bluuebunny/crossref_metadata_2025_split huggingface dataset:
Caring for Spirit: Developing online dementia education and training with Aboriginal and Torres Strait Islander Australians
Authors: Poulos Lauren , Wall Sharon , Pross Eliza , Daylight Gail , Delbaere Kim , Broe Gerald A , Radford Kylie 
Published: February 2022

AbstractBackgroundAboriginal and Torres Strait Islander peoples (hereafter Aboriginal Australians) experience rates of dementia 3‐5 times higher than the general Australian population. Promoting dementia health literacy in culturally appropriate ways has been identified by Aboriginal communities as a priority to improve awareness of dementia and support timely diagnosis and service access, enhance care, and support wellbeing for family caregivers. This project aimed to respond to the need for culturally relevant, evidence‐based and accessible dementia education with this population.MethodThe ‘Caring for Spirit’ project sought to develop a dementia education website and online training program for the Aboriginal health and aged care workforce, as well as for family caregivers. Resource development applied the following overarching principles: (1)Dementia information was produced from Aboriginal cultural perspectives, foregrounding research evidence and lived experience in Aboriginal populations; (2)The project was led by an Aboriginal project manager and engaged Aboriginal consultants throughout; (3)Training was designed using appropriate and engaging pedagogical methods; (4)Resources needed to be accessible to a wide range of users, taking into consideration cultural diversity, technology access, disabilities and literacy levels; (5)Extensive community consultation and engagement guided the development and dissemination of the resources. The project team worked beside Aboriginal Elders, colleagues and wider networks, established community research partners, and dementia researchers.ResultWorkshops were conducted with community‐based researchers and service providers to scope education needs and plan the content, and later, seek feedback on initial website content and design. Infographics and videos were important elements, including interviews with Aboriginal and non‐Aboriginal dementia experts from across Australia, and scripted drama to illustrate key concepts in understanding dementia. To complement the website, an interactive online training program featured exploratory non‐linear progression across four modules, reflective exercises, voice‐over narrations, and multimedia. Training modules were evaluated by our network of project collaborators to ensure that information provided was accurate, supported by research, and presented in a way that is culturally safe, respectful, practical, and easy to understand and apply.ConclusionThese resources, designed with older Aboriginal people and their communities, will contribute to reducing the high burden of dementia in this population, and enhance dementia care.
Brain MRI signatures of atrophy in genetic frontotemporal lobar degeneration
Authors: Spinelli Edoardo Gioele , Ghirelli Alma , Basaia Silvia , Cividini Camilla , Riva Nilo , Magnani Giuseppe , Caso Francesca , Caroppo Paola , Prioni Sara , Rossi Giacomina , Tremolizzo Lucio , Appollonio Ildebrando , Silani Vincenzo , Carrera Paola , Filippi Massimo , Agosta Federica 
Published: February 2022

AbstractBackgroundGenetic heterogeneity underlying different clinical presentations of the frontotemporal lobar degeneration (FTLD) spectrum hampers the identification of useful biomarkers that may be able to monitor disease progression and/or facilitate the enrolment in clinical trials. The aim of this study was to assess cortical, subcortical and cerebellar grey matter (GM) atrophy using magnetic resonance imaging (MRI) in patients affected by disorders of the FTLD spectrum with known genetic mutations.MethodSixty‐six patients carrying FTLD‐related mutations were enrolled, including 44 with pure motor neuron disease (MND) and 22 with frontotemporal dementia (FTD). Sixty‐one patients with sporadic FTLD (sFTLD) matched for age, sex and disease severity with genetic FTLD (gFTLD) were also included, as well as 52 healthy controls (HC). A whole‐brain voxel‐based morphometry (VBM) analysis was performed. GM volumes of subcortical and cerebellar structures were also obtained.ResultCompared with HC, GM volume loss on VBM was generally greater and more diffuse in genetic FTD (gFTD) cases, followed by sporadic FTD (sFTD) cases and genetic MND (gMND) cases, whereas sporadic MND (sMND) showed very focal atrophy of the motor cortex. Patients carrying GRN and C9ORF72 mutations showed the most widespread cortical volume loss, whereas SOD1 and TARDBP patients were the least atrophic. Greater atrophy of the parietal cortices and thalami was found, globally, in genetic FTLD patients compared with sporadic FTLD and, particularly, in C9‐MND patients compared with sMND. When assessing deep GM volumes, genetic FTLD patients showed significant volume loss compared with sporadic FTLD in the caudate nuclei and thalami. In particular, greater atrophy of the left caudate and right thalamus was found in C9MND compared with sMND. At the cerebellar level, greater atrophy of the right lobule VIIb could discriminate genetic FTLD from sporadic FTLD patients.ConclusionOur data suggest that measures of deep GM and cerebellar involvement might be useful markers of genetic FTLD, particularly C9ORF72‐related disorders, regardless of the clinical presentation within the FTLD spectrum.Supported by: Italian Ministry of Health (RF‐2011‐02351193; GR‐2011‐02351217) and European Research Council (StG‐2016_714388_NeuroTRACK).
Quantification of neocortical sulcal widening as a rapidly‐computable brain atrophy metric: Application to frontotemporal dementia
Authors: Yeh Chien‐Lin , Clarke Mica T.M. , Takano Akihiro , Rabiner Eugenii A. , Rohrer Jonathan D. , Schwarz Adam J. 
Published: February 2022

