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Age-related assessment of diffusion parameters in specific brain tracts correlated with cortical thinning

Abstract

The aging process is associated with many brain structural alterations. These changes are not associated with neuronal loss but can be due to cortical structural changes that may be related to white matter (WM) structural alterations. In this study, we evaluated age-related changes in WM and gray matter (GM) parameters and how they correlate for specific brain tracts in a cohort of 158 healthy individuals, aged between 18 and 83 years old. In the tract-cortical analysis, cortical regions connected by tracts demonstrated similar thinning patterns for the majority of tracts. Additionally, a significant relationship was found between mean cortical thinning rate with fractional anisotropy (FA) and mean diffusivity (MD) alteration rates. For all tracts, age was the main effect controlling diffusion parameter alterations. We found no direct correlations between cortical thickness and FA or MD, except for in the fornix, for which the subcallosal gyrus thickness was significantly correlated to FA and MD (p < 0.05 FDR corrected). Our findings lead to the conclusion that alterations in the WM diffusion parameters are explained by the aging process, also associated with cortical thickness changes. Also, the alteration rates of the structural parameters are correlated to the different brain tracts in the aging process.

Introduction

The brain microstructure changes during the life span. Alterations in the brain are defined in two stages: the developmental process of maturation, followed by the natural aging process of degeneration [1, 2]. The structural and functional changes in the brain can be studied using magnetic resonance imaging (MRI) techniques.

Studies on the thinning pattern of cortical regions throughout aging show that age-related cortical thickness atrophy and age-related differences in functional connectivity are associated [3]. Aligning with the healthy aging theories, structural and functional deterioration may relate to the complex alterations associated with aging to preserve cognitive function [3]. Cortical thinning presents itself since middle age, beginning from the third decade of life, in a widespread way, with significant thinning in primary sensory, primary somatosensory, and motor and association cortices, at rates of at least 0.01 mm/decade [4]. Strong age-related cortical thinning in frontal and temporal regions are presented in literature, with shrinkage range of − 0.8%/year in the frontal cortex and −0.67%/year in the temporal cortex [5, 6]. The evidence age-associated regional brain shrinkage and cortical thinning is consistent across multiple studies [4, 5, 7].

Age-related changes in microstructural properties of cerebral white matter (WM) can be explored using diffusion-weighted imaging (DWI), which enables the investigation of WM microstructure [8, 9]. Many pioneer studies reported age-related differences in microstructural integrity of cerebral white matter, showing widespread declines in fractional anisotropy (FA) and increasing mean diffusivity (MD) [10,11,12]. The increased MD and reduced FA associated with normal aging may be related to a number of changes in WM, as reduction in axon density, rupture of connections, reorganization of the fibers, and decline in number and length of myelinated fibers [12, 13], among others. WM changes in the aging process are presumed to also play a role in cognitive decline [14].

Tractography techniques are used to segment WM fiber bundles, and then diffusion quantitative metrics can be estimated along the tracts. Those measures reflect underlying structural integrity for those specific WM regions. Some tract-specific studies have observed significant FA/MD alterations in several brain tracts during life span, as, for example, the corpus callosum [15,16,17,18], uncinate fasciculus [19,20,21], internal capsule [22], corticospinal tract [23, 24] , fornix [1, 25], inferior longitudinal fasciculus [18, 26, 27], and inferior occipitofrontal fasciculus [26, 27].

In the last decade, several researchers have suggested FA decrease and MD increase in WM as sensitive markers of aging, related to atrophy in several brain regions [14, 16,17,18, 28,29,30,31]. In the life span, there are heterogeneous cortical thinning across the cortex and a wide distribution of white matter degeneration, with specific tract patterns. However, literature relating to both of these parameters of the human brain is very scarce and it is of great interest to find an association between regional cortical thickness and diffusion parameters of the connecting brain tracts in the normal aging process.

Changes in cortical structure can be associated with WM structural changes, in a bidirectional way, i.e., GM damage, related to neuronal functioning, is expected to lead to WM degeneration, and vice versa [32,33,34]. Since GM is responsible for the information processing and WM responsible for the structural connections between the brain regions, instinctively one might presume that there should be some kind of association between their alterations in the normal aging process; however, the causality between them is still undefined.

