Identifying novel data-driven subgroups in congenital heart disease using multi-modal measures of brain structure

Marlee M Vandewouw; Ami Norris-Brilliant; Anum Rahman; Stephania Assimopoulos; Sarah U Morton; Azadeh Kushki; Sean Cunningham; Eileen King; Elizabeth Goldmuntz; Thomas A Miller; Nina H Thomas; Heather R Adams; John Cleveland; James F Cnota; P Ellen Grant; Caren S Goldberg; Hao Huang; Jennifer S Li; Patrick McQuillen; George A Porter; Amy E Roberts; Mark W Russell; Christine E Seidman; Madalina E Tivarus; Wendy K Chung; Donald J Hagler; Jane W Newburger; Ashok Panigrahy; Jason P Lerch; Bruce D Gelb; Evdokia Anagnostou

doi:10.1016/j.neuroimage.2024.120721

. Author manuscript; available in PMC: 2026 Mar 31.

Published in final edited form as: Neuroimage. 2024 Jul 4;297:120721. doi: 10.1016/j.neuroimage.2024.120721

Identifying novel data-driven subgroups in congenital heart disease using multi-modal measures of brain structure

Marlee M Vandewouw ^a,^b,^*, Ami Norris-Brilliant ^c, Anum Rahman ^d,^e,^f, Stephania Assimopoulos ^d,^f, Sarah U Morton ^g,^h,^i,^j, Azadeh Kushki ^a,^b, Sean Cunningham ^k, Eileen King ^l,^m, Elizabeth Goldmuntz ^n,^o, Thomas A Miller ^p, Nina H Thomas ^q,^r, Heather R Adams ^s, John Cleveland ^t, James F Cnota ^l,^u, P Ellen Grant ^g,^h,^i,^v,^w, Caren S Goldberg ^x, Hao Huang ^y,^z, Jennifer S Li ^aa, Patrick McQuillen ^ab, George A Porter ^s, Amy E Roberts ^i,^ac,^ad, Mark W Russell ^x, Christine E Seidman ^j,^ae,^af, Madalina E Tivarus ^ag, Wendy K Chung ^ah, Donald J Hagler ^ai,^aj,^ak, Jane W Newburger ^i,^ac, Ashok Panigrahy ^al, Jason P Lerch ^f,^am,^an, Bruce D Gelb ^ao,¹, Evdokia Anagnostou ^a,^an,^ap,¹

^aAutism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada

^bInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada

^cDepartment of Psychiatry, Icahn School of Medicine, Mount Sinai, New York, USA

^dMouse Imaging Centre, The Hospital for Sick Children, Toronto, ON, Canada

^eTranslational Medicine, The Hospital for Sick Children, Toronto, ON, Canada

^fDepartment of Medical Biophysics, University of Toronto, Toronto, ON, Canada

^gDivision of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, MA, USA

^hFetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, USA

ⁱDepartment of Pediatrics, Harvard Medical School, Boston, MA, USA

^jDepartment of Genetics, Harvard Medical School, Boston, MA, USA

^kDepartment of Pediatrics, Division of General Pediatrics, University of Utah, Salt Lake City, UT, USA

^lDepartment of Pediatrics, University of Cincinnati, Cincinnati, OH, USA

^mDivision of Biostatistics and Epidemiology, Cincinnati Children’s Hospital Medical Centre, Cincinnati, OH, USA

ⁿDivision of Cardiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

^oDepartment of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

^pDepartment of Pediatrics, Maine Medical Center, Portland, ME, USA

^qDepartment of Child and Adolescent Psychiatry and Behavioral Sciences and Center for Human Phenomic Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

^rDepartment of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA

^sDepartments of Neurology and Pediatrics, University of Rochester Medical Center, Rochester, NY, USA

^tDepartments of Surgery and Pediatrics, Keck School of Medicine, University of Southern California, LA, USA

^uHeart Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

^vDepartment of Radiology, Boston Children’s Hospital, Boston, MA, USA

^wDepartment of Radiology, Harvard Medical School, Boston, MA, USA

^xDepartment of Pediatrics, C.S. Mott Children’s Hospital, University of Michigan, Ann Arbor, MI, USA

^yDepartment of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

^zDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

^aaDepartment of Pediatrics, Duke University Medical Center, Durham, NC, USA

^abDepartments of Pediatrics and Neurology, University of California San Francisco, San Francisco, CA, USA

^acDepartment of Cardiology, Boston Children’s Hospital, Boston, MA USA

^adDivision of Genetics and Genomics, Boston Children’s Hospital, Boston, MA, USA

^aeCardiovascular Division, Brigham and Women’s Hospital, Boston, MA, USA

^afHoward Hughes Medical Institute, Chevy Chase, MD, USA

^agDepartment of Imaging Sciences and Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, USA

^ahDepartments of Pediatrics and Medicine, Columbia University, New York, NY, USA

^aiCenter for Multimodal Imaging and Genetics, University of California San Diego, USA

^ajDepartment of Radiology, School of Medicine, University of California San Diego, USA

^akDepartments of Cognitive Science and Neuroscience, University of California San Diego, USA

^alDepartment of Pediatric Radiology, Children’s Hospital of Pittsburgh, University of Pittsburgh Medical Center, Pittsburgh, PA USA

^amProgram in Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, ON, Canada

^anWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK

^aoMindich Child Health and Development Institute and Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY USA

^apInstitute of Medical Science, University of Toronto, Toronto, ON, Canada

These authors contributed equally to the work.

Corresponding author at: Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada. mvandewouw@hollandbloorview.ca (M.M. Vandewouw).

