{"title":"Distinct roles of mTORC2 in excitatory and inhibitory neurons in inflammatory and neuropathic pain.","authors":"Wei He,Xin Ge,Ru-Rong Ji","doi":"10.1093/brain/awaf004","DOIUrl":"https://doi.org/10.1093/brain/awaf004","url":null,"abstract":"","PeriodicalId":9063,"journal":{"name":"Brain","volume":"28 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991834","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}
Marie-Sophie von Braun, Kristin Starke, Lucas Peter, Daniel Kürsten, Florian Welle, Hans Ralf Schneider, Max Wawrzyniak, Daniel P O Kaiser, Gordian Prasse, Cindy Richter, Elias Kellner, Marco Reisert, Julian Klingbeil, Anika Stockert, Karl-Titus Hoffmann, Gerik Scheuermann, Christina Gillmann, Dorothee Saur
The advent of endovascular thrombectomy has significantly improved outcomes for stroke patients with intracranial large vessel occlusion, yet individual benefits can vary widely. As demand for thrombectomy rises and geographic disparities in stroke care access persist, there is a growing need for predictive models that quantify individual benefits. However, current imaging methods for estimating outcomes may not fully capture the dynamic nature of cerebral ischemia and lack a patient-specific assessment of thrombectomy benefits. Our study introduces a deep learning approach to predict individual responses to thrombectomy in acute ischemic stroke patients. The proposed models provide predictions for both tissue and clinical outcomes under two scenarios: one assuming successful reperfusion and another assuming unsuccessful reperfusion. The resulting simulations of penumbral salvage and difference in NIHSS at discharge quantify the potential individual benefits of the intervention. Our models were developed on an extensive dataset from routine stroke care, which included 405 ischemic stroke patients who underwent thrombectomy. We used acute data for training (n = 304), including multimodal CT imaging and clinical characteristics, along with post hoc markers like thrombectomy success, final infarct localization, and NIHSS at discharge. We benchmarked our tissue outcome predictions under the observed reperfusion scenario against a thresholding-based clinical method and a generalised linear model. Our deep-learning model showed significant superiority, with a mean Dice score of 0.48 on internal (n = 50) and 0.52 on external (n = 51) test data, versus 0.26/0.36 and 0.34/0.35 for the baselines, respectively. The NIHSS sum score prediction achieved median absolute errors of 1.5 NIHSS points on the internal test dataset and 3.0 NIHSS points on the external test dataset, outperforming other machine learning models. By predicting the patient-specific response to thrombectomy for both tissue and clinical outcomes, our approach offers an innovative biomarker that captures the dynamics of cerebral ischemia. We believe this method holds significant potential to enhance personalised therapeutic strategies and to facilitate efficient resource allocation in acute stroke care.
{"title":"Prediction of tissue and clinical thrombectomy outcome in acute ischaemic stroke using deep learning","authors":"Marie-Sophie von Braun, Kristin Starke, Lucas Peter, Daniel Kürsten, Florian Welle, Hans Ralf Schneider, Max Wawrzyniak, Daniel P O Kaiser, Gordian Prasse, Cindy Richter, Elias Kellner, Marco Reisert, Julian Klingbeil, Anika Stockert, Karl-Titus Hoffmann, Gerik Scheuermann, Christina Gillmann, Dorothee Saur","doi":"10.1093/brain/awaf013","DOIUrl":"https://doi.org/10.1093/brain/awaf013","url":null,"abstract":"The advent of endovascular thrombectomy has significantly improved outcomes for stroke patients with intracranial large vessel occlusion, yet individual benefits can vary widely. As demand for thrombectomy rises and geographic disparities in stroke care access persist, there is a growing need for predictive models that quantify individual benefits. However, current imaging methods for estimating outcomes may not fully capture the dynamic nature of cerebral ischemia and lack a patient-specific assessment of thrombectomy benefits. Our study introduces a deep learning approach to predict individual responses to thrombectomy in acute ischemic stroke patients. The proposed models provide predictions for both tissue and clinical outcomes under two scenarios: one assuming successful reperfusion and another assuming unsuccessful reperfusion. The resulting simulations of penumbral salvage and difference in NIHSS at discharge quantify the potential individual benefits of the intervention. Our models were developed on an extensive dataset from routine stroke care, which included 405 ischemic stroke patients who underwent thrombectomy. We used acute data for training (n = 304), including multimodal CT imaging and clinical characteristics, along with post hoc markers like thrombectomy success, final infarct localization, and NIHSS at discharge. We benchmarked our tissue outcome predictions under the observed reperfusion scenario against a thresholding-based clinical method and a generalised linear model. Our deep-learning model showed significant superiority, with a mean Dice score of 0.48 on internal (n = 50) and 0.52 on external (n = 51) test data, versus 0.26/0.36 and 0.34/0.35 for the baselines, respectively. The NIHSS sum score prediction achieved median absolute errors of 1.5 NIHSS points on the internal test dataset and 3.0 NIHSS points on the external test dataset, outperforming other machine learning models. By predicting the patient-specific response to thrombectomy for both tissue and clinical outcomes, our approach offers an innovative biomarker that captures the dynamics of cerebral ischemia. We believe this method holds significant potential to enhance personalised therapeutic strategies and to facilitate efficient resource allocation in acute stroke care.","PeriodicalId":9063,"journal":{"name":"Brain","volume":"52 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989763","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}
Shih-Pin Chen, Ya-Hsuan Chang, Yen-Feng Wang, Hsuan-Yu Chen, Shuu-Jiun Wang