In the literature, whole-brain analyses of the relationship between age-related alterations of FA and cortical GM thickness described a linear and positive relationship across the life span [35]. Another study examined the heterogeneous patterns of age-related GM and WM changes in the limbic system, proving that the diffusion alterations in the limbic tracts were consistent with the GM degeneration patterns identified [36]. A longitudinal study with a 3.6-year follow-up on this relationship in specific brain tracts demonstrated that changes in WM microstructure and cortical thickness are correlated with healthy adults in multiple cortical areas [37]. A recent article demonstrated a coupled association of tissue integrity in a life span sample of healthy aging adults, suggesting a differential time course of aging within each tissue type [38]. These results lead to the understanding that WM and GM relations in healthy aging are global and tract-specific; however, the age was not considered a variable in the regression analysis, a confounder factor, or in evaluating the aging rates of the involved parameters.

The main goal of this article is to evaluate the relation, if any, between white matter and cortical gray matter integrity parameter alterations, in major brain tracts and its cortical endings, knowing that both are strongly driven by the age effect. Cortical thickness measurements and diffusion parameters were taken as integrity biomarkers for the aging process and the correlation between those was assessed, removing the age effect. According to our best knowledge, the evaluation of this interaction between gray and white matter integrities has not been performed yet.

Materials and methods

Participants

The study was approved by the local Research Ethics Committee. The data consists of DWI and T1-weighted images, acquired in a single session, of 164 healthy individuals, aged between 18 and 83 years old, acquired in a 3 T Philips Achieva MRI scanner. The images were collected by fellow researchers, between 2009 and 2016, at the local university hospital.

The individual data were included in our analysis based on three inclusion criteria: (1) no previous neurological diseases, (2) having both DWI and T1-weighted images, and (3) sufficient image quality for both images, performed by visual quality check. Additionally, elderly subjects with images that contained WM hyperintensities were included considering it is a normal factor in the aging process. From the initial 164 individuals, one was excluded by the criteria number one, four by number two and one by number three. Thus, 158 healthy subjects were included in this age study; the subject cohort is described in Table 1.

Table 1 Number of subjects for each age range stratified by gender

Data acquisition protocol

Anatomical images: high-resolution 3D GE sequence T1-weighted, MPRAGE with the following parameters: TR/TE = 2500/3.2 ms, time echo spacing 7.0 ms, inversion time 900 ms, voxel size 1 mm isotropic, flip angle = 8°, FOV = 240 × 240 mm2, 160 slices.

DWI: SE sequence combined with EPI acquisition, with the following parameters: TR/TE = 9300/54 ms, voxel size 2 mm isotropic, EPI factor = 67, FOV = 256 × 256 mm2, 60 slices, 33 diffusion gradients, being 32 with b = 1000 s/mm2 and one with b = 0, overplus = no.

The PAR/REC images collected from the scanner were converted to NIfTI format using the dcm2nii extension MRIcroGL (http://www.mccauslandcenter.sc.edu/mricrogl/).

T1-weighted images processing

Structural 3D-T1 weighted (3D-T1w) images were processed using the recon-all routine in Freesurfer v5.3.0 [39]. The Destrieux atlas [40] was used for cortical parcellation, with 74 regions per hemisphere, which has a good anatomical specificity by dividing the cortex into gyri and sulci. The cortical parcellation and thicknesses estimates were obtained individually, and visual check was performed. Cortical labeled volumes were created. The FSL FLIRT boundary-based registration [41, 42] was used to align the DWI and structural images. 3D-T1w and the DWI were coregistered into DWI space, with 2-mm isotropic resolution.

DWI processing

Images were motion corrected by standard Philips processing in the scanner, and a preprocessing pipeline was performed on the DWI data. The preprocessing pipeline in FSL software (FMRIB, Oxford, England) [41] includes correction for eddy current distortions and motion [43], brain extraction [44], bias field corrections, tensor model fitting using ordinary least squares, and FA and MD scalar map calculation using FDT toolbox [41].

