The Structural and Functional Connectome and Prediction of Risk for Cognitive Impairment in Older Adults

The human connectome refers to a comprehensive description of the brain's structural and functional connections in terms of brain networks. As the field of brain connectomics has developed, data acquisition, subsequent processing and modeling, and ultimately the representation of the connectome have become better defined and integrated with network science approaches. In this way, the human connectome has provided a way to elucidate key features of not only the healthy brain but also diseased brains. The field has quickly evolved, offering insights into network disruptions that are characteristic for specific neurodegenerative disorders. In this paper, we provide a brief review of the field of brain connectomics, as well as a more in-depth survey of recent studies that have provided new insights into brain network pathologies, including those found in Alzheimer's disease (AD), patients with mild cognitive impairment (MCI), and finally in people classified as being "at risk". Until the emergence of brain connectomics, most previous studies had assessed neurodegenerative diseases mainly by focusing on specific and dispersed locales in the brain. Connectomics-based approaches allow us to model the brain as a network, which allows for inferences about how dynamic changes in brain function would be affected in relation to structural changes. In fact, looking at diseases using network theory gives rise to new hypotheses on mechanisms of pathophysiology and clinical symptoms. Finally, we discuss the future of this field and how understanding both the functional and structural connectome can aid in gaining sharper insight into changes in biological brain networks associated with cognitive impairment and dementia

Axonal diameter and density estimated with 7-Tesla hybrid diffusion imaging in transgenic Alzheimer rats

Diffusion-weighted MR imaging (DWI) is a powerful tool to study brain tissue microstructure. DWI is sensitive to subtle changes in the white matter (WM), and can provide insight into abnormal brain changes in diseases such as Alzheimer’s disease (AD). In this study, we used 7-Tesla hybrid diffusion imaging (HYDI) to scan 3 transgenic rats (line TgF344-AD; that model the full clinico-pathological spectrum of the human disease) ex vivo at 10, 15 and 24 months. We acquired 300 DWI volumes across 5 q-sampling shells (b=1000, 3000, 4000, 8000, 12000 s/mm^2). From the top three b-value shells with highest signal-to-noise ratios, we reconstructed markers of WM disease, including indices of axon density and diameter in the corpus callosum (CC) – directly quantifying processes that occur in AD. As expected, apparent anisotropy progressively decreased with age; there were also decreases in the intra- and extra-axonal MR signal along axons. Axonal diameters were larger in segments of the CC (splenium and body, but not genu), possibly indicating neuritic dystrophy – characterized by enlarged axons and dendrites as previously observed at the ultrastructural level (see Cohen et al., J. Neurosci. 2013). This was further supported by increases in MR signals trapped in glial cells, CSF and possibly other small compartments in WM structures. Finally, tractography detected fewer fibers in the CC at 10 versus 24 months of age. These novel findings offer great potential to provide technical and scientific insight into the biology of brain disease

Axonal diameter and density estimated with 7-Tesla hybrid diffusion imaging in transgenic Alzheimer rats

Diffusion-weighted MR imaging (DWI) is a powerful tool to study brain tissue microstructure. DWI is sensitive to subtle changes in the white matter (WM), and can provide insight into abnormal brain changes in diseases such as Alzheimer’s disease (AD). In this study, we used 7-Tesla hybrid diffusion imaging (HYDI) to scan 3 transgenic rats (line TgF344-AD; that model the full clinico-pathological spectrum of the human disease) ex vivo at 10, 15 and 24 months. We acquired 300 DWI volumes across 5 q-sampling shells (b=1000, 3000, 4000, 8000, 12000 s/mm^2). From the top three b-value shells with highest signal-to-noise ratios, we reconstructed markers of WM disease, including indices of axon density and diameter in the corpus callosum (CC) – directly quantifying processes that occur in AD. As expected, apparent anisotropy progressively decreased with age; there were also decreases in the intra- and extra-axonal MR signal along axons. Axonal diameters were larger in segments of the CC (splenium and body, but not genu), possibly indicating neuritic dystrophy – characterized by enlarged axons and dendrites as previously observed at the ultrastructural level (see Cohen et al., J. Neurosci. 2013). This was further supported by increases in MR signals trapped in glial cells, CSF and possibly other small compartments in WM structures. Finally, tractography detected fewer fibers in the CC at 10 versus 24 months of age. These novel findings offer great potential to provide technical and scientific insight into the biology of brain disease

