Evaluating P300 Latency as a Physiological Marker for Asymptomatic and Prodromal Alzheimer’s Disease

Evaluating P300 Latency as a Physiological Marker for Asymptomatic and Prodromal Alzheimer’s Disease | 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 Evaluating P300 Latency as a Physiological Marker for Asymptomatic and Prodromal Alzheimer’s Disease Manal Mohamed, Nourelhuda Mohamed, Jang Jae Lee, Kyu Yeong Choi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6525416/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Alzheimer's disease (AD) is a progressive neurodegenerative disorder that poses significant challenges in terms of early diagnosis and intervention. While various biomarkers have been explored, few studies have utilized electroencephalography (EEG) with a focus on P300 peak latency to distinguish between the preclinical stages of AD, specifically Asymptomatic AD (AAD) and Prodromal AD (PAD). Methods In this study, we investigated P300 latency during an oddball task. EEG data was collected from a total of 117 participants with 39 Healthy Controls (HCs) (mean age = 72.08 ± 4.08 years), 39 AAD (mean age = 73.08 ± 4.75 years), and 39 PAD (mean age = 74.95 ± 4.29 years). Statistical analyses involved ANOVA tests to assess group differences in neurophysiological and neuropsychological data. With a focus on regional differences across the left, middle, and right brain hemispheres, a mixed-design ANOVA examined P300 peak latency, followed by post-hoc tests and ROC analysis to evaluate classification performance at the individual level. Results Our results showed that P300 peak latency can effectively differentiate HC from both AAD and PAD, with the left hemisphere providing the most significant distinction between HC and AAD, with a sensitivity of 74.3% and specificity of 55.6%. P300 latency from the middle region demonstrated a sensitivity of 77.4% and specificity of 72.2% for distinguishing HC from PAD, while the right region showed the highest sensitivity (80%) but lower specificity (63.9%) for HC vs PAD. However, no clear distinction was observed between AAD and PAD, except for a borderline significance in the middle region. Conclusions These results suggest that P300 latency from the left hemisphere is capable of differentiating HCs from AAD, and latency in any brain region distinguishes HCs from PAD. Accordingly, we concluded that P300 latency could serve as a useful biomarker for the early detection and classification of AD, particularly in its preclinical stages. Physical sciences/Engineering/Biomedical engineering Biological sciences/Neuroscience/Cognitive ageing Biological sciences/Neuroscience/Cognitive neuroscience Health sciences/Diseases/Neurological disorders P300 ERP asymptomatic AD prodromal AD left hemisphere Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and behavioral changes [ 1 , 2 ]. As of 2025, more than 55 million people worldwide are living with AD and other dementias, a number projected to rise to 139 million by 2050. This increasing prevalence represents a significant global health challenge, imposing substantial emotional and financial burdens on patients, families, and healthcare systems. The global economic impact is considerable, with the cost of dementia estimated at $ 818 billion in 2015 [ 3 ]. These statistics highlight the urgent need for early detection, diagnosis, and treatment of AD in its preclinical stages, including asymptomatic AD (AAD) and prodromal AD (PAD), before progression to full-blown dementia occurs. A variety of alterations in the brain are believed to play a role in the development of AD, such as the buildup of beta-amyloid plaques outside of neurons and deposition of tau tangles, an abnormal form of tau protein [ 4 ], leading to inflammation, neuronal atrophy, and cell death [ 5 , 6 ]. In AD, brain atrophy in the left and right hemispheres is asymmetrical [ 7 ], with the left hemisphere having more severe cortical thinning, amyloid-beta plaques, and loss of neurite connections [ 8 ]. A recent study discovered that cortical thinning occurs asymmetrically throughout adulthood and that AD causes this thinning to occur more quickly than normal aging [ 9 ]. In another study, scientists found that the white matter networks of AD patients exhibited rightward asymmetry. They suggested that impairment in the left hemisphere was the cause of this rightward asymmetry [ 10 ]. Additionally, [ 11 ] concluded that in preclinical AD, the left hemisphere exhibits differences compared to the right, including potential selective vulnerability to neurodegeneration and asymmetric amyloid deposition. Various neuroimaging techniques, such as magnetic resonance imaging (MRI), have been employed to investigate the early stages of AD while focusing on brain asymmetry [ 12 ]. Among many EEG-related biomarkers, P300 latency—a component of event-related potentials (ERPs)—has shown promise [ 13 – 15 ], however, to our knowledge, no study has explored its use as biomarkers to distinguish between the preclinical phases of AD —specifically AAD and PAD—while taking into consideration brain asymmetry in both healthy and pathological conditions. The majority of studies that have used the P300 peak latency acquired during oddball tasks compared PAD to healthy controls (HCs) [ 16 – 25 ] or AD to HCs [ 26 – 31 ] without considering asymmetry, focusing mostly on Fz, Cz, and/or Pz electrodes. Notably, none of these studies have included AAD subjects. In 2024, our research team explored the potential of P300 peak latency acquired during the visual oddball task as a biomarker for distinguishing the HC, AAD, and PAD groups from one another [ 32 ]. The findings indicated that P300 peak latency at central electrodes (C3, Cz, and C4) can effectively differentiate PAD from both AAD and HCs. However, it was inadequate to differentiate AAD from HCs. To address this limitation, the current study aims to explore broader regional differences by analyzing the left, middle, and right brain regions achieved by averaging signals from three electrodes within each region. This methodological shift is motivated by the observed hemispheric asymmetry in AD pathology, which may enhance the differentiation between AAD, PAD, and HC groups [ 6 ]. Methods Participants EEG data were collected from senior citizens residing in Gwangju, recruited by the National Research Center for Dementia and Chonnam National University Hospital (Gwangju, South Korea). A series of medical examinations, including MMSE, PET, MRI, and patient interviews, were conducted to determine the disease stage. The recruitment and diagnostic procedures followed in this study align with those described in our previous work [ 32 ]. The participants in this study were categorized into three groups: healthy controls (HCs), who were cognitively normal; asymptomatic Alzheimer's disease (AAD), who exhibited amyloid positivity on PET scans; and prodromal Alzheimer's disease (PAD), who presented with mild cognitive impairment. A total of 117 participants were enrolled, with 39 individuals in each group: HC (mean age = 72.08 ± 4.08 years), AAD (mean age = 73.08 ± 4.75 years), and PAD (mean age = 74.95 ± 4.29 years). Table 1 provides a summary of the demographic and psychometric factors of the participants, along with the results of statistical comparisons between the three groups. These differences were assessed using a one-way analysis of variance (ANOVA). Table 1 Demographics, psychometric and statistical comparisons among the three groups. HC AAD PAD p value Post-hoc HC vs. AAD HC vs. PAD AAD vs. PAD Count 39 39 39 - - - - Age (years) 72.051 ± 4.104 73.077 ± 4.748 74.949 ± 4.292 0.015 0.913 0.013 0.187 Education (years) 9.462 ± 4.246 9.795 ± 4.047 10.449 ± 5.049 0.612 1.00 0.994 1.00 Gender (M/F) 13/26 22/17 23/16 0.045* - - CDR 26.128 ± 6.404 27.051 ± 2.224 25.436 ± 3.362 0.265 1.00 1.00 0.316 MMSE 26.821 ± 4.715 27.051 ± 2.224 25.462 ± 3.363 0.113 1.00 0.294 0.160 CDR, Clinical Dementia Rating; MMSE, Mini-Mental State Examination. *Chi-square test. Neuropsychological tests The Seoul Neuropsychological Screening Battery (SNSB-II) was utilized for neuropsychological screening. It consists of 18 subsets across five cognitive domains: attention, language, memory, visuospatial functions, and executive functions, as summarized in Table 2 . For a more detailed description of these tests and their standards, please refer to [ 32 , 33 ]. Table 2 Cognitive Domains and Subtests of the Seoul Neuropsychological Screening Battery (SNSB-II). Domain Tests Attention Digit Span Test Forward (DST-F) and Digit Span Test Backward (DST-B). Language and related functions Repetition, Korean-Boston Naming Test (K-BNT) and its short form (S-K-BNT), Praxis Ideomotor test, Calculation test, and Comprehension test. Visuospatial functions Copy of the Rey Complex Figure Test (Rey CFT) including copy score and time. Memory Seoul Verbal Learning Test (SVLT), including Immediate recall, Delayed recall, Recognition, and Recognition discriminability index; and Rey Complex Figure Test (RCFT), including