PMCID: PMC13034073 NIHMSID: NIHMS2145483 PMID: 38968977

Abstract

Individuals with congenital heart disease (CHD) have an increased risk of neurodevelopmental impairments. Given the hypothesized complexity linking genomics, atypical brain structure, cardiac diagnoses and their management, and neurodevelopmental outcomes, unsupervised methods may provide unique insight into neurodevelopmental variability in CHD. Using data from the Pediatric Cardiac Genomics Consortium Brain and Genes study, we identified data-driven subgroups of individuals with CHD from measures of brain structure. Using structural magnetic resonance imaging (MRI; N = 93; cortical thickness, cortical volume, and subcortical volume), we identified subgroups that differed primarily on cardiac anatomic lesion and language ability. In contrast, using diffusion MRI (N = 88; white matter connectivity strength), we identified subgroups that were characterized by differences in associations with rare genetic variants and visual-motor function. This work provides insight into the differential impacts of cardiac lesions and genomic variation on brain growth and architecture in patients with CHD, with potentially distinct effects on neurodevelopmental outcomes.

Keywords: Congenital heart disease, Brain structure, Genetics, Unsupervised methods, Neurodevelopmental outcomes

1. Introduction

Congenital heart disease (CHD) is the most common congenital anomaly (Hoffman and Kaplan, 2002; Wu et al., 2020), accounting for approximately 3 % of infant deaths (Sadowski, 2009). Significant advances in surgical and medical treatments in the past several decades have greatly reduced mortality for individuals born with CHD (Khairy et al., 2010), making their increased likelihood for neurodevelopmental differences more apparent (Liamlahi and Latal, 2019). Approximately 10 % of children with CHD present with impairments in domains including cognition, motor, language, executive function, social interaction, attention, and impulsivity, increasing to 50 % among individuals with severe forms of CHD (Liamlahi and Latal, 2019; Marino et al., 2012). While patient-specific, postoperative, and preoperative factors have been found to be more predictive of poor neurodevelopmental outcomes in CHD than modifiable intraoperative techniques (Gaynor et al., 2007; Limperopoulos et al., 2010; Miller and McQuillen, 2007; Ortinau et al., 2018), our ability to explain much of the variance remains limited (Gaynor et al., 2007; Goldberg et al., 2019; Marino et al., 2012). Genetic and epigenetic factors have been proposed as possible contributing factors (Limperopoulos et al., 2010), with CHD and neuro-developmental differences potentially sharing genetic etiology (Homsy et al., 2015; Jin et al., 2017).

Neuroimaging is a promising approach to bridge the gap between the diverse neurodevelopmental pathways modulated by CHD-related genetic and non-genetic factors and the variability of neurodevelopmental outcomes in CHD. Magnetic resonance imaging (MRI) studies have shown altered brain maturation in utero (Limperopoulos et al., 2010; Ortinau et al., 2018; Rollins et al., 2021), thought to be caused by genetic factors and oxygen and nutrient deficiencies caused by disease-related disruptions in blood flow (Rollins et al., 2021; Sun et al., 2015). This atypical maturation has persistent effects on brain structure, such as reductions in global and regional brain volumes (Bonthrone et al., 2021), predominantly decreased cortical thickness (Verrall et al., 2021; Watson et al., 2017, 2016), reduced white matter integrity (Ehrler et al., 2021; 2020; Rollins et al., 2014; Watson et al., 2018) and altered white matter connectivity (Panigrahy et al., 2015; Schmithorst et al., 2016), which have been shown to be associated with neuro-developmental outcomes (Bonthrone et al., 2021; Ma et al., 2020; Panigrahy et al., 2015; Schmithorst et al., 2016; Verrall et al., 2021; Watson et al., 2018).

Investigations linking the atypical brain structure in CHD and the subsequent behavioral and cognitive sequelae with their genetic underpinnings are an important first step in understanding the complex relationship between genetics, brain, and behavior in CHD. Recent work from the Pediatric Cardiac Genomics Consortium’s (PCGC) Brain and Genes study (ClinicalTrials.gov #NCT01196182) was the first study to compare neurodevelopmental outcomes and brain structure in CHD individuals with (cases) and without (controls) predicted deleterious de novo variants (Morton et al., 2022). The study excluded participants with a damaging variant in a gene associated with neurodevelopmental disabilities, designed particularly to identify new genetic sources of variance in neuropsychiatric measures. Although they reported no differences in neurological outcomes between cases and controls, a small group of individuals with rare, loss-of-function (LOF) variants were found to have worse behavioral outcomes and exhibit differences in some measures of brain structure and function(Morton et al., 2022).

Studies investigating CHD have thus far employed classic case-control approaches, where differences in brain structure and/or outcome are compared between pre-specified groups. Given the hypothesized complexity of the relationships linking genomics, the various measures of brain structure, the types of cardiac lesion and their management, and subsequent neurodevelopmental outcomes, unsupervised methods with no a priori assumptions may provide unique, complementary insights into the structure of such multivariable data.

In this secondary analysis of the CHD Brain and Genes study, we used clustering techniques on measures of brain structure to identify subgroups of individuals with CHD, beyond case and control definitions. We compared the subgroups on their genetic, clinical, and behavioral characteristics to provide insights into how genomic, cardiac lesion and clinical care features may impact brain structure and ultimately neurodevelopmental outcomes. We performed clustering separately for measures derived from structural MRI (thickness and volume) and diffusion MRI (connectivity strength), allowing for different neural mechanisms and embryological origins to differentially relate to CHD pathology and neurodevelopmental outcomes.

