Paul D. Coleman, Elaine Delvaux, Ashley Boehringer, Carol J. Huseby
<p>In our review article titled “Massive changes in gene expression and their cause(s) can be a unifying principle in the pathobiology of Alzheimer's disease,”<span><sup>1</sup></span> we posed an answer to the question, “What mechanisms might be responsible for the massive changes in gene expression encompassing over 90% of the known KEGG pathways?” <span><sup>2, 3</sup></span> This question has confounded the neurodegenerative disease field for years, complicating efforts focusing on single pathways for the development of therapies to slow or halt the progression of Alzheimer's disease (AD). A model that begins with cellular stress response (CSR) offers a unifying mechanism, which when chronic, results in massive gene expression changes in most functional pathways.</p> <p>CSR occurs within all cells, from simple bacteria to highly differentiated animal cells, when encountering perturbations to homeostatic conditions either through extracellular or intracellular stressors. The CSR program is evolutionarily conserved and reacts to counteract insults, cope, and repair damage. Stress granules (SGs) are a central contributor to the response to shut down macromolecular synthesis and focus on increasing synthesis of stress proteins to mitigate and repair damage. Once indications of the cell stress are gone, the SGs are disassembled via autophagic vesicles and fusion with lysosomes for breakdown<span><sup>4</sup></span> so the cell can return to homeostasis. If survival strategies are unsuccessful, the CSR can promote programmed cell death to eliminate damaged cells.<span><sup>5, 6</sup></span></p> <p>CSRs are conserved across all types of cells with a universal response having dynamic and flexible complexity to offer compensatory mechanisms. The universality of the response comes about through a system of stress monitoring that is based on the damage, thus signaling the appropriate response.<span><sup>7</sup></span> This important aspect of the system, although conserved, can in theory be affected by variations in the molecules involved in the sensing, shift in cellular priorities, and repair of affected molecules.</p> <p>Dr. Rudroff has outlined evidence for population differences in a letter titled “Population diversity validation for Alzheimer's disease ‘unifying’ models” citing research for genes having significant allele frequency differences across populations. Under our unifying model, molecules that are sequestered in SGs and those with synthesis paused should not affect CSR by population variations; however, those molecules monitoring, initiating, and taking part in damage repair, for example, eIF2α and apolipoprotein E, having variations central to their stress response activity, may enhance or impede the effectiveness of the CSR.<span><sup>8</sup></span> Our model, although having substantial supportive evidence, remains incomplete. Further research is needed to validate multiple aspects of the model. Dr. Rudroff has graciously outlined ste
{"title":"Regarding the importance of population diversity validation in Alzheimer's disease models","authors":"Paul D. Coleman, Elaine Delvaux, Ashley Boehringer, Carol J. Huseby","doi":"10.1002/alz.71071","DOIUrl":"https://doi.org/10.1002/alz.71071","url":null,"abstract":"<p>In our review article titled “Massive changes in gene expression and their cause(s) can be a unifying principle in the pathobiology of Alzheimer's disease,”<span><sup>1</sup></span> we posed an answer to the question, “What mechanisms might be responsible for the massive changes in gene expression encompassing over 90% of the known KEGG pathways?” <span><sup>2, 3</sup></span> This question has confounded the neurodegenerative disease field for years, complicating efforts focusing on single pathways for the development of therapies to slow or halt the progression of Alzheimer's disease (AD). A model that begins with cellular stress response (CSR) offers a unifying mechanism, which when chronic, results in massive gene expression changes in most functional pathways.</p>\u0000<p>CSR occurs within all cells, from simple bacteria to highly differentiated animal cells, when encountering perturbations to homeostatic conditions either through extracellular or intracellular stressors. The CSR program is evolutionarily conserved and reacts to counteract insults, cope, and repair damage. Stress granules (SGs) are a central contributor to the response to shut down macromolecular synthesis and focus on increasing synthesis of stress proteins to mitigate and repair damage. Once indications of the cell stress are gone, the SGs are disassembled via autophagic vesicles and fusion with lysosomes for breakdown<span><sup>4</sup></span> so the cell can return to homeostasis. If survival strategies are unsuccessful, the CSR can promote programmed cell death to eliminate damaged cells.<span><sup>5, 6</sup></span></p>\u0000<p>CSRs are conserved across all types of cells with a universal response having dynamic and flexible complexity to offer compensatory mechanisms. The universality of the response comes about through a system of stress monitoring that is based on the damage, thus signaling the appropriate response.<span><sup>7</sup></span> This important aspect of the system, although conserved, can in theory be affected by variations in the molecules involved in the sensing, shift in cellular priorities, and repair of affected molecules.</p>\u0000<p>Dr. Rudroff has outlined evidence for population differences in a letter titled “Population diversity validation for Alzheimer's disease ‘unifying’ models” citing research for genes having significant allele frequency differences across populations. Under our unifying model, molecules that are sequestered in SGs and those with synthesis paused should not affect CSR by population variations; however, those molecules monitoring, initiating, and taking part in damage repair, for example, eIF2α and apolipoprotein E, having variations central to their stress response activity, may enhance or impede the effectiveness of the CSR.<span><sup>8</sup></span> Our model, although having substantial supportive evidence, remains incomplete. Further research is needed to validate multiple aspects of the model. Dr. Rudroff has graciously outlined ste","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"12 1","pages":"e71071"},"PeriodicalIF":14.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jeevitha GR. Development and implementation of PHC-CST: A cognitive screening tool for early detection of dementia in primary health care settings in India. Alzheimers Dement. 2025;21(Suppl 6):e096673. doi:10.1002/alz70860_096673
In the Author Information section, the text “Jeevitha G R, M S Ramaiah University of Applied Sciences, Bangalore, Karnataka, India” was incorrect. This should have read: “Jeevitha Gowda R, Alliance University, Bangalore, Karnataka, India.”
This change is important to ensure accurate academic attribution, institutional reporting, and indexing. I sincerely apologize for any inconvenience this may cause and greatly appreciate the editorial team's understanding and consideration.
We apologize for this error.
