Longitudinal impact of cholinesterase inhibitors on cholinergic white matter integrity in mild cognitive impairment: A diffusion MRI study

Abstract

Background

Early degeneration of the cholinergic nucleus basalis of Meynert contributes to cognitive decline in Alzheimer's disease (AD). Microstructural damage in downstream cholinergic tracts—the cingulum bundle (CGC), entorhinal cortex (EC), and uncinate fasciculus (UNC)—often precedes volumetric atrophy. While cholinesterase inhibitors (ChEIs) can preserve cortical and hippocampal volume, their influence on white-matter integrity is unclear.

Objective

To determine whether ChEIs slow microstructural decline in four cholinergic tracts (CGC, EC, UNC, posterior thalamic radiation [PTR]) in mild cognitive impairment (MCI), and whether baseline cognitive status modulates this effect.

Methods

Diffusion-tensor imaging from the Alzheimer's Disease Neuroimaging Initiative was analyzed in 46 MCI participants receiving donepezil or rivastigmine and 62 untreated MCI controls, each scanned serially over two years. Fractional anisotropy (FA) and mean diffusivity (MD) indexed tract integrity. Linear mixed-effects models tested time × medication × baseline cognition (ADAS-Cog13) interactions, adjusting for age, sex, APOE ε4, and white-matter hyperintensity burden.

Results

Across groups, CGC showed progressive degeneration (FA↓, MD↑; p < 0.001). Significant three-way interactions emerged for MD in bilateral CGC, FA in right EC, and MD in left PTR (all p < 0.01). ChEI users with milder baseline impairment (lower ADAS-Cog13) exhibited attenuated FA loss and MD increase, indicating slower microstructural decline; those with greater initial impairment derived minimal benefit. No medication effect was detected in UNC.

Conclusions

ChEIs confer tract-specific, stage-dependent protection of cholinergic white matter, particularly in early MCI. The findings underscore the value of initiating ChEI therapy before substantial cognitive deterioration and highlight the need for stage-tailored interventions aimed at preserving white-matter integrity in prodromal AD.

Keywords

alzheimer's disease cholinergic tracts cholinesterase inhibitors diffusion tensor imaging mild cognitive impairment neurodegeneration white matter integrity

Introduction

There is great evidence in the literature of Alzheimer's disease (AD) indicating the critical role of the cholinergic system in cognitive functions including memory and attention. Nucleus basalis of Meynert (NBM) as a part of the basal forebrain responsible for projecting cholinergic tracts to cortex is among the first structures that deteriorate in the course of AD continuum.^1-4 Recent evidence supports that volumetric changes in NBM occur sooner than hippocampus in AD continuum.^5,6 In the case of cholinergic tracts such as the cingulum bundle (CGC), entorhinal cortex (EC), and uncinate fasciculus (UNC), microstructural biomarkers such as mean diffusivity (MD) and fractional anisotropy (FA) could detect signs of neurodegeneration even sooner than volumetric changes in the forebrain nuclei. For example, MD in the EC outperformed NBM and hippocampus volume for distinguishing normal patients versus patients with subjective cognitive decline (SCD) with area under the curve (AUC) of 73%. While the AUC values for EC and CGC increases over the course of disease, its superior predictive power drops below volumetric biomarkers AUC as the disease progresses to mild cognitive impairment (MCI) and AD showing that microstructural changes in cholinergic tracts can be first markers of cognitive changes in the brain.⁵

In addition to amyloid hypothesis of AD, a newly proposed phospho-tau (p-tau) cascade proposes that p-tau accumulation can break down microtubules in cholinergic axons projecting from NBM and basal forebrain to the cortex, and as a result, basal forebrain loses its cholinergic phenotype and acetylcholine (ACh) supply to the cortex diminishes severely causing cognitive dysfunction. As these cortical areas secret neural growth factor (NGF) to those cholinergic synapses only when activated, a drop in NGF level would occur. NGF is critical for stimulating ACh release from the basal forebrain and sustains its viability. This malicious loop would eventually cause severe atrophy and cognitive dysfunction seen in AD.^7,8

