Biology subjects

Tong, E.

Publications and source records attributed to Tong, E..

5 recordsLinked to original sources

bioRxiv2025

Elevated Thalamic Blood Flow in Self-Limited Epilepsy with Centrotemporal Spikes

Children with self-limited epilepsy syndrome with centrotemporal spikes (SeLECTS) exhibit altered thalamocortical connectivity, but whether thalamic function itself is abnormal remains unclear. We investigated whether thalamic blood flow, a marker of metabolism, differs between children with SeLECTS and controls, and examined the effects of spike distribution and antiseizure medications (ASMs) on thalamic perfusion. In this retrospective cohort study, we identified consecutive children with SeLECTS who underwent magnetic resonance imaging (MRI) for epilepsy evaluation (n = 44) and age- and sex-matched children who underwent MRI for non-epilepsy indications (n = 35). We quantified thalamic blood flow via manual segmentation of cerebral blood flow (CBF) sequences obtained from arterial spin labeling MRI. Clinical variables including sedation use during MRI, daily ASM use, and spike distribution (unilateral or bilateral) were extracted from medical records. Children with SeLECTS demonstrated elevated thalamic blood flow compared to controls, with the most pronounced differences in specific subgroups. Children with unilateral spikes showed the highest CBF, particularly in the thalamus contralateral to spike activity. ASM use significantly modulated thalamic blood flow: children taking oxcarbazepine showed the highest CBF, while those on levetiracetam had CBF similar to controls. Unmedicated children showed intermediate elevations. These findings demonstrate that elevated thalamic blood flow may be intrinsic to SeLECTS pathophysiology, with different ASMs producing distinct neurobiological effects. The differential medication effects may relate to their clinical efficacy and provide neurobiological rationale for treatment selection in this common childhood epilepsy syndrome.

neuroscience↗

bioRxiv2023

Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction

IntroductionSpatio-temporal MRI methods enable whole-brain multi-parametric mapping at ultra-fast acquisition times through efficient k-space encoding, but can have very long reconstruction times, which limit their integration into clinical practice. Deep learning (DL) is a promising approach to accelerate reconstruction, but can be computationally intensive to train and deploy due to the large dimensionality of spatio-temporal MRI. DL methods also need large training data sets and can produce results that dont match the acquired data if data consistency is not enforced. The aim of this project is to reduce reconstruction time using DL whilst simultaneously limiting the risk of deep learning induced hallucinations, all with modest hardware requirements. MethodsDeep Learning Initialized Compressed Sensing (Deli-CS) is proposed to reduce the reconstruction time of iterative reconstructions by "kick-starting" the iterative reconstruction with a DL generated starting point. The proposed framework is applied to volumetric multi-axis spiral projection MRF that achieves whole-brain T1 and T2 mapping at 1-mm isotropic resolution for a 2-minute acquisition. First, the traditional reconstruction is optimized from over two hours to less than 40 minutes while using more than 90% less RAM and only 4.7 GB GPU memory, by using a memory-efficient GPU implementation. The Deli-CS framework is then implemented and evaluated against the above reconstruction. ResultsDeli-CS achieves comparable reconstruction quality with 50% fewer iterations bringing the full reconstruction time to 20 minutes. ConclusionDeli-CS reduces the reconstruction time of subspace reconstruction of volumetric spatio-temporal acquisitions by providing a warm start to the iterative reconstruction algorithm.

neuroscience↗

bioRxiv2023

Discovery of multi-state gene cluster switches determining the adaptive mitochondrial and metabolic landscape of breast cancer

Adaptive metabolic switches are proposed to underlie conversions between cellular states during normal development as well as in cancer evolution, where they represent important therapeutic targets. However, the full spectrum, characteristics and regulation of existing metabolic switches are unknown. We propose that metabolic switches can be recognised by locating large alternating gene expression patterns and associate them with specific metabolic states. We developed a method to identify interspersed genesets by massive correlated biclustering (MCbiclust) and to predict their metabolic wiring. Testing the method on major breast cancer transcriptome datasets we discovered a series of gene sets with switch-like behaviour, predicting mitochondrial content, activity and central carbon fluxes in tumours associated with different switch positions. The predictions were experimentally validated by bioenergetic profiling and metabolic flux analysis of 13C-labelled substrates and were ultimately extended by geneset analysis to link metabolic alterations to cellular states, thus predicting tumour pathology, prognosis and chemosensitivity. The method is applicable to any large and heterogeneous transcriptome dataset to discover metabolic and associated pathophysiological states. Statement of significanceWe present a novel method to identify the transcriptomic signatures of metabolic switches underlying divergent routes of cellular transformation. We illustrate the power of the method by stratifying breast cancer into metabolic subtypes, predicting their biology, architecture and clinical outcome.

systems biology↗

bioRxiv2022

Effort Foraging Task reveals positive correlation between individual differences in the cost of cognitive and physical effort in humans and relationship to self-reported motivation and affect

Effort-based decisions, in which people weigh potential future rewards against effort costs required to achieve those rewards, have largely been studied separately for cognitive or physical effort, yet most real-world actions incur both effort costs. What is the relationship between cognitive and physical effort costs? Here we attempt to formalize the mechanisms underlying effort-based decisions and address methodological challenges to isolate and measure these mechanisms. Patch foraging is an ecologically valid reward rate maximization problem with well-developed theoretical tools to understand choices. We developed the Effort Foraging Task, which embedded cognitive or physical effort into a patch foraging sequential decision task, to isolate and quantify the cost of both cognitive and physical effort using a computational model. Participants chose between harvesting a depleting patch, or traveling to a new patch that was costly in time and effort. Participants exit thresholds (reflecting the reward they expected to receive by harvesting when they chose to travel to a new patch) were sensitive to cognitive and physical effort demands, allowing us to quantify the perceived effort cost in monetary terms. Individual differences in cognitive and physical effort costs were positively correlated, suggesting that these are perceived and processed in common terms across different domains. We found patterns of correlation of both cognitive and physical effort costs with self-reported anxiety, cognitive function, behavioral activation, and self-efficacy. This suggests the task captures decision mechanisms associated with real-world motivation and can be used to study individual variation in effort-based decisions across domains of cost.

neuroscience↗

bioRxiv2017

Polygenic hazard scores in preclinical Alzheimer’s disease

Identifying asymptomatic older individuals at elevated risk for developing Alzheimers disease (AD) is of clinical importance. Among 1,081 asymptomatic older adults, a recently validated polygenic hazard score (PHS) significantly predicted time to AD dementia and steeper longitudinal cognitive decline, even after controlling for APOE {varepsilon}4 carrier status. Older individuals in the highest PHS percentiles showed the highest AD incidence rates. PHS predicted longitudinal clinical decline among older individuals with moderate to high CERAD (amyloid) and Braak (tau) scores at autopsy, even among APOE {varepsilon}4 non-carriers. Beyond APOE, PHS may help identify asymptomatic individuals at highest risk for developing Alzheimers neurodegeneration.

genetics↗