ApoE4 effects on the structural covariance brain networks topology in Mild Cognitive Impairment

The Apolipoprotein E isoform E4 (ApoE4) is consistently associated with an elevated risk of developing late-onset Alzheimer's Disease (AD). However, little is known about his potential genetic modulation on the structural covariance brain networks during prodromal stages like Mild Cognitive Impairment (MCI). The covariance phenomenon is based on the observation that regions correlating in morphometric descriptors are often part of the same brain system. In a first study, I assessed the ApoE4-related changes on the brain network topology in 256 MCI patients, using the regional cortical thickness to define the covariance network. The cross-sectional sample selected from the ADNI database was subdivided into ApoE4-positive (Carriers) and negative (non-Carriers). At the group-level, the results showed a significant decrease in characteristic path length, clustering index, local efficiency, global connectivity, modularity, and increased global efficiency for Carriers compared to non-Carriers. Overall, I found that ApoE4 in MCI shaped the topological organization of cortical thickness covariance networks. In the second project, I investigated the impact of ApoE4 on the single-subject gray matter networks in a sample of 200 MCI from the ADNI database. The patients were classified based on clinical outcome (stable MCI versus converters to AD) and ApoE4 status (Carriers versus non-Carriers). The effects of ApoE4 and disease progression on the network measures at baseline and rate of change were explored. The topological network attributes were correlated with AD biomarkers. The main findings showed that gray matter network topology is affected independently by ApoE4 and the disease progression (to AD) in late-MCI. The network measures alterations showed a more random organization in Carriers compared to non-Carriers. Finally, as additional research, I investigated whether a network-based approach combined with the graph theory is able to detect cerebrovascular reactivity (CVR) changes in MCI. Our findings suggest that this experimental approach is more sensitive to identifying subtle cerebrovascular alterations than the classical experimental designs. This study paves the way for a future investigation on the ApoE4-cerebrovascular interaction effects on the brain networks during AD progression. In summary, my thesis results provide evidence of the value of the structural covariance brain network measures to capture subtle neurodegenerative changes associated with ApoE4 in MCI. Together with other biomarkers, these variables may help predict disease progression, providing additional reliable intermediate phenotypes

Using brain connectomics to detect functional connectivity differences in Alzheimer's disease

Indiana University-Purdue University Indianapolis (IUPUI)Prodromal Alzheimer’s disease (AD) has recently been identified as a disease state where pathophysiological changes may progress despite the absence of significant clinical symptoms. Yet, the specific processes of neural dysfunction occurring during this preclinical phase remain unclear. Resting state fMRI (RS-fMRI) in combination with brain connectomic measurements may be able to provide ways to measure subtle connectivity changes in different neurological disease states. For instance, RS-fMRI scans allow us to determine functionally connected yet spatially distinct brain regions that can then be separated into resting-state networks (RSNs). More recently, the exploration of RSNs in disease states have proved promising since they have been reliably altered when compared to a control population. By using brain connectomic approaches to assess functional connectivity we can evaluate the human connectome from a different and more global perspective to help us better understand and detect prodromal neurodegenerative disease states

