Amanda Morris, Min Jung Kim, Michael Pontecorvo, Sergey Shcherbinin, Leonardo Iaccarino, Samantha C. Burnham, John R. Sims, Dawn A. Brooks, Emily C. Collins, Mark A. Mintun, Ming Lu
Background
In the TRAILBLAZER-ALZ 2 study (NCT04437511), tau pathology at screening was evaluated with flortaucipir Positron Emission Tomography (PET) with a combination of visual read (V) and quantitative (Q) standardized uptake value ratio (SUVr) approaches. In most cases, participants had an advanced Alzheimer's disease (AD) tau visual pattern (AD++) with an AD-signature weighted neocortical SUVr>1.1 (V++/Q+). A minority of participants presented other V/Q profiles, namely an AD++ visual read with an SUVr≤1.1 (V++/Q-) and a moderate AD tau pattern (AD+) with an SUVr>1.1 (V+/Q+). The objective of these analyses was to characterise these populations.
Method
All available TRAILBLAZER-ALZ 2 placebo-treated early symptomatic AD participants were included. Baseline characteristics and progression on both clinical scales (Clinical Dementia Rating - Sum of Boxes [CDR-SB], integrated Alzheimer Disease Rating Scale [iADRS]) and biomarkers (p-tau217, glial fibrillary acidic protein [GFAP], neurofilament light chain [NfL]) at Week 76 (using mixed model repeated measures methodology), of the V++/Q- and V+/Q+ groups were compared to each other and to the rest of the study population (V++/Q+).
Result
The V++/Q- group comprised 15.7% (126/802) and the V+/Q+ group 2.9% (23/802) of the placebo-treated study population with characteristics as shown in the Table. At Week 76, all groups were at risk of clinical progression, although changes from baseline in CDR-SB and iADRS for the V++/Q- and V+/Q+ groups were less compared to the rest of the population (significantly different between the V++/Q- and V++/Q+ groups; p <0.0001 for both CDR-SB and iADRS) (Figure). Progression for both AD-specific and non-AD specific biomarker progression was similar across all groups.
Conclusion
The observation of V++/Q- and V+/Q+ participants being older, milder in AD pathology while at a similar clinical stage at screening compared to V++/Q+ participants may suggest the effect of age-related comorbidities. Participants in the V++/Q- and the V+/Q+ groups showed clinical/pathological progression, although not at the level of those in the V++/Q+ group.
{"title":"Characterizing early symptomatic AD patients by visual and quantitative tau profiles","authors":"Amanda Morris, Min Jung Kim, Michael Pontecorvo, Sergey Shcherbinin, Leonardo Iaccarino, Samantha C. Burnham, John R. Sims, Dawn A. Brooks, Emily C. Collins, Mark A. Mintun, Ming Lu","doi":"10.1002/alz70856_106941","DOIUrl":"10.1002/alz70856_106941","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>In the TRAILBLAZER-ALZ 2 study (NCT04437511), tau pathology at screening was evaluated with flortaucipir Positron Emission Tomography (PET) with a combination of visual read (V) and quantitative (Q) standardized uptake value ratio (SUVr) approaches. In most cases, participants had an advanced Alzheimer's disease (AD) tau visual pattern (AD++) with an AD-signature weighted neocortical SUVr>1.1 (V++/Q+). A minority of participants presented other V/Q profiles, namely an AD++ visual read with an SUVr<b>≤</b>1.1 (V++/Q-) and a moderate AD tau pattern (AD+) with an SUVr>1.1 (V+/Q+). The objective of these analyses was to characterise these populations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>All available TRAILBLAZER-ALZ 2 placebo-treated early symptomatic AD participants were included. Baseline characteristics and progression on both clinical scales (Clinical Dementia Rating - Sum of Boxes [CDR-SB], integrated Alzheimer Disease Rating Scale [iADRS]) and biomarkers (<i>p</i>-tau217, glial fibrillary acidic protein [GFAP], neurofilament light chain [NfL]) at Week 76 (using mixed model repeated measures methodology), of the V++/Q- and V+/Q+ groups were compared to each other and to the rest of the study population (V++/Q+).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Result</h3>\u0000 \u0000 <p>The V++/Q- group comprised 15.7% (126/802) and the V+/Q+ group 2.9% (23/802) of the placebo-treated study population with characteristics as shown in the Table. At Week 76, all groups were at risk of clinical progression, although changes from baseline in CDR-SB and iADRS for the V++/Q- and V+/Q+ groups were less compared to the rest of the population (significantly different between the V++/Q- and V++/Q+ groups; <i>p</i> <0.0001 for both CDR-SB and iADRS) (Figure). Progression for both AD-specific and non-AD specific biomarker progression was similar across all groups.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The observation of V++/Q- and V+/Q+ participants being older, milder in AD pathology while at a similar clinical stage at screening compared to V++/Q+ participants may suggest the effect of age-related comorbidities. Participants in the V++/Q- and the V+/Q+ groups showed clinical/pathological progression, although not at the level of those in the V++/Q+ group.</p>\u0000 </section>\u0000 </div>","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"21 S2","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://alz-journals.onlinelibrary.wiley.com/doi/epdf/10.1002/alz70856_106941","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jeremy A. Tanner, Diefei Chen, Min Soo Byun, Evgeny J. Chumin, Ileana De Anda-Duran, Kacie D Deters, Martine Elbejjani, Evan Fletcher, A. Zarina Kraal, Dong Young Lee, Silvia Mejia-Arango, Stefanie Pina-Escudero, Kwangsik Nho, Talia L. Robinson, C. Elizabeth Shaaban, Adam M. Staffaroni, Andrew J. Saykin, Paul K Crane, Dahyun Yi, Shannon L Risacher, the Alzheimer's Disease Neuroimaging Initiative; the KBASE Research Group
<div> <section> <h3> Background</h3> <p>Recent diagnosis and staging criteria have been proposed for AD based on amyloid(“A”), tau(“T”), and neurodegeneration(“N”), with consideration of comorbid vascular(“V”) pathology. However, challenges remain in their application and interpretation for research and clinical use. Additionally, current criteria have been largely informed by data derived from samples of highly educated, non-Hispanic White cohorts in the US. We compared methods to operationalize multimodal ATNV measures across two cohorts to meaningfully inform future research and clinical practice.</p> </section> <section> <h3> Method</h3> <p>Participants with amyloid PET, tau PET, and brain MRI were included from two prospective cohort studies with similar study designs: ADNI3 in the US and KBASE in Korea. ATNV measures were operationalized as continuous (A=centiloid; T=meta-temporal ROI; <i>N</i> = AD signature region cortical thickness; V=white matter hyperintensity volume) and as binary (+/-) variables by replicating approaches applied in other cohorts. Clinical diagnoses of cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia were determined by experts at multidisciplinary consensus conferences. Parallel cross-sectional analyses were performed within each cohort. Multivariate logistic regressions with ATNV modeled as continuous or binary predictors (16 possible combinations) adjusting for age, sex, and education were performed to compare model fit and predictive power for distinguishing participants with dementia versus CU.