Madelaine Daianu

Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network

Diffusion imaging can assess the white matter connections within the brain, revealing how neural pathways break down in Alzheimers disease (AD). We analyzed 3‐Tesla whole‐brain diffusion‐weighted images from 202 participants scanned by the Alzheimers Disease Neuroimaging Initiative–50 healthy controls, 110 with mild cognitive impairment (MCI) and 42 AD patients. From whole‐brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We tested whether AD disrupts the “rich club” – a network property where high‐degree network nodes are more interconnected than expected by chance. We calculated the rich club properties at a range of degree thresholds, as well as other network topology measures including global degree, clustering coefficient, path length, and efficiency. Network disruptions predominated in the low‐degree regions of the connectome in patients, relative to controls. The other metrics also showed alterations, suggesting a distinctive pattern of disruption in AD, less pronounced in MCI, targeting global brain connectivity, and focusing on more remotely connected nodes rather than the central core of the network. AD involves severely reduced structural connectivity; our step‐wise rich club coefficients analyze points to disruptions predominantly in the peripheral network components; other modalities of data are needed to know if this indicates impaired communication among non rich club regions. The highly connected core was relatively preserved, offering new evidence on the neural basis of progressive risk for cognitive decline. Hum Brain Mapp 36:3087–3103, 2015.

Pericyte degeneration causes white matter dysfunction in the mouse central nervous system

Diffuse white-matter disease associated with small-vessel disease and dementia is prevalent in the elderly. The biological mechanisms, however, remain elusive. Using pericyte-deficient mice, magnetic resonance imaging, viral-based tract-tracing, and behavior and tissue analysis, we found that pericyte degeneration disrupted white-matter microcirculation, resulting in an accumulation of toxic blood-derived fibrin(ogen) deposits and blood-flow reductions, which triggered a loss of myelin, axons and oligodendrocytes. This disrupted brain circuits, leading to white-matter functional deficits before neuronal loss occurs. Fibrinogen and fibrin fibrils initiated autophagy-dependent cell death in oligodendrocyte and pericyte cultures, whereas pharmacological and genetic manipulations of systemic fibrinogen levels in pericyte-deficient, but not control mice, influenced the degree of white-matter fibrin(ogen) deposition, pericyte degeneration, vascular pathology and white-matter changes. Thus, our data indicate that pericytes control white-matter structure and function, which has implications for the pathogenesis and treatment of human white-matter disease associated with small-vessel disease.

An advanced white matter tract analysis in frontotemporal dementia and early-onset Alzheimer’s disease

Cortical and subcortical nuclei degenerate in the dementias, but less is known about changes in the white matter tracts that connect them. To better understand white matter changes in behavioral variant frontotemporal dementia (bvFTD) and early-onset Alzheimer’s disease (EOAD), we used a novel approach to extract full 3D profiles of fiber bundles from diffusion-weighted MRI (DWI) and map white matter abnormalities onto detailed models of each pathway. The result is a spatially complex picture of tract-by-tract microstructural changes. Our atlas of tracts for each disease consists of 21 anatomically clustered and recognizable white matter tracts generated from whole-brain tractography in 20 patients with bvFTD, 23 with age-matched EOAD, and 33 healthy elderly controls. To analyze the landscape of white matter abnormalities, we used a point-wise tract correspondence method along the 3D profiles of the tracts and quantified the pathway disruptions using common diffusion metrics – fractional anisotropy, mean, radial, and axial diffusivity. We tested the hypothesis that bvFTD and EOAD are associated with preferential degeneration in specific neural networks. We mapped axonal tract damage that was best detected with mean and radial diffusivity metrics, supporting our network hypothesis, highly statistically significant and more sensitive than widely studied fractional anisotropy reductions. From white matter diffusivity, we identified abnormalities in bvFTD in all 21 tracts of interest but especially in the bilateral uncinate fasciculus, frontal callosum, anterior thalamic radiations, cingulum bundles and left superior longitudinal fasciculus. This network of white matter alterations extends beyond the most commonly studied tracts, showing greater white matter abnormalities in bvFTD versus controls and EOAD patients. In EOAD, network alterations involved more posterior white matter – the parietal sector of the corpus callosum and parahipoccampal cingulum bilaterally. Widespread but distinctive white matter alterations are a key feature of the pathophysiology of these two forms of dementia.

