Prefrontal cortical asymmetry and motor slowing in older women: EEG evidence that fear of falling modulates emotional valence and reaction time

Prefrontal cortical asymmetry and motor slowing in older women: EEG evidence that fear of falling modulates emotional valence and reaction time | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 30 October 2025 V1 Latest version Share on Prefrontal cortical asymmetry and motor slowing in older women: EEG evidence that fear of falling modulates emotional valence and reaction time Authors : Guilherme Augusto Santos Bueno 0000-0002-7924-3886 [email protected] , Murielle Celestino da Costa , Katarine Souza Costa , Renato Canevari Dutra da Silva , Elton Camargo Júnior 0000-0001-5148-1703 , Germano Gabriel Lima Esteves , and Ruth Losada de Menezes Authors Info & Affiliations https://doi.org/10.22541/au.176179700.02174534/v1 Published Psychogeriatrics Version of record Peer review timeline 212 views 136 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Fear of falling (FoF) is a neuroemotional phenomenon that compromises motor control and cortical efficiency in older adults. This study investigated the relationship between cortical activation and motor performance in older women with varying FoF levels and fall histories. Fifty-five participants were evaluated, including 40 older adults divided into four groups (NotFall-LFoF, NotFall-HFoF, Fall-LFoF, Fall-HFoF) and 15 younger controls. Motor reaction time was measured using adapted TRT_S2012 software, while cortical activity was recorded via EEG (EMOTIV EPOC+). Cortical arousal was indexed by the β/α ratio, and valence by αF4−αF3 asymmetry. Statistical analyses included ANOVA and Pearson’s correlation (α ≤ 0.05). Groups were homogeneous in demographic and cognitive characteristics. Significant differences were found in cortical arousal (p = 0.014) and valence (p = 0.004), with higher FoF linked to reduced prefrontal symmetry and slower reaction times. Strong negative correlations were observed between valence and reaction times (r > −0.9), while FES-I scores correlated positively with motor latency (r ≈ 0.8–0.9) and negatively with cortical indices (r ≈ −0.7 to −0.9). The findings suggest that FoF modulates prefrontal activation, shifting control from automatic to conscious domains, thereby impairing motor efficiency. FoF emerges as a cortical biomarker of motor vulnerability, emphasizing the importance of neurorehabilitation strategies that integrate emotional and cortical regulation to improve mobility and reduce fall risk in aging populations. Prefrontal cortical asymmetry and motor slowing in older women: EEG evidence that fear of falling modulates emotional valence and reaction time Abstract Fear of falling (FoF) is a neuroemotional phenomenon that compromises motor control and cortical efficiency in older adults. This study investigated the relationship between cortical activation and motor performance in older women with varying FoF levels and fall histories. Fifty-five participants were evaluated, including 40 older adults divided into four groups (NotFall-LFoF, NotFall-HFoF, Fall-LFoF, Fall-HFoF) and 15 younger controls. Motor reaction time was measured using adapted TRT_S2012 software, while cortical activity was recorded via EEG (EMOTIV EPOC+). Cortical arousal was indexed by the β/α ratio, and valence by αF4−αF3 asymmetry. Statistical analyses included ANOVA and Pearson’s correlation (α ≤ 0.05). Groups were homogeneous in demographic and cognitive characteristics. Significant differences were found in cortical arousal (p = 0.014) and valence (p = 0.004), with higher FoF linked to reduced prefrontal symmetry and slower reaction times. Strong negative correlations were observed between valence and reaction times (r > −0.9), while FES-I scores correlated positively with motor latency (r ≈ 0.8–0.9) and negatively with cortical indices (r ≈ −0.7 to −0.9). The findings suggest that FoF modulates prefrontal activation, shifting control from automatic to conscious domains, thereby impairing motor efficiency. FoF emerges as a cortical biomarker of motor vulnerability, emphasizing the importance of neurorehabilitation strategies that integrate emotional and cortical regulation to improve mobility and reduce fall risk in aging populations. Guilherme Augusto Santos Bueno a,b* ; [email protected] Murielle