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. 2025 Sep 23;46(14):e70321. doi: 10.1002/hbm.70321

Microstructural Changes in Aging Hippocampal Pathways: Insights From the HCP‐Aging Diffusion MRI Study

Huize Pang 1, Zhe Sun 1, Zifei Liang 1, Chenyang Li 1, Jiangyang Zhang 1, Yulin Ge 1,✉
PMCID: PMC12455248  PMID: 40985250

ABSTRACT

While hippocampal atrophy in Alzheimer's disease is well‐documented, research on microstructural integrity of hippocampal pathways to selected cortical regions in healthy aging populations remains limited. Four hundred seventy‐five healthy individuals aged 36–90 from the Human Connectome Project Aging (HCP‐A) dataset were analyzed. Hippocampal fiber pathways, including the “Papez,” “Prefrontal,” “Occipital,” and “Parietal” pathways, were extracted from whole‐brain tractography and characterized by fractional anisotropy (FA) and mean diffusivity (MD), neurite density index (NDI), and orientation distribution index (ODI). Partial linear and quadratic nonlinear correlation analyses were conducted to examine the relationship between age, cognition, and diffusion metrics, adjusted by hippocampus volumes. While FA, MD, and ODI demonstrated linear age‐related changes, NDI exhibited a quadratic pattern. MD was identified as the most age‐sensitive parameter. Among all pathways, the “Prefrontal” pathway showed the most pronounced microstructural changes in both males and females, characterized by reduced FA and NDI and increased MD and ODI with age (FA: r = −0.31 to −0.40; NDI: r 2 = 0.30–0.31; MD/ODI: r = 0.23–0.48; p < 0.01). Similar changes were observed in the “Occipital” pathway (FA: r = −0.28 to −0.39; MD/ODI: r = 0.32–0.50; p < 0.01), with NDI reduction present only in females (r 2 = 0.18, p < 0.01). In the “Parietal” pathway, changes were detected only in females, with lower FA (r = −0.29, p < 0.01) and higher ODI (r = 0.24, p < 0.01). Additionally, age‐related cognitive decline was significantly associated with microstructural changes in the “Occipital” (FA: r = 0.29; MD: r = −0.28; ODI: r = −0.25; p < 0.001) and “Prefrontal” pathways (FA: r = 0.27; MD: r = −0.25; NDI: r = 0.25; ODI: r = −0.22; p < 0.01) in females. This study revealed age‐ and cognition‐related changes in hippocampal pathways across the adult lifespan. These findings provide normative references for hippocampal‐cortical connectivity changes associated with healthy aging and its potential relevance to Alzheimer's disease and related dementias.

Keywords: aging, diffusion characteristics, hippocampal connectivity, human connectome project, microstructural degeneration

Diffusion MRI data from the HCP‐Aging dataset was used to explore the hippocampal pathways and their microstructural changes across the adult lifespan. The results suggest that diffusion MRI‐based measurements of the hippocampal pathways degeneration are associated with both age and cognition and exhibit distinct aging trajectories.

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1. Introduction

Aging is a natural biological process characterized by a gradual decline in physical and cognitive function and is a major risk factor for neurodegenerative disorders such as Alzheimer's disease (AD) (Hou et al. 2019). Understanding the neurobiological mechanisms of brain aging is essential to distinguish normal age‐related trajectories from pathological alterations leading to dementia. While aging affects whole brain volume (Ge et al. 2002) involving multiple brain regions, the hippocampus, located in the medial temporal lobe, is particularly susceptible to age‐related pathological alterations and cognitive decline (Bartsch and Wulff 2015; Bettio et al. 2017). Hence, elucidating the hippocampus's connectivity characteristics is essential for understanding the aging process and its link to AD.

Previous MRI studies have consistently reported hippocampal alterations in both normal aging and AD, with hippocampal atrophy recognized as a hallmark feature for AD (Pini et al. 2016; van de Pol et al. 2006). Beyond volumetric changes, quantitative MRI techniques have identified additional aging‐ and AD‐related hippocampal changes, including iron deposition (Zhou et al. 2024) and disrupted microstructural integrity (Ofori et al. 2019; Sathe et al. 2023). The hippocampus, however, does not function in isolation but extensively coordinates with other cortical regions. Therefore, investigating its structural connectivity with other regions is essential for understanding age‐related neurodegenerative changes. Resting‐state functional MRI (rs‐fMRI) studies have revealed age‐related alterations in hippocampal connectivity, particularly with the prefrontal (Gluth et al. 2015), parietal, occipital, and temporal cortices (Raud et al. 2023; Setton et al. 2022), as well as with large‐scale networks such as the default mode network (Salami et al. 2014). While these studies underscore the significance of hippocampal connectivity in aging, direct evidence of age‐related changes in hippocampal structural pathways and their microstructural integrity remains scarce.

