A data-driven model of brain volume changes in progressive supranuclear palsy

Supplementary material: Supplementary material is available at Brain Communications online.Copyright © The Author(s) 2022. The most common clinical phenotype of progressive supranuclear palsy is Richardson syndrome, characterized by levodopa unresponsive symmetric parkinsonism, with a vertical supranuclear gaze palsy, early falls and cognitive impairment. There is currently no detailed understanding of the full sequence of disease pathophysiology in progressive supranuclear palsy. Determining the sequence of brain atrophy in progressive supranuclear palsy could provide important insights into the mechanisms of disease progression, as well as guide patient stratification and monitoring for clinical trials. We used a probabilistic event-based model applied to cross-sectional structural MRI scans in a large international cohort, to determine the sequence of brain atrophy in clinically diagnosed progressive supranuclear palsy Richardson syndrome. A total of 341 people with Richardson syndrome (of whom 255 had 12-month follow-up imaging) and 260 controls were included in the study. We used a combination of 12-month follow-up MRI scans, and a validated clinical rating score (progressive supranuclear palsy rating scale) to demonstrate the longitudinal consistency and utility of the event-based model’s staging system. The event-based model estimated that the earliest atrophy occurs in the brainstem and subcortical regions followed by progression caudally into the superior cerebellar peduncle and deep cerebellar nuclei, and rostrally to the cortex. The sequence of cortical atrophy progresses in an anterior to posterior direction, beginning in the insula and then the frontal lobe before spreading to the temporal, parietal and finally the occipital lobe. This in vivo ordering accords with the post-mortem neuropathological staging of progressive supranuclear palsy and was robust under cross-validation. Using longitudinal information from 12-month follow-up scans, we demonstrate that subjects consistently move to later stages over this time interval, supporting the validity of the model. In addition, both clinical severity (progressive supranuclear palsy rating scale) and disease duration were significantly correlated with the predicted subject event-based model stage (P < 0.01). Our results provide new insights into the sequence of atrophy progression in progressive supranuclear palsy and offer potential utility to stratify people with this disease on entry into clinical trials based on disease stage, as well as track disease progression.We thank the research participants for their contribution to the study. The Dementia Research Centre is supported by Alzheimer’s Research UK, Alzheimer’s Society, Brain Research UK and The Wolfson Foundation. This work was supported by the National Institute of Health Research UCLH Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre Clinical Research Facility and the UK Dementia Research Institute (DRI), which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK. The Progressive Supranuclear Palsy-Cortico-Basal Syndrome-Multiple System Atrophy (PROSPECT) study is funded by the PSP Association and CBD Solutions. The 4-repeat tauopathy neuroimaging initiative (4RTNI) and frontotemporal lobar degeneration neuroimaging initiative (FTLDNI) are funded by the National Institutes of Health Grant R01 AG038791 and through generous contributions from the Tau Research Consortium. Both are coordinated through the University of California, San Francisco, Memory and Aging Center. 4RTNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. W.J.S. is supported by a Wellcome Trust Clinical PhD fellowship (220582/Z/20/Z). M.B. is supported by a Fellowship award from the Alzheimer’s Society, UK (AS-JF-19a-004-517) and the UK Dementia Research Institute. N.P.O. is a UK Research and Innovation Future Leaders Fellow (MR/S03546X/1). D.C.A. is supported by the Engineering and Physical Sciences Research Council (EP/M020533/1); Medical Research Council (MR/T046422/1); Wellcome Trust (UNS113739). D.M.C. is supported by the UK Dementia Research Institute, as well as Alzheimer’s Research UK (ARUK-PG2017-1946) and the UCL/UCLH National Institute of Health Research Biomedical Research Centre. H.R.M. is supported by Parkinson’s UK, Cure Parkinson’s Trust, PSP Association, CBD Solutions, Drake Foundation, Medical Research Council, and the Michael J Fox Foundation. H.H. is supported by the National Institute of Health (R01AG038791, U19AG063911). L.V.V. is supported by National Institute of Health (R01AG038791). J.B.R. is supported by the Wellcome Trust (220258); National Institute of Health Research Cambridge Biomedical Research Centre (BRC-1215-20014); PSP Association; Evelyn Trust; Medical Research Council (SUAG051 R101400). A.B. is supported by National Institute of U19AG063911, R01AG038791, R01AG073482, U24AG057437, the Rainwater Charitable Foundation, the Bluefield Project to Cure FTD, the Alzheimer’s Association and the Association for Frontotemporal Degeneration. J.D.R. is supported by the Miriam Marks Brain Research UK Senior Fellowship and has received funding from a Medical Research Council Clinician Scientist Fellowship (MR/M008525/1) and the National Institute of Health Research Rare Disease Translational Research Collaboration (BRC149/NS/MH). P.A.W. is supported by a Medical Research Council Skills Development Fellowship (MR/T027770/1)