2. Materials and methods

2.1. Participants

Participants’ data were extracted from the PCGC CHD Brain and Genes study, details of which are outlined in the primary study (Morton et al., 2022). The Pediatric Cardiac Genomics Consortium (PCGC) Brain and Genes study, a cross-sectional study performed from September 2017 to June 2020, included individuals with CHD who were at least eight years of age and who had available research-based genetic sequencing data, and excluded those who had a history of cardiac transplant, underwent a cardiac surgical procedure within six months of enrollment, had a known clinical genetic syndrome, had brain injury that a center investigator determined would overshadow the effect of a genetic variant on neurodevelopmental outcomes, lacked ability to communicate in English or Spanish, or were unable to undergo MRI scanning. Participants were also excluded if they had the presence of a clinically pathogenic copy number variant or damaging coding variant in a gene associated with neurodevelopmental delay; this criterion included both autosomal dominant genes with a single damaging variant or autosomal recessive genes where two variants were present. One hundred participants were recruited for MRI scanning at one of seven participating institutions: Boston Children’s Hospital (BCH; Boston, United States), Children’s Hospital of Philadelphia (CHOP; Philadelphia, United States), the Icahn School of Medicine at Mount Sinai (ISMMS; New York, United States), the University of California, San Francisco (UCSF; San Francisco, United States), the University of Rochester (ROC; Rochester, United States), the University of Utah (UTAH; Salt Lake City, United States), Yale University (YALE; New Haven, United States). Informed consent and assent were obtained from all participants and/or their parents or guardians. A central institutional research ethics board approved the trial’s protocol, and participating sites approved reliance agreements. This secondary analysis was also approved by the Holland Bloorview Kids Rehabilitation Hospital’s research ethics board.

2.2. Data acquisition

Information on the nature of the participant’s cardiac lesions and medical histories was obtained from chart review and participant interviews, and included the total number of cardiac surgeries, the total number of open cardiac surgeries, age at first hospital admission, and age of first open cardiac surgery. Participants were classified clinically as having either single ventricle (SV) or biventricular (BV) lesions, and the Fyler coding system was also used to categorize the lesions.

Neuroimaging data were acquired at one of seven participating institutions in the United States. Structural MRI (sMRI) data were obtained with a T1-weighted acquisition and diffusion MRI (dMRI) data were obtained with a multi-shell sequence (see the Supplemental Information and Supplemental Table 1). A neuroradiology reader assessed the structural MRIs for brain injury, providing the number of focal infarcts, the size of focal infarcts if present, and the number of foci with low and high T2 signal; a participant was determined to have MRI findings based on the presence of infarcts and/or foci. Genomic results are described in the primary study (Morton et al., 2022). Participants were classified by heterozygous status for the presence (case) or absence (control) of a damaging de novo variant in a gene that was not recognized as a definitive cause of neurodevelopmental disability (NDD; Homsy et al., 2015; Jin et al., 2017) at the time participant stratification (June 2017). Cases were further classified based on whether variants are predicted to cause loss of function (pLOF; Jin et al., 2017; Seidman et al., 2021) in a gene involved in chromatin remodelling (CHR), altered a gene that is highly expressed in the developing brain (HBE), altered a constrained gene with a high probability of intolerance to loss (H-pLI), caused a single abnormal allele in an autosomal recessive NDD gene, or occurred in a gene newly associated with NDD, after participant stratification. Note that this is distinct from the exclusion criteria at study enrollment. The NDD gene list used in this analysis phase does contain NDD genes discovered since enrollment, but most of the individuals with NDD gene variants have a single variant in a NDD gene with an autosomal recessive mechanism (while exclusion criteria required two variants). NDD gene variant status was included in our analysis to fully consider the potential impact of known NDD genes in the cohort, based on updated information at the time of analysis.

Neurocognitive testing was performed in conjunction with, or within six months of the MRI data. For the purposes of this study, we examined primary and secondary measures pre-specified in the original Brain and Genes study (Morton et al., 2022) capturing academic achievement, visual-motor integration, intelligence, memory, executive function and added the Vineland Adaptive Behavior Scales, Third Edition (Vineland-3; Sparrow et al., 2016) from exploratory measures to capture overall adaptive function.

2.3. Preprocessing

For the sMRI preprocessing, the Adolescent Brain Cognitive Development (ABCD) Study pipeline was performed (Hagler et al., 2019). The dMRI data were preprocessed and reconstructed using QSIPrep (version 0.14.3; Cieslak et al., 2021), followed by tractography to generate structural connectivity networks of streamline count between pairs of regions in the FreeSurfer cortical and subcortical parcellation. Further details are provided in the Supplemental Information. ComBat harmonization (Fortin et al., 2018) was used to correct for site effects, and the effects of age at imaging (linear and quadratic), sex, and intracranial volume were regressed from each measure. For both steps, bootstrapping (63.2 % of the sample (Efron, 1979), 100,000 iterations) was used to increase robustness (Da-ano et al., 2020). The residual data were z-scored for the clustering analysis.

2.4. Clustering

The clustering pipeline was run separately for the sMRI and dMRI data. The pipeline consisted of 100,000 bootstrap iterations, each with 63.2 % of the sample, the square root of the number of features (Efron, 1979), and the number of clusters pre-selected as a value between two and five. The difference between pairs of participants was computed for each selected feature, converted to a similarity matrix (Ontanon, 2020), and averaged across all features; spectral clustering (Ng et al., 2001) was then performed using the pre-selected number of clusters. These 100,000 clustering solutions were collected and used to generate a participant co-assignment matrix containing the probability pairs of participants were clustered together across the bootstrap iterations. Finally, hierarchical clustering was performed on the co-assignment matrix. Hierarchical clustering produces a dendrogram, or clustering decision tree, where for each layer (two to five considered in this study) a root node is split into two leaves. Further details on the clustering pipeline are provided in the Supplemental Information.

2.5. Statistics

Differences in participant demographics, CHD lesion categories, genetic categories, MRI findings, and behavioural measures between pairs of leaves in each layer of the dendrograms were evaluated using chi-squared tests for categorical variables, ordinal logistic regression for ordinal variables, and either one-way analysis of variances (ANOVAs) or Kruskal-Wallis tests for continuous variables depending on normality; participants with missing data were excluded on a case-by-case basis, and significance was held at p < 0.05, uncorrected. Differences in the brain measures between the leaf clusters were also tested and resulting p-values were corrected across the brain regions (Benjamini and Hochberg, 1995), holding significance at p_corr<0.05.

