scholarly journals The effect of network thresholding and weighting on structural brain networks in the UK Biobank

2019 ◽  
Author(s):  
Colin R. Buchanan ◽  
Mark E. Bastin ◽  
Stuart J. Ritchie ◽  
David C. Liewald ◽  
James Madole ◽  
...  
Keyword(s):  
White Matter ◽  
Mean Diffusivity ◽  
Clustering Coefficient ◽  
Uk Biobank ◽  
Whole Brain ◽  
Threshold Levels ◽  
Network Measures ◽  
Structural Networks ◽  
The Uk ◽  
Structural Brain

AbstractWhole-brain structural networks can be constructed using diffusion MRI and probabilistic tractography. However, measurement noise and the probabilistic nature of the tracking procedure result in an unknown proportion of spurious white matter connections. Faithful disentanglement of spurious and genuine connections is hindered by a lack of comprehensive anatomical information at the network-level. Therefore, network thresholding methods are widely used to remove ostensibly false connections, but it is not yet clear how different thresholding strategies affect basic network properties and their associations with meaningful demographic variables, such as age. In a sample of 3,153 generally healthy volunteers from the UK Biobank Imaging Study (aged 44—77 years), we constructed 85 × 85 node whole-brain structural networks and applied two principled network thresholding approaches (consistency and proportional thresholding). These were applied over a broad range of threshold levels across six alternative network weightings (streamline count, fractional anisotropy, mean diffusivity and three novel weightings from neurite orientation dispersion and density imaging) and for four common network measures (mean edge weight, characteristic path length, network efficiency and network clustering coefficient). We compared network measures against age associations and found that the most commonly-used level of proportional-thresholding from the literature (retaining 68.7% of all possible connections) yielded significantly weaker age-associations (0.070 ≤ |β| ≤ 0.406) than the consistency-based approach which retained only 30% of connections (0.140 ≤ |β| ≤ 0.409). However, we determined that the stringency of the threshold was a stronger determinant of the network-age association than the choice of threshold method and the two thresholding approaches identified a highly overlapping set of connections (ICC = 0.84) when matched at a plausible level of network sparsity (70%). Generally, more stringent thresholding resulted in more age-sensitive network measures in five of the six network weightings, except at the highest levels of sparsity (>90%), where crucial connections were then removed. At two commonly-used threshold levels, the age-associations of the connections that were discarded (mean β ≤ |0.068|) were significantly smaller in magnitude than the corresponding age-associations of the connections that were retained (mean β ≤ |0.219|, p < 0.001, uncorrected). Given histological evidence of widespread degeneration of structural brain connectivity with increasing age, these results indicate that stringent thresholding methods may be most accurate in identifying true white matter connections.

scholarly journals Associations of cigarette smoking with gray and white matter in the UK Biobank

2019 ◽  
Author(s):  
Joshua Gray ◽  
Matthew Thompson ◽  
Chelsie Benca-Bachman ◽  
Max Michael Owens ◽  
Mikela Murphy ◽  
...  
Keyword(s):  
White Matter ◽  
Cigarette Smoking ◽  
Gray Matter ◽  
Brain Structure ◽  
Mean Diffusivity ◽  
Medial Lemniscus ◽  
Uk Biobank ◽  
Matter Volume ◽  
Increased Risk ◽  
The Uk

