Optimized Electrical Field Strength of tDCS and White Matter Microstructural Integrity in MCI: Role of Individual Factors Associated with AD

Optimized Electrical Field Strength of tDCS and White Matter Microstructural Integrity in MCI: Role of Individual Factors Associated with AD | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Optimized Electrical Field Strength of tDCS and White Matter Microstructural Integrity in MCI: Role of Individual Factors Associated with AD Jung-Won Lee, Sunghwan Kim, Suhyung Kim, Yoo Hyun Um, Sheng-Min Wang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6541880/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Transcranial direct current stimulation (tDCS) is a promising non-invasive intervention for mild cognitive impairment (MCI). This prospective study investigated the relationship between optimized electrical field (EF) strength of tDCS and white matter (WM) microstructural changes in 63 individuals with MCI following personalized tDCS. Magnetic resonance imaging (MRI)-based computational modeling was used to optimize EF strength targeting the left dorsolateral prefrontal cortex (DLPFC). Diffusion tensor imaging (DTI) assessed WM integrity through fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD). Higher EF strength was significantly associated with increased FA and reduced MD and RD in specific left-lateralized tracts, including the anterior thalamic radiation, corticospinal tract, inferior fronto-occipital fasciculus, and inferior longitudinal fasciculus. These EF-dependent WM changes were moderated by Alzheimer’s disease (AD)-related factors. Greater WM plasticity was observed in Aβ-positive individuals, APOE ε4 non-carriers, and BDNF Met non-carriers. Moreover, APOE ε4 status significantly moderated the relationship between EF strength and executive function; in non-carriers, stronger EF strength was associated with improved Stroop performance, potentially reflecting enhanced WM integrity in the right superior longitudinal fasciculus. However, no significant associations were observed between EF-sensitive tracts and short-term cognitive changes in the full sample, suggesting that structural modifications may precede functional improvements or require longer follow-up. These findings emphasize the importance of individual AD-related factors in shaping neuromodulatory responses and support further longitudinal, sham-controlled studies to clarify the clinical implications of EF strength in personalized tDCS for MCI. Health sciences/Medical research/Biomarkers/Prognostic markers Biological sciences/Neuroscience/Diseases of the nervous system/Dementia Health sciences/Diseases/Neurological disorders/Dementia Transcranial Direct Current Stimulation Mild Cognitive Impairment White Matter Diffusion Tensor Imaging Alzheimer Disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Alzheimer’s disease (AD) is the most prevalent cause of dementia, characterized by amyloid-beta (Aβ) and tau protein accumulation, leading to progressive cognitive decline and functional impairment 1 . Mild cognitive impairment (MCI) is considered a prodromal stage of AD, distinguished by preserved independence despite measurable cognitive deficits. Approximately 10–15% of individuals with MCI progress to AD annually 2 , with a preclinical phase lasting around a decade, followed by an MCI phase of approximately four years before conversion to AD 3 . Despite extensive efforts to identify interventions that slow or prevent AD progression, treatment options remain limited. Pharmacological approaches, including cholinesterase inhibitors, memantine, and vitamin E, have not demonstrated definitive efficacy in preventing cognitive decline 2 . Non-pharmacological strategies, such as cognitive training 4 , physical exercise 5 , and dietary modifications 6 , have shown some promise but face challenges related to adherence and scalability. These limitations underscore the need for alternative interventions that are both effective and sustainable 7 . Transcranial direct current stimulation (tDCS) has emerged as a potential non-invasive therapy for MCI due to its accessibility, cost-effectiveness, and favorable safety profile 8 . By delivering low-intensity electrical currents through scalp electrodes, tDCS modulates cortical excitability 9 and enhances synaptic plasticity 10 , mechanisms that are critical for cognitive function. Evidence suggests that repeated tDCS sessions may improve cognition in AD by promoting Aβ clearance, modulating blood-brain barrier integrity 11 , and increasing brain-derived neurotrophic factor (BDNF) levels 12 . Preliminary studies indicate that tDCS may enhance episodic memory in MCI 13 . Importantly, the effectiveness of tDCS is not solely dependent on the stimulation site or duration, but also on the electrical field (EF) strength generated within the brain 14 . Higher EF intensity has been linked to greater cognitive improvements in cognitively impaired individuals 15 , 16 . However, variability in outcomes under identical stimulation protocols suggests that individual anatomical differences influence treatment effects. Recent advances have enabled optimized, personalized tDCS, designed to enhance EF strength by incorporating individual brain structural variability 17 . These developments highlight EF strength as a key mediator of the clinical efficacy of tDCS, making it a critical variable for investigation in neurodegenerative conditions such as MCI. A key biomarker for evaluating treatment efficacy in MCI and AD is white matter (WM) microstructural integrity. Diffusion tensor imaging (DTI) allows for quantitative assessment of WM integrity through metrics such as fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD), which serve as early diagnostic markers and indicators of therapeutic response 18 , 19 . Prior studies have shown that AD patients exhibit lower FA and higher MD in key WM regions, including the splenium, fornix, and parahippocampal cingulum 20 . Aβ burden has also been linked to WM microstructural deterioration 21 , which in turn predicts the speed of conversion from normal aging to MCI 22 . Individual factors related to AD, such as Aβ deposition, APOE ε4 allele status, and the BDNF Val66Met polymorphism, may influence the effects of tDCS on WM integrity. Aβ accumulation disrupts neuronal connectivity and synaptic function, potentially modulating neuroplastic responses to tDCS 23 , 24 . The APOE ε4 allele exacerbates Aβ-related damage, impairs synaptic plasticity, and compromises blood-brain barrier function, which may alter the neurophysiological effects of tDCS 25 . Additionally, the BDNF Val66Met polymorphism affects neuroplasticity, with the Met allele associated with reduced BDNF secretion, potentially diminishing tDCS-induced synaptic modulation 12 , 26 . Despite their significance, these individual factors remain underexplored in the context of tDCS treatment for MCI. Recent findings suggest that tDCS may influence WM integrity. Specifically, our prior research demonstrated that APOE ε4 carriers exhibited greater FA increases in the right uncinate fasciculus after tDCS. Additionally, Val66 homozygotes showed increased MD in the right uncinate fasciculus and decreased MD and RD in the left cingulum 27 . These results highlight the necessity of considering individual factors related to AD when evaluating tDCS efficacy. Building upon this foundation, the present study focuses on the role of EF strength as the main predictor of tDCS-induced changes in WM microstructure. The primary aim of this study is to investigate the effects of optimized EF strength on WM microstructural integrity in MCI patients and to examine how these effects vary according to individual factors related to AD. To achieve this, we applied a two-week protocol consisting of ten consecutive tDCS sessions and assessed WM integrity changes using DTI metrics. We hypothesize that stronger EF strength will be associated with enhanced WM microstructural integrity and that this association will be modulated by AD-related individual characteristics. Materials and Methods Participants Participants were recruited from the Brain Health Center at Yeoui-do St. Mary’s Hospital, affiliated with the College of Medicine, the Catholic University of Korea. The inclusion criteria were as follows: (1) Diagnosis in accordance with Petersen’s criteria for mild cognitive impairment (MCI) 28 , with scores below 8 on the Seoul-Instrumental Activities of Daily Living (S-IADL) scale to confirm independent functioning in daily life 29 , and (2) a Clinical Dementia Rating (CDR) score of 0.5. The exclusion criteria were: (1) A history of alcohol or drug abuse, head trauma, or psychiatric disorders; (2) use of psychotropic medications, including cholinesterase inhibitors, N-Methyl-D-Aspartate receptor antagonists, antidepressants, benzodiazepines, or antipsychotics; (3) contraindications for tDCS or MRI, such as the presence of ferromagnetic or coiled metal implants; and (4) any dermatological condition affecting scalp integrity. Additionally, only individuals with a Hamilton Depression Rating Scale (HAMD) score of 7 or lower were included to ensure depressive symptoms remained within the normal range 30 . All participants were tDCS-naive, meaning they had never previously received tDCS treatment. The selection process was supervised by two geriatric psychiatry specialists. Participants consented to a medical record review, and all assessments were conducted at the Brain Health Center, Yeoui-do St. Mary’s Hospital, the Catholic University of Korea. The study was conducted in compliance with the Declaration of Helsinki and received approval from the Institutional Review Board (IRB) of the Catholic University of Korea (SC19DEST0012). Written informed consent was obtained from all participants before their enrollment. Study protocol This single-arm, prospective study was conducted without a sham control condition. Participants received ten sessions of tDCS, administered in their homes at a frequency of five sessions per week over two weeks. The ten-session protocol was selected based on previous clinical research, which demonstrated its effectiveness in treating AD and MCI 13 , 31 – 33 , while also considering adherence feasibility in older adults. Neuropsychological assessments and MRI scans were performed at the Brain Health Center of Yeoui-do St. Mary’s Hospital, both within two weeks prior to the first tDCS session and after the completion of the tenth session. Additionally, participants underwent [ 18 F] flutemetamol (FMM) positron emission tomography-computed tomography (PET-CT) imaging and genetic testing for APOE and BDNF variants, all completed within four weeks before the initiation of tDCS. To maintain blinding, neither the participants nor the neuropsychological examiners had access to the results of the FMM-PET, APOE genotyping, or BDNF testing. A detailed schematic outlining the experimental procedures, previously presented in an earlier study, is available for reference 27 . This research was registered with the Clinical Research Information Service of the Korea Disease Control and Prevention Agency (KCT0006020) and was conducted from May 2020 to February 2022 at the Brain Health Center. The authors declare that they have no ethical or financial conflicts of interest related to any of the equipment manufacturers used in this study. Transcranial direct current stimulation application In this procedure, a stable direct current of 2 mA was administered for 20 minutes using an MRI-compatible stimulator (YDS-301N, YBrain, Seoul, Republic of Korea). The NEUROPHET tES LAB software (version 3.0; Neurophet, Seoul, Republic of Korea) was employed to construct individualized brain models, estimate the tDCS-induced EF strength, and determine each participant’s optimized electrode positioning based on their unique brain structural characteristics to ensure targeted stimulation. A prior simulation study revealed that optimizing the electrode placement using simulation software enhanced the EF intensity over the left DLPFC by 55.28% compared to traditional placements based on the 10–20 EEG system 34 . This highlights the potential of computational modeling in enhancing stimulation efficiency