孤独症脑自发活动动态性及其整合的异常机制
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摘要
孤独症谱系障碍(autism spectrum disorder,ASD)是一种病因未明,患病率日益增高的神经发育障碍.目前大多数静息态功能磁共振成像(resting-state f MRI,R-f MRI)研究仅考察了ASD患者脑活动的静态特征,忽视了动态特征.近期研究发现,不同R-f MRI局部指标的动态性之间存在一致性.本研究基于ASD公开数据库,使用滑动时间窗方法系统计算了480名ASD患者和539名健康对照(typical control,TC)被试的主流R-f MRI局部指标的动态性及R-f MRI局部指标之间的一致性动态特征,并考察了这些指标与ASD行为指标之间的关系.我们发现ASD患者在外侧额叶呈现出动态性显著升高,该现象在低频振幅(amplitude of low-frequency fluctuations,ALFF)和度中心性(degree centrality,DC)的动态性中都有体现.我们也发现ALFF,DC和局部一致性(regional homogeneity,Re Ho)的动态性在视觉相关脑区,如梭状回、距状沟和舌回呈现下降.在进一步考察了这些指标之间的一致性动态特征后,发现ASD患者的一致性动态特征显著低于TC组被试,表现出显著下降的功能整合能力.我们的结果表明ASD患者存在着脑自发活动动态性及其整合的异常.
Autism spectrum disorder(ASD) is a neurodevelopmental disease with unknown etiology but high incidence. The objective biomarkers of ASD are urgently awaited to be developed using neuroimaging method including resting-state f MRI(R-f MRI). However, the majority of current R-f MRI studies only examined the static features of brain activity in ASD patients, but neglected the dynamic aspects especially for regional metrics. Furthermore, the concordance of the dynamic regional indices was reported imbedded in human intrinsic brain activity, while its abnormality in ASD is largely unknown. In order to shed light on the abnormity of ASD from dynamic perspective, we analyzed R-f MRI data of 480 ASD male patients and 539 healthy male controls(HC) gathered from ASD public database(ABIDE I/ABIDE II). We used sliding window method to calculate the dynamics of mainstream regional indices of R-f MRI(amplitude of low frequency fluctuations(ALFF), fractional ALFF(f ALFF), regional homogeneity(Re Ho), voxel-mirrored homotopic connectivity(VMHC), degree centrality(DC) and the correlation with global signal(GSCorr)) and generated the SD statistic maps of these six dynamic regional indices. We performed z-standardization and smoothing on the SD statistic maps. After that, two-sample t-test between the SD statistic maps of ASD group and HC group was performed. We also calculated the concordance of dynamic regional indices for each time point, which is the Kendall's coefficient of the ALFF, Re Ho, DC, GSCorr and VMHC maps across voxels. Two-sample t-test between the SD and mean of concordance time series of ASD group and HC group was performed. We found a significant increase in the dynamics of ALFF and DC in the lateral frontal cortices in ASD patients as compared to HCs. Dorsal lateral frontal cortex(dl PFC) is a critical brain area for cognitive control and execution network. The abnormal activities in dl PFC indicate the disruption of control execution system and the impairment of relevant cognitive function. In the visual related brain areas, the dynamics of ALFF, DC and Re Ho showed a decrease in fusiform gyrus, calcarine and lingual gyrus in ASD patients. Recent studies have indicated that the abnormal face processing in children with autism may be related with the impairment in social cognition of them. The deficits in face information processing of ASD patients may stem from the inflexible intrinsic brain activity in visual processing brain area(especially in fusiform gyrus). After further examining the concordance among these dynamic indices, the mean and SD of concordance of patients with ASD was found to be significantly lower than that of the HCs, demonstrating that the ASD patients' inferior integration ability for different aspects of brain functions. These findings suggest that there exists abnormality in dynamics of spontaneous brain activity and its integration in ASD patients. Dynamic R-f MRI regional indices and the concordance of them could be efficient neuroimaging biomarkers for ASD.
