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. 2026 Jan 20;6(1):100321. doi: 10.1016/j.ynirp.2026.100321

Impact of SNAP-25 MnlI polymorphism on brain activity patterns in children with ADHD: Insights from fractional amplitude of low-frequency fluctuation analysis

Diangang Fang a,1, Wenxian Huang b,1, Tong Mo a, Xiaojing Lv a, Guohua Liang a, Binrang Yang b, Hongwu Zeng a,⁎
PMCID: PMC12856436  PMID: 41624630

Abstract

Objective

SNAP-25, a synaptic vesicle docking protein, carries a polymorphism (rs3746544) in its 3′-UTR region that is associated with ADHD, yet its functional mechanism remains unknown. The purpose of this study is to evaluate the impact of synaptosomal-associated protein 25 (SNAP-25) gene MnlI polymorphism (rs3746544) on spontaneous brain activity in children with attention deficit hyperactivity disorder (ADHD), employing the fractional amplitude of low-frequency fluctuation (fALFF) analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data, to explore its potential neurobiological mechanisms and neuroimaging biomarkers.

Methods

This study enrolled 56 boys with ADHD (aged 8–10 years) and 21 age-matched healthy boys as healthy controls (HCs). According to the SNAP-25 MnlI genotype, ADHD patients were divided into two groups: the TT homozygote group (TT group, n = 36) and the G-allele carrier group (TG group, n = 20). Rs-fMRI data were acquired and analyzed using fALFF to measure spontaneous brain activity.

One-sample t-tests were performed to calculate fALFF maps for each group, setting the threshold as a cluster greater than 20 voxels, with P < 0.01 after AlphaSim correction. Two-sample t-tests were performed to calculate the differences in fALFF values among the TT, TG, and HCs groups, with age as a covariate. A cluster of greater than 20 voxels, with P < 0.01 after AlphaSim correction, was considered to have statistically significant differences. Assessed the Working Memory Index (WMI) using the Wechsler Intelligence Scale for Children-IV (WISC-IV) in children with ADHD from the TT and TG groups.

Results

One-sample t-tests revealed that children with ADHD group (both TT and TG group) exhibited significantly lower fALFF values in the default mode network (DMN) and parieto-occipital cortex compared to HCs, while showing increased fALFF located in the posterior cerebellar lobe; Two-sample t-tests demonstrated that: (a) Compared to HCs, the ADHD group (both TT and TG group) showed widespread reductions of fALFF values across multiple brain regions, including the posterior cingulate cortex and precuneus. The TG group showed more pronounced decreases when compared with the TT group. (b) In comparison to the TG group, the TT group exhibited higher fALFF values in higher-order cognitive regions, such as the right superior frontal gyrus and left medial frontal gyrus, but lower fALFF values in the posterior cerebellar lobe and posterior cingulate cortex. The TT group had significantly higher WMI compared to the TG group (t = 2.098, P < 0.05).

Conclusions

The SNAP-25 gene MnlI polymorphism has an impact on spontaneous brain activity in children with ADHD, as measured by fALFF. This study reveals the potential mechanisms from the perspective of brain networks, demonstrates how ADHD genotypes affect neural function, and provides a new approach for clinical decision-making and efficacy monitoring.

Keywords: SNAP-25, ADHD, Resting-state fMRI, fALFF, Brain activity

Highlights

  • •

    Genotype-Specific Differences: Compared to G-allele carriers, TT homozygotes showed enhanced activity in the right superior frontal and left medial frontal gyri, but reduced activity in the posterior cerebellar lobe and posterior cingulate cortex.

  • •

    Cognitive Performance: TT homozygotes had significantly higher Working Memory Index (WMI) scores, indicating superior cognitive performance.

  • •

    Neurobiological Mechanisms: The SNAP-25 MnlI polymorphism may influence cognition by modulating the frontoparietal executive network and the default mode network (DMN).

1. Introduction

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder in childhood, characterized by developmentally inappropriate symptoms of inattention, hyperactivity, and impulsivity. According to a 2023 study published in The Lancet Child & Adolescent Health, the global prevalence of ADHD was approximately 5 % among children and adolescents (Sibley et al., 2023), increasing to 7.6 % in the 3–12-year age group (Salari et al., 2023). Clinically, 30–50 % of patients exhibit significant symptoms that persist into adulthood and even throughout their lifespan (Fayyad et al., 2017). According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5, 2013), ADHD was comprised of three clinical subtypes: 1) Predominantly inattentive presentation (ADHD-I), 2) Predominantly hyperactive/impulsive presentation (ADHD-H), and 3) Combined presentation (ADHD-C). ADHD can cause widespread functional impairments across multiple domains. Its manifestations included learning disabilities, cognitive dysfunction, language development delays, social skill deficits, occupational adaptation difficulties and so on. These impairments deteriorate the life quality of ADHD patients and impose enormous burdens on families and society (Rajaprakash and Leppert, 2022; Shen and Zhou, 2024). Currently, cognitive dysfunction has been identified as a core feature of ADHD, primarily presenting as impaired inhibitory control, working memory deficits, sustained attention impairment, abnormal reward processing, temporal perception dysfunction and so on (Fried et al., 2016, 2019; Lee et al., 2022; Soderqvist et al., 2010). Nevertheless, the precise neurobiological mechanisms underlying these cognitive function deficits remain incompletely understood, warranting further investigation.