through personalized electrode adjustments. To generate the brain model and analyze the tDCS-induced EF, all participants underwent baseline T1-weighted MRI scans. The software segmented these images into distinct anatomical structures, including skin, skull, cerebral gray and white matter, cerebellar gray and white matter, cerebrospinal fluid (CSF), and ventricles, creating a 3D brain reconstruction for each individual. The electrical conductivity values predefined in the software were: skin (0.465 S/m), skull (0.010 S/m), cerebral and cerebellar gray matter (0.276 S/m), cerebral and cerebellar white matter (0.126 S/m), and CSF/ventricles (1.65 S/m) 35 . After the brain model was generated, an investigator assigned anatomical landmarks (nasion, inion, and preauricular points) to facilitate accurate electrode placement. The stimulation was directed at the DLPFC, with the anode positioned over the left DLPFC and the cathode placed over the contralateral supraorbital region. Disk-shaped electrodes with a 3 cm radius were used. The tDCS intensity (2 mA) was input into the software, which then computed optimized electrode locations by simulating the EF distribution across the participant’s brain. The software systematically adjusted electrode positions around the target region to identify the placement that maximized the EF strength induced by tDCS. The software then generated precise placement guides, allowing trained personnel to correctly position the electrodes. The stimulation was administered by trained staff, who conducted home visits for each session. The electrode positions were measured using distances from preauricular points to the electrode center, aligning with a reference line extending from the vertex to the nasion. Before each session, the electrode positioning was carefully verified using these anatomical landmarks. Additionally, 15 minutes into the session, staff reassessed electrode positioning to ensure accuracy. Each participant was consistently monitored by the same staff member across all ten sessions. For each participant, the individualized EF distribution was quantitatively assessed using the peak EF strength (V/m) within the left DLPFC target region. This optimized EF strength value was extracted and used as a continuous variable for subsequent statistical analysis to examine its relationship with changes in WM microstructural integrity. Neuropsychological assessment All participants underwent cognitive assessments utilizing the Korean adaptation of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD-K) 36 . This battery included the Korean versions of several tests: Verbal Fluency (VF), the 15-item Boston Naming Test, and the Korean version of the Mini-Mental State Examination (MMSE-K) 37 , along with assessments for word list memory (WLM), recall, recognition, constructional praxis, and constructional recall (CR). The comprehensive CERAD-K score was calculated as the sum of all test scores, excluding the MMSE-K and CR. To assess executive function, we employed the Korean Stroop Word-Color Test (K-SWCT), which measures response inhibition in both letter and color reading tasks 38 , and the Trail Making Test B (TMT-B), which evaluates processing speed and cognitive flexibility by measuring the time required to connect numbers and letters in sequential order. A detailed description of these assessments is available in the Supplementary Material. MRI Acquisition and processing Detailed information regarding MRI acquisition and processing is available in the Supplementary Material. Processing procedures of the DTI images Details of the MRI acquisition procedures are provided in the Supplementary Material. The imaging data were preprocessed using Statistical Parametric Mapping 12 (SPM12) ( https://www.fil.ion.ucl.ac.uk/spm/software/spm12 ), running on MATLAB version 2018b, along with the PANDA toolbox ( https://www.nitrc.org/projects/panda/ ) and the FMRIB Software Library (FSL) version 6.0 ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL ). The PANDA processing pipeline consisted of two primary stages: (1) preprocessing and (2) generating diffusion metrics. The preprocessing workflow included the following five steps: (1) estimating the brain mask, (2) cropping raw images, (3) correcting for eddy-current distortions, (4) averaging multiple acquisitions, and (5) computing diffusion tensor (DT) metrics. The DT metrics analyzed in this study included FA, MD, and RD. The resulting diffusion metric images were then normalized to the Montreal Neurological Institute (MNI) standard space for further analysis. To facilitate regional analysis, diffusion metric images with a voxel size of 1.0×1.0×1.0 mm³ in standard space were processed using a WM probabilistic tract atlas. This atlas consists of 20 white matter tracts, which were identified probabilistically through deterministic tractography performed on a cohort of 28 healthy individuals 39 . The WM probabilistic tract atlas includes the following WM tracts: (1) anterior thalamic radiation (ATR), (2) cingulum in the cingulate cortex, (3) cingulum in the hippocampal area, (4) corticospinal tract (CST), (5) forceps major, (6) forceps minor, (7) inferior fronto-occipital fasciculus (IFOF), (8) superior longitudinal fasciculus (SLF), (9) temporal projection of the SLF, (10) inferior longitudinal fasciculus (ILF), and (11) uncinate fasciculus (UF). For each WM tract, separate statistical results were obtained for the left and right hemispheres, except for the forceps major and minor, which were analyzed as a unified region. These statistical files contained values for FA, MD, and RD, each of which provides distinct insights into WM integrity 40 : FA (Fractional Anisotropy): Represents the degree of directional water diffusion within a voxel. Higher FA values suggest greater microstructural integrity of WM. MD (Mean Diffusivity): Reflects the overall magnitude of water diffusion, irrespective of direction. Higher MD values indicate increased water mobility, which may suggest lower WM density, structural degradation, or potential injury. RD (Radial Diffusivity): Measures water diffusion perpendicular to the principal direction. Increased RD values are often interpreted as markers of demyelination, providing insight into myelin integrity. Additional details on the PANDA processing pipeline can be found in a previous study 41 . Aβ deposition The methodology for acquiring and processing [ 18 F]-FMM PET images to evaluate Aβ deposition, as well as the calculation of standardized uptake value ratios (SUVRs) to quantify deposition intensity, is comprehensively detailed in the Supplementary Material. A threshold of 0.62 was employed to differentiate between Aβ positive (Aβ +) and Aβ negative (Aβ -) accumulations, aligning with previous FMM-PET studies 42 . It is crucial to note that 'negative accumulation' denotes subthreshold deposition rather than a complete absence of amyloid deposition. APOE genotyping The approach for APOE genotyping is detailed in the Supplementary Material. According to our protocol, we would exclude subjects who possessed the APOE ε2 allele due to its observed protective role 43 . Participants were classified based on the presence of the APOE ε4 allele; those with at least one ε4 allele were grouped as APOE ε4 carriers, whereas those without any ε4 alleles were designated as non-carriers. BDNF genotyping The methodology for BDNF genotyping is described in the Supplementary Material. In the context of the BDNF Val66Met polymorphism (rs6265), we categorized participants based on existing genetic research 44 , 45 into groups: those carrying at least one Met66 allele were labeled as Met carriers, whereas those without any Met66 alleles were considered Met non-carriers. Statistical analysis Statistical analyses were performed using R software (version 4.3.0), jamovi (version 2.6.19), and SPM 12 ( https://www.fil.ion.ucl.ac.uk/spm/ ). The normality of continuous variables was assessed using the Kolmogorov–Smirnov test, and data were standardized using z-score transformation prior to analysis. Partial correlation analyses were conducted to investigate associations between optimized EF strength and changes in WM microstructural integrity metrics, including FA, MD, and RD. These analyses adjusted for covariates such as age, sex, years of education, Aβ deposition, APOE ε4 carrier status, and BDNF Val66Met polymorphism. Additionally, correlation analyses using Pearson’s product-moment correlation were performed to evaluate the relationships between changes in FA across tracts of interest. These tracts of interest were identified based on their relevance to the optimized EF strength and included WM tracts with significant FA changes. Based on the findings from these partial correlation analyses, correlation analyses using Pearson’s product-moment correlation were performed to evaluate the relationships between changes in DTI parameters within the identified tracts of interest and changes in total and domain scores of the CERAD-K. Multiple regression analyses were applied to examine the interaction effects of optimized EF strength and individual modifiers (e.g., Aβ deposition, APOE ε4 carrier status, and BDNF polymorphism) on WM microstructural integrity metrics. In addition, to investigate whether these interaction effects extended to cognitive outcomes, multiple regression analyses were also conducted to test the interaction between optimized EF strength and each effect modifier for changes in total and domain scores of the CERAD-K. Covariates included age, sex, years of education, and the effect modifiers not included in the specific interaction model under evaluation. All statistical analyses were conducted using a two-tailed approach, with significance thresholds set at p < 0.05. Results Baseline demographic and clinical data A total of 70 participants were assessed for eligibility. Of these, 63 completed the intervention and post-tDCS assessments. Seven participants discontinued the intervention: six withdrew informed consent, and one experienced a mild adverse event (tingling under the electrode). Among the 63 participants who completed the intervention, eight were excluded from the analysis due to missing EF strength values. Therefore, the final analyses were conducted on 55 participants. Figure 1 provides a detailed flowchart of participant recruitment, retention, and inclusion. Table 1 shows the baseline demographic data for the participants who completed the study. The number and distribution of participants based on individual factors are described in the Supplementary Material. Table 1 Baseline demographic and clinical characteristics of the study participants Demographic and clinical characteristics (N = 55) Age (years) 72.7 ± 8.2 Sex - Male 17 (30.9%) - Female 38 (69.1%) Years of education 12.2 ± 4.9 [ 18 F] Flutemetamol deposition (positivity, %) 20 (36.4%) Global [ 18 F] Flutemetamol SUVR PONS 0.61 ± 0.15 APOE ε4 carrier status (carrier. %) 26 (47.3%) BDNF polymorphism (Val/Met or Met/Met, %) 45 (81.8%) CERAD-K VF 11.9 ± 5.2 BNT 10.7 ± 3.2 MMSE 23.1 ± 5.2 WLM 14.4 ± 4.8 CP 10.1 ± 1.6 WLR 3.8 ± 2.6 WLRc 6.8 ± 2.9 CR 4.6 ± 3.5 TMT B 223.1 ± 79.1 Stroop word-color 25.9 ± 14.0 Total CERAD-K 57.7 ± 16.1 Optimized EF strength (mV/mm) 0.24 ± 0.07 Data are presented as the mean ± SD unless indicated otherwise. SUVR PONS , standardized uptake value ratio of [ 18 F] Flutemetamol, using the pons as a reference region; CERAD-K, Korean version of Consortium to Establish a Registry for Alzheimer’s Disease; VF, verbal fluency; BNT, Boston Naming Test; MMSE, the Korean version of the Mini-Mental Status Examination; WLM, Word List Memory; CP, Constructional Praxis; WLR, Word List Recall; WLRc, Word List Recognition; CR, constructional recall; TMT B, Trail Making Test B; Total CERAD-K, composite score summing scores of the CERAD-K VF, BNT, WLM, CP, WLR, and WLRc domains; EF, electrical field. Changes in WM Microstructural Integrity and Optimized EF Strength After 10 sessions of anodal tDCS, significant associations were observed between optimized EF strength and changes in WM microstructural integrity metrics, including FA, MD, and RD. Partial correlation analyses, adjusted for age, sex, education years, Aβ deposition, APOE ε4 carrier status, and BDNF Val66Met polymorphism, demonstrated significant correlations between optimized EF strength and changes in these metrics across tracts of interest (Fig. 2). For FA, significant positive correlations were identified in the following tracts: left ATR (r = 0.417, P = 0.003), left CST (r = 0.434, P = 0.002), left IFOF (r = 0.392, P = 0.005), and left ILF (r = 0.374, P = 0.008). Significant correlations were identified between changes in FA across the WM tracts of interest (Fig. 2A). All correlations were statistically significant with P < 0.001, indicating positive associations among changes in the left CST, left IFOF, and left ILF. For MD and RD, significant negative correlations were observed in the same tracts of interest. Specifically, the correlations for MD and RD were as follows: left CST (r = -0.336 and − 0.425, respectively; P = 0.018 and 0.002), and left IFOF (r = -0.378 and − 0.413, respectively; P = 0.007 and 0.003) (Fig. 2B). However, no significant associations were observed between optimized EF strength and changes in WM microstructural integrity metrics in other WM tracts included in the probtract atlas. Additionally, correlation analyses using Pearson’s product-moment correlation did not reveal any significant associations between changes in DTI parameters within the identified tracts of interest and changes in total and domain scores of the CERAD-K. Interactions Between Optimized EF Strength and Individual Factors Associated with AD Significant associations were identified between optimized EF strength and changes in WM microstructural integrity, with these effects varying based on individual effect modifiers (Fig. 3 ). Notably, Aβ deposition moderated the relationship between EF strength and microstructural changes in the right SLF (MD; P = 0.014 and RD; P = 0.012). Specifically, increased EF strength was associated with reductions in MD and RD in Aβ-positive patients. In addition, a significant interaction was observed between EF strength and APOE ε4 carrier status in predicting WM microstructural changes in the right SLF. Specifically, EF strength was associated with greater reductions in RD ( P = 0.021) and MD ( P = 0.027) among non-carriers compared to carriers. Additionally, BDNF polymorphism influenced the relationship between EF strength and WM integrity ( P = 0.002). Higher EF strength was associated with increased FA in the right UF among Met allele non-carriers. Regarding cognitive outcomes, interaction analyses revealed that APOE ε4 status moderated the relationship between EF strength and cognitive change (Fig. 4 ). Specifically, a significant interaction was found for the CERAD SWCT ( P = 0.008), indicating that higher EF strength was associated with improved Stroop performance in non-carriers, whereas carriers showed no improvement or a decline. No significant interactions were observed between EF strength and APOE ε4 status for the CERAD-K total score or other domain scores. Discussion This study investigated the effects of two weeks of consecutive tDCS on WM microstructural integrity in individuals with MCI and examined how these effects varied according to individual factors related to AD, including Aβ deposition, APOE ε4 carrier status, and BDNF Val66Met polymorphism. We observed significant associations between optimized EF strength and WM integrity changes. Higher EF strength was linked to greater increases in FA and reductions in mean MD and RD in left-lateralized tracts, including the ATR, CST, IFOF, and ILF. These findings suggest that a greater EF strength may facilitate more robust WM plasticity, potentially reflecting increased axonal coherence and myelination 46 . Previous studies have similarly reported FA increases following tDCS, supporting its potential to induce WM structural modification 27 . Each affected tract plays a distinct role in cognitive and neural function. The ATR is critical for executive function and working memory, and its integrity has been associated with cognitive recovery in individuals with MCI 47 , 48 . The left CST, primarily responsible for motor function 49 , showed the strongest correlation with EF strength, suggesting that optimized tDCS may exert effects on motor-related pathways. The left IFOF and ILF, involved in visuospatial processing 50 and memory function 51 , respectively, also exhibited EF strength-dependent changes. Furthermore, the current study identified significant correlations among FA changes across these tracts. This suggests that tDCS may have induced synchronized structural changes across these pathways. It is plausible that as EF strength increased, axonal coherence and myelination improved concurrently across these tracts. Previous studies have reported synchronized WM changes following tDCS, particularly in pathways such as the CST and IFOF, which may contribute to network-level neuroplasticity and functional integration 52 , 53 . These findings highlight the potential for tDCS to influence broader cognitive networks rather than exerting purely localized cortical effects, emphasizing the importance of optimizing EF strength to enhance network-level plasticity while avoiding potential overstimulation effects. Beyond FA changes, significant reductions in MD and RD were found in the left CST and left IFOF. Lower MD and RD values indicate reduced extracellular diffusion, typically associated with improved axonal integrity and myelination 54 , 55 . Given that MD and RD are sensitive markers of demyelination and overall tissue integrity, these findings suggest that tDCS may contribute to structural stabilization or repair. The specificity of these changes in certain tracts may be attributed to their proximity to the stimulation site or differential sensitivity to tDCS-induced plasticity. The left IFOF and CST, being anatomically closer to the DLPFC stimulation site, exhibited more pronounced effects, whereas deeper tracts such as the ATR and ILF showed comparatively less change. As EF intensity is strongest at the cortical surface and diminishes with depth 56 , these variations may reflect structural and functional differences in tract responsiveness to tDCS. Future studies should explore how variations in EF strength and electrode configuration influence WM integrity across different pathways. The influence of individual AD-related factors was evident in this study. In Aβ-positive individuals, higher EF strength correlated with greater reductions in MD and RD in the right SLF, suggesting that tDCS may promote compensatory plasticity in regions affected by Aβ pathology. The SLF is a key pathway linking the frontal and parietal lobes and plays a critical role in working memory and executive function 57 . Previous studies have reported FA reductions in the SLF in AD patients, linking such changes to cognitive decline 58 . Our findings suggest that tDCS may enhance WM integrity in Aβ-positive individuals, potentially mitigating structural deterioration associated with AD pathology. Similarly, APOE ε4 carrier status significantly modulated the effects of tDCS. Non-carriers exhibited greater MD and RD reductions in the right SLF compared to carriers, indicating that APOE ε4 allele may attenuate the neuroplastic effects of tDCS. APOE ε4 allele is a well-established genetic risk factor for AD, and prior studies have shown that carriers experience more severe WM damage and reduced neuroplastic potential 59 , 60 . In addition, a significant interaction between optimized EF strength and APOE ε4 status was observed in relation to executive function, as measured by the CERAD-K SWCT. Specifically, higher EF strength was associated with greater improvement in Stroop performance among non-carriers compared with carriers. Considering the established involvement of the SLF in executive function via frontoparietal connectivity, it is plausible that EF strength-related enhancements in WM microstructural integrity within the right SLF contributed to the observed cognitive improvement in non-carriers. These findings indicate a potential structure–function relationship whereby optimized EF strength may facilitate both WM plasticity and functional gains in individuals without the APOE ε4 allele. Therefore, APOE ε4 carriers may require adjusted stimulation protocols to achieve comparable neuroplastic and cognitive benefits. BDNF Val66Met polymorphism also influenced FA changes, with non-carriers of the Met allele exhibiting greater FA increases in the right UF. The UF is a major tract connecting the prefrontal cortex and temporal lobe and is essential for memory and emotional regulation 61 . Reduced FA in the UF has been reported in AD and is associated with disease progression 62 . Given that BDNF plays a central role in neuroplasticity 63 and that the Met allele is associated with reduced BDNF secretion 12 , 64 , our findings suggest that BDNF-mediated mechanisms influence the neurophysiological response to tDCS. These results underscore the relevance of genetic factors in designing personalized neuromodulation approaches. The present study demonstrated a significant association between optimized EF strength and specific WM microstructural changes. However, these structural modifications were not accompanied by measurable improvements in cognitive performance when analyzed within tracts that showed significant associations with EF strength. Notably, no direct relationship was observed between EF-sensitive WM changes and cognitive outcomes within the scope of the current two-week stimulation protocol. These findings suggest that, although optimized EF strength may induce physiologically meaningful WM plasticity, such changes may not immediately translate into short-term cognitive benefits in a tract-specific manner. In contrast, previous findings from the same cohort reported tDCS-induced cognitive improvements in specific subgroups, including Aβ-negative individuals and those without the BDNF Met allele 27 , 65 . Additionally, prior analyses showed that baseline Aβ burden was significantly associated with FA in the left hippocampal cingulum, which in turn predicted delayed memory improvement 27 . This contrast highlights a critical distinction: while certain WM tracts, including the left hippocampal cingulum, have been linked to both AD-related pathology and cognitive outcomes, the EF-sensitive tracts identified in the present study did not show such associations. This dissociation suggests that EF strength-related WM plasticity may influence cognitive outcomes through longer-term or more distributed network-level mechanisms, which may not be fully captured within the short duration of the current study. It also raises the possibility that structural changes may precede functional improvements, which could emerge only after extended follow-up. Several limitations should be acknowledged. First, the absence of a sham control group restricts the ability to draw causal inferences about the effects of tDCS, as placebo effects or nonspecific stimulation-related influences cannot be excluded. Second, the relatively short stimulation period may have limited the detection of delayed or cumulative cognitive effects related to WM plasticity. Finally, given the interindividual variability in response to stimulation, future studies should further explore optimal EF thresholds and stimulation parameters tailored to individual characteristics. In conclusion, this study confirms that optimized EF strength is significantly associated with WM microstructural changes in individuals with MCI. It also emphasizes the importance of considering individual AD-related factors when evaluating the efficacy of tDCS. These findings support the development of personalized neuromodulation approaches and highlight the potential of precision medicine in non-invasive brain stimulation strategies for the prevention and management of AD. Declarations Conflicts of interest Author Hyun Kook Lim, TaeYeong Kim, and Donghyeon Kim are employed by NEUROPHET Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The data processing services provided by NEUROPHET Inc. were utilized to enhance the quality and analysis of the brain imaging data collected during the study. Authors declare that the research outcomes and conclusions remain unbiased and are not influenced by any commercial interests associated with the NEUROPHET Inc.'s products or services. Author contributions Jung-Won Lee: Methodology, Data Curation, Visualization, Writing - Original Draft. Sunghwan Kim: Methodology, Data Curation. Suhyung Kim : Data Curation. Yoo Hyun Um: Software, Investigation, Writing - Review & Editing. Sheng-Min Wang: Methodology, Data Curation, Writing - Review & Editing. TaeYeong Kim: Methodology, Data Curation. Donghyeon Kim: Methodology, Data Curation, Funding Acquisition. Hyun Kook Lim: Conceptualization, Methodology, Writing - Review & Editing, Supervision, Funding Acquisition. Chang Uk Lee: Conceptualization, Supervision. Dong Woo Kang: Conceptualization, Methodology, Data Curation, Formal Analysis, Project Administration, Funding Acquisition. Funding This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (No. 2019R1C1C1007608) and a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (No. HI22C0467). Availability of data and materials The datasets generated or analyzed during the current study are not publicly available due to the Patient Data Management Protocol of Yeouido Saint Mary’s Hospital but are available from the corresponding author upon reasonable request. References Scheltens, P. et al. Alzheimer's disease. 