Multi-Modal Approaches to Alzheimer’s Diagnosis: Combining Cognitive assessments with Biomarkers and Imaging

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Early and accurate diagnosis is essential for timely intervention and effective disease management. While PET-CT imaging is considered the gold standard for detecting Alzheimer’s pathology, its high cost and limited accessibility necessitates the exploration of alternative diagnostic tools. Cognitive assessments, serum biomarkers, EEG, and MRI each offer unique insights into the disease process. When used in combination, these modalities may enhance diagnostic accuracy and provide a more comprehensive understanding of Alzheimer’s progression. Results: Among 384 participants, PET-CT confirmed Alzheimer’s in 192 cases (50%). Serum biomarkers showed the highest individual sensitivity (77.60%), followed by MRI (69.79%), EEG (66.67%), and cognitive tests (62.50%). All modalities had a specificity of 84.90%. When combined using the addition rule of probability, diagnostic sensitivity increased to 99.15% and specificity to 99.95%. ROC curve analysis showed serum biomarkers and MRI had the highest diagnostic accuracy. The multi-modal approach significantly improved early diagnostic performance compared to single modalities. Conclusion: Individual diagnostic accuracy after serum biomarkers and MRI was the best, whereas when all four modalities were combined, sensitivity (up to 99.15%) and specificity (up to 99.95%) showed a significant increment through the addition rule. The evidence used will provide greater early detection and decision-making in Alzheimer's disease that promotes the employment of a multi-modal diagnostic strategy. Alzheimer’s disease cognitive tests serum biomarkers EEG MRI PET-CT diagnostic accuracy multi-modal diagnosis Figures Figure 1 Background Alzheimer's disease (AD) is a progressive, neurodegenerative disorder characterized by the loss of behavioral changes, memory loss, and mental capacity [ 1 – 3 ]. The aging of the world population is steadily increasing the prevalence of Alzheimer's in the population, and therefore it is of utmost importance to clinical practice and research to diagnose this condition as early and as precisely as possible [ 4 , 5 ]. Timely diagnosis can help to provide early treatment and planning of care and improve the potency of evolving curing approaches, which strive to drastically reduce the speed of the disease process [ 6 ]. Clinical assessment as well as neuropsychological testing has long been a major element of diagnosing Alzheimer's [ 7 ], the current revolution in biomedical technology has already presented various diagnostic tools with significant insight into the structural, functional, and molecular changes involved in the disease [ 8 ]. Positron Emission Tomography–Computed Tomography (PET-CT) has been identified as the gold standard in confirmation of pathology of Alzheimer's disease, especially with visualization of amyloid plaques and tau tangles [ 9 – 12 ]. PET-CT, although it has high specificity, is costly and not accessible, and radioactive tracers are used, and these factors present limitations to PET-CT being used as a routine procedure in most clinical practices [ 13 ]. To overcome these shortcomings, the idea of a multi-modal diagnostic system has been popularized, which consists of combining different non-invasive and inexpensive tools to increase diagnostic sensitivity. Simple mental status examinations, including the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA), are also needed to assess the cognitive status and hamper the onset of changes [ 14 ]. Serum biomarkers, such as beta-amyloid, tau proteins, and so on, are viewed as a window to the biochemical process of the disease and are gaining broader acceptance in terms of early detection [ 15 ]. The electroencephalography (EEG) can give real-time data about brain activity and neural associations such that functional disruptions are easily uncovered before structural damage becomes apparent [ 16 , 17 ]. Magnetic Resonance Imaging (MRI), on the other hand, helps in the visualization of brain atrophy and other structural abnormalities linked to Alzheimer's [ 18 – 20 ]. All these modalities add complementary and unique information. Cognitive tests record the existence of the lesser behavioral and functional performance, serum biomarkers indicate underlying disease pathology, EEG electrophysiologic abnormalities, and MRI anatomical abnormalities. In combination, the use of these tools can make a significant difference in the precision of diagnosis, especially during the early phase of the disease, where the cost of intervention is highest. This research study is aimed at comparing the diagnostic performance of cognitive tests, serum biomarkers, EEG, and MRI alone and in combination with PET-CT findings as alternatives to the early diagnostics of Alzheimer's disease. The proposed research will attempt to establish an evidence-based framework by comparing the sensitivity, specificity, and predictive value of each single modality and by combining the performance levels of each modality using both probabilistic addition and probabilistic product rules. An effective and practical multi-modal diagnosis of Alzheimer's can be established. Methodology The aim of this prospective diagnostic accuracy study was to determine the sensitivity of the four diagnostic modalities—cognitive tests (MMSE and MoCA), serum biomarkers, Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI)—in identifying Alzheimer's disease against the results of PET-CT, which was the gold standard [ 21 ]. The ethics committee of the institution approved the study, and all the participants were informed of their consent. Suspected cases of early-stage Alzheimer's disease were recruited, and their clinical and demographic information, such as age, gender, and symptoms related to thinking, was documented in a systematic manner. The cognitive assessment was carried out using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). MoCA has more executive function-related and abstraction tasks than MMSE and supports orientation, attention, memory, language, and visuospatial abilities. Each of the tests was done by certified neuropsychologists in a clinical environment that was standardized. The scores were interpreted as known cutoffs of cognitive impairment. The aseptic collection of venous blood was made and measured by immunoassay standards against tau protein and beta-amyloid (A 42), total tau (t-tau), and phosphorylated tau (p-tau). The reduced ratio between Abeta 42 and Abeta 40 and the increased tau were deemed to be the symbols of Alzheimer pathology. EEGs in a resting state were recorded according to the international 10–20 electrode montage. Studies advised the participants to stay comfortably seated with their eyes closed. Alzheimer-specific EEG data were examined to include more theta and delta activity and less alpha and beta activity. Where available, event-related potentials (ERPs), like delayed P300 responses, were also assessed. To determine structural brain changes, high-resolution T1-weighted MRI scans were carried out. The emphasis was made on hippocampal atrophy and cortical thinning. Other images (T2-weighted and FLAIR) were taken to identify white matter hyperintensities. Radiologists who read all of the scans were blinded to the PET-CT results. PET-CT was used as the benchmark. The presence of amyloid plaque was observed applying amyloid PET tracers. There was radiotracer injection of the participants and capture via hybrid PET-CT scanner. It was established that Alzheimer's is positive after the presence of amyloid plaques or tau tangles. The individual modalities were evaluated with respect to their objective performance on the portrait directly in standard statistical terms: sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Sensitivity was established to be the ratio of the true positive earlier recognized by the test, and specificity had been termed as the ratio of true negative earlier recognized by the test. PPV showed the true likelihood that a positive test was an indication of the disease, and NPV showed the true likelihood that a negative test indicated a lack of disease. Such measures were computed with the help of 2 x 2 contingency tables, and the results of PET-CT provided the gold standard. Also, the Receiver Operating Characteristic (ROC) curves were calculated to account for how well each modality could perform diagnoses, and the area under the curve (AUC) was taken as an aggregate of precision. With IBM SPSS version 27, all statistical calculations were done. To assess the diagnostic strength of using multiple modalities, it would follow that the addition rule of probability and the product rule of probability will be utilized as probabilistic models. These designs can be used to aid in estimation of the accuracy of tests that improve the overall accuracy of testing compared to using the single tests individually. The likelihood of stating that at least one of the tests will identify Alzheimer's disease accurately was estimated using the addition rule of probability. This strategy is more applicable where the settings are clinical, where a high level of sensitivity is required to avoid missing diagnoses. The formula for combined sensitivity using the additional rule is: 1 - (1 - S_Cognitive)(1 - S_Biomarker)(1 - S_EEG)(1 - S_MRI). Similarly, the combined specificity using the addition rule is 1 - (1 - Sp_Cognitive)(1 - Sp_Biomarker)(1 - Sp_EEG)(1 - Sp_MRI). These formulas assume that the tests are statistically independent and provide a more inclusive estimate of diagnostic capability. In contrast, the product rule of probability was applied to estimate the likelihood that all tests would simultaneously agree on the diagnosis. This method is more conservative and is typically used when high specificity is required, such as in confirmatory testing where false positives must be minimized. The formulas for combined sensitivity and specificity using the product rule are S_Cognitive × S_Biomarker × S_EEG × S_MRI and Sp_Cognitive × Sp_Biomarker × Sp_EEG × Sp_MRI . These calculations provide a comprehensive understanding of how combining diagnostic tools can enhance or limit diagnostic accuracy depending on the clinical context. Results A total of 384 participants with suspected early-stage Alzheimer’s disease were enrolled in the study, comprising 193 males (50.26%) and 191 females (49.74%). The mean age was 74.56 ± 8.60 years. PET-CT findings revealed that 192 participants (50.00%) were positive for Alzheimer’s disease, while the remaining 192 were classified as normal individuals (Table 1 ). Table 1 Descriptive Statistics of Variables Variable Value Age (years) 74.56 ± 8.60 Gender Male 193 (50.26%) Female 191 (49.74%) PET-CT Findings* Alzheimer’s Disease Positive 192 (50.00%) Normal Individuals 192 (50.00%) *PET-CT findings used as the diagnostic gold standard. Diagnostic accuracy of cognitive tests, EEG, serum biomarkers, and MRI against PET-CT (gold standard) findings, stratified by gender and overall. Across all modalities, positive test results aligned more frequently with PET-CT–confirmed Alzheimer’s disease than with normal individuals, indicating good sensitivity. Serum biomarkers showed the highest alignment (83.71% in AD-positive cases), followed closely by MRI (82.21%) and EEG (81.53%), while cognitive tests had the lowest (80.54%). Negative results showed stronger alignment with normal individuals (around 70–79% across tests), reflecting good specificity. Gender differences were minimal, suggesting that diagnostic accuracy was consistent between males and females. Overall, the table highlights that serum biomarkers and MRI were the most reliable individual tests compared with PET-CT, though all modalities demonstrated clinically meaningful diagnostic value (Table 2 ). Table 2 Cognitive Tests, EEG, Serum Biomarkers, and MRI Compared with PET-CT Findings Diagnostic Test Gender Result Alzheimer’s Disease Positive (n, %) Normal Individuals (n, %) Cognitive Tests Male Positive 60 (81.08%) 14 (18.92%) Negative 38 (31.93%) 81 (68.07%) Female Positive 60 (80.00%) 15 (20.00%) Negative 34 (29.31%) 82 (70.69%) Total Positive 120 (80.54%) 29 (19.46%) Negative 72 (30.64%) 163 (69.36%) EEG Male Positive 66 (82.50%) 14 (17.50%) Negative 32 (28.32%) 81 (71.68%) Female Positive 62 (80.52%) 15 (19.48%) Negative 32 (28.07%) 82 (71.93%) Total Positive 128 (81.53%) 29 (18.47%) Negative 64 (28.19%) 163 (71.81%) Serum Biomarkers Male Positive 76 (84.44%) 14 (15.56%) Negative 22 (21.36%) 81 (78.64%) Female Positive 73 (82.95%) 15 (17.05%) Negative 21 (20.39%) 82 (79.61%) Total Positive 149 (83.71%) 29 (16.29%) Negative 43 (20.87%) 163 (79.13%) MRI Male Positive 69 (83.13%) 14 (16.87%) Negative 29 (26.36%) 81 (73.64%) Female Positive 65 (81.25%) 15 (18.75%) Negative 29 (26.13%) 82 (73.87%) Total Positive 134 (82.21%) 29 (17.79%) Negative 58 (26.24%) 163 (73.76%) These percentages reflect how often each test result (positive or negative) aligns with PET-CT findings, broken down by gender and overall. The diagnostic accuracy of individual modalities showed that Serum Biomarkers had the highest sensitivity at 77.60%, followed by MRI at 69.79%, EEG at 66.67%, and Cognitive Tests at 62.50%. All four modalities demonstrated the same specificity of 84.90%. Positive predictive values ranged from 80.54% for Cognitive Tests to 83.71% for Serum Biomarkers, while negative predictive values ranged from 69.36–79.13% (Table 3 ). Table 3 Diagnostic Accuracy of Cognitive Tests, EEG, Serum Biomarkers, and MRI Metric Cognitive Tests (%) EEG (%) Serum Biomarkers (%) MRI (%) Sensitivity 62.50 66.67 77.60 69.79 Specificity 84.90 84.90 84.90 84.90 Positive Predictive Value (PPV) 80.54 81.53 83.71 82.21 Negative Predictive Value (NPV) 69.36 71.81 79.13 73.76 Accuracy 73.70 75.78 81.25 77.34 When combining diagnostic modalities, the addition rule of probability significantly improved sensitivity. The combination of Serum Biomarkers and Cognitive Tests yielded a combined sensitivity of 91.60%, which increased to 97.20% when EEG was added, and further to 99.15% with the inclusion of MRI. Conversely, the product rule, which requires all tests to be positive, resulted in lower combined sensitivities: 48.50% for Serum Biomarkers and Cognitive Tests, 32.33% with EEG added, and 22.57% when MRI was also included. Combined specificity, calculated