Study on amygdala volume and amygdala white matter fiber connection in preschool children with autism spectrum disorder

doi:10.3877/cma.j.issn.1673-5250.2025.01.004

Objective

To investigate the amygdala volume and amygdala white matter fiber connection intensity in preschool children with autism spectrum disorder （ASD），and their correlation with ASD assessment scale scores.

Methods

A total of 64 children with ASD diagnosed in the Department of Pediatrics，First Medical Center，Chinese PLA General Hospital from September 2022 to September 2023 were enrolled into ASD group（n=64），And other 23 typically development（TD）children recruited from Beijing during the same period were prospectively enrolled into control group （n=23）.Brain diffusion tensor imaging（DTI）image data of children in two groups were acquired using a 3.0 T MRI scanner by prospective research method.The deep diffusion parcellation（DDParcel）tool and the DK atlas brain template in FreeSurfer were employed for precise segmentation and volumetric measurement of the amygdala.White matter connections with the amygdala were tracked with two-tensor unscented Kalman filter （t UKF）tractography to evaluate the strength of amygdala white matter fiber connectivity.Two chief physician of pediatrics conducted double-blind assessments on two groups of subjects using the DiagnosticandStatisticalManualof MentalDisorders5thEdition （DSM-5），ChildhoodAutismRatingScale （CARS），and Autism BehaviorChecklist（ABC）.The comparison of the amygdala volume and the intensity of amygdala white matter fiber connection between two groups was performed by independent-samples t test.Pearson correlation analysis was used to analyze the correlation between the amygdala volume and amygdala white matter fiber connection intensity with the ABC and CARS scores in ASD group.The procedures followed in this study were in accordance with the standards set by the Medical Ethics Committee of the Chinese PLA General Hospital and were approved by this committee （Approval No.S2022-646-01）.Written informed consents were obtained from all guardians of the children before their inclusion in the study.There was no significant difference in the age of the subjects between the two groups （P＞0.05）.

Results

①The DSM-5 score of children in ASD group was （4.0±1.5）points，the CARS score was（31.1±5.6）points，and the total ABC score was （45.7±18.0）points.The DSM-5 score，the scores of each dimension and the total score of ABC，and the CARS score of children in ASD group were significantly higher than those in control group，and the differences were statistically significant （P＜0.001）.②The volume of right amygdala in ASD group was larger than that of the left amygdala，and the difference was statistically significant （t=-3.36，P=0.001）.There was no statistically significant difference in the volume of the left and right amygdala between two groups （P＞0.05）.③The white matter fiber connection intensity between the left amygdala and the left orbitofrontal lobe and the left temporal pole，as well as the white matter fiber connection intensity between the right amygdala and the right paracalcarine gyrus in ASD group were significantly lower than those in control group （t=2.30，3.02，2.70；P=0.024，0.003，0.008）.④In ASD group，the white matter fiber connection intensity between the left amygdala and the left orbitofrontal lobe was negatively correlated with the physical motor ability dimension score of the ABC（r=-0.269，P=0.032）.

Conclusions

The volume of right amygdala in children with ASD is larger than that of the left amygdala.The fiber connection intensity between the amygdala and the temporal pole，orbitofrontal lobe，paracalcarine gyrus and other brain regions in children with ASD is abnormal.The abnormal white matter fiber connection intensity between the amygdala and orbitofrontal lobe is associated with somatic movement disorders in children with ASD.

