The human cerebral cortex undergoes progressive structural changes throughout the lifespan, with cortical thickness serving as a key morphometric indicator of neural integrity and brain health [1]. Magnetic resonance imaging (MRI) studies have consistently demonstrated that cortical thinning is a hallmark feature of normative aging, with certain regions exhibiting greater vulnerability to age-related atrophy than others [2]. In particular, the prefrontal, temporal, and parietal association cortices have been identified as areas of pronounced thinning, and these changes appear to accelerate beyond the fourth decade of life [3, 4]. Longitudinal neuroimaging research has confirmed that cortical thickness reductions are not uniform; rather, they follow region-specific trajectories that vary considerably across individuals[5].Concurrently, a growing body of epidemiological evidence has demonstrated that cognitive abilities, including processing speed, executive function, and episodic memory, begin to decline measurably during middle adulthood, well before the onset of clinically recognized dementia [6, 7]. This intersection of structural brain change and functional cognitive deterioration during midlife represents a critical kalayet insufficiently investigated period in the trajectory of brain aging [8]. Despite substantial research into cortical atrophy in elderly populations, relatively fewer studies have specifically examined the relationship between cortical thickness variations and cognitive performance in middle-aged adults, typically defined as those between 40 and 65years of age [9, 10]. This represents a significant gap in the current understanding of neurocognitive aging, as middle adulthood is increasingly recognized as a window during which early neurodegenerative processes may be initiated or modulated by genetic, cardiovascular, and lifestyle factors [11, 12]. Previous cross-sectional and longitudinal studies have suggested that cortical thickness in the prefrontal and medial temporal regions is positively correlated with performance on tasks of memory, attention, and executive control; however, these findings have predominantly been derived from samples of older adults or clinical populations [13]. Moreover, advances in high-resolution structural MRI and automated cortical parcellation techniques, such as Free Surfer, have now enabled more precise quantification of regional cortical thickness, allowing researchers to detect subtle variations that were previously undetectable [14, 15]. There remains a pressing need to characterize the normative patterns of cortical thinning during midlife and to determine whether these structural changes are meaningfully predictive of concurrent or future cognitive decline [16, 17]. The primary objective of this study was to investigate the association between regional cortical thickness variations and cognitive performance in a cohort of middle-aged adults aged 40 to 65 years. Specifically, this study aimed to: (a) characterize the distribution and magnitude of cortical thickness across key brain regions using high-resolution structural MRI; (b) assess cognitive functioning through a standardized neuropsychological battery encompassing domains of memory, executive function, attention, and processing speed; and (c) evaluate the extent to which regional cortical thickness independently predicts cognitive performance after controlling for age, sex, education, and cardiovascular risk factors [18]. Based on existing literature and neuroanatomical theory, we hypothesized that thinner cortices in the prefrontal and medial temporal regions will be significantly associated with poorer performance on tasks of executive function and episodic memory, respectively, and that these associations will remain significant after adjustment for relevant demographic and clinical covariates. Furthermore, we hypothesized that middle-aged adults demonstrating an accelerated pattern of cortical thinning relative to age- matched norms will exhibit measurably lower cognitive performance, thereby supporting the premise that cortical thickness serves as a sensitive structural biomarker of early cognitive vulnerability in this population.