Tract reconstruction was performed using the Diffusion Toolkit [45]. The processing occurs in two steps: (1) reconstruction—diagonalization of diffusion data, in which the input data are the corrected 4D DW image, bvec table, and value of bval; (2) tracking—reconstruction of whole-brain fibers, considering angle threshold of 35° and FA threshold of 0.2, using fiber assignment by continuous tracking (FACT) propagation algorithm [46], with inversion of the X diffusion gradient direction. Tract visualization and processing were performed with TrackVis visualization program [45], in which track files generated in the prior step were uploaded, together with FA grayscale, FA color, and MD maps, for tract selection and analysis.

Several tracts were manually segmented using TrackVis, based on known anatomical information in the literature, namely: left/right (L/R) arcuate fasciculus (AF), L/R cingulate part of the cingulum (CGC), L/R corticospinal tract (CST), L/R fornix (FX), L/R inferior longitudinal fasciculus (ILF), L/R inferior occipitofrontal fasciculus (IFOF), L/R uncinate fasciculus (UF), and corpus callosum (CC) fibers subdivided in two ways, based on main structural parts such as genu (GCC), body (BCC), and splenium (SCC) and based on interhemispheric connections between cortical regions of the Destrieux atlas, i.e., transverse frontopolar gyri and sulci (CC_frontopol), superior frontal gyrus (CC_frontsup), paracentral lobule and sulcus (CC_paracentral), precuneus (CC_precuneus), and superior occipital gyrus (CC_occipitalsup). More details on the manual segmentation are available in the supplementary material. Twenty-two tracts were individually and manually selected. Next, for each subject, the average FA and MD across the brain tracts were estimated, overlaying the diffusion metric maps the tractography data using TrackVis.

For the tract-cortical relationship, the cortical regions selected for each tract were chosen considering the area that most of the fibers (more than 50%) of each tract were terminating in, according to Table 2 and Fig. 1.

Table 2 Cortical regions selected for each tract considering the area that most of the fibers of each tract were terminating in, based on the Destrieux atlas [left/right (L/R) arcuate fasciculus (AF), L/R cingulate part of the cingulum (CGC), L/R corticospinal tract (CST), L/R fornix (FX), L/R inferior longitudinal fasciculus (ILF), L/R inferior occipitofrontal fasciculus (IFOF), L/R uncinate fasciculus (UF), and corpus callosum (CC) fibers subdivided in two ways, based on the main structural parts such as genu (GCC), body (BCC), and splenium (SCC) and based on interhemispheric connections between cortical regions of the Destrieux atlas: transverse frontopolar gyri and sulci (CC_frontopol), superior frontal gyrus (CC_frontsup), paracentral lobule and sulcus (CC_paracentral), precuneus (CC_precuneus), and superior occipital gyrus (CC_occipitalsup)]
Fig. 1
figure1

Cortical regions selected for each tract considering the area that most of the fibers of each tract were terminating in. Sagittal, coronal, and axial views for the tracts: (a) arcuate fasciculus (AF); (b) corpus callosum divided in genu (GCC), body (BCC), and splenium (SCC); (c) corpus callosum fibers selected by the interhemispheric cortical regions of the Destrieux atlas—CC_frontopol, CC_frontsup, CC_paracentral, CC_precuneus, and CC_occipitalsup; (d) L/R cingulate part of the cingulum (CGC); (e) L/R corticospinal tract (CST); (f) L/R fornix (FX); (g) L/R inferior longitudinal fasciculus (ILF); (h) L/R inferior occipitofrontal fasciculus (IFO); and (i) L/R uncinate fasciculus (UF)

Statistical analyses

Statistical analyses were performed in R 3.3.0 statistical environment and language (https://www.r-project.org/). The structural and diffusion values were used to performed linear regressions and statistical analysis of the structural information (cortical thickness and diffusion parameters) in relation to age and its correlation.

The cortical thickness (CT) of the 148 parcellated cortical regions and the mean values of FA and MD, calculated for each brain tract, were evaluated with respect to the subject age. A linear fitting was used for modeling these relationships of the structural parameters with aging.

The change rates per decade, of CT for each cortical region and FA/MD parameters for each brain tract, were calculated as the percentage of change from an initial value at 18 years, using the slope and intercept of the linear regression. The modulus of the difference of the CT change rates was calculated for each brain tract connection and compared with the mean change rate difference of CT for each hemisphere. The FA/MD change rates for each tract were also evaluated in relation to the mean CT change rate in a linear fit for the assessment of the association between them.

