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The Reproducibility on the Estimation of Brain Small World Metrics using Probabilistic Diffusion Tractography
Wang, Wei Ping
Tsai, Shang Yueh
Wang, Wei Ping
diffusion tensor imaging (DTI)
|Issue Date: ||2014-07-29 16:12:04 (UTC+8)|
|Abstract: ||擴散權重影像與神經纖維追蹤可以用來探討腦區域之間的連結性，目前透過網路分析方式已經證實腦網路是有小世界的特性，最近也有研究不同受試者或者是病人之間的網路連結量測集中程度，但是擴散權重影像所運算出來的網路參數中間要經過很多步驟，這些中間步驟可能會影響到網路參數。所以有必要對於量測網路參數的受試者間變異性和重複量測重現性進行研究。本研究的目標是利用機率式神經纖維追蹤術量測大腦網路參數的重現性，探討三個會影響計算網路參數的重現性的變因，分別是，路徑定義方式、有無損耗正規化、受試者群體的網路連結篩選機制。變異係數定義（Coefficient of Variance, CV）為標準差除以平均值，分別計算二次量測之間的變異係數（CVwithin），以及受試者之間的變異係數（CVbetween），另外也計算組內相關係數（Intraclass correlation coefficient, ICC）。
掃描30受試者（15男，15女，年齡20~26）。每人掃描二次，並利用機率式神經纖維追蹤術計算網路連結，網路節點則是使用AAL標準模板定義的節點。若使用Wij = 1 – Pij定義長度，三項網路參數（區域效率、全域效率及損耗）重現性皆可接受（CVwithin<1.08%, CVwithin ≤ 10% and ICC > 0.7）。如果使用Wij=1/Pij定義長度，其損耗的CVwithin相較於Wij = 1 – Pij的大。如果長度的全距大，區域效率會不尋常地增加。如果二次掃描分別實施連結篩選，全域效率的CVwithin會較大。
Diffusion tensor imaging (DTI) with associate tractography can be used to access the connectivity of cortical regions in brain. Network analysis applied to connectivity matrix has demonstrated that brain has small world property. Recent studies also use network analysis to study the variation of concentricity among different group of subjects and patients. However the estimation of network metrics from DTI takes sophisticated processing steps. These intermediate steps may influence the estimation of network metric. It is therefore needed to investigate the potential variation of estimated network metrics using reproducibility test. The goal is to study the reproducibility of network properties derived from diffusion connectivity matrix constructed using probabilistic tractography. The effects of three factors on the reproducibility of network metrics estimation were studied. They are definition of path lengths of network matrix, path with and without cost normalization, the application of threshold to subjects groups. Coefficient of Variation (CV) defined as standard deviation divided by mean is used to test the intra-session (CVwithin) and inter subject (CVbetween) variability. Intra-class correlation coefficient (ICC) was also calculated.
Images were acquired from 30 healthy participants (15 male, 15 female, aged 20-26 years). Each subject was scanned twice, denoted as N1 and N2. Probabilistic tractography was performed to mapping of cortico-cortical anatomical connections between regions defined from an anatomical atlas. All three of the tested network metrics (local efficiency, global efficiency and cost) were identified as acceptable (CVwithin < 1.08%, CVwithin ≤ 10% and ICC > 0.7) using path length defined as Wij = 1 – Pij. When the path length is defined as Wij = 1/Pij, cost showed higher CVwithin compared to Wij = 1 – Pij. It is unusual that local efficiency increase when the range of path length of edges is large. Global efficiency showed higher CVwithin as threshold is applied to N1 and N2 separately compared to both scans together.
The present study revealed that different ways to construct cortical network had an effect on intra-session reproducibility. Our study also showed that despite evaluation of identical subjects using the same MRI system, variation of network metrics may be found by different research groups due to the potential errors from tractography. Replication of the experiment need to be carefully analyzed and interpreted.
|Reference: ||Behrens, T., et al. (2003). "Characterization and propagation of uncertainty in diffusion‐weighted MR imaging." Magnetic resonance in medicine 50(5): 1077-1088.
Bush, G., et al. (2005). "Functional neuroimaging of attention-deficit/hyperactivity disorder: a review and suggested future directions." Biological psychiatry 57(11): 1273-1284.
Cao, Q., et al. (2013). "Probabilistic diffusion tractography and graph theory analysis reveal abnormal white matter structural connectivity networks in drug-naive boys with attention deficit/hyperactivity disorder." The Journal of Neuroscience 33(26): 10676-10687.
Glasser, M. F. and J. K. Rilling (2008). "DTI tractography of the human brain's language pathways." Cerebral cortex 18(11): 2471-2482.
Gong, G., et al. (2009). "Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography." Cerebral cortex 19(3): 524-536.
Gong, G., et al. (2009). "Age-and gender-related differences in the cortical anatomical network." The Journal of Neuroscience 29(50): 15684-15693.
Hagmann, P., et al. (2008). "Mapping the structural core of human cerebral cortex." PLoS biology 6(7): e159.
Hagmann, P., et al. (2006). "Hand preference and sex shape the architecture of language networks." Human brain mapping 27(10): 828-835.
Iturria-Medina, Y., et al. (2008). "Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory." Neuroimage 40(3): 1064-1076.
Li, Y., et al. (2009). "Brain anatomical network and intelligence." PLoS computational biology 5(5): e1000395.
Liu, Y., et al. (2008). "Disrupted small-world networks in schizophrenia." Brain 131(4): 945-961.
Marenco, S., et al. (2006). "Regional distribution of measurement error in diffusion tensor imaging." Psychiatry Research: Neuroimaging 147(1): 69-78.
Maslov, S. and K. Sneppen (2002). "Specificity and stability in topology of protein networks." Science 296(5569): 910-913.
Meunier, D., et al. (2009). "Age-related changes in modular organization of human brain functional networks." Neuroimage 44(3): 715-723.
Parker, G. J., et al. (2003). "A framework for a streamline‐based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements." Journal of Magnetic Resonance Imaging 18(2): 242-254.
Reijneveld, J. C., et al. (2007). "The application of graph theoretical analysis to complex networks in the brain." Clinical Neurophysiology 118(11): 2317-2331.
Seidman, L. J., et al. (2005). "Structural brain imaging of attention-deficit/hyperactivity disorder." Biological psychiatry 57(11): 1263-1272.
Shu, N., et al. (2009). "Altered anatomical network in early blindness revealed by diffusion tensor tractography." PLoS One 4(9): e7228.
Sporns, O. (2002). "Network analysis, complexity, and brain function." Complexity 8(1): 56-60.
Stam, C. J. and J. C. Reijneveld (2007). "Graph theoretical analysis of complex networks in the brain." Nonlinear biomedical physics 1(1): 3.
Tzourio-Mazoyer, N., et al. (2002). "Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain." Neuroimage 15(1): 273-289.
Vaessen, M., et al. (2010). "The effect and reproducibility of different clinical DTI gradient sets on small world brain connectivity measures." Neuroimage 51(3): 1106-1116.
Wang, J. Y., et al. (2012). "A comprehensive reliability assessment of quantitative diffusion tensor tractography." Neuroimage 60(2): 1127-1138.
Watts, D. J. and S. H. Strogatz (1998). "Collective dynamics of ‘small-world’networks." nature 393(6684): 440-442.
|Source URI: ||http://thesis.lib.nccu.edu.tw/record/#G0101755004|
|Data Type: ||thesis|
|Appears in Collections:||[應用物理研究所 ] 學位論文|
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