以“世界毒品之王”而著称的海洛因是一种成瘾性极强、戒断难度极大的阿片类药物。长期使用海洛因会导致成瘾患者执行控制能力差，抑制渴求能力下降等，这也是海洛因成瘾戒治较难的原因。因此，了解海洛因使用和长期戒断所导致的大脑可塑性改变的机制，可能为海洛因成瘾机制研究和成瘾治疗提供新的科学证据。本文通过功能磁共振技术采集海洛因成瘾患者的影像学数据，并从不同的维度研究了海洛因成瘾患者大脑回路因果连接与网络间动态功能连接的异常模式，并通过纵向跟踪探究海洛因成瘾患者大脑回路因果连接与网络间动态功能连接恢复重构现象。

研究一中，此内容探究了海洛因成瘾患者长期戒断后背外侧前额叶（dorsolateral prefrontal cortex ，DLPFC）相关回路格兰杰因果连接（Granger causality analysis ，GCA）的恢复现象。本研究招募了 29 名海洛因成瘾患者和 30 名健康对照，并进行了为期 8 个月的纵向研究。通过选择被海洛因线索激活的区域作为种子点，在基线时使用 Granger 因果分析比较了健康对照组和海洛因成瘾患者之间不同的大脑因果连接模式。并采用配对 t 检验来探究长期戒断后左侧 DLPFC 回路的恢复现象。本实验使用视觉模拟量表（visual analog scale ，VAS）和线索测试-A（trail-making test-A ，TMT-A）来分别评估海洛因成瘾患者的渴求和认知控制能力，并将神经影像学变化与行为改善做相关分析。研究结果发现，在基线状态时海洛因成瘾患者相较于健康对照组从左侧DLPFC 到双侧辅助运动区（supplementary motor area ，SMA）和右侧壳核的 GCA 系数显著降低，以及从双侧眶额叶（orbitofrontal cortex，OFC）到左侧 DLPFC 的 GCA系数显著降低。在纵向研究中，长期戒断后的海洛因成瘾患者在左侧 DLPFC 到右侧脑岛回路中观察到 GCA 系数恢复，并且其回路的因果连接与渴求得分的变化呈显著相关。本文的研究结果将大脑恢复现象扩展到了海洛因成瘾领域，并表明长期戒断后DLPFC 对于脑岛的正常调节对于降低海洛因成瘾患者的渴求十分重要。

研究二中，此内容探究了海洛因成瘾患者长期戒断后的脑网络动态功能连接的重构现象。本研究对 40 名海洛因成瘾患者进行为期 10 个月的纵向跟踪。采用组独立成分分析（group independent component analysis，GICA）和动态功能连接（dynamic functional network connectivity ，dFNC）来检测海洛因成瘾患者在成瘾相关独立成分网络中的不同动态功能连接模式。计算了 dFNC 时间特性和拓扑理论特性。并探究了长期戒断后海洛因成瘾患者中的脑网络间动态功能连接异常是否会重构恢复。本文从GICA提取的独立成分中选择了8个与成瘾相关的网络（基底神经节网络（basal ganglia network ，BG）、前显著性网络（anterior salience network ，aSN）、后显著性网络（posterior salience network ，pSN）、左执行控制网络（left executive-control network，LECN）、右执行控制网络（right executive-control network，RECN）、腹侧默认模式网络（ventral default-mode network ，vDMN）、背侧默认网络（dorsal default-mode network ，pDMN）、感觉运动网络（sensorimotor network ，SMN）），并通过 k-means聚类确定了 4 个 State。发现在 State4 中，海洛因成瘾患者的平均停留时间和占比较低，并在长期戒断后此指标与正常人无显著差异。同时，也是在此状态中，海洛因成瘾患者在基线状态时显示出较高的 RECN-aSN、aSN-aSN 和 dDMN-pSN 的dFNC，且在长期戒断后向健康人趋近。同时，长期戒断后的海洛因成瘾患者在全局效率和路径长度方面也发现了类似的恢复现象。本研究的相关分析也表明，异常网络间的dFNC与基线和戒断后的渴求得分呈显著相关。此项纵向研究从动态角度观察了长期戒断后海洛因成瘾患者的大规模脑网络重构现象，提高了对海洛因成瘾患者长期戒断神经生物学的理解。

综上所述，本文从因果连接和脑网络动态功能连接两个维度探究了海洛因成瘾患者戒断前后大脑回路以及网络的恢复现象。并认为长期戒断后 SN 在 ECN 和 DMN中可以重新配置异常的信号，使得成瘾患者渴求降低以及认知控制能力提高。并且，本文通过网络中重要节点的因果分析，也发现了长期戒断后海洛因成瘾患者左侧DLPFC 到右侧脑岛的这种自上而下控制的恢复伴随着渴求分数的降低。本文的研究提高了对海洛因成瘾患者长期戒断神经生物学的理解，也为未来成瘾的治疗提供了新的靶点。

Heroin, known as the "king of drugs in the world," is a highly addictive and difficult opioid drug to abstain from. Long term use of heroin can lead to a decrease in the executive control and suppress cravings ability, which is also the reason why it is difficult to treat heroin addiction. Therefore, understanding the mechanism of brain plasticity changes induced by heroin use and prolonged abstinence may provide novel scientific evidence into the pathogenesis of heroin addiction and addiction treatment. This article uses functional magnetic resonance imaging technology to collect imaging data from heroin addicts, and studies the abnormal connectivity between brain circuits and networks in heroin addicts from different dimensions. Through longitudinal tracking, it explores the phenomenon of the recovery and reconstruction of brain circuits and network connections in heroin users.