ADHD is a neurodevelopmental disorder with complex multifactorial pathogenesis. Current research demonstrates that its etiology involves multiple interacting mechanisms, including polygenic mutations, trace element imbalances, gut microbiota dysbiosis, 5-hydroxytryptamine (5-HT) and catecholaminergic signaling pathways (Aarts et al., 2017; Demontis et al., 2019; Li et al., 2020, 2022). Among these, genetic factors constitute one of the most essential pathogenic components (Demontis et al., 2023). ADHD exhibits high heritability (Demontis et al., 2019; Faraone et al., 2024), ranking among the most inheritable psychiatric disorders in childhood, with genetic risk variants accounting for approximately 60 %–90 % of disease susceptibility (Liu et al., 2017). These genetic influences may mediate ADHD's core symptoms of inattention, hyperactivity, and impulsivity by disrupting neurotransmitter metabolism, synaptic plasticity, and neural circuit development (Faraone et al., 2005). Molecular genetic studies have identified several candidate genes associated with ADHD susceptibility, including: synaptosomal-associated protein 25 (SNAP-25), dopamine receptor D4 protein (DRD4), dopamine receptor D5 protein (DRD5), dopamine transporter (DAT), dopamine beta-hydroxylase (DBH), 5-hydroxytryptamine transporter (5-HTT) and 5-hydroxytryptamine (serotonin) receptor 1B (5HT1B) (Bolat et al., 2022; Faraone et al., 2005; Gizer et al., 2009; Russell, 2011b; Zhong et al., 2024). Notably, the SNAP-25 gene, which encodes a crucial regulator of synaptic vesicle fusion, plays a particularly significant role in the pathogenesis of ADHD (Liu et al., 2017).

SNAP-25 serves as a core structural component of the soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex. Together with syntaxin-1 (Stx1) and vesicle-associated membrane protein (VAMP), it forms the synaptic vesicle docking and fusion complex that mediates neurotransmitter release (Liu et al., 2017). As a critical regulator of synaptic transmission, SNAP-25 participates in nervous system development and function through the following mechanisms: 1) Modulating axonal growth and synaptic plasticity, 2) Precisely regulating synaptic vesicle fusion dynamics, 3) Influencing the release of neurotransmitters including dopamine and glutamate (Catsicas et al., 1991; Liu et al., 2017; Oyler et al., 1989). The SNAP-25 gene, located at chromosomal band 20p11.2, harbors multiple functional single-nucleotide polymorphisms (SNPs), including rs3746544, rs363006, rs1051312, rs8636, rs362549, and rs362998. Notably, the rs3746544 variant (designated as the MnlI polymorphism) located within the 3′-untranslated region (3′-UTR) represents a characteristic SNP associated with ADHD (Barr et al., 2000; Liu et al., 2017). Pathophysiological consequences of genetic variants: molecular evidence confirmed that these genetic variations may: 1) Downregulate SNAP-25 expression levels, 2) Induce structural brain abnormalities, 3) Impair cognitive functioning, thereby conferring increased susceptibility to ADHD (Grasby et al., 2020; Hawi et al., 2013; Thapar et al., 2005). The SNAP-25 gene carries a biallelic T/G polymorphism (rs3746544), which can be genotyped via MnlI restriction enzyme digestion, yielding three possible genotypes: 1) TT homozygotes, 2) TG heterozygotes, 3) GG homozygotes. Up to date, evidences demonstrate significant inter-genotype differences among ADHD patients in: 1) Clinical manifestations, 2) Cognitive performance (particularly executive function and working memory). These phenotypic distinctions may originate from: 1) divergent patterns of neurodevelopmental structural maturation, 2) altered functional connectivity profiles, and 3) modified neurotransmitter release dynamics (Fang et al., 2022; Huang et al., 2023).

Blood Oxygenation Level-Dependent Functional Magnetic Resonance Imaging (BOLD-fMRI) is an advanced neuroimaging technique based on the magnetic susceptibility difference between oxygenated and deoxygenated hemoglobin. By detecting changes in blood oxygen level-dependent signals within cerebral microvasculature, it indirectly reflects neuronal activity (Turner et al., 1991). Because of its excellent temporal resolution (typically within 1 s), this technique has become a vital tool for investigating brain functional activity during both task-based and resting-state conditions, playing a significant role in elucidating the pathogenesis of neurological disorders (Finn et al., 2023). Task-based fMRI employs specific experimental paradigms to monitor dynamic changes in cerebral blood flow and oxygenation levels while subjects perform cognitive tasks, enabling functional localization and connectivity analysis (Yang et al., 2019). Studies have demonstrated significant functional abnormalities in children with ADHD, particularly in the DMN, motor pathways, emotion regulation networks, and reward systems (Hammer et al., 2015; Lee et al., 2022; Wang et al., 2025). However, since task-based fMRI requires subjects to maintain high compliance in performing specific cognitive tasks, its application in ADHD children, who exhibit prominent inattention and hyperactivity, poses substantial challenges, leading to relatively slow progress in related research. On the contrary, rs-fMRI only requires subjects to remain awake and motionless without performing any specific task, making it particularly suitable for investigating brain function in children, especially those with ADHD. Currently, rs-fMRI has been widely applied in studying brain function in children with ADHD and other diseases. Rs-fMRI data analysis methods include: 1) amplitude of low-frequency fluctuation (ALFF), 2) fractional amplitude of low-frequency fluctuation (fALFF), 3) regional homogeneity (ReHo), 4) local functional connectivity density (Local FCD), 5) long-range functional connectivity density (Long-range FCD) and so on. ALFF quantifies spontaneous fluctuations in BOLD signal intensity, reflecting the magnitude of spontaneous brain activity during the resting state (Yang et al., 2007). The fALFF method normalizes ALFF by dividing the low-frequency amplitude (0.01–0.08 Hz) by the total amplitude across all frequency bands. This approach provides a more accurate representation of the relative contribution of low-frequency oscillations while selectively suppressing physiological noise and non-specific signals, demonstrating superior sensitivity and specificity compared to conventional ALFF (Zou et al., 2008). Due to its superior signal-to-noise ratio (SNR) and frequency specificity, fALFF has emerged as a crucial technique in psychiatric research for searching neuroimaging biomarkers. Accumulating evidences indicate that fALFF exhibits characteristic alterations in various disorders, including major depressive disorder (MDD) (Fattahi et al., 2024), autism spectrum disorder (ASD) (Wang et al., 2025), schizophrenia (Fortier et al., 2024), Alzheimer's disease (AD) (Zhang et al., 2024), obsessive-compulsive disorder (OCD) (Yu et al., 2024), and ADHD (Hu et al., 2025). Particularly in ADHD research, fALFF has provided novel insights into the neural mechanisms underlying disease subtypes by revealing abnormal patterns of neural oscillations across different brain regions (Chen et al., 2024; Hu et al., 2025).