Celestino da Costa c [email protected] Katarine Souza Costa c [email protected] Renato Canevari Dutra da Silva d [email protected] Elton Brás Camargo Júnior e [email protected] Germano Gabriel Lima Esteves f [email protected] Ruth Losada de Menezes c [email protected] AUTHOR AFFILIATIONS: a Department of Medicine, University of Rio Verde, Goiás, GO, Brazil. b Postgraduate Program in Health Sciences and Technologies, University of Brasília, Federal District, DF, Brazil. c Graduate Program in Health Sciences, Faculty of Medicine, Interdisciplinary Center on Aging, Federal University of Goiás, Goiânia, Brazil. d Department of Odontology, University of Rio Verde, Goiás, GO, Brazil. e Department of Postgraduate, University of Rio Verde, Goiás, GO, Brazil. f Department of Psychology, University of Rio Verde, Goiás, GO, Brazil. CORRESPONDING AUTHOR: Guilherme Augusto Santos Bueno, Centro Metropolitano, conjunto A, lote 01, Brasília - DF. CEP: 72220-275. Unidade de Ensino e Docência - UED, 1º andar, sala A1-04/67. E-mail: [email protected] Author contributions GB: Conceptualized the study, analyzed and interpreted the data, and wrote the manuscript. MC, KC and RS: Analyzed the data and critically revised the manuscript for important intellectual content. RM, EJ and GG: Contributed to the study’s concept and design, supervised the research, and critically revised the manuscript for significant intellectual content. Funding “Coordenação de Aperfeiçoamento de Pessoal de Nível superior – Brasil (CAPES) – Finance Code 001 and FAPDF – Fundação de Apoio a Pesquisa do Distrito Federal, Finance Code - 00193.00001697/2019-6. Data availability statement The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Patient consent statement All participants were informed about the study procedures and objectives and provided written informed consent prior to participation. Ethical Statement This cross-sectional analytical study was conducted in a controlled laboratory environment. The research protocol was approved by the Research Ethics Committee of the University of Brasília, Faculty of Ceilândia (approval number 2.109.807). All procedures were performed in accordance with the principles of the Declaration of Helsinki. All participants were informed about the study objectives and provided written informed consent prior to participation. Introduction Falls represent one of the most disabling clinical events of aging, resulting in injuries, loss of independence, and increased mortality (1,2). Although frequent, falls are not part of the physiological course of aging but rather result from the interaction between intrinsic factors such as deficits in motor control, muscle strength, and sensorimotor integration and extrinsic factors, including environmental hazards and drug-related iatrogenesis. Consequently, falls are considered a marker of functional and cognitive vulnerability, reflecting fragility in postural and motor control systems (3–5). Over recent decades, understanding of fall mechanisms has expanded beyond the musculoskeletal domain to include cognitive and emotional dimensions. Functions such as attention, decision-making, and inhibitory control contribute to gait and balance regulation, while affective factors such as fear of falling (FoF) modulate motor behavior and the risk of future falls (6–8). This conceptual integration has driven the development of multidisciplinary prevention strategies that combine physical, cognitive, and emotional approaches. Fear of falling has emerged as one of the most relevant and paradoxical psychogenic factors. Although it initially serves a protective purpose, FoF often persists even in the absence of prior falls, negatively influencing body perception and motor control (7,9). Fear alters gait automaticity and fluency, leading to segmental rigidity and movement slowing, paradoxically increasing the likelihood of future falls (10,11). Previous findings also suggest that FoF predicts motor alterations more strongly than an actual history of falls (10,12). Recent neuroscientific evidence indicates that FoF is not merely a behavioral phenomenon but rather a neuroemotional state mediated by prefrontal circuits (7,9,10). The prefrontal cortex (PFC) plays a central role in fear and anxiety regulation, modulating emotional and motor responses (13,14). Hyperactivity of the right and