Introduction

ADHD is a neurodevelopmental disorder that typically begins in childhood and is characterized by symptoms of inattention, hyperactivity, and impulsivity that are inconsistent with an individual’s developmental stage (American Psychiatric Association, 2013). As a lifelong condition, approximately 30–50 % of individuals diagnosed with ADHD in childhood continue to experience its effects into adulthood, facing persistent challenges in academic, occupational, and social contexts (Franke et al., 2018; Kooij et al., 2010; Kessler et al., 2006). In recent years, researchers have increasingly conceptualized ADHD as a continuum, focusing on symptom severity rather than a categorical distinction between affected and unaffected individuals (Posner et al., 2020; Marcus and Barry, 2011; Frazier et al., 2007; Haslam et al., 2006). Therefore, even those who do not meet the clinical diagnostic criteria may still exhibit varying levels of ADHD traits, including difficulties with attention, hyperactivity, and impulsivity. Individuals exhibiting high levels of ADHD traits frequently report more problematic behaviors, including substance abuse, reckless driving, and poor social adaptability (Graziano et al., 2015; Norwalk et al., 2009). Furthermore, emerging evidence indicates a potential link between ADHD traits and Future Time Perspective (FTP), a cognitive framework that involves an individual’s orientation and planning for the future (Boyd and Zimbardo, 2006). Previous studies have emphasized a negative association between levels of FTP and adult ADHD traits (Carelli and Wiberg, 2012; Weissenberger et al., 2016; Weissenberger et al., 2020). However, the neural basis underlying this relationship remains unexplored, leaving an important gap in understanding the interplay between ADHD traits and temporal cognition.

Individuals possess an internal framework of time that categorizes subjective experiences as past, present, and future, imparting meaning to these events (Zimbardo and Boyd, 2014). FTP refers to the unconscious process by which individuals encode experiences within this future-oriented framework (Boyd and Zimbardo, 2006). FTP is key to self-regulation, helping individuals set goals, plan, and execute actions (Baird et al., 2021). Those with higher FTP are more likely to adopt future-oriented behaviors, using proximal subgoals to connect present actions with long-term objectives (Nuttin, 1964; Miller and Brickman, 2004; Padawer et al., 2007). Conversely, failing to align future goals with current actions can disrupt the self-regulatory process, contributing to problems such as task-related distraction and an increased inclination towards immediate rewards, both of which are central manifestations of ADHD (Barkley and Murphy, 2006; Cosenza and Nigro, 2015). According to the dual-pathway model of ADHD, deficits in executive functioning—a critical self-regulatory process that supports goal-directed behavior—are central to attention difficulties in ADHD (Barkley, 1997; Sonuga-Barke, 2002; Barkley, 2011). Specifically, research on ADHD has shown that due to a lack of focus on future goals, individuals with AD are easily distracted by immediate stimuli in the environment, further affecting their task persistence and completion (Barkley, 2001). Additionally, Jensen et al. (2021) demonstrated that enhancing goal-directed behavior through goal management training led to improvements in ADHD symptoms, highlighting the role of impaired goal maintenance in attentional and executive dysfunctions associated with the disorder. Therefore, individuals with low FTP are more likely to exhibit a higher tendency towards AD traits. Besides, FTP is closely linked to valuing future goals, with individuals having higher FTP attributing greater importance to them (De Volder and Lens, 1982; Kooij et al., 2018). This suggests that low FTP may also contribute to heightened HD traits. Previous research has shown that both children and adults with ADHD exhibit impairments in value evaluation, particularly in intertemporal choice tasks, where they consistently undervalue larger future rewards compared to control groups (Jackson and MacKillop, 2016; Patros et al., 2016). The devaluation towards large rewards may be a key factor contributing to their symptoms of hyperactive-impulsive disorder (Sonuga-Barke, 2003). This suggests that abnormalities in value assessment may be a crucial cause of HD traits. In contrast, individuals with higher FTP can more effectively balance current and future benefits, thereby inhibiting impulsive behavior and mitigating its adverse effects (Tabernero and Hernández, 2011). Therefore, FTP may influence HD traits by affecting the evaluation of future goals’ value. Collectively, we hypothesize that higher FTP may be typically associated with lower AD and HD traits respectively.

