Brain communications最新文献

{"title":"Elevated circulating cell-free mitochondrial DNA level in cerebrospinal fluid of narcolepsy type 1.","authors":"Monica Moresco, Concetta Valentina Tropeano, Martina Romagnoli, Giulia Neccia, Alessandro Rapone, Fabio Pizza, Stefano Vandi, Emmanuel Mignot, Alessandra Maresca, Valerio Carelli, Giuseppe Plazzi","doi":"10.1093/braincomms/fcaf125","DOIUrl":"https://doi.org/10.1093/braincomms/fcaf125","url":null,"abstract":"<p><p>Narcolepsy type 1 (NT1) is a rare neurological disorder characterized by excessive daytime sleepiness and cataplexy, thought to result from an autoimmune process targeting the hypothalamic hypocretin-producing neurons. Aiming to add clues to the latter hypothesis, we investigated circulating cell-free mitochondrial DNA (ccf-mtDNA) levels in cerebrospinal fluid (CSF), a possible biomarker for neurodegeneration, neuroinflammation or immune activation, from 46 NT1 patients with low CSF hypocretin-1, compared with 32 controls. We found significantly increased ccf-mtDNA levels in NT1 patients compared with controls, which negatively correlated with CSF hypocretin-1 concentrations. Additionally, higher ccf-mtDNA levels were observed in patients with elevated number of sleep onset rapid eye movement periods. These observations imply that increased levels of ccf-mtDNA associate with reduced CSF hypocretin-1 concentrations leading to greater alteration in sleep architecture. Furthermore, cytokine profiling in CSF revealed significant changes in interleukins 6 and 18 in NT1 patients, suggesting an active neuroinflammatory process possibly linked to ccf-mtDNA release, thus pointing to a specific inflammatory signature in NT1. These findings hint a potential mitochondrial dysfunction and neuroinflammation in NT1. Further studies are needed to elucidate the underlying mechanisms and how this may reflect on therapy.</p>","PeriodicalId":93915,"journal":{"name":"Brain communications","volume":"7 2","pages":"fcaf125"},"PeriodicalIF":4.1,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12003949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross brain reshaping in congenital visual or hearing impairment: triple-network dysfunction.","authors":"Jiahong Li, Binbin Xiong, Suijun Chen, Jing Li, Yingting Luo, Yu-Chen Chen, Jae-Jin Song, Fei Zhao, Jing Yang, Chenlong Li, Yiqing Zheng, Lan Gui, Huanling Feng, Weirong Chen, Yuexin Cai, Wan Chen","doi":"10.1093/braincomms/fcaf150","DOIUrl":"https://doi.org/10.1093/braincomms/fcaf150","url":null,"abstract":"<p><p>This research examines how congenital visual or hearing impairment reshapes brain function using EEG. The study involved 40 children with congenital visual impairment, 40 with hearing impairment and 42 age and gender-matched normal children as controls. The investigation included assessments of visual and auditory abilities, along with comprehensive EEG evaluations. Techniques such as source localization, functional connectivity and cross-frequency coupling were used to analyse variations in brain activity. Machine learning methods, specifically support vector machines, were utilized to identify key reshaping characteristics associated with congenital impairments. Results showed reduced activation in the visual cortex for visually impaired children and decreased activation in the auditory cortex for hearing-impaired children compared with the control group. Both impairment groups demonstrated significant reductions in functional connectivity across various brain regions, including the visual and auditory cortices, insula, parahippocampal gyrus, posterior cingulate gyrus and frontal cortex. The machine learning model highlighted aberrant connectivity between the visual/auditory cortex and the right insula, the medial prefrontal cortex and dorsolateral prefrontal cortex and the visual and auditory cortex in children with these impairments in the alpha frequency band. Spatially similar patterns of cross-frequency coupling of rhythmic activity were also observed. The study concludes that congenital visual and hearing impairments significantly impact brain development, identifying distinct functional characteristics and shared reshaping patterns. The consistent presence of dysrhythmic activity and reduced functional connectivity suggest the existence of a triple network anomaly.