Study Design and Participants This cross-sectional study enrolled 180 cognitively healthy adults aged 40 to 65 years (mean age = 52.7 ± 7.4 years; 98 female, 82 male), recruited from a community-based population cohort between January 2023 and December 2024. Participants were included if they had no history of neurological or psychiatric illness, no contraindications to MRI, and a Mini- Mental State Examination (MMSE) score ≥ 27, consistent with established screening thresholds for normal cognition [6, 8]. Exclusion criteria included a prior diagnosis of dementia or mild cognitive impairment, history of traumatic brain injury with loss of consciousness exceeding 30 minutes, current use of psychotropic medications and the presence of significant cerebrovascular disease confirmed on neuroimaging [11]. Demographic and clinical data, including age, sex, years of formal education, body mass index (BMI), systolic and diastolic blood pressure, and self-reported cardiovascular risk factors (hypertension, diabetes mellitus, hyperlipidemia, and smoking status), were collected through structured interviews and medical record review [9, 12]. The study protocol was approved by the Kalasalingam Medical College, Kalasalingam University, Krishnankoil, Srivilliputhur, Tamil Nadu, Institutional Review Board, and all participants provided written informed consent prior to enrollment, in accordance with the Declaration of Helsinki [18]. High-resolution T1-weighted structural MRI scans were acquired for each participant using a 3.0 Tesla Siemens Magnetom Prisma scanner equipped with a 64-channel head coil, employing a three-dimensional magnetization-prepared rapid gradient echo (MPRAGE) sequence with the following parameters: repetition time (TR) = 2300 ms, echo time (TE) = 2.98 ms, inversion time (TI) = 900 ms, flip angle = 9°, field of view (FOV) = 256 × 256 mm, matrix size = 256 × 256, slice thickness = 1.0 mm, and 176 sagittal slices providing full brain coverage with isotropic 1.0 mm³ voxel resolution [2, 14]. All images were visually inspected for motion artifacts; scans with excessive movement were repeated or excluded from analysis. Image Processing, Cognitive Assessment and Statistical Analysis Cortical reconstruction and volumetric segmentation were performed using the FreeSurfer image analysis suite (version 7.3.2; http://surfer.nmr.mgh.harvard.edu), following the fully automated processing pipeline described by Fischl and Dale [14] and the Desikan–Killiany cortical atlas for parcellation into anatomically defined regions of interest [15]. The processing stream included motion correction, intensity normalization, removal of non-brain tissue, Talairach transformation, tessellation of the gray–white matter boundary, and surface-based inflation and registration to a spherical atlas [3, 14]. Cortical thickness was computed as the shortest distance between the white matter surface and the pial surface at each vertex across the cortical mantle [14]. Six bilateral regions of interest (ROIs) were selected a priori based on their established sensitivity to age-related thinning and cognitive relevance: the dorsolateral prefrontal cortex (DLPFC), medial prefrontal cortex (MPFC), medial temporal lobe (MTL; including entorhinal and parahippocampal cortices), inferior parietal lobule (IPL), superior temporal cortex (STC), and orbitofrontal cortex (OFC) [2, 5, 10]. All cortical reconstructions were visually inspected by a trained neuroimaging analyst and manually corrected where necessary to ensure topological accuracy [15]. Cognitive performance was assessed using a comprehensive neuropsychological battery administered by licensed clinical neuropsychologists and included the Trail Making Test Parts A and B for processing speed and executive function [12], the Rey Auditory Verbal Learning Test (RAVLT) for episodic memory [7], the Digit Span Forward and Backward from the Wechsler Adult Intelligence Scale–IV for attention and working memory [8], and the Stroop Color–Word Interference Test for inhibitory control and cognitive flexibility [6, 13]. Raw scores were converted to age-adjusted z-scores, and composite domain scores were calculated for executive function, episodic memory, processing speed, and attention [7]. Statistical analyses were performed using IBM SPSS Statistics (version 28.0) and R (version 4.3.1). Descriptive statistics were computed for demographic, clinical, and neuroimaging variables. Pearson product-moment correlation coefficients were used to assess bivariate associations between regional cortical thickness and cognitive domain scores [4, 10]. Independent-samples t-tests were employed to compare cortical