TY - JOUR
AU - Clark, David, J
AB - Abstract Background and Objectives The influence of interindividual differences on brain activation during obstacle negotiation and the implications for walking performance are poorly understood in older adults. This study investigated the extent to which prefrontal recruitment during obstacle negotiation is explained by differences in age, executive function, and sex. These data were interpreted according to the Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH) framework of brain aging. We also tested the association between prefrontal recruitment and walking performance. Research Design and Methods Prefrontal oxygenated hemoglobin concentration (O2Hb) was measured during typical walking (Typical) and obstacle negotiation (Obstacles) tasks in 50 adults aged 65 years and older using functional near-infrared spectroscopy. The primary outcome was the change in prefrontal recruitment (∆PFR), measured as Obstacles ∆O2Hb minus Typical ∆O2Hb. Multiple regression was used to test the relationship between ∆PFR and age, executive function measured by the Trail Making Test, and sex. Pearson’s correlation coefficient was used to investigate the association between ∆PFR and the cost of Obstacles walking speed relative to Typical walking. Results Age, executive function, and their interaction significantly predicted greater ∆PFR (R2 = 0.34, p = .01). Participants were subgrouped according to age and executive function to examine the interaction effects. Adults of lower age and with lower executive function exhibited greater ∆PFR during Obstacles compared to their peers with higher executive function (p = .03). Adults of advanced age exhibited a ceiling of prefrontal recruitment during obstacle negotiation, regardless of executive function level (p = .87). Greater ∆PFR was significantly associated with a smaller cost of Obstacles (r = 0.3, p = .03). Discussion and Implications These findings are consistent with the CRUNCH framework: neural inefficiency where a greater amount of brain activation is needed for task performance at a similar level, compensatory overactivation to prevent a steeper decline in task performance, and capacity limitation with a recruitment ceiling effect. Aging, Complex walking, Executive function, Near-infrared spectroscopy, Prefrontal cortex Translational Significance: In older adults, the demand on prefrontal/executive resources during walking can be affected by various factors that may alter study results and interpretation. Poor executive function may increase the perceived difficulty of complex walking leading to prefrontal brain overactivation. Older age may contribute to reduced availability of brain recruitment resources. These factors should be considered when interpreting brain activity data and when designing intervention studies that seek to target mechanisms of walking dysfunction in older adults. Background and Objectives Walking deficits in older adults have traditionally been viewed as a consequence of motor and sensory dysfunction. However, evidence from the past several years shows a strong link between walking function and cognition, particularly in the cognitive domain of executive function (1–3). Furthermore, mobile brain imaging studies have recently revealed that brain recruitment during walking is altered substantially with aging in a manner that indicates substantial cognitive demand (4–6). Age-related change in cognitive control of walking has therefore become an important area of research because of the potential for intervention opportunities that may preserve walking function. Control of complex walking tasks is highly relevant to real-world home and community ambulation (7,8). Complex walking tasks can be broadly divided into two major categories of cognitive demand: task-irrelevant and task-relevant. A prior publication used the analogous terms “internally driven” and “externally driven” (5). Task-irrelevant cognitive demands divide attention away from the walking task, such as performing word fluency or serial subtractions. Task-relevant cognitive demands direct attention toward a feature of the movement or the environment that is directly applicable to performing the walking task. Obstacle negotiation is a commonly studied complex walking task with task-relevant cognitive demands that exceed the demands of typical walking. For example, planning the motor strategy during approach, dynamic balance during single-limb support, and endpoint control to prevent striking the obstacle during swing (9–11). Older age has been linked to the deterioration of the cognitive mechanisms underlying obstacle negotiation including attention, planning, visuospatial processing, and sensorimotor integration (10–13). The challenge of obstacle negotiation for older adults is confirmed by the substantial slowing of walking speed during this task (4,6,9,14). While both young and older adults have been shown to recruit the prefrontal cortex during obstacle negotiation, older adults have been shown to exhibit a higher magnitude of prefrontal activation, consistent with a greater reliance on executive control resources (4,6,15). However, the extent to which interindividual differences influence the magnitude of prefrontal recruitment in older adults during obstacle negotiation, and its association with walking performance, is not well understood. Frameworks of brain aging from the cognitive neuroscience literature may inform the interpretation of brain activity data during walking tasks. One such framework is the Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH), which consolidates influences from age-related neural inefficiency, compensatory overactivation, and recruitment ceiling effects (16). Briefly, CRUNCH posits that (a) compared to young adults, older adults exhibit brain overactivation at low levels of task difficulty (e.g., during typical walking) and (b) older adults exhibit reduced availability of brain recruitment resources (16). Collectively, these factors might cause older adults to reach a ceiling in brain resources at high levels of task complexity (e.g., during obstacle negotiation). The primary objective of this study was to investigate the extent to which prefrontal recruitment during typical walking and obstacle negotiation is explained by interindividual differences in age, executive function, and sex. We hypothesized that changes in prefrontal recruitment due to obstacle negotiation would be significantly associated with executive function, supporting that prefrontal executive resources are important to this task. Furthermore, these data were interpreted in the context