In the first study, the 8-month longitudinal study was carried out in 29 heroin users (HUs) and 30 healthy controls (HCs). By choosing the left dorsolateral prefrontal cortex (DLPFC), which was activated by the heroin cue as the seeding region, different brain connection patterns were compared between healthy controls and heroin users by using Granger causality analysis (GCA) at baseline. Then, a paired-t test was employed to detect the potential recovery of L_DLPFC circuits after prolonged abstinence. The visual analog scale (VAS) and trail-making test-A (TMT-A) were adopted to investigate craving and cognitive control impairment, respectively. The neuroimaging changes were then correlated with behavioral improvements. Similar analyses were applied for the mirrored right DLPFC to verify the lateralization hypothesis of the DLPFC in addiction. In the longitudinal study, enhanced GCA coefcients were observed in the L_DLPFC-R_insula circuit of heroin users after long-term abstinence and were associated with craving score changes. At baseline, decreased GCA coeffcients from the left DLPFC to the bilateral SMA and right putamen, together with the reduced GCA strength from the bilateral orbiotofrontal cortex (OFC) to the left DLPFC, were found between HUs and HCs. Our findings extended the brain recovery phenomenon into the field of heroin and suggested that the increased regulation of the L_DLPFC over the insula after prolonged abstinence was important for craving inhibition.

In the second study, we explored the brain network dynamic connection reconfigurations after prolonged abstinence in heroin users. The 10-month longitudinal design was carried out for 40 HUs. Group independent component analysis (GICA) and dynamic functional network connectivity analysis (dFNC) were employed to detect the different dFNC patterns of addiction-related networks between HUs and HCs. The temporal properties and the graph-theoretical properties were calculated. Based on eight functional networks extracted from GICA(basal ganglia network (BG), anterior salience network (aSN), posterior salience network (pSN), left executive-control network (LECN), right executive-control network (RECN), ventral default-mode network (vDMN), dorsal default-mode network (dDMN), sensorimotor network (SMN)), four states were identified by the dFNC analysis. Lower mean dwell time and fraction rate in state4 were found for HUs, which were increased toward HCs after prolonged abstinence. In this state, HUs at baseline showed higher dFNC of RECN-aSN, aSN-aSN and dDMN-pSN, which decreased after protracted abstinence. A similar recovery phenomenon was found for the global efficiency and path length in abstinence HUs. Mean while, the abnormal dFNC strength was correlated with craving both at baseline and after abstinence. Our longitudinal study observed the large-scale brain network reconfiguration from the dynamic perspective in HUs after prolonged abstinence and improved the understanding of the neurobiology of prolonged abstinence in HUs.

