Exploring Brain Activation as a Predictor of Intention to Use Cognitive Training Digital Therapeutics: A Functional Near-Infrared Spectroscopy Study

Abstract

This study examines the neural underpinnings of the intention to use cognitive training digital therapeutics among older adults with subjective memory complaints. A total of 23 older adults with a clinical dementia rating score of 0.5—commonly interpreted as indicative of very mild cognitive impairment—participated in cognitive training using a smartphone application. Leveraging functional near-infrared spectroscopy during cognitive training and the technology acceptance model, this research investigates the predictive role of neural activation on the intention to use. Results revealed that higher activation in the right medial prefrontal cortex and reduced activation in the right dorsolateral prefrontal cortex during cognitive training were significantly associated with increased intention to use. The interplay between neural mechanisms in the motivational system and the cognitive control system at the prefrontal cortex level plays a critical role in shaping the intention to use.

Introduction

Digital therapeutics (DTx) has emerged as a transformative paradigm in contemporary health care, offering evidence-based, software-driven interventions designed to prevent, manage, and treat medical conditions. This is particularly significant in the context of an aging global population. The global population aged 60 years and older is expected to almost double, increasing from 12 percent in 2015 to 22 percent by 2050.¹ This demographic shift is associated with a rise in age-related medical conditions, including cognitive decline, which imposes substantial societal and economic burdens.² Addressing these challenges necessitates the development of accessible, cost-effective DTx solutions tailored to the unique needs of older adults. DTx holds particular promise for older adults with memory complaints due to its ability to deliver interventions without spatial or temporal constraints.³

The success of DTx depends on user engagement and adherence, particularly as it shifts from being practitioner-led to user-driven, requiring individuals to manage their own care.⁴ This paradigm shift underscores the need to understand the factors influencing users’ motivation and intention to use DTx. One framework frequently employed to explore technology adoption is the Technology Acceptance Model (TAM).⁵ TAM provides a theoretical foundation for examining users’ behavioral intentions to adopt new technologies. The model posits that three primary constructs—perceived usefulness (PU), perceived ease of use (PEoU), and intention to use (IU)—predict technology adoption. PU reflects the degree to which a user believes a technology will enhance their performance or well-being, while PEoU refers to the perceived effort required to use the technology. IU, in turn, represents the likelihood or motivation of a user to engage with and continue using the technology.⁶ PEoU is typically viewed as an antecedent of PU, which, in turn, predicts IU. This hierarchical structure highlights that users’ perception of ease contributes to their belief in the usefulness of the technology, ultimately influencing their motivation to use it. This study builds on this theoretical foundation by exploring these relationships in the context of cognitive training DTx and examining their neural underpinnings through functional near-infrared spectroscopy (fNIRS).

Neural biomarkers, which objectively capture cognitive and emotional processes, could offer a novel complement to behavioral models like TAM.⁷ Recent studies integrating neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), fNIRS, and electroencephalography (EEG), with TAM have shown significant correlations with brain regions associated with decision-making and motivation.^8,9 Building on these advancements, the current study aimed to integrate neural data into the TAM by leveraging fNIRS to investigate the neural correlations of intention to use DTx.

To provide a stronger theoretical grounding for this integration, the current study drew on the cognitive-motivation system framework, which conceptualizes goal-directed behavior as emerging from the interaction between motivational and cognitive control processes.¹⁰ Within this framework, the medial prefrontal cortex (mPFC) is involved in motivational functions such as value-based decision-making, while the dorsolateral prefrontal cortex (dlPFC) supports cognitive effort and control. Therefore, it was hypothesized that the interplay of activation in these regions might reflect processes relevant to the formation of intention to use, such as motivational engagement. This perspective offers a valuable lens for understanding the neural mechanisms underlying users’ adoption of DTx, particularly in populations experiencing cognitive concern.