</p> </section> <section> <h3> Result</h3> <p>A total of 508 participants in ADNI (mean age=71±7, female=55%, education(yrs)=16.5±2.3) and 165 in KBASE (<i>n</i> = 165; age=73±8, female=64%, education(yrs)=11.0±4.6) were included (Table 1). In ADNI, the continuous ATNV and continuous ATN+binary V explained the most variance in dementia diagnosis (highest pseudo-R<sup>2</sup>, lowest AIC/BIC; Table 2). In KBASE, models had higher overall predictive power for distinguishing dementia. The best-performing models included continuous A and T, binary N, and continuous V, or binary A, continuous T, binary N, and continuous V (Table 3).</p> </section> <section> <h3> Conclusion</h3> <p>Models including ATNV were highly predictive of dementia in both cohorts. Models with predominantly continuous variables explained more variability in dementia diagnosis, and are optimal for research use due to their simplicity. In Korea, binary N and binary A increased predictive power. Ongoing analyses will assess the relative
近年来，人们提出了基于淀粉样蛋白（“A”）、tau蛋白（“T”）和神经变性（“N”）的AD诊断和分期标准，并考虑了合并症血管（“V”）病理。然而，它们在研究和临床应用中的应用和解释仍然存在挑战。此外，目前的标准在很大程度上是由美国受过高等教育的非西班牙裔白人群体的样本数据提供的。我们比较了在两个队列中实施多模式ATNV措施的方法，以便为未来的研究和临床实践提供有意义的信息。方法：两项具有相似研究设计的前瞻性队列研究（美国的ADNI3和韩国的KBASE）纳入了淀粉样蛋白PET、tau PET和脑MRI的参与者。ATNV测量被操作为连续（A=centiloid； T=meta-temporal ROI; N = AD特征区皮质厚度；V=白质高强度体积）和二元（+/-）变量，通过复制应用于其他队列的方法。认知无损伤（CU）、轻度认知损伤（MCI）和痴呆的临床诊断由多学科共识会议上的专家确定。在每个队列中进行平行横断面分析。将ATNV建模为连续或二元预测因子（16种可能的组合），进行多变量logistic回归，调整年龄、性别和教育程度，比较模型拟合和预测能力，以区分痴呆和CU的参与者。结果共纳入ADNI患者508例（平均年龄71±7岁，女性=55%，学历（年龄）=16.5±2.3），KBASE患者165例（n = 165，年龄=73±8岁，女性=64%，学历（年龄）=11.0±4.6）（表1）。在ADNI中，连续ATNV和连续ATN+二元V解释了痴呆诊断的最大方差（伪r2最高，AIC/BIC最低；表2）。在KBASE中，模型在区分痴呆方面具有更高的总体预测能力。表现最好的模型包括连续A和T，二元N和连续V，或者二元A，连续T，二元N和连续V（表3）。结论包括ATNV在内的模型在两组患者中均可高度预测痴呆。以连续变量为主的模型解释了痴呆症诊断中更多的可变性，并且由于其简单性而最适合研究使用。在韩国，二进制N和二进制A提高了预测能力。正在进行的分析将评估ATNV对每个队列的认知表现和诊断的相对贡献，以便更好地为跨国队列的适当研究和临床应用提供信息。
{"title":"Cross-National Study to Operationalize Amyloid, Tau, Neurodegeneration, and Vascular Contributions to Dementia","authors":"Jeremy A. Tanner, Diefei Chen, Min Soo Byun, Evgeny J. Chumin, Ileana De Anda-Duran, Kacie D Deters, Martine Elbejjani, Evan Fletcher, A. Zarina Kraal, Dong Young Lee, Silvia Mejia-Arango, Stefanie Pina-Escudero, Kwangsik Nho, Talia L. Robinson, C. Elizabeth Shaaban, Adam M. Staffaroni, Andrew J. Saykin, Paul K Crane, Dahyun Yi, Shannon L Risacher, the Alzheimer's Disease Neuroimaging Initiative; the KBASE Research Group","doi":"10.1002/alz70856_106635","DOIUrl":"https://doi.org/10.1002/alz70856_106635","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Recent diagnosis and staging criteria have been proposed for AD based on amyloid(“A”), tau(“T”), and neurodegeneration(“N”), with consideration of comorbid vascular(“V”) pathology. However, challenges remain in their application and interpretation for research and clinical use. Additionally, current criteria have been largely informed by data derived from samples of highly educated, non-Hispanic White cohorts in the US. We compared methods to operationalize multimodal ATNV measures across two cohorts to meaningfully inform future research and clinical practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>Participants with amyloid PET, tau PET, and brain MRI were included from two prospective cohort studies with similar study designs: ADNI3 in the US and KBASE in Korea. ATNV measures were operationalized as continuous (A=centiloid; T=meta-temporal ROI; <i>N</i> = AD signature region cortical thickness; V=white matter hyperintensity volume) and as binary (+/-) variables by replicating approaches applied in other cohorts. Clinical diagnoses of cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia were determined by experts at multidisciplinary consensus conferences. Parallel cross-sectional analyses were performed within each cohort. Multivariate logistic regressions with ATNV modeled as continuous or binary predictors (16 possible combinations) adjusting for age, sex, and education were performed to compare model fit and predictive power for distinguishing participants with dementia versus CU.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Result</h3>\u0000 \u0000 <p>A total of 508 participants in ADNI (mean age=71±7, female=55%, education(yrs)=16.5±2.3) and 165 in KBASE (<i>n</i> = 165; age=73±8, female=64%, education(yrs)=11.0±4.6) were included (Table 1). In ADNI, the continuous ATNV and continuous ATN+binary V explained the most variance in dementia diagnosis (highest pseudo-R<sup>2</sup>, lowest AIC/BIC; Table 2). In KBASE, models had higher overall predictive power for distinguishing dementia. The best-performing models included continuous A and T, binary N, and continuous V, or binary A, continuous T, binary N, and continuous V (Table 3).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Models including ATNV were highly predictive of dementia in both cohorts. Models with predominantly continuous variables explained more variability in dementia diagnosis, and are optimal for research use due to their simplicity. In Korea, binary N and binary A increased predictive power. Ongoing analyses will assess the relative ","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"21 S2","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://alz-journals.onlinelibrary.wiley.com/doi/epdf/10.1002/alz70856_106635","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145970036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pamela C.L. Ferreira, Tevy Chan, Bruna Bellaver, Guilherme Povala, Guilherme Bauer-Negrini, Emma Ruppert, Carolina Soares, Cynthia Felix, Andreia Rocha, Marina Scop Madeiros, Livia Amaral, Juli Cehula, Firoza Z Lussier, Joseph C. Masdeu, Dana L Tudorascu, Thomas K Karikari, David N. Soleimani-Meigooni, Juan Fortea, Val J Lowe, Hwamee Oh, Belen Pascual, Brian A. Gordon, Pedro Rosa-Neto, Suzanne L. Baker, Tharick A Pascoal