3. Results

3.1. Participant demographics

Of the 100 participants in the CHD Brain and Genes clinical trial, 93 and 88 were included in the sMRI and dMRI analyses, respectively (see Fig. 1). Sample descriptors are summarized for the sMRI and dMRI datasets in Table 1. To provide consistency with the PCGC CHD Brain and Genes study, sample characteristics summarized by case (damaging de novo variant) and control (no such variant) are also presented in Supplemental Tables 2 and 3. MRI findings and descriptive statistics of the behavioral and medical history measures for the sMRI and dMRI datasets are provided in Supplemental Tables 4 and 5, respectively.

Table 1.

Sample characteristics for the sMRI and dMRI datasets.

		sMRI	dMRI
N		93	88
Age¹ range (years)		7 – 41	7 – 41
Median age¹ (± IQR)		16.1 ± 12.4	16.6 ± 12.5
Sex (M:F)		37:56	38:50
Race (White:Black/African American:Asian:Mixed)		88:2:1:2	83:2:1:2
Ethnicity (Not Hispanic/Latino:Hispanic/Latino)		84:9	80:8
Household income level (Level 1:2:3:4:5:6)²		3:7:10:10:16:24	2:6:11:9:15:22
Mother’s education level (Level 1:2:3)³		13:53:26	11:51:25
Damaging de novo variant present		46	44
Single ventricle (SV)		24	22
Biventricular (BV)		69	66
pLOF variant present	HBE gene	58	55
	CHR gene	8	8
	NDD gene	7	6
	H-pLI gene	36	37

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sMRI: structural magnetic resonance imaging; dMRI: diffusion magnetic resonance imaging; IQR: interquartile range; M: male; F: female; CHD: congenital heart disease; BV: biventricular; SV: single-ventricle; pLOF: putative loss-of-function; HBE: high brain expressed gene; CHR: chromatin remodelling gene; NDD: gene newly identified as a dominant neurodevelopmental disability gene or a single variant in a recessive NDD gene; H-pLI: constrained gene with a high probability of intolerance to loss

Age at imaging

1=<$24,999/year, 2=$25,000-$49,999/year, 3=$50,000-$74,999/year, 4=$75,000-$99,999/year, 5=$100,000-$149,999/year, 6=>$150,000.

1=high school or under/other, 2=some college/college, 3=post graduate degree.

3.2. sMRI clustering

Clustering results for the sMRI dataset are presented in Fig. 2. The emergence of the five clusters is presented using the hierarchical clustering dendrogram (Fig. 2A), and the effect sizes of significant differences (p_corr < 0.05) in cortical thickness (Fig. 2B) and cortical and subcortical volume (Fig. 2C) are also presented. Significant differences (p < 0.05) in the participant demographics, CHD lesion categories, genetic categories, and behavioral measures between each pair of leaf clusters are illustrated, with descriptive statistics of significant findings summarized in Table 2; full details are provided in Supplemental Table 6.

Table 2.

Summary of the significant differences in the sample characteristics for each layer of the sMRI dendrogram.

Dendrogram layer	Measure	Statistics¹			Subgroup	Descriptive statistics
Dendrogram layer	Measure	Test statistic	p-value	Effect size	Subgroup	Descriptive statistics
2-cluster solution	CHD category (SV/BV)	5.91	0.02	0.25	A1	17 SV, 29 BV
2-cluster solution	CHD category (SV/BV)				B1	7 SV, 40 BV
	Oral language composite²	7.28	0.01	0.08	A1	103.25 ± 12.75
	Oral language composite²				B1	110.80 ± 13.71
3-cluster solution	D-TGA	8.88	2.89 × 10⁻³	0.44	C1	0 with, 22 without
3-cluster solution					D1	8 with, 16 without
4-cluster solution	CHD category (SV/BV)	5.22	0.02	0.33	E1	33 BV, 3 SV
4-cluster solution	CHD category (SV/BV)				F1	7 BV, 4 SV
	Household income level³	7.10	0.01	0.19	E1	2:2:6:7:4:6
	Household income level³				F1	1:0:0:0:3:7
	HBE gene	6.65	0.01	0.38	E1	28 with, 8 without
					F1	4 with, 7 without
	H-pLI gene	4.56	0.03	0.31	E1	16 with, 20 without
					F1	1 with, 10 without
	Socialization⁴	17.62	1.41 × 10⁻⁴	0.30	E1	104.41 ± 8.81
					F1	90.56 ± 8.76
5-cluster solution	NDD gene	5.79	0.02	0.40	G1	3 with, 10 without
5-cluster solution					H1	0 with, 23 without
	Category fluency⁴	4.64	0.04	0.12	G1	10.08 ± 2.75
	Category fluency⁴				H1	12.35 ± 3.19
	Communication⁴	5.63	0.02	0.04	G1	96.85 ± 13.07
					H1	105.47 ± 6.50

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CHD: congenital heart disease; d-TGA: d-transposition of the great arteries; HBE: high brain expressed gene; H-pLI: constrained gene with a high probability of intolerance to loss; NDD: gene newly identified as a dominant neurodevelopmental disability gene or a single variant in a recessive NDD gene.

Normally distributed and continuous: one-way ANOVA F-statistic, eta-squared effect size, and mean ± standard deviation, non-normally distributed and continuous: Kruskal-Wallis H-statistic, eta-squared effect size, and median [interquartile range], categorical: Pearson chi-squared test statistic, Cramer’s V effect size, and sample size per group, ordinal: Ward’s test statistic, pseudo R² effect size, and sample size per group.

