Pub Date : 2024-09-04DOI: 10.1101/2024.09.03.24313019
Jean Ikanga, Kharine Jean, Priscilla Medina, Saranya Sundaram Patel, Megan Schwinne, Emmanuel Epenge, Guy Gikelekele, Nathan Tshengele, Immaculee Kavugho, Samuel Mampunza, Lelo Mananga, Charlotte E. Teunissen, Anthony Stringer, Julio C. Rojas, Brandon Chan, Argentina Lario Lago, Joel H. Kramer, Adam L. Boxer, Andreas Jeromin, Bernard Hanseeuw, Alden L. Gross, Alvaro Alonso
Objective The hippocampus is one of the first brain structures affected by Alzheimer’s disease (AD), and its atrophy is a strong indicator of the disease. This study investigates the ability of plasma biomarkers of AD and AD-related dementias— amyloid-β (Aβ42/40), phosphorylated tau-181 (p-tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP)—to predict hippocampal atrophy in adult individuals in Kinshasa, Democratic Republic of Congo (DRC).
{"title":"Neurodegenerative Plasma Biomarkers for Prediction of Hippocampal Atrophy in Older Adults with Suspected Alzheimer’s Disease in Kinshasa, Democratic Republic of Congo","authors":"Jean Ikanga, Kharine Jean, Priscilla Medina, Saranya Sundaram Patel, Megan Schwinne, Emmanuel Epenge, Guy Gikelekele, Nathan Tshengele, Immaculee Kavugho, Samuel Mampunza, Lelo Mananga, Charlotte E. Teunissen, Anthony Stringer, Julio C. Rojas, Brandon Chan, Argentina Lario Lago, Joel H. Kramer, Adam L. Boxer, Andreas Jeromin, Bernard Hanseeuw, Alden L. Gross, Alvaro Alonso","doi":"10.1101/2024.09.03.24313019","DOIUrl":"https://doi.org/10.1101/2024.09.03.24313019","url":null,"abstract":"<strong>Objective</strong> The hippocampus is one of the first brain structures affected by Alzheimer’s disease (AD), and its atrophy is a strong indicator of the disease. This study investigates the ability of plasma biomarkers of AD and AD-related dementias— amyloid-β (Aβ42/40), phosphorylated tau-181 (p-tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP)—to predict hippocampal atrophy in adult individuals in Kinshasa, Democratic Republic of Congo (DRC).","PeriodicalId":501367,"journal":{"name":"medRxiv - Neurology","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1101/2024.08.30.24312864
Thomas G. Beach, Geidy E. Serrano, Nan Zhang, Erika D. Driver-Dunckley, Lucia I. Sue, Holly A. Shill, Shyamal H. Mehta, Christine Belden, Cecilia Tremblay, Parichita Choudhury, Alireza Atri, Charles H. Adler
In recent years, proposals have been advanced to redefine or reclassify Lewy body disorders by merging the long-established entities of Parkinson’s disease (PD), Parkinson’s disease dementia (PDD) and dementia with Lewy bodies (DLB). These proposals reject the International DLB Consortium classification system that has evolved over three decades of consensus collaborations between neurologists, neuropsychologists and neuropathologists. While the Consortium’s “one year rule” for separating PD and DLB has been criticized as arbitrary, it has been a pragmatic and effective tool for splitting the continuum between the two entities. In addition to the decades of literature supporting the non-homogeneity of PD and DLB, it has become increasingly apparent that Lewy body disorders may fundamentally differ in their etiology. Most PD subjects, as well as most clinically-presenting DLB subjects, might best be classified as having a “primary synucleinopathy” while most clinically-unidentified DLB subjects, who also have concurrent neuropathology-criteria AD (AD/DLB), as well as those with neuropathological AD and amygdala-predominant LBD insufficient for a DLB diagnosis, may best be classified as having a “secondary synucleinopathy. Importantly, the DLB Consortium recognized the importance of comorbid AD pathology by defining “Low”, “Intermediate” and “High” subdivisions of DLB based on the relative brain stages of both Lewy body and AD pathology. If the one-year rule for separating PD from DLB, and for then dividing DLB into subtypes based on the presence and severity of comorbid AD pathology, is effective, then the divided groups should statistically differ in important ways. In this study we used the comprehensive clinicopathological database of the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND) to empirically test this hypothesis. Furthermore, we used multivariable statistical models to test the hypothesis that comorbid AD neuropathology is a major predictor of the presence and severity of postmortem Lewy synucleinopathy. The results confirm the clinicopathological heterogeneity of Lewy body disorders as well as the profound influence of comorbid AD pathology.