using the product rule decreased as more tests were added: 72.08% for two tests, 61.20% for three, and 51.96% for all four. However, when using the additional rule, combined specificity increased dramatically, reaching 99.95% for the full combination of all four modalities (Table 4 ). Table 4: Combined Statistics of Cognitive Tests, EEG, Serum Biomarkers, and MRI Combination Combined Sensitivity* (%) Combined Specificity** (%) Combined Specificity* (%) Combined Sensitivity** (%) Serum Biomarkers + Cognitive Tests 91.60 72.08 97.72 48.50 Serum Biomarkers + Cognitive Tests + EEG 97.20 61.20 99.66 32.33 Serum Biomarkers + Cognitive Tests + EEG + MRI 99.15 51.96 99.95 22.57 *Addition rule of probability. **Product of all tests. ROC curve analysis (Fig. 1 ) visually confirmed the diagnostic performance of each modality. Serum Biomarkers and MRI demonstrated the highest area under the curve (AUC), indicating strong diagnostic capabilities. EEG and Cognitive Tests, while slightly lower in AUC, provided valuable complementary information, especially when used in combination with other modalities. These findings support the use of a multi-modal diagnostic approach for Alzheimer’s disease, where combining cognitive, biochemical, functional, and structural assessments significantly enhances diagnostic accuracy compared to any single modality alone. Discussion This study evaluated the diagnostic accuracy of cognitive tests, serum biomarkers, EEG, and MRI—individually and in combination—against PET-CT findings in the early detection of Alzheimer’s disease. Our findings support the growing body of literature advocating for a multi-modal diagnostic approach to improve sensitivity and specificity in clinical settings. In our research, the sensitivity and specificity of cognitive tests were also similar to recent meta-analyses (62.50% and 84.90%, respectively). In a 2023 Bayesian study of the ADNI data set, the MoCA presented a sensitivity of 91.2% with a specificity of 90.1% in detecting Alzheimer's, whereas, compared to the sensitivity, the MMSE reported almost similar values of sensitivity with a higher specificity of 92.2% [ 22 ]. Likewise, the 2022 systematic review results revealed pooled sensitivity and specificity of MMSE as 73 and 83, respectively [ 23 ]. These results confirm the usefulness of cognitive tests as easily performed and sensitive screening measures, but the diagnostic sensitivity can be enhanced when administered in conjunction with objective biomarkers. Serum biomarkers proved to have the highest individual sensitivity in our research (77.60%), as it corresponds to current progress in blood-based diagnostics. A post-2024 NIH-funded study discussed how plasma p-tau217 and the Aβ42/Aβ40 ratio attained diagnostic accuracy in more than 90% contingent upon contrasting with PET and CSF results [ 24 ]. Such findings reinforce the ongoing promise of blood biomarkers as a non-invasive, blood-based alternative to the CSF test, especially in early diagnosis. EEG, which has a sensitivity of 66.67% and specificity of 84.90% in our study, has also become promising in the recent literature. In a 2025 systematic review, EEG was shown to pick up early changes in the functioning of the mind in Alzheimer's as a result of elevated theta and reduced alpha activity [ 25 ]. These electrophysiological changes correlate with ours and make a case for EEG as a relatively inexpensive tool that can help in real-time diagnostics. MRI, which obtained 69.79% and 84.90% sensitivity and specificity, respectively, in our study, is one of the pillars of structural imaging in Alzheimer's. A 2024 study utilizing machine learning on MRI characteristics (including hippocampus volume and cortical thickness) found that 95% of the participants could be successfully classified based on the Alzheimer's diagnosis and normal aging by utilizing the MRI tools [ 26 ]. In another review of 2021, the authors stressed that although MRI without other tests is not a reliable method to reveal amyloid pathology, it still helps to find structural atrophy and exclude other dementia-related diseases [ 27 ]. The most important result of our work was an improvement in the diagnostic performance with the help of multi-modal combinations. With the addition rule of probability, the combination of serum parameters, cognitive tests, EEG, and MRI provided a sensitivity of 99.15% and a specificity of 99.95%. These findings matched a 2025 systematic review of AI-based multimodal diagnostics, which found that biomarker-enhanced multimodal diagnostics combining imaging and biomarker and cognitive data exposed substantially better classification performance and clinical value [ 28 ]. A study published in 2024, which was based on SeaLM and used a Transformer-based model to fuse MRI and PET imaging data, reached an accuracy of 98.19% when classifying people with Alzheimer's, while healthy people were classified at 99.43% [ 29 ]. The former, though, had a sensitivity of only 22.57%, as opposed to that of the latter, whereas the specificity was high. This can be more suited under confirming circumstances when it is necessary to reduce the false positives. A more recently published multimodal study based on longitudinal data alluded to the same trade-off between sensitivity and specificity in fusion models by arguing in favor of diagnostic thresholds set on contextual grounds [ 30 ]. Our results are consistent with the existing trend of Alzheimer's research that significantly incorporates multi-modal, data-driven modalities. Using an assembly of cognitive, biochemical, electrophysiological, and structural information, clinicians will be able to make more appropriate and earlier diagnoses, which will lead to better patient outcomes and allow more specific interventions to be made. Conclusion The study establishes that individual use of diagnostic modalities of cognitive tests, serum biomarkers, EEG, and MRI contributes highly to insights into Alzheimer's disease, but their combinational use contributes highly to diagnostic accuracy. All four modalities were superior when used individually and especially when combined, as the sensitivity and specificity performed nearly perfectly using the additional rule of probability, with serum biomarkers and MRI having the highest performance rates. The above findings confirm the utilization of a multi-modal diagnostic framework in clinical settings, which can assist in early diagnosis and treatment of Alzheimer's, as it is a more comprehensive, accessible, and precise diagnosis model. Declarations Ethics approval and consent to participate The study was conducted in accordance with the ethical standards of the Ethics Committee of Bolan Medical College. Ethical approval was obtained (Approval No: EC/92/2025). Written informed consent was obtained from all participants prior to their inclusion in the study. Consent for publication : Written informed consent for publication of anonymized data was obtained from all participants prior to their inclusion in the study. Availability of data and materials: The datasets generated 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 received no external funding. Authors’ contributions Muhammad Arif Afridi designed the study and supervised data collection. Malik Mairaj Khalid performed the statistical analysis and drafted the manuscript. Rahman Ud Din contributed to the interpretation of results and critical revisions. All authors read and approved of the final manuscript. References Monzio Compagnoni G, Di Fonzo A, Corti S, Comi GP, Bresolin N, Masliah E. The Role of Mitochondria in Neurodegenerative Diseases: the Lesson from Alzheimer’s Disease and Parkinson’s Disease. Mol Neurobiol 2020;57:2959–80. https://doi.org/10.1007/s12035-020-01926-1. Pathak N, Vimal SK, Tandon I, Agrawal L, Hongyi C, Bhattacharyya S. Neurodegenerative Disorders of Alzheimer, Parkinsonism, Amyotrophic Lateral Sclerosis and Multiple Sclerosis: An Early Diagnostic Approach for Precision Treatment. Metab Brain Dis 2022;37:67–104. https://doi.org/10.1007/s11011-021-00800-w. 