The association between CT of the regions in Table 2 and the mean values of FA and MD of the connecting tracts were studied using a linear partial mediation model in Eqs. 1 and 2. In this model, age was the independent variable, cortical thickness of both structural connected cortical areas were the mediator variable, and diffusion metrics were the dependent variable. The CT indices do not indicate directionality; both thicknesses are equivalent and of equal weight for the analysis:

$$ {FA}_i={\alpha}_0+{\alpha}_{1\cdot }{Thickness}_{1,i}+{\alpha}_{2\cdot }{Thickness}_{2,i}+{\alpha}_{3\cdot }{Age}_i+{\in}_i $$
(1)
$$ {MD}_i={\beta}_0+{\beta}_{1\cdot }{Thickness}_{1,i}+{\beta}_{2\cdot }{Thickness}_{2,i}+{\beta}_{3\cdot }{Age}_i+{\in}_i $$
(2)

The p-values were adjusted considering multiple comparisons using the Benjamini-Hochberg false discovery rate (FDR) controlling procedure [47] in all statistical analyses.

Results

An analysis of cortical thickness information was carried out in relation to age for the 148 cortical regions based on the Destrieux atlas; thus 131 regions show a significant reduction in healthy aging (p < 0.05 FDR corrected) (Fig. 2). In general, the affected regions represent 88.5% of the cortical structures, 90.4% of the cortical surface area, and 88.3% of the cortical volume. The regions of most cortical thinning are left subcallosal gyrus (0.12 mm/decade) and right transverse temporal sulcus (0.11 mm/decade). Other brain regions show high thinning rates, as the right anterior part of the cingulate gyrus and sulcus, left orbital part of the inferior frontal gyrus, and left triangular part of the inferior frontal gyrus. Based on the high R2 values of the fittings, the linear model seems an adequate approach to characterize the relationship between cortical thickness and age in the interval used in this work.

Fig. 2
figure2

Regions of significant cortical age-related thinning based on the Destrieux atlas in a blue scale. Left and right hemispheres of the brain (p < 0.05, FDR correction)

From the DWI, it was possible to verify, through the estimates for the corpus callosum (CC), an anteroposterior gradient of decrease in FA as widely reported in the literature [11, 12, 28, 48]. All selected tracts showed a significant (p < 0.05 FDR corrected) decrease of FA values and increased MD with age; the rate of change of this parameters are shown in Table 3.

Table 3 Rate of the change of FA and MD per decade as a result of linear regression analysis vs. age for the manually selected tracts, p-value < 0.05 FDR corrected. Percentage indicates the rate of change from an initial value at 18 years [left/right (L/R) arcuate fasciculus (AF), L/R cingulate part of the cingulum (CGC), L/R corticospinal tract (CST), L/R fornix (FX), L/R inferior longitudinal fasciculus (ILF), L/R inferior occipitofrontal fasciculus (IFOF), L/R uncinate fasciculus (UF), and corpus callosum (CC) fibers subdivided in two ways, based on main structural parts such as genu (GCC), body (BCC), and splenium (SCC) and based on interhemispheric connections between cortical regions of the Destrieux atlas: transverse frontopolar gyri and sulci (CC_frontopol), superior frontal gyrus (CC_frontsup), paracentral lobule and sulcus (CC_paracentral), precuneus (CC_precuneus), and superior occipital gyrus (CC_occipitalsup)]

The corpus callosum tracts and the fornix are the ones with higher alteration rates of FA and MD among the selected ones for this study. The cortical thickness change rates were evaluated for the connecting regions of each tract. Figure 3 demonstrates that the absolute differences between the cortical thinning rates of the cortical regions connected by the tracts are usually under or near the mean of this difference considering all the cortical regions of each hemisphere (dash line in Fig. 3).

Fig. 3
figure3

Modulus of the cortical thinning rate differences between both cortical areas linked by each tract. Dashed lines demonstrate the mean of the thinning rate absolute differences between all the cortical regions of each hemisphere

The averages of the cortical thickness change rates between the cortical regions linked by the tracts are significantly correlated with the alteration rates of the WM diffusion parameters (FA and MD) of the corresponding tracts (Fig. 4). This result motivates and we try to explore if the cortical thickness of specific areas has a true mediation relationship with diffusion metrics of specific tracts during aging process.

