Identifying differential brain structures and genetic mechanisms between Alzheimer's disease and idiopathic normal pressure hydrocephalus

Abstract

Background

Idiopathic normal pressure hydrocephalus (INPH) is a reversible neurological disorder presenting with cognitive decline, urinary incontinence, and gait disturbance, yet it is often misdiagnosed as Alzheimer's disease (AD) due to overlapping features. Magnetic resonance imaging (MRI) highlights structural differences, but their causal links to disease manifestations remain unclear.

Objective

To investigate the causal relationships between brain structures and INPH/AD through Mendelian randomization (MR) and to explore genetic mechanisms underlying structural variations.

Methods

We analyzed 83 brain phenotypes from the UK Biobank and INPH/AD data from the FinnGen cohort using bidirectional MR. Differentially expressed genes (DEGs) in MR-identified brain regions were obtained from the Allen Human Brain Atlas and examined via bioinformatics analyses.

Results

MR revealed 12 brain structures associated with INPH and 5 with AD, identifying 13 distinct regions differentiating the two disorders across temporal, frontal, occipital, and parietal lobes, as well as the basal ganglia and limbic system. Genetic analyses identified 205 DEGs linked to these regions, enriched in pathways regulating neurodevelopment, neuronal differentiation, and synaptic plasticity. Notably, the neuroactive ligand–receptor interaction pathway was significantly implicated, suggesting a mechanism contributing to cerebrospinal fluid circulation abnormalities in INPH.

Conclusions

This study integrates MR and bioinformatics to reveal structural and genetic factors distinguishing INPH from AD. These findings provide new insights into the pathogenesis of INPH, improve diagnostic precision, and may inform targeted therapeutic strategies.

Keywords

Alzheimer's disease bioinformatics brain structure genetic mechanisms idiopathic normal pressure hydrocephalus Mendelian randomization

Introduction

Idiopathic normal pressure hydrocephalus (INPH) is an invertible neurological condition, although its exact pathogenesis remains incompletely understood. Its hallmark clinical features include the classic triad of cognitive decline, urinary incontinence, and gait disturbance.¹ Cerebrospinal fluid (CSF) shunting is the preferred clinical therapy.² Alzheimer's disease (AD), the leading cause of dementia among the elderly, also manifests with cognitive decline and gait abnormalities.^3,4 Moreover, ventriculomegaly is frequently observed in AD.⁵ Consequently, INPH is often misdiagnosed as AD, making it critical to distinguish between these two conditions for optimal preoperative decision-making and improved prognosis.

In light of the overlapping clinical presentations between INPH and AD, particularly with respect to cognitive decline and gait disturbances, AD was selected as the control condition in this study. This design choice reflects a clinically relevant scenario in which INPH is frequently misdiagnosed as AD, often resulting in delayed or inappropriate treatment. By using AD as a comparative reference, the study aims to identify structural and genetic features uniquely associated with INPH, thereby enhancing the specificity and utility of potential biomarkers for differential diagnosis and therapeutic stratification.

Magnetic resonance imaging (MRI) is an essential technique for visualizing brain structure and function. Recent MRI-based studies have thoroughly investigated the connection between brain structure and both INPH and AD, highlighting important neurobiological underpinnings. Evidence suggests that patients with INPH exhibit notable dilation of the aqueduct of Sylvius, a reduced angle of the aqueduct, and a diminished sagittal cross-sectional area of the brainstem.⁶ In contrast, AD patients typically present with lower hippocampal and amygdala volumes.⁷ Despite substantial evidence indicating structural distinctions between INPH and AD, the causal links between these alterations and the clinical manifestations of the two diseases have yet to be fully clarified. Furthermore, identifying differential brain structures between INPH and AD may provide valuable insights for improving the differential diagnosis of these conditions.

Mendelian randomization (MR) is a genetic epidemiology technique widely employed to assess the causal impact of exposures on outcomes.⁸ It minimizes the impact of confounding factors and mitigates the risk of reverse causality inherent in observational studies.⁹ This study aims to employ MR to investigate the causal relationship between specific brain structures and the development of INPH and AD, thereby identifying differential brain structures between the two diseases. Furthermore, the study will identify differentially expressed genes (DEGs) linked to these structural changes and conduct bioinformatics analyses to elucidate possible pathophysiological processes. Overall, this study fills a critical research gap by exploring potential mechanisms through genetic analysis, thereby advancing the neurobiological understanding of INPH. This extensive analysis aims to enrich our knowledge of the biological and genetic foundations of INPH and may contribute to the advancement of more effective diagnostic and therapeutic strategies.