Several studies have investigated hippocampal structural connectivity in vivo using diffusion MRI, spanning from local intra‐hippocampal circuits to long‐range connections with distributed brain regions. For instance, Yassa et al. (2011) combined high‐resolution fMRI and diffusion MRI to examine age‐related alterations in intra‐hippocampal connectivity, with a particular emphasis on the perforant pathway‐a major input route from the entorhinal cortex to the hippocampus. Subsequent studies using high‐resolution diffusion MRI have delineated the subregions of the medial temporal lobe and their associated white matter pathways, contributing to a more precise understanding of the hippocampus's microstructural connectivity integration within the medial temporal network (Zeineh et al. 2012). Extending beyond local circuits, Maller et al. (2019) used a super‐resolution 1150‐direction diffusion tensor imaging to map a comprehensive hippocampal connectome, demonstrating extensive long‐range connections beyond conventional pathways, including strong connections to temporal, subcortical, occipital, and frontal regions. Despite its widespread connectivity, the hippocampus is primarily linked through four key pathways (Huang et al. 2021): the “Papez” pathway, “Parietal” pathway, “Occipital” pathway, and “Prefrontal” pathway. The “Papez” circuit, a neural loop that originates and returns to the hippocampus involving the fornix and cingulate, plays a central role in memory processing (Kamali et al. 2023). The “Parietal” pathway connects the hippocampus with the medial and lateral parietal cortex, supporting episodic memory retrieval (Zheng et al. 2021). The “Occipital” pathway links the hippocampus with the ventral visual stream for spatial and visual processing (Fridriksson et al. 2016). The “Prefrontal” pathway facilitates hippocampal‐PFC interactions, crucial for episodic memory and executive functions (Jin and Maren 2015). However, few studies have comprehensively reconstructed these pathways in vivo using 3.0T DTI or investigated age‐related alterations in a large‐scale dataset. The HCP‐A dataset offers high‐resolution, multi‐shell diffusion imaging with exceptional data quality and harmonized cognitive assessments, making it an ideal resource for mapping hippocampal fiber pathways and investigating their trajectories of age‐related changes across the adult lifespan.

We hypothesized that age‐related microstructural changes in hippocampal fiber pathways follow tract‐specific degenerative patterns and contribute to cognitive decline throughout the adult lifespan. To test this, we aimed to: (1) track and delineate hippocampal fiber pathways using a whole‐brain DTI connectome approach from a large‐scale, population‐based HCP‐Aging cohort; (2) examine the associations between hippocampal pathway diffusion characteristics, age, and cognition and (3) provide normative references for identifying hippocampal pathway degeneration in future studies of AD and AD‐related dementias.

2. Materials and Methods

2.1. Participants

This study utilized imaging data from the Human Connectome Project Aging (HCP‐A) dataset including individuals across the adult lifespan. All participants provided informed written consent for this Institution Review Board (IRB) approved cross‐sectional study. “Healthy participants” were defined as individuals without cognitive impairments, with detailed inclusion and exclusion criteria reported by Bookheimer et al. (2019). In the current study, additional exclusion criteria were applied. Specifically, participants were excluded if they: (1) were aged over 90 years; (2) had poor image quality on either diffusion MRI or T1‐weighted MRI; (3) failed to generate a whole‐brain tractography under the tracking parameters; (4) exhibited poor quality hippocampal pathway reconstructions based on visual inspection; or (5) lacked cognitive assessments. After applying these criteria, a total of 475 individuals (age: 36–90 years, mean ± SD = 59.43 ± 14.68; females/males = 276/199) who underwent T1‐weighted and diffusion MRI were included in this study for postprocessing and analyses. Cognitive performance was assessed using the Cognitive‐Functional Composite, which combines seven cognitive tests of memory and executive function with the Amsterdam Instrumental Activities of Daily Living Questionnaire (A‐IADL‐Q). Scores are computed as the weighted z‐score of cognitive tests and equally averaged with the A‐IADL‐Q z‐score. The cognitive‐functional composite is a sensitive and reliable tool for detecting functional decline in aging and early neurodegeneration (Jutten et al. 2019).

2.2. Image Acquisition

The MRI data were acquired using a Siemens Prisma 3 T scanner (Siemens; Erlangen, Germany) with a 32‐channel head coil. Diffusion data were acquired using four consecutive runs of a pulsed‐gradient spin‐echo EPI sequence with opposing phase‐encoding directions: b values = 0, 1500, 3000 s/mm2, 99 and 98 diffusion encoding directions, TR = 3230 ms, TE = 89.20 ms, flip angle = 78°, FOV = 210 × 210 mm2, spatial resolution = 1.5 mm isotropic, and 92 axial slices. Besides, T1‐weighted imaging was performed using multi‐echo MPRAGE: TR = 2500 ms, TE = 1.81/3.6/5.39/7.18 ms, TI = 1000 ms, flip angle = 8°, FOV = 256 × 256 mm2, spatial resolution = 0.8 mm isotropic, and 208 sagittal slices.

2.3. Diffusion MRI Preprocessing and Metrics Estimation

The preprocessing procedures for Diffusion data followed a standard pipeline using Diffusion parameter EStimation with Gibbs and NoisE Removal (DESIGNER) (Ades‐Aron et al. 2018), including corrections for thermal noise, Gibbs ringing, Rician bias, EPI and eddy current, and motion‐related artifacts. The native T1w image was then registered to the mean b0 image using FLIRT with boundary‐based registration and bbregister, followed by the affine transformation of T1 data to the Diffusion MRI data. The T1 image registered to diffusion space was then processed with the MRICloud platform (https://mricloud.org/) for tissue segmentation and brain parcellation, yielding 286 brain regions.

