was to compare the accuracy of four automated segmentation pipelines following surgical resection in a mixed cohort of subjects following temporal or extra temporal epilepsy surgery. We identified 4 open-source automated segmentation pipelines. Epic-CHOP and ResectVol utilise SPM-12 within MATLAB, while Resseg and Deep Resection utilise 3D U-net convolutional neural networks. We manually segmented the resection cavity of 50 consecutive subjects who underwent epilepsy surgery (30 temporal, 20 extratemporal). We calculated Dice similarity coefficient (DSC) for each algorithm compared to the manual segmentation. No algorithm identified all resection cavities. ResectVol (n=44, 88%) and Epic-CHOP (n=43, 86%) were able to detect more resection cavities than Resseg (n=22, 44%, P<0.001) and Deep Resection (n=21, 42%, P<0.001). The SPM-based pipelines (Epic-CHOP and ResectVol) performed better than the deep learning-based pipelines in the overall and extratemporal surgery cohorts, however there was no difference between methods in the temporal surgery cohort. These pipelines could be applied to machine learning studies of outcome prediction to improve efficiency in pre-processing data, however human quality control is still required.

neuroscience↗

bioRxiv2024

A worldwide ENIGMA study on epilepsy-related gray and white matter compromise across the adult lifespan

ObjectivesTemporal lobe epilepsy (TLE) is commonly associated with mesiotemporal pathology and widespread alterations of grey and white matter structures. Evidence supports a progressive condition although the temporal evolution of TLE is poorly defined. This ENIGMA-Epilepsy study utilized multimodal magnetic resonance imaging (MRI) data to investigate structural alterations in TLE patients across the adult lifespan. We charted both grey and white matter changes and explored the covariance of age-related alterations in both compartments. MethodsWe studied 769 TLE patients and 885 healthy controls across an age range of 17-73 years, from multiple international sites. To assess potentially non-linear lifespan changes in TLE, we harmonized data and combined median split assessments with cross-sectional sliding window analyses of grey and white matter age-related changes. Covariance analyses examined the coupling of grey and white matter lifespan curves. ResultsIn TLE, age was associated with a robust grey matter thickness/volume decline across a broad cortico-subcortical territory, extending beyond the mesiotemporal disease epicentre. White matter changes were also widespread across multiple tracts with peak effects in temporo-limbic fibers. While changes spanned the adult time window, changes accelerated in cortical thickness, subcortical volume, and fractional anisotropy (all decreased), and mean diffusivity (increased) after age 55 years. Covariance analyses revealed strong limbic associations between white matter tracts and subcortical structures with cortical regions. ConclusionsThis study highlights the profound impact of TLE on lifespan changes in grey and white matter structures, with an acceleration of aging-related processes in later decades of life. Our findings motivate future longitudinal studies across the lifespan and emphasize the importance of prompt diagnosis as well as intervention in patients.

neuroscience↗

bioRxiv2020

U-Infuse: Democratization of Customizable AI for Object Detection

O_LIImage data is one of the primary sources of ecological data used in biodiversity conservation and management worldwide. However, classifying and interpreting large numbers of images is time and resource expensive, particularly in the context of camera trapping. Deep learning models have been used to achieve this task but are often not suited to specific applications due to their inability to generalise to new environments and inconsistent performance. Models need to be developed for specific species cohorts and environments, but the technical skills required to achieve this are a key barrier to the accessibility of this technology to ecologists. There is a strong need to democratise access to deep learning technologies by providing an easy to use software application allowing non-technical users to custom train custom object detectors. C_LIO_LIU-Infuse addresses this issue by putting the power of AI into the hands of ecologists. U-Infuse provides ecologists with the ability to train customised models using publicly available images and/or their own camera trap images, without the constraints of annotating and pre-processing large numbers of images, or specific technical expertise. U-Infuse is a free and open-source software solution that supports both multiclass and single class training and inference, allowing ecologists to access state of the art AI on their own device, customised to their application without sharing IP or sensitive data. C_LIO_LIU-Infuse provides ecological practitioners with the ability to (i) easily achieve camera trap object detection within a user-friendly GUI, generating a species distribution report, and other useful statistics, (ii) custom train deep learning models using publicly available and custom training data, (iii) achieve supervised auto-annotation of images for further training, with the benefit of editing annotations to ensure quality datasets. C_LIO_LIBroad adoption of U-Infuse by ecological practitioners will improve camera trap image analysis and processing by allowing significantly more image data to be processed with minimal expenditure of time and resources. Ease of training and reliance on transfer learning means domain-specific models can be trained rapidly, and frequently updated without the need for computer science expertise, or data sharing, protecting intellectual property and privacy. C_LI

ecology↗

bioRxiv2020

Location Invariant Animal Recognition UsingMixed Source Datasets and Deep Learning