Blockwise Human Brain Network Visual Comparison Using NodeTrix Representation

Visually comparing human brain networks from multiple population groups serves as an important task in the field of brain connectomics. The commonly used brain network representation, consisting of nodes and edges, may not be able to reveal the most compelling network differences when the reconstructed networks are dense and homogeneous. In this paper, we leveraged the block information on the Region Of Interest (ROI) based brain networks and studied the problem of blockwise brain network visual comparison. An integrated visual analytics framework was proposed. In the first stage, a two-level ROI block hierarchy was detected by optimizing the anatomical structure and the predictive comparison performance simultaneously. In the second stage, the NodeTrix representation was adopted and customized to visualize the brain network with block information. We conducted controlled user experiments and case studies to evaluate our proposed solution. Results indicated that our visual analytics method outperformed the commonly used node-link graph and adjacency matrix design in the blockwise network comparison tasks. We have shown compelling findings from two real-world brain network data sets, which are consistent with the prior connectomics studies.

An investigation of care-based vs. rule-based morality in frontotemporal dementia, Alzheimer's disease, and healthy controls.

Behavioral changes in dementia, especially behavioral variant frontotemporal dementia (bvFTD), may result in alterations in moral reasoning. Investigators have not clarified whether these alterations reflect differential impairment of care-based vs. rule-based moral behavior. This study investigated 18 bvFTD patients, 22 early onset Alzheimers disease (eAD) patients, and 20 healthy age-matched controls on care-based and rule-based items from the Moral Behavioral Inventory and the Social Norms Questionnaire, neuropsychological measures, and magnetic resonance imaging (MRI) regions of interest. There were significant group differences with the bvFTD patients rating care-based morality transgressions less severely than the eAD group and rule-based moral behavioral transgressions more severely than controls. Across groups, higher care-based morality ratings correlated with phonemic fluency on neuropsychological tests, whereas higher rule-based morality ratings correlated with increased difficulty set-shifting and learning new rules to tasks. On neuroimaging, severe care-based reasoning correlated with cortical volume in right anterior temporal lobe, and rule-based reasoning correlated with decreased cortical volume in the right orbitofrontal cortex. Together, these findings suggest that frontotemporal disease decreases care-based morality and facilitates rule-based morality possibly from disturbed contextual abstraction and set-shifting. Future research can examine whether frontal lobe disorders and bvFTD result in a shift from empathic morality to the strong adherence to conventional rules.

Neuroimaging and genetic risk for Alzheimer’s disease and addiction-related degenerative brain disorders

Neuroimaging offers a powerful means to assess the trajectory of brain degeneration in a variety of disorders, including Alzheimer’s disease (AD). Here we describe how multi-modal imaging can be used to study the changing brain during the different stages of AD. We integrate findings from a range of studies using magnetic resonance imaging (MRI), positron emission tomography (PET), functional MRI (fMRI) and diffusion weighted imaging (DWI). Neuroimaging reveals how risk genes for degenerative disorders affect the brain, including several recently discovered genetic variants that may disrupt brain connectivity. We review some recent neuroimaging studies of genetic polymorphisms associated with increased risk for late-onset Alzheimer’s disease (LOAD). Some genetic variants that increase risk for drug addiction may overlap with those associated with degenerative brain disorders. These common associations offer new insight into mechanisms underlying neurodegeneration and addictive behaviors, and may offer new leads for treating them before severe and irreversible neurological symptoms appear.

Disrupted rich club network in behavioral variant frontotemporal dementia and early-onset Alzheimer's disease