orbitofrontal PFC, observed in older women with high fear levels, reflects increased cognitive involvement in movement control, resulting in greater attentional cost and slower responses (14–16). During aging, compensatory neural patterns emerge, characterized by enhanced activation of motor and somatosensory cortical regions, as demonstrated by functional neuroimaging and near-infrared spectroscopy studies (17–21). Although adaptive, this increased cortical recruitment is associated with loss of motor automaticity and greater reliance on cognitive networks (22). Electroencephalography (EEG) provides a sensitive, real-time measure of these cortical alterations. Oscillations in the alpha (8–12 Hz) and beta (13–30 Hz) bands are recognized as indicators of cortical state, with the beta/alpha ratio reflecting neural arousal and the frontal asymmetry (F3–F4) index representing emotional valence (23,24). Right-hemispheric dominance, resulting in reduced valence, has been associated with anxiety and avoidance states and correlates with motor slowing and longer reaction times (25–27). Within this framework, understanding how FoF influences cortical activity and reaction time in older women may reveal subtle physiological mechanisms that precede falls, enabling more precise, cortex-centered prevention strategies. Therefore, this study aimed to assess prefrontal cortical activity (valence and arousal) and motor performance (simple and fatigue reaction times) in older women with varying levels of FoF and fall history, using EEG. The central hypothesis was that FoF acts as a cortical modulator of movement, manifested by reduced prefrontal valence and increased motor latency, even in the absence of structural or cognitive deficits. Materials and Methods Study design and ethics This was a cross-sectional and analytical study conducted in a controlled laboratory environment. The protocol was approved by the Research Ethics Committee of the University of Brasília, Faculty of Ceilândia (approval number 2.109.807). All procedures were performed in accordance with the principles of the Declaration of Helsinki. Participants were informed about the study objectives and signed an informed consent form prior to participation. Study population Participants were invited to take part in the research and were screened for eligibility before enrollment. The inclusion criteria were: (i) female sex; (ii) age ≥ 65 years; (iii) ability to ambulate independently in the community without walking aids; (iv) absence of previous surgeries on the lower limbs, pelvis or spine; (v) body mass index (BMI) < 28 kg/m² (28); (vi) preserved cognition, assessed by the Mini-Mental State Examination (MMSE) (29), with a minimum score of 18 adjusted for education level (30); (vii) absence of medical diagnosis of rheumatoid arthritis, neuromuscular or neurodegenerative disease, including diabetes mellitus; (viii) absence of visual impairment; (ix) abstinence from alcohol intake in the 24 hours preceding data collection; and (x) no prior contact with the instrumented gait analysis laboratory. All eligible participants were instructed about the procedures and signed the informed consent form. The sample size was estimated using G*Power 3.1.9.2 software (Franz Faul, Universität Kiel, Germany) (31), based on a one-way analysis of variance (ANOVA) for simple reaction time. A total sample of 40 older women (n = 10 per group) was required to detect a statistically and clinically significant difference related to exposure to fear of falling, considering an effect size (ω²) of 0.82, α = 0.05 and power = 0.99. An additional 15 younger adult women were included to compose the control group. Measurements and definitions Participants were classified according to fall history and fear of falling (FoF), resulting in two experimental groups of older women (fallers and non-fallers) and one control group of younger women. The experimental sample was further divided into four subgroups: fallers with low fear of falling (Fall-LFOF), fallers with high fear of falling (Fall-HFOF), non-fallers with low fear of falling (NotFall-LFOF) and non-fallers with high fear of falling (NotFall-HFOF). Fall history was determined based on the definition proposed by Lamb, Ellen and Hauer (2005) (32), as “an unexpected event in which the participant comes to rest on the ground, floor