The Structural Basis for Brain Health

Cardiovascular disease (CVD) remains the leading cause of mortality in the United States. Stroke and dementia are the leading causes of adult disability worldwide, and the 5th and 6th leading causes of mortality respectively in the United States. Furthermore, CVD annually accounts for approximately $330 billion in direct and indirect costs in the United States: approximately one in seven health care dollars is spent on CVD. While these diseases have different etiologies, and present with different clinical manifestations and prognosis, converging evidence increasingly supports the idea of CVD as a common pathophysiological origin of cerebrovascular disease, potentially indicating a complex interplay between brain health and cardiovascular health. In this thesis, we leverage methodological advancements in systems and computational neurosciences related to the human brain connectome to assess individual topological network organization and integrity in acute and chronic stroke cohorts, and in a non-stroke cohort with varying CV risk factor burden, using graph theory and network analysis. We propose measures that underly neuroanatomical mechanisms that constitute efficient transfer of information and brain health. We demonstrate the impact of cardiovascular risk factors on brain health, even before overt clinical manifestation, and the resulting impact on cognitive performance, and further determine the underlying pathophysiology relating white matter disease and post-stroke outcomes

How to build a functional connectomic biomarker for mild cognitive impairment from source reconstructed MEG resting-state activity: the combination of ROI representation and connectivity estimator matters

Our work aimed to demonstrate the combination of machine learning and graph theory for the designing of a connectomic biomarker for mild cognitive impairment (MCI) subjects using eyes-closed neuromagnetic recordings. The whole analysis based on source-reconstructed neuromagnetic activity. As ROI representation, we employed the principal component analysis (PCA) and centroid approaches. As representative bi-variate connectivity estimators for the estimation of intra and cross-frequency interactions, we adopted the phase locking value (PLV), the imaginary part (iPLV) and the correlation of the envelope (CorrEnv). Both intra and cross-frequency interactions (CFC) have been estimated with the three connectivity estimators within the seven frequency bands (intra-frequency) and in pairs (CFC), correspondingly. We demonstrated how different versions of functional connectivity graphs single-layer (SL-FCG) and multi-layer (ML-FCG) can give us a different view of the functional interactions across the brain areas. Finally, we applied machine learning techniques with main scope to build a reliable connectomic biomarker by analyzing both SL-FCG and ML-FCG in two different options: as a whole unit using a tensorial extraction algorithm and as single pair-wise coupling estimations. We concluded that edge-weighed feature selection strategy outperformed the tensorial treatment of SL-FCG and ML-FCG. The highest classification performance was obtained with the centroid ROI representation and edge-weighted analysis of the SL-FCG reaching the 98% for the CorrEnv in α1:α2 and 94% for the iPLV in α2. Classification performance based on the multi-layer participation coefficient, a multiplexity index reached 52% for iPLV and 52% for CorrEnv. Selected functional connections that build the multivariate connectomic biomarker in the edge-weighted scenario are located in default-mode, fronto-parietal and cingulo-opercular network. Our analysis supports the notion of analysing FCG simultaneously in intra and cross-frequency whole brain interactions with various connectivity estimators in beamformed recordings

How do morphological alterations caused by chronic pain distribute across the brain? A meta-analytic co-alteration study

It was recently suggested that in brain disorders neuronal alterations does not occur randomly, but tend to form patterns that resemble those of cerebral connectivity. Following this hypothesis, we studied the network formed by co-altered brain regions in patients with chronic pain. We used a meta-analytical network approach in order to: i) find out whether the neuronal alterations distribute randomly across the brain; ii) find out (in the case of a non-random pattern of distribution) whether a disease-specific pattern of brain co-alterations can be identified and characterized in terms of altered areas (nodes) and propagation links between them (edges); iii) verify whether the co-alteration pattern overlaps with the pattern of functional connectivity; iv) describe the topological properties of the co-alteration network and identify the highly connected nodes that are supposed to have a pre-eminent role in the diffusion timing of neuronal alterations across the brain. Our results indicate that: i) gray matter (GM) alterations do not occur randomly; ii) a symptom-related pattern of structural co-alterations can be identified for chronic pain; iii) this co-alteration pattern resembles the pattern of brain functional connectivity; iv) within the co-alteration network a set of highly connected nodes can be identified.This study provides further support to the hypothesis that neuronal alterations may spread according to the logic of a network-like diffusion suggesting that this type of distribution may also apply to chronic pain. Keywords: Chronic pain, Neuronal alterations, Pathoconnectomics, Co-alteration network, Network analysis, Voxel-based morphometr