Biology subjects

Ward, P. G. D.

Publications and source records attributed to Ward, P. G. D..

9 recordsLinked to original sources

bioRxiv2020

Neuroprotective effects of motherhood on brain function in late-life: a resting state fMRI study.

The maternal brain undergoes structural and functional plasticity during pregnancy and the postpartum period. Little is known about functional plasticity outside caregiving-specific contexts, and whether changes persist across the lifespan. Structural neuroimaging studies suggest that parenthood may confer a protective effect against the ageing process, however it is unknown whether parenthood is associated with functional brain differences in late-life. We examined the relationship between resting state functional connectivity and number of children parented in 220 healthy older females (73.82{+/-}3.53years) and 252 healthy older males (73.95{+/-}3.50years). We compared patterns of resting state functional connectivity with three different models of age-related functional change to assess whether these effects may be functionally neuroprotective for the ageing human parental brain. No relationship between functional connectivity and number of children was obtained for males. For females, we found widespread decreasing functional connectivity with increasing number of children parented, with increased segregation between networks, decreased connectivity between hemispheres, and decreased connectivity between anterior and posterior regions. The patterns of functional connectivity related to the number of children an older woman has parented were in the opposite direction to those usually associated with age-related cognitive decline, suggesting that motherhood may be beneficial for brain function in late-life.

neuroscience↗

bioRxiv2020

Metabolic and haemodynamic resting-state connectivity of the human brain: a high-temporal resolution simultaneous BOLD-fMRI and FDG-fPET multimodality study

Simultaneous FDG-PET/fMRI ([18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging) provides the capacity to image two sources of energetic dynamics in the brain - glucose metabolism and haemodynamic response. Functional fMRI connectivity has been enormously useful for characterising interactions between distributed brain networks in humans. Metabolic connectivity based on static FDG-PET has been proposed as a biomarker for neurological disease; but static FDG-PET cannot be used to estimate subjectlevel measures of connectivity, only across-subject covariance. Here, we applied high-temporal resolution constant infusion fPET to measure subject-level metabolic connectivity simultaneously with fMRI connectivity. fPET metabolic connectivity was characterised by fronto-parietal connectivity within and between hemispheres. fPET metabolic connectivity showed moderate similarity with fMRI primarily in superior cortex and frontoparietal regions. Significantly, fPET metabolic connectivity showed little similarity with static FDG-PET metabolic covariance, indicating that metabolic brain connectivity is a non-ergodic process whereby individual brain connectivity cannot be inferred from group level metabolic covariance. Our results highlight the complementary strengths of fPET and fMRI in measuring the intrinsic connectivity of the brain, and open up the opportunity for novel fundamental studies of human brain connectivity as well as multi-modality biomarkers of neurological diseases.

neuroscience↗

bioRxiv2019

Serial assessment of iron in the motor cortex in limb-onset Amyotrophic Lateral Sclerosis using Quantitative Susceptibility Mapping

ObjectiveDysregulation of iron in the cerebral motor areas has been hypothesized to occur in individuals with Amyotrophic Lateral Sclerosis (ALS). There is still limited knowledge regarding iron dysregulation in the progression of ALS pathology. Our objectives were to use magnetic resonance based Quantitative Susceptibility Mapping (QSM) to investigate the association between iron dysregulation in the motor cortex and clinical manifestations in patients with limb-onset ALS, and to examine changes in the iron concentration in the motor cortex in these patients over a six-month period. MethodsIron concentration was investigated using magnetic resonance based -QSM in the primary motor cortex and the pre-motor area in thirteen limb-onset ALS patients (including five lumbar onset, six cervical onset and two flail arm patients), and eleven age and sex-matched healthy controls. Nine ALS patients underwent follow-up scans at six months. ResultsSignificantly increased QSM was observed in the left posterior primary motor area (p = 0.02, Cohens d = 0.9) and right anterior primary motor area (p = 0.02, Cohens d = 0.92) in all individuals with limb-onset ALS compared to healthy controls. Increased QSM was observed in the primary motor and pre-motor area at baseline in patients with lumbar onset ALS patients, but not cervical limb-onset ALS patients, compared to healthy controls. No significant change in QSM was observed at the six-month follow-up scans in the ALS patients. ConclusionsThe findings suggest that iron dysregulation can be detected in the motor cortex in limb-onset ALS, which does not appreciably change over a further 6 months. Individuals with lumbar onset ALS appear to be more susceptible to motor cortex iron dysregulation compared to the individuals with cervical onset ALS. Importantly, this study highlights the potential use of QSM as a radiological indicator in disease diagnosis, and in clinical trials in limb-onset ALS and its subtypes. HighlightsO_LISerial measurement of QSM in the motor cortex in limb-onset ALS was performed C_LIO_LIQSM changes in the motor cortex in ALS sub-groups were investigated C_LIO_LIHigher QSM was observed in the motor cortex in Lumbar ALS relative to controls C_LIO_LIQSM is sensitive to iron dysregulation in the motor cortex in limb-onset ALS C_LI

neuroscience↗

bioRxiv2019

Individual differences in haemoglobin concentration influence BOLD fMRI functional connectivity and its correlation with cognition

