Abstract

Introduction

Tetralogy of Fallot (TOF) represents the most prevalent form of cyanotic congenital heart disease (CCHD), comprising about 3.5% of all CHD instances [1, 2]. TOF is characterized by distinct anatomical anomalies such as ventricular septal defect (VSD), right ventricular outflow tract obstruction (RVOTO), overriding aorta (AO) and right ventricular hypertrophy. These features result in hemodynamic irregularities that precipitate a cascade of hypoxic-ischemic incidents [3]. Research indicates that TOF patients undergoing corrective surgery before reaching five years of age can achieve a 30-year survival rate of up to 90% [4]. Although surgical mortality rates have declined, survivors encounter numerous complications [5]. Notably, brain injuries during the perioperative period of TOF have garnered heightened scrutiny due to their potential link to deteriorating neurocognitive, psychosocial, and behavioral functions. Prolonged lack of intervention could detrimentally impact patients’ long-term quality of life [6–8]. According to the World Health Organization [9, 10], health-related quality of life is a multidimensional concept that integrates various facets, including physical health, psychological state, social interaction, and occupational capability. While the aspect of physical health in patients receiving comprehensive TOF repair has been profoundly studied, the domain of social cognitive function still requires further exploration.

Developmental anomalies in children with CHD have been substantially investigated [11]. Studies have suggested that children with CCHD are susceptible to neural damage during prenatal, pre-postpartum, perioperative, and postoperative phases. This damage is chiefly attributed to cerebral circulation disturbances, cardiopulmonary bypass procedures, anesthesia, reduced cardiac output, and socioeconomic factors. The prenatal and perinatal periods align with crucial stages of brain development and are deemed to present the greatest risk for neurological impairment [11–13]. The incidence of atypical cerebral alterations in children with CHD is around 10%, but this figure may escalate to 50% in those with CCHD. Magnetic resonance imaging (MRI) can reveal these aberrant cerebral modifications, which are evidenced by diminished brain size, retarded neural development, white matter injury, and cerebrovascular accidents [14–19]. Moreover, reports of delayed cortical maturation in CCHD are on the rise, primarily characterized by postponed cortical gyrification, uneven cortical depth, and diminished cortical density [20–23]. Concurrently, these children might exhibit lower intelligence quotients (IQ) and a higher incidence of behavioral issues and executive function deficiencies compared to their counterparts [24, 25]. Nonetheless, these investigations have primarily focused on preoperative and short-term postoperative morphological changes. There is a need for additional research to monitor long-term changes in subcortical nuclei and to delve deeper into the connection between these changes and cognitive functions. However, the effect of volume of subcortical nuclei changes on postoperative cognitive ability in children with TOF is still unclear. Consequently, we assessed the subcortical nuclei volume in TOF patients via MRI to appraise their cognitive capabilities, in conjunction with the anatomical alterations associated with TOF, and further scrutinized the interrelation between these factors.

Materials and methods

Multimodal MRI acquisition and processing

All participants underwent scans during natural sleep using a 3.0 Tesla MRI system (Ingenia 3.0, Philips Healthcare, The Netherlands) in the radiology department of our hospital using a 16-channel head coil. Subjects who could not complete the MRI during natural sleep were sedated with chloral hydrate (1 ml/kg), with parental consent. Earplugs were used to reduce scanning noise, and foam was employed to minimize head motion. The scanning parameters for the 3D T1-weighted high-resolution structural images were: echo time (TE): 3.5 ms; repetition time (TR): 7.9 ms; field of view (FOV): 200×200×200 mm; slice thickness: 1 mm and acquisition time: 4 min 24 s. The scanning parameters for conventional axial T2-weighted images are as follows: TE: 110 ms; TR: 4000 ms; FOV: 200×200×119 mm; slice thickness: 5 mm and acquisition time: 1 min 28 s. Two experienced pediatric neuroradiologists, blinded to each participant’s medical history, independently reviewed all images. In cases where there was a difference of opinion, a consensus was reached through discussion. The 3D T1-weighted images were processed using FreeSurfer, V6.0 (https://surfer.nmr.mgh.harvard.edu), employing an automated recon-all pipeline that included motion correction and conform, intensity normalization, talairach transformation computation, skull stripping, removing neck, brain extraction, segment of subcortical white and gray matter, smooth, inflating, topology correction and surface deformation, spherical registration, cortical and subcortical parcellation. Subsequently, the quality of the preprocessed images was verified, including checks for skull stripping, Talairach transformation, and segmentation. Finally, the subcortical nuclei were segmented into 10 subregions (Fig. 1): left thalamus proper nucleus (LTHA), left caudate nucleus (LCAU), left putamen nucleus (LPU), left pallidum nucleus (LPA), left amygdala nucleus (LAM), right thalamus proper nucleus (RTHA), right caudate nucleus (RCAU), right putamen nucleus (RPU), right pallidum nucleus (RPA) and right amygdala nucleus (RAM).