<div> <section> <h3> Background</h3> <p>Tau phosphorylation and aggregation are hallmark features of Alzheimer's disease pathology(AD). Previous studies have shown that plasma phosphorylated tau(<i>p</i>-tau) at threonine 217 can detect AD-related tau tangle pathology. However, the head-to-head association between <i>p</i>-tau217 and different tau PET tracers remains unexplored. Here, we conducted a head-to-head comparison of the association between plasma <i>p</i>-tau217 and four distinct tau PET tracers[MK6240, Flortaucipir(FTP), PI2620, RO948].</p> </section> <section> <h3> Method</h3> <p>We studied 338 individuals across the AD continuum from the HEAD study[190 cognitively unimpaired elderly(CU), 109 with mild cognitive impairment(MCI), and 39 with dementia] with available FTP, MK6240, and plasma <i>p</i>-tau217 measured by ALZpath assay. A subset of 64 individuals(28 CU, 25 MCI, and 11 dementia) also had PI2620 and RO948, and <i>p</i>-tau217 measured by Lumipulse. The strength of the association was determined using t-value maps from regression models testing the association between each tau PET tracer and plasma <i>p</i>-tau217, and the extent was measured by the percentage of significant voxels(t-value>3.2). R-square metric was used to determine how much of the variance in tau PET tracer estimates was explained by plasma <i>p</i>-tau217 concentrations. Associations between <i>p</i>-tau217 with tau PET were evaluated using the Spearman test.</p> </section> <section> <h3> Result</h3> <p>We found that in CU, both the extent and magnitude of the association with plasma <i>p</i>-tau217 were higher for MK6240 compared to FTP (Figure 1A-D). Plasma <i>p</i>-tau217 explained a greater proportion of the variance in MK6240 than in FTP (Figure 1E-F). In CI, the extent and magnitude of the association were similar for MK6240 and FTP. Additionally, plasma <i>p</i>-tau217 explained a similar proportion of the variance in MK6240 and FTP. Using the subset of individuals who had all tau tracers, MK6240 showed a slightly higher extent of association with <i>p</i>-tau217, followed by FTP, PI2620, and RO948 (Figure 2A-B). The magnitude of association was stronger for MK6240, followed by PI2620, RO948, and FTP (Figure 2C-D). Plasma <i>p</i>-tau217 explained a greater proportion of the variance in MK6240, followed by FTP, RO948, and PI2620 (Figure 2E-F). We compared the associations between different <i>p</i>-tau217 assays, and both assays presented similar associations with all tau PET tracers (Figure 3).</p> </section> <section> <h3> Conclusion</h3> <p>In sum
{"title":"Head-to-Head evaluation of plasma p-tau217 associations with MK6240, Flortaucipir, PI2620, and RO948 tau PET tracers","authors":"Pamela C.L. Ferreira, Tevy Chan, Bruna Bellaver, Guilherme Povala, Guilherme Bauer-Negrini, Emma Ruppert, Carolina Soares, Cynthia Felix, Andreia Rocha, Marina Scop Madeiros, Livia Amaral, Juli Cehula, Firoza Z Lussier, Joseph C. Masdeu, Dana L Tudorascu, Thomas K Karikari, David N. Soleimani-Meigooni, Juan Fortea, Val J Lowe, Hwamee Oh, Belen Pascual, Brian A. Gordon, Pedro Rosa-Neto, Suzanne L. Baker, Tharick A Pascoal","doi":"10.1002/alz70856_106115","DOIUrl":"10.1002/alz70856_106115","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Tau phosphorylation and aggregation are hallmark features of Alzheimer's disease pathology(AD). Previous studies have shown that plasma phosphorylated tau(<i>p</i>-tau) at threonine 217 can detect AD-related tau tangle pathology. However, the head-to-head association between <i>p</i>-tau217 and different tau PET tracers remains unexplored. Here, we conducted a head-to-head comparison of the association between plasma <i>p</i>-tau217 and four distinct tau PET tracers[MK6240, Flortaucipir(FTP), PI2620, RO948].</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>We studied 338 individuals across the AD continuum from the HEAD study[190 cognitively unimpaired elderly(CU), 109 with mild cognitive impairment(MCI), and 39 with dementia] with available FTP, MK6240, and plasma <i>p</i>-tau217 measured by ALZpath assay. A subset of 64 individuals(28 CU, 25 MCI, and 11 dementia) also had PI2620 and RO948, and <i>p</i>-tau217 measured by Lumipulse. The strength of the association was determined using t-value maps from regression models testing the association between each tau PET tracer and plasma <i>p</i>-tau217, and the extent was measured by the percentage of significant voxels(t-value>3.2). R-square metric was used to determine how much of the variance in tau PET tracer estimates was explained by plasma <i>p</i>-tau217 concentrations. Associations between <i>p</i>-tau217 with tau PET were evaluated using the Spearman test.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Result</h3>\u0000 \u0000 <p>We found that in CU, both the extent and magnitude of the association with plasma <i>p</i>-tau217 were higher for MK6240 compared to FTP (Figure 1A-D). Plasma <i>p</i>-tau217 explained a greater proportion of the variance in MK6240 than in FTP (Figure 1E-F). In CI, the extent and magnitude of the association were similar for MK6240 and FTP. Additionally, plasma <i>p</i>-tau217 explained a similar proportion of the variance in MK6240 and FTP. Using the subset of individuals who had all tau tracers, MK6240 showed a slightly higher extent of association with <i>p</i>-tau217, followed by FTP, PI2620, and RO948 (Figure 2A-B). The magnitude of association was stronger for MK6240, followed by PI2620, RO948, and FTP (Figure 2C-D). Plasma <i>p</i>-tau217 explained a greater proportion of the variance in MK6240, followed by FTP, RO948, and PI2620 (Figure 2E-F). We compared the associations between different <i>p</i>-tau217 assays, and both assays presented similar associations with all tau PET tracers (Figure 3).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>In sum","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"21 S2","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://alz-journals.onlinelibrary.wiley.com/doi/epdf/10.1002/alz70856_106115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fabricio Nery Garrafiel, Ricardo Benardi Soder, Ricardo Pessini Paganin, Maria Rosa Alves da Silva, Andrei Bieger Sr., Vitor Verlindo Vidaletti, Cristiano Aguzzoli, Lucas Porcello Schilling
Background
Magnetic resonance imaging (MRI) is a tool used in the evaluation of patients with cognitive deficits, capable of identifying characteristic changes of neurodegenerative processes, such as hippocampal atrophy. The most common assessment methods include scoring systems using the Fazekas and MTA scales. The present study aims to evaluate the relationship between the classification data from the MTA scale, obtained through the analysis of MRI images by two experienced neuroradiologists, and the data obtained from hippocampal volumetry of the same image sample.
Method
677 MRI images were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and the hippocampal volume quantification values were extracted using Freesurfer. The degree of atrophy, according to the MTA scale, on both sides of the hippocampus were classified by two independent specialized radiologists.
Result
The correlation between MTA classifications was 0.85, and a negative correlation (-0.64) was found between volumetry and atrophy grade (fgure 1). Despite these results correlating the MTA grade with automatic volumetry, approximately 10% of the individuals were classified as outliers, being determined to be outside 2 standard deviations from the mean volume of each atrophy grade (Figure 2).