or a lower level.” The presence of at least one fall in the past 12 months was used as a criterion, producing a dichotomous classification of “faller” or “non-faller.” Fear of falling was assessed using the Falls Efficacy Scale–International (FES-I) (33),in its Brazilian cross-cultural validation (34). The instrument evaluates concern about falling during 16 daily activities, rated on a 4-point scale ranging from 1 (“not at all concerned”) to 4 (“very concerned”), with total scores ranging from 16 to 64. Based on previous studies, participants were classified as having low fear of falling (≤ 27) or high fear of falling (> 27) (35). Motor reaction time assessment Motor reaction time and coordination were assessed using the TRT_S2012 software, adapted with a “pedal-type” joystick as the response interface. Participants were instructed to respond as quickly as possible to the visual stimuli generated by the software (Figure 1). Two types of stimuli were presented: a brief, single stimulus to measure simple motor reaction time, and a sustained, randomly presented stimulus to measure motor reaction time under fatigue (36). In the Simple Reaction Time Test (TRT-Simple), a red square appeared at the center of the monitor at predetermined intervals ranging from 1.5 to 6.5 seconds, with randomized timing between trials. Upon the appearance of the stimulus, participants were required to respond as quickly as possible by pressing the pedal joystick (Figure 1). The Fatigue Reaction Time Test (TRT-Fatigue) involved following the movement of a red bar displayed on the screen, shifting horizontally from left to right. Participants were instructed to press the pedal joystick immediately when the red bar appeared and to keep it pressed while the bar moved across the screen. The button was to be released precisely when the red bar disappeared. Two variables were recorded: TRTi-Fatigue, corresponding to the pressing latency, and TRTf-Fatigue, corresponding to the release latency (36). The software protocol included two familiarization trials, followed by five TRT-Simple trials and a sequence of TRT-Fatigue trials. Figure 1. Motor reaction time assessment combined with electroencephalography. Source: author. Electroencephalography (EEG) recording and processing In this study, electroencephalographic signals were recorded using the EMOTIV EPOC+ system (Emotiv Inc., San Francisco, USA) (37,38), as illustrated in Figure 2. The EMOTIV EPOC+ is a high-resolution, portable EEG system equipped with 14 active data acquisition electrodes (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8 and AF4) and two reference electrodes (P3 and P4). Several studies have validated this system for scientific research applications (39–41). EEG enables real-time monitoring of neural activity on a millisecond scale and provides a highly sensitive measure for detecting subtle differences in neural oscillations (42). Figure 2. EEG preparation protocol for the motor reaction time task. Source: author. To extract relevant neural features, preprocessing of the raw EEG data was performed to remove noise and trivial information. Offline analysis was carried out using the EEGLAB toolbox (43) in MATLAB R2019b (The MathWorks, Natick, MA, USA). The preprocessing pipeline included downsampling the signal to 250 Hz and applying a 0.01–45 Hz Butterworth filter. The Independent Component Analysis (ICA) algorithm was used to identify and remove artifacts (44,45) . Artifact-related components were manually inspected for each participant and excluded based on their energy spectra, topographic distribution and visual characteristics before performing the inverse ICA reconstruction. The cleaned datasets were then filtered into the conventional EEG frequency bands: δ (0.5–4 Hz), θ (4–8 Hz), α (8–13 Hz), β (13–30 Hz) and γ (30–45 Hz), as recommended by Stam and De Bruin (2004) (46) . Cortical arousal levels were determined by calculating the ratio of beta (12–28 Hz) to alpha (8–12 Hz) power. EEG signals were extracted from four prefrontal electrode sites (AF3, AF4, F3 and F4). Beta activity (β) is typically associated with alertness and engagement, while alpha activity (α) is dominant during relaxed or idle states. The beta-to-alpha ratio is therefore considered a reliable index of cortical arousal (47) . and was computed as follows: Arousal