Sialic Acid Glycobiology Unveils Trypanosoma cruzi Trypomastigote Membrane Physiology.

Trypanosoma cruzi, the flagellate protozoan agent of Chagas disease or American trypanosomiasis, is unable to synthesize sialic acids de novo. Mucins and trans-sialidase (TS) are substrate and enzyme, respectively, of the glycobiological system that scavenges sialic acid from the host in a crucial interplay for T. cruzi life cycle. The acquisition of the sialyl residue allows the parasite to avoid lysis by serum factors and to interact with the host cell. A major drawback to studying the sialylation kinetics and turnover of the trypomastigote glycoconjugates is the difficulty to identify and follow the recently acquired sialyl residues. To tackle this issue, we followed an unnatural sugar approach as bioorthogonal chemical reporters, where the use of azidosialyl residues allowed identifying the acquired sugar. Advanced microscopy techniques, together with biochemical methods, were used to study the trypomastigote membrane from its glycobiological perspective. Main sialyl acceptors were identified as mucins by biochemical procedures and protein markers. Together with determining their shedding and turnover rates, we also report that several membrane proteins, including TS and its substrates, both glycosylphosphatidylinositol-anchored proteins, are separately distributed on parasite surface and contained in different and highly stable membrane microdomains. Notably, labeling for α(1,3)Galactosyl residues only partially colocalize with sialylated mucins, indicating that two species of glycosylated mucins do exist, which are segregated at the parasite surface. Moreover, sialylated mucins were included in lipid-raft-domains, whereas TS molecules are not. The location of the surface-anchored TS resulted too far off as to be capable to sialylate mucins, a role played by the shed TS instead. Phosphatidylinositol-phospholipase-C activity is actually not present in trypomastigotes. Therefore, shedding of TS occurs via microvesicles instead of as a fully soluble form

Accurate Bayesian segmentation of thalamic nuclei using diffusion MRI and an improved histological atlas

Data availability: Data will be made available on request.Supplementary materials are available online at: https://www.sciencedirect.com/science/article/pii/S1053811923002744?via%3Dihub#sec0024 .Research data are available online at: https://www.sciencedirect.com/science/article/pii/S1053811923002744?via%3Dihub#ec-research-data .Copyright © 2023 The Author(s). The human thalamus is a highly connected brain structure, which is key for the control of numerous functions and is involved in several neurological disorders. Recently, neuroimaging studies have increasingly focused on the volume and connectivity of the specific nuclei comprising this structure, rather than looking at the thalamus as a whole. However, accurate identification of cytoarchitectonically designed histological nuclei on standard in vivo structural MRI is hampered by the lack of image contrast that can be used to distinguish nuclei from each other and from surrounding white matter tracts. While diffusion MRI may offer such contrast, it has lower resolution and lacks some boundaries visible in structural imaging. In this work, we present a Bayesian segmentation algorithm for the thalamus. This algorithm combines prior information from a probabilistic atlas with likelihood models for both structural and diffusion MRI, allowing segmentation of 25 thalamic labels per hemisphere informed by both modalities. We present an improved probabilistic atlas, incorporating thalamic nuclei identified from histology and 45 white matter tracts surrounding the thalamus identified in ultra-high gradient strength diffusion imaging. We present a family of likelihood models for diffusion tensor imaging, ensuring compatibility with the vast majority of neuroimaging datasets that include diffusion MRI data. The use of these diffusion likelihood models greatly improves identification of nuclear groups versus segmentation based solely on structural MRI. Dice comparison of 5 manually identifiable groups of nuclei to ground truth segmentations show improvements of up to 10 percentage points. Additionally, our chosen model shows a high degree of reliability, with median test-retest Dice scores above 0.85 for four out of five nuclei groups, whilst also offering improved detection of differential thalamic involvement in Alzheimer’s disease (AUROC 81.98%). The probabilistic atlas and segmentation tool will be made publicly available as part of the neuroimaging package FreeSurfer.This work was primarily funded by Alzheimers Research UK (ARUK-IRG2019A003). PGs work in this area was supported by NIH NIBIB NAC P41EB015902 AYs work in this area was supported by NIH grants R01 EB021265 and R56 MH121426. DCAs work in this area was supported by EPSRC grant EP/R006032/1 and Wellcome Trust award 221915/Z/20/Z. The Dementia Research Centre is supported by Alzheimer’s Research UK, Alzheimer’s Society, Brain Research UK, and The Wolfson Foundation. This work was supported by the National Institute for Health Research (NIHR) Queen Square Dementia Biomedical Research Unit and the University College London Hospitals Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre (LWENC) Clinical Research Facility, and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK. This project has received funding from the European Unions Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 765148, as well as from the National Institutes Of Health under project number R01NS112161. MB is supported by a Fellowship award from the Alzheimers Society, UK (AS-JF-19a-004-517). MBs work was also supported by the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimers Society and Alzheimers Research UK. JDR is supported by the Miriam Marks Brain Research UK Senior Fellowship and has received funding from an MRC Clinician Scientist Fellowship (MR/M008525/1) and the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH). JEI is supported by the European Research Council (Starting Grant 677697, project BUNGEE-TOOLS) and the NIH (1RF1MH123195-01 and 1R01AG070988-01). The collection and sharing of the ADNI data was funded by the Alzheimer’s Disease Neuroimaging Initiative (National Institutes of Health Grant U01 AG024904) and Department of Defence (W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds for ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health. The grantee is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI is disseminated by the Laboratory for Neuro Imaging at the University of Southern California