All diffusion metrics were estimated in native space using the preprocessed diffusion data. Considering the potential bias in tensor estimation from multi‐shell acquisitions and the non‐Gaussian nature of diffusion at high b values, tensor‐derived metrics were computed exclusively from the b = 1500 s/mm2 shells. The diffusion tensor model was fitted using MRtrix3, with dwi2tensor for tensor reconstruction and tensor2metric for deriving scalar maps, yielding maps of fractional anisotropy (FA) and mean diffusivity (MD). In addition, higher‐order multi‐compartment metrics were derived using data from all b value shells. The Neurite Orientation Dispersion and Density Imaging (NODDI) model was fitted via the Accelerated Microstructure Imaging via Convex Optimization (AMICO) in Python (https://github.com/daducci/AMICO), resulting in voxel‐wise maps of Neurite Density Index (NDI) and Orientation Dispersion Index (ODI) (Daducci et al. 2015).

2.4. Fiber Tractography

The T1w images were segmented into five tissue types: cortical gray matter (GM), subcortical GM, white matter (WM), cerebrospinal fluid (CSF), and pathological tissue using the 5TT algorithm. Meanwhile, response functions for WM, GM, and CSF were estimated directly from the preprocessed DWI data using a multi‐shell multi‐tissue (MSMT) approach, with spherical harmonic orders determined based on the diffusion acquisition scheme. Fiber orientation distributions (FODs) were estimated using an MSMT‐Constrained Spherical Deconvolution (MSMT‐CSD) model applied to the preprocessed DWI data, along with the tissue‐specific response functions. Based on the voxel‐wise FODs, whole‐brain tractography was performed using the second‐order integration over FODs (iFOD2) algorithm in conjunction with Anatomically Constrained Tractography (ACT) and seeding at the gray matter‐white matter interface (GMWMI) to improve cortical coverage. For initial streamline generation, 100 million streamlines were generated in native space, with a minimum length of 2 mm, a maximum length of 200 mm, an FOD amplitude cutoff of 0.1, and a curvature threshold of 45°. The “‐backtrack” option was enabled to allow streamlines to reverse at incorrect termination points, improving anatomical accuracy. To reduce false‐positive streamlines and ensure biologically plausible tract densities, we applied Spherical‐deconvolution Informed Filtering of Tractograms (SIFT) to assign biologically meaningful weights to each streamline, thereby producing more accurate estimates of structural connection density (Smith et al. 2012). Subsequently, weighted streamlines were mapped onto the JHU atlas‐based parcellated brain regions to construct a biologically meaningful whole‐brain structural connectome.

2.5. Hippocampal Fiber Pathways Reconstruction

Based on the established whole‐brain connectome, all regions connected to the hippocampus were identified. Streamlines corresponding to hippocampus‐related ROI pairs were extracted and merged to construct a hippocampus‐to‐whole‐brain tractogram. To further isolate the major hippocampal pathways, predefined anatomical landmarks were applied to filter specific tracts according to previous studies (Huang et al. 2021; Maller et al. 2019; Takahashi et al. 2007; Metzler‐Baddeley et al. 2011). Specifically, the “Prefrontal” pathway was defined as streamlines connecting the hippocampus with subdivisions of the prefrontal cortex, including the superior, middle, and inferior frontal gyri, as well as the dorsolateral and orbitofrontal cortices; the “Parietal” pathway targeted the posterior parietal cortex, particularly the precuneus and posterior cingulate cortex, which are implicated in visuospatial processing and episodic memory; the “Occipital” pathway projected toward ventral occipital and medial visual areas, including the cuneus, lingual gyrus, fusiform, and middle and inferior occipital gyri, regions involved in visual scene perception; the “Papez” pathway included fibers passing through the fornix and cingulum. In addition, a hemisphere‐specific mask was used to constrain streamlines within each hemisphere. To extract hippocampal connectivity in an objective and standardized manner, a data‐driven, connectome‐guided approach was employed. This strategy minimizes the subjective bias associated with ROI selection and streamline counts inherent to seed‐based tractography and allows for a comprehensive and anatomically informed characterization of hippocampal fiber pathways. DTI metrics (FA and MD) and NODDI metrics (NDI and ODI) were calculated by averaging values across voxels along the fiber pathways. The workflow of the current study is illustrated in Figure 1.

FIGURE 1.