level=(ΒF3+ΒF4+ΒAF3+ΒAF4)/(αF3+αF4+αAF3+ αAF4) Cortical valence was determined by comparing the relative activation of the two hemispheres. Numerous EEG studies (48–50) have demonstrated that the left frontal area is associated with positive affect and memory, whereas the right frontal region is more involved in negative emotions. The F3 and F4 electrodes were selected because they are widely used to assess prefrontal alpha/beta asymmetry, a reliable index of emotional valence. Valence values were calculated by comparing the alpha and beta power between the left and right hemispheres (47) , using the following formula: Valence = (αF4/βF4)−(αF3/βF3) Covariates Potential confounding factors were controlled for, including age, sex, body weight, height, and body mass index. Additional covariates known to be associated with both fall risk and fear of falling were also evaluated, namely cognitive level (51); and fall history (51–53). Statistical analysis All analyses were performed using SPSS version 23.0 (IBM Corp., Armonk, NY, USA). The Shapiro–Wilk test was used to verify data normality. Continuous variables were expressed as mean ± standard deviation (SD). Group comparisons were conducted using one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test. Effect sizes were calculated using ω² and r. The relationships between reaction time and cortical measures were examined using Pearson’s correlation coefficient, interpreted as weak (r ≤ 0.30), moderate (0.31–0.69), or strong (r ≥ 0.70) (Aday & Cornelius, 2006). The level of statistical significance was set at p ≤ 0.05. Results A total of 55 participants were included, consisting of one control group of younger women and four experimental groups of older adults classified by fall history and fear of falling: non-fallers with low fear (NotFall-LFOF), non-fallers with high fear (NotFall-HFOF), fallers with low fear (Fall-LFOF), and fallers with high fear (Fall-HFOF). Groups were homogeneous in weight, height, and cognitive status (p > 0.05). However, there were significant differences in age (p < 0.001) and body mass index (BMI) (p = 0.007), with higher BMI among older participants. Fear of falling, measured by the FES-I Brazil, differed markedly among groups (p < 0.001; ω = 0.56), being higher in NotFall-HFOF and Fall-HFOF groups (34.3 ± 5.9 and 32.2 ± 4.5) compared with controls (20.6 ± 3.7) (Table 1). Table 1. Sociodemographic characteristics, cognitive status, and fear of falling comparisons among experimental groups and the control group. A/B (r) A/C (r) A/D (r) A/E (r) B/C (r) B/D (r) B/E (r) C/D (r) C/E (r) D/E (r) Age (years) Control group 24.81 ± 6.82 18.21 – 29.21 <0.001 (0.47) - - - <0.001 (0.68) - - <0.001 (0.64) - <0.001 (0.71) <0.001 (0.71) NotFall-LFOF 72.50 ± 6.04 68.66 – 76.34 NotFall-HFOF 72.67 ± 7.59 68.46 – 76.87 Fall-LFOF 70.83 ± 5.59 67.28 – 74.38 Fall-HFOF 73.90 ± 6.56 69.21 – 78.59 Body weight (Kg) Control group 55.68 ± 6.62 48.31 – 61.02 0.530 (-0.04) - - - - - - - - - - NotFall-LFOF 61.61 ± 6.37 57.56 – 65.66 NotFall-HFOF 58.05 ± 5.03 52.50 – 63.66 Fall-LFOF 60.53 ± 7.78 54.98 – 66.07 Fall-HFOF 63.27 ± 6.62 54.96 – 71.58 Height (m) Control group 1.59 ± 0.04 1.54 – 1.67 0.154 (-0.06) - - - - - - - - - - NotFall-LFOF 1.55 ± 0.05 1.52 – 1.59 NotFall-HFOF 1.54 ± 0.05 1.51 – 1.56 Fall-LFOF 1.56 ± 0.08 1.51 – 1.60 Fall-HFOF 1.53 ± 0.06 1.48 – 1.57 Body mass index (kg/m²) Control group 20.83 ± 2.28 19.45 – 23.20 0.007 (0.12) - - - 0.019 (0.25) - - 0.035 (0.18) - - - NotFall-LFOF 25.57 ± 2.65 23.88 – 27.25 NotFall-HFOF 24.67 ± 4.53 22.16 – 27.18 Fall-LFOF 24.91 ± 2.34 23.42 – 26.39 Fall-HFOF 27.04 ± 3.47 24.20 – 29.88 Mini-Mental State Examination (score) Control group 25.65 ± 3.46 22.72 – 28.52 0.882 (-0.07) - - - - - - - - - - NotFall-LFOF 26.50 ± 3.15 24.50 – 28.50 NotFall-HFOF 26.93 ± 2.49 25.55 – 28.31 Fall-LFOF 25.00 ± 3.19 22.97 – 27.03 Fall-HFOF 27.70 ± 2.87 25.65 – 29.75 FES-I (score) Control group 20.63 ± 3.74 17.50 – 20.14 <0.001 (0.56) <0.001 (0.77) - <0.001 (0.77) - <0.001 (0.75) - <0.001 (0.79) <0.001 (0.75) - <0.001 (0.72) NotFall-LFOF 22.33 ± 3.87 19.88 – 24.79 NotFall-HFOF 34.27 ± 5.87 31.01 – 37.52 Fall-LFOF 23.17 ± 3.74 20.79 – 25.54 Fall-HFOF 32.20 ± 4.49 28.99 – 35.41 Note: A = NotFall-LFoF; B = NotFall-HFoF; C = Fall-LFoF; D = Fall-HFoF. Comparative analysis was performed using one-way ANOVA, considering effect size (ω) and