O_LIA time-consuming challenge faced by camera trap practitioners all over the world is the extraction of meaningful data from images to inform ecological management. The primary methods of image processing used by practitioners includes manual analysis and citizen science. An increasingly popular alternative is automated image classification software. However, most automated solutions are not sufficiently robust to be deployed on a large scale. Key challenges include limited access to images for each species and lack of location invariance when transferring models between sites. This prevents optimal use of ecological data and results in significant expenditure of time and resources to annotate and retrain deep learning models. C_LIO_LIIn this study, we aimed to (a) assess the value of publicly available non-iconic FlickR images in the training of deep learning models for camera trap object detection, (b) develop an out-of-the-box location invariant automated camera trap image processing solution for ecologist using deep transfer learning and (c) explore the use of small subsets of camera trap images in optimisation of a FlickR trained deep learning model for high precision ecological object detection. C_LIO_LIWe collected and annotated a dataset of images of "pigs" (Sus scrofa and Phacochoerus africanus) from the consumer image sharing website FlickR. These images were used to achieve transfer learning using a RetinaNet model in the task of object detection. We compared the performance of this model to the performance of models trained on combinations of camera trap images obtained from five different projects, each characterised by 5 different geographical regions. Furthermore, we explored optimisation of the FlickR model via infusion of small subsets of camera trap images to increase robustness in difficult images. C_LIO_LIIn most cases, the mean Average Precision (mAP) of the FlickR trained model when tested on out of sample camera trap sites (67.21-91.92%) was significantly higher than the mAP achieved by models trained on only one geographical location (4.42-90.8%) and rivalled the mAP of models trained on mixed camera trap datasets (68.96-92.75%). The infusion of camera trap images into the FlickR training further improved AP by 5.10-22.32% to 83.60-97.02%. C_LIO_LIEcology researchers can use FlickR images in the training of automated deep learning solutions for camera trap image processing to significantly reduce time and resource expenditure by allowing the development of location invariant, highly robust out-of-the-box solutions. This would allow AI technologies to be deployed on a large scale in ecological applications. C_LI

ecology↗

bioRxiv2019

White matter abnormalities across different epilepsy syndromes in adults: an ENIGMA Epilepsy study

The epilepsies are commonly accompanied by widespread abnormalities in cerebral white matter. ENIGMA-Epilepsy is a large quantitative brain imaging consortium, aggregating data to investigate patterns of neuroimaging abnormalities in common epilepsy syndromes, including temporal lobe epilepsy, extratemporal epilepsy, and genetic generalized epilepsy. Our goal was to rank the most robust white matter microstructural differences across and within syndromes in a multicentre sample of adult epilepsy patients. Diffusion-weighted MRI data were analyzed from 1,069 non-epileptic controls and 1,249 patients: temporal lobe epilepsy with hippocampal sclerosis (N=599), temporal lobe epilepsy with normal MRI (N=275), genetic generalized epilepsy (N=182) and nonlesional extratemporal epilepsy (N=193). A harmonized protocol using tract-based spatial statistics was used to derive skeletonized maps of fractional anisotropy and mean diffusivity for each participant, and fiber tracts were segmented using a diffusion MRI atlas. Data were harmonized to correct for scanner-specific variations in diffusion measures using a batch-effect correction tool (ComBat). Analyses of covariance, adjusting for age and sex, examined differences between each epilepsy syndrome and controls for each white matter tract (Bonferroni corrected at p<0.001). Across "all epilepsies" lower fractional anisotropy was observed in most fiber tracts with small to medium effect sizes, especially in the corpus callosum, cingulum and external capsule. Less robust effects were seen with mean diffusivity. Syndrome-specific fractional anisotropy and mean diffusivity differences were most pronounced in patients with hippocampal sclerosis in the ipsilateral parahippocampal cingulum and external capsule, with smaller effects across most other tracts. Those with temporal lobe epilepsy and normal MRI showed a similar pattern of greater ipsilateral than contralateral abnormalities, but less marked than those in patients with hippocampal sclerosis. Patients with generalized and extratemporal epilepsies had pronounced differences in fractional anisotropy in the corpus callosum, corona radiata and external capsule, and in mean diffusivity of the anterior corona radiata. Earlier age of seizure onset and longer disease duration were associated with a greater extent of microstructural abnormalities in patients with hippocampal sclerosis. We demonstrate microstructural abnormalities across major association, commissural, and projection fibers in a large multicentre study of epilepsy. Overall, epilepsy patients showed white matter abnormalities in the corpus callosum, cingulum and external capsule, with differing severity across epilepsy syndromes. These data further define the spectrum of white matter abnormalities in common epilepsy syndromes, yielding new insights into pathological substrates that may be used to guide future therapeutic and genetic studies.

neuroscience↗

bioRxiv2019

Abbreviated assessment of psychopathology in patients with suspected seizure disorders

