Human brain structures are not static across the lifespan but exhibit morphology changes with age, which are possibly linked to neurodevelopment or neurodegeneration (1,2). These changes impact cognitive performance. Cortical gray matter (GM) structure significantly reduces with age at multiple locations, including the frontal and temporal lobes, insular areas, and cerebellum (3-8). Age effects on the brain structures related to cognitive function may be important for understanding the role of aging in mild cognitive impairment (MCI) and Alzheimer disease (AD) (9-13). For instance, the decrease in episodic memory is related to volume alterations of the entorhinal cortex in healthy older adults (14). Executive function deficits in healthy aging are associated with greater atrophy of the prefrontal regions (15). Additionally, several studies have documented anatomical differences in the brain across the sexes. Epidemiological studies have shown that females have a higher risk of developing AD, but the reasons why are unclear (16). In particular, there is some evidence for the cerebral cortex imaging markers of sex differences in the aging process. A study of the thinning of the cerebral cortex during aging found that global thickness thinning was apparent by middle age (1). Males and females showed a similar degree of global thinning and did not differ in mean thickness in the younger or older groups (1). Another study showed that males and females have different age trajectories regarding changes in brain structures; males over 45 years old showed an earlier acceleration of change in global and lobar volumes compared with females (16). In addition, age-related subcortical volume reductions are more rapid among males. For example, in one study, compared with females at approximately 70 years old, males showed a steeper reduction in thalamic volume (after 25 years of age) and a faster hippocampus atrophy speed (17). Two studies have shown that males have larger brains than do females (18,19). In contrast, other studies have observed greater cortical thickness in females than in males (20,21). One study also observed a significantly greater global cortical thickness in healthy young females than in males (22). However, the influence of sex on brain aging and cognitive changes remains unclear, especially in the transition from middle age to older adulthood. Therefore, systematic research of the sex differences in brain structure and cognition among middle-aged and older adults groups could deepen our understanding of the healthy aging of the brain structure and provide a clearer understanding of the possible neuroanatomical differences between sexes.
However, to date, previous studies have mainly used the voxel-based morphometry (VBM) method to analyze structural brain differences in aging and sex (5,7,23,24). This method is not particularly suited for considering intersubject macro-anatomical modifiability in gyral and sulcal folding patterns and the specific brain tissue property behind the differences in GM density (25). Surface-based morphometry (SBM) offers more information for brain structural analysis. The SBM approach provides measurements of several GM properties, including cortical thickness, gyrification index, and surface complexity, that potentially play different roles in brain function (26,27). Cortical thickness is estimated as the distance between the gray–white boundary and the outer cortical surface (28). The gyrification index is a metric that quantifies the amount of cortex buried within the sulcal folds compared to the amount of cortex on the outer cortex (29). Surface complexity is represented by the fractal dimension, which may be seen as an estimation of gyrification, through a combination of sulcal depth, the frequency of cortical folding, and the convolution of gyral shape (30). These measures have been successfully used to study aging (1,31,32), AD (33,34), sex differences, and cognitive functioning (35). The advantage of SBM over the VBM method is that SBM is theoretically quantitative because it measures and compares absolute distances and shapes rather than magnetic resonance imaging (MRI) intensities (2).
We used the SBM technique to conduct a cross-sectional study that evaluated sex and age differences and age and sex interactions and their relationships with cortical morphology differences (cortical thickness, gyrification index, and surface complexity) in middle-aged and older adult participants. In addition, we further investigated the correlation between cortical morphology differences and processing speed and working memory to explore the possible influence of cortical morphology changes on cognitive function. We present the following article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-22-583/rc).
Participants were selected from wave 1 of the Dallas Lifespan Brain Study (DLBS). As an initiative of the Center for Vital Longevity, School of Behavioral and Brain Sciences at the University of Texas in Dallas, the DLBS is a major effort designed to understand the antecedents of preservation and decline of cognitive function at different stages of the adult lifespan. Wave 1 of the DLBS data collection was completed from 2008 to 2014. The data are available on the International Neuroimaging Data-sharing Initiative, including apolipoprotein E gene information, cognitive tests, structural MRI, and positron emission tomography (PET) data (from https://fcon_1000.projects.nitrc.org/indi/retro/dlbs.html). The participants comprised 315 healthy adults (198 females; 117 males) aged 20–89 years. The Mini-Mental State Examination (MMSE) served as a general cognitive function test. All participants had an MMSE score greater than or equal to 26 and 12 or more years of education. All were native English speakers and right-handed. Participants were excluded if screening showed that they had experienced neurologic or psychiatric disorders, loss of consciousness for more than 10 min, drug or alcohol abuse, major heart surgery, or chemotherapy within 5 years of DLBS. Finally, 204 participants aged 45 years and older (127 females; 77 males) were included in the current study. Analyses were stratified by 2 age groups: 45–64 years (middle-aged adults) and 65 years or older (older adults) (36). The participants were classified into four groups: middle-aged males (n=32), middle-aged females (n=56), older adult males (n=45), and older adult females (n=71; Figure 1). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The basic information of these participants is shown in Table 1.
|Characteristics||Middle-aged males||Middle-aged females||Older adult males||Older adult females||F value||P value|
|Number of participants||32||56||45||71||n.d.||n.d.|
|American Indian/Alaskan Native||1||–||–||2||–||–|
#, data are presented as mean ± standard deviation; &, one-way ANOVA (analysis of variance); ***, P<0.001. n.d., not done; MMSE, mini-mental state examination; DC, digit comparison; DS, Wechsler Adult Intelligence Scale-III digit symbol; LNS, Wechsler Adult Intelligence Scale-III Letter-Number Sequencing; CANTAB-SWM, Cambridge Neuropsychological Test Automated Battery Spatial Working Memory.
Neuropsychological examination for cognitive function
All participants in the present study were evaluated with a battery of neuropsychological tests. These tests were used to assess 2 cognitive domains: the speed of processing and working memory. The Digit Comparison (DC) (37) Task and Wechsler Adult Intelligence Scale third edition (WAIS-III) Digit Symbol (DS) (38) were used to evaluate the speed of processing. Working memory function was assessed using the WAIS-III Letter-Number Sequencing (LNS) (38) and Cambridge Neuropsychological Test Automated Battery (CANTAB) Spatial Working Memory (SWM) tests (39). Details on the cognitive function tests are available online (https://sites.utdallas.edu/dlbs/data-collection).
Structural MRI data acquisition
All structural MRI data images were collected using the DLBS with a Philips Achieva 3.0 T MR scanner with an 8-channel head coil. High-resolution T1-weighted, sagittal 3D magnetization-prepared rapid gradient-echo (MP-RAGE) sequences were acquired and covered the entire brain. The parameters used were as follows: 160 sagittal slices, repetition time (TR) =8.1 ms, echo time (TE) =3.7 ms, slice thickness =1 mm, flip angle =12°, field of view (FOV) =204×256 mm2, and acquisition matrix =256×256.
