Functional Near-Infrared Spectroscopy Evidence of Prefrontal Regulation of Cognitive Flexibility in Adults With ADHD

Abstract

Background:

Executive function (EF) deficit is considered to be a core cognitive deficit in ADHD. The current study combined functional near-infrared spectroscopy (fNIRS) and numerical switching tasks to investigate the cognitive flexibility of adult ADHD as an important part of EF.

Material and Methods:

The Wender Utah Rating Scale and the adult ADHD self-rating scale were respectively used to assess ADHD symptoms in childhood and adulthood. A 22 adults with ADHD and 24 healthy controls (HCs) participated in the large/small and odd/even switching tasks.

Results:

Behavioral results were indicative of the ADHD switch costs being lower than the HCs. The fNIRS results also showed that ADHD’s frontal eye field was over-activated both in magnitude and switched judgment tasks. The dorsolateral prefrontal cortex was also over-activated in magnitude judgment tasks.

Conclusion:

These results revealed that adults with ADHD’s cognitive flexibility performed better than the HCs, which result is different from mainstream ideas that EF is a core deficit in ADHD.

Keywords

ADHD cognitive flexibility functional near-infrared spectroscopy

Introduction

ADHD is the most common neurodevelopmental disorder characterized by symptoms of attention, hyperactivity, and impulsivity in childhood (Sachs, 2000; Spencer, 2006). Symptoms of ADHD may arise from executive function deficit (Barkley, 1997; Martel et al., 2007). Approximately two-third of children with ADHD continue to have impaired characteristics of ADHD symptoms until adulthood (Biederman et al., 2021; Faraone & Larsson, 2019) with a prevalence of 2%–5% in adults (Brandt & Fischer, 2017). However, adults with ADHD have not received a valid diagnosis (Moncrieff & Timimi, 2010), because diagnosis of adult ADHD is based on symptoms during childhood.

Executive function comprises three interrelated key components: inhibitory control, working memory, and cognitive flexibility (Diamond, 2013). Although previous studies have focused so much on inhibitory control and working memory in ADHD, cognitive flexibility has been ignored (Ludyga et al., 2020). Therefore, we further examined how adults with ADHD performed in terms of cognitive flexibility, which is an important part of the execution function (Miyake et al., 2000).

Previous studies have demonstrated that the switching task paradigm is a valid method for assessing individual cognitive flexibility (Petruo et al., 2019). Task-switching studies often use stimulus materials, such as numbers, letters, symbols, and graphics (Taube-Schiff & Segalowitz, 2005). Task switching occurs between unidimensional properties of different materials (e.g., switching between alphabetic proto-consonant judgments and numerical parity judgments; Rogers & Monsell, 1995), or between bi-dimensional properties of the same material (e.g., switching between numerical parity judgments and numerical size judgments; Koch, 2005). Previous studies that adopted a switch between large/small or odd/even judgments have proven the reliability of this research paradigm (Schneider & Logan, 2006; Wilckens et al., 2020).

Therefore, this study used switching judgments of the size and parity of numbers. Individuals need to make a parity judgment on whether the presented stimulus is an “odd” or “even” number, or make a magnitude judgment whether the presented stimulus is larger or smaller than “5.” The typical task-switching paradigm requires individuals to switch between two or more repeated cognitive tasks and compare the differences in performance between the repeated and switched tasks. Individuals performing a repeated task (AA or BB) have shorter reaction times (RT) than those performing a switched task (AB or BA). The difference in RT between repeated and switched tasks is known as “switch cost” (switch cost RT = switch trial RT − non-switch trial RT), which is an important indicator of switching ability (Koch, 2005). The current experiment was divided into three parts. The first and second parts were repeated tasks, and the third part was a switched task. In the first part, the individuals were required to determine whether the number was greater than or less than 5; in the second part, the individuals were required to determine whether the number was odd or even; in the third part, the individuals were required to make a flexible switch judgment between large/small or odd/even.

Cognitive flexibility is assumed to be mainly processed in the prefrontal cortex (PFC; Kim et al., 2011; Spellman et al., 2021). Deficits in ADHD are often linked to neurological deficits in the PFC (Filipek et al., 1997; Schulz et al., 2005). Thus, the PFC performance of adult patients with ADHD was explored during the switching task in the current study. However, research on adult ADHD has been shown to have low ecological validity, and that the adult ADHD diagnosis relies on the symptoms of childhood ADHD (LeRoy et al., 2019).

Consequently, the present study combined high ecological validity near-infrared brain imaging techniques (fNIRS, functional near-infrared spectroscopy; Rosenbaum et al., 2018) to examine the behavioral and neurological characteristics of adults with ADHD during the switching task. fNIRS also has other advantages: (1) it has a low cost and high tolerance for head movement (Perlman et al., 2014), which are more applicable to the hyperactive characteristics of adult ADHD in clinical and experimental studies and (2) fNIRS has a higher spatial resolution than functional magnetic resonance imaging, and a higher temporal resolution than ERP (Koike et al., 2015). Previously, conclusions about neurological deficits in ADHD relied solely on behavioral features, which are incomplete. fNIRS can be combined with behavioral experiments to examine neuropsychological features of adult ADHD in terms of both the temporal and spatial characteristics of brain activation.

In this study, prefrontal oxygenation changes were investigated using fNIRS in adults with ADHD during a switching task. Our study is the first to assess specific cognitive flexibility functions of adult ADHD from a behavioral perspective, as well as spatial and temporal prefrontal oxygenation activation. We expected diminished performance and altered brain activity in adults with ADHD compared to age- and sex-matched controls in the PFC during the task-switching paradigm.

Materials and Methods

Participants

To detect an effect size of 0.25, α = .05, power (1 − β) = .95; Cohen & Jacob, 1992, and two groups, G-Power 3.1.9.4 software indicated that a size of 44 was required for three measurements to verify the sufficiency of the sample size. Additionally, we referenced the study by Ludyga et al. (2020). Finally, we examined a total of 48 participants: 22 adults with ADHD (gender: 9 males and 13 females; age: M ± SD = 21.14 ± 2.59 years, range 18–27 years) and 24 healthy controls (gender: 11 males and 13 females; age: M ± SD = 20.21 ± 2.13 years, range 18–25 years), and one participant abandoned the experimental task. The bad channel (one participant) was excluded. Age and sex were not significantly different between the two groups (p > .05; Table 1).

Table 1.

Participant Descriptive Characteristics.