近年来，有人提出将帕金森病（PD）、帕金森病性痴呆（PDD）和路易体痴呆（DLB）这几个历史悠久的实体合并起来，重新定义或重新分类路易体疾病。这些建议拒绝了国际路易体痴呆联盟的分类系统，该系统是神经学家、神经心理学家和神经病理学家经过三十年的共识合作发展而来的。虽然国际 DLB 联盟将 PD 和 DLB 区分开来的 "一年规则 "被批评为武断，但它一直是划分两个实体之间连续性的实用而有效的工具。除了数十年来支持帕金森氏症和多发性硬化症非同一性的文献外，越来越明显的是路易体失调症在病因学上可能存在根本性差异。大多数帕金森病患者和大多数临床表现的 DLB 患者最好被归类为患有 "原发性突触核蛋白病"，而大多数临床未确定的 DLB 患者，如果同时患有神经病理学标准的 AD（AD/DLB），以及那些神经病理学标准的 AD 和杏仁核为主的路易体疾病不足以诊断为 DLB 的患者，最好被归类为患有 "继发性突触核蛋白病"。重要的是，DLB联盟认识到了合并AD病理的重要性，根据路易体和AD病理在大脑中的相对阶段，定义了DLB的 "低度"、"中度 "和 "高度 "分类。如果将 PD 与 DLB 区分开来，然后根据合并 AD 病变的存在和严重程度将 DLB 划分为亚型的一年规则是有效的，那么在统计学上，被划分的组别应该存在重要差异。在本研究中，我们利用亚利桑那州衰老与神经退行性疾病研究（AZSAND）的综合临床病理数据库对这一假设进行了实证检验。此外，我们还使用多变量统计模型检验了以下假设：合并 AD 神经病理学是存在路易突触核蛋白病及其严重程度的主要预测因素。结果证实了路易体疾病的临床病理异质性以及合并 AD 病理的深远影响。
{"title":"Clinicopathological Heterogeneity of Lewy Body Diseases: The Profound Influence of Comorbid Alzheimer’s Disease","authors":"Thomas G. Beach, Geidy E. Serrano, Nan Zhang, Erika D. Driver-Dunckley, Lucia I. Sue, Holly A. Shill, Shyamal H. Mehta, Christine Belden, Cecilia Tremblay, Parichita Choudhury, Alireza Atri, Charles H. Adler","doi":"10.1101/2024.08.30.24312864","DOIUrl":"https://doi.org/10.1101/2024.08.30.24312864","url":null,"abstract":"In recent years, proposals have been advanced to redefine or reclassify Lewy body disorders by merging the long-established entities of Parkinson’s disease (PD), Parkinson’s disease dementia (PDD) and dementia with Lewy bodies (DLB). These proposals reject the International DLB Consortium classification system that has evolved over three decades of consensus collaborations between neurologists, neuropsychologists and neuropathologists. While the Consortium’s “one year rule” for separating PD and DLB has been criticized as arbitrary, it has been a pragmatic and effective tool for splitting the continuum between the two entities. In addition to the decades of literature supporting the non-homogeneity of PD and DLB, it has become increasingly apparent that Lewy body disorders may fundamentally differ in their etiology. Most PD subjects, as well as most clinically-presenting DLB subjects, might best be classified as having a “primary synucleinopathy” while most clinically-unidentified DLB subjects, who also have concurrent neuropathology-criteria AD (AD/DLB), as well as those with neuropathological AD and amygdala-predominant LBD insufficient for a DLB diagnosis, may best be classified as having a “secondary synucleinopathy. Importantly, the DLB Consortium recognized the importance of comorbid AD pathology by defining “Low”, “Intermediate” and “High” subdivisions of DLB based on the relative brain stages of both Lewy body and AD pathology. If the one-year rule for separating PD from DLB, and for then dividing DLB into subtypes based on the presence and severity of comorbid AD pathology, is effective, then the divided groups should statistically differ in important ways. In this study we used the comprehensive clinicopathological database of the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND) to empirically test this hypothesis. Furthermore, we used multivariable statistical models to test the hypothesis that comorbid AD neuropathology is a major predictor of the presence and severity of postmortem Lewy synucleinopathy. The results confirm the clinicopathological heterogeneity of Lewy body disorders as well as the profound influence of comorbid AD pathology.","PeriodicalId":501367,"journal":{"name":"medRxiv - Neurology","volume":"261 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1101/2024.09.03.24313022
Emile Omba Yohe, Alvaro Alonso, Daniel L. Drane, Saranya Sundaram Patel, Megan Schwinne, Emmanuel Epenge, Guy Gikelekele, Esambo Herve, Immaculee Kavugho, Nathan Tshengele, Samuel Mampunza, Lelo Mananga, Liping Zhao, Deqiang Qiu, Anthony Stringer, Amit M Saindane, Jean Ikanga
Background White matter hyperintensities (WMHs) are strongly linked to cardiovascular risk factors and other health conditions such as Alzheimer’s disease. However, there is a dearth of research on this topic in low-income countries and underserved populations, especially in the Democratic Republic of Congo (DRC) where the population is aging rapidly with increasing cardiovascular risk factors and dementia-related diseases. This study evaluates health factors associated with WMH in the elderly Sub-Saharan Africa (SSA), specifically Congolese adults.