Methods

Participants

A total of 23 older adults (aged from 55 to 85) with subjective memory complaints were recruited from the Department of Psychiatry at Boramae Medical Center. Eligible participants had a clinical dementia rating (CDR) score between 0.5 and 1.0 and scored below −1.0 SD compared with age-adjusted norms on at least one memory domain of the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease Assessment Packet neuropsychological test battery.¹¹ Exclusion criteria included the presence of any disease or psychiatric condition that could interfere with study procedures, as well as the recent initiation of medications with cholinergic or non-cholinergic effects that might influence cognitive functioning. Demographic information for all participants was presented in Table 1. All participants had a CDR score of 0.5, indicating questionable cognitive decline.

Table 1.

Socio-Demographical Characteristics of Participants

Variables	Total (N = 23)
Age (years)
Mean (standard deviation)	73.04 (7.00)
Range	58–82
Gender, n (percent)
Male	8 (35 percent)
Female	15 (65 percent)
Education (years)
Mean (standard deviation)	11.78 (5.54)
[1] No education (0 year)	1 (4 percent)^a
[2] Elementary school (1–6 years)	3 (12 percent)
[3] Middle school (7–9 years)	4 (16 percent)
[4] High school (10–12 years)	4 (16 percent)
[5] University (13 ≤ years)	11 (52 percent)

All participants scored 0.5 on the Clinical Dementia Rating (CDR).

n (percent).

Recruitment procedure

Participants were recruited through physician referral during clinical visits to Boramae Medical Center. After receiving information about the study, those who met the eligibility criteria and voluntarily agreed to participate were enrolled in a digital therapeutic usability evaluation.

Experimental procedures

Upon enrollment, participants completed a basic demographic questionnaire and were then fitted with an fNIRS device (NIRSIT; OBELAB Inc., Korea). Once the setup was complete, participants engaged in cognitive training using the DTx application while fNIRS data were continuously recorded.

Measurements

Clinical dementia rating

The CDR is a standardized measure used to assess dementia severity based on clinician ratings across six functional domains: memory, orientation, judgment and problem solving, community affairs, home and hobbies, and personal care. The composite rating consists of five levels: 0 (none), 0.5 (questionable), 1 (mild), 2 (moderate), and 3 (severe). This tool was originally developed by Hughes et al.¹² and later revised by Morris.¹³ A Korean version of the test has been validated by Choi and colleagues.¹⁴

TAM measures

Items to measure PU, PEoU, and IU were adopted from the Measurement Scales for perceived usefulness and perceived ease of use by Davis (1989).^15,16 To align with the context of a digital cognitive training application for older adults, the items were modified to emphasize memory-related outcomes, age-relevant usability, and long-term engagement.

For example, the original PU item “Using [the system] would improve my job performance” was adapted to “Using this application would improve my memory,” reflecting the goal of cognitive enhancement rather than work-related productivity. A PEoU item such as “I would find [the system] easy to use” was conceptually retained but rephrased as “Overall, I find this application easy to use,” to better capture user experience among older adults. Similarly, the original IU item “Assuming I have access to [the system], I intend to use it” was adapted to “I intend to use this application for cognitive training,” emphasizing sustained engagement for cognitive improvement. All items were rated on a 5-point Likert scale (1 = strongly disagree, 3 = neutral, 5 = strongly agree). The internal consistency reliability coefficients (Cronbach’s α) for PU, PEoU, and IU were 0.906, 0.893, and 0.822, respectively.

fNIRS data acquisition and preprocessing

Neural activity was recorded using the NIRSIT device (OBELAB Inc., Korea), a wearable fNIRS system equipped with 24 light sources and 32 detectors, creating a total of 48 channels. The inter-optode distances were set within 3 cm to optimize signal capture, and data were collected at a sampling rate of 8.138 Hz.

All fNIRS data acquisition was conducted with participants seated comfortably. During the baseline recording, participants were instructed to sit quietly with their eyes closed, avoid any deliberate thoughts, and remain awake. Previous studies using fNIRS for baseline measures have reported durations ranging from 2 seconds to 10 minutes.¹⁷ Based on these findings, baseline signals in this study were recorded for 5 minutes.

