9～12歲兒童應(yīng)激與額顳區(qū)的關(guān)聯(lián):來自多模態(tài)腦影像的證據(jù)*

李 為 邊子茗 陳曦梅 王俊杰 羅一君 劉 永,2 宋詩情 高 笑,2 陳 紅,2

9～12歲兒童應(yīng)激與額顳區(qū)的關(guān)聯(lián):來自多模態(tài)腦影像的證據(jù)*

李 為1邊子茗1陳曦梅1王俊杰1羅一君1劉 永1,2宋詩情1高 笑1,2陳 紅1,2

(1西南大學(xué)心理學(xué)部;2西南大學(xué)認(rèn)知與人格教育部重點實驗室, 重慶 400715)

首次采用多模態(tài)數(shù)據(jù)結(jié)合機器學(xué)習(xí)的方法考察了78名學(xué)齡兒童(女性39名, 平均年齡10.18歲)應(yīng)激的神經(jīng)關(guān)聯(lián)。結(jié)果表明, 兒童應(yīng)激水平與內(nèi)側(cè)眶額葉、腦島、顳上回和輔助運動區(qū)的灰質(zhì)體積呈顯著正相關(guān); 而與腦島和頂下小葉之間的功能連接強度呈顯著負(fù)相關(guān)。這表明涉及情緒加工的前額葉?邊緣?顳葉腦區(qū)可能在兒童應(yīng)激的個體差異中起著關(guān)鍵作用, 而負(fù)責(zé)整合內(nèi)外部信息(如, 積極的自我評價和外部消極刺激)的腦島與頂下小葉之間功能同步性的增加與兒童應(yīng)激的降低有密切關(guān)聯(lián)?；诮Y(jié)構(gòu)網(wǎng)絡(luò)的預(yù)測分析顯示, 感覺運動、額頂、突顯、視覺和小腦網(wǎng)絡(luò)對兒童應(yīng)激水平具有較好的預(yù)測能力。研究不僅豐富了兒童應(yīng)激神經(jīng)基礎(chǔ)的實證證據(jù), 而且對兒童應(yīng)激的早期預(yù)防策略和干預(yù)手段具有啟示意義。

應(yīng)激, 兒童, 灰質(zhì)體積, 靜息態(tài)功能連接, 機器學(xué)習(xí), 結(jié)構(gòu)網(wǎng)絡(luò)

1 引言

應(yīng)激(stress)是指個體感知需求與滿足需求的能力出現(xiàn)不平衡時的緊張狀態(tài), 個體通常會改變自身的心理和生理反應(yīng)以適應(yīng)這一狀態(tài)(Lazarus & Folkman, 1984)。人們普遍認(rèn)為應(yīng)激主要出現(xiàn)在成年群體中(Vanaelst et al., 2012), 但學(xué)齡兒童也會面對應(yīng)激性事件, 這些消極事件主要來自于家庭生活、學(xué)習(xí)生活、同伴關(guān)系以及生活適應(yīng)(Calem et al., 2017; 劉舒丹等, 2016)。學(xué)齡時期的兒童開始參與學(xué)校社會活動, 主要接觸者從父母變?yōu)槔蠋熀屯瑢W(xué), 這種更加復(fù)雜的外部環(huán)境使得消極事件的發(fā)生率增加(Vanaelst et al., 2012)。與此同時, 兒童的大腦發(fā)育和心理功能發(fā)展還欠成熟, 這使得他們在面對消極事件時往往不能作出恰當(dāng)反應(yīng), 并更易受到消極事件的影響從而出現(xiàn)應(yīng)激癥狀(McLaughlin et al., 2009)。更重要的是, 生命早期應(yīng)激會對兒童認(rèn)知和情感等方面造成影響, 甚至是個體畢生精神疾病的有力預(yù)測因子(Berens et al., 2017; Smith & Pollak, 2020)。正如應(yīng)激敏感模型(the stress sensitization model)所揭示的那樣, 童年期逆境增加了個體生命后期對應(yīng)激生活事件的易感性, 從而增加了患精神疾病的風(fēng)險(Hammen et al., 2000; McLaughlin et al., 2010)。目前已有學(xué)者從神經(jīng)科學(xué)的角度對成人應(yīng)激的神經(jīng)機制進行了大量的探索, 但關(guān)于兒童應(yīng)激的神經(jīng)基礎(chǔ)的研究一定程度上還不夠充分。因此, 探討兒童時期應(yīng)激的神經(jīng)生物學(xué)標(biāo)記是了解應(yīng)激對個體產(chǎn)生消極影響的一個關(guān)鍵問題, 進而為針對兒童應(yīng)激的早期預(yù)防和干預(yù)策略的開發(fā)提供有力證據(jù)(McLaughlin et al., 2017; Shonkoff et al., 2009)。

近年來, 越來越多形態(tài)學(xué)(voxel-based morpho-metry, VBM)和靜息態(tài)磁共振(resting-state functional MRI, rs-fMRI)的研究從神經(jīng)基礎(chǔ)的角度揭示了兒童應(yīng)激與大腦結(jié)構(gòu)和功能差異的關(guān)系。VBM技術(shù)主要根據(jù)大腦形態(tài)學(xué)指標(biāo)來考察腦區(qū)結(jié)構(gòu)的變化, 可用于研究心理現(xiàn)象與局部腦組織變化的關(guān)系, 其中灰質(zhì)體積是VBM的常用指標(biāo)之一(Ashburner & Friston, 2000; Mechelli et al., 2005)?，F(xiàn)有VBM證據(jù)主要聚焦于具有嚴(yán)重創(chuàng)傷經(jīng)歷(如暴力、虐待等)的個體或患有應(yīng)激相關(guān)障礙(如創(chuàng)傷應(yīng)激障礙[post- traumatic stress disorder, PTSD]、焦慮癥和抑郁癥等)的臨床病人。具體而言, Lim等人通過基于體素的元分析研究表明, 在經(jīng)歷童年虐待的個體中, 灰質(zhì)的異常出現(xiàn)在發(fā)育相對較晚的前額葉?邊緣?顳葉區(qū), 該區(qū)域涉及情緒和認(rèn)知控制功能(Lim et al., 2014)。這一發(fā)現(xiàn)在PTSD、焦慮癥和重度抑郁癥患者中也得到了印證(見綜述, Serra-Blasco et al., 2021)。鑒于極端應(yīng)激事件較少發(fā)生于日常生活中(Demir-Lira et al., 2016), 因此有學(xué)者開始關(guān)注更具普遍性的應(yīng)激生活事件以探究一般性應(yīng)激水平的神經(jīng)基礎(chǔ)。譬如, Ringwald等人采用應(yīng)激生活事件量表評估個體的應(yīng)激水平(如, 更換工作類型、金融危機等), 并表明成人應(yīng)激水平越高, 內(nèi)側(cè)眶額葉(medial orbitofrontal cortex, mOFC)的灰質(zhì)體積越小(Ringwald et al., 2021)。一項比較了青少年和成人與應(yīng)激有關(guān)的灰質(zhì)體積的差異研究進一步發(fā)現(xiàn), 應(yīng)激水平與OFC、腦島和杏仁核的灰質(zhì)體積在成人中呈負(fù)相關(guān), 而在青少年中呈正相關(guān)(Wu et al., 2021), 這為探究一般性生活應(yīng)激與兒童腦結(jié)構(gòu)的關(guān)系提供了部分實證支持。

Rs-fMRI能測量在休息狀態(tài)下大腦自發(fā)神經(jīng)活動的變化(Zuo et al., 2010); 相比于任務(wù)態(tài)fMRI, 該方法的結(jié)果獨立于實驗任務(wù)。功能連接(functional connectivity, FC)是rs-fMRI研究中常用的有效指標(biāo)之一, 被用于反映腦區(qū)之間的協(xié)同性以及這種協(xié)同性與某種特定的心理和行為之間的關(guān)系(Mennes et al., 2010)。近年來, 該技術(shù)在應(yīng)激相關(guān)的研究領(lǐng)域中已取得一定成果, 許多證據(jù)表明童年創(chuàng)傷應(yīng)激與腦區(qū)間的連接強度改變有關(guān), 包括OFC、海馬、杏仁核和腦島(Goetschius et al., 2020; Lu et al., 2017; Sheynin et al., 2020)。Thomason等人的研究表明, 9～15歲高創(chuàng)傷組被試在腦島?杏仁核、OFC-腦島的FC強度顯著高于控制組(Thomason et al., 2015)。對15～17歲的青少年而言, 童年暴力暴露程度越高, 腦島和頂下小葉(inferior parietal lobule, IPL)之間的連接強度越弱(Goetschius et al., 2020)。與此同時, 任務(wù)態(tài)fMRI研究表明兒童應(yīng)激與上述腦區(qū)的異常激活有關(guān)。相比于控制組, PTSD兒童在情緒加工任務(wù)中(如, 情緒識別任務(wù)和內(nèi)隱情緒任務(wù)), 其前額葉、腦島和顳上回(superior temporal gyrus, STG)表現(xiàn)出異常激活, 并在行為上表現(xiàn)出對恐懼表情的識別更快以及對消極詞匯顏色判斷的正確率更低(Calderon-Delgado et al., 2020; Hart et al., 2018)。

綜上所述, 雖然已有不少研究探討了應(yīng)激和大腦結(jié)構(gòu)和功能的關(guān)系, 但僅有部分研究關(guān)注兒童應(yīng)激的神經(jīng)關(guān)聯(lián), 更鮮少有研究直接考察兒童日常生活應(yīng)激事件與神經(jīng)結(jié)構(gòu)和功能的關(guān)系。大多數(shù)研究皆關(guān)注符合PTSD診斷標(biāo)準(zhǔn)的臨床兒童(Sheynin et al., 2020)或是經(jīng)歷了嚴(yán)重創(chuàng)傷事件(如, 性虐待、身體虐待和被撫養(yǎng)者遺棄等)的兒童(Hart et al., 2018; Thomason et al., 2015)。這類嚴(yán)重的應(yīng)激事件較少發(fā)生在日常生活中, 并沒有囊括來自家庭生活、學(xué)習(xí)生活、同伴關(guān)系以及生活適應(yīng)等方面的應(yīng)激事件, 致使研究結(jié)果難以推廣到一般兒童群體中。為此, 本研究將采用全面涵蓋了兒童日常應(yīng)激事件的小學(xué)生應(yīng)激性生活事件量表以測量兒童應(yīng)激水平(劉舒丹等, 2016)。其次, 目前兒童應(yīng)激的神經(jīng)證據(jù)大多都是基于單一神經(jīng)模態(tài)的分析方法得到的, 鮮少有研究采用多模態(tài)數(shù)據(jù)探究兒童應(yīng)激的神經(jīng)基礎(chǔ)。有研究者指出, 大腦結(jié)構(gòu)和功能網(wǎng)絡(luò)同步發(fā)展, 結(jié)構(gòu)網(wǎng)絡(luò)為功能網(wǎng)絡(luò)的發(fā)展和整合的提供了內(nèi)部生理框架, 因此在考察不良心理現(xiàn)象對個體發(fā)展的潛在危害時, 要同時考慮結(jié)構(gòu)和功能兩種模態(tài)(Grayson & Fair, 2017)。例如, Sp?ti等人(2015)通過分析重度抑郁癥患者的皮質(zhì)厚度和FC表明, 在抑郁發(fā)作期間前額葉皮質(zhì)變薄可能會損害前扣帶回的神經(jīng)活動(Sp?ti et al., 2015)?；诖? 本研究將結(jié)合結(jié)構(gòu)態(tài)(sMRI)和靜息態(tài)(rs-fMRI)來探究兒童應(yīng)激的潛在神經(jīng)關(guān)聯(lián)。首先, 以灰質(zhì)體積作為VBM的指標(biāo)分析與兒童應(yīng)激水平顯著相關(guān)的腦區(qū)。隨后, 以這些腦區(qū)為感興趣區(qū)(region of interest, ROI)估計與兒童應(yīng)激顯著相關(guān)的FC。鑒于海馬和杏仁核是兒童應(yīng)激重要腦區(qū)(Agorastos, et al., 2018; McLaughlin et al., 2017), 因此也考察了海馬和杏仁核與全腦的FC。根據(jù)前人研究, 我們假設(shè): 兒童應(yīng)激水平主要與前額葉?邊緣?顳葉腦區(qū)的灰質(zhì)體積和功能連接有關(guān), 如OFC、腦島、海馬和杏仁核。除上述節(jié)點水平的分析外, 本研究還采用了機器學(xué)習(xí)的方法在網(wǎng)絡(luò)水平上對兒童應(yīng)激的個體差異進行預(yù)測, 進而為腦與應(yīng)激的關(guān)系提供穩(wěn)健性支持。綜上, 本次研究從多模態(tài)的視角考察兒童應(yīng)激的神經(jīng)基礎(chǔ), 并為兒童日常應(yīng)激的干預(yù)策略和創(chuàng)傷應(yīng)激的治療方案提供更具有針對性的證據(jù)(Insel, 2009; Shonkoff et al., 2009)。