{"title":"Predictors of white matter hyperintensities in the elderly Congolese population","authors":"Emile Omba Yohe, Alvaro Alonso, Daniel L. Drane, Saranya Sundaram Patel, Megan Schwinne, Emmanuel Epenge, Guy Gikelekele, Esambo Herve, Immaculee Kavugho, Nathan Tshengele, Samuel Mampunza, Lelo Mananga, Liping Zhao, Deqiang Qiu, Anthony Stringer, Amit M Saindane, Jean Ikanga","doi":"10.1101/2024.09.03.24313022","DOIUrl":"https://doi.org/10.1101/2024.09.03.24313022","url":null,"abstract":"<strong>Background</strong> White matter hyperintensities (WMHs) are strongly linked to cardiovascular risk factors and other health conditions such as Alzheimer’s disease. However, there is a dearth of research on this topic in low-income countries and underserved populations, especially in the Democratic Republic of Congo (DRC) where the population is aging rapidly with increasing cardiovascular risk factors and dementia-related diseases. This study evaluates health factors associated with WMH in the elderly Sub-Saharan Africa (SSA), specifically Congolese adults.","PeriodicalId":501367,"journal":{"name":"medRxiv - Neurology","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1101/2024.09.03.24313005
Mohamed F Doheim, Abdulrahman Ibrahim Hagrass
Background Association of endovascular therapy (EVT) with clinical outcomes beyond 24 hours remains unclear. We conducted a meta-analysis to answer this question.
{"title":"Not Too Late to Intervene? A Meta-analysis of 13 Studies Evaluating the Association of Endovascular Therapy with Clinical Outcomes in Stroke Patients Presenting Beyond 24 Hours","authors":"Mohamed F Doheim, Abdulrahman Ibrahim Hagrass","doi":"10.1101/2024.09.03.24313005","DOIUrl":"https://doi.org/10.1101/2024.09.03.24313005","url":null,"abstract":"<strong>Background</strong> Association of endovascular therapy (EVT) with clinical outcomes beyond 24 hours remains unclear. We conducted a meta-analysis to answer this question.","PeriodicalId":501367,"journal":{"name":"medRxiv - Neurology","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1101/2024.09.03.24312481
Bhargav T. Nallapu, Kellen K. Petersen, Tianchen Qian, Idris Demirsoy, Elham Ghanbarian, Christos Davatzikos, Richard B. Lipton, Ali Ezzati, Alzheimer’s Disease Neuroimaging Initiative
Background Of persons randomized to the placebo arm of Alzheimer’s Disease (AD) treatment trials, 40% do not show cognitive decline over 80 weeks of follow-up. Identifying and excluding these individuals from both arms of randomized clinical trials (RCTs) of AD has the potential to increase power to detect treatment effects.