Fig. 4
figure4

Association between FA and MD change rates and mean cortical thickness change rates for the respective tract. Each point in both graphs is associated with the specific brain tracts calculated across the study cohort

In order to analyze the associations between WM diffusion parameters (FA/MD) and cortical thickness information, Eqs. 1 and 2 were used to fit the data. In our linear partial mediation model, age was the independent variable, cortical thickness was the mediator variable, and diffusion metrics were the dependent variable. The results demonstrate that age was the most significant parameter for all of the tracts, with a p-value < 0.05 (FDR corrected). The cortical thickness was not significant in the regression for the majority of the tracts to explain FA and MD alterations, except for the fornix, in which the subcallosal gyrus cortical thickness was significantly (p < 0.05 FDR corrected) correlated with the FA and MD alterations, in both hemispheres for MD and in the left hemisphere for FA (Fig. 5). The left subcallosal gyrus was the region with greater cortical thinning.

Fig. 5
figure5

FA and MD effect in the left/right fornix versus the thickness of the subcallosal gyrus, accounting for the age and the cortical thickness of the temporal pole. The presented p-values are FDR corrected

Discussion

Several studies indicated whole-brain age-related cortical thinning, in the prefrontal cortex areas [4], superior and inferior frontal gyrus and temporal lobe [5, 49], and precentral, paracentral, postcentral, and orbitofrontal gyrus [49, 50]. The frontal and temporal patterns of cortical thinning were demonstrated (Fig. 2) in accordance with the literature [4, 6, 49]. The regions of most cortical thinning were the left subcallosal gyrus and right transverse temporal sulcus, with decline rates rather high compared with the average previous findings [4, 49]. However, in the literature, a significant reduction in cortical thickness is shown in the postcentral gyrus and primary somatosensory areas [49, 50], while our results did not show any significant reduction in the left or right postcentral regions. These disagreements could be attributed to our sample (demographic characteristic and acquisition parameters) and some methodological issues in the segmentation procedure.

Cortical thinning with age does not necessarily correspond to neuronal loss; instead it implies in alterations in neuronal architecture, reduced synapses, and lower metabolic cell activity [6]. The analysis of white matter integrity provides additional information about the structural alterations occurring during the brain aging process.

In the tract-specific analysis, it was possible to verify FA reductions, and MD increases in the life span (Table 3) in accordance with the literature [16, 28]. FA reduction was verified for all tracts, demonstrating, once more, that these are age-sensitive parameters that are useful for aging studies, agreeing with the literature [1, 16, 51]. From these findings, we hypothesize that the aging process affects the brain structural connectivity in a tract-specific way, due to known structural changes in the white matter microstructure and degeneration of fiber integrity, such as alterations in the axonal count, integrity, and density, degree of myelination, and fiber organization [52]. The differences in the order of change rate of these parameters with age could be related to the different structural alterations in the WM connections of these specific fibers, producing different patterns of alteration of FA and MD.

The fornix presented a high rate of change in the aging process, in agreement with previous report; the limbic regions connected by this tract are also more affected by normal aging [51]. Additionally, when comparing the change rates inter-hemispherically for the selected tracts, those rates presented higher percentages in the left hemisphere. Moreover, intra-hemispherically, the order of most to least affected tracts FA in the aging process, considering FA change rate, is FX, CGC, AF, ILF, UF, IFOF, and CST. However, the pattern is not the same for MD changes, due to the different structural information that is evaluated with that metric. The corpus callosum (CC) exhibit an anteroposterior gradient of FA decrease, as previously reported by others [10, 15, 28], and the use of cortical regions for CC streamlines selection is very useful for studying the interhemispheric connections [53]. These results are concordant with previous results in which the anteroposterior degeneration pattern is not observed for the MD parameter in the aging process [15].

Relating to the cortical thickness information for each tract connections, Fig. 3 shows that for most of the tracts, the connected regions age with similar alteration rates. For five tracts (left/right inferior fronto-occipital fasciculus, left fornix, right cingulate part of the cingulum, and right arcuate fasciculus), the differences are higher, demonstrating that for these connections one of the cortical areas is more affected than the other in the aging process, which could be due to the degree of WM degradation.

