Objective: Alzheimer's disease (AD) is the most common neurodegenerative disease. The predominantly sporadic form of AD is age-related, but the underlying pathogenic mechanisms remain not fully understood. Current efforts to combat the disease focus on the main pathological hallmarks, in particular beta-amyloid (Aβ) plaque pathology. According to the amyloid cascade hypothesis, Aβ is the critical early initiator of AD pathogenesis. Plaque pathology is very heterogeneous, where a subset of plaques, neuritic plaques (NPs), are considered most neurotoxic rendering their in-depth characterization essential to understand Aβ pathogenicity. Methods: To delineate the chemical traits specific to NP types, we investigated senile Aβ pathology in the postmortem, human sporadic AD brain using advanced correlative biochemical imaging based on immunofluorescence (IF) microscopy and mass spectrometry imaging (MSI). Results: Immunostaining-guided MSI identified distinct Aβ signatures of NPs characterized by increased Aβ1-42(ox) and Aβ2-42. Moreover, correlation with a marker of dystrophy (reticulon 3 [RTN3]) identified key Aβ species that both delineate NPs and display association with neuritic dystrophy. Conclusion: Together, these correlative imaging data shed light on the complex biochemical architecture of NPs and associated dystrophic neurites. These in turn are obvious targets for disease-modifying treatment strategies, as well as novel biomarkers of Aβ pathogenicity.

Background: Multivoxel pattern analysis (MVPA) has emerged as a powerful unbiased approach for generating seed regions of interest (ROIs) in resting-state functional connectivity (RSFC) analysis in a data-driven manner. Studies exploring RSFC in multiple sclerosis have produced diverse and often incongruent results. Objectives: The aim of the present study was to investigate RSFC differences between people with relapsing-remitting multiple sclerosis (RRMS) and healthy controls (HC). Methods: We performed a whole-brain connectome-wide MVPA in 50 RRMS patients with expanded disability status scale ≤4 and 50 age and gender-matched HCs. Results: Significant group differences were noted in RSFC in three clusters distributed in the following regions: anterior cingulate gyrus, right middle frontal gyrus, and frontal medial cortex. Whole-brain seed-to-voxel RSFC characterization of these clusters as seed ROIs revealed network-specific abnormalities, specifically in the anterior cingulate cortex and the default mode network. Conclusions: The network-wide RSFC abnormalities we report agree with the previous findings in RRMS, the cognitive and clinical implications of which are discussed herein. Impact statement This study investigated resting-state functional connectivity (RSFC) in relapsing-remitting multiple sclerosis (RRMS) people with mild disability (expanded disability status scale ≤4). Whole-brain connectome-wide multivoxel pattern analysis was used for assessing RSFC. Compared with healthy controls, we were able to identify three regions of interest for significant differences in connectivity patterns, which were then extracted as a mask for whole-brain seed-to-voxel analysis. A reduced connectivity was noted in the RRMS group, particularly in the anterior cingulate cortex and the default mode network regions, providing insights into the RSFC abnormalities in RRMS.

Brain ConnectivityVol. 12, No. 2 CorrectionOpen AccessCorrection to: Increased Functional Connectivity Within and Between Cognitive-Control Networks from Early Infancy to Nine Years During Story Listening, by Farah and Horowitz-Kraus Brain Connect 2019;9(3):285–295. DOI: 10.1089/brain.2018.0625is erratum ofIncreased Functional Connectivity Within and Between Cognitive-Control Networks from Early Infancy to Nine Years During Story ListeningPublished Online:14 Mar 2022AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail In the April 2019 issue of Brain Connectivity (vol. 9, no. 3; 285–295), the article titled Increased Functional Connectivity Within and Between Cognitive-Control Networks from Early Infancy to Nine Years During Story Listening, by Farah and Horowitz-Kraus requires correction.In the original article, author Tzipi Horowitz-Kraus's affiliations were incomplete. Authors and affiliations were listed as: Rola Farah1,2 and Tzipi Horowitz-Kraus1,31Faculty of Biomedical Engineering, Educational Neuroimaging Center, Technion, Haifa, Israel.2Faculty of Education in Science and Technology, Educational Neuroimaging Center, Technion, Haifa, Israel.3Division of General and Community Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.Tzipi Horowitz-Kraus should be listed with all three institutions. Thus, it should have appeared as: Rola Farah1,2 and Tzipi Horowitz-Kraus1-3The online version has been corrected to reflect this. The publisher apologize for this error.FiguresReferencesRelatedDetailsRelated articlesIncreased Functional Connectivity Within and Between Cognitive-Control Networks from Early Infancy to Nine Years During Story Listening5 Apr 2019Brain Connectivity Volume 12Issue 2Mar 2022 InformationCopyright 2022, Mary Ann Liebert, Inc., publishersTo cite this article:Correction to: Increased Functional Connectivity Within and Between Cognitive-Control Networks from Early Infancy to Nine Years During Story Listening, by Farah and Horowitz-Kraus Brain Connect 2019;9(3):285–295. DOI: 10.1089/brain.2018.0625.Brain Connectivity.Mar 2022.206-206.http://doi.org/10.1089/brain.2018.0625.correxcreative commons licensePublished in Volume: 12 Issue 2: March 14, 2022Online Ahead of Print:March 2, 2022PDF download