PHC-CST的开发和实施：印度初级卫生保健机构早期发现痴呆的认知筛查工具。阿尔茨海默病。2025；21 (6): e096673。在作者信息部分，文本“Jeevitha G R, M S Ramaiah University of Applied Sciences, Bangalore, Karnataka, India”是不正确的。这封信应该是这样写的：“Jeevitha Gowda R，印度卡纳塔克邦班加罗尔联合大学。”这一变化对于确保准确的学术归属、机构报告和索引非常重要。对于由此造成的任何不便，我深表歉意，并感谢编辑团队的理解和考虑。我们为这个错误道歉。
{"title":"Correction to “Development and implementation of PHC-CST: A cognitive screening tool for early detection of dementia in primary health care settings in India”","authors":"","doi":"10.1002/alz.71138","DOIUrl":"https://doi.org/10.1002/alz.71138","url":null,"abstract":"<p>Jeevitha GR. Development and implementation of PHC-CST: A cognitive screening tool for early detection of dementia in primary health care settings in India. <i>Alzheimers Dement</i>. 2025;21(Suppl 6):e096673. doi:10.1002/alz70860_096673</p>\u0000<p>In the Author Information section, the text “Jeevitha G R, M S Ramaiah University of Applied Sciences, Bangalore, Karnataka, India” was incorrect. This should have read: “Jeevitha Gowda R, Alliance University, Bangalore, Karnataka, India.”</p>\u0000<p>This change is important to ensure accurate academic attribution, institutional reporting, and indexing. I sincerely apologize for any inconvenience this may cause and greatly appreciate the editorial team's understanding and consideration.</p>\u0000<p>We apologize for this error.</p>","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"261 1","pages":""},"PeriodicalIF":14.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julian V. Pentchev, Trever Jackson, Naazneen Khan, Thea J. Rosewood, Yen-Ning Huang, Kwangsik Nho, Andrew J. Saykin, Ani Eloyan, Alexander Taurone, Maryanne Thangarajah, Meghan Riddle, Steven Salloway, Alireza Atri, Lawrence S. Honig, Erik C. B. Johnson, Raymond Scott Turner, Joseph C. Masdeu, Tatiana M. Foroud, David Clark, Dustin B. Hammers, Jeffrey L. Dage, Kala Kirby, Bernardino Ghetti, Kathy Newell, Chiadi U. Onyike, Gregory S. Day, Neill R. Graff-Radford, Melissa E. Murray, David T. Jones, Clifford R. Jack Jr., Prashanthi Vemuri, Alexandra Touroutoglou, Ranjan Duara, Ian Grant, Sharon Sha, Thomas S. Wingo, Laurel A. Beckett, Emily Rogalski, Mario F. Mendez, Joel Kramer, Renaud La Joie, Ganna Blazhenets, Lea T. Grinberg, Robert Koeppe, David A. Wolk, Paul Aisen, Rema Raman, Arthur Toga, Walter A. Kukull, Erik Musiek, Kyle B. Womack, Maria C. Carrillo, Gil D. Rabinovici, Bradford C. Dickerson, Liana G. Apostolova, Kelly N. H. Nudelman, for the Longitudinal Early-Onset Alzheimer's Disease Study Consortium and the Alzheimer's Disease Neuroimaging Initiative
INTRODUCTION
The genetic basis of sporadic early-onset Alzheimer's disease (EOAD) remains largely unknown, prompting evaluation of late-onset Alzheimer's disease (LOAD) polygenic risk in EOAD.
METHODS
A LOAD polygenic score (PGS) was calculated in the Longitudinal Early-onset Alzheimer's Disease Study (LEADS) and Alzheimer's Disease Neuroimaging Initiative (ADNI) study and tested for associations with AD risk, cognitive performance, and imaging and fluid biomarkers.
RESULTS
Though PGS was elevated in LOAD and EOAD, it was not a significant predictor of EOAD adjusting for APOE ε4 carrier status and was not associated with age of EOAD onset (p = 0.106) or with cognitive performance (p = 0.417). In LEADS, greater LOAD PGS was associated with differences in neuroimaging and fluid biomarkers, including elevated synaptosomal-associated protein 25 (SNAP-25) (p = 2.3 × 10−5).
DISCUSSION
While LOAD polygenic risk contributed minimally to EOAD onset and cognitive dysfunction, PGS association with fluid biomarkers in LEADS suggests a role for LOAD polygenic risk in EOAD pathophysiology.
Highlights
LOAD PGSs were elevated in both LOAD and EOAD compared to controls; however, LOAD PGS did not significantly predict EOAD risk, age at onset, or cognitive performance independent of APOE ε4 in the LEADS.
Higher LOAD PGS was associated with lower amyloid PET Centiloids (less brain amyloid deposition) as well as lower CSF biomarker Aβ42 in LEADS (proxy marker suggesting higher brain amyloid deposition) in LEADS; these contradictory findings support the need for larger studies to further investigate whether LOAD PGS is associated with increased amyloid deposition in EOAD.
Higher LOAD PGS was also associated with higher levels of CSF synaptosomal-associated protein 25 (SNAP-25), a key component of the SNARE complex, suggesting that LOAD genetic factors may contribute to dysregulation of synaptic transmission and/or pathological protein aggregation in EOAD.
