在一篇發(fā)表于期刊Neuron上的論文中,，舊金山退伍軍人醫(yī)療中心（SFVCMC）和加利福尼亞大學(xué)的研究人員指出，一項(xiàng)分析腦圖像的新技術(shù)可提供預(yù)測(cè)許多退行性大腦疾病進(jìn)展程度和生理途徑的可能性,。這項(xiàng)新技術(shù)就是通過使用核磁共振成像（MIR）技術(shù),。該技術(shù)提供了表明癡呆以與感染性蛋白質(zhì)疾病（prion diseases）相同的方式沿特定神經(jīng)元通路蔓延至大腦的證據(jù),。
科學(xué)家們應(yīng)用新的計(jì)算機(jī)模建技術(shù)真實(shí)地預(yù)測(cè)了阿爾茨海默病和額顳葉癡呆（FTD）的生理進(jìn)展程度,。此模型以全腦成像為基礎(chǔ)，即繪制連接大腦不同區(qū)域的神經(jīng)通路或"通信電線"的核磁共振成像技術(shù),。在18個(gè)阿爾茨海默病患者和FTD患者中，沿這些途徑的疾病傳播,，正如模型預(yù)測(cè)的那樣,，與大腦退化的實(shí)際核磁共振圖像幾乎完全吻合。相關(guān)研究結(jié)果發(fā)表在期刊。
盡管此研究結(jié)果還需要重復(fù)驗(yàn)證,，但是通過這種方法可預(yù)測(cè)阿爾茨海默病,、FTD和其他退化性腦疾病未來腦萎縮的位置和過程，只根據(jù)疾病一開始時(shí)所做的核磁共振成像,。在計(jì)劃治療和幫助患者及其家屬了解老年癡呆癥進(jìn)展是非常有用的,。
而且，該結(jié)果與一個(gè)新興觀念是一致的,，此新興觀念認(rèn)為,，腦損傷出現(xiàn)在這些彌散的感染性蛋白質(zhì)樣傳播的神經(jīng)退行性疾病中。
感染性蛋白質(zhì)是正常蛋白質(zhì)的一種有傳染性的,、錯(cuò)誤折疊形式,。這些蛋白質(zhì)在它們發(fā)生的大腦中留下破壞性的淀粉樣沉著物，引起退化,，最后至死亡,。它們負(fù)責(zé)人克雅氏（Creutzfeldt-Jakob disease）和牛海綿狀腦病（俗稱瘋牛病,，bovine spongiform encephalopathy）,。1997年，舊金山加利福尼亞大學(xué)（UCSF）神經(jīng)學(xué)家Stanley B. Prusiner因感染性蛋白質(zhì)的發(fā)現(xiàn)和特征化而獲諾貝爾醫(yī)學(xué)獎(jiǎng),。他的發(fā)現(xiàn)推翻了現(xiàn)代生物學(xué)的一個(gè)信條,，表明蛋白質(zhì)能導(dǎo)致感染。
現(xiàn)在,，越來越多科學(xué)家開始支持一種觀點(diǎn),，即癡呆進(jìn)展的感染性蛋白質(zhì)樣模型觀點(diǎn)，它是指一個(gè)神經(jīng)元中的錯(cuò)誤折疊蛋白質(zhì)會(huì)傳染鄰近腦細(xì)胞,，依次地引起鄰近腦細(xì)胞中蛋白質(zhì)錯(cuò)誤折疊,，這些錯(cuò)誤折疊蛋白質(zhì)沿著大腦中某一網(wǎng)狀系統(tǒng)蔓延傳播。例如,，在阿爾茨海默病中,，淀粉狀蛋白質(zhì)沿著記憶網(wǎng)狀系統(tǒng)蔓延傳播。該論文對(duì)此新興觀點(diǎn)進(jìn)行了補(bǔ)充說明,。（生物谷bioon.com）
doi:10.1016/j.neuron.2011.12.040
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A Network Diffusion Model of Disease Progression in Dementia
Ashish Raj, Amy Kuceyeski, Michael Weiner
Patterns of dementia are known to fall into dissociated but dispersed brain networks, suggesting that the disease is transmitted along neuronal pathways rather than by proximity. This view is supported by neuropathological evidence for "prion-like" transsynaptic transmission of disease agents like misfolded tau and beta amyloid. We mathematically model this transmission by a diffusive mechanism mediated by the brain's connectivity network obtained from tractography of 14 healthy-brain MRIs. Subsequent graph theoretic analysis provides a fully quantitative, testable, predictive model of dementia. Specifically, we predict spatially distinct "persistent modes," which, we found, recapitulate known patterns of dementia and match recent reports of selectively vulnerable dissociated brain networks. Model predictions also closely match T1-weighted MRI volumetrics of 18 Alzheimer's and 18 frontotemporal dementia subjects. Prevalence rates predicted by the model strongly agree with published data. This work has many important implications, including dimensionality reduction, differential diagnosis, and especially prediction of future atrophy using baseline MRI morphometrics.