Methods

Study design

Our research utilized the MR method to examine the causal associations between 83 MRI phenotypes and both INPH and AD. This analysis identified brain structures significantly associated with INPH and AD, allowing for the identification of differential brain structures between the two diseases. Subsequently, the Allen Human Brain Atlas¹⁰ was utilized to determine the DEGs within these differential brain structures. These genes were then subjected to a series of bioinformatics analyses to further explore the neuropathophysiological mechanisms underlying INPH (Figure 1).

Figure 1.

Flow chart of Mendelian randomization study design.

Data source

Genome-wide association study (GWAS) data encompassing 83 brain morphometric phenotypes were obtained from the UK Biobank (UKB) cohort, which included 36,778 participants of European descent (54% female).¹¹ The neuroimaging dataset comprised bilateral measurements of 33 cortical regions, 8 subcortical structures per hemisphere, and brainstem volumetry. This integrative analytical approach facilitates a systematic investigation of the genetic architecture underlying gray matter volume variations across both cortical and subcortical domains by incorporating multimodal MRI-genetics data. The methodology establishes a quantitative framework for elucidating neuroanatomical heritability patterns through the synergistic integration of structural neuroimaging and polygenic risk profiling.

The GWAS summary datasets for INPH and AD were sourced from the FinnGen study of European populations, a global research initiative that integrates genomic information with digital health data, approved by the FinnGen Ethics Committee.¹² The 12th round of FinnGen research includes 1680 INPH cases and 497,451 controls, as well as 13,964 AD cases and 486,384 controls. The ICD-10 code for INPH is G91.2, and for AD, it is G30. Both datasets were publicly retrieved from the FinnGen GWAS summary database (https://r12.finngen.fi/). Detailed information on these datasets is provided in Table 1.

Table 1.

Details of GWAS summary data. GWAS: genome-wide association study; MRI: Magnetic Resonance Imaging.

GWAS	Author	Database	Sample size	Population	Year	Sexes
83 MRI phenotypes	Stuart et al.	GWAS catalogue	8428	European	2023	males and females
INPH	Kurki et al.	Finngen Study (R12)	1680	European	2025	males and females
AD	Kurki et al.	Finngen Study (R12)	13964	European	2025	males and females

Selection of instrumental variables (IVs)

The limited number of single nucleotide polymorphisms (SNPs) achieving genome-wide significance (p < 5 × 10⁻⁸) for MRI phenotypes posed challenges for downstream analyses. Therefore, in the forward MR analysis between MRI and INPH/AD, a more relaxed threshold of p < 5 × 10⁻⁶ was adopted for the selection of IVs.¹³ For the reverse MR analysis between INPH, AD, and MRI, the conventional threshold of p < 5 × 10⁻⁸ was applied to obtain IVs for subsequent analysis. To guarantee the independence of these IVs, PLINK was used to eliminate linkage disequilibrium (LD), with parameters set as LD = 10,000 kb and R² < 0.001.¹⁴ Additionally, F-statistics were calculated, and SNPs with F-statistics < 10 were excluded. An F-statistic > 10 indicates that all SNPs included in the subsequent analysis are strong instrumental variables.¹⁵

Bidirectional Mendelian randomization analysis

Bidirectional MR analysis was performed to investigate the causal associations between brain structure and INPH as well as AD. Five MR techniques were utilized to assess the results, including simple mode, inverse-variance weighted (IVW), weighted mode, weighted median, and MR-Egger.¹⁶ Consistency in the direction of effect across these methods indicates robust findings, with IVW serving as the main approach for causal inference. To account for multiple testing, the false discovery rate (FDR) was employed to adjust p-values,¹⁷ and exposures with an FDR < 0.05 were considered statistically significant.

Several sensitivity analyses were conducted to ensure the robustness of the MR findings. First, Rucker's Q and Cochran's Q statistics were computed to assess heterogeneity in the MR-Egger and IVW methods, respectively. A p-value > 0.05 suggests the absence of heterogeneity. Second, horizontal pleiotropy was assessed by calculating the intercept of MR-Egger regression, where a p-value < 0.05 indicating the presence of horizontal pleiotropy. Third, MR-PRESSO was used to identify outliers, and any detected outlier SNPs were excluded before reanalyzing the data to ensure the validity of the results.