</p>","PeriodicalId":93915,"journal":{"name":"Brain communications","volume":"7 2","pages":"fcaf150"},"PeriodicalIF":4.1,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12038346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144014323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging interictal multimodal features and graph neural networks for automated planning of epilepsy surgery.","authors":"Petr Nejedly, Valentina Hrtonova, Martin Pail, Jan Cimbalnik, Pavel Daniel, Vojtech Travnicek, Irena Dolezalova, Filip Mivalt, Vaclav Kremen, Pavel Jurak, Gregory A Worrell, Birgit Frauscher, Petr Klimes, Milan Brazdil","doi":"10.1093/braincomms/fcaf140","DOIUrl":"https://doi.org/10.1093/braincomms/fcaf140","url":null,"abstract":"<p><p>Precise localization of the epileptogenic zone is pivotal for planning minimally invasive surgeries in drug-resistant epilepsy. Here, we present a graph neural network (GNN) framework that integrates interictal intracranial EEG features, electrode topology, and MRI features to automate epilepsy surgery planning. We retrospectively evaluated the model using leave-one-patient-out cross-validation on a dataset of 80 drug-resistant epilepsy patients treated at St. Anne's University Hospital (Brno, Czech Republic), comprising 31 patients with good postsurgical outcomes (Engel I) and 49 with poor outcomes (Engel II-IV). The GNN predictions demonstrated a significantly better (<i>P</i> < 0.05, Mann-Whitney-U test) area under the precision-recall curve in patients with good outcomes (area under the precision-recall curve: 0.69) compared with those with poor outcomes (area under the precision-recall curve: 0.33), indicating that the model captures clinically relevant targets in successful cases. In patients with poor outcomes, the graph neural network proposed alternative intervention sites that diverged from the original clinical plans, highlighting its potential to identify alternative therapeutic targets. We show that topology-aware GNNs significantly outperformed (<i>P</i> < 0.05, Wilcoxon signed-rank test) traditional neural networks while using the same intracranial EEG features, emphasizing the importance of incorporating implantation topology into predictive models. These findings uncover the potential of GNNs to automatically suggest targets for epilepsy surgery, which can assist the clinical team during the planning process.</p>","PeriodicalId":93915,"journal":{"name":"Brain communications","volume":"7 3","pages":"fcaf140"},"PeriodicalIF":4.1,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12066826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microglia positron emission tomography and progression in multiple sclerosis: thalamus on fire.","authors":"Burcu Zeydan, Nur Neyal, Jiye Son, Christopher G Schwarz, June C Kendall Thomas, Holly A Morrison, Melissa L Bush, Robert I Reid, Scott A Przybelski, Angela J Fought, Clifford R Jack, Ronald C Petersen, Kejal Kantarci, Val J Lowe, Laura Airas, Orhun H Kantarci","doi":"10.1093/braincomms/fcaf141","DOIUrl":"https://doi.org/10.1093/braincomms/fcaf141","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Increased innate immune activity promotes neurodegeneration and contributes to progression in multiple sclerosis. This prospective case-control study aims to investigate thalamic microglia density on 18kDa translocator protein PET in patients with multiple sclerosis using a third-generation radioligand, &lt;sup&gt;11&lt;/sup&gt;C-ER176, and investigate the associations of &lt;sup&gt;11&lt;/sup&gt;C-ER176 PET uptake with imaging and clinical measures of progression in multiple sclerosis. Patients with multiple sclerosis (&lt;i&gt;n&lt;/i&gt; = 50) and controls (&lt;i&gt;n&lt;/i&gt; = 55) were prospectively enrolled and they underwent &lt;sup&gt;11&lt;/sup&gt;C-ER176 PET and MRI including diffusion MRI with neurite orientation dispersion and density imaging. Disease characteristics, expanded disability status