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Electric field strength-based anode positioning of transcranial direct current stimulation targeting at left dorsolateral prefrontal cortex: in silico study. Brain Stimulation: Basic. Translational Clin. Res. Neuromodulation . 16 , 284 (2023). Mendonca, M. E. et al. Transcranial DC stimulation in fibromyalgia: optimized cortical target supported by high-resolution computational models. J. Pain . 12 , 610–617 (2011). Lee, J. H. et al. Development of the Korean Version of the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet (CERAD-K) clinical and neuropsychological assessment batteries. Journals Gerontol. Ser. B: Psychol. Sci. Social Sci. 57 , P47–P53 (2002). JH, P. Standardization of Korean version of the Mini-Mental State Examination (MMSE-K) for use in the elderly. Part II. Diagnostic validity. Korean J. Neuropsych Assoc. 28 , 125–135 (1989). Kim, T. Y. et al. Development of the Korean Stroop Test and Study of the Validity and the Reliability. J. 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Reduced fractional anisotropy in the uncinate fasciculus in patients with major depression carrying the met-allele of the Val66Met brain‐derived neurotrophic factor genotype. Am. J. Med. Genet. Part. B: Neuropsychiatric Genet. 159 , 537–548 (2012). Han, E. J. et al. Evidence for association between the brain-derived neurotrophic factor gene and panic disorder: a novel haplotype analysis. Psychiatry Invest. 12 , 112 (2014). Alba-Ferrara, L. & de Erausquin, G. A. What does anisotropy measure? Insights from increased and decreased anisotropy in selective fiber tracts in schizophrenia. Front. Integr. Nuerosci. 7 , 9 (2013). Niida, R. et al. Aberrant anterior thalamic radiation structure in bipolar disorder: a diffusion tensor tractography study. Front. Psychiatry . 9 , 522 (2018). Chua, T. C., Wen, W., Slavin, M. J. & Sachdev, P. S. Diffusion tensor imaging in mild cognitive impairment and Alzheimer's disease: a review. Curr. Opin. Neurol. 21 , 83–92 (2008). Laundre, B. J., Jellison, B. J., Badie, B., Alexander, A. L. & Field, A. S. Diffusion tensor imaging of the corticospinal tract before and after mass resection as correlated with clinical motor findings: preliminary data. Am. J. Neuroradiol. 26 , 791–796 (2005). Almairac, F., Herbet, G., Moritz-Gasser, S., de Champfleur, N. M. & Duffau, H. The left inferior fronto-occipital fasciculus subserves language semantics: a multilevel lesion study. Brain Struct. Function . 220 , 1983–1995 (2015). Herbet, G., Zemmoura, I. & Duffau, H. Functional anatomy of the inferior longitudinal fasciculus: from historical reports to current hypotheses. Front Neuroanat. 12 , 77 (2018). Lindenberg, R., Nachtigall, L., Meinzer, M., Sieg, M. M. & Flöel, A. Differential effects of dual and unihemispheric motor cortex stimulation in older adults. J. Neurosci. 33 , 9176–9183 (2013). Kunze, T., Hunold, A., Haueisen, J., Jirsa, V. & Spiegler, A. Transcranial direct current stimulation changes resting state functional connectivity: A large-scale brain network modeling study. Neuroimage 140 , 174–187 (2016). Clark, K. A. et al. Mean diffusivity and fractional anisotropy as indicators of disease and genetic liability to schizophrenia. J. Psychiatr. Res. 45 , 980–988 (2011). Becerra-Laparra, I., Cortez-Conradis, D., Garcia-Lazaro, H. G., Martinez-Lopez, M. & Roldan-Valadez, E. Radial diffusivity is the best global biomarker able to discriminate healthy elders, mild cognitive impairment, and Alzheimer's disease: A diagnostic study of DTI-derived data. Neurol. India . 68 , 427–434 (2020). Parazzini, M., Fiocchi, S., Rossi, E., Paglialonga, A. & Ravazzani, P. Transcranial direct current stimulation: estimation of the electric field and of the current density in an anatomical human head model. IEEE Trans. Biomed. Eng. 58 , 1773–1780 (2011). Koshiyama, D. et al. Association between the superior longitudinal fasciculus and perceptual organization and working memory: A diffusion tensor imaging study. Neurosci. Lett. 738 , 135349 (2020). Collij, L. E. et al. White matter microstructure disruption in early stage amyloid pathology. Alzheimer's Dementia: Diagnosis Assess. Disease Monit. 13 , e12124 (2021). Westlye, L. T., Reinvang, I., Rootwelt, H. & Espeseth, T. Effects of APOE on brain white matter microstructure in healthy adults. Neurology 79 , 1961–1969 (2012). Sun, J. et al. APOE ε4 allele accelerates age-related multi-cognitive decline and white matter damage in non-demented elderly. Aging (Albany NY) . 12 , 12019 (2020). Von Heide, D., Skipper, R. J., Klobusicky, L. M., Olson & E. & I. R. Dissecting the uncinate fasciculus: disorders, controversies and a hypothesis. Brain 136 , 1692–1707 (2013). Hiyoshi-Taniguchi, K. et al. The uncinate fasciculus as a predictor of conversion from amnestic mild cognitive impairment to Alzheimer disease. J. Neuroimaging . 25 , 748–753 (2015). Park, C. et al. The BDNF Val66Met polymorphism affects the vulnerability of the brain structural network. Front. Hum. Neurosci. 11 , 400 (2017). Jung, D. H. et al. Therapeutic effects of anodal transcranial direct current stimulation in a rat model of ADHD. Elife 9 , e56359 (2020). Kang, D. W. et al. Effects of transcranial direct current stimulation on cognition in MCI with Alzheimer's disease risk factors using Bayesian analysis. Sci. Rep. 14 , 18818 (2024). Additional Declarations Competing interest reported. Author Hyun Kook Lim, TaeYeong Kim, and Donghyeon Kim are employed by NEUROPHET Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The data processing services provided by NEUROPHET Inc. were utilized to enhance the quality and analysis of the brain imaging data collected during the study. Authors declare that the research outcomes and conclusions remain unbiased and are not influenced by any commercial interests associated with the NEUROPHET Inc.'s products or services. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6541880","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":482093313,"identity":"fb7dc07d-bac3-48fc-9ef4-3d7c4e888544","order_by":0,"name":"Jung-Won Lee","email":"","orcid":"","institution":"The Catholic University of Korea","correspondingAuthor":false,"prefix":"","firstName":"Jung-Won","middleName":"","lastName":"Lee","suffix":""},{"id":482093314,"identity":"0abaf13f-0bf9-417a-891a-a22fa1bb957a","order_by":1,"name":"Sunghwan Kim","email":"","orcid":"","institution":"The Catholic University of 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study\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6541880/v1/de124b1365d4dbf599437bdb.jpg"},{"id":86388797,"identity":"625662c5-a9ef-4b05-b974-31e9e0edeed2","added_by":"auto","created_at":"2025-07-10 06:24:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":178591,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between optimized electrical field strength and changes in white matter microstructural integrity after 10 sessions of anodal tDCS\u003c/p\u003e\n\u003cp\u003ePartial correlation analysis adjusting for age, sex, education years, Aβ deposition,\u003cem\u003e APOE\u003c/em\u003e \u0026nbsp;ε4 carrier status, and BDNF polymorphism. (A) Right panel: a correlation analysis between changes in FA (tracks of interest); the r values by Pearson’s product-moment correlation; ***, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. FA, fractional anisotropy; MD, mean diffusivity; RD, radial diffusivity.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6541880/v1/dc631341751daf61ea572c36.png"},{"id":86388796,"identity":"1221ddab-2b21-4e62-a11b-ff8f6dd13338","added_by":"auto","created_at":"2025-07-10 06:24:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92320,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between optimized electrical field strength and changes in white matter microstructural integrity according to Aβ deposition,\u003cem\u003e APOE\u003c/em\u003e \u0026nbsp;ε4 carrier status, and BDNF polymorphism\u003c/p\u003e\n\u003cp\u003eMultiple regression analysis was used to predict the impact of effect modifier-by-optimized electrical field strength (effect modifier*EF) for white matter microstructural integrity (effect modifiers: Aβ deposition, \u003cem\u003eAPOE\u003c/em\u003e \u0026nbsp;ε4 carrier status, and BDNF polymorphism), adjusting for age, sex, education years, and effect modifier not included in each interaction evaluation. FA, fractional anisotropy; MD, mean diffusivity; RD, radial diffusivity.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6541880/v1/7bd81f1b114f83abcfa5fd39.jpg"},{"id":86388807,"identity":"054a2107-544f-45ce-bd4e-090b48aa1170","added_by":"auto","created_at":"2025-07-10 06:24:15","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":67001,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between optimized electrical field strength and changes in cognitive function according to \u003cem\u003eAPOE\u003c/em\u003e \u0026nbsp;ε4 carrier status\u003c/p\u003e\n\u003cp\u003eMultiple regression analysis was used to predict the impact of effect modifier-by-optimized electrical field strength (effect modifier*EF) for cognitive function (effect modifiers: Aβ deposition, \u003cem\u003eAPOE\u003c/em\u003e \u0026nbsp;ε4 carrier status, and BDNF polymorphism), adjusting for age, sex, education years, and effect modifier not included in each interaction evaluation.