{"title":"Alzheimer's disease polygenic risk in early- and late-onset Alzheimer's disease","authors":"Julian V. Pentchev, Trever Jackson, Naazneen Khan, Thea J. Rosewood, Yen-Ning Huang, Kwangsik Nho, Andrew J. Saykin, Ani Eloyan, Alexander Taurone, Maryanne Thangarajah, Meghan Riddle, Steven Salloway, Alireza Atri, Lawrence S. Honig, Erik C. B. Johnson, Raymond Scott Turner, Joseph C. Masdeu, Tatiana M. Foroud, David Clark, Dustin B. Hammers, Jeffrey L. Dage, Kala Kirby, Bernardino Ghetti, Kathy Newell, Chiadi U. Onyike, Gregory S. Day, Neill R. Graff-Radford, Melissa E. Murray, David T. Jones, Clifford R. Jack Jr., Prashanthi Vemuri, Alexandra Touroutoglou, Ranjan Duara, Ian Grant, Sharon Sha, Thomas S. Wingo, Laurel A. Beckett, Emily Rogalski, Mario F. Mendez, Joel Kramer, Renaud La Joie, Ganna Blazhenets, Lea T. Grinberg, Robert Koeppe, David A. Wolk, Paul Aisen, Rema Raman, Arthur Toga, Walter A. Kukull, Erik Musiek, Kyle B. Womack, Maria C. Carrillo, Gil D. Rabinovici, Bradford C. Dickerson, Liana G. Apostolova, Kelly N. H. Nudelman, for the Longitudinal Early-Onset Alzheimer's Disease Study Consortium and the Alzheimer's Disease Neuroimaging Initiative","doi":"10.1002/alz.71066","DOIUrl":"10.1002/alz.71066","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> INTRODUCTION</h3>\u0000 \u0000 <p>The genetic basis of sporadic early-onset Alzheimer's disease (EOAD) remains largely unknown, prompting evaluation of late-onset Alzheimer's disease (LOAD) polygenic risk in EOAD.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> METHODS</h3>\u0000 \u0000 <p>A LOAD polygenic score (PGS) was calculated in the Longitudinal Early-onset Alzheimer's Disease Study (LEADS) and Alzheimer's Disease Neuroimaging Initiative (ADNI) study and tested for associations with AD risk, cognitive performance, and imaging and fluid biomarkers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> RESULTS</h3>\u0000 \u0000 <p>Though PGS was elevated in LOAD and EOAD, it was not a significant predictor of EOAD adjusting for <i>APOE</i> ε4 carrier status and was not associated with age of EOAD onset (<i>p</i> = 0.106) or with cognitive performance (<i>p</i> = 0.417). In LEADS, greater LOAD PGS was associated with differences in neuroimaging and fluid biomarkers, including elevated synaptosomal-associated protein 25 (SNAP-25) (<i>p</i> = 2.3 × 10<sup>−5</sup>).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> DISCUSSION</h3>\u0000 \u0000 <p>While LOAD polygenic risk contributed minimally to EOAD onset and cognitive dysfunction, PGS association with fluid biomarkers in LEADS suggests a role for LOAD polygenic risk in EOAD pathophysiology.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Highlights</h3>\u0000 \u0000 <div>\u0000 <ul>\u0000 \u0000 <li>LOAD PGSs were elevated in both LOAD and EOAD compared to controls; however, LOAD PGS did not significantly predict EOAD risk, age at onset, or cognitive performance independent of <i>APOE</i> ε4 in the LEADS.</li>\u0000 \u0000 <li>Higher LOAD PGS was associated with lower amyloid PET Centiloids (less brain amyloid deposition) as well as lower CSF biomarker Aβ42 in LEADS (proxy marker suggesting higher brain amyloid deposition) in LEADS; these contradictory findings support the need for larger studies to further investigate whether LOAD PGS is associated with increased amyloid deposition in EOAD.</li>\u0000 \u0000 <li>Higher LOAD PGS was also associated with higher levels of CSF synaptosomal-associated protein 25 (SNAP-25), a key component of the SNARE complex, suggesting that LOAD genetic factors may contribute to dysregulation of synaptic transmission and/or pathological protein aggregation in EOAD.</li>\u0000 </ul>\u0000 </div>\u0000 </section>\u0000 </div>","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"22 1","pages":""},"PeriodicalIF":11.1,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://alz-journals.onlinelibrary.wiley.com/doi/epdf/10.1002/alz.71066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961653","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}
Xuemei Zeng, Rebecca A Deek, Michel N Nafash, Jeremy M. Gu, Lamia Choity, Tara K Lafferty, Marissa F Farinas, Margaret A Bedison, Rocco B Mercurio, Cristy Matan, Alexandra Gogola, Julia K. Kofler, Dana L Tudorascu, C. Elizabeth Shaaban, Jennifer H Lingler, Tharick A Pascoal, William E Klunk, Victor L. Villemagne, Milos D. Ikonomovic, Sarah B Berman, Robert Sweet, Beth E. Snitz, Ann D Cohen, M. Ilyas Kamboh, Oscar L Lopez, Thomas K Karikari
Background Plasma biomarkers have demonstrated excellent performances in detecting AD/ADRD‐related brain pathology. However, their relationship with cognitive decline remains unclear. We examined this in a large memory clinic cohort with baseline plasma biomarkers and repeated cognitive assessments over approximately three decades. Method Participants at the University of Pittsburgh Alzheimer's Disease Research Center underwent blood collection and Clinical Dementia Rating (CDR) Sum of Boxes‐based cognitive assessment cross‐sectionally, followed by annual CDR assessments for up to 29 years (3.0 [IQR 1.9‐5.9]). Plasma p ‐tau181, p ‐tau217, brain‐derived tau (BD‐tau), GFAP and NfL, were measured using SIMOA assays. Linear/logistic regression and Fisher's exact were employed for statistical inference. Result We included 4,073 participants (59.9% female; 90.2% self‐identified non‐Hispanic White), aged 71.9 ± 9.8 years, with 2160 being non‐demented (CDR≤0.5) at baseline. Cross‐sectionally, higher levels of all biomarkers were significantly associated with worse CDR scores. Longitudinally, baseline p ‐tau181 and GFAP levels best predicted cognitive decline at 0‐2, 2‐5, 5‐10, and >10 years. In contrast, p ‐tau217 was superior at predicting whether cognitive decline would happen at all within 2, 5, or 10 years, with AUCs up to 0.810. Participants with above‐median p ‐tau217 levels had the highest odds of cognitive decline (2.57, 4.53, and 10.34 times within 2, 5 and 10 years, respectively). p ‐tau217, and p ‐tau217/BD‐tau ratio accounting for CNS‐derived p ‐tau217, were most effective in predicting cognitive decline in participants who were cognitively non‐demented at baseline. Importantly, cognitively stable individuals had lower levels of all plasma biomarkers vs. progressors, with p ‐tau217 best at separating these groups. Conclusion Leveraging a large cohort with extensive longitudinal data, our findings underscore the significant value of blood‐based biomarkers in predicting cognitive decline to aid personalized clinical management and ultimately improve patient outcomes.