Explore genetic associations within the measured differences in brain structures

AD was used as the control group and INPH as the experimental group to determine brain structures that differ between the two conditions. Subsequently, the Allen Human Brain Atlas, which integrates gene expression profiles with human brain structural and functional data, was employed to investigate the genetic mechanisms driving these structural differences related to INPH. The “differential search” function in AHBA was employed to targeted brain regions against the whole brain, aiming to determine DEGs. Genes meeting the criteria of absolute log fold change > 2 and p-value < 0.05 were defined as statistically significant DEGs. Enrichment analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, disease ontology (DO), and gene ontology (GO) analyses, were subsequently conducted on these DEGs,^18,19 followed by FDR correction, where a corrected p-value < 0.05 was as statistically significant. The DEGs were subsequently uploaded to the STRING database (https://cn.string-db.org/) to generate a protein-protein interaction (PPI) network.²⁰ These comprehensive bioinformatics analyses provide valuable insights into the potential genetic associations involved in the pathogenesis of INPH. All analyses were performed using R version 4.4.3. Major software packages include TwoSampleMR (v0.5.6), MR-PRESSO, etc.

Results

Information on IVs

Based on GWAS data comprising 83 MRI phenotypes, we systematically identified 7272 SNPs that met the criteria for IV strength. The strength of these IVs was assessed utilizing F-statistics, with a range of 19.511 to 500.544, significantly exceeding the weak IV threshold (F > 10), thereby confirming that all selected SNPs possessed sufficient statistical power. Across phenotypes, the number of associated SNPs varied substantially, spanning from 34 to 173, with a mean value close to 87 (Supplemental Table 1).

The causal relationship between brain structure and INPH and AD

After performing MR analysis and applying FDR correction, we identified 12 brain structures that exhibited significant positive correlations with INPH (FDR < 0.05 using the IVW method). These structures include the left caudal middle frontal volume, left pericalcarine volume, right rostral anterior cingulate volume, and left caudate volume, among others, primarily localized in the frontal and occipital lobes, limbic system, and basal ganglia. Conversely, reverse MR analysis between INPH and 83 brain structures revealed no significant causal relationships.

Similarly, in the MR analysis of AD data, we identified 5 brain structures that exhibited significant positive correlations, including the left caudal middle frontal volume, left precuneus volume, and right inferior temporal volume, among others, primarily localized in the temporal, frontal, and parietal lobes. Conversely, reverse MR analysis between AD and 83 brain structures revealed no significant causal relationships. Figure 2 presents the positive results from these two MR analyses, while the outcomes of the sensitivity analyses are detailed in Supplemental Table 2.

Figure 2.

Forest plot of Mendelian randomization results integrating 83 MRI phenotypic and (A) INPH, (B) AD stratified by brain structure. SNP: single nucleotide polymorphism; FDR: false discovery rate; OR: odds ratios; CI: confidence interval.

We designated AD as the control group and INPH as the experimental group, identifying 13 distinct brain structures with significant differences. These structures include the left pars orbitalis volume, left pericalcarine volume, left rostral middle frontal volume, left rostral anterior cingulate volume, left pars opercularis volume, left caudate volume, left lateral orbitofrontal volume, right lateral orbitofrontal volume, right lingual volume, left caudal middle frontal volume, left precuneus volume, right rostral anterior cingulate volume, right pars opercularis volume, and right inferior temporal volume.

Differential expression gene in identified differential brain structures

After identifying the differentially expressed brain structural regions, we retrieved DEGs from the Allen Human Brain Atlas database. The top 2000 genes ranked by statistical significance were extracted. Following the elimination of duplicate records and genes lacking chromosome information, 963 genes were retained, as summarized in Supplemental Table 3. Applying the criteria of p-value < 0.05 and log fold change > 2, a total of 205 genes were subsequently selected for further analysis (Figure 3A). The outcomes of the gene enrichment analyses are illustrated in Figures 3B–D. In the DO enrichment analysis, these genes were strongly linked to cognitive disorders, mood disorders, cerebral infarction, and AD, suggesting their involvement in psychiatric and behavioral abnormalities as well as neurodegenerative processes. GO enrichment analysis of biological processes emphasized “axonogenesis” and “forebrain development,” suggesting that altered gene expression may predominantly impact ventricular architecture and cortical regions. This disruption may interfere with cerebrospinal fluid circulation by influencing neuronal differentiation and synaptic plasticity, ultimately contributing to the pathogenesis of INPH. Notably, KEGG pathway enrichment analysis demonstrated the most significant enrichment in the “Neuroactive ligand–receptor interaction” pathway, with approximately twice the number of annotated genes compared to the next most enriched pathway. This result strongly aligns with the findings from the GO analysis, thereby enhancing the robustness of our conclusions through consistent validation across complementary analytical approaches.