In summary, this article explores the recovery of brain circuits and networks in HUs before and after long-term abstinence from two dimensions: GCA and dFNC. We believe that after long-term abstinence, SN networks can reconfigure abnormal signals in ECN and DMN networks, resulting in reduced cravings and improved cognitive control abilities in addicts. Moreover, through GCA analysis of important nodes in the network, we also found that the recovery of GCA coefficients between the left DLPFC to the right insula in HUs was accompanied by a decrease in craving scores after long-term abstinence. The study improved our understanding of the neurobiology of long-term heroin abstinence. It also provides a new target for the treatment of addiction in the future.

[1]MAYER K H, KHALSA J H, ELKASHEF A. Interventions for HIV and hepatitis C virus infections in recreational drug users [J]. Clin Infect Dis, 2010, 50(11): 1505-11.
[2] BABOR T F, CAETANO R. Subtypes of substance dependence and abuse: implications for diagnostic classification and empirical research [J]. Addiction, 2006, 101: 104-10.
[3] 郑继旺, 张开镐. 丁丙诺啡治疗阿片依赖性的研究进展 [J]. 中国药物依赖性通报, 1992, 1(2): 78-82.
[4] BOSSERT J M, POLES G C, WIHBEY K A, et al. Differential effects of blockade of dopamine D1-family receptors in nucleus accumbens core or shell on reinstatement of heroin seeking induced by contextual and discrete cues [J]. J Neurosci, 2007, 27(46): 12655-63.
[5] VOLKOW N D, WANG G-J, FOWLER J S, et al. Addiction: beyond dopamine reward circuitry [J]. Proc Natl Acad Sci U S A 2011, 2011, 108(37): 15037-42.
[6] MENON V. Large-scale brain networks and psychopathology: a unifying triple network model [J]. Trends Cogn Sci, 2011, 15(10): 483-506.
[7] VOLKOW N D, FOWLER J S, WANG G J, et al. Decreased dopamine D2 receptor availability is associated with reduced frontal metabolism in cocaine abusers [J]. Synapse, 1993, 14(2): 169-77.
[8] VOLKOW N D, FOWLER J S, WANG G-J. The addicted human brain: insights from imaging studies [J]. J Clin Invest, 2003, 111(10): 1444-51.
[9] FELTENSTEIN M, SEE R. The neurocircuitry of addiction: an overview [J]. Br J Pharmacol, 2008, 154(2): 261-74.
[10] KOOB G F, LE MOAL M. Plasticity of reward neurocircuitry and the'dark side'of drug addiction [J]. Nat Neurosci, 2005, 8(11): 1442-4.
[11] DI CHIARA G, IMPERATO A. Drugs abused by humans preferentially increase synaptic dopamine concentrations in the mesolimbic system of freely moving rats [J]. Proc Natl Acad Sci U S A, 1988, 85(14): 5274-8.
[12] VOLKOW N, MORALES M. The Brain on Drugs: From Reward to Addiction [J]. Cell, 2015, 162(4): 712-25.
[13] ZHONG W-J, ZHOU Z-M, ZHAO J-N, et al. Abnormal spontaneous brain activity in minimal hepatic encephalopathy: resting-state fMRI study [J]. Diagn Interv Radiol, 2016, 22(2): 196.
[14] XIANG Y, GONG T, WU J, et al. Subtypes evaluation of motor dysfunction in Parkinson’s disease using neuromelanin-sensitive magnetic resonance imaging [J]. Neurosci Lett, 2017, 638: 145-50.
[15] WIEGELL M R, LARSSON H B, WEDEEN V J. Fiber crossing in human brain depicted with diffusion tensor MR imaging [J]. Radiology, 2000, 217(3): 897-903.
[16] LYOO I K, POLLACK M H, SILVERI M M, et al. Prefrontal and temporal gray matter density decreases in opiate dependence [J]. Psychopharmacology, 2006, 184: 139-44.
[17] BACH P, FRISCHKNECHT U, REINHARD I, et al. Impaired working memory performance in opioid-dependent patients is related to reduced insula gray matter volume: a voxel-based morphometric study [J]. Eur Arch Psychiatry Clin Neurosci, 2021, 271: 813-22.
