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. 2026 Feb 26;244(4):51. doi: 10.1007/s00221-026-07248-3

Cortical activation in precision grip and its association with manual dexterity: a foundational fNIRS study

Shumpei Toriyama 1, Nozomi Sasaki 1, Takumu Yamaguchi 1, Hiroki Sato 1,✉
PMCID: PMC12946292  PMID: 41746380

Abstract

Fine motor tasks that involve precision grip depend on both motor control and cognitive processes. However, the neural mechanisms underlying individual differences in manual dexterity remain incompletely understood, particularly under ecologically valid task conditions. The present study aimed to establish a controlled functional near-infrared spectroscopy (fNIRS) paradigm capable of isolating cortical activation specifically elicited by precision grip, and to examine whether such activation is associated with individual differences in manual dexterity. Manual dexterity was assessed using the Purdue Pegboard test (PPT). Cortical activity was measured using 44-channel fNIRS while 40 young adult participants performed temporally controlled precision grip tasks derived from the PPT with either their right or left hand. Precision grip tasks elicited significant increases in oxyhemoglobin signal in bilateral prefrontal and sensorimotor regions compared with a control task. Furthermore, higher PPT assembly scores were associated with greater task-related activation in the lateral prefrontal cortex during right-hand precision grip task. In applied terms, greater task-evoked activation should be interpreted as increased cortical recruitment required to meet precision demands, rather than as inherently superior performance. Taken together, these preliminary findings provide a foundational reference for understanding precision grip–related cortical recruitment and its association with individual differences in manual dexterity, and demonstrate the feasibility of using fNIRS to study cognitive–motor integration under realistic task conditions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00221-026-07248-3.

Keywords: Fine motor skill, Manual dexterity, Precision grip, Prefrontal cortex, fNIRS

Introduction

Fine motor skills are essential for everyday activities such as writing, manipulating small objects, and performing self-care tasks. They represent a fundamental component of human motor control, requiring the integration of muscular strength, dexterity, and agility to achieve precise and efficient movements (Makofske 2011).

Among these, precision grip refers to the delicate modulation of force between the thumb and index finger to pick up or pinch small objects. It is a prototypical fine motor action critical for object manipulation (Simmons et al. 2024). Executing such movements engages higher-order sensorimotor networks, including the supplementary motor area (SMA) and frontoparietal cortex, which support temporal and force-related adjustments (Haller et al. 2009). In addition to sensorimotor control, cognitive processes such as motor planning, sequencing, and executive attention also play important roles (Park and Reuter-Lorenz 2009). Functional magnetic resonance imaging (fMRI) studies have shown that even simple precision grip tasks activate the dorsolateral prefrontal cortex (DLPFC) and parietal cortices, reflecting cognitive-motor integration processes such as coordinated movement, visuomotor perception, and attention (Ehrsson et al. 2000). Neurophysiological evidence also indicates that projections from the ventral premotor cortex (PMv) to the primary motor cortex (M1) modulate corticospinal excitability during grasping (Davare et al. 2008; Casarotto et al. 2023).

fMRI has provided valuable insights into the neural basis of fine motor control; however, the requirement for participant immobility limits its ecological validity, particularly for dynamic, naturalistic hand movements. In contrast, functional near-infrared spectroscopy (fNIRS) offers a noninvasive, motion-tolerant alternative that can detect prefrontal and parietal activation during realistic motor tasks (Ferrari and Quaresima 2012; Fishburn et al. 2014). Ecological validity refers to the extent to which experimental conditions approximate real-world behavior and natural postures. fNIRS is well suited for such contexts because it can be used in seated, task-engaged settings and is relatively tolerant of head and body motion compared with MRI-based methods. fNIRS noninvasively infers cortical activation by measuring task-evoked changes in oxygenated and deoxygenated hemoglobin in superficial cortical tissue using near-infrared light (Obrig and Villringer 2003; Ferrari and Quaresima 2012). fNIRS has limited spatial resolution and cannot directly measure activity in deeper structures (Ferrari and Quaresima 2012); however, its portability makes it suitable for investigating fine motor behaviors in ecologically valid contexts.