Key words: Autism spectrum disorder, Amygdala, Neuroimaging, Two-tensor unscented Kalman filter, Tractography, Diagnostic and statistical manual of mental disorders, Child,preschool

表1 2组受试儿DSM-5、ABC与CARS评分比较（分，

±s）

组别	例数	DSM-5评分	ABC						CARS评分
组别	例数	DSM-5评分	感觉能力维度评分	交往能力维度评分	躯体运动能力维度评分	语言能力维度评分	社会生活自理能力维度评分	总评分	CARS评分
ASD组	64	4.0±1.5	7.2±5.4	12.2±7.0	6.5±4.8	14.8±5.7	6.7±4.4	45.7±18.0	31.1±5.6
对照组	23	0	0	0	0	0	0	0	15.0±0
t 值		-11.88	-6.68	-8.54	-5.85	-12.89	-7.29	-12.50	-29.54
P值		＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001

表1 2组受试儿DSM-5、ABC与CARS评分比较（分，

±s）

组别	例数	DSM-5评分	ABC						CARS评分
组别	例数	DSM-5评分	感觉能力维度评分	交往能力维度评分	躯体运动能力维度评分	语言能力维度评分	社会生活自理能力维度评分	总评分	CARS评分
ASD组	64	4.0±1.5	7.2±5.4	12.2±7.0	6.5±4.8	14.8±5.7	6.7±4.4	45.7±18.0	31.1±5.6
对照组	23	0	0	0	0	0	0	0	15.0±0
t 值		-11.88	-6.68	-8.54	-5.85	-12.89	-7.29	-12.50	-29.54
P值		＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001	＜0.001

表2 2组受试儿双侧杏仁核体积比较（mm³，

±s）

组别	例数	左侧杏仁核	右侧杏仁核	t 值	P值
ASD组	64	209.5±3.4	226.3±3.7	-3.36	0.001
对照组	23	216.4±6.6	217.4±6.6	-0.11	0.915
t 值		1.00	-1.23
P值		0.322	0.223

表2 2组受试儿双侧杏仁核体积比较（mm³，

±s）

组别	例数	左侧杏仁核	右侧杏仁核	t 值	P值
ASD组	64	209.5±3.4	226.3±3.7	-3.36	0.001
对照组	23	216.4±6.6	217.4±6.6	-0.11	0.915
t 值		1.00	-1.23
P值		0.322	0.223

表3 ASD 组患儿杏仁核体积与CRAS评分、ABC 总评分及其不同维度评分的相关性分析（n=64）

量表评分	左侧杏仁核体积		右侧杏仁核体积
量表评分	r值	P值	r值	P值
CARS评分	-0.107	0.401	-0.148	0.242
ABC总评分	-0.093	0.463	-0.150	0.236
感觉能力维度评分	-0.034	0.792	-0.146	0.248
交往能力维度评分	-0.142	0.263	-0.124	0.329
躯体运动能力维度评分	-0.080	0.530	-0.061	0.631
语言能力维度评分	-0.160	0.206	-0.094	0.459
社会生活自理能力维度评分	-0.102	0.423	-0.239	0.057

表3 ASD 组患儿杏仁核体积与CRAS评分、ABC 总评分及其不同维度评分的相关性分析（n=64）

量表评分	左侧杏仁核体积		右侧杏仁核体积
量表评分	r值	P值	r值	P值
CARS评分	-0.107	0.401	-0.148	0.242
ABC总评分	-0.093	0.463	-0.150	0.236
感觉能力维度评分	-0.034	0.792	-0.146	0.248
交往能力维度评分	-0.142	0.263	-0.124	0.329
躯体运动能力维度评分	-0.080	0.530	-0.061	0.631
语言能力维度评分	-0.160	0.206	-0.094	0.459
社会生活自理能力维度评分	-0.102	0.423	-0.239	0.057

图1 ASD 组与对照组儿童双侧杏仁核白质纤维连接示意图（图1A、1B：左侧杏仁核与左侧眶额叶、左侧颞极间的白质纤维连接示意图；图1C、1D：右侧杏仁核与右侧右侧距状旁回间的白质纤维连接示意图）注：对照组为典型发育儿童。ASD为孤独症谱系障碍。AMY.L为左侧杏仁核，SPG.L为左侧眶额叶，TPO.L为左侧颞极，AMY.R为右侧杏仁核，PCA.R 为右侧距状旁回。连线表示纤维连接。图1采用MATLAB软件绘制，仅绘制ASD组与对照组双侧杏仁核白质纤维连接强度差异有统计学意义（P＜0.05）的纤维连接