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6541880/v1/717c46686f636b30b7e1e1e1.jpg"},{"id":98814168,"identity":"dcc25928-5be0-40e0-bc73-1e0bafbcf225","added_by":"auto","created_at":"2025-12-22 16:11:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1435909,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6541880/v1/eb5034fb-9c9d-48f0-8c53-f580290ed31c.pdf"},{"id":86388800,"identity":"b9c41ccf-0244-42a7-af09-bf9c76f64ea7","added_by":"auto","created_at":"2025-07-10 06:24:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":39821,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6541880/v1/e9bb41b1fe7941452d7df51f.docx"}],"financialInterests":"Competing interest reported. Author Hyun Kook Lim, TaeYeong Kim, and Donghyeon Kim are employed by NEUROPHET Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The data processing services provided by NEUROPHET Inc. were utilized to enhance the quality and analysis of the brain imaging data collected during the study. Authors declare that the research outcomes and conclusions remain unbiased and are not influenced by any commercial interests associated with the NEUROPHET Inc.'s products or services.","formattedTitle":"Optimized Electrical Field Strength of tDCS and White Matter Microstructural Integrity in MCI: Role of Individual Factors Associated with AD","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is the most prevalent cause of dementia, characterized by amyloid-beta (Aβ) and tau protein accumulation, leading to progressive cognitive decline and functional impairment\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Mild cognitive impairment (MCI) is considered a prodromal stage of AD, distinguished by preserved independence despite measurable cognitive deficits. Approximately 10\u0026ndash;15% of individuals with MCI progress to AD annually\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, with a preclinical phase lasting around a decade, followed by an MCI phase of approximately four years before conversion to AD\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite extensive efforts to identify interventions that slow or prevent AD progression, treatment options remain limited. Pharmacological approaches, including cholinesterase inhibitors, memantine, and vitamin E, have not demonstrated definitive efficacy in preventing cognitive decline\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Non-pharmacological strategies, such as cognitive training\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, physical exercise\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, and dietary modifications\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, have shown some promise but face challenges related to adherence and scalability. These limitations underscore the need for alternative interventions that are both effective and sustainable\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTranscranial direct current stimulation (tDCS) has emerged as a potential non-invasive therapy for MCI due to its accessibility, cost-effectiveness, and favorable safety profile\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. By delivering low-intensity electrical currents through scalp electrodes, tDCS modulates cortical excitability\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and enhances synaptic plasticity\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, mechanisms that are critical for cognitive function. Evidence suggests that repeated tDCS sessions may improve cognition in AD by promoting Aβ clearance, modulating blood-brain barrier integrity \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and increasing brain-derived neurotrophic factor (BDNF) levels\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Preliminary studies indicate that tDCS may enhance episodic memory in MCI\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eImportantly, the effectiveness of tDCS is not solely dependent on the stimulation site or duration, but also on the electrical field (EF) strength generated within the brain\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Higher EF intensity has been linked to greater cognitive improvements in cognitively impaired individuals\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, variability in outcomes under identical stimulation protocols suggests that individual anatomical differences influence treatment effects. Recent advances have enabled optimized, personalized tDCS, designed to enhance EF strength by incorporating individual brain structural variability\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. These developments highlight EF strength as a key mediator of the clinical efficacy of tDCS, making it a critical variable for investigation in neurodegenerative conditions such as MCI.\u003c/p\u003e\u003cp\u003eA key biomarker for evaluating treatment efficacy in MCI and AD is white matter (WM) microstructural integrity. Diffusion tensor imaging (DTI) allows for quantitative assessment of WM integrity through metrics such as fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD), which serve as early diagnostic markers and indicators of therapeutic response\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Prior studies have shown that AD patients exhibit lower FA and higher MD in key WM regions, including the splenium, fornix, and parahippocampal cingulum\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Aβ burden has also been linked to WM microstructural deterioration\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, which in turn predicts the speed of conversion from normal aging to MCI\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIndividual factors related to AD, such as Aβ deposition, \u003cem\u003eAPOE\u003c/em\u003e ε4 allele status, and the \u003cem\u003eBDNF\u003c/em\u003e Val66Met polymorphism, may influence the effects of tDCS on WM integrity. Aβ accumulation disrupts neuronal connectivity and synaptic function, potentially modulating neuroplastic responses to tDCS\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The \u003cem\u003eAPOE\u003c/em\u003e ε4 allele exacerbates Aβ-related damage, impairs synaptic plasticity, and compromises blood-brain barrier function, which may alter the neurophysiological effects of tDCS\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Additionally, the \u003cem\u003eBDNF\u003c/em\u003e Val66Met polymorphism affects neuroplasticity, with the Met allele associated with reduced BDNF secretion, potentially diminishing tDCS-induced synaptic modulation\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Despite their significance, these individual factors remain underexplored in the context of tDCS treatment for MCI. Recent findings suggest that tDCS may influence WM integrity. Specifically, our prior research demonstrated that \u003cem\u003eAPOE\u003c/em\u003e ε4 carriers exhibited greater FA increases in the right uncinate fasciculus after tDCS. Additionally, Val66 homozygotes showed increased MD in the right uncinate fasciculus and decreased MD and RD in the left cingulum\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. These results highlight the necessity of considering individual factors related to AD when evaluating tDCS efficacy.\u003c/p\u003e\u003cp\u003eBuilding upon this foundation, the present study focuses on the role of EF strength as the main predictor of tDCS-induced changes in WM microstructure. The primary aim of this study is to investigate the effects of optimized EF strength on WM microstructural integrity in MCI patients and to examine how these effects vary according to individual factors related to AD. To achieve this, we applied a two-week protocol consisting of ten consecutive tDCS sessions and assessed WM integrity changes using DTI metrics. We hypothesize that stronger EF strength will be associated with enhanced WM microstructural integrity and that this association will be modulated by AD-related individual characteristics.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003e Participants were recruited from the Brain Health Center at Yeoui-do St. Mary\u0026rsquo;s Hospital, affiliated with the College of Medicine, the Catholic University of Korea. The inclusion criteria were as follows: (1) Diagnosis in accordance with Petersen\u0026rsquo;s criteria for mild cognitive impairment (MCI)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, with scores below 8 on the Seoul-Instrumental Activities of Daily Living (S-IADL) scale to confirm independent functioning in daily life\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, and (2) a Clinical Dementia Rating (CDR) score of 0.5. The exclusion criteria were: (1) A history of alcohol or drug abuse, head trauma, or psychiatric disorders; (2) use of psychotropic medications, including cholinesterase inhibitors, N-Methyl-D-Aspartate receptor antagonists, antidepressants, benzodiazepines, or antipsychotics; (3) contraindications for tDCS or MRI, such as the presence of ferromagnetic or coiled metal implants; and (4) any dermatological condition affecting scalp integrity.\u003c/p\u003e\u003cp\u003eAdditionally, only individuals with a Hamilton Depression Rating Scale (HAMD) score of 7 or lower were included to ensure depressive symptoms remained within the normal range\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. All participants were tDCS-naive, meaning they had never previously received tDCS treatment. The selection process was supervised by two geriatric psychiatry specialists. Participants consented to a medical record review, and all assessments were conducted at the Brain Health Center, Yeoui-do St. Mary\u0026rsquo;s Hospital, the Catholic University of Korea. The study was conducted in compliance with the Declaration of Helsinki and received approval from the Institutional Review Board (IRB) of the Catholic University of Korea (SC19DEST0012). Written informed consent was obtained from all participants before their enrollment.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy protocol\u003c/h3\u003e\n\u003cp\u003e This single-arm, prospective study was conducted without a sham control condition. Participants received ten sessions of tDCS, administered in their homes at a frequency of five sessions per week over two weeks. The ten-session protocol was selected based on previous clinical research, which demonstrated its effectiveness in treating AD and MCI\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, while also considering adherence feasibility in older adults.\u003c/p\u003e\u003cp\u003eNeuropsychological assessments and MRI scans were performed at the Brain Health Center of Yeoui-do St. Mary\u0026rsquo;s Hospital, both within two weeks prior to the first tDCS session and after the completion of the tenth session. Additionally, participants underwent [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF] flutemetamol (FMM) positron emission tomography-computed tomography (PET-CT) imaging and genetic testing for \u003cem\u003eAPOE\u003c/em\u003e and \u003cem\u003eBDNF\u003c/em\u003e variants, all completed within four weeks before the initiation of tDCS. To maintain blinding, neither the participants nor the neuropsychological examiners had access to the results of the FMM-PET, \u003cem\u003eAPOE\u003c/em\u003e genotyping, or \u003cem\u003eBDNF\u003c/em\u003e testing.\u003c/p\u003e\u003cp\u003eA detailed schematic outlining the experimental procedures, previously presented in an earlier study, is available for reference\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This research was registered with the Clinical Research Information Service of the Korea Disease Control and Prevention Agency (KCT0006020) and was conducted from May 2020 to February 2022 at the Brain Health Center. The authors declare that they have no ethical or financial conflicts of interest related to any of the equipment manufacturers used in this study.\u003c/p\u003e\n\u003ch3\u003eTranscranial direct current stimulation application\u003c/h3\u003e\n\u003cp\u003e In this procedure, a stable direct current of 2 mA was administered for 20 minutes using an MRI-compatible stimulator (YDS-301N, YBrain, Seoul, Republic of Korea). The NEUROPHET tES LAB software (version 3.0; Neurophet, Seoul, Republic of Korea) was employed to construct individualized brain models, estimate the tDCS-induced EF strength, and determine each participant\u0026rsquo;s optimized electrode positioning based on their unique brain structural characteristics to ensure targeted stimulation. A prior simulation study revealed that optimizing the electrode placement using simulation software enhanced the EF intensity over the left DLPFC by 55.28% compared to traditional placements based on the 10\u0026ndash;20 EEG system\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This highlights the potential of computational modeling in enhancing stimulation efficiency through personalized electrode adjustments.\u003c/p\u003e\u003cp\u003eTo generate the brain model and analyze the tDCS-induced EF, all participants underwent baseline T1-weighted MRI scans. The software segmented these images into distinct anatomical structures, including skin, skull, cerebral gray and white matter, cerebellar gray and white matter, cerebrospinal fluid (CSF), and ventricles, creating a 3D brain reconstruction for each individual. The electrical conductivity values predefined in the software were: skin (0.465 S/m), skull (0.010 S/m), cerebral and cerebellar gray matter (0.276 S/m), cerebral and cerebellar white matter (0.126 S/m), and CSF/ventricles (1.65 S/m)\u003csup\u003e35\u003c/sup\u003e. After the brain model was generated, an investigator assigned anatomical landmarks (nasion, inion, and preauricular points) to facilitate accurate electrode placement.