1,720,967 research outputs found
The neural substrates of social cognition deficits in newly diagnosed multiple sclerosis patients
Full text linkObjective: Cognitive and affective symptoms in multiple sclerosis (MS) can be independently impaired and have different pathways of progression. Cognitive alterations have been described since the earliest MS stages; by contrast, the social cognition (SC) domain has never been investigated in the first year from MS diagnosis. We aimed to evaluate SC and unravel its neural bases in newly diagnosed MS patients. Methods: Seventy MS patients underwent at diagnosis a 3 T-MRI and a neuropsychological/SC assessment (median time between diagnosis and MRI/cognitive evaluation = 0 months). We tested two matched reference samples: 31 relapsing-remitting MS patients with longer course (mean ± SD disease duration = 7.0 ± 4.5 years) and 38 healthy controls (HCs). Cortical thicknesses (CTh) and volumes of brain regions were calculated. Results: Newly diagnosed MS patients performed significantly lower than HCs in facial emotion recognition (global: p < 0.001; happiness: p = 0.041, anger: p = 0.007; fear: p < 0.001; disgust: p = 0.004) and theory of mind (p = 0.005), while no difference was found between newly diagnosed and longer MS patients. Compared to lower performers, higher performers in facial emotion recognition showed greater volume of amygdala (p = 0.032) and caudate (p = 0.036); higher performers in theory of mind showed greater CTh in lingual gyrus (p = 0.006), cuneus (p = 0.024), isthmus cingulate (p = 0.038), greater volumes of putamen (p = 0.016), pallidum (p = 0.029), and amygdala (p = 0.032); patients with higher empathy showed lower cuneus CTh (p = 0.042) and putamen volume (p = 0.007). Interpretations: SC deficits are present in MS patients since the time of diagnosis and remain persistent along the disease course. Specific basal, limbic, and occipital areas play a significant role in the pathogenesis of these alterations
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Treatment Effect on Brain Atrophy Correlates with Treatment Effect on Cognition in Multiple Sclerosis
No full textObjective: The purpose of this study was to evaluate the extent to which treatment effect on magnetic resonance imaging (MRI)-derived measures of brain atrophy and focal lesions can mediate, at the trial level, the treatment effect on cognitive outcomes in multiple sclerosis (MS).Methods: We collected all published randomized clinical trials in MS lasting at least 2 years and including as end points: active MRI lesions (defined as new/enlarging T2 lesions), brain atrophy (defined as a change in brain volume between month 12 and month 24), and change in cognitive performance (assessed by the Paced Auditory Serial Addition Test [PASAT]). Relative reductions were used to quantify the treatment effect on MRI markers (lesions and atrophy), whereas the standardized mean difference (Hedges g) between baseline and follow-up cognitive assessment was used to quantify the treatment effects on cognition. A linear regression, weighted for trial size, was used to assess the relationship between the treatment effects on MRI markers and cognition.Results: Fourteen trials including more than 8,813 patients with MS were included in the meta-regression. Treatment effect on cognition was strongly associated with the treatment effect on brain atrophy (R-2 = 0.79, p < 0.001), but was not correlated with the treatment effect on active MRI lesions (R-2 = 0.16, p = 0.14).Interpretation: Results reported here suggest that brain atrophy, a well-established MRI marker in MS clinical trials, can be used as a main outcome for clinical trials with drugs targeting cognitive impairment and neurodegeneration
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Network-based magnetic resonance imaging measures for clinical trials in multiple sclerosis
Full text linkMy work, presented in this thesis, aimed to define MRI markers to be used in clinical trials for identifying participants most likely to worsen, monitoring disease progression, and assessing treatment effects. With my first study (Chapter 3), I identified from T1-weighted sequences data-driven patterns of grey matter covarying volumes that predicted physical and cognitive disability in a large cohort of participants with secondary progressive multiple sclerosis. Moreover, some of the identified components were better correlated with concurrent disability, and some better predicted disability progression than conventionally used MRI measures (i.e. regional and whole-brain volume). Therefore, with this study, I identified clinically relevant structural patterns that could be used in clinical trials to stratify participants that are most likely to progress.
With my second study (Chapter 4), I expanded on the first project by investigating the involvement of microstructural WM and GM damage as prognostic markers of clinical disability and cognitive dysfunctions in multiple sclerosis. I found networks of microstructural changes predictive of clinical progression and cognitive worsening. Moreover, this was the first study to use standardised T1-weighted/T2-weighted measures of white and grey matter to identify patterns of covarying microstructural
damage changes and use them to predict clinical and cognitive worsening in multiple sclerosis. Finally, because these measures were obtained from MRI sequences routinely acquired in clinical trials, they hold promises to be broadly used in future clinical trials.
With the third and last study (Chapter 5), I have developed a new paradigm to obtain longitudinal individual-level network-based measures of grey matter regional volume changes by applying independent component analysis (ICA) and a self-supervised machine learning model. The identified networks were clinically relevant as they discriminated among multiple sclerosis phenotypes, explained clinical disability, and
showed treatment effect. Moreover, while the ICA needs to be run on the whole cohort, the approach I developed allows retrieving network-based measures at the individual level without re-estimating model parameters on the whole population when applied to new data (e.g. participants and time-points). These measures could be used in future clinical trials to complement conventional MRI measures and open the possibility of estimating network measures prospectively and at the individual
level
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