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Wahul RM, Ambadekar S, Dhanvijay DM, Dhanvijay MM, Dudhedia MA, Gaikwad V, et al. Multimodal approaches and AI-driven innovations in dementia diagnosis: a systematic review. Discov Artif Intell 2025;5:96. https://doi.org/10.1007/s44163-025-00358-x. Tang Y, Xiong X, Tong G, Yang Y, Zhang H. Multimodal diagnosis model of Alzheimer’s disease based on improved Transformer. Biomed Eng OnLine 2024;23:8. https://doi.org/10.1186/s12938-024-01204-4. Multi-Modal Fusion and Longitudinal Analysis for Alzheimer’s Disease Classification Using Deep Learning n.d. https://www.mdpi.com/2075-4418/15/6/717 (accessed July 17, 2025). 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7404405","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513457670,"identity":"0b7de898-00a6-4ab2-b879-17442456dce3","order_by":0,"name":"Muhammad Arif Afridi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIie3PvQrCMBDA8QsH7VLrmoLgK1Q6iODHq0QEJwXBUdCCUBd11slnEMHZksGl+AJZnJwjLkUcTHVysNZNMH+4wMH9hgDodL/YTg2rQh4Q3J1Ui2FmIm1wfEXCRUIwC4GEALhoJcsnYgt+kkdWp+UJrnn1ui3aCESeO++Jc2h7C8ZatMCNHu/ORSlAQGe5fU/cCDxoxjikaLm8OxVEEQNzqcS8AGMj+iCVqWhkIJanCH8SiEXzI3Eiq6/IXhGjF8580QqQjFP/YkfmhsRsQGmeb2R8E7XVZBzKcwp5jQSP1896n3T75lin0+n+pTtQT03C88f5SgAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Arif","lastName":"Afridi","suffix":""},{"id":513457676,"identity":"5ef878d4-1983-4b00-a575-acdfb58e47e5","order_by":1,"name":"Malik Mairaj Khalid","email":"","orcid":"","institution":"Bolan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Malik","middleName":"Mairaj","lastName":"Khalid","suffix":""},{"id":513457678,"identity":"279a89a6-df37-4171-87e5-6b037636b343","order_by":2,"name":"Rahman Ud Din","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Rahman","middleName":"Ud","lastName":"Din","suffix":""}],"badges":[],"createdAt":"2025-08-19 04:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7404405/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7404405/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91195471,"identity":"0755acb3-5fb4-46ea-8ef7-f4026b3da848","added_by":"auto","created_at":"2025-09-12 14:58:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":111279,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for the diagnostic modalities—MRI, EEG, Serum Biomarkers, and Cognitive Tests—evaluated against the PET-CT gold standard\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7404405/v1/0b103dc5f13ca94cb892bb33.jpg"},{"id":108005907,"identity":"24757f09-4bcb-4b35-89b2-14ebf5f24af0","added_by":"auto","created_at":"2026-04-28 12:50:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":391241,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7404405/v1/b6e7b02b-35a9-462d-9871-e3e53a98b094.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Modal Approaches to Alzheimer’s Diagnosis: Combining Cognitive assessments with Biomarkers and Imaging","fulltext":[{"header":"Background","content":"\u003cp\u003eAlzheimer's disease (AD) is a progressive, neurodegenerative disorder characterized by the loss of behavioral changes, memory loss, and mental capacity [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The aging of the world population is steadily increasing the prevalence of Alzheimer's in the population, and therefore it is of utmost importance to clinical practice and research to diagnose this condition as early and as precisely as possible [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Timely diagnosis can help to provide early treatment and planning of care and improve the potency of evolving curing approaches, which strive to drastically reduce the speed of the disease process [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eClinical assessment as well as neuropsychological testing has long been a major element of diagnosing Alzheimer's [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], the current revolution in biomedical technology has already presented various diagnostic tools with significant insight into the structural, functional, and molecular changes involved in the disease [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Positron Emission Tomography\u0026ndash;Computed Tomography (PET-CT) has been identified as the gold standard in confirmation of pathology of Alzheimer's disease, especially with visualization of amyloid plaques and tau tangles [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. PET-CT, although it has high specificity, is costly and not accessible, and radioactive tracers are used, and these factors present limitations to PET-CT being used as a routine procedure in most clinical practices [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo overcome these shortcomings, the idea of a multi-modal diagnostic system has been popularized, which consists of combining different non-invasive and inexpensive tools to increase diagnostic sensitivity. Simple mental status examinations, including the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA), are also needed to assess the cognitive status and hamper the onset of changes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Serum biomarkers, such as beta-amyloid, tau proteins, and so on, are viewed as a window to the biochemical process of the disease and are gaining broader acceptance in terms of early detection [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The electroencephalography (EEG) can give real-time data about brain activity and neural associations such that functional disruptions are easily uncovered before structural damage becomes apparent [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Magnetic Resonance Imaging (MRI), on the other hand, helps in the visualization of brain atrophy and other structural abnormalities linked to Alzheimer's [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAll these modalities add complementary and unique information. Cognitive tests record the existence of the lesser behavioral and functional performance, serum biomarkers indicate underlying disease pathology, EEG electrophysiologic abnormalities, and MRI anatomical abnormalities. In combination, the use of these tools can make a significant difference in the precision of diagnosis, especially during the early phase of the disease, where the cost of intervention is highest.