The neurobiological mechanisms driving the ictal-interictal fluctuations and the chronification of migraine remain elusive. We aimed to construct a composite genetic-microRNA model that could reflect the dynamic perturbations of the disease course and inform the pathogenesis of migraine. We prospectively recruited four groups of participants, including interictal episodic migraine (i.e., headache-free for > 72 hrs apart from prior and subsequent attacks), ictal episodic migraine (i.e., during moderate to severe migraine attacks), chronic migraine, and controls in the discovery cohort. Next-generation sequencing (NGS) was used for microRNA profiling. The candidate microRNAs were validated with quantitative PCR (qPCR) in an independent validation cohort. Biological pathways associated with the microRNA regulome and interaction networks were explored. In addition, all participants received genotyping with the Axiom Genome-Wide Array TWB chip. A composite model was established, combining disease-associated microRNAs and genetic risk scores (GRS) indicative of genetic susceptibility, with the objective of differentiating migraine from controls using a binary outcome. From a total of 120 participants in the discovery cohort and 197 participants in the validation cohort, we identified disease-state microRNA signatures (including miR-183, miR-25, and miR-320) that were ubiquitously higher or lower in patients with migraine compared to controls. We have also validated four disease-activity miRNA signatures (miR-1307-5p, miR-6810-5p, let-7e, and miR-140-3p) that were differentially expressed only during the ictal stage of episodic migraine. Functional analysis suggested that prolactin and estrogen signaling pathways might play important roles in the pathogenesis. Moreover, the composite microRNA-GRS model differentiated patients from controls, achieving a positive predictive value of over 90%. To conclude, we developed a composite microRNA-genetic risk score model, which may serve as a predictive tool for identifying high-risk individuals. Our findings may help illuminate potential pathogenic mechanisms underlying the dysfunctional allostasis of migraine and pave the way for future precision medicine.
{"title":"Composite microRNA-genetic risk score model links to migraine and implicates its pathogenesis","authors":"Shih-Pin Chen, Ya-Hsuan Chang, Yen-Feng Wang, Hsuan-Yu Chen, Shuu-Jiun Wang","doi":"10.1093/brain/awaf005","DOIUrl":"https://doi.org/10.1093/brain/awaf005","url":null,"abstract":"The neurobiological mechanisms driving the ictal-interictal fluctuations and the chronification of migraine remain elusive. We aimed to construct a composite genetic-microRNA model that could reflect the dynamic perturbations of the disease course and inform the pathogenesis of migraine. We prospectively recruited four groups of participants, including interictal episodic migraine (i.e., headache-free for > 72 hrs apart from prior and subsequent attacks), ictal episodic migraine (i.e., during moderate to severe migraine attacks), chronic migraine, and controls in the discovery cohort. Next-generation sequencing (NGS) was used for microRNA profiling. The candidate microRNAs were validated with quantitative PCR (qPCR) in an independent validation cohort. Biological pathways associated with the microRNA regulome and interaction networks were explored. In addition, all participants received genotyping with the Axiom Genome-Wide Array TWB chip. A composite model was established, combining disease-associated microRNAs and genetic risk scores (GRS) indicative of genetic susceptibility, with the objective of differentiating migraine from controls using a binary outcome. From a total of 120 participants in the discovery cohort and 197 participants in the validation cohort, we identified disease-state microRNA signatures (including miR-183, miR-25, and miR-320) that were ubiquitously higher or lower in patients with migraine compared to controls. We have also validated four disease-activity miRNA signatures (miR-1307-5p, miR-6810-5p, let-7e, and miR-140-3p) that were differentially expressed only during the ictal stage of episodic migraine. Functional analysis suggested that prolactin and estrogen signaling pathways might play important roles in the pathogenesis. Moreover, the composite microRNA-GRS model differentiated patients from controls, achieving a positive predictive value of over 90%. To conclude, we developed a composite microRNA-genetic risk score model, which may serve as a predictive tool for identifying high-risk individuals. Our findings may help illuminate potential pathogenic mechanisms underlying the dysfunctional allostasis of migraine and pave the way for future precision medicine.","PeriodicalId":9063,"journal":{"name":"Brain","volume":"30 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142988843","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}
Zeynep Kaya, Nevin Belder, Melike Sever-Bahçekapılı, Şefik Evren Erdener, Buket Dönmez-Demir, Canan Bağcı, Merve Nur Köroğlu, Kaya Bilguvar, Turgay Dalkara
Cortical spreading depolarization (CSD), the neurophysiological event believed to underlie aura, may trigger migraine headaches through inflammatory signaling that originates in neurons and spreads to the meninges via astrocytes. Increasing evidence from studies on rodents and migraine patients supports this hypothesis. The transition from pro-inflammatory to anti-inflammatory mechanisms is crucial for resolving inflammation. However, the resolution of inflammation in the context of CSD and migraine headaches remains poorly understood. This study aims to elucidate the progression of post-CSD inflammatory signaling and its resolution in neurons, astrocytes, and microglia in mouse brains. CSD was triggered optogenetically or by pinprick. HMGB1 release, caspase-1 activation, and cell-specific activation of NF-κB pairs, along with ensuing transcriptomic changes, were evaluated using immunofluorescence, Western blotting, co-immunoprecipitation, FRET analysis, and cell-specific transcriptomics. Our findings indicate that after the initial burst, HMGB1 release from neurons ceased, and caspase-1 activation, which peaked 1-hour post-CSD, diminished within 3-5 hours. This suggests that pro-inflammatory stimuli driving inflammatory signaling decreased within hours after CSD. Pro-inflammatory NF-κB p65:p50 pairs, along with anti-inflammatory cRel:p65 pairs, were detected in astrocyte nuclei shortly after CSD. However, 24 hours post-CSD, the former had disappeared while the latter persisted, indicating a shift from pro-inflammatory to anti-inflammatory activity in astrocytes. Pathway analysis of cell-specific transcriptomic data confirmed NF-κB-related pro-inflammatory transcription in astrocytes 1-hour post-CSD, while no such activity was observed in neurons. Detailed transcriptomic analysis with Bayesian cell proportion reconstruction revealed that microglia exhibited transcriptional changes trending towards an anti-inflammatory profile, along with upregulation of several chemokines and cytokines (e.g., TNF). This suggests that microglia may play a role in supporting the inflammatory responses in astrocytes through the release of these mediators. The upregulation of genes involved in chemotaxis (e.g., Ccl3) and spine pruning (e.g., C1q) in microglia implies that microglia may contribute to synaptic repair, while inflammatory signaling in astrocytes could potentially modulate meningeal nociceptor activity through an extensive astrocyte endfeet syncytium abutting subarachnoid and perivascular spaces although direct evidence remains incomplete. This nuanced understanding of the inflammatory response in CNS cell types highlights the intricate cellular interactions and responses to CSD. Following a single CSD, distinct transcriptomic responses occur in neurons, astrocytes, and microglia, driving inflammatory and anti-inflammatory responses, potentially contributing to headache initiation and resolution.