Autism spectrum disorder(ASD) is a neurodevelopmental disease with unknown etiology but high incidence. The objective biomarkers of ASD are urgently awaited to be developed using neuroimaging method including resting-state f MRI(R-f MRI). However, the majority of current R-f MRI studies only examined the static features of brain activity in ASD patients, but neglected the dynamic aspects especially for regional metrics. Furthermore, the concordance of the dynamic regional indices was reported imbedded in human intrinsic brain activity, while its abnormality in ASD is largely unknown. In order to shed light on the abnormity of ASD from dynamic perspective, we analyzed R-f MRI data of 480 ASD male patients and 539 healthy male controls(HC) gathered from ASD public database(ABIDE I/ABIDE II). We used sliding window method to calculate the dynamics of mainstream regional indices of R-f MRI(amplitude of low frequency fluctuations(ALFF), fractional ALFF(f ALFF), regional homogeneity(Re Ho), voxel-mirrored homotopic connectivity(VMHC), degree centrality(DC) and the correlation with global signal(GSCorr)) and generated the SD statistic maps of these six dynamic regional indices. We performed z-standardization and smoothing on the SD statistic maps. After that, two-sample t-test between the SD statistic maps of ASD group and HC group was performed. We also calculated the concordance of dynamic regional indices for each time point, which is the Kendall's coefficient of the ALFF, Re Ho, DC, GSCorr and VMHC maps across voxels. Two-sample t-test between the SD and mean of concordance time series of ASD group and HC group was performed. We found a significant increase in the dynamics of ALFF and DC in the lateral frontal cortices in ASD patients as compared to HCs. Dorsal lateral frontal cortex(dl PFC) is a critical brain area for cognitive control and execution network. The abnormal activities in dl PFC indicate the disruption of control execution system and the impairment of relevant cognitive function. In the visual related brain areas, the dynamics of ALFF, DC and Re Ho showed a decrease in fusiform gyrus, calcarine and lingual gyrus in ASD patients. Recent studies have indicated that the abnormal face processing in children with autism may be related with the impairment in social cognition of them. The deficits in face information processing of ASD patients may stem from the inflexible intrinsic brain activity in visual processing brain area(especially in fusiform gyrus). After further examining the concordance among these dynamic indices, the mean and SD of concordance of patients with ASD was found to be significantly lower than that of the HCs, demonstrating that the ASD patients' inferior integration ability for different aspects of brain functions. These findings suggest that there exists abnormality in dynamics of spontaneous brain activity and its integration in ASD patients. Dynamic R-f MRI regional indices and the concordance of them could be efficient neuroimaging biomarkers for ASD.
引文
1 Association A P.Diagnostic and Statistical Manual of Mental Disorders.5th ed.Washington,DC:American Psychiatric Association,2013
2 Christensen D L,Baio J,Braun K V,et al.Prevalence and characteristics of autism spectrum disorder among children aged 8 yearsAutism and developmental disabilities monitoring network,11 sites,united states,2012.MMWR Surveill Summ,2014,63:1
3 Wong V C N,Hui S L H.Epidemiological study of autism spectrum disorder in China.J Child Neurol,2007,23:67-72