{"title":"A Machine Learning Approach to Predict Cognitive Decline in Alzheimer’s Disease Clinical Trials","authors":"Bhargav T. Nallapu, Kellen K. Petersen, Tianchen Qian, Idris Demirsoy, Elham Ghanbarian, Christos Davatzikos, Richard B. Lipton, Ali Ezzati, Alzheimer’s Disease Neuroimaging Initiative","doi":"10.1101/2024.09.03.24312481","DOIUrl":"https://doi.org/10.1101/2024.09.03.24312481","url":null,"abstract":"<strong>Background</strong> Of persons randomized to the placebo arm of Alzheimer’s Disease (AD) treatment trials, 40% do not show cognitive decline over 80 weeks of follow-up. Identifying and excluding these individuals from both arms of randomized clinical trials (RCTs) of AD has the potential to increase power to detect treatment effects.","PeriodicalId":501367,"journal":{"name":"medRxiv - Neurology","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1101/2024.09.02.24312694
Martin Schuler, Grischa Gerwert, Marvin Mann, Nathalie Woitzik, Lennart Langenhoff, Diana Hubert, Deniz Duman, Adrian Höveler, Sandy Galkowski, Jonas Simon, Robin Denz, Sandrina Weber, Eun-Hae Kwon, Robin Wanka, Carsten Kötting, Jörn Güldenhaupt, Léon Beyer, Lars Tönges, Brit Mollenhauer, Klaus Gerwert
Misfolding and aggregation of alpha-synuclein (αSyn) plays a key role in the pathophysiology of Parkinson’s disease (PD). It induces cellular and axonal damage already in the early stages of the disease. Despite considerable advances in PD diagnostics by αSyn seed-amplification assays (SAAs), an early and differential diagnosis of PD still represents a major challenge. Here, we extended the immuno-infrared sensor (iRS) platform technology from Alzheimer’s disease (AD), in which β-amyloid misfolding was monitored as a fluid biomarker towards αSyn misfolding in PD. Using the iRS platform technology, we analyzed cerebrospinal fluid (CSF) from two independent cohorts, a discovery and a validation cohort comprising clinically diagnosed PD (n=57), atypical Parkinsonian disorders with αSyn pathology (multiple system atrophy (MSA), n= 5) or Tau pathology (corticobasal degeneration (CBD), n=5, progressive supranuclear palsy (PSP) n=9), and further disease controls (frontotemporal dementia (FTD) n=7 and other, n=51). In the discovery cohort, an AUC of 0.90, 95 %-CL 0.85 – 0.96 is obtained for the differentiation of PD/MSA vs. all controls, and in the validation cohort, an AUC of 0.86, 95 %-CL 0.80 - 0.93, respectively. In the combined dataset, the αSyn misfolding classifies PD/MSA from controls with an AUC of 0.90 (n=134, 95 %-CL 0.85 - 0.96). Using two threshold values instead of one identified people in the continuum between clearly unaffected (low misfolding group) and affected by PD/MSA (high misfolding group) with an intermediate area in between. The controls versus PD/MSA in the low vs. high misfolding group were classified with 97% sensitivity and 92% specificity.
{"title":"Alpha-synuclein misfolding as a fluid biomarker for Parkinson’s disease and synucleinopathies measured with the iRS platform","authors":"Martin Schuler, Grischa Gerwert, Marvin Mann, Nathalie Woitzik, Lennart Langenhoff, Diana Hubert, Deniz Duman, Adrian Höveler, Sandy Galkowski, Jonas Simon, Robin Denz, Sandrina Weber, Eun-Hae Kwon, Robin Wanka, Carsten Kötting, Jörn Güldenhaupt, Léon Beyer, Lars Tönges, Brit Mollenhauer, Klaus Gerwert","doi":"10.1101/2024.09.02.24312694","DOIUrl":"https://doi.org/10.1101/2024.09.02.24312694","url":null,"abstract":"Misfolding and aggregation of alpha-synuclein (αSyn) plays a key role in the pathophysiology of Parkinson’s disease (PD). It induces cellular and axonal damage already in the early stages of the disease. Despite considerable advances in PD diagnostics by αSyn seed-amplification assays (SAAs), an early and differential diagnosis of PD still represents a major challenge. Here, we extended the immuno-infrared sensor (iRS) platform technology from Alzheimer’s disease (AD), in which β-amyloid misfolding was monitored as a fluid biomarker towards αSyn misfolding in PD. Using the iRS platform technology, we analyzed cerebrospinal fluid (CSF) from two independent cohorts, a discovery and a validation cohort comprising clinically diagnosed PD (n=57), atypical Parkinsonian disorders with αSyn pathology (multiple system atrophy (MSA), n= 5) or Tau pathology (corticobasal degeneration (CBD), n=5, progressive supranuclear palsy (PSP) n=9), and further disease controls (frontotemporal dementia (FTD) n=7 and other, n=51). In the discovery cohort, an AUC of 0.90, 95 %-CL 0.85 – 0.96 is obtained for the differentiation of PD/MSA vs. all controls, and in the validation cohort, an AUC of 0.86, 95 %-CL 0.80 - 0.93, respectively. In the combined dataset, the αSyn misfolding classifies PD/MSA from controls with an AUC of 0.90 (n=134, 95 %-CL 0.85 - 0.96). Using two threshold values instead of one identified people in the continuum between clearly unaffected (low misfolding group) and affected by PD/MSA (high misfolding group) with an intermediate area in between. The controls versus PD/MSA in the low vs. high misfolding group were classified with 97% sensitivity and 92% specificity.","PeriodicalId":501367,"journal":{"name":"medRxiv - Neurology","volume":"202 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1101/2024.09.04.24313055