Based on accepted knowledge, famous studies, and our previous research foundation, we safely propose the following scientific hypothesis: Children with ADHD carrying different SNAP-25 MnlI genetic polymorphisms exhibit significant differences in spontaneous neural activity patterns, and these neurofunctional differences may constitute an essential neurobiological basis for cognitive dysfunction. As a reliable quantitative indicator for characterizing spontaneous brain activity, fALFF could provide direct evidence for elucidating this neural mechanism and shows promise as an objective neuroimaging biomarker for ADHD subtyping.

To further elucidate the neurobiological mechanisms of how the SNAP-25 MnlI polymorphism influences ADHD, we conducted a compound study on ADHD with Healthy Controls (HCs), including genetic polymorphism, cognitive function, and rs-fMRI. This study first classified children with ADHD into two molecular subtypes based on their SNAP-25 gene MnlI polymorphism genotypes and evaluated their cognitive function using the Wechsler Intelligence Scale for Children-IV (WISC-IV). Subsequently, fALFF analysis was applied to rs-fMRI data. Finally, statistical analyses were performed to identify group differences and characterize neural activity patterns. HCs were included to quantitatively assess how the SNAP-25 MnlI polymorphism modulates brain functional activity in children with ADHD. This research approach established critical theoretical foundations for the precise subtyping of ADHD and the development of personalized therapeutic interventions.

2. Materials and methods

2.1. Study participants

A total of 66 boys diagnosed with ADHD at Shenzhen Children's Hospital between July 2013 and July 2017 were recruited for this study. The inclusion criteria for participants were as follows: 1) aged 8–10 years; 2) an intelligence quotient (IQ) exceeding 70, as assessed by the WISC-IV; 3) normal hearing and vision; 4) Han Chinese ethnicity; and 5) right-handed. Each participant underwent evaluation using the Schedule for Affective Disorder and Schizophrenia for School-Aged Children Present and Lifetime Version (K-SADS-PL) (Kaufman et al., 1997). Exclusion criteria included a history of mental retardation, tic disorder, learning disabilities, conduct disorder, or any other medical conditions, as well as an inability to cooperate during MRI examinations or contraindications to MRI. Children receiving medication, such as stimulants (e.g., methylphenidate), or undergoing treatment by psychiatrists or psychologists were also excluded. Additionally, the study included 21 HCs boys, all of whom were right-handed, aged 8–10 years. Given that the primary objective of the research was to explore the relationship between brain function and specific genotypes in children with ADHD, genotyping analysis was not conducted on the healthy control group. The study protocol was approved by the Medical Research Ethics Committee of Shenzhen Children's Hospital, China, and informed consent was obtained from all participants and their legal guardians.

2.2. Genotyping

SNAP-25 is a neurotransmitter vesicle docking protein, and its MnlI polymorphism (rs3746544) is located within the 3′-untranslated region (3′-UTR). Genotyping of the SNAP-25 MnlI polymorphism was performed using peripheral venous blood samples obtained from the participants. DNA extraction was carried out using the FlexiGene DNA Kit (QIAGEN, Germany), following the manufacturer's instructions precisely. The genotyping process employed the following primer pairs:

forward, 5′ TTCTCCTCCAAATGCTGTCG 3′, and reverse, 5′ CCACCGAGGAGAGAAAATG 3’. After DNA extraction, polymerase chain reaction (PCR) amplification was performed with EX-Taq polymerase and GC buffer (Takara, Dalian, China). The PCR protocol started with a denaturing cycle at 94 °C for 2 min, followed by 30 cycles of 94 °C for 30 s, 52 °C for 30 s, 72 °C for 45 s and, finally, an extension step at 72 °C for 8 min. According to the results, ADHD participants were divided into two groups: the TT homozygote group (TT group) and the G-allele carrier group (TG group).

2.3. MRI acquisition and preprocessing

All MRI scans were conducted using a 3.0T Skyra MRI scanner (Siemens, Germany), encompassing both structural imaging and rs-fMRI. The parameters for the three-dimensional (3D) T1-weighted isotropy volumetric sequence were set as follows: repetition time (TR) = 2300 ms, echo time (TE) = 2.26 ms, inversion time (TI) = 900 ms, flip angle (FA) = 8°, field of view (FOV) = 256 × 200 mm2, acquisition matrix = 256 × 200, GRAPPA acceleration factor = 2, slice thickness = 1 mm, gap = 0 mm, total slices = 176, and acquisition time (TA) = 3 min 48 s. For the rs-fMRI data acquisition, an echo-planar imaging sequence was employed with the following parameters: TE = 30 ms, TR = 2000 ms, phase-encoding direction = anterior to posterior (A ≫ P), FA = 90°, acquisition matrix = 94 × 94, GRAPPA acceleration factor = 2, slice thickness = 3 mm, gap = 0 mm, 32 transversal slices, 130 vol, and TA = 4 min 28 s. Before the rs-fMRI scan, participants were instructed to remain relaxed, lie still, keep their eyes closed, and stay awake throughout the procedure.

2.4. fMRI analysis

Preprocessing of the raw fMRI data was conducted by a radiologist (M.D., Ph.D.) with 10 years of experience in brain function studies, who remained blinded to the genotypic information. The preprocessing was performed using the Data Processing Assistant for Resting-State fMRI (DPARSF 2.3; http://www.restfmri.net/forum/DPARSF), the RS-fMRI Data Analysis Toolkit (REST 1.2; http://www.restfmri.net/forum/index.php), and MATLAB-based Statistical Parameter Mapping (SPM 8; http://www.fil.ion.ucl.ac.uk/spm). The first 10 time points of each functional time series were discarded to eliminate scanner calibration and magnetization equilibration. Subsequent preprocessing steps included slice timing correction, head motion correction, spatial normalization to the Montreal Neurological Institute template, resampling to a voxel size of 3 × 3 × 3 mm3, temporal band-pass filtering, and regression of nuisance signals, such as the six head motion parameters, white matter, and cerebrospinal fluid signals. Another group of 10 subjects was excluded because their data had a maximum displacement of >3 mm in any of the cardinal directions (x, y, z) or a maximum spin (x, y, z) of >1°.