We used the CAT12 toolbox (revision 1830, http://dbm.neuro.uni-jena.de/cat/) for SPM12 (revision 7771; http://www.fil.ion.ucl.ac.uk/spm/software/spm12/) for segmentation of all 3D-T1 images. The CAT12 toolbox runs within SPM12; that is, SPM12 must be installed and added to the Matlab search path before the CAT12 toolbox can be installed. Structural MRI processing in CAT12 can be separated into two main processes: voxel-based processing and surface-based processing. Voxel-based processing comprises skull-stripping of the brain, spatial adaptive nonlocal means denoising filter, bias correction, and affine registration. The images were then segmented into GM, white matter (WM), and cerebrospinal fluid (CSF), and the tissue segments were spatially normalized to Montreal Neurological Institute (MNI) standard space using diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL) (40). In the surface-based processing, the cortical thickness estimation and reconstruction of the central surface were conducted using a projection-based thickness method (28). Subsequently, surface reconstruction, topological correction, and surface refinement were performed, which resulted in the central surface mesh (41). The individual central surfaces were spatially registered to the FsAverage template in FreeSurfer (Laboratory for Computational Neuroimaging at the Athinoula A. Martinos Center for Biomedical Imaging), and then the local thickness values were transferred onto the same template. Cortical thickness measurements were obtained by reconstructing representations of the GM/WM boundary. The gyrification index was defined as the ratio between the inner surface size to the outer surface size of a convex hull (29,30). Following this, the cortical thickness, fractal dimension, and gyrification index of each participant in the standard space were obtained. Finally, cortical thickness maps were smoothed with a 15 mm full width at half maximum of the Gaussian smoothing kernel, and fractal dimension and gyrification index maps were smoothed at 20 mm (Figure S1). All structural images were visually inspected after the automated analyses to assess appropriate segmentation and labeling by a single user, and all data passed this quality control.
In this cross-sectional study, the middle-aged and older adult participants were divided into four groups: middle-aged males, middle-aged females, older adult males, and older adult females. Age, education years, MMSE, DC, DS, LNS, and CANTAB-SWM scores are presented as mean ± standard deviation for each group. Differences in age, education years, and cognitive tests score were evaluated using the 1-way analysis of variance (ANOVA) test (Table 1). For indices with significant differences across the four groups, we examined the post hoc differences. Pairwise comparisons across all participants for age, education years, and cognitive tests were evaluated using Bonferroni correction (P<0.05) for multiple comparisons (Table 2). Comparisons of cortical morphology differences were calculated by 2-way ANOVA as implemented in CAT12 using education years as a covariate (42). The dependent variables in this study were the cortical thickness, fractal dimension, and gyrification index. The independent variables in this study were the (I) 2 levels of sex, males and females, and (II) the 2 levels of age, middle-aged and older adults. Subsequently, we analyzed the main age effect and the main sex effect separately. Finally, we calculated cortical morphology differences between the middle-aged and older adult groups based on the main effect of age, and the differences between the males and females based on the main effect of sex. All obtained clusters of each comparison were corrected post hoc by an extent threshold of 100 contiguous vertices and reported after family-wise error (FWE) correction on a cluster level of a 5% alpha error. Spearman correlation coefficient was used to investigate the association between cortical morphology differences and cognitive test scores. The correlation tests were considered significant at a threshold of P<0.05 with the false discovery rate (FDR) correction. Statistical analysis was performed with SPSS version 25.0 (IBM Corporation, Armonk, NY, USA).
|Characteristics||G1 vs. G2||G1 vs. G3||G1 vs. G4||G2 vs. G3||G2 vs. G4||G3 vs. G4|
The expressed data are P values of the pairwise comparisons between groups under Bonferroni correction. *, P<0.05; **, P<0.01; ***, P<0.001. G1, middle-aged male group; G2, middle-aged female group; G3, older adult male group; G4, older adult female group. MMSE, mini-mental state examination; DC, digit comparison; DS, Wechsler Adult Intelligence Scale-III Digit Symbol; LNS, Wechsler Adult Intelligence Scale-III Letter-Number Sequencing; CANTAB-SWM, Cambridge Neuropsychological Test Automated Battery Spatial Working Memory.
Demographic and cognitive function
The demographic and cognitive test scores of participants for each group are presented in Table 1, and the pairwise comparisons of demographic and cognitive test scores between the four groups are listed in Table 2. The four groups of individuals did not differ with respect to years of education (F=1.752; P=0.158); However, they did differ in average scores of MMSE (F=9.968; P<0.001), DC (F=22.42; P<0.001), DS (F=33.56; P<0.001), LNS (F=13.46; P<0.001), and CANTAB-SWM (F=13.28; P<0.001). Compared to older adult participants, the middle-aged group showed higher scores on MMSE, DC, DS, and LNS, and lower scores on CANTAB-SWM. There were no significant differences in any of the cognitive tests between the two middle-aged groups, whereas MMSE (P=0.016) and DS (P=0.011) scores were significantly different between the two groups of older adults. These scores were higher in the older adult male group than in the older adult female group.
There was no significant interaction effect in cortical thickness, fractal dimension, or gyrification index. However, we observed a significant main age effect in cortical thickness, fractal dimension, and gyrification index, as well as main sex effect in fractal dimension and gyrification index.
A significant main effect of age on cortical thickness was observed (Figure 2 and Table S1). The post hoc results showed a significantly thinner cortical thickness in the older adult male group, mainly including the bilateral superior frontal gyrus (SFG), superior temporal gyrus (STG), rostral middle frontal gyrus (rMFG), transverse temporal gyrus (TTG), postcentral gyrus (PostCG), lingual gyrus (LG), the left precentral gyrus (PreCG), and precuneus (PreCUN), compared with that of the middle-aged male group. As further illustrated in Figure 2 (second and third columns) and Table 3, compared with the male group, females showed significant differences in cortical thickness related to aging in almost the whole brain, with more pronounced aging effects on cortical thickness in the female group than in the male group.