In regard to ACh deficit in AD, previous studies have shown cholinesterase inhibitor (ChEI) drugs such as rivastigmine and donepezil, can restore cholinergic phenotype and amplify ACh-dependent release of NGF which itself stimulates more ACh and slows neurodegeneration process.⁹ Donepezil, rivastigmine, and galantamine are ChEIs that enhance synaptic ACh but differ in their specificity and secondary mechanisms. Donepezil acts as a highly selective, reversible inhibitor of acetylcholinesterase (AChE) within the central nervous system, minimally affecting butyrylcholinesterase (BuChE). It crosses the blood–brain barrier effectively and sustains elevated cortical ACh levels, improving cholinergic neurotransmission at muscarinic and nicotinic receptors.¹⁰ Beyond inhibiting cholinesterase, donepezil also allosterically modulates nicotinic receptors, a feature thought to contribute to neuroprotective effects.¹¹ Rivastigmine, a carbamate inhibitor, targets both AChE and BuChE, maintaining ACh levels even in late-stage AD through a pseudo-irreversible binding mechanism. However, its dual inhibition may increase the incidence of cholinergic side effects. Galantamine, a plant alkaloid, reversibly inhibits AChE while also acting allosterically on nicotinic receptors, enhancing receptor function and supporting cognitive circuits linked to attention and memory.¹²

In addition to that, here is a body of evidence proposing the effect of AchEI on brain structures. For example, double-blinded randomized control trials have shown that a dose of 10 mg/day of donepezil can decrease annual rate of atrophy of hippocampus, basal forebrain (specially in NBM and medial septum / diagonal band areas) and regional cortical thickness in anterior cingulate, orbitofrontal, right inferior frontal and right insula in suspected prodromal AD cases after 1 year follow-up.^13-15 In addition to gray matter, cross-sectional studies have shown the effect of ChEIs on hippocampal-inferior temporal gyrus and thalamo-cortical white matter connectivity.¹⁶ However, no study has investigated the effect of ChEIs on microstructural changes of the cholinergic tracts longitudinally. Besides that, whether it is possible to restore the cholinergic phenotype once lost during advanced AD continuum stages is a question.

In this study, I aimed to compare integrity of four cholinergic tracts—CGC, EC, UNC and posterior thalamic radiation (PTR)—in two groups of MCI patients (medication group and non-medication group) over the course of 2-year follow-up and investigate whether the stage of cognitive impairment can determine beneficial effect of ChEIs, i.e., which patients benefit from ChEIs more.

Methods

Study design

I conducted a longitudinal study on ADNI subjects with previously analyzed diffusion tensor imaging (DTI) data whose status on medication usage was specified. For being included in the medication group, subjects had to have their first DTI assessment after the initiation of drug and last imaging before ending drug consumption or they may have continued the drug. 280 subjects had the previously analyzed DTI data, from whom 104 subjects met the inclusion criteria for the medication group, and 166 subjects were not on the medication during study time. Numbers of subjects based on their diagnosis were 46 MCI, 43 dementia and 15 control subjects in the medication group and 62 MCI, 2 dementia and 102 control subjects for the non-medication group. As there was a significant diagnosis imbalance between medication and non-medication groups with only 2 dementia subjects in non-medication and 15 control subjects in the medication group, I decided to narrow-down the sample size with only adding MCI subjects for further analysis. For the chronological aspect of follow-up, we included screening, month 3, month 6, month 12 and month 24 as the number of subjects for other visit points were too small to be included in the analysis (Figure 1).

Figure 1.

Longitudinal cohort group distribution.

Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The original goal of ADNI was to test whether serial magnetic resonance imaging (MRI), positron emission tomography, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. The current goals include validating biomarkers for clinical trials, improving the generalizability of ADNI data by increasing diversity in the participant cohort, and to provide data concerning the diagnosis and progression of AD to the scientific community. For up-to-date information, see adni.loni.usc.edu.