Immediate recall, Delayed recall, Recognition, and Recognition discriminability index. Frontal/executive functions Contrasting program, Go-no-go, Controlled Oral Word Association Test (COWAT), including animal, supermarket, and phonemic tests; and Korean-Color Word Stroop Test (K-CWST), including Word reading, Color reading, and Interference score, Digit Symbol Coding (DSC), and Korean-Trail Making Test-Elderly’s version (K-TMT-E) SNSB-II total SNSB-II domain attention, domain language, domain visuospatial, domain memory, domain frontal, and SNSB-C Stimuli and task: P300 visual oddball task In this study, we employed the same P300 visual oddball task described in [ 32 ], instructing participants to focus on the center of the screen while responding to the occasional yellow circle among a succession of blue circles. The stimulus presentation parameters included a ratio of 25:75 for target and non-target stimuli, respectively. While our previous study used the data from Oddball Task trial 1, the current study utilized data from trial 2 with a larger number of participants. Figure 1 presents a schematic layout of the task stimulus sequence and timing. Electroencephalography (EEG) Data EEG signals were recorded using a 32-channel dry electrode wireless bio-signal acquisition system (g. Nautilus, g.tec, Austria). The electrode positioning followed the 10–20 international electrode positioning system, with the left and right mastoids serving as reference sites. Data were recorded at a sampling frequency of 500 Hz using BCI2000 software, alongside fNIRS signals, which are not within the scope of the current study. For data processing, MATLAB EEGLAB/ERPLAB toolboxes were used to perform resampling, filtering, independent component analysis (ICA) artifact removal, and epoching to obtain the P300 waveform. Further details can be found in [ 32 ]. P300 peak latency was extracted with a time window of 300–600 ms. post-stimulus from electrodes of interest (EOI), namely, AF3, Fz, AF4, C3, Cz, C4, PO3, Pz, and PO4. For statistical modeling, these EOIs were grouped into three regions representing Left Hemisphere (L: AF3, C3, PO3), Middle (M: Fz, Cz, Pz), and Right Hemisphere (R: AF4, C4, PO4), as shown in Fig. 2 . Statistical Analysis Statistical analyses were conducted using JASP 0.18.3.0. ANOVA tests assessed group differences based on neurophysiological data and P300 latency. The neuropsychological data was analyzed using a one-way ANOVA with the groups (HC, AAD, PAD) as the independent variable and each neuropsychological test as the dependent variable to assess their effectiveness in differentiating the groups. Post-hoc tests with Bonferroni correction were applied to examine significant differences further. A mixed-design ANOVA was performed to investigate the ability of P300 peak latency from the L, M, and R regions to differentiate between the three groups, with groups (HC, AAD, PAD) serving as the between-subjects factor and the regions (L, M, R) as the repeated measure. Age and gender, which differed significantly across groups, were included as covariates. To further explore significant differences, group effects were analyzed using post-hoc tests with Bonferroni correction followed by the analysis of groups and region interaction (groups * region) effects to assess significance within each region. Following the mixed ANOVA, receiver operating characteristic (ROC) analysis was performed to determine the cut-off scores, sensitivity, specificity, area under the curve (AUC), and 95% confidence intervals for the P300 peak latencies that showed significant group and region interaction effects. This analysis is crucial for evaluating whether the classification power of P300 peak latencies, which demonstrated significant group * region effects, holds at the individual level. Results Neuropsychology The analysis of neuropsychological tests revealed significant differences in the SNSB-C ( p = 0.004) and the memory and frontal domains of the SNSB-II ( p < 0.001), as shown in Table 3 . Post-hoc comparisons for these domains indicated a significant difference between PAD and both HC and AAD, while no significant difference was observed between AAD and HC (Table 3 ). These findings suggest that SNSB-II fails to differentiate AAD from HC, aligning with the results reported in [ 32 ]. Event Related Potentials (ERPs) The analysis of P300 peak latency from the L, M, and R regions revealed a significant difference in the between-subjects effect ( p < 0.001). The post-hoc analysis of the group effects showed significantly shorter latency in the HC group compared to both the AAD and PAD groups ( p < 0.001) and in the AAD compared to the PAD group ( p = 0.003), as shown in Table 4 . These results demonstrate the ability of P300 peak latency from L, M, and R regions to differentiate the three groups. The grand average waveforms from L, M, and R regions during target and non-target processing are as illustrated in Fig. 3 . Table 3 Groups performance on the neuropsychological battery and the statistical analysis results. HC AAD PAD p value Post-hoc HC vs AAD HC vs PAD AAD vs PAD SNSB-II total Attention domain 9.641 ± 2.254 9.641 ± 1.530 9.000 ± 1.850 0.232 1.00 0.418 0.418 Language domain 0.148 ± 0.342 0.157 ± 0.321 -0.017 ± 0.425 0.065 1.00 0.147 0.113 Visuospatial domain 0.449 ± 0.482 0.435 ± 0.415 1.153 ± 5.222 0.493 1.00 0.925 0.896 Memory domain 0.251 ± 0.584 0.055 ± 0.681 -0.603 ± 0.732 < 0.001 0.589 < 0.001 < 0.001 Frontal domain 0.220 ± 0.528 0.121 ± 0.602 -0.290 ± 0.692 < 0.001 1.00 0.001 0.011 SNSB-C 25.077 ± 6.960 25.237 ± 7.379 20.316 ± 7.189 0.004 1.00 0.013 0.010 HC, Healthy Control; AAD, Asymptomatic AD; PAD, Prodromal AD; SNSB-II, Seoul Neuropsychological Screening Battery, 2nd Edition; SNSB-C, Seoul Neuropsychological Screening Battery-Core. Table 4 Statistical results of the 3 × 3 mixed ANOVA of P300 peak latency during target stimuli across the three regions (L, M, and R) among the three groups. variable HC (N = 39) AAD (N = 39) PAD (N = 39) F P η2 Post-hoc L 432.068 ± 41.772 466.872 ± 52.784 491.214 ± 39.970 24.003 < 0.001 0.222 HC < AAD < PAD M 436.718 ± 48.992 465.744 ± 62.153 496.752 ± 45.087 R 441.333 ± 46.321 465.470 ± 45.958 491.145 ± 33.509 Post-hoc analysis of groups * region effects showed a significant difference between HC and AAD in the L region ( p = 0.007), with no significant difference observed in the M and R regions. These findings suggest that P300 peak latency from the L region can effectively classify AAD from HCs. Conversely, significant differences between HC and PAD were observed across all regions ( p < 0.001), indicating that P300 peak latency from any of the three regions can effectively distinguish PAD from HCs. For AAD vs. PAD, no significant differences were observed in any region except for a trending significance in the M region ( p = 0.056), suggesting that P300 latency from the L, M, and R regions fails to differentiate these two groups. Table 5 provides a summary of these results. The ROC results for P300 peak latencies that showed significant group * region effects are presented in Fig. 4 and Table 6 . For HCs vs. AAD, the peak latency in the L region demonstrated a sensitivity of 74.3% and a specificity of 55.6% with a cut-off of 431.33 ms. For classifying HCs vs. PAD, the peak latency in L and M regions showed a sensitivity of 74.2% and 77.4%, respectively, and a specificity of 77.2%, while it showed the highest sensitivity (80%) in the R region. Table 5 The post-hoc analysis for the interaction between groups and brain regions (groups * regions). Mean difference P bonf L HC vs AAD − 40.576 ± 10.690 0.007 HC vs PAD -69.615 ± 11.021 < 0.001 AAD vs PAD -29.039 ± 10.629 0.246 M HC vs AAD -32.737 ± 10.690 0.089 HC vs PAD -66.827 ± 11.021 < 0.001 AAD vs PAD -34.090 ± 10.6920 0.056 R HC vs AAD -27.989 ± 10.690 0.341 HC vs PAD -55.041 ± 11.021 < 0.001 AAD vs PAD -27.053 ± 10.6920 0.419 Table 6 Results of ROC analysis of peak P300 latency at left, middle, and right regions. Cut-off AUC SE CI 95% Sensitivity Specificity L HC vs AAD 431.33 0.702 0.062 0.58–0.83 74.3 55.6 HC vs PAD 468.67 0.844 0.047 0.752–0.935 74.2 72.2 M HC vs PAD 448.67 0.833 0.051 0.733–0.933 77.4 72.2 R HC vs PAD 468 0.778 0.055 0.67–0.886 80.6 63.9 Discussion This study aimed to evaluate the potential of P300 peak latency, measured using EEG during an oddball task, as a biomarker for distinguishing between Healthy Controls (HCs), Asymptomatic Alzheimer's Disease (AAD), and Prodromal Alzheimer's Disease (PAD). Our findings suggest that P300 latency can effectively differentiate between these groups, especially when considering brain region-specific differences. First, the neuropsychological assessments revealed significant differences between the PAD group and both HC and AAD, particularly in the memory and frontal domains. These cognitive domains are crucial in the early stages of AD, as they reflect disruptions in attention, executive function, and memory processing—areas that are typically affected in AD [ 34 – 36 ]. The significant