2 研究方法

2.1 被試

本實驗被試來自于中國西南地區(qū)的兩所公立小學(xué), 一共招募了139名小學(xué)生被試。所有被試均為右利手, 根據(jù)自我報告和學(xué)校學(xué)生檔案記錄, 所有被試精神狀態(tài)良好, 無精神或神經(jīng)疾病史或服用精神類藥物。查看學(xué)生學(xué)校檔案記錄在被試參加實驗之前已經(jīng)告知被試及其家長, 并得到了他們的同意。共回收有效問卷139份, 其中130名被試進行了rs-MRI掃描, 數(shù)據(jù)采集時間為2018年4月至2018年10月。剔除有質(zhì)量問題和頭動過大的被試后(見2.3.3節(jié)), 剩余78人納入正式分析(女性39人, 平均年齡 = 10.18 ± 1.02歲)。保證所有參與者及其父母在實驗前均簽署了知情同意書, 每位被試在實驗結(jié)束后獲得文具作為獎勵, 并為家長提供項目指標(biāo)檢測報告。本研究所有實驗程序均通過了西南大學(xué)學(xué)術(shù)倫理委員會的批準(zhǔn)。

2.2 量表工具

2.2.1 小學(xué)生應(yīng)激性生活事件量表

采用由劉舒丹等人(2016)編制的適用于我國小學(xué)生的應(yīng)激性生活事件量表(the Scale of Stressful Life Events for Primary School Students, SSLEPSS)。該量表包含5個維度, 共30個題項。其中, 家庭生活應(yīng)激維度包含9個題項(如, “爸爸媽媽吵架或打架”)、師生關(guān)系應(yīng)激維度包含5個題項(如, “老師嚴(yán)厲的批評我”)、學(xué)習(xí)適應(yīng)應(yīng)激維度包含5個題項(如, “作業(yè)不會做”)、同伴關(guān)系應(yīng)激維度包含7個題項(如, “和好朋友鬧別扭”)和生活適應(yīng)應(yīng)激維度包含4個題項(如, “中午或晚上睡不著覺”)。參與者首先對“近半年有無該應(yīng)激事件”進行“有”或“無”的報告, 再根據(jù)實際經(jīng)歷或感受對其影響進行5點評分, 1表示“沒有影響”, 5表示“超級難過”?？偡衷礁叽響?yīng)激水平越高。該量表包含了對兒童具有較大影響的創(chuàng)傷應(yīng)激事件(如, “父母分居或離婚”)以及具有潛在影響的日常應(yīng)激事件(如, “被迫睡覺”), 以便更全面地測量兒童應(yīng)激水平。本研究中, 該量表的Cronbach’s α系數(shù)為0.92。

2.2.2 積極消極情緒量表

采用由Watson等人(1988)編制的積極消極情緒量表(Positive and Negative Affect Schedule, PANAS)。該量表包括積極情緒和消極情緒兩個分量表, 采用Likert 5點計分, 從1到5分別代表“完全沒有”至“非常多”, 分?jǐn)?shù)越高代表個體體驗到積極或消極情緒越強烈。本研究中, 積極情緒分量表的Cronbach’s α系數(shù)為0.79, 消極情緒分量表的Cronbach’s系數(shù)為0.75。本研究只使用了消極情緒分量表的得分以分析消極情緒與兒童激應(yīng)水平和應(yīng)激相關(guān)腦指標(biāo)的關(guān)聯(lián)。

2.3 腦影像數(shù)據(jù)采集與預(yù)處理

2.3.1 數(shù)據(jù)采集

所有被試均完成了5分鐘的結(jié)構(gòu)像掃描和8分鐘的靜息功能像掃描, 所有影像數(shù)據(jù)均通過西南大學(xué)腦成像中心的西門子掃描儀(3.0 T Siemens Trio MRI)進行采集。在正式掃描前, 每名被試均進行了模擬掃描練習(xí)以適應(yīng)掃描環(huán)境, 從而減少頭動影響。在正式掃描前, 調(diào)整被試頭部至舒適的位置, 再用海綿進行固定; 在掃描過程中, 要求被試睜開眼睛, 平躺休息, 不做思考也不要睡著。采用快速梯度回波成像(Magnetization Prepared Rapid Acquisition Gradient Echo Sequences, MPRAGE)采集T1加權(quán)結(jié)構(gòu)像, 掃描參數(shù)為: 回波時間(echo time, TE) = 3.48 ms, 重復(fù)時間(repetition time, TR) = 2530 ms, 反轉(zhuǎn)時間(inversion time, TI) = 1900 ms, 翻轉(zhuǎn)角(flip angle, FA) = 7°, 視野大小(field of vision, FOV) = 256 mm × 256 mm, 成像矩陣(matrix size) = 256 × 256, 層間距(distance between slice) = 1.0 mm, 體素大小(voxel size) = 1 mm × 1 mm × 1 mm。采用梯度回波平面成像掃描序列(Gradient echo-echo planar imaging, GRE-EPI)采集靜息功能圖像, 掃描參數(shù)為: TE = 30 ms, TR = 2000 ms, FA = 90°, FOV = 224 × 224 mm2, matrix size = 64 × 64, 層數(shù)(Slice) = 33, 厚度(thickness) = 3.5 mm, distance between slice = 1 mm, voxel size = 3.5 mm × 3.5 mm × 3.5 mm。一共獲得180個時間點的連續(xù)圖像。

2.3.2 結(jié)構(gòu)態(tài)與靜息態(tài)數(shù)據(jù)預(yù)處理

使用基于Matlab R2014a平臺上運行的SPM 12 (http://www.fil.ion.ucl.ac.uk/spm/)軟件中的CAT 12插件(http://www.neuro.uni-jena.de/cat12-html/cat. html)對sMRI數(shù)據(jù)進行預(yù)處理。預(yù)處理步驟如下: (1)圖像質(zhì)量較差或有解剖學(xué)異常的大腦圖像予以剔除; (2)組織分割, 將T1加權(quán)圖像分割成灰質(zhì)、白質(zhì)和腦脊液三種組織類型; (3)采用DARTEL (diff-eo-morphic anatomical registration through exponen-tiated Lie algebra)配準(zhǔn)方式將結(jié)構(gòu)像數(shù)據(jù)配準(zhǔn)到MNI (montreal neurological institute)空間, 并使用雅可比行列式對圖像進行非線性調(diào)節(jié), 標(biāo)準(zhǔn)化后的體素大小是1.5 mm × 1.5 mm × 1.5 mm; (4)采用 8 mm平滑核進行平滑(smooth)。

使用基于Matlab R2014a平臺上運行的DPARSF 5.2軟件(http://www.restfmri.net/forum/ DPARSF)對rs-MRI數(shù)據(jù)進行預(yù)處理(Yan et al., 2016)。預(yù)處理步驟如下: (1)數(shù)據(jù)質(zhì)量檢查; (2)剔除前10個時間點, 對剩余層數(shù)進行時間層校正(slice timing); (3)頭動校正(realignment); (4)采用DARTEL方法將處理后數(shù)據(jù)標(biāo)準(zhǔn)化到MNI標(biāo)準(zhǔn)空間模板, 標(biāo)準(zhǔn)化后的體素大小是3 mm × 3 mm × 3 mm; (5)采用6 mm平滑核進行平滑(smooth); (6)采用Friston 24方法回歸掉頭動信號以及白質(zhì)與腦脊液信號, 所有被試頭動(mean framewise displacement, mean FD)大小均小于0.5 mm; (7)提取低頻段0.01～0.1 Hz的信號。

2.3.3 數(shù)據(jù)質(zhì)量監(jiān)控和頭動控制

在預(yù)處理前, 由兩名心理學(xué)專業(yè)博士對所有被試(139名)的結(jié)構(gòu)態(tài)數(shù)據(jù)進行初級的視覺篩查(對數(shù)據(jù)質(zhì)量進行總共四次的主觀評分), 據(jù)此剔除了49名存在結(jié)構(gòu)異?；虼罅總斡暗谋辉?其中女性15人)。其次, 根據(jù)廣泛使用的靜息態(tài)頭動標(biāo)準(zhǔn)對剩余81名被試進行頭動控制, 即mean FD_Jenkinson值≧0.5 mm的被試會被剔除(Jenkinson et al., 2002)。本研究進一步剔除了3名應(yīng)激分?jǐn)?shù)為0的被試, 最終78名被試(其中女性39人)納入正式統(tǒng)計分析。隨后, 鑒于掃描過程中頭部運動可能會對感興趣變量的神經(jīng)基礎(chǔ)造成潛在影響(Shen et al., 2017), 因此需要確保感興趣變量與頭動指標(biāo)不存在顯著相關(guān)。當(dāng)前數(shù)據(jù)顯示, 頭動指標(biāo)(mean FD)與應(yīng)激分?jǐn)?shù)的相關(guān)系數(shù)不顯著(頭動與應(yīng)激原始分?jǐn)?shù)的相關(guān)結(jié)果:= 0.04,= 0.71; 頭動與應(yīng)激正態(tài)轉(zhuǎn)換分?jǐn)?shù)[應(yīng)激水平(stress_sqrt); 見3.1節(jié)]的相關(guān)結(jié)果:= 0.09,= 0.46; 見表1)。最后, 在統(tǒng)計分析中, 將頭動納入?yún)f(xié)變量以進一步控制其對結(jié)果的影響(Horien et al., 2018; Waller et al., 2017)。