of the CRUNCH framework. This study also examined the association between prefrontal recruitment and walking performance. Research Design and Methods Participants This study recruited 50 community-dwelling older adults aged 65 years and older (mean age = 74.62 years ± 6.71; 34 females) who could walk independently (or with a cane), but who self-reported difficulty with walking tasks, such as becoming tired when walking a quarter mile, or when climbing two flights of stairs, or when performing household chores. Participants with a history of neurological disorders such as stroke, dementia, or Parkinson’s disease were excluded from the study. Also excluded were serious medical conditions affecting walking ability including recent history of musculoskeletal injury, heart or lung disease, or severe pain. All participants provided written informed consent at the time of enrollment and the University of Florida Institutional Review Board approved the study procedures. Protocol and Equipment Walking tasks All assessments were conducted in a research laboratory located in a university setting. The participants walked at their preferred speed with no assistive device for typical unobstructed walking (Typical) and while stepping over obstacles (Obstacles). The walking tasks were presented in a randomized order, and all participants performed three trials of each task. Walking tasks were performed on a 19.16-m rectangular course with 10 foam obstacles. Obstacle dimensions (length × width × height) were 12 × 4 × 4 inches (30.48 × 10.16 × 10.16 cm). The obstacles were placed equidistantly along each of the long sides of the course (Figure 1A), and a stopwatch was used to measure the time to walk across each long side. The participants were instructed to step directly over the foam blocks and to avoid circumventing the blocks with either leg. Depending on their walking speed, the participants encountered approximately 12–18 foam blocks during each Obstacles trial. All participants performed the obstacle negotiation task safely and successfully, with infrequent striking of obstacles. Figure 1. Open in new tabDownload slide Setup for the walking course for the obstacle negotiation task (Obstacles; A) and functional near-infrared spectroscopy to measure prefrontal hemodynamic activity (B). Figure 1. Open in new tabDownload slide Setup for the walking course for the obstacle negotiation task (Obstacles; A) and functional near-infrared spectroscopy to measure prefrontal hemodynamic activity (B). Functional near-infrared spectroscopy setup and procedures Prefrontal recruitment was measured during the Typical and Obstacles walking tasks using a commercially available multichannel continuous-wave functional near-infrared spectroscopy (fNIRS) unit (OctaMon; Artinis Medical Systems, Nijmegen, The Netherlands). Participants wore a headband with embedded light sources that emitted near-infrared light at continuous wavelengths of 760 and 850 nm, along with two near-infrared light detectors (Figure 1B). Separate recording channels were distinguished by time-division multiplexing. The bottom of the headband was positioned approximately just above the eyebrows and the middle of the headband was aligned with the midline of the face. The source-detector optode location on the headband was fixed and was separated by a distance of 3.5 cm. Consistent with prior studies, a differential pathlength factor value of 6 was used (17). In order to report estimated anatomical recording sites for each channel, we measured the midpoint location between each light emitter–detector pair and report this location in reference to the international 10–10 system (18). Horizontal placement was measured in the transverse plane as a percentage of head circumference. Vertical placement was measured in the sagittal plane as a percentage of the nasion to inion distance. The horizontal and vertical recording sites relative to the nasion are as follows: 4.4% ± 0.2 and 11% ± 1.8 (for the medial optodes); 8.9% ± 0.3 and 11% ± 1.8 (for the lateral optodes). The medial left and right fNIRS optodes were approximately aligned with the landmarks of Fp1 and Fp2. The lateral left and right optodes were approximately aligned with the landmarks of AF7 and AF8. The measurement locations correspond to medial and lateral subregions of Brodmann Area 10 (18). Functional near-infrared spectroscopy data were acquired using a block design, where active periods of walking were alternated with reference periods. During the reference periods, participants stood still while counting slowly from 1 to 30 (approximately at the rate of 1 number/second). Following the reference period, the participants performed the walking task for 30 seconds. For each walking task, three consecutive trials of reference and active periods were performed. A wireless remote device (PortaSync; Artinis Medical Systems) was used to mark the reference and active periods in the fNIRS signal during the data collection. Assessments of executive functions Executive functions were assessed with the NIH EXAMINER and the Trail Making Test (TMT). The NIH EXAMINER is a computer-based standardized assessment that tests executive domains of planning, set shifting, working memory, inhibition, and fluency (19). Table 1 reports the group mean NIH EXAMINER scores for executive function composite, fluency factor, cognitive control factor, and working memory factor. The TMT is a well-established measure of executive functions such as attention, working memory, inhibition, and set shifting in older adults (20). The test required the participants to connect a sequence of 24 consecutive targets on paper. The test consists of two parts: in the first part of the test (TMT A), the participants connect targets that are all numbers (1, 2, 3, 4, etc.) in a sequential manner. In the second part of the test (TMT B), the participants are required to alternate between numbers and letters (1, A, 2, B, etc.). The participants were instructed to complete both components of the test as quickly as they could and the time to complete each component was measured with a stopwatch. Executive function was measured as the difference between the TMT B and A completion times (i.e., ΔTMT = TMT B – A) and is reported in Table 1. We used TMT as our primary measure of executive function because this test has been widely used to assess executive function in prior walking studies (21,22). Furthermore, prior work has reported that TMT is an independent predictor of mobility disability in older adults and longer ΔTMT has been previously associated with slower obstacle negotiation speed, shorter step