difference between PAD and HC supports studies highlighting the high sensitivity of neurophysiological tests in distinguishing MCI (PAD) from normal cognition and dementia [ 37 – 39 ], and the difference between PAD and AAD likely reflects varying declines in attention and working memory across AD’s preclinical stages [ 40 – 42 ]. The lack of significant differences between AAD and HC, however, highlights the subtlety of cognitive changes in AAD, supporting the idea that AAD may not show overt cognitive deficits in standard neuropsychological testing. This aligns with previous findings that suggest individuals with AAD are cognitively intact despite underlying pathological changes in the brain [ 43 – 45 ]. The ERP results, however, offered a more sensitive measure for distinguishing between the groups. Considering the brain regions (left (L), middle (M), and right (R)), the 3 × 3 mixed ANOVA revealed significant differences in P300 peak latency between HC, AAD, and PAD. HCs exhibited the shortest latencies across all regions, while PAD participants showed the longest. This finding aligns with previous research indicating that P300 latency increases with cognitive decline in AD [ 16 , 17 , 19 – 25 , 46 – 49 ]. The intermediate latency observed in the AAD group could be due to the fact that AAD is a transitional stage between HC and PAD. The aforementioned results suggest that analyzing P300 peak latency across the L, M, and R brain regions provides a comprehensive regional classification by capturing a broader spectrum of neural activity. This approach differs from that used in our previous study [ 32 ], where P300 peak latency recorded from central electrodes (C3, Cz, and C4) was insufficient to distinguish AAD from HCs which confirms that relying solely on specific electrode sites may not fully capture the extent of cognitive decline impact on P300 latency. Our analysis also revealed that P300 latency from the L region was particularly effective in distinguishing ADD from HC, while the R and M regions showed less pronounced differences. This finding is in line with the growing body of literature suggesting that the left hemisphere is more affected in the early stages of AD, particularly in regions associated with memory and executive function [ 6 , 50 , 51 ]. Our results corroborate these observations, emphasizing the role of brain asymmetry in AD pathology. The increased P300 latency in the left hemisphere among AAD and PAD individuals could be reflective of early cortical thinning, neuronal loss, or other pathological changes that are characteristic of AD [ 52 – 55 ]. Interestingly, while P300 latency from any brain region effectively distinguished between HC and PAD, it failed to differentiate AAD from PAD, except in the middle region, where a borderline significance was observed. This finding indicates that although P300 latency could serve as a useful marker for distinguishing healthy individuals from those with neurodegenerative changes, the shared neural mechanisms—such as amyloid-beta (Aβ) and tau protein accumulation, synaptic dysfunction, and disruptions in brain networks like the default mode network [ 56 , 57 ]—present in preclinical stages of AD (AAD and PAD) might be too subtle for P300 to resolve, at least with the electrode sites and task conditions used in this study. In addition to the significant differences observed in the group comparisons, the ROC analysis further supports the potential utility of P300 latency as a diagnostic tool. The AUC values for distinguishing HC from AAD and HC from PAD were generally high, with the left region showing the highest classification accuracy. These findings are promising for the use of P300 latency as a diagnostic marker for early-stage AD, particularly when considering the practical advantages of EEG in clinical settings, such as its non-invasive nature, relatively low cost, and ability to assess brain function in real time [ 58 – 61 ]. Strengths and Limitations Investigating the ability of P300 latency in distinguishing the very early stage of AD (AAD) from HCs using the visual oddball paradigm is an inherent strength of the study. While many studies focused on differentiating MCI (PAD) subjects from HCs, to our knowledge, no previous study has explored the use of P300 latency for the AAD classification, except by our group in 2024 [ 32 ]. On the other hand, the modest sample size in each group, which may affect the generalizability of the findings, and the simplicity and singleness of the task are the main limitations of this study. Also, as this study is not a longitudinal study, it cannot track changes in P300 latency over time that could reveal whether P300 latency changes precede or correlate with clinical progression in AAD and PAD individuals. Future Work In future work, larger and more diverse populations would be needed to confirm the results of this study. This would improve the applicability and Inferential strength of the study findings. Additionally, further investigation into the relationship between P300 latency and other biomarkers of AD, such as amyloid plaques and tau tangles, could help provide a more comprehensive understanding of how these markers interact in the preclinical and prodromal stages of the disease. Also, integrating P300 latency measurements, while focusing on the left hemisphere, with multimodal imaging techniques, such as functional near-infrared spectroscopy (fNIRS) or MRI, may improve the diagnostic value of this biomarker. Conclusions This study provides compelling evidence for the use of P300 peak latency as a biomarker for distinguishing healthy controls (HCs), asymptomatic Alzheimer’s disease (AAD), and prodromal Alzheimer’s disease (PAD). Our results revealed that the P300 peak latency from left, middle, and right brain regions differs significantly between HCs, AAD, and PAD groups. Further analysis using ROC demonstrated a high classification accuracy for HCs compared to both AAD and PAD, particularly in the left hemisphere. These results aligned with the current understanding of AD pathology and suggest that EEG could serve as a valuable tool for the early detection of AD. With further validation, EEG-based biomarkers could become valuable tools in early diagnosis and intervention planning. Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Gwangju Institute of Science and Technology (20201124-HR-57-02-04, on 24 November 2020). Informed consent was obtained from all subjects involved in the study. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research was supported by the National Research Foundation of Korea (2016M3C7A1905475 and 2022R1A2C3009749), the Healthcare AI Convergence Research & Development Program through the National IT Industry Promotion Agency of Korea (NIPA) funded by the Ministry of Science and ICT (No. S1601-20-1016), and the KBRI Basic Research Program through the Korea Brain Research Institute, funded by the Ministry of Science and ICT (24-BR-03-05). Author Contributions J.G.K. conceptualized and supervised the study and managed project administration and funding acquisition. N.M. and M.M. developed the methodology, performed data curation, validation, formal analysis, and investigation. M.M. and N.M. also contributed to software development and visualization. JJL and KYC contributed to subject recruitment. BCK contributed to the diagnosis of subjects. N.M. contributed to data collection. JG and KHL contributed to project management. 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Cold Spring Harb Perspect Med, 2012. 2 (4): p. a006171. Tse, C.S., et al., The utility of placing recollection in opposition to familiarity in early discrimination of healthy aging and very mild dementia of the Alzheimer's type. Neuropsychology, 2010. 24 (1): p. 49-67. Twamley, E.W., S.A. Ropacki, and M.W. Bondi, Neuropsychological and neuroimaging changes in preclinical Alzheimer's disease. J Int Neuropsychol Soc, 2006. 12 (5): p. 707-35. Tarawneh, R. and D.M. Holtzman, The clinical problem of symptomatic Alzheimer disease and mild cognitive impairment. Cold Spring Harb Perspect Med, 2012. 2 (5): p. a006148. Dubois, B., et al., " Advancing research diagnostic criteria for Alzheimer’s disease: The IWG-2 criteria.": Correction. 2014. Jack Jr, C.R., et al., NIA‐AA research framework: toward a biological definition of Alzheimer's disease. Alzheimer's & dementia, 2018. 14 (4): p. 535-562. Caselli, R.J. and E.M. Reiman, Characterizing the preclinical stages of Alzheimer's disease and the prospect of presymptomatic intervention. J Alzheimers Dis, 2013. 33 Suppl 1 (0 1): p. S405-16. Chiang, H.-S., et al., Age effects on event-related potentials in individuals with amnestic Mild Cognitive Impairment during semantic categorization Go/NoGo tasks. Neuroscience Letters, 2018. 670 : p. 19-21. Cid-Fernández, S., et al., Neurocognitive and Behavioral Indexes for Identifying the Amnestic Subtypes of Mild Cognitive Impairment. Journal of Alzheimer’s Disease, 2017. 