Introduction
Ageing is a complex nonlinear process that has negative impacts on the structures and functions of central neural system (CNS) [
1]. Over the last decades, brain morphometry, derived from structural magnetic resonance imaging (sMRI), has emerged as a robust measure to quantify the cortical features [
2,
3], which can enhance the accuracy of clinical diagnosis and prognosis, and further facilitate brain-based interventions in elderly population, such as in Alzheimer’s disease (AD) [
4].
Advanced age is consistently associated with the reduction in total brain volume and cortical thickness [
5,
6]; however, evidence regarding regional cortical changes measured using different scales is inconsistent [
3]. For instance, the volumetric MRI-based measures of prefrontal cortex could discriminate the effect of age from the early morphometric changes resulting from neurodegeneration instead of global brain volume [
7‐
9]. Meanwhile, studies that focused on surface-based measures, such as cortical thickness and folding, demonstrate more promising results in identifying the adults with mild cognitive impairment (MCI) from normal ageing adults and dementia patients [
10]. Indeed, increasing evidence has confirmed that cortical regions exhibit ageing-related changes in morphometric features that are no larger than those measured across the whole cortex [
11], implying a lack of anatomical specificity of progressive brain changes during normal ageing. Especially, the grey matter of lateral prefrontal cortex (PFC), as the vulnerable cortical region to AD pathology, has shown an inverted U-shaped across time [
12], suggesting that global brain atrophy may not depict the regional brain changes of increased vulnerability due to pathological ageing.
Importantly, identifications of regional cortical features that are affected during ageing also emphasize on the key step toward developing personalized therapy for neurodegenerative diseases. Recently, transcranial current stimulation that delivers a small amount of electric current to the scalp and modulates the cortical activities is increasingly considered as a safe and effective intervention for individuals suffering from age-related brain diseases, such as late-life depression [
13], MCI [
14], and stroke [
15]. Clinically, left primary motor cortex (M1) and left dorsolateral prefrontal cortex (DLPFC) are two commonly used therapeutic targets. The cortical features of the stimulation targets have been found to be related to the discrepancies in treatment outcome [
16]. Except for cortical features, scalp-to-cortex distance (SCD), as an important parameter, could prominently influence the focality and strength of the electric field induced by brain stimulation and lead to heterogeneous treatment outcome [
17‐
19]. For example, our previous studies have shown that older adults with dementia have an increased SCD of left M1 and DLPFC; therefore, same stimulation output may be insufficient to induce the desired therapeutic response in older adults with brain atrophy [
20,
21]. Notably, when adjusting the stimulation output with the SCD of left M1, the SCD-adjusted output can induce effective stimulation intensity and improve the treatment response accordingly [
22].
As mentioned, the links between region-specific cortical features, SCD, and electric field support the complex biophysical nature in ageing brains [
20,
23,
24]. Considering the continuum of ageing to ageing-related neurodegenerative diseases [
25], whether ageing has similar or differential effect on region-specific cortical features and SCD in individuals with increased risk of developing dementia is still unclear. Hence, the main purpose of this study was to determine the ageing effect on the cortical features and SCD of left M1 and DLPFC in normal ageing adults and mild cognitive impairment converters. A second objective was to examine and quantify the effect of SCD on the electric field through computational simulation model.
Discussion
Using the MRI scans collected over a 3-year observational period, we examined and quantified the ageing effect on region-specific morphometric and geometric measures in older adults with different cognitive statuses. Generally, different from global measures, longitudinal volumetric, geometric, and surface-based measures showed nonlinear trajectories as represented by region-specific structural changes in normal ageing adults and MCI converters over time. In the context of global brain atrophy, ageing has a pronounced, but differential effect on regional cortical features, especially on cortical thickness, folding, and scalp-to-cortex distance (SCD). Moreover, the SCD change of left DLPFC has shown a significant impact on the electric field induced by transcranial current stimulation.
For decades, a majority of the cross-sectional studies has reported the structural brain changes with a linear pattern of age-related global decline [
37]. Given the distributed nature of structural changes across the adulthood [
38,
39], the marked atrophy in parenchymal volumes (i.e., grey matter and white matter) was mostly reported in older adults relative to younger adults [
40]. Of note, even in late adulthood, a steeper and more rapid cortical atrophy was found in old–old adults (+ 90 years) compared to young–old adults (60–66 years) [
41,
42]. Subsequently, longitudinal studies appeared to confirm these structural brain changes occurring at different rates for different cortical regions and tissue types [
3], of which a greater annualized decline was found in total white matter volume rather than grey matter volume during normal ageing [
43]. Indeed, a consistent pattern of brain atrophy in parenchymal volumes was observed in normal ageing adults, but an inversed pattern was detected in MCI converters during a 3-year period. Additionally, our results lead directly to the hypothesis that not all cortical regions demonstrate the similar patterns of volumetric brain changes [
44]. In several targeted investigations, the most prominent regional reduction was observed in the fronto-parietal neocortex [
45], particularly in the grey matter of prefrontal cortex [
7,
46]. In accordance with these findings, regional grey matter loss over time was identical in left M1 and DLPFC among normal ageing adults compared to MCI converters, indicating that accelerated regional volume loss is not simply an effect of ageing on brain.