PurposePsychopathology is common in patients undergoing investigation for seizure-related disorders. Psychometric examination using self-report instruments, such as the SCL-90-R, can assist diagnosis. The SCL-90-R, however, is a lengthy instrument and might not be tolerated by all patients. We assessed several abbreviated forms of the SCL-90-R in patients undergoing video encephalographic monitoring (VEM).\n\nMethod687 patients completed the SCL-90-R and scores were computed for the full SCL-90-R and five abbreviated forms. Correlations and mean differences were computed between different forms. Classification accuracy was assessed via receiver operating characteristic (ROC) curves, and measurements models were examined using confirmatory factor analysis (CFA).\n\nResultsAll abbreviated forms were strongly correlated with the SCL-90-R for general psychopathology (r = .93 - .99), depression (r = .89 - .95), anxiety (r = .97 - .98), psychosis (r = .95 - .99), and obsessive-compulsive symptoms (r = .97). Classification performance was similar across forms for depression and anxiety, with high negative predictive values (.90 - .94) and lower positive predictive values (.34 - .38). Classification performance for psychotic and obsessive-compulsive disorders was poor. Differences were observed between the full SCL-90-R and its abbreviated forms across most domains (d = 0.00 - 0.65). The published measurement model was most strongly validated for the SCL-27, SCL-14, and the SCL-K-9.\n\nConclusionsThese five SCL-90-R abbreviated forms show high convergent validity with the full version. In patients undergoing investigation for seizure-related disorders, the BSI or BSI-18 are most appropriate where screening for both depression and anxiety is required. The SCL-K-9 is appropriate when only a single measure of global psychological distress is required. None of the instruments were able to detect psychotic or obsessive-compulsive symptoms with great accuracy. Caution should be exercised when making direct comparisons across the different forms.

neuroscience↗

bioRxiv2019

Improved HLA-based prediction of coeliac disease identifies two novel HLA risk modifiers, DQ6.2 and DQ7.3

PurposeHuman Leukocyte Antigen (HLA) testing is useful in the clinical work-up of coeliac disease (CD), with high negative but low positive predictive value. We construct a genomic risk score (GRS) using HLA risk loci to improve CD prediction and guide exclusion criteria.\n\nMethodsImputed HLA genotypes for five European CD case-control GWAS (n>15,000) were used to construct and validate an HLA based risk models (HDQ15). Conditioning on this score, we identified novel HLA interactions which modified CD risk, and integrated these novel alleles into a new risk score (HDQ17).\n\nResultsA GRS from HLA risk allele genotypes yields performance equivalent to a state-of-the-art GRS (GRS228) using 228 single nucleotide polymorphisms (SNPs) and significantly improves upon all previous HLA based risk models. Conditioning on this model, we find two novel associations, HLA-DQ6.2 and HLA-DQ7.3, that interact significantly with HLA-DQ2.5 (p = 2.51 x 10-9, 1.99 x 10-7 for DQ6.2 and DQ7.3 respectively). These epistatic interactions yield the best performing risk score (HDQ17) which retains performance when implemented using 6 tag SNPs. Using the HDQ17 model, the positive predictive value of CD testing in high risk populations increases from 17.5% to 27.1% while maintaining a negative predictive value above 99%.\n\nConclusionOur proposed HLA-based GRS achieves state-of-the-art risk prediction, helps elucidate further risk factors and improves HLA typing exclusionary criteria, which may reduce the number of patients requiring unnecessary endoscopies.

genetics↗

bioRxiv2019

Ultra-rare genetic variation in the epilepsies: a whole-exome sequencing study of 17,606 individuals

Sequencing-based studies have identified novel risk genes for rare, severe epilepsies and revealed a role of rare deleterious variation in common epilepsies. To identify the shared and distinct ultra-rare genetic risk factors for rare and common epilepsies, we performed a whole-exome sequencing (WES) analysis of 9,170 epilepsy-affected individuals and 8,364 controls of European ancestry. We focused on three phenotypic groups; the rare but severe developmental and epileptic encephalopathies (DEE), and the commoner phenotypes of genetic generalized epilepsy (GGE) and non-acquired focal epilepsy (NAFE). We observed that compared to controls, individuals with any type of epilepsy carried an excess of ultra-rare, deleterious variants in constrained genes and in genes previously associated with epilepsy, with the strongest enrichment seen in DEE and the least in NAFE. Moreover, we found that inhibitory GABAA receptor genes were enriched for missense variants across all three classes of epilepsy, while no enrichment was seen in excitatory receptor genes. The larger gene groups for the GABAergic pathway or cation channels also showed a significant mutational burden in DEE and GGE. Although no single gene surpassed exome-wide significance among individuals with GGE or NAFE, highly constrained genes and genes encoding ion channels were among the top associations, including CACNA1G, EEF1A2, and GABRG2 for GGE and LGI1, TRIM3, and GABRG2 for NAFE. Our study confirms a convergence in the genetics of common and rare epilepsies associated with ultra-rare coding variation and highlights a ubiquitous role for GABAergic inhibition in epilepsy etiology in the largest epilepsy WES study to date.

genetics↗