Yilan Dong, Dafnis Batalle, Maria Deprez
Autism is a neurodevelopmental condition affecting ∼1% of the population. Recently, machine learning models have been trained to classify participants with autism using their neuroimaging features, though the performance of these models varies in the literature. Differences in experimental setup hamper the direct comparison of different machine-learning approaches. In this paper, five of the most widely used and best-performing machine learning models in the field were trained to classify participants with autism and typically developing (TD) participants, using functional connectivity matrices, structural volumetric measures and phenotypic information from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Their performance was compared under the same evaluation standard. The models implemented included: graph convolutional networks (GCN), edge-variational graph convolutional networks (EV-GCN), fully connected networks (FCN), auto-encoder followed by a fully connected network (AE-FCN) and support vector machine (SVM). Our results show that all models performed similarly, achieving a classification accuracy around 70%. Our results suggest that different inclusion criteria, data modalities and evaluation pipelines rather than different machine learning models may explain variations in accuracy in published literature. The highest accuracy in our framework was obtained by an ensemble of GCN models trained on combination of functional MRI and structural MRI features, reaching classification accuracy of 72.2% and AUC = 0.78 on the test set. The combined structural and functional modalities exhibited higher predictive ability compared to using single modality features alone. Ensemble methods were found to be helpful to improve the performance of the models. Furthermore, we also investigated the stability of features identified by the different machine learning models using the SmoothGrad interpretation method. The FCN model demonstrated the highest stability selecting relevant features contributing to model decision making. Code available at: https://github.com/YilanDong19/Machine-learning-with-ABIDE.
{"title":"Reproducible comparison and interpretation of machine learning classifiers to predict autism on the ABIDE multimodal dataset","authors":"Yilan Dong, Dafnis Batalle, Maria Deprez","doi":"10.1101/2024.09.04.24313055","DOIUrl":"https://doi.org/10.1101/2024.09.04.24313055","url":null,"abstract":"Autism is a neurodevelopmental condition affecting ∼1% of the population. Recently, machine learning models have been trained to classify participants with autism using their neuroimaging features, though the performance of these models varies in the literature. Differences in experimental setup hamper the direct comparison of different machine-learning approaches. In this paper, five of the most widely used and best-performing machine learning models in the field were trained to classify participants with autism and typically developing (TD) participants, using functional connectivity matrices, structural volumetric measures and phenotypic information from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Their performance was compared under the same evaluation standard. The models implemented included: graph convolutional networks (GCN), edge-variational graph convolutional networks (EV-GCN), fully connected networks (FCN), auto-encoder followed by a fully connected network (AE-FCN) and support vector machine (SVM). Our results show that all models performed similarly, achieving a classification accuracy around 70%. Our results suggest that different inclusion criteria, data modalities and evaluation pipelines rather than different machine learning models may explain variations in accuracy in published literature. The highest accuracy in our framework was obtained by an ensemble of GCN models trained on combination of functional MRI and structural MRI features, reaching classification accuracy of 72.2% and AUC = 0.78 on the test set. The combined structural and functional modalities exhibited higher predictive ability compared to using single modality features alone. Ensemble methods were found to be helpful to improve the performance of the models. Furthermore, we also investigated the stability of features identified by the different machine learning models using the SmoothGrad interpretation method. The FCN model demonstrated the highest stability selecting relevant features contributing to model decision making. Code available at: https://github.com/YilanDong19/Machine-learning-with-ABIDE.","PeriodicalId":501367,"journal":{"name":"medRxiv - Neurology","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-03DOI: 10.1101/2024.09.01.24312913
Vaibhav Tripathi, Joshua Fox-Fuller, Vincent Malotaux, Ana Baena, Nikole Bonillas Felix, Sergio Alvarez, David Aguillon, Francisco Lopera, David C Somers, Yakeel T. Quiroz
INTRODUCTION Autosomal Dominant Alzheimer’s Disease (ADAD) through genetic mutations can result in near complete expression of the disease. Tracking AD pathology development in an ADAD cohort of Presenilin-1 (PSEN1) E280A carriers’ mutation has allowed us to observe incipient tau tangles accumulation as early as 6 years prior to symptom onset.