FIGURE 1

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Study workflow. (A) Diffusion MRI (dMRI) preprocessing and tractography. Raw dMRI data was preprocessed using the DESIGNER pipeline, registered to T1WI, and used to estimate fiber orientation distributions (FODs) via a multi‐shell multi‐tissue constrained spherical deconvolution (MSMT‐CSD). Five tissue segmented maps from 5TTgen algorithm were used to guide anatomically‐constrained tractography (ACT). The whole‐brain fiber tracking connectome was then constructed by mapping the tractography results onto the JHU brain parcellation atlas. (B) Hippocampal fiber pathways. From whole‐brain connectome, relative streamlines were extracted and merged to construct hippocampal tractography, from which major pathways were identified based on anatomical landmarks. (C) Hippocampal pathways diagram. The “Prefrontal” pathway connects the hippocampus with key subdivisions of the prefrontal cortex. The “Parietal” pathway targets the posterior parietal cortex, including the precuneus and posterior cingulate cortex, which are involved in visuospatial and episodic memory. The “Occipital” pathway projects to ventral occipital and medial visual areas associated with visual scene processing. The “Papez” pathway comprises fibers passing through the fornix and cingulum.

2.6. Hippocampus Volume Measures

Hippocampus volume was calculated considering its central role in aging and dementia. T1w images were parcellated by an automated pipeline deployed at MRI Cloud (https://www.mricloud.org/), following the standard pipeline that included: bias field correction, skull stripping, multi‐atlas label fusion segmentation, and atlas registration. Finally, the hippocampus was segmented, and both the hippocampal volume and total intracranial volume (TIV) were calculated. Hippocampal volume was analyzed using TIV as a correction factor.

2.7. Test–Retest Reliability of Hippocampal Fiber Pathways

To evaluate the test–retest reliability of the connectome‐guided hippocampal tractography, a complementary seed‐based tractography was performed using the same ROI definitions and number of streamlines in a subset of 30 randomly selected subjects spanning the full age range. Spatial overlap between the two approaches was quantified using Dice similarity coefficients (Table S1), and representative visualizations of the hippocampal pathways are provided in Figure S1.

2.8. Statistical Analysis

Age, cognitive‐functional Composite scores, and hippocampal imaging characteristics (i.e., hippocampal volume and diffusion measures of hippocampal pathways) were compared between male and female subjects using independent t‐tests. Before correlation analysis, extra‐sum‐of‐squares F tests were conducted to determine the best‐fitting model for the data. The results indicated that a linear model best described the relationship of FA, MD, and ODI with age, whereas a nonlinear model provided a better fit for NDI. In contrast, all four metrics—FA, MD, NDI, and ODI—exhibited linear associations with cognitive performance. Accordingly, partial linear and quadratic nonlinear analyses were performed between age, cognitive‐functional Composite scores, and diffusion metrics of hippocampal pathways in male and female subjects, while controlling for hippocampal volume as a covariate. The Bonferroni correction was applied to adjust for multiple comparisons, with a significance threshold set at corrected p < 0.0031. The statistical analysis was performed using SPSS 28.0 software (IBM Software Analytics, New York, NY).

3. Results

3.1. Demographic Characteristics

We analyzed 475 healthy participants aged 36–90 years (59.43 ± 14.68) from the HCP‐Aging dataset. Males showed a significantly lower fractional hippocampal volume than female subjects (2.70 ± 0.23 vs. 2.83 ± 0.23, p < 0.001). No significant differences were found between male and female subjects in age (60.14 ± 14.43 vs. 59.05 ± 14.86, p = 0.43) or cognitive measurements (104.69 ± 10.70 vs. 105.46 ± 10.51, p = 0.47). Diffusion metrics were calculated separately for the left and right hippocampal pathways. Since no significant hemispheric differences were observed, bilateral values were averaged and used for subsequent analyses (Table S2). In males, FA values were significantly reduced in the “Parietal” and “Occipital” pathways, while ODI values were significantly increased across all fiber pathways compared to females (p < 0.01). Females demonstrated significantly lower NDI in the “Parietal” and “Papez” pathways than males (p < 0.001). No significant sex differences were observed in the MD values of hippocampal pathways (p > 0.05) (Table 1).

TABLE 1.

Demographics and hippocampal imaging characteristics in male and female subjects.

Male (n = 199) Female (n = 276) t p
Age 60.14 ± 14.43 59.05 ± 14.86 −0.80 0.43
Cognitive‐functional Composite 104.69 ± 10.70 105.46 ± 10.51 0.72 0.47
Hippocampal volume (%) 2.70 ± 0.23 2.83 ± 0.23 6.10 < 0.001*
FA
“Prefrontal” pathway 0.39 ± 0.04 0.40 ± 0.04 −2.02 0.04
“Parietal” pathway 0.31 ± 0.03 0.32 ± 0.03 −3.41 0.001*
“Occipital” pathway 0.37 ± 0.03 0.38 ± 0.03 −3.08 0.002*
“Papez” pathway 0.37 ± 0.02 0.37 ± 0.02 −2.20 0.03
MD (×10−3 mm2/s)
“Prefrontal” pathway 0.74 ± 0.04 0.74 ± 0.04 0.26 0.79
“Parietal” pathway 0.81 ± 0.05 0.80 ± 0.05 0.79 0.43
“Occipital” pathway 0.78 ± 0.05 0.77 ± 0.05 2.17 0.03
“Papez” pathway 0.92 ± 0.06 0.92 ± 0.06 0.79 0.43
NDI
“Prefrontal” pathway 0.54 ± 0.05 0.54 ± 0.05 1.31 0.19
“Parietal” pathway 0.51 ± 0.04 0.49 ± 0.04 5.11 < 0.001*
“Occipital” pathway 0.50 ± 0.03 0.50 ± 0.04 0.74 0.46
“Papez” pathway 0.54 ± 0.04 0.52 ± 0.03 8.83 < 0.001*
ODI
“Prefrontal” pathway 0.28 ± 0.03 0.27 ± 0.03 3.37 0.001*
“Parietal” pathway 0.36 ± 0.03 0.34 ± 0.03 7.15 < 0.001*
“Occipital” pathway 0.31 ± 0.02 0.30 ± 0.02 3.92 < 0.001*
“Papez” pathway 0.31 ± 0.03 0.30 ± 0.03 6.11 < 0.001*
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Note: * denotes a significant difference after Bonferroni correction.