[18] LIU H, HAO Y, KANEKO Y, et al. Frontal and cingulate gray matter volume reduction in heroin dependence: Optimized voxel‐based morphometry [J]. Psychiatry Clin Neurosci, 2009, 63(4): 563-8.
[19] YUAN K, QIN W, DONG M, et al. Gray matter deficits and resting-state abnormalities in abstinent heroin-dependent individuals [J]. Neurosci Lett, 2010, 482(2): 101-5.
[20] YUAN Y, ZHU Z, SHI J, et al. Gray matter density negatively correlates with duration of heroin use in young lifetime heroin-dependent individuals [J]. Brain Cogn, 2009, 71(3): 223-8.
[21] LI M, TIAN J, ZHANG R, et al. Abnormal cortical thickness in heroin-dependent individuals [J]. Neuroimage, 2014, 88: 295-307.
[22] LIU H, LI L, HAO Y, et al. Disrupted white matter integrity in heroin dependence: a controlled study utilizing diffusion tensor imaging [J]. Am J Drug Alcohol Abuse, 2008, 34(5): 562-75.
[23] MA X, QIU Y, TIAN J, et al. Aberrant default-mode functional and structural connectivity in heroin-dependent individuals [J]. PLoS One, 2015, 10(4): e0120861.
[24] ZHANG M, LIU S, WANG S, et al. Reduced thalamic resting‐state functional connectivity and impaired cognition in acute abstinent heroin users [J]. Hum Brain Mapp, 2021, 42(7): 2077-88.
[25] SCHMIDT A, DENIER N, MAGON S, et al. Increased functional connectivity in the resting-state basal ganglia network after acute heroin substitution [J]. Transl Psychiatry, 2015, 5(3): e533-e.
[26] LIU S, WANG S, ZHANG M, et al. Brain responses to drug cues predict craving changes in abstinent heroin users: a preliminary study [J]. Neuroimage, 2021, 237: 118169.
[27] LIN H C, WANG P W, WU H C, et al. Altered gray matter volume and disrupted functional connectivity of dorsolateral prefrontal cortex in men with heroin dependence [J]. Psychiatry Clin Neurosci, 2018, 72(6): 435-44.
[28] LI Q, LI Z, LI W, et al. Disrupted default mode network and basal craving in male heroin-dependent individuals: a resting-state fMRI study [J]. J Clin Psychiatry, 2016, 77(10): 4560.
[29] LI Q, LIU J, WANG W, et al. Disrupted coupling of large-scale networks is associated with relapse behaviour in heroin-dependent men [J]. J Psychiatry Neurosci, 2018, 43(1): 48-57.
[30] YANG W, ZHANG M, TANG F, et al. Recovery of superior frontal gyrus cortical thickness and resting‐state functional connectivity in abstinent heroin users after 8 months of follow‐up [J]. Hum Brain Mapp, 2022, 43(10): 3164-75.
[31] XU Y, WANG S, CHEN L, et al. Reduced midbrain functional connectivity and recovery in abstinent heroin users [J]. J Psychiatr Res, 2021, 144: 168-76.
[32] CHEN J, WANG F, ZHU J, et al. Assessing effect of long‐term abstinence on coupling of three core brain networks in male heroin addicts: A resting‐state functional magnetic resonance imaging study [J]. Addict Biol, 2021, 26(4): e12982.
[33] ZANG Z-X, YAN C-G, DONG Z-Y, et al. Granger causality analysis implementation on MATLAB: a graphic user interface toolkit for fMRI data processing [J]. J Neurosci Methods, 2012, 203(2): 418-26.
[34] CALHOUN V D, ADALI T, PEARLSON G D, et al. A method for making group inferences from functional MRI data using independent component analysis [J]. Hum Brain Mapp, 2001, 14(3): 140-51.
[35] MCKEOWN M J, MAKEIG S, BROWN G G, et al. Analysis of fMRI data by blind separation into independent spatial components [J]. Hum Brain Mapp, 1998, 6(3): 160-88.
[36] CALHOUN V D, ADALI T, PEARLSON G, et al. Spatial and temporal independent component analysis of functional MRI data containing a pair of task‐related waveforms [J]. Hum Brain Mapp, 2001, 13(1): 43-53.