60 (2): p. 633-649. Mudar, R.A., et al., The Effects of Amnestic Mild Cognitive Impairment on Go/NoGo Semantic Categorization Task Performance and Event-Related Potentials. Journal of Alzheimer’s Disease, 2016. 50 (2): p. 577-590. Tsai, C.-L., et al., The Role of Physical Fitness in the Neurocognitive Performance of Task Switching in Older Persons with Mild Cognitive Impairment. Journal of Alzheimer’s Disease, 2016. 53 (1): p. 143-159. Yang, C., et al., The Abnormality of Topological Asymmetry between Hemispheric Brain White Matter Networks in Alzheimer's Disease and Mild Cognitive Impairment. Front Aging Neurosci, 2017. 9 : p. 261. Lubben, N., et al., The enigma and implications of brain hemispheric asymmetry in neurodegenerative diseases. Brain Commun, 2021. 3 (3): p. fcab211. Yang, H., et al., Study of brain morphology change in Alzheimer's disease and amnestic mild cognitive impairment compared with normal controls. Gen Psychiatr, 2019. 32 (2): p. e100005. Tang, R., et al., Early Cortical Microstructural Changes in Aging Are Linked to Vulnerability to Alzheimer’s Disease Pathology. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2024. 9 (10): p. 975-985. Krumm, S., et al., Cortical thinning of parahippocampal subregions in very early Alzheimer's disease. Neurobiology of Aging, 2016. 38 : p. 188-196. Du, A.T., et al., Different regional patterns of cortical thinning in Alzheimer's disease and frontotemporal dementia. Brain, 2007. 130 (Pt 4): p. 1159-66. Sperling, R.A., et al., Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement, 2011. 7 (3): p. 280-92. Sperling, R., E. Mormino, and K. Johnson, The evolution of preclinical Alzheimer's disease: implications for prevention trials. Neuron, 2014. 84 (3): p. 608-22. Zhang, H., et al., The applied principles of EEG analysis methods in neuroscience and clinical neurology. Military Medical Research, 2023. 10 (1): p. 67. Veciana de las Heras, M., et al., Utility of Quantitative EEG in Neurological Emergencies and ICU Clinical Practice. Brain Sciences, 2024. 14 (9): p. 939. Biasiucci, A., B. Franceschiello, and M.M. Murray, Electroencephalography. Current Biology, 2019. 29 (3): p. R80-R85. Jadhav, C., et al., Clinical applications of EEG as an excellent tool for event related potentials in psychiatric and neurotic disorders. Int J Physiol Pathophysiol Pharmacol, 2022. 14 (2): p. 73-83. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6525416","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":456538810,"identity":"8dde43b2-731d-4c1c-8a54-f4c409e2caad","order_by":0,"name":"Manal Mohamed","email":"","orcid":"","institution":"Gwangju Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Manal","middleName":"","lastName":"Mohamed","suffix":""},{"id":456538811,"identity":"5cb8a7f7-3b17-4a2c-a565-f0d46a64a4bc","order_by":1,"name":"Nourelhuda Mohamed","email":"","orcid":"","institution":"Gwangju Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Nourelhuda","middleName":"","lastName":"Mohamed","suffix":""},{"id":456538812,"identity":"39817496-7f80-4be5-9e59-993487043aee","order_by":2,"name":"Jang Jae Lee","email":"","orcid":"","institution":"Chosun University","correspondingAuthor":false,"prefix":"","firstName":"Jang","middleName":"Jae","lastName":"Lee","suffix":""},{"id":456538813,"identity":"a8ef36a5-b972-48e7-856e-8c7de446878d","order_by":3,"name":"Kyu Yeong Choi","email":"","orcid":"","institution":"Chosun University","correspondingAuthor":false,"prefix":"","firstName":"Kyu","middleName":"Yeong","lastName":"Choi","suffix":""},{"id":456538814,"identity":"7d5b0e27-6325-4113-ae5f-e5cb94f37bfe","order_by":4,"name":"Byeong C. Kim","email":"","orcid":"","institution":"Chonnam National University Medical School","correspondingAuthor":false,"prefix":"","firstName":"Byeong","middleName":"C.","lastName":"Kim","suffix":""},{"id":456538815,"identity":"c4839a5f-7f0e-4ee2-84f3-df98131499ed","order_by":5,"name":"Jeonghwan Gwak","email":"","orcid":"","institution":"Korea National University of Transportation","correspondingAuthor":false,"prefix":"","firstName":"Jeonghwan","middleName":"","lastName":"Gwak","suffix":""},{"id":456538816,"identity":"c13e8afb-b852-471e-af66-effdcc4729f2","order_by":6,"name":"Kun Ho Lee","email":"","orcid":"","institution":"Chosun University","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"Ho","lastName":"Lee","suffix":""},{"id":456538817,"identity":"59b46d47-98ed-445b-8133-e0bb0bbec5e2","order_by":7,"name":"Jae Gwan Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIie2OsQrCMBRFbwlkk64RBH8hUtCl1F9peeDUT+iQSRexq1/SORLoqB/gVAQnwcFFB8EUuyejYM5yeY93eBcIBH6QSIEhRwo+LKSvsrIK81Qs/akZ0kdhG2NkV56yXXzSeFZIFspVbLuivGjOtBZkhxbziXYpqkx0r3Bhi40UUuEqFtX3h1WOxGOD6O2l7Etmi+mMg8Dsl7mHck1k0VDOBUkzaUXiVGY1XcavJltO60PX3ap0tncq6ptFnxpw/gCmQy7dp4FAIPC3fACdyzmtxP1XkwAAAABJRU5ErkJggg==","orcid":"","institution":"Gwangju Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Jae","middleName":"Gwan","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2025-04-25 05:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6525416/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6525416/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82887816,"identity":"6d89a62e-fda7-4cf4-9a86-73caf21fd82d","added_by":"auto","created_at":"2025-05-16 11:58:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68808,"visible":true,"origin":"","legend":"\u003cp\u003eA\u003cstrong\u003e \u003c/strong\u003eschematic diagram shows the sequence of the oddball task [32].\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6525416/v1/986cd5dc929ca60148230497.png"},{"id":82889577,"identity":"1dded5c9-5ce7-4f16-9f20-8b7f14f3d79e","added_by":"auto","created_at":"2025-05-16 12:06:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3589299,"visible":true,"origin":"","legend":"\u003cp\u003eElectrodes of interest (EOIs): Right Hemisphere (R: AF4, C4, PO4), Middle (M: Fz, Cz, Pz), and Left Hemisphere (L: AF3, C3, PO3).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6525416/v1/b8623eb0167933af20d80c60.png"},{"id":82887823,"identity":"4f26648d-d72c-4be5-8936-07a5eeb26d2c","added_by":"auto","created_at":"2025-05-16 11:59:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2406079,"visible":true,"origin":"","legend":"\u003cp\u003eGrand average waveforms from left, middle, and right regions for (a) HC, (b) AAD, and (c) PAD during target (black) and non-target (red) stimulus processing.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6525416/v1/5f802d0ae98e51618c73d961.png"},{"id":82887821,"identity":"ec16f319-32f6-4366-bccd-6a684931a01c","added_by":"auto","created_at":"2025-05-16 11:58:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1863847,"visible":true,"origin":"","legend":"\u003cp\u003e(a) HC vs AAD at left region (b) HC vs PAD at Left, middle and Right region.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6525416/v1/ea4a1a6d5c0c2cb21974de81.png"},{"id":83758186,"identity":"127e0bb4-ea5e-4abc-8fea-a18302b2351f","added_by":"auto","created_at":"2025-06-02 08:39:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9348116,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6525416/v1/10f06930-f6ed-4d05-8e9b-601c56c8a6d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating P300 Latency as a Physiological Marker for Asymptomatic and Prodromal Alzheimer’s Disease","fulltext":[{"header":"Background","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAlzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and behavioral changes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As of 2025, more than 55\u0026nbsp;million people worldwide are living with AD and other dementias, a number projected to rise to 139\u0026nbsp;million by 2050. This increasing prevalence represents a significant global health challenge, imposing substantial emotional and financial burdens on patients, families, and healthcare systems. The global economic impact is considerable, with the cost of dementia estimated at \u003cspan\u003e$\u003c/span\u003e818\u0026nbsp;billion in 2015 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These statistics highlight the urgent need for early detection, diagnosis, and treatment of AD in its preclinical stages, including asymptomatic AD (AAD) and prodromal AD (PAD), before progression to full-blown dementia occurs.\u003c/p\u003e \u003cp\u003eA variety of alterations in the brain are believed to play a role in the development of AD, such as the buildup of beta-amyloid plaques outside of neurons and deposition of tau tangles, an abnormal form of tau protein [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], leading to inflammation, neuronal atrophy, and cell death [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In AD, brain atrophy in the left and right hemispheres is asymmetrical [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], with the left hemisphere having more severe cortical thinning, amyloid-beta plaques, and loss of neurite connections [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A recent study discovered that cortical thinning occurs asymmetrically throughout adulthood and that AD causes this thinning to occur more quickly than normal aging [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In another study, scientists found that the white matter networks of AD patients exhibited rightward asymmetry. They suggested that impairment in the left hemisphere was the cause of this rightward asymmetry [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] concluded that in preclinical AD, the left hemisphere exhibits differences compared to the right, including potential selective vulnerability to neurodegeneration and asymmetric amyloid deposition.