\u003c/p\u003e\u003cp\u003eThe stimulation was directed at the DLPFC, with the anode positioned over the left DLPFC and the cathode placed over the contralateral supraorbital region. Disk-shaped electrodes with a 3 cm radius were used. The tDCS intensity (2 mA) was input into the software, which then computed optimized electrode locations by simulating the EF distribution across the participant\u0026rsquo;s brain. The software systematically adjusted electrode positions around the target region to identify the placement that maximized the EF strength induced by tDCS. The software then generated precise placement guides, allowing trained personnel to correctly position the electrodes.\u003c/p\u003e\u003cp\u003eThe stimulation was administered by trained staff, who conducted home visits for each session. The electrode positions were measured using distances from preauricular points to the electrode center, aligning with a reference line extending from the vertex to the nasion. Before each session, the electrode positioning was carefully verified using these anatomical landmarks. Additionally, 15 minutes into the session, staff reassessed electrode positioning to ensure accuracy. Each participant was consistently monitored by the same staff member across all ten sessions.\u003c/p\u003e\u003cp\u003eFor each participant, the individualized EF distribution was quantitatively assessed using the peak EF strength (V/m) within the left DLPFC target region. This optimized EF strength value was extracted and used as a continuous variable for subsequent statistical analysis to examine its relationship with changes in WM microstructural integrity.\u003c/p\u003e\n\u003ch3\u003eNeuropsychological assessment\u003c/h3\u003e\n\u003cp\u003eAll participants underwent cognitive assessments utilizing the Korean adaptation of the Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease (CERAD-K)\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. This battery included the Korean versions of several tests: Verbal Fluency (VF), the 15-item Boston Naming Test, and the Korean version of the Mini-Mental State Examination (MMSE-K)\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, along with assessments for word list memory (WLM), recall, recognition, constructional praxis, and constructional recall (CR). The comprehensive CERAD-K score was calculated as the sum of all test scores, excluding the MMSE-K and CR.\u003c/p\u003e\u003cp\u003eTo assess executive function, we employed the Korean Stroop Word-Color Test (K-SWCT), which measures response inhibition in both letter and color reading tasks\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, and the Trail Making Test B (TMT-B), which evaluates processing speed and cognitive flexibility by measuring the time required to connect numbers and letters in sequential order. A detailed description of these assessments is available in the Supplementary Material.\u003c/p\u003e\n\u003ch3\u003eMRI Acquisition and processing\u003c/h3\u003e\n\u003cp\u003eDetailed information regarding MRI acquisition and processing is available in the Supplementary Material.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e Processing procedures of the DTI images\u003c/h2\u003e\u003cp\u003eDetails of the MRI acquisition procedures are provided in the Supplementary Material. The imaging data were preprocessed using Statistical Parametric Mapping 12 (SPM12) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/software/spm12\u003c/span\u003e\u003cspan address=\"https://www.fil.ion.ucl.ac.uk/spm/software/spm12\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), running on MATLAB version 2018b, along with the PANDA toolbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nitrc.org/projects/panda/\u003c/span\u003e\u003cspan address=\"https://www.nitrc.org/projects/panda/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the FMRIB Software Library (FSL) version 6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL\u003c/span\u003e\u003cspan address=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe PANDA processing pipeline consisted of two primary stages: (1) preprocessing and (2) generating diffusion metrics. The preprocessing workflow included the following five steps: (1) estimating the brain mask, (2) cropping raw images, (3) correcting for eddy-current distortions, (4) averaging multiple acquisitions, and (5) computing diffusion tensor (DT) metrics. The DT metrics analyzed in this study included FA, MD, and RD. The resulting diffusion metric images were then normalized to the Montreal Neurological Institute (MNI) standard space for further analysis.\u003c/p\u003e\u003cp\u003eTo facilitate regional analysis, diffusion metric images with a voxel size of 1.0\u0026times;1.0\u0026times;1.0 mm\u0026sup3; in standard space were processed using a WM probabilistic tract atlas. This atlas consists of 20 white matter tracts, which were identified probabilistically through deterministic tractography performed on a cohort of 28 healthy individuals\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The WM probabilistic tract atlas includes the following WM tracts: (1) anterior thalamic radiation (ATR), (2) cingulum in the cingulate cortex, (3) cingulum in the hippocampal area, (4) corticospinal tract (CST), (5) forceps major, (6) forceps minor, (7) inferior fronto-occipital fasciculus (IFOF), (8) superior longitudinal fasciculus (SLF), (9) temporal projection of the SLF, (10) inferior longitudinal fasciculus (ILF), and (11) uncinate fasciculus (UF).\u003c/p\u003e\u003cp\u003eFor each WM tract, separate statistical results were obtained for the left and right hemispheres, except for the forceps major and minor, which were analyzed as a unified region. These statistical files contained values for FA, MD, and RD, each of which provides distinct insights into WM integrity \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eFA (Fractional Anisotropy): Represents the degree of directional water diffusion within a voxel. Higher FA values suggest greater microstructural integrity of WM.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eMD (Mean Diffusivity): Reflects the overall magnitude of water diffusion, irrespective of direction. Higher MD values indicate increased water mobility, which may suggest lower WM density, structural degradation, or potential injury.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eRD (Radial Diffusivity): Measures water diffusion perpendicular to the principal direction. Increased RD values are often interpreted as markers of demyelination, providing insight into myelin integrity.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eAdditional details on the PANDA processing pipeline can be found in a previous study\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAβ deposition\u003c/h3\u003e\n\u003cp\u003eThe methodology for acquiring and processing [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]-FMM PET images to evaluate Aβ deposition, as well as the calculation of standardized uptake value ratios (SUVRs) to quantify deposition intensity, is comprehensively detailed in the Supplementary Material. A threshold of 0.62 was employed to differentiate between Aβ positive (Aβ +) and Aβ negative (Aβ -) accumulations, aligning with previous FMM-PET studies \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. It is crucial to note that 'negative accumulation' denotes subthreshold deposition rather than a complete absence of amyloid deposition.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAPOE\u003c/b\u003e \u003cb\u003egenotyping\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe approach for \u003cem\u003eAPOE\u003c/em\u003e genotyping is detailed in the Supplementary Material. According to our protocol, we would exclude subjects who possessed the \u003cem\u003eAPOE\u003c/em\u003e ε2 allele due to its observed protective role\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Participants were classified based on the presence of the \u003cem\u003eAPOE\u003c/em\u003e ε4 allele; those with at least one ε4 allele were grouped as \u003cem\u003eAPOE\u003c/em\u003e ε4 carriers, whereas those without any ε4 alleles were designated as non-carriers.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBDNF\u003c/b\u003e \u003cb\u003egenotyping\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe methodology for \u003cem\u003eBDNF\u003c/em\u003e genotyping is described in the Supplementary Material. In the context of the \u003cem\u003eBDNF\u003c/em\u003e Val66Met polymorphism (rs6265), we categorized participants based on existing genetic research\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e into groups: those carrying at least one Met66 allele were labeled as Met carriers, whereas those without any Met66 alleles were considered Met non-carriers.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using R software (version 4.3.0), jamovi (version 2.6.19), and SPM 12 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/\u003c/span\u003e\u003cspan address=\"https://www.fil.ion.ucl.ac.uk/spm/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The normality of continuous variables was assessed using the Kolmogorov\u0026ndash;Smirnov test, and data were standardized using z-score transformation prior to analysis.\u003c/p\u003e\u003cp\u003ePartial correlation analyses were conducted to investigate associations between optimized EF strength and changes in WM microstructural integrity metrics, including FA, MD, and RD. These analyses adjusted for covariates such as age, sex, years of education, Aβ deposition, \u003cem\u003eAPOE\u003c/em\u003e ε4 carrier status, and \u003cem\u003eBDNF\u003c/em\u003e Val66Met polymorphism. Additionally, correlation analyses using Pearson\u0026rsquo;s product-moment correlation were performed to evaluate the relationships between changes in FA across tracts of interest. These tracts of interest were identified based on their relevance to the optimized EF strength and included WM tracts with significant FA changes.\u003c/p\u003e\u003cp\u003eBased on the findings from these partial correlation analyses, correlation analyses using Pearson\u0026rsquo;s product-moment correlation were performed to evaluate the relationships between changes in DTI parameters within the identified tracts of interest and changes in total and domain scores of the CERAD-K.\u003c/p\u003e\u003cp\u003eMultiple regression analyses were applied to examine the interaction effects of optimized EF strength and individual modifiers (e.g., Aβ deposition, \u003cem\u003eAPOE\u003c/em\u003e ε4 carrier status, and \u003cem\u003eBDNF\u003c/em\u003e polymorphism) on WM microstructural integrity metrics. In addition, to investigate whether these interaction effects extended to cognitive outcomes, multiple regression analyses were also conducted to test the interaction between optimized EF strength and each effect modifier for changes in total and domain scores of the CERAD-K. Covariates included age, sex, years of education, and the effect modifiers not included in the specific interaction model under evaluation. All statistical analyses were conducted using a two-tailed approach, with significance thresholds set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eBaseline demographic and clinical data\u003c/h2\u003e\u003cp\u003eA total of 70 participants were assessed for eligibility. Of these, 63 completed the intervention and post-tDCS assessments. Seven participants discontinued the intervention: six withdrew informed consent, and one experienced a mild adverse event (tingling under the electrode). Among the 63 participants who completed the intervention, eight were excluded from the analysis due to missing EF strength values. Therefore, the final analyses were conducted on 55 participants. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a detailed flowchart of participant recruitment, retention, and inclusion. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline demographic data for the participants who completed the study. The number and distribution of participants based on individual factors are described in the Supplementary Material.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline demographic and clinical characteristics of the study participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eDemographic and clinical characteristics (N\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e72.