表4 2组受试儿双侧杏仁核白质纤维连接强度NoS比较（

±s）

组别	例数	AMY-SPG.L	AMY-TPO.L	AMY-PCA.R
ASD组	64	43.7±5.3	11.4±1.4	0.36±0.08
对照组	23	69.5±11.4	22.2±4.6	0.91±0.27
t 值		2.30	3.02	2.70
P值		0.024	0.003	0.008

表4 2组受试儿双侧杏仁核白质纤维连接强度NoS比较（

±s）

组别	例数	AMY-SPG.L	AMY-TPO.L	AMY-PCA.R
ASD组	64	43.7±5.3	11.4±1.4	0.36±0.08
对照组	23	69.5±11.4	22.2±4.6	0.91±0.27
t 值		2.30	3.02	2.70
P值		0.024	0.003	0.008

图2 ASD 组患儿左侧杏仁核与左侧眶额叶间的白质纤维连接强度NoS与ABC 的躯体运动能力维度评分相关性散点图注：ASD为孤独症谱系障碍，NoS为流线数量，ABC 为《孤独症行为量表》

表5 差异性杏仁核白质纤维连接强度NoS与CARS、ABC评分的相关性分析（n=64）

量表评分	AMY-SPG.L		AMY-TPO.L		AMY-PCA.R
量表评分	r值	P值	r值	P值	r值	P值
CARS评分	-0.073	0.564	-0.245	0.051	-0.002	0.985
ABC总评分	-0.132	0.299	-0.069	0.588	0.289	0.134
感觉能力维度评分	-0.083	0.513	-0.022	0.864	-0.056	0.662
交往能力维度评分	0.126	0.322	0.041	0.745	0.079	0.537
躯体运动能力维度评分	-0.269	0.032	-0.155	0.221	0.020	0.873
语言能力维度评分	-0.037	0.769	0.058	0.647	0.162	0.201
社会生活自理能力维度评分	-0.176	0.163	-0.125	0.327	0.035	0.781

表5 差异性杏仁核白质纤维连接强度NoS与CARS、ABC评分的相关性分析（n=64）

量表评分	AMY-SPG.L		AMY-TPO.L		AMY-PCA.R
量表评分	r值	P值	r值	P值	r值	P值
CARS评分	-0.073	0.564	-0.245	0.051	-0.002	0.985
ABC总评分	-0.132	0.299	-0.069	0.588	0.289	0.134
感觉能力维度评分	-0.083	0.513	-0.022	0.864	-0.056	0.662
交往能力维度评分	0.126	0.322	0.041	0.745	0.079	0.537
躯体运动能力维度评分	-0.269	0.032	-0.155	0.221	0.020	0.873
语言能力维度评分	-0.037	0.769	0.058	0.647	0.162	0.201
社会生活自理能力维度评分	-0.176	0.163	-0.125	0.327	0.035	0.781