Figure 3.

(A) Volcano plot of differentially expressed genes. (B) Disease ontology enrichment analysis. (C) Kyoto encyclopedia of genes and genomes pathway analysis. (D) Gene ontology enrichment analysis. BP: biological process; CC: cellular component; MF: molecular function.

We constructed a PPI network (Figure 4) using the STRING database. In this network, proteins are depicted as nodes, and their functional and physical interactions are represented by the edges connecting them. Different types of protein interactions are represented by the color of the connecting lines: blue lines to gene co-occurrence, green lines correspond to gene neighborhood, and pink lines to gene fusion. The network consists of 189 nodes connected by 339 edges, yielding an average node degree of 3.59. Central proteins, defined as nodes with the highest degree of connectivity within the network, are crucial for preserving the stability and connectivity of the network. These key proteins, includingTBR1, FOXG1, BCL11B, SATB2, FEZF2, DLX1, DLX2, LHX6, LHX2, and EMX2, form an interactive network with other proteins, highlighting their crucial roles in modulating downstream pathways.

Figure 4.

(A) Protein-protein interaction network analysis derived from the STRING database. (B) The top 10 hub genes ranked by cytoHubba. Different edge colors represent varying levels of evidence for protein connections.

Discussion

Our study was conducted in three distinct phases. In the first phase, a bidirectional MR analysis was performed to investigate the potential causal relationships between INPH, AD, and brain structures. During the second phase of the analysis, brain regions exhibiting structural differences were first identified. Subsequently, the spatial expression patterns of DEGs within these regions were mapped using data from the Allen Human Brain Atlas. In the third phase, a comprehensive suite of bioinformatics analyses was conducted on the DEGs, including DO analysis, GO enrichment analysis, KEGG pathway analysis, and PPI network construction. These sequential analyses led to the identification of 13 brain structures with significant differences between INPH and AD.

DEGs, including DO analysis, GO analysis, KEGG pathway enrichment analysis, and the construction of a PPI network. Through these sequential steps, we identified 13 brain structures exhibiting significant differences between INPH and AD. These structures are primarily distributed across the parietal, temporal, occipital, and frontal lobes, as well as the limbic system and basal ganglia. Subsequent genetic analyses revealed that DEGs may primarily affect ventricular structures and the cerebral cortex, potentially disrupting cerebrospinal fluid circulation pathways by influencing neuronal differentiation and modulating synaptic plasticity, ultimately contributing to the development of INPH. Furthermore, neuroactive ligand–receptor interaction pathways may shed new light on the underlying mechanisms of INPH, potentially revealing novel therapeutic targets.

As the largest component of the cerebral cortex, the frontal lobe is located at the front of the brain and comprises about one-third of its total volume. It plays a pivotal role in higher cognitive functions, social behavior, and emotional regulation. Structurally, it is divided into the orbitofrontal cortex, medial prefrontal cortex, and dorsolateral prefrontal cortex. Damage to the frontal lobe can lead to profound personality changes and impaired social functioning. There is a strong association between frontal lobe dysfunction and INPH, as the hallmark symptoms of INPH, such as cognitive impairment and gait disturbances, are closely linked to impaired frontal lobe function.^21,22 In INPH patients, anterior horn enlargement of the lateral ventricles directly compresses the frontal cortex, notably affecting the prefrontal and premotor areas.²³ Disruption of the frontal lobe–basal ganglia circuit contributes to gait disturbances and executive dysfunction. Cognitive impairment in INPH is predominantly characterized by frontal lobe dementia, which presents as impaired executive function with relatively preserved memory.²⁴ Moreover, the apathy frequently observed in INPH patients has been linked to impairments in frontal lobe function.²⁵ Characteristic MRI/CT findings include anterior horn enlargement of the lateral ventricles (Evans index > 0.3) and periventricular low-density white matter near the frontal horns, suggestive of cerebrospinal fluid accumulation and compressive effects.²⁶

