Resting-State Functional Connectivity in Late-Life Depression: Higher Global Connectivity and More Long Distance Connections

Functional magnetic resonance imaging recordings in the resting-state (RS) from the human brain are characterized by spontaneous low-frequency fluctuations in the blood oxygenation level dependent signal that reveal functional connectivity (FC) via their spatial synchronicity. This RS study applied network analysis to compare FC between late-life depression (LLD) patients and control subjects. Raw cross-correlation matrices (CM) for LLD were characterized by higher FC. We analyzed the small-world (SW) and modular organization of these networks consisting of 110 nodes each as well as the connectivity patterns of individual nodes of the basal ganglia. Topological network measures showed no significant differences between groups. The composition of top hubs was similar between LLD and control subjects, however in the LLD group posterior medial-parietal regions were more highly connected compared to controls. In LLD, a number of brain regions showed connections with more distant neighbors leading to an increase of the average Euclidean distance between connected regions compared to controls. In addition, right caudate nucleus connectivity was more diffuse in LLD. In summary, LLD was associated with overall increased FC strength and changes in the average distance between connected nodes, but did not lead to global changes in SW or modular organization

Compressed representation of brain genetic transcription

The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. Established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods-PCA, kernel PCA, non-negative matrix factorization (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and predictive utility with respect to signalling, microstructural, and metabolic targets. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain.Comment: 21 pages, 5 main figures, 1 supplementary figur

Aging-Sensitive Networks Within the Human Structural Connectome Are Implicated in Late-Life Cognitive Declines

BACKGROUND: Aging-related cognitive decline is a primary risk factor for Alzheimer’s disease and related dementias. More precise identification of the neurobiological bases of cognitive decline in aging populations may provide critical insights into the precursors of late-life dementias. METHODS: Using structural and diffusion brain MRI data from the UK Biobank (UKB; N = 8,185, ages 45–78 years), we examined aging of regional grey matter volumes (nodes) and white matter structural connectivity (edges) within nine well-characterized networks-of-interest in the human brain connectome. In the independent Lothian Birth Cohort 1936 (LBC1936; N = 534, all age 73 years), we tested whether aging-sensitive connectome elements are enriched for key domains of cognitive function, before and after controlling for early-life cognitive ability. RESULTS: In UKB, age-differences in individual connectome elements corresponded closely with principal component loadings reflecting connectome-wide integrity (|r(nodes)| = 0.420; |r(edges)| = 0.583), suggesting that connectome aging occurs on broad dimensions of variation in brain architecture. In LBC1936, composite indices of node integrity were predictive of all domains of cognitive function, whereas composite indices of edge integrity were associated specifically with processing speed. Elements within the Central Executive network were disproportionately predictive of late-life cognitive function relative to the network’s small size. Associations with processing speed and visuospatial ability remained after controlling for childhood cognitive ability. CONCLUSIONS: These results implicate global dimensions of variation in the human structural connectome in aging-related cognitive decline. The Central Executive network may demarcate a constellation of elements that are centrally important to age-related cognitive impairments

Disruption of rich club organisation in cerebral small vessel disease.

Cerebral small vessel disease (SVD) is an important cause of vascular cognitive impairment. Recent studies have demonstrated that structural connectivity of brain networks in SVD is disrupted. However, little is known about the extent and location of the reduced connectivity in SVD. Here they investigate the rich club organisation-a set of highly connected and interconnected regions-and investigate whether there is preferential rich club disruption in SVD. Diffusion tensor imaging (DTI) and cognitive assessment were performed in a discovery sample of SVD patients (n = 115) and healthy control subjects (n = 50). Results were replicated in an independent dataset (49 SVD with confluent WMH cases and 108 SVD controls) with SVD patients having a similar SVD phenotype to that of the discovery cases. Rich club organisation was examined in structural networks derived from DTI followed by deterministic tractography. Structural networks in SVD patients were less dense with lower network strength and efficiency. Reduced connectivity was found in SVD, which was preferentially located in the connectivity between the rich club nodes rather than in the feeder and peripheral connections, a finding confirmed in both datasets. In discovery dataset, lower rich club connectivity was associated with lower scores on psychomotor speed (β = 0.29, P < 0.001) and executive functions (β = 0.20, P = 0.009). These results suggest that SVD is characterized by abnormal connectivity between rich club hubs in SVD and provide evidence that abnormal rich club organisation might contribute to the development of cognitive impairment in SVD