{"title":"Plasma biomarkers predict incident cognitive decline up to 29 years prior to disease onset: a memory clinic cohort study of 4,073 participants","authors":"Xuemei Zeng, Rebecca A Deek, Michel N Nafash, Jeremy M. Gu, Lamia Choity, Tara K Lafferty, Marissa F Farinas, Margaret A Bedison, Rocco B Mercurio, Cristy Matan, Alexandra Gogola, Julia K. Kofler, Dana L Tudorascu, C. Elizabeth Shaaban, Jennifer H Lingler, Tharick A Pascoal, William E Klunk, Victor L. Villemagne, Milos D. Ikonomovic, Sarah B Berman, Robert Sweet, Beth E. Snitz, Ann D Cohen, M. Ilyas Kamboh, Oscar L Lopez, Thomas K Karikari","doi":"10.1002/alz70856_106702","DOIUrl":"https://doi.org/10.1002/alz70856_106702","url":null,"abstract":"Background Plasma biomarkers have demonstrated excellent performances in detecting AD/ADRD‐related brain pathology. However, their relationship with cognitive decline remains unclear. We examined this in a large memory clinic cohort with baseline plasma biomarkers and repeated cognitive assessments over approximately three decades. Method Participants at the University of Pittsburgh Alzheimer's Disease Research Center underwent blood collection and Clinical Dementia Rating (CDR) Sum of Boxes‐based cognitive assessment cross‐sectionally, followed by annual CDR assessments for up to 29 years (3.0 [IQR 1.9‐5.9]). Plasma <jats:italic>p</jats:italic> ‐tau181, <jats:italic>p</jats:italic> ‐tau217, brain‐derived tau (BD‐tau), GFAP and NfL, were measured using SIMOA assays. Linear/logistic regression and Fisher's exact were employed for statistical inference. Result We included 4,073 participants (59.9% female; 90.2% self‐identified non‐Hispanic White), aged 71.9 ± 9.8 years, with 2160 being non‐demented (CDR≤0.5) at baseline. Cross‐sectionally, higher levels of all biomarkers were significantly associated with worse CDR scores. Longitudinally, baseline <jats:italic>p</jats:italic> ‐tau181 and GFAP levels best predicted cognitive decline at 0‐2, 2‐5, 5‐10, and >10 years. In contrast, <jats:italic>p</jats:italic> ‐tau217 was superior at predicting whether cognitive decline would happen at all within 2, 5, or 10 years, with AUCs up to 0.810. Participants with above‐median <jats:italic>p</jats:italic> ‐tau217 levels had the highest odds of cognitive decline (2.57, 4.53, and 10.34 times within 2, 5 and 10 years, respectively). <jats:italic>p</jats:italic> ‐tau217, and <jats:italic>p</jats:italic> ‐tau217/BD‐tau ratio accounting for CNS‐derived <jats:italic>p</jats:italic> ‐tau217, were most effective in predicting cognitive decline in participants who were cognitively non‐demented at baseline. Importantly, cognitively stable individuals had lower levels of all plasma biomarkers vs. progressors, with <jats:italic>p</jats:italic> ‐tau217 best at separating these groups. Conclusion Leveraging a large cohort with extensive longitudinal data, our findings underscore the significant value of blood‐based biomarkers in predicting cognitive decline to aid personalized clinical management and ultimately improve patient outcomes.","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"82 1","pages":""},"PeriodicalIF":14.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gustavo E Juantorena, Agustín Penas, Francisco Figari, Agustin Petroni, Juan E Kamienkowski
Background In recent years, several prototypes of remote, webcam‐based eye‐tracking have emerged, exploring their feasibility and potential for web‐based experiments. This growing interest is largely driven by the ability to conduct tasks remotely, enabling research on larger and hard‐to‐reach populations. However, its use has been primarily limited to proof‐of‐concept studies in basic cognitive science. These studies have generally reported a decrease in precision, compounded by lower camera quality and noisier environments, posing new implementation challenges. Additionally, webcam‐based eye‐tracking has been applied in human‐computer interaction and marketing, where only qualitative results are typically required. Here, we aim to extend its application to cognitive tasks relevant to mental health and telemedicine. Methods We present a novel prototype of a remote, webcam‐based eye‐tracker with key enhancements over existing systems, including a reliable sampling rate, screen distance detection, head movement tracking, blink detection, and improved calibration without requiring constant mouse interactions. We first evaluated its spatiotemporal resolution and reliability. Next, we tested its functionality in a cognitive experiment using the anti‐saccade task, a well‐established paradigm in various patient populations. This task assesses inhibitory control by comparing horizontal eye movements toward (pro‐saccades) and away from (anti‐saccades) a target. Results The proposed improvements resulted in a robust remote eye‐tracking system that performed consistently across different hardware setups and maintained stable calibration over time. We also introduced novel capabilities and provided a discussion on their limitations. Furthermore, our results replicated key findings from high‐precision laboratory‐based eye‐trackers: response times and error rates were higher in anti‐saccades than in pro‐saccades, and incorrect responses were associated with shorter reaction times. Conclusions We demonstrate the potential of this prototype for both cognitive research and clinical applications, providing a comprehensive evaluation of its capabilities and limitations. Our findings indicate that remote webcam‐based eye‐tracking can successfully replicate classical results from the anti‐saccade task in an online setting.