Wechsler Individual Achievement Test’s oral language composite.

1=<$24,999/year, 2=$25,000-$49,999/year, 3=$50,000-$74,999/year, 4=$75,000-$99,999/year, 5=$100,000-$149,999/year, 6=>$150,000.

⁴

Vineland Adaptive Behaviour Scales, Third Edition 4Delis Kaplan Executive Function System.

In the first layer of the dendrogram, the sample is split into Subgroup-A1 and Subgroup-B1, which show differences in CHD category and language abilities. Specifically, a higher proportion of the individuals with SV lesions were classified into Subgroup-A1 compared to Subgroup-B1, and the SV-enriched subgroup performed worse on all three language measures compared to the BV-dominant subgroup. The SV-enriched subgroup was characterized by widespread decreases in cortical thickness, cortical volume, and subcortical volume, with nearly all regions showing significant differences. As an exploratory analysis, we also performed within-lesion comparisons between the leaf clusters; results are presented in the Supplemental Information.

In the second layer of the dendrogram, the SV-enriched Subgroup-A1 was split into Subgroup-D1 and Subgroup-C1, who differed in the proportion of individuals with a d-transposition of the great arteries: all eight of the participants with this lesion belonged to Subgroup-D1. Localized increases in cortical thickness and cortical volume were observed in Subgroup-D1.

In the next layer, the BV-dominant Subgroup-B1 was split into Subgroup-E1 and Subgroup-F1, which differed in CHD diagnosis, the presence of a pLOF variant in a HBE gene, the presence of a pLOF variant in a H-pLI gene, and the socialization domain of adaptive functioning. Subgroup-F1 contained over half the SV individuals, and Subgroup-E1 contained over 80 % of the BV individuals as well as most of the individuals with a pLOF variant in HBE and H-pLI genes (14 of these individuals had co-occurring variants, eight of whom had a single variant that met criteria for both). However, despite having a decreased genetic impact, the subgroup with most SV individuals had a 14-point reduction in the socialization domain of adaptive functioning compared to Subgroup-E1. Subgroup-F1 had widespread decreased cortical thickness in all lobes, and localized increased volume compared to Subgroup-E1.

Finally, in the last layer of the dendrogram presented, the BV-dominant subgroup with more individuals with pLOF variants (Subgroup-E1) was split into Subgroup-G1 and Subgroup-H1, which differed in the presence of a pLOF variant in an gene newly identified as a dominant neurodevelopmental disability gene or a single variant in a recessive NDD gene (detailed in Data acquisitions), in executive functioning and in the communication domain of adaptive functioning. However, the sample size at this layer is small, and no differences in thickness and volume survived multiple comparisons.

3.3. dMRI clustering

Clustering results for the dMRI dataset are presented in Fig. 3, with a summary of significant differences between leaf clusters in the sample characteristics provided in Table 3 (full details provided in Supplemental Table 7).

Table 3.

Summary of the significant differences in the sample characteristics for each layer of the dMRI dendrogram.

Dendrogram layer	Measure	Statistics¹			Subgroup	Descriptive statistics
Dendrogram layer	Measure	Test statistic	p-value	Effect size	Subgroup	Descriptive statistics
2-cluster solution	HBE gene	7.99	4.95 × 10⁻³	0.30	A2	15 with, 19 without
					B2	40 with, 14 without
	Visual motor integration²	4.26	0.04	0.05	A2	82.29 ± 13.42
					B2	89.00 ± 15.66
3-cluster solution	Household income level³	4.25	0.04	0.10	C2	0:1:5:1:9:11
					D2	1:1:4:4:2:3
	HBE and H-pLI gene	4.13	0.04	0.28	C2	9 with, 22 without
					D2	13 with, 10 without
4-cluster solution	Processing speed index⁴	4.82	0.04	0.14	E2	89.31 ± 13.62
					F2	100.67 ± 15.16
	Letter fluency⁵	5.45	0.03	0.16	E2	8.69 ± 2.33
					F2	11.20 ± 3.57
	Category fluency⁵	7.26	0.01	0.20	E2	8.94 ± 2.33
					F2	12.20 ± 4.07
5-cluster solution	Heterotaxy findings	4.11	0.04	0.42	G2	0 with, 15 without
					H2	2 with, 6 without
	Age of 1st admission	4.69	0.03	0.25	G2	2.00 [0.00, 36.00]
					H2	217.00 [45.75, 4458.25]
	Age of 1st open heart surgery	7.09	0.01	0.26	G2	7.50 [3.50, 58.50]
					H2	218.00 [185.50, 4458.25]
	Word reading⁶	12.51	1.95 × 10⁻³	0.37	G2	100.27 ± 14.80
					H2	120.75 ± 9.33
	Sentence comprehension⁶	7.14	0.01	0.25	G2	101.33 ± 14.41
					H2	116.12 ± 8.03
	Math computation⁶	6.54	0.02	0.24	G2	88.73 ± 18.36
					H2	106.12 ± 7.04
	Reading composite⁶	11.60	2.66 × 10⁻³	0.36	G2	100.13 ± 15.27
					H2	119.38 ± 5.73
	Visual motor integration²	4.70	0.04	0.18	G2	84.80 ± 16.96
					H2	99.50 ± 11.99
	Verbal comprehension⁴	4.92	0.04	0.19	G2	103.80 ± 14.71
					H2	118.00 ± 14.46
	Category fluency⁵	4.71	0.04	0.18	G2	9.73 ± 3.61
					H2	13.12 ± 3.48
	Category switching⁵	5.22	0.03	0.20	G2	9.13 ± 3.83
					H2	12.50 ± 2.14
	Number sequencing⁵	5.24	0.02	0.21	G2	9.00 [6.50, 11.00]
					H2	11.50 [11.00, 12.50]
	Letter sequencing⁵	8.26	0.01	0.28	G2	8.80 ± 3.19
					H2	12.25 ± 1.49

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HBE: high brain expressed gene; H-pLI: constrained gene with a high probability of intolerance to loss.