AbstractBackgroundRegional patterns of brain atrophy are characteristic features of neurodegenerative disorders. Radiological assessments of cortical brain atrophy tend to reflect sulcal widening and brain shrinkage, whereas quantitative assessments based on grey matter volume or cortical thickness can be hampered by poor SNR and inaccurate estimation of grey/white matter boundaries. Our aim was to define a simple and rapidly‐computable marker of localized sulcal widening based on standard brain tissue segmentation tools and assess it in a cohort of individuals with frontotemporal dementia (FTD) and SV2A PET scans.Method3DT1 MRI and [11C]UCB‐J PET scans were analyzed from individuals with FTD (N=8, MMSE range 10‐28) and age‐matched healthy volunteers (HV, N=17) within the MIND‐MAPS consortium. 3DT1 images were segmented into gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) using SPM12. The lobar CSF occupancy fraction (CSFOF) was calculated as CSF/(GM+WM+CSF) in the frontal, parietal, temporal and occipital lobes. The tissue type masks were created by a threshold of total tissue occupancy > 50% then assigning each voxel to the tissue for which it has the maximum probability. 3DT1 scans were also assessed using cortical thickness (Freesurfer v6.0). SV2A binding was quantified as distribution volume ratio (DVR‐1) from [11C]UCB‐J using centrum semiovale as reference region.ResultThe CSFOF computation time was approximately 6 minutes per scan on a 2019 MacBook pro. CSFOF was significantly higher in FTD than in HV (ANOVA F=19.6, p<0.0001, Figure 1). Corrected, non‐parametric linear relationships between CSFOF and MMSE, cortical thickness and [11C]UCB‐J DVR‐1 are summarized in Table 1 and Figure 2 for the frontal lobe. The relationships were weaker in the FTD cohort alone, compared with the full sample. Cortical thickness was the only nominally significant association remained after correction for age and MMSE, suggesting that CSFOF and SV2A density may reflect different aspects of the disease.ConclusionThe CSFOF provides a simple, rapid and robust quantification of lobar brain atrophy, reflecting sulcal widening and both grey and white matter atrophy. It complements other imaging markers of neurodegeneration and extends the concepts of ventricular volume and hippocampal occupancy fraction to the wider neocortex.
Converging longitudinal patterns of atrophy in clinical variants of frontotemporal lobar degeneration
Authors: Spinelli Edoardo Gioele , Basaia Silvia , Cividini Camilla , Riva Nilo , Magnani Giuseppe , Caso Francesca , Caroppo Paola , Prioni Sara , Tremolizzo Lucio , Appollonio Ildebrando , Silani Vincenzo , Carrera Paola , Filippi Massimo , Agosta Federica 
Published: February 2022