scale and multiple sclerosis functional composite scores were obtained in patients with multiple sclerosis. Age at imaging (mean ± standard deviation: patients = 49.6 ± 12.9 years, controls = 48.2 ± 15.4 years, &lt;i&gt;P&lt;/i&gt; = 0.63) and sex (female ratio; patients = 72%, controls = 65%, &lt;i&gt;P&lt;/i&gt; = 0.47) were not different between the groups. Thalamus &lt;sup&gt;11&lt;/sup&gt;C-ER176 PET uptake was highest in patients with progressive multiple sclerosis (1.272 ± 0.072 standardized uptake value ratio), followed by patients with relapsing multiple sclerosis (1.209 ± 0.074 standardized uptake value ratio) and lowest in controls (1.162 ± 0.067 standardized uptake value ratio, &lt;i&gt;P&lt;/i&gt; &lt; 0.001). Patients with thalamic lesions had higher thalamus &lt;sup&gt;11&lt;/sup&gt;C-ER176 PET uptake than those without thalamic lesions in both relapsing multiple sclerosis and progressive multiple sclerosis (&lt;i&gt;P&lt;/i&gt; &lt; 0.001). In patients with multiple sclerosis, higher thalamus &lt;sup&gt;11&lt;/sup&gt;C-ER176 PET uptake correlated with lower thalamic volume (&lt;i&gt;r&lt;/i&gt; = -0.45, &lt;i&gt;P&lt;/i&gt; = 0.001), higher mean diffusivity (&lt;i&gt;r&lt;/i&gt; = 0.56, &lt;i&gt;P&lt;/i&gt; &lt; 0.001), lower neurite density index (&lt;i&gt;r&lt;/i&gt; = -0.43, &lt;i&gt;P&lt;/i&gt; = 0.002), lower orientation dispersion index (&lt;i&gt;r&lt;/i&gt; = -0.40, &lt;i&gt;P&lt;/i&gt; = 0.005) and higher free water fraction (&lt;i&gt;r&lt;/i&gt; = 0.42, &lt;i&gt;P&lt;/i&gt; = 0.003) in the thalamus. In patients with multiple sclerosis, higher thalamus &lt;sup&gt;11&lt;/sup&gt;C-ER176 PET uptake also correlated with higher mean diffusivity (&lt;i&gt;r&lt;/i&gt; = 0.47, &lt;i&gt;P&lt;/i&gt; &lt; 0.001) and lower neurite density index (&lt;i&gt;r&lt;/i&gt; = -0.36, &lt;i&gt;P&lt;/i&gt; = 0.012) in the corpus callosum. In patients with multiple sclerosis, higher thalamus &lt;sup&gt;11&lt;/sup&gt;C-ER176 PET uptake correlated with worse expanded disability status scale scores (&lt;i&gt;r&lt;/i&gt; = 0.33, &lt;i&gt;P&lt;/i&gt; = 0.02), paced auditory serial addition test scores (&lt;i&gt;r&lt;/i&gt; = -0.43, &lt;i&gt;P&lt;/i&gt; = 0.003) and multiple sclerosis functional composite &lt;i&gt;z&lt;/i&gt;-scores (&lt;i&gt;r&lt;/i&gt; = -0.46, &lt;i&gt;P&lt;/i&gt; = 0.001). Microglia density in the thalamus is highest in patients with progressive multiple sclerosis and is associated with imaging biomarkers of neurodegeneration and clinical disease severity. As a signature imaging biomarker of progression in multiple sclerosis, effectively reflecti","PeriodicalId":93915,"journal":{"name":"Brain communications","volume":"7 3","pages":"fcaf141"},"PeriodicalIF":4.1,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12046125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of chronic pain on regional brain volume reduction in a general older Japanese population: a longitudinal imaging analysis from the Hisayama Study.","authors":"Yuri Nakamura, Mao Shibata, Naoki Hirabayashi, Taro Nakazawa, Yoshihiko Furuta, Jun Hata, Masako Hosoi, Nobuyuki Sudo, Ken Yamaura, Toshiharu Ninomiya","doi":"10.1093/braincomms/fcaf149","DOIUrl":"https://doi.org/10.1093/braincomms/fcaf149","url":null,"abstract":"<p><p>Longitudinal analyses of the influence of chronic pain on pain-related regional brain volumes in general populations are warranted. This prospective cohort study investigated the association between the presence of chronic pain at baseline and the subsequent changes in pain-related regional brain volumes among Japanese community-dwelling older residents. Participants aged 65 years or older who underwent brain magnetic resonance imaging (MRI) scans in both 2012 and 2017 were included. According to the presence or absence of chronic pain (defined as pain lasting for longer than 3 months) in 2012, participants were categorized into a 'chronic pain' group and 'no chronic pain' group. Region-of-interest analyses for the ventrolateral prefrontal cortex, dorsolateral prefrontal cortex, orbitofrontal cortex, postcentral gyrus, insular cortex, thalamus, anterior cingulate cortex, posterior