Characterizing the Clinical Features and Atrophy Patterns of MAPT-Related Frontotemporal Dementia With Disease Progression Modeling

BACKGROUND AND OBJECTIVE: Mutations in the MAPT gene cause frontotemporal dementia (FTD). Most previous studies investigating the neuroanatomical signature of MAPT mutations have grouped all different mutations together and shown an association with focal atrophy of the temporal lobe. However, the variability in atrophy patterns between each particular MAPT mutation is less well characterised. We aimed to investigate whether there were distinct groups of MAPT mutation carriers based on their neuroanatomical signature. METHODS: We applied Subtype and Stage Inference (SuStaIn), an unsupervised machine learning technique that identifies groups of individuals with distinct progression patterns, to characterise patterns of regional atrophy in MAPT-associated FTD within the Genetic FTD Initiative (GENFI) cohort study. RESULTS: 82 MAPT mutation carriers were analysed, the majority of whom had P301L, IVS10+16 or R406W mutations, along with 48 healthy non-carriers. SuStaIn identified two groups of MAPT mutation carriers with distinct atrophy patterns: a 'temporal' subtype in which atrophy was most prominent in the hippocampus, amygdala, temporal cortex and insula, and a 'frontotemporal' subtype in which atrophy was more localised to the lateral temporal lobe and anterior insula, as well as the orbitofrontal and ventromedial prefrontal cortex and anterior cingulate. There was a one-to-one mapping between IVS10+16 and R406W mutations and the temporal subtype, and a near one-to-one mapping between P301L mutations and the frontotemporal subtype. There were differences in clinical symptoms and neuropsychological test scores between subtypes: the temporal subtype was associated with amnestic symptoms, whereas the frontotemporal subtype was associated with executive dysfunction. DISCUSSION: Our results demonstrate that different MAPT mutations give rise to distinct atrophy patterns and clinical phenotype, providing insights into the underlying disease biology, and potential utility for patient stratification in therapeutic trials

Teaching the science of learning

The science of learning has made a considerable contribution to our understanding of effective teaching and learning strategies. However, few instructors outside of the field are privy to this research. In this Tutorial Review, we focus on six specific cognitive strategies that have received robust support from decades of research: spaced practice, interleaving, retrieval practice, elaboration, concrete examples, and dual coding. We describe the basic research behind each strategy and relevant applied research, present examples of existing and suggested implementation, and make recommendations for further research that would broaden the reach of these strategies