\u003c/p\u003e \u003cp\u003eVarious neuroimaging techniques, such as magnetic resonance imaging (MRI), have been employed to investigate the early stages of AD while focusing on brain asymmetry [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Among many EEG-related biomarkers, P300 latency\u0026mdash;a component of event-related potentials (ERPs)\u0026mdash;has shown promise [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], however, to our knowledge, no study has explored its use as biomarkers to distinguish between the preclinical phases of AD \u0026mdash;specifically AAD and PAD\u0026mdash;while taking into consideration brain asymmetry in both healthy and pathological conditions. The majority of studies that have used the P300 peak latency acquired during oddball tasks compared PAD to healthy controls (HCs) [\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] or AD to HCs [\u003cspan additionalcitationids=\"CR27 CR28 CR29 CR30\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] without considering asymmetry, focusing mostly on Fz, Cz, and/or Pz electrodes. Notably, none of these studies have included AAD subjects.\u003c/p\u003e \u003cp\u003eIn 2024, our research team explored the potential of P300 peak latency acquired during the visual oddball task as a biomarker for distinguishing the HC, AAD, and PAD groups from one another [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The findings indicated that P300 peak latency at central electrodes (C3, Cz, and C4) can effectively differentiate PAD from both AAD and HCs. However, it was inadequate to differentiate AAD from HCs. To address this limitation, the current study aims to explore broader regional differences by analyzing the left, middle, and right brain regions achieved by averaging signals from three electrodes within each region. This methodological shift is motivated by the observed hemispheric asymmetry in AD pathology, which may enhance the differentiation between AAD, PAD, and HC groups [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eEEG data were collected from senior citizens residing in Gwangju, recruited by the National Research Center for Dementia and Chonnam National University Hospital (Gwangju, South Korea). A series of medical examinations, including MMSE, PET, MRI, and patient interviews, were conducted to determine the disease stage. The recruitment and diagnostic procedures followed in this study align with those described in our previous work [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe participants in this study were categorized into three groups: healthy controls (HCs), who were cognitively normal; asymptomatic Alzheimer's disease (AAD), who exhibited amyloid positivity on PET scans; and prodromal Alzheimer's disease (PAD), who presented with mild cognitive impairment. A total of 117 participants were enrolled, with 39 individuals in each group: HC (mean age\u0026thinsp;=\u0026thinsp;72.08\u0026thinsp;\u0026plusmn;\u0026thinsp;4.08 years), AAD (mean age\u0026thinsp;=\u0026thinsp;73.08\u0026thinsp;\u0026plusmn;\u0026thinsp;4.75 years), and PAD (mean age\u0026thinsp;=\u0026thinsp;74.95\u0026thinsp;\u0026plusmn;\u0026thinsp;4.29 years). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a summary of the demographic and psychometric factors of the participants, along with the results of statistical comparisons between the three groups. These differences were assessed using a one-way analysis of variance (ANOVA).\u003c/p\u003e\u003c/div\u003e\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\u003eDemographics, psychometric and statistical comparisons among the three groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003ePost-hoc\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHC vs. AAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHC vs. PAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAAD vs. PAD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCount\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.051\u0026thinsp;\u0026plusmn;\u0026thinsp;4.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.077\u0026thinsp;\u0026plusmn;\u0026thinsp;4.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.949\u0026thinsp;\u0026plusmn;\u0026thinsp;4.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.462\u0026thinsp;\u0026plusmn;\u0026thinsp;4.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.795\u0026thinsp;\u0026plusmn;\u0026thinsp;4.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.449\u0026thinsp;\u0026plusmn;\u0026thinsp;5.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (M/F)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13/26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22/17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23/16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.045*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCDR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.128\u0026thinsp;\u0026plusmn;\u0026thinsp;6.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.051\u0026thinsp;\u0026plusmn;\u0026thinsp;2.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.436\u0026thinsp;\u0026plusmn;\u0026thinsp;3.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMMSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.821\u0026thinsp;\u0026plusmn;\u0026thinsp;4.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.051\u0026thinsp;\u0026plusmn;\u0026thinsp;2.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.462\u0026thinsp;\u0026plusmn;\u0026thinsp;3.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCDR, Clinical Dementia Rating; MMSE, Mini-Mental State Examination. *Chi-square test.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNeuropsychological tests\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe Seoul Neuropsychological Screening Battery (SNSB-II) was utilized for neuropsychological screening. It consists of 18 subsets across five cognitive domains: attention, language, memory, visuospatial functions, and executive functions, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. For a more detailed description of these tests and their standards, please refer to [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCognitive Domains and Subtests of the Seoul Neuropsychological Screening Battery (SNSB-II).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTests\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAttention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigit Span Test Forward (DST-F) and Digit Span Test Backward (DST-B).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLanguage and\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003erelated functions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRepetition, Korean-Boston Naming Test (K-BNT) and its short form (S-K-BNT), Praxis Ideomotor test, Calculation test, and Comprehension test.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVisuospatial\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003efunctions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCopy of the Rey Complex Figure Test (Rey CFT) including copy score and time.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMemory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeoul Verbal Learning Test (SVLT), including Immediate recall, Delayed recall, Recognition, and Recognition discriminability index; and Rey Complex Figure Test (RCFT), including Immediate recall, Delayed recall, Recognition, and Recognition discriminability index.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrontal/executive functions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContrasting program, Go-no-go, Controlled Oral Word Association Test (COWAT), including animal, supermarket, and phonemic tests; and Korean-Color Word Stroop Test (K-CWST), including Word reading, Color reading, and Interference score, Digit Symbol Coding (DSC), and Korean-Trail Making Test-Elderly\u0026rsquo;s version (K-TMT-E)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSNSB-II total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNSB-II domain attention, domain language, domain visuospatial, domain memory, domain frontal, and SNSB-C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eStimuli and task: P300 visual oddball task\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this study, we employed the same P300 visual oddball task described in [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], instructing participants to focus on the center of the screen while responding to the occasional yellow circle among a succession of blue circles. The stimulus presentation parameters included a ratio of 25:75 for target and non-target stimuli, respectively. While our previous study used the data from Oddball Task trial 1, the current study utilized data from trial 2 with a larger number of participants. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a schematic layout of the task stimulus sequence and timing.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eElectroencephalography (EEG) Data\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eEEG signals were recorded using a 32-channel dry electrode wireless bio-signal acquisition system (g. Nautilus, g.tec, Austria). The electrode positioning followed the 10\u0026ndash;20 international electrode positioning system, with the left and right mastoids serving as reference sites. Data were recorded at a sampling frequency of 500 Hz using BCI2000 software, alongside fNIRS signals, which are not within the scope of the current study.\u003c/p\u003e \u003cp\u003eFor data processing, MATLAB EEGLAB/ERPLAB toolboxes were used to perform resampling, filtering, independent component analysis (ICA) artifact removal, and epoching to obtain the P300 waveform. Further details can be found in [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eP300 peak latency was extracted with a time window of 300\u0026ndash;600 ms. post-stimulus from electrodes of interest (EOI), namely, AF3, Fz, AF4, C3, Cz, C4, PO3, Pz, and PO4. For statistical modeling, these EOIs were grouped into three regions representing Left Hemisphere (L: AF3, C3, PO3), Middle (M: Fz, Cz, Pz), and Right Hemisphere (R: AF4, C4, PO4), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eStatistical analyses were conducted using JASP 0.18.3.0. ANOVA tests assessed group differences based on neurophysiological data and P300 latency.\u003c/p\u003e \u003cp\u003eThe neuropsychological data was analyzed using a one-way ANOVA with the groups (HC, AAD, PAD) as the independent variable and each neuropsychological test as the dependent variable to assess their effectiveness in differentiating the groups. Post-hoc tests with Bonferroni correction were applied to examine significant differences further.\u003c/p\u003e \u003cp\u003eA mixed-design ANOVA was performed to investigate the ability of P300 peak latency from the L, M, and R regions to differentiate between the three groups, with groups (HC, AAD, PAD) serving as the between-subjects factor and the regions (L, M, R) as the repeated measure. Age and gender, which differed significantly across groups, were included as covariates. To further explore significant differences, group effects were analyzed using post-hoc tests with Bonferroni correction followed by the analysis of groups and region interaction (groups * region) effects to assess significance within each region.\u003c/p\u003e \u003cp\u003eFollowing the mixed ANOVA, receiver operating characteristic (ROC) analysis was performed to determine the cut-off scores, sensitivity, specificity, area under the curve (AUC), and 95% confidence intervals for the P300 peak latencies that showed significant group and region interaction effects. This analysis is crucial for evaluating whether the classification power of P300 peak latencies, which demonstrated significant group * region effects, holds at the individual level.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eNeuropsychology\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe analysis of neuropsychological tests revealed significant differences in the SNSB-C (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and the memory and frontal domains of the SNSB-II (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Post-hoc comparisons for these domains indicated a significant difference between PAD and both HC and AAD, while no significant difference was observed between AAD and HC (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These findings suggest that SNSB-II fails to differentiate AAD from HC, aligning with the results reported in [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEvent Related Potentials (ERPs)\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe analysis of P300 peak latency from the L, M, and R regions revealed a significant difference in the between-subjects effect (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The post-hoc analysis of the group effects showed significantly shorter latency in the HC group compared to both the AAD and PAD groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and in the AAD compared to the PAD group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. These results demonstrate the ability of P300 peak latency from L, M, and R regions to differentiate the three groups. The grand average waveforms from L, M, and R regions during target and non-target processing are as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGroups performance on the neuropsychological battery and the statistical analysis results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003ePost-hoc\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHC vs\u003c/p\u003e \u003cp\u003eAAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHC vs\u003c/p\u003e \u003cp\u003ePAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAAD vs\u003c/p\u003e \u003cp\u003ePAD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNSB-II total\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttention domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.641\u0026thinsp;\u0026plusmn;\u0026thinsp;2.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.641\u0026thinsp;\u0026plusmn;\u0026thinsp;1.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.000\u0026thinsp;\u0026plusmn;\u0026thinsp;1.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanguage domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.148\u0026thinsp;\u0026plusmn;\u0026thinsp;0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.157\u0026thinsp;\u0026plusmn;\u0026thinsp;0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-0.017\u0026thinsp;\u0026plusmn;\u0026thinsp;0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisuospatial domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.449\u0026thinsp;\u0026plusmn;\u0026thinsp;0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.435\u0026thinsp;\u0026plusmn;\u0026thinsp;0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.153\u0026thinsp;\u0026plusmn;\u0026thinsp;5.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMemory domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.251\u0026thinsp;\u0026plusmn;\u0026thinsp;0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.055\u0026thinsp;\u0026plusmn;\u0026thinsp;0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-0.603\u0026thinsp;\u0026plusmn;\u0026thinsp;0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrontal domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.220\u0026thinsp;\u0026plusmn;\u0026thinsp;0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.121\u0026thinsp;\u0026plusmn;\u0026thinsp;0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-0.290\u0026thinsp;\u0026plusmn;\u0026thinsp;0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNSB-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e25.077\u0026thinsp;\u0026plusmn;\u0026thinsp;6.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e25.237\u0026thinsp;\u0026plusmn;\u0026thinsp;7.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e20.316\u0026thinsp;\u0026plusmn;\u0026thinsp;7.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eHC, Healthy Control; AAD, Asymptomatic AD; PAD, Prodromal AD; SNSB-II, Seoul Neuropsychological Screening Battery, 2nd Edition; SNSB-C, Seoul Neuropsychological Screening Battery-Core.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical results of the 3 \u0026times; 3 mixed ANOVA of P300 peak latency during target stimuli across the three regions (L, M, and R) among the three groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAD\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePAD\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eη2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePost-hoc\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e432.068\u0026thinsp;\u0026plusmn;\u0026thinsp;41.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e466.872\u0026thinsp;\u0026plusmn;\u0026thinsp;52.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e491.214\u0026thinsp;\u0026plusmn;\u0026thinsp;39.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e24.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003cp\u003e\u0026lt;\u003c/p\u003e \u003cp\u003eAAD\u003c/p\u003e \u003cp\u003e\u0026lt;\u003c/p\u003e \u003cp\u003ePAD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e436.718\u0026thinsp;\u0026plusmn;\u0026thinsp;48.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e465.744\u0026thinsp;\u0026plusmn;\u0026thinsp;62.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e496.752\u0026thinsp;\u0026plusmn;\u0026thinsp;45.