length, and lower stepping accuracy (12,21,23–25). Table 1. Demographic and Functional Assessment Data for All Participants . Mean/SD . Range . Age (in years) 74.62 ± 6.71 65 to 92 Sex (Male/Female) 16 M/34 F — Executive Function (ΔTMT in seconds) 60.93 ± 44.54 −1.42 to 183.94 NIH EXAMINER composite score 0.26 ± 0.61 −1.65 to 1.47 NIH EXAMINER fluency score 0.40 ± 0.65 −1.15 to 2.34 NIH EXAMINER cognitive control score 0.13 ± 0.61 −1.75 to 1.55 NIH EXAMINER working memory score −0.14 ± 0.87 −2.24 to 1.54 10MWS (m/s) 1.02 ± 0.17 0.67 to 1.45 ABC scale (%) 81.22 ± 12.52 43.56 to 98.87 BBS (out of 56 points) 49.2 ± 5.13 32 to 56 Typical speed (m/s) 1.03 ± 0.19* 0.64 to 1.46 Obstacles speed (m/s) 0.84 ± 0.21 0.42 to 1.28 . Mean/SD . Range . Age (in years) 74.62 ± 6.71 65 to 92 Sex (Male/Female) 16 M/34 F — Executive Function (ΔTMT in seconds) 60.93 ± 44.54 −1.42 to 183.94 NIH EXAMINER composite score 0.26 ± 0.61 −1.65 to 1.47 NIH EXAMINER fluency score 0.40 ± 0.65 −1.15 to 2.34 NIH EXAMINER cognitive control score 0.13 ± 0.61 −1.75 to 1.55 NIH EXAMINER working memory score −0.14 ± 0.87 −2.24 to 1.54 10MWS (m/s) 1.02 ± 0.17 0.67 to 1.45 ABC scale (%) 81.22 ± 12.52 43.56 to 98.87 BBS (out of 56 points) 49.2 ± 5.13 32 to 56 Typical speed (m/s) 1.03 ± 0.19* 0.64 to 1.46 Obstacles speed (m/s) 0.84 ± 0.21 0.42 to 1.28 Notes: ABC = Activities-specific Balance Confidence scale; BBS = Berg Balance Scale; 10MWS = 10-Meter Walking Speed; TMT = Trail Making Test; ΔTMT = TMT B − TMT A. Group means, standard deviations, and the range of values are reported. Walking speed during Typical was significantly faster compared to Obstacles. *p < .001. Open in new tab Table 1. Demographic and Functional Assessment Data for All Participants . Mean/SD . Range . Age (in years) 74.62 ± 6.71 65 to 92 Sex (Male/Female) 16 M/34 F — Executive Function (ΔTMT in seconds) 60.93 ± 44.54 −1.42 to 183.94 NIH EXAMINER composite score 0.26 ± 0.61 −1.65 to 1.47 NIH EXAMINER fluency score 0.40 ± 0.65 −1.15 to 2.34 NIH EXAMINER cognitive control score 0.13 ± 0.61 −1.75 to 1.55 NIH EXAMINER working memory score −0.14 ± 0.87 −2.24 to 1.54 10MWS (m/s) 1.02 ± 0.17 0.67 to 1.45 ABC scale (%) 81.22 ± 12.52 43.56 to 98.87 BBS (out of 56 points) 49.2 ± 5.13 32 to 56 Typical speed (m/s) 1.03 ± 0.19* 0.64 to 1.46 Obstacles speed (m/s) 0.84 ± 0.21 0.42 to 1.28 . Mean/SD . Range . Age (in years) 74.62 ± 6.71 65 to 92 Sex (Male/Female) 16 M/34 F — Executive Function (ΔTMT in seconds) 60.93 ± 44.54 −1.42 to 183.94 NIH EXAMINER composite score 0.26 ± 0.61 −1.65 to 1.47 NIH EXAMINER fluency score 0.40 ± 0.65 −1.15 to 2.34 NIH EXAMINER cognitive control score 0.13 ± 0.61 −1.75 to 1.55 NIH EXAMINER working memory score −0.14 ± 0.87 −2.24 to 1.54 10MWS (m/s) 1.02 ± 0.17 0.67 to 1.45 ABC scale (%) 81.22 ± 12.52 43.56 to 98.87 BBS (out of 56 points) 49.2 ± 5.13 32 to 56 Typical speed (m/s) 1.03 ± 0.19* 0.64 to 1.46 Obstacles speed (m/s) 0.84 ± 0.21 0.42 to 1.28 Notes: ABC = Activities-specific Balance Confidence scale; BBS = Berg Balance Scale; 10MWS = 10-Meter Walking Speed; TMT = Trail Making Test; ΔTMT = TMT B − TMT A. Group means, standard deviations, and the range of values are reported. Walking speed during Typical was significantly faster compared to Obstacles. *p < .001. Open in new tab Assessments of mobility and balance Preferred walking speed was measured by the 10-Meter Walk Test. Functional balance was measured by the Berg Balance Scale (26). Balance Confidence was measured by self-report using the Activities-specific Balance Confidence scale (27). The demographic, executive function, mobility, and balance function data for all participants are presented in Table 1. Data Analysis Walking performance The walking speed during Obstacles was compared to the walking speed during Typical to calculate the cost of obstacle negotiation using the formula: % Obstacles cost = [(Typical speed – Obstacles speed)/Typical speed] × 100. Prefrontal activity measurement Functional near-infrared spectroscopy estimates regional cortical neural activity by detecting changes in the quantity of infrared light that passes between the source and detector. These changes are considered to be due to the absorption and scattering of light within the tissue caused by task-related changes in blood flow and hemoglobin concentrations in response to neural activity. The data were sampled at 10 Hz and later exported to a computer for analysis. Prefrontal O2Hb concentrations were calculated according to the modified Beer–Lambert law then analyzed with custom programs in Matlab version R2015a (Mathworks, Natick, MA). Preprocessing of the raw fNIRS signals included detrending the signal and using a low-pass filter with cutoff frequency at 0.14 Hz to reduce the physiological noise (28,29). A wavelet filter was used to reduce the influence of motion artifacts (30). Additionally, a trained team member visually examined the data and excluded any channels with obvious deficiencies in signal quality. Task-related change in prefrontal O2Hb (∆O2Hb) was calculated for each person using the formula: ∆O2Hb = Active O2Hb – Reference O2Hb. The primary outcome measure was the magnitude of change in prefrontal recruitment (∆PFR) during Obstacles compared to Typical calculated for each person using the formula: ∆PFR = Obstacles ∆O2Hb – Typical ∆O2Hb. This approach is useful for isolating the changes in prefrontal activity that are due to the demands of obstacle negotiation, as well as reducing variability in the data set caused by natural interindividual variability in the magnitude of ∆O2Hb. Statistical Analysis Statistical analysis was conducted using JMP software (JMP 11; SAS Institute Inc., Cary, NC). For all analyses, statistical significance was set at p < .05. Separate paired t tests were used to compare the walking speeds between Obstacles and Typical (Table 1) and to compare ΔO2Hb between Obstacles and Typical. Effect of the Prefrontal Recording Site For each walking task, a paired t test was used to compare left and right ΔO2Hb to examine whether the data were affected by the laterality of the prefrontal recording site. Analysis of Prefrontal Recruitment and Explanatory Variables A multiple regression model was used to examine what variables explained ∆PFR, including age, executive function, sex, and interaction effects. Interactions were examined post hoc with independent t tests. Association Between Prefrontal Recruitment and Obstacle Negotiation Performance Pearson’s correlation was conducted to examine the association between ∆PFR and the performance cost of obstacle negotiation (i.e., the percent drop in walking speed compared to Typical). Results