Depression and anxiety as affective disorders are the most prevalent mental disorders in the general population (Steel et al., 2014). Numerous cognitive neuroscience studies have shown a reduction in the cognitive ability, as well as changes in brain structure, in depression and anxiety, even at a subthreshold level (Besteher et al., 2020; Bierman et al., 2005; Dotson, 2014). Decreased cognitive function (i.e., attention, memory, thinking, decision-making, planning, reasoning, remembering, and problem-solving) is associated with personal and family suffering, and has a devastating impact on daily living(Mehta et al., 2002). Particularly, previous studies have demonstrated that depressed and anxious mood influences how the individual weighs the risks in decision-making, and is associated with dysfunctional risk-taking (Blanco et al., 2013; Cobb-Clark et al., 2022; Giorgetta et al., 2012).

Risk-taking (RT) as a decision component refers to situations in which an individual is faced with a choice that involves both high risk and high profit (Krain et al., 2006; Mellers et al., 1997). Risk-taking behavior is situated on a continuum that spans from healthy behavior (constructive) to maladaptive behavior (risk avoidance or impulsivity)(Peris and Galván, 2021). People's risk-taking behaviors can be affected by various factors, such as age, mental health, genetics, and social context (de-Juan-Ripoll et al., 2021). Specifically, previous studies have shown that depressed and anxious individuals often exhibit impaired function in risk-taking (Cobb-Clark et al., 2022; Smith et al., 2016). In general, according to the studies conducted so far, there is a hypothesis that higher anxiety and depression symptoms are associated with a decrease in risk-taking. However, few studies have been conducted on how the neural basis of anxiety and depression affect risk-taking.

Neuroimaging studies support brain changes in depression and anxiety even at the subclinical level (psychiatrically healthy)(Besteher et al., 2020). In general, studies conducted at the non-clinical level show that people with depressive symptoms show changes in the hippocampus(O’Shea et al., 2018; Spalletta et al., 2014) and anterior cingulate cortex (McLaren et al., 2016; Webb et al., 2014), similar to people with major depressive disorder, and people with anxiety symptoms also show changes in the amygdala(Hu et al., 2020), similar to anxiety disorders (Besteher et al., 2020). In particular, new studies show a negative correlation between hippocampal volume and subclinical depression symptoms (Osler et al., 2018; Szymkowicz et al., 2019), unlike older studies (Dotson et al., 2009; Goveas et al., 2011). Also, studies show a decrease in gray matter (GM) in the anterior cingulate cortex (Lai, 2013; Webb et al., 2014), and a lower GM in the posterior cingulate cortex (McLaren et al., 2016) in people with mild depression symptoms. In addition, studies show structural changes in the regions such as the anterior insula (Stratmann et al., 2014), pallidum (Price and Drevets, 2010), cerebellum (Dai et al., 2022), hippocampus (Tartt et al., 2022), and thalamus (Zhang et al., 2022) in depressive disorder. Similarly, studies conducted on subclinical anxiety show both positive (Baur et al., 2012) and negative (Hu et al., 2020) correlations with amygdala volume. Specifically, a new study showed that trait anxiety is associated with an increase in GM volume in the thalamus, and a decrease in the hypothalamus (Modi et al., 2019). In addition, studies emphasize the role of the hippocampus (Cherbuin et al., 2008), anterior cingulate cortex (Spampinato et al., 2009), and orbitofrontal cortex (Blackmon et al., 2011) in subclinical anxiety. Importantly, new studies point to the cerebellum's possible role in anxiety and depression (Chin and Augustine, 2023; Minichino et al., 2014). In fact, clinical studies now demonstrated that the cerebellum has diverse roles ranging from motor function to cognitive function (Klein et al., 2016; Stoodley and Schmahmann, 2010). Additionally, cerebellar stimulation has an effect on mood improvement (Schutter et al., 2003), and therefore it could be considered as a treatment target for mood (Lupo et al., 2019) and anxiety disorders (Moreno-Rius, 2018).

More importantly, anxiety and depression are associated with a specific type of dysfunction in the resting state networks (RSNs) (Kaiser et al., 2015; Sylvester et al., 2012). To be more specific, a recent review study found that patients with depression are affected by four fundamental networks, including the default mode network, reward network, affective network, and cognitive control network (Li et al., 2018). Moreover, a specific network dysfunction pattern is connected to anxiety disorders, including higher levels of activation in the salience and ventral attention networks, and lower levels of activation in the default mode and executive control networks (Sylvester et al., 2012).

It is well known that the neural structures and functions that underlie cognitive ability can be investigated using techniques such as voxel-based morphometric (VBM) and resting-state networks (RSN) analysis. Of note, the effect of depression and anxiety on brain structure or neural networks may significantly affect the cognitive functions such as risk-taking. Hence, in this study, by collecting data from 245 individuals checked for their physical and mental health, using the depression anxiety stress scale test and the balloon analog risk task, and using a 3T MRI scanner with a 64-channel head coil, we aimed to investigate how the neural basis of anxiety and depression affect risk-taking. The analysis of the MRI data was aimed to extract the resting state networks, as well as the structures of the brain, which might be influential in this subclinical condition.