Within the broad domain of fine motor skills, manual dexterity refers to an individual’s ability to efficiently perform fine motor actions such as precision grip and object manipulation in goal-directed situations. This construct reflects the integration of motor coordination and cognitive control, including working memory, sequencing, and attentional regulation (Seidler et al. 2010; Makofske 2011). Cortical mechanisms may offer a more comprehensive model for understanding manual dexterity, as precision grip represents a fundamental component of skilled manual performance.

The Purdue pegboard test (PPT) is a widely used measure of manual dexterity. It includes subtasks requiring sequential bimanual precision grip actions such as peg placement and object assembly (Tiffin and Asher 1948; Chen et al. 2024). The neural correlates of manual dexterity during these tasks have recently been explored using fNIRS. For example, Bonzano et al. (2023) compared the Nine-Hole Peg Test (9-HPT) and the simplified One-Hole Peg Test (1-HPT) and identified increased prefrontal activation in participants with slower 9-HPT performance. However, both tasks involve identical pin-insertion movements, making it difficult to attribute neural differences specifically to finger motor control. Similarly, Bakhshipour et al. (2021) reported greater prefrontal activation during PPT assembly subtasks. While these findings provide valuable insights into cognitive load and task complexity, variations in task duration, bimanual coordination, and sequencing demands make it difficult to isolate activation specific to precision grip.

To date, few fNIRS studies have directly isolated and examined cortical activation specifically elicited by precision grip under realistic conditions. This suggests a gap in foundational knowledge regarding the assessment of manual dexterity and rehabilitation practices using fNIRS.

Accordingly, the primary aim of this study was not to test competing hypotheses, but to establish a controlled fNIRS paradigm capable of isolating precision grip–related cortical activity under ecologically valid conditions. To achieve this aim, we designed an fNIRS paradigm involving temporally controlled peg manipulation with counterbalanced task order and hand use to dissociate cortical responses specifically related to fine finger movements from general task-related or attentional effects. Our secondary objective was to determine whether individual differences in manual dexterity, measured using the PPT, were reflected in task-evoked cortical activation. Overall, our preliminary findings provide a foundational reference for understanding the cortical mechanisms underlying precision grip and their potential association with individual differences in prefrontal activation and manual dexterity.

Methods

Participants

We recruited 40 right-handed young adults (male: n = 20; female: n = 20; mean age ± SD: 20.8 ± 1.36 years) from Shibaura Institute of Technology. Handedness was assessed using the Japanese version of the FLANDERS Handedness Test (Okubo et al. 2014). All participants provided written informed consent before participation. We included only right-handed participants to reduce inter-individual variability in hemispheric motor dominance and to facilitate interpretation of hand-related effects on cortical activation. The Research Ethics Committee at Shibaura Institute of Technology approved the study protocol (Approval No: 22-037), which complied with the Ethical Guidelines for Medical and Biological Research Involving Human Subjects (Japan, 2021).

We conducted an a priori power analysis using G*Power 3.1 (Faul et al. 2007) to determine the required sample size for a two-way repeated-measures analysis of variance (ANOVA). Assuming a medium effect size (partial ηp2 = 0.06; Cohen’s f = 0.25; Cohen 1988), an α = 0.05, and a statistical power of 0.80, the analysis indicated a minimum of 34 participants. To account for potential data loss of ~ 15%, we recruited six additional participants, resulting in an initial sample of 40. One participant was excluded due to experimental interruption, leaving 39 participants for the final analysis.

Experimental procedure

All experimental sessions began with manual dexterity assessments using the PPT, a standardized measure of fine motor skills and bimanual coordination in clinical and research settings (Desrosiers et al. 1995). Participants then performed precision grip and control tasks with their right and left hands in a counterbalanced order during ~ 45-min fNIRS recording sessions conducted in a sound-attenuated room. Thereafter, changes in hemoglobin concentrations reflecting brain activity were recorded.

Manual dexterity assessment using PPT

Manual dexterity was assessed using a pegboard with two vertical rows of 25 holes and four cups at the top containing pegs, washers, and collars (standard PPT).