{"title":"Remote Eye‐Tracking for Cognitive Science and Health Applications: Validating the Anti‐Saccade Task in a Web‐Based Setting","authors":"Gustavo E Juantorena, Agustín Penas, Francisco Figari, Agustin Petroni, Juan E Kamienkowski","doi":"10.1002/alz70856_107106","DOIUrl":"https://doi.org/10.1002/alz70856_107106","url":null,"abstract":"Background In recent years, several prototypes of remote, webcam‐based eye‐tracking have emerged, exploring their feasibility and potential for web‐based experiments. This growing interest is largely driven by the ability to conduct tasks remotely, enabling research on larger and hard‐to‐reach populations. However, its use has been primarily limited to proof‐of‐concept studies in basic cognitive science. These studies have generally reported a decrease in precision, compounded by lower camera quality and noisier environments, posing new implementation challenges. Additionally, webcam‐based eye‐tracking has been applied in human‐computer interaction and marketing, where only qualitative results are typically required. Here, we aim to extend its application to cognitive tasks relevant to mental health and telemedicine. Methods We present a novel prototype of a remote, webcam‐based eye‐tracker with key enhancements over existing systems, including a reliable sampling rate, screen distance detection, head movement tracking, blink detection, and improved calibration without requiring constant mouse interactions. We first evaluated its spatiotemporal resolution and reliability. Next, we tested its functionality in a cognitive experiment using the anti‐saccade task, a well‐established paradigm in various patient populations. This task assesses inhibitory control by comparing horizontal eye movements toward (pro‐saccades) and away from (anti‐saccades) a target. Results The proposed improvements resulted in a robust remote eye‐tracking system that performed consistently across different hardware setups and maintained stable calibration over time. We also introduced novel capabilities and provided a discussion on their limitations. Furthermore, our results replicated key findings from high‐precision laboratory‐based eye‐trackers: response times and error rates were higher in anti‐saccades than in pro‐saccades, and incorrect responses were associated with shorter reaction times. Conclusions We demonstrate the potential of this prototype for both cognitive research and clinical applications, providing a comprehensive evaluation of its capabilities and limitations. Our findings indicate that remote webcam‐based eye‐tracking can successfully replicate classical results from the anti‐saccade task in an online setting.","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"30 1","pages":""},"PeriodicalIF":14.0,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Laura Alejandra Ramirez Tirado, Ann D Cohen, C. Elizabeth Shaaban, Cristy Matan, Brian J Lopresti, Milos D. Ikonomovic, Thomas K Karikari, Victor L Villemagne, Oscar L Lopez, Oscar L Lopez
Background We recently reported synergistic effects of TNFR1 and TNFR2 with astrogliosis resulting in greater vascular burden and neurodegeneration, particularly in Aβ+ participants. Here, we aimed to assess the interactions of peripheral inflammation and astrogliosis on incidence of AD dementia and mortality. We hypothesized synergistic effects with astrogliosis resulting in increased incidence of dementia, and mortality, particularly in Aβ+ participants. Methods Participants of the Gingko evaluation of memory (GEM) study underwent PiB‐PET scans between 2009‐2018. GFAP and peripheral inflammatory markers were measured in 2009 by immunoassay. We evaluated the relationship of peripheral markers of inflammation (TNFR1, TNFR2) and astrogliosis (GFAP</≥196pg/mL) on AD dementia incidence and mortality using Cox proportional hazards models adjusted for age, sex, education, APOEε4 , cystatin C and baseline Aβ status. Results We included 190 participants (mean age:86±2.8 yrs., 40.8% women, 96.9% White); 53% developed AD dementia over a median time of 15.78 years (95% CI:12.16–19.26). After full adjustment, Aβ+ status was associated with greater hazards for dementia HR:1.73(95%CI:1.10–2.70);p=.016. High levels of TNFR1 HR:1.41(95%CI:0.88–2.27);p=.145 or TNFR2 HR:1.04(95%CI:0.65–1.65);p=.855 were not associated with higher hazards for dementia after full adjustment. Likewise, among Aβ+ participants, high levels of TNFR1 and TNFR2 were also not associated with greater hazards for AD dementia. However, among Aβ+ participants, fully adjusted models showed greater hazards of dementia and significant additive and multiplicative interactions for the joint effects of GFAP with TNFR1 HR:4.55(95%CI:1.14–18.19);p=.032 and with TNFR2 HR:4.07(95%CI:1.35–12.24);p=.012. Regarding mortality, median survival was 16 years(95%CI:13.39‐18.58). We found no difference in mortality by amyloid status HR:1.18(95%CI:0.79–1.75);p=.405, GFAP or NfL status. High levels of TNFR1 HR:1.50(95%CI:1.00‐2.24);p=.046, but not TNFR2 HR:1.20(95%CI:0.80–1.80);p=.365, were associated with greater hazards of death after adjustment. Among Aβ+ participants high levels of TNFR1 were also associated with worse survival. Fully adjusted models also showed greater hazards of death for the joint effects of GFAP with TNFR1 and TNFR2, and a synergistic effect on the additive scale was found between GFAP and TNFR2 (Excess relative risk due to interaction:1.43;95%CI:‐.09–2.76;p=0.036). Conclusion Interaction of GFAP with TNFR1 and TNFR2 seems to be clinically relevant for progression to AD dementia and mortality in Aβ+ participants.