[37] FRISTON K J. Functional and effective connectivity: a review [J]. Brain Connect, 2011, 1(1): 13-36.
[38] TU Y, FU Z, MAO C, et al. Distinct thalamocortical network dynamics are associated with the pathophysiology of chronic low back pain [J]. Nat Commun, 2020, 11(1): 1-12.
[39] LIU F, WANG Y, LI M, et al. Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic–clonic seizure [J]. Hum Brain Mapp, 2017, 38(2): 957-73.
[40] XUE K, LIANG S, YANG B, et al. Local dynamic spontaneous brain activity changes in first-episode, treatment-naïve patients with major depressive disorder and their associated gene expression profiles [J]. Psychol Med, 2022, 52(11): 2052-61.
[41] ALLEN E A, DAMARAJU E, PLIS S M, et al. Tracking whole-brain connectivity dynamics in the resting state [J]. Cereb Cortex, 2014, 24(3): 663-76.
[42] FRIEDMAN J, HASTIE T, TIBSHIRANI R. Sparse inverse covariance estimation with the graphical lasso [J]. Biostatistics, 2008, 9(3): 432-41.
[43] VAROQUAUX G, GRAMFORT A, POLINE J-B, et al. Brain covariance selection: better individual functional connectivity models using population prior [J]. Adv Neural Inf Process Syst, 2010, 23.
[44] ALLEN E A, ERHARDT E B, DAMARAJU E, et al. A baseline for the multivariate comparison of resting-state networks [J]. Front Syst Neurosci, 2011, 5: 2.
[45] GOLDSTEIN R Z, VOLKOW N D. Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex [J]. Am J Psychiatry, 2002, 159(10): 1642-52.
[46] HE Q, HUANG X, TUREL O, et al. Presumed structural and functional neural recovery after long-term abstinence from cocaine in male military veterans [J]. Prog Neuropsychopharmacol Biol Psychiatry, 2018, 84: 18-29.
[47] PARVAZ M A, MOELLER S J, D'OLEIRE UQUILLAS F, et al. Prefrontal gray matter volume recovery in treatment‐seeking cocaine‐addicted individuals: A longitudinal study [J]. Addict Biol, 2017, 22(5): 1391-401.
[48] CHOU Y-H, HUANG W-S, SU T-P, et al. Dopamine transporters and cognitive function in methamphetamine abuser after a short abstinence: A SPECT study [J]. European Neuropsychopharmacology, 2007, 17(1): 46-52.
[49] VOLKOW N D, CHANG L, WANG G-J, et al. Loss of dopamine transporters in methamphetamine abusers recovers with protracted abstinence [J]. J Neurosci, 2001, 21(23): 9414-8.
[50] WANG G-J, VOLKOW N D, CHANG L, et al. Partial recovery of brain metabolism in methamphetamine abusers after protracted abstinence [J]. Am J Psychiatry, 2004, 161(2): 242-8.
[51] KOZLOWSKI L T, MANN R E, WILKINSON D, et al. “Cravings” are amibiguous: Ask about urges or desires [J]. Addict Behav, 1989, 14(4): 443-5.
[52] PENG S-Y, SHI Z, ZHOU D-S, et al. Reduced motor cortex GABABR function following chronic alcohol exposure [J]. Mol Psychiatry, 2021, 26(2): 383-95.
[53] ZHAO D, ZHANG M, TIAN W, et al. Neurophysiological correlate of incubation of craving in individuals with methamphetamine use disorder [J]. Mol Psychiatry, 2021, 26(11): 6198-208.
[54] LIU X, ZHAO X, LIU T, et al. The effects of repetitive transcranial magnetic stimulation on cue-induced craving in male patients with heroin use disorder [J]. EBioMedicine, 2020, 56: 102809.
[55] ROHSENOW D J, MARTIN R A, EATON C A, et al. Cocaine craving as a predictor of treatment attrition and outcomes after residential treatment for cocaine dependence [J]. J Stud Alcohol Drugs, 2007, 68(5): 641-8.