Following the baseline session, participants viewed a tutorial video embedded in the cognitive training application to familiarize themselves with its use. Once adequately trained, participants completed cognitive training tasks provided by the application. To control potential order effects, a counterbalancing method was employed to vary the sequence of tasks across participants.¹⁸

The time required for hemodynamic responses to peak after stimulus onset varies depending on the task but typically takes several seconds (e.g., ∼6 seconds).^19–21 Hemodynamic responses generally return to baseline within 10–16 seconds after stimulus termination.^22,23 To minimize interference between tasks, a minimum interval of 30 seconds was provided between task completions.

Data acquisition and preprocessing were performed using the NIRSIT PC Tool v.2.8 and the NIRSIT Analysis Tool v.3.7.5, provided by OBELAB. Raw optical data collected by the fNIRS device were converted into Oxyhemoglobin (HbO) and Deoxyhemoglobin (HbR) concentrations using the modified Beer–Lambert law.²⁴ Physiological noise caused by motion artifacts and systemic signals was minimized by applying a discrete cosine transform (DCT) filter with a cutoff frequency of 0.005–0.100 Hz. Channels were excluded from analysis if they met any of the following criteria: Over 5 percent of frames contained missing values, Negative values exceeded 5 percent of total data, or the signal-to-noise ratio (SNR) was below 30 dB. Excluded channels were interpolated following a padding procedure: Padding with data from backup channels, averaging values from channels in the same Brodmann area, or using the overall mean of the dataset for padding. An example of an activation map during cognitive training was presented in Figure 1.

FIG. 1.

Activation map during the cognitive training. Left, 2D; Right, 3D.

Supplementary Figure S1 was included to provide the channel distribution map of the NIRSIT system used in this study. This map indicates the channels corresponding to major prefrontal subregions, including the mPFC, dlPFC, ventrolateral prefrontal cortex, orbitofrontal cortex, and frontopolar prefrontal cortex, as identified by anatomical labels and Montreal Neurological Institute (MNI) coordinates provided by the manufacturer²⁵ (see Supplementary Figure S1). A complete list of channel-wise MNI coordinates is provided in Supplementary Table S1.

Ethical considerations

All participants provided written informed consent, and the study was conducted in accordance with the ethical guidelines and regulations approved by the Boramae Medical Center Institutional Review Board (IRB No. 20-2022-48).

Statistical analysis

SPSS 20.0 for WINDOWS was used for data analyses. Descriptive statistics for demographic variables (age, gender, and education) and Pearson correlations were computed to examine the relationships between demographic variables and TAM constructs (PEoU, PU, IU). To analyze the TAM, mediation analysis was conducted using the PROCESS macro for SPSS (Version 3.1),²⁶ applying Model 4. This approach allowed for the examination of the mediating role of PU in the relationship between PEoU and IU. Age was included as a covariate in all mediation paths. Forward multiple linear regression analyses were conducted to explore the predictive role of neural activation in three dependent variables.

Results

Associations among perceived usefulness, perceived ease of use, and intention to use

Correlations

Associations among the three constructs of the TAM and their relationships with demographic characteristics are presented in Table 2.

Table 2.

Descriptive Statistics and Correlation Between the Variables

	(1)	(2)	Age	Gender	Education	M (SD)
(1) Perceived ease of use	—		−0.022 ^ns	−0.323 ^ns	0.141 ^ns	4.39 (0.85)
(2) Perceived usefulness	−0.035 ^ns	—	0.481*	−0.270 ^ns	−0.052 ^ns	4.24 (0.98)
(3) Intention to use	0.124 ^ns	0.676^***	0.581^**	0.327 ^ns	−0.095 ^ns	4.08 (1.05)

Gender was coded as male = 0 and female = 1.

^*p < 0.05, ^**p < 0.01, ^***p < 0.001.

M, mean; ns, nonsignificant; SD, standard deviation.