3.2. Age‐Related Alterations in Hippocampal Pathways

Participants were first categorized into three age groups: early midlife (36–49 years), late midlife (50–69 years), and older adulthood (70–90 years). Group‐wise average FA and MD values of hippocampal fiber pathways across these subgroups are shown in Figures 2 and 3; group‐wise average NDI and ODI values are provided in Figures S2 and S3. FA and NDI values decreased, while MD and ODI values increased with age. We also conducted partial linear and quadratic analyses, revealing that age‐related microstructural alterations were most pronounced in the “Prefrontal” pathway, with decreased FA (r = −0.31 for males and −0.40 for females) and NDI (r 2 = 0.30 for males and r 2 = 0.31 for females), and increased MD (r = 0.37 for males and r = 0.48 for females) and ODI (r = 0.23 for males and r = 0.30 for females). Moreover, age‐related declines in FA and increases in MD and NDI were observed in both males and females in the “Occipital” pathway, with a reduced NDI specifically observed in females (r 2 = 0.18, p < 0.01). In the “Parietal” pathway, decreased FA (r = −0.29, p < 0.001) and increased ODI (r = 0.24, p < 0.01) were exclusively found in females. However, the “Papez” pathway showed minimal microstructural changes with age, with only increases in MD (Figure 4; Tables 2 and 3). Age‐related changes in fiber metrics showed no significant hemispheric differences (Table S3). In addition, the age‐related changes across hippocampal pathways were plotted by averaging z‐score normalization diffusion metrics for each decade (Figure S4). The “Prefrontal” pathway exhibited a greater reduction in NDI during late lifespan, accelerating after the age of 70.

FIGURE 2.

FIGURE 2

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Group‐wise comparison of average FA values of four hippocampal pathways at early midlife, late midlife, and older adulthood in the left and right hemispheres. The color maps depict the mean FA values at each voxel along the fiber pathways. FA declines as group age increasing across all four pathways, with more pronounced reductions in the “Prefrontal” and “Occipital” pathways, indicating age‐related microstructural alterations.

FIGURE 3.

FIGURE 3

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Group‐wise comparison of average MD values of four hippocampal pathways at early midlife, late midlife, and older adulthood in the left and right hemispheres. The color maps depict the mean MD values at each voxel along the fiber pathways. MD increases as group age increasing across all four pathways, indicating age‐related microstructural alterations.

FIGURE 4.

FIGURE 4

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Partial correlation analysis between diffusion metrics (FA, MD, NDI, and ODI) of four hippocampal fiber pathways and age.

TABLE 2.

Correlations between diffusion measures of hippocampal pathways and age.

R/R 2 (Male) p (Male) R/R 2 (Female) p (Female)
FA
“Prefrontal” pathway −0.31 < 0.001* −0.40 < 0.001*
“Parietal” pathway −0.13 0.08 −0.29 < 0.001*
“Occipital” pathway −0.28 < 0.001* −0.39 < 0.001*
“Papez” pathway −0.15 0.04 −0.12 0.06
MD
“Prefrontal” pathway 0.37 < 0.001* 0.48 < 0.001*
“Parietal” pathway 0.28 < 0.001* 0.31 < 0.001*
“Occipital” pathway 0.47 < 0.001* 0.50 < 0.001*
“Papez” pathway 0.40 < 0.001* 0.41 < 0.001*
NDI
“Prefrontal” pathway 0.30 0.002* 0.31 < 0.001*
“Parietal” pathway 0.03 0.21 0.12 0.02
“Occipital” pathway 0.11 0.05 0.18 0.003*
“Papez” pathway 0.04 0.03 0.03 0.06
ODI
“Prefrontal” pathway 0.23 0.001* 0.30 < 0.001*
“Parietal” pathway 0.15 0.03 0.24 0.001*
“Occipital” pathway 0.33 < 0.001* 0.32 < 0.001*
“Papez” pathway 0.19 0.005 0.16 0.01
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Note: * denotes a statistical significance after Bonferroni correction. R values represent linear correlation coefficients between age and diffusion metrics (FA, MD, and ODI), while R 2 values represent coefficients of determination from quadratic regression models for NDI. The type of correlation model was determined using extra‐sum‐of‐squares F tests.

TABLE 3.

Correlations between diffusion measures of hippocampal pathways and age‐related cognitive function.