\u003c/p\u003e\u003cp\u003eThis research study is aimed at comparing the diagnostic performance of cognitive tests, serum biomarkers, EEG, and MRI alone and in combination with PET-CT findings as alternatives to the early diagnostics of Alzheimer's disease. The proposed research will attempt to establish an evidence-based framework by comparing the sensitivity, specificity, and predictive value of each single modality and by combining the performance levels of each modality using both probabilistic addition and probabilistic product rules. An effective and practical multi-modal diagnosis of Alzheimer's can be established.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThe aim of this prospective diagnostic accuracy study was to determine the sensitivity of the four diagnostic modalities\u0026mdash;cognitive tests (MMSE and MoCA), serum biomarkers, Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI)\u0026mdash;in identifying Alzheimer's disease against the results of PET-CT, which was the gold standard [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The ethics committee of the institution approved the study, and all the participants were informed of their consent. Suspected cases of early-stage Alzheimer's disease were recruited, and their clinical and demographic information, such as age, gender, and symptoms related to thinking, was documented in a systematic manner.\u003c/p\u003e\u003cp\u003eThe cognitive assessment was carried out using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). MoCA has more executive function-related and abstraction tasks than MMSE and supports orientation, attention, memory, language, and visuospatial abilities. Each of the tests was done by certified neuropsychologists in a clinical environment that was standardized. The scores were interpreted as known cutoffs of cognitive impairment.\u003c/p\u003e\u003cp\u003eThe aseptic collection of venous blood was made and measured by immunoassay standards against tau protein and beta-amyloid (A 42), total tau (t-tau), and phosphorylated tau (p-tau). The reduced ratio between Abeta 42 and Abeta 40 and the increased tau were deemed to be the symbols of Alzheimer pathology.\u003c/p\u003e\u003cp\u003eEEGs in a resting state were recorded according to the international 10\u0026ndash;20 electrode montage. Studies advised the participants to stay comfortably seated with their eyes closed. Alzheimer-specific EEG data were examined to include more theta and delta activity and less alpha and beta activity. Where available, event-related potentials (ERPs), like delayed P300 responses, were also assessed.\u003c/p\u003e\u003cp\u003eTo determine structural brain changes, high-resolution T1-weighted MRI scans were carried out. The emphasis was made on hippocampal atrophy and cortical thinning. Other images (T2-weighted and FLAIR) were taken to identify white matter hyperintensities. Radiologists who read all of the scans were blinded to the PET-CT results.\u003c/p\u003e\u003cp\u003ePET-CT was used as the benchmark. The presence of amyloid plaque was observed applying amyloid PET tracers. There was radiotracer injection of the participants and capture via hybrid PET-CT scanner. It was established that Alzheimer's is positive after the presence of amyloid plaques or tau tangles.\u003c/p\u003e\u003cp\u003eThe individual modalities were evaluated with respect to their objective performance on the portrait directly in standard statistical terms: sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Sensitivity was established to be the ratio of the true positive earlier recognized by the test, and specificity had been termed as the ratio of true negative earlier recognized by the test. PPV showed the true likelihood that a positive test was an indication of the disease, and NPV showed the true likelihood that a negative test indicated a lack of disease. Such measures were computed with the help of 2 x 2 contingency tables, and the results of PET-CT provided the gold standard. Also, the Receiver Operating Characteristic (ROC) curves were calculated to account for how well each modality could perform diagnoses, and the area under the curve (AUC) was taken as an aggregate of precision. With IBM SPSS version 27, all statistical calculations were done.\u003c/p\u003e\u003cp\u003eTo assess the diagnostic strength of using multiple modalities, it would follow that the addition rule of probability and the product rule of probability will be utilized as probabilistic models. These designs can be used to aid in estimation of the accuracy of tests that improve the overall accuracy of testing compared to using the single tests individually.\u003c/p\u003e\u003cp\u003eThe likelihood of stating that at least one of the tests will identify Alzheimer's disease accurately was estimated using the addition rule of probability. This strategy is more applicable where the settings are clinical, where a high level of sensitivity is required to avoid missing diagnoses. The formula for combined sensitivity using the additional rule is: 1 - (1 - S_Cognitive)(1 - S_Biomarker)(1 - S_EEG)(1 - S_MRI). Similarly, the combined specificity using the addition rule is 1 - (1 - Sp_Cognitive)(1 - Sp_Biomarker)(1 - Sp_EEG)(1 - Sp_MRI). These formulas assume that the tests are statistically independent and provide a more inclusive estimate of diagnostic capability.\u003c/p\u003e\u003cp\u003eIn contrast, the product rule of probability was applied to estimate the likelihood that all tests would simultaneously agree on the diagnosis. This method is more conservative and is typically used when high specificity is required, such as in confirmatory testing where false positives must be minimized. The formulas for combined sensitivity and specificity using the product rule are \u003cem\u003eS_Cognitive \u0026times; S_Biomarker \u0026times; S_EEG \u0026times; S_MRI\u003c/em\u003e and \u003cem\u003eSp_Cognitive \u0026times; Sp_Biomarker \u0026times; Sp_EEG \u0026times; Sp_MRI\u003c/em\u003e. These calculations provide a comprehensive understanding of how combining diagnostic tools can enhance or limit diagnostic accuracy depending on the clinical context.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 384 participants with suspected early-stage Alzheimer\u0026rsquo;s disease were enrolled in the study, comprising 193 males (50.26%) and 191 females (49.74%). The mean age was 74.56\u0026thinsp;\u0026plusmn;\u0026thinsp;8.60 years. PET-CT findings revealed that 192 participants (50.00%) were positive for Alzheimer\u0026rsquo;s disease, while the remaining 192 were classified as normal individuals (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eDescriptive Statistics of Variables\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\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74.56\u0026thinsp;\u0026plusmn;\u0026thinsp;8.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e193 (50.26%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e191 (49.74%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePET-CT Findings*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlzheimer\u0026rsquo;s Disease Positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e192 (50.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal Individuals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e192 (50.