Recent neuroimaging studies have highlighted that the neural substrates of FTP encompass multiple brain regions and involve specific functional networks. These brain regions include the prefrontal cortex, dorsal parietal lobe, and parahippocampal gyrus (Carelli and Olsson, 2014; Guo et al., 2017; Liu and Feng, 2019). Notably, several studies have established a strong association between the gray matter volume of the medial prefrontal cortex (mPFC) and FTP (Guo et al., 2017; Chen et al., 2018; Liu and Feng, 2019). Within the mPFC, distinct subregions contribute specialized functions: the dorsomedial PFC is implicated in the formation and maintenance of future goals (Haynes et al., 2007), while the ventromedial PFC plays a role in evaluating the value of these goals (Lieberman et al., 2019). The parietal lobe further contributes to FTP by facilitating the construction of future scenarios through dorsal activation and by enabling the understanding of goal-directed intentions (Maria et al., 2014). In addition, the parahippocampal gyrus is key to FTP, showing increased activity during future-oriented tasks and supporting the envisioning and planning of future events (Liu and Feng, 2019). Additionally, FTP relies on specific brain networks that coordinate these regions. FTP involves the ability to envision future scenarios and to engage in goal-oriented self-regulation. Previous research has demonstrated that recalling the past or imagining the future activates the default mode network (DMN), which includes the medial prefrontal cortex, medial and lateral temporal lobes, and the posterior inferior parietal lobule (Spreng et al., 2009). This suggests that the DMN plays a central role in envisioning future scenarios. In parallel, the frontoparietal control network—comprising regions such as the lateral prefrontal cortex, anterior cingulate gyrus, and inferior parietal lobule—supports executive control functions by enabling the conscious guidance of actions based on set goals (Dixon et al., 2018). Importantly, the coupling between the activity of the DMN and the frontoparietal control network facilitates the establishment and maintenance of goal-directed behavior (Spreng et al., 2010). Thus, the functional integration between key nodes of the DMN (e.g., mPFC) and the frontoparietal control network (e.g., IPL) may constitute the neural basis of FTP. Therefore, the neural basis of FTP likely involves the functional integration of core nodes in the DMN (e.g., mPFC) with the frontoparietal control network (e.g., IPL), allowing for the effective regulation of goal-directed behavior.

Although direct evidence on the neural basis linking FTP and ADHD traits is limited, existing literature suggests potential overlaps in their neurobiological mechanisms. For example, the mPFC is thought to play a crucial role in influencing traits of attention deficit and hyperactivity-impulsivity. Research suggests that reduced gray matter volume (GMV) in the vmPFC during adolescence can predict the greater severity of attention deficit and hyperactivity symptoms in adulthood (Albaugh et al., 2017; Albaugh et al., 2019). Ducharme et al. (2012) found that subclinical attention symptoms in the normal population are associated with decreased cortical thinning rates in the medial prefrontal and parietal regions. Furthermore, Shaw et al. (2011) reported a correlation between subclinical hyperactivity and impulsivity symptoms in normally developing adolescents and delays in the maturation of mPFC cortical thickness. Additionally, a large number of studies have found abnormalities in specific functional networks in both children and adults with ADHD. For example, ADHD patients are unable to effectively suppress DMN activity during tasks, leading to distracted attention and diminished cognitive control capabilities (Sonuga-Barke and Castellanos, 2007; Castellanos et al., 2009; Salavert et al., 2018). Further research has shown that excessive activation in hubs from DMN network among participants with ADHD is closely related to reduce functional connectivity with the executive control network (Sripada et al., 2014). Functional abnormalities in the frontoparietal control network and the DMN are regarded as the primary neurological underpinnings of attention deficits and hyperactive-impulsive behaviors in individuals with adult ADHD (Kaboodvand et al., 2020). Furthermore, Franzen et al. (2013) directly found reduced IPL-mPFC connectivity in untreated adult ADHD patients, which was normalized following stimulant medication, suggesting a link between lower IPL-mPFC connectivity and ADHD symptoms like inattention. Taken together, these findings support the notion that overlapping neural mechanisms—particularly within the mPFC and IPL—may underlie the relationship between FTP and ADHD traits. Specifically, weakened mPFC–IPL functional connectivity may disrupt self-regulatory mechanisms critical for sustaining goal-directed, thereby manifesting as reduced FTP and elevated ADHD traits.