R (Male) p (Male) R (Female) p (Female)
FA
“Prefrontal” pathway 0.17 0.03 0.27 < 0.001*
“Parietal” pathway 0.04 0.66 0.06 0.36
“Occipital” pathway 0.17 0.03 0.29 < 0.001*
“Papez” pathway 0.05 0.52 0.07 0.26
MD
“Prefrontal” pathway −0.13 0.11 −0.25 < 0.001*
“Parietal” pathway −0.02 0.78 −0.14 0.03
“Occipital” pathway −0.17 0.03 −0.28 < 0.001*
“Papez” pathway −0.05 0.52 −0.16 0.01
NDI
“Prefrontal” pathway 0.12 0.13 0.25 < 0.001*
“Parietal” pathway 0.11 0.19 0.05 0.45
“Occipital” pathway 0.15 0.06 0.16 0.01
“Papez” pathway 0.01 0.96 0.05 0.45
ODI
“Prefrontal” pathway −0.18 0.03 −0.22 0.001*
“Parietal” pathway −0.04 0.24 −0.05 0.41
“Occipital” pathway −0.14 0.09 −0.25 < 0.001*
“Papez” pathway −0.05 0.57 −0.03 0.64
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Note: * denotes a statistical significance after Bonferroni correction.

3.3. Correlations Between Hippocampal Pathway Diffusion and Cognition

Significant associations with cognitive‐functional composite scores were observed in the “Occipital” and “Prefrontal” pathways in females (Figure 5). Specifically, as shown in Tables 2 and 3, MD and ODI in the “Prefrontal” and “Occipital” pathways were negatively correlated with cognition (r = −0.22 to −0.28, p < 0.01), while FA showed a significant positive correlation (r = 0.27 to 0.29, p < 0.001). NDI was positively correlated with cognition only in the “Prefrontal” pathway (r = 0.25, p < 0.001).

FIGURE 5.

FIGURE 5

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Partial correlation analysis between diffusion metrics (FA, MD, NDI, and ODI) of four hippocampal fiber pathway and age‐related cognitive measures.

3.4. Correlations Between Hippocampal Pathway Diffusion and Hippocampal Volume

The FA values of all hippocampal pathways were positively correlated with hippocampal volume in both males and females (r = 0.19–0.44, p < 0.05), while MD and ODI values showed a negative correlation with volume (r = −0.34 to −0.64, p < 0.001). NDI in the “Prefrontal” pathway showed significant positive correlations with volume in both sexes (r = 0.35–0.43, p < 0.001), whereas significant associations in the “Parietal” and “Occipital” pathways were observed only in females (r = 0.18–0.22, p < 0.01) (Table 4).

TABLE 4.

Correlations between diffusion measures of hippocampal fiber pathways and hippocampal volume.

R (Male) p (Male) R (Female) p (Female)
FA
“Prefrontal” pathway 0.44 < 0.001* 0.40 < 0.001*
“Parietal” pathway 0.33 < 0.001* 0.27 < 0.001*
“Occipital” pathway 0.31 < 0.001* 0.26 < 0.001*
“Papez” pathway 0.35 < 0.001* 0.19 0.002*
MD
“Prefrontal” pathway −0.53 < 0.001* −0.41 < 0.001***
“Parietal” pathway −0.55 < 0.001* −0.47 < 0.001***
“Occipital” pathway −0.64 < 0.001* −0.52 < 0.001***
“Papez” pathway −0.57 < 0.001* −0.50 < 0.001***
NDI
“Prefrontal” pathway 0.43 < 0.001* 0.35 < 0.001*
“Parietal” pathway 0.18 0.01 0.18 0.003*
“Occipital” pathway 0.19 0.008 0.22 < 0.001*
“Papez” pathway 0.11 0.12 0.03 0.61
ODI
“Prefrontal” pathway −0.36 < 0.001* −0.38 < 0.001*
“Parietal” pathway −0.40 < 0.001* −0.34 < 0.001*
“Occipital” pathway −0.41 < 0.001* −0.34 < 0.001*
“Papez” pathway −0.46 < 0.001* −0.34 < 0.001*
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Note: * denotes a statistical significance after Bonferroni correction.

4. Discussion

This study examined age‐ and cognition‐related changes associated with hippocampal fiber connectivity across the adult lifespan using a large‐scale dataset of healthy participants. With aging, hippocampal fibers demonstrated microstructural degeneration, characterized by decreases in FA and NDI and increases in MD and ODI. Among these metrics, MD was the most sensitive to age‐related changes, while NDI demonstrated a nonlinear association with age. Among the major hippocampal pathways, the “Prefrontal” pathway exhibited the most pronounced degeneration in both males and females, followed by the “Occipital” pathway and the “Parietal” pathway, both of which showed female‐specific alterations in diffusion measures. The “Papez” pathway, however, showed minimal age‐related changes and was the least affected. In addition, cognition‐related changes in the “Prefrontal” and the “Occipital” pathways were observed only in females. By characterizing these patterns using diffusion MRI at 3T, our findings offer insights into normal hippocampal pathway aging and may serve as normative references for identifying abnormal degeneration in future studies of neurodegenerative diseases.