The assessment comprised the following subtests:

  1. Right hand: Insert as many pegs as possible into the right column within 30 s.

  2. Left hand: Insert as many pegs as possible into the left column within 30 s.

  3. Both hands: Simultaneously insert pegs into both columns within 30 s.

  4. Assembly: Sequentially assemble units of pegs, washers, and collars using both hands within 60 s.

The PPT is a reliable and valid tool for assessing manual dexterity across various populations (Tiffin and Asher 1948; Desrosiers et al. 1995) and is particularly effective for detecting impairments associated with neurological and musculoskeletal conditions (Rasova et al. 2012; Amirjani et al. 2011; Chaytor and Schmitter-Edgecombe 2003).

Motor tasks for fNIRS measurement

We designed one control and two precision grip tasks to measure brain activity associated with precision grip while minimizing fine motor demands in the control condition. Each task was repeated six times with each hand, and the order of tasks and hand use was counterbalanced across participants. The tasks followed a block design, alternating between rest (15 s) and task (14 s) periods for six trials. Participants followed on-screen instructions with auditory cues presented every 2 s to guide movement timing. Figure 1A illustrates the overall experimental design.

  1. Control task.

Fig. 1.

Fig. 1

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Experimental setup. A Task design. Left and right images respectively show control task (non-precision movement and precision grip task involving peg manipulation. B Configuration of fNIRS probe arrangement. Red and blue circles respectively indicate source and detector positions. Channels were positioned to cover the prefrontal and sensorimotor cortices

Participants closed their hands into a fist and moved them in synchrony with the auditory cues. This task served as a simple motor-control condition without fine motor demands.

  • b.

    Precision grip task.

The standard PPT was used for behavioral assessment of manual dexterity. For fNIRS measurements, a modified version of the PPT was implemented to allow temporally controlled task execution suitable for block-design analysis of cortical activity.

All motor tasks during fNIRS recording were performed using a block design consisting of a 14-s task period repeated six times. Two experimental runs of the precision grip task were performed during fNIRS measurement. In the first run (Run 1), participants repeatedly performed a simple unilateral peg manipulation derived from the standard PPT. To enable continuous execution within each block, peg insertion and peg removal were alternated in synchrony with the auditory cues. In the second run (Run 2), the first three blocks were identical to Run 1. In blocks 4–6, elements of the standard PPT assembly task were incorporated: participants inserted a peg in block 4, placed a washer in block 5, and added a collar in block 6. This run increased sequencing demands while preserving comparable fingertip precision grip kinematics.

Because both runs required similar precision grip movements and were identically paced, and because preliminary analyses revealed no significant differences in task-evoked cortical activation between Run 1 and Run 2 (Online Resource 1), the data from both runs were averaged and treated as a single precision grip condition in the subsequent analyses.

fNIRS instruments and probe placement

Cortical hemodynamic activity was recorded using the portable continuous-wave NIRSport2 system (NIRx Medical Technologies, LLC, Glen Head, NY, USA), designed for cognitive neuroscience applications (Pinti et al. 2020). This system utilizes dual-wavelength light-emitting diodes (760 and 850 nm) and silicon photodiodes (SiPDs) to measure changes in oxyhemoglobin (oxy-Hb) and deoxyhemoglobin (deoxy-Hb) concentrations (Ferrari and Quaresima 2012).

Participants were comfortably seated in a quiet, dimly lit room. The probe array was placed on the scalp according to the international 10–20 system, a standard procedure in fNIRS and electroencephalography (EEG) research (Jurcak et al. 2007). The array comprised 16 sources and 16 detectors spaced 30 mm apart, forming 44 measurement channels that covered the bilateral frontal and parietal cortices (Fig. 1B).

To minimize contamination from extracerebral signals, 16 short-separation channels were included using source-detector pairs spaced 5 mm apart. Anatomical labels were assigned to each channel based on Montreal Neurological Institute (MNI) coordinates provided by the NIRx system and referenced to a standard brain atlas (Mai et al. (2015) (Online Resource 2).

Data analysis

The fNIRS data were preprocessed using the Platform for Optical Topography Analysis Tools (PoTATo) toolbox in MATLAB R2023a (Sutoko et al. 2016). Raw light attenuation signals were converted into oxy-Hb and deoxy-Hb concentration changes using the modified Beer–Lambert law (Casarotto et al. 2023). Scalp-derived blood flow was attenuated by regressing out signals from the short-separation channels provided by the NIRx system (Saager and Berger 2005; Scholkmann et al. 2014).