2.5. fALFF analysis

Both ALFF and fALFF were computed using the DPARSF software. ALFF (Yang et al., 2007), which quantifies the absolute strength or intensity of spontaneous brain activity, was calculated as the average square root of the power spectrum within the frequency range of 0.01–0.08 Hz fALFF, a normalized version of ALFF, was defined as the ratio of the low-frequency power (0.01–0.08 Hz) to the total power across the entire detectable frequency range. Compared to ALFF, fALFF is considered a more specific and sensitive measure of spontaneous brain activity (Chao-Gan and Yu-Feng, 2010). Individual maps were normalized by the global mean of the ALFF and fALFF values to reduce the variation across participants (Zang et al., 2007).

2.6. WM capacity analysis

The WM capacity of each child with ADHD was assessed by professionals certified by the Psychometric Professional Committee of the Chinese Psychological Association using WISC-IV. The Working Memory Index (WMI) was derived from the standardized scores of the Digit Span and Letter-Number Sequencing subtests.

2.7. Statistical analysis

2.7.1. Demographic information analysis

Statistical analyses of the two MnlI polymorphism genotypic groups (TT group and TG group) and the HCs group were performed using SPSS version 22.0 (SPSS Inc., IBM, USA) for Windows. One-way ANOVA was used to compare the differences among three groups. Data were presented as the mean ± standard deviation (SD). The two-sample t-test was used to compare the differences in WMI between the TT and TG groups. Data were presented as the mean ± standard deviation (SD). The value of P < 0.05 for the WMI of the two genotypic groups was considered statistically significant. Genotyping quality was evaluated by assessing the Hardy-Weinberg equilibrium (HWE) in the total sample. Deviations from HWE were tested using a chi-square (χ2) goodness-of-fit test, comparing observed genotype frequencies with expected frequencies derived from allele frequencies based on the formulas p2, 2pq, and q2 for wild-type homozygotes, heterozygotes, and variant homozygotes, respectively (where p and q represent allele frequencies). A P-value <0.05 was considered indicative of significant deviation, suggesting potential genotyping error or population stratification.

2.7.2. fALFF analysis

Whole-brain voxel-wise statistical analyses were conducted on the fALFF maps. Within-group fALFF distributions were characterised using one-sample t-tests (two-tailed) performed separately for the HC, TT, and TG cohorts. Between-group differences in fALFF values were evaluated using two-sample t-tests (two-tailed), with age included as a covariate, to compare the TT group, TG group, and HCs group. Multiple comparisons correction: The statistical significance of fALFF differences was determined through cluster-extent thresholding, implemented via the AlphaSim Monte Carlo simulation algorithm within the DPARSF/REST pipeline. The following parameters were applied to establish the minimum cluster size necessary to maintain a family-wise error (FWE) rate of P < 0.01: Voxel-wise cluster-forming threshold: P < 0.01 (uncorrected, two-tailed). Estimated spatial smoothness: Residual smoothness was estimated from the pre-processed data across all participants using the 2 times voxel size for FWHM(6 mm (x), 6 mm (y), and 6 mm (z)). Analysis mask: Analyses were constrained to a whole-brain gray matter mask in Montreal Neurological Institute (MNI) space (61 × 73 × 61 voxels; voxel size 3 × 3 × 3 mm3), comprising 49,089 voxels. Cluster connectivity criterion: Clusters were defined using 26-nearest neighbour connectivity (including all face-, edge-, and corner-adjacent voxels). Monte Carlo iterations: 10,000 simulations were performed. Based on these parameters, the Monte Carlo simulation indicated that a cluster comprising more than 20 contiguous voxels would survive a cluster-level FWE-corrected threshold of P < 0.01. Thus, clusters exceeding both the voxel-wise threshold (P < 0.01) and the cluster-extent threshold (>20 voxels) were deemed statistically significant.

3. Results

3.1. Demographic and clinical characteristics

The study included 56 boys with ADHD (mean age = 8.7 ± 1.2 years) and 21 HCs (HCs; mean age = 8.8 ± 1.5 years). ADHD participants were stratified into the TT group (n = 36; mean age = 8.93 ± 0.63 years) and the TG group (n = 20; mean age = 8.71 ± 0.53 years) based on SNAP-25 MnlI genotypes. Genotyping for the SNAP-25 MnlI polymorphism (rs3746544) was successful in all 56 study participants. The observed genotype distribution was as follows: 36 individuals (64.3 %) were homozygous for the T allele (T/T), 18 (32.1 %) were heterozygous (T/G), and 2 (3.6 %) were homozygous for the G allele (G/G). The calculated allele frequencies were 0.804 for the T allele and 0.196 for the G allele. A chi-square test for Hardy-Weinberg equilibrium revealed no significant deviation from the expected genotype distribution (χ2 = 0.019, df = 1, P > 0.05). This result confirms that the genotyping data for this SNP are reliable and free from significant technical artifacts or major population substructure within the studied cohort. One-way ANOVA revealed no statistically significant difference in age among the three groups (F = 0.34, P = 0.711). Based on preliminary findings from this study, the TT group had significantly higher WMI compared to the TG group (t = 2.098, P < 0.05) (Fang et al., 2022).