AbstractBackgroundThe aim of this study was to assess longitudinal patterns of atrophy shown by magnetic resonance imaging (MRI) in the whole‐brain GM of patients affected by different clinical variants of the FTLD spectrum.MethodFifty‐nine patients, including 26 with behavioral variant of frontotemporal dementia (bvFTD), 10 non‐fluent/agrammatic variant of primary progressive aphasia (nfvPPA), 12 semantic variant of PPA (svPPA), and 11 MND, in the absence of known pathogenic mutations, underwent MRI on a 3T scanner at 6‐month intervals for one year. Thirty‐three healthy controls underwent the same protocol. 3D T1‐weighted MRI sequences were analyzed using voxel‐based morphometry to assess the longitudinal evolution of GM atrophy in patients, compared with HC.ResultAt baseline, severe diffuse atrophy of frontotemporal cortical regions and basal ganglia was found in bvFTD, nfvPPA and svPPA groups, whereas MND did not show significant GM atrophy. At 6‐month follow‐up, bvFTD and PPA groups showed significant progression of atrophy in the insular (bvFTD, nfvPPA and svPPA) and anterior cingulate cortex (bvFTD and nfvPPA), bilaterally, as well as in the left caudate nucleus and middle temporal cortex (svPPA). At 12‐month follow‐up, similar patterns of atrophy progression were found, with the additional involvement of the superior frontal cortical gyri in nfvPPA, bilaterally, and the right hippocampus in svPPA. No significant progression of atrophy was found in MND patients.ConclusionOur data suggest that atrophy of insular and anterior cingulate cortical regions closely reflects the progression of neurodegeneration across the behavioral and linguistic presentations of frontotemporal dementia (FTD), in contrast with a substantial sparing of GM in MND. Supported by: European Research Council (StG‐2016_714388_NeuroTRACK).
Baseline MRI and CSF measurements in cognitively normal individuals as prognostic markers of progression to mild cognitive impairment
Authors: Prosser Lloyd , Macdougall Amy , Fiford Cassidy M. , Sudre Carole H. , Manning Emily N. , Malone Ian B. , Walsh Phoebe , Goodkin Olivia , Pemberton Hugh , Barkhof Frederik , Biessels Geert Jan , Cash David M. , Barnes Jo 
Published: February 2022

AbstractBackgroundAlzheimer’s disease is currently one of our greatest socioeconomic challenges. Understanding the ability of biomarkers to predict clinical progression, prior to significant cognitive decline, is critical when considering secondary prevention strategies. Here we consider the association between a range of single time‐point baseline biomarkers in a cognitively‐normal group and the subsequent risk of developing Mild Cognitive Impairment (MCI).MethodData from Alzheimer’s Disease Neuroimaging Initiative (ADNIGO/2/3) were used. Baseline markers (CSF amyloid 1‐42, CSF ptau, white matter hyperintensities [WMH], microbleeds, whole‐brain volume, and hippocampal volume) were obtained for 192 cognitively‐normal (CN) individuals (controls and with significant memory concern; see table). Longitudinal diagnostic data to calculate conversion time was also collected. Cox regression models were fitted for time to diagnosis of MCI. Models were fitted for each marker separately, and in a fully‐adjusted model (containing all markers); adjustments for total intracranial volume (TIV) were performed when needed. Models were refitted adjusting for baseline age.ResultThere was strong evidence that increased WMH volume and lower hippocampal volume were associated with conversion to MCI. This was found in both individual models, and in the fully‐adjusted model (see figure). Decreased whole brain volume, CSF amyloid, and increased microbleeds were found to be associated with conversion to MCI when considered individually but not in a fully‐adjusted model. CSF ptau was not associated with conversion, either individually or in the fully‐adjusted model. Adjustments for baseline age did not materially change results.ConclusionSingle time‐point baseline measurements of increased WMH and decreased hippocampal volume were independently predictive of subsequent progression to MCI. Hippocampal volume and WMH may represent different processes; lower hippocampal volumes representing greater neurodegeneration and WMH as a surrogate marker of numerous pathologies, including cerebrovascular disease. While decreased whole brain volume, decreased CSF amyloid, and increased microbleeds were predictive of conversion to MCI when assessed separately, their non‐significant association in the fully‐adjusted model suggests that these are not as strongly predictive of conversion.
Identifying imaging genetics biomarkers in Alzheimer’s disease via integrating graph convolutional neural network and canonical correlation analysis
Authors: Kim Mansu , Yao Xiaohui , Saykin Andrew J. , Moore Jason H. , Long Qi , Kim Dokyoon , Shen Li 
Published: February 2022