significance at α ≤ 0.05. Post hoc comparisons were conducted using Tukey’s test, with effect size (r) and significance set at α ≤ 0.05. For motor performance, significant group differences were found in simple reaction time (TRTSimple) (p < 0.001; ω = 0.17). Controls exhibited faster responses (352.18 ± 98.21 ms) than older women, whose mean latencies increased progressively according to fear of falling: 855.66 ms (NotFall-LFOF), 1055.81 ms (Fall-LFOF), 1971.22 ms (NotFall-HFOF), and 2187.11 ms (Fall-HFOF). Reaction time under fatigue (TRTiFatigue and TRTfFatigue) showed a similar trend (p < 0.001), with the longest latencies in high-fear groups (Table 2). Regarding cortical activation, the arousal index (β/α ratio) was slightly reduced in high-fear groups (0.96 ± 0.14) compared with controls (0.98 ± 0.11) (p = 0.034). More pronounced differences appeared in prefrontal valence (αF4/βF4 − αF3/βF3) (p < 0.001; ω = 0.71), with lower values in NotFall-HFOF (0.73 ± 0.12) and Fall-HFOF (0.71 ± 0.13) than in controls (0.96 ± 0.09), indicating a relative dominance of right-hemispheric activity (Table 2). Table 2 - Motor reaction time, cortical arousal, and valence comparisons among experimental groups and the control group. A/B (r) A/C (r) A/D (r) A/E (r) B/C (r) B/D (r) B/E (r) C/D (r) C/E (r) D/E (r) Simple reaction time (ms) Control group 352.18 ± 98.21 296.65 – 615.21 0.007 (0.17) 0.029 (0.28) - <0.001 (0.61) 0.008 (0.58) 0.022 (0.22) - <0.001 (0.78) 0.011 (0.39) 0.014 (0.34) <0.001 (0.84) NotFall-LFOF 855.66 ± 103.54 724.19 – 928.21 NotFall-HFOF 1971.22 ± 109.28 1429.68 – 2101.45 Fall-LFOF 1055.81 ± 149.57 786.40 – 1214.23 Fall-HFOF 2187.11 ± 141.04 1876.67 – 2354.41 Initial fatigue reaction time (ms) Control group 578.84 ± 87.98 459.87 – 625.24 <0.001 (0.78) 0.021 (0.19) - <0.001 (0.64) <0.001 (0.69) <0.001 (0.70) - <0.001 (0.75) <0.001 (0.69) <0.001 (0.54) <0.001 (0.88) NotFall-LFOF 1501.84 ± 191.70 1395.44 – 1785.25 NotFall-HFOF 2310.11 ± 181.85 1865.25 – 2547.37 Fall-LFOF 1232.98 ±175.11 913.30 – 1547.25 Fall-HFOF 2316.04 ± 165.68 1931.27 – 2547.69 Final fatigue reaction time (ms) Control group 417.44 ± 78.61 314.26 – 509.32 <0.001 (0.69) <0.001 (0.67) - <0.001 (0.56) <0.001 (0.51) <0.001 (0.51) - <0.001 (0.79) <0.001 (0.53) <0.001 (0.51) <0.001 (0.89) NotFall-LFOF 817.85 ± 119.18 615.19 – 958.74 NotFall-HFOF 1850.34 ± 158.71 1656.45 – 2025.86 Fall-LFOF 864.41 ± 121.51 650.77 – 958.46 Fall-HFOF 1935.37 ± 151.13 1702.74 – 2102.54 Cortical arousal a Control group 0.98 ± 0.11 0.97 – 0.99 0.034 (0.12) 0.029 (0.19) - 0.024 (0.27) - 0.032 (0.18) - 0.031 (0.19) 0.025 (0.24) - 0.026 (0.24) NotFall-LFOF 0.98 ± 0.19 0.97 – 0.99 NotFall-HFOF 0.96 ± 0.14 0.95 – 0.98 Fall-LFOF 0.98 ± 0.17 0.97 – 0.99 Fall-HFOF 0.96 ± 0.12 0.94 – 0.97 Cortical valence b Control group 0.96 ± 0.09 0.95 – 0.98 <0.001 (0.71) <0.001 (0.81) - <0.001 (0.86) 0.004 (0.67) <0.001 (0.79) - <0.001 (0.89) <0.001 (0.79) 0.010 (0.68) <0.001 (0.84) NotFall-LFOF 0.92 ± 0.11 0.90 – 0.95 NotFall-HFOF 0.73 ± 0.12 0.70 – 0.77 Fall-LFOF 0.91 ± 0.09 0.88 – 0.93 Fall-HFOF 0.71 ± 0.13 0.68 – 0.79 Note: a (ΒF3+ΒF4+ΒAF3+ΒAF4)/(αF3+αF4+αAF3+ αAF4); b (αF4−αF3); A - NotFall-LFOF; B-NotFall-HFOF; C-Fall-LFOF; D-Fall-HFOF. Comparative analysis was performed using one-way ANOVA, considering effect size (ω) and significance at α ≤ 0.05. Post hoc Tukey pairwise comparisons were conducted considering effect size (r) and significance set at α ≤ 0.05. Pearson’s correlation analysis revealed strong and consistent associations among cortical, motor, and emotional variables. Valence showed a strong negative correlation with all reaction time parameters (r > −0.9), indicating that lower valence, meaning greater right-hemispheric asymmetry, was associated with slower motor responses. Complementarily, the FES-I score displayed high positive correlations with reaction times (r ≈ 0.8–0.9) and negative correlations with both arousal and valence (r ≈ −0.7 to −0.9) (Table 3). Table 3 - Correlation between motor reaction time, cortical arousal, valence, and fear of falling assessed by the Brazilian version of the FES-I Cortical arousal a Control group -0.325 -0.224 -0.354 -0.378 NotFall-LFOF -0.308 -0.287 -0.396 -0.794 NotFall-HFOF -0.513 -0.547 -0.345 -0.845 Fall-LFOF -0.397 -0.415 -0.361 -0.764 Fall-HFOF -0.689 -0.598 -0.305 -0.831 Cortical valence b Control group -0.854 -0.865 -0.798 -0.863 NotFall-LFOF -0.886 -0.897 -0.831 -0.801 NotFall-HFOF -0.924 -0.934 -0.921 -0.896 Fall-LFOF -0.914 -0.925 -0.942 -0.798 Fall-HFOF -0.932 -0.915 -0.905 -0.901 FES-I (escore) Control group 0.834 0.513 0.868 - NotFall-LFOF 0.414 0.251 0.248 - NotFall-HFOF 0.874 0.664 0.749 - Fall-LFOF 0.421 0.542 0.712 - Fall-HFOF 0.896 0.625 0.654 - Nota: a (ΒF3+ΒF4+ΒAF3+ΒAF4)/(αF3+αF4+αAF3+ αAF4); b (αF4−αF3); A - NotFall-LFOF; B-NotFall-HFOF; C-Fall-LFOF; D-Fall-HFOF. - no significant correlations were observed (p ≤ 0.05). Pearson’s correlation test was applied, considering significance at p < 0.05. Correlations were interpreted as weak (r 0.60). Discussion The results of this study demonstrate that fear of falling (FoF) directly influences cortical activation and motor performance in older women, independently of previous fall events. The significant prolongation of reaction times and the reduction in cortical valence indicate that fear acts as a neuroemotional modulator, reducing the efficiency of prefrontal–motor circuits. The right-hemispheric asymmetry observed in participants with high FoF is consistent with negative emotional dominance patterns reported in studies linking anxiety and risk anticipation to altered motor performance (7,54). According to the perceived control of falling model , FoF and balance confidence interact through perceived control over balance-threatening situations, which mediates the relation between fear and fall risk. Neuroimaging studies indicate that the prefrontal cortex plays a crucial role in balance and motor control. High prefrontal–motor coherence during postural reactions has been associated with greater cognitive–motor interference and increased fall risk (55). Older adults show higher prefrontal activation during balance tasks compared with younger adults, reflecting neural inefficiency. Lower prefrontal activation, conversely, correlates with better postural performance, suggesting that prefrontal asymmetry may represent a compensatory mechanism in aging (56). Decline in inhibitory control, a key executive function, is also associated with impaired balance and greater fall risk, even in pathological aging such as Alzheimer’s disease (57). Moreover, dual-task paradigms reveal that brain activity during simultaneous motor–cognitive tasks independently predicts fall profiles in older adults (58), while functional overlap between vestibular and fear-related networks may modulate postural performance (59). The progressive slowing of reaction time among participants with high FoF suggests an imbalance between automatic and conscious control of movement. Older adults frequently show compensatory prefrontal hyperactivation in motor and cognitive tasks, consistent with the CRUNCH and STAC-r models of aging, which propose the recruitment of additional neural resources to maintain performance (60). However, under conditions of perceived threat, this compensatory mechanism becomes maladaptive, leading to excessive motor monitoring and attentional interference that delay responses (61). Functional near-infrared spectroscopy (fNIRS) findings corroborate this pattern, showing that older adults exhibit higher frontal activation during dual tasks as a compensatory attempt to preserve motor control (62). The association between FoF and reaction time under fatigue reinforces this interpretation. Sustained attention and cortical self-regulation are essential for daily activities and tend to deteriorate with age, contributing to frailty and increased fall risk (63). Event-related potentials and EEG spectral analyses demonstrate that attentional decline and cognitive fatigue are marked by altered oscillatory patterns, such as reduced occipital alpha power and frontal theta variability (65,66). Older adults with high FoF also allocate attentional resources to worrisome thoughts and environmental monitoring, which diminishes postural control efficiency (Ellmers; Maslivec; Young, 2020). The reduction in prefrontal valence observed in the high-FoF groups supports the hypothesis of emotional asymmetry. Lower valence (F3–F4) reflects greater right-hemispheric activity, consistent with patterns associated with negative emotion, risk anticipation, and avoidance behaviors. Although some evidence suggests that older adults exhibit increased left cortical activity compared with younger