Variables and measurements

MRI acquisition and analysis

Diffusion MRI data were acquired and processed following standard protocols to ensure high-quality DTI analysis. Raw diffusion-weighted images were corrected for head motion and eddy current distortions using FSL's eddy tool. Non-brain tissue was removed using the Brain Extraction Tool (BET) and ROBEX, followed by intensity inhomogeneity correction. Structural T1-weighted images were registered to a standardized template using FSL flirt, and diffusion data were aligned to correct for susceptibility-induced distortions. Diffusion tensors were estimated using FSL's dtifit, from which FA and MD maps were derived. A standardized white matter atlas was used to extract regional summary measures for FA and MD. Additionally, tract-based spatial statistics was employed to assess white matter integrity across subjects. Further methodological details can be found in the original documentation.^17,18

Cholinergic tracts

Based on previous literature on brain cholinergic system, these tracts were chosen for further analysis, including PTR, CGC, EC, and U for both right and left sides.⁵ FA and MD measures were used as the final outcome for each of the tracts.

CGC, EC, UNC, and PTR are key cholinergic projection pathways from the basal forebrain and have been implicated in AD pathology. Early microstructural alterations in these tracts, such as increased MD, have been observed in individuals with SCD and MCI, suggesting they are affected early in the disease continuum.⁵ Specifically, the CGC supports memory and attention, the EC mediates neocortical cholinergic innervation, the UNC connects medial temporal regions to the frontal cortex facilitating memory and emotional regulation, and the PTR links lateral cholinergic fibers to parietal and occipital cortices, impacting visuospatial and attentional functions. Degeneration of these tracts is believed to reflect cholinergic denervation contributing to cognitive deficits characteristic of AD.^6,19

Other variables

I used the 13-item version of the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog13) as a cognitive measure for subjects in the study. Age, gender and interval between initiation of medication and first MRI scan were included as covariates to the analysis.

In addition, previous studies have shown how the amount of small vessel disease in the brain can influence the integrity of white matter tracts,^6,20 therefore, I included white matter hyperintensity (WMH) as a measure of small vessel disease status. WMHs in T2/FLAIR images are strongly correlated with WMHs in T1 images. Segmentation and volumetric measurements of WMHs were performed using Freesurfer protocols on T2/FLAIR images; details about the methodology of the work were published elsewhere.²¹ APOE genotyping was conducted and having either ε4/ε4 or ε3/ε4 genotype was counted as having the ε4 allele in the analysis.²²

Statistical analysis

For numerical variables, mean and standard deviation were used and for categorical variables frequency was used. For comparative analysis, T-test (for normal distribution) or Wilcoxon rank sum test (for skewed distribution) and chi-square were used for numerical and categorical variables, respectively. Data was normalized using standard normalization and outliers were removed. In order to compare DTI measure longitudinal changes over time, a set of mixed-linear effects models were implemented, for each right and left tract, FA and MD measures were used as outcome variables of the model, therefore, there were 4 types of models for each tract, with MD and FA values on both right and left side which resulted in total number of 16 models. To prevent error, time points were included as numerical measures in the models as the interval between the time points were not the same. Interaction of time, group and ADAS-Cog13 were included in the models and age and gender and APOE genotype (number of ε4 allele) were included as other fixed variables to account for their confounding effects. For random effect, we used a random intercept, random slope combination.

DTI measure ∼ Time * Group * ADAS-Cog13 + Age + Gender + Genotype + (1 + Time | Subject)

Linear-mixed effect model was used for the analysis because 1) It can handle missing values and imbalances in data, 2) It models between and within subject variability by adding random subject effects (slope and intercept), and 3) It is flexible for modeling trajectory of time-varying covariates and their interactions with time.²³

As shown in Figure 1, not all subjects have data measures for all the visits; however, as the missingness was not systematic, the linear-mixed effect models can handle that by excluding that data point and fitting the model anyway. All the analyses were performed in Python 3.12.4 and R 4.4.2 With packages including Pandas,²⁴ StatsModels,²⁵ lme4,²⁶ggplot2²⁷ and emmeans.²⁸

Results

Descriptive statistics

In this study, I aimed to find whether taking ChEIs changed the trend of changes in four major cholinergic tracts in the brain and whether patients’ cognitive status intervenes this interaction or not.