In this study, we employed a whole‐brain connectome‐guided approach to identify the major hippocampal pathways, in alignment with previous research (Huang et al. 2021; Maller et al. 2019). Specifically, we first extracted hippocampal‐to‐whole‐brain streamlines from the whole‐brain connectome and subsequently isolated four anatomically relevant pathways using predefined ROIs. Compared to conventional seed‐based tractography, this approach offers two key advantages. First, it minimizes subjective bias arising from manual ROI selection and arbitrary streamline thresholds. Second, by embedding hippocampal connectivity within the context of global brain architecture, it enables data‐driven and anatomically informed fiber selection. This strategy not only mitigates false‐positive tracking but also improves the anatomical validity and reproducibility of hippocampal pathways, thereby providing a more robust foundation for subsequent analyses of age‐ and cognition‐related effects on hippocampal connectivity across the lifespan. Based on the defined hippocampal pathways, we extracted conventional measures of FA and mean diffusivity MD as well as advanced NODDI‐derived measures (e.g., NDI, ODI), which offer more specific insights into white matter degeneration. The observed age‐related decline in FA may reflect a combination of multiple factors, including reduced neurite density and/or increased orientation dispersion that FA alone cannot differentiate. In contrast, NODDI provides separate estimates of neurite density and tract complexity/fanning, offering more nuanced and mechanistic insights into white matter aging.

Among all diffusion metrics, MD was found to be the most sensitive to age‐related changes. This may be due to its ability to quantify the overall magnitude of water diffusion across all directions, making it particularly responsive to a wide spectrum of microstructural alterations. Our findings are in line with a previous large‐scale study by Cox et al. (2016), which identified MD as the most age‐sensitive diffusion metric for white matter changes. Additionally, we found that, compared to FA, MD, and ODI, which exhibited linear relationships with age, NDI demonstrated a nonlinear association. This pattern is consistent with previous studies reporting a quadratic decline in NDI and a modest linear increase in ODI across the adult lifespan (Henriques et al. 2023; Schilling et al. 2023). Similarly, a recent study using mean apparent propagator model (MAP‐MRI) features from the HCP‐Aging dataset reported a linear association between age and propagator anisotropy (PA), a metric sensitive to neurite orientation dispersion (Singh et al. 2024). These findings suggest that neurite density may be more susceptible to late‐life decline, while FA, MD, and ODI reflect more gradual, cumulative microstructural changes over time. However, longitudinal studies are needed to confirm the sensitivity of these metrics in capturing age‐related microstructural alterations.

Aging is a major risk factor for neurodegenerative diseases, with early changes preceding overt pathology. The hippocampus is particularly vulnerable, and its atrophy is a well‐established marker of cognitive decline and dementia (Fotuhi et al. 2012). Our study revealed strong associations between hippocampal atrophy and its pathway disruptions. In males, reduced FA in parietal and occipital pathways and increased ODI in hippocampal tracts may reflect more pronounced hippocampal atrophy. However, hippocampal connectivity decreases even after adjusting for volume, suggesting independent age‐related degeneration. The hippocampal‐prefrontal pathway exhibited the most pronounced age‐related changes, with decreased FA and NDI and increased MD and ODI in males and females, suggesting complex neurodegenerative processes involving axonal thinning and loss, myelin breakdown, and fiber disorganization. This pathway follows the trajectory of the uncinate fasciculus, which links limbic and frontal regions crucial for emotion and episodic memory. Previous rodent studies identified direct anatomical hippocampal‐prefrontal connections (Hoover and Vertes 2007), and this pathway likely plays a critical role in memory and executive functions (Preston and Eichenbaum 2013). Older adults may increasingly rely on prefrontal cortex (PFC) regions to compensate for medial temporal engagement (Dennis et al. 2007; Gutchess et al. 2005). RS‐fMRI studies also show age‐related alterations in hippocampal‐PFC functional connectivity, further confirming its potential role in episodic memory (Ankudowich et al. 2019; L. Zheng et al. 2023). Our findings in this study extend this by demonstrating microstructural degeneration in the hippocampal‐prefrontal pathway, closely linked to aging and cognitive decline.

In addition, the “Occipital” pathway showed age‐related reductions in FA and increases in MD and ODI in both sexes, while NDI declined only in females, suggesting greater microstructural vulnerability in females. In males, the “occipital” pathway changes may reflect increased neurite dispersion without significant density loss. As part of the inferior longitudinal fasciculus (ILF), the “Occipital” pathway connects the hippocampus with the visual cortex and supports visual processing, memory function, and spatial navigation (Kravitz et al. 2013). Visual deficits are common in aging and AD (Chung et al. 2021; Springer et al. 2023), often linked to cortical rather than retinal or optic nerve alterations (Cronin‐Golomb et al. 1991). The ventral visual pathway, particularly the ILF, also shows functional dedifferentiation with age, and its integrity disruption is associated with age‐related cognitive decline (Park et al. 2004). The cognitive‐functional composite score used in this study is particularly sensitive to early AD‐related semantic memory impairment, which is believed to involve the occipital‐temporal system and its downstream hippocampal projections (Binder and Desai 2011). These findings enhance our understanding of hippocampal‐occipital connectivity, particularly through the ILF, in aging and AD.