|Comparison between two groups||Hemisphere||Overlap of atlas region||Cluster size (vertices)||Peak MNI coordinate||t value||P value (FWE)|
|G1 > G3||Left||57% SFG; 27% ParaCG; 16% PCC||5,455||–10||–18||46||6.5||<0.001***|
|71% STG; 23% TTG; 6% INS||3,345||–46||–19||–3||7.1||<0.001***|
|59% rMFG; 27% pTRI; 13% pOPER; 1% pORB||3,291||–36||52||14||5.1||0.001**|
|83% PostCG; 17% PreCG||1,896||–50||–12||28||6.2||<0.001***|
|35% MFGor; 27% SFG; 25% Fpole; 10% rMFG; 3% LFGor||895||–5||–57||–12||5.0||0.002**|
|82% LG; 18% PeriCAL||819||–18||–68||2||5.9||0.002**|
|64% CUN; 24% PreCUN; 12% PeriCAL||783||–11||–67||14||5.8||0.003**|
|94% pOPER; 3% cMFG; 3% rMFG||712||–42||13||25||5.0||0.004**|
|87% rMFG; 13% cMFG||637||–42||28||35||4.9||0.008**|
|49% PreCG; 33% pOPER; 18% PostCG||521||–44||–6||12||4.7||0.010*|
|71% PreCG; 29% cMFG||428||–46||0||31||4.8||0.039*|
|57% LG; 34% FG; 9% ParaHIPP||238||–33||–45||–8||4.8||0.005**|
|Right||68% SFG; 12% CAR; 11% rMFG; 9% RAC||2,927||9||52||7||5.4||<0.001***|
|40% STG; 31% MTG; 23% bSTS; 6% SupraMG||2,449||51||–30||0||5.3||0.001**|
|50% STG; 31% TTG; 15% SupraMG; 4% INS||1,996||44||–21||–2||5.6||<0.001***|
|84% LG; 16% PeriCAL||804||19||–60||2||6.1||<0.001***|
|94% IPG; 6% SPG||794||35||–62||46||5.3||0.001**|
|91% rMFG; 9% cMFG||749||35||30||40||5.0||0.002**|
|97% PostCG; 3% PreCG||736||41||–18||47||4.8||0.004**|
|60% ParaCG; 40% SFG||638||6||–7||57||4.6||0.008**|
|88% FG; 11% LG; 1% ParaHIPP||576||33||–51||–7||4.7||0.006**|
|94% PostCG; 6% PreCG||315||52||–10||25||4.5||0.013*|
|86% CUN; 14% SPG||189||4||–81||32||4.4||0.020*|
|80% pTRI; 20% rMFG||186||47||31||12||4.6||0.010*|
|G2 > G4||Left||16% SFG; 8% PreCG; 8% SPG; 8% PostCG; 8% SupraMG; 7% rMFG; 6% STG; 6% IPG; 5% PreCUN; 4% cMFG; 3% LOG; 3% pOPER; 3% LG; 2% LFGor; 2% pTRI; 2% ParaCG; 2% PCC; 2% INS; 1% PeriCAL; 1% FG; 1% MTG; 1% CUN; 1% TTG||98,332||–46||–18||1||10.8||<0.001***|
|87% MTG; 13% bSTS||915||–64||–44||–6||5.5||<0.001***|
|Right||17% SFG; 9% PreCG; 8% SPG; 8% IPG; 8% PostCG; 8% rMFG; 7% SupraMG; 6% STG; 4% LOG; 4% cMFG; 3% MTG; 3% PreCUN; 3% ParaCG; 2% pOPER; 2% LG; 2% pTRI; 2% LFGor; 1% PCC; 1% PeriCAL; 1% INS; 1% CUN||97,215||42||–34||13||9.9||<0.001***|
*, P<0.05; **, P<0.01; ***, P<0.001. G1, middle-aged male group; G2, middle-aged female group; G3, older adult male group; G4, older adult female group. SFG, superior frontal gyrus; ParaCG, paracentral gyrus; PCC, posterior cingulate; STG, superior temporal gyrus; TTG, transverse temporal gyrus; INS, insula; rMFG, rostral middle frontal gyrus; pTRI, pars triangularis; pOPER, pars opercularis; pORB, pars orbitalis; PostCG, postcentral gyrus; PreCG, precentral gyrus; MFGor, rostral middle frontal gyrus; Fpole, frontal pole; rMFG, rostral middle frontal gyrus; LFGor, lateral orbitofrontal gyrus; LG, lingual gyrus; PeriCAL, pericalcarine cortex; cMFG, caudal middle frontal gyrus; FG, fusiform gyrus; ParaHIPP; parahippocampal gyrus; SupraMG, supramarginal gyrus; IPG, inferior parietal gyrus; RAC, rostral anterior cingulate; CAR, caudal anterior cingulate; MTG, middle temporal gyrus; bSTS, banks of the superior temporal sulcus; SPG, superior parietal gyrus; LOG, lateral occipital gyrus; CUN, cuneus; PreCUN, precuneus; MNI, Montreal Neurological Institute; FWE, family-wise error.
The fractal dimension in the middle-aged female group was significantly higher than that of the older adult female group bilaterally in the insula (INS), in the lateral orbitofrontal gyrus (LFGor), and inferior temporal gyrus (ITG) in the right hemisphere. The middle-aged males had significantly higher surface complexity in the left STG compared to the older adult males. In addition, we detected significant sex differences between the middle-aged female and middle-aged male groups for the fractal dimension in the fusiform gyrus (FG) and LG in the right hemisphere. These regions are highlighted in Figure 3, Table 4, and Table S2 and Table S3.
|Comparison between two groups||Hemisphere||Overlap of atlas region||Cluster size (vertices)||Peak MNI coordinate||t value||P value (FWE)|
|G1 > G3||Left||99% STG; 1% TTG||270||–51||–6||–3||5.1||0.002**|
|G2 > G4||Left||100% INS||528||–35||7||3||5.9||<0.001***|
|62% INS; 38% LFGor||433||31||17||–11||5.3||0.001**|
|G2 > G1||Right||80% FG; 20% LG||571||28||–68||–6||5.2||0.001**|
*, P<0.05; **, P<0.01; ***, P<0.001. G1, middle-aged male group; G2, middle-aged female group; G3, older adult male group; G4, older adult female group. STG, superior temporal gyrus; TTG, transverse temporal gyrus; INS, insula; LFGor, lateral orbitofrontal gyrus; ITG, inferior temporal gyrus; FG, fusiform gyrus; LG, lingual gyrus; MNI, Montreal Neurological Institute; FWE, family-wise error.