A band-pass filter (0.01–0.2 Hz) was applied to remove low-frequency drifts and high-frequency noise (Pinti et al. 2020). Motion artifacts were identified using a threshold of three standard deviations (SDs) from the signal amplitude, thereby minimizing the risk of excluding physiologically relevant fluctuations. Data were sampled at 5.1 Hz.

Z-scores for oxy-Hb and deoxy-Hb were calculated for each participant and channel by standardizing task-related hemoglobin concentration changes against a pre-onset baseline (Otsuka et al. 2007). The mean concentration during each task period (5–19 s post-onset) was compared with the mean baseline value (2 s pre-onset) and transformed into Z-scores as follows:

graphic file with name d33e426.gif

where Mtask is the mean value during the task period, Mbase is the mean baseline value, and Sbase is the SD of the baseline. To enhance the signal-to-noise ratio (SNR), task-related Z-scores were averaged within each functional channel across trials. The resulting channel-wise averaged Z-scores, referred to as “activation values,” served as indices of task-related brain activity.

Statistical analysis were conducted in two stages. First, task-related Z-scores were analyzed using a two-way repeated-measures ANOVA, with task type (precision vs. control) and hand (right vs. left) as within-subject factors. Post hoc comparisons were conducted where appropriate, with multiple comparisons corrected using the false discovery rate (FDR) method (Benjamini and Hochberg 1995). Effect sizes were reported as partial eta squared (ηp2).

Second, correlations between task-related activation and PPT assembly scores were examined using Spearman’s rank correlation analyses to determine individual differences in manual dexterity. Task-related activation was defined as the difference in mean Z-scores between precision and control tasks for the significant channels identified in the ANOVA. Statistical significance was determined using FDR-adjusted p-values (Schober et al. 2018).

Results

Handedness and manual dexterity

All included participants were right-handed according to FLANDERS scores (mean ± SD = 9.56 ± 0.87; range = 6–10). FLANDERS scores were used as a screening and descriptive measure of handedness.

Manual dexterity was evaluated using the PPT, with scores obtained for the right-hand, left-hand, both-hands, and assembly subtests (Table 1). Mean scores were relatively similar across subtests; however, variability was higher for the assembly task. This finding suggests that the assembly task was more sensitive to inter-individual differences in dexterity, likely due to its greater complexity and higher demands for cognitive–motor integration. Therefore, assembly scores were used as the representative measure of manual dexterity. No significant correlation was observed between assembly scores and FLANDERS scores (Spearman’s rho = − 0.08, p = 0.60).

Table 1.

Performance of Purdue Pegboard Test and task-wise variability

Right Left Both Assembly
Average (score) 15.5 14.7 12.5 38.9
SD 1.39 1.55 1.34 6.70
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Mean (± SD) scores for each subtest of Purdue Pegboard tests: right, left, and both hands, and assembly. Standard deviation was largest for assembly tasks, suggesting greater interindividual variability, possibly reflecting increased task complexity

Precision grip-related changes in brain activity

The effects of task precision and hand performance were analyzed using two-way repeated-measures ANOVA of the Z-transformed oxy-Hb and deoxy-Hb signals. Figure 2A presents the statistical maps of main effects and interactions across all 44 fNIRS channels.

Fig. 2.

Fig. 2

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Task-related cortical activation. A Statistical maps of results of two-way repeated measures ANOVA. Left to right: main effect of task precision, main effect of hand performance, and their interaction. Colors indicate statistical significance of each channel: red (q < 0.005), orange (q < 0.05), and yellow (p < 0.05, uncorrected). B Grand-averaged time courses of oxy-Hb and deoxy-Hb signals in channels show significant task precision effects, along with contralateral homologs. Time 0 represents task onset

Main effect

Significant main effects of task precision (precision grip vs. control) were observed for oxy-Hb in several cortical regions. Specifically, activation during the precision grip task was higher in the middle frontal gyrus (MFG; channel 4 [CH4], CH5, CH19, CH23), superior frontopolar gyrus (CH8, CH15, CH16), precentral gyrus (CH31), postcentral gyrus (CH32, CH41), inferior frontal gyrus, opercular part (CH38), prefrontal areas (CH10, CH25), and inferior frontopolar gyrus (CH39, CH40). The time courses of oxy-Hb in these channels consistently demonstrated greater hemodynamic responses under precision grip conditions (Fig. 2B). Table 2 summarizes the corresponding ANOVA statistics, including F values, FDR-corrected q values, and partial η2.