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e*PET-CT findings used as the diagnostic gold standard.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDiagnostic accuracy of cognitive tests, EEG, serum biomarkers, and MRI against PET-CT (gold standard) findings, stratified by gender and overall. Across all modalities, positive test results aligned more frequently with PET-CT\u0026ndash;confirmed Alzheimer\u0026rsquo;s disease than with normal individuals, indicating good sensitivity. Serum biomarkers showed the highest alignment (83.71% in AD-positive cases), followed closely by MRI (82.21%) and EEG (81.53%), while cognitive tests had the lowest (80.54%). Negative results showed stronger alignment with normal individuals (around 70\u0026ndash;79% across tests), reflecting good specificity. Gender differences were minimal, suggesting that diagnostic accuracy was consistent between males and females. Overall, the table highlights that serum biomarkers and MRI were the most reliable individual tests compared with PET-CT, though all modalities demonstrated clinically meaningful diagnostic value (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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 Tests, EEG, Serum Biomarkers, and MRI Compared with PET-CT Findings\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnostic Test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResult\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAlzheimer\u0026rsquo;s Disease Positive (n, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNormal Individuals (n, %)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eCognitive Tests\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60 (81.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14 (18.92%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38 (31.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81 (68.07%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60 (80.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15 (20.00%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34 (29.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82 (70.69%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e120 (80.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29 (19.46%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e72 (30.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e163 (69.36%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eEEG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e66 (82.50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14 (17.50%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32 (28.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81 (71.68%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62 (80.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15 (19.48%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32 (28.07%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82 (71.93%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e128 (81.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29 (18.47%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64 (28.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e163 (71.81%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eSerum Biomarkers\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e76 (84.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14 (15.56%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22 (21.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81 (78.64%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73 (82.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15 (17.05%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21 (20.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82 (79.61%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e149 (83.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29 (16.29%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43 (20.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e163 (79.13%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eMRI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69 (83.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14 (16.87%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29 (26.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81 (73.64%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65 (81.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15 (18.75%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29 (26.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82 (73.87%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e134 (82.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29 (17.79%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58 (26.24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e163 (73.76%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eThese percentages reflect how often each test result (positive or negative) aligns with PET-CT findings, broken down by gender and overall.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe diagnostic accuracy of individual modalities showed that Serum Biomarkers had the highest sensitivity at 77.60%, followed by MRI at 69.79%, EEG at 66.67%, and Cognitive Tests at 62.50%. All four modalities demonstrated the same specificity of 84.90%. Positive predictive values ranged from 80.54% for Cognitive Tests to 83.71% for Serum Biomarkers, while negative predictive values ranged from 69.36\u0026ndash;79.13% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eDiagnostic Accuracy of Cognitive Tests, EEG, Serum Biomarkers, and MRI\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetric\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCognitive Tests (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEEG (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSerum Biomarkers (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMRI (%)\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\u003eSensitivity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e69.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpecificity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e84.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePositive Predictive Value (PPV)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e83.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNegative Predictive Value (NPV)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e79.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e73.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAccuracy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e73.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e77.34\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\u003eWhen combining diagnostic modalities, the addition rule of probability significantly improved sensitivity. The combination of Serum Biomarkers and Cognitive Tests yielded a combined sensitivity of 91.60%, which increased to 97.20% when EEG was added, and further to 99.15% with the inclusion of MRI. Conversely, the product rule, which requires all tests to be positive, resulted in lower combined sensitivities: 48.50% for Serum Biomarkers and Cognitive Tests, 32.33% with EEG added, and 22.57% when MRI was also included.