Biology subjects

Daws, R. E.

Publications and source records attributed to Daws, R. E..

5 recordsLinked to original sources

bioRxiv2022

Tissue volume estimation and age prediction using rapid structural brain scans

The multicontrast EPImix sequence generates 6 contrasts, including a T1-weighted scan, in [~]1 minute. EPImix shows comparable diagnostic performance to conventional scans under qualitative clinical evaluation, and similarities in simple quantitative measures including contrast intensity. However, EPImix scans have not yet been compared to standard MRI scans using established quantitative measures. In this study, we compared conventional and EPImix-derived T1-weighted scans of 64 healthy participants using tissue volume estimates and predicted brain-age. All scans were pre-processed using the SPM12 DARTEL pipeline, generating measures of grey matter, white matter and cerebrospinal fluid volume. Brain-age was predicted using brainageR, a Gaussian process regression model previously trained on a large sample of standard T1-weighted scans. Estimates of both global and voxel-wise tissue volume showed significantly similar results between standard and EPImix-derived T1-weighted scans. Brain-age estimates from both sequences were significantly correlated, although EPImix T1-weighted scans showed a systematic offset in predictions of chronological age. Supplementary analyses suggest that this is likely caused by the reduced field of view of EPImix scans, and the use of a brain-age model trained using conventional T1-weighted scans. However, this systematic error can be corrected using additional regression of T1-predicted brain-age onto EPImix-predicted brain-age. Finally, retest EPImix scans acquired for 10 participants demonstrated high test-retest reliability in all evaluated quantitative measurements. Quantitative analysis of EPImix scans holds potential to reduce scanning time, increasing participant comfort and reducing cost, as well as to support automation of scanning, utilising active learning for faster and individually-tailored (neuro)imaging.

neuroscience↗

bioRxiv2021

Rapid processing and quantitative evaluation of multicontrast EPImix scans for adaptive multimodal imaging

Current neuroimaging acquisition and processing approaches tend to be optimised for quality rather than speed. However, rapid acquisition and processing of neuroimaging data can lead to novel neuroimaging paradigms, such as adaptive acquisition, where rapidly processed data is used to inform subsequent image acquisition steps. Here we first evaluate the impact of several processing steps on the processing time and quality of registration of manually labelled T1-weighted MRI scans. Subsequently, we apply the selected rapid processing pipeline both to rapidly acquired multicontrast EPImix scans of 95 participants (which include T1-FLAIR, T2, T2*, T2-FLAIR, DWI & ADC contrasts, acquired in [~]1 minute), as well as to slower, more standard single-contrast T1-weighted scans of a subset of 66 participants. We quantify the correspondence between EPImix and single-contrast T1-weighted scans, using correlations between voxels and regions of interest across participants, measures of within- and between-participant identifiability as well as regional structural covariance networks. Furthermore, we explore the use of EPImix for the rapid construction of morphometric similarity networks. Finally, we quantify the reliability of EPImix-derived data using test-retest scans of 10 participants. Our results demonstrate that quantitative information can be derived from a neuroimaging scan acquired and processed within minutes, which could further be used to implement adaptive multimodal imaging and tailor neuroimaging examinations to individual patients. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/430956v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@3ae0e9org.highwire.dtl.DTLVardef@1840d39org.highwire.dtl.DTLVardef@80339borg.highwire.dtl.DTLVardef@bc16cf_HPS_FORMAT_FIGEXP M_FIG Graphical abstract. C_FIG

neuroscience↗

bioRxiv2020

Preferential activation of the posterior Default-Mode Network with sequentially predictable task switches.

The default-mode network (DMN) has been primarily associated with internally-directed and self-relevant cognition. This perspective is expanding to recognise its importance in executive behaviours like switching. We investigated the effect different task-switching manipulations have on DMN activation in two studies with novel fMRI paradigms. In the first study, the paradigm manipulated visual discriminability, visuo-perceptual distance and sequential predictability during switching. Increased posterior cingulate/precuneus (PCC/PrCC) activity was evident during switching; critically, this was strongest when the occurrence of the switch was predictable. In the second study, we sought to replicate and further investigate this switch-related effect with a fully factorial design manipulating sequential, spatial and visual-feature predictability. Whole-brain analysis again identified a PCC/PrCC-centred cluster that was more active for sequentially predictable versus unpredictable switches, but not for the other predictability dimensions. We propose PCC/PrCC DMN subregions may play a prominent executive role in mapping the sequential structure of complex tasks.

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↗