	Group		T	p Value
	ADHD (n = 22)	HCs (n = 24)	T	p Value
Age (M ± SD)	21.14 ± 2.59	20.21 ± 2.13	1.33	.19
Sex
Male (n)	9	11	N/A	N/A
Female (n)	13	13	N/A	N/A
WURS (M ± SD)	45.23 ± 7.19	17.21 ± 2.09	18.29	.00

Note. HCs = healthy controls; WURS = Wender Utah Rating Scale; N/A = not available; M = mean; p = p-value; SD = standard deviation.

Adult ADHD participants were recruited and diagnosed in three steps: The Chinese short version of the Wender Utah Rating Scale (WURS; Zhang & Zhang, 2011), adult ADHD self-rating scale (ADHD-SR; Yeh et al., 2008), and Structured Clinical Interview for the DSM-IV (SCID) were used to assess ADHD symptoms during childhood, review the presence of ADHD symptoms in adulthood, and exclude individuals with other psychiatric symptoms and a history of brain damage, such as mood disorders, psychotic disorders, substance use disorders, and anxiety disorders. A participant was described as having adult ADHD if there was a history of ADHD during childhood (a total WURS score of ≥30) and a current ADHD symptomatology according to the diagnostic checklist of ADHD-SR. The Chinese versions of the WURS, ADHD-SR, and SCID have been found to have high reliability and validity (Liao et al., 2012; Tsai et al., 2011; Tseng et al., 2007; Yeh et al., 2008; Zhang & Zhang, 2011).

Participants rated their ADHD symptoms during childhood retrospectively, using the Chinese version of the short version of the WURS, which consists of 15 items rated on a five-point Likert scale (0–4, “not at all” to “severe”). The Chinese version of the WURS was revised based on the diagnostic criteria for adult ADHD, as standardized in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and the Conners Adult ADHD Self-Rating Scale (CAARS-S). The Cronbach’s α of the WURS in our sample was .96. The WURS scores in the ADHD group (WURS = 45.23 ± 7.19) were significantly higher than those in the healthy controls (HCs; WURS = 17.21 ± 2.09), indicating valid grouping (t₍₄₄₎ = 18.29, p < .001; Table 1).

The ADHD-SR is an observer-rating scale based on the diagnostic criteria of DSM-IV, which includes the 18 DSM-V items of inattention (1–9 items, A1) and hyperactivity/impulsivity (10–18 items, A2) to be rated on a two-point scale (“no” scoring 0 and “yes” scoring 1). A total score of six or more, lasting at least six months was used to define the presence of distress/impairment. The Cronbach’s α of ADHD-SR in our sample were .92 and .81, respectively, for A1 and A2. All the participants were right-handed, had normal corrected vision, and had not taken psychotropic drugs, such as Ritalin within the last 6 months.

The participants were recruited from a university in Wuhan, China, and had not previously majored in neuropsychology. Ethical approval for this study was granted by the institute’s ethical committee. Participants provided written informed consent to ensure that they could decide not to participate, or stop participating at any time, but would still receive treatment. All the participants received 30 RMB at the end of data collection.

Experimental Design

Arabic numerals 1 to 9 not including 5 (Arial font in 64-point size), were displayed centrally and randomly on a computer screen (size: 10 cm; refresh rate: 90 Hz; resolution: 1,024 × 768 pixels) at a viewing distance of 60 cm. E-Prime software (version 3.0) was used to program a computer version of the switching task (large/small or odd/even).

Before the experiment began, the participants were asked to respond to a keystroke as quickly as possible to ensure accuracy, and to press “F” for larger than 5 or even numbers, or “J” for smaller than 5 or odd numbers. The task comprised a 10-second pre-task baseline period and three blocks (Figure 1b).

Figure 1.

Experimental design and block structure: (a) the yellow number is to judge the magnitude whether it is larger or smaller than 5 in the first block. The blue number is to judge the parity whether it is odd or even in the second block. Switched block 3 consisted of both yellow and blue numbers presented in random order and (b) the switching task in block design. Block 1 = make a magnitude judgment, Block 2 = make a parity judgment, Block 3 = a switched condition.

In the first and second blocks, the participants were asked to make a magnitude and parity judgment about whether the numbers were larger or smaller than 5 or odd or even. In the third block, the participants were asked to make a size or parity judgment based on the color of the number. If the number was yellow, its size was determined. If the number was blue, parity was assessed.

Before the formal task, there was a practice task. A practice task accuracy of 75% or more was required to proceed to the formal task. The per practice task consisted of 16 trials. The formal part of the first, second, and third tasks comprised 32, 32, and 96 trials (50% odd and even; 50% large and small), respectively (Figure 1a). After each block, the individuals were asked to rest for at least 30 seconds and then press any key to start the next block (Figure 1b).

Functional Near-Infrared Spectroscopy Method

Hardware and Probe Design

The participants were seated in a quiet room during the experiment. An fNIRS imaging system (NIRScout; NIRx Medical Technologies, Brooklyn, NY) emits at 780 and 830 nm to measure the change of oxyhemoglobin (oxyHb) concentration on the PFC during the switching task. The distance between the eight detectors and seven sources was approximately 3 cm. A headpiece was placed at the lowest probe line along Fp1/Fp2 using the 10/20 system with the probe center positioned at Fpz.

A three-dimensional (3D) digitizer (Patriot, Polhemus, Brooklyn, NY) was used to collect one individual’s 3D coordinates of the head and cap points. Subsequently, 20 channel positions were projected into the Montreal Neurological Institute (MNI) space coordinated to the MRIcro space using the predefined model cap and SPM for fNIRS (Table 2 and Figure 2).

Table 2.

Montreal Neurological Institute (MNI) Channel Positions Registered to the Brodmann’s Areas After Corrected t-Tests of the Significant (p < .05) Group Contrasts of ΔHbO in Each Condition.

CH	MNI coordinates			Brodmann’s area
CH	X	Y	Z	MNI Label	Percentage (%)
6	−27.4	65.5	2.95	Frontopolar area	0.94
				Orbitofrontal area	0.04
				Dorsolateral prefrontal cortex	0.02
8	−11.15	31.4	61.45	Includes frontal eye fields	0.94
8	−11.15	31.4	61.45	Pre-motor and supplementary motor cortex	0.06
9	0.1	49.4	48.6	Includes frontal eye fields	0.51
9	0.1	49.4	48.6	Dorsolateral prefrontal cortex	0.49
10	12.5	33.1	61.5	Includes frontal eye fields	0.94
10	12.5	33.1	61.5	Pre-motor and supplementary motor cortex	0.06
11	−12.0	69.7	−9.0	Frontopolar area	0.68
11	−12.0	69.7	−9.0	Orbitofrontal area	0.32

Note. The fNIRS observation channels cover multiple brain regions, and this table only lists the channels after corrected t-tests of the ΔHbO’s significance (p < .05) between the two groups.