[56] YUAN J, LIU W, LIANG Q, et al. Effect of low-frequency repetitive transcranial magnetic stimulation on impulse inhibition in abstinent patients with methamphetamine addiction: a randomized clinical trial [J]. JAMA Netw Open, 2020, 3(3): e200910-e.
[57] JIANG G-H, QIU Y-W, ZHANG X-L, et al. Amplitude low-frequency oscillation abnormalities in the heroin users: a resting state fMRI study [J]. Neuroimage, 2011, 57(1): 149-54.
[58] MAKANI R, PRADHAN B, SHAH U, et al. Role of repetitive transcranial magnetic stimulation (rTMS) in treatment of addiction and related disorders: a systematic review [J]. Curr Drug Abuse Rev, 2017, 10(1): 31-43.
[59] WANG S, ZHANG M, LIU S, et al. Impulsivity in heroin‐dependent individuals: structural and functional abnormalities within frontostriatal circuits [J]. Brain Imaging Behav, 2021: 1-10.
[60] YUAN K, YU D, BI Y, et al. The left dorsolateral prefrontal cortex and caudate pathway: New evidence for cue‐induced craving of smokers [J]. Hum Brain Mapp, 2017, 38(9): 4644-56.
[61] BALCONI M, FINOCCHIARO R, CANAVESIO Y. Reward-system effect (BAS rating), left hemispheric “unbalance”(alpha band oscillations) and decisional impairments in drug addiction [J]. Addict Behav, 2014, 39(6): 1026-32.
[62] KOBER H, MENDE-SIEDLECKI P, KROSS E F, et al. Prefrontal–striatal pathway underlies cognitive regulation of craving [J]. Proc Natl Acad Sci U S A, 2010, 107(33): 14811-6.
[63] SAUNDERS J B, AASLAND O G, BABOR T F, et al. Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption‐II [J]. Addiction, 1993, 88(6): 791-804.
[64] HEATHERTON T F, KOZLOWSKI L T, FRECKER R C, et al. The Fagerström test for nicotine dependence: a revision of the Fagerstrom Tolerance Questionnaire [J]. Br J Addict, 1991, 86(9): 1119-27.
[65] LI Q, WANG Y, ZHANG Y, et al. Craving correlates with mesolimbic responses to heroin-related cues in short-term abstinence from heroin: an event-related fMRI study [J]. Brain Res, 2012, 1469: 63-72.
[66] SONG X-W, DONG Z-Y, LONG X-Y, et al. REST: a toolkit for resting-state functional magnetic resonance imaging data processing [J]. PloS one, 2011, 6(9): e25031.
[67] PREACHER K J, HAYES A F. SPSS and SAS procedures for estimating indirect effects in simple mediation models [J]. Behav Res Methods Instrum Comput, 2004, 36: 717-31.
[68] ZHANG S, YANG W, LI M, et al. Partial recovery of the left DLPFC-right insula circuit with reduced craving in abstinent heroin users: a longitudinal study [J]. Brain Imaging Behav, 2022, 16(6): 2647-56.
[69] VOLKOW N D, WANG G J, FOWLER J S, et al. Addiction: beyond dopamine reward circuitry [J]. Proc Natl Acad Sci U S A, 2011, 108(37): 15037-42.
[70] ZHANG Y, LI Q, WEN X, et al. Granger causality reveals a dominant role of memory circuit in chronic opioid dependence [J]. Addict Biol, 2017, 22(4): 1068-80.
[71] FAKHRIEH‐ASL G, SADR S S, KARIMIAN S M, et al. Deep brain stimulation of the orbitofrontal cortex prevents the development and reinstatement of morphine place preference [J]. Addict Biol, 2020, 25(4): e12780.
[72] WILSON S J, SAYETTE M A, FIEZ J A. Prefrontal responses to drug cues: a neurocognitive analysis [J]. Nat Neurosci, 2004, 7(3): 211-4.
[73] LÜSCHER C, ROBBINS T W, EVERITT B J. The transition to compulsion in addiction [J]. Nat Rev Neurosci, 2020, 21(5): 247-63.
[74] SONG K, LI J, ZHU Y, et al. Altered small-world functional network topology in patients with optic neuritis: A resting-state fMRI study [J]. Dis Markers, 2021, 2021.