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Fig. 1

Segmentation of the subcortical structures. 3D T1-weighted image of a participant with delineation of the subcortical structures: cerebral white matter (color 1), cerebral cortex (color 2), lateral ventricle (color 3), thalamus (color 4), caudate(color 5), putamen (color 6), pallidum (color 7), 3rd -Ventricle (color 8), hippocampus (color 9) and amygdala(color 10) in the axial (A) and coronal (B) planes

Results

Demographic data for the TOF and HC groups is presented in Table 1. There were no significant differences in gender or family income between the two groups. A summary of essential hospitalization records for the TOF group is also provided.

Table 1

Characteristic information of healthy children and tetralogy of fallot children

Variables	TOF (n=36)	HC (n=29)	p-value
Age at MRI, year	3.98±1.23	4.41±0.91	0.104
Sex, male, %	20 (55.56%)	19 (65.52%)	0.415
Family income, ten thousand yuan per year	8.93±6.78	11.97±5.58	0.057
BMI, kg/m2	15.87±1.84	17.71±1.74	<0.001
Age of surgery, month (n=31)	12.45±8.89	/
Stay in hospital, day (n=31)	25.90±7.53	/
Stay in ICU, day (n=31)	5.48±2.08	/
Preoperative RVOT pressure, mmHg (n=31)	66.39±14.78	/
VSD, mm (n=31)	14.44±3.06	/
Overriding aorta, % (n=31)	48.55±7.09	/
McGoon ratio (n=31)	1.71±0.33	/
Time of surgery, min (n=31)	201.77±59.03	/
Time of CPB, min (n=31)	77.48±18.69	/
Time of ACC, min (n=31)	54.54±18.00	/
Verbal comprehension index (n=31)	87.32±13.90	/
Visual spaces index (n=31)	95.32±12.41	/
Working memory index (n=31)	93.13±11.81	/
Verbal acceptive index (n=31)	91.13±15.07	/
Non-verbal index (n=31)	92.87±12.31	/
General ability index (n=31)	90.10±12.35	/
Fully scale intelligence quotient (n=31)	90.35±12.68	/

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Mean±standard deviation (SD); n (percentage). Bold value represents data having statistical significance

Healthy children, HC; tetralogy of Fallot, TOF; magnetic resonance imaging, MRI; body mass index, BMI; intensive care unit, ICU; right ventricular outflow tract, RVOT; ventricular septal defect, VSD; cardiopulmonary bypass, CPB; aortic crossclamp, ACC; systolic blood pressure, SBP; diastolic blood pressure, DBP

Differences in the volume of subcortical nuclei between the TOF and HC groups are shown in Table 2. HC group exhibited higher volumes in the left caudate nucleus (LTHA) (7435.36±532.84 mm³), left amygdala nucleus (LAM) (1436.27±140.62mm³), right thalamus proper nucleus (RTHA) (7162.94±554.60 mm³), right caudate nucleus (RCAU) (3777.89±394.71 mm³), right putamen nucleus (RPU) (5215.91±473.41mm³), right pallidum nucleus (RPA) (1794.82±194.04 mm³), and right amygdala nucleus (RAM) (1601.27±185.08 mm³).

Table 2

Volume of subcortical nuclei in patients of TOF and HC

Variables	TOF (n=36)	HC (n=29)	FDR
LTHA (mm3)	6771.54±666.03	7435.36±532.84	<0.001
LCAU (mm3)	3433.84±454.68	3668.40±444.28	0.057
LPU (mm3)	4837.86±676.95	5137.60±551.15	0.073
LPA (mm3)	1725.89±273.54	1876.21±276.71	0.052
LAM (mm3)	1292.60±155.57	1436.27±140.62	<0.001
RTHA (mm3)	6514.61±715.23	7162.94±554.60	<0.001
RCAU (mm3)	3455.57±478.70	3777.89±394.71	0.015
RPU (mm3)	4846.28±643.00	5215.91±473.41	0.023
RPA (mm3)	1651.15±230.35	1794.82±194.04	0.023
RAM (mm3)	1469.20±172.47	1601.27±185.08	0.015