{"title":"Spreading depolarization triggers pro- and anti-inflammatory signalling: a potential link to headache","authors":"Zeynep Kaya, Nevin Belder, Melike Sever-Bahçekapılı, Şefik Evren Erdener, Buket Dönmez-Demir, Canan Bağcı, Merve Nur Köroğlu, Kaya Bilguvar, Turgay Dalkara","doi":"10.1093/brain/awaf015","DOIUrl":"https://doi.org/10.1093/brain/awaf015","url":null,"abstract":"Cortical spreading depolarization (CSD), the neurophysiological event believed to underlie aura, may trigger migraine headaches through inflammatory signaling that originates in neurons and spreads to the meninges via astrocytes. Increasing evidence from studies on rodents and migraine patients supports this hypothesis. The transition from pro-inflammatory to anti-inflammatory mechanisms is crucial for resolving inflammation. However, the resolution of inflammation in the context of CSD and migraine headaches remains poorly understood. This study aims to elucidate the progression of post-CSD inflammatory signaling and its resolution in neurons, astrocytes, and microglia in mouse brains. CSD was triggered optogenetically or by pinprick. HMGB1 release, caspase-1 activation, and cell-specific activation of NF-κB pairs, along with ensuing transcriptomic changes, were evaluated using immunofluorescence, Western blotting, co-immunoprecipitation, FRET analysis, and cell-specific transcriptomics. Our findings indicate that after the initial burst, HMGB1 release from neurons ceased, and caspase-1 activation, which peaked 1-hour post-CSD, diminished within 3-5 hours. This suggests that pro-inflammatory stimuli driving inflammatory signaling decreased within hours after CSD. Pro-inflammatory NF-κB p65:p50 pairs, along with anti-inflammatory cRel:p65 pairs, were detected in astrocyte nuclei shortly after CSD. However, 24 hours post-CSD, the former had disappeared while the latter persisted, indicating a shift from pro-inflammatory to anti-inflammatory activity in astrocytes. Pathway analysis of cell-specific transcriptomic data confirmed NF-κB-related pro-inflammatory transcription in astrocytes 1-hour post-CSD, while no such activity was observed in neurons. Detailed transcriptomic analysis with Bayesian cell proportion reconstruction revealed that microglia exhibited transcriptional changes trending towards an anti-inflammatory profile, along with upregulation of several chemokines and cytokines (e.g., TNF). This suggests that microglia may play a role in supporting the inflammatory responses in astrocytes through the release of these mediators. The upregulation of genes involved in chemotaxis (e.g., Ccl3) and spine pruning (e.g., C1q) in microglia implies that microglia may contribute to synaptic repair, while inflammatory signaling in astrocytes could potentially modulate meningeal nociceptor activity through an extensive astrocyte endfeet syncytium abutting subarachnoid and perivascular spaces although direct evidence remains incomplete. This nuanced understanding of the inflammatory response in CNS cell types highlights the intricate cellular interactions and responses to CSD. Following a single CSD, distinct transcriptomic responses occur in neurons, astrocytes, and microglia, driving inflammatory and anti-inflammatory responses, potentially contributing to headache initiation and resolution.","PeriodicalId":9063,"journal":{"name":"Brain","volume":"119 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142988847","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}
Ifrah Zawar, Aleksander Luniewski, Rithvik Gundlapalli, Carol Manning, Prachi Parikh, Jaideep Kapur, Mark Quigg
Seizures in people with dementia (PWD) are associated with faster cognitive decline and worse clinical outcomes. However, the relationship between ongoing seizure activity and postmortem neuropathology in PWD remains unexplored. We compared post-mortem findings in PWD with active, remote, and no seizures using multicentre data from 39 Alzheimer’s Disease Centres from 2005 to 2021. PWD were grouped by seizure status into active (seizures over the preceding one year), remote (prior seizures but none in the preceding year), and no seizures (controls). Baseline demographics, cognition, mortality, and postmortem findings of primary and contributing(co-pathologies) Alzheimer’s Disease(AD), Frontotemporal lobar degeneration(FTD), Lewy body, vascular pathologies and neurodegeneration were compared among the groups using Pearson’s Chi-squared test, fisher’s exact test, t-test, and ANOVA tests. Of 10,474 deceased PWD, active seizure participants suffered the highest mortality among the groups(proportion deceased among the groups: active=56%remote=35%, controls=34%, p<0.001). Among 6085 (58.1% of deceased) who underwent autopsy, 294 had active, 151 had remote, and 5640 had no seizures. PWD and active seizures died at a younger age (Active=75.8, remote=77.9, controls: 80.8 years, p <0.001) and had more severe dementia (CDR-Global: active=2.36, remote=1.90, controls=1.69, p<0.001). In post hoc analyses, those with primary postmortem diagnosis of AD with active seizures had more severe and later stages of AD pathology and ATN (amyloid, tau, and neurodegeneration) as evidenced by Braak stage for neurofibrillary(tau) degeneration and CERAD score density of neuritic(amyloid) plaques than remote seizure participants and controls. Active seizure participants had more neurodegeneration, evidenced by cerebral atrophy, hippocampal atrophy, and locus coeruleus hypopigmentation than controls. Among participants with primary postmortem diagnosis of non-AD, in posthoc analyses, active seizure participants had worse AD co-pathology evidenced by higher Braak stages than remote seizures and controls and a higher thal phase of beta-amyloid plaques than controls. Neurodegeneration (cerebral/hippocampal atrophy) and LC hypopigmentation were comparable among the groups. In both primary postmortem AD and non-AD diagnoses, FTD (co)pathology was less prevalent among active seizure participants than controls, while vascular pathology, Circle of Willis atherosclerosis, Lewy body pathology, lobar atrophy, and substantia nigra hypopigmentation were comparable among the three groups. This study shows that active, compared to remote seizures, are associated with earlier death and postmortem evidence of more severe ATN pathology. Active seizures are associated with more advanced AD pathology in AD and worse AD co-pathology in non-AD dementias. Therefore, clinicians should be vigilant in detecting ongoing seizures as this could reflect a worse prognosis in PWD.