4 Abrams D A,Lynch C J,Cheng K M,et al.Underconnectivity between voice-selective cortex and reward circuitry in children with autism.Proc Natl Acad Sci USA,2013,110:12060
5 Alaerts K,Woolley D G,Steyaert J,et al.Underconnectivity of the superior temporal sulcus predicts emotion recognition deficits in autism.Soc Cogn Affect Neurosci,2014,9:1589
6 Uddin L Q,Supekar K,Menon V.Reconceptualizing functional brain connectivity in autism from a developmental perspective.Front Hum Neurosci,2013,7:458
7 Itahashi T,Yamada T,Watanabe H,et al.Alterations of local spontaneous brain activity and connectivity in adults with high-functioning autism spectrum disorder.Mol Autism,2015,6:30
8 Guo X,Chen H,Long Z,et al.Atypical developmental trajectory of local spontaneous brain activity in autism spectrum disorder.Sci Rep,2017,7:39822
9 Paakki J J,Rahko J,Long X,et al.Alterations in regional homogeneity of resting-state brain activity in autism spectrum disorders.Brain Res,2010,1321:169-179
10 Di Martino A,Zuo X N,Kelly C,et al.Shared and distinct intrinsic functional network centrality in autism and attention-deficit/hyperactivity disorder.Biol Psychiatry,2013,74:623-632
11 Anderson J S,Druzgal T J,Froehlich A,et al.Decreased interhemispheric functional connectivity in autism.Cereb Cortex,2011,21:1134-1146
12 Chen X,Lu B,Yan C G.Reproducibility of R‐f MRI metrics on the impact of different strategies for multiple comparison correction and sample sizes.Hum Brain Map,2018,39:300-318
13 Di Martino A,Yan C G,Li Q,et al.The autism brain imaging data exchange:Towards a large-scale evaluation of the intrinsic brain architecture in autism.Mol Psychiatry,2014,19:659-667
14 Hutchison R M,Womelsdorf T,Allen E A,et al.Dynamic functional connectivity:Promise,issues,and interpretations.Neuro Image,2013,80:360-378
15 Voytek B,Knight R T.Dynamic network communication as a unifying neural basis for cognition,development,aging,and disease.Biol Psychiatry,2015,77:1089-1097
16 Hutchison R M,Womelsdorf T,Gati J S,et al.Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques.Hum Brain Map,2013,34:2154-2177
17 Sako?luü,Pearlson G D,Kiehl K A,et al.A method for evaluating dynamic functional network connectivity and task-modulation:Application to schizophrenia.Magn Reson Mater Phys,2010,23:351-366
18 de Vos F,Koini M,Schouten T M,et al.A comprehensive analysis of resting state f MRI measures to classify individual patients with alzheimer’s disease.Neuro Image,2018,167:62-72
19 Hosenfeld B,Bos E H,Wardenaar K J,et al.Major depressive disorder as a nonlinear dynamic system:Bimodality in the frequency distribution of depressive symptoms over time.BMC Psychiatry,2015,15:222
20 Liu F,Wang Y,Li M,et al.Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic-clonic seizure.Hum Brain Mapp,2017,38:957-973
21 Zhang J,Cheng W,Liu Z,et al.Neural,electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders.Brain,2016,139:2307-2321
22 Chen H,Nomi J S,Uddin L Q,et al.Intrinsic functional connectivity variance and state-specific under-connectivity in autism.Hum Brain Mapp,2017,38:5740-5755
23 Fu Z,Tu Y,Di X,et al.Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity:An application to schizophrenia.Neuro Image,2017,doi:10.1016/j.neuroimage.2017.09.035
24 Hudetz A G,Liu X,Pillay S.Dynamic repertoire of intrinsic brain states is reduced in propofol-induced unconsciousness.Brain Connect,2015,5:10
25 Liao X,Yuan L,Zhao T,et al.Spontaneous functional network dynamics and associated structural substrates in the human brain.Front Hum Neurosci,2015,9:478