Chronic cigarette smoking is associated with increased risk for myriad health consequences including cognitive decline and dementia, but research on the link between smoking and brain structure is nascent. We assessed the relationship of cigarette smoking (ever smoked, cigarettes per day, and duration) with gray and white matter using the UK Biobank cohort (gray matter N = 19,615; white matter N = 17,760), adjusting for numerous demographic and health confounders. Ever smoked and duration were associated with smaller total gray matter volume. Ever smoked was associated with reduced volume of the right VIIIa cerebellum, as well as elevated white matter hyperintensity volumes. Smoking duration was associated with reduced total white matter volume. With regard to specific tracts, ever smoked was associated with reduced fractional anisotropy in the left cingulate gyrus part of the cingulum, left posterior thalamic radiation, and bilateral superior thalamic radiation and increased mean diffusivity in the middle cerebellar peduncle, right medial lemniscus, bilateral posterior thalamic radiation, and bilateral superior thalamic radiation. Overall, we found significant associations of cigarette exposure with global measures of gray and white matter. Furthermore, we found select associations of ever smoked, but not cigarettes per day or duration, with specific gray and white matter regions. These findings inform our understanding of the connections between smoking and variation in brain structure and clarify potential mechanisms of risk for common neurological sequelae.

scholarly journals Adapting the UK Biobank brain imaging protocol and analysis pipeline for the C-MORE multi-organ study of COVID-19 survivors

2021 ◽  
Author(s):  
Ludovica Griffanti ◽  
Betty Raman ◽  
Fidel Alfaro-Almagro ◽  
Nicola Filippini ◽  
Mark Philip Cassar ◽  
...  
Keyword(s):  
White Matter ◽  
Brain Mri ◽  
Mean Diffusivity ◽  
Brain Anatomy ◽  
Uk Biobank ◽  
Analysis Pipeline ◽  
Imaging Protocol ◽  
Diffusion Weighted Images ◽  
Radiology Reports ◽  
The Uk

SARS-CoV-2 infection has been shown to damage multiple organs, including the brain. Multiorgan MRI can provide further insight on the repercussions of COVID-19 on organ health but requires a balance between richness and quality of data acquisition and total scan duration. We adapted the UK Biobank brain MRI protocol to produce high-quality images while being suitable as part of a post-COVID-19 multiorgan MRI exam. The analysis pipeline, also adapted from UK Biobank, includes new imaging-derived phenotypes (IDPs) designed to assess the effects of COVID-19. A first application of the protocol and pipeline was performed in 51 COVID-19 patients post-hospital discharge and 25 controls participating in the Oxford C-MORE study. The protocol acquires high resolution T1, T2-FLAIR, diffusion weighted images, susceptibility weighted images, and arterial spin labelling data in 17 minutes. The automated imaging pipeline derives 1575 IDPs, assessing brain anatomy (including olfactory bulb volume and intensity) and tissue perfusion, hyperintensities, diffusivity, and susceptibility. In the C-MORE data, these quantitative measures were consistent with clinical radiology reports. Our exploratory analysis tentatively revealed that recovered COVID-19 patients had a decrease in frontal grey matter volumes, an increased burden of white matter hyperintensities, and reduced mean diffusivity in the total and normal appearing white matter in the posterior thalamic radiation and sagittal stratum, relative to controls. These differences were generally more prominent in patients who received organ support. Increased T2* in the thalamus was also observed in recovered COVID-19 patients, with a more prominent increase for non-critical patients. This initial evidence of brain changes in COVID-19 survivors prompts the need for further investigations. Follow-up imaging in the C-MORE study is currently ongoing, and this protocol is now being used in large-scale studies. The pipeline is widely applicable and will contribute to new analyses to hopefully clarify the medium to long-term effects of COVID-19.

scholarly journals White matter, cognition and psychotic-like experiences in UK Biobank

2021 ◽  
pp. 1-10
Author(s):  
M. J. Bosma ◽  
S. R. Cox ◽  
T. Ziermans ◽  
C. R. Buchanan ◽  
X. Shen ◽  
...  
Keyword(s):  
White Matter ◽  
Processing Speed ◽  
Mean Diffusivity ◽  
Psychotic Symptoms ◽  
Uk Biobank ◽  
Whole Brain ◽  
G Factor ◽  
Brain White Matter ◽  
White Matter Microstructure ◽  
The Relationship