\u003c/p\u003e\u003cp\u003eCombined specificity, calculated using the product rule decreased as more tests were added: 72.08% for two tests, 61.20% for three, and 51.96% for all four. However, when using the additional rule, combined specificity increased dramatically, reaching 99.95% for the full combination of all four modalities (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTable 4: Combined Statistics of Cognitive Tests, EEG, Serum Biomarkers, and MRI\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"738\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2683%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombination\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined Sensitivity* (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5122%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined Specificity** (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0732%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined Specificity* (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3442%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined Sensitivity** (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2683%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum Biomarkers + Cognitive Tests\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8022%;\"\u003e\n \u003cp\u003e91.60\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5122%;\"\u003e\n \u003cp\u003e72.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0732%;\"\u003e\n \u003cp\u003e97.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3442%;\"\u003e\n \u003cp\u003e48.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2683%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum Biomarkers + Cognitive Tests + EEG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8022%;\"\u003e\n \u003cp\u003e97.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5122%;\"\u003e\n \u003cp\u003e61.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0732%;\"\u003e\n \u003cp\u003e99.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3442%;\"\u003e\n \u003cp\u003e32.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.2683%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum Biomarkers + Cognitive Tests + EEG + MRI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8022%;\"\u003e\n \u003cp\u003e99.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5122%;\"\u003e\n \u003cp\u003e51.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.0732%;\"\u003e\n \u003cp\u003e99.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3442%;\"\u003e\n \u003cp\u003e22.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Addition rule of probability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e**Product of all tests.\u003c/p\u003e\u003cp\u003eROC curve analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) visually confirmed the diagnostic performance of each modality. Serum Biomarkers and MRI demonstrated the highest area under the curve (AUC), indicating strong diagnostic capabilities. EEG and Cognitive Tests, while slightly lower in AUC, provided valuable complementary information, especially when used in combination with other modalities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese findings support the use of a multi-modal diagnostic approach for Alzheimer\u0026rsquo;s disease, where combining cognitive, biochemical, functional, and structural assessments significantly enhances diagnostic accuracy compared to any single modality alone.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the diagnostic accuracy of cognitive tests, serum biomarkers, EEG, and MRI\u0026mdash;individually and in combination\u0026mdash;against PET-CT findings in the early detection of Alzheimer\u0026rsquo;s disease. Our findings support the growing body of literature advocating for a multi-modal diagnostic approach to improve sensitivity and specificity in clinical settings.\u003c/p\u003e\u003cp\u003eIn our research, the sensitivity and specificity of cognitive tests were also similar to recent meta-analyses (62.50% and 84.90%, respectively). In a 2023 Bayesian study of the ADNI data set, the MoCA presented a sensitivity of 91.2% with a specificity of 90.1% in detecting Alzheimer's, whereas, compared to the sensitivity, the MMSE reported almost similar values of sensitivity with a higher specificity of 92.2% [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Likewise, the 2022 systematic review results revealed pooled sensitivity and specificity of MMSE as 73 and 83, respectively [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These results confirm the usefulness of cognitive tests as easily performed and sensitive screening measures, but the diagnostic sensitivity can be enhanced when administered in conjunction with objective biomarkers.\u003c/p\u003e\u003cp\u003eSerum biomarkers proved to have the highest individual sensitivity in our research (77.60%), as it corresponds to current progress in blood-based diagnostics. A post-2024 NIH-funded study discussed how plasma p-tau217 and the Aβ42/Aβ40 ratio attained diagnostic accuracy in more than 90% contingent upon contrasting with PET and CSF results [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Such findings reinforce the ongoing promise of blood biomarkers as a non-invasive, blood-based alternative to the CSF test, especially in early diagnosis.\u003c/p\u003e\u003cp\u003eEEG, which has a sensitivity of 66.67% and specificity of 84.90% in our study, has also become promising in the recent literature. In a 2025 systematic review, EEG was shown to pick up early changes in the functioning of the mind in Alzheimer's as a result of elevated theta and reduced alpha activity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These electrophysiological changes correlate with ours and make a case for EEG as a relatively inexpensive tool that can help in real-time diagnostics.\u003c/p\u003e\u003cp\u003eMRI, which obtained 69.79% and 84.90% sensitivity and specificity, respectively, in our study, is one of the pillars of structural imaging in Alzheimer's. A 2024 study utilizing machine learning on MRI characteristics (including hippocampus volume and cortical thickness) found that 95% of the participants could be successfully classified based on the Alzheimer's diagnosis and normal aging by utilizing the MRI tools [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In another review of 2021, the authors stressed that although MRI without other tests is not a reliable method to reveal amyloid pathology, it still helps to find structural atrophy and exclude other dementia-related diseases [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe most important result of our work was an improvement in the diagnostic performance with the help of multi-modal combinations. With the addition rule of probability, the combination of serum parameters, cognitive tests, EEG, and MRI provided a sensitivity of 99.15% and a specificity of 99.95%. These findings matched a 2025 systematic review of AI-based multimodal diagnostics, which found that biomarker-enhanced multimodal diagnostics combining imaging and biomarker and cognitive data exposed substantially better classification performance