{"title":"Synergistic effects of GFAP with TNFR1 and TNFR2 on conversion to AD dementia and overall survival across the Alzheimer's disease spectrum","authors":"Laura Alejandra Ramirez Tirado, Ann D Cohen, C. Elizabeth Shaaban, Cristy Matan, Brian J Lopresti, Milos D. Ikonomovic, Thomas K Karikari, Victor L Villemagne, Oscar L Lopez, Oscar L Lopez","doi":"10.1002/alz70856_106860","DOIUrl":"https://doi.org/10.1002/alz70856_106860","url":null,"abstract":"Background We recently reported synergistic effects of TNFR1 and TNFR2 with astrogliosis resulting in greater vascular burden and neurodegeneration, particularly in Aβ+ participants. Here, we aimed to assess the interactions of peripheral inflammation and astrogliosis on incidence of AD dementia and mortality. We hypothesized synergistic effects with astrogliosis resulting in increased incidence of dementia, and mortality, particularly in Aβ+ participants. Methods Participants of the Gingko evaluation of memory (GEM) study underwent PiB‐PET scans between 2009‐2018. GFAP and peripheral inflammatory markers were measured in 2009 by immunoassay. We evaluated the relationship of peripheral markers of inflammation (TNFR1, TNFR2) and astrogliosis (GFAP</≥196pg/mL) on AD dementia incidence and mortality using Cox proportional hazards models adjusted for age, sex, education, <jats:italic>APOEε4</jats:italic> , cystatin C and baseline Aβ status. Results We included 190 participants (mean age:86±2.8 yrs., 40.8% women, 96.9% White); 53% developed AD dementia over a median time of 15.78 years (95% CI:12.16–19.26). After full adjustment, Aβ+ status was associated with greater hazards for dementia HR:1.73(95%CI:1.10–2.70);p=.016. High levels of TNFR1 HR:1.41(95%CI:0.88–2.27);p=.145 or TNFR2 HR:1.04(95%CI:0.65–1.65);p=.855 were not associated with higher hazards for dementia after full adjustment. Likewise, among Aβ+ participants, high levels of TNFR1 and TNFR2 were also not associated with greater hazards for AD dementia. However, among Aβ+ participants, fully adjusted models showed greater hazards of dementia and significant additive and multiplicative interactions for the joint effects of GFAP with TNFR1 HR:4.55(95%CI:1.14–18.19);p=.032 and with TNFR2 HR:4.07(95%CI:1.35–12.24);p=.012. Regarding mortality, median survival was 16 years(95%CI:13.39‐18.58). We found no difference in mortality by amyloid status HR:1.18(95%CI:0.79–1.75);p=.405, GFAP or NfL status. High levels of TNFR1 HR:1.50(95%CI:1.00‐2.24);p=.046, but not TNFR2 HR:1.20(95%CI:0.80–1.80);p=.365, were associated with greater hazards of death after adjustment. Among Aβ+ participants high levels of TNFR1 were also associated with worse survival. Fully adjusted models also showed greater hazards of death for the joint effects of GFAP with TNFR1 and TNFR2, and a synergistic effect on the additive scale was found between GFAP and TNFR2 (Excess relative risk due to interaction:1.43;95%CI:‐.09–2.76;p=0.036). Conclusion Interaction of GFAP with TNFR1 and TNFR2 seems to be clinically relevant for progression to AD dementia and mortality in Aβ+ participants.","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"38 1","pages":""},"PeriodicalIF":14.0,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Timothy I. Richardson, Rebecca C. Klein, Kun Huang, Jie Zhang, Andrew D. Mesecar, Jeffrey L. Dage, Brent Clayton, Bruce T. Lamb, Alan D. Palkowitz
The incidence of Alzheimer's disease (AD) continues to increase, despite decades of effort to develop disease‐modifying therapies. In response, the National Institute on Aging (NIA) established the TaRget Enablement to Accelerate Therapy Development for Alzheimer's Disease (TREAT‐AD) centers to address the gap between basic research and translational drug discovery. Situated within a robust AD research environment, the Indiana University School of Medicine (IUSM)–Purdue University TREAT‐AD Center is one of two National Institutes of Health (NIH)‐supported centers funded to accomplish this mission. With a focus on novel biological targets beyond amyloid and tau, our center has assembled the necessary components of a drug discovery engine: project and data management, bioinformatics and computational science, structural biology and biochemistry, assay development and pharmacology, and molecular design and synthesis of small molecules, antibodies, and oligonucleotides. Our objective is to deliver Target Enabling Packages (TEPs) within an open science framework, making data, methods, and research tools broadly accessible through the AD Knowledge Portal. Highlights The Indiana University School of Medicine (IUSM)–Purdue TREAT‐AD Center develops Target Enabling Packages (TEPs) to advance novel targets for the treatment of Alzheimer's disease (AD). The center is overseen by an administrative core and operates through four technical cores – bioinformatics, structural biology, assay development, and medicinal chemistry – within a milestone‐driven and open science framework. Multi‐omics, systems biology, and machine learning (ML) approaches guide the nomination of high‐priority targets beyond amyloid and tau. Cross‐core workflows provide structural insights into novel biological targets, validated assays, biomarkers, and molecular probes that enable lead optimization. All data, methods, and tools are openly shared through the AD Knowledge Portal to accelerate global efforts in AD drug discovery.
{"title":"Next‐generation Alzheimer's therapeutics: target assessment and enablement at the Indiana University School of Medicine–Purdue University TREAT‐AD Center","authors":"Timothy I. Richardson, Rebecca C. Klein, Kun Huang, Jie Zhang, Andrew D. Mesecar, Jeffrey L. Dage, Brent Clayton, Bruce T. Lamb, Alan D. Palkowitz","doi":"10.1002/alz.70964","DOIUrl":"https://doi.org/10.1002/alz.70964","url":null,"abstract":"<jats:label/> The incidence of Alzheimer's disease (AD) continues to increase, despite decades of effort to develop disease‐modifying therapies. In response, the National Institute on Aging (NIA) established the TaRget Enablement to Accelerate Therapy Development for Alzheimer's Disease (TREAT‐AD) centers to address the gap between basic research and translational drug discovery. Situated within a robust AD research environment, the Indiana University School of Medicine (IUSM)–Purdue University TREAT‐AD Center is one of two National Institutes of Health (NIH)‐supported centers funded to accomplish this mission. With a focus on novel biological targets beyond amyloid and tau, our center has assembled the necessary components of a drug discovery engine: project and data management, bioinformatics and computational science, structural biology and biochemistry, assay development and pharmacology, and molecular design and synthesis of small molecules, antibodies, and oligonucleotides. Our objective is to deliver Target Enabling Packages (TEPs) within an open science framework, making data, methods, and research tools broadly accessible through the AD Knowledge Portal. Highlights <jats:list list-type=\"bullet\"> <jats:list-item> The Indiana University School of Medicine (IUSM)–Purdue TREAT‐AD Center develops Target Enabling Packages (TEPs) to advance novel targets for the treatment of Alzheimer's disease (AD). </jats:list-item> <jats:list-item> The center is overseen by an administrative core and operates through four technical cores – bioinformatics, structural biology, assay development, and medicinal chemistry – within a milestone‐driven and open science framework. </jats:list-item> <jats:list-item> Multi‐omics, systems biology, and machine learning (ML) approaches guide the nomination of high‐priority targets beyond amyloid and tau. </jats:list-item> <jats:list-item> Cross‐core workflows provide structural insights into novel biological targets, validated assays, biomarkers, and molecular probes that enable lead optimization. </jats:list-item> <jats:list-item> All data, methods, and tools are openly shared through the AD Knowledge Portal to accelerate global efforts in AD drug discovery. </jats:list-item> </jats:list>","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"243 1","pages":""},"PeriodicalIF":14.0,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marc D. Rudolph, Melissa M. Rundle, Kathryn H Alphin, Richard A. Barcus, Timothy M. Hughes, Trey R. Bateman, Kiran K. Solingapuram Sai, Christopher T Whitlow, Suzanne Craft, Da Ma