Abstract Background Psychotic-like experiences (PLEs) are risk factors for the development of psychiatric conditions like schizophrenia, particularly if associated with distress. As PLEs have been related to alterations in both white matter and cognition, we investigated whether cognition (g-factor and processing speed) mediates the relationship between white matter and PLEs. Methods We investigated two independent samples (6170 and 19 891) from the UK Biobank, through path analysis. For both samples, measures of whole-brain fractional anisotropy (gFA) and mean diffusivity (gMD), as indications of white matter microstructure, were derived from probabilistic tractography. For the smaller sample, variables whole-brain white matter network efficiency and microstructure were also derived from structural connectome data. Results The mediation of cognition on the relationships between white matter properties and PLEs was non-significant. However, lower gFA was associated with having PLEs in combination with distress in the full available sample (standardized β = −0.053, p = 0.011). Additionally, lower gFA/higher gMD was associated with lower g-factor (standardized β = 0.049, p < 0.001; standardized β = −0.027, p = 0.003), and partially mediated by processing speed with a proportion mediated of 7% (p = < 0.001) for gFA and 11% (p < 0.001) for gMD. Conclusions We show that lower global white matter microstructure is associated with having PLEs in combination with distress, which suggests a direction of future research that could help clarify how and why individuals progress from subclinical to clinical psychotic symptoms. Furthermore, we replicated that processing speed mediates the relationship between white matter microstructure and g-factor.

scholarly journals Associations of cigarette smoking with gray and white matter in the UK Biobank

2020 ◽  
Vol 45 (7) ◽  
pp. 1215-1222
Author(s):  
Joshua C. Gray ◽  
Matthew Thompson ◽  
Chelsie Bachman ◽  
Max M. Owens ◽  
Mikela Murphy ◽  
...  
Keyword(s):  
White Matter ◽  
Cigarette Smoking ◽  
Uk Biobank ◽  
The Uk

scholarly journals Association between APOE e4 and white matter hyperintensity volume, but not total brain volume or white matter integrity

2019 ◽  
Vol 14 (5) ◽  
pp. 1468-1476 ◽  
Author(s):  
Donald M. Lyall ◽  
Simon R. Cox ◽  
Laura M. Lyall ◽  
Carlos Celis-Morales ◽  
Breda Cullen ◽  
...  
Keyword(s):  
White Matter ◽  
Cognitive Aging ◽  
Social Deprivation ◽  
Mean Diffusivity ◽  
Brain Magnetic Resonance Imaging ◽  
Smoking History ◽  
White Matter Hyperintensity ◽  
Future Research ◽  
Uk Biobank ◽  
Intermediate Phenotypes

Abstract Apolipoprotein (APOE) e4 genotype is an accepted risk factor for accelerated cognitive aging and dementia, though its neurostructural substrates are unclear. The deleterious effects of this genotype on brain structure may increase in magnitude into older age. This study aimed to investigate in UK Biobank the association between APOE e4 allele presence vs. absence and brain imaging variables that have been associated with worse cognitive abilities; and whether this association varies by cross-sectional age. We used brain magnetic resonance imaging (MRI) and genetic data from a general-population cohort: the UK Biobank (N = 8395 after exclusions). We adjusted for the covariates of age in years, sex, Townsend social deprivation scores, smoking history and cardiometabolic diseases. There was a statistically significant association between APOE e4 genotype and increased (i.e. worse) white matter (WM) hyperintensity volumes (standardised beta = 0.088, 95% confidence intervals = 0.036 to 0.139, P = 0.001), a marker of poorer cerebrovascular health. There were no associations with left or right hippocampal, total grey matter (GM) or WM volumes, or WM tract integrity indexed by fractional anisotropy (FA) and mean diffusivity (MD). There were no statistically significant interactions with age. Future research in UK Biobank utilising intermediate phenotypes and longitudinal imaging hold significant promise for this area, particularly pertaining to APOE e4’s potential link with cerebrovascular contributions to cognitive aging.

scholarly journals Brain peak width of skeletonised mean diffusivity (PSMD), processing speed, and other cognitive domains