[75] YUAN K, WEN X, WANG S, et al. Potential neural mechanism of single session transcranial magnetic stimulation on smoking craving [J]. Science China Information Sciences, 2020, 63: 1-3.
[76] MOHEBI N, ARAB M, MOGHADDASI M, et al. Stroke in supplementary motor area mimicking functional disorder: a case report [J]. J Neurol, 2019, 266: 2584-6.
[77] EVERITT B J, ROBBINS T W. Neural systems of reinforcement for drug addiction: from actions to habits to compulsion [J]. Nat Neurosci, 2005, 8(11): 1481-9.
[78] DAMASIO A R, GRABOWSKI T J, BECHARA A, et al. Subcortical and cortical brain activity during the feeling of self-generated emotions [J]. Nat Neurosci, 2000, 3(10): 1049-56.
[79] CRAIG A D. How do you feel? Interoception: the sense of the physiological condition of the body [J]. Nat Rev Neurosci, 2002, 3(8): 655-66.
[80] MOSCA A. A review essay on Antonio Damasio's the feeling of what Happens: Body and emotion in the making of consciousness [J]. Psyche, 2000, 6(10): 1-13.
[81] HOSHI E. Functional specialization within the dorsolateral prefrontal cortex: a review of anatomical and physiological studies of non-human primates [J]. Neuroscience research, 2006, 54(2): 73-84.
[82] LI X, CHEN L, MA R, et al. The top‐down regulation from the prefrontal cortex to insula via hypnotic aversion suggestions reduces smoking craving [J]. Hum Brain Mapp, 2019, 40(6): 1718-28.
[83] LI X, CHEN L, MA R, et al. The neural mechanisms of immediate and follow-up of the treatment effect of hypnosis on smoking craving [J]. Brain Imaging Behav, 2020, 14: 1487-97.
[84] WANG X, XUE T, DONG F, et al. The changes of brain functional networks in young adult smokers based on independent component analysis [J]. Brain Imaging Behav, 2021, 15: 788-97.
[85] WANG C, WANG S, SHEN Z, et al. Increased thalamic volume and decreased thalamo-precuneus functional connectivity are associated with smoking relapse [J]. NeuroImage: Clinical, 2020, 28: 102451.
[86] 袁杰. 海洛因依赖者和冰毒依赖者脑多巴胺转运体改变的研究 [D]; 上海: 复旦大学, 2013.
[87] POWER J D, COHEN A L, NELSON S M, et al. Functional network organization of the human brain [J]. Neuron, 2011, 72(4): 665-78.
[88] HEILIG M, MACKILLOP J, MARTINEZ D, et al. Addiction as a brain disease revised: why it still matters, and the need for consilience [J]. Neuropsychopharmacology, 2021, 46(10): 1715-23.
[89] LESHNER A I. Addiction is a brain disease, and it matters [J]. Science, 1997, 278(5335): 45-7.
[90] YUAN K, QIN W, DONG M, et al. Combining spatial and temporal information to explore resting-state networks changes in abstinent heroin-dependent individuals [J]. Neurosci Lett, 2010, 475(1): 20-4.
[91] LERMAN C, GU H, LOUGHEAD J, et al. Large-scale brain network coupling predicts acute nicotine abstinence effects on craving and cognitive function [J]. JAMA Psychiatry, 2014, 71(5): 523-30.
[92] SUTHERLAND M T, MCHUGH M J, PARIYADATH V, et al. Resting state functional connectivity in addiction: Lessons learned and a road ahead [J]. Neuroimage, 2012, 62(4): 2281-95.
[93] MENON V, UDDIN L Q. Saliency, switching, attention and control: a network model of insula function [J]. Brain Struct Funct, 2010, 214: 655-67.
[94] MATSUI T, MURAKAMI T, OHKI K. Neuronal origin of the temporal dynamics of spontaneous BOLD activity correlation [J]. Cereb Cortex, 2019, 29(4): 1496-508.
[95] MA Z, ZHANG N. Temporal transitions of spontaneous brain activity [J]. Elife, 2018, 7: e33562.
[96] VARJACIC A, MANTINI D, DEMEYERE N, et al. Neural signatures of Trail Making Test performance: Evidence from lesion-mapping and neuroimaging studies [J]. Neuropsychologia, 2018, 115: 78-87.