2.3.4 結(jié)構(gòu)網(wǎng)絡(luò)構(gòu)建

使用彌散張量成像構(gòu)建個體水平網(wǎng)絡(luò)可能重建出不存在的偽連接(梁夏等, 2010; Jones & Cercignani, 2010); 而使用T1像構(gòu)建組水平結(jié)構(gòu)協(xié)變網(wǎng)絡(luò)不僅忽略了個體差異, 還忽略了各個腦區(qū)形狀和大小的差異。鑒于上述缺點, 本次基于Shen等人(2013)的模板構(gòu)建個體水平的結(jié)構(gòu)腦網(wǎng)絡(luò), 采用KL散度相似度(Kullback-Leibler divergence- based similarity, KLS)來量化腦區(qū)之間的結(jié)構(gòu)連接值, 作為腦網(wǎng)絡(luò)中邊的值。該方法可以估計不同形狀和大小腦區(qū)之間的關(guān)系, 是一種表征大腦組織的可靠方法(Kong et al., 2014)。首先, 提取出腦模板中268個腦區(qū)的灰質(zhì)體積的值。其次, 使用核密度估計(kernel density estimation, KDE)計算出每個腦區(qū)灰質(zhì)體積的概率密度函數(shù), 根據(jù)概率密度函數(shù)得到每個腦區(qū)灰質(zhì)體積值的概率分布函數(shù)(Wang et al., 2016)。隨后, 計算每對腦區(qū)概率分布函數(shù)之間的差異即為每對腦區(qū)KL散度的值以此作為結(jié)構(gòu)連接的數(shù)值。最后, 獲得268×268的結(jié)構(gòu)矩陣。為了在網(wǎng)絡(luò)水平上解釋本次結(jié)果, 使用了Noble等人(2017)對腦網(wǎng)絡(luò)的劃分, 包括8個網(wǎng)絡(luò), 即內(nèi)側(cè)額葉網(wǎng)絡(luò)(medial frontal network, MFN)、額頂網(wǎng)絡(luò)(frontoparietal network, FPN)、默認(rèn)網(wǎng)絡(luò)(default mode network, DMN)、感覺運動網(wǎng)絡(luò)(sensorimotor network, SMN)、突顯網(wǎng)絡(luò)(salience network, SAN)、視覺網(wǎng)絡(luò)(包含視覺1、2區(qū)和視覺聯(lián)合區(qū); visual network, VN)、皮下網(wǎng)絡(luò)(subcortical network, SCN)和小腦網(wǎng)絡(luò)(cerebellar network)。

2.4 統(tǒng)計分析

2.4.1 全腦水平分析

灰質(zhì)體積分析。在全腦水平上, 使用SPM 12軟件對兒童應(yīng)激水平與灰質(zhì)體積進行多重線性回歸分析, 并以年齡、性別和顱內(nèi)總體積(total incranial volume, TIV)作為協(xié)變量。采用體素水平< 0.005, 團塊水平< 0.05的高斯隨機場(Gaussian Random- Field, GRF)多重比較矯正, 以獲得與兒童應(yīng)激顯著相關(guān)的灰質(zhì)體積腦區(qū)。

基于種子點的FC分析。首先, 采用與應(yīng)激顯著相關(guān)的灰質(zhì)體積的腦區(qū)峰值坐標(biāo)作為ROI, 畫半徑為5 mm的小球。同時, 選擇自動解剖標(biāo)記圖譜(Automated Anatomical Labeling, AAL; Tzourio- Mazoyer et al., 2002)中的杏仁核和海馬(與兒童應(yīng)激相關(guān)的重要腦區(qū); Agorastos, et al., 2018; McLaughlin et al., 2017)作為ROI。其次, 提取每個被試ROI內(nèi)體素的時間序列, 用基于體素的相關(guān)分析法計算ROI與全腦其他腦區(qū)之間的皮爾遜相關(guān)系數(shù), 將值進行Fisher轉(zhuǎn)化。最后, 使用DPABI軟件計算兒童應(yīng)激水平與功能連接強度的相關(guān)。鑒于年齡和性別被證實與FC有關(guān)(Feng et al., 2018; Hsu et al., 2018), 以及核磁掃描過程中的頭部運動是影響FC的主要因素之一(Horien et al., 2018; Waller et al., 2017)。因此, 本研究不僅回歸了年齡和性別的影響(如童丹丹等, 2020), 同時還對被試頭動進行了控制, 采用體素水平< 0.005, 團塊水平< 0.05的GRF多重比較矯正, 以獲得與兒童應(yīng)激顯著相關(guān)的FC。

2.4.2 機器學(xué)習(xí)的預(yù)測模型分析

運用留一交叉驗證(leave-one-out cross- validation, LOOCV)建立RVR預(yù)測模型。具體過程如下: 首先, 將總數(shù)據(jù)樣本隨機分為訓(xùn)練集(training set)和測試集(test set), 每次隨機挑選一個數(shù)據(jù)樣本(即,–1名被試)作為訓(xùn)練集用于建立RVR預(yù)測模型, 剩下的數(shù)據(jù)樣本(1名被試)均為測試集用于測試RVR模型的預(yù)測能力。隨后, 再計算測試集觀測值和預(yù)測值之間的相關(guān)系數(shù)(predicted, observed)。最后運用置換檢驗(permutation test)將上述步驟重復(fù)2000次, 獲得一個含有2000個的零分布, 根據(jù)(predicted, observed)在零分布的位置來評估其顯著性值。值 = 置換值大于或等于(predicted, observed)的次數(shù)/總置換次數(shù)。

特征向量的權(quán)重絕對值用于衡量該特征對預(yù)測模型的貢獻率, 絕對值越大代表該特征對模型的貢獻率越大。本研究取貢獻率在前10%的腦區(qū)。此外, 根據(jù)公式= cov () ×× cov ()?1對權(quán)重進行了“激活模式”轉(zhuǎn)換, 以闡明腦區(qū)與預(yù)測的行為變量之間的相關(guān)(Haufe et al., 2014, Zhou et al., 2021), 即正網(wǎng)絡(luò)與預(yù)測變量正相關(guān), 負(fù)網(wǎng)絡(luò)與預(yù)測變量負(fù)相關(guān)。

2.5 探索性分析

2.5.1 應(yīng)激與腦指標(biāo)相關(guān)的性別差異

在全腦水平上, 使用SPM 12軟件對性別、應(yīng)激水平(stress_sqrt)以及兩者的交互項構(gòu)建多重線性回歸模型, 分析性別與應(yīng)激水平在灰質(zhì)體積上是否存在交互作用, 協(xié)變量為年齡和TIV。采用體素水平< 0.005, 團塊水平< 0.05的GRF多重比較矯正。隨后, 提取出顯著的灰質(zhì)體積腦區(qū)的信號值, 采用SPSS 26.0軟件, 分別在男性和女性組計算兒童應(yīng)激水平(stress_sqrt)與對應(yīng)腦區(qū)的相關(guān)系數(shù), 以進一步考察兒童應(yīng)激與腦指標(biāo)的顯著相關(guān)是否具有性別差異。若存在顯著腦區(qū), 則以該腦區(qū)的峰值坐標(biāo)為圓心畫5 mm半徑的小球, 作為功能連接的種子點。同樣采用多重線性回歸模型分析性別與應(yīng)激水平在該腦區(qū)功能連接上的交互作用。

2.5.2 應(yīng)激與腦指標(biāo)相關(guān)的年齡特征

在全腦水平上, 使用SPM 12軟件對年齡、應(yīng)激水平(stress_sqrt)以及兩者的交互項構(gòu)建多重線性回歸模型, 分析年齡與應(yīng)激水平在灰質(zhì)體積上是否存在交互作用, 協(xié)變量為性別和TIV。采用體素水平< 0.005, 團塊水平< 0.05的GRF多重比較矯正。隨后, 提取出顯著的灰質(zhì)體積腦區(qū)的信號值, 采用SPSS 26.0軟件, 分別在不同年齡組計算兒童應(yīng)激水平(stress_sqrt)與對應(yīng)腦區(qū)的相關(guān)系數(shù), 以進一步考察哪一年齡組的應(yīng)激與腦指標(biāo)顯著相關(guān)。若存在顯著腦區(qū), 則以該腦區(qū)的峰值坐標(biāo)為圓心畫5 mm半徑的小球, 作為功能連接的種子點。同樣采用多重線性回歸模型分析年齡與應(yīng)激水平在該腦區(qū)的功能連接上的交互作用。

3 結(jié)果

3.1 正態(tài)性檢驗與行為結(jié)果

首先, 根據(jù)以往研究中所采用的方法(如Wallace et al., 2020), 本次實驗去掉了3名在應(yīng)激總分上為0分的被試, 最終78名被試納入統(tǒng)計分析。其次, 鑒于計算腦指標(biāo)與應(yīng)激的皮爾遜相關(guān)要求數(shù)據(jù)呈正態(tài)分布, 因此對兒童應(yīng)激得分進行正態(tài)性檢驗(Kolmogorov-Smirnov test, K-S檢驗), 結(jié)果顯示應(yīng)激原始分?jǐn)?shù)為非正態(tài)分布(K-S檢驗,< 0.001; 見圖1A)。隨后, 對原始應(yīng)激分?jǐn)?shù)進行平方根變換(square root transformation) (如Ferketich et al., 2005; Song, 2013)以得到服從正態(tài)分布的應(yīng)激分?jǐn)?shù)(stress_sqrt) (K-S檢驗,0.20; 見圖1B)。后續(xù)統(tǒng)計分析均使用應(yīng)激水平(stress_sqrt)作為自變量。

樣本的人口學(xué)信息和兒童應(yīng)激得分的描述性統(tǒng)計見表1和圖1C。應(yīng)激原始分?jǐn)?shù)無顯著性別差異,(76) = 1.66,= 0.1。應(yīng)激原始分?jǐn)?shù)與年齡相關(guān)不顯著,= –0.04,= 0.72。

表1 描述性統(tǒng)計與相關(guān)分析結(jié)果(N = 78)

注:*< 0.05,**< 0.01。

圖1 (A)兒童應(yīng)激原始分?jǐn)?shù)分布直方圖; (B)兒童應(yīng)激轉(zhuǎn)換分?jǐn)?shù)分布直方圖; (C)樣本年齡和性別分布圖

注: K-S test為Kolmogorov-Smirnov正態(tài)性檢驗。

3.2 腦成像數(shù)據(jù)結(jié)果

3.2.1 兒童應(yīng)激與灰質(zhì)體積的關(guān)系

對兒童stress_sqrt分?jǐn)?shù)與全腦灰質(zhì)體積進行多重線性回歸分析, 將性別、年齡和TIV作為協(xié)變量。結(jié)果表明, 兒童應(yīng)激水平(stress_sqrt)分?jǐn)?shù)與左側(cè)mOFC、右側(cè)腦島、左側(cè)STG和右側(cè)輔助運動區(qū)(supplementary motor area, SMA)的灰質(zhì)體積呈顯著正相關(guān)。具體腦區(qū)的坐標(biāo)、體素量和相關(guān)值見表2和圖2A。消極情緒得分與SMA灰質(zhì)體積顯著正相關(guān),= 0.25,< 0.05 (詳見網(wǎng)絡(luò)版附錄表1)。

表2 兒童應(yīng)激與灰質(zhì)體積和靜息功能連接相關(guān)分析結(jié)果

注: mOFC = medial orbitofrontal cortex; STG = superior temporal gyrus; SMA = supplementary motor area; IPL = inferior parietal lobule; MNI = Montreal Neurological Institute; 采用體素水平< 0.005, 團塊水平< 0.05的GRF多重比較矯正; 協(xié)變量: 性別、年齡和頭動。

3.2.2 兒童應(yīng)激與靜息態(tài)功能連接的關(guān)系

選擇灰質(zhì)體積分析中顯著的腦區(qū)作為ROI, 功能連接分析結(jié)果表明, 右側(cè)腦島和左側(cè)IPL之間的功能連接強度與兒童應(yīng)激水平(stress_sqrt)顯著負(fù)相關(guān), 即兒童應(yīng)激水平(stress_sqrt)越高, 右側(cè)腦島和左側(cè)IPL之間的功能連接強度越低; 但兒童應(yīng)激水平(stress_sqrt)與海馬和杏仁核的FC沒有顯著相關(guān)(表2和圖2B)。