Technology Acceptance Model

A regression analysis was conducted to examine the relationships among PEoU, PU, and IU (Figure 2). PEoU did not significantly predict PU (β = –0.024, p = 0.903), nor did it directly predict IU (β = 0.150, p = 0.349). In contrast, PU significantly predicted IU (β = 0.554, p = 0.006), indicating that users’ perceptions of usefulness were strongly associated with their intention to use the digital therapeutic application. Age was included as a covariate and showed a significant effect on PU (β = 0.069, p = 0.024), but not on IU (β = 0.038, p = 0.155).

FIG. 2.

Technology acceptance model testing the indirect effect of perceived ease of use on intention to use via perceived usefulness, with age included as a covariate. Bold lines represent statistically significant paths (p < 0.05).

Neural predictors of perceived usefulness, perceived ease of use, and intention to use

Forward multiple regression analyses were conducted separately for each of the dependent variables (i.e., IU, PU, and PEoU), using brain activation values during cognitive training as predictors. The results revealed that the right mPFC (β = 0.670; p = 0.006) and right dlPFC (β = −0.479; p = 0.038) significantly predicted the IU [adjusted R² = 0.298; F(2, 18) = 5.249; p = 0.016] (Table 3). Specifically, higher activation in the mPFC and reduced activation in the dlPFC during cognitive training were associated with a subsequent increase in IU. In contrast, neither PU nor PEoU was significantly predicted by brain activity.

Table 3.

Forward Multiple Regression Results Showing Brain Activity Predicting Intention to Use the Cognitive Training

Brain activity during cognitive training	b	SE	β	t	p
Right medical prefrontal cortex	67224.84	21469.72	0.670	3.131	0.006
Right dorsolateral prefrontal cortex	−530312.88	23672.35	−0.479	2.240	0.038

Discussion

The current study investigated the associations among three constructs (i.e., PEoU, PU, IU) of TAM and their neural predictors during cognitive training in older adults. Results revealed that PU significantly predicted IU, and higher activation in the right mPFC alongside reduced activity in the right dlPFC during cognitive training were significantly correlated with increased IU. By focusing on older adults with subjective memory complaints, this study contributes to a basic understanding of how cognitive training can be effectively administered using DTx adoption in populations particularly vulnerable to cognitive decline. Theoretically, this research advances TAM by uncovering its neural underpinnings. This study provides critical insights into the neural and psychological mechanisms underlying IU, thereby laying the groundwork for future research aimed at optimizing user-centered interventions and promoting sustained engagement in DTx solutions.

The TAM for older adults with subjective memory complaints was partially supported by the findings of this study. Specifically, PEoU did not significantly predict PU, but PU emerged as a critical factor in predicting IU. These findings underscore the unique role of PU in encouraging older adults to continue using cognitive training DTx, suggesting that the perceived benefits of DTx outweigh considerations of ease of use for this population.²⁷

This result can be further interpreted through the lens of Expectancy-Value Theory, which posits that motivation arises from the interplay of two factors: expectancy (the belief that one can succeed or perform the task) and value (the perceived importance or benefit of the outcome).²⁸ Within this framework, PEoU aligns with “expectancy,” as it reflects the effort required to use the system, while PU aligns with “value,” as it represents the benefits users perceive from engaging with the technology. For older adults with subjective memory complaints, the findings suggest that value (e.g., PU) is far more influential than expectancy (e.g., PEoU) in predicting motivation for further use of DTx. Individuals in this population, who often experience heightened concerns about cognitive decline, prioritize interventions that offer tangible benefits, such as improving memory or maintaining cognitive function, over ease of use. This finding aligns with research suggesting that for older adults, the PU of a technology (e.g., tangible benefits) plays a more critical role than ease of use in forming their intention to adopt.²⁹ For instance, in a study examining elderly users’ intention to adopt wearable robots, PU positively predicted intention to use, whereas PEoU showed a negative association when controlling for other factors such as anxiety. These results highlight the importance of prioritizing interventions that clearly communicate functional benefits, especially in aging populations.