2018 ◽  
Author(s):  
Ian J. Deary ◽  
Stuart J. Ritchie ◽  
Susana Muñoz Maniega ◽  
Simon R. Cox ◽  
Maria C. Valdés Hernández ◽  
...  
Keyword(s):  
White Matter ◽  
Brain Imaging ◽  
Processing Speed ◽  
Brain Mri ◽  
Mean Diffusivity ◽  
Perivascular Spaces ◽  
Peak Width ◽  
Cognitive Domains ◽  
Diffusion Parameters ◽  
Structural Brain

AbstractIt is suggested that the brain’s peak width of skeletonised water mean diffusivity (PSMD) is a neuro-biomarker of processing speed, a crucial contributor to cognitive ageing. We tested whether PSMD is more strongly correlated with processing speed than with other cognitive domains, and more strongly than other structural brain MRI indices. Participants were 731 Lothian Birth Cohort 1936 members, mean age 73 years (SD=0.7); analytical sample was 656-680. Cognitive domains tested were: processing speed (5 tests), visuospatial (3), memory (3), and verbal (3). Brain-imaging variables included PSMD, white matter diffusion parameters and hyperintensity volumes, grey and white matter volumes, and perivascular spaces. PSMD was significantly associated with all processing speed tests; absolute standardised beta values were 0.11 to 0.23 (mean = 0.17). Other structural brain-imaging variables correlated as or more strongly. PSMD was significantly associated with processing speed (−0.27), visuospatial (−0.23), memory (−0.17), and general cognitive ability (−0.25). PSMD correlated with processing speed: but not more strongly than with other cognitive domains; and not more strongly than other brain-imaging measures.

scholarly journals Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Wenbin Li ◽  
Qianqian Wei ◽  
Yanbing Hou ◽  
Du Lei ◽  
Yuan Ai ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
White Matter ◽  
Clustering Coefficient ◽  
Single Subject ◽  
Progression Rate ◽  
Motor Systems ◽  
Structural Network ◽  
Structural Networks ◽  
Healthy Participants ◽  
Lateral Sclerosis

Abstract Objective There is increasing evidence that amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease impacting large-scale brain networks. However, it is still unclear which structural networks are associated with the disease and whether the network connectomics are associated with disease progression. This study was aimed to characterize the network abnormalities in ALS and to identify the network-based biomarkers that predict the ALS baseline progression rate. Methods Magnetic resonance imaging was performed on 73 patients with sporadic ALS and 100 healthy participants to acquire diffusion-weighted magnetic resonance images and construct white matter (WM) networks using tractography methods. The global and regional network properties were compared between ALS and healthy subjects. The single-subject WM network matrices of patients were used to predict the ALS baseline progression rate using machine learning algorithms. Results Compared with the healthy participants, the patients with ALS showed significantly decreased clustering coefficient Cp (P = 0.0034, t = 2.98), normalized clustering coefficient γ (P = 0.039, t = 2.08), and small‐worldness σ (P = 0.038, t = 2.10) at the global network level. The patients also showed decreased regional centralities in motor and non-motor systems including the frontal, temporal and subcortical regions. Using the single-subject structural connection matrix, our classification model could distinguish patients with fast versus slow progression rate with an average accuracy of 85%. Conclusion Disruption of the WM structural networks in ALS is indicated by weaker small-worldness and disturbances in regions outside of the motor systems, extending the classical pathophysiological understanding of ALS as a motor disorder. The individual WM structural network matrices of ALS patients are potential neuroimaging biomarkers for the baseline disease progression in clinical practice.

scholarly journals Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference

2018 ◽  
Author(s):  
Vaanathi Sundaresan ◽  
Ludovica Griffanti ◽  
Petya Kindalova ◽  
Fidel Alfaro-Almagro ◽  
Giovanna Zamboni ◽  
...  
Keyword(s):  
White Matter ◽  
Vascular Disease ◽  
White Matter Hyperintensities ◽  
Age Groups ◽  
Clinical Importance ◽  
Population Level ◽  
Distribution Model ◽  
Ageing Population ◽  
Uk Biobank ◽  
The Uk