In network analysis, the so‐called “rich club” describes the core areas of the brain that are more densely interconnected among themselves than expected by chance, and has been identified as a fundamental aspect of the human brain connectome. This is the first in‐depth diffusion imaging study to investigate the rich club along with other organizational changes in the brains anatomical network in behavioral frontotemporal dementia (bvFTD), and a matched cohort with early‐onset Alzheimers disease (EOAD). Our study sheds light on how bvFTD and EOAD affect connectivity of white matter fiber pathways in the brain, revealing differences and commonalities in the connectome among the dementias. To analyze the breakdown in connectivity, we studied three groups: 20 bvFTD, 23 EOAD, and 37 healthy elderly controls. All participants were scanned with diffusion‐weighted magnetic resonance imaging (MRI), and based on whole‐brain probabilistic tractography and cortical parcellations, we analyzed the rich club of the brains connectivity network. This revealed distinct patterns of disruption in both forms of dementia. In the connectome, we detected less disruption overall in EOAD than in bvFTD [false discovery rate (FDR) critical Pperm = 5.7 × 10−3, 10,000 permutations], with more involvement of richly interconnected areas of the brain (chi‐squared P = 1.4 × 10−4)—predominantly posterior cognitive alterations. In bvFTD, we found a greater spread of disruption including the rich club (FDR critical Pperm = 6 × 10−4), but especially more peripheral alterations (chi‐squared P = 6.5 × 10−3), particularly in medial frontal areas of the brain, in line with the known behavioral socioemotional deficits seen in these patients. Hum Brain Mapp 37:868–883, 2016.

Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer's disease

Measures of network topology and connectivity aid the understanding of network breakdown as the brain degenerates in Alzheimers disease (AD). We analyzed 3-Tesla diffusion-weighted images from 202 patients scanned by the Alzheimers Disease Neuroimaging Initiative - 50 healthy controls, 72 with early- and 38 with late-stage mild cognitive impairment (eMCI/lMCI) and 42 with AD. Using whole-brain tractography, we reconstructed structural connectivity networks representing connections between pairs of cortical regions. We examined, for the first time in this context, the networks Laplacian matrix and its Fiedler value, describing the networks algebraic connectivity, and the Fiedler vector, used to partition a graph. We assessed algebraic connectivity and four additional supporting metrics, revealing a decrease in network robustness and increasing disarray among nodes as dementia progressed. Network components became more disconnected and segregated, and their modularity increased. These measures are sensitive to diagnostic group differences, and may help understand the complex changes in AD.

Left versus right hemisphere differences in brain connectivity: 4-Tesla HARDI tractography in 569 twins

Diffusion imaging can map anatomical connectivity in the living brain, offering new insights into fundamental questions such as how the left and right brain hemispheres differ. Anatomical brain asymmetries are related to speech and language abilities, but less is known about left/right hemisphere differences in brain wiring. To assess this, we scanned 457 young adults (age 23.4±2.0 SD years) and 112 adolescents (age 12-16) with 4-Tesla 105-gradient high-angular resolution diffusion imaging. We extracted fiber tracts throughout the brain with a Hough transform method. A 70×70 connectivity matrix was created, for each subject, based on the proportion of fibers intersecting 70 cortical regions. We identified significant differences in the proportions of fibers intersecting left and right hemisphere cortical regions. The degree of asymmetry in the connectivity matrices varied with age, as did the asymmetry in network topology measures such as the small-world effect.

3D tract-specific local and global analysis of white matter integrity in Alzheimer's disease

Alzheimers disease (AD) is a chronic neurodegenerative disease characterized by progressive decline in memory and other aspects of cognitive function. Diffusion‐weighted imaging (DWI) offers a non‐invasive approach to delineate the effects of AD on white matter (WM) integrity. Previous studies calculated either some summary statistics over regions of interest (ROI analysis) or some statistics along mean skeleton lines (Tract Based Spatial Statistic [TBSS]), so they cannot quantify subtle local WM alterations along major tracts. Here, a comprehensive WM analysis framework to map disease effects on 3D tracts both locally and globally, based on a study of 200 subjects: 49 healthy elderly normal controls, 110 with mild cognitive impairment, and 41 AD patients has been presented. 18 major WM tracts were extracted with our automated clustering algorithm—autoMATE (automated Multi‐Atlas Tract Extraction); we then extracted multiple DWI‐derived parameters of WM integrity along the WM tracts across all subjects. A novel statistical functional analysis method—FADTTS (Functional Analysis for Diffusion Tensor Tract Statistics) was applied to quantify degenerative patterns along WM tracts across different stages of AD. Gradually increasing WM alterations were found in all tracts in successive stages of AD. Among all 18 WM tracts, the fornix was most adversely affected. Among all the parameters, mean diffusivity (MD) was the most sensitive to WM alterations in AD. This study provides a systematic workflow to examine WM integrity across automatically computed 3D tracts in AD and may be useful in studying other neurological and psychiatric disorders. Hum Brain Mapp 38:1191–1207, 2017.