3.2. fALFF analysis

The one-sample t-test (AlphaSim corrected, voxel-wise P < 0.01, cluster size >20 voxels) revealed significant differences in the spatial distribution patterns of fALFF between the whole ADHD group (both the TT group and the TG group) and the HCs group. In HCs, fALFF clusters were predominantly distributed within the DMN, including bilateral medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), precuneus, and angular gyrus (Fig. 1). Compared with HCs, the whole ADHD group (both the TT group and the TG group) exhibited significantly decreased fALFF in parieto-occipital regions and DMN areas, particularly in the PCC, precuneus, and angular gyrus. Conversely, these two groups demonstrated significantly increased fALFF in the bilateral posterior lobes of the cerebellum (Fig. 1).

Fig. 1.

Fig. 1

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fALFF maps across the HCs, TT group and TG group (one-sample t-tests; voxel-wise P < 0.01, cluster size >20 voxels, AlphaSim corrected). For visualization purposes, a more stringent display threshold is applied. Colour scale indicates increased fALFF values; TT, TT group; TG, TG group; HCs, HCs group.

The two-sample t-test (AlphaSim corrected, voxel-wise P < 0.01, cluster size >20 voxels) revealed significant differences in fALFF values among the TT group, TG group, and HCs group. (1) Compared with HCs, the TT group exhibited globally reduced fALFF values throughout the brain, with particularly pronounced decreases in the Medial Frontal Gyrus_R, Occipital Lobe_R, Cuneus_R/L, Lingual Gyrus_L, Posterior Cingulate_R, and Postcentral Gyrus_L. Notably, this group demonstrated significantly elevated fALFF in the bilateral cerebellar posterior lobes (Fig. 2, Table 1). (2) Compared with HCs, the TG group similarly demonstrated a generalized reduction of fALFF values in the whole brain. These alterations were predominantly localized to several key brain regions, including the Medial Frontal Gyrus_L, Parahippocampal Gyrus_R, Lingual Gyrus_L, and Cuneus_R. Notably, this group exhibited a region-specific increase in fALFF values in the Cerebellum Posterior Lobe_R (Fig. 2, Table 2). (3) Compared with the TG group, the TT group exhibited significantly higher fALFF values in several brain regions associated with higher-order cognitive functions, including the Middle Frontal Gyrus_R, Insula_L, Posterior Cingulate_L, Superior Frontal Gyrus_R/L, Cingulate Gyrus_L, and Inferior Frontal Gyrus_L. Conversely, this group showed relatively reduced fALFF values in the Cerebellum_Posterior Lobe_R, Posterior Cingulate_R, Superior Temporal Gyrus_R, Precuneus_R, and Postcentral Gyrus_R (Fig. 2, Table 3).

Fig. 2.

Fig. 2

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t-map showing the difference of fALFF between the TT group and HCs; the TG group and HCs; the TT group and TG group (two-sample t-tests; voxel-wise P < 0.01, cluster size >20 voxels, AlphaSim corrected). Cold colors indicated the TT group < HCs; the TG group < HCs; the TT group < the TG group; warm colors indicate the opposite side; TT, TT group; TG, TG group; HCs, HCs group.

Table 1.

Brain regions showing different fALFF between TT group and HCs.

Brain regions Cluster size MNI coordinates
 T values
x y z
Cerebellum Posterior Lobe_L 43 −24 −87 −33 4.2236
Cerebellum Posterior Lobe_R 62 24 −75 −33 3.7514
Occipital Lobe_R 117 24 −57 −6 −4.1466
Cuneus_R 163 12 −90 12 −5.383
Cuneus_L 44 −12 −99 6 −4.1909
Cuneus_L 22 −9 −84 30 −4.2483
Lingual Gyrus_L 59 −18 −72 −6 −5.3666
Posterior Cingulate_R 22 12 −51 18 −4.3373
Postcentral Gyrus_L 27 −45 −24 57 −3.9703
Medial Frontal Gyrus_R 565 9 −15 78 −6.2933
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Abbreviations: MNI, Montreal Neurological Institute; L, left; R, right; AlphaSim correction P < 0.01; cluster>20 voxels; The T values represent the difference in fALFF between TT group and HCs; A positive T value indicates that the fALFF value of TT group is greater than that of HCs, while a negative T value indicates that the fALFF value of TT group is less than that of HCs.

Table 2.

Brain regions showing different fALFF between TG group and HCs.

Brain regions Cluster size MNI coordinates
 T values
x y z
Cerebellum Posterior Lobe_R 71 27 −75 −39 3.9386
Cerebellum Posterior Lobe_R 39 18 −75 −18 3.9418
Parahippocampal Gyrus_R 142 21 −51 −6 −4.9546
Lingual Gyrus_L 152 −18 −60 −6 −5.5559
Cuneus_R 121 6 −81 21 −4.0751
Medial Frontal Gyrus_L 176 −6 −15 75 −4.6933
Precentral Gyrus_R 23 12 −21 72 −4.1399
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Abbreviations: MNI, Montreal Neurological Institute; L, left; R, right; AlphaSim correction P < 0.01; cluster>20 voxels; The T values represent the difference in fALFF between TG group and HCs group; A positive T value indicates that the fALFF value of TG group is greater than that of HCs group, while a negative T value indicates that the fALFF value of TG group is less than that of HCs group.

Table 3.

Brain regions showing different fALFF between TT group and TG group.

Brain regions Cluster size MNI coordinates
 T values
x y z
Cerebellum_Posterior Lobe_R 24 27 −75 −42 −3.9877
Middle Frontal Gyrus_R 21 36 51 −3 2.9071
Middle Frontal Gyrus_R 27 48 42 18 3.5332
Middle Frontal Gyrus_R 22 27 60 12 3.0969
Insula_L 28 −30 21 3 4.1395
Posterior Cingulate_L 28 −18 −60 12 3.6217
Posterior Cingulate_R 72 12 −45 24 −3.8
Superior Frontal Gyrus_R 23 30 60 0 3.2966
Superior Frontal Gyrus_L 36 −21 54 12 3.7578
Superior Temporal Gyrus_R 21 51 −60 27 −3.3959
Cingulate Gyrus_L 38 −9 18 30 4.2766
Inferior Frontal Gyrus_L 23 −45 3 36 3.1097
Precuneus_R 64 3 −69 42 −3.7305
Postcentral Gyrus_R 44 21 −30 72 −3.8161
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Abbreviations: MNI, Montreal Neurological Institute; L, left; R, right; AlphaSim correction P < 0.01; cluster>20 voxels; The T values represent the difference in fALFF between TT group and TG group; A positive T value indicates that the fALFF value of TT group is greater than that of TG group, while a negative T value indicates that the fALFF value of TT group is less than that of TG group carriers.