Conclusion
The results showed a strong correlation between the MTA scale and volumetry, as well as a consistent correlation between the radiologists' MTA classification. Albeit we identified 10% of individuals classified as outliers, highlighting the existence of specific cases that should be better investigated in order to understand the efficiency of the qualitative method and the accuracy of its classifications.
{"title":"Analysis of the relationship between volumetry and visual classification of hippocampal atrophy on magnetic ressonance image","authors":"Fabricio Nery Garrafiel, Ricardo Benardi Soder, Ricardo Pessini Paganin, Maria Rosa Alves da Silva, Andrei Bieger Sr., Vitor Verlindo Vidaletti, Cristiano Aguzzoli, Lucas Porcello Schilling","doi":"10.1002/alz70856_106082","DOIUrl":"10.1002/alz70856_106082","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Magnetic resonance imaging (MRI) is a tool used in the evaluation of patients with cognitive deficits, capable of identifying characteristic changes of neurodegenerative processes, such as hippocampal atrophy. The most common assessment methods include scoring systems using the Fazekas and MTA scales. The present study aims to evaluate the relationship between the classification data from the MTA scale, obtained through the analysis of MRI images by two experienced neuroradiologists, and the data obtained from hippocampal volumetry of the same image sample.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>677 MRI images were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and the hippocampal volume quantification values were extracted using Freesurfer. The degree of atrophy, according to the MTA scale, on both sides of the hippocampus were classified by two independent specialized radiologists.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Result</h3>\u0000 \u0000 <p>The correlation between MTA classifications was 0.85, and a negative correlation (-0.64) was found between volumetry and atrophy grade (fgure 1). Despite these results correlating the MTA grade with automatic volumetry, approximately 10% of the individuals were classified as outliers, being determined to be outside 2 standard deviations from the mean volume of each atrophy grade (Figure 2).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The results showed a strong correlation between the MTA scale and volumetry, as well as a consistent correlation between the radiologists' MTA classification. Albeit we identified 10% of individuals classified as outliers, highlighting the existence of specific cases that should be better investigated in order to understand the efficiency of the qualitative method and the accuracy of its classifications.</p>\u0000 </section>\u0000 </div>","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"21 S2","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://alz-journals.onlinelibrary.wiley.com/doi/epdf/10.1002/alz70856_106082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rufeyda Yagci, Tatjana Schmidt, Marcella Montagnese, Timothy Rittman
Background
White matter (WM) degeneration has been proposed as an early indicator of Alzheimer's disease (AD). Alterations in WM integrity, such as reductions in fractional anisotropy (FA) and increases in mean diffusivity (MD), may reflect the underlying pathological processes associated with AD. This study aims to investigate microstructural changes in AD utilizing Diffusion Tensor Imaging (DTI) and its applicability to a real world setting.
Method
A total of 30 participants diagnosed with AD with a mean age of 68.5 ± 8.4 and 42 control participants with a mean age of 58.7 ± 7.7 were included, from the Quantitative MRI in NHS Memory Clinics (QMIN-MC) study, recruited from NHS Memory Assessment Services. Diffusion-weighted imaging (DWI) data were preprocessed using the Micapipe preprocessing pipeline to generate fractional anisotropy (FA) and mean diffusivity (MD) values. Twenty-five regions of interest (ROIs) were selected from the JHU ICBM DTI-81 Atlas and analyzed using FMRIB Software Library (FSL) tools.
Results
We analyzed 25 ROIs to identify differences in FA and MD between AD and control groups. FA values showed significant differences in 17 regions (Figure 1), including the hippocampus, fornix, and cingulate gyrus, while MD values revealed significant differences in 18 regions (Figure 2). Normalization was performed using reference tracts considered least affected by age-related changes: the superior longitudinal fasciculus (SLF) for FA and the superior fronto-occipital fasciculus (SFO) for MD. After normalization, 6 regions showed significant differences for FA, and 18 regions for MD (Figure 3).
Conclusion
Our findings support DTI measures as potential diagnostic tools for evaluation of AD patients as part of routine clinical practice. The involvement of key structures of the limbic system (hippocampus, fornix, and cingulate gyrus) along with white matter pathways such as the tapetum and stria terminalis provide evidence for the disruption of memory and executive processing, alongside inter-hemispheric communication in Alzheimer's disease. Such insights provide a basis for further research into the microstructural changes underlying AD pathology and their real world diagnostic application.
{"title":"Microstructural White Matter Alterations in Alzheimer's Disease: A Diffusion Tensor Imaging Study","authors":"Rufeyda Yagci, Tatjana Schmidt, Marcella Montagnese, Timothy Rittman","doi":"10.1002/alz70856_105992","DOIUrl":"10.1002/alz70856_105992","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>White matter (WM) degeneration has been proposed as an early indicator of Alzheimer's disease (AD). Alterations in WM integrity, such as reductions in fractional anisotropy (FA) and increases in mean diffusivity (MD), may reflect the underlying pathological processes associated with AD. This study aims to investigate microstructural changes in AD utilizing Diffusion Tensor Imaging (DTI) and its applicability to a real world setting.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>A total of 30 participants diagnosed with AD with a mean age of 68.5 ± 8.4 and 42 control participants with a mean age of 58.7 ± 7.7 were included, from the Quantitative MRI in NHS Memory Clinics (QMIN-MC) study, recruited from NHS Memory Assessment Services. Diffusion-weighted imaging (DWI) data were preprocessed using the Micapipe preprocessing pipeline to generate fractional anisotropy (FA) and mean diffusivity (MD) values. Twenty-five regions of interest (ROIs) were selected from the JHU ICBM DTI-81 Atlas and analyzed using FMRIB Software Library (FSL) tools.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We analyzed 25 ROIs to identify differences in FA and MD between AD and control groups. FA values showed significant differences in 17 regions (Figure 1), including the hippocampus, fornix, and cingulate gyrus, while MD values revealed significant differences in 18 regions (Figure 2). Normalization was performed using reference tracts considered least affected by age-related changes: the superior longitudinal fasciculus (SLF) for FA and the superior fronto-occipital fasciculus (SFO) for MD. After normalization, 6 regions showed significant differences for FA, and 18 regions for MD (Figure 3).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our findings support DTI measures as potential diagnostic tools for evaluation of AD patients as part of routine clinical practice. The involvement of key structures of the limbic system (hippocampus, fornix, and cingulate gyrus) along with white matter pathways such as the tapetum and stria terminalis provide evidence for the disruption of memory and executive processing, alongside inter-hemispheric communication in Alzheimer's disease. Such insights provide a basis for further research into the microstructural changes underlying AD pathology and their real world diagnostic application.</p>\u0000 </section>\u0000 </div>","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"21 S2","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://alz-journals.onlinelibrary.wiley.com/doi/epdf/10.1002/alz70856_105992","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Patricia Genius, Blanca Rodríguez-Fernández, Mahnaz Shekari, Gonzalo Sánchez-Benavides, Gwendlyn Kollmorgen, Clara Quijano Rubio, Carole H Sudre, Marta Cirach, Mark Nieuwenhuijsen, Marc Suárez-Calvet, Arcadi Navarro, Juan Domingo Gispert, Natalia Vilor-Tejedor
