PMC:4718081 / 19203-24762
Annnotations
{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4718081","sourcedb":"PMC","sourceid":"4718081","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4718081","text":"Discussion\nIn this study, we propose a disease progression model termed as mini network balance model. The mini network balance model evaluates the disease progression in three ways. Firstly, the mini network balance map describes mini network imbalance visually. Secondly, the robustness of mini network is measured by mini network disruption probability which is a proxy for the disease progression. Thirdly, the integral variation of mini network is evaluated by the mini network disruption parameters U, K, and φ. The variation of mini network is usually complex. These three parameters decompose the complicated variation into simple variation. Firstly, parameter K is response to the consistency variation of biomarkers in mini network. Secondly, parameter φ represents the inconsistency variation of biomarkers in mini network. Parameter U is response to the total variation. With value of mini network integral parameters greater, the disease risk gets higher. The clinical relevance of mini network integral disruption parameters is that they can help enhance the accuracy and specificity of AD and MCI diagnosis. Especially, parameter U has the greatest contribution to the accuracy and specificity of the classification.\nCompared to several previous researches based on CSF markers list in Table 4, the mini network balance model achieves relatively high performance on the classification of AD vs. normal and MCI vs. normal. Furthermore, the comparison to researches based on imaging markers shows that CSF markers and imaging markers may play similar roles in classification of AD vs. normal. Because, AD may cause atrophy in multiple region in brain and CSF markers reflect these change (Reiman and Jagust, 2012; Rosenmann, 2012). However, these two different types may play different roles in classification of MCI vs. normal. On one hand, imaging methods are often recommended to help rule out potentially reversible brain abnormalities like tumors or subdural hematomas in patients with MCI (Reiman and Jagust, 2012). On the other hand, CSF markers have better accuracy and specificity in classification of MCI vs. normal and they can be employed to identify the prodromal AD (Blennow and Zetterberg, 2015).\nTable 4 Comparison to previous researches.\nResearch Biomarkers Normal vs. AD Normal vs. MCI\nACC (%) SEN (%) SPE (%) ACC (%) SEN (%) SPE (%)\nSVM based on network disruption model (this study) CSF 95 95 95 90 95 84\nSVM (Apostolova et al., 2014) CSF 82 – – 74 – –\nMulti-modal multi-task (M3T) learning (Zhang et al., 2012) CSF 93 – – 83 – –\nLogistic Regression (Teipel et al., 2007) CSF 81 78 83 – – –\nLarge-scale regularized logistic regression (Casanova et al., 2013) CSF 75 71 78 64 50 74\nMulti-modal multi-task (M3T) learning (Zhang et al., 2012) MRI 93 – – 83 – –\nSVM (Salvatore et al., 2015) MRI 76 – – 72 – –\nSparse representation (Xu et al., 2015) MRI 95 96 90 75 66 82\nImage-level hierarchical classifier learning (Suk et al., 2014) MRI 92 92 95 84 99 54\nMulti-modal multi-task (M3T) learning (Zhang et al., 2012) PET 93 – – 83 – –\nSparse representation (Xu et al., 2015) PET 91 89 93 72 65 79\nImage-level hierarchical classifier learning (Suk et al., 2014) PET 92 88 96 84 99 57\nACC, accuracy; SEN, sensitivity; SPE, specificity. The amyloid cascade hypothesis has been widely accepted as AD etiology. However, our model shows that Aβ accumulation may not be the sole factor in AD etiology. Joint contribution of P-tau and Aβ may be another potential major contributor to the AD progression. Consistent with Prior studies, Aβ toxicity is P-tau dependent (Kayed, 2010; Desikan et al., 2012). In other word, Aβ in the absence of P-tau is not necessarily associated with loss of cognitive function. Previous researches have proposed a possible mechanism for the interaction of P-tau and Aβ. On one hand, P-tau can increase the activity of AChE which can elevate the level of PS-1 and then accelerate the Aβ deposition (García-Ayllón et al., 2011; Silveyra et al., 2012). On the other hand, Aβ can activate tau hyperphosphorylation pathways (García-Ayllón et al., 2011). The crosstalk between Aβ and P-tau forms a vicious cycle in which they elevate each other and trigger cognitive decline.\nFurthermore, more and more evidences in clinical trials suggest the multifactorial nature of AD. For example, Anti-bodies bapineuzumab and solanezumab targeted at Aβ have failed to meet their primary endpoints in the high-profile phase 3 clinical trials (Castello et al., 2014). Doody et al. attribute the failure to administering the therapy at late stages and propose that it should be instituted in early stages (Doody et al., 2014). However, the proposed model suggests that even at early stage the therapies targeted at Aβ only may have limited effect on AD. The presented results suggest that P-tau may also play a vital role in AD progression. Several prior studies have proved that immunotherapies targeted at P-tau can reduce cognitive impairment in animal model (Boutajangout et al., 2010; Kayed, 2010; Lim et al., 2013). In addition, our results suggested that single marker might play a limited role in disease progression and joint contribution of P-tau and Aβ might be the potential vital factors in the disease progression. Therefore, the single-target immunotherapies may have limited effects on AD. According to our results, combination therapy of reducing both P-tau and Aβ may be an effective strategy for AD 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