Effect of Prefrontal Recording Site and Task on ∆O2Hb The group mean ∆O2Hb was not significantly different across recording sites (i.e., channels) for either walking task. When channels were averaged within participant by right and left prefrontal cortex, the group mean ∆O2Hb remained nonsignificant. Therefore, ∆O2Hb data from all channels were averaged within each participant for each task prior to all subsequent analyses. The group mean ∆O2Hb was significantly greater during Obstacles compared to Typical (1.38 ± 0.95 vs 1.00 ± 0.83, p < .001). Prefrontal Recruitment and Explanatory Variables All assumptions for the multiple regression model were met. The multiple regression model (F (7, 41) = 3.06, R2 = 0.34, p = .01) revealed that age (p = .003), executive function (p = .02), and age × executive function interaction (p = .04) were significantly associated with ∆PFR. The association between sex and ΔPFR was not significant (p = .75). Comparisons of ∆PFR based on aging and executive function groups To further investigate the interaction effect of age and executive function on ΔPFR, the participants were subgrouped according to age and executive function. Participants were divided into Early Aging (age <75 years; n = 27) and Late Aging (age ≥75 years; n = 23) groups based on the median split of age. Participants were divided into High (ΔTMT ≤44.33 s; n = 25) and Low (ΔTMT >44.33 s; n = 25) Executive Function groups based on the median split of ΔTMT. There was a significant association between the ΔTMT and NIH EXAMINER executive function composite score (r = 0.68, p < .0001), suggesting a strong association between both measures of executive function. Post hoc examination of the age × executive function interaction showed that ∆PFR was significantly greater in the Early Aging group with Low Executive Function compared to their Early Aging peers with High Executive Function (∆PFR = 0.92 ± 0.75 vs 0.32 ± 0.83, p = .03; Figure 2A). In contrast, ∆PFR did not differ significantly by executive function within the Late Aging group (∆PFR = 0.16 ± 0.32 vs 0.12 ± 0.57, p = .87; Figure 2B). Figure 2. Open in new tabDownload slide Experimental data of prefrontal recruitment (A and B) and conceptual interpretation of the data based on the Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH) framework (C and D). The asterisk (*) symbol denotes significant differences between the magnitude of change in task-related prefrontal recruitment (∆PFR) for Obstacles compared to Typical. Group differences in ∆PFR are reported as Cohen’s d. Obstacles ∆O2Hb for each task and ∆PFR may be explained by the CRUNCH framework. Older age may contribute to a lower functional range of brain activity due to greater recruitment at low levels of task demand (Typical) and ceiling effects at high levels of task demand (Obstacles). This results in compression of the CRUNCH curve on the y-axis in the conceptual figure in D. Poor executive function (EF) may increase the perceived complexity of Obstacles. This is shown as a rightward shift on the x-axis for Obstacles in the conceptual figures, such that prefrontal recruitment is higher in people with poorer executive function (particularly within the Early Aging subgroup). Visual comparison of experimental data with the conceptual figure suggests agreement. Figure 2. Open in new tabDownload slide Experimental data of prefrontal recruitment (A and B) and conceptual interpretation of the data based on the Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH) framework (C and D). The asterisk (*) symbol denotes significant differences between the magnitude of change in task-related prefrontal recruitment (∆PFR) for Obstacles compared to Typical. Group differences in ∆PFR are reported as Cohen’s d. Obstacles ∆O2Hb for each task and ∆PFR may be explained by the CRUNCH framework. Older age may contribute to a lower functional range of brain activity due to greater recruitment at low levels of task demand (Typical) and ceiling effects at high levels of task demand (Obstacles). This results in compression of the CRUNCH curve on the y-axis in the conceptual figure in D. Poor executive function (EF) may increase the perceived complexity of Obstacles. This is shown as a rightward shift on the x-axis for Obstacles in the conceptual figures, such that prefrontal recruitment is higher in people with poorer executive function (particularly within the Early Aging subgroup). Visual comparison of experimental data with the conceptual figure suggests agreement. Typical ∆O2Hb was not significantly different for either pair of Early or Late Aging subgroups (p > .34). Likewise, Obstacles ∆O2Hb was not significantly different for either pair of Early or Late Aging subgroups (p > .49). Table 2 reports the findings of independent t tests comparing the demographic, executive function, mobility, balance function, and hemodynamic data within the Early and Late Aging executive function groups, respectively. Table 2. Group Characteristics by Age and Executive Function . High EF (n = 16) . Low EF (n = 11) . t statistic . p Value . Early aging Executive function (ΔTMT in seconds) 25.29 ± 11.96 (−1.42 to 40.52) 80.11 ± 33.09 (44.43 to 163.79) 6.11 <.0001* NIH EXAMINER composite score 0.73 ± 0.43 (−0.04 to 1.47) −0.004 ± 0.47 (−0.66 to 0.58) −4.15 <.001* NIH EXAMINER working memory score 0.40 ± 0.71 (−1.06 to 1.54) −0.74 ± 0.97 (−1.92 to 0.80) −3.51 <.001* NIH EXAMINER cognitive control score 0.43 ± 0.41 (−0.44 to 1.16) −0.19 ± 0.52 (−1.20 to 0.76) −3.39 .001* ABC scale (%) 77.37 ± 13.41 (43.56 to 94.37) 86.15 ± 9.20 (70 to 98.56) 1.88 .03* ΔPFR 0.32 ± 0.83 (−1.15 to 1.85) 0.92 ± 0.75 (−0.11 to 2.73) 1.85 .03* NIH EXAMINER fluency score 0.74 ± 0.69 (−0.24 to 2.34) 0.42 ± 0.54 (−0.55 to 1.10) −1.29 .20 Typical ΔO2Hb 1.05 ± 0.98 (−0.59 to 2.72) 0.70 ± 0.87 (−0.65 to 2.11) −0.94 .35 BBS (out of 56 points) 51.37 ± 3.40 (45 to 56) 50.09 ± 4.94 (41 to 56) −0.80 .43 Obstacles ΔO2Hb 1.37 ± 0.64 (0.28 to 2.53) 1.66 ± 1.51 (−0.015 to 4.85) 0.67 .50 Obstacles speed (m/s) 0.88 ± 0.22 (0.48 to 1.28) 0.90 ± 0.21 (0.62 to 1.27) 0.26 .78 Age (in years) 69.31 ± 2.98 (65 to 74) 69.63 ± 2.97 (66 to 74) 0.27 .78 10MWS (m/s) 1.06 ± 0.13 (0.78 to 1.32) 1.07 ± 0.18 (0.86 to 1.45) 0.26 .79 Typical speed (m/s) 1.08 ± 0.17 (0.66 to 1.34) 1.09 ± 0.20 (0.70 to 1.46) 0.06 .94 . High EF (n = 9) . Low EF (n = 14) . t statistic . p Value . . Late aging . . . . Executive function (ΔTMT in seconds) 37.03 ± 5.08 (30.48 to 44.24) 101.93 ± 48.71 (49.04 to 183.94) 4.94 <.001* 10MWS (m/s) 1.08 ± 0.15 (0.90 to 1.35) 0.88 ± 0.14 (0.67 to 1.21) −3.12 .002* Typical speed (m/s) 1.10 ± 0.18 (0.77 to 1.34) 0.90 ± 0.16 (0.65 to 1.14) −2.70 .006* NIH EXAMINER working memory score 0.18 ± 0.38 (−0.41 to 0.60) −0.52 ± 0.73 (−2.24 to 0.36) −2.64 .007* Obstacles speed (m/s) 0.90 ± 0.12 (0.63 to 1.06) 0.71 ± 0.21 (0.42 to 1.07) −2.42 .01* NIH EXAMINER composite score 0.40 ± 0.36 (−0.22 to 1.08) −0.13 ± 0.64 (−1.65 to 0.63) −2.23 .01* NIH EXAMINER cognitive control score 0.46 ± 0.50 (−0.14 to 1.54) −0.14 ± 0.71 (−1.75 to 0.70) −2.25 .01* NIH EXAMINER fluency score 0.33 ± 0.51 (−0.31 to 1.07) 0.06 ± 0.65 (−1.15 to 1.44) −1.03 .31 ABC scale (%) 85.25 ± 10.77 (61.25 to 98.87) 79.17 ± 13.87 (49.06 to 94.37) −1.11 .27 Typical ΔO2Hb 0.96 ± 0.56 (0.28 to 1.72) 1.22 ± 0.78 (0.03 to 2.40) 0.86 .39 Obstacles ΔO2Hb 1.12 ± 0.52 (0.37 to 1.98) 1.35 ± 1.00 (0.07 to 3.31) 0.62 .53 BBS (out of 56 points) 48 ± 4.71 (41 to 56) 46.78 ± 6.31 (32 to 56) −0.49 .62 ΔPFR 0.16 ± 0.32 (−0.34 to 0.68) 0.12 ± 0.57 (−1.24 to 0.90) −0.15 .87 Age (in years) 80.66 ± 3.80 (75 to 85) 80.71 ± 4.79 (75 to 92) 0.02 .98 . High EF (n = 16) . Low EF (n = 11) . t statistic . p Value . Early aging Executive function (ΔTMT in seconds) 25.29 ± 11.96 (−1.42 to 40.52) 80.11 ± 33.09 (44.43 to 163.79) 6.11 <.0001* NIH EXAMINER composite score 0.73 ± 0.43 (−0.04 to 1.47) −0.004 ± 0.47 (−0.66 to 0.58) −4.15 <.001* NIH EXAMINER working memory score 0.40 ± 0.71 (−1.06 to 1.54) −0.74 ± 0.97 (−1.92 to 0.80) −3.51 <.001* NIH EXAMINER cognitive control score 0.43 ± 0.41 (−0.44 to 1.16) −0.19 ± 0.52 (−1.20 to 0.76) −3.39 .001* ABC scale (%) 77.37 ± 13.41 (43.56 to 94.37) 86.15 ± 9.20 (70 to 98.56) 1.88 .03* ΔPFR 0.32 ± 0.83 (−1.15 to 1.85) 0.92 ± 0.75 (−0.11 to 2.73) 1.85 .03* NIH EXAMINER fluency score 0.74 ± 0.69 (−0.24 to 2.34) 0.42 ± 0.54 (−0.55 to 1.10) −1.29 .20 Typical ΔO2Hb 1.05 ± 0.98 (−0.59 to 2.72) 0.70 ± 0.87 (−0.65 to 2.11) −0.94 .35 BBS (out of 56 points) 51.37 ± 3.40 (45 to 56) 50.09 ± 4.94 (41 to 56) −0.80 .43 Obstacles ΔO2Hb 1.37 ± 0.64 (0.28 to 2.53) 1.66 ± 1.51 (−0.015 to 4.85) 0.67 .50 Obstacles speed (m/s) 0.88 ± 0.22 (0.48 to 1.28) 0.90 ± 0.21 (0.62 to 1.27) 0.26 .78 Age (in years) 69.31 ± 2.98 (65 to 74) 69.63 ± 2.97 (66 to 74) 0.27 .78 10MWS (m/s) 1.06 ± 0.13 (0.78 to 1.32) 1.07 ± 0.18 (0.86 to 1.45) 0.26 .79 Typical speed (m/s) 1.08 ± 0.17 (0.66 to 1.34) 1.09 ± 0.20 (0.70 to 1.46) 0.06 .94 . High EF (n = 9) . Low EF (n = 14) . t statistic . p Value . . Late aging . . . . Executive function (ΔTMT in seconds) 37.03 ± 5.08 (30.48 to 44.24) 101.93 ± 48.71 (49.04 to 183.94) 4.94 <.001* 10MWS (m/s) 1.08 ± 0.15 (0.90 to 1.35) 0.88 ± 0.14 (0.67 to 1.21) −3.12 .002* Typical speed (m/s) 1.10 ± 0.18 (0.77 to 1.34) 0.90 ± 0.16 (0.65 to 1.14) −2.70 .006* NIH EXAMINER working memory score 0.18 ± 0.38 (−0.41 to 0.60) −0.52 ± 0.73 (−2.24 to 0.36) −2.64 .007* Obstacles speed (m/s) 0.90 ± 0.12 (0.63 to 1.06) 0.71 ± 0.21 (0.42 to 1.07) −2.42 .01* NIH EXAMINER composite score 0.40 ± 0.36 (−0.22 to 1.08) −0.13 ± 0.64 (−1.65 to 0.63) −2.23 .01* NIH EXAMINER cognitive control score 0.46 ± 0.50 (−0.14 to 1.54) −0.14 ± 0.71 (−1.75 to 0.70) −2.25 .01* NIH EXAMINER fluency score 0.33 ± 0.51 (−0.31 to 1.07) 0.06 ± 0.65 (−1.15 to 1.44) −1.03 .31 ABC scale (%) 85.25 ± 10.77 (61.25 to 98.87) 79.17 ± 13.87 (49.06 to 94.37) −1.11 .27 Typical ΔO2Hb 0.96 ± 0.56 (0.28 to 1.72) 1.22 ± 0.78 (0.03 to 2.40) 0.86 .39 Obstacles ΔO2Hb 1.12 ± 0.52 (0.37 to 1.98) 1.35 ± 1.00 (0.07 to 3.31) 0.62 .53 BBS (out of 56 points) 48 ± 4.71 (41 to 56) 46.78 ± 6.31 (32 to 56) −0.49 .62 ΔPFR 0.16 ± 0.32 (−0.34 to 0.68) 0.12 ± 0.57 (−1.24 to 0.90) −0.15 .87 Age (in years) 80.66 ± 3.80 (75 to 85) 80.71 ± 4.79 (75 to 92) 0.02 .98 Notes: ABC = Activities-specific Balance Confidence Scale; BBS = Berg Balance Scale; EF = executive function; ΔPFR = change in prefrontal recruitment; TMT = Trail Making Test; ΔTMT = TMT B − TMT A; 10MWS = 10 Meter Walking Speed. Group means and standard deviations are reported. The range of values are reported in parentheses. Group means were compared between the High and Low Executive Function groups within the Early Aging group and the Late Aging group, respectively. *p < .05. Open in new tab Table 2. Group Characteristics by Age and Executive Function . High EF (n = 16) . Low EF (n = 11) . t statistic . p Value . Early aging Executive function (ΔTMT in seconds) 25.29 ± 11.96 (−1.42 to 40.52) 80.11 ± 33.09 (44.43 to 163.79) 6.11 <.0001* NIH EXAMINER composite score 0.73 ± 0.43 (−0.04 to 1.47) −0.004 ± 0.47 (−0.66 to 0.58) −4.15 <.001* NIH EXAMINER working memory score 0.40 ± 0.71 (−1.06 to 1.54) −0.74 ± 0.97 (−1.92 to 0.80) −3.51 <.001* NIH EXAMINER cognitive control score 0.43 ± 0.41 (−0.44 to 1.16) −0.19 ± 0.52 (−1.20 to 0.76) −3.39 .001* ABC scale (%) 77.37 ± 13.41 (43.56 to 94.37) 86.15 ± 9.20 (70 to 98.56) 1.88 .03* ΔPFR 0.32 ± 0.83 (−1.15 to 1.85) 0.92 ± 0.75 (−0.11 to 2.73) 1.85 .03* NIH EXAMINER fluency score 0.74 ± 0.69 (−0.24 to 2.34) 0.42 ± 0.54 (−0.55 to 1.10) −1.29 .20 Typical ΔO2Hb 1.05 ± 0.98 (−0.59 to 2.72) 0.70 ± 0.87 (−0.65 to 2.11) −0.94 .35 BBS (out of 56 points) 51.37 ± 3.40 (45 to 56) 50.09 ± 4.94 (41 to 56) −0.80 .43 Obstacles ΔO2Hb 1.37 ± 0.64 (0.28 to 2.53) 1.66 ± 1.51 (−0.015 to 4.85) 0.67 .50 Obstacles speed (m/s) 0.88 ± 0.22 (0.48 to 1.28) 0.90 ± 0.21 (0.62 to 1.27) 0.26 .78 Age (in years) 69.31 ± 2.98 (65 to 74) 69.63 ± 2.97 (66 to 74) 0.27 .78 10MWS (m/s) 1.06 ± 0.13 (0.78 to 1.32) 1.07 ± 0.18 (0.86 to 1.45) 0.26 .79 Typical speed (m/s) 1.08 ± 0.17 (0.66 to 1.34) 1.09 ± 0.20 (0.70 to 1.46) 0.06 .94 . High EF (n = 9) . Low EF (n = 14) . t statistic . p Value . . Late aging . . . . Executive function (ΔTMT in seconds) 37.03 ± 5.08 (30.48 to 44.24) 101.93 ± 48.71 (49.04 to 183.94) 4.94 <.001* 10MWS (m/s) 1.08 ± 0.15 (0.90 to 1.35) 0.88 ± 0.14 (0.67 to 1.21) −3.12 .002* Typical speed (m/s) 1.10 ± 0.18 (0.77 to 1.34) 0.90 ± 0.16 (0.65 to 1.14) −2.70 .006* NIH EXAMINER working memory score 0.18 ± 0.38 (−0.41 to 0.60) −0.52 ± 0.73 (−2.24 to 0.36) −2.64 .007* Obstacles speed (m/s) 0.90 ± 0.12 (0.63 to 1.06) 0.71 ± 0.21 (0.42 to 1.07) −2.42 .01* NIH EXAMINER composite score 0.40 ± 0.36 (−0.22 to 1.08) −0.13 ± 0.64 (−1.65 to 0.63) −2.23 .01* NIH EXAMINER cognitive control score 0.46 ± 0.50 (−0.14 to 1.54) −0.14 ± 0.71 (−1.75 to 0.70) −2.25 .01* NIH EXAMINER fluency score 0.33 ± 0.51 (−0.31 to 1.07) 0.06 ± 0.65 (−1.15 to 1.44) −1.03 .31 ABC scale (%) 85.25 ± 10.77 (61.25 to 98.87) 79.17 ± 13.87 (49.06 to 94.37) −1.11 .27 Typical ΔO2Hb 0.96 ± 0.56 (0.28 to 1.72) 1.22 ± 0.78 (0.03 to 2.40) 0.86 .39 Obstacles ΔO2Hb 1.12 ± 0.52 (0.37 to 1.98) 1.35 ± 1.00 (0.07 to 3.31) 0.62 .53 BBS (out of 56 points) 48 ± 4.71 (41 to 56) 46.78 ± 6.31 (32 to 56) −0.49 .62 ΔPFR 0.16 ± 0.32 (−0.34 to 0.68) 0.12 ± 0.57 (−1.24 to 