Compared with the older adult male group, the middle-aged male group had a higher gyrification index in the bilateral INS and pars opercularis (pOPER) and the right PreCG. There were also significant differences between the 2 female groups for the gyrification index in both hemispheres. The gyrification index was higher for the bilateral STG, INS, supramarginal gyrus (SupraMG), TTG, pars triangularis (pTRI), pOPER, and right PreCG and lower for the left isthmus cingulate (IC) in the middle-aged female group. Furthermore, significant sex differences between the older adult male and older adult female groups were also detected. The gyrification index was higher for the bilateral caudal middle frontal gyrus (cMFG), rMFG, SFG, and right lateral occipital gyrus (LOG) and lower for the right SupraMG in the older adult female group. These regions are highlighted in Figure 4, Table 5, and Table S4 and Table S5.
|Comparison between two groups||Hemisphere||Overlap of atlas region||Cluster size (vertices)||Peak MNI coordinate||t value||P value (FWE)|
|G1 > G3||Left||86% INS; 10% pOPER; 4% TTG||1,286||–35||–19||19||5.0||0.002**|
|Right||51% STG; 48% INS; 1% TTG||444||40||–23||–1||5.2||0.001**|
|54% PreCG; 28% pOPER; 15% INS; 3% PostCG||268||37||5||13||4.5||0.015*|
|G2 > G4||Left||56% STG; 44% INS||1,414||–41||–19||–8||5.9||<0.001***|
|28% INS; 27% SupraMG; 23% TTG; 22% STG||1,066||–35||–33||15||5.9||<0.001***|
|60% pTRI; 40% pOPER||167||–33||23||10||4.7||0.010*|
|Right||40% INS; 35% SupraMG; 13% STG; 11% TTG||3,723||43||–22||–2||6.7||<0.001***|
|96% PreCG; 4% PostCG||271||43||–10||31||4.8||0.006**|
|52% pOPER; 33% INS; 15% pTRI||144||33||19||11||4.6||0.015*|
|G4 > G2||Left||98% IC; 2% PreCUN||152||–4||–50||18||4.7||0.013*|
|G3 > G4||Right||71% SupraMG; 21% STG; 8% bSTS||643||63||–41||21||5.0||0.002**|
|G4 > G3||Left||96% cMFG; 4% rMFG||397||–44||19||38||4.5||0.017*|
|91% cMFG; 9% rMFG||376||–33||22||48||4.6||0.013*|
|Right||51% SFG; 34% rMFG; 15% cMFG||770||12||36||17||5.3||0.001**|
|44% IPG; 43% LOG; 13% MTG||181||44||–64||5||4.6||0.013*|
*, P<0.05; **, P<0.01; ***, P<0.001. G1, middle-aged male group; G2, middle-aged female group; G3, older adult male group; G4, older adult female group. INS, insula; pOPER, pars opercularis; TTG, transverse temporal gyrus; STG, superior temporal gyrus; PreCG, precentral gyrus; PostCG, postcentral gyrus; SupraMG, supramarginal gyrus; pTRI, pars triangularis; IC, isthmus cingulate; PreCUN, precuneus; bSTS, banks of the superior temporal sulcus; cMFG, caudal middle frontal gyrus; rMFG, rostral middle frontal gyrus; SFG, superior frontal gyrus; IPG, inferior parietal gyrus; LOG, lateral occipital gyrus; MTG, middle temporal gyrus; MNI, Montreal Neurological Institute; FWE, family-wise error.
The relationship between cortical morphology and cognitive ability is shown in Figure 5. During the aging process, females showed more significant positive correlations between the cortical thickness of the right SFG and LNS test scores (females: r=0.394; P<0.001; 95% CI for r values 0.216–0.577; Figure 5A) than did males (males: r=0.344; P<0.001; 95% CI for r values 0.197–0.491; Figure 5B). In addition, a significant relationship between the gyrification index of the right SupraMG and DS test scores was observed in the older adult groups (r=0.375; P<0.001; 95% CI for r values 0.203–0.522; Figure 5C). However, there was no significant correlation found between fractal dimension and cognitive function in this study.
Based on the DLBS cohort, this study explored the differences in cortical morphology of middle-aged and older adults and evaluated the relationship between the cortical thickness, gyrification index, fractal dimension, cognitive abilities during the aging process and sex differences. The study made three main findings. First, cortical morphology and cognitive abilities show significant age associations, and general cognitive function, speed of processing, and working memory were significantly decreased in older adults compared with middle-aged adults. The cortical morphology of SFG, MFG, PCC, INS, PreCUN, TTG, STG, PreCG, and PostCG changed significantly with aging. Furthermore, there were significant positive correlations between the cortical thickness of the right SFG and LNS test scores. Second, there were significant differences in fractal dimension and gyrification index between sexes but no significant differences in cortical thickness. Sex differences in the fractal dimension were found in the middle-aged group, and gyrification index differences were found in the older group. Notably, a significant relationship between the gyrification index of the right SupraMG and DS scores was observed in the older groups. Third, results relating to cortical thickness, fractal dimension, or gyrification index showed that cortical differences in females were more affected by aging than were those in males.
Differences in cognitive function
The present study explored the differences in cognitive function in middle-aged and older adult participants from two aspects: speed of processing and working memory. Processing speed and working memory are mechanisms that play important explanatory roles in the age-related decline of cognitive abilities (43). The processing-speed theory indicates that a major factor contributing to age-related differences in memory and other aspects of cognitive functioning is a reduction with increased age in the speed with which many cognitive operations can be executed (44). Processing speed performance, especially inspection time, might be useful as a biological marker of cognitive aging (43). In the present study, DC and DS tests were used to evaluate the processing speed of middle-aged and older adult participants. Consistent with previous studies, we found that processing speed declined on average as people grew older (P<0.001) (45-48). Both verbal working memory (LNS test) and spatial working memory (CANTAB-SWM test) showed significant differences between the middle-aged group and the older adult group (P<0.001). Furthermore, we found that a thinner right SFG was related to worse working memory in aging. Older adults show working memory deficiencies and slowing due to the selection of irrelevant information into the contents of working memory, along with inefficient deletion of working memory contents that are no longer relevant to task performance (49). According to previous studies, females in their 60s show a significantly faster age-related decline and greater deterioration of cognition than do men (50,51). Our results also indicated the same trend, especially in processing speed. We found significant sex differences in the DS test in the older adult group (older adult males: 47.50±10.83; older adult females: 41.27±10.19; P=0.011). In contrast, there was no significant difference in working memory between different sexes during aging. Studies on sex differences in working memory in middle-aged and older adults people often report conflicting results. Some studies have found that males have advantages in verbal and visuospatial working memory (52,53). In contrast, other studies have reported that females have advantages in these aspects or that there are no sex differences in verbal working memory and visuospatial working memory (54,55). These inconsistent findings might be due to methodological differences, such as in sample size, age groups, and working memory tasks. Overall, in this study, the cognitive level of females was slightly lower than that of males. However, age was found to be a more important factor than sex in the cognitive changes of middle-aged and older adult participants.