A significant relationship between FA and MD change rates with mean cortical thinning rates of the regions connected by the tracts was found (Fig. 4), supporting the nexus between WM microstructure and cortical organization. This evidence confirms previous studies indicating FA decrease and MD increase in WM as sensitive markers of aging [16,17,18, 30, 31].

The main factor that explains the FA and MD variations between subjects is the age (p-value < 0.05, FDR corrected). Most tracts did not show a significant association with cortical thickness in the multiple regressions after removing the age effect. Only the left and right fornix demonstrated a significant direct association between FA/MD with cortical thicknesses of one of its ending cortical areas (Fig. 5). Thus, the aging process affects the brain structural connectivity in a tract-specific way [38]. The findings could be extended in a cognitive study to assess the relation with the cognitive decline in the elderly, as reported in the literature [21, 34]. Considering that the diffusion integrity of this limbic tract is related to the GM structure and therefore, to cognitive functions [36], numerous alterations in the brain structure and function with age affect brain volume and WM integrity and could predict cognitive decline. Studies demonstrated that cognitive decline related to the aging process is expressed after a threshold of structural deterioration [54].

Some limitations can be noted in our study. A bigger cohort and nonlinear models to evaluate the tract-cortical relation could generate a more robust study. Also, a more homogeneous distribution of data across all ages would improve the study and reduce a possible age bias in the results. Furthermore, a future study would indeed benefit from gender-specific analysis to identify any age-related structural changes that differ between the male and female subjects. An additional longitudinal study would be of great value to assess the tract-cortical relations in the aging process to evaluate if the findings are valid for individual subjects. Additionally, one big constraint to our analysis is the lack of clinical/neuropsychological data that would be very valuable to assess whether and how structural degeneration is related with age-related cognitive changes. Another limitation is related to the data, which was acquired with a standard clinical protocol for DWI, limiting the correction for susceptibility artifacts and the possible models to characterize the intravoxel diffusion. For further studies, one should use more sophisticated acquisition protocols for multi-shell data to overcome some bias due to the limitation of one direction of streamline per voxel. Additionally, evaluating more brain tracts for the tract-cortical relations would be beneficial to draw stronger conclusions.

Conclusion

The normal aging process changes the microstructure of the brain tissues; it was seen in this study a significant cortical thinning in many regions and alterations in diffusion parameters of many brain tracts. A tract-specific change rate for the diffusion parameters was observed, describing that the WM integrity degeneration is different for each tract, being the most affected the corpus callosum, mainly the anterior part, and the fornix. For the tract-cortical analysis, the cortical regions connected by tracts demonstrated similar thinning patterns for the majority of tracts, and a significant relationship between mean cortical thinning rate and FA/MD alteration rates was found. Based on the linear model here described, cortical thickness of the specific cortical areas has not a true mediation relationship with diffusion metrics (FA or MD) of the tracts linking these cortical areas in the aging process, except for in the fornix. In this tract, the subcallosal gyrus thinning is mediating FA and MD variations.

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Acknowledgments

Data was provided from the Center of Image Sciences and Medical Physics, in the Faculty of Medicine of Ribeirao Preto, University of Sao Paulo (CCIFM/HC-FMRP), with the approval of the Radiology Department head.

This work was presented in part at the ISMRM Meeting in Honolulu, April 2017, and at the OHBM Meeting in Vancouver, June 2017.

Funding

This study was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), grant number 2015/26227-7. CEGS received financial support from the CNPq (National Council for Scientific and Technological Development), grant number 311703/2014-3.

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MSP wrote the manuscript with comments from CEGS and ACS. All authors read and approved the final manuscript.

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Correspondence to Maíra Siqueira Pinto.

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The authors declare no conflict of interest.

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The use of the retrospective data was approved by the local Research Ethics Committee (CAAE: 55268616.3.3001.5440 and 55268616.3.0000.5407). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

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Pinto, M.S., dos Santos, A.C. & Salmon, C.E.G. Age-related assessment of diffusion parameters in specific brain tracts correlated with cortical thinning. Neurol Sci 42, 1799–1809 (2021). https://doi.org/10.1007/s10072-020-04688-9

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Keywords

  • Aging
  • Diffusion MRI
  • Structural connectivity
  • Cortical thickness
  • Tractography