Beery-Buktenica Developmental Test of Visual-Motor Integration.

1=<$24,999/year, 2=$25,000-$49,999/year, 3=$50,000-$74,999/year, 4=$75,000-$99,999/year, 5=$100,000-$149,999/year, 6=>$150,000.

⁴

Wechsler intelligence quotient scales.

⁵

Delis Kaplan Executive Function System.

⁶

Wide Range Achievement Test, Fourth Edition.

In the first layer of the dendrogram, the sample is split into Subgroup-A2 and Subgroup-B2, which differ in the presence of a rare pLOF variant in an HBE gene and visual motor integration. Subgroup-B2 contained a higher proportion of those with an HBE gene and had better visual motor integration compared to Subgroup-A2. Subgroup-B2 had decreased structural connectivity strength in regions throughout the brain, spanning all lobes and subcortical structures.

In the next layer of the dendrogram, the HBE-dominant Subgroup-B2 was split into Subgroup-C2 and -D2 who differed in household income level. Given the trend (p < 0.07) for the proportion of individuals with a rare pLOF variant in HBE and H-pLI genes, we examined individuals with rare variants in both types of genes (a “combined hit”; 22 individuals, 12 of whom had a single variant that met criteria for both). A significant difference was observed (X²(54) = 4.13, p = 0.04), with Subgroup-D2 having a higher proportion of individuals with a rare HBE and H-pLI variant compared to Subgroup-C2. Subgroup-C2 had decreased structural connectivity strength in cortical regions (primarily in the bilateral parietal and frontal lobes) and increased strength in subcortical regions compared to Subgroup-D2.

In the fourth layer of the dendrogram, Subgroup-C2, the group with fewer individuals with combined HBE and H-pLI genetic hits, was split into Subgroup-E2 and -F2, which differed in processing speed index and executive functioning. Subgroup-F2 performed better on these measures compared to Subgroup-E2 and had localized increases in connectivity strength compared to Subgroup-E2.

In the final layer of the dendrogram, the HBE and H-pLI-dominant subgroup (Subgroup-D2) was split into Subgroup-G2 and -H2, which differed in several clinical and behavioural variables. However, results should be interpreted with the small sample size at this stage of the dendrogram in mind.

4. Discussion

Here, we undertook an unsupervised data-driven approach to show that in patients with CHD, specific measures of brain structure can delineate subgroups that have differential associations with cardiac diagnosis and related clinical characteristics, genetics, and ultimately neurodevelopmental outcomes. In general, groups based on cortical thickness and volume are more likely to reflect differences in basic cardiac anatomy and are associated with differences in language performance, whereas groups based on brain structural connectivity are more likely to reflect genomic variation and are mostly associated with differences in visual-motor integration.

Our study uses the first cohort of individuals with CHD for whom extensive genetic and neuroimaging measures had been obtained. While cortical and subcortical morphology and white matter connectivity are known to be influenced by both genetic, environmental, and CHD-specific factors (Bonthrone et al., 2021; Panigrahy et al., 2015; Verrall et al., 2021; Winkler et al., 2010; Yeh et al., 2016), we demonstrate that unsupervised data-driven approaches can identify associations between distinct brain measures and lesion- and genetic-specific influences of CHD on neurodevelopmental outcomes. These observations improve our understanding of the complex and heterogeneous relations between genetics, brain, and behavior in CHD which have not yet been apparent using classic case-control approaches. Our data contributes to the growing body of literature advocating for data-driven approaches to enhance our understanding of differences in brain development and the associated heterogeneity in both the factors contributing to its origins and downstream developmental trajectories of behavior and cognition (Zhang et al., 2020).

When using sMRI measures (thickness and volume), the primary division revealed two subgroups characterized by significantly different proportions of the single-ventricle and biventricular participants, with the subgroup with more single-ventricle individuals being associated with decreased thicknesses and volumes and decreased performance on language measures. The observation confirms the idea that the presence of a single ventricle has profound effect on brain structure which may be independent of a genetic effect and aligns with previous literature reporting widespread decreases in volumes and thickness across the lifespan as well as decreased language abilities in single ventricle cohorts (Brosig et al., 2013; Burns et al., 2021; Claessens et al., 2016; Clouchoux et al., 2013; Hiraiwa et al., 2020; Hövels-Gürich et al., 2008; Mahle et al., 2000; Rajagopalan et al., 2018; Singh et al., 2018; Verrall et al., 2021; Watson et al., 2017). Lower in utero substrate delivery to the brain may contribute, in part, to diminished brain volume and cortical thickness, which can increase the likelihood of neurodevelopmental differences. This is supported by the observation that most of the individuals with d-TGA, who are expected to also have poor substrate and oxygen delivery, were clustered into the subgroup that was single-ventricle enriched.