and clinical value [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. A study published in 2024, which was based on SeaLM and used a Transformer-based model to fuse MRI and PET imaging data, reached an accuracy of 98.19% when classifying people with Alzheimer's, while healthy people were classified at 99.43% [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe former, though, had a sensitivity of only 22.57%, as opposed to that of the latter, whereas the specificity was high. This can be more suited under confirming circumstances when it is necessary to reduce the false positives. A more recently published multimodal study based on longitudinal data alluded to the same trade-off between sensitivity and specificity in fusion models by arguing in favor of diagnostic thresholds set on contextual grounds [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur results are consistent with the existing trend of Alzheimer's research that significantly incorporates multi-modal, data-driven modalities. Using an assembly of cognitive, biochemical, electrophysiological, and structural information, clinicians will be able to make more appropriate and earlier diagnoses, which will lead to better patient outcomes and allow more specific interventions to be made.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study establishes that individual use of diagnostic modalities of cognitive tests, serum biomarkers, EEG, and MRI contributes highly to insights into Alzheimer's disease, but their combinational use contributes highly to diagnostic accuracy. All four modalities were superior when used individually and especially when combined, as the sensitivity and specificity performed nearly perfectly using the additional rule of probability, with serum biomarkers and MRI having the highest performance rates. The above findings confirm the utilization of a multi-modal diagnostic framework in clinical settings, which can assist in early diagnosis and treatment of Alzheimer's, as it is a more comprehensive, accessible, and precise diagnosis model.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The study was conducted in accordance with the ethical standards of the Ethics Committee of Bolan Medical College. Ethical approval was obtained (Approval No: EC/92/2025). Written informed consent was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Written informed consent for publication of anonymized data was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMuhammad Arif Afridi designed the study and supervised data collection. Malik Mairaj Khalid performed the statistical analysis and drafted the manuscript. Rahman Ud Din contributed to the interpretation of results and critical revisions. All authors read and approved of the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMonzio Compagnoni G, Di Fonzo A, Corti S, Comi GP, Bresolin N, Masliah E. The Role of Mitochondria in Neurodegenerative Diseases: the Lesson from Alzheimer\u0026rsquo;s Disease and Parkinson\u0026rsquo;s Disease. Mol Neurobiol 2020;57:2959\u0026ndash;80. https://doi.org/10.1007/s12035-020-01926-1.\u003c/li\u003e\n\u003cli\u003ePathak N, Vimal SK, Tandon I, Agrawal L, Hongyi C, Bhattacharyya S. Neurodegenerative Disorders of Alzheimer, Parkinsonism, Amyotrophic Lateral Sclerosis and Multiple Sclerosis: An Early Diagnostic Approach for Precision Treatment. 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Discov Appl Sci 2024;7:27. https://doi.org/10.1007/s42452-024-06440-w.\u003c/li\u003e\n\u003cli\u003eComplete Evaluation of Dementia: PET and MRI Correlation and Diagnosis for the Neuroradiologist | American Journal of Neuroradiology n.d. https://www.ajnr.org/content/early/2021/04/29/ajnr.A7079 (accessed July 17, 2025).\u003c/li\u003e\n\u003cli\u003eWahul RM, Ambadekar S, Dhanvijay DM, Dhanvijay MM, Dudhedia MA, Gaikwad V, et al. Multimodal approaches and AI-driven innovations in dementia diagnosis: a systematic review. Discov Artif Intell 2025;5:96. https://doi.org/10.1007/s44163-025-00358-x.\u003c/li\u003e\n\u003cli\u003eTang Y, Xiong X, Tong G, Yang Y, Zhang H. Multimodal diagnosis model of Alzheimer\u0026rsquo;s disease based on improved Transformer. Biomed Eng OnLine 2024;23:8. https://doi.org/10.1186/s12938-024-01204-4.\u003c/li\u003e\n\u003cli\u003eMulti-Modal Fusion and Longitudinal Analysis for Alzheimer\u0026rsquo;s Disease Classification Using Deep Learning n.d. https://www.mdpi.com/2075-4418/15/6/717 (accessed July 17, 2025).\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":"Alzheimer’s disease, cognitive tests, serum biomarkers, EEG, MRI, PET-CT, diagnostic accuracy, multi-modal diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-7404405/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7404405/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is a progressive neurodegenerative disorder that significantly impairs memory, cognition, and daily functioning. Early and accurate diagnosis is essential for timely intervention and effective disease management. While PET-CT imaging is considered the gold standard for detecting Alzheimer\u0026rsquo;s pathology, its high cost and limited accessibility necessitates the exploration of alternative diagnostic tools. Cognitive assessments, serum biomarkers, EEG, and MRI each offer unique insights into the disease process. When used in combination, these modalities may enhance diagnostic accuracy and provide a more comprehensive understanding of Alzheimer\u0026rsquo;s progression.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eAmong 384 participants, PET-CT confirmed Alzheimer\u0026rsquo;s in 192 cases (50%). Serum biomarkers showed the highest individual sensitivity (77.60%), followed by MRI (69.79%), EEG (66.67%), and cognitive tests (62.50%). All modalities had a specificity of 84.90%. When combined using the addition rule of probability, diagnostic sensitivity increased to 99.15% and specificity to 99.95%. ROC curve analysis showed serum biomarkers and MRI had the highest diagnostic accuracy. The multi-modal approach significantly improved early diagnostic performance compared to single modalities.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eIndividual diagnostic accuracy after serum biomarkers and MRI was the best, whereas when all four modalities were combined, sensitivity (up to 99.15%) and specificity (up to 99.95%) showed a significant increment through the addition rule. The evidence used will provide greater early detection and decision-making in Alzheimer's disease that promotes the employment of a multi-modal diagnostic strategy.\u003c/p\u003e","manuscriptTitle":"Multi-Modal Approaches to Alzheimer’s Diagnosis: Combining Cognitive assessments with Biomarkers and Imaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 14:58:14","doi":"10.21203/rs.3.rs-7404405/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":"9b3a4ca5-f497-4b9c-ab6c-e8d7727499bb","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-16T00:39:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-12 14:58:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7404405","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7404405","identity":"rs-7404405","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}