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Male\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e17 (30.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Female\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e38 (69.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e12.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e[\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF] Flutemetamol deposition (positivity, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e20 (36.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlobal [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF] Flutemetamol SUVR\u003csub\u003ePONS\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e ε4 carrier status (carrier. %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e26 (47.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBDNF\u003c/em\u003e polymorphism (Val/Met or Met/Met, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e45 (81.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCERAD-K\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBNT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMMSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWLM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e14.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWLRc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTMT B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e223.1\u0026thinsp;\u0026plusmn;\u0026thinsp;79.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroop word-color\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal CERAD-K\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e57.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOptimized EF strength (mV/mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eData are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD unless indicated otherwise. SUVR\u003csub\u003ePONS\u003c/sub\u003e, standardized uptake value ratio of [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF] Flutemetamol, using the pons as a reference region; CERAD-K, Korean version of Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease; VF, verbal fluency; BNT, Boston Naming Test; MMSE, the Korean version of the Mini-Mental Status Examination; WLM, Word List Memory; CP, Constructional Praxis; WLR, Word List Recall; WLRc, Word List Recognition; CR, constructional recall; TMT B, Trail Making Test B; Total CERAD-K, composite score summing scores of the CERAD-K VF, BNT, WLM, CP, WLR, and WLRc domains; EF, electrical field.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eChanges in WM Microstructural Integrity and Optimized EF Strength\u003c/h2\u003e\u003cp\u003eAfter 10 sessions of anodal tDCS, significant associations were observed between optimized EF strength and changes in WM microstructural integrity metrics, including FA, MD, and RD. Partial correlation analyses, adjusted for age, sex, education years, Aβ deposition, \u003cem\u003eAPOE\u003c/em\u003e ε4 carrier status, and \u003cem\u003eBDNF\u003c/em\u003e Val66Met polymorphism, demonstrated significant correlations between optimized EF strength and changes in these metrics across tracts of interest (Fig.\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eFor FA, significant positive correlations were identified in the following tracts: left ATR (r\u0026thinsp;=\u0026thinsp;0.417, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), left CST (r\u0026thinsp;=\u0026thinsp;0.434, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), left IFOF (r\u0026thinsp;=\u0026thinsp;0.392, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), and left ILF (r\u0026thinsp;=\u0026thinsp;0.374, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). Significant correlations were identified between changes in FA across the WM tracts of interest (Fig.\u0026nbsp;2A). All correlations were statistically significant with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating positive associations among changes in the left CST, left IFOF, and left ILF.\u003c/p\u003e\u003cp\u003eFor MD and RD, significant negative correlations were observed in the same tracts of interest. Specifically, the correlations for MD and RD were as follows: left CST (r = -0.336 and \u0026minus;\u0026thinsp;0.425, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018 and 0.002), and left IFOF (r = -0.378 and \u0026minus;\u0026thinsp;0.413, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007 and 0.003) (Fig.\u0026nbsp;2B). However, no significant associations were observed between optimized EF strength and changes in WM microstructural integrity metrics in other WM tracts included in the probtract atlas.\u003c/p\u003e\u003cp\u003eAdditionally, correlation analyses using Pearson\u0026rsquo;s product-moment correlation did not reveal any significant associations between changes in DTI parameters within the identified tracts of interest and changes in total and domain scores of the CERAD-K.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eInteractions Between Optimized EF Strength and Individual Factors Associated with AD\u003c/h2\u003e\u003cp\u003eSignificant associations were identified between optimized EF strength and changes in WM microstructural integrity, with these effects varying based on individual effect modifiers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, Aβ deposition moderated the relationship between EF strength and microstructural changes in the right SLF (MD; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014 and RD; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012). Specifically, increased EF strength was associated with reductions in MD and RD in Aβ-positive patients.\u003c/p\u003e\u003cp\u003eIn addition, a significant interaction was observed between EF strength and \u003cem\u003eAPOE\u003c/em\u003e ε4 carrier status in predicting WM microstructural changes in the right SLF. Specifically, EF strength was associated with greater reductions in RD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021) and MD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) among non-carriers compared to carriers.\u003c/p\u003e\u003cp\u003eAdditionally, \u003cem\u003eBDNF\u003c/em\u003e polymorphism influenced the relationship between EF strength and WM integrity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Higher EF strength was associated with increased FA in the right UF among Met allele non-carriers.\u003c/p\u003e\u003cp\u003eRegarding cognitive outcomes, interaction analyses revealed that \u003cem\u003eAPOE\u003c/em\u003e ε4 status moderated the relationship between EF strength and cognitive change (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Specifically, a significant interaction was found for the CERAD SWCT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), indicating that higher EF strength was associated with improved Stroop performance in non-carriers, whereas carriers showed no improvement or a decline. No significant interactions were observed between EF strength and \u003cem\u003eAPOE\u003c/em\u003e ε4 status for the CERAD-K total score or other domain scores.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the effects of two weeks of consecutive tDCS on WM microstructural integrity in individuals with MCI and examined how these effects varied according to individual factors related to AD, including Aβ deposition, \u003cem\u003eAPOE\u003c/em\u003e ε4 carrier status, and \u003cem\u003eBDNF\u003c/em\u003e Val66Met polymorphism.\u003c/p\u003e\u003cp\u003eWe observed significant associations between optimized EF strength and WM integrity changes. Higher EF strength was linked to greater increases in FA and reductions in mean MD and RD in left-lateralized tracts, including the ATR, CST, IFOF, and ILF. These findings suggest that a greater EF strength may facilitate more robust WM plasticity, potentially reflecting increased axonal coherence and myelination\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Previous studies have similarly reported FA increases following tDCS, supporting its potential to induce WM structural modification\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eEach affected tract plays a distinct role in cognitive and neural function. The ATR is critical for executive function and working memory, and its integrity has been associated with cognitive recovery in individuals with MCI\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. The left CST, primarily responsible for motor function\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, showed the strongest correlation with EF strength, suggesting that optimized tDCS may exert effects on motor-related pathways. The left IFOF and ILF, involved in visuospatial processing\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e and memory function\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, respectively, also exhibited EF strength-dependent changes. Furthermore, the current study identified significant correlations among FA changes across these tracts. This suggests that tDCS may have induced synchronized structural changes across these pathways. It is plausible that as EF strength increased, axonal coherence and myelination improved concurrently across these tracts. Previous studies have reported synchronized WM changes following tDCS, particularly in pathways such as the CST and IFOF, which may contribute to network-level neuroplasticity and functional integration\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. These findings highlight the potential for tDCS to influence broader cognitive networks rather than exerting purely localized cortical effects, emphasizing the importance of optimizing EF strength to enhance network-level plasticity while avoiding potential overstimulation effects.\u003c/p\u003e\u003cp\u003eBeyond FA changes, significant reductions in MD and RD were found in the left CST and left IFOF. Lower MD and RD values indicate reduced extracellular diffusion, typically associated with improved axonal integrity and myelination\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Given that MD and RD are sensitive markers of demyelination and overall tissue integrity, these findings suggest that tDCS may contribute to structural stabilization or repair. The specificity of these changes in certain tracts may be attributed to their proximity to the stimulation site or differential sensitivity to tDCS-induced plasticity. The left IFOF and CST, being anatomically closer to the DLPFC stimulation site, exhibited more pronounced effects, whereas deeper tracts such as the ATR and ILF showed comparatively less change. As EF intensity is strongest at the cortical surface and diminishes with depth\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, these variations may reflect structural and functional differences in tract responsiveness to tDCS. Future studies should explore how variations in EF strength and electrode configuration influence WM integrity across different pathways.\u003c/p\u003e\u003cp\u003eThe influence of individual AD-related factors was evident in this study. In Aβ-positive individuals, higher EF strength correlated with greater reductions in MD and RD in the right SLF, suggesting that tDCS may promote compensatory plasticity in regions affected by Aβ pathology. The SLF is a key pathway linking the frontal and parietal lobes and plays a critical role in working memory and executive function\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Previous studies have reported FA reductions in the SLF in AD patients, linking such changes to cognitive decline\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Our findings suggest that tDCS may enhance WM integrity in Aβ-positive individuals, potentially mitigating structural deterioration associated with AD pathology.