Pierre Vrignaud

Principle of structural equation modeling for exploring functional interactivity within a putative network of interconnected brain areas

Functional neuroimaging first allowed researchers to describe the functional segregation of regionally activated areas during a variety of experimental tasks. More recently, functional integration studies have described how these functionally specialized areas, interact within a highly distributed neural network. When applied to the field of neurosciences, structural equation modeling (SEM) uses theoretical and/or empirical hypotheses to estimate the effects of an experimental task within a putative network. SEM represents a linear technique for multivariate analysis of neuroimaging data and has been developed to simultaneously examine ratios of multiple causality in an experimental design; the method attempts to explain a covariance structure within an anatomical constrained model. This method, when combined with the concept of effective connectivity, can provide information on the strength and direction of the functional interactions that take place between identified brain regions of a putative network.

Differences in effective connectivity between dyslexic children and normal readers during a pseudoword reading task: an fMRI study.

PURPOSE This fMRI study investigated phonological and lexicosemantic processing in dyslexic and in chronological age- and reading level-matched children in a pseudoword reading task. MATERIALS AND METHODS The effective connectivity network was compared between the three groups using a structural model including the supramarginal cortex (BA 40; BA: Brodmann area), fusiform cortex (BA 37) and inferior frontal cortex (BA 44/45) areas of the left hemisphere. RESULTS The results revealed differences in connectivity patterns. In dyslexic patients, in contrast with chronological age- and reading level-matched groups, no causal relationship was demonstrated between BA 40 and BA 44/45. However, a significant causal relationship was demonstrated between BA 37 and BA 44/45 both in dyslexic children and in the reading level-matched group. CONCLUSIONS These findings were interpreted as evidence for a phonological deficit in developmental dyslexia.

Social interactions between people with dementia: pilot evaluation of an observational instrument in a nursing home

BACKGROUND In dementia, cognitive and psychological disorders might interfere with maintaining social interactions. We have little information about the nature of these interactions of people with dementia in nursing homes. The aim of this study is to investigate social interactions between people with dementia and to validate an observation grid of them. METHODS Fifty-six institutionalized people with dementia took part in this study. Residents had not met beforehand and were divided into groups of four to six. Social behaviors were videotaped and analyzed by two independent raters with an observation grid measuring frequency of occurrence. The ethogram was the conceptual tool that became the Social Observation Behaviors Residents Index (SOBRI). RESULTS Two-thousand-six-hundred-seventy instances of behavior were collected. Behaviors directed at others represented 50.90% and self-centered behaviors 47.83%. No negative behaviors were observed. Principal Component Analysis (PCA) was used to validate the SOBRI and showed two components of social behaviors that explained about 30.56% of the total variance: social interactions with other residents (18.36%) and with care staff (12.20%). The grid showed a good internal consistency with a Cronbachs α of 0.90 for the first component and 0.85 for the second one. CONCLUSIONS The SOBRI presents robust psychometric validity. This pilot study indicates that people with dementia spontaneously interact with other residents. These results contradict the stigma of non-communication and the stereotypes about dementia. More studies and validations are needed to contribute to the knowledge of social interactions in dementia.

Anxiety, emotional processing and depression in people with multiple sclerosis

BackgroundDespite the high comorbidity of anxiety and depression in people with multiple sclerosis (MS), little is known about their inter-relationships. Both involve emotional perturbations and the way in which emotions are processed is likely central to both. The aim of the