Quantifying the Link between Anatomical Connectivity, Gray Matter Volume and Regional Cerebral Blood Flow: An Integrative MRI Study

Background In the graph theoretical analysis of anatomical brain connectivity, the white matter connections between regions of the brain are identified and serve as basis for the assessment of regional connectivity profiles, for example, to locate the hubs of the brain. But regions of the brain can be characterised further with respect to their gray matter volume or resting state perfusion. Local anatomical connectivity, gray matter volume and perfusion are traits of each brain region that are likely to be interdependent, however, particular patterns of systematic covariation have not yet been identified. Methodology/Principal Findings We quantified the covariation of these traits by conducting an integrative MRI study on 23 subjects, utilising a combination of Diffusion Tensor Imaging, Arterial Spin Labeling and anatomical imaging. Based on our hypothesis that local connectivity, gray matter volume and perfusion are linked, we correlated these measures and particularly isolated the covariation of connectivity and perfusion by statistically controlling for gray matter volume. We found significant levels of covariation on the group- and regionwise level, particularly in regions of the Default Brain Mode Network. Conclusions/Significance Connectivity and perfusion are systematically linked throughout a number of brain regions, thus we discuss these results as a starting point for further research on the role of homology in the formation of functional connectivity networks and on how structure/function relationships can manifest in the form of such trait interdependency

Multimodal neuroimaging of vestibular and postural networks: Investigating the pathophysiology of idiopathic dizziness in older adults

Successful ageing - the preservation of good performance into old age, is an aspiration for many and a challenge for society. Modifiable factors which account for ageing-related functional decline should thus be identified and reduced. As life expectancy increases, brain ageing and its functional consequences become an increasingly important target for research and intervention. Cerebral small vessel disease, largely driven by vascular risk factors, has emerged as a strong contributor to cognitive and balance decline in late life. Though the early effects of cerebral small vessel disease on cognition are increasingly better understood, its symptomatic effects on other functional systems are not well characterised. In this thesis, I investigated the long recognised, but pathophysiologically enigmatic syndrome of dizziness in older adults, not accounted for by neurological disease or vestibular dysfunction. I considered the hypothesis that this ‘idiopathic dizziness’ is secondary to cerebral small vessel disease through its deleterious effects on white matter networks which subserve vestibular perceptual processes and/or the control of balance. I first defined the functional anatomy of the core human vestibular cortex by its functional connectivity (Chapter 3). I related the resulting anatomical subregions to behavioural and task neuroimaging data to define a vestibular network involved in self-motion perception. I proceeded to characterise the syndrome of idiopathic dizziness using clinical, cognitive and behavioural (vestibular function, balance and gait) data from patients and controls (Chapter 4). I combined this data with structural and diffusion magnetic resonance imaging data to investigate the pathophysiology of idiopathic dizziness. I found that frontal white matter tracts relevant to the control of balance had lower integrity in patients with idiopathic dizziness than controls. These findings occurred in the context of excess vascular risk, and markers of cerebral small vessel disease. Additionally, I found vestibular function and perception were normal in patients with idiopathic dizziness. The results suggest disrupted balance control may underpin idiopathic dizziness in cerebral small vessel disease. I proceeded to investigate whether neural correlates of balance control were altered in idiopathic dizziness as a model for mild balance impairment in cerebral small vessel disease (Chapter 5). To do this, I applied electroencephalography during quiet standing and related brain activity to spontaneous sway. I showed idiopathic dizziness was linked to altered cortical activity in relation to balance control, and this cortical activity was influenced by the burden of cerebral small vessel disease. Additionally, patients with idiopathic dizziness uniquely engaged a low frequency postural connectivity network, consistent with a different mode of postural control. Overall, the results within this thesis show a relationship between idiopathic dizziness and vascular injury to frontal tracts involved in the control of balance in cerebral small vessel disease. Small vessel disease may disrupt the cortical control of balance as a basis for symptoms in this syndrome.Open Acces