3.3 基于機器學(xué)習(xí)的預(yù)測分析結(jié)果

基于結(jié)構(gòu)網(wǎng)絡(luò)的RVR模型可以邊緣顯著地預(yù)測兒童應(yīng)激水平(= 0.24,= 0.07, 見圖3)。在節(jié)點水平上, OFC、STG和SMA位于前10%貢獻率的腦區(qū)中(詳見網(wǎng)絡(luò)版附錄表2), 這與上述灰質(zhì)體積結(jié)果相一致。在網(wǎng)絡(luò)水平上, SMN內(nèi)部的結(jié)構(gòu)連接、SMN-SAN、SMN-VN以及SMN-FPN之間的結(jié)構(gòu)連接構(gòu)成了正網(wǎng)絡(luò), 其結(jié)構(gòu)連接越強, 應(yīng)激水平越高; 小腦-FPN、小腦-VN、SCN-VN之間的結(jié)構(gòu)連接構(gòu)成了負(fù)網(wǎng)絡(luò), 其結(jié)構(gòu)連接越弱, 所預(yù)測的應(yīng)激水平越高。

3.4 探索性分析結(jié)果

3.4.1 應(yīng)激與腦指標(biāo)相關(guān)的性別差異

多重回歸分析顯示, 性別與應(yīng)激水平(stress_ sqrt)在全腦灰質(zhì)體積上不存在顯著的交互作用。

圖2 (A)兒童應(yīng)激相關(guān)的灰質(zhì)體積結(jié)果; (B)兒童應(yīng)激相關(guān)的靜息功能連接結(jié)果

注: 采用體素水平< 0.005, 團塊水平< 0.05的GRF多重比較矯正。mOFC = 內(nèi)側(cè)眶額葉(medial orbitofrontal cortex); Insular = 腦島; STG = 顳上回(superior temporal gyrus); SMA = 輔助運動區(qū)(supplementary motor area); IPL = 頂下小葉(inferior parietal lobule); L = Left hemisphere; R = Right hemisphere。

3.4.2 應(yīng)激與腦指標(biāo)相關(guān)的年齡特征

多重回歸分析表明, 年齡與應(yīng)激水平在枕下回(inferior occipital gyrus, IOG)的灰質(zhì)體積存在顯著的交互作用(坐標(biāo): x = ?53, y = ?66, z = ?11; 體素量 = 249;= ?4.08)。將腦結(jié)果信號值提取出來計算與每個年齡組應(yīng)激水平(stress_sqrt)的偏相關(guān), 協(xié)變量為性別和TIV。結(jié)果表明, 9歲組(= 24)的應(yīng)激水平(stress_sqrt)與IOG的灰質(zhì)體積顯著正相關(guān); 10歲組(= 26)和11歲組(= 18)的應(yīng)激水平(stress_sqrt)與IOG的灰質(zhì)體積無顯著相關(guān); 而12歲組(= 10)的應(yīng)激水平(stress_sqrt)與IOG的灰質(zhì)體積顯著負(fù)相關(guān), 如圖4所示。此外, 年齡與應(yīng)激水平在IOG功能連接上的交互作用不顯著。

4 討論

本研究采用多模態(tài)數(shù)據(jù)考察了與兒童應(yīng)激有關(guān)的大腦結(jié)構(gòu)和功能基礎(chǔ), 并探索了其性別差異和年齡特征。與我們的研究假設(shè)一致, VBM分析表明兒童應(yīng)激與mOFC、腦島、STG和SMA的灰質(zhì)體積呈正相關(guān)。以VBM的顯著腦區(qū)為ROI進行FC分析, 結(jié)果顯示, 兒童應(yīng)激水平越高, 腦島與IPL的連接強度越低弱。這些結(jié)果不受被試年齡、性別和頭動/TIV的影響; 機器學(xué)習(xí)建模預(yù)測分析進一步顯示, 上述腦區(qū)均能有效預(yù)測兒童應(yīng)激水平, 表明了研究結(jié)果的穩(wěn)健性。

VBM分析表明, 兒童應(yīng)激越高, mOFC、腦島、STG和SMA灰質(zhì)體積越大。首先, mOFC被證實與情緒加工有關(guān), 如調(diào)節(jié)對消極刺激的反應(yīng)和評估消極情緒狀態(tài)(Rolls, 2017; O’Doherty et al., 2001; Phillips et al., 2003)。實證研究表明, 較差的情緒管理能力與更大的OFC灰質(zhì)體積有關(guān)(Wabnegger et al., 2018); 那些傾向于報告更高應(yīng)激水平的青少年, 其OFC灰質(zhì)體積也越大(Wu et al., 2021)。據(jù)此, 兒童應(yīng)激與OFC灰質(zhì)體積呈顯著正相關(guān)的結(jié)果表明, 由OFC所參與的情緒加工(如, 情緒管理與調(diào)節(jié))的異常(表現(xiàn)為OFC灰質(zhì)體積的增大)可能與兒童應(yīng)激水平的增加有密切關(guān)聯(lián)(Kautz et al., 2021; McEwen et al., 2016); 這種神經(jīng)易感性可能與兒童期OFC還未完全發(fā)育成熟有關(guān)(Gogtay et al. 2004)。其次, 腦島的功能和結(jié)構(gòu)在個體應(yīng)激和自我情緒意識中發(fā)揮著重要作用(Etkin & Wager, 2007; Craig 2009; Gu et al. 2013)。多項任務(wù)態(tài)研究發(fā)現(xiàn), 相較于健康被試, PTSD患者在面對恐懼和痛苦刺激時, 腦島出現(xiàn)更強激活(Aupperle et al., 2012; Simmons et al., 2008; Strigo et al., 2010)。一項新近的基于青少年的結(jié)構(gòu)像研究發(fā)現(xiàn), 個體應(yīng)激水平越高, 腦島灰質(zhì)體積越大(Wu et al., 2021)。因此, 腦島灰質(zhì)體積的增大可能代表了應(yīng)激的重要神經(jīng)基礎(chǔ), 以解釋高水平應(yīng)激兒童對消極刺激的過度敏感性。

此外, 結(jié)果還表明了兒童應(yīng)激與STG和SMA灰質(zhì)體積呈正相關(guān)。STG與情景記憶和語音理解有關(guān)(Brunet et al., 2000; Howard et al., 1992)。一項結(jié)構(gòu)態(tài)研究發(fā)現(xiàn), STG灰質(zhì)體積增加與童年期父母言語訓(xùn)斥有關(guān), 而言語訓(xùn)斥是生命早期應(yīng)激的來源之一(Tomoda et al., 2011)。相比于控制組, PTSD兒童STG的灰質(zhì)體積也更大(de Bellis, Keshavan, Frustaci et al., 2002)。據(jù)此, 兒童應(yīng)激水平與STG灰質(zhì)體積的顯著正相關(guān)可能表明, 言語理解加工的異常(表現(xiàn)為STG灰質(zhì)體積增大)可能致力于對消極言語(如, 父母的訓(xùn)斥)的高度敏感化, 這與兒童應(yīng)激水平的增加有關(guān)(de Bellis, Keshavan, Frustaci et al., 2002)。另外, 以往研究表明SMA在對錯誤行為的監(jiān)控過程中起著主要作用(Bonini et al., 2014; Roger et al., 2010), 同時該腦區(qū)還參與對情緒信息的傳遞(Aybek et al., 2014; Oliveri et al., 2003)。Lim等人(2015)研究發(fā)現(xiàn), 相比于健康控制組和童年無應(yīng)激事件的精神病組, 經(jīng)歷童年應(yīng)激事件的成人對錯誤行為(如, 對目標(biāo)刺激的抑制失敗)的高度敏感表現(xiàn)為SMA的激活更強; 這種神經(jīng)功能的變化能夠幫助個體在應(yīng)激狀態(tài)下監(jiān)控自身行為, 從而避免錯誤行為帶來的消極懲罰(Lim et al., 2015)。目前關(guān)于SMA灰質(zhì)體積與兒童應(yīng)激關(guān)系的證據(jù)較少, 有一項結(jié)構(gòu)態(tài)研究發(fā)現(xiàn), PTSD兒童的SMA灰質(zhì)體積與PTSD得分呈正相關(guān)(黎磊等, 2017)。此外, 基于功能性神經(jīng)癥狀障礙(functional neurological symptom disorder, FND)的研究也能為二者關(guān)系提供佐證。FND是指身體出現(xiàn)醫(yī)學(xué)無法解釋的感覺或運動功能喪失, 如失明或四肢麻痹等(Ani et al., 2013)。一項基于兒童FND的結(jié)構(gòu)態(tài)研究表明, 由先前應(yīng)激所致的FND兒童患者對情緒信號具有高度警覺, 表現(xiàn)為對情緒識別的反應(yīng)時減短, 而這與SMA灰質(zhì)體積的增加有關(guān)(Kozlowska et al., 2015; Kozlowska et al., 2017)。據(jù)此, 我們推測, SMA灰質(zhì)體積越大可能與高水平應(yīng)激兒童對情緒刺激和個體行為的過度監(jiān)控有關(guān), 以避免錯誤行為帶來的消極后果(McEwen et al., 2012; Menon & Uddin, 2010)。值得注意的是, 有綜述研究表明, 由于大腦結(jié)構(gòu)具有可塑性特征, 因此由應(yīng)激導(dǎo)致的灰質(zhì)體積變化可能反映了一種依賴于經(jīng)驗的結(jié)構(gòu)適應(yīng), 可以暫時改善個體在應(yīng)激環(huán)境下的適應(yīng)能力(McEwen et al., 2012)。對兒童而言, 若長期保持這種短期適應(yīng)性功能, 可能會降低大腦可塑性, 并促進大腦早熟(Tooley et al., 2021), 甚至帶來消極后果, 如焦慮、抑郁和PTSD等(Berens et al., 2017; Smith & Pollak, 2020)。同時, 有研究表明兒童前額葉、STG (包含腦島)和SMA灰質(zhì)體積的增加可能作為了精神疾病風(fēng)險的預(yù)兆因子(Kozlowska et al., 2017)。綜上, 應(yīng)激的大腦結(jié)構(gòu)基礎(chǔ)對于理解應(yīng)激的異常情緒和認(rèn)知模式有著重要意義, 為進一步揭示兒童應(yīng)激的神經(jīng)關(guān)聯(lián)提供了實證支持。

注: 圈圖: 腦區(qū)以解剖順序呈現(xiàn), 連線長度代表連接腦區(qū)的距離; 腦圖: 節(jié)點大小代表節(jié)點對模型的貢獻度; 矩陣圖: 數(shù)字代表網(wǎng)絡(luò)內(nèi)或網(wǎng)絡(luò)間連接的數(shù)量。特征選擇閾值< 0.01, 預(yù)測模型顯著性采用2000次置換檢驗< 0.05。

圖4 兒童應(yīng)激神經(jīng)基礎(chǔ)的年齡特征

注: 采用體素水平< 0.005, 團塊水平< 0.05的GRF多重比較矯正。GMV = 灰質(zhì)體積(gray matter volume); IOG = inferior occipital gyrus; L = left hemisphere;*< 0.05;**< 0.01。