Demographic characteristics of two groups were not comparable with mean age in the medication group 74.6 ± 7.3 and in the non-medication group 73.1 ± 6.8 (p = 0.83). Females comprised 30% and 37% of patients in medication and non-medication groups respectively (p = 0.58). From all patients in the medication group, 80% were on donepezil, 11% on rivastigmine and 9% on galantamine during the study course. The medication group had worse cognitive status with an ADAS-Cog13 mean of 23.6 ± 10.9 and 11 ± 6.55 in the non-medication group (p = 0.00). The distributions of WMH values in both groups were skewed to the right side and median and interquartile range are 7.07, 10.27 for medication and 5.29, 9.11 for non-medication group (p = 0.34). Median interval between initiation of medication and acquisition of the first MRI scan was 1 year with an interquartile range of 7 months. In the medication group, 64% had APOE ε4 allele and in the non-medication group 43% had the ε4 in their genotype (p = 0.02).

General findings

In the course of time, the only tracts with significant changes were left and right CGC, with MD with increasing trend and FA with decreasing trend. MD for right CGC (β = −0.05, p = 0.03), and for left CGC (β = −0.07, p = 0.00). FA for right CGC (β = −0.08, p = 0.01) and for left CGC (β = −0.06, p = 0.02) (Table 1).

Table 1.

Mixed-linear effect models results.

Tract	Predictor	Estimate	Std Error	p	Adjusted p
MD_CGC R	(Intercept)	−0.25	0.17	0.14	-
	Time	0.05	0.02	0.03*	-
	Group Non-medication	0.5	0.16	0.003**	-
	ADAS13	−0.21	0.11	0.061	-
	Age	0.34	0.07	0.00**	-
	Gender Male	−0.05	0.13	0.721	-
	Genotype	−0.13	0.14	0.349	-
	Time:Group Non-medication	−0.04	0.03	0.164	-
	Time:ADAS13	0.08	0.03	0.009**	-
	Group Non-medication:ADAS13	0.24	0.14	0.09	-
	Time:Group Non-medication:ADAS13	−0.11	0.04	0.003**	0.016*
MD_CGC_L	(Intercept)	−0.49	0.18	0.007**	-
	Time	0.08	0.03	0.008**	-
	Group Non-medication	0.45	0.18	0.011	-
	ADAS13	−0.28	0.13	0.035*	-
	Age	0.37	0.07	0.00**	-
	Gender Male	0.24	0.14	0.092	-
	Genotype	−0.05	0.15	0.716	-
	Time:Group Non-medication	−0.05	0.03	0.114	-
	Time:ADAS13	0.12	0.03	0.001**	-
	Group Non-medication:ADAS13	0.51	0.16	0.002**	-
	Time:Group Non-medication:ADAS13	−0.17	0.04	0.00**	0.00**
FA_CGC_R	(Intercept)	0.15	0.23	0.53	-
	Time	−0.07	0.03	0.026*	-
	Group Non-medication	0.1	0.23	0.668	-
	ADAS13	0.36	0.15	0.02*	-
	Age	−0.13	0.09	0.156	-
	Gender Male	0.21	0.18	0.251	-
	Genotype	0.14	0.19	0.458	-
	Time:Group Non-medication	−0.02	0.04	0.642	-
	Time:ADAS13	−0.08	0.04	0.051	-
	Group Non-medication:ADAS13	−0.57	0.19	0.004**	-
	Time:Group Non-medication:ADAS13	0.14	0.05	0.003**	0.016*
FA_CGC_L	(Intercept)	0.31	0.23	0.181	-
	Time	−0.08	0.03	0.016*	-
	Group Non-medication	−0.03	0.22	0.885	-
	ADAS13	0.19	0.16	0.223	-
	Age	−0.12	0.09	0.174	-
	Gender Male	0.06	0.18	0.764	-
	Genotype	−0.02	0.19	0.922	-
	Time:Group Non-medication	0.02	0.04	0.614	-
	Time:ADAS13	−0.06	0.04	0.174	-
	Group Non-medication:ADAS13	−0.43	0.2	0.029*	-
	Time:Group Non-medication:ADAS13	0.12	0.05	0.023*	0.072
FA_EC_R	(Intercept)	−0.39	0.25	0.122	-
	Time	−0.05	0.04	0.184	-
	Group Non-medication	0.08	0.24	0.754	-
	ADAS13	0.12	0.18	0.512	-
	Age	−0.03	0.09	0.724	-
	Gender Male	0.26	0.19	0.181	-
	Genotype	0.35	0.2	0.081	-
	Time:Group Non-medication	0.02	0.05	0.733	-
	Time:ADAS13	−0.06	0.05	0.169	-
	Group Non-medication:ADAS13	−0.25	0.22	0.266	-
	Time:Group Non-medication:ADAS13	0.13	0.06	0.027*	0.072
MD_PTR_L	(Intercept)	−0.27	0.2	0.168	-
	Time	0.0	0.02	0.858	-
	Group Non-medication	0.31	0.18	0.086	-
	ADAS13	0.18	0.1	0.074	-
	Age	0.44	0.08	0.00**	-
	Gender Male	0.17	0.17	0.311	-
	Genotype	−0.05	0.17	0.774	-
	Time:Group Non-medication	0.01	0.03	0.724	-
	Time:ADAS13	−0.07	0.03	0.008**	-
	Group Non-medication:ADAS13	−0.22	0.12	0.078**	-
	Time:Group Non-medication:ADAS13	0.09	0.03	0.004**	0.016*