Background Acquisition and participant‐related artifacts (atrophy, motion) can degrade the quality of acquired images resulting in poor segmentation of tissue compartments. This can bias estimates of brain volume and thickness used quantify age and disease‐related atrophy, a problem particularly salient in clinical populations. In some cases, poor quality scans may be discarded or repeated incurring additional costs. Method Participants ( <jats:italic>n</jats:italic> = 624; cognitively normal [CU; <jats:italic>n</jats:italic> = 330]; mild cognitive impairment [MCI; <jats:italic>n</jats:italic> = 214]; dementia [DEM; <jats:italic>n</jats:italic> = 75]; otherwise not classified [OTHER; <jats:italic>n</jats:italic> = 5)] enrolled in the Wake Forest ADRC Clinical Cohort (Table 1). Structural T1‐MRI scans were processed using FreeSurfer (v7.2) recon‐all and FreeSurfer (v7.4) recon‐all‐clinical (SynthSeg) pipelines. Tau‐PET (FTP) images were acquired; global, meta‐temporal, and entorhinal tau‐PET (white+gray matter; SUVr) was quantified. Measures of (1) cortical thickness: entorhinal cortex, inferior temporal lobe, temporal pole, and meta‐temporal; (2) volume: gray matter, white matter, and hippocampi; and (3) tau deposition (global and entorhinal SUVr) were compared between pipelines and by cognitive status (Figure 2). GLMs (R <jats:sup>2</jats:sup> =shared variance) and gaussian‐mixture modeling (cohort‐specific atrophy cutpoints) were performed. Result Overall, FreeSurfer (v7.2) recon‐all tended to undersgement, producing smaller volume and thickness estimates (and a wider range of estimates), as compared to FreeSurfer recon‐all‐clinical (e.g., SynthSeg; Figure 1a). For cortical thickness, we observed poor‐to‐moderate associations in signature regions for age‐related dementias including: temporal pole [R <jats:sup>2</jats:sup> : Left=3%; Right=7%]); inferior temporal lobe (R <jats:sup>2</jats:sup> : Left=34%; Right=24%), entorhinal cortex (R2: Left=32%; Right=27%), posterior cingulate (R <jats:sup>2</jats:sup> : Left=40%; Right=39%), and precuneus (R <jats:sup>2</jats:sup> : Left=39%; Right=36%). Conversely, volumetric estimates were largely comparable across pipelines, except for hippocampi (R <jats:sup>2</jats:sup> : Left=65%; Right=64%), where we observed a modest drop in agreement for classification of atrophy (N; Figure 1b). ∼13% (hippocampal volume) and 36% (meta‐temporal cortical thickness) of cases were discordant when classifying atrophy (Figure 2: cognitive status). Quantification of tau‐PET (global/regional deposition) was not impacted. Conclusion Volumetric estimates were comparable across FreeSurfer segmentation algorithms in our cohort; however, cortical thickness estimates were impacted contributing to discrepant classification of atrophy. SynthSeg segmentations were robust to scan quality (not shown) and recovered susceptible regions (e.g., temporal pole). Deep learning‐based segmentation algorithms (e.g., SynthSeg) require less proc
背景采集和参与者相关的伪影（萎缩、运动）会降低获取图像的质量，导致组织区室分割不良。这可能会对用于量化年龄和疾病相关萎缩的脑容量和厚度的估计产生偏差，这是一个在临床人群中特别突出的问题。在某些情况下，质量差的扫描可能会被丢弃或重复产生额外的费用。方法纳入Wake Forest ADRC临床队列的参与者（n = 624，认知正常[CU； n = 330]，轻度认知障碍[MCI； n = 214]，痴呆[DEM； n = 75]，其他未分类[OTHER； n = 5）)（表1）。结构T1 - MRI扫描使用FreeSurfer (v7.2) recon - all和FreeSurfer (v7.4) recon - all - clinical （SynthSeg）管道进行处理。获取Tau‐PET （FTP）图像；对全脑、后颞和内鼻的tau - PET（白质+灰质；SUVr）进行量化。(1)皮质厚度测量：内嗅皮质、下颞叶、颞极和后颞叶；(2)体积：灰质、白质、海马；(3)比较管道和认知状态之间的tau沉积（整体和内嗅SUVr）（图2）。进行了GLMs （r2 =共享方差）和高斯混合模型（队列特定萎缩切点）。总体而言，与FreeSurfer recon - all - clinical（例如，SynthSeg；图1a）相比，FreeSurfer (v7.2) recon - all倾向于低估，产生更小的体积和厚度估计（以及更大的估计范围）。对于皮质厚度，我们观察到年龄相关痴呆的特征区域有弱到中度的关联，包括：颞极[r2：左=3%；正确的= 7%);下颞叶（R2：左=34%；右=24%）、内鼻皮层（R2：左=32%；右=27%）、后扣带（R2：左=40%；右=39%）和楔前叶（R2：左=39%；右=36%）。相反，除了海马体（r2：左=65%；右=64%），我们观察到在萎缩分类上的一致性略有下降（N；图1b），其他管道的体积估计基本上是可比性的。约13%（海马体积）和36%（颞叶皮质厚度）的病例在分类萎缩时不一致（图2：认知状态）。tau‐PET（全球/区域沉积）的定量不受影响。结论：在我们的队列中，FreeSurfer分割算法的体积估计值具有可比性；然而，皮质厚度估计受到影响，导致不同的萎缩分类。SynthSeg分割对扫描质量（未显示）和恢复敏感区域（例如，颞极）具有鲁棒性。基于深度学习的分割算法（例如SynthSeg）需要更少的处理时间，并可能最终减少丢弃低质量扫描或执行手动分割的需要。
{"title":"Preliminary comparison of FreeSurfer segmentation algorithms in the Wake Forest community‐based cohort and potential impact on ATN classification","authors":"Marc D. Rudolph, Melissa M. Rundle, Kathryn H Alphin, Richard A. Barcus, Timothy M. Hughes, Trey R. Bateman, Kiran K. Solingapuram Sai, Christopher T Whitlow, Suzanne Craft, Da Ma","doi":"10.1002/alz70856_107027","DOIUrl":"https://doi.org/10.1002/alz70856_107027","url":null,"abstract":"Background Acquisition and participant‐related artifacts (atrophy, motion) can degrade the quality of acquired images resulting in poor segmentation of tissue compartments. This can bias estimates of brain volume and thickness used quantify age and disease‐related atrophy, a problem particularly salient in clinical populations. In some cases, poor quality scans may be discarded or repeated incurring additional costs. Method Participants ( <jats:italic>n</jats:italic> = 624; cognitively normal [CU; <jats:italic>n</jats:italic> = 330]; mild cognitive impairment [MCI; <jats:italic>n</jats:italic> = 214]; dementia [DEM; <jats:italic>n</jats:italic> = 75]; otherwise not classified [OTHER; <jats:italic>n</jats:italic> = 5)] enrolled in the Wake Forest ADRC Clinical Cohort (Table 1). Structural T1‐MRI scans were processed using FreeSurfer (v7.2) recon‐all and FreeSurfer (v7.4) recon‐all‐clinical (SynthSeg) pipelines. Tau‐PET (FTP) images were acquired; global, meta‐temporal, and entorhinal tau‐PET (white+gray matter; SUVr) was quantified. Measures of (1) cortical thickness: entorhinal cortex, inferior temporal lobe, temporal pole, and meta‐temporal; (2) volume: gray