{"title":"The association of seizure control with neuropathology in dementia","authors":"Ifrah Zawar, Aleksander Luniewski, Rithvik Gundlapalli, Carol Manning, Prachi Parikh, Jaideep Kapur, Mark Quigg","doi":"10.1093/brain/awaf017","DOIUrl":"https://doi.org/10.1093/brain/awaf017","url":null,"abstract":"Seizures in people with dementia (PWD) are associated with faster cognitive decline and worse clinical outcomes. However, the relationship between ongoing seizure activity and postmortem neuropathology in PWD remains unexplored. We compared post-mortem findings in PWD with active, remote, and no seizures using multicentre data from 39 Alzheimer’s Disease Centres from 2005 to 2021. PWD were grouped by seizure status into active (seizures over the preceding one year), remote (prior seizures but none in the preceding year), and no seizures (controls). Baseline demographics, cognition, mortality, and postmortem findings of primary and contributing(co-pathologies) Alzheimer’s Disease(AD), Frontotemporal lobar degeneration(FTD), Lewy body, vascular pathologies and neurodegeneration were compared among the groups using Pearson’s Chi-squared test, fisher’s exact test, t-test, and ANOVA tests. Of 10,474 deceased PWD, active seizure participants suffered the highest mortality among the groups(proportion deceased among the groups: active=56%remote=35%, controls=34%, p&lt;0.001). Among 6085 (58.1% of deceased) who underwent autopsy, 294 had active, 151 had remote, and 5640 had no seizures. PWD and active seizures died at a younger age (Active=75.8, remote=77.9, controls: 80.8 years, p &lt;0.001) and had more severe dementia (CDR-Global: active=2.36, remote=1.90, controls=1.69, p&lt;0.001). In post hoc analyses, those with primary postmortem diagnosis of AD with active seizures had more severe and later stages of AD pathology and ATN (amyloid, tau, and neurodegeneration) as evidenced by Braak stage for neurofibrillary(tau) degeneration and CERAD score density of neuritic(amyloid) plaques than remote seizure participants and controls. Active seizure participants had more neurodegeneration, evidenced by cerebral atrophy, hippocampal atrophy, and locus coeruleus hypopigmentation than controls. Among participants with primary postmortem diagnosis of non-AD, in posthoc analyses, active seizure participants had worse AD co-pathology evidenced by higher Braak stages than remote seizures and controls and a higher thal phase of beta-amyloid plaques than controls. Neurodegeneration (cerebral/hippocampal atrophy) and LC hypopigmentation were comparable among the groups. In both primary postmortem AD and non-AD diagnoses, FTD (co)pathology was less prevalent among active seizure participants than controls, while vascular pathology, Circle of Willis atherosclerosis, Lewy body pathology, lobar atrophy, and substantia nigra hypopigmentation were comparable among the three groups. This study shows that active, compared to remote seizures, are associated with earlier death and postmortem evidence of more severe ATN pathology. Active seizures are associated with more advanced AD pathology in AD and worse AD co-pathology in non-AD dementias. Therefore, clinicians should be vigilant in detecting ongoing seizures as this could reflect a worse prognosis in PWD.","PeriodicalId":9063,"journal":{"name":"Brain","volume":"131 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142987672","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}
Claudia Cicognola, Gemma Salvadó, Ruben Smith, Sebastian Palmqvist, Erik Stomrud, Tobey Betthauser, Sterling Johnson, Shorena Janelidze, Niklas Mattsson-Carlgren, Oskar Hansson, Alexa Pichet Binette
The APOE4 allele is the strongest genetic risk factor for sporadic Alzheimer’s disease (AD). While APOE4 is strongly associated with amyloid-beta (Aβ), its relationship with tau accumulation is less understood. Studies evaluating the role of APOE4 on tau accumulation showed conflicting results, particularly regarding the independence of these associations from Aβ load. In this study, we examined three independent longitudinal cohorts (BioFINDER-1, BioFINDER-2 and WRAP) in which participants had cross-sectional and longitudinal measures of tau tangles (tau-PET; temporal meta-ROI and entorhinal) or soluble p-tau (p-tau217), Aβ-PET and APOE genotype. The study included a total of 1370 cognitively unimpaired (CU) and 449 mild cognitive impairment (MCI) subjects, followed longitudinally with tau-PET and p-tau217. APOE4 carriers accounted for 40.2-50% of the cohorts. Different linear regressions (cross-sectional) and linear mixed-effect models (longitudinal) with tau measures as outcomes were fitted to test the effect of APOE4 as independent predictor, as well as in combination with baseline Aβ load (including interaction). All models included age, sex and cognitive status as covariates. We found no independent effects of the APOE4 carriership on insoluble tau in either cohort (BioFINDER-2 or WRAP), both on cross-sectional and longitudinal tau-PET in the temporal meta-ROI, when