We also observed increased MD in the “Parietal” pathway in both sexes, while reduced FA and increased ODI were shown only in females. As part of the posterior segment of the cingulum bundle, this pathway connects the medial and posterior parietal cortices (Fanselow and Dong 2010; Vann et al. 2009) with key regions of the default mode network (Vincent et al. 2006) implicated in spatial navigation, memory retrieval, and self‐ or goal‐oriented processing (Whitlock et al. 2008; Zheng et al. 2025). Disruption in hippocampal‐parietal connectivity has been linked to neocortical tau pathology and memory deficits in AD (Ziontz et al. 2021), with age‐related parietal deactivation and compensatory hippocampal activity (Miller et al. 2008). The “Papez” pathway, part of episodic memory circuits connecting the hippocampus to subcortical regions, showed age‐related MD increases, while FA, NDI, and ODI remained relatively stable. This mild microstructural change aligns with previous studies reporting on the fornix and cingulum (Hou et al. 2024; Metzler‐Baddeley et al. 2012; Tinney et al. 2024) and may be attributed to its shorter anatomical trajectory and tightly packed fiber organization, making it less susceptible than other long‐range hippocampal‐cortical tracts (Meijer et al. 2020).

In this study, sex differences were observed, with females showing more pronounced hippocampal pathway alterations, particularly in parietal and occipital connections, and significant associations with cognitive function in connections involving the prefrontal and occipital lobes. These findings may be partially explained by the role of estrogen in regulating memory circuitry (Luine and Frankfurt 2020) and its decline after menopause in females, while males' compensatory potential remains (O'Dwyer et al. 2012). Women also face higher AD risk and faster progression (Austad and Fischer 2016; Lin et al. 2015). However, as this study focused on healthy aging and used a composite score rather than domain‐specific neuropsychological assessments, interpretations should be cautious. Future research with detailed domain‐specific cognitive assessment is needed to clarify sex‐specific effects.

Several limitations should be noted. First, this study focused on major hippocampal pathways captured with the current diffusion MRI resolution from the HCP‐A dataset; while higher resolution MRI is needed to examine finer and subfield‐specific fiber tracts. Second, the cross‐sectional design of these datasets limits the ability to assess individual trajectories over time; longitudinal studies are necessary to track the microstructural degenerative progression. Third, although the HCP‐A dataset was acquired across multiple sites using standardized protocols, site effects were not explicitly modeled and should be addressed in future analyses. Fourth, cognitive function measured with a composite score may lack sensitivity to domain‐specific changes; more comprehensive neuropsychological testing is needed. Lastly, as the HCP‐Aging dataset includes only cognitively healthy individuals, further studies are warranted to examine hippocampal pathway changes in AD and AD‐related dementias, particularly in relation to pathological biomarkers.

5. Conclusion

This study demonstrated age‐related degeneration in major fiber pathways connecting the hippocampus to the prefrontal, parietal, occipital cortices, and subcortical structures, with the hippocampal‐prefrontal pathways showing the most pronounced microstructural changes. These findings support the hippocampal disconnection hypothesis in aging and provide structural connectivity insights and normative references that may be relevant to studies of AD and AD‐related dementias, identifying potential in vivo biomarkers for early detection of age‐related cognitive decline.

Author Contributions

Conceptualization: Y.G., J.Z., and H.P. Methodology: H.P., Z.S., Z.L., and J.Z. Software: H.P., C.L., and Z.L. Investigation: H.P., C.L., and J.Z. Resources: Y.G., and J.Z. Original draft: H.P. Writing – review and editing: Y.G., J.Z., Z.S., and C.L. Supervision: Y.G. and J.Z.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Data S1: Supporting information.

HBM-46-e70321-s001.docx (1.4MB, docx)

Acknowledgements

The HCP‐Aging 2.0 Release data used in this report came from DOI: 10.15154/1520707, supported by NIH grants U01AG052564 (HCP‐A: Mapping the Human Connectome During Typical Aging), 5R24MH108315 (Connectome Coordination Facility I), 1R24MH122820 (Connectome Coordination Facility II), NIH/NINDS U24 NS141774, and NIH/NINDS R01 NS108491.

Pang, H. , Sun Z., Liang Z., Li C., Zhang J., and Ge Y.. 2025. “Microstructural Changes in Aging Hippocampal Pathways: Insights From the HCP‐Aging Diffusion MRI Study.” Human Brain Mapping 46, no. 14: e70321. 10.1002/hbm.70321.

Funding: This work was supported by National Institutes of Health, 1R24MH122820, 5R24MH108315, U01AG052564.

Data Availability Statement

The data that support the findings of this study are openly available in Human Connectome Project at https://www.humanconnectome.org/study/hcp‐aging.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1: Supporting information.

HBM-46-e70321-s001.docx (1.4MB, docx)

Data Availability Statement

The data that support the findings of this study are openly available in Human Connectome Project at https://www.humanconnectome.org/study/hcp‐aging.

Articles from Human Brain Mapping are provided here courtesy of Wiley

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