Table 2.

Results of two-way repeated measures ANOVA on task-related fNIRS signals

Channel Oxy-Hb Deoxy-Hb
F(1, 38) p q ηp2 F(1, 38) p q ηp2
CH4 11.72 0.002 0.027 0.15
CH5 13.31 0.001 0.012 0.16
CH8 9.51 0.005 0.023 0.12
CH10 7.12 0.012 0.040 0.09
CH15 6.71 0.014 0.045 0.08
CH16 23.31  < .001 0.002 0.24
CH18 13.28  < .001 0.020 0.12
CH19 8.20 0.008 0.032 0.07
CH21 14.43  < .001 0.028 0.15
CH23 7.76 0.009 0.035 0.06
CH25 10.33 0.004 0.022 0.08
CH29 8.68 0.008 0.049 0.14
CH30 9.49 0.006 0.042 0.20
CH31 9.59 0.005 0.023 0.19
CH32 20.54  < .001 0.003 0.27 9.53 0.005 0.043 0.12
CH38 11.94 0.003 0.018 0.21
CH39 11.44 0.003 0.018 0.22
CH40 14.10  < .001 0.013 0.26
CH41 14.46  < .001 0.013 0.27 12.51 0.002 0.026 0.18
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F-values and the corresponding FDR-corrected q-values for main effects of task precision, hand performance, and their interaction are shown for channels that exhibited significant effects after FDR correction (q < 0.05).. Oxyhemoglobin (oxy-Hb) and deoxyhemoglobin (deoxy-Hb) signals were separately analyzed.

Deoxy-Hb concentrations were significantly lower during the precision grip relative to the control task in the middle frontopolar gyrus (CH18), MFG (CH21), inferior frontal gyrus, opercular part (CH29), precentral gyrus (CH30), and postcentral gyrus (CH32 and CH41). No channels showed significant main effects of hand performance.

Interaction effects

No significant interactions between task precision and hand performance were detected after FDR correction (q < 0.05). Exploratory analyses at uncorrected thresholds indicated trends (p < 0.05); however, these did not remain significant after correction.

Brain activity involved in manual dexterity

Assembly scores and task-related brain activation across participants were analyzed using Spearman’s rank correlation to identify cortical regions specifically associated with manual dexterity. Task-related activation for each channel was defined as the difference in average Z-scores between the precision and control tasks.

The correlation analysis was limited to channels with a statistically significant main effect of task type (precision grip) in the preceding two-way ANOVA, with oxy-Hb and deoxy-Hb signals analyzed separately.

Significant positive correlations were observed only for oxy-Hb signals, suggesting that higher activation during the precision grip task was associated with greater cortical recruitment during task performance. Among the significant channels, the right-hand condition at CH5 in the MFG showed a notable correlation (Spearman’s rho [ρ] = 0.51, p = 0.003), whereas no significant correlations were found for the left-hand condition. A scatter plot illustrates the relationship between prefrontal activation and assembly scores (Fig. 3).

Fig. 3.

Fig. 3

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Correlations between prefrontal activation and manual dexterity. Scatter plot of relationship between task-related activation in CH5 (MFG) during right-hand precision grip task and assembly scores from Purdue pegboard tests. Significant positive correlation reveals association between prefrontal activation and better manual dexterity (ρ = 0.51, p = 0.003)

Discussion

The present study was designed as a foundational investigation rather than a definitive test of competing cognitive–motor hypotheses. This study investigated cortical responses during precision grip, a key component of manual dexterity, and examined their relationship with PPT performance. The fNIRS results revealed task-evoked increases in oxy-Hb across the bilateral prefrontal and sensorimotor regions. Activation in the left MFG during right-hand use positively correlated with dexterity scores, suggesting an association between prefrontal function and cognitive-motor integration.