進一步地, 兒童應(yīng)激水平與腦島-IPL的FC強度呈負(fù)相關(guān)。眾所周知, IPL是DMN的核心節(jié)點(Lu et al., 2017; Menon, 2011), 該網(wǎng)絡(luò)獨立于任務(wù)加工, 致力于自我參照加工和自傳體記憶(Buckner et al., 2008)。腦島是SAN的關(guān)鍵區(qū)域, 負(fù)責(zé)監(jiān)控和整合內(nèi)外部突顯刺激(Manoliu et al., 2014; Uddin, 2017)。實證研究表明, 生命早期應(yīng)激不僅與DMN和SAN內(nèi)部FC顯著相關(guān)(Menon, 2011; Uddin, 2017), 還與SAN和DMN之間的FC強度減弱有關(guān), 尤其是腦島和IPL之間的FC (Goetschius et al., 2020; Lu et al., 2017; Marusak et al., 2015)。據(jù)此, 本研究所發(fā)現(xiàn)的兒童應(yīng)激與腦島-IPL的FC強度呈負(fù)相關(guān)表明, 以腦島和IPL為關(guān)鍵節(jié)點的雙網(wǎng)絡(luò)間(即DMN和SAN)功能同步性的降低可能反映了自我參照加工(如, 積極的自我評價)與外部刺激反應(yīng)性(如, 對消極刺激的高度敏感性)的異常交互, 而這對兒童應(yīng)激水平的升高具有重要作用(Vuper et al., 2021; Etkin & Wager, 2007; Manoliu et al., 2014)。然而, 以海馬和杏仁核為ROI的FC分析沒有顯示與兒童應(yīng)激水平顯著相關(guān)的結(jié)果。一項基于青年被試的靜息態(tài)研究也未發(fā)現(xiàn)童年創(chuàng)傷組與控制組在海馬和杏仁核的FC上存在差異(Paquola et al., 2017), 但有證據(jù)表明海馬和杏仁核的FC異常與青少年的童年創(chuàng)傷經(jīng)歷有關(guān)(Saxbe et al., 2018; Thomason et al., 2015)。上述不一致結(jié)果可能受到了樣本量大小(即小于或大于30被試; Saxbe et al., 2018; Thomason et al., 2015)、海馬/杏仁核興趣區(qū)的選取方式(即AAL或Jülich模板, 或手動分割法; Paquola et al., 2017; Thomason et al., 2015; Saxbe et al., 2018)以及偏態(tài)數(shù)據(jù)轉(zhuǎn)換與否的影響(Morey et al., 2009; Hahm et al., 2019)。

機器學(xué)習(xí)預(yù)測分析表明, SMN內(nèi)部的結(jié)構(gòu)連接、SMN-VN、SMN-SAN以及SMN-FPN之間的結(jié)構(gòu)連接構(gòu)成了預(yù)測兒童應(yīng)激水平的正網(wǎng)絡(luò); 小腦-FPN、小腦-VN、SCN-VN之間的結(jié)構(gòu)連接構(gòu)成了預(yù)測兒童應(yīng)激水平的負(fù)網(wǎng)絡(luò)。對于正網(wǎng)絡(luò), SMN和VN屬于感覺系統(tǒng), 負(fù)責(zé)感覺加工、運動控制以及錯誤行為的監(jiān)控(Bonini et al., 2014; Roger et al., 2010; Tomoda et al., 2009); SAN負(fù)責(zé)監(jiān)控和整合內(nèi)外部突顯刺激(Manoliu et al., 2014; Uddin, 2017); 而FPN主要涉及執(zhí)行控制功能, 通過改變與其他功能系統(tǒng)的交互模式靈活快速地適應(yīng)正在進行的任務(wù)(Cole et al., 2013)。實證研究表明, 成人在經(jīng)歷應(yīng)激事件后, 除了感覺系統(tǒng)的結(jié)構(gòu)整合性與灰質(zhì)體積會發(fā)生改變外(Choi et al., 2012; Tomoda et al., 2011), SMN與VN、SAN和FPN之間的FC強度也會出現(xiàn)異常, 這與個體警覺性提高以及難以從應(yīng)激事件中恢復(fù)有關(guān)(Soares et al., 2013; Yu et al., 2019; Zhutovsky et al., 2021)。關(guān)于負(fù)網(wǎng)絡(luò)結(jié)果, 小腦不僅與自主運動有關(guān), 還與高級認(rèn)知功能如情緒加工和錯誤預(yù)期有關(guān)(Blithikioti et al., 2022); 而SCN負(fù)責(zé)加工由感覺系統(tǒng)(如, VN)傳入的威脅刺激, 并迅速對其做出反應(yīng)(Teicher et al., 2016)。此外, 較高的應(yīng)激癥狀與小腦-FPN、小腦-VN和SCN-VN的異常連接有關(guān), 說明高應(yīng)激個體對內(nèi)外部刺激的調(diào)節(jié)能力下降, 并進一步導(dǎo)致過度警覺和泛化的恐懼反應(yīng)(Holmes et al., 2018; Rabellino et al., 2018; Teicher et al., 2016)。當(dāng)前結(jié)果表明, 結(jié)構(gòu)網(wǎng)絡(luò)相似性可能與兒童應(yīng)激的個體差異有關(guān); 雖然其預(yù)測能力在統(tǒng)計上為邊緣顯著, 但仍有助于理解應(yīng)激與大腦結(jié)構(gòu)關(guān)聯(lián)的本質(zhì)與程度。

探索性分析顯示, 與兒童應(yīng)激有關(guān)的灰質(zhì)體積和FC均不存在性別差異。一項前瞻性研究表明, 兒童和青少年P(guān)TSD癥狀和發(fā)展軌跡不存在性別差異(Maikovich et al., 2009)。在腦發(fā)育水平上, 綜述研究表明大腦發(fā)展出兩性差異是在青春期以后(Stevens et al., 2018)。一項包含兒童、青少年和成人的大樣本研究表明, 在青春期前海馬和杏仁核的體積并沒有表現(xiàn)出性別差異, 但隨著青春期的成熟, 男性相應(yīng)腦區(qū)體積減少而女性增加(Hu et al., 2013)?？v向研究顯示, 在青少年時期, 生命早期應(yīng)激會降低女性杏仁核與前額葉之間的連接強度, 但對男性沒有影響(Burghy et al., 2012; Herringa et al., 2013)。據(jù)此, 本次兒童應(yīng)激腦關(guān)聯(lián)不存在性別差異可能是由于應(yīng)激在兒童期未出現(xiàn)兩性差異以及應(yīng)激神經(jīng)基礎(chǔ)的性別特異性主要表現(xiàn)在青春期。最近, 有學(xué)者對經(jīng)歷生命早期應(yīng)激的兒童激素分泌的性別差異進行了縱向追蹤調(diào)查, 結(jié)果顯示在3～6歲經(jīng)歷應(yīng)激事件會影響女孩9歲時皮質(zhì)醇與睪酮素的耦合模式(即激素分泌水平的同步性), 但不會影響男孩(Black et al., 2018)。未來研究可以從神經(jīng)內(nèi)分泌的角度進一步探索兒童應(yīng)激在神經(jīng)生物基礎(chǔ)上的性別差異。另外, 年齡特征分析表明, 應(yīng)激水平與IOG灰質(zhì)體積的顯著正相關(guān)只出現(xiàn)在9歲組中。IOG是初級視覺皮層的一部分, 負(fù)責(zé)加工和傳遞消極信息的視覺皮層是一個高度可塑性的結(jié)構(gòu), 但這種可塑性會隨著青春期的到來而減緩(Hubel & Wiesel, 1998; Shimada et al., 2015)。前人研究表明, 應(yīng)激相關(guān)的經(jīng)歷(如, 經(jīng)歷暴力虐待事件或目睹家庭暴力)可能會影響初級視覺皮層的發(fā)育(Cwik et al., 2020; Tomoda et al., 2012)。這一結(jié)果可能支持了以往的觀點, 即童年中期是神經(jīng)系統(tǒng)易受到應(yīng)激所帶來消極影響的時期(Stevens et al., 2018)。雖然12歲組的應(yīng)激水平與IOG的灰質(zhì)體積顯著負(fù)相關(guān), 但這一結(jié)果可能是由于該年齡階段被試量較少(= 10)所致。未來可進一步基于大樣本數(shù)據(jù)從發(fā)展角度考察應(yīng)激與腦出現(xiàn)關(guān)聯(lián)的關(guān)鍵年齡點以及性別差異, 并探明兒童應(yīng)激的神經(jīng)標(biāo)記是否能夠穩(wěn)定預(yù)測其未來的應(yīng)激水平和消極情緒。

另外, 已有大量證據(jù)支持了兒童應(yīng)激與焦慮之間的緊密關(guān)聯(lián), 二者皆具有高警覺性和恐慌不安等特征(Price et al., 2019)。當(dāng)前研究也發(fā)現(xiàn)兒童應(yīng)激水平與更多敵意和痛苦等消極情緒存在正相關(guān)。同時, 患有焦慮癥的兒童其應(yīng)激系統(tǒng)也會發(fā)生紊亂, 如皮質(zhì)醇升高(Funke et al., 2017)。在神經(jīng)層面, 焦慮癥兒童在前額?邊緣?顳葉處也表現(xiàn)出腦結(jié)構(gòu)與功能的異常。譬如, VBM研究發(fā)現(xiàn), 兒童焦慮癥患者在OFC、STG和腦島的GMV顯著大于健康兒童(Albaugh et al., 2017; de Bellis, Keshavan, Shifflett et al., 2002; Liu et al., 2021)。一項新近的基于DTI結(jié)構(gòu)網(wǎng)絡(luò)的連接組預(yù)測模型的研究發(fā)現(xiàn), 前額?邊緣?顳葉的結(jié)構(gòu)網(wǎng)絡(luò)還可預(yù)測成年早期的特質(zhì)焦慮水平(Yoo, Park & Kim, 2022)。此外, rs-fMRI研究也發(fā)現(xiàn)上述腦區(qū)間FC的異常與兒童焦慮水平有關(guān)。具體而言, 邊緣系統(tǒng)(如, 腦島和杏仁核)與突顯網(wǎng)絡(luò)和執(zhí)行控制網(wǎng)絡(luò)(如, 前額葉)之間的FC強度與兒童焦慮水平呈正相關(guān)(Perino et al., 2021), 并能顯著預(yù)測兒童特質(zhì)焦慮水平(Qin et al., 2014)。與上述發(fā)現(xiàn)類似, 本研究中兒童應(yīng)激的神經(jīng)關(guān)聯(lián)也主要聚焦于前額?邊緣?顳葉腦區(qū), 這表明兒童應(yīng)激與焦慮的神經(jīng)生物學(xué)基礎(chǔ)可能存在重疊, 其在一定程度上與這些疾病的高共病性有關(guān)(Kribakaran et al., 2020)。

本研究發(fā)現(xiàn)兒童應(yīng)激與涉及情緒調(diào)節(jié)和內(nèi)外部刺激整合(即整合積極自我評價與外部消極情緒刺激)的前額葉?邊緣?顳葉腦區(qū)有關(guān), 這對兒童應(yīng)激的早期預(yù)防和干預(yù)策略具有實踐意義。大量證據(jù)已表明兒童應(yīng)激會導(dǎo)致身心發(fā)展異常, 甚至出現(xiàn)精神疾病癥狀, 將嚴(yán)重影響個體正常生活(Berens et al., 2017; Smith & Pollak, 2020)。從行為認(rèn)知層面來看, 鑒于情緒調(diào)節(jié)訓(xùn)練和正念訓(xùn)練能有效改善個體對應(yīng)激源的情緒調(diào)節(jié), 并已在青少年和成人群體中得到廣泛應(yīng)用(Bai et al., 2020; Saedpanah et al., 2016)。因此, 未來研究可采用上述心理訓(xùn)練技術(shù)來調(diào)節(jié)兒童的情緒功能, 并促進其積極的自我參照功能, 以此緩解和預(yù)防應(yīng)激對兒童的消極影響。從神經(jīng)層面來看, 還可采用神經(jīng)生物手段, 如生物反饋、經(jīng)顱直流電刺激和深部腦刺激等, 刺激前額葉等腦區(qū)來干預(yù)應(yīng)激癥狀更為嚴(yán)重的臨床個體(Bari et al., 2014; Hamani et al., 2020; Schlatter et al., 2021)。