Figure 2.

Optodes and channels placement: (a) optodes placement. Red and blue circles indicate the near-infrared light emitters and detectors, respectively and (b) channels placement. Twenty channels are superimposed in the MNI coordinates to MRIcro space over the prefrontal cortex, colored in blue. Brain images were visualized with the BrainNet Viewer (Xia et al., 2013, http://www.nitrc.org/projects/bnv/).

Analysis Procedures

The NIRS-SPM (Ye et al., 2009; http://www.fil.ion.ucl.ac.uk/spm/soft-ware/) and HOMER2 (www.nmr.mgh.harvard.edu/PMI/resources/homer2; Huppert et al., 2009) toolboxes in MATLAB were used to analyze the fNIRS data.

In the SPM analysis, the general linear model (GLM) with a canonical hemodynamic response was used to model the estimated oxyHb signals. After filtering high-frequency noise using a wavelet minimum description length detrending algorithm to decompose the signal into a global trend, a GLM with a canonical hemodynamic response was used to model the estimated oxyHb signals to obtain the weights of beta (β) values.

In the HOMER2 analysis, the raw signal intensity was demeaned and converted to an optical intensity measure (function hmrIntensity2OD). We then used the modified wavelet-filtering technique (hmrMotionCorrectWavelet) with an IQR threshold of 1.5. Additionlly, data were then filtered with Butterworth 0.1 Hz low-pass filter and 0.01 Hz high-pass filter to further enhance the signal-to-noise-ratio (functions hmrBandpassFilt). Moreover, the optical density data were converted into oxyHb concentration changes using the Modified Beer-Lambert law (functions hmrOD2Conc). Furthermore, oxyHb concentrations were averaged for each group using the hmrBlockAvg function with a tRange of [−5.0, 35.0] for Blocks 1 and 2 and [−5.0, 150.0] for Block 3 within the duration of the stimulus. Finally, the timing of the peaks and the peak value were extracted directly from the group average oxyHb concentration during the stimulus duration.

Data Analysis

Behavioral and fNIRS data were analyzed using the SPSS 22 (IBM) statistical software. Behavioral (RT and accuracy) and fNIRS data (β) for oxyHb were analyzed using a two-groups (adult ADHD patients and HCs) × three conditions (large/small, odd/even, and switched) mixed subject design. Follow-up analyses were performed using two-way mixed-design analysis of variance (ANOVA) to analyze the behavioral data and β values in the 20 channels, with the group and conditions as factors and repeated measures for the three conditions. Subsequently, multiple-testing correction (false discovery rate [FDR]) was applied to the resulting p-values among the 20 channels for each condition using the R p.adjust() function and “FDR” method. If the channels were significantly activated between ADHD patients and HCs, it would be the ROI. Homer2 was used to further analyze the temporal activation characteristics of the ROIs.

Additionally, the average RT of the three blocks was recorded, and the switching cost was calculated as an indicator of behavioral performance, with the switching cost being the average RT of the third block minus the average RT of the first and second blocks.

Results

Behavioral Data

The dependent variables were RT and accuracy. The two-way repeated-measures ANOVA on RT revealed a main effect of conditions (F_(2,43) = 417.264, p = .000, η² = .951), due to considerably longer RTs in Block 3 than Block 1 or Block 2 (1,015.13 ms vs. 526.99 ms; 1,015.13 ms vs. 589.34 ms). The participants committed more errors in Block 3 as compared to Block 1 and Block 2, resulting in a main effect of Block (F_(2,43) = 24.545, p = .000, η² = 0.533; 87.7% vs. 98.3%; 87.7% vs. 96.7%). Moreover, in the repeated-measures ANOVA for RT and accuracy, there was no group × condition interaction (F_(2,43) = 2.253, p = .117, η² = .095; F_(2,43) = 0.097, p = .908, η² = .004). There were no significant differences in RT and accuracy between the two groups (p < .050) for each condition. The adult ADHD patients’ switching cost was significantly lower than that of the HCs (t₍₄₄₎ = 2.131, p = .039, 95% confidence interval [CI] [3.67523, 132.10646]; M ± SD: 423.02 ± 113.87 [ADHD] vs. 490.91 ± 102.25 [HC]).

Functional Near-Infrared Spectroscopy Data

Spatial Analysis

In the two-way repeated-measures ANOVA model, we observed a statistically significant interaction effect between condition and group (Channel 9: F_{(2, 43)} = 3.527, p = .038, η² = .141), and there was a main effect of condition (Channel 13: F_{(2, 43)} = 6.261, p = .004, η² = .226). After using the FDR method to correct p-values between the 20 channels, the corrected p-values were >.05. Therefore, simple effects analyses were not used. Instead, the mean oxyHb changes between ADHD patients and HCs were further tested using the independent-samples T test in the 20 channels for each condition.

The results revealed that the ADHD patients’ oxyHb change was significantly higher than HCs during the magnitude judgment task in the dorsolateral prefrontal cortex and frontal eye field (channel 10: t₍₄₄₎ = −3.741, corrected p = .011, Cohen’s d = −1.859; 95% CI [−0.00147, 0.00038]; β = .00019 [ADHD] vs. −.00022 [HCs]; Figure 3a). Additionally, in the switched condition, oxyHb’s group differences were apparent and occurred predominantly in the frontal eye field, due to considerably higher in ADHD patients than HCs (channel 9: t (44) = −3.444, corrected p = .025, Cohen’s d = −1.770, 95% CI [−0.00147, 0.00038]; β = .00009 [ADHD] vs. −.00017 [HCs]; Figure 3c).

Figure 3.