Behavioral variability in manual dexterity

Manual dexterity, broadly defined as the ability to execute goal-directed fine motor actions, depends on the coordinated involvement of the motor and cognitive systems. All PPT subtests were applied, including the assembly task, which imposes greater demands on sequencing, bimanual coordination, and executive control than simpler peg-placement tasks. The wider inter-individual variability observed in assembly performance may therefore reflect its sensitivity to these higher-level cognitive–motor demands (Chen et al. 2024).

This interpretation aligns with earlier findings that skilled manual performance engages cognitive control processes such as planning and attention (Tiffin and Asher 1948; Chaytor and Schmitter-Edgecombe 2003). Accordingly, the PPT may be considered not only a measure of motor speed but also an index of cognitive-motor integration.

Task-related cortical activation during precision grip

The precision grip elicited activation within a distributed cortical network encompassing regions associated with both motor and executive functions. Increased oxy-Hb concentrations were observed in the MFG, superior frontopolar (SFPG), precentral (PrG), and postcentral (PoG) gyri, a pattern consistent with frontoparietal recruitment during dexterous actions (Tiffin and Asher 1948). The MFG, part of the DLPFC, likely supports attentional control and working memory under time constraints (Boettiger and D’esposito 2005), whereas PrG and PoG activity reflect motor execution and somatosensory engagement, respectively (Indovina and Sanes 2001).

No significant main effect of hand performance was found; however, uncorrected contrasts revealed lateralized activation patterns, such as CH29/32 during right-hand tasks, consistent with contralateral motor control (Klöppel et al. 2007). However, these trends require validation in larger, more sensitive studies. Overall, the findings align with existing models proposing that precision grip engages integrated cognitive-motor networks.

Prefrontal activation associated with dexterity performance

We further examined whether individual differences in manual dexterity were associated with cortical activation during the grip tasks. Right-hand assembly scores showed a significant positive correlation with increased oxy-Hb concentrations in the left MFG (CH5). This association aligns with the established role of the MFG in rule-based planning and top-down control, processes particularly relevant to structured, temporally ordered behaviors (Boettiger and D’esposito 2005). Given the dominant role of the left hemisphere in right-handed individuals (Serrien et al. 2006), this lateralized relationship may reflect hemisphere-specific efficiency in cognitive-motor coordination. The specificity of the oxy-Hb signal, rather than deoxy-Hb or left-hand performance, likely reflects its greater sensitivity to task-evoked cerebral hemodynamics (Sato et al. 2005).

It should be noted that the present study did not include direct behavioral or neuropsychological measures of cognitive function. Accordingly, the interpretation of prefrontal activation in relation to manual dexterity is based on established theoretical frameworks and prior evidence linking prefrontal cortex activity to attentional control, working memory, and cognitive–motor integration (Miller & Cohen 2001; D’Esposito et al. 2000; Ridderinkhof et al. 2004; Duncan 2010), rather than on direct assessment of cognitive performance in the current sample. In this context, the observed association between lateral prefrontal activation and PPT assembly performance is interpreted as reflecting differences in the degree of cortical recruitment required to meet the combined motor and cognitive demands of the task. However, this interpretation remains indirect, and future studies should incorporate explicit cognitive assessments alongside precision grip tasks to more directly examine the relationship between cognitive function and task-related cortical activation.

In addition, although the present paradigm was informed by elements of the Purdue Pegboard Test, it should be regarded as a methodological adaptation for neuroimaging purposes rather than as a modification of the PPT as a clinical assessment tool. The standard PPT was retained exclusively for behavioral evaluation of manual dexterity, whereas the modified peg manipulation task was designed to enable temporally controlled execution suitable for block-design fNIRS analysis and to isolate precision grip–related cortical activation from more general motor or attentional effects. In applied and clinical contexts, fNIRS-based measures of precision grip–related cortical recruitment may therefore serve as a baseline reference for identifying inefficient or compensatory recruitment patterns. In such settings, within-individual changes in activation (e.g., before and after training or rehabilitation) may be more informative than absolute activation levels.