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e441.333\u0026thinsp;\u0026plusmn;\u0026thinsp;46.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e465.470\u0026thinsp;\u0026plusmn;\u0026thinsp;45.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e491.145\u0026thinsp;\u0026plusmn;\u0026thinsp;33.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePost-hoc analysis of groups * region effects showed a significant difference between HC and AAD in the L region (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), with no significant difference observed in the M and R regions. These findings suggest that P300 peak latency from the L region can effectively classify AAD from HCs. Conversely, significant differences between HC and PAD were observed across all regions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that P300 peak latency from any of the three regions can effectively distinguish PAD from HCs. For AAD vs. PAD, no significant differences were observed in any region except for a trending significance in the M region (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.056), suggesting that P300 latency from the L, M, and R regions fails to differentiate these two groups. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e provides a summary of these results.\u003c/p\u003e \u003cp\u003eThe ROC results for P300 peak latencies that showed significant group * region effects are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. For HCs vs. AAD, the peak latency in the L region demonstrated a sensitivity of 74.3% and a specificity of 55.6% with a cut-off of 431.33 ms. For classifying HCs vs. PAD, the peak latency in L and M regions showed a sensitivity of 74.2% and 77.4%, respectively, and a specificity of 77.2%, while it showed the highest sensitivity (80%) in the R region.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe post-hoc analysis for the interaction between groups and brain regions (groups * regions).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003ebonf\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHC vs AAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;40.576\u0026thinsp;\u0026plusmn;\u0026thinsp;10.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHC vs PAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-69.615\u0026thinsp;\u0026plusmn;\u0026thinsp;11.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAAD vs PAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-29.039\u0026thinsp;\u0026plusmn;\u0026thinsp;10.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHC vs AAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-32.737\u0026thinsp;\u0026plusmn;\u0026thinsp;10.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHC vs PAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-66.827\u0026thinsp;\u0026plusmn;\u0026thinsp;11.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAAD vs PAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-34.090\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHC vs AAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-27.989\u0026thinsp;\u0026plusmn;\u0026thinsp;10.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHC vs PAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-55.041\u0026thinsp;\u0026plusmn;\u0026thinsp;11.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAAD vs PAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-27.053\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of ROC analysis of peak P300 latency at left, middle, and right regions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut-off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI 95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHC vs AAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e431.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.58\u0026ndash;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e74.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e55.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHC vs PAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e468.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.752\u0026ndash;0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e74.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e72.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHC vs PAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e448.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.733\u0026ndash;0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e77.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e72.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHC vs PAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.67\u0026ndash;0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e63.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study aimed to evaluate the potential of P300 peak latency, measured using EEG during an oddball task, as a biomarker for distinguishing between Healthy Controls (HCs), Asymptomatic Alzheimer's Disease (AAD), and Prodromal Alzheimer's Disease (PAD). Our findings suggest that P300 latency can effectively differentiate between these groups, especially when considering brain region-specific differences.\u003c/p\u003e \u003cp\u003eFirst, the neuropsychological assessments revealed significant differences between the PAD group and both HC and AAD, particularly in the memory and frontal domains. These cognitive domains are crucial in the early stages of AD, as they reflect disruptions in attention, executive function, and memory processing\u0026mdash;areas that are typically affected in AD [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The significant difference between PAD and HC supports studies highlighting the high sensitivity of neurophysiological tests in distinguishing MCI (PAD) from normal cognition and dementia [\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and the difference between PAD and AAD likely reflects varying declines in attention and working memory across AD\u0026rsquo;s preclinical stages [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The lack of significant differences between AAD and HC, however, highlights the subtlety of cognitive changes in AAD, supporting the idea that AAD may not show overt cognitive deficits in standard neuropsychological testing. This aligns with previous findings that suggest individuals with AAD are cognitively intact despite underlying pathological changes in the brain [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe ERP results, however, offered a more sensitive measure for distinguishing between the groups. Considering the brain regions (left (L), middle (M), and right (R)), the 3 \u0026times; 3 mixed ANOVA revealed significant differences in P300 peak latency between HC, AAD, and PAD. HCs exhibited the shortest latencies across all regions, while PAD participants showed the longest. This finding aligns with previous research indicating that P300 latency increases with cognitive decline in AD [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR47 CR48\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The intermediate latency observed in the AAD group could be due to the fact that AAD is a transitional stage between HC and PAD. The aforementioned results suggest that analyzing P300 peak latency across the L, M, and R brain regions provides a comprehensive regional classification by capturing a broader spectrum of neural activity. This approach differs from that used in our previous study [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], where P300 peak latency recorded from central electrodes (C3, Cz, and C4) was insufficient to distinguish AAD from HCs which confirms that relying solely on specific electrode sites may not fully capture the extent of cognitive decline impact on P300 latency.\u003c/p\u003e \u003cp\u003eOur analysis also revealed that P300 latency from the L region was particularly effective in distinguishing ADD from HC, while the R and M regions showed less pronounced differences. This finding is in line with the growing body of literature suggesting that the left hemisphere is more affected in the early stages of AD, particularly in regions associated with memory and executive function [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Our results corroborate these observations, emphasizing the role of brain asymmetry in AD pathology. The increased P300 latency in the left hemisphere among AAD and PAD individuals could be reflective of early cortical thinning, neuronal loss, or other pathological changes that are characteristic of AD [\u003cspan additionalcitationids=\"CR53 CR54\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, while P300 latency from any brain region effectively distinguished between HC and PAD, it failed to differentiate AAD from PAD, except in the middle region, where a borderline significance was observed. This finding indicates that although P300 latency could serve as a useful marker for distinguishing healthy individuals from those with neurodegenerative changes, the shared neural mechanisms\u0026mdash;such as amyloid-beta (Aβ) and tau protein accumulation, synaptic dysfunction, and disruptions in brain networks like the default mode network [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u0026mdash;present in preclinical stages of AD (AAD and PAD) might be too subtle for P300 to resolve, at least with the electrode sites and task conditions used in this study.