Differences in cortical morphology and their relationship with cognition
We here report on the age and sex differences in cortical thickness, fractal dimension, and gyrification index. We found no significant age-sex interaction effect for these morphological differences. However, we observed that these parameters changed significantly with age. Numerous studies have identified widespread age-related reductions in cortical thickness. Madan (56) found that age-related differences in brain structure are systematic enough to enable reliable age prediction based on metrics of cortical morphology. The strongest consistent effects of age are reported for the prefrontal, temporal, and parietal regions (57-61). Consistent with these studies, we found that the cortical thickness of older adult participants decreased significantly in the whole brain, excluding the occipital lobe and mainly including the SFG, rMFG, STG, middle temporal gyru (MTG), superior parietal gyrus (SPG), inferior parietal gyrus (IPG), and LG. Furthermore, significant age differences were found in the cortical thickness of PreCG, PostCG, and INS, which were considered insensitive to age in previous studies (58,60). In addition, the decrease in cortical thickness during the aging process was more prominent in females than in males. Of the studies that have examined the relationship between cortical thickness and cognition in cognitively healthy older adults, most have reported positive correlations between regional thickness and cognitive performance. For example, a study found that a thicker IFG and INS were related to better letter fluency while a greater thickness of other frontal regions and the IPG was positively correlated with category fluency (62). Westlye et al. (63) reported that anterior cingulate cortex and right IFG cortical thickness was correlated with the attention function. Sun et al. (64) reported that the thickness of the anterior temporal, rostral medial prefrontal, and anterior midcingulate cortex was correlated with memory performance. Similarly, we found a significant positive correlation between the thickness of the right SFG and verbal working memory in this study. Research has also linked working memory changes to age-related changes in the prefrontal cortex (PFC), a region important for working memory (65). The decline of working memory in the middle-aged and older adults is related to the thinning of SFG cortical thickness, which is consistent with the explanation of the hemispheric asymmetry reduction in older adults (HAROLD) model (66). Findings in support of the HAROLD model reveal that, during working memory tasks, young adults display left PFC activation during verbal working memory tasks, while older adults display bilateral activation of the PFC. Older adults compensate for age-related decline by recruiting additional neural networks to keep on the task; however, with the gradual atrophy of the PFC in older adults, even if the bilateral PFC is activated, their working memory performance of still decreases significantly (66). However, no significant sex difference in cortical thickness was found in middle-aged or older adults.
Compared with widespread age-related reductions of cortical thickness, the differences in fractal dimension and gyrification index were regionally heterogeneous. The fractal dimension provides an important additional measure of brain structures that gives the means to consider differences in the shape of structures rather than the volume or thickness. Our findings differed from previous studies reporting that the GM fractal dimension is more sensitive to age-related differences (32,67). In this study, we only found that the fractal dimensions of the left STG in the male group and the bilateral INS and right ITG in the female group decreased significantly with age. An interesting finding of our study was that the fractal dimension of right FG and LG in middle-aged females was significantly higher than that in the male group, but this feature was not found in the older adult group. The lingual and fusiform gyri play important roles in visual processing. A previous study has shown that prosopagnosia is associated with damage to the fusiform and lingual gyri (68). Chao found that damage to the fusiform/lingual gyri correlates with a loss in color perception (69). This tentative finding of sex differences limited to the middle-aged group could suggest that the fractal dimension is influenced by sex hormones, as females are likely to undergo menopause during this period and thus experience an alteration in hormone levels. A recent study exploring the difference in spontaneous brain activity between premenopausal and perimenopausal females reported increased regional homogeneity value in the right LG in perimenopausal females compared with premenopausal females (70). Therefore, we speculate that the structural and functional differences of the right LG can be used as imaging markers of brain differences between middle-aged males and females. However, further research is needed to prove this speculation.
In the present study, gyrification index values changed during aging in the bilateral INS, TTG, and pOPER in both males and females. In addition, the gyrification index of the bilateral STG, pTRI, and SupraMG was only different between middle-aged and older adults females. These results suggest that the process of cortical aging is more complex in the female brain than it is in males. Compared with the differences in cortical thickness and fractal dimension, the gyrification index showed more sex differences but only in the older adult group. The gyrification index of the right SupraMG, STG, and bSTS was higher in older adult males than in the older adult female group In contrast, the gyrification index of the bilateral SFG, rMFG, and cMFG was higher in older females than in the the older adult male group. More importantly, a significant relationship between the gyrification index of the right SupraMG and DS scores was observed in the older adults groups. The SupraMG is a portion of the parietal lobe. Evidence from neuroimaging experiments suggests that the SupraMG is functionally involved in action execution, simulation, and observation (71). Previous rTMS studies have demonstrated that the SupraMG is functionally involved in visual word recognition (72), verbal working memory (73), and regulating egocentricity (74). In a recent study, the volume of the right SupraMG was found to be associated with the maintenance of emotion recognition ability (75). Combined with our findings, we believe that the right SupraMG plays an important role in the change of processing speed in older adults. The decrease of the gyrification index of the right SupraMG was correlated with the reduced processing speed of older adult females compared to males. Therefore, the right SupraMG can be used as an imaging marker of sexual cognitive differences between males and females in older adults.
There are several limitations to this study. First, this study used cross-sectional data. All data included in the present study were acquired from the DLBS data set, so we were unable to obtain further information, such as on body mass index, chronic diseases, lifestyles, and socioeconomic status (76), which are risk factors for impaired cognitive function in middle-aged and older adults. A multimodal investigation suggested diverse aspects of neurocognition were associated with obesity, particularly deficits in executive function and ineffective suppression of the default mode network (77). According to a recent cross-sectional study, more than 20 well-known and emerging diseases are associated with smaller brain volumes (78). Therefore, the influence of comorbidities, lifestyle, and socioeconomic status should be considered in future studies of brain aging and cognitive decline. In addition, the sample size in this study was not balanced between the four groups, which might have influenced our results. Therefore, it is unclear whether the results of this study will be consistent with other studies. Further validation is needed through evaluating more detailed clinical data and performing larger cross-sectional and longitudinal studies.
The results of this study indicate that aging has a more significant impact on cognitive function alteration than does sex and that sex differences in cognitive function only appear in the older adult stage. The alterations of cortical morphology parameters had different correlations with age and sex. The alterations of cortical thickness were more sensitive to aging than were the other parameters. There were significant sex differences in the fractal dimension in middle-aged participants and the gyrification index in older adults. These parameters showed more significant differences in females than in males during the aging process, which might be related to the higher incidence rate of cognitive impairment in older females.
The authors are very grateful to the owner of the DLBS data set and the International Neuroimaging Data-Sharing Initiative Group for sharing this data publicly. The data will be available from the corresponding author upon reasonable request.
Funding: This study was supported by the National Natural Science Foundation of China (No. 52007199 to J Jin and No. 81901730 to W Cao) and the CAMS Initiative for Innovative Medicine (No. 2021-I2M-1-058 to T Yin).
Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-22-583/rc
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-583/coif). The authors have no conflicts of interest to declare.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).
Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
- Salat DH, Buckner RL, Snyder AZ, Greve DN, Desikan RS, Busa E, Morris JC, Dale AM, Fischl B. Thinning of the cerebral cortex in aging. Cereb Cortex 2004;14:721-30. [Crossref] [PubMed]
- Canna A, Russo AG, Ponticorvo S, Manara R, Pepino A, Sansone M, Di Salle F, Esposito F. Automated search of control points in surface-based morphometry. Neuroimage 2018;176:56-70. [Crossref] [PubMed]
- Ramanoël S, Hoyau E, Kauffmann L, Renard F, Pichat C, Boudiaf N, Krainik A, Jaillard A, Baciu M. Gray Matter Volume and Cognitive Performance During Normal Aging. A Voxel-Based Morphometry Study. Front Aging Neurosci 2018;10:235. [Crossref] [PubMed]
- Matsuda H. Voxel-based Morphometry of Brain MRI in Normal Aging and Alzheimer's Disease. Aging Dis 2013;4:29-37. [PubMed]
- Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 2001;14:21-36. [Crossref] [PubMed]
- Tisserand DJ, van Boxtel MP, Pruessner JC, Hofman P, Evans AC, Jolles J. A voxel-based morphometric study to determine individual differences in gray matter density associated with age and cognitive change over time. Cereb Cortex 2004;14:966-73. [Crossref] [PubMed]
- Smith CD, Chebrolu H, Wekstein DR, Schmitt FA, Markesbery WR. Age and gender effects on human brain anatomy: a voxel-based morphometric study in healthy elderly. Neurobiol Aging 2007;28:1075-87. [Crossref] [PubMed]
- Terribilli D, Schaufelberger MS, Duran FL, Zanetti MV, Curiati PK, Menezes PR, Scazufca M, Amaro E Jr, Leite CC, Busatto GF. Age-related gray matter volume changes in the brain during non-elderly adulthood. Neurobiol Aging 2011;32:354-68. [Crossref] [PubMed]
- Apostolova LG, Green AE, Babakchanian S, Hwang KS, Chou YY, Toga AW, Thompson PM. Hippocampal atrophy and ventricular enlargement in normal aging, mild cognitive impairment (MCI), and Alzheimer Disease. Alzheimer Dis Assoc Disord 2012;26:17-27. [Crossref] [PubMed]
- Kirova AM, Bays RB, Lagalwar S. Working memory and executive function decline across normal aging, mild cognitive impairment, and Alzheimer's disease. Biomed Res Int 2015;2015:748212. [Crossref] [PubMed]
- Solé-Padullés C, Bartrés-Faz D, Junqué C, Vendrell P, Rami L, Clemente IC, Bosch B, Villar A, Bargalló N, Jurado MA, Barrios M, Molinuevo JL. Brain structure and function related to cognitive reserve variables in normal aging, mild cognitive impairment and Alzheimer's disease. Neurobiol Aging 2009;30:1114-24. [Crossref] [PubMed]
- Guo S, Xiao B, Wu CAlzheimer’s Disease Neuroimaging Initiative. Identifying subtypes of mild cognitive impairment from healthy aging based on multiple cortical features combined with volumetric measurements of the hippocampal subfields. Quant Imaging Med Surg 2020;10:1477-89. [Crossref] [PubMed]
- Kuhn T, Becerra S, Duncan J, Spivak N, Dang BH, Habelhah B, Mahdavi KD, Mamoun M, Whitney M, Pereles FS, Bystritsky A, Jordan SE. Translating state-of-the-art brain magnetic resonance imaging (MRI) techniques into clinical practice: multimodal MRI differentiates dementia subtypes in a traditional clinical setting. Quant Imaging Med Surg 2021;11:4056-73. [Crossref] [PubMed]
- Trivedi MA, Stoub TR, Murphy CM, George S, deToledo-Morrell L, Shah RC, Whitfield-Gabrieli S, Gabrieli JD, Stebbins GT. Entorhinal cortex volume is associated with episodic memory related brain activation in normal aging and amnesic mild cognitive impairment. Brain Imaging Behav 2011;5:126-36. [Crossref] [PubMed]
- Raz N, Rodrigue KM. Differential aging of the brain: patterns, cognitive correlates and modifiers. Neurosci Biobehav Rev 2006;30:730-48. [Crossref] [PubMed]
- Vinke EJ, de Groot M, Venkatraghavan V, Klein S, Niessen WJ, Ikram MA, Vernooij MW. Trajectories of imaging markers in brain aging: the Rotterdam Study. Neurobiol Aging 2018;71:32-40. [Crossref] [PubMed]
- Wang Y, Xu Q, Luo J, Hu M, Zuo C. Effects of Age and Sex on Subcortical Volumes. Front Aging Neurosci 2019;11:259. [Crossref] [PubMed]
- Ruigrok AN, Salimi-Khorshidi G, Lai MC, Baron-Cohen S, Lombardo MV, Tait RJ, Suckling J. A meta-analysis of sex differences in human brain structure. Neurosci Biobehav Rev 2014;39:34-50. [Crossref] [PubMed]
- Rushton JP, Ankney CD. Whole brain size and general mental ability: a review. Int J Neurosci 2009;119:691-731. [Crossref] [PubMed]
- Sowell ER, Peterson BS, Kan E, Woods RP, Yoshii J, Bansal R, Xu D, Zhu H, Thompson PM, Toga AW. Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age. Cereb Cortex 2007;17:1550-60. [Crossref] [PubMed]
- Lv B, Li J, He H, Li M, Zhao M, Ai L, Yan F, Xian J, Wang Z. Gender consistency and difference in healthy adults revealed by cortical thickness. Neuroimage 2010;53:373-82. [Crossref] [PubMed]
- Im K, Lee JM, Lee J, Shin YW, Kim IY, Kwon JS, Kim SI. Gender difference analysis of cortical thickness in healthy young adults with surface-based methods. Neuroimage 2006;31:31-8. [Crossref] [PubMed]
- Chen X, Sachdev PS, Wen W, Anstey KJ. Sex differences in regional gray matter in healthy individuals aged 44-48 years: a voxel-based morphometric study. Neuroimage 2007;36:691-9. [Crossref] [PubMed]
- Takahashi R, Ishii K, Kakigi T, Yokoyama K. Gender and age differences in normal adult human brain: voxel-based morphometric study. Hum Brain Mapp 2011;32:1050-8. [Crossref] [PubMed]
- Escorial S, Román FJ, Martínez K, Burgaleta M, Karama S, Colom R. Sex differences in neocortical structure and cognitive performance: A surface-based morphometry study. Neuroimage 2015;104:355-65. [Crossref] [PubMed]