［1］	Paulsen B，Velasco S，Kedaigle AJ，et al.Autism genes converge on asynchronous development of shared neuron classes［J］.Nature，2022，602（7896）：268-273.DOI：10.1038/s41586-021-04358-6.
［2］	Zeidan J，Fombonne E，Scorah J，et al.Global prevalence of autism：a systematic review update［J］.Autism Res，2022，15（5）：778-790.DOI：10.1002/aur.2696.
［3］	Fombonne E，MacFarlane H，Salem AC，Epidemiological surveys of ASD：advances and remaining challenges［J］.J Autism Dev Disord，2021，51（12）：4271-4290.DOI：10.1007/s10803-021-05005-9.
［4］	Baron-Cohen S，Ring HA，Bullmore ET，et al.The amygdala theory of autism［J］.Neurosci Biobehav Rev，2000，24（3）：355-364.DOI：10.1016/S0149-7634（00）00011-7.
［5］	Dougherty CC，Evans DW，Myers SM，et al.A comparison of structural brain imaging findings in autism spectrum disorder and attention-deficit hyperactivity disorder［J］.Neuropsychol Rev，2016，26（1）：25-43.DOI：10.1007/s11065-015-9300-2.
［6］	Zhu Z，Fang X，Chen H，et al.Alterations in volumes and MRI features of amygdala in Chinese autistic preschoolers associated with social and behavioral deficits［J］.Brain Imaging B ehav，2018，12（6）：1814-1821.DOI：10.1007/s11682-018-9853-9.
［7］	Zagorski N.Children with autism，fragile X show distinct early brain changes［J］.Psychiatr News，2022，57（9）：appi.pn.2022.09.6.11.DOI：10.1176/appi.pn.2022.09.6.11.
［8］	Libero LE，Burge WK，Deshpande HD，et al.White matter diffusion of major fiber tracts implicated in autism spectrum disorder［J］.Brain Connect，2016，6（9）：691-699.DOI：10.1089/brain.2016.0442.
［9］	Kryza-Lacombe M，Iturri N，Monk CS，et al.Face emotion processing in pediatric irritability：neural mechanisms in a sample enriched for irritability with autism spectrum disorder［J］.J Am Acad Child Adolesc Psychiatry，2020，59（12）：1380-1391.DOI：10.1016/j.jaac.2019.09.002.
［10］	Sato W，Uono S，Kochiyama T.Neurocognitive mechanisms underlying social atypicalities in autism：weak amygdala's emotional modulation hypothesis［J］.Front Psychiatry，2020，11：864.DOI：10.3389/fpsyt.2020.00864.
［11］	Kovacevic M，Macuzic IZ，Milosavljevic J，et al.Amygdala volumes in autism spectrum disorders：Meta-analysis of magnetic resonance imaging s tudies［J］.Rev J Autism Dev Disord，2023，10（1）：169-183.DOI：10.1007/s40489-021-00281-8.
［12］	Azad A，Cabeen RP，Sepehrband F，et al.Microstructural properties within the amygdala and affiliated white matter tracts across adolescence［J］.Neuroimage，2021，243：118489.DOI：10.1016/j.neuroimage.2021.118489.
［13］	Paszke A，Gross S，Massa F，et al.Py Torch：an imperative style，high-performance deep learning library［C］//Advances in Neural Information Processing Systems （NeurIPS 2019），2019：8026-8037.DOI：10.48550/ar Xiv.1912.01703.
［14］	Malcolm JG，Shenton ME，Rathi Y.Filtered multitensor tractography［J］.IEEE Trans Med Imaging，2010，29（9）：1664-1675.DOI：10.1109/tmi.2010.2048121.
［15］	Rathi Y，Kubicki M，Bouix S，et al.Statistical analysis of fiber bundles using multi-tensor tractography：application to first-episode schizophrenia［J］.Magn Reson Imaging，2011，29（4）：507-515.DOI：10.1016/j.mri.2010.10.005.
［16］	He J，Zhang F，Xie G，et al.Comparison of multiple tractography methods for reconstruction of the retinogeniculate visual pathway using diffusion MRI［J］.Hum Brain Mapp，2021，42（12）：3887-3904.DOI：10.1002/hbm.25472.
［17］	First MB.DSM-5-TR^®handbook of differential diagnosis［M］.Washington，DC：American Psychiatric Association，2013.
［18］	Wadden NP，Bryson SE，Rodger RS.A closer look at the autism behavior checklist：discriminant validity and factor structure［J］.J Autism Dev Disord，1991，21（4）：529-541.DOI：10.1007/BF02206875.