4. Discussion

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorders in childhood, with cognitive dysfunction as its core deficit. We conduct this study to clarify the potential neural mechanisms by which the SNAP-25 MnlI polymorphism affects cognitive function, employing fALFF methodology to investigate spontaneous brain activity in children with ADHD. One-sample t-test analysis revealed significantly reduced fALFF values in the parieto-occipital regions and DMN of the ADHD group (both the TT group and the TG group) compared to HCs. Two-sample t-test results demonstrated that, except for bilateral posterior cerebellar lobes, both the TT group and the TG group exhibited decreased spontaneous neural activity across most brain regions relative to HCs. Notably, this aberrant pattern was more pronounced in the TG group. Further comparative analysis between genotypes showed that compared to the TG group, the TT group exhibited: 1) Enhanced spontaneous neural activity in higher-order cognitive brain regions; 2) Significantly reduced activity in specific areas, including the right posterior cerebellar lobe and right posterior cingulate cortex.

In the HCs group, the peak fALFF values predominantly localized in key brain regions, including the left superior frontal gyrus, right precuneus, and left lingual gyrus. Functionally, these regions are core components of the DMN and are critically responsible for higher-order cognitive processes. This spatial distribution pattern of this study closely aligns with the characteristic resting-state neural activity profiles reported in previous studies of typically developing populations, accurately reflecting the baseline neuro-functional architecture of healthy children during resting-state conditions (Hu et al., 2025).

The TT group exhibited a distinctive fALFF distribution pattern, characterized by: 1) prominent peak values in the right cerebellar posterior lobe and right inferior frontal gyrus (IFG), and 2) significantly suppressed neural activity in bilateral superior frontal gyri (SFG). This unique neuro-functional profile suggests two key characteristics of resting-state brain activity in this genotype: 1) compensatory activation of the cerebellar-right frontal pathway, and 2) functional imbalance between DMN and executive control network. Classical research has demonstrated that the cerebellar posterior lobe serves as a critical hub for integrating multimodal information, playing pivotal roles in higher-order neural functions including cognitive processing, motor coordination, and emotional regulation. Convergent evidence from multiple studies has established significant associations between structural-functional abnormalities in cerebellar posterior lobe and core ADHD symptoms (i.e., inattention, impulsivity, and executive dysfunction) (Baldacara et al., 2008; Passarelli et al., 2013). Notably, our findings reveal a potential neural mechanism underlying the phenotypic expression of ADHD. In the TT group, the co-occurrence of hyperactivation in the right IFG (a key node for behavioral inhibition) and hypoactivation in the SFG (central to higher-order executive functions) may collaboratively reflect pathological overcompensation in motor-related circuits and diminished regulatory capacity of prefrontal networks, ultimately contributing to the manifestation of clinical symptoms (Depue et al., 2010).

In the TG group, results demonstrated a distinct spatial distribution pattern of fALFF, with peak values predominantly localized in the left middle occipital gyrus (MOG_L), the right precuneus (Precuneus_R), and the left precentral gyrus (PreCG_L). This activation profile exhibited two salient characteristics: 1) coordinated activation within the left-hemispheric visual-motor-language network, and 2) aberrant engagement of the right DMN core node (precuneus). This cross-hemispheric, trans-network functional reorganization may represent a compensatory mechanism for visuo-motor integration in this genotype (Buckner et al., 2008). Notably, the abnormal activity observed in the left precentral gyrus among the TG group was statistically significant. As a crucial component of the premotor cortex, dysregulated activation in this region suggests potential dysfunction in motor preparation and execution processes. This finding aligns with established neural mechanisms underlying impulsive behaviours in ADHD, thereby providing a neurofunctional basis for understanding the association between this genotype and specific ADHD symptom dimensions (particularly motor impulsivity) (Chen et al., 2024).

The two-sample t-test results revealed essential neural activity differences between the TT group and TG group. Specifically, the TT group exhibited significantly higher fALFF values in key nodes of the executive control network, including the left medial frontal gyrus and the right superior frontal gyrus, while showing significantly suppressed neural activity in regions such as the right cerebellar posterior lobe, the right postcentral gyrus, and the right posterior cingulate cortex. This finding conveys important neurobiological implications: Firstly, the abnormally elevated activity in frontal regions (notably the dorsolateral prefrontal cortex) among the TT group may reflect compensatory enhancement within their executive function network (Hammer et al., 2015; Soderqvist et al., 2010). This result is closely consistent with the critical role of the SNAP-25 gene in regulating synaptic plasticity in the prefrontal cortex (Hawi et al., 2013). Secondly, the reduced activity in the parietal-posterior cingulate system implies a dual dysfunction: 1) as a core hub of the DMN, impaired posterior cingulate function may lead to compromised network integration (Hu et al., 2025); 2) Aberrant neural activity in the cerebellar-parietal pathway may constitute a pathophysiological substrate contributing to both motor coordination deficits and hyperactive symptoms in children with ADHD (Lee et al., 2022; Wang et al., 2025). Notably, our prior study (Fang et al., 2022) found that TT homozygote children with ADHD performed significantly better on the WMI than G-allele carriers. Integrating the current fALFF findings, we propose the following mechanistic hypothesis: the SNAP-25 MnlI polymorphism may interfere with cognitive performance in children with ADHD by modulating 1) the efficiency of the frontoparietal executive network and 2) the integrative function of the DMN. These findings provide additional supported evidence for understanding the gene-brain-behavior relationship in ADHD and suggest that resting-state fALFF metrics may serve as potential biomarkers for endophenotype research in ADHD.