總之, 探明個體從適應(yīng)性到適應(yīng)不良行為的神經(jīng)關(guān)聯(lián)有助于理解從非臨床到臨床全動態(tài)范圍的神經(jīng)變化過程。本研究從多模態(tài)的視角拓展了非臨床兒童應(yīng)激神經(jīng)基礎(chǔ)的實證研究, 促進了對應(yīng)激易感兒童的結(jié)構(gòu)和功能神經(jīng)特征的深入了解。然而, 當(dāng)前研究也存在一些不足。首先, 由于本研究為橫斷設(shè)計, 只能確定應(yīng)激與兒童大腦的相關(guān)關(guān)系, 而無法確定變量之間的因果關(guān)系。目前尚不清楚究竟是更高的應(yīng)激水平導(dǎo)致了兒童更大的灰質(zhì)體積, 還是由于更大的灰質(zhì)體積致使了兒童更高的應(yīng)激水平。未來研究可采用前瞻性縱向隊列設(shè)計, 進一步細(xì)分兒童年齡階段以回答這些科學(xué)問題。其次, 尚不清楚灰質(zhì)體積對應(yīng)的是什么成分的改變, 它們可能與細(xì)胞大小的變化、神經(jīng)元或膠質(zhì)細(xì)胞的生長或萎縮以及突觸產(chǎn)生有關(guān)(May & Gaser, 2006)。因此, 未來研究可以采用白質(zhì)纖維束等指標(biāo)以便從更微觀的角度了解兒童應(yīng)激的神經(jīng)生物學(xué)基礎(chǔ)。再次, 本研究僅通過兒童自我報告量表的方式測量了兒童的應(yīng)激水平。然而, 與應(yīng)激水平相關(guān)的重要因素可能會影響兒童應(yīng)激的神經(jīng)關(guān)聯(lián), 譬如, 應(yīng)激事件的強度、持續(xù)時間、應(yīng)激事件的數(shù)量以及不同應(yīng)激事件之間的相互作用等(Nelson et al., 2020)。盡管本次使用的問卷可以測量兒童應(yīng)激水平和應(yīng)激事件數(shù)量兩種指標(biāo), 但由于樣本量較小未能探究兩變量的交互作用。未來研究可以深入考察高應(yīng)激事件?低應(yīng)激水平和低應(yīng)激事件?高應(yīng)激水平這兩類兒童的腦差異。最后, 神經(jīng)區(qū)域和心理功能之間似乎不是一一對應(yīng)的關(guān)系(Anderson, 2014), 因此當(dāng)前研究結(jié)果也存在其他可能的推論。未來研究需要進一步證明本研究所發(fā)現(xiàn)的兒童應(yīng)激與大腦的關(guān)聯(lián)能否被情緒調(diào)節(jié)與認(rèn)知控制等功能得以最好地解釋。

5 結(jié)論

本研究首次采用多模態(tài)數(shù)據(jù)并結(jié)合機器學(xué)習(xí)的方法探討了兒童應(yīng)激相關(guān)的神經(jīng)基礎(chǔ)。結(jié)果表明, 兒童應(yīng)激與涉及情緒功能的mOFC、腦島、STG和SMA的灰質(zhì)體積存在正相關(guān); 與涉及內(nèi)外部信息整合的腦島-IPL的FC呈負(fù)相關(guān)。當(dāng)前結(jié)果不受被試年齡、性別、頭動和TIV的影響, 表明了研究發(fā)現(xiàn)的特異性; 機器學(xué)習(xí)預(yù)測分析顯示, 上述腦區(qū)均能有效預(yù)測兒童應(yīng)激水平, 表明了研究結(jié)果的穩(wěn)健性。研究發(fā)現(xiàn)揭示了異常的情緒功能與內(nèi)外信息整合與兒童應(yīng)激水平的增加有關(guān)。本研究不僅拓展了目前有關(guān)兒童應(yīng)激神經(jīng)基礎(chǔ)的實證證據(jù), 還為開發(fā)出兒童應(yīng)激的早期預(yù)防策略和干預(yù)手段提供了有力支持。

Agorastos, A., Pervanidou, P., Chrousos, G. P., & Kolaitis, G. (2018). Early life stress and trauma: Developmental neuroe-ndocrine aspects of prolonged stress system dysregulation.(4), 507–520.

Albaugh, M. D., Nguyen, T. V., Ducharme, S., Collins, D. L., Botteron, K. N., D’Alberto, N., ... Brain Development Cooperative Group. (2017). Age-related volumetric change of limbic structures and subclinical anxious/depressed symptomatology in typically developing children and adolescents., 133–140.

Anderson, M. L. (2014).MIT Press.

Ani, C., Reading, R., Lynn, R., Forlee, S., & Garralda, E. (2013). Incidence and 12-month outcome of non-transient childhood conversion disorder in the U.K. and Ireland.(6), 413–418.

Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry— the methods.805–821.

Aupperle, R. L., Allard, C. B., Grimes, E. M., Simmons, A. N., Flagan, T., Behrooznia, M., ... Stein, M. B. (2012). Dorsolateral prefrontal cortex activation during emotional anticipation and neuropsychological performance in posttraumatic stress disorder.,(4), 360– 371.

Aybek, S., Nicholson, T. R., Draganski, B., Daly, E., Murphy, D. G., David, A. S., & Kanaan, R. A. (2014). Grey matter changes in motor conversion disorder.(2), 236–238.

Bai, S., Elavsky, S., Kishida, M., Dvo?áková, K., & Greenberg, M. T. (2020). Effects of mindfulness training on daily stress response in college students: Ecological momentary assessment of a randomized controlled trial.(6), 1433–1445.

Bari, A., Niu, T., Langevin, J. P., & Fried, I. (2014). Limbic neuromodulation: Implications for addiction, posttraumatic stress disorder, and memory.(1), 137–145.

Berens, A. E., Jensen, S., & Nelson, C. A. (2017). Biological embedding of childhood adversity: From physiological mechanisms to clinical implications.(1), 135.

Black, S. R., Lerner, M. D., Shirtcliff, E. A., & Klein, D. N. (2018). Patterns of neuroendocrine coupling in 9-year-old children: Effects of sex, body-mass index, and life stress.252–259.

Blithikioti, C., Nu?o, L., Guell, X., Pascual-Diaz, S., Gual, A., Balcells-Olivero, Μ., & Miquel, L. (2022). The cerebellum and psychological trauma: A systematic review of neuroimaging studies.100429.

Bonini, F., Burle, B., Liégeois-Chauvel, C., Régis, J., Chauvel, P., & Vidal, F. (2014). Action monitoring and medial frontal cortex: Leading role of supplementary motor area.(6173), 888–891.

Brunet, E., Sarfati, Y., Hardy-Baylé, M. C., & Decety, J. (2000). A PET investigation of the attribution of intentions with a nonverbal task.(2), 157–166.

Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain's default network: Anatomy, function, and relevance to disease., 1–38.

Burghy, C. A., Stodola, D. E., Ruttle, P. L., Molloy, E. K., Armstrong, J. M., Oler, J. A., ... Birn, R. M. (2012). Developmental pathways to amygdala-prefrontal function and internalizing symptoms in adolescence.(12), 1736–1741.

Calderon-Delgado, L., Barrera-Valencia, M., Noriega, I., Al-Khalil, K., Trejos-Castillo, E., Mosi, J., Chavez, B., Galvan, M., & O'Boyle, M. W. (2020). Implicit processing of emotional words by children with Post-Traumatic Stress Disorder: An fMRI investigation.(1), 46–53.

Calem, M., Bromis, K., McGuire, P., Morgan, C., & Kempton, M. J. (2017). Meta-analysis of associations between childhood adversity and hippocampus and amygdala volume in non-clinical and general population samples., 471–479.

Choi, J., Jeong, B., Polcari, A., Rohan, M. L., & Teicher, M. H. (2012). Reduced fractional anisotropy in the visual limbic pathway of young adults witnessing domestic violence in childhood.(2), 1071–1079.

Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013). Multi-task connectivity reveals flexible hubs for adaptive task control.(9), 1348–1355.

Craig, A. D. (2009). How do you feel–now? The anterior insula and human awareness.(1), 59–70.

Cwik, J. C., Vahle, N., Woud, M. L., Potthoff, D., Kessler, H., Sartory, G., & Seitz, R. J. (2020). Reduced gray matter volume in the left prefrontal, occipital, and temporal regions as predictors for posttraumatic stress disorder: A voxel-based morphometric study.(5), 577–588.

de Bellis, M. D., Keshavan, M. S., Frustaci, K., Shifflett, H., Iyengar, S., Beers, S. R., & Hall, J. (2002). Superior temporal gyrus volumes in maltreated children and adolescents with PTSD.,(7), 544–552.

de Bellis, M. D., Keshavan, M. S., Shifflett, H., Iyengar, S., Dahl, R. E., Axelson, D. A., ... Ryan, N. D. (2002). Superior temporal gyrus volumes in pediatric generalized anxiety disorder.(7), 553–562.

Demir-Lira, ?. E., Voss, J. L., O'Neil, J. T., Briggs-Gowan, M. J., Wakschlag, L. S., & Booth, J. R. (2016). Early-life stress exposure associated with altered prefrontal resting-state fMRI connectivity in young children., 107–114.

Etkin, A., & Wager, T. D. (2007). Functional neuroimaging of anxiety: A meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia.(10), 1476–1488.

Feng, C., Yuan, J., Geng, H., Gu, R., Zhou, H., Wu, X., & Luo, Y. (2018). Individualized prediction of trait narcissism from whole-brain resting-state functional connectivity.(9), 3701–3712.

Ferketich, A. K., Ferguson, J. P., & Binkley, P. F. (2005). Depressive symptoms and inflammation among heart failure patients.(1), 132–136.

Funke, R., Eichler, A., Distler, J., Golub, Y., Kratz, O., & Moll, G. H. (2017). Stress system dysregulation in pediatric generalized anxiety disorder associated with comorbid depression.(5), 518–529.

Grayson, D. S., & Fair, D. A. (2017). Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature., 15?31.

Goetschius, L. G., Hein, T. C., McLanahan, S. S., Brooks- Gunn, J., McLoyd, V. C., Dotterer, H. L., ... Beltz, A. M. (2020). Association of childhood violence exposure with adolescent neural network density.(9), e2017850.

Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., ... Thompson, P. M. (2004). Dynamic mapping of human cortical development during childhood through early adulthood.(21), 8174–8179.

Gu, X., Hof, P. R., Friston, K. J., & Fan, J. (2013). Anterior insular cortex and emotional awareness.(15), 3371–3388.

Hahm, S., Lotze, M., Domin, M., & Schmidt, S. (2019). The association of health-related quality of life and cerebral gray matter volume in the context of aging: A voxel-based morphometry study with a general population sample., 470–480.