Oxyhemoglobin (oxyHb) activation between adult ADHD patients and healthy controls (HCs): (a) spatial activation. Corrected p-value significance map for oxyHb increased significantly higher in ADHD patients compared with HCs during the large/small task. Corrected p-values inside the yellow circle channels are lower than.05 (channel 10), whereas corrected p-values are higher than.05, (b) temporal activation. Group averaged temporal oxyHb concentrations changes measured in channel 10 during the large/small task. Grand average waveforms of oxyHb in ADHD patients (solid lines) and HCs (dashed lines), (c) spatial activation. Corrected p-value significance map for oxyHb increased significantly higher in ADHD patients compared with HCs during the switched task. Corrected p-values inside the yellow circle channels are lower than.05 (channel 9), whereas corrected p-values are higher than.05, and (d) temporal activation. Group averaged temporal oxyHb concentrations changes measured in channel 9 in the switched task. Grand average waveforms of oxyHb in ADHD patients (solid lines) and HCs (dashed lines). Brain images were visualized with the BrainNet Viewer (Xia et al., 2013, http://www.nitrc.org/projects/bnv/).

Consequently, we adopted channel 10 in the large/small task and channel 9 in the switched task as statistically specific ROIs to represent the temporal activation of the oxyHb concentration.

Temporal Analysis

As shown in Figure 3, the adult ADHD group’s change of oxyHb concentration largest peak occurred later than the HC group both in channel 10 during the large/small task (34.56 s vs. 0 s; Figure 3b) and in channel 9 during the switched task (83.2 s vs. 80.77 s; Figure 3d).

Additionally, the result of hemoglobin temporal activation was consistent with spatial activation that the ADHD group’s oxyHb peak-value was higher than in the HC group both during the large/small (0.618 µmol/L vs. −0.028 µmol/L; Figure 3b) and switched task (0.840 µmol/L vs. 0.49 µmol/L; Figure 3d).

Brain-Behavior Correlations

We conducted a Pearson correlation analysis to examine the behavior correlations. Significant brain-behavior correlations are shown in Table 3. In a preliminary analysis, it was shown that: (1) the ADHD switch cost was positively associated with the oxyHb response in the included frontal eye fields (channel 8: p = .004) during the large/small task; (2) ADHD switch cost was negatively correlated with the oxyHb response in the dorsolateral prefrontal cortex and included frontal eye fields (channel 9: p = .032) during the odd/even task; and (3) the ADHD switch cost was positively correlated with oxyHb response in the frontopolar area and orbitofrontal cortex (channel 6: p = .027; channel 11: p = .040) during the switched task.

Table 3.

Brain-Behavior Correlations and Test Statistics (n = 22).

Condition	Channel	r	p value
Large/small task	8	.583	.004
Odd/even task	9	−.459	.032
Switched task	6	.471	.027
Switched task	11	.441	.040

Discussion

The present study combined fNIRS and switching tasks to analyze the cognitive flexibility performance of adult patients with ADHD. Adult ADHD patients had a lower switch cost than HCs. From spatial activation, adult ADHD patients had higher oxyHb concentration changes than HCs both in large/small and switched conditions. Adult ADHD can maintain a high level of temporal activation throughout the task. These results are crucial because cognitive flexibility is an important component of executive function, and the fact that ADHD patients performed better than HCs in terms of cognitive flexibility suggests that we have overfocused on executive function deficits in adults with ADHD in the past.

Behaviorally, switch costs were lower in adults with ADHD than in the HCs. The results of the children’s sample were inconsistent with those of the current study (Cepeda et al., 2000), which might be due to the fact that prefrontal development in children is incomplete (Hoogman et al., 2017; Nakao et al., 2011; Polanczyk & Rohde, 2007; Valera et al., 2007). In contrast, prefrontal development is completed in adulthood. Consequently, although adult ADHD continues the symptoms of childhood ADHD, adult ADHD may have different behavioral and cognitive performance characteristics compared to childhood ADHD (Frodl & Skokauskas, 2012; Hoogman et al., 2017; Nakao et al., 2011). Additionally, the relationship between behavioral performance and cognitive neurology is complex. Furthermore, previous studies found that cognitive processing compensates for behavioral performance (Geary et al., 2021). Specifically, cognitive flexibility is thought to be endogenous top-down cognitive processing (Monsell, 2003; Rogers & Monsell, 1995), whereas ADHD is thought to be a deficit in endogenous inhibition (Lansbergen et al., 2007) and excessive responses to external, direct, and novel stimuli (Pliszka et al., 1996; Sikström & Söderlund, 2007; Tegelbeckers et al., 2022; Tripp & Wickens, 2008). Individuals with ADHD have a lower switching cost, as some brain regions responsible for exogenous stimulus responses may overcompensate for other exogenous bottom-up cognitive processing. Therefore, further exploration of neurocognitive processing in adults with ADHD is needed.

Spatially, the adult ADHD’s dorsolateral prefrontal cortex and frontal eye field were over-activated during the magnitude (repeated) judgment task, and the adult ADHD’s frontal eye field was over-activated during the switched judgment task. Regardless of whether the task was repeated or switched, individuals were required to make response judgments to direct visual stimuli. Moreover, adults with ADHD are more responsive to novel external visual stimuli (Bellato et al., 2020). Thus, in both repeated and switched tasks, the oxyHb activation of adult ADHD was significantly higher than that of HCs in the frontal eye field responsible for purely rapid visual processing (Fassbender & Schweitzer, 2006; Kirchner et al., 2009). The dorsolateral prefrontal cortex plays an important role in complex decision-making (Fellows, 2004). Therefore, the dorsolateral prefrontal cortex was essential for making choice judgments in both repetitive magnitude judgments and switched tasks, indicating its involvement in higher cognitive processing.

Temporally, on repeated magnitude judgments, adult ADHD activation levels remained high until the end of the task, in contrast to HCs. The HCs peaked at the beginning of the task, but had lower activation levels in subsequent judgment tasks. During the switched task, adult ADHD reached peak time approximately at the same time as the normal group, but activation levels were consistently higher than those in HCs. This suggests that adult ADHD is indeed an overreaction to rapid external visual stimuli.