The parietal lobe plays a central role in the pathological progression of INPH, where structural damage (such as disruption of white matter fibers and gray matter atrophy) and functional abnormalities (including reduced metabolism and dysregulation of neural networks) are closely associated with its hallmark symptoms: gait disturbances,²⁶ cognitive decline,²⁷ and urinary dysfunction.²⁸ These findings provide a theoretical basis for developing targeted interventions aimed at restoring parietal lobe function. The medial temporal lobe, which includes the hippocampus, amygdala, and surrounding cortex, is also susceptible to the effects of ventricular enlargement in INPH. As the ventricles enlarge, they may exert pressure on the hippocampus and amygdala, which are located near the inferior horn of the lateral ventricle. Hippocampal atrophy may exacerbate memory impairment,²⁹ while injury to temporal lobe white matter fibers may impair interregional communication with key structures such as the frontal lobe and limbic system.^30,31 Dysfunction of the amygdala may contribute to apathy, anxiety, and mood fluctuations. Studies have demonstrated that CSF shunt surgery can reduce ventricular size, potentially alleviating pressure on the temporal lobe and improving memory and language function.^32,33

In patients with INPH, MRI frequently reveals dilation of the posterior horns of the lateral ventricles and compression of the occipital lobes.³⁴ CSF shunt surgery can effectively reduce ventricular size and alleviate occipital lobe compression, thereby improving associated symptoms.³⁵ The limbic system, a critical network regulating emotions, memory, motivation, and autonomic functions, includes structures such as the hippocampus, amygdala, cingulate gyrus, fornix, and mammillary bodies. Ventricular enlargement in INPH often leads to hippocampal atrophy, contributing to cognitive impairment,²⁹ while dysfunction of the amygdala and cingulate gyrus may result in emotional blunting.^23,36,37 Moreover, previous studies have observed a reduction in caudate nucleus volume in INPH patients.³⁸ The caudate nucleus plays a significant role in modulating cognitive and emotional changes associated with INPH,³⁹ and patients with INPH often exhibit marked local hypometabolism in the bilateral caudate nuclei.⁴⁰ Notably, hypometabolism in the caudate nucleus has emerged as a promising biomarker for diagnosing INPH.⁴¹

Changes in brain structure may impact the development of INPH by altering the function and expression of relevant proteins. Our findings suggest that these structural changes are associated with specific genes, which may contribute to the development of INPH and the manifestation of associated symptoms by regulating key proteins involved in brain development, synaptic regulation, and signal transduction pathways. These proteins are enriched in essential biological pathways, including brain development, neuronal differentiation, synaptic plasticity, and G protein-coupled receptor signaling, all of which are closely linked to the pathogenesis of INPH. Furthermore, our findings suggest that INPH-related gene products are involved in diverse biological pathways, where their interactions contribute to a sophisticated and interconnected regulatory network. Altered regulation of these pathways may cause neurodevelopmental defects, potentially leading to the development of INPH. For example, disturbances in synaptic plasticity may disrupt neuronal network function through multiple mechanisms, including alterations in CSF dynamics, neuroinflammation, and metabolic imbalances, ultimately contributing to cognitive impairment and motor symptoms in INPH patients.^42,43 Similarly, dysregulation of the G protein-coupled receptor signaling pathway may modulate INPH progression by affecting CSF dynamics, neuronal excitability, and neuroinflammatory responses.^44,45 Key genes identified within the PPI network, including TBR1, FOXG1, BCL11B, SATB2, FEZF2, DLX1, DLX2, LHX6, LHX2, and EMX2, have been recognized as significant contributors to these regulatory processes.

TBR1, FOXG1, BCL11B, SATB2, FEZF2, DLX1, DLX2, LHX6, LHX2, and EMX2 are all critical transcription factors involved in cerebral cortex development and neurodevelopmental processes. Previous studies have indicated that TBR1 may be associated with cognitive impairment⁴⁶ and ischemic brain injury.⁴⁷ FOXG1 is essential for brain structure and function, serving as a master regulatory gene for forebrain development by directing the differentiation of the telencephalon into the cerebral cortex, basal ganglia, and olfactory bulb.⁴⁸ Deficiency in LHX6 leads to a reduction in GABAergic interneurons, potentially disrupting the function of the cortical-basal ganglia circuit and indirectly impairing gait regulation. Additionally, LHX2, SATB2, FEZF2, and EMX2 are primarily associated with various structural abnormalities in the brain, such as cortical layering defects, axon guidance errors, cortical thinning, and hippocampal hypoplasia. These structural abnormalities may alter ventricular morphology and CSF dynamics, contributing to the pathogenesis and progression of INPH.

