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Objective: This study is one of the first to investigate social cognition in participants with traumatic brain injury (TBI) using a task that actively engaged the participant in a real interaction with a partner. Previous results have reported altered social cognition in TBI patients, but social cognition was mostly assessed through traditional tasks involving conscious and deliberate reasoning about characters' mental states (i.e., a third‐person perspective). Our goal was to present a new paradigm which allowed the assessment of social cognition in conditions closer to real life meaning that participants were actively engaged in an interaction (i.e., second‐person perspective) in order to capture more implicit use of social cognition processes. Method: This study used three tasks to evaluate social cognition. We designed a task, called EVICog, in which participants were engaged in real audio‐visual conversations with two virtual humans who expressed emotions and produced speech content that required the participants to make inferences about the characters' mental states. The two other tasks are standard in the literature; they use photographs to test participants' recognition of emotions and short comic strips to test their attribution of intentions. Results: Our results showed that TBI participants presented a significant deficit of social cognition compared to control participants. The ROC analysis showed that EVICog has a high discrimination power compared to the other tests. Conclusion: These results further confirm that social cognition is altered in TBI participants even in real interactions and further support the use of ecological settings to investigate social cognition. [ABSTRACT FROM AUTHOR]

Cognitive impairment (CI) affects 40–65% of patients with multiple sclerosis (MS). CI can have a negative impact on a patient's everyday activities, such as engaging in conversations. Speech production planning ability is crucial for successful verbal interactions and thus for preserving social and occupational skills. This study investigates the effect of cognitive‐linguistic demand and CI on speech production planning in MS, as reflected in speech prosody. A secondary aim is to explore the clinical potential of prosodic features for the prediction of an individual's cognitive status in MS. A total of 45 subjects, that is 22 healthy controls (HC) and 23 patients in early stages of relapsing‐remitting MS, underwent neuropsychological tests probing specific cognitive processes involved in speech production planning. All subjects also performed a read speech task, in which they had to read isolated sentences manipulated as for phonological length. Results show that the speech of MS patients with CI is mainly affected at the temporal level (articulation and speech rate, pause duration). Regression analyses further indicate that rate measures are correlated with working memory scores. In addition, linear discriminant analysis shows the ROC AUC of identifying MS patients with CI is 0.70 (95% confidence interval: 0.68–0.73). Our findings indicate that prosodic planning is deficient in patients with MS‐CI and that the scope of planning depends on patients' cognitive abilities. We discuss how speech‐based approaches could be used as an ecological method for the assessment and monitoring of CI in MS. [ABSTRACT FROM AUTHOR]

Simple Summary: In the recent years, several deep learning methods for medical image segmentation have been developed for different purposes such as diagnosis, radiotherapy planning or to correlate images findings with other clinical data. However, few studies focus on longitudinal images and response assessment. To the best of our knowledge, this is the first study to date evaluating the use of automatic segmentation to obtain imaging biomarkers that can be used to assess treatment response in patients with metastatic breast cancer. Moreover, the statistical analysis of the different biomarkers shows that automatic segmentation can be successfully used for their computation, reaching similar performances compared to manual segmentation. Analysis also demonstrated the potential of the different biomarkers including novel/original ones to determine treatment response. Metastatic breast cancer patients receive lifelong medication and are regularly monitored for disease progression. The aim of this work was to (1) propose networks to segment breast cancer metastatic lesions on longitudinal whole-body PET/CT and (2) extract imaging biomarkers from the segmentations and evaluate their potential to determine treatment response. Baseline and follow-up PET/CT images of 60 patients from the EPICURE s e i n m e t a study were used to train two deep-learning models to segment breast cancer metastatic lesions: One for baseline images and one for follow-up images. From the automatic segmentations, four imaging biomarkers were computed and evaluated: SUL p e a k , Total Lesion Glycolysis (TLG), PET Bone Index (PBI) and PET Liver Index (PLI). The first network obtained a mean Dice score of 0.66 on baseline acquisitions. The second network obtained a mean Dice score of 0.58 on follow-up acquisitions. SUL p e a k , with a 32% decrease between baseline and follow-up, was the biomarker best able to assess patients' response (sensitivity 87%, specificity 87%), followed by TLG (43% decrease, sensitivity 73%, specificity 81%) and PBI (8% decrease, sensitivity 69%, specificity 69%). Our networks constitute promising tools for the automatic segmentation of lesions in patients with metastatic breast cancer allowing treatment response assessment with several biomarkers. [ABSTRACT FROM AUTHOR]

Metastatic breast cancer patients receive lifelong medication and are regularly monitored for disease progression. The aim of this work was to (1) propose networks to segment breast cancer metastatic lesions on longitudinal whole-body PET/CT and (2) extract imaging biomarkers from the segmentations and evaluate their potential to determine treatment response. Baseline and follow-up PET/CT images of 60 patients from the EPICUREseinmeta study were used to train two deep-learning models to segment breast cancer metastatic lesions: One for baseline images and one for follow-up images. From the automatic segmentations, four imaging biomarkers were computed and evaluated: SULpeak, Total Lesion Glycolysis (TLG), PET Bone Index (PBI) and PET Liver Index (PLI). The first network obtained a mean Dice score of 0.66 on baseline acquisitions. The second network obtained a mean Dice score of 0.58 on follow-up acquisitions. SULpeak, with a 32% decrease between baseline and follow-up, was the biomarker best able to assess patients' response (sensitivity 87%, specificity 87%), followed by TLG (43% decrease, sensitivity 73%, specificity 81%) and PBI (8% decrease, sensitivity 69%, specificity 69%). Our networks constitute promising tools for the automatic segmentation of lesions in patients with metastatic breast cancer allowing treatment response assessment with several biomarkers.

Theory of Mind refers to the ability to infer other’s mental states, their beliefs, intentions, or knowledge. To date, only two studies have reported the presence of Theory of Mind impairment in mild cognitive impairment (MCI). In the present study,we evaluated 20 MCI patients and compared them with 25 healthy control participants using two Theory of Mind tasks. The first task was a false belief paradigm as frequently used in the literature, and the second one was a referential communication task,assessing Theory of Mind in a real situation of interaction and which had never been used before in this population. The results showed that MCI patients presented difficulties inferring another person’s beliefs about reality and attributing knowledge to them in a situation of real-life interaction. Two different patterns of Theory of Mind emerged among the patients. In comparison with the control group, some MCI patients demonstrated impairment only in the interaction task and presented isolated episodicmemory impairment, while others were impaired in both Theory of Mind tasks and presented cognitive impairment impacting both episodic memory and executive functioning. Theory of Mind is thus altered in the very early stages of cognitive impairment even in real social interaction, which could impact precociously relationships in daily life.