26 Yan C G,Yang Z,Colcombe S J,et al.Concordance among indices of intrinsic brain function:Insights from inter-individual variation and temporal dynamics.Sci Bull,2017,62:1572-1584
27 Jenkinson M,Bannister P,Brady M,et al.Improved optimization for the robust and accurate linear registration and motion correction of brain images.Neuro Image,2002,17:825-841
28 Yan C G,Zang Y F.Dparsf:A matlab toolbox for“pipeline”data analysis of resting-state f MRI.Front Syst Neurosci,2010,4:13
29 Yan C G,Wang X D,Zuo X N,et al.Dpabi:Data processing&analysis for(resting-state)brain imaging.Neuroinformatics,2016,14:339-351
30 Yan C G,Craddock R C,He Y,et al.Addressing head motion dependencies for small-world topologies in functional connectomics.Front Hum Neurosci,2013,7:910
31 Power J D,Barnes K A,Snyder A Z,et al.Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.Neuroimage,2012,59:2142-2154
32 Van Dijk K R,Sabuncu M R,Buckner R L.The influence of head motion on intrinsic functional connectivity MRI.Neuro Image,2012,59:431-438
33 Yan C G,Cheung B,Kelly C,et al.A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics.Neuro Image,2013,76:183-201
34 Satterthwaite T D,Elliott M A,Gerraty R T,et al.An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.Neuro Image,2013,64:240-256
35 Murphy K,Birn R M,Handwerker D A,et al.The impact of global signal regression on resting state correlations:Are anti-correlated networks introduced?Neuro Image,2009,44:893-905
36 Murphy K,Fox M D.Towards a consensus regarding global signal regression for resting state functional connectivity MRI.Neuro Image,2017,154:169-173
37 Saad Z S,Gotts S J,Murphy K,et al.Trouble at rest:How correlation patterns and group differences become distorted after global signal regression.Brain Connect,2012,2:25-32
38 Zuo X N,Kelly C,Di Martino A,et al.Growing together and growing apart:Regional and sex differences in the lifespan developmental trajectories of functional homotopy.J Neurosci,2010,30:15034-15043
39 Power J D,Plitt M,Laumann T O,et al.Sources and implications of whole-brain f MRI signals in humans.Neuro Image,2017,146:609-625
40 Smith S M,Nichols T E.Threshold-free cluster enhancement:Addressing problems of smoothing,threshold dependence and localisation in cluster inference.Neuro Image,2009,44:83-98
41 Zuo X N,Di Martino A,Kelly C,et al.The oscillating brain:Complex and reliable.Neuro Image,2010,49:1432-1445
42 Supekar K,Uddin Lucina Q,Khouzam A,et al.Brain hyperconnectivity in children with autism and its links to social deficits.Cell Rep,2013,5:738-747
43 Lai M C,Lombardo M V,Chakrabarti B,et al.A shift to randomness of brain oscillations in people with autism.Biol Psychiatry,2010,68:1092-1099
44 Dosenbach N U,Fair D A,Cohen A L,et al.A dual-networks architecture of top-down control.Trends Cogn Sci,2008,12:99-105
45 South M,Ozonoff S,Mc Mahon W M.The relationship between executive functioning,central coherence,and repetitive behaviors in the high-functioning autism spectrum.Autism,2007,11:437-451
46 Lopez B R,Lincoln A J,Ozonoff S,et al.Examining the relationship between executive functions and restricted,repetitive symptoms of autistic disorder.J Autism Dev Disord,2005,35:445-460
47 Cheng W,Rolls E T,Gu H,et al.Autism:Reduced connectivity between cortical areas involved in face expression,theory of mind,and the sense of self.Brain,2015,138:1382-1393
48 Doyle-Thomas K A,Card D,Soorya L V,et al.Metabolic mapping of deep brain structures and associations with symptomatology in autism spectrum disorders.Res Autism Spectr Disord,2014,8:44-51