FTLD-TDP with motor neuron disease, visuospatial impairment and a progressive supranuclear palsy-like syndrome: broadening the clinical phenotype of TDP-43 proteinopathies. A report of three cases

<p>Abstract</p> <p>Background</p> <p>Frontotemporal lobar degeneration with ubiquitin and TDP-43 positive neuronal inclusions represents a novel entity (FTLD-TDP) that may be associated with motor neuron disease (FTLD-MND); involvement of extrapyramidal and other systems has also been reported.</p> <p>Case presentation</p> <p>We present three cases with similar clinical symptoms, including Parkinsonism, supranuclear gaze palsy, visuospatial impairment and a behavioral variant of frontotemporal dementia, associated with either clinically possible or definite MND. Neuropathological examination revealed hallmarks of FTLD-TDP with major involvement of subcortical and, in particular, mesencephalic structures. These cases differed in onset and progression of clinical manifestations as well as distribution of histopathological changes in the brain and spinal cord. Two cases were sporadic, whereas the third case had a pathological variation in the progranulin gene 102 delC.</p> <p>Conclusions</p> <p>Association of a "progressive supranuclear palsy-like" syndrome with marked visuospatial impairment, motor neuron disease and early behavioral disturbances may represent a clinically distinct phenotype of FTLD-TDP. Our observations further support the concept that TDP-43 proteinopathies represent a spectrum of disorders, where preferential localization of pathogenetic inclusions and neuronal cell loss defines clinical phenotypes ranging from frontotemporal dementia with or without motor neuron disease, to corticobasal syndrome and to a progressive supranuclear palsy-like syndrome.</p

Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012

Physicochemical Characterization, and Relaxometry Studies of Micro-Graphite Oxide, Graphene Nanoplatelets, and Nanoribbons

The chemistry of high-performance magnetic resonance imaging contrast agents remains an active area of research. In this work, we demonstrate that the potassium permanganate-based oxidative chemical procedures used to synthesize graphite oxide or graphene nanoparticles leads to the confinement (intercalation) of trace amounts of Mn2+ ions between the graphene sheets, and that these manganese intercalated graphitic and graphene structures show disparate structural, chemical and magnetic properties, and high relaxivity (up to 2 order) and distinctly different nuclear magnetic resonance dispersion profiles compared to paramagnetic chelate compounds. The results taken together with other published reports on confinement of paramagnetic metal ions within single-walled carbon nanotubes (a rolled up graphene sheet) show that confinement (encapsulation or intercalation) of paramagnetic metal ions within graphene sheets, and not the size, shape or architecture of the graphitic carbon particles is the key determinant for increasing relaxivity, and thus, identifies nano confinement of paramagnetic ions as novel general strategy to develop paramagnetic metal-ion graphitic-carbon complexes as high relaxivity MRI contrast agents

Parkinson Phenotype in Aged PINK1-Deficient Mice Is Accompanied by Progressive Mitochondrial Dysfunction in Absence of Neurodegeneration

Background Parkinson's disease (PD) is an adult-onset movement disorder of largely unknown etiology. We have previously shown that loss-of-function mutations of the mitochondrial protein kinase PINK1 (PTEN induced putative kinase 1) cause the recessive PARK6 variant of PD. Methodology/Principal Findings Now we generated a PINK1 deficient mouse and observed several novel phenotypes: A progressive reduction of weight and of locomotor activity selectively for spontaneous movements occurred at old age. As in PD, abnormal dopamine levels in the aged nigrostriatal projection accompanied the reduced movements. Possibly in line with the PARK6 syndrome but in contrast to sporadic PD, a reduced lifespan, dysfunction of brainstem and sympathetic nerves, visible aggregates of alpha-synuclein within Lewy bodies or nigrostriatal neurodegeneration were not present in aged PINK1-deficient mice. However, we demonstrate PINK1 mutant mice to exhibit a progressive reduction in mitochondrial preprotein import correlating with defects of core mitochondrial functions like ATP-generation and respiration. In contrast to the strong effect of PINK1 on mitochondrial dynamics in Drosophila melanogaster and in spite of reduced expression of fission factor Mtp18, we show reduced fission and increased aggregation of mitochondria only under stress in PINK1-deficient mouse neurons. Conclusion Thus, aging Pink1 -/- mice show increasing mitochondrial dysfunction resulting in impaired neural activity similar to PD, in absence of overt neuronal death