［19］	Schopler E，Reichler RJ，DeVellis RF，et al.Toward objective classification of childhood autism：Childhood Autism Rating Scale （CARS）［J］.J Autism Dev Disord，1980，10（1）：91-103.DOI：10.1007/BF02408436.
［20］	Zhang F，Cho KIK，Seitz-Holland J，et al.DDParcel：deep learning anatomical brain parcellation from diffusion MRI［J］.IEEE Trans Med Imaging，2024，43（3）：1191-1202.DOI：10.1109/TMI.2023.3331691.
［21］	Desikan RS，Ségonne F，Fischl B，et al.An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest［J］.Neuroimage，2006，31（3）：968-980.DOI：10.1016/j.neuroimage.2006.01.021.
［22］	Zhang F，Savadjiev P，Cai W，et al.Whole brain white matter connectivity analysis using machine learning：an application to autism［J］.NeuroImage，2018，172：826-837.DOI：10.1016/j.neuroimage.2017.10.029.
［23］	Mosconi MW，Cody-Hazlett H，Poe MD，et al.Longitudinal study of amygdala volume and joint attention in 2-to 4-yearold children with autism［J］.Arch Gen Psychiatry，2009，66（5）：509-516.DOI：10.1001/archgenpsychiatry.2009.19.
［24］	Nordahl CW，Scholz R，Yang X，et al.Increased rate of amygdala growth in children aged 2 to 4 years with autism spectrum disorders：a longitudinal study［J］.Arch Gen Psychiatry，2012，69（1）：53-61.DOI：10.1001/archgen psychiatry.2011.145.
［25］	Munson J，Dawson G，Abbott R，et al.Amygdalar volume and behavioral development in autism ［J］.Arch Gen Psychiatry，2006，63 （6）：686-693.DOI：10.1001/archpsyc.63.6.686.
［26］	Joo SW，Kim H，Jo YT，et al.Shared and distinct white matter abnormalities in schizophrenia and bipolar disorder［J］.Prog Neuro Psychopharmacol Biol Psychiatry，2021，108：110175.DOI：10.1016/j.pnpbp.2020.110175.
［27］	Gong L，Xu R，Yang D，et al.Orbitofrontal cortex functional connectivity-based classification for chronic insomnia disorder patients with depression symptoms［J］.Front Psychiatry，2022，13：907978.DOI：10.3389/fpsyt.2022.907978.
［28］	Cheng W，Rolls ET，Qiu J，et al.Functional connectivity of the human amygdala in health and in depression［J］.Soc Cogn Affect Neurosci，2018，13（6）：557-568.DOI：10.1093/scan/nsy032.
［29］	Jezzini A，Bromberg-Martin ES，Trambaiolli LR，et al.A prefrontal network integrates preferences for advance information about uncertain rewards and punishments［J］.Neuron，2021，109（14）：2339-2352.e5.DOI：10.1016/j.neuron.2021.05.013.
［30］	Defenderfer J，Kerr-German A，Hedrick M，et al.Investigating the role of temporal lobe activation in speech perception accuracy with normal hearing adults：an eventrelated f NIRS study［J］.Neuropsychologia，2017，106：31-41.DOI：10.1016/j.neuropsychologia.2017.09.004.
［31］	Bai C，Wang Y，Zhang Y，et al.Abnormal gray matter volume and functional connectivity patterns in social cognition-related brain regions of young children with autism spectrum disorder［J］.Autism Res，2023，16（6）：1124-1137.DOI：10.1002/aur.2936.
［32］	Chen YC，Chen C，Martínez RM，et al.An amygdalacentered hyper-connectivity signature of threatening face processing predicts anxiety in youths with autism spectrum conditions［J］.Autism Res，2021，14（11）：2287-2299.DOI：10.1002/aur.2595.
［33］	Kahl M，Wagner G，de la Cruz F，et al.Resilience and cortical thickness：a MRI study［J］.Eur Arch Psychiatry Clin Neurosci，2020，270（5）：533-539.DOI：10.1007/s00406-018-0963-6.
［34］	Little JA.Vision in children with autism spectrum disorder：a critical review［J］.Clin Exp Optom，2018，101（4）：504-513.DOI：10.1111/cxo.12651.
［35］	Liu J，Girault JB，Nishino T，et al.Atypical functional connectivity between the amygdala and visual，salience regions in infants with genetic liability for autism［J］.Cereb Cortex，2024，34（13）：30-39.DOI：10.1093/cercor/bhae092.

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