thickness between male and female participants, and one-way analysis of variance (ANOVA) was used to evaluate differences in cortical thickness across three age groups (40–49, 50–59, and 60–65 years), with Bonferroni-corrected post hoc comparisons [5, 9]. Hierarchical multiple regression analyses were conducted to determine the independent contribution of regional cortical thickness to cognitive performance, entering age, sex, education, and cardiovascular risk factors in the first block and cortical thickness measures in the second block [16, 17]. Statistical significance was set at p < 0.05 for all analyses, and effect sizes were reported using Cohen’s d for group comparisons and adjusted R² for regression models

A total of 180 participants met all inclusion criteria and were included in the final analysis (98 female, 82 male; mean age = 52.7 ± 7.4 years). The sample was distributed across three age strata: 40–49 years (n = 64, 35.6%), 50–59 years (n = 68, 37.8%), and 60–65 years (n = 48, 26.7%). The mean years of education were 15.2 ± 2.8 years, and 43.9% of participants reported at least one cardiovascular risk factor [9, 11]. Table 1 presents the full demographic and clinical characteristics of the study sample. Table 1: DemographicandClinicalCharacteristicsofStudyParticipantsbyAgeGroup Characteristic Total (N=180) 40–49(n=64) 50–59(n=68) 60–65(n=48) Age, years(mean ± SD) 52.7 ±7.4 44.8 ±2.9 54.3 ±2.7 62.1 ±1.6 Female, n (%) 98 (54.4) 36 (56.3) 38 (55.9) 24 (50.0) Education, years(mean ± SD) 15.2 ±2.8 15.8 ±2.6 15.1 ±2.9 14.5 ±3.0 BMI, kg/m²(mean ±SD) 26.4 ±4.1 25.8 ±3.9 26.6 ±4.2 27.1 ±4.3 Hypertension, n (%) 52 (28.9) 12 (18.8) 20 (29.4) 20 (41.7) Diabetes mellitus, n (%) 24 (13.3) 5 (7.8) 9 (13.2) 10 (20.8) Current smoker, n(%) 21 (11.7) 8 (12.5) 7 (10.3) 6 (12.5) MMSE score(mean±SD) 28.8 ±0.9 29.1 ±0.7 28.8 ±0.8 28.3 ±1.0 Regional Cortical Thickness across Age Groups Mean cortical thickness values across the six regions of interest, stratified by age group, are presented in Table 2 and Figure 1. One-way ANOVA revealed statistically significant differences in cortical thickness across the three age groups for all six regions examined (all p < 0.001) [2, 5]. The most pronounced age-related reductions were observed in the DLPFC (F(2, 177) =28.41, p<0.001, η²=0.243) and the MTL(F(2, 177)=24.67, p<0.001, η²=0.218), consistent with previous reports of preferential vulnerability of prefrontal and temporal association cortices to age-related atrophy [2, 4]. Bonferroni-corrected post hoc comparisons indicated that the 60–65 age group exhibited significantly thinner cortex relative to the 40–49 group in all regions (allp<0.001), and the 50–59 group also differed significantly from the 40–49 group in the DLPFC(p=0.002), MPFC(p=0.004), and MTL(p=0.003)[5, 10]. Independent-samples t-tests comparing male and female participants revealed no statistically significant sex differences in cortical thickness for any region after correction for multiple comparisons (all p > 0.05), although a trend toward slightly greater thickness in females was observed in the MTL (t(178) = 1.89, p = 0.061) [18]. Table 2: Mean Cortical Thickness (mm±SD) by Brain Region and Age Group with ANOVA Results Brain Region 40–49(mm) 50–59(mm) 60–65(mm) F-statistic p-value η² DLPFC 2.78 ±0.14 2.61 ±0.16 2.43 ±0.18 28.41 <0.001 0.243 MPFC 2.71 ±0.13 2.54 ±0.15 2.38 ±0.17 23.18 <0.001 0.208 MTL 3.42 ±0.18 3.21 ±0.20 2.98 ±0.22 24.67 <0.001 0.218 IPL 2.65 ±0.12 2.52 ±0.14 2.41 ±0.15 17.92 <0.001 0.168 STC 2.89 ±0.15 2.74 ±0.17 2.58 ±0.19 16.45 <0.001 0.157 OFC 2.82 ±0.13 2.68 ±0.16 2.53 ±0.17 19.73 <0.001 0.182 Associations Between Cortical Thickness and Cognitive Performance Table 3 displays the Pearson correlation coefficients between regional cortical thickness and cognitive domain scores. Significant positive correlations were observed between DLPFC thickness and executive function (r = 0.58, p < 0.001), DLPFC thickness and processing speed (r = 0.41, p < 0.001), MTL thickness and episodic memory (r = 0.52, p < 0.001), IPL thickness and attention (r = 0.39, p < 0.001), and MPFC thickness and executive function (r = 0.44, p < 0.001) [1, 10, 13]. These associations are illustrated in Figures 2 and 3, which display scatterplots for the two strongest correlations: DLPFC thickness versus executive function and MTL thickness versus episodic memory, respectively. The OFC and STC showed moderate but statistically significant correlations with processing speed (r = 0.33, p < 0.001) and attention (r = 0.36, p < 0.001), respectively [4, 7]. Notably, the strength of the association between cortical thickness and cognitive performance increased across