Hamani, C., Davidson, B., Levitt, A., Meng, Y., Corchs, F., Abrahao, A., ... Lipsman, N. (2020). Patient with posttraumatic stress disorder successfully treated with deep brain stimulation of the medial prefrontal cortex and uncinate fasciculus.(11), e57–e59.

Hammen, C., Henry, R., & Daley, S. E. (2000). Depression and sensitization to stressors among young women as a function of childhood adversity.(5), 782–787.

Hart, H., Lim, L., Mehta, M. A., Simmons, A., Mirza, K., & Rubia, K. (2018). Altered fear processing in adolescents with a history of severe childhood maltreatment: An fMRI study.(7), 1092–1101.

Haufe, S., Meinecke, F., G?rgen, K., D?hne, S., Haynes, J. D., Blankertz, B., & Bie?mann, F. (2014). On the interpretation of weight vectors of linear models in multivariate neuroimaging., 96–110.

Herringa, R. J., Birn, R. M., Ruttle, P. L., Burghy, C. A., Stodola, D. E., Davidson, R. J., & Essex, M. J. (2013). Childhood maltreatment is associated with altered fear circuitry and increased internalizing symptoms by late adolescence.(47), 19119– 19124.

Holmes, S. E., Scheinost, D., DellaGioia, N., Davis, M. T., Matuskey, D., Pietrzak, R. H., ... Esterlis, I. (2018). Cerebellar and prefrontal cortical alterations in PTSD: Structural and functional evidence., 2470547018786390.

Horien, C., Noble, S., Finn, E. S., Shen, X., Scheinost, D., & Constable, R. T. (2018). Considering factors affecting the connectome-based identification process: Comment on Waller et al., 172–175.

Howard, D., Patterson, K., Wise, R., Brown, W. D., Friston, K., Weiller, C., & Frackowiak, R. (1992). The cortical localization of the lexicons: Positron emission tomography evidence.,(6), 1769–1782.

Hsu, W. T., Rosenberg, M. D., Scheinost, D., Constable, R. T., & Chun, M. M. (2018). Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals.(2), 224–232.

Hu, S., Pruessner, J. C., Coupé, P., & Collins, D. L. (2013). Volumetric analysis of medial temporal lobe structures in brain development from childhood to adolescence., 276–287.

Hubel, D. H., & Wiesel, T. N. (1998). Early exploration of the visual cortex.(3), 401–412.

Insel, T. R. (2009). Translating scientific opportunity into public health impact: A strategic plan for research on mental illness.(2), 128–133.

Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images.,(2), 825–841.

Jones, D. K., & Cercignani, M. (2010). Twenty‐five pitfalls in the analysis of diffusion MRI data.(7), 803–820.

Kautz, M., Ered, A., Nielsen, J., Olino, T., & Alloy, L. (2021). The effect of early life stress during sensitive exposure periods on orbitofrontal cortex thickness.(9), S278–S279.

Kong, X. Z., Wang, X., Huang, L., Pu, Y., Yang, Z., Dang, X., ... Liu, J. (2014). Measuring individual morphological relationship of cortical regions., 103–107.

Kozlowska, K., Palmer, D. M., Brown, K. J., McLean, L., Scher, S., Gevirtz, R., ... Williams, L. M. (2015). Reduction of autonomic regulation in children and adolescents with conversion disorders.(4), 356– 370.

Kozlowska, K., Griffiths, K. R., Foster, S. L., Linton, J., Williams, L. M., & Korgaonkar, M. S. (2017). Grey matter abnormalities in children and adolescents with functional neurological symptom disorder.306–314.

Kribakaran, S., Danese, A., Bromis, K., Kempton, M. J., & Gee, D. G. (2020). Meta-analysis of structural magnetic resonance imaging studies in pediatric posttraumatic stress disorder and comparison with related conditions.(1), 23–34.

Lazarus, R. S., & Folkman, S. (1984).Springer publishing company.

Li, L., Zhang, H., Hu, X., Suo, X., Lei, D., Wu, M., ... Gong, Q. (2017). Voxel-based morphometry-diffeomorphic anatomical registration through exponentiated lie algebra algorithm evaluation of brain structural of recent-onset pediatric post-traumatic stress disorder patients.(3), 210–213.

[黎磊, 張華為, 胡新宇, 索學(xué)玲, 雷都, 吳敏, ... 龔啟勇. (2017). 基于體素的形態(tài)學(xué)測量與自建模板及微分同胚圖像融合方法對近期發(fā)病的創(chuàng)傷后應(yīng)激障礙兒童的腦結(jié)構(gòu)研究.(3), 210–213.]

Lim, L., Hart, H., Mehta, M. A., Simmons, A., Mirza, K., & Rubia, K. (2015). Neural correlates of error processing in young people with a history of severe childhood abuse: An fMRI study.(9), 892–900.

Lim, L., Radua, J., & Rubia, K. (2014). Gray matter abnormalities in childhood maltreatment: A voxel-wise meta-analysis.(8), 854–863.

Liu, Z., Hu, Y., Zhang, Y., Liu, W., Zhang, L., Wang, Y., ... Yang, Z. (2021). Altered gray matter volume and structural co-variance in adolescents with social anxiety disorder: Evidence for a delayed and unsynchronized development of the fronto-limbic system.(10), 1742?1751.

Liu, S. D., Liu, Q., Luo, Y., & Wen, Y. (2016). Development of the stressful life events for primary school students.(10), 745–751.

[劉舒丹, 劉琴, 羅燕, 文一. (2016). 小學(xué)生應(yīng)激性生活事件量表的編制.(10), 745–751.]

Lu, S., Gao, W., Wei, Z., Wang, D., Hu, S., Huang, M., ... Li, L. (2017). Intrinsic brain abnormalities in young healthy adults with childhood trauma: A resting-state functional magnetic resonance imaging study of regional homogeneity and functional connectivity.(6), 614–623.

Maikovich, A. K., Koenen, K. C., & Jaffee, S. R. (2009). Posttraumatic stress symptoms and trajectories in child sexual abuse victims: An analysis of sex differences using the national survey of child and adolescent well-being.(5), 727–737.

Manoliu, A., Meng, C., Brandl, F., Doll, A., Tahmasian, M., Scherr, M., ... Sorg, C. (2014). Insular dysfunction within the salience network is associated with severity of symptoms and aberrant inter-network connectivity in major depressive disorder.930.

Marusak, H. A., Etkin, A., & Thomason, M. E. (2015). Disrupted insula-based neural circuit organization and conflict interference in trauma-exposed youth., 516–525.

May, A., & Gaser, C. (2006). Magnetic resonance-based morphometry: A window into structural plasticity of the brain.(4), 407–411.

McEwen, B. S., Eiland, L., Hunter, R. G., & Miller, M. M. (2012). Stress and anxiety: Structural plasticity and epigenetic regulation as a consequence of stress.(1), 3–12.

McEwen, B. S., Nasca, C., & Gray, J. D. (2016). Stress effects on neuronal structure: Hippocampus, amygdala, and prefrontal cortex.(1), 3–23.

McLaughlin, K. A., Conron, K. J., Koenen, K. C., & Gilman, S. E. (2010). Childhood adversity, adult stressful life events, and risk of past-year psychiatric disorder: A test of the stress sensitization hypothesis in a population-based sample of adults.(10), 1647–1658.

McLaughlin, K. A., & Hatzenbuehler, M. L. (2009). Stressful life events, anxiety sensitivity, and internalizing symptoms in adolescents.(3), 659–669.

McLaughlin, K. A., Sheridan, M. A., & Nelson, C. A. (2017). Neglect as a violation of species-expectant experience: Neurodevelopmental consequences.(7), 462–471.

Mechelli, A., Price, C. J., Friston, K. J., Ashburner, J. (2005). Voxel-based morphometry of the human brain: Methods and applications.(2), 105–113.

Mennes, M., Kelly, C., Zuo, X. N., Martino, A. D., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2010). Interindividual differences in resting-state functional connectivity predict task-induced BOLD activity.,(4), 1690–1701.

Menon, V. (2011)Large-scale brain networks and psychopathology: A unifying triple network model.(10), 483–506.

Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: A network model of insula function.(5-6), 655–667.

Morey, R. A., Petty, C. M., Xu, Y., Hayes, J. P., Wagner II, H. R., Lewis, D. V., ... McCarthy, G. (2009). A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes.(3), 855–866.

Nelson, C. A., Scott, R. D., Bhutta, Z. A., Harris, N. B., Danese, A., & Samara, M. (2020). Adversity in childhood is linked to mental and physical health throughout life., 3048.

Noble, S., Spann, M. N., Tokoglu, F., Shen, X., Constable, R. T., & Scheinost, D. (2017). Influences on the test-retest reliability of functional connectivity MRI and its relationship with behavioral utility.(11), 5415– 5429.

O'Doherty, J., Kringelbach, M. L., Rolls, E. T., Hornak, J., & Andrews, C. (2001). Abstract reward and punishment representations in the human orbitofrontal cortex.(1), 95–102.

Oliveri, M., Babiloni, C., Filippi, M. M., Caltagirone, C., Babiloni, F., Cicinelli, P., ... Rossini, P. M. (2003). Influence of the supplementary motor area on primary motor cortex excitability during movements triggered by neutral or emotionally unpleasant visual cues.(2), 214–221.

Paquola, C., Bennett, M. R., Hatton, S. N., Hermens, D. F., & Lagopoulos, J. (2017). Utility of the cumulative stress and mismatch hypotheses in understanding the neurobiological impacts of childhood abuse and recent stress in youth with emerging mental disorder.(5), 2709–2721.

Perino, M. T., Myers, M. J., Wheelock, M. D., Yu, Q., Harper, J. C., Manhart, M. F., ... Sylvester, C. M. (2021). Whole-brain resting-state functional connectivity patterns associated with pediatric anxiety and involuntary attention capture.(3), 229–238.

Phillips, M. L., Drevets, W. C., Rauch, S. L., & Lane, R. (2003). Neurobiology of emotion perception I: The neural basis of normal emotion perception.(5), 504–514.

Price, M., Legrand, A. C., Brier, Z. M., & Hébert-Dufresne, L. (2019). The symptoms at the center: Examining the comorbidity of posttraumatic stress disorder, generalized anxiety disorder, and depression with network analysis., 52–58.

Qin, S., Young, C. B., Duan, X., Chen, T., Supekar, K., & Menon, V. (2014). Amygdala subregional structure and intrinsic functional connectivity predicts individual differences in anxiety during early childhood.(11), 892–900.

Rabellino, D., Densmore, M., Théberge, J., McKinnon, M. C., & Lanius, R. A. (2018). The cerebellum after trauma: Resting-state functional connectivity of the cerebellum in posttraumatic stress disorder and its dissociative subtype.(8), 3354–3374.

Ringwald, K. G., Meller, T., Schmitt, S., Andlauer, T., Stein, F., Brosch, K., … Kircher, T. (2021). Interaction of developmental factors and ordinary stressful life events on brain structure in adults., 102683.

Roger, C., Bénar, C. G., Vidal, F., Hasbroucq, T., & Burle, B. (2010). Rostral Cingulate Zone and correct response monitoring: ICA and source localization evidences for the unicity of correct- and error-negativities.(1), 391–403.

Rolls, E. T. (2017). The orbitofrontal cortex and emotion in health and disease, including depression.,, 14–43.

Saedpanah, D., Salehi, S., & Moghaddam, L. F. (2016). The effect of emotion regulation training on occupational stress of critical care nurses.(12), VC01–VC04.

Saxbe, D., Khoddam, H., Piero, L. D., Stoycos, S. A., Gimbel, S. I., Margolin, G., & Kaplan, J. T. (2018). Community violence exposure in early adolescence: Longitudinal associations with hippocampal and amygdala volume and resting state connectivity.(6), e12686.