matter, white matter, and hippocampi; and (3) tau deposition (global and entorhinal SUVr) were compared between pipelines and by cognitive status (Figure 2). GLMs (R <jats:sup>2</jats:sup> =shared variance) and gaussian‐mixture modeling (cohort‐specific atrophy cutpoints) were performed. Result Overall, FreeSurfer (v7.2) recon‐all tended to undersgement, producing smaller volume and thickness estimates (and a wider range of estimates), as compared to FreeSurfer recon‐all‐clinical (e.g., SynthSeg; Figure 1a). For cortical thickness, we observed poor‐to‐moderate associations in signature regions for age‐related dementias including: temporal pole [R <jats:sup>2</jats:sup> : Left=3%; Right=7%]); inferior temporal lobe (R <jats:sup>2</jats:sup> : Left=34%; Right=24%), entorhinal cortex (R2: Left=32%; Right=27%), posterior cingulate (R <jats:sup>2</jats:sup> : Left=40%; Right=39%), and precuneus (R <jats:sup>2</jats:sup> : Left=39%; Right=36%). Conversely, volumetric estimates were largely comparable across pipelines, except for hippocampi (R <jats:sup>2</jats:sup> : Left=65%; Right=64%), where we observed a modest drop in agreement for classification of atrophy (N; Figure 1b). ∼13% (hippocampal volume) and 36% (meta‐temporal cortical thickness) of cases were discordant when classifying atrophy (Figure 2: cognitive status). Quantification of tau‐PET (global/regional deposition) was not impacted. Conclusion Volumetric estimates were comparable across FreeSurfer segmentation algorithms in our cohort; however, cortical thickness estimates were impacted contributing to discrepant classification of atrophy. SynthSeg segmentations were robust to scan quality (not shown) and recovered susceptible regions (e.g., temporal pole). Deep learning‐based segmentation algorithms (e.g., SynthSeg) require less proc","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"45 1","pages":""},"PeriodicalIF":14.0,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qiwen Cheng, Jennifer Nolz, Timothy Karr, Nicole Dorn, Benjamin Readhead, Rosa Krajmalnik‐Brown, Diego Mastroeni
INTRODUCTION Alzheimer's disease (AD) has been regarded as a brain‐first disorder. Emerging evidence suggests that the gut may influence central nervous system pathology, but the mechanisms remain unclear. METHODS We conducted a proteomic and microbial analysis of transverse colon samples from clinically and pathologically confirmed AD and control cases. RESULTS In the AD gut samples, antimicrobial humoral response and oxidative stress response were downregulated, while catabolic processes and insulin signaling were upregulated. Several complement (e.g., C5) and synaptic (e.g., synaptophysin) proteins were downregulated. Amyloid beta 42 was detected at higher levels. Christensenellaceae , Desulfovibrio , and Candida tropicalis amplicon sequence variants were higher in abundance, while Streptococcus , Lachnospiraceae , Blautia , and Nakaseomyces were lower. In general, bacterial composition correlated with AD clinical variables such as plaque and tangle burden. DISCUSSION These findings underscore the gut's possible involvement in AD pathogenesis and provide new insights into potential biomarkers and therapeutic targets. Highlights This study provides the first in‐depth analysis of the proteome and microbiome in AD transverse colon tissues. Multiple immune and oxidative stress response pathways were downregulated in AD, while metabolic pathways were upregulated. Synaptic protein, complement protein, and Aβ42 levels were significantly different between AD and controls. Transverse colon microbial composition was associated with AD clinical variables.
{"title":"Gut proteome and microbiome alterations: Analysis of transverse colon samples from pathologically confirmed Alzheimer's disease patients","authors":"Qiwen Cheng, Jennifer Nolz, Timothy Karr, Nicole Dorn, Benjamin Readhead, Rosa Krajmalnik‐Brown, Diego Mastroeni","doi":"10.1002/alz.71021","DOIUrl":"https://doi.org/10.1002/alz.71021","url":null,"abstract":"INTRODUCTION Alzheimer's disease (AD) has been regarded as a brain‐first disorder. Emerging evidence suggests that the gut may influence central nervous system pathology, but the mechanisms remain unclear. METHODS We conducted a proteomic and microbial analysis of transverse colon samples from clinically and pathologically confirmed AD and control cases. RESULTS In the AD gut samples, antimicrobial humoral response and oxidative stress response were downregulated, while catabolic processes and insulin signaling were upregulated. Several complement (e.g., C5) and synaptic (e.g., synaptophysin) proteins were downregulated. Amyloid beta 42 was detected at higher levels. <jats:italic>Christensenellaceae</jats:italic> , <jats:italic>Desulfovibrio</jats:italic> , and <jats:italic>Candida tropicalis</jats:italic> amplicon sequence variants were higher in abundance, while <jats:italic>Streptococcus</jats:italic> , <jats:italic>Lachnospiraceae</jats:italic> , <jats:italic>Blautia</jats:italic> , and <jats:italic>Nakaseomyces</jats:italic> were lower. In general, bacterial composition correlated with AD clinical variables such as plaque and tangle burden. DISCUSSION These findings underscore the gut's possible involvement in AD pathogenesis and provide new insights into potential biomarkers and therapeutic targets. Highlights <jats:list list-type=\"bullet\"> <jats:list-item> This study provides the first in‐depth analysis of the proteome and microbiome in AD transverse colon tissues. </jats:list-item> <jats:list-item> Multiple immune and oxidative stress response pathways were downregulated in AD, while metabolic pathways were upregulated. </jats:list-item> <jats:list-item> Synaptic protein, complement protein, and Aβ42 levels were significantly different between AD and controls. </jats:list-item> <jats:list-item> Transverse colon microbial composition was associated with AD clinical variables. </jats:list-item> </jats:list>","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"28 1","pages":""},"PeriodicalIF":14.0,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Correction to “PP2A methylesterase, PME‐1, and PP2A methyltransferase, LCMT‐1, control sensitivity to impairments caused by injury‐related oligomeric tau”","authors":"","doi":"10.1002/alz.71119","DOIUrl":"https://doi.org/10.1002/alz.71119","url":null,"abstract":"","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"29 1","pages":""},"PeriodicalIF":14.0,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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