Higher activation in the mPFC and reduced activation in the dlPFC were significantly correlated with increased IU. The findings of this study can be understood within the framework of the cognitive-motivation system, which integrates motivational and cognitive processes to drive goal-directed behaviors.¹⁰ The mPFC is a critical region for motivational processing, including self-referential thought and reward-based decision-making.^30,31 Furthermore, the higher mPFC activation observed in this study reflects the engagement of motivational pathways essential for “exploration.”³² The decision to adopt DTx involves navigating a shift from conventional approaches (exploitation) to embracing new tools and interventions (exploration). As highlighted in Alexander and Brown’s Predictive Response–Outcome model, the mPFC plays a pivotal role in monitoring and integrating the predicted outcomes of new actions, particularly when individuals evaluate the value of novel interventions like DTx.³³

In contrast, the dlPFC, a region associated with cognitive control and effort regulation,^34,35 exhibited reduced activation in participants with higher IU. This pattern suggests that experiencing cognitive burden during the intervention may reduce participants’ motivation to engage with the program in the future. Similarly, findings from recent consumer neuroscience study indicate that reduced dlPFC activity can reflect a state of “cognitive relief,” where decreased cognitive control allows for more intuitive or value-driven decision-making.³⁶

The interplay between mPFC-mediated motivation and dlPFC-regulated cognitive effort reflects the balance within the cognitive-motivation system.¹⁰ In this study, participants with higher IU relied more on motivational processes while perceiving minimal cognitive demands. This highlights the importance of emphasizing value-driven motivation in interventions while minimizing cognitive complexity, particularly for older adults with cognitive concerns. The dominance of motivational processing (mPFC) over effort regulation (dlPFC) also aligns with the Expectancy-Value Theory, which posits that when the perceived value (PU) is high, cognitive effort (PEoU) becomes less relevant. This dynamic likely explains why PU, but not PEoU, emerged as a critical predictor of IU in this study.

Limitations and future directions

Several limitations of this study should be acknowledged. First, the sample size was relatively small, which may limit the generalizability of the findings. Future studies with larger and more diverse samples are needed to confirm the observed neural correlates of IU. Second, the current study did not apply multiple comparison corrections due to the limited sample size. This issue should be addressed in future studies through appropriate statistical adjustments or increased statistical power. Third, although the CDR was used to ensure clinical consistency across participants, subjective memory complaint were not directly measured. As subjective memory complaint levels may influence motivation and engagement with DTx, incorporating validated subjective memory complaint assessments in future research could enhance the robustness of the findings. Moreover, while age was considered as a covariate in the neural regression model predicting IU, its inclusion attenuated the primary effect of brain activation. Due to the small sample size and the exploratory nature of the analysis, age was not included in the final model. Future studies with larger and more diverse samples should incorporate age as a covariate to further clarify its role.

In addition to the limitations of this study, it is important to note potential challenges that future research may face when attempting to integrate real-time fNIRS analysis into DTx systems. Current portable fNIRS devices may be limited in terms of spatial resolution, signal stability, and processing latency, which can hinder their ability to provide reliable real-time neural feedback. Furthermore, practical constraints such as user comfort, device calibration, and the lack of standardized protocols for real-time neural signal interpretation may pose additional barriers to clinical implementation. Addressing these limitations will be essential for the successful integration of neurophysiological data into next-generation DTx platforms.

Conclusions

This study provides preliminary neural evidence that prefrontal activation during cognitive training predicts older adults’ intention to use a DTx application. Higher activation in the right mPFC and lower activation in the right dlPFC were associated with a stronger intention to use, suggesting that motivational and cognitive control mechanisms both play important roles in DTx acceptance among aging populations. These findings contribute to the growing literature on neuro-informed technology acceptance by demonstrating the relevance of brain-based predictors in user engagement.

This work also highlights the potential of adaptive DTx systems that integrate real-time neural feedback to enhance personalization and effectiveness. For instance, if fNIRS signals indicate low user motivation, the system could dynamically adjust its content or interaction mode to better align with user needs—an especially important feature for older adults with memory complaints. Future research should build on these findings using larger and more diverse samples, incorporating real-time feedback mechanisms, and exploring longitudinal effects on adherence and outcomes.