cingulate cortex, amygdala and hippocampus were performed using FreeSurfer software. Whole-brain analysis was conducted by voxel-based morphometry. Rates of change in regional brain volume at 5 years after baseline were estimated using analysis of covariance. Among the 766 participants included in the FreeSurfer analysis, 444 (58%) were female and 287 (37%) were categorized into the chronic pain group. The results of FreeSurfer analysis showed that the chronic pain group had significantly greater decreases in regional volume in the postcentral gyrus (-2.187% in the chronic pain group versus -1.681% in the no chronic pain group, <i>P</i> = 0.01), thalamus (-4.400% versus -3.897%, <i>P</i> = 0.006), anterior cingulate cortex (-2.507% versus -1.941%, <i>P</i> = 0.004) and amygdala (-4.739% versus -4.022%, <i>P</i> = 0.03) compared to the no chronic pain group after adjusting for age, sex, education attainment, marital status, hypertension, diabetes, serum total cholesterol level, body mass index, current smoking, current drinking, regular exercise, cerebrovascular lesions on MRI, activities in daily living disability and depressive symptoms. Among the 730 participants included in the voxel-based morphometry analysis, 433 (59%) were female and 272 (37%) were categorized into the chronic pain group. The voxel-based morphometry analysis showed that the chronic pain group had a significantly greater regional volume decrease in the right anterior insula than the no chronic pain group. Our findings suggest that the presence of chronic pain at baseline is associated with a significantly greater decrease in the volume of pain-related brain regions at 5 years after baseline in community-dwelling older Japanese.</p>","PeriodicalId":93915,"journal":{"name":"Brain communications","volume":"7 2","pages":"fcaf149"},"PeriodicalIF":4.1,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12018798/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuroimaging-based data-driven subtypes of spatiotemporal atrophy due to Parkinson's disease.","authors":"Zeena Shawa, Cameron Shand, Beatrice Taylor, Henk W Berendse, Chris Vriend, Tim D van Balkom, Odile A van den Heuvel, Ysbrand D van der Werf, Jiun-Jie Wang, Chih-Chien Tsai, Jason Druzgal, Benjamin T Newman, Tracy R Melzer, Toni L Pitcher, John C Dalrymple-Alford, Tim J Anderson, Gaëtan Garraux, Mario Rango, Petra Schwingenschuh, Melanie Suette, Laura M Parkes, Sarah Al-Bachari, Johannes Klein, Michele T M Hu, Corey T McMillan, Fabrizio Piras, Daniela Vecchio, Clelia Pellicano, Chengcheng Zhang, Kathleen L Poston, Elnaz Ghasemi, Fernando Cendes, Clarissa L Yasuda, Duygu Tosun, Philip Mosley, Paul M Thompson, Neda Jahanshad, Conor Owens-Walton, Emile d'Angremont, Eva M van Heese, Max A Laansma, Andre Altmann, Rimona S Weil, Neil P Oxtoby","doi":"10.1093/braincomms/fcaf146","DOIUrl":"https://doi.org/10.1093/braincomms/fcaf146","url":null,"abstract":"<p><p>Parkinson's disease is the second most common neurodegenerative disease. Despite this, there are no robust biomarkers to predict progression, and understanding of disease mechanisms is limited. We used the Subtype and Stage Inference algorithm to characterize Parkinson's disease heterogeneity in terms of spatiotemporal subtypes of macroscopic atrophy detectable on T1-weighted MRI-a successful approach used in other neurodegenerative diseases. We trained the model on covariate-adjusted cortical thicknesses and subcortical volumes from the largest known T1-weighted MRI dataset in Parkinson's disease, Enhancing Neuroimaging through Meta-Analysis consortium Parkinson's Disease dataset (<i>n</i> = 1100 cases). We tested the model by analyzing clinical progression over up to 9 years in openly-available data from people with Parkinson's disease from the Parkinson's Progression Markers Initiative (<i>n</i> = 584 cases). Under cross-validation, our analysis supported three spatiotemporal atrophy subtypes, named for the location of the earliest affected regions as: '<i>Subcortical</i>' (<i>n</i> = 359, 33%), '<i>Limbic</i>' (<i>n</i> = 237, 22%) and '<i>Cortical</i>' (<i>n</i> = 187, 17%). A fourth subgroup having sub-threshold/no atrophy was named '<i>Sub-threshold atrophy</i>' (<i>n</i> = 317, 29%). Statistical differences in clinical scores existed between the no-atrophy subgroup and the atrophy subtypes, but not among the atrophy subtypes. This suggests that the prime T1-weighted MRI delineator of clinical differences in Parkinson's disease is atrophy severity, rather than atrophy location. Future work on unravelling the biological and clinical heterogeneity of Parkinson's disease should leverage more sensitive neuroimaging modalities and multimodal data.