49 Jeste S S,Nelson C A.Event related potentials in the understanding of autism spectrum disorders:An analytical review.J Autism Dev Disord,2009,39:495-510
50 Critchley H D,Daly E M,Bullmore E T,et al.The functional neuroanatomy of social behaviour:Changes in cerebral blood flow when people with autistic disorder process facial expressions.Brain,2000,123(Pt 11):2203-2212
51 Schultz R T,Gauthier I,Klin A,et al.Abnormal ventral temporal cortical activity during face discrimination among individuals with autism and asperger syndrome.Arch Gen Psychiatry,2000,57:331-340
52 Keown C L,Shih P,Nair A,et al.Local functional overconnectivity in posterior brain regions is associated with symptom severity in autism spectrum disorders.Cell Rep,2013,5:567-572
53 Maximo J O,Keown C L,Nair A,et al.Approaches to local connectivity in autism using resting state functional connectivity MRI.Front Hum Neurosci,2013,7:605
54 Cheng W,Rolls E T,Zhang J,et al.Functional connectivity decreases in autism in emotion,self,and face circuits identified by knowledge-based enrichment analysis.Neuro Image,2017,148:169-178
55 Koshino H,Kana R K,Keller T A,et al.FMRI investigation of working memory for faces in autism:Visual coding and underconnectivity with frontal areas.Cereb Cortex,2008,18:289-300
56 Villalobos M E,Mizuno A,Dahl B C,et al.Reduced functional connectivity between v1 and inferior frontal cortex associated with visuomotor performance in autism.Neuro Image,2005,25:916-925
1)DC和VMHC本质上基于功能连接进行计算.不过,和普通的种子点功能连接不同,DC和VMHC的异常区域具有定位的作用,如表明该异常脑区与全脑的整合不正常,或表明该异常脑区的不对称性有异常.而普通的种子点功能连接不具有这个作用,只能表明该异常脑区与种子点的功能连接有异常,但无法确认是哪方的异常.从这个意义上说,我们认为这些指标在某种程度上也具有“局部指标”的作用
2 Christensen D L,Baio J,Braun K V,et al.Prevalence and characteristics of autism spectrum disorder among children aged 8 yearsAutism and developmental disabilities monitoring network,11 sites,united states,2012.MMWR Surveill Summ,2014,63:1
3 Wong V C N,Hui S L H.Epidemiological study of autism spectrum disorder in China.J Child Neurol,2007,23:67-72
4 Abrams D A,Lynch C J,Cheng K M,et al.Underconnectivity between voice-selective cortex and reward circuitry in children with autism.Proc Natl Acad Sci USA,2013,110:12060
5 Alaerts K,Woolley D G,Steyaert J,et al.Underconnectivity of the superior temporal sulcus predicts emotion recognition deficits in autism.Soc Cogn Affect Neurosci,2014,9:1589
6 Uddin L Q,Supekar K,Menon V.Reconceptualizing functional brain connectivity in autism from a developmental perspective.Front Hum Neurosci,2013,7:458
7 Itahashi T,Yamada T,Watanabe H,et al.Alterations of local spontaneous brain activity and connectivity in adults with high-functioning autism spectrum disorder.Mol Autism,2015,6:30
8 Guo X,Chen H,Long Z,et al.Atypical developmental trajectory of local spontaneous brain activity in autism spectrum disorder.Sci Rep,2017,7:39822
9 Paakki J J,Rahko J,Long X,et al.Alterations in regional homogeneity of resting-state brain activity in autism spectrum disorders.Brain Res,2010,1321:169-179
10 Di Martino A,Zuo X N,Kelly C,et al.Shared and distinct intrinsic functional network centrality in autism and attention-deficit/hyperactivity disorder.Biol Psychiatry,2013,74:623-632
11 Anderson J S,Druzgal T J,Froehlich A,et al.Decreased interhemispheric functional connectivity in autism.Cereb Cortex,2011,21:1134-1146
12 Chen X,Lu B,Yan C G.Reproducibility of R‐f MRI metrics on the impact of different strategies for multiple comparison correction and sample sizes.Hum Brain Map,2018,39:300-318
13 Di Martino A,Yan C G,Li Q,et al.The autism brain imaging data exchange:Towards a large-scale evaluation of the intrinsic brain architecture in autism.Mol Psychiatry,2014,19:659-667