SNAP-25, as a core structural component of the SNARE complex, precisely regulates the synaptic release of dopaminergic and glutamatergic neurotransmitters by mediating the fusion process of synaptic vesicles with the plasma membrane (Liu et al., 2017; Oyler et al., 1989). Molecular biological studies have demonstrated that SNAP-25 forms a stable four-helix bundle structure with syntaxin and VAMP2 through its C-terminal domain, a process critical for synaptic plasticity [48]. Transgenic animal models revealed that SNAP-25 deficiency leads to multi-level impairments in neurotransmitter release, including: (1) dysfunction of calcium-dependent vesicle fusion; (2) significant reduction in the readily releasable pool of synaptic vesicles; and (3) disruption of glutamatergic/GABAergic neurotransmission balance (Sudhof and Rothman, 2009). These molecular abnormalities ultimately result in behavioral phenotypes that are highly consistent with core ADHD symptoms, such as hyperactivity, increased impulsivity, and impaired spatial working memory (Barr et al., 2000; Russell, 2011a). Human genetic studies further confirm a significant association between the SNAP-25 gene MnlI polymorphism and ADHD susceptibility (Guan et al., 2009), with the underlying mechanism potentially involving a cascade: reduced synaptic transmission efficiency → abnormal functional integration of prefrontal-striatal circuits → behavioral inhibition deficits (Faraone et al., 2024). Our fMRI findings provide novel evidence supporting this framework: the significantly elevated fALFF values in the frontal lobe in the TT group, which may reflect genotype-specific enhancement of dopaminergic signaling, whereas the distributed activation pattern in the TG group suggests functional impairments in synaptic stability (Huang et al., 2023). These discoveries not only expand our understanding of the relationships between SNAP-25 polymorphism and brain function at the systems neuroscience level but also offer direct evidence for establishing genotype-neuroimaging biomarkers for ADHD.

Furthermore, to delve deeper into the biological mechanisms underlying the genotype-specific fALFF patterns observed in this study and to contextualize our findings within the existing literature. Firstly, a deeper exploration of the molecular pathway: The more pronounced reductions in fALFF within higher-order cognitive regions among G-allele carriers (TG group) may be directly linked to the functional impact of the rs3746544 polymorphism. Previous studies have suggested that the G allele, located in the 3′-UTR of the SNAP-25 gene, may potentially downregulate SNAP-25 mRNA expression or translational efficiency by affecting microRNA binding (Guan et al., 2009; Ye et al., 2016). Other studies have also confirmed that such downregulation can impair the assembly and function of the SNARE complex, leading to reduced synaptic vesicle fusion efficiency and the aberrant release of key neurotransmitters, such as dopamine and glutamate (Liu et al., 2017; Sudhof and Rothman, 2009). Altered synaptic transmission efficacy may subsequently disrupt the synchronization of neural network activity in cognitive circuits such as the prefrontal-striatal pathways, ultimately manifesting at the systems level as the specific fALFF alteration patterns captured in our study. This hypothesized cascade-from genetic variant, to molecular expression, to synaptic function, and finally to neural network activity-was supported by animal model studies. For instance, rodent models with partial SNAP-25 deficiency or specific mutations exhibit not only ADHD-like behavioral phenotypes (hyperactivity, impulsivity, working memory deficits) but also concomitant alterations in neurotransmitter release dynamics and aberrant network activity in the brain (Oyler et al., 1989; Russell, 2011). Secondly, we compare and position our results with other brain network studies that also employ dynamic fALFF. Hu et al. (2025) also find abnormalities in spontaneous neural activity within the DMN (particularly the posterior cingulate cortex/precuneus) and the cerebellum in children with ADHD (Hu et al., 2025), reinforcing the view of ADHD as a disorder of large-scale brain network dysfunction. However, by employing genetic stratification, our study reveals the genetic modulation effect underlying these widespread abnormalities. Specifically, the differential fALFF patterns between TT and TG groups, particularly the unique activity pattern in the sensorimotor cortex (e.g., left precentral gyrus) in the TG group, resonate with findings by Chen et al. (2024) suggesting heterogeneous neural activity bases for different clinical presentations of ADHD (Chen et al., 2024). This indicates that the SNAP-25 MnlI polymorphism may be one crucial genetic factor contributing to the neuroimaging heterogeneity in ADHD. In summary, our study not only double-confirmed some core features of brain functional abnormalities in ADHD, but more importantly, linked them to a specific genetic variant, provided a new evidential layer for understanding the diversity of neural mechanisms in ADHD and suggests the potential of fALFF as an endophenotypic biomarker connecting genetic risk to macro-scale brain function.

Notably, there are a few discrepancies between our study and previous research. The cross-ethnic meta-analysis conducted by Ye et al. (2016) demonstrated significant ethnic specificity in the association between the rs3746544 polymorphism and ADHD, revealing a significant association in Asian populations but no statistically significant association in Caucasian populations. Another study investigated Korean children with ADHD from Kim et al. (2017), found that carriers of the TT genotype for the rs3746544 polymorphism displayed increased omission errors in the Continuous Performance Test (CPT), suggesting this genetic locus may contribute to ADHD pathogenesis through its effects on attentional network functioning. The observed heterogeneity across studies may originate from multiple sources: 1) study design factors, including population genetic background differences and inadequate sample sizes; 2) variations in coverage of potentially functional SNPs; and 3) clinical confounding variables such as comorbid conditions and medication history, all of which may affect the comparability of research outcomes. More significantly, as a complex neurodevelopmental disorder, ADHD's genetic architecture involves the cumulative micro-effects of multiple genes across the dopaminergic, noradrenergic, and serotonergic systems, with individual polymorphisms demonstrating limited effect sizes and marked population heterogeneity (Demontis et al., 2023). Consequently, our future research would focus on the following approaches to deepen understanding: 1) conducting large-scale genome-wide association studies (GWAS) to identify risk loci comprehensively; 2) establishing multi-center prospective cohorts to enhance statistical power; and 3) integrating multi-omics data, including neuroimaging genomics and epigenomics, to systematically elucidate the polygenic brain-behavior regulatory networks underlying ADHD.