- Winkler AM, Kochunov P, Blangero J, Almasy L, Zilles K, Fox PT, Duggirala R, Glahn DC. Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. Neuroimage 2010;53:1135-46. [Crossref] [PubMed]
- Panizzon MS, Fennema-Notestine C, Eyler LT, Jernigan TL, Prom-Wormley E, Neale M, Jacobson K, Lyons MJ, Grant MD, Franz CE, Xian H, Tsuang M, Fischl B, Seidman L, Dale A, Kremen WS. Distinct genetic influences on cortical surface area and cortical thickness. Cereb Cortex 2009;19:2728-35. [Crossref] [PubMed]
- Dahnke R, Yotter RA, Gaser C. Cortical thickness and central surface estimation. Neuroimage 2013;65:336-48. [Crossref] [PubMed]
- Luders E, Thompson PM, Narr KL, Toga AW, Jancke L, Gaser C. A curvature-based approach to estimate local gyrification on the cortical surface. Neuroimage 2006;29:1224-30. [Crossref] [PubMed]
- Yotter RA, Nenadic I, Ziegler G, Thompson PM, Gaser C. Local cortical surface complexity maps from spherical harmonic reconstructions. Neuroimage 2011;56:961-73. [Crossref] [PubMed]
- Madan CR. Age-related decrements in cortical gyrification: Evidence from an accelerated longitudinal dataset. Eur J Neurosci 2021;53:1661-71. [Crossref] [PubMed]
- Madan CR, Kensinger EA. Cortical complexity as a measure of age-related brain atrophy. Neuroimage 2016;134:617-29. [Crossref] [PubMed]
- Ruiz de Miras J, Costumero V, Belloch V, Escudero J, Ávila C, Sepulcre J. Complexity analysis of cortical surface detects changes in future Alzheimer's disease converters. Hum Brain Mapp 2017;38:5905-18. [Crossref] [PubMed]
- Vuksanović V, Staff RT, Ahearn T, Murray AD, Wischik CM. Cortical Thickness and Surface Area Networks in Healthy Aging, Alzheimer's Disease and Behavioral Variant Fronto-Temporal Dementia. Int J Neural Syst 2019;29:1850055. [Crossref] [PubMed]
- Crespo-Facorro B, Roiz-Santiáñez R, Pérez-Iglesias R, Mata I, Rodríguez-Sánchez JM, Tordesillas-Gutiérrez D, Ortíz-García de la Foz V, Tabarés-Seisdedos R, Sánchez E, Andreasen N, Magnotta V, Vázquez-Barquero JL. Sex-specific variation of MRI-based cortical morphometry in adult healthy volunteers: the effect on cognitive functioning. Prog Neuropsychopharmacol Biol Psychiatry 2011;35:616-23. [Crossref] [PubMed]
- Okoro CA, Hollis ND, Cyrus AC, Griffin-Blake S. Prevalence of Disabilities and Health Care Access by Disability Status and Type Among Adults - United States, 2016. MMWR Morb Mortal Wkly Rep 2018;67:882-7. [Crossref] [PubMed]
- Hedden T, Park DC, Nisbett R, Ji LJ, Jing Q, Jiao S. Cultural variation in verbal versus spatial neuropsychological function across the life span. Neuropsychology 2002;16:65-73. [Crossref] [PubMed]
- Yamamoto D, Kazui H, Takeda M. Wechsler Adult Intelligence Scale-III (WAIS-III). Nihon Rinsho 2011;69:403-7. [PubMed]
- Robbins TW, James M, Owen AM, Sahakian BJ, McInnes L, Rabbitt P. Cambridge Neuropsychological Test Automated Battery (CANTAB): a factor analytic study of a large sample of normal elderly volunteers. Dementia 1994;5:266-81. [PubMed]
- Kurth F, Gaser C, Luders E. A 12-step user guide for analyzing voxel-wise gray matter asymmetries in statistical parametric mapping (SPM). Nat Protoc 2015;10:293-304. [Crossref] [PubMed]
- Yotter RA, Dahnke R, Thompson PM, Gaser C. Topological correction of brain surface meshes using spherical harmonics. Hum Brain Mapp 2011;32:1109-24. [Crossref] [PubMed]
- Kim JP, Seo SW, Shin HY, Ye BS, Yang JJ, Kim C, Kang M, Jeon S, Kim HJ, Cho H, Kim JH, Lee JM, Kim ST, Na DL, Guallar E. Effects of education on aging-related cortical thinning among cognitively normal individuals. Neurology 2015;85:806-12. [Crossref] [PubMed]
- Deary IJ, Johnson W, Starr JM. Are processing speed tasks biomarkers of cognitive aging? Psychol Aging 2010;25:219-28. [Crossref] [PubMed]
- Salthouse TA. The processing-speed theory of adult age differences in cognition. Psychol Rev 1996;103:403-28. [Crossref] [PubMed]
- Salthouse TA. Aging and measures of processing speed. Biol Psychol 2000;54:35-54. [Crossref] [PubMed]
- Finkel D, Reynolds CA, McArdle JJ, Pedersen NL. Age changes in processing speed as a leading indicator of cognitive aging. Psychol Aging 2007;22:558-68. [Crossref] [PubMed]
- Oschwald J, Mérillat S, Liem F, Röcke C, Martin M, Jäncke L. Lagged Coupled Changes Between White Matter Microstructure and Processing Speed in Healthy Aging: A Longitudinal Investigation. Front Aging Neurosci 2019;11:298. [Crossref] [PubMed]
- Faroqi-Shah Y, Gehman M. The Role of Processing Speed and Cognitive Control on Word Retrieval in Aging and Aphasia. J Speech Lang Hear Res 2021;64:949-64. [Crossref] [PubMed]
- Park DC, Reuter-Lorenz P. The adaptive brain: aging and neurocognitive scaffolding. Annu Rev Psychol 2009;60:173-96. [Crossref] [PubMed]
- Henderson VW, Buckwalter JG. Cognitive deficits of men and women with Alzheimer's disease. Neurology 1994;44:90-6. [Crossref] [PubMed]
- Cavedo E, Chiesa PA, Houot M, Ferretti MT, Grothe MJ, Teipel SJ, Lista S, Habert MO, Potier MC, Dubois B, Hampel HAlzheimer Precision Medicine Initiative (APMI). Sex differences in functional and molecular neuroimaging biomarkers of Alzheimer's disease in cognitively normal older adults with subjective memory complaints. Alzheimers Dement 2018;14:1204-15. [Crossref] [PubMed]
- Fournet N, Roulin JL, Vallet F, Beaudoin M, Agrigoroaei S, Paignon A, Dantzer C, Desrichard O. Evaluating short-term and working memory in older adults: French normative data. Aging Ment Health 2012;16:922-30. [Crossref] [PubMed]