adults (68), the present findings point to a context-dependent shift toward right dominance under fear conditions. Age-related changes in cortical oscillations also affect motor control, as increased theta activity and reduced mu–beta power during balance tasks have been described in older adults (69), and alterations in excitatory–inhibitory balance modulate both cognition and movement (70). The reduction in cortical arousal (β/α ratio) observed in high-FoF participants indicates diminished prefrontal engagement and possible top-down inhibition of motor networks. Age-related alterations in alpha–gamma synchronization reveal compromised top-down attentional mechanisms, even when bottom-up processes remain relatively preserved (71). The beta/alpha ratio has been proposed as a sensitive index of attentional engagement, predicting lapses in sustained attention (72). Changes in the excitatory–inhibitory balance, particularly reduced inhibitory activity, contribute to cognitive and motor inefficiency in aging (70). Strong negative correlations between valence and reaction time (r > −0.9) support the link between emotion and motor performance: greater right-hemispheric asymmetry was associated with slower responses. Likewise, positive correlations between FES-I and reaction times, together with negative correlations with arousal and valence, indicate that perceived fear translates into measurable behavioral slowing. Variability in reaction time has been proposed as a marker of cognitive control efficiency, as frontal midline theta power correlates with control demands and response variability (72). Moreover, emotional processing asymmetry influences response speed, with left-frontal activation associated with faster emotional reactivity (74). Recent evidence also highlights the role of beta oscillations in motor planning and inhibition. Age-related declines in inhibitory control are reflected in altered beta bursts and reduced motor synchronization (75). The movement-related beta desynchronization (MRBD) becomes exaggerated with aging, indicating inefficient motor planning and increased cognitive effort during movement (76). The present findings align with this literature, suggesting that inefficient beta modulation contributes to the observed motor slowing in older women with high FoF. Taken together, these results indicate that fear of falling functions as a neuroemotional marker of cortical inefficiency, capable of predicting motor decline before falls occur. This perspective has clinical implications, as traditional prevention programs focused solely on strength and balance may be insufficient without addressing the affective and cortical dimension of movement. Interventions targeting prefrontal symmetry and emotional regulation, such as non-invasive neuromodulation (tDCS, TMS), immersive virtual reality, and cognitive–motor training, have shown promising results in enhancing motor performance and reducing fall risk. In summary, fear of falling should be understood not merely as a psychological risk factor but as a neurofunctional mechanism that reshapes the cortical dynamics of aging. The combination of motor slowing and prefrontal hypoactivation observed in this study suggests that fear acts as an emotional brake, altering timing and motor efficiency. 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Keywords aging fear motor behavior motor control Authors Affiliations Guilherme Augusto Santos Bueno 0000-0002-7924-3886 [email protected] Universidade de Rio Verde Curso de Medicina View all articles by this author Murielle Celestino da Costa Universidade Federal de Goias View all articles by this author Katarine Souza Costa Universidade Federal de Goias View all articles by this author Renato Canevari Dutra da Silva Universidade Federal de Goias View all articles by this author Elton Camargo Júnior 0000-0001-5148-1703 Universidade de Rio Verde View all articles by this author Germano Gabriel Lima Esteves Universidade de Rio Verde View all articles by this author Ruth Losada de Menezes Universidade Federal de Goias View all articles by this author Metrics & Citations Metrics Article Usage 212 views 136 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Guilherme Augusto Santos Bueno, Murielle Celestino da Costa, Katarine Souza Costa, et al. 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