Detailed result from Mixed-linear effect models conducted for each of the metrics. Semicolon between variables in the predictor column shows interaction between the variables. Full table including models for other metrices are provided in Supplemental Table 1. ADAS13: 13-item version of the Alzheimer's Disease Assessment Scale-Cognitive Subscale; *p < 0.05, **p < 0.01.

In the baseline, some of the measures showed significant different levels between two groups including right CGC MD (β = 0.52, p = 0.00), left CGC MD (β = 0.46, p = 0.00), left EC MD (β = 0.43, p = 0.03), right UNC FA (β = −0.51, p = 0.04), right UNC MD (β = 0.42, p = 0.03) and left UNC MD (β = 0.63, p = 0.00). With the medication group having lower levels in all the measures except for the right UNC FA with higher baseline values (Supplemental Figures).

In linear-mixed effect models, age was a significant confounder in almost all the models, i.e., as subjects grew older in both groups, all MD measures increased and FA measures decreased. However, gender appeared marginally significant only in the left UNC FA with male having higher levels than females (β = 0.32, p = 0.046).

ADAS-Cog13 as the only measure of cognitive status in the study, showed significant effect on changing trend for MD in CGC in both right and left sides, as the ADAS-Cog13 score increased, MD levels tend to have increase more steeply over time with (β = 0.07, p = 0.00 and β = 0.11, p = 0.00) for right and left sides, respectively.

Effect of medication on microstructural measures of the tracts

The main question of the study was how taking medication affects cholinergic tracts microstructure over time, the results show that the answer is complicated, when accounting only for the effect of medication status on tracts measures, only FA of right UNC appears significant with marginal p-value (β = 0.12, p = 0.048). However, when adding ADAS-Cog13 to the interaction, results would change for MD of right and left CGC tracts (β = −0.10, p = 0.00 and β = −0.16, p = 0.00), respectively. In concordance with MD, FA showed reciprocal results as FA for CGC right and left were 0.13 with p-value of 0.00 and 0.11 with p-value of 0.02, respectively. In addition, FA for right EC seemed significant (β = 0.12, p = 0.03). In contrast to previous tract's MD estimates, MD for left PTR showed significant positive estimate of 0.09 with p-value of 0.00.