Schlatter, S., Guillot, A., Schmidt, L., Mura, M., Trama, R., Di Rienzo, F., ... Debarnot, U. (2021). Combining proactive transcranial stimulation and cardiac biofeedback to substantially manage harmful stress effects.(5), 1384–1392.

Serra-Blasco, M., Radua, J., Soriano-Mas, C., Gómez- Benlloch, A., Porta-Casteràs, D., Carulla-Roig, M., ... Cardoner, N. (2021). Structural brain correlates in major depression, anxiety disorders and post-traumatic stress disorder: A voxel-based morphometry meta-analysis., 269–281.

Shen, X., Finn, E. S., Scheinost, D., Rosenberg, M. D., Chun, M. M., Papademetris, X., & Constable, R. T. (2017). Using connectome-based predictive modeling to predict individual behavior from brain connectivity.(3), 506–518.

Shen, X., Tokoglu, F., Papademetris, X., & Constable, R. T. (2013). Groupwise whole-brain parcellation from resting- state fMRI data for network node identification., 403–415.

Sheynin, J., Duval, E. R., Lokshina, Y., Scott, J. C., Angstadt, M., Kessler, D., ... Liberzon, I. (2020). Altered resting-state functional connectivity in adolescents is associated with PTSD symptoms and trauma exposure., 102215.

Shimada, K., Takiguchi, S., Mizushima, S., Fujisawa, T. X., Saito, D. N., Kosaka, H., ... Tomoda, A. (2015). Reduced visual cortex grey matter volume in children and adolescents with reactive attachment disorder., 13–19.

Shonkoff, J. P., Boyce, W. T., & McEwen, B. S. (2009). Neuroscience, molecular biology, and the childhood roots of health disparities: Building a new framework for health promotion and disease prevention.(21), 2252– 2259.

Simmons, A. N., Paulus, M. P., Thorp, S. R., Matthews, S. C., Norman, S. B., & Stein, M. B. (2008). Functional activation and neural networks in women with posttraumatic stress disorder related to intimate partner violence.(8), 681–690.

Smith, K. E., & Pollak, S. D. (2020). Early life stress and development: Potential mechanisms for adverse outcomes.(1), 34.

Soares, J. M., Sampaio, A., Ferreira, L. M., Santos, N. C., Marques, P., Marques, F., ... Sousa, N. (2013). Stress impact on resting state brain networks.(6), e66500.

Song, K. (2013). Asymptotic relative efficiency and exact variance stabilizing transformation for the generalized Gaussian distribution.,(7), 4389–4396.

Sp?ti, J., H?nggi, J., Doerig, N., Ernst, J., Sambataro, F., Brakowski, J., ... Spinelli, S. (2015). Prefrontal thinning affects functional connectivity and regional homogeneity of the anterior cingulate cortex in depression.(7), 1640–1648.

Stevens, J. S., van Rooij, S., & Jovanovic, T. (2018). Developmental contributors to trauma response: The importance of sensitive periods, early environment, and sex differences., 1–22.

Strigo, I. A., Simmons, A. N., Matthews, S. C., Grimes, E. M., Allard, C. B., Reinhardt, L. E., ... Stein, M. B. (2010). Neural correlates of altered pain response in women with posttraumatic stress disorder from intimate partner violence.,(5), 442–450.

Teicher, M. H., Samson, J. A., Anderson, C. M., & Ohashi, K. (2016). The effects of childhood maltreatment on brain structure, function and connectivity.(10), 652–666.

Thomason, M. E., Marusak, H. A., Tocco, M. A., Vila, A. M., McGarragle, O., & Rosenberg, D. R. (2015). Altered amygdala connectivity in urban youth exposed to trauma.(11), 1460– 1468.

Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine., 211–244.

Tomoda, A., Navalta, C. P., Polcari, A., Sadato, N., & Teicher, M. H. (2009). Childhood sexual abuse is associated with reduced gray matter volume in visual cortex of young women.(7), 642–648.

Tomoda, A., Sheu, Y. S., Rabi, K., Suzuki, H., Navalta, C. P., Polcari, A., & Teicher, M. H. (2011). Exposure to parental verbal abuse is associated with increased gray matter volume in superior temporal gyrus.(suppl. 1), S280–S286.

Tomoda, A., Polcari, A., Anderson, C. M., & Teicher, M. H. (2012). Reduced visual cortex gray matter volume and thickness in young adults who witnessed domestic violence during childhood.(12), e52528.

Tong, D. D., Li, W. F., Lu, P., Yang, W. J., Yang, D., Zhang, Q. L., & Qiu, J. (2020). The neural basis of scientific innovation problem finding.,(11), 1253–1265.

[童丹丹, 李文福, 祿鵬, 楊文靜, 楊東, 張慶林, 邱江. (2020). 科學(xué)發(fā)明情境中問題提出的腦機制再探.(11), 1253–1265.]

Tooley, U. A., Bassett, D. S., & Mackey, A. P. (2021). Environmental influences on the pace of brain development.(6), 372–384.

Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., ... Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.(1), 273–289.

Uddin, L.Q. (2017).(pp.11–16)Academic Press.

Vanaelst, B., Huybrechts, I., de Bourdeaudhuij, I., Bammann, K., Hadjigeorgiou, C., Eiben, G., ... de Henauw, S. (2012). Prevalence of negative life events and chronic adversities in European pre-and primary-school children: Results from the IDEFICS study.(1), 1–11.

Vuper, T. C., Philippi, C. L., & Bruce, S. E. (2021). Altered resting-state functional connectivity of the default mode and central executive networks following cognitive processing therapy for PTSD.113312.

Wabnegger, A., übel, S., & Schienle, A. (2018). Voxel-based morphometry of disgust sensitivity.(2), 241–245.

Wallace, G. L., Richard, E., Peng, C. S., Knodt, A. R., & Hariri, A. R. (2020). Subclinical eating disorder traits are correlated with cortical thickness in regions associated with food reward and perception.,(2), 346–352.

Waller, L., Walter, H., Kruschwitz, J. D., Reuter, L., Müller, S., Erk, S., Veer, I. M. (2017). Evaluating the replicability, specificity, and generalizability of connectome fingerprints., 371–377.

Wang, H., Jin, X., Zhang, Y., & Wang, J. (2016). Single‐subject morphological brain networks: Connectivity mapping, topological characterization and test–retest reliability.(4), e00448.

Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales.(6), 1063–1070.

Wu, J., Tong, H., Liu, Z., Tao, J., Chen, L., Chan, C., & Lee, T. (2021). Neurobiological effects of perceived stress are different between adolescents and middle-aged adults.(2), 846–854.

Xia, L., Wang, J. L., & He, Y. (2010). Human connectome: Structural and functional brain networks.(16)1565–1583.

[梁夏, 王金輝, 賀永. (2010). 人腦連接組研究: 腦結(jié)構(gòu)網(wǎng)絡(luò)和腦功能網(wǎng)絡(luò).(16), 1565?1583.]

Yan, C. G., Wang, X. D., Zuo, X. N., & Zang, Y. F. (2016). DPABI: data processing & analysis for (resting-state) brain imaging.(3), 339?351.

Yoo, C., Park, S., & Kim, M. J. (2022). Structural connectome- based prediction of trait anxiety., https://doi.org/10.1007/s11682-022- 00700-2.

Yu, M., Linn, K. A., Shinohara, R. T., Oathes, D. J., Cook, P. A., Duprat, R., ... Sheline, Y. I. (2019). Childhood trauma history is linked to abnormal brain connectivity in major depression.(17), 8582?8590.

Zhou, F., Zhao, W., Qi, Z., Geng, Y., Yao, S., Kendrick, K. M., ... Becker, B. (2021). A distributed fMRI-based signature for the subjective experience of fear.(1), 1?16.

Zhutovsky, P., Zantvoord, J. B., Ensink, J. B., op den Kelder, R., Lindauer, R. J., & van Wingen, G. A. (2021). Individual prediction of trauma-focused psychotherapy response in youth with posttraumatic stress disorder using resting-state functional connectivity., 102898.

Zuo, X. N., Di Martino, A., Kelly, C., Shehzad, Z. E., Gee, D. G., Klein, D. F., ... Milham, M. P. (2010). The oscillating brain: Complex and reliable.(2), 1432?1445.

The relationship between frontotemporal regions and early life stress in children aged 9 to 12: Evidence from multimodal fMRI

LI Wei1, BIAN Ziming1, CHEN Ximei1, WANG Junjie1, LUO Yijun1, LIU Yong1,2, SONG Shiqing1, GAO Xiao1,2, CHEN Hong1,2

(1Faculty of Psychology, Southwest University;2Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China)

Early life stress (ELS) has been used to describe a broad spectrum of adverse and stressful events, including childhood trauma occurring during neonatal life, early and late childhood, and adolescence. Childhood is a vulnerable time point for stressful events due to an immature brain, which increases the risk of psychopathology in later life. However, to date, studies have focused almost exclusively on adolescents and adults, and little is known about the relationship between ELS and the structural and functional brain changes in children. Here, we adopted a multimodal approach combining voxel-based morphometry (VBM) and functional connectivity (FC) to examine the neural substrates of ELS in children aged 9～12 years.

A total of 139 children were recruited for this study. For each participant, the ELS level was assessed and an 8-minute rs-fMRI scan was performed using a 3T Trio scanner. Participants with unqualified data were excluded, resulting in a final sample of 78 participants (39 females; mean age = 10.18). For statistical analysis, we used the gray matter volume (GMV) and FC to explore the brain structural and functional correlates of children’s ELS and then used a machine learning method to investigate whether and how structural connectivity profiles in predefined brain networks can predict ELS levels. Additionally, exploratory analyses were performed to investigate potential sex differences and age characteristics in GMV and FC associated with children’s ELS.

VBM analysis showed that greater ELS was associated with a larger GMV in the left medial orbitofrontal cortex, right insular cortex, left superior temporal gyrus, and left supplementary motor area. Subsequently, we used these clusters as seed regions to analyze the correlation between FC and stress in children. We found that greater ELS was associated with lower insular-inferior parietal lobule (IPL) connectivity. The results were not influenced by sex, age, total intracranial volume, or head motion. Furthermore, the predictive analysis of machine learning reported that the sensorimotor, frontoparietal, salience, visual, and cerebellar networks could marginally predict ELS scores. Finally, exploratory analyses showed that there were no significant sex differences in the GMV or FC associated with ELS and that significant correlations of ELS with the GMV of the inferior occipital gyrus were mainly manifested in 9-year-old children.

Using VBM and FC analyses, we detected structural and functional brain alterations associated with ELS in children aged 9～12 years. Specifically, the VBM analysis mainly reflected that children with high ELS may have abnormal emotional and cognitive functions, such as hypersensitivity to emotional stimuli and over-monitoring of their own behavior. In addition, FC analysis indicated that aberrant interaction of internal and external information may contribute to high ELS in childhood. This study not only provides unique insights into the neural substrates of ELS but may also help identify children who are susceptible to ELS within the general population, which may be advantageous for early prevention strategies and interventions for children.

early life stress, children, gray matter volume, resting-state functional connectivity, machine learning, structural network

2022-03-15

* 國家自然科學(xué)基金項目(31771237, 32271087); 中央高?；究蒲袠I(yè)務(wù)費專項資金創(chuàng)新團隊項目(SWU1709106)資助。

李為和邊子茗為共同第一作者。

陳紅, E-mail: chenhg@swu.edu.cn

B845