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Mean±standard deviation (SD). Bold value represents data having statistical significance. Data were adjusted for age and sex using FDR

Healthy children, HC; tetralogy of Fallot, TOF; left thalamus proper nucleus, LTHA; left caudate nucleus, LCAU; left putamen nucleus, LPU; left pallidum nucleus, LPA; left amygdala nucleus LAM; right thalamus proper nucleus, RTHA; right caudate nucleus, RCAU; right putamen nucleus, RPU; right pallidum nucleus, RPA; right amygdala nucleus RAM

After adjusting for sex, body mass index (BMI), age at MRI, family income, age of surgery, stay in intensive care unit (ICU), stay in hospital, time of surgery, time of cardiopulmonary bypass (CPB) and time of aortic cross-clamp (ACC), the volumes of LTHA (β: 0.007, 95% CI: 0.0001, 0.014), LAM CAT (β: 0.033, 95% CI: 0.004, 0.062), RTHA (β: 0.008, 95% CI: 0.002, 0.014) and RPA (β: 0.021, 95% CI: 0.0001, 0.041) showed associations with WMI; the volume of RTHA (β: 0.008, 95% CI: 0.0001, 0.016) was associated with VSI; the volume of RTH (β: 0.009, 95% CI: 0.002, 0.016) was linked to NVI; and the volume of RTHA (β: 0.008, 95% CI: 0.001, 0.016) was correlated with FSIQ (Table 3).

Furthermore, linear regression analyses examining preoperative cardiac structural changes and subcortical nuclei volume are presented in Table 4. The size of VSD, the degree of overriding aorta and the McGoon ratio were significantly associated with LAM, RCAU and RPU and these associations were independent of sex, BMI, age at MRI and family income. Notably, the volume of LAM (β: -19.828, 95% CI: -36.462, -3.193) was inversely related to the size of VSD.

Discussion

Our population-based follow-up study is pioneering in identifying the correlation between changes in subcortical nuclei volume and cognitive functions in preschool-aged children with TOF and the relationship between the size of VSD and the volume of subcortical nuclei change in LAM. The findings reveal a relationship among anatomical changes in TOF, subcortical nuclei volume, and cognitive behavior.

Reports have indicated delayed cortical development, compromised microstructural integrity, and abnormalities in cerebral perfusion and metabolism in the CHD population [26–28]. Functional MRI studies have also observed connectivity disorders across multiple brain regions in this demographic [29]. Research has substantiated that brain abnormalities in children with CHD correlate with negative neurodevelopmental outcomes [30]. Andropoulos et al. discovered that preoperative brain injuries (white matter damage, infarction, and hemorrhage) correlated with impaired motor and language functions in early infancy [31]. It is noteworthy that the subcortical nuclei act as gatekeepers of the central nervous system, responsible for regulating and controlling the rapid and massive information exchange between the cerebral cortex and the rest of the body, directly or indirectly affecting the change of cognitive behavior. Consequently, drawing on our results and existing literature, we hypothesize about the mechanisms underlying reduced WMI due to diminished subcortical nuclei volume in postoperative pre-school-aged children with TOF. Our study demonstrates a close correlation between the volume of LAM and the reduction in WMI, which suggests that the subcortical nuclei of TOF patients are related to postoperative cognitive and behavioral changes. Hence, it is crucial to prioritize the monitoring and protection of these subcortical nuclei.

Additionally, studies have linked reduced brain volume in infants and adolescents with CHD to deficits in cognitive, motor, and language development [32]. Furthermore, diffusion tensor imaging has revealed that delayed maturation of white matter microstructures in adolescents with CHD can adversely affect working memory, attention, and learning capabilities [32]. Children with TOF experience reduced oxygen delivery and consumption [32], making their cerebral cortex especially vulnerable to related adverse effects [20]. The primary microstructural changes in the hypoxic brain include impaired dendritic branching of neurons [33] and suppressed formation of glial cells [34–36].

The amygdala, a key structure within the limbic system, is crucial for emotional and cognitive functions [37]. Amygdala dysfunction is associated with a range of neurodevelopmental disorders and psychological conditions [38–40], including depression [41], social anxiety [42], post-traumatic stress disorder [43], dementia [44], and schizophrenia [45]. Earlier research has also shown the amygdala’s role in higher cognitive functions [39, 46], including memory [47–50], learning [51, 52], decision-making [53], reward regulation [54], and intelligence [46, 55]. Recently, more studies have identified connections between the amygdala and various memory functions, such as emotional memory processing, memory consolidation, working memory, state-dependent memory retrieval, autobiographical memory encoding/recall, and episodic memory formation/retrieval [56–60]. Although often considered a single entity, it’s important to recognize that the amygdala comprises multiple nuclei, each with unique roles and connections within the limbic system and the broader brain network.