In this context, the present findings may also be considered in relation to the Hemispheric Asymmetry Reduction in Older Adults (HAROLD) model, which proposes reduced lateralization of prefrontal activation with aging (Cabeza 2002). Although the current study did not examine age-related differences or directly test the HAROLD hypothesis, the observed patterns of prefrontal activation in healthy young adults may provide a useful baseline reference for future aging and clinical studies. From this perspective, the fNIRS paradigm employed here could be applied to older populations to examine whether precision grip tasks elicit more bilateral or less lateralized prefrontal recruitment, potentially reflecting compensatory or inefficient cortical mechanisms. Thus, rather than testing the HAROLD model directly, the present study establishes a methodological and empirical foundation upon which age-related changes in cortical recruitment during precision grip can be systematically investigated.

Collectively, these findings support the view that fNIRS can detect individual variability in prefrontal activity associated with manual dexterity.

Limitations and future directions

This study had some limitations. The limited spatial resolution and penetration depth of fNIRS precluded measurements of deeper brain regions, such as the supplementary motor area and medial prefrontal cortex. Our peg manipulation task provided temporal control for isolating precision grip; however, it might not have captured the full range of naturalistic manual behaviors. Moreover, because the study involved only healthy young adults, the generalizability of our findings to other age groups or clinical populations remains uncertain. In addition, restricting the sample to right-handers improves interpretability but limits generalizability to left-handed individuals and precludes direct tests of handedness-related neural organization. Future studies should include left-handers and directly compare dominance groups to clarify laterality effects.

We used block-averaged hemoglobin signals to estimate activation, which was appropriate for the fixed trial structure. However, future studies using general linear modeling could provide better temporal resolution and greater sensitivity to trial-level effects. Finally, the modest sample size of 39 participants limited our ability to detect subtle lateralization or interaction effects.

Future investigations should examine these associations in pediatric, older, or clinical populations to determine whether interventions targeting dexterity can modulate prefrontal engagement. Multimodal approaches, such as combining fNIRS with fMRI or EEG, may further elucidate the dynamics of cognitive-motor integration.

Conclusion

The present findings indicate that precision grip elicits activation across prefrontal and sensorimotor regions, with prefrontal engagement varying according to individual manual dexterity. These results demonstrate the feasibility of using fNIRS to investigate cognitive-motor integration in ecologically relevant contexts. Although preliminary, such insights could inform future assessments of fine motor function and executive control, particularly in applied or clinical settings.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (14.5KB, docx)
Supplementary Material 2 (19.1KB, docx)

Acknowledgements

The authors are grateful to all those who participated in this study. We also thank the laboratory staff and student volunteers who helped with data collection and preprocessing.

Author contributions

Shumpei Toriyama: Conceptualization, methodology, investigation, data curation, formal analysis, visualization, writing—original draft, writing—review and editing, project administration. Nozomi Sasaki: Investigation. Takumu Yamaguchi: Formal analysis, supervision, writing—review and editing. Hiroki Sato: Supervision, funding acquisition, writing—review and editing, corresponding author. All authors have read and approved the final manuscript.

Funding

This study was supported by the Japan Society for the Promotion of Science (JSPS), KAKENHI (Grant Number 23K25652).

Data availability

The raw fNIRS data for each participant used in this study are available from Figshare at the following private link (provided for peer review purposes only): https://figshare.com/s/35131143ff0cd854ccf3. This link provides temporary access for peer review and should not be shared or cited. The MATLAB scripts used for data preprocessing and statistical analysis are available from the corresponding author upon reasonable request.

Declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval and consent to participate

All participants provided written informed consent before the study started, which complied with the ethical principles enshrined in the Declaration of Helsinki (2013 amendment). The Shibaura Institute of Technology Research Ethics Committee approved the study protocol (Approval No: 22-037).

Consent for publication

Not applicable. This study did not include any identifiable individual data, such as personal details, images, or videos.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (14.5KB, docx)
Supplementary Material 2 (19.1KB, docx)

Data Availability Statement

The raw fNIRS data for each participant used in this study are available from Figshare at the following private link (provided for peer review purposes only): https://figshare.com/s/35131143ff0cd854ccf3. This link provides temporary access for peer review and should not be shared or cited. The MATLAB scripts used for data preprocessing and statistical analysis are available from the corresponding author upon reasonable request.

Articles from Experimental Brain Research are provided here courtesy of Springer

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