Building on this hypothesis, current study sought to explore the functional neural underpinning of how FTP links to ADHD traits. To this end, participants were required to undergo a resting-state MRI scan and completed a series of psychometric measurement. We first employed voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) analyses to comprehensively investigate the neural substrates potentially implicated in FTP. Specifically, VBM analysis was used to identify the neuroanatomical structures underlying FTP. Abnormalities in gray matter volume might be linked to changes in functional connectivity (Gili et al., 2011). Resting-state functional connectivity (RSFC) could examine spontaneous brain activity and inter-regional functional connectivity (Kempton et al., 2011). Thus, we used survival brain regions identified in the VBM analysis as seed regions and conducted an RSFC analysis to identify the functional connectivity related to FTP. Finally, a mediation analysis was performed to verify the relationships between functional connectivity, FTP, and ADHD traits. Investigating the neural mechanisms through which FTP relates to ADHD may contribute to advancing theoretical models of ADHD by integrating temporal cognition with existing neurocognitive frameworks and could support the development of time-based therapeutic approaches targeting ADHD symptoms.

Section snippets

Participants and procedure

This study involved 241 students from Southwest University in Chongqing, China, including 176 females, with an average age of 20.04 years (SD = 1.87). One participant was excluded from the RSFC analysis due to excessive head motion (>2 mm translation in axis and >2 angular rotation in axis), resulting in 240 participants for further analysis. All participants were healthy university students, all of whom were right-handed, had normal or corrected-to-normal vision, and reported no history of

Behavioral results

The demographics and behavioral measurements information is shown in Table 1. The distributions of FTP, AD, and HD dimensions are presented in Fig. 1. The skewness and kurtosis values for each variable were as follows: FTP (skewness = 0.10, kurtosis = 0.287), AD (skewness = 0.341, kurtosis = −0.296), and HD (skewness = 0.234, kurtosis = −0.355). The absolute values of skewness and kurtosis for all three variables were less than 2, indicating that the data were approximately normally distributed

Discussion

In this study, we investigated the neural basis of the relationship between FTP and AD, HD traits using VBM and RSFC analyses. The behavioral results indicated that FTP was negatively correlated with AD and HD respectively. VBM analysis revealed that gray matter volume in the SMFG, PG, IPL, and STG was significantly correlated with FTP. Furthermore, RSFC results showed that the functional connectivity between IPL-dmPFC and IPL-vmPFC was positively correlated with FTP. More importantly,

Conclusions

The current study demonstrated that FTP was significantly negatively correlated with levels of AD and HD, respectively. VBM results indicated that the gray matter volume in the SMFG and the PG is significantly positively correlated with FTP, while the gray matter volume in the IPL and the STG is significantly negatively correlated with FTP. Based on the findings from VBM, RSFC results further found that the functional connectivity between the IPL-dmPFC, and IPL-vmPFC is positively correlated

CRediT authorship contribution statement

Mingzhen Ding: Writing – original draft, Visualization, Methodology, Data curation. Rong Zhang: Writing – review & editing, Methodology, Formal analysis, Data curation. Tingyong Feng: Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition, Conceptualization.

Consent for publication

All participants provided written informed consent.

Ethics approval and consent to participate

This study had been reviewed and approved by the IRB of Faculty of Psychology, Southwest University (No.H24239).

Funding

This work was supported by the National Natural Science Foundation of China (32271123,31971026), the National Key Research and Development Program of China (2022YFC2705201), the Fundamental Research Funds for the Central Universities (SWU2009104) and Innovation Research 2035 Pilot Plan of Southwest University (SWUPilotPlan006).

Declaration of competing interest

The authors declare no competing interests.

1

Mingzhen Ding and Rong Zhang contributed equally to this work.

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