</p>","PeriodicalId":93915,"journal":{"name":"Brain communications","volume":"7 2","pages":"fcaf146"},"PeriodicalIF":4.1,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12037470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG-based graph network analysis in relation to regional tau in asymptomatic Alzheimer's disease.","authors":"Laure Spruyt, Tjaša Mlinarič, Nathalie Dusart, Mariska Reinartz, Gabriela Meade, Marc M Van Hulle, Koen Van Laere, Patrick Dupont, Rik Vandenberghe","doi":"10.1093/braincomms/fcaf138","DOIUrl":"https://doi.org/10.1093/braincomms/fcaf138","url":null,"abstract":"<p><p>Tau aggregation in early affected regions in the asymptomatic stage of Alzheimer's disease marks a transitional phase between stable asymptomatic amyloid positivity and the clinically manifest stage. How this early region tau aggregation covertly affects brain function during this asymptomatic stage remains unclear. In this study, 83 participants underwent a 128 electrodes resting-state EEG, a dynamic 100 min tau PET scan (¹⁸F-MK6240), an amyloid PET scan, a structural T1 MRI scan and neuropsychological assessment. Tau PET data quality control led to a final sample of 66 subjects. Based on the clinical and cognitive status, amyloid and tau PET biomarkers, the group was composed of 37 cognitively unimpaired amyloid negative subjects, 14 cognitively unimpaired amyloid positive subjects and 15 patients with prodromal Alzheimer's disease. We calculated the average undirected weighted Phase Lag Index in the alpha frequency band with eyes closed and used this as weights for the graph and analysed the global clustering coefficient and characteristic path length in sensor space. As a primary objective, we assessed how these global graph measures correlated with tau PET values, in an <i>a priori</i> defined early metaVOI, comprised of the entorhinal and perirhinal cortex, hippocampus, parahippocampus and fusiform cortex. As secondary analyses, we investigated which specific brain regions were mainly implicated, what the contribution was of amyloid, the effect of electrode density and the relation to cognitive performance. In the overall group and within the cognitively unimpaired amyloid positive subgroup, tau aggregation was associated with a decrease in global clustering coefficient and an increase in characteristic path length. These changes reflect the initial disintegration of the small-world brain network during the transitional phase, even before clinical symptoms are apparent. The correlations are most prominent in the perirhinal cortex, indicating that global deterioration of the network is already present early in the Alzheimer's disease pathology. We obtained similar results with only taking 64 electrodes into account. To conclude, we found that in the asymptomatic stage of Alzheimer's disease, tau PET load in medial temporal cortex is associated with global electrophysiological measures of network disintegration. The study demonstrates the potential value of high-density EEG in the era of biologically defined Alzheimer's disease for characterizing brain function in the asymptomatic stage.