The linear mixed-effects model revealed a significant three-way interaction between time, cognitive impairment (ADAS-Cog13 score), and medication status on MD in right CGC (β = −0.10, p = 0.004) and left CGC (β = −0.16, p = 0.004), FA in right CGC (β = 0.13, p = 0.00) and left CGC (β = 0.11, p = 0.02), FA in right EC (β = 0.12, p = 0.03) and MD in left PTR (β = 0.09, p = 0.00).This interaction indicates that the effect of time on these measures depends on both the level of cognitive impairment and whether participants were on medication. Specifically, the relationship between time and DTI measures varied across different levels of cognitive impairment, and this variation was further moderated by medication status. The interaction is visualized in Figure 2, which shows the predicted values of measures over time for participants with low (13.09), medium (19.36), and high (25.62) ADAS-Cog13 scores, stratified by medication status. The plot suggests that the trajectory of indices over time differs between those on and off medication, particularly at higher levels of cognitive impairment (Figure 2).

Figure 2.

Three-way interaction between the 13-item version of the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS13), DTI measure and time, R and L in DTI measure names indicated right and left side, y axis indicates normalized values for DTI measures, numbers in Time axis show the visit number; 1: screening, 2: month 3, 3: month 6, 4: month 12 and 5: month 24, predicted values for measure were plotted for three levels of cognitive impairment shown by three mean ADAS13 values.

Discussion

The present study aimed to investigate the impact of ChEIs on the microstructural integrity of four major cholinergic tracts in the brain and assess whether cognitive impairment modulates these effects. Our findings provide novel insights into how medication status and cognitive decline influence the trajectory of white matter microstructure over time.

Effect of ChEIs on cholinergic tract microstructure

My results suggest a complex relationship between medication use and the microstructural integrity of cholinergic tracts. At baseline, significant differences in microstructural measures were observed between the medication and non-medication groups. Specifically, MD values in right and left CGC, left EC, and left and right UNC were lower in the medication group, except for right UNC FA, which was higher. These baseline differences indicate that individuals on medication had distinct microstructural characteristics prior to the study, potentially reflecting differences in disease severity.

Over time, the most significant changes were observed in the left and right CGC, with MD showing an increasing trend and FA showing a decreasing trend. These changes are consistent with progressive white matter degeneration, which is expected in neurodegenerative conditions such as AD. However, when accounting for medication effects, only right UNC FA showed a marginally significant association, suggesting that the direct impact of ChEIs on white matter integrity is not straightforward.

The role of cognitive impairment in medication effects

An important finding of this study is the interaction between cognitive impairment (as measured by ADAS-Cog13), medication status, and time on cholinergic tract microstructure. When cognitive status was included in the model, significant effects emerged for MD and FA in CGC bilaterally, FA in right EC, and MD in left PTR. These results suggest that the influence of medication on white matter microstructure is not uniform but is moderated by the level of cognitive impairment. As it is clear in Figure 2(a) and (b) for MD in right and left CGC, patients in the medication group benefit from the drug, especially for those with lower levels of cognitive impairment in whom we see improvement in tract microstructure over time. Meanwhile, in the non-medication group, those with better cognition show steeper increases in MD, indicating how initiation of treatment for this group can reverse the process of CGC tract degeneration. Reciprocal changes can be seen in FA measure (Figure 2(c) and (d)), as both groups, all patients experience a decrease in FA, except for those patients in the medication group with better cognitive status who seem to stay solid over time. For FA in the right EC (Figure 2(e)), these patients even show an increase in FA overtime. For MD in the left PTR (Figure 2(f)), a different pattern is observed. Those with worse cognitive status in the medication group experienced improvement in tract microstructure while those with better cognition showed increase in their MD. In the non-medication group however, as the cognition worsens, the tract deteriorates in terms of MD.

Implications and mechanistic interpretations

The observed patterns suggest that ChEIs may exert a neuroprotective effect on cholinergic tracts, but this effect is conditional on disease severity. One possible explanation is that in early stages of cognitive decline, ChEIs may help preserve white matter integrity by enhancing cholinergic neurotransmission, reducing neuroinflammation, and preventing axonal degeneration. However, as cognitive impairment progresses, the structural benefits of medication may be diminished due to the overarching neurodegenerative process. The increase in MD and decrease in FA in CGC over time align with previous studies indicating that white matter damage is a hallmark of AD progression.²⁹ Kim et al. showed increased structural connectivity between hippocampus and inferior temporal gyrus following 6-month donepezil treatment in a small group of early diagnosed AD patients.¹⁶ However, he failed to show any significant changes in a thalamocortical WM connectivity following in another analysis.³⁰

Additionally, the finding that left PTR MD exhibited a significant positive estimate suggests a potential region-specific vulnerability or compensatory mechanism. Unlike CGC, which is directly involved in cholinergic signaling, PTR may undergo distinct neurodegenerative changes influenced by broader disease mechanisms beyond cholinergic dysfunction.