<div> <section> <h3> Background</h3> <p>White matter hyperintensities (WMHs) are a hallmark of cerebral small vessel disease (CSVD) linked to cognitive impairment and risk of dementia. Several risk factors are associated with WMH, but their contribution to WMH progression remains unclear. This study aimed to identify risk factors driving WMHs changes in middle-aged individuals and to examine their impact on cognitive decline.</p> </section> <section> <h3> Method</h3> <p>Global WMHs and cognitive composites were evaluated at baseline and after three years on 75 cognitively unimpaired individuals at risk of Alzheimer's disease (AD) whose WMH volumes increased (Progressors: ΔWMHv2-v1 > 10+Percentile ΔWMHv2-v1=0). CSF biomarkers, exposure to air pollutants, AD pathway-specific polygenic risk scores (PRS) and cardiovascular risk factors were evaluated as predictors of the annual rate of change of global WMH, which was assessed as a predictor of cognitive performance. Partial least squares regression identified latent factors describing predictor-outcome covariance. Latent factors and WMH change were used in independent linear regression models to predict cognitive performance.</p> </section> <section> <h3> Result</h3> <p>Global WMHs increased by 9.5%, while cognitive composites declined annually by 1–11% [Table 1]. Five latent factors explained 56% of the variance in risk factors, though their stability was limited, with only a few significant loadings after bootstrapping (CI 95%) [Figure 1A]. Key predictors of WMH progression included higher genetic risks for WMH and AD (via the stimulus signaling pathway) and notably lower educational attainment, even when paired with low levels of other risk factors [Figure 1B]. Component 3, linked to baseline risk factors (e.g. Centiloid, CSF Aβ42/40)[Figure 1C], predicted lower language and PACC scores after 3 years [Figure 2A]. While WMH change did not mediate this association, it correlated with worse PACC (<i>p</i> = 0.057) and memory performance (ꞵ= -0.1 [-0.188, -0.011], <i>p</i> = 0.028) [Figure 2B].</p> </section> <section> <h3> Conclusion</h3> <p>WMHs progression is linked to subclinical cognitive decline in cognitively unimpaired individuals, for which low educational attainment and risk of AD via signaling pathways drive WMH progression. Remarkably, when combined with other risk factors, these variables predict worse PACC outcomes. These findings highlight the need to work with larger sample sizes to identify covariance patterns in modifiable and non-modifiable risk factors related to cerebrovascular d
{"title":"Distinct risk factors drive white matter hyperintensity progression which relates to cognitive performance in individuals at risk of Alzheimer's disease","authors":"Patricia Genius, Blanca Rodríguez-Fernández, Mahnaz Shekari, Gonzalo Sánchez-Benavides, Gwendlyn Kollmorgen, Clara Quijano Rubio, Carole H Sudre, Marta Cirach, Mark Nieuwenhuijsen, Marc Suárez-Calvet, Arcadi Navarro, Juan Domingo Gispert, Natalia Vilor-Tejedor","doi":"10.1002/alz70856_106766","DOIUrl":"10.1002/alz70856_106766","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>White matter hyperintensities (WMHs) are a hallmark of cerebral small vessel disease (CSVD) linked to cognitive impairment and risk of dementia. Several risk factors are associated with WMH, but their contribution to WMH progression remains unclear. This study aimed to identify risk factors driving WMHs changes in middle-aged individuals and to examine their impact on cognitive decline.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>Global WMHs and cognitive composites were evaluated at baseline and after three years on 75 cognitively unimpaired individuals at risk of Alzheimer's disease (AD) whose WMH volumes increased (Progressors: ΔWMHv2-v1 > 10+Percentile ΔWMHv2-v1=0). CSF biomarkers, exposure to air pollutants, AD pathway-specific polygenic risk scores (PRS) and cardiovascular risk factors were evaluated as predictors of the annual rate of change of global WMH, which was assessed as a predictor of cognitive performance. Partial least squares regression identified latent factors describing predictor-outcome covariance. Latent factors and WMH change were used in independent linear regression models to predict cognitive performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Result</h3>\u0000 \u0000 <p>Global WMHs increased by 9.5%, while cognitive composites declined annually by 1–11% [Table 1]. Five latent factors explained 56% of the variance in risk factors, though their stability was limited, with only a few significant loadings after bootstrapping (CI 95%) [Figure 1A]. Key predictors of WMH progression included higher genetic risks for WMH and AD (via the stimulus signaling pathway) and notably lower educational attainment, even when paired with low levels of other risk factors [Figure 1B]. Component 3, linked to baseline risk factors (e.g. Centiloid, CSF Aβ42/40)[Figure 1C], predicted lower language and PACC scores after 3 years [Figure 2A]. While WMH change did not mediate this association, it correlated with worse PACC (<i>p</i> = 0.057) and memory performance (ꞵ= -0.1 [-0.188, -0.011], <i>p</i> = 0.028) [Figure 2B].</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>WMHs progression is linked to subclinical cognitive decline in cognitively unimpaired individuals, for which low educational attainment and risk of AD via signaling pathways drive WMH progression. Remarkably, when combined with other risk factors, these variables predict worse PACC outcomes. These findings highlight the need to work with larger sample sizes to identify covariance patterns in modifiable and non-modifiable risk factors related to cerebrovascular d","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"21 S2","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://alz-journals.onlinelibrary.wiley.com/doi/epdf/10.1002/alz70856_106766","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145947319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Luca Villa, Nicolas Guizard, Mathilde Borrot, Ayoub Gueddou, Audrey Gabelle, Elizabeth Gordon, Olivier Courreges
Background
The heterogeneity of patient clinical progression is a key challenge in Alzheimer's disease (AD). This is especially the case when conducting disease-modifying clinical trials in patients with mild cognitive impairment (MCI) and early AD where, historically, large proportions of patients in placebo groups show subtle cognitive decline within the period of the trials, hindering the ability to detect treatment effects. Recent advances in Artificial Intelligence (AI) have allowed rapid progress to be made in the field of predictive modeling, enabling the integration of clinical, demographic, genetic, and neuroimaging data to provide accurate personalized predictions of prognosis at an individual level. So far, these methods have remained in the domain of academic research but have huge potential if applied to the enrichment of real-world clinical trials in AD, as well as in the prioritization of medication prescription for newly approved treatments.
Method
We have developed QyScore®, an advanced fully-automated CE-Marked and FDA-cleared medical imaging platform that is certified for grey and white matter segmentation, and was used to identify a reduction in grey matter atrophy associated with treatment in a recent AD clinical trial. Moreover, we have developed QyPredict®, an AI-based predictive modelling platform designed to predict clinical progression in central nervous system diseases.