AbstractWhite matter hyperintensities (WMH), also known as white matter lesions, are localised white matter areas that appear hyperintense on MRI scans. WMH commonly occur in the ageing population, and are often associated with several factors such as cognitive disorders, cardiovascular risk factors, cerebrovascular and neurodegenerative diseases. Despite the fact that some links between lesion location and parametric factors such as age have already been established, the relationship between voxel-wise spatial distribution of lesions and these factors is not yet well understood. Hence, it would be of clinical importance to model the distribution of lesions at the population-level and quantitatively analyse the effect of various factors on the lesion distribution model.In this work we compare various methods, including our proposed method, to generate voxel-wise distributions of WMH within a population with respect to various factors. Our proposed Bayesian spline method models the spatio-temporal distribution of WMH with respect to a parametric factor of interest, in this case age, within a population. Our probabilistic model takes as input the lesion segmentation binary maps of subjects belonging to various age groups and provides a population-level parametric lesion probability map as output. We used a spline representation to ensure a degree of smoothness in space and the dimension associated with the parameter, and formulated our model using a Bayesian framework.We tested our algorithm output on simulated data and compared our results with those obtained using various existing methods with different levels of algorithmic and computational complexity. We then compared the better performing methods on a real dataset, consisting of 1000 subjects of the UK Biobank, divided in two groups based on hypertension diagnosis. Finally, we applied our method on a clinical dataset of patients with vascular disease.On simulated dataset, the results from our algorithm showed a mean square error (MSE) value of 7.27 × 10−5, which was lower than the MSE value reported in the literature, with the advantage of being robust and computationally efficient. In the UK Biobank data, we found that the lesion probabilities are higher for the hypertension group compared to the non-hypertension group and further verified this finding using a statistical t-test. Finally, when applying our method on patients with vascular disease, we observed that the overall probability of lesions is significantly higher in later age groups, which is in line with the current literature.

scholarly journals Investigation of Whole-Brain White Matter Identifies Altered Water Mobility in the Pathogenesis of High-Altitude Headache

2013 ◽  
Vol 33 (8) ◽  
pp. 1286-1294 ◽  
Author(s):  
Justin S Lawley ◽  
Samuel J Oliver ◽  
Paul G Mullins ◽  
Jamie H Macdonald
Keyword(s):  
White Matter ◽  
High Altitude ◽  
Brain Edema ◽  
Fractional Anisotropy ◽  
Mean Diffusivity ◽  
Cerebral White Matter ◽  
Water Mobility ◽  
Whole Brain ◽  
Brain White Matter ◽  
Brain Water

Elevated brain water is a common finding in individuals with severe forms of altitude illness. However, the location, nature, and a causative link between brain edema and symptoms of acute mountain sickness such as headache remains unknown. We examined indices of brain white matter water mobility in 13 participants after 2 and 10 hours in normoxia (21% O2) and hypoxia (12% O2) using magnetic resonance imaging. Using a whole-brain analysis (tract-based spatial statistics (TBSS)), mean diffusivity was reduced in the left posterior hemisphere after 2 hours and globally reduced throughout cerebral white matter by 10 hours in hypoxia. However, no changes in T2 relaxation time (T2) or fractional anisotropy were observed. The TBSS identified an association between changes in mean diffusivity, fractional anisotropy, and T2 both supra and subtentorially after 2 and 10 hours, with headache score after 10 hours in hypoxia. Region of interest-based analyses generally confirmed these results. These data indicate that acute periods of hypoxemia cause a shift of water into the intracellular space within the cerebral white matter, whereas no evidence of brain edema (a volumetric enlargement) is identifiable. Furthermore, these changes in brain water mobility are related to the intensity of high-altitude headache.

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