0.90) −0.15 .87 Age (in years) 80.66 ± 3.80 (75 to 85) 80.71 ± 4.79 (75 to 92) 0.02 .98 . High EF (n = 16) . Low EF (n = 11) . t statistic . p Value . Early aging Executive function (ΔTMT in seconds) 25.29 ± 11.96 (−1.42 to 40.52) 80.11 ± 33.09 (44.43 to 163.79) 6.11 <.0001* NIH EXAMINER composite score 0.73 ± 0.43 (−0.04 to 1.47) −0.004 ± 0.47 (−0.66 to 0.58) −4.15 <.001* NIH EXAMINER working memory score 0.40 ± 0.71 (−1.06 to 1.54) −0.74 ± 0.97 (−1.92 to 0.80) −3.51 <.001* NIH EXAMINER cognitive control score 0.43 ± 0.41 (−0.44 to 1.16) −0.19 ± 0.52 (−1.20 to 0.76) −3.39 .001* ABC scale (%) 77.37 ± 13.41 (43.56 to 94.37) 86.15 ± 9.20 (70 to 98.56) 1.88 .03* ΔPFR 0.32 ± 0.83 (−1.15 to 1.85) 0.92 ± 0.75 (−0.11 to 2.73) 1.85 .03* NIH EXAMINER fluency score 0.74 ± 0.69 (−0.24 to 2.34) 0.42 ± 0.54 (−0.55 to 1.10) −1.29 .20 Typical ΔO2Hb 1.05 ± 0.98 (−0.59 to 2.72) 0.70 ± 0.87 (−0.65 to 2.11) −0.94 .35 BBS (out of 56 points) 51.37 ± 3.40 (45 to 56) 50.09 ± 4.94 (41 to 56) −0.80 .43 Obstacles ΔO2Hb 1.37 ± 0.64 (0.28 to 2.53) 1.66 ± 1.51 (−0.015 to 4.85) 0.67 .50 Obstacles speed (m/s) 0.88 ± 0.22 (0.48 to 1.28) 0.90 ± 0.21 (0.62 to 1.27) 0.26 .78 Age (in years) 69.31 ± 2.98 (65 to 74) 69.63 ± 2.97 (66 to 74) 0.27 .78 10MWS (m/s) 1.06 ± 0.13 (0.78 to 1.32) 1.07 ± 0.18 (0.86 to 1.45) 0.26 .79 Typical speed (m/s) 1.08 ± 0.17 (0.66 to 1.34) 1.09 ± 0.20 (0.70 to 1.46) 0.06 .94 . High EF (n = 9) . Low EF (n = 14) . t statistic . p Value . . Late aging . . . . Executive function (ΔTMT in seconds) 37.03 ± 5.08 (30.48 to 44.24) 101.93 ± 48.71 (49.04 to 183.94) 4.94 <.001* 10MWS (m/s) 1.08 ± 0.15 (0.90 to 1.35) 0.88 ± 0.14 (0.67 to 1.21) −3.12 .002* Typical speed (m/s) 1.10 ± 0.18 (0.77 to 1.34) 0.90 ± 0.16 (0.65 to 1.14) −2.70 .006* NIH EXAMINER working memory score 0.18 ± 0.38 (−0.41 to 0.60) −0.52 ± 0.73 (−2.24 to 0.36) −2.64 .007* Obstacles speed (m/s) 0.90 ± 0.12 (0.63 to 1.06) 0.71 ± 0.21 (0.42 to 1.07) −2.42 .01* NIH EXAMINER composite score 0.40 ± 0.36 (−0.22 to 1.08) −0.13 ± 0.64 (−1.65 to 0.63) −2.23 .01* NIH EXAMINER cognitive control score 0.46 ± 0.50 (−0.14 to 1.54) −0.14 ± 0.71 (−1.75 to 0.70) −2.25 .01* NIH EXAMINER fluency score 0.33 ± 0.51 (−0.31 to 1.07) 0.06 ± 0.65 (−1.15 to 1.44) −1.03 .31 ABC scale (%) 85.25 ± 10.77 (61.25 to 98.87) 79.17 ± 13.87 (49.06 to 94.37) −1.11 .27 Typical ΔO2Hb 0.96 ± 0.56 (0.28 to 1.72) 1.22 ± 0.78 (0.03 to 2.40) 0.86 .39 Obstacles ΔO2Hb 1.12 ± 0.52 (0.37 to 1.98) 1.35 ± 1.00 (0.07 to 3.31) 0.62 .53 BBS (out of 56 points) 48 ± 4.71 (41 to 56) 46.78 ± 6.31 (32 to 56) −0.49 .62 ΔPFR 0.16 ± 0.32 (−0.34 to 0.68) 0.12 ± 0.57 (−1.24 to 0.90) −0.15 .87 Age (in years) 80.66 ± 3.80 (75 to 85) 80.71 ± 4.79 (75 to 92) 0.02 .98 Notes: ABC = Activities-specific Balance Confidence Scale; BBS = Berg Balance Scale; EF = executive function; ΔPFR = change in prefrontal recruitment; TMT = Trail Making Test; ΔTMT = TMT B − TMT A; 10MWS = 10 Meter Walking Speed. Group means and standard deviations are reported. The range of values are reported in parentheses. Group means were compared between the High and Low Executive Function groups within the Early Aging group and the Late Aging group, respectively. *p < .05. Open in new tab Association Between Prefrontal Recruitment and Obstacle Negotiation Performance Consistent with the known challenge of obstacle negotiation, the walking speed during Obstacles was significantly slower than the walking speed during Typical (0.84 ± 0.21 vs 1.03 ± 0.19 m/s, p < .001). There was a significant association between greater ΔPFR and lower Obstacles cost measured as a smaller reduction in walking speed during Obstacles compared to Typical (% Obstacles cost; r = 0.3, p = .03; Figure 3). Figure 3. Open in new tabDownload slide A significant association between greater change in task-related prefrontal recruitment (ΔPFR) and lower Obstacles cost measured by a percent change in walking speed (% Obstacle cost; Obstacles vs Typical). Figure 3. Open in new tabDownload slide A significant association between greater change in task-related prefrontal recruitment (ΔPFR) and lower Obstacles cost measured by a percent change in walking speed (% Obstacle cost; Obstacles vs Typical). Discussion and Implications The main objective of this study was to examine how task-related changes in prefrontal recruitment (∆PFR; calculated as Obstacles ∆O2Hb − Typical walking ∆O2Hb) were explained by interindividual differences in age, executive function, and sex in older adults. The primary findings were that lower age, poor executive function, and their interaction were significantly associated with greater ∆PFR. Greater ∆PFR was also associated with a lower reduction in walking speed during Obstacles compared to Typical. In our model, sex was not significantly associated with prefrontal recruitment during obstacle negotiation. This finding is in contrast to prior work that has reported gender-specific differences in prefrontal hemodynamic response at rest and during executively demanding tasks (verbal N-back task) (31,32). Brain Activity Interpreted Through Cognitive Models of Brain Aging Several evidence-based models of task-related brain activity exist in the field of cognitive aging and have begun to be applied to understand brain control of walking (6). Among these models are neural inefficiency, neural compensation, and computational capacity limitation (33). A broader framework that consolidates some of the models is the CRUNCH framework, which is conceptualized in Figure 2. According to Reuter-Lorenz et al. (16,34), CRUNCH posits that neural inefficiency leads older adults to engage more neural circuits than young adults to meet task demands (i.e., neural compensation). Therefore, older adults are likely to show overactivation at low levels of cognitive demand. As the task becomes more complex and cognitive demand increases, younger adults exhibit continued heightened activation to meet task demands. At some level of task complexity, which would differ for each individual, brain activity should peak, then plateau or decline (35). In older adults who may have already maxed out their neural resources (i.e., capacity limitation; absence of cognitive reserve), this peak would occur earlier, resulting in underactivation and performance decline. Below we consider CRUNCH and its constituent models of brain activity with respect to our present results and prior obstacle negotiation studies. Neural inefficiency Neural inefficiency is defined as higher levels of brain activity required to achieve a similar or lower level of task performance. According to the CRUNCH framework, neural inefficiency is one possible factor driving