8月25日,，《神經(jīng)學(xué)雜志》（Journal of Neurology）在線發(fā)表的一項(xiàng)研究中，研究人員聲稱,，由于注意力缺陷多動(dòng)障礙（ADHD）,，胎兒酒精譜系障礙（FASD）和帕金森?。≒D）都涉及眼控制和注意功能障礙,，通過評價(jià)患者看電視時(shí)怎樣移動(dòng)他們的眼睛，可輕松診斷出這些疾病,。
“自然的關(guān)注和眼球運(yùn)動(dòng)行為 - 就像一滴唾液 - 包含個(gè)體以及他/她的腦功能或功能障礙狀態(tài)的生物特征識(shí)別”,，文章指出， “然而,，這樣的個(gè)體特征,，特別是神經(jīng)系統(tǒng)疾病可能含有潛在的生物標(biāo)志物，尚未被成功解碼,。”
典型的檢測方法——臨床評價(jià),、結(jié)構(gòu)化行為任務(wù)、神經(jīng)影像,，花費(fèi)人力,、物力，并且受患者理解力和依從性的限制,。為了解決這個(gè)問題,， 南加州大學(xué)維特比工程學(xué)院計(jì)算機(jī)科學(xué)系Po-He Tseng 博士生和Laurent Itti教授與加拿大Queen大學(xué)的合作者，已經(jīng)設(shè)計(jì)出一種新的篩查方法,。
參與者,，遵循簡單指示“觀看和享受”20分鐘的電視短片，記錄他們的眼動(dòng)。眼球追蹤數(shù)據(jù),，然后再結(jié)合規(guī)范的眼球追蹤數(shù)據(jù)和視覺注意計(jì)算模型提取的224個(gè)量化特征,，來確定區(qū)別于對照組患者的關(guān)鍵特征。
結(jié)合108例的眼動(dòng)數(shù)據(jù)得出,，該小組能夠識(shí)別帕金森病的老年人有89.6％的準(zhǔn)確率,，兒童多動(dòng)癥或FASD 有77.3％的準(zhǔn)確率。
該團(tuán)隊(duì)的方法在關(guān)注和凝視控制是影響特定的疾病這方面提供了新的見解,，提供了相當(dāng)有前景的,，容易實(shí)施，低成本的,，高通量篩選工具,，特別是對于傳統(tǒng)測試依從性低的年幼的兒童和老年人。
“這是我們第一次通過個(gè)人的日常行為,，精確破譯他們的神經(jīng)狀態(tài)”Itti 說道,。（生物谷Bioon.com）
doi:10.1007/s00415-012-6631-2
PMC:
PMID:
High-throughput classification of clinical populations from natural viewing eye movements.
Tseng PH, Cameron IG, Pari G, Reynolds JN, Munoz DP, Itti L.
Many high-prevalence neurological disorders involve dysfunctions of oculomotor control and attention, including attention deficit hyperactivity disorder (ADHD), fetal alcohol spectrum disorder (FASD), and Parkinson's disease (PD). Previous studies have examined these deficits with clinical neurological evaluation, structured behavioral tasks, and neuroimaging. Yet, time and monetary costs prevent deploying these evaluations to large at-risk populations, which is critically important for earlier detection and better treatment. We devised a high-throughput, low-cost method where participants simply watched television while we recorded their eye movements. We combined eye-tracking data from patients and controls with a computational model of visual attention to extract 224 quantitative features. Using machine learning in a workflow inspired by microarray analysis, we identified critical features that differentiate patients from control subjects. With eye movement traces recorded from only 15 min of videos, we classified PD versus age-matched controls with 89.6 % accuracy (chance 63.2 %), and ADHD versus FASD versus control children with 77.3 % accuracy (chance 40.4 %). Our technique provides new quantitative insights into which aspects of attention and gaze control are affected by specific disorders. There is considerable promise in using this approach as a potential screening tool that is easily deployed, low-cost, and high-throughput for clinical disorders, especially in young children and elderly populations who may be less compliant to traditional evaluation tests.