Analysis of brain-behavior correlations provides additional evidence for the correlation between the switch cost of ADHD and changes in oxyHb levels in the prefrontal cortex. The results showed that the switching cost in ADHD was positively associated with increased oxyHb activation of the frontal eye fields during the large/small task and increased oxyHb activation of the orbitofrontal cortex and frontopolar areas during the switched task. Rapid visual stimulation is associated with frontal eye fields (Campana et al., 2007; Murthy et al., 2007; Wardak et al., 2006), while deficits in the orbitofrontal cortex are linked to impulsivity in ADHD (Spinella, 2004; Wilbertz et al., 2012). Both direct automatic visual stimulus and impulse responses are correlated with bottom-up cognitive processing (Leshem, 2016; Nigg, 2016; Ubaldi et al., 2015). Additionally, the frontopolar cortex can be used to coordinate multiple cognitive processes in other associated cortical areas, and its role as part of the top-down executive system can interact with the bottom-up sensory processing system and maintain task performance (Ono et al., 2014; Ramnani & Owen, 2004; Sakai et al., 2002). Thus, the switching cost of ADHD is positively correlated with automatic visual and impulse responses. However, in the odd/even task, the switching costs of ADHD were negatively correlated with oxyHb activation in the dorsolateral prefrontal cortex, which is responsible for top-down executive processing of decision making and choice judgments (Christakou et al., 2009; Tassy et al., 2012). The cognitive task we perform each moment and effectiveness with which we perform it is the result of the interaction between goal-oriented control (endogenous) and external stimuli (exogenous; Monsell, 2003). Effective cognition is essential to have a balanced level of endogenous control, which is sufficient to protect the task from disruption, yet flexible enough to allow us to quickly switch to other tasks when necessary (Monsell, 2003). It has been shown that bottom-up attentional selection is triggered by external stimuli, whereas top-down attentional selection is actively directed by humans according to behavioral goals (Müller & Rabbitt, 1989a, 1989b). Cognitive flexibility is a top-down cognitive process (Monsell, 2003; Rogers & Monsell, 1995), and previous research revealed that ADHD suffered from an endogenous control deficit that is controlled by the top-down processing system and is more prone to inhibitory interference (Lansbergen et al., 2007). Consequently, it is possible that the lower switching cost of adult ADHD was a result of bottom-up cognitive processing, compensating for top-down cognitive processing rather than better top-down cognitive processing.

The findings of this study suggest that adults with ADHD have a lower switching cost in terms of behavioral performance than HCs do. Additionally, the results showed that adults with ADHD had higher levels of oxyHb activation in the frontal eye field, which is responsible for rapid visual processing (Campana et al., 2007; Murthy et al., 2007; Wardak et al., 2006). This study supplements the previous study that focused so much on executive function deficits in adult ADHD, as it did not show that adult ADHD patients had deficits in cognitive flexibility compared to HCs. The results of previous studies are consistent with the current study that adults with ADHD may not have executive function deficits. For example, Sergeant (2005) found no deficits in executive function in ADHD after controlling for age and intelligence level. Few studies (Fassbender & Schweitzer, 2006; Primich & Iennaco, 2012) suggest that this may be due to the existence of compensatory behavior in adult ADHD. ADHD’s purely sensory processing could compensate for higher-order processing because ADHD is more sensitive to external direct visual stimuli. Additionally, Sergeant et al. (2000, 2005) held the view that cognitive deficits in ADHD may be a dysfunction of cognitive energy. Activation, as a part of the energy factor, can be influenced by novel stimuli. The results of the present study also support the finding that adults with ADHD are over-activated in response to external novel stimuli.

Limitations

The results presented herein should be considered in the context of certain limitations. First, the adults in this study only focused on the university population; therefore, future studies need to pay attention to the diversity of the population to improve the generalizability of the results. Second, this study only examined cognitive flexibility in adults with ADHD cross-sectionally, and there is a need to expand the age range to examine the cognitive-developmental characteristics longitudinally. Third, the present study only examined the oxyHb activation characteristics of individuals in the prefrontal cortex. Thus, the behavioral-cognitive compensation relationship in adult ADHD should be examined by expanding the range of brain areas in the future. Fourth, ADHD symptoms were divided into two subtypes (attention and impulsivity/hyperactivity deficits), but the present study did not distinguish between subtypes of adult ADHD. In future studies, adult ADHD subtypes should be distinguished to examine cognitive neuropsychological features. Finally, in this study, the time to peak for each group was extracted directly after group average analysis using the Homer2 toolboxes, and the time to peak values for the two groups were compared. The times to peak values could not be analyzed for group variation.

Conclusion

In terms of cognitive flexibility, adult ADHD may not have a deficit in executive function, but rather a performance of overreacting to external visual stimuli. Additionally, adult ADHD is more adept at lower-order, purely visual processing. In more difficult switched tasks, adult ADHD can compensate for deficits in higher-order cognitive processing with purely visual processing. This provides a direction for future clinical interventions in the treatment of adult ADHD where external visual stimulation may promote cognitive flexibility in adults with ADHD. Alternatively, as suggested by Sergeant et al. (2000, 2005), executive function deficits are only superficial in adult ADHD, and further exploration of whether adult ADHD is dysfunctional to novel stimuli is needed. Accordingly, previous studies have paid so much attention to executive function deficits in adults with ADHD.

Footnotes

Author Contributions

The statements of specific contributions of each author are as follows: Yaojin Li contributed substantially to data curation, writing-original draft preparation, conceptualization, methodology, software, and visualization; Jianwen Chen gave final approval of the version to be published and contributed to conceptualization; Xintong Zheng revised the article critically; Jianxiu Liu analyzed the data; Cong Peng contributed to the conception; Youguo Liao contributed to the conception.

Declaration of Conflicting Interests

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

Funding

This work was supported by the Fundamental Research Funds for the Central Universities in China [Grant No. 2017wkzdj015].

ORCID iD

Yaojin Li

Author Biographies

Yaojin Li is a Ph.D. student at the Huazhong University of Science and Technology (School of Educational Sciences) in the P.R. China, and a student assistant at the Educational Neuroscience Research Center (HUST).

Jianwen Chen, Ph.D., is a Professor at the Huazhong University of Science and Technology (School of Educational Sciences) in the P.R. China. He has obtained a Bachelor of Psychology, and a Ph.D. in School of Psychology at Southwest University in P.R. China. He has published numerous articles and book chapters on specific mental health and education for college students, psychological measurement, personality health and education, social adjustment, and mental health. His research interests are the psychological mechanisms of mental health and education for college students, and the assessment of mental health and education.

Xintong Zheng is a Ph.D. student at the Huazhong University of Science and Technology (School of Educational Sciences) in the P.R. China, and a student assistant at the Educational Neuroscience Research Center (HUST).

Jianxiu Liu is a postdoctoral fellow at the Tsinghua University (Vanke School of Public Health) in P.R. China.

Cong Peng is a Ph.D. student at the Huazhong University of Science and Technology (School of Educational Sciences) in the P.R. China, and a student assistant at the Educational Neuroscience Research Center (HUST).

Youguo Liao is a Ph.D. student at the Huazhong University of Science and Technology (School of Educational Sciences) in the P.R. China, and a student assistant at the Educational Neuroscience Research Center (HUST).