\u003c/p\u003e \u003cp\u003eIn addition to the significant differences observed in the group comparisons, the ROC analysis further supports the potential utility of P300 latency as a diagnostic tool. The AUC values for distinguishing HC from AAD and HC from PAD were generally high, with the left region showing the highest classification accuracy. These findings are promising for the use of P300 latency as a diagnostic marker for early-stage AD, particularly when considering the practical advantages of EEG in clinical settings, such as its non-invasive nature, relatively low cost, and ability to assess brain function in real time [\u003cspan additionalcitationids=\"CR59 CR60\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eInvestigating the ability of P300 latency in distinguishing the very early stage of AD (AAD) from HCs using the visual oddball paradigm is an inherent strength of the study. While many studies focused on differentiating MCI (PAD) subjects from HCs, to our knowledge, no previous study has explored the use of P300 latency for the AAD classification, except by our group in 2024 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. On the other hand, the modest sample size in each group, which may affect the generalizability of the findings, and the simplicity and singleness of the task are the main limitations of this study. Also, as this study is not a longitudinal study, it cannot track changes in P300 latency over time that could reveal whether P300 latency changes precede or correlate with clinical progression in AAD and PAD individuals.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFuture Work\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn future work, larger and more diverse populations would be needed to confirm the results of this study. This would improve the applicability and Inferential strength of the study findings. Additionally, further investigation into the relationship between P300 latency and other biomarkers of AD, such as amyloid plaques and tau tangles, could help provide a more comprehensive understanding of how these markers interact in the preclinical and prodromal stages of the disease. Also, integrating P300 latency measurements, while focusing on the left hemisphere, with multimodal imaging techniques, such as functional near-infrared spectroscopy (fNIRS) or MRI, may improve the diagnostic value of this biomarker.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study provides compelling evidence for the use of P300 peak latency as a biomarker for distinguishing healthy controls (HCs), asymptomatic Alzheimer\u0026rsquo;s disease (AAD), and prodromal Alzheimer\u0026rsquo;s disease (PAD). Our results revealed that the P300 peak latency from left, middle, and right brain regions differs significantly between HCs, AAD, and PAD groups. Further analysis using ROC demonstrated a high classification accuracy for HCs compared to both AAD and PAD, particularly in the left hemisphere. These results aligned with the current understanding of AD pathology and suggest that EEG could serve as a valuable tool for the early detection of AD. With further validation, EEG-based biomarkers could become valuable tools in early diagnosis and intervention planning.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Gwangju Institute of Science and Technology (20201124-HR-57-02-04, on 24 November 2020). Informed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Research Foundation of Korea (2016M3C7A1905475 and 2022R1A2C3009749), the Healthcare AI Convergence Research \u0026amp; Development Program through the National IT Industry Promotion Agency of Korea (NIPA) funded by the Ministry of Science and ICT (No. S1601-20-1016), and the KBRI Basic Research Program through the Korea Brain Research Institute, funded by the Ministry of Science and ICT (24-BR-03-05).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJ.G.K. conceptualized and supervised the study and managed project administration and funding acquisition. N.M. and M.M. developed the methodology, performed data curation, validation, formal analysis, and investigation. M.M. and N.M. also contributed to software development and visualization. JJL and KYC contributed to subject recruitment. BCK contributed to the diagnosis of subjects. N.M. contributed to data collection. JG and KHL contributed to project management. M.M. and N.M. drafted the original manuscript, while J.G.K., N.M., and M.M. reviewed and edited it. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZvěřov\u0026aacute;, M., \u003cem\u003eClinical aspects of Alzheimer\u0026apos;s disease.\u003c/em\u003e Clinical Biochemistry, 2019. \u003cstrong\u003e72\u003c/strong\u003e: p. 3-6.\u003c/li\u003e\n\u003cli\u003eJicha, G.A. and S.A. 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R80-R85.\u003c/li\u003e\n\u003cli\u003eJadhav, C., et al., \u003cem\u003eClinical applications of EEG as an excellent tool for event related potentials in psychiatric and neurotic disorders.\u003c/em\u003e Int J Physiol Pathophysiol Pharmacol, 2022. \u003cstrong\u003e14\u003c/strong\u003e(2): p. 73-83.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"P300 ERP, asymptomatic AD, prodromal AD, left hemisphere","lastPublishedDoi":"10.21203/rs.3.rs-6525416/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6525416/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlzheimer's disease (AD) is a progressive neurodegenerative disorder that poses significant challenges in terms of early diagnosis and intervention. While various biomarkers have been explored, few studies have utilized electroencephalography (EEG) with a focus on P300 peak latency to distinguish between the preclinical stages of AD, specifically Asymptomatic AD (AAD) and Prodromal AD (PAD).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this study, we investigated P300 latency during an oddball task. EEG data was collected from a total of 117 participants with 39 Healthy Controls (HCs) (mean age\u0026thinsp;=\u0026thinsp;72.08\u0026thinsp;\u0026plusmn;\u0026thinsp;4.08 years), 39 AAD (mean age\u0026thinsp;=\u0026thinsp;73.08\u0026thinsp;\u0026plusmn;\u0026thinsp;4.75 years), and 39 PAD (mean age\u0026thinsp;=\u0026thinsp;74.95\u0026thinsp;\u0026plusmn;\u0026thinsp;4.29 years). Statistical analyses involved ANOVA tests to assess group differences in neurophysiological and neuropsychological data. With a focus on regional differences across the left, middle, and right brain hemispheres, a mixed-design ANOVA examined P300 peak latency, followed by post-hoc tests and ROC analysis to evaluate classification performance at the individual level.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur results showed that P300 peak latency can effectively differentiate HC from both AAD and PAD, with the left hemisphere providing the most significant distinction between HC and AAD, with a sensitivity of 74.3% and specificity of 55.6%. P300 latency from the middle region demonstrated a sensitivity of 77.4% and specificity of 72.2% for distinguishing HC from PAD, while the right region showed the highest sensitivity (80%) but lower specificity (63.9%) for HC vs PAD. However, no clear distinction was observed between AAD and PAD, except for a borderline significance in the middle region.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese results suggest that P300 latency from the left hemisphere is capable of differentiating HCs from AAD, and latency in any brain region distinguishes HCs from PAD. Accordingly, we concluded that P300 latency could serve as a useful biomarker for the early detection and classification of AD, particularly in its preclinical stages.\u003c/p\u003e","manuscriptTitle":"Evaluating P300 Latency as a Physiological Marker for Asymptomatic and Prodromal Alzheimer’s Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 11:58:55","doi":"10.21203/rs.3.rs-6525416/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4c6a4ce5-1d30-4599-9e0f-4ea8cd713109","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48525093,"name":"Physical sciences/Engineering/Biomedical engineering"},{"id":48525094,"name":"Biological sciences/Neuroscience/Cognitive ageing"},{"id":48525095,"name":"Biological sciences/Neuroscience/Cognitive neuroscience"},{"id":48525096,"name":"Health sciences/Diseases/Neurological disorders"}],"tags":[],"updatedAt":"2025-06-02T08:38:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 11:58:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6525416","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6525416","identity":"rs-6525416","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}