Decreased cortical thickness and cortical volume were observed in the single-compared to bi-ventricular subgroup throughout the brain. Decreased cerebral volume in individuals with CHD compared to typical development has been reported throughout the lifespan, with some evidence of larger reductions in individuals with single-ventricular and cyanotic CHD (see (Bonthrone et al., 2021) for a review), aligning with our findings. For cortical thickness, the largest effects were observed in bilateral entorhinal cortices (part of the medial temporal lobe), pars orbitales (part of the inferior frontal gyrus), and isthmus of the cingulate gyri, and for cortical volume, some of the largest effects were also observed in similar regions (specifically, the right caudal anterior cingulate gyri and the right entorhinal cortex). Decreases in the volumes and thicknesses of frontal, temporal, and cingulate regions have been reported in severe CHD compared to controls throughout development (Cordina et al., 2014; Ortinau et al., 2012; Singh et al., 2018). The pars orbitalis is a well-established region in the language processing network (Price, 2010). Furthermore, the entorhinal cortices and cingulate gyri are part of the limbic system, which is frequently implicated memory (Rolls, 2015), a function that has classically been viewed as distinct from language. However, recent advances in our understanding of cross-function relationships have proposed a language-and-memory network (Banjac et al., 2021b, 2021a; Roger et al., 2022; 2020). This network is thought to be responsible for the intersection of language and declarative memory and includes the entorhinal cortex and isthmus of the cingulate gyri (Roger et al., 2022). Together, this suggests that alterations in the cortical thickness and volume of these brain regions may be the underlying mechanism accompanying differences in language ability. This is supported by evidence in the existing literature, which has shown associations between whole brain and temporal lobe volumes and language ability in CHD (Jakab et al., 2019; Meuwly et al., 2019).

Further division of the biventricular-dominant group revealed two subgroups that contained statistically different proportions of individuals with rare pLOF variants in addition to lesion type. In this higher performing group with predominantly more benign cardiac lesions, the influence of genomic variation starts to become apparent. However, even within this group, those with single ventricles had fewer rare genetic variants and decreased performance, highlighting, again, the impact of single ventricle physiology in the fetal, perinatal, and peri- and postoperative periods on brain development (Clouchoux et al., 2013; Kelly et al., 2017; Newburger et al., 2012; Sun et al., 2015; Zeng et al., 2015).

We chose to investigate structural connectivity separately from brain volume and cortical thickness given their distinct embryological origin, developmental timing, and effects on development. In the first division of the structural connectivity analysis, we identified two subgroups that differed in the proportion of those with rare pLOF variants in HBE genes. The subgroup with more genetic variants was associated with decreased white matter connectivity strength compared to the group with fewer such variants. Despite both groups scoring below average in visual-motor integration, as expected from previous studies (Bellinger and Newburger, 2016; Schaefer et al., 2013; Teixeira et al., 2021), the subgroup with more genetic variants performed better on visuomotor integration. This is the first report of structural connectivity differences between subgroups with differing proportions of pLOF variants. While the primary study examined measures of white matter integrity, finding differences between those with and without a variants in an NDD risk gene, white matter connectivity was not examined (Morton et al., 2022). Some of the largest effect sizes of connectivity strength differences between these two subgroups were observed in the bilateral precentral gyri, right inferior parietal lobule, left lateral occipital cortex and bilateral insulae, amongst others. The primary motor cortex is located within the precentral gyrus (Banker and Tadi, 2021), while the lateral occipital cortex is associated with both the dorsal and ventral visual streams (Goodale and Milner, 2018, 1992), and the parietal lobules and insulae have been shown to be important visuomotor integration (Andersen, 1987; Cauda et al., 2011; Sepulcre, 2014). Together, our results suggest a link between pLOF variants in HBE genes in individuals with CHD with white matter connectivity strength in visuomotor-related regions, which has functional implications. In the subsequent division, the subgroups again separate based on proportion of individuals with pLOF variants, further underscoring that white matter connectivity measures track with genetic influences in CHD.

This study takes a step towards personalized approaches to the care of children with CHD and potential neurodevelopmental concerns. Our current ability to explain the variability of neurodevelopmental conditions in CHD is limited (Gaynor et al., 2007; Goldberg et al., 2019; Marino et al., 2012). This has meant that we are currently unable to confidently determine who is at risk of developing poor outcomes, challenging our ability to deliver anticipatory guidance and secondary prevention interventions before challenges emerge. This calls for a precision medicine approach that takes into consideration each patient’s neurobiology alongside their clinical and genetic factors. As a first step, we identified subgroups of individuals with CHD with specific patterns of brain structure that are associated with less optimal neurodevelopmental outcomes. Although this is not ready for clinical use at this point, as precision medicine advances, subgroups based on brain and other biological markers which predict specific developmental trajectories, will be identified to facilitate the early detection of patients with a high risk of poor neurodevelopmental outcomes. Clinicians then will develop anticipatory guidelines and proactively intervene to optimize outcomes, ultimately improving the quality of life of those affected.

We acknowledge the sample sizes required for reproducible results in neuroimaging studies and the necessity to replicate data-driven approaches in independent samples to enable the clinical translation of findings (Fröhlich et al., 2018; Klapwijk et al., 2021; Marek et al., 2022), and thus the sample size and lack of a replication cohort is a limitation to our study. The sample size increases the risk of type II errors and has also restricted our diffusion features to connectivity strength rather than the raw connectivity values, as otherwise, the number of features would exceed the number of samples, which can lead to less meaningful and interpretable subgroups (Assent, 2012). We also took an exploratory approach to this study given its novelty and our limited sample size, and thus did not correct for multiple comparisons across the behavioural measures, to avoid missing important signal to be explored in future studies. However, we are the first to use such approaches with extensive neuroimaging and genetic measures collected on a cohort of individuals with congenital heart disease and as such no replication cohort is available yet. Our work highlights the importance of understanding both genomic influences and heart anatomy and related factors to influence neurodevelopmental outcomes in individuals with CHD, and advocates for future studies to collect neuroimaging, genetic, clinical, and behavioural measures to allow for further delineation of such influences in large samples and the ability to independently replicate.