14 Hutchison R M,Womelsdorf T,Allen E A,et al.Dynamic functional connectivity:Promise,issues,and interpretations.Neuro Image,2013,80:360-378
15 Voytek B,Knight R T.Dynamic network communication as a unifying neural basis for cognition,development,aging,and disease.Biol Psychiatry,2015,77:1089-1097
16 Hutchison R M,Womelsdorf T,Gati J S,et al.Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques.Hum Brain Map,2013,34:2154-2177
17 Sako?luü,Pearlson G D,Kiehl K A,et al.A method for evaluating dynamic functional network connectivity and task-modulation:Application to schizophrenia.Magn Reson Mater Phys,2010,23:351-366
18 de Vos F,Koini M,Schouten T M,et al.A comprehensive analysis of resting state f MRI measures to classify individual patients with alzheimer’s disease.Neuro Image,2018,167:62-72
19 Hosenfeld B,Bos E H,Wardenaar K J,et al.Major depressive disorder as a nonlinear dynamic system:Bimodality in the frequency distribution of depressive symptoms over time.BMC Psychiatry,2015,15:222
20 Liu F,Wang Y,Li M,et al.Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic-clonic seizure.Hum Brain Mapp,2017,38:957-973
21 Zhang J,Cheng W,Liu Z,et al.Neural,electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders.Brain,2016,139:2307-2321
22 Chen H,Nomi J S,Uddin L Q,et al.Intrinsic functional connectivity variance and state-specific under-connectivity in autism.Hum Brain Mapp,2017,38:5740-5755
23 Fu Z,Tu Y,Di X,et al.Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity:An application to schizophrenia.Neuro Image,2017,doi:10.1016/j.neuroimage.2017.09.035
24 Hudetz A G,Liu X,Pillay S.Dynamic repertoire of intrinsic brain states is reduced in propofol-induced unconsciousness.Brain Connect,2015,5:10
25 Liao X,Yuan L,Zhao T,et al.Spontaneous functional network dynamics and associated structural substrates in the human brain.Front Hum Neurosci,2015,9:478
26 Yan C G,Yang Z,Colcombe S J,et al.Concordance among indices of intrinsic brain function:Insights from inter-individual variation and temporal dynamics.Sci Bull,2017,62:1572-1584
27 Jenkinson M,Bannister P,Brady M,et al.Improved optimization for the robust and accurate linear registration and motion correction of brain images.Neuro Image,2002,17:825-841
28 Yan C G,Zang Y F.Dparsf:A matlab toolbox for“pipeline”data analysis of resting-state f MRI.Front Syst Neurosci,2010,4:13
29 Yan C G,Wang X D,Zuo X N,et al.Dpabi:Data processing&analysis for(resting-state)brain imaging.Neuroinformatics,2016,14:339-351
30 Yan C G,Craddock R C,He Y,et al.Addressing head motion dependencies for small-world topologies in functional connectomics.Front Hum Neurosci,2013,7:910
31 Power J D,Barnes K A,Snyder A Z,et al.Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.Neuroimage,2012,59:2142-2154
32 Van Dijk K R,Sabuncu M R,Buckner R L.The influence of head motion on intrinsic functional connectivity MRI.Neuro Image,2012,59:431-438
33 Yan C G,Cheung B,Kelly C,et al.A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics.Neuro Image,2013,76:183-201
34 Satterthwaite T D,Elliott M A,Gerraty R T,et al.An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.Neuro Image,2013,64:240-256
35 Murphy K,Birn R M,Handwerker D A,et al.The impact of global signal regression on resting state correlations:Are anti-correlated networks introduced?Neuro Image,2009,44:893-905
36 Murphy K,Fox M D.Towards a consensus regarding global signal regression for resting state functional connectivity MRI.Neuro Image,2017,154:169-173