References

Barkley

R. A.

(1997). Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD. Psychological Bulletin, 121(1), 65–94. https://doi.org/10.1037/0033-2909.121.1.65

Bellato

Arora

Hollis

Groom

M. J.

(2020). Is autonomic nervous system function atypical in attention deficit hyperactivity disorder (ADHD)? A systematic review of the evidence. Neuroscience & Biobehavioral Reviews, 108, 182–206. https://doi.org/10.1016/j.neubiorev.2019.11.001

Biederman

DiSalvo

Vaudreuil

Wozniak

Uchida

Woodworth

K. Y.

Green

Farrell

Faraone

S. V.

(2021). The child behavior checklist can aid in characterizing suspected comorbid psychopathology in clinically referred youth with ADHD. Journal of Psychiatric Research, 138, 477–484. https://doi.org/10.1016/j.jpsychires.2021.04.028

Brandt

Fischer

(2017). The prevalence of adult attention deficit/hyperactivity disorder among treatment-seeking problem gamblers. Drug and Alcohol Dependence, 171, e26. https://doi.org/10.1016/j.drugalcdep.2016.08.085

Campana

Cowey

Casco

Oudsen

Walsh

(2007). Left frontal eye field remembers “where” but not “what”. Neuropsychologia, 45(10), 2340–2345. https://doi.org/10.1016/j.neuropsychologia.2007.02.009

Cepeda

N. J.

Cepeda

M. L.

Kramer

A. F.

(2000). Task Switching and Attention Deficit Hyperactivity Disorder. Journal of Abnormal Child Psychology, 28(3), 213–226. https://doi.org/10.1023/A:1005143419092

Christakou

Brammer

Giampietro

Rubia

(2009). Right ventromedial and dorsolateral prefrontal cortices mediate adaptive decisions under ambiguity by integrating choice utility and outcome evaluation. Journal of Neuroscience, 29(35), 11020–11028. https://doi.org/10.1523/JNEUROSCI.1279-09.2009

Cohen

(1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155

Diamond

(2013). Executive functions. Annual Review of Psychology, 64(1), 135–168. https://doi.org/10.1146/annurev-psych-113011-143750

10.

Faraone

S. V.

Larsson

(2019). Genetics of attention deficit hyperactivity disorder. Molecular Psychiatry, 24(4), 562–575. https://doi.org/10.1038/s41380-018-0070-0

11.

Fassbender

Schweitzer

J. B.

(2006). Is there evidence for neural compensation in attention deficit hyperactivity disorder? A review of the functional neuroimaging literature. Clinical Psychology Review, 26(4), 445–465. https://doi.org/10.1016/j.cpr.2006.01.003

12.

Fellows

L. K.

(2004). Different underlying impairments in decision-making following ventromedial and dorsolateral frontal lobe damage in humans. Cerebral Cortex, 15(1), 58–63. https://doi.org/10.1093/cercor/bhh108

13.

Filipek

P. A.

Semrud-Clikeman

Steingard

R. J.

Renshaw

P. F.

Kennedy

D. N.

Biederman

(1997). Volumetric MRI analysis comparing subjects having attention-deficit hyperactivity disorder with normal controls. Neurology, 48(3), 589–601. https://doi.org/10.1212/WNL.48.3.589

14.

Frodl

Skokauskas

(2012). Meta-analysis of structural MRI studies in children and adults with attention deficit hyperactivity disorder indicates treatment effects: Meta-analysis of structural MRI ADHD studies. Acta Psychiatrica Scandinavica, 125(2), 114–126. https://doi.org/10.1111/j.1600-0447.2011.01786.x

15.

Geary

D. C.

Hoard

M. K.

Nugent

(2021). Boys’ visuospatial abilities compensate for their relatively poor in-class attentive behavior in learning mathematics. Journal of Experimental Child Psychology, 211, 105222. https://doi.org/10.1016/j.jecp.2021.105222

16.

Hoogman

Bralten

Hibar

D. P.

Mennes

Zwiers

M. P.

Schweren

L. S. J.

van Hulzen

K. J. E.

Medland

S. E.

Shumskaya

Jahanshad

Zeeuw

Szekely

Sudre

Wolfers

Onnink

A. M. H.

Dammers

J. T.

Mostert

J. C.

Vives-Gilabert

Kohls

. . . Franke

(2017). Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: A cross-sectional mega-analysis. The Lancet Psychiatry, 4(4), 310–319. https://doi.org/10.1016/S2215-0366(17)30049-4

17.

Huppert

T. J.

Diamond

S. G.

Franceschini

M. A.

Boas

D. A.

(2009). HomER: A review of time-series analysis methods for near-infrared spectroscopy of the brain. Applied Optics, 48(10), D280–D298. https://doi.org/10.1364/AO.48.00D280

18.

Kim

Johnson

N. F.

Cilles

S. E.

Gold

B. T.

(2011). Common and distinct mechanisms of cognitive flexibility in prefrontal cortex. Journal of Neuroscience, 31(13), 4771–4779. https://doi.org/10.1523/JNEUROSCI.5923-10.2011

19.

Kirchner

Barbeau

E. J.

Thorpe

S. J.

Regis

Liegeois-Chauvel

(2009). Ultra-rapid sensory responses in the human frontal eye field region. Journal of Neuroscience, 29(23), 7599–7606. https://doi.org/10.1523/JNEUROSCI.1233-09.2009

20.

Koch

(2005). Sequential task predictability in task switching. Psychonomic Bulletin & Review, 12(1), 107–112. https://doi.org/10.3758/BF03196354

21.

Koike

Tanabe

H. C.

Sadato

(2015). Hyperscanning neuroimaging technique to reveal the “two-in-one” system in social interactions. Neuroscience Research, 90, 25–32. https://doi.org/10.1016/j.neures.2014.11.006

22.

Lansbergen

M. M.

Kenemans

J. L.

van Engeland

(2007). Stroop interference and attention-deficit/hyperactivity disorder: A review and meta-analysis. Neuropsychology, 21(2), 251–262. https://doi.org/10.1037/0894-4105.21.2.251

23.

LeRoy

Jacova

Young

(2019). Neuropsychological performance patterns of adult ADHD subtypes. Journal of Attention Disorders, 23(10), 1136–1147. https://doi.org/10.1177/1087054718773927

24.

Leshem

(2016). Using dual process models to examine impulsivity throughout neural maturation. Developmental Neuropsychology, 41(1–2), 125–143. https://doi.org/10.1080/87565641.2016.1178266

25.