Aβ was present in the model (p=0.531-0.949). Aβ alone was the best predictor of insoluble tau accumulation, with no interaction between APOE4 and Aβ on tau-PET. In BioFINDER-2, there was a significant interaction between APOE4 and Aβ (b=0.166, p<0.001) in the entorhinal cortex at baseline. However, the interaction was not present in WRAP PET. No independent effects of the APOE4 carriership on baseline (p=0.683-0.708) and longitudinal (p=0.188-0.570) soluble p-tau217 were observed when Aβ was included in the model in BioFINDER-1 and WRAP. Similarly, no interaction between APOE4 and Aβ on soluble p-tau217 was observed. Mediation analysis revealed that Aβ load fully mediated most associations between APOE4 and tau (46-112%, either cross-sectional or longitudinal tau-PET or soluble p-tau217). In the largest cohort (BioFINDER-2), looking at APOE4 groups by number of ε4 alleles, we found an interaction between APOE4 homozygotes and Aβ on tau-PET levels at baseline and over time in the temporal meta-ROI, while in the entorhinal cortex this effect was observed only at baseline. In conclusion, although APOE4 is strongly associated with Aβ aggregation, it seems to be minimally associated with longitudinal changes in soluble or insoluble p-tau levels at a given level of Aβ pathology, confirming the primacy of Aβ in driving tau pathology.
{"title":"APOE4 impact on soluble and insoluble tau pathology is mostly influenced by amyloid-beta","authors":"Claudia Cicognola, Gemma Salvadó, Ruben Smith, Sebastian Palmqvist, Erik Stomrud, Tobey Betthauser, Sterling Johnson, Shorena Janelidze, Niklas Mattsson-Carlgren, Oskar Hansson, Alexa Pichet Binette","doi":"10.1093/brain/awaf016","DOIUrl":"https://doi.org/10.1093/brain/awaf016","url":null,"abstract":"The APOE4 allele is the strongest genetic risk factor for sporadic Alzheimer’s disease (AD). While APOE4 is strongly associated with amyloid-beta (Aβ), its relationship with tau accumulation is less understood. Studies evaluating the role of APOE4 on tau accumulation showed conflicting results, particularly regarding the independence of these associations from Aβ load. In this study, we examined three independent longitudinal cohorts (BioFINDER-1, BioFINDER-2 and WRAP) in which participants had cross-sectional and longitudinal measures of tau tangles (tau-PET; temporal meta-ROI and entorhinal) or soluble p-tau (p-tau217), Aβ-PET and APOE genotype. The study included a total of 1370 cognitively unimpaired (CU) and 449 mild cognitive impairment (MCI) subjects, followed longitudinally with tau-PET and p-tau217. APOE4 carriers accounted for 40.2-50% of the cohorts. Different linear regressions (cross-sectional) and linear mixed-effect models (longitudinal) with tau measures as outcomes were fitted to test the effect of APOE4 as independent predictor, as well as in combination with baseline Aβ load (including interaction). All models included age, sex and cognitive status as covariates. We found no independent effects of the APOE4 carriership on insoluble tau in either cohort (BioFINDER-2 or WRAP), both on cross-sectional and longitudinal tau-PET in the temporal meta-ROI, when Aβ was present in the model (p=0.531-0.949). Aβ alone was the best predictor of insoluble tau accumulation, with no interaction between APOE4 and Aβ on tau-PET. In BioFINDER-2, there was a significant interaction between APOE4 and Aβ (b=0.166, p&lt;0.001) in the entorhinal cortex at baseline. However, the interaction was not present in WRAP PET. No independent effects of the APOE4 carriership on baseline (p=0.683-0.708) and longitudinal (p=0.188-0.570) soluble p-tau217 were observed when Aβ was included in the model in BioFINDER-1 and WRAP. Similarly, no interaction between APOE4 and Aβ on soluble p-tau217 was observed. Mediation analysis revealed that Aβ load fully mediated most associations between APOE4 and tau (46-112%, either cross-sectional or longitudinal tau-PET or soluble p-tau217). In the largest cohort (BioFINDER-2), looking at APOE4 groups by number of ε4 alleles, we found an interaction between APOE4 homozygotes and Aβ on tau-PET levels at baseline and over time in the temporal meta-ROI, while in the entorhinal cortex this effect was observed only at baseline. In conclusion, although APOE4 is strongly associated with Aβ aggregation, it seems to be minimally associated with longitudinal changes in soluble or insoluble p-tau levels at a given level of Aβ pathology, confirming the primacy of Aβ in driving tau pathology.","PeriodicalId":9063,"journal":{"name":"Brain","volume":"23 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986803","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}
Veith Weilnhammer, Marcus Rothkirch, Deniz Yilmaz, Merve Fritsch, Lena Esther Ptasczynski, Katrin Reichenbach, Lukas Rödiger, Philip Corlett, Philipp Sterzer
Perception integrates external sensory signals with internal predictions that reflect prior knowledge about the world. Previous research suggests that this integration is governed by slow alternations between an external mode, driven by sensory signals, and an internal mode, shaped by prior knowledge. Using a double-blind, placebo-controlled, cross-over experiment in healthy human participants, we investigated the effects of the N-Methyl-D-aspartate receptor (NMDAR) antagonist S-ketamine on the balance between external and internal modes. We found that S-ketamine causes a shift of perception toward the external mode. A case-control study revealed that individuals with paranoid Scz, a disorder repeatedly associated with NMDAR hypofunction, spend more time in the external mode. This NMDAR-dependent increase in the external mode suggests that the symptoms of schizophrenia are caused by recurring dissociations of perception from prior knowledge about the world.