AbstractBackgroundBrain imaging genetics is an emerging research topic in the study of Alzheimer’s disease (AD). The conventional approach, such as canonical correlation analysis (CCA), has been widely used to identify imaging genetic associations. A deep learning model has recently been proposed to better understand the roots of the complex association between imaging and genetic measures. We propose a graph convolutional neural network (GCN) with CCA loss function to integrate and identify the complex imaging genetics associations in AD.MethodWe proposed a spectral GCN approach with CCA loss function (GCN‐CCA) to extract feature representations from imaging and genetics data. Briefly, the graph embeddings on the graph nodes were filtered in the Fourier domain. We used two hidden layers with 64 hidden units for extracting imaging and genetic data. ReLU activations were used after each graph convolution layer. A canonical correlation loss function was optimized based on Adam optimizer. We compared our model with the deep CCA model (DCCA) for AD classification.ResultWe downloaded data for 310 participants (103 AD and 207 Cognitive Normal [CN]) including neuroimaging and genetic data from the ADNI database. We used average structural connectivity based on the AAL atlas as the graph in our GCN model, three imaging measurements (VBM, FDG, FBR) as initial attributes on the graph nodes. We selected 2,644 candidate SNPs from the GWAS catalog (https://www.ebi.ac.uk/gwas/). The proposed model obtained 82.25 % test accuracy for the AD/CN classification, outperforming the DCCA model (77.41%). For interpretation, we generated the saliency maps using guided gradient backpropagation (Figs 1 and 2). We observed the imaging phenotypes from left middle temporal gyrus, superior temporal, frontal inferior triangularis, putamen, paracentral lobule, frontal medial orbital, and pallidum, and right posterior cingulum, and genetic markers from ABCA13 (rs2163935, rs6955132, rs4024044) and APOE (rs429358) contributed to the AD outcome prediction.ConclusionHere, we demonstrated the utility of GCN‐CCA model and its interpretability. The GCN‐CCA not only obtained higher prediction performance but also highlighted important regions for AD classification. We plan to apply our algorithm to other AD cohorts to see if our algorithm generalizes to independent data sets.
The impact of accuracy on functional neural correlates of the useful field of view (UFOV) task
Authors: Kraft Jessica N. , Albizu Alejandro , O'Shea Andrew , Hausman Hanna K. , Evangelista Nicole D. , Boutzoukas Emanuel , Hardcastle Cheshire , Van Etten Emily J. , Bharadwaj Pradyumna K. , Song Hyun , Smith Samantha G. , Porges Eric , DeKosky Steven T. , Hishaw Georg A. , Wu Samuel S. , Marsiske Michael , Cohen Ronald , Alexander Gene , Woods Adam J. 
Published: February 2022

AbstractBackgroundRapid declines in processing speed performance occur in aging and serve as a critical marker of cognitive abilities. Studies suggest that Useful Field of View (UFOV) training may ameliorate cognitive decline. The ACTIVE trial, consisting of over 2,800 participants, found a 29% reduction in ADRD risk 10 years following intervention. Recent clinical trials aimed at slowing cognitive decline in AD have focused on UFOV‐based interventions. Despite its efficacy, little is known about its neural correlates. The current study investigated blood‐oxygen level dependent (BOLD) activation patterns of the UFOV task.Method233 older adults completed the UFOV task while undergoing task‐based fMRI. The UFOV task requires participants to identify a central target, as well as a peripheral target amongst distractors, presented for a brief period of time. First‐level contrasts modeled hemodynamic response, while second‐level analyses assessed unique BOLD activation associated with encoding and recall components of the task. Age, education, sex and scanner were included as covariates. Significant clusters were defined as >10 voxels, and all clusters were corrected at a false discovery rate (FDR) of p<0.05.ResultPrevious data have shown widespread bilateral whole‐brain activation associated with the encoding component, whereas discrete activation patterns were related to the recall component of the task. Additionally, numerous shared brain regions were critical to both the encoding and recall components of the UFOV task. We further investigated cortical activation patterns associated with age‐related differences in UFOV performance. Variability in performance was related to widespread patterns of activation during the encoding component of the UFOV task, namely the superior parietal lobule, insula, hippocampus and DLPFC. Assessing UFOV performance variability within recall, no significant clusters survived FDR correction. Furthermore, the inferior parietal lobule, cerebellum, and inferior frontal gyrus were critical in both encoding and recall in UFOV performance.ConclusionThe present study elucidates pivotal regions associated with UFOV task performance. Notably, we found that variability in UFOV performance is directly associated with neural activation during the presentation/apprehension of the stimuli, but not during recall. These findings suggest that variation in neural activation during speeded attentional processing is central to age‐related UFOV performance differences, and interventions targeting these neural processes may be of benefit.
The Overlap Index: A new means for early detection of serial tau PET signal change
Authors: Lee Jeyeon , Min Hoon‐Ki , Lundt Emily S. , Albertson Sabrina M. , Botha Hugo , Senjem Matthew L. , Gunter Jeffrey L. , Schwarz Christopher G. , Jones David T. , Knopman David S. , Jack Clifford R. , Petersen Ronald C. , Lowe Val J. 
Published: February 2022

