Effects of Brain Parcellation on the Characterization of Topological Deterioration in Alzheimer's Disease

Wu, Zhanxiong; Xu, Dong; Potter, Thomas; Zhang, Yingchun

doi:10.3389/fnagi.2019.00113

Cited by 38 publications

(37 citation statements)

References 62 publications

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“…In contrast, CPL, GE, and NS showed better robustness and sensitivity to the whole-brain parcellation schemes among graph theoretical measures. These results are consistent with existing graph-theoretic findings in [2,35,40]. Moreover, only two persistent features IPF and BNP can recognize (p < 0.05) or almost recognize (p < 0.1) all group differences across all tested parcellations.…”

Section: Validation On Various Parcellationssupporting

confidence: 91%

“…In general, the network measures are sensitive to the brain parcellation strategies [2,35]. In order to evaluate the robustness of our findings, we performed identity experiments on other three parcellation schemes.…”

Section: Validation On Various Parcellationsmentioning

confidence: 99%

“…As a transitional stage between normal aging and AD, mild cognitive impairment (MCI) increases the risk of developing dementia. Understanding the physiological deterioration caused by AD and MCI provides an opportunity to develop future drugs and predict AD onset [2,3]. The human brain is interconnected by a large number of neurons through synapses, forming a highly complex network system that realizes various intelligent behaviors of human beings.…”

Section: Introductionmentioning

confidence: 99%

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White Matter Brain Network Research in Alzheimer’s Disease Using Persistent Features

et al. 2020

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Despite the severe social burden caused by Alzheimer’s disease (AD), no drug than can change the disease progression has been identified yet. The structural brain network research provides an opportunity to understand physiological deterioration caused by AD and its precursor, mild cognitive impairment (MCI). Recently, persistent homology has been used to study brain network dynamics and characterize the global network organization. However, it is unclear how these parameters reflect changes in structural brain networks of patients with AD or MCI. In this study, our previously proposed persistent features and various traditional graph-theoretical measures are used to quantify the topological property of white matter (WM) network in 150 subjects with diffusion tensor imaging (DTI). We found significant differences in these measures among AD, MCI, and normal controls (NC) under different brain parcellation schemes. The decreased network integration and increased network segregation are presented in AD and MCI. Moreover, the persistent homology-based measures demonstrated stronger statistical capability and robustness than traditional graph-theoretic measures, suggesting that they represent a more sensitive approach to detect altered brain structures and to better understand AD symptomology at the network level. These findings contribute to an increased understanding of structural connectome in AD and provide a novel approach to potentially track the progression of AD.

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Section: Validation On Various Parcellationssupporting

confidence: 91%

Section: Validation On Various Parcellationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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White Matter Brain Network Research in Alzheimer’s Disease Using Persistent Features

et al. 2020

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“…Finally, although we used the best whole-brain parcellation currently available, there has been limited validation of the CAB-NP due to its recent release. However, all cortical boundaries were taken directly from the multimodal surface parcellation meticulously derived by the HCP (9), which has been found to outperform other contemporary atlases and is widely considered a gold standard of human brain segmentation (64,65). Subcortical parcels in the CAB-NP were then determined using a consensus partitioning approach based on data from over 300 HCP subjects divided into independent discovery and validation sets to ensure reproducibility and reliability (10).…”

Section: Discussionmentioning

confidence: 99%

Neural Correlates of Positive and Negative Valence System Dysfunction in Adolescents Revealed by Data-Driven Parcellation and Resting-State Network Modeling