感知将外部感官信号与反映对世界先验知识的内部预测结合起来。先前的研究表明，这种整合是由由感官信号驱动的外部模式和由先验知识形成的内部模式之间的缓慢交替控制的。采用双盲、安慰剂对照、交叉实验，研究了n -甲基- d -天冬氨酸受体（NMDAR）拮抗剂s-氯胺酮对内外部模式平衡的影响。我们发现s -氯胺酮导致感知转向外部模式。一项病例对照研究显示，患有偏执型Scz（一种反复与NMDAR功能减退相关的疾病）的个体在外部模式下花费的时间更多。这种依赖于nmdar的外部模式的增加表明，精神分裂症的症状是由对世界的先验知识的感知反复分离引起的。
{"title":"N-methyl-d-aspartate receptor hypofunction causes recurrent and transient failures of perceptual inference","authors":"Veith Weilnhammer, Marcus Rothkirch, Deniz Yilmaz, Merve Fritsch, Lena Esther Ptasczynski, Katrin Reichenbach, Lukas Rödiger, Philip Corlett, Philipp Sterzer","doi":"10.1093/brain/awaf011","DOIUrl":"https://doi.org/10.1093/brain/awaf011","url":null,"abstract":"Perception integrates external sensory signals with internal predictions that reflect prior knowledge about the world. Previous research suggests that this integration is governed by slow alternations between an external mode, driven by sensory signals, and an internal mode, shaped by prior knowledge. Using a double-blind, placebo-controlled, cross-over experiment in healthy human participants, we investigated the effects of the N-Methyl-D-aspartate receptor (NMDAR) antagonist S-ketamine on the balance between external and internal modes. We found that S-ketamine causes a shift of perception toward the external mode. A case-control study revealed that individuals with paranoid Scz, a disorder repeatedly associated with NMDAR hypofunction, spend more time in the external mode. This NMDAR-dependent increase in the external mode suggests that the symptoms of schizophrenia are caused by recurring dissociations of perception from prior knowledge about the world.","PeriodicalId":9063,"journal":{"name":"Brain","volume":"54 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142987674","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":"Intrinsic neuronal resilience as a tool for therapeutic discovery.","authors":"Stefania Corti,Eva Hedlund","doi":"10.1093/brain/awaf010","DOIUrl":"https://doi.org/10.1093/brain/awaf010","url":null,"abstract":"","PeriodicalId":9063,"journal":{"name":"Brain","volume":"142 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142988901","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}
Léa Chauveau, Brigitte Landeau, Sophie Dautricourt, Anne-Laure Turpin, Marion Delarue, Oriane Hébert, Vincent de La Sayette, Gaël Chételat, Robin de Flores
Curing Alzheimer’s disease remains hampered by an incomplete understanding of its pathophysiology and progression. Exploring dysfunction in medial temporal lobe networks, particularly the anterior-temporal (AT) and posterior-medial (PM) systems, may provide key insights, as these networks exhibit functional connectivity alterations along the entire Alzheimer’s continuum, potentially influencing disease propagation. However, the specific changes in each network and their clinical relevance across stages are not yet fully understood. This requires considering commonly used biomarkers, clinical progression, individual variability, and age confounds. Here, we leveraged monocentric longitudinal data from 261 participants spanning the adult lifespan and the Alzheimer’s continuum. The sample included cognitively unimpaired adults aged 19 to 85 years (n = 209; eight out of 64 older adults over 60 were Aβ-positive) and Aβ-positive patients fulfilling diagnostic criteria for mild cognitive impairment (MCI, n = 26; 18 progressed to Alzheimer-dementia within seven years) or Alzheimer’s type dementia (n = 26). Participants underwent structural and resting-state functional (f) MRI, florbetapir and FDG-PET, and global cognitive assessments, with up to three visits over a maximum period of 47 months. Network connectivity was assessed using seed-based analyses with the perirhinal and parahippocampal cortices as seeds, within data-driven masks reflecting the AT and PM networks. Generalized additive and linear mixed models were run to assess age-specific effects and Alzheimer’s-related alterations. In this context, we explored various markers of pathological and clinical severity, including cerebral amyloid uptake, glucose metabolism, hippocampal volume, global cognition, diagnostic staging, and time to dementia onset. Our findings revealed distinct patterns of connectivity linked to normal aging or Alzheimer’s disease. Advancing age throughout adulthood was associated with lower PM connectivity and more subtle changes in AT connectivity, while Alzheimer’s disease was characterised by AT hyperconnectivity without global changes in PM connectivity. Specifically, AT connectivity was higher in MCI and Alzheimer-dementia patients compared to older controls and was positively associated with amyloid burden, glucose hypometabolism, hippocampal atrophy, and global cognitive deficits in older adults, ranging from unimpaired to demented. Additionally, higher AT connectivity correlated with faster progression to Alzheimer-dementia in MCI patients. This comprehensive approach allowed to reveal that excessive connectivity within the AT network is intrinsically linked to the pathological and clinical progression of Alzheimer’s disease. These insights may guide future research to better understand cascading events leading to the disease and hold promise for developing prognostic tools and therapeutic interventions targeting these specific network alterations.