[97] YAN C, ZANG Y. DPARSF: a MATLAB toolbox for" pipeline" data analysis of resting-state fMRI [J]. Front Syst Neurosci, 2010: 13.
[98] BELL A J, SEJNOWSKI T J. An information-maximization approach to blind separation and blind deconvolution [J]. Neural Comput, 1995, 7(6): 1129-59.
[99] HIMBERG J, HYVÄRINEN A, ESPOSITO F. Validating the independent components of neuroimaging time series via clustering and visualization [J]. Neuroimage, 2004, 22(3): 1214-22.
[100] SHIRER W R, RYALI S, RYKHLEVSKAIA E, et al. Decoding subject-driven cognitive states with whole-brain connectivity patterns [J]. Cereb Cortex, 2012, 22(1): 158-65.
[101] CORDES D, HAUGHTON V M, ARFANAKIS K, et al. Mapping functionally related regions of brain with functional connectivity MR imaging [J]. AJNR Am J Neuroradiol, 2000, 21(9): 1636-44.
[102] ZHANG S, YANG W, LI M, et al. Reconfigurations of Dynamic Functional Network Connectivity in Large-scale Brain Network after Prolonged Abstinence in Heroin Users [J]. Current Neuropharmacology, 2022.
[103] TU Y, FU Z, ZENG F, et al. Abnormal thalamocortical network dynamics in migraine [J]. Neurology, 2019, 92(23): e2706-e16.
[104] DAMARAJU E, ALLEN E A, BELGER A, et al. Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia [J]. Neuroimage Clin, 2014, 5: 298-308.
[105] LI Y, YUAN K, GUAN Y, et al. The implication of salience network abnormalities in young male adult smokers [J]. Brain Imaging Behav, 2017, 11: 943-53.
[106] MCHUGH M J, GU H, YANG Y, et al. Executive control network connectivity strength protects against relapse to cocaine use [J]. Addict Biol, 2017, 22(6): 1790-801.
[107] LU L, YANG W, ZHANG X, et al. Potential brain recovery of frontostriatal circuits in heroin users after prolonged abstinence: A preliminary study [J]. J Psychiatr Res, 2022, 152: 326-34.
[108] MANOLIU A, MENG C, BRANDL F, et al. Insular dysfunction within the salience network is associated with severity of symptoms and aberrant inter-network connectivity in major depressive disorder [J]. Front Hum Neurosci, 2014, 7: 930.
[109] LI Q, WANG Y, ZHANG Y, et al. Craving correlates with mesolimbic responses to heroin-related cues in short-term abstinence from heroin: an event-related fMRI study [J]. Brain Res, 2012, 1469: 63-72.
[110] MA N, LIU Y, FU X-M, et al. Abnormal brain default-mode network functional connectivity in drug addicts [J]. PloS one, 2011, 6(1): e16560.
[111] LI Q, YANG W-C, WANG Y-R, et al. Abnormal function of the posterior cingulate cortex in heroin addicted users during resting-state and drug-cue stimulation task [J]. Chin Med J, 2013, 126(04): 734-9.
[112] WANG W, WANG Y-R, QIN W, et al. Changes in functional connectivity of ventral anterior cingulate cortex in heroin abusers [J]. Chin Med J, 2010, 123(12): 1582-8.
[113] HABER S. Parallel and integrative processing through the Basal Ganglia reward circuit: lessons from addiction [J]. Biol Psychiatry, 2008, 64(3): 173-4.
[114] GREMEL C M, LOVINGER D M. Associative and sensorimotor cortico‐basal ganglia circuit roles in effects of abused drugs [J]. Genes Brain Behav, 2017, 16(1): 71-85.
[115] ZHANG R, JIANG G, TIAN J, et al. Abnormal white matter structural networks characterize heroin‐dependent individuals: A network analysis [J]. Addict Biol, 2016, 21(3): 667-78.
[116] YUAN K, QIN W, LIU J, et al. Altered small-world brain functional networks and duration of heroin use in male abstinent heroin-dependent individuals [J]. Neurosci Lett, 2010, 477(1): 37-42.