Liao

S.-C.

Chen

W. J.

Lee

M.-B.

Lung

F.-W.

Lai

T.-J.

Liu

C.-Y.

Lin

C.-Y.

Yang

M.-J.

Chen

C.-C.

(2012). Low prevalence of major depressive disorder in Taiwanese adults: Possible explanations and implications. Psychological Medicine, 42(6), 1227–1237. https://doi.org/10.1017/S0033291711002364

26.

Ludyga

Gerber

Mücke

Brand

Weber

Brotzmann

Pühse

(2020). The acute effects of aerobic exercise on cognitive flexibility and task-related heart rate variability in children with ADHD and healthy controls. Journal of Attention Disorders, 24(5), 693–703. https://doi.org/10.1177/1087054718757647

27.

Martel

Nikolas

Nigg

J. T.

(2007). Executive function in adolescents with ADHD. Journal of the American Academy of Child & Adolescent Psychiatry, 46(11), 1437–1444. https://doi.org/10.1097/chi.0b013e31814cf953

28.

Miyake

Friedman

N. P.

Emerson

M. J.

Witzki

A. H.

Howerter

Wager

T. D.

(2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49–100. https://doi.org/10.1006/cogp.1999.0734

29.

Moncrieff

Timimi

(2010). Is ADHD a valid diagnosis in adults? No. BMJ, 340(mar26 1), c547–c547. https://doi.org/10.1136/bmj.c547

30.

Monsell

(2003). Task switching. Trends in Cognitive Sciences, 7(3), 134–140. https://doi.org/10.1016/S1364-6613(03)00028-7

31.

Müller

H. J.

Rabbitt

P. M.

(1989a). Reflexive and voluntary orienting of visual attention: Time course of activation and resistance to interruption. Journal of Experimental Psychology: Human Perception and Performance, 15(2), 315–330. https://doi.org/10.1037/0096-1523.15.2.315

32.

Müller

H. J.

Rabbitt

P. M. A.

(1989b). Spatial cueing and the relation between the accuracy of “where” and “what” decisions in visual search. The Quarterly Journal of Experimental Psychology Section A, 41(4), 747–773. https://doi.org/10.1080/14640748908402392

33.

Murthy

Ray

Shorter

S. M.

Priddy

E. G.

Schall

J. D.

Thompson

K. G.

(2007). Frontal eye field contributions to rapid corrective saccades. Journal of Neurophysiology, 97(2), 1457–1469. https://doi.org/10.1152/jn.00433.2006

34.

Nakao

Radua

Rubia

Mataix-Cols

(2011). Gray matter volume abnormalities in ADHD: Voxel-based meta-analysis exploring the effects of age and stimulant medication. American Journal of Psychiatry, 168(11), 1154–1163. https://doi.org/10.1176/appi.ajp.2011.11020281

35.

Nigg

J. T.

(2016). Attention and Impulsivity. In Cicchetti

(Ed.), Developmental Psychopathology (pp. 1–56). Hoboken, NJ, USA: John Wiley & Sons, Inc. https://doi.org/10.1002/9781119125556.devpsy314

36.

Ono

Nomoto

Tanaka

Sato

Shimada

Tachibana

Bronner

Noah

J. A.

(2014). Frontotemporal oxyhemoglobin dynamics predict performance accuracy of dance simulation gameplay: Temporal characteristics of top-down and bottom-up cortical activities. NeuroImage, 85, 461–470. https://doi.org/10.1016/j.neuroimage.2013.05.071

37.

Perlman

S. B.

Luna

Hein

T. C.

Huppert

T. J.

(2014). FNIRS evidence of prefrontal regulation of frustration in early childhood. NeuroImage, 85, 326–334. https://doi.org/10.1016/j.neuroimage.2013.04.057

38.

Petruo

V. A.

Mueckschel

Beste

(2019). Numbers in action during cognitive flexibility—A neurophysiological approach on numerical operations underlying task switching. Cortex: A Journal Devoted to the Study of the Nervous System and Behavior, 120. https://www.zhangqiaokeyan.com/journal-foreign-detail/0704022292403.html

39.

Pliszka

S. R.

McCracken

J. T.

Maas

J. W.

(1996). Catecholamines in attention-deficit hyperactivity disorder: Current perspectives. Journal of the American Academy of Child & Adolescent Psychiatry, 35(3), 264–272. https://doi.org/10.1097/00004583-199603000-00006

40.

Polanczyk

Rohde

L. A.

(2007). Epidemiology of attention-deficit/hyperactivity disorder across the lifespan. Current Opinion in Psychiatry, 20(4), 386–392. https://doi.org/10.1097/YCO.0b013e3281568d7a

41.

Primich

Iennaco

(2012). Diagnosing adult attention-deficit hyperactivity disorder: The importance of establishing daily life contexts for symptoms and impairments: Life context in adult ADHD diagnosis. Journal of Psychiatric and Mental Health Nursing, 19(4), 362–373. https://doi.org/10.1111/j.1365-2850.2011.01845.x

42.

Ramnani

Owen

A. M.

(2004). Anterior prefrontal cortex: Insights into function from anatomy and neuroimaging. Nature Reviews Neuroscience, 5(3), 184–194. https://doi.org/10.1038/nrn1343

43.

Rogers

R. D.

Monsell

(1995). Costs of a predictible switch between simple cognitive tasks. Journal of Experimental Psychology: General, 124, 207–231. https://doi.org/10.1037/0096-3445.124.2.207

44.

Rosenbaum

Hilsendegen

Thomas

Haeussinger

F. B.

Metzger

F. G.

Nuerk

H.-C.

Fallgatter

A. J.

Nieratschker

Ehlis

A.-C.

(2018). Cortical hemodynamic changes during the trier social stress Test: An fNIRS study. NeuroImage, 171, 107–115. https://doi.org/10.1016/j.neuroimage.2017.12.061

45.

Sachs

G. S.

(2000). Comorbidity of attention deficit hyperactivity disorder with early- and late-onset bipolar disorder. American Journal of Psychiatry, 157(3), 466–468. https://doi.org/10.1176/appi.ajp.157.3.466

46.

Sakai

Ramnani

Passingham

R. E.

(2002). Learning of sequences of finger movements and timing: Frontal lobe and action-oriented representation. Journal of Neurophysiology, 88(4), 2035–2046. https://doi.org/10.1152/jn.2002.88.4.2035

47.