successive age groups, suggesting that the coupling between structure and function becomes tighter with advancing age within the midlife period [8, 16]. Table 3: Pearson Correlation Coefficients between Regional Cortical Thickness and Cognitive Domain Scores Region Exec. Function EpisodicMemory Proc.Speed Attention DLPFC 0.58*** 0.31*** 0.41*** 0.28*** MPFC 0.44*** 0.29*** 0.33*** 0.25** MTL 0.34*** 0.52*** 0.28*** 0.30*** IPL 0.30*** 0.26** 0.35*** 0.39*** STC 0.27** 0.24** 0.29*** 0.36*** OFC 0.32*** 0.22** 0.33*** 0.26** Note: **p <0.01;***p < 0.001 Hierarchical Regression Analysis Hierarchical multiple regression results are summarized in Table 4. In the first step, demographic and cardiovascular covariates (age, sex, years of education, and composite cardiovascular risk score) were entered, collectively accounting for 21.6% of the variance in the cognitive composite score (adjusted R² = 0.216, F(4, 175) = 13.21, p < 0.001) [6, 9]. Age was the strongest predictor in this step (β = −0.38, p < 0.001), followed by years of education (β = 0.22, p = 0.002). In the second step, cortical thickness values for the DLPFC and MTL were added to the model, which resulted in a significant increment in explained variance (ΔR² = 0.168, ΔF(2, 173) = 21.84, p < 0.001), yielding a total adjusted R² of 0.384 [14, 16]. DLPFC thickness was the most robust independent predictor of cognitive composite scores (β = 0.34, p < 0.001), followed by MTL thickness (β = 0.27, p < 0.001), while the effect of age was substantially attenuated (β = −0.19, p = 0.014), suggesting that cortical thickness partially mediates the relationship between chronological age and cognitive performance [3, 17]. An exploratory mediation analysis using the PROCESS macro (Model 4) confirmed that DLPFC thickness significantly mediated the age–executive function association (indirect effect: β = −0.14, 95% CI [−0.21, −0.08]), accounting for approximately 37% of the total effect of age on executive function [5, 15]. Table 4: Hierarchical Multiple Regression Predicting Cognitive Composite Score Predictor β SE t p-value Step1 (R² = 0.216) Age −0.38 0.06 −6.42 <0.001 Sex 0.04 0.07 0.58 0.563 Education(years) 0.22 0.07 3.18 0.002 CV risk score −0.14 0.06 −2.24 0.026 Step2(ΔR²=0.168;TotalR²= 0.384) Age −0.19 0.06 −3.12 0.014 Sex 0.03 0.06 0.48 0.634 Education(years) 0.18 0.06 2.84 0.005 CV risk score −0.10 0.06 −1.72 0.088 DLPFC thickness 0.34 0.07 5.14 <0.001 MTL thickness 0.27 0.07 4.02 <0.001

The present study investigated the association between regional cortical thickness and cognitive performance in a cohort of 180 middle-aged adults, and the findings provide robust evidence that cortical thinning in specific brain regions is significantly related to cognitive decline during midlife. The observation that the dorsolateral prefrontal cortex (DLPFC) and medial temporal lobe (MTL) exhibited the most pronounced age-related cortical thinning is consistent with the well-established frontal aging hypothesis, which assumes that prefrontal regions are disproportionately vulnerable to age-related structural decline, and with evidence of early medial temporal involvement in the trajectory toward cognitive impairment [1, 2, 8]. The magnitude of cortical thickness reduction observed between the youngest (40–49) and oldest (60–65) age groups, averaging approximately 0.35 mm in the DLPFC and 0.44 mm in the MTL, aligns with longitudinal estimates reported by Storsve et al. [5] and Thambisetty et al. [10], who documented annual cortical thinning rates of 0.01 to 0.03 mm per year in these regions among healthy adults. A central finding of this study was that DLPFC thickness was the strongest independent predictor of executive function and cognitive composite scores, even after adjusting for demographic and cardiovascular confounders. This result converges with prior research by Grieve et al. [12], who demonstrated that prefrontal cortical integrity is critically linked to executive control capacities, and with findings from Karama et al. [13], who reported that cortical thickness in frontal regions uniquely predicted cognitive ability in aging populations. Similarly, the significant association between MTL thickness and episodic memory performance is in agreement with the extensive body of literature implicating the hippocampal and parahippocampal regions in memory encoding and retrieval processes [3, 9]. The correlation coefficients observed in this study (r = 0.58 for DLPFC–executive function; r = 0.52 for MTL–episodic memory) are moderately large and comparable to those reported in studies of older adults [16, 17], suggesting that the structure–function relationship in