{"title":"Anterior-temporal network hyperconnectivity is key to Alzheimer's disease: from ageing to dementia","authors":"Léa Chauveau, Brigitte Landeau, Sophie Dautricourt, Anne-Laure Turpin, Marion Delarue, Oriane Hébert, Vincent de La Sayette, Gaël Chételat, Robin de Flores","doi":"10.1093/brain/awaf008","DOIUrl":"https://doi.org/10.1093/brain/awaf008","url":null,"abstract":"Curing Alzheimer’s disease remains hampered by an incomplete understanding of its pathophysiology and progression. Exploring dysfunction in medial temporal lobe networks, particularly the anterior-temporal (AT) and posterior-medial (PM) systems, may provide key insights, as these networks exhibit functional connectivity alterations along the entire Alzheimer’s continuum, potentially influencing disease propagation. However, the specific changes in each network and their clinical relevance across stages are not yet fully understood. This requires considering commonly used biomarkers, clinical progression, individual variability, and age confounds. Here, we leveraged monocentric longitudinal data from 261 participants spanning the adult lifespan and the Alzheimer’s continuum. The sample included cognitively unimpaired adults aged 19 to 85 years (n = 209; eight out of 64 older adults over 60 were Aβ-positive) and Aβ-positive patients fulfilling diagnostic criteria for mild cognitive impairment (MCI, n = 26; 18 progressed to Alzheimer-dementia within seven years) or Alzheimer’s type dementia (n = 26). Participants underwent structural and resting-state functional (f) MRI, florbetapir and FDG-PET, and global cognitive assessments, with up to three visits over a maximum period of 47 months. Network connectivity was assessed using seed-based analyses with the perirhinal and parahippocampal cortices as seeds, within data-driven masks reflecting the AT and PM networks. Generalized additive and linear mixed models were run to assess age-specific effects and Alzheimer’s-related alterations. In this context, we explored various markers of pathological and clinical severity, including cerebral amyloid uptake, glucose metabolism, hippocampal volume, global cognition, diagnostic staging, and time to dementia onset. Our findings revealed distinct patterns of connectivity linked to normal aging or Alzheimer’s disease. Advancing age throughout adulthood was associated with lower PM connectivity and more subtle changes in AT connectivity, while Alzheimer’s disease was characterised by AT hyperconnectivity without global changes in PM connectivity. Specifically, AT connectivity was higher in MCI and Alzheimer-dementia patients compared to older controls and was positively associated with amyloid burden, glucose hypometabolism, hippocampal atrophy, and global cognitive deficits in older adults, ranging from unimpaired to demented. Additionally, higher AT connectivity correlated with faster progression to Alzheimer-dementia in MCI patients. This comprehensive approach allowed to reveal that excessive connectivity within the AT network is intrinsically linked to the pathological and clinical progression of Alzheimer’s disease. These insights may guide future research to better understand cascading events leading to the disease and hold promise for developing prognostic tools and therapeutic interventions targeting these specific network alterations.","PeriodicalId":9063,"journal":{"name":"Brain","volume":"22 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986808","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}
Jennifer M Martinez-Thompson, Kevin A Mazurek, Carolina Parra Cantu, Elie Naddaf, Venkatsampath Gogineni, Hugo Botha, David T Jones, Ruple S Laughlin, Leland Barnard, Nathan P Staff
Nerve conduction F-wave studies contain critical information about subclinical motor dysfunction which may be used to diagnose patients with amyotrophic lateral sclerosis (ALS). However, F-wave responses are highly variable in morphology, making waveform interpretation challenging. Artificial Intelligence techniques can extract time-frequency features to provide new insights into ALS diagnosis and prognosis. A retrospective analysis was performed on F-wave responses from 46,802 patients. Discrete wavelet transforms were applied to time-series waveform responses after stimulating ulnar, median, fibular, and tibial nerves. Wavelet coefficient statistics, onset age, sex, and BMI were features for training a Gradient Boosting Machine model on 40,095 (5,329 diagnosed with motor neuron disease). Model performance was tested on responses from 689 ALS patients meeting Gold Coast criteria and 689 age- and sex-matched controls. An exploratory analysis examined model performance on cohorts of patients with inclusion body myositis (IBM), cervical radiculopathy, lumbar radiculopathy, or peripheral neuropathy which can mimic ALS symptoms. Factors affecting survival were estimated through cox proportional hazards regression. The model trained using wavelet-features on the full waveform had 90% recall, 87% precision, and 88% accuracy. Similar model performance was measured using features only from the M-Wave or F-Wave. Classification probabilities for ALS patients were statistically different from the diagnoses mimicking ALS symptoms (p<0.001, ANOVA, Tukey’s post-hoc), Higher model classification probabilities of ALS, older age at onset, and family history of ALS alone or with frontotemporal dementia were factors decreasing survival. Longer diagnostic delay and upper limb onset site were factors increasing survival. Model scores two standard deviations below the mean had 4 months increased survival (two standard deviations below had 3 months decreased survival). Artificial intelligence techniques extracted important information from F-wave responses to estimate a patient’s likelihood of ALS and their survival risks. Although the model can make predictions at specific decision threshold as presented here, the true strength of such a model lies in its ability to provide probabilities about whether a patient is likely to have ALS compared to other mimicking diagnoses such as IBM, cervical or lumbar radiculopathy, or peripheral neuropathy. These probabilities provide clinicians with additional information they can use to make the final diagnosis with greater confidence and precision. Integrating such a model into the clinical workflow could help clinicians diagnose ALS sooner and manage treatment based on estimated survival, which may improve outcomes and patients’ quality of life.