Result
By integrating the neuroimaging quantifications generated by QyScore® into QyPredict®, alongside key demographic, clinical, and genetic data from a wide variety of patients and healthy controls, we have achieved high prediction accuracies when predicting clinical decline – with the following range of performance metrics, depending on clinical population and prediction target: Balanced Accuracy: 0.70-0.81, Sensitivity: 0.73-0.80, Specificity: 0.60-0.88, Precision: 0.77-0.81, Negative Predictive Value: 0.59-0.87, F1: 0.74-0.81.
Conclusion
Given the ability of this predictive modelling platform to identify patients at the highest risk of clinical decline, it could be invaluable for the stratification of the right patient’ target. Moreover, its ability to predict patients with the greatest likelihood for remaining clinically stable could be invaluable for the identification and exclusion of patients in future clinical trials that are unlikely to show clinical decline – therefore improving the ability of future trials to detect treatment effects.
{"title":"The relevance of MRI-based AI Predictive Modelling for Treatment's response sand personalized Clinical Trials design","authors":"Luca Villa, Nicolas Guizard, Mathilde Borrot, Ayoub Gueddou, Audrey Gabelle, Elizabeth Gordon, Olivier Courreges","doi":"10.1002/alz70856_106596","DOIUrl":"10.1002/alz70856_106596","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The heterogeneity of patient clinical progression is a key challenge in Alzheimer's disease (AD). This is especially the case when conducting disease-modifying clinical trials in patients with mild cognitive impairment (MCI) and early AD where, historically, large proportions of patients in placebo groups show subtle cognitive decline within the period of the trials, hindering the ability to detect treatment effects. Recent advances in Artificial Intelligence (AI) have allowed rapid progress to be made in the field of predictive modeling, enabling the integration of clinical, demographic, genetic, and neuroimaging data to provide accurate personalized predictions of prognosis at an individual level. So far, these methods have remained in the domain of academic research but have huge potential if applied to the enrichment of real-world clinical trials in AD, as well as in the prioritization of medication prescription for newly approved treatments.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>We have developed QyScore®, an advanced fully-automated CE-Marked and FDA-cleared medical imaging platform that is certified for grey and white matter segmentation, and was used to identify a reduction in grey matter atrophy associated with treatment in a recent AD clinical trial. Moreover, we have developed QyPredict®, an AI-based predictive modelling platform designed to predict clinical progression in central nervous system diseases.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Result</h3>\u0000 \u0000 <p>By integrating the neuroimaging quantifications generated by QyScore® into QyPredict®, alongside key demographic, clinical, and genetic data from a wide variety of patients and healthy controls, we have achieved high prediction accuracies when predicting clinical decline – with the following range of performance metrics, depending on clinical population and prediction target: Balanced Accuracy: 0.70-0.81, Sensitivity: 0.73-0.80, Specificity: 0.60-0.88, Precision: 0.77-0.81, Negative Predictive Value: 0.59-0.87, F1: 0.74-0.81.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Given the ability of this predictive modelling platform to identify patients at the highest risk of clinical decline, it could be invaluable for the stratification of the right patient’ target. Moreover, its ability to predict patients with the greatest likelihood for remaining clinically stable could be invaluable for the identification and exclusion of patients in future clinical trials that are unlikely to show clinical decline – therefore improving the ability of future trials to detect treatment effects.</p>\u0000 </section>\u0000 </div>","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"21 S2","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://alz-journals.onlinelibrary.wiley.com/doi/epdf/10.1002/alz70856_106596","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145947340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<div> <section> <h3> Background</h3> <p>Elevated plasma neurofilament light chain (NfL) is a promising biomarker for dementia diagnosis. However, it has also been reported to be associated with age, body mass index (BMI), and comorbidities such as diabetes, hypertension, chronic kidney disease (CKD), stroke, and cardiovascular diseases. This study aimed to investigate the relationship between plasma NfL levels, cognitive performance, and activities of daily living (ADL) in a Thai population, as well as the comorbidities influencing NfL levels.</p> </section> <section> <h3> Method</h3> <p>A pilot study was conducted with 48 participants categorized into normal cognition (<i>n</i> = 4), mild cognitive impairment (MCI)(<i>n</i> = 18), and dementia (<i>n</i> = 26) groups. Plasma NfL levels were analyzed using Single Molecule Arrays (SiMoA) technology. Statistical analyses, including Mann-Whitney U Test and Spearman's rho correlations, were used to examine associations between NfL levels and other variables.</p> </section> <section> <h3> Result</h3> <p>The mean age of participants was 70.8±12.0 years, and 58.3% were female. Median plasma NFL levels were significantly higher (<i>p</i> <0.05) in the dementia group (44.3, IQR45.9) compared to the MCI (18.7, IQR13.9) and normal cognition groups (8.6, IQR15.8). Higher NFL levels were significantly associated with lower cognitive scores, including Thai Mental State Examination scores (r=-0.58, <i>p</i> <0.001) and Montreal Cognitive Assessment scores (r=-0.55, <i>p</i> <0.001). They also correlated with poorer function, as measured by the sum of basic and instrumental ADL scores, and Clinical Dementia Rating scale-sum of boxes (r=0.61 and 0.64, respectively, <i>p</i> <0.001). Plasma NFL levels were significantly correlated with serum albumin (r=-0.37, <i>p</i> = 0.028) and BMI (<i>r</i> =-0.37, <i>p</i> = 0.023). Age and comorbidities, including diabetes, hypertension, dyslipidemia, CKD, cardiovascular and cerebrovascular disease, levels of blood urea nitrogen, creatinine, liver enzymes, lipid profiles, and thyroid functions, did not significantly influence NfL concentrations. However, higher creatinine (r=0.31, <i>p</i> = 0.051) and a history of CKD (<i>p</i> = 0.054) showed a trend toward an associate with elevated plasma NfL levels.</p> </section> <section> <h3> Conclusion</h3> <p>In our pilot study, plasma NfL levels are significantly associated with dementia stages, ADL, serum albumin, and BMI and showed a trend toward an associated with renal dysfunction. The interplay between systemic health and NfL levels underscores the need for comorbidity assessments in dementia resear