Footnotes

Authors' Contributions

Y.B. and J.-Y.L.: Conceptualization. Y.B. and J.-I.L.: Methodology. S.P., Y.B., and J.-I.L.: Formal analysis. S.P. and Y.B.: Investigation. Y.B. and J.I.L.: Data curation. Y.B. and H.K.: Writing—original draft. S.P., H.K., and J.-Y.L.: Writing—review & editing. S.P.: Visualization. J.-Y.L.: Supervision.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

This work was supported by the Korea Medical Device Development Fund grant funded by the Korea government. (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: 2710002631, RS-2023-00253694).

Supplementary Material

References

WHO. Ageing and Health. 2024.

Skaria

. The economic and societal burden of Alzheimer disease: Managed care considerations. Am J Manag Care, 2022; 28,(Suppl 10):S188–S196; doi: 10.37765/ajmc.2022.89236

Park

, Ha

. Effect of digital technology interventions for cognitive function improvement in mild cognitive impairment and dementia: A systematic review and meta-analysis. Res Nurs Health, 2024; 47(4):409–422; doi: 10.1002/nur.22383

Lee

, Jung

, Ma

E-Y

, et al. Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions. 2022.

Davis

. Technology Acceptance Model. Massachusetts Institute of Technology; 1985.

Nadal

, Sas

, Doherty

. Technology acceptance in mobile health: Scoping review of definitions, models, and measurement. J Med Internet Res, 2020; 22(7):e17256; doi: 10.2196/17256

Dimoka

, Davis

. Where Does TAM Reside in the Brain? The Neural Mechanisms Underlying Technology Adoption. In: ICIS 2008 Proceedings 2008; p. 1.

Nissen

, Krampe

, Kenning

, et al. Utilizing Mobile FNIRS to Investigate Neural Correlates of the TAM in ECommerce. 2019.

Tolgay

, Dell’orco

, Trojano

. EEGs as Potential Predictors of Virtual Agents’ Acceptance. In: 10th IEEE International Conference on Cognitive Infocommunications IEEE; 2019; pp. 433–438.

10.

Kok

. Cognitive control, motivation and fatigue: A cognitive neuroscience perspective. Brain Cogn, 2022; 160:105880; doi: 10.1016/j.bandc.2022.105880

11.

Lee

, Lee

, et al. Development of the Korean version of the consortium to establish a registry for Alzheimer’s disease assessment packet (CERAD-K): Clinical and neuropsychological assessment batteries. J Gerontol B Psychol Sci Soc Sci, 2002; 57(1):47–53; doi: 10.1016/S0197-4580(00)82818-3

12.

Hughes

, Berg

, Danziger

, et al. A new clinical scale for the staging of dementia. Br J Psychiatry, 1982; 140(6):566–572.

13.

Morris

. The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology, 1993; 43(11):2412–2414.

14.

Choi

, Na

, Lee

, et al. Estimating the validity of the Korean version of expanded clinical dementia rating (CDR) scale. Journal of the Korean Neurological Association, 2001.

15.

Davis

. Perceived usefulness, perceived ease of use, and user acceptance of information. Source: MIS Quarterly, 1989; 13(3):319–340.

16.

Venkatesh

, Davis

. A theoretical extension of the technology acceptance model: Four Longitudinal field studies. Manage Sci, 2000; 46(2):186–204.

17.

Herold

, Wiegel

, Scholkmann

, et al. Applications of functional near-infrared spectroscopy (FNIRS) neuroimaging in exercise–cognition science: A systematic, methodology-focused review. JCM, 2018; 7(12):466; doi: 10.3390/jcm7120466

18.

Pollatsek

, Well

. On the Use of Counterbalanced Designs in Cognitive Research: A Suggestion for a Better and More Powerful Analysis. 1995.

19.

Orihuela-Espina

, Leff

, James

DRC

, et al. Quality control and assurance in functional near infrared spectroscopy (fNIRS) experimentation. Phys Med Biol, 2010; 55(13):3701–3724; doi: 10.1088/0031-9155/55/13/009

20.