</p>","PeriodicalId":93915,"journal":{"name":"Brain communications","volume":"7 2","pages":"fcaf138"},"PeriodicalIF":4.1,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12008720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144048459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning determines the incidence of Alzheimer's disease based on population gut microbiome profile.","authors":"Amedra Basgaran, Eva Lymberopoulos, Ella Burchill, Maryam Reis-Dehabadi, Nikhil Sharma","doi":"10.1093/braincomms/fcaf059","DOIUrl":"https://doi.org/10.1093/braincomms/fcaf059","url":null,"abstract":"<p><p>The human microbiome is a complex and dynamic community of microbes, thought to have symbiotic benefit to its host. Influences of the gut microbiome on brain microglia have been identified as a potential mechanism contributing to neurodegenerative diseases, such as Alzheimer's disease, motor neurone disease and Parkinson's disease (Boddy SL, Giovannelli I, Sassani M, <i>et al.</i> The gut microbiome: A key player in the complexity of amyotrophic lateral sclerosis (ALS). <i>BMC Med.</i> 2021;19(1):13). We hypothesize that population level differences in the gut microbiome will predict the incidence of Alzheimer's disease using machine learning methods. Cross-sectional analyses were performed in R, using two large, open-access microbiome datasets (<i>n</i> = 959 and <i>n</i> = 2012). Countries in these datasets were grouped based on Alzheimer's disease incidence and the gut microbiome profiles compared. In countries with a high incidence of Alzheimer's disease, there is a significantly lower diversity of the gut microbiome (<i>P</i> < 0.05). A permutational analysis of variance test (<i>P</i> < 0.05) revealed significant differences in the microbiome profile between countries with high versus low incidence of Alzheimer's disease with several contributing taxa identified: at a species level <i>Escherichia coli,</i> and at a genus level <i>Haemophilus and Akkermansia</i> were found to be reproducibly protective in both datasets. Additionally, using machine learning, we were able to predict the incidence of Alzheimer's disease within a country based on the microbiome profile (mean area under the curve 0.889 and 0.927). We conclude that differences in the microbiome can predict the varying incidence of Alzheimer's disease between countries. Our results support a key role of the gut microbiome in neurodegeneration at a population level.</p>","PeriodicalId":93915,"journal":{"name":"Brain communications","volume":"7 2","pages":"fcaf059"},"PeriodicalIF":4.1,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11999016/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical predictors for efficacy of erenumab for migraine: a Registry for Migraine (REFORM) study.","authors":"William Kristian Karlsson, Messoud Ashina, Rune Häckert Christensen, Haidar Muhsen Al-Khazali, Håkan Ashina","doi":"10.1093/braincomms/fcaf147","DOIUrl":"https://doi.org/10.1093/braincomms/fcaf147","url":null,"abstract":"<p><p>Erenumab has proven effective for migraine prevention; however, a substantial proportion of people with migraine do not benefit from treatment, and among those who do, there is considerable variability in response. This study aimed to identify clinical predictors of therapeutic response to erenumab and evaluate their predictive value in a large cohort of people with migraine. We conducted a single-centre, prospective, longitudinal cohort study of adults with migraine, experiencing ≥4 monthly migraine days. All participants received erenumab 140 mg monthly for 24 weeks and recorded their response in a headache diary with daily entries. A semi-structured interview was conducted at enrolment, and patient-reported outcome measures were collected before and after treatment. Treatment responders were classified as participants achieving a reduction from baseline of ≥50% in average monthly migraine days across weeks 13 through 24. Clinical predictors were analysed using logistic regression analysis. In total, 570 participants with migraine provided data eligible for analysis. Of these, 298 (52.3%) participants were classified as treatment responders, and the remaining 272 (47.7%) were non-responders. Independent predictors associated with a lower likelihood of response to erenumab were chronic migraine (odds ratio 0.63, 95% confidence interval 0.43-0.91; <i>P</i> = 0.030), daily headache (odds ratio 0.41, 95% confidence interval 0.24-0.67; <i>P</i> = 0.003) and previous failure of ≥3 preventive migraine medications (odds ratio 0.54, 95% confidence interval 