A negative correlation was found between the size of the VSD and changes in LAM volume, while positive correlations were observed between the aorta and RCAU volume changes, and between the McGoon index and RPU volume changes. These anatomical variations correlate with the extent of ischemia and hypoxia experienced by children with TOF. This aligns with existing literature suggesting that hemodynamic irregularities in TOF are primary contributors to brain structural alterations, particularly damage to subcortical nuclei. Thus, preoperative cardiac structural factors in TOF patients play a role in forecasting postoperative cognitive outcomes. Even after correction of TOF’s intracardiac malformations, careful attention must be given to the potential for brain injury post-surgery. Meanwhile, postoperative working memory training for children with TOF should be intensified. In our study, preoperative cardiac structural changes appear to predict adverse neurodevelopmental outcomes. A large sample size may provide a more definitive determination of the link between cortical structure and neurodevelopmental outcomes in children with TOF.

This study has the following highlights: 1) The long-term cognitive function of the children with TOF was analyzed and studied. 2)MRI data is relatively stable, and we are strict on MRI data screening, which greatly ensures the integrity and reliability of data. 30) We carried out a single blind test on the experimenters who conducted the scale evaluation, and a professional supervised the implementation process to ensure the authenticity of the scale data. Nevertheless, this study has several limitations. (1) The sample size included in the analysis was limited. Being an exploratory study, we endeavored to include as many eligible cases as feasible. We aim to incorporate additional cases for validation in the future to enhance the reliability and generalizability of our findings. (2) The study lacked cognition assessments for healthy controls, as parents reported normal intelligence levels and declined cognitive testing for their children. This presented an unavoidable challenge within the scope of our research. Consequently, our study concentrated on examining brain MRI discrepancies between healthy controls and TOF subjects, exploring the potential correlations with cognitive function, and investigating underlying causes. We intend to conduct standardized tests on healthy participants in future studies. (3) An adult template was used for image normalization, which could introduce bias and limitations, potentially overlooking significant inter-population differences. In the future, we will integrate perioperative risk factors and neuroimaging analyses to identify a more suitable age-specific template. (4) While this study represents a single follow-up, conducting multimodal brain MRI and neurocognitive testing at various time points pre- and post-surgery is crucial for a deeper understanding of morphological changes and the cognitive impairment process in TOF patients.

Conclusion

This study concludes that preschool children with TOF face a heightened risk of cognitive impairment, corroborating evidence from existing research. The study found a strong association between reduced WMI in preschoolers post-TOF surgery and morphological damage to the left amygdala, indicating that pre-correction brain damage in TOF children persists in influencing cortical development and cognitive abilities during the preschool years. Our findings propose that changes in the volume of subcortical nuclei in LAM could serve as a potential biomarker for neurocognitive deficits in TOF and may predict future neurodevelopmental outcomes.

Acknowledgements

Author contributions

Mo XM and Yang M designed the study protocol and proofread the manuscript. Wu KD and Fu MC evaluated the levels of cognition of participants. Zhang YQ, Tang MH, Lin Y and Zhang Z collected the information and performed the data analysis under the close supervision of An J, Cai XY and Wu KD. Li HJ and Lu F performed brain MRI scans on participants. MRI of patients was analyzed by Li HJ and the results were verified by Zhu MJ. Hu L drafted and proofread the manuscript. Nishant P took charge of the modification of english language. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by the National Natural Science Foundation of China (82270310), a Sub-project of the National Key R&D Program “The recognition and identification of genetic pathogenic genes for structural birth defects” (2021YFC2701002), Jiangsu Provincial key research and development program (BE2023662),  General project of Nanjing Health Commission (YKK22164), the postdoctoral start-up funds of Chil- dren’s Hospital of Nanjing Medical University (QDJJ2022006, QDJJ2023002, QDJJ2024003) and the National Natural Science Foundation cultivation program of Children’s Hospital of Nanjing Medical University (QNPY2024007, QNPY2024010).

Data availability

Please contact the author for data requests.

Declarations

Footnotes

Contributor Information

Ming Yang, Email: moc.361@71201791gnimgnay.

Xuming Mo, Email: nc.ude.umjn@51gnimushom.

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