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Van de Ville, D.

Publications and source records attributed to Van de Ville, D..

2 recordsLinked to original sources

Emo-FilM: A multimodal dataset for affective neuroscience using naturalistic stimuli

The extensive Emo-FilM dataset stands for Emotion research using Films and fMRI in healthy participants. This dataset includes detailed emotion annotations by 44 raters for 14 short films with a combined duration of over 2[1/2] hours, as well as recordings of respiration, heart rate, and functional magnetic resonance imaging (fMRI) from a different sample of 30 individuals watching the same films. The detailed annotations of experienced emotion evaluated 50 items including ratings of discrete emotions and emotion components from the domains of appraisal, motivation, motor expression, physiological response, and feeling. Quality assessment for the behavioural data shows a mean inter-rater agreement of 0.38. The parallel fMRI data was acquired at 3 Tesla in four sessions, accompanied with a high-resolution structural (T1) and resting state fMRI scans for each participant. Physiological recordings during fMRI included heart rate, respiration, and electrodermal activity (EDA). Quality assessment indicators confirm acceptable quality of the MRI data. This dataset is designed, but not limited, to studying the dynamic neural processes involved in emotion experience. A particular strength of this data is the high temporal resolution of behavioural annotations, as well as the inclusion of a validation study in the fMRI sample. This high-quality behavioural data in combination with continuous physiological and MRI measurements makes this dataset a treasure trove for researching human emotion in response to naturalistic stimulation in a multimodal framework.

neuroscience↗

Differential impact of brain network efficiency on post-stroke motor and attentional deficits

BackgroundMost studies on stroke have been designed to examine one deficit in isolation, yet survivors often have multiple deficits in different domains. While the mechanisms underlying multiple-domain deficits remain poorly understood, network-theoretical methods may open new avenues of understanding. Methods50 subacute stroke patients (7{+/-}3days post-stroke) underwent diffusion-weighted magnetic resonance imaging and a battery of clinical tests of motor and cognitive functions. We defined indices of impairment in strength, dexterity, and attention. We also computed imaging-based probabilistic tractography and whole brain connectomes. Overlaying individual lesion masks onto the tractograms enabled us to split the connectomes into their affected and unaffected parts and associate them to impairment. ResultsTo efficiently integrate inputs from different sources, brain networks rely on a "rich-club" of a few hub nodes. Lesions harm efficiency, particularly when they target the rich-club. We computed efficiency of the unaffected connectome, and found it was more strongly correlated to impairment in strength, dexterity and attention than efficiency of the total connectome. The magnitude of the correlation between efficiency and impairment followed the order attention > dexterity {approx} strength. Network weights associated with the rich-club were more strongly correlated to efficiency than non-rich-club weights. ConclusionsAttentional impairment is more sensitive to disruption of coordinated network activity between brain regions than motor impairment, which is sensitive to disruption of localized network activity. Providing more accurate reflections of actually functioning parts of the network enables the incorporation of information about the impact of brain lesions on connectomics contributing to a better understanding of underlying stroke mechanisms.

neuroscience↗