Resting-state connectivity measures the temporal coherence of the spontaneous neural activity of spatially distinct regions, and is commonly measured using BOLD-fMRI. The BOLD response follows neuronal activity, when changes in the relative concentration of oxygenated and deoxygenated haemoglobin cause fluctuations in the MRI T2* signal. Since the BOLD signal detects changes in relative concentrations of oxy/deoxy-haemoglobin, individual differences in haemoglobin levels may influence the BOLD signal-to-noise ratio in a manner independent of the degree of neural activity. In this study, we examined whether group differences in haemoglobin may confound measures of functional connectivity. We investigated whether relationships between measures of functional connectivity and cognitive performance could be influenced by individual variability in haemoglobin. Finally, we mapped the neuroanatomical distribution of the influence of haemoglobin on functional connectivity to determine where group differences in functional connectivity are manifest. In a cohort of 518 healthy elderly subjects (259 men) each sex group was median split into two groups with high and low haemoglobin concentration. Significant differences were obtained in functional connectivity between the high and low haemoglobin groups for both men and women (Cohens d 0.17 and 0.03 for men and women respectively). The haemoglobin connectome in males showed a widespread systematic increase in functional connectivity correlational scores, whilst the female connectome showed predominantly parietal and subcortical increases and temporo-parietal decreases. Despite the haemoglobin groups having no differences in cognitive measures, significant differences in the linear relationships between cognitive performance and functional connectivity were obtained for all 5 cognitive tests in males, and 4 out of 5 tests in females. Our findings confirm that individual variability in haemoglobin levels that give rise to group differences are an important confounding variable in BOLD-fMRI-based studies of functional connectivity. Controlling for haemoglobin variability as a potentially confounding variable is crucial to ensure the reproducibility of human brain connectome studies, especially in studies that compare groups of individuals, compare sexes, or examine connectivity-cognition relationships. HighlightsO_LIIndividual differences in haemoglobin significantly impact measures of functional connectivity in the elderly. C_LIO_LISignificant differences in connectivity-cognition relationships are shown between groups separated by haemoglobin values without accompanying cognitive differences. C_LIO_LIThe influence of haemoglobin on functional connectivity differs between men and women. C_LI

neuroscience↗

bioRxiv2019

Analysis of continuous infusion functional PET (fPET) in the human brain

Functional positron emission tomography (fPET) is a neuroimaging method involving continuous infusion of 18-F-fluorodeoxyglucose (FDG) radiotracer during the course of the PET examination. Compared with the conventional bolus administered static FDG PET which provides only a snapshot of the averaged glucose uptake into the brain in a limited dynamic time window, fPET offers a significantly wider time window to study the dynamics of glucose uptake. Several earlier studies have applied fPET to investigate brain FDG uptake and study its relationship with functional magnetic resonance imaging (fMRI). However, due to the unique characteristics of fPET signals, modelling of the fPET signal is a complex task and poses challenges for accurate interpretation of the results. This study applies independent component analysis (ICA) to analyze resting state fPET data, and to compare the performance of ICA and general linear modelling (GLM) for estimation of brain activation in response to tasks. The fPET signal characteristics were compared using GLM and ICA methods to model the fPET visual activation data. Our aim was to evaluate GLM and ICA methods for analyzing task fPET datasets and present ICA method in the analysis of resting state fPET datasets. Using both simulation and in-vivo experimental datasets, we show that both methods can successfully identify task related brain activation. We report fPET metabolic resting state brain networks analyzed using the fPET ICA method in a cohort of healthy subjects. Functional PET provides a unique method to map dynamic changes of glucose uptake in the resting human brain and in response to extrinsic stimulation.

neuroscience↗

bioRxiv2019

Cortical changes associated with parenthood are present in late life

Early parenthood results in changes in cortical thickness in regions related to parental care. However, the enduring effects of this period on the structure of the human brain, and cognition in late-life, is unknown. In an elderly sample, we examined the relationship between the number of children parented (here, 1-6 children) and cortical thickness in 267 males (74.0 {+/-}3.5 years) and 231 females (73.8{+/-}3.5 years). We also compared cognition and cortical thickness between parents of one child and non-parents, in n=36 males (73.4{+/-}3.7 years), and n=46 females (72.8{+/-}3.3 years). We obtained a positive relationship between number of children parented and verbal memory performance, showing increasing memory performance with number of children. For mothers, number of children positively correlated with cortical thickness in the parahippocampal gyrus and negatively correlated with regions of the visual cortex. Mothers of one child showed thinner cortical thickness in the dorsolateral prefrontal cortex and visual cortex compared with childless women. Fathers of one child showed thinner cortical thickness in the anterior cingulate cortex and thicker cortical thickness in the temporal pole compared with childless men. Our results are the first to reveal distributed differences in cortical thickness related to parenthood that are evident beyond the postpartum period. Our findings overlap substantially with the areas found to be altered across pregnancy and the postpartum period, suggesting that neural changes associated with early parenthood persist into older age, and are potentially cognitively beneficial.