\u003c/p\u003e\u003cp\u003eSimilarly, \u003cem\u003eAPOE\u003c/em\u003e ε4 carrier status significantly modulated the effects of tDCS. Non-carriers exhibited greater MD and RD reductions in the right SLF compared to carriers, indicating that \u003cem\u003eAPOE\u003c/em\u003e ε4 allele may attenuate the neuroplastic effects of tDCS. \u003cem\u003eAPOE\u003c/em\u003e ε4 allele is a well-established genetic risk factor for AD, and prior studies have shown that carriers experience more severe WM damage and reduced neuroplastic potential\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. In addition, a significant interaction between optimized EF strength and \u003cem\u003eAPOE\u003c/em\u003e ε4 status was observed in relation to executive function, as measured by the CERAD-K SWCT. Specifically, higher EF strength was associated with greater improvement in Stroop performance among non-carriers compared with carriers. Considering the established involvement of the SLF in executive function via frontoparietal connectivity, it is plausible that EF strength-related enhancements in WM microstructural integrity within the right SLF contributed to the observed cognitive improvement in non-carriers. These findings indicate a potential structure\u0026ndash;function relationship whereby optimized EF strength may facilitate both WM plasticity and functional gains in individuals without the \u003cem\u003eAPOE\u003c/em\u003e ε4 allele. Therefore, \u003cem\u003eAPOE\u003c/em\u003e ε4 carriers may require adjusted stimulation protocols to achieve comparable neuroplastic and cognitive benefits.\u003c/p\u003e\u003cp\u003e\u003cem\u003eBDNF\u003c/em\u003e Val66Met polymorphism also influenced FA changes, with non-carriers of the Met allele exhibiting greater FA increases in the right UF. The UF is a major tract connecting the prefrontal cortex and temporal lobe and is essential for memory and emotional regulation\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Reduced FA in the UF has been reported in AD and is associated with disease progression \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Given that BDNF plays a central role in neuroplasticity\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e and that the Met allele is associated with reduced BDNF secretion\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, our findings suggest that BDNF-mediated mechanisms influence the neurophysiological response to tDCS. These results underscore the relevance of genetic factors in designing personalized neuromodulation approaches.\u003c/p\u003e\u003cp\u003eThe present study demonstrated a significant association between optimized EF strength and specific WM microstructural changes. However, these structural modifications were not accompanied by measurable improvements in cognitive performance when analyzed within tracts that showed significant associations with EF strength. Notably, no direct relationship was observed between EF-sensitive WM changes and cognitive outcomes within the scope of the current two-week stimulation protocol. These findings suggest that, although optimized EF strength may induce physiologically meaningful WM plasticity, such changes may not immediately translate into short-term cognitive benefits in a tract-specific manner.\u003c/p\u003e\u003cp\u003eIn contrast, previous findings from the same cohort reported tDCS-induced cognitive improvements in specific subgroups, including Aβ-negative individuals and those without the \u003cem\u003eBDNF\u003c/em\u003e Met allele\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Additionally, prior analyses showed that baseline Aβ burden was significantly associated with FA in the left hippocampal cingulum, which in turn predicted delayed memory improvement\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This contrast highlights a critical distinction: while certain WM tracts, including the left hippocampal cingulum, have been linked to both AD-related pathology and cognitive outcomes, the EF-sensitive tracts identified in the present study did not show such associations. This dissociation suggests that EF strength-related WM plasticity may influence cognitive outcomes through longer-term or more distributed network-level mechanisms, which may not be fully captured within the short duration of the current study. It also raises the possibility that structural changes may precede functional improvements, which could emerge only after extended follow-up.\u003c/p\u003e\u003cp\u003eSeveral limitations should be acknowledged. First, the absence of a sham control group restricts the ability to draw causal inferences about the effects of tDCS, as placebo effects or nonspecific stimulation-related influences cannot be excluded. Second, the relatively short stimulation period may have limited the detection of delayed or cumulative cognitive effects related to WM plasticity. Finally, given the interindividual variability in response to stimulation, future studies should further explore optimal EF thresholds and stimulation parameters tailored to individual characteristics.\u003c/p\u003e\u003cp\u003eIn conclusion, this study confirms that optimized EF strength is significantly associated with WM microstructural changes in individuals with MCI. It also emphasizes the importance of considering individual AD-related factors when evaluating the efficacy of tDCS. These findings support the development of personalized neuromodulation approaches and highlight the potential of precision medicine in non-invasive brain stimulation strategies for the prevention and management of AD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eConflicts of interest\u003c/p\u003e\n\u003cp\u003eAuthor Hyun Kook Lim, TaeYeong Kim, and Donghyeon Kim are employed by NEUROPHET Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The data processing services provided by NEUROPHET Inc. were utilized to enhance the quality and analysis of the brain imaging data collected during the study. Authors declare that the research outcomes and conclusions remain unbiased and are not influenced by any commercial interests associated with the NEUROPHET Inc.\u0026apos;s products or services.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJung-Won Lee:\u003c/strong\u003e Methodology, Data Curation, Visualization, Writing - Original Draft.\u0026nbsp;\u003cstrong\u003eSunghwan Kim:\u003c/strong\u003e Methodology, Data Curation.\u0026nbsp;\u003cstrong\u003eSuhyung Kim\u003c/strong\u003e:\u0026nbsp;Data Curation.\u0026nbsp;\u003cstrong\u003eYoo Hyun Um:\u003c/strong\u003e Software, Investigation, Writing - Review \u0026amp; Editing. \u003cstrong\u003eSheng-Min Wang:\u003c/strong\u003e Methodology, Data Curation, Writing - Review \u0026amp; Editing. \u003cstrong\u003eTaeYeong Kim:\u003c/strong\u003e Methodology, Data Curation. \u003cstrong\u003eDonghyeon Kim:\u003c/strong\u003e Methodology, Data Curation,\u0026nbsp;Funding Acquisition. \u003cstrong\u003eHyun Kook Lim:\u003c/strong\u003e Conceptualization, Methodology, Writing - Review \u0026amp; Editing, Supervision,\u0026nbsp;Funding Acquisition. \u003cstrong\u003eChang Uk Lee:\u003c/strong\u003e Conceptualization, Supervision. \u003cstrong\u003eDong Woo Kang:\u003c/strong\u003e Conceptualization, Methodology, Data Curation, Formal Analysis, Project Administration, Funding Acquisition.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (No. 2019R1C1C1007608) and a grant of the Korea Health Technology R\u0026amp;D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health \u0026amp; Welfare, Republic of Korea (No. HI22C0467).\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets generated or analyzed during the current study are not publicly available due to the Patient Data Management Protocol of Yeouido Saint Mary\u0026rsquo;s Hospital but are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eScheltens, P. et al. 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Rep.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 18818 (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Transcranial Direct Current Stimulation, Mild Cognitive Impairment, White Matter, Diffusion Tensor Imaging, Alzheimer Disease","lastPublishedDoi":"10.21203/rs.3.rs-6541880/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6541880/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTranscranial direct current stimulation (tDCS) is a promising non-invasive intervention for mild cognitive impairment (MCI). This prospective study investigated the relationship between optimized electrical field (EF) strength of tDCS and white matter (WM) microstructural changes in 63 individuals with MCI following personalized tDCS. Magnetic resonance imaging (MRI)-based computational modeling was used to optimize EF strength targeting the left dorsolateral prefrontal cortex (DLPFC). Diffusion tensor imaging (DTI) assessed WM integrity through fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD). Higher EF strength was significantly associated with increased FA and reduced MD and RD in specific left-lateralized tracts, including the anterior thalamic radiation, corticospinal tract, inferior fronto-occipital fasciculus, and inferior longitudinal fasciculus. These EF-dependent WM changes were moderated by Alzheimer\u0026rsquo;s disease (AD)-related factors. Greater WM plasticity was observed in Aβ-positive individuals, \u003cem\u003eAPOE\u003c/em\u003e ε4 non-carriers, and \u003cem\u003eBDNF\u003c/em\u003e Met non-carriers. Moreover, \u003cem\u003eAPOE\u003c/em\u003e ε4 status significantly moderated the relationship between EF strength and executive function; in non-carriers, stronger EF strength was associated with improved Stroop performance, potentially reflecting enhanced WM integrity in the right superior longitudinal fasciculus. However, no significant associations were observed between EF-sensitive tracts and short-term cognitive changes in the full sample, suggesting that structural modifications may precede functional improvements or require longer follow-up. These findings emphasize the importance of individual AD-related factors in shaping neuromodulatory responses and support further longitudinal, sham-controlled studies to clarify the clinical implications of EF strength in personalized tDCS for MCI.\u003c/p\u003e","manuscriptTitle":"Optimized Electrical Field Strength of tDCS and White Matter Microstructural Integrity in MCI: Role of Individual Factors Associated with AD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-10 06:24:09","doi":"10.21203/rs.3.rs-6541880/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-29T03:48:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-18T15:55:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-18T07:46:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213926744142191765743168915105351707237","date":"2025-07-09T13:42:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71814019489991530588515860296884047688","date":"2025-07-08T03:22:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19592291953936610172708003125669098959","date":"2025-07-08T02:39:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211487973160632549029696149583229779561","date":"2025-07-06T12:13:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-04T11:57:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-08T08:55:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-30T11:07:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-28T07:34:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-27T17:41:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"302330c9-182a-4160-9c62-4b6bcfb2e004","owner":[],"postedDate":"July 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51185625,"name":"Health sciences/Medical research/Biomarkers/Prognostic markers"},{"id":51185626,"name":"Biological sciences/Neuroscience/Diseases of the nervous system/Dementia"},{"id":51185627,"name":"Health sciences/Diseases/Neurological disorders/Dementia"}],"tags":[],"updatedAt":"2025-12-22T16:07:16+00:00","versionOfRecord":{"articleIdentity":"rs-6541880","link":"https://doi.org/10.1038/s41598-025-27612-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-17 15:58:39","publishedOnDateReadable":"December 17th, 2025"},"versionCreatedAt":"2025-07-10 06:24:09","video":"","vorDoi":"10.1038/s41598-025-27612-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-27612-7","workflowStages":[]},"version":"v1","identity":"rs-6541880","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6541880","identity":"rs-6541880","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}