Emerging evidence highlights that central white-matter cells, including oligodendrocytes and their precursors (OPCs), express muscarinic and nicotinic acetylcholine receptors (AChRs), establishing a link between cholinergic signaling and myelination.³¹ Functional M3-type muscarinic receptors on OPCs promote proliferation via MAPK (mitogen-activated protein kinase) pathways, while nicotinic α4β2 and α7 receptors influence the maturation of oligodendrocyte lineage cells.³² In vitro studies have demonstrated that donepezil, significantly enhances OPC differentiation and upregulates key myelin-related genes. Notably, the promyelinating effect of donepezil is blocked by nicotinic but not muscarinic antagonists, suggesting mediation via AChRs. Additional research using demyelination models supports the idea that donepezil fosters remyelination independently of its AChE inhibition, further implicating nicotinic pathways. Conversely, disruptions in ACh levels can hinder OPC maturation, emphasizing the necessity of adequate cholinergic tone for effective myelin repair. These findings have significant implications for AD.³³ Cholinergic deficits in AD, driven by basal forebrain degeneration, may deprive white matter of essential trophic support, exacerbating oligodendrocyte dysfunction and myelin loss. Importantly, ChEIs, such as donepezil, have demonstrated the ability to reduce neuroinflammation and demyelination in multiple sclerosis models, suggesting potential therapeutic effects on white matter integrity in AD.³⁴ Overall, the presence of functional nicotinic and muscarinic receptors on OLs and OPCs underscores the critical role of cholinergic signaling in promoting myelin formation and maintenance, with impaired transmission likely contributing to white matter pathology in AD.

Previous studies have shown that the effect of APOE genotyping on ChEI response is negligible³⁵; however, an experimental study by Bott et al. shows that APOE ε4 allele is associated with impaired remodeling and sprouting of cholinergic tracts after EC lesions.³⁶ In our study, the medication group had more frequency of APOE ε4 alleles than the non-medication group; however, adding APOE status in the analysis did not change any of the results and APOE status never emerged significant, showing no moderating effect on cholinergic structural integrity.

There was no significant difference between medication and non-medication groups in case of WMH in this study. A recent study by Lee et al. discovered that early MCI patients with higher levels of cholinergic tracts WMH have less response to ChEI over a course of a year.³⁷ The definition of response in this study was not to have deterioration in Mini-Mental State Examination and Clinical Dementia Rating scores while other studies showed general WMH cannot be a predictor of ChEI response.³⁸ The results from this study show those MCI subjects with lower cognitive disability benefit the most from ChEI in case cholinergic tracts integrity. In general, underlining the importance of starting ChEI in early stages and before degeneration becomes irreversible.

Clinical implications

DTI findings suggest that cholinergic white-matter tracts are disrupted early in the course of AD. Studies in individuals with MCI have shown reduced FA and increased diffusivity within the basal forebrain cholinergic pathways and associated limbic circuits.³⁹ This early “damage in the cholinergic pathway in MCI” has been cited as a rationale for initiating ChEI treatment at the MCI stage, potentially helping to preserve white-matter integrity before significant degeneration occurs. In support of this, longitudinal studies have reported that patients with AD treated with galantamine exhibited increased FA over six months, while those receiving placebo showed FA declines, suggesting DTI measures may serve as biomarkers for ChEI response.⁴⁰ However, extensive white matter lesions, particularly within cholinergic tracts, have been associated with poorer outcomes following ChEI therapy.³⁷ Taken together, these findings argue for early ChEI therapy in prodromal AD/MCI, ideally before severe white matter damage accumulates. Treatment timing is critical: intervening when white matter is still relatively intact could maximize benefit. Furthermore, diffusion-MRI may serve as a useful biomarker to stratify and monitor patients. For example, DTI or advanced tractography could identify MCI patients with intact cholinergic tracts who are most likely to benefit, or track changes in FA/MD over time as a pharmacodynamic readout.