Previous studies investigating the relationship between brain structure and INPH have been primarily observational. Through MR analyses, we have identified causal relationships between specific brain structures and INPH. Furthermore, by comparing INPH with AD, we identified differential brain structures and employed a gene-anchoring approach to infer underlying mechanisms, thereby providing a theoretical foundation for a deeper understanding of INPH. However, this study has several limitations: (1) The genetic imaging-based analytical framework did not account for environmental exposure parameters, potentially overlooking a critical dimension of multifactorial interactions involved in INPH. (2) The research was confined to bioinformatics-level analysis, lacking validation through clinical trials or prospective cohort studies, which limits its translational value in clinical practice. (3) The study sample exhibited significant ethnic bias, being predominantly composed of individuals of European descent. This genetic heterogeneity across populations may influence cerebrospinal fluid dynamics through gene-environment interaction mechanisms, potentially leading to cross-ethnic phenotypic variations.

Conclusion

In conclusion, our finding highlights the critical role of combining genetic information with MRI findings to investigate the structural and functional mechanisms underlying INPH. By identifying critical brain structures and associated genetic pathways, particularly those related to neuroactive ligand-receptor interactions, we provide a theoretical basis for potential novel therapeutic interventions.

Supplemental Material

sj-docx-1-alz-10.1177_13872877251379046 - Supplemental material for Identifying differential brain structures and genetic mechanisms between Alzheimer's disease and idiopathic normal pressure hydrocephalus

Supplemental material, sj-docx-1-alz-10.1177_13872877251379046 for Identifying differential brain structures and genetic mechanisms between Alzheimer's disease and idiopathic normal pressure hydrocephalus by Wencai Wang, Hui Liu, Yinuo Chen, Zijie Xiong, Menghao Liu, Zun Wang, Wei Ye and Xianfeng Li in Journal of Alzheimer's Disease

Supplemental Material

sj-xlsx-2-alz-10.1177_13872877251379046 - Supplemental material for Identifying differential brain structures and genetic mechanisms between Alzheimer's disease and idiopathic normal pressure hydrocephalus

Supplemental material, sj-xlsx-2-alz-10.1177_13872877251379046 for Identifying differential brain structures and genetic mechanisms between Alzheimer's disease and idiopathic normal pressure hydrocephalus by Wencai Wang, Hui Liu, Yinuo Chen, Zijie Xiong, Menghao Liu, Zun Wang, Wei Ye and Xianfeng Li in Journal of Alzheimer's Disease

Supplemental Material

sj-xlsx-3-alz-10.1177_13872877251379046 - Supplemental material for Identifying differential brain structures and genetic mechanisms between Alzheimer's disease and idiopathic normal pressure hydrocephalus

Supplemental material, sj-xlsx-3-alz-10.1177_13872877251379046 for Identifying differential brain structures and genetic mechanisms between Alzheimer's disease and idiopathic normal pressure hydrocephalus by Wencai Wang, Hui Liu, Yinuo Chen, Zijie Xiong, Menghao Liu, Zun Wang, Wei Ye and Xianfeng Li in Journal of Alzheimer's Disease

Footnotes

Acknowledgements

We extend our gratitude to the researchers who generously provided the data utilized in this study. Appreciation is also extended to the UK Biobank (UKB) cohort project, the FinnGen database, and the Allen Human Brain Atlas for their invaluable contributions in making data publicly accessible. Their efforts greatly enhance the scope of scientific inquiry and collaboration, thereby advancing progress in the field.

ORCID iD

Wencai Wang

Ethical considerations

In accordance with local legislative and institutional requirements, this research involving human participants did not necessitate ethical approval.

Consent to participate

Per national legislation and institutional directives, the study was exempt from the requirement to obtain written informed consent from the subjects or their legal guardians/close relatives.

Author contribution(s)

Wencai Wang: Data curation; Methodology.

Hui Liu: Data curation; Software; Visualization.

Yinuo Chen: Investigation; Software; Validation; Visualization; Writing – original draft.

Zijie Xiong: Formal analysis; Investigation; Software.

Menghao Liu: Data curation; Software; Visualization.

Zun Wang: Data curation; Methodology.

Wei Ye: Conceptualization; Data curation; Writing – review & editing.

Xianfeng Li: Conceptualization; Funding acquisition; Project administration; Supervision; Writing – review & editing.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by grants from Neurosurgery First-class Discipline Funding Project of the Second Affiliated Hospital of Harbin Medical University.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The datasets supporting the conclusions of this article are included within the article and its additional files GWAS data for 83 brain morphometric phenotypes from the the UK Biobank (UKB) cohort website (https://www.nealelab.is/uk-biobank). Additionally, GWAS data for INPH and AD were obtained at the Finngen GWAS summary statistics website ().

Supplemental material

Supplemental material for this article is available online.

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