In addition to local spontaneous neural activity measures such as fALFF, insights emerging from the mapping of intrinsic brain connectivity networks (ICNs) offer a potentially more mechanistic framework for understanding aspects of human behavior and mental disorders, including ADHD. Research indicates that cognitive functions and behavioral symptoms do not arise from isolated abnormalities in single brain regions, but rather from dynamic imbalances in interactions within and between large-scale functional networks, such as the default mode, executive control, and salience networks (Mo et al., 2024; Zhang et al., 2024). Of particular relevance, studies suggest that specific genetic variants (e.g., those related to neurotransmitter systems) may shape individual differences and confer psychopathological risk by altering the connectional efficiency or topological properties of critical network hubs (Cheng, 2025, Li, 2025). This macroscale connectomics perspective on gene-network-behavior relationships is highly complementary to the findings of the present study. The spatial pattern of SNAP-25 genotype-specific fALFF alterations in this study, particularly in the prefrontal cortex, posterior cingulate cortex, and cerebellum, directly maps onto key nodes of several core intrinsic networks repeatedly implicated in ADHD pathophysiology (Xu et al., 2024; Yao et al., 2025). Therefore, future investigations that integrate local fALFF metrics with whole-brain functional connectivity analyses (e.g., within-network connectivity, between-network interactions) will provide a more comprehensive understanding of how the SNAP-25 polymorphism may contribute to ADHD-related cognitive and behavioral deficits. This would help to understand the mechanisms of cooperation and competition among large-scale brain networks, explicating a deeper mechanistic explanation at the systems neuroscience level.

This study elucidated the critical role of the SNAP-25 MnlI polymorphism in modulating brain functional activity in children with ADHD, demonstrating significant clinical value. Key findings reveal that: 1) Compared with HCs, both the TT group and the TG group exhibit significantly reduced fALFF values in multiple key brain regions accountable for higher-order neural functions, including cognitive control, attentional regulation, emotional processing, and motor coordination (Fang et al., 2022; Huang et al., 2023). These results suggest that the SNAP-25 MnlI polymorphism may constitute a key molecular determinant underlying aberrant brain function in ADHD; 2) More importantly, significant fALFF differences were observed between TT homozygotes and G-allele carriers across several brain regions. These genotype-specific functional signatures probably stem from genetic variations in SNAP-25 protein function, ultimately contributing to clinical heterogeneity in ADHD symptomatology and treatment response (Oner et al., 2011). To sum up, these findings not only provide objective evidence for understanding the neurobiological mechanisms of SNAP-25 MnlI polymorphism in ADHD, but also make it possible to identify different gene subtypes through distinct fALFF patterns. This advances a theoretical framework for developing personalized therapeutic strategies, including the selection of targeted pharmacotherapy and genotype-specific cognitive training protocols (Li et al., 2021). Longitudinal multimodal neuroimaging studies integrating genomic data are needed to characterize how SNAP-25 MnlI polymorphism modulates functional connectome organization, thereby informing stratified intervention strategies for ADHD.

The limitations of our study include the following: 1) The relatively modest sample size for the MRI study, attributable to the challenges of instructing children with ADHD to comply with scanning procedures. 2) The exclusive inclusion of males aged 8–11 years limits the generalizability of our findings. Future studies should expand sample diversity by incorporating female participants, broader age ranges, and multi-ethnic populations to validate the robustness of the results. 3) The absence of SNAP-25 genotyping in HCs limits direct comparison of brain activity between ADHD and typically developing children within the same genotype. Future work should genotype controls to separate the gene's baseline neurophysiological effects from its ADHD-specific contributions, clarifying SNAP-25's role in disease mechanisms. 4) As a cross-sectional study, it cannot yet reflect the long-term state of the disease. Longitudinal investigations are warranted to track dynamic interactions between genotype-dependent neural functional changes and symptom progression over time.

5. Conclusion

We use fALFF to analyse rs-fMRI data and find that genotype-dependent modulation of spontaneous brain activity in children with ADHD affected by the SNAP-25 MnlI polymorphism. This study reveals the potential mechanisms from the view of brain networks, how ADHD genotypes affect neural function, and provides a new approach for clinical decision-making and efficacy monitoring. Additionally, it may provide a theoretical framework for the future development of precision interventions tailored to individual genetic profiles.

CRediT authorship contribution statement

Diangang Fang: Writing – original draft, Visualization, Data curation, Conceptualization. Wenxian Huang: Writing – original draft, Software, Investigation, Formal analysis, Conceptualization. Tong Mo: Validation, Software. Xiaojing Lv: Formal analysis, Data curation. Guohua Liang: Visualization, Formal analysis. Binrang Yang: Resources, Funding acquisition. Hongwu Zeng: Writing – review & editing, Supervision, Project administration, Methodology, Funding acquisition, Data curation, Conceptualization.

Submission declaration

We have not published these data elsewhere, and it is not under consideration by any other journal. There are no conflicts of interest to declare, and all the listed authors have read and approved the final version of the manuscript.

Ethics statement

The studies involving human participants were reviewed and approved by the Medical Ethics Committee of the Shenzhen Children's Hospital. Written informed consent to participate in this study was provided by the participants' legal guardian.

Funding

This work was supported by Natural Science Foundation of Guangdong Province (No. 2022A1515011427), Sciences and Technology Project of Shenzhen Municipality (Grant No. JCYJ2022053015580501), Sanming Project of Medicine in Shenzhen(No. SZSM202011005 and SZSM201612036)from Shenzhen Medical and Health Project, Guangdong High-level Hospital Construction Fund(ynkt2021-zz47), and Natural Science Foundation of China (Grant No. 81271512).

Declaration of competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.

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