Confounding factors and limitations

Age was a significant confounder in almost all models, with older individuals showing expected increases in MD and decreases in FA, reflecting age-related white matter degeneration. Gender appeared to have a marginal effect, with males showing higher FA in left UNC. While these demographic variables were accounted for, other potential confounders such as vascular risk factors and medication adherence were not explicitly controlled, which could influence results.

Another limitation is the observational nature of the study, which precludes causal inferences. While our models adjust for confounders, unmeasured variables may still contribute to the observed effects. Additionally, the median interval between medication initiation and MRI acquisition was one year. Previous studies indicated that the best cognitive response would occur in about 6 month from the initiation of drug⁴¹; however, they focused on symptomatic aspects of administration, in case of structural changes, either 6-month or one-year interval may not capture long-term neuroprotective effects of ChEIs. Most of the subjects in this study were using donepezil while the effect of other ChEIs such as rivastigmine and galantamine may be different as for example rivastigmine's effect on receptors is irreversible. Future studies should consider these factors. Future longitudinal studies with larger sample sizes and multimodal imaging approaches are needed to further elucidate the mechanistic underpinnings of these findings and optimize therapeutic strategies for AD. In addition, clinical trials should be conducted to confirm the best window interval for initiation of ChEI in MCI patients.

Conclusion

This study highlights the complex interplay between ChEIs, cognitive decline, and white matter microstructure in cholinergic pathways. While direct medication effects on tract integrity were limited, the interaction with cognitive impairment underscores the potential role of ChEIs in modulating disease progression in a way that early initiation of ChEI in patients can reverse the degenerative process happening in cholinergic tracts. As later in the disease continuum, the effect of ChEI would become only symptomatic and barely change the degeneration trajectory.

Supplemental Material

sj-docx-1-alr-10.1177_25424823251374681 - Supplemental material for Longitudinal impact of cholinesterase inhibitors on cholinergic white matter integrity in mild cognitive impairment: A diffusion MRI study

Supplemental material, sj-docx-1-alr-10.1177_25424823251374681 for Longitudinal impact of cholinesterase inhibitors on cholinergic white matter integrity in mild cognitive impairment: A diffusion MRI study by Elham Ramezannezhad and in Journal of Alzheimer's Disease Reports

Supplemental Material

sj-pptx-2-alr-10.1177_25424823251374681 - Supplemental material for Longitudinal impact of cholinesterase inhibitors on cholinergic white matter integrity in mild cognitive impairment: A diffusion MRI study

Supplemental material, sj-pptx-2-alr-10.1177_25424823251374681 for Longitudinal impact of cholinesterase inhibitors on cholinergic white matter integrity in mild cognitive impairment: A diffusion MRI study by Elham Ramezannezhad and in Journal of Alzheimer's Disease Reports

Footnotes

Acknowledgements

Data collection and sharing for the Alzheimer's Disease Neuroimaging Initiative (ADNI) is funded by the National Institute on Aging (National Institutes of Health Grant U19AG024904). The grantee organization is the Northern California Institute for Research and Education. In the past, ADNI has also received funding from the National Institute of Biomedical Imaging and Bioengineering, the Canadian Institutes of Health Research, and private sector contributions through the Foundation for the National Institutes of Health (FNIH) including generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The author acknowledges the use of ChatGPT (OpenAI) for assistance in drafting and editing sections of this manuscript (about 20%). All AI-generated content was subsequently reviewed, edited, and refined by the authors, who take full responsibility for the final content of the manuscript.

ORCID iD

Elham Ramezannezhad

Author contribution(s)

Elham Ramezannezhad: Conceptualization; Formal analysis; Methodology; Project administration; Software; Validation; Visualization; Writing – original draft; Writing – review & editing.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI). The funding source had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.

Declaration of conflicting interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The data supporting the findings of this study are available upon request from the ADNI database ().

Supplemental material

Supplemental material for this article is available online.

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Supplementary Material

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