- Cansino S, Hernández-Ramos E, Estrada-Manilla C, Torres-Trejo F, Martínez-Galindo JG, Ayala-Hernández M, Gómez-Fernández T, Osorio D, Cedillo-Tinoco M, Garcés-Flores L, Beltrán-Palacios K, García-Lázaro HG, García-Gutiérrez F, Cadena-Arenas Y, Fernández-Apan L, Bärtschi A, Rodríguez-Ortiz MD. The decline of verbal and visuospatial working memory across the adult life span. Age (Dordr) 2013;35:2283-302. [Crossref] [PubMed]
- Pliatsikas C, Veríssimo J, Babcock L, Pullman MY, Glei DA, Weinstein M, Goldman N, Ullman MT. Working memory in older adults declines with age, but is modulated by sex and education. Q J Exp Psychol (Hove) 2019;72:1308-27. [Crossref] [PubMed]
- Doppelt JE, Wallace WL. Standardization of the Wechsler adult intelligence scale for older persons. J Abnorm Psychol 1955;51:312-30. [PubMed]
- Madan CR, Kensinger EA. Predicting age from cortical structure across the lifespan. Eur J Neurosci 2018;47:399-416. [Crossref] [PubMed]
- Fjell AM, Westlye LT, Grydeland H, Amlien I, Espeseth T, Reinvang I, Raz N, Dale AM, Walhovd KBAlzheimer Disease Neuroimaging Initiative. Accelerating cortical thinning: unique to dementia or universal in aging? Cereb Cortex 2014;24:919-34. [Crossref] [PubMed]
- Fjell AM, Westlye LT, Amlien I, Espeseth T, Reinvang I, Raz N, Agartz I, Salat DH, Greve DN, Fischl B, Dale AM, Walhovd KB. High consistency of regional cortical thinning in aging across multiple samples. Cereb Cortex 2009;19:2001-12. [Crossref] [PubMed]
- Fjell AM, Walhovd KB, Reinvang I, Lundervold A, Salat D, Quinn BT, Fischl B, Dale AM. Selective increase of cortical thickness in high-performing elderly--structural indices of optimal cognitive aging. Neuroimage 2006;29:984-94. [Crossref] [PubMed]
- Zhao L, Matloff W, Ning K, Kim H, Dinov ID, Toga AW. Age-Related Differences in Brain Morphology and the Modifiers in Middle-Aged and Older Adults. Cereb Cortex 2019;29:4169-93. [Crossref] [PubMed]
- Hou M, de Chastelaine M, Donley BE, Rugg MD. Specific and general relationships between cortical thickness and cognition in older adults: a longitudinal study. Neurobiol Aging 2021;102:89-101. [Crossref] [PubMed]
- Vonk JMJ, Rizvi B, Lao PJ, Budge M, Manly JJ, Mayeux R, Brickman AM. Letter and Category Fluency Performance Correlates with Distinct Patterns of Cortical Thickness in Older Adults. Cereb Cortex 2019;29:2694-700. [Crossref] [PubMed]
- Westlye LT, Grydeland H, Walhovd KB, Fjell AM. Associations between regional cortical thickness and attentional networks as measured by the attention network test. Cereb Cortex 2011;21:345-56. [Crossref] [PubMed]
- Sun FW, Stepanovic MR, Andreano J, Barrett LF, Touroutoglou A, Dickerson BC. Youthful Brains in Older Adults: Preserved Neuroanatomy in the Default Mode and Salience Networks Contributes to Youthful Memory in Superaging. J Neurosci 2016;36:9659-68. [Crossref] [PubMed]
- Funahashi S. Working Memory in the Prefrontal Cortex. Brain Sci 2017;7:49. [Crossref] [PubMed]
- Cabeza R. Hemispheric asymmetry reduction in older adults: the HAROLD model. Psychol Aging 2002;17:85-100. [Crossref] [PubMed]
- Liu H, Liu T, Jiang J, Cheng J, Liu Y, Li D, Dong C, Niu H, Li S, Zhang J, Brodaty H, Sachdev P, Wen W. Differential longitudinal changes in structural complexity and volumetric measures in community-dwelling older individuals. Neurobiol Aging 2020;91:26-35. [Crossref] [PubMed]
- De Renzi E. Disorders of visual recognition. Semin Neurol 2000;20:479-85. [Crossref] [PubMed]
- Chao LL, Martin A. Cortical regions associated with perceiving, naming and knowing about colors. J Cogn Neurosci 1999;11:25-35. [Crossref] [PubMed]
- He L, Guo W, Qiu J, An X, Lu W. Altered Spontaneous Brain Activity in Women During Menopause Transition and Its Association With Cognitive Function and Serum Estradiol Level. Front Endocrinol (Lausanne) 2021;12:652512. [Crossref] [PubMed]
- Grèzes J, Decety J. Functional anatomy of execution, mental simulation, observation, and verb generation of actions: a meta-analysis. Hum Brain Mapp 2001;12:1-19. [Crossref] [PubMed]
- Stoeckel C, Gough PM, Watkins KE, Devlin JT. Supramarginal gyrus involvement in visual word recognition. Cortex 2009;45:1091-6. [Crossref] [PubMed]
- Romero L, Walsh V, Papagno C. The neural correlates of phonological short-term memory: a repetitive transcranial magnetic stimulation study. J Cogn Neurosci 2006;18:1147-55. [Crossref] [PubMed]
- Silani G, Lamm C, Ruff CC, Singer T. Right supramarginal gyrus is crucial to overcome emotional egocentricity bias in social judgments. J Neurosci 2013;33:15466-76. [Crossref] [PubMed]
- Wada S, Honma M, Masaoka Y, Yoshida M, Koiwa N, Sugiyama H, Iizuka N, Kubota S, Kokudai Y, Yoshikawa A, Kamijo S, Kamimura S, Ida M, Ono K, Onda H, Izumizaki M. Volume of the right supramarginal gyrus is associated with a maintenance of emotion recognition ability. PLoS One 2021;16:e0254623. [Crossref] [PubMed]
- Chan MY, Na J, Agres PF, Savalia NK, Park DC, Wig GS. Socioeconomic status moderates age-related differences in the brain's functional network organization and anatomy across the adult lifespan. Proc Natl Acad Sci U S A 2018;115:E5144-53. [Crossref] [PubMed]
- Syan SK, Owens MM, Goodman B, Epstein LH, Meyre D, Sweet LH, MacKillop J. Deficits in executive function and suppression of default mode network in obesity. Neuroimage Clin 2019;24:102015. [Crossref] [PubMed]
- Shang X, Zhang X, Huang Y, Zhu Z, Zhang X, Liu J, Wang W, Tang S, Yu H, Ge Z, Yang X, He M. Association of a wide range of individual chronic diseases and their multimorbidity with brain volumes in the UK Biobank: A cross-sectional study. EClinicalMedicine 2022;47:101413. [Crossref] [PubMed]