these regions is already robustly established during middle adulthood. The hierarchical regression findings merit particular attention. The fact that cortical thickness measures explained an additional 16.8% of variance in cognitive composite scores beyond what was accounted for by age, sex, education, and cardiovascular risk factors underscores the independent contribution of brain structure to cognitive performance in this population [14, 16]. Moreover, the attenuation of the age coefficient upon inclusion of cortical thickness variables in the regression model suggests that cortical thinning may partially mediate the relationship between chronological age and cognitive decline, a finding supported by the exploratory mediation analysis and consistent with the theoretical framework proposed by Fjell and Walhovd [1]. The absence of significant sex differences in cortical thickness in this sample contrasts with some earlier studies [18] but is consistent with reports indicating that sex differences in cortical morphometry diminish in middle adulthood when controlling for total intracranial volume [4]. The observation that cardiovascular risk factors were significantly associated with both cortical thinning and cognitive performance aligns with the vascular hypothesis of cognitive aging, which posits that microvascular damage contributes to cortical atrophy and subsequent cognitive impairment [11]. This finding has implications for preventive strategies, as modifiable vascular risk factors may represent tractable targets for interventions aimed at preserving both cortical structure and cognitive function during midlife [6, 7]. Several limitations of this study should be acknowledged. First, the cross-sectional design precludes causal inference regarding the directionality of the association between cortical thickness and cognitive performance; it remains possible that cognitive engagement and lifestyle factors influence cortical maintenance, rather than the reverse [13]. Second, although FreeSurfer provides highly reliable cortical thickness estimates, measurement error at the individual vertex level may introduce noise, particularly in regions with complex folding geometry [14, 15]. Third, the sample was drawn from a community-based cohort and may not be fully representative of the broader population, particularly with respect to ethnic and socioeconomic diversity [9]. Fourth, the cognitive battery, while comprehensive, did not include measures of all cognitive domains, such as visuospatial processing or language, which may also be sensitive to cortical thickness variations [7, 8]. Future longitudinal studies with repeated neuroimaging and cognitive assessments are needed to establish the temporal dynamics of cortical thinning and cognitive change, and to determine whether baseline cortical thickness in midlife prospectively predicts the onset of clinically significant cognitive impairment or dementia [5, 10, 16].

In conclusion, this study provides compelling evidence that regional cortical thickness variations are significantly and independently associated with cognitive performance in middle-aged adults. The dorsolateral prefrontal cortex and medial temporal lobe emerge as the regions most strongly linked to executive function and episodic memory, respectively, and cortical thinning in these areas explains a substantial proportion of variance in cognitive outcomes beyond what is attributable to demographic and cardiovascular factors alone. These findings underscore the importance of middle adulthood as a critical period for the detection of early structural brain changes that may foreshadow subsequent cognitive decline. From a clinical perspective, these results suggest that cortical thickness measurement may warrant further investigation as a supplementary screening tool for individuals at elevated risk of cognitive impairment, particularly those with coexisting cardiovascular risk factors. The observed association between modifiable vascular risk factors and cortical thinning further suggests that interventions targeting cardiovascular health during midlife could potentially confer neuroprotective benefits, although longitudinal and interventional studies are needed to confirm this hypothesis. Future research should prioritize longitudinal designs with repeated neuroimaging and cognitive assessments to establish temporal dynamics and predictive validity, as well as the development of multimodal biomarker panels combining cortical thickness with white matter integrity metrics and fluid-based biomarkers to improve early cognitive risk stratification.

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