neuroscience↗

bioRxiv2018

Simultaneous task-based BOLD-fMRI and FDG functional PET for measurement of neuronal metabolism in the human visual cortex

Studies of task-evoked brain activity are the cornerstone of cognitive neuroscience, and unravel the spatial and temporal brain dynamics of cognition in health and disease. Blood oxygenation level dependent functional magnetic resonance imaging (BOLD-fMRI) is one of the most common methods of studying brain function in humans. BOLD-fMRI indirectly infers neuronal activity from regional changes in blood oxygenation and is not a quantitative metric of brain function. Regional variation in glucose metabolism, measured using [18-F] fluorodeoxyglucose positron emission tomography (FDG-PET), provides a more direct and interpretable measure of neuronal activity. However, while the temporal resolution of BOLD-fMRI is in the order of seconds, standard FDG-PET protocols provide a static snapshot of glucose metabolism. Here, we develop a novel experimental design for measurement of task-evoked changes in regional blood oxygenation and glucose metabolism with high temporal resolution. Over a 90-min simultaneous BOLD-fMRI/FDG-PET scan, [18F] FDG was constantly infused to 10 healthy volunteers, who viewed a flickering checkerboard presented in a hierarchical block design. Dynamic task-related changes in blood oxygenation and glucose metabolism were examined with temporal resolution of 2.5sec and 1-min, respectively. Task-related, temporally coherent brain networks of haemodynamic and metabolic connectivity were maximally related in the visual cortex, as expected. Results demonstrate that the hierarchical block design, together with the infusion FDG-PET technique, enabled both modalities to track task-related neural responses with high temporal resolution. The simultaneous MR-PET approach has the potential to provide unique insights into the dynamic haemodynamic and metabolic interactions that underlie cognition in health and disease.

neuroscience↗

bioRxiv2018

A comprehensive framework to capture the arcana of neuroimaging analysis

Mastering the \"arcana of neuroimaging analysis\", the obscure knowledge required to apply an appropriate combination of software tools and parameters to analyse a given neuroimaging dataset, is a time consuming process. Therefore, it is not typically feasible to invest the additional effort required generalise workflow implementations to accommodate for the various acquisition parameters, data storage conventions and computing environments in use at different research sites, limiting the reusability of published workflows.\n\nWe present a novel software framework, Abstraction of Repository-Centric ANAlysis (Arcana), which enables the development of complex, \"end-to-end\" workflows that are adaptable to new analyses and portable to a wide range of computing infrastructures. Analysis templates for specific image types (e.g. MRI contrast) are implemented as Python classes, which define a range of potential derivatives and analysis methods. Arcana retrieves data from imaging repositories, which can be BIDS datasets, XNAT instances or plain directories, and stores selected derivatives and associated provenance back into a repository for reuse by subsequent analyses. Workflows are constructed using Nipype and can be executed on local workstations or in high performance computing environments. Generic analysis methods can be consolidated within common base classes to facilitate code-reuse and collaborative development, which can be specialised for study-specific requirements via class inheritance. Arcana provides a framework in which to develop unified neuroimaging workflows that can be reused across a wide range of research studies and sites.

bioinformatics↗

bioRxiv2017

Combining images and anatomical knowledge to improve automated vein segmentation in MRI

PurposeTo improve the accuracy of automated vein segmentation by combining susceptibility-weighted images (SWI), quantitative susceptibility maps (QSM), and a vein atlas to produce a resultant image called a composite vein image (CV image).\n\nMethodAn atlas was constructed in common space from 1072 manually traced 2D-slices. The composite vein image was derived for each subject as a weighted sum of three inputs; a SWI image, a QSM image and the vein atlas. The weights for each input and each anatomical location, called template priors, were derived by assessing the accuracy of each input over an independent data set. The accuracy of venograms derived automatically from each of the CV image, SWI, and QSM image sets was assessed by comparison with manual tracings. Three different automated vein segmentation techniques were used, and ten performance metrics evaluated.\n\nResultsVein segmentations using the CV image were comprehensively better than those derived from SWI or QSM images (mean Cohens d = 1.1). Sixty permutations of performance metric and automated segmentation technique were evaluated. Vein identification improvements that were both large and significant (Cohens d>0.80, p<0.05) were found in 77% of the permutations, compared to no improvement in 5%.\n\nConclusionThe accuracy of automated venograms derived from the composite vein image was overwhelmingly superior to venograms derived from SWI or QSM alone.

neuroscience↗