This study had several other limitations. We have used cross-sectional data, which only captures neurobiology and cognition at a specific point in time. Future studies should take a longitudinal approach, tracking developmental trajectories of brain structure and connectivity to better understand their impact on neurodevelopmental outcomes in CHD; in particular, the stability of data-driven subgroups across development should be investigated. Other limitations include only studying rare pLOF variants in four gene categories and the exclusion of participants with genetic variants known to be associated with neurodevelopmental delay or impairment. We also did not evaluate for subdural hemorrhage and other known confounding morbidities that affect neurodevelopmental outcomes such as history of prematurity, access to care, race, ethnicity, and the cumulative burden of ionizing radiation from medical imaging procedures (Pasqual et al., 2021). We did consider whether household income and maternal education level as a proxy for social determinants of health contributes to the subgroupings; however, in our dataset we see its contributions further in the dendrogram suggesting that much larger sample sizes may be required to understand the impact of social determinants of health. We also used the FreeSurfer gray matter cortical and subcortical parcellation for diffusion tractography, which may not be the most suitable due to the unreliability of tensor fitting near gray matter regions; however, using the same parcellation for the sMRI and dMRI clustering procedure ensured we were able to make cross-modality comparisons. Of note, we have only used measures of neurobiology to derive the subgroups; while neurobiology acts as an intermediary between the diverse neurodevelopmental pathways modulated by clinical, genetic, and environmental factors and the variability of outcomes in CHD, future work could also use these factors to inform the subgroups.

In conclusion, we used a data-driven approach to investigate the relation between patterns of brain structure/connectivity, rare genomic variation and types of cardiac lesions and behaviour and cognition in individuals with CHD. We identified that whereas measures of cortical thickness and cortical and subcortical volumes mostly relate to cardiac lesion anatomical type and subsequent language outcomes, white matter connectivity strength was more associated with the presence or absence of rare loss-of-function genetic variants and subsequent visuo-motor integration differences. Such insights are foundational to our understanding of distinct impacts of cardiac anatomy versus genomic variation on brain structure and associated neurodevelopmental effects.

Supplementary Material

Supplementary materials

NIHMS2145483-supplement-Supplementary_materials.docx^{(351.7KB, docx)}

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.neuroimage.2024.120721.

Acknowledgments

We would like to thank all participants and their families for their involvement in this study. Funding has been provided by the National Centers for Research Resources (U01-HL098153) and Advancing Translational Sciences (UL1TR000003, 1TR002541) the National Institutes of Health (U01-HL098188, U01-HL098147, U01-HL098153, U01-HL098163, U01-HL098123, U01-HL098162, and U01-HL131003), the NIH Centers for Mendelian Genomics (5U54HG006504), and the Howard Hughes Medical Institute.

Declaration of competing interest

A. Kushki received grants from the National Science and Engineering Research Council during the conduct of the study and holds a patent for Anxiety Meter with royalties paid from Awake Labs. E. King reported receiving grants from the National Institutes of Health (NIH) during the conduct of the study. Dr Goldmuntz reported receiving grants from the NIH during the conduct of the study. T. A. Miller reported receiving grants from the NIH during the conduct of the study. N. H. Thomas reported receiving grants from the NIH, including the Clinical and Translational Science Awards Program, during the conduct of the study. J. F. Cnota reported receiving grants from the NIH during the conduct of the study and from Additional Ventures outside the submitted work. C. S. Goldberg reported receiving grants from the NIH related to work for the Pediatric Heart Network during the conduct of the study. P. McQuillen reported receiving grants from the NIH during the conduct of the study. G. A. Porter reported receiving grants from the University of Rochester Medical Center. A. E. Roberts reported receiving grants from the NIH during the conduct of the study. C. E. Seidman serves as a consultant for Maze Therapeutics and on the board of directors for Merck and the Burroughs Wellcome Fund; these companies had no role in the design, execution, or analyses of this study. J. W. Newburger has served as a consultant to and received grant funds from Pfizer: received honoraria from Daiichi-Sankyo for membership on a trial Steering Committee; and chaired Independent Clinical Events Committees for Bristol Myer Squibb, Pfizer, and Novartis. E. Anagnostou has received grants from Roche and Anavex, served as a consultant to Roche, Quadrant Therapeutics, Ono, and Impel Pharmaceuticals, has received in-kind support from AMO Pharma and CRA-Simons Foundation, holds a patent for the device, “Anxiety Meter”, has received royalties from APPI and Springer, and has received an editorial honorarium from Wiley. The remaining authors have reported no potential conflicts of interest.

Data availability statement

Data from the Pediatric Cardiac Genomics Consortium Brain and Genes study will be available at the time of publication as per NIH policy.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary materials

NIHMS2145483-supplement-Supplementary_materials.docx^{(351.7KB, docx)}

Data Availability Statement

Data from the Pediatric Cardiac Genomics Consortium Brain and Genes study will be available at the time of publication as per NIH policy.

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PERMALINK

Identifying novel data-driven subgroups in congenital heart disease using multi-modal measures of brain structure

Marlee M Vandewouw

Ami Norris-Brilliant

Anum Rahman

Stephania Assimopoulos

Sarah U Morton

Azadeh Kushki

Sean Cunningham

Eileen King

Elizabeth Goldmuntz

Thomas A Miller

Nina H Thomas

Heather R Adams

John Cleveland

James F Cnota

P Ellen Grant

Caren S Goldberg

Hao Huang

Jennifer S Li

Patrick McQuillen

George A Porter

Amy E Roberts

Mark W Russell

Christine E Seidman

Madalina E Tivarus

Wendy K Chung

Donald J Hagler

Jane W Newburger

Ashok Panigrahy

Jason P Lerch

Bruce D Gelb

Evdokia Anagnostou

Abstract

1. Introduction

2. Materials and methods

2.1. Participants

2.2. Data acquisition

2.3. Preprocessing

2.4. Clustering

2.5. Statistics

3. Results

3.1. Participant demographics

Fig. 1.

Table 1.

3.2. sMRI clustering

Fig. 2.

Table 2.

3.3. dMRI clustering

Fig. 3.

Table 3.

4. Discussion

Supplementary Material

Acknowledgments

Declaration of competing interest

Data availability statement

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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