{"title":"Connectome-based predictive modeling of brain pathology and cognition in Autosomal Dominant Alzheimer’s Disease","authors":"Vaibhav Tripathi, Joshua Fox-Fuller, Vincent Malotaux, Ana Baena, Nikole Bonillas Felix, Sergio Alvarez, David Aguillon, Francisco Lopera, David C Somers, Yakeel T. Quiroz","doi":"10.1101/2024.09.01.24312913","DOIUrl":"https://doi.org/10.1101/2024.09.01.24312913","url":null,"abstract":"<strong>INTRODUCTION</strong> Autosomal Dominant Alzheimer’s Disease (ADAD) through genetic mutations can result in near complete expression of the disease. Tracking AD pathology development in an ADAD cohort of Presenilin-1 (<em>PSEN1)</em> E280A carriers’ mutation has allowed us to observe incipient tau tangles accumulation as early as 6 years prior to symptom onset.","PeriodicalId":501367,"journal":{"name":"medRxiv - Neurology","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-03DOI: 10.1101/2024.09.01.24311959
Andrew J. Kwok, Babak Soleimani, Bo Sun, Andrew Fower, Mateusz Makuch, Thomas Johnson, Julian C. Knight, Ho Ko, Belinda Lennox, Sarosh Irani, Lahiru Handunnetthi
We performed single-cell RNA and immune receptor repertoire sequencing of an N-methyl-D-aspartate receptor encephalitis (NMDARE) patient in relapse and remission states, as well as an autoimmune psychosis (AP) patient with anti-NMDAR antibodies. We leveraged publicly available cerebrospinal fluid (CSF) single-cell sequencing data from other neurological disorders to contextualise our findings. Results highlight a key role for T-cells in NMDARE pathogenesis with clonal expansion of both cytotoxic CD4+ and CD8+ effector memory cells in CSF. We further identified interferon responsive B-cells in the CSF during the acute phase of NMDARE and a higher proportion of mononuclear phagocytes in the CSF of AP. Collectively, our work sheds light into the immunobiology of anti-NMDAR antibody-mediated disease.
我们对一名处于复发和缓解状态的N-甲基-D-天冬氨酸受体脑炎（NMDARE）患者以及一名患有抗NMDAR抗体的自身免疫性精神病（AP）患者进行了单细胞RNA和免疫受体组测序。我们利用公开的其他神经系统疾病的脑脊液（CSF）单细胞测序数据，为我们的研究结果提供了背景信息。结果凸显了T细胞在NMDARE发病机制中的关键作用，CSF中细胞毒性CD4+和CD8+效应记忆细胞均出现克隆性扩增。我们还在 NMDARE 急性期的 CSF 中发现了干扰素反应性 B 细胞，并在 AP 的 CSF 中发现了更高比例的单核吞噬细胞。总之，我们的工作揭示了抗 NMDAR 抗体介导疾病的免疫生物学。
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Pub Date : 2024-09-03DOI: 10.1101/2024.09.02.24312935
Meher Lad, Charlotte Deasy, John-Paul Taylor, Tim Griffiths
Background Speech-in-noise hearing is impaired in early symptomatic Alzheimer’s disease. However, most tests involve the use of verbal stimuli where performance measures may be confounded by linguistic and cultural factors. Non-verbal auditory measures may overcome these issues.
{"title":"Beyond Words: Cross-Sectional Analysis of Non-Verbal Auditory Measures Across Cognitive Health, Mild Cognitive Impairment and Alzheimer’s Disease Dementia","authors":"Meher Lad, Charlotte Deasy, John-Paul Taylor, Tim Griffiths","doi":"10.1101/2024.09.02.24312935","DOIUrl":"https://doi.org/10.1101/2024.09.02.24312935","url":null,"abstract":"<strong>Background</strong> Speech-in-noise hearing is impaired in early symptomatic Alzheimer’s disease. However, most tests involve the use of verbal stimuli where performance measures may be confounded by linguistic and cultural factors. Non-verbal auditory measures may overcome these issues.","PeriodicalId":501367,"journal":{"name":"medRxiv - Neurology","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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