Pinti

, Aichelburg

, Gilbert

, et al. A review on the use of wearable functional near-infrared spectroscopy in naturalistic environments. Jpn Psychol Res, 2018; 60(4):347–373; doi: 10.1111/jpr.12206

21.

Tong

, Frederick B de

. Time lag dependent multimodal processing of concurrent fMRI and near-infrared spectroscopy (NIRS) data suggests a global circulatory origin for low-frequency oscillation signals in human brain. Neuroimage, 2010; 53(2):553–564; doi: 10.1016/j.neuroimage.2010.06.049

22.

Friston

, Josephs

, Rees

, et al. Nonlinear event-related responses in fMRI. Magn Reson Med, 1998; 39(1):41–52; doi: 10.1002/mrm.1910390109

23.

Villringer

, Dirnagl

. OPTICAL IMAGING OF BRAIN FUNCTION AND METABOLISM 2 Physiological Basis and Comparison to Other Functional Neuroimaging Methods. Springer Science: New York; 1997.

24.

Badiola

, Blazek

, Kumar

. A Monte Carlo Investigation of Optical Pathlength in Inhomogeneous Tissue and Its Application to Near-Infrared Spectroscopy You May Also like Accuracy Enhancement in Reflective Pulse Oximetry by Considering Wavelength-Dependent Pathlengths. 1993.

25.

OBELAB Inc. NIRSIT Channel Information Brochure. Seoul, Korea; 2022.

26.

Hayes

. PROCESS: A Versatile Computational Tool for Observed Variable Mediation, Moderation, and Conditional Process Modeling. 2012:1–39.

27.

Guisado-Fernández

, Giunti

, Mackey

, et al. Factors influencing the adoption of smart health technologies for people with dementia and their informal caregivers: Scoping review and design framework. JMIR Aging, 2019; 2(1):e12192; doi: 10.2196/12192

28.

Wigfield

, Eccles

. Expectancy-value theory of achievement motivation. Contemp Educ Psychol, 2000; 25(1):68–81; doi: 10.1006/ceps.1999.1015

29.

Jung

, Kim

Y-M

. Prediction of the elderly’s usage intention of wearable robots: Focusing on the technology acceptance model. KSW, 2022; 17(2):271–278; doi: 10.21097/ksw.2022.5.17.2.271

30.

Yang

, Hu

, Talishinsky

, et al. Medial prefrontal cortex and anteromedial thalamus interaction regulates goal-directed behavior and dopaminergic neuron activity. Nat Commun, 2022; 13(1):1386; doi: 10.1038/s41467-022-28892-7

31.

Segalowitz

, van Noordt

. Performance monitoring and the medial prefrontal cortex: A review of individual differences and context effects as a window on self-regulation. Front Hum Neurosci, 2012; 6:197; doi: 10.3389/fnhum.2012.00197

32.

Domenech

, Rheims

, Koechlin

. Neural mechanisms resolving exploitation-exploration dilemmas in the medial prefrontal cortex. Science, 2020; 369(6507); doi: 10.1126/science.abb0184

33.

Alexander

, Brown

. Medial prefrontal cortex as an action-outcome predictor. Nat Neurosci, 2011; 14(10):1338–1344; doi: 10.1038/nn.2921

34.

Nejati

, Majdi

, Salehinejad

, et al. The role of dorsolateral and ventromedial prefrontal cortex in the processing of emotional dimensions. Sci Rep, 2021; 11(1):1971; doi: 10.1038/s41598-021-81454-7

35.

Gbadeyan

, McMahon

, Steinhauser

, et al. Stimulation of dorsolateral prefrontal cortex enhances adaptive cognitive control: A high-definition transcranial direct current stimulation study. J Neurosci, 2016; 36(50):12530–12536; doi: 10.1523/JNEUROSCI.2450-16.2016

36.

Gier

, Strelow

, Krampe

. Measuring dlPFC signals to predict the success of merchandising elements at the point-of-sale—A fNIRS approach. Front Neurosci, 2020; 14:575494; doi: 10.3389/fnins.2020.575494

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.23 MB

3.02 MB