AbstractBackgroundIn longitudinal tau (flortaucipir) positron emission tomography (PET), a reliable method to detect a tau accumulation while minimizing a random variability is crucial. Variability can be a problem especially in the early stages of tau pathology in which only small regions and low levels of accumulation may exist relative to the range of random noise fluctuation.MethodWe recruited 76 participants who had serial scans for a total of 234 tau PET scans. In this cohort we tested a PET signal measurement method we developed called the “overlap index” (OI). The OI was calculated by identifying voxels that have elevated tau PET signal that overlap on two successive tau PET scans in the same participant (Figure 1). This analysis procedure tests the hypothesis that voxel‐wise, elevated‐uptake consistency might be more sensitive for early detection of tau tangles than standard ROI based measures. We compared OI to ROI based measures for early detection of increasing tau PET signal and detection clinical progression. A meta ROI used in this analysis included the amygdala, entorhinal cortex, fusiform, parahippocampal, and inferior temporal and middle temporal gyri.ResultOI showed regional progression of tau in the temporal lobe followed by increases throughout the brain at relatively low standardized uptake value ratio (SUVr) levels (Figure 2A and B). The topographical pattern of OI was in agreement in some respects with the sequential appearance of tau aggregation (Figure 2B and C). The high‐OI group (OI>0.5) had a significantly higher ΔSUVr compared to the low‐OI group (OI<0.5) (Figure 3A and B). OI identified subjects that did or didn’t have tau accumulation within meta SUVr subgroups even when below the meta ROI SUVr threshold. OI was associated with early clinical disease stage progression vs meta‐ROI SUVr measures (Figure 3C‐E).ConclusionIn this study, we found that OI tau PET measurement was able to quantify voxel‐wise consistency using a set of serial scans. OI may detect earlier tau tangle accumulation and earlier disease progression than standard SUVr measurements in tau PET.
Automatic brain extraction using deep learning
Authors: Weatheritt Jack , Vega Barbara Brito , Reinwald Michael , Joules Richard , Lorenzini Luigi , Barkhof Frederik , Mutsaerts Henk‐Jan , Wink Alle Meije , Wolz Robin 
Published: February 2022

AbstractBackgroundBrain extraction from cranial MR images is an important precursor to many medical‐imaging algorithms prominent in Alzheimer’s disease research, including image registration, atlas‐based segmentation and longitudinal analyses. Moreover, skull stripping can be an important step in deidentification by removing facial features from the image. One widely used method is PiNCRAM, which provides reliable results, however, is computationally intensive and time consuming. This is a bottleneck for scalable clinical trial data processing and potential clinical applications. The emergence of deep‐learning segmentation tools (convolutional neural networks – CNNs) provide a framework for drastically reducing the processing time and potentially increasing the reproducibility. In this work, we develop a CNN‐based skull‐stripping method that mimics PiNCRAM, but runs in a fraction of the time.MethodA cohort of 2300 T1 weighted scans from healthy participants with a slightly increased risk for Alzheimer’s Disease, drawn from the European Prediction of Alzheimer’s Dementia (EPAD) consortium was used to train a CNN. The examples fed to the model are generated from PiNCRAM masks. The network architecture is a 3D U‐NET consisting of around 200,000 parameters and was trained using the ADAM optimiser with default settings (as implemented in Tensorflow 2).We used 50 T1 weighted scans from the Open Access Series of Imaging Studies (OASIS) test‐retest cohort as a validation set.ResultVisually, the CNN is seen to segment the brain well (Fig 1). The mean dice overlap score on the validation set is 0.9811 (std. 0.002791). The median CNN test‐retest volume change percentage (expressed between 0 and 1) was ‐0.000854 (std. 0.006703), whilst PiNCRAM was 0.00883 (std. 0.0115; Fig. 2, Table 1). From the superior reproducibility, one could infer that dice overlap less than unity does not imply CNN error (Fig 1.)Our CNN pipeline takes approximately 1.1% of the time taken by PiNCRAM. Each brain takes approximately 20 seconds (when in batch mode) versus 30 minutes for PiNCRAM.ConclusionThe PiNCRAM‐CNN provides a robust and highly accurate brain extraction alternative to PiNCRAM. Not only is it two orders of magnitude faster to run, but it also displays an order of magnitude better reproducibility.