Gabbay

Liu

DeWitt

et al. 2020

Preprint

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Objective: Adolescence is a period of rapid brain development when symptoms of mood, anxiety, and other disorders often first emerge, suggesting disruptions in maturing reward circuitry may play a role in mental illness onset. Here, we characterized associations between resting-state network properties and psychiatric symptomatology in medication-free adolescents with a wide range of symptom severity.Methods: Adolescents (age 12-20) with mood and/or anxiety symptoms (n=68) and healthy controls (n=19) completed diagnostic interviews, depression/anhedonia/anxiety questionnaires, and 3T resting-state fMRI (10min/2.3mm/TR=1s). Data were preprocessed (HCP Pipelines), aligned (MSMAll), and parcellated into 750 nodes encompassing the entire cortex/subcortex (Cole-Anticevic Brain-wide Network Partition). Weighted graph theoretical metrics (Strength Centrality=CStr; Eigenvector Centrality=CEig; Local Efficiency=ELoc) were estimated within Whole Brain and task-derived Reward Anticipation/Attainment/Prediction Error networks. Associations with clinical status and symptoms were assessed non-parametrically (two-tailed pFWE<0.05).Results: Relative to controls, clinical adolescents had increased ventral striatum CEig within the Reward Attainment network. Across subjects, depression correlated with subgenual cingulate CStr and ELoc, anhedonia correlated with ventromedial prefrontal CStr and lateral amygdala ELoc, and anxiety negatively correlated with parietal operculum CEig and medial amygdala ELoc within the Whole Brain network.Conclusions: Using a data-driven analysis approach, high-quality parcellation, and clinically diverse adolescent cohort, we found that symptoms within positive and negative valence system constructs differentially associated with resting-state network abnormalities: depression and anhedonia, as well as clinical status, involved greater influence and communication efficiency in prefrontal and limbic reward areas, whereas anxiety was linked to reduced influence/efficiency in amygdala and cortical regions involved in stimulus monitoring.

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“…The similarity between groups of atlases, such as the various Schaefer atlases, the Yeo liberal atlases, and the DS atlases, is immediately apparent. Recent work highlighting the complementary information provided by disparate parcellations stresses the importance of the availability and ease-of-use of a collection of parcellations from heterogeneous sources [43,[49][50][51].…”

Section: Dice Coefficientmentioning

confidence: 99%

Standardizing Human Brain Parcellations

Myers

Arvapalli

Ramachandran

et al. 2019

Preprint

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Using brain atlases to localize regions of interest is a required for making neuroscientifically valid statistical inferences. These atlases, represented in volumetric or surface coordinate spaces, can describe brain topology from a variety of perspectives. Although many human brain atlases have circulated the field over the past fifty years, limited effort has been devoted to their standardization and specification. The purpose of standardization and specification is to ensure consistency and transparency with respect to orientation, resolution, labeling scheme, file storage format, and coordinate space designation. Consequently, researchers are often confronted with limited knowledge about a given atlas's organization, which make analytic comparisons more difficult. To fill this gap, we consolidate an extensive selection of popular human brain atlases into a single, curated open-source library, where they are stored following a standardized protocol with accompanying metadata. We propose that this protocol serves as the basis for storing future atlases. To demonstrate the utility of storing and standardizing these atlases following a common protocol, we conduct an experiment using data from the Healthy Brain Network whereby we quantify the statistical dependence of each atlas label on several key phenotypic variables. The repository containing the atlases, the specification, as well as relevant transformation functions is available at https://neurodata.io/mriBackground & Summary Understanding the brain's organization is one of the key challenges in human neuroscience [1] and is critical for clinical translation [2]. Parcellation of the brain into functionally and structurally distinct regions has seen impressive advances in recent years [3], and has grown the field of network neuroscience [4,5]. Through a range of techniques such as clustering [6-9], multivariate decomposition [10,11] , gradient based connectivity [1,[12][13][14][15][16], and multimodal neuroimaging [1], parcellations have enabled fundamental insights into the brain's topological organization and network properties [17]. In turn, these properties have allowed researchers to investigate brain-behavioral associations with developmental [18,19], cognitive [20,21], and clinical phenotypes [22][23][24].More recently, researchers interested in understanding brain organization are presented with a variety of brain atlases that can be used to define nodes of network-based analyses [25]. While this variety is a boon to researchers, the use of different parcellations across studies makes assessing reproducibility of brain-behavior relationships difficult (e.g. comparing across parcellations with different organizations and numbers of nodes; [5]). Amalgamating multiple brain parcellations into a single, standardized, curated list would offer researchers a valuable resource for evaluating replication of neuroimaging studies.Some efforts to consolidate these atlases is already underway. For example, Nilearn is a popular Python package that provides machine-...

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Effects of Brain Parcellation on the Characterization of Topological Deterioration in Alzheimer's Disease

Cited by 38 publications

References 62 publications

White Matter Brain Network Research in Alzheimer’s Disease Using Persistent Features

White Matter Brain Network Research in Alzheimer’s Disease Using Persistent Features

Neural Correlates of Positive and Negative Valence System Dysfunction in Adolescents Revealed by Data-Driven Parcellation and Resting-State Network Modeling

Standardizing Human Brain Parcellations

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