{"title":"Plasma NfL Levels Across Cognitive Stages and the Impact of Comorbidities in a Thai Cohort","authors":"Chatchawan Rattanabannakit, Kristsana Khuranae, Natthamon Wongkom, Pathitta Dujada, Paphawadee Phoyoo, Leatchai Wachirutmanggur, Vorapun Senanarong","doi":"10.1002/alz70856_106886","DOIUrl":"10.1002/alz70856_106886","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Elevated plasma neurofilament light chain (NfL) is a promising biomarker for dementia diagnosis. However, it has also been reported to be associated with age, body mass index (BMI), and comorbidities such as diabetes, hypertension, chronic kidney disease (CKD), stroke, and cardiovascular diseases. This study aimed to investigate the relationship between plasma NfL levels, cognitive performance, and activities of daily living (ADL) in a Thai population, as well as the comorbidities influencing NfL levels.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>A pilot study was conducted with 48 participants categorized into normal cognition (<i>n</i> = 4), mild cognitive impairment (MCI)(<i>n</i> = 18), and dementia (<i>n</i> = 26) groups. Plasma NfL levels were analyzed using Single Molecule Arrays (SiMoA) technology. Statistical analyses, including Mann-Whitney U Test and Spearman's rho correlations, were used to examine associations between NfL levels and other variables.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Result</h3>\u0000 \u0000 <p>The mean age of participants was 70.8±12.0 years, and 58.3% were female. Median plasma NFL levels were significantly higher (<i>p</i> <0.05) in the dementia group (44.3, IQR45.9) compared to the MCI (18.7, IQR13.9) and normal cognition groups (8.6, IQR15.8). Higher NFL levels were significantly associated with lower cognitive scores, including Thai Mental State Examination scores (r=-0.58, <i>p</i> <0.001) and Montreal Cognitive Assessment scores (r=-0.55, <i>p</i> <0.001). They also correlated with poorer function, as measured by the sum of basic and instrumental ADL scores, and Clinical Dementia Rating scale-sum of boxes (r=0.61 and 0.64, respectively, <i>p</i> <0.001). Plasma NFL levels were significantly correlated with serum albumin (r=-0.37, <i>p</i> = 0.028) and BMI (<i>r</i> =-0.37, <i>p</i> = 0.023). Age and comorbidities, including diabetes, hypertension, dyslipidemia, CKD, cardiovascular and cerebrovascular disease, levels of blood urea nitrogen, creatinine, liver enzymes, lipid profiles, and thyroid functions, did not significantly influence NfL concentrations. However, higher creatinine (r=0.31, <i>p</i> = 0.051) and a history of CKD (<i>p</i> = 0.054) showed a trend toward an associate with elevated plasma NfL levels.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>In our pilot study, plasma NfL levels are significantly associated with dementia stages, ADL, serum albumin, and BMI and showed a trend toward an associated with renal dysfunction. The interplay between systemic health and NfL levels underscores the need for comorbidity assessments in dementia resear","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"21 S2","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://alz-journals.onlinelibrary.wiley.com/doi/epdf/10.1002/alz70856_106886","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Roy Massett, Ziwei Zhang, Micaela Chan, Gagan S Wig, Alzheimer's Disease Neuroimaging Initiative (ADNI), Wig Neuroimaging Lab
<div> <section> <h3> Background</h3> <p>Alzheimer's disease (AD) is a highly heterogeneous condition both in terms of the distribution of pathology deposition and clinical manifestation. The organization of functional brain networks is related to AD pathology, with recent work showing that higher dementia severity is related to the desegregation of brain networks (Zhang <i>et al</i>., 2023). However, the spatiotemporal heterogeneity of these changes in network segregation and how they contribute to different cognitive deficit profiles remains an open area of research. To contribute to this question, we applied a clustering-based data-driven disease progression model to system-level measures of network organization.</p> </section> <section> <h3> Method</h3> <p>We included 754 resting-state functional magnetic resonance imaging (fMRI) scans from cognitively impaired individuals (CDR > 0) enrolled in the Alzheimer's Disease Neuroimaging Initiative. The correlations of fMRI resting-state time series between nodes were used to construct brain networks (Chan <i>et al</i>., 2014). Nodes were assigned to a functional system based on a previously defined functional atlas (Power <i>et al</i>., 2011). Network segregation was calculated for each brain system as measures of system-level organization. The SuStaIn algorithm was used to simultaneously stage and subtype subjects according to their patterns of network segregation (Young <i>et al</i>., 2018). Functional systems that were clustered together were further analyzed collectively using linear regression models.</p> </section> <section> <h3> Result</h3> <p>SuStaIn identified two subtypes of alterations in system-level network segregation, which were upheld in <i>k</i>-fold cross validation. One cluster showed decreases in the segregation of sensory-motor systems and several additional systems, including the superior temporal gyrus, salience, and medial temporal parietal systems. The other cluster showed decreases primarily in segregation of association systems including the default mode network, task control systems, attention systems, and memory retrieval systems. Further, we observed a significant interaction effect between system-type and model-estimated disease stage and subtype.</p> </section> <section> <h3> Conclusion</h3> <p>Resting-state fMRI signals can be used to identify dissociable patterns of AD-related brain network desegregation relating to different profiles of brain network dysfunction and cognitive impairment. These findings begin to describe the spatiotemporal heterogeneity in brain network decline, and their relation to cognitive trajectories, which is relevant not o
背景阿尔茨海默病（AD）无论在病理沉积分布还是临床表现上都是一个高度异质性的疾病。功能性脑网络的组织与AD病理有关，最近的研究表明，较高的痴呆严重程度与脑网络的去隔离有关（Zhang et al., 2023）。然而，这些网络隔离变化的时空异质性以及它们如何导致不同的认知缺陷概况仍然是一个开放的研究领域。为了回答这个问题，我们将一个基于聚类的数据驱动的疾病进展模型应用于网络组织的系统级度量。方法：我们纳入了参加阿尔茨海默病神经影像学计划的认知受损个体（CDR > 0）的754张静息状态功能磁共振成像（fMRI）扫描。利用节点间fMRI静息状态时间序列的相关性构建脑网络（Chan et al., 2014）。节点被分配到基于先前定义的功能图谱的功能系统（Power et al., 2011）。计算每个大脑系统的网络隔离，作为系统级组织的度量。使用SuStaIn算法根据受试者的网络隔离模式同时对其进行分期和分型（Young等人，2018）。聚在一起的功能系统进一步使用线性回归模型进行集体分析。结果SuStaIn在系统级网络隔离中发现了两种亚型的改变，这在k-fold交叉验证中得到了证实。一个集群显示感觉-运动系统和其他几个系统的分离减少，包括颞上回、显著性和内侧颞顶叶系统。另一组主要表现为关联分离系统的减少，包括默认模式网络、任务控制系统、注意系统和记忆检索系统。此外，我们观察到系统类型和模型估计的疾病分期和亚型之间存在显著的相互作用效应。结论静息状态fMRI信号可用于识别ad相关脑网络分离的可解离模式，该模式与脑网络功能障碍和认知障碍的不同特征有关。这些发现开始描述大脑网络衰退的时空异质性及其与认知轨迹的关系，这不仅与阿尔茨海默病的预后有关，而且还与评估认知不同患者群体的临床试验结果有关。
{"title":"Spatiotemporal Heterogeneity of Alzheimer's Disease-Related Functional Brain Network Dedifferentiation","authors":"Roy Massett, Ziwei Zhang, Micaela Chan, Gagan S Wig, Alzheimer's Disease Neuroimaging Initiative (ADNI), Wig Neuroimaging Lab","doi":"10.1002/alz70856_107430","DOIUrl":"https://doi.org/10.1002/alz70856_107430","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Alzheimer's disease (AD) is a highly heterogeneous condition both in terms of the distribution of pathology deposition and clinical manifestation. The organization of functional brain networks is related to AD pathology, with recent work showing that higher dementia severity is related to the desegregation of brain networks (Zhang <i>et al</i>., 2023). However, the spatiotemporal heterogeneity of these changes in network segregation and how they contribute to different cognitive deficit profiles remains an open area of research. To contribute to this question, we applied a clustering-based data-driven disease progression model to system-level measures of network organization.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>We included 754 resting-state functional magnetic resonance imaging (fMRI) scans from cognitively impaired individuals (CDR > 0) enrolled in the Alzheimer's Disease Neuroimaging Initiative. The correlations of fMRI resting-state time series between nodes were used to construct brain networks (Chan <i>et al</i>., 2014). Nodes were assigned to a functional system based on a previously defined functional atlas (Power <i>et al</i>., 2011). Network segregation was calculated for each brain system as measures of system-level organization. The SuStaIn algorithm was used to simultaneously stage and subtype subjects according to their patterns of network segregation (Young <i>et al</i>., 2018). Functional systems that were clustered together were further analyzed collectively using linear regression models.