# White Matter Network Alterations in Alzheimer's Disease Patients
## 1 Introduction  
Alzheimer's disease (AD), an irreversible and progressive brain disorder, is the most common cause of dementia [1; 2; 3; 4]. The deposition of two abnormal protein fragments known as plagues and tangles causes the death of neuron cells [2]. As such, neuronal death and white matter degeneration characterized by cortical atrophy take place primarily in medial temporal region and gradually spread over the entorhinal cortex and the limbic system and eventually affect the neocortical regions through synapses and neural connections [5; 6]. Mild cognitive impairment (MCI) is the stage between healthy controls (HC) aging and serious cognitive decline of dementia [7; 8].  
Different imaging models, such as electroencephalography (EEG) [9], functional magnetic resonance imaging (fMRI) [10] and positron emission tomography (PET) [11], have been used to study the progression of disease. The majority of studies have investigated using the structural magnetic resonance imaging (MRI) [12; 13; 14] that assists in the visualization of degenerative histological changes caused by neurological disorders. The feature extracted from MRI is typically grey matter volumes. Although grey matter volume of the hippocampus has been an important biomarker of medial temporal lobe neurodegeneration, alterations of hippocampal white matter pathways are often observed in AD [15].  
Recent studies [16; 17; 18; 19] suggest a direct role of white matter (WM) degeneration in AD/MCI pathogenesis. Similarly, diffusion tensor imaging (DTI)-based studies [17] show lower fractional anisotropy (FA) and higher mean diffusivity (MD) in MCI and AD patients compared to controls.  
AD and MCI patients exhibit alterations in widely distributed connectivity pairs. This line of evidence suggests that AD is a disconnection syndrome characterized by network disruptions [20; 21].  
In recent years, many studies based on graph theory using WM features have provided metrics such as small world properties, clustering and modularity, characterizing relevant properties of networks such as efficiency and the integration and segregation of the brain network in the AD and MCI cohorts [22; 23; 24; 25]. Graph theory is a mathematical approach for the analysis of complex networks constructed of \"nodes\" i.e., in our case brain regions of interest (ROI), which are interconnected via \"edges\". Graph theory has emerged as a powerful tool for identifying anatomically localized subnetworks associated with neuronal alterations in psychiatric conditions [26].  
Similarly, network-based statistics (NBS) [27; 28] has been widely used for testing hypotheses about the human connectome. More specifically, it has been used to identify networks comprising the connectome associated with between-group difference. Initially, an arbitrary threshold is set to determine the network cluster. Next, the permutations are performed on the determined network. NBS potentially yields notably large power to provide the altered clustering network structure in AD/MCI.  
In the current study, we use NBS to investigate the abnormal network components and clustering structure of connectivity alterations in AD and MCI. We employ NBS on the DTI model to explore the alterations in the network structure of white matter connectome. Further, we perform an integrated analysis of graph theoretical analysis with NBS to identify the subtle topological changes that occur in specific regions connected by the altered brain network. The overall flow chart of this study is shown in Figure 1.