神经传导 F 波研究包含亚临床运动功能障碍的重要信息，可用于诊断肌萎缩侧索硬化症（ALS）患者。然而，F 波反应在形态上变化很大，因此波形解读具有挑战性。人工智能技术可以提取时间频率特性，为肌萎缩侧索硬化症的诊断和预后提供新的见解。我们对 46802 名患者的 F 波反应进行了回顾性分析。在刺激尺神经、正中神经、腓神经和胫神经后，对时间序列波形响应进行离散小波变换。小波系数统计、发病年龄、性别和体重指数是对 40,095 人（5,329 人确诊为运动神经元疾病）进行梯度提升机模型训练的特征。对 689 名符合黄金海岸标准的 ALS 患者和 689 名年龄和性别匹配的对照者的反应进行了模型性能测试。一项探索性分析检验了模型在患有包涵体肌炎 (IBM)、颈椎病、腰椎病或周围神经病变（可模仿 ALS 症状）的患者群中的表现。影响存活率的因素通过 cox 比例危险回归进行估计。使用完整波形的小波特征训练的模型具有 90% 的召回率、87% 的精确率和 88% 的准确率。仅使用 M 波或 F 波的特征也能测出类似的模型性能。ALS患者的分类概率与模仿ALS症状的诊断结果存在统计学差异（p<0.001，方差分析，Tukey's post-hoc），较高的ALS模型分类概率、较高的发病年龄以及单独或伴有额颞叶痴呆的ALS家族史是降低存活率的因素。而较长的诊断延迟和上肢发病部位则是提高存活率的因素。模型得分低于平均值两个标准差，生存期会延长 4 个月（低于两个标准差，生存期会缩短 3 个月）。人工智能技术从 F 波反应中提取了重要信息，以估计患者患 ALS 的可能性及其生存风险。虽然该模型可以在特定的决策阈值（如本文所述）上进行预测，但这种模型的真正优势在于它能够提供患者是否有可能患有 ALS 的概率，而不是其他模仿诊断，如 IBM、颈椎或腰椎病或周围神经病变。这些概率为临床医生提供了额外的信息，他们可以利用这些信息更有把握、更准确地做出最终诊断。将这样一个模型整合到临床工作流程中，可以帮助临床医生更快地诊断出 ALS，并根据估计的存活率进行治疗，从而改善治疗效果和患者的生活质量。
{"title":"Artificial intelligence models using F-wave responses predict amyotrophic lateral sclerosis","authors":"Jennifer M Martinez-Thompson, Kevin A Mazurek, Carolina Parra Cantu, Elie Naddaf, Venkatsampath Gogineni, Hugo Botha, David T Jones, Ruple S Laughlin, Leland Barnard, Nathan P Staff","doi":"10.1093/brain/awaf014","DOIUrl":"https://doi.org/10.1093/brain/awaf014","url":null,"abstract":"Nerve conduction F-wave studies contain critical information about subclinical motor dysfunction which may be used to diagnose patients with amyotrophic lateral sclerosis (ALS). However, F-wave responses are highly variable in morphology, making waveform interpretation challenging. Artificial Intelligence techniques can extract time-frequency features to provide new insights into ALS diagnosis and prognosis. A retrospective analysis was performed on F-wave responses from 46,802 patients. Discrete wavelet transforms were applied to time-series waveform responses after stimulating ulnar, median, fibular, and tibial nerves. Wavelet coefficient statistics, onset age, sex, and BMI were features for training a Gradient Boosting Machine model on 40,095 (5,329 diagnosed with motor neuron disease). Model performance was tested on responses from 689 ALS patients meeting Gold Coast criteria and 689 age- and sex-matched controls. An exploratory analysis examined model performance on cohorts of patients with inclusion body myositis (IBM), cervical radiculopathy, lumbar radiculopathy, or peripheral neuropathy which can mimic ALS symptoms. Factors affecting survival were estimated through cox proportional hazards regression. The model trained using wavelet-features on the full waveform had 90% recall, 87% precision, and 88% accuracy. Similar model performance was measured using features only from the M-Wave or F-Wave. Classification probabilities for ALS patients were statistically different from the diagnoses mimicking ALS symptoms (p&lt;0.001, ANOVA, Tukey’s post-hoc), Higher model classification probabilities of ALS, older age at onset, and family history of ALS alone or with frontotemporal dementia were factors decreasing survival. Longer diagnostic delay and upper limb onset site were factors increasing survival. Model scores two standard deviations below the mean had 4 months increased survival (two standard deviations below had 3 months decreased survival). Artificial intelligence techniques extracted important information from F-wave responses to estimate a patient’s likelihood of ALS and their survival risks. Although the model can make predictions at specific decision threshold as presented here, the true strength of such a model lies in its ability to provide probabilities about whether a patient is likely to have ALS compared to other mimicking diagnoses such as IBM, cervical or lumbar radiculopathy, or peripheral neuropathy. These probabilities provide clinicians with additional information they can use to make the final diagnosis with greater confidence and precision. Integrating such a model into the clinical workflow could help clinicians diagnose ALS sooner and manage treatment based on estimated survival, which may improve outcomes and patients’ quality of life.","PeriodicalId":9063,"journal":{"name":"Brain","volume":"68 1","pages":""},"PeriodicalIF":14.5,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142987680","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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