Background: Physiological and pathological brain aging plays a central role in brain network modulation. The aim of the present article was to assess the stability of a proposed method for evaluation of small-world (SW) characteristics for the study of the human connectome. Subjects and Methods: Eighty subjects were recruited: 36 young healthy controls, 32 elderly healthy controls, and 12 patients affected by Alzheimer's disease (AD). Electroencephalograms (EEGs) were recorded during six separate sessions (480 recordings) at an average intersession interval of 3.8 ± 0.2 days. We applied graph theory functions to the weighted and undirected networks obtained by the lagged linear coherence estimated by exact low-resolution electromagnetic tomography (eLORETA). We explored the following frequency bands: delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-40 Hz). Results: The proposed method for evaluation of SW characteristics exhibited good reproducibility and stability. Furthermore, results showed the pattern, Young>Elderly>AD, in low-frequency delta and theta bands and vice versa in the higher alpha band. Finally, a correlation with age was confirmed in healthy subjects, showing that the older the age, the higher the SW values for alpha 2. Discussion: Evidences from the present study confirm the stability of the SW index and suggest that the analysis of connectivity patterns evaluated from EEGs can be supported by the graph theory. The proposed method for evaluation of SW characteristics has shown good reproducibility and stability. This technique, applied to patient data, could provide more information on the pathophysiological processes underlying age-related brain disconnection, as well as on administration of rehabilitation treatments at the right time, which could allow patients to avoid unnecessary interventions. Impact statement The graph analysis tools described in this study represent an interesting approach to study the distinctive characteristics of physiological aging by focusing on functional connectivity networks. The proposed method for evaluation of small-world characteristics has shown good reproducibility and stability. This technique, applied to patient data, could provide more information on the pathophysiological processes underlying age-related brain disconnection, as well as on delivery of rehabilitation treatments at the right time, which could allow patients to avoid unnecessary interventions.

Introduction: The importance of the amygdala/medial orbitofrontal cortex (OFC) network during processing of emotional stimuli, emotional faces in particular, is well established. This premise is supported by converging evidence from animal models, human neuroanatomical results, and neuroimaging studies. However, there is missing evidence from human brain connectivity studies that the OFC and no other prefrontal brain areas such as the dorsolateral prefrontal cortex (DLPFC) or ventrolateral prefrontal cortex (VLPFC) are responsible for amygdala regulation in the functional context of emotional face stimuli. Methods: Dynamic causal modeling of ultrahigh-field functional magnetic resonance imaging data acquired at 7 Tesla in 38 healthy subjects and a well-established paradigm for emotional face processing were used to assess the central role of the OFC to provide empirical validation for the assumed network architecture. Results: Using Bayesian model selection, it is demonstrated that indeed the OFC, and not the VLPFC and the DLPFC, downregulates amygdala activation during the emotion discrimination task. In addition, Bayesian model averaging group results were rigorously tested using bootstrapping, further corroborating these findings and providing an estimator for robustness and optimal sample sizes. Discussion: While it is true that VLPFC and DLPFC are relevant for the processing of emotional faces and are connected to the OFC, the OFC appears to be a central hub for the prefrontal/amygdala interaction. Impact statement Using dynamic causal modeling (DCM), abnormal effective connectivity in the orbitofrontal cortex (OFC)/amygdala network has been repeatedly observed in the pathophysiology of psychiatric disorders. However, it has to be considered that these findings are all based on the a priori assumption of the OFC being the central area for prefrontal control regulating amygdala activation. This is particularly important, as DCM results conditionally depend on the underlying model space used for model selection. Using Bayesian model comparison methods, it is shown that the OFC (and not the dorsolateral prefrontal cortex or ventrolateral prefrontal cortex) engages in amygdala downregulation in the context emotional face processing.

Brain ConnectivityVol. 11, No. 10 Calls for PapersFree AccessCall for Special Issue Papers: Advances in Alzheimer's DiseaseDeadline for Manuscript Submission: January 31, 2022Guest Editor: Michael SchollGuest Editor: Michael SchollMichael Scholl, PhD, Associate Professor; University of Gothenburg, Sweden, University College LondonSearch for more papers by this authorPublished Online:8 Dec 2021https://doi.org/10.1089/brain.2021.29029.cfp2AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail Someone in the world develops dementia every 3 seconds. There are over 55 million people worldwide living with dementia in 2020. This number will almost double every 20 years, reaching 139 million in 2050. This is set to surpass due to the impact of COVID-19. The annual global cost of dementia is now above US$ 1.3 trillion and is expected to rise to US$ 2.8 trillion by 2050. Most dementia cases are caused by Alzheimer's disease, with only very minimal treatment options available.While we have made some progress in understanding the Alzheimer's disease process, we are still a long way away from a cure and even from preventing the progression of the disease.This prominent special issue will consider articles from the full breadth of evidence from original research papers and reviews, to thoughtful perspectives and commentaries.Areas of interest include, but are not limited to: Advances in amyloid imagingAdvances in tau imagingNeuroinflammation, synaptic function, mitochondrial functionFluid biomarkersAdvances in structural and functional imagingNovel therapeutic strategiesQuestions or pre-submission queries? Please contact Guest Editor Dr. Michael Scholl at michael.scholl@neuro.gu.sePlease visit the Brain Connectivity website www.liebertpub.com/brain to learn about the benefits of submitting, to view our Instructions for Authors, and for manuscript submission guidelines.Visit the Instructions for Authors:https://www.liebertpub.com/brainSubmit your paper for peer review online:https://mc.manuscriptcentral.com/brainconnectivityFiguresReferencesRelatedDetails Volume 11Issue 10Dec 2021 InformationCopyright 2021, Mary Ann Liebert, Inc., publishersTo cite this article:Guest Editor: Michael Scholl.Call for Special Issue Papers: Advances in Alzheimer's Disease.Brain Connectivity.Dec 2021.784-784.http://doi.org/10.1089/brain.2021.29029.cfp2Published in Volume: 11 Issue 10: December 8, 2021Online Ahead of Print:November 22, 2021PDF download