Whereas earlier studies measured cortical changes almost exclusively in terms of volumetric measures, more recent work adopted with surface-based measure reveals a more complex picture of ageing brain, with region-specific cortical differences demonstrating exclusive and often nonlinear trajectories [
3]. Similar to regional volumetric changes, ageing-related cortical thinning was also mainly observed in the fronto-parietal cortex, including prefrontal cortex and motor cortex, in normal ageing adults and dementia patients [
3,
7]. The nonlinear trajectories of cortical atrophy are normally represented with two morphometric features: (1) cortical thinning and (2) sulcal widening. In our observations, an overall accelerated cortical thinning was found in both left M1 and DLPFC. However, a region-specific pattern of cortical thinning was only detected in MCI converters during a mid-term follow-up period, of which the reduced cortical thickness is disproportionately greater in left M1 than left DLPFC at 1-year follow-up, but the cortical thickness was decreased rapidly in left DLPFC than left M1 at 3-year follow-up. During ageing, neuron loss and cortical atrophy may lead to cortical thinning [
7], while decreased cortical folding might mainly reflect the underlying white matter change [
47,
48]. Recent evidence highlighted that the age-related differences in the sulcal width of the frontal and central sulci were significantly associated with differences in the adjacent parenchymal volumes in older adults [
49] and AD patients [
50]. It seems that the sulcal morphometry measured by gyrification index (i.e., folding) is closely related to the cortical features in frontal and parietal cortex. Indeed, the anti-correlated trajectories of gyrification index and regional white matter volume observed in MCI converters also support this assumption. Moreover, the change of cortical folding, not white matter, showed the highest discriminative value in differentiating MCI converters from normal controls, indicating that the differences in cortical folding are progressive and could be detected and monitored during the long-term management of neurodegenerative diseases.
Combined with surface-based measures, scalp-to-cortex distance, as a geometric measure in the field of transcranial brain stimulation, is first studied to detect the accelerated structural brain changes in the region with a high degree of folding. Interestingly, the trajectories of SCD and cortical folding were nearly paralleled in normal ageing adults, but anti-correlated in MCI converters. The results may reflect the complicated dynamic changes in cortical features during pathological ageing. Different from other morphometric measures, significant ageing effect was only found in the SCD of left DLPFC in MCI converters, of which the SCD change of left DLPFC from baseline to 3-year follow-up also showed a modest power to discriminate MCI converters from normal ageing adults. In addition, with SCD as the key parameter of interest, we constructed the head model of anodal transcranial direct current stimulation (tDCS) over left DLPFC. Both strength and spatial distribution of the SCD-associated E-field were prominently decreased in MCI converters at 3-year follow-up. Therefore, the geometric simulation model demonstrates a novel and useful way to describe and quantify the complex biophysical interactions between SCD and E-field.
Moreover, SCD may essentially “scale-up” the collection of MRI-based morphometric measures (i.e., scalar variables) to serve as a vector in transcranial brain stimulation therapies. For example, despite cortical thickness and SCD share the same measurement scale (i.e., mm), SCD can provide additional geometric information from 3D space, even four-dimensional space (i.e., time dimension). The dynamic changes of region-specific SCD and the simulated E-field featured with quantitative measures may open the way for optimizing the technical parameters of brain stimulation, such as tDCS electrode montages, and TMS coil configurations, when conducting the brain-based interventions in age- or disease-specific populations.
Implications for intervention
Understanding the trajectories of region-specific brain morphometry may allow us to harness the dynamic cortical changes to combat the cognitive decline during pathological ageing. Facilitating the scale-dependent cortical features, such as cortical thickness and scalp-to-cortex distance, shown to subserve brain-based interventions may expand the functional range of individuals contending with brain atrophy. Another potential application of MRI-based measures is to serve as an imaging marker to guide and inform the personalized rehabilitation and intervention.
Limitations and future directions
The findings in this study should be interpreted with caution due to its limitations, of which the major one is the low number of participants. Nevertheless, it is somewhat remarkable that we found relatively robust ageing effect on cortical features, which further alludes to region-specific brain changes in the context of global brain atrophy. Furthermore, we were unable to examine the associations between morphometric measures and the domain-specific cognitive function during normal and pathological ageing in the present study due to the limitations of OASIS dataset.
Future research will aim to examine the cortical features and the functioning of core domains of cognition concurrently, so as to obtain a specific picture on the pattern of brain morphometry in different types of age-related neurodegenerative diseases, such as frontotemporal dementia (FTD), Alzheimer’s disease (AD), and Parkinson’s disease (PD). This will help to construct disease-specific brain atlas and further expand the applications of MRI-based measures in tracking the progression of cognitive decline and providing valuable information facilitated the personalized rehabilitation. Also, methodologies such as closed-loop transcranial brain stimulation with combined imaging modalities (e.g., TMS-EEG, tDCS-EEG) may represent the next frontier of neurotherapeutics in decoding the complex interlinks between ageing, brain morphometry, and disease progression.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.