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Result</h3>\u0000 \u0000 <p>SuStaIn identified two subtypes of alterations in system-level network segregation, which were upheld in <i>k</i>-fold cross validation. One cluster showed decreases in the segregation of sensory-motor systems and several additional systems, including the superior temporal gyrus, salience, and medial temporal parietal systems. The other cluster showed decreases primarily in segregation of association systems including the default mode network, task control systems, attention systems, and memory retrieval systems. Further, we observed a significant interaction effect between system-type and model-estimated disease stage and subtype.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Resting-state fMRI signals can be used to identify dissociable patterns of AD-related brain network desegregation relating to different profiles of brain network dysfunction and cognitive impairment. These findings begin to describe the spatiotemporal heterogeneity in brain network decline, and their relation to cognitive trajectories, which is relevant not o","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"21 S2","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://alz-journals.onlinelibrary.wiley.com/doi/epdf/10.1002/alz70856_107430","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145964253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Timothy Lawn, Ibai Diez, Elisenda Bueichekú2, Maxime Van Egroo, Gillian T Coughlan, Rachel F. Buckley, Dorene M. Rentz, Keith A. Johnson, Reisa A. Sperling, Jorge Sepulcre, Heidi I.L. Jacobs
<div> <section> <h3> Background</h3> <p>Brainstem nuclei such as the locus coeruleus (LC) are amongst the earliest regions affected by tau pathology in Alzheimer's disease (AD). The LC's extensive noradrenergic projections shape brain network architecture and its structural integrity is associated with resilience against cognitive decline. However, the role of LC network connectivity in cognitive resilience remains unclear, as does its potential differential impact across distinct population subgroups who might specifically benefit from its augmentation.</p> </section> <section> <h3> Methods</h3> <p>We included 393 cognitively unimpaired Aβ+ individuals (centiloid > 19) from the A4 study who underwent baseline resting-state fMRI and florbetapir Aβ-PET as well as longitudinal Preclinical Alzheimer's Cognitive Composite (PACC) assessment. Pearsons's correlation coefficient was used to create maps of LC functional connectivity (LC-FC) to 454 parcels of the Schaefer-Tian parcellation which were harmonised across scanners using NeuroCombat. Mixed-effects models with natural cubic splines (2 DOF) were used to test whether global, Yeo17 network, and individual parcel level LC-FC moderated PACC decline over time as well as in interaction with centiloid, sex, and APOΕ4 status, controlling for age, education, framewise displacement, treatment, and sex (when not interacted). Analyses were deemed significant following Benjamini-Hochberg false-discovery rate correction for multiple comparisons.</p> </section> <section> <h3> Results</h3> <p>The LC showed widespread connectivity that was strongest within the limbic regions, hippocampus, and thalamus (Figure 1a). Greater global LC-FC was associated with attenuated cognitive decline (Figure 1b, <i>p</i> = 0.014), especially at higher levels of Aβ (Figure 1c, <i>p</i> <0.001). At the network level, LC-FC resilience was predominantly related to task-positive (control and dorsal/ventral attentional) networks, whilst in the default mode network greater LC-FC was associated with reduced cognitive decline at lower Aβ (Figure 1b/c). The effect of LC-FC on PACC decline was particularly pronounced in females at high Aβ levels (Figure 2b) and APOE-e4 carriers (Figure 2c), demonstrating a synergistic effect of sex and genetic risk on LC-mediated cognitive resilience (Figure 2d).</p> </section> <section> <h3> Conclusions</h3> <p>Our findings further support the role of the LC in cognitive resilience and suggest this particularly manifests from modulation of task-positive attentional and frontoparietal control networks. M
{"title":"Sex and APOE status moderate locus coeruleus network related resilience in preclinical Alzheimer's disease","authors":"Timothy Lawn, Ibai Diez, Elisenda Bueichekú2, Maxime Van Egroo, Gillian T Coughlan, Rachel F. Buckley, Dorene M. Rentz, Keith A. Johnson, Reisa A. Sperling, Jorge Sepulcre, Heidi I.L. Jacobs","doi":"10.1002/alz70856_107257","DOIUrl":"10.1002/alz70856_107257","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Brainstem nuclei such as the locus coeruleus (LC) are amongst the earliest regions affected by tau pathology in Alzheimer's disease (AD). The LC's extensive noradrenergic projections shape brain network architecture and its structural integrity is associated with resilience against cognitive decline. However, the role of LC network connectivity in cognitive resilience remains unclear, as does its potential differential impact across distinct population subgroups who might specifically benefit from its augmentation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We included 393 cognitively unimpaired Aβ+ individuals (centiloid > 19) from the A4 study who underwent baseline resting-state fMRI and florbetapir Aβ-PET as well as longitudinal Preclinical Alzheimer's Cognitive Composite (PACC) assessment. Pearsons's correlation coefficient was used to create maps of LC functional connectivity (LC-FC) to 454 parcels of the Schaefer-Tian parcellation which were harmonised across scanners using NeuroCombat. Mixed-effects models with natural cubic splines (2 DOF) were used to test whether global, Yeo17 network, and individual parcel level LC-FC moderated PACC decline over time as well as in interaction with centiloid, sex, and APOΕ4 status, controlling for age, education, framewise displacement, treatment, and sex (when not interacted). Analyses were deemed significant following Benjamini-Hochberg false-discovery rate correction for multiple comparisons.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The LC showed widespread connectivity that was strongest within the limbic regions, hippocampus, and thalamus (Figure 1a). Greater global LC-FC was associated with attenuated cognitive decline (Figure 1b, <i>p</i> = 0.014), especially at higher levels of Aβ (Figure 1c, <i>p</i> <0.001). At the network level, LC-FC resilience was predominantly related to task-positive (control and dorsal/ventral attentional) networks, whilst in the default mode network greater LC-FC was associated with reduced cognitive decline at lower Aβ (Figure 1b/c). The effect of LC-FC on PACC decline was particularly pronounced in females at high Aβ levels (Figure 2b) and APOE-e4 carriers (Figure 2c), demonstrating a synergistic effect of sex and genetic risk on LC-mediated cognitive resilience (Figure 2d).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our findings further support the role of the LC in cognitive resilience and suggest this particularly manifests from modulation of task-positive attentional and frontoparietal control networks. M","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"21 S2","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://alz-journals.onlinelibrary.wiley.com/doi/epdf/10.1002/alz70856_107257","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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