37 Saad Z S,Gotts S J,Murphy K,et al.Trouble at rest:How correlation patterns and group differences become distorted after global signal regression.Brain Connect,2012,2:25-32
38 Zuo X N,Kelly C,Di Martino A,et al.Growing together and growing apart:Regional and sex differences in the lifespan developmental trajectories of functional homotopy.J Neurosci,2010,30:15034-15043
39 Power J D,Plitt M,Laumann T O,et al.Sources and implications of whole-brain f MRI signals in humans.Neuro Image,2017,146:609-625
40 Smith S M,Nichols T E.Threshold-free cluster enhancement:Addressing problems of smoothing,threshold dependence and localisation in cluster inference.Neuro Image,2009,44:83-98
41 Zuo X N,Di Martino A,Kelly C,et al.The oscillating brain:Complex and reliable.Neuro Image,2010,49:1432-1445
42 Supekar K,Uddin Lucina Q,Khouzam A,et al.Brain hyperconnectivity in children with autism and its links to social deficits.Cell Rep,2013,5:738-747
43 Lai M C,Lombardo M V,Chakrabarti B,et al.A shift to randomness of brain oscillations in people with autism.Biol Psychiatry,2010,68:1092-1099
44 Dosenbach N U,Fair D A,Cohen A L,et al.A dual-networks architecture of top-down control.Trends Cogn Sci,2008,12:99-105
45 South M,Ozonoff S,Mc Mahon W M.The relationship between executive functioning,central coherence,and repetitive behaviors in the high-functioning autism spectrum.Autism,2007,11:437-451
46 Lopez B R,Lincoln A J,Ozonoff S,et al.Examining the relationship between executive functions and restricted,repetitive symptoms of autistic disorder.J Autism Dev Disord,2005,35:445-460
47 Cheng W,Rolls E T,Gu H,et al.Autism:Reduced connectivity between cortical areas involved in face expression,theory of mind,and the sense of self.Brain,2015,138:1382-1393
48 Doyle-Thomas K A,Card D,Soorya L V,et al.Metabolic mapping of deep brain structures and associations with symptomatology in autism spectrum disorders.Res Autism Spectr Disord,2014,8:44-51
49 Jeste S S,Nelson C A.Event related potentials in the understanding of autism spectrum disorders:An analytical review.J Autism Dev Disord,2009,39:495-510
50 Critchley H D,Daly E M,Bullmore E T,et al.The functional neuroanatomy of social behaviour:Changes in cerebral blood flow when people with autistic disorder process facial expressions.Brain,2000,123(Pt 11):2203-2212
51 Schultz R T,Gauthier I,Klin A,et al.Abnormal ventral temporal cortical activity during face discrimination among individuals with autism and asperger syndrome.Arch Gen Psychiatry,2000,57:331-340
52 Keown C L,Shih P,Nair A,et al.Local functional overconnectivity in posterior brain regions is associated with symptom severity in autism spectrum disorders.Cell Rep,2013,5:567-572
53 Maximo J O,Keown C L,Nair A,et al.Approaches to local connectivity in autism using resting state functional connectivity MRI.Front Hum Neurosci,2013,7:605
54 Cheng W,Rolls E T,Zhang J,et al.Functional connectivity decreases in autism in emotion,self,and face circuits identified by knowledge-based enrichment analysis.Neuro Image,2017,148:169-178
55 Koshino H,Kana R K,Keller T A,et al.FMRI investigation of working memory for faces in autism:Visual coding and underconnectivity with frontal areas.Cereb Cortex,2008,18:289-300
56 Villalobos M E,Mizuno A,Dahl B C,et al.Reduced functional connectivity between v1 and inferior frontal cortex associated with visuomotor performance in autism.Neuro Image,2005,25:916-925
1)DC和VMHC本质上基于功能连接进行计算.不过,和普通的种子点功能连接不同,DC和VMHC的异常区域具有定位的作用,如表明该异常脑区与全脑的整合不正常,或表明该异常脑区的不对称性有异常.而普通的种子点功能连接不具有这个作用,只能表明该异常脑区与种子点的功能连接有异常,但无法确认是哪方的异常.从这个意义上说,我们认为这些指标在某种程度上也具有“局部指标”的作用