Biology subjects

Soreq, E.

Publications and source records attributed to Soreq, E..

4 recordsLinked to original sources

bioRxiv2024

High-dimensional proteomic analysis for pathophysiological classification of Traumatic Brain Injury

Pathophysiology and outcomes after Traumatic Brain Injury (TBI) are complex and highly heterogenous. Current classifications are uninformative about pathophysiology, which limits prognostication and treatment. Fluid-based biomarkers can identify pathways and proteins relevant to TBI pathophysiology. Proteomic approaches are well suited to exploring complex mechanisms of disease, as they enable sensitive assessment of an expansive range of proteins. We used novel high-dimensional, multiplex proteomic assays to study changes in plasma protein expression in acute moderate-severe TBI. We analysed samples from 88 participants in the longitudinal BIO-AX-TBI cohort (n=38 TBI within 10 days of injury, n=22 non-TBI trauma, n=28 non-injured controls) on two platforms: Alamar NULISA CNS Diseases and OLINK(R) Target 96 Inflammation. Participants also had data available from Simoa(R) (neurofilament light, GFAP, total tau, UCHL1) and Millipore (S100B). The Alamar panel assesses 120 proteins, most of which have not been investigated before in TBI, as well as proteins, such as GFAP, which differentiate TBI from non-injured and non-TBI trauma controls. A subset (n=29 TBI, n=24 non-injured controls) also had subacute 3T MRI measures of lesion volume and white matter injury (fractional anisotropy, scanned 10 days to 6 weeks after injury). Differential Expression analysis identified 16 proteins with TBI-specific significantly different plasma expression. These were neuronal markers (calbindin2, UCHL1, visinin-like protein1), astroglial markers (S100B, GFAP), tau and other neurodegenerative disease proteins (total tau, pTau231, PSEN1, amyloid beta42, 14-3-3{gamma}), inflammatory cytokines (IL16, CCL2, ficolin2), cell signalling (SFRP1), cell metabolism (MDH1) and autophagy related (sequestome1) proteins. Acute plasma levels of UCHL1, PSEN1, total tau and pTau231 correlated with subacute lesion volume, while sequestome1 was correlated with whole white matter skeleton fractional anisotropy and CCL2 was inversely correlated with corpus callosum FA. Neuronal, astroglial, tau and neurodegenerative proteins correlated with each other, and IL16, MDH1 and sequestome1. Clustering (k means) by acute protein expression identified 3 TBI subgroups which had differential injury patterns, but did not differ in age or outcome. Proteins that overlapped on two platforms had excellent (r>0.8) correlations between values. We identified TBI-specific changes in acute plasma levels of proteins involved in amyloid processing, inflammatory and cellular processes such as autophagy. These changes were related to patterns of injury, thus demonstrating that processes previously only studied in animal models are also relevant in human TBI pathophysiology. Our study highlights the potential of proteomic analysis to improve the classification and understanding of TBI pathophysiology, with implications for prognostication and treatment development.

neuroscience↗

bioRxiv2020

Mapping the common and distinct neural correlates of visual, rule and motor conflict

Flexible behaviour requires cognitive-control mechanisms to efficiently resolve conflict between competing information and alternative actions. Whether a global neural resource mediates all forms of conflict or this is achieved within domainspecific systems remains debated. We use a novel fMRI paradigm to orthogonally manipulate rule, response and stimulus-based conflict within a full-factorial design. Whole-brain voxelwise analyses show that activation patterns associated with these conflict types are distinct but partially overlapping within Multiple Demand Cortex (MDC), the brain regions that are most commonly active during cognitive tasks. Region of interest analysis shows that most MDC sub-regions are activated for all conflict types, but to significantly varying levels. We propose that conflict resolution is an emergent property of distributed brain networks, the functional-anatomical components of which place on a continuous, not categorical, scale from domain-specialised to domain general. MDC brain regions place towards one end of that scale but display considerable functional heterogeneity.

neuroscience↗

bioRxiv2020

Contrasting hierarchical and multiple-demand accounts of frontal lobe functional organisation during task-switching

There is an unresolved discrepancy between popular hierarchical and multiple-demand perspectives on the functional organisation of the human frontal lobes. Here, we tested alternative predictions of these perspectives with a novel fMRI switching paradigm. Each trial involved switching attention between stimuli, but at different levels of difficulty and abstraction. As expected, increasing response times were evident when comparing low-level perceptual switching to more abstract dimension, rule and task-switching. However, there was no evidence of an abstraction hierarchy within the prefrontal cortex (PFC). Nor was there recruitment of additional anterior PFC regions under increased switching demand. Instead, switching activated a widespread network of frontoparietal and cerebellar regions. Critically, the activity within PFC sub-regions uniformly increased with behavioural switch costs. We propose that both perspectives have some validity, but neither is complete. Too many studies have reported dissociations within MD for this volume to be functionally uniform, and the recruitment of more anterior regions with increased general difficulty cannot explain those results. Conversely, whilst reproducible evidence for a hierarchical functional organisation has been reported, this cannot be explained in terms of abstraction of representation or reconfiguration per se, because those interpretations generalise poorly to other task contexts.

neuroscience↗

bioRxiv2020

Concurrent optimisation of brain states and behavioural strategies when learning complex tasks

We developed two novel self-ordered switching (SOS) fMRI paradigms to investigate how human behaviour and underlying network resources are optimised when learning to perform complex tasks with multiple goals. SOS was performed with detailed feedback and minimal pretraining (study 1) or with minimal feedback and substantial pretraining (study 2). In study 1, multiple-demand (MD) system activation became less responsive to routine trial demands but more responsive to the executive switching events with practice. Default Mode Network (DMN) activation showed the opposite relationship. Concomitantly, reaction time learning curves correlated with increased connectivity between functional brain networks and subcortical regions. This fine-tuning of network resources correlated with progressively more routine and lower complexity behavioural structure. Furthermore, overall task performance was superior for people who applied structured behavioural routines with low algorithmic complexity. These behavioural and network signatures of learning were less evident in study 2, where task structure was established prior to entering the scanner. Together, these studies demonstrate how detailed feedback monitoring enables network resources to be progressively redeployed in order to efficiently manage concurrent demands. HighlightsO_LIWe examine the optimisation of behaviour and brain-network resources during a novel "self-ordered switching" (SOS) paradigm. C_LIO_LITask performance depended on generating behavioural routines with low algorithmic complexity (i.e., structured behaviours). C_LIO_LIBehaviour became more structured and reaction time decreased as SOS was practised. C_LIO_LIAs behaviour became more structured, activation in multiple-demand regions decreased for simple trial events but increased for executive switching events C_LIO_LIIncreases in between-network functional connectivity correlate with reaction time decreases. C_LI

neuroscience↗