compensatory overactivation, with the goal of attenuating decrements in task performance. In the present study, we see a possible example of neural inefficiency from the large increase in ∆PFR for the Obstacles task in the Early Aging: Low Executive Function subgroup (Figure 2A). Given the aforementioned executive control demands of obstacle negotiation and this subgroup’s low executive function (i.e., poor TMT performance), we propose that obstacle negotiation required substantially more prefrontal recruitment than their counterparts in the Early Aging: High Executive Function subgroup. This is conceptualized in Figure 2C as a larger rightward shift on the x-axis, representing the perceived complexity of Obstacles compared to Typical. The same rightward shift is also conceptualized for the Late Aging subgroups (Figure 2D), but the prefrontal activity data do not exhibit the same dependency on executive function (Figure 2B). We attribute this to capacity limitation with advancing age, consistent with CRUNCH and discussed in “Capacity limitation and recruitment ceiling effect” section. A prior study has also proposed neural inefficiency as a possible explanation for higher prefrontal activity during obstacle negotiation and dual-tasking in older adults who had poorer mobility function (i.e., slower walkers) (6). Age-related loss of prefrontal gray matter volume is a possible mechanism of neural inefficiency leading to overactivation during walking, as measured by fNIRS (36). An observation that should be acknowledged is that large ∆PFR in the Early Aging: Low Executive Function subgroup (compared to Early Aging: High Executive Function subgroup) is partially due to lower ∆O2Hb during Typical walking (although the subgroups were not statistically different; p = .35). A possible explanation is that the Early Aging: Low Executive Function subgroup also reported significantly higher levels of balance confidence (Table 2). Although speculative, this would be consistent with a prior study that reported a relationship between higher self-reported balance confidence and lower prefrontal activity during typical walking in adults post-stroke (37). Neural compensation A common finding with older age is broader, often bilateral, activation of brain regions during task performance. This might indicate a detrimental loss of network specificity that compromises task performance (dedifferentiation; possibly impaired network inhibition) or might instead indicate a beneficial compensatory reorganization that supports task performance. The present study was not designed to test overactivation across multiple regions, given that only prefrontal activity was assessed. However, another form of overactivation that can be addressed is the magnitude of the prefrontal hemodynamic response. The compensation hypothesis explains that overactivation in a particular brain region indicates that older adult brains are “working harder” (16). According to CRUNCH, overactivation in older adults relative to younger adults is particularly evident at lower cognitive loads. Indeed, compensatory prefrontal overactivation has been widely reported during typical steady-state walking in older adults and other mobility-compromised populations (stroke, multiple sclerosis, and Parkinson’s disease) (4–6,37–41). Overactivation may serve to overcome network inefficiency as described in the previous section or instead may make up for compromised input from other areas of the nervous system. One such example is impaired peripheral somatosensation (42,43). CRUNCH explains that while overactivation is potentially beneficial to task performance, the consequence is a reduced functional range of brain activity. Specifically, the lower range of activity is shifted upward toward the recruitment ceiling as conceptualized in Figure 2D. Support for this assertion in the context of walking was presented by Hawkins et al. (4), where prefrontal overactivation during typical walking and obstacle negotiation was accompanied by lower cognitive reserves and a more pronounced cost (slowing of walking speed) for obstacle negotiation. Importantly, prefrontal compensatory overactivation is a modifiable target for behavioral interventions that seek to improve brain control of walking. And indeed, prior studies suggest that various forms of walking rehabilitation can reduce prefrontal overactivation (44,45). This has also been established in the context of cognitive tasks, such as shown in a recent study of working memory training in older adults. The study used the CRUNCH framework to interpret reduced overactivation in the frontoparietal regions at lower task loads and increased neural efficiency following the working memory training. The authors reported a rightward shift in the brain activation peak on the CRUNCH curve (i.e., at higher task difficulty) and improved performance on a verbal working memory task following 10 sessions of training (46). Capacity limitation and recruitment ceiling effect Capacity limitation refers to the throughput that is possible in a brain circuit, or the amount of reserve (33). Some evidence supports that older adults reach a peak in brain activation at lower cognitive loads than younger adults (35,46). This capacity limitation reduces the functional range of brain activity by lowering the ceiling of available resources, as conceptualized in Figure 2D (16). The combination of a lower resource ceiling with the aforementioned overactivation compresses the range of functional brain activity, as described by CRUNCH. This can lead to underactivation, which seems consistent with the ∆PFR data from our Late Aging subgroup. Unlike the Early Aging group who showed a substantial increase in brain activity for Obstacles relative to Typical, the Late Aging group showed only modest, nonsignificant changes. This finding might be indicative of a capacity limitation and recruitment ceiling effect with advancing age. Relationship of prefrontal activity to walking performance Across our full cohort in the present study, there was a significant association between greater ΔPFR and lower Obstacles cost (% difference in walking speed; Figure 3). With respect to the brain aging models discussed above, higher ΔPFR is consistent with compensation to overcome inefficiency, as well as the absence of a capacity-limiting ceiling of prefrontal recruitment. Similarly, Clark et al. (47) measured the change in prefrontal activity between a resting control period and several complex walking tasks including obstacle negotiation (similar to our ∆PFR) in older adults. Those who had a larger increase in prefrontal activity (i.e., possible absence of recruitment ceiling effect) had a lower cost of obstacle negotiation based on walking speed. In contrast, Mirelman et al. (5) reported in older adults that higher prefrontal activity (∆O2Hb) during obstacle negotiation was associated with higher gait variability. Higher variability is often considered a sign of poor motor control. The authors proposed that higher prefrontal activity compensated for inefficiency, but ultimately may have been insufficient to meet the motor control demands. Additional evidence is needed to better understand the links between brain activity and walking performance. Although the definition of compensatory overactivation assumes a benefit to task performance, the expression of this benefit must be considered carefully. For any particular individual, it is not possible to measure performance enhancement from compensatory prefrontal activity relative to its theoretical absence. Furthermore, whatever benefit is gained by a particular individual may not necessarily translate to performing a task better than other individuals. Moreover, walking performance (e.g., speed and related metrics) is highly dependent on multiple body systems, not just the nervous system. For these reasons, it is difficult for cross-sectional studies to strongly translate brain activity findings to walking performance outcomes. Better study designs that use walking tasks with several levels of incremental task difficulty (increased cognitive demand) may be more useful, particularly when evaluating CRUNCH (48). Furthermore, better standardization of tasks and outcome measurements would be valuable to the field. This includes being careful to distinguish between complex walking tasks that impose task-relevant versus task-irrelevant cognitive loads, as these may have considerable differences for brain activity and data interpretation. Finally, there is a need for more intervention and longitudinal studies to allow for the within-subject evaluation of changes in brain activity and walking performance (5). Study Limitations A limitation of this study is that we recorded brain activity only from the prefrontal cortex. Many other cortical and subcortical regions are known to be involved in the control of walking (43,49). Neuroimaging approaches such as multichannel fNIRS and high-density electroencephalography (EEG) can be used to measure activity from additional brain regions during walking (48,50). For instance, using EEG prior work has implicated the supplementary motor area and the premotor and posterior parietal cortical regions in the motor planning and execution of foot placement during obstacle negotiation (51). Likewise, using EEG prior work has shown increased activity in the frontal, premotor, and somatosensory cortical regions in response to higher demands on the processing and integration of sensory information to control posture during walking (52). Nevertheless, in the context of the primary objective of relating walking to executive function, there is strong evidence to expect that the prefrontal cortex is among the most crucial brain regions for assessment (53). The prefrontal cortex is highly susceptible to age-related brain neurodegeneration (36,54–56), is preferentially recruited for compensatory processing in older adults (5,6,16,42,47,54), and is strongly linked to executive function which in turn is associated with age-related walking decrements (3,21,57,58) and obstacle negotiation (2,10,25,59). A limitation for assessing the cognitive control of obstacle walking using CRUNCH is that this study did not include walking tasks with multiple gradations (>2) in the level of difficulty. Future studies should use a greater number of difficulty levels to better model the CRUNCH relationship (16). Additionally, this study did not include healthy young adults to serve as a reference for the relationship between task complexity and brain activity. Because prior work has reported age-related differences in task-related brain activity, future studies should consider including young adults in their experimental designs (4). Finally, this study did not account for the influence of life course variables including genetics, education, and active/sedentary lifestyle on brain structure and activity. These factors can protect against or exacerbate the neural and functional decline in older age (60). Conclusions Prefrontal brain activity during obstacle negotiation in older adults is significantly related to age, executive function, and their interaction. Interpreting brain activity in older adults during walking is not straightforward because multiple intersecting factors are at play. These include the influence of neural factors described by brain aging models of neural inefficiency, compensatory overactivation, capacity limitation with a ceiling effect, and potentially others. This is particularly true for tasks with a higher cognitive load such as obstacle negotiation. Frameworks that consolidate multiple brain aging models, such as CRUNCH, are valuable for data interpretation. Future research studies should consider novel study designs to better establish how brain activity relates to underlying mechanisms and to walking deficits, so that targeted interventions can be developed and tested. Funding This work was supported by the National Institutes of Health [1R21AG053736 to D.C.]. Conflict of Interest None declared. Acknowledgments All authors (S.C., R.S., J.S., P.L., C.S., S.W., R.C., D.R., A.W., and D.C.) contributed toward the preparation of the manuscript. Resources for this study were provided by the Claude D. Pepper Older Americans Independence Center at the University of Florida Institute on Aging. Resources were also provided by the North Florida/South Georgia Veterans Health System and the VA Brain Rehabilitation Research Center. The contents of this article do not represent the views of the U.S. Department of Veterans Affairs or the United States Government. References 1. Tinetti ME , Speechley M, Ginter SF. Risk factors for falls among elderly persons living in the community . N Engl J Med . 1988 ; 319 ( 26 ): 1701 – 1707 . doi:10.1056/NEJM198812293192604 Google Scholar PubMed OpenURL Placeholder Text WorldCat 2. 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TI - Obstacle Negotiation in Older Adults: Prefrontal Activation Interpreted Through Conceptual Models of Brain Aging
JF - Innovation in Aging
DO - 10.1093/geroni/igaa034
DA - 2020-08-01
UR - https://www.deepdyve.com/lp/oxford-university-press/obstacle-negotiation-in-older-adults-prefrontal-activation-interpreted-uRRfTu11g7
VL - 4
IS - 4
DP - DeepDyve
ER -