0.37-0.77; <i>P</i> = 0.005). Conversely, better outcomes were observed with higher age (10-year increase: odds ratio 1.22, 95% confidence interval 1.06-1.41; <i>P</i> = 0.017). Multivariate model area under curve was 64.6% (60.0-69.2%). Participants with an early response to erenumab (≥50% reduction within weeks 1-12) were less likely than late responders to have chronic migraine [119/217 (57.1%) versus 61/79 (77.2%); <i>P</i> < 0.001], had lower Migraine Disability Assessment Scores [median (IQR): 52 (30-85) versus 65 (35-120); <i>P</i> = 0.029], more often had unilateral headache [193 (88.9%) versus 63/79 (79.7%); <i>P</i> = 0.041], and experienced less ictal allodynia measured by Allodynia Symptom Checklist-12 scores [median (IQR): 4 (0-8) versus 6 (2-8) versus; <i>P</i> = 0.024]. In conclusion, chronic migraine, experiencing daily headache, and having ≥3 preventive medication failures were independently associated with a lower likelihood of response to erenumab. Moreover, patients with a more severe clinical phenotype were more likely to respond later. Prediction of treatment responses might be improved by incorporating machine learning models and multimodal biomarkers, facilitating a shift towards personalized medicine.</p>","PeriodicalId":93915,"journal":{"name":"Brain communications","volume":"7 2","pages":"fcaf147"},"PeriodicalIF":4.1,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12015094/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144044130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining the genetic relationship between Alzheimer's disease, schizophrenia and their shared risk factors using genomic structural equation modelling.","authors":"Inês Almeida E Sousa, Andrew D Grotzinger, Jeremy M Lawrence, Sophie Breunig, Charles R Marshall, Ania Korszun, Isabelle F Foote","doi":"10.1093/braincomms/fcaf112","DOIUrl":"https://doi.org/10.1093/braincomms/fcaf112","url":null,"abstract":"<p><p>Epidemiological studies have demonstrated an association between dementia and schizophrenia. There is a significant symptom overlap between the two disorders-psychosis is seen in 50% of patients with Alzheimer's disease and cognitive impairment is a key feature of schizophrenia. Whether these overlapping clinical presentations reflect shared aetiology is unclear. Therefore, we aimed to model the genetic correlation between Alzheimer's disease, schizophrenia and their shared risk factors using genomic structural equation modelling to identify potentially overlapping biological pathways between these traits. We measured genetic correlation between Alzheimer's disease, schizophrenia and 13 shared risk factors, including body fat percentage, less education, alcohol intake, insomnia, loneliness, less social/leisure activity, major depression, mean arterial pressure, smoking, socioeconomic deprivation, low-density lipoprotein cholesterol, eye problems and type 2 diabetes mellitus. Schizophrenia and Alzheimer's disease were not significantly genetically correlated but were both significantly associated with loneliness. Colocalization suggested that the association between loneliness and Alzheimer's disease was predominantly driven by a shared causal variant on Chromosome 11. Factor analysis of shared risk factors produced four latent factors representing clusters of shared genetics between socioeconomic traits, psychiatric traits, cardiometabolic traits and smoking-related traits. Both Alzheimer's disease and schizophrenia were significantly associated with the socioeconomic latent factor. Although there is little direct genetic overlap between schizophrenia and Alzheimer's disease, loneliness may play an important role in the association between these two disorders. In addition, the shared genetics between socioeconomic traits may affect susceptibility to both Alzheimer's disease and schizophrenia to a greater extent than trait-specific pathways.</p>","PeriodicalId":93915,"journal":{"name":"Brain communications","volume":"7 2","pages":"fcaf112"},"PeriodicalIF":4.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11981896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}