Schneider

D. W.

Logan

G. D.

(2006). Priming cue encoding by manipulating transition frequency in explicitly cued task switching. Psychonomic Bulletin & Review, 13(1), 145–151. https://doi.org/10.3758/BF03193826

48.

Schulz

K. P.

Newcorn

J. H.

Fan

Tang

C. Y.

Halperin

J. M.

(2005). Brain activation gradients in Ventrolateral prefrontal cortex related to persistence of ADHD in adolescent boys. Journal of the American Academy of Child & Adolescent Psychiatry, 44(1), 47–54. https://doi.org/10.1097/01.chi.0000145551.26813.f9

49.

Sergeant

J. A.

(2000). The cognitive-energetic model: An empirical approach to Attention-Deficit Hyperactivity Disorder. Neuroscience & Biobehavioral Reviews, 24(1), 7–12. https://doi.org/10.1016/S0149-7634(99)00060-3

50.

Sergeant

J. A.

(2005). Modeling attention-deficit/hyperactivity disorder: A critical appraisal of the cognitive-energetic model. Biological Psychiatry, 57(11), 1248–1255. https://doi.org/10.1016/j.biopsych.2004.09.010

51.

Sikström

Söderlund

(2007). Stimulus-dependent dopamine release in attention-deficit/hyperactivity disorder. Psychological Review, 114, 1047–1075. https://doi.org/10.1037/0033-295X.114.4.1047

52.

Spellman

Svei

Kaminsky

Manzano-Nieves

Liston

(2021). Prefrontal deep projection neurons enable cognitive flexibility via persistent feedback monitoring. Cell, 184(10), 2750–2766.e17. https://doi.org/10.1016/j.cell.2021.03.047

53.

Spencer

T. J.

(2006). ADHD and comorbidity in childhood. Journal of Clinical Psychiatry, 67, 27.

54.

Spinella

(2004). Neurobehavioral correlates of impulsivity: Evidence of prefrontal involvement. International Journal of Neuroscience, 114(1), 95–104. https://doi.org/10.1080/00207450490249347

55.

Tassy

Oullier

Duclos

Coulon

Mancini

Deruelle

Attarian

Felician

Wicker

(2012). Disrupting the right prefrontal cortex alters moral judgement. Social Cognitive and Affective Neuroscience, 7(3), 282–288. https://doi.org/10.1093/scan/nsr008

56.

Taube-Schiff

Segalowitz

(2005). Linguistic attention control: Attention shifting governed by grammaticized elements of language. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31(3), 508–519. https://doi.org/10.1037/0278-7393.31.3.508

57.

Tegelbeckers

Brechmann

Breitling-Ziegler

Bonath

Flechtner

H.-H.

Krauel

(2022). Neural mechanisms underlying the effects of novel sounds on task performance in children with and without ADHD. Frontiers in Human Neuroscience, 16, 878994. https://doi.org/10.3389/fnhum.2022.878994

58.

Tripp

Wickens

J. R.

(2008). Research review: Dopamine transfer deficit: a neurobiological theory of altered reinforcement mechanisms in ADHD. Journal of Child Psychology and Psychiatry, 49(7), 691–704. https://doi.org/10.1111/j.1469-7610.2007.01851.x

59.

Tsai

C.-W.

Gunnell

Chou

Y.-H.

Kuo

C.-J.

Lee

M.-B.

Chen

Y.-Y.

(2011). Why do people choose charcoal burning as a method of suicide? An interview based study of survivors in Taiwan. Journal of Affective Disorders, 131(1), 402–407. https://doi.org/10.1016/j.jad.2010.12.013

60.

Tseng

M. M.-C.

Fang

Lee

M.-B.

Chie

W.-C.

Liu

J.-P.

Chen

W. J.

(2007). Two-phase survey of eating disorders in gifted dance and non-dance high-school students in Taiwan. Psychological Medicine, 37(8), 1085–1096. https://doi.org/10.1017/S0033291707000323

61.

Ubaldi

Barchiesi

Cattaneo

(2015). Bottom-up and top-down visuomotor responses to action observation. Cerebral Cortex, 25(4), 1032–1041. https://doi.org/10.1093/cercor/bht295

62.

Valera

E. M.

Faraone

S. V.

Murray

K. E.

Seidman

L. J.

(2007). Meta-analysis of structural imaging findings in attention-deficit/hyperactivity disorder. Biological Psychiatry, 61(12), 1361–1369. https://doi.org/10.1016/j.biopsych.2006.06.011

63.

Wardak

Ibos

Duhamel

J.-R.

Olivier

(2006). Contribution of the monkey frontal eye field to covert visual attention. Journal of Neuroscience, 26(16), 4228–4235. https://doi.org/10.1523/JNEUROSCI.3336-05.2006

64.

Wilbertz

Tebartz van Elst

Delgado

M. R.

Maier

Feige

Philipsen

Blechert

(2012). Orbitofrontal reward sensitivity and impulsivity in adult attention deficit hyperactivity disorder. NeuroImage, 60(1), 353–361. https://doi.org/10.1016/j.neuroimage.2011.12.011

65.

Wilckens

K. A.

Kline

C. E.

Bowman

M. A.

Brindle

R. C.

Cribbet

M. R.

Thayer

J. F.

Hall

M. H.

(2020). Does objectively-assessed sleep moderate the association between history of major depressive disorder and task-switching? Journal of Affective Disorders, 265, 216–223. https://doi.org/10.1016/j.jad.2020.01.003

66.

Xia

Wang

(2013). BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics. PLOS ONE, 8(7), e68910. https://doi.org/10.1371/journal.pone.0068910

67.

J. C.

Tak

Jang

K. E.

Jung

Jang

(2009). NIRS-SPM: Statistical parametric mapping for near-infrared spectroscopy. NeuroImage, 44(2), 428–447. https://doi.org/10.1016/j.neuroimage.2008.08.036

68.

Yeh

C.-B.

Gau

S. S.-F.

Kessler

R. C.

Y.-Y.

(2008). Psychometric properties of the Chinese version of the adult ADHD self-report scale. International Journal of Methods in Psychiatric Research, 17(1), 45–54. https://doi.org/10.1002/mpr.241

69.

Zhang

(2011). Notice of retraction: The Chinese revised edition of Wender Utah rating scale for attention deficit/hyperactivity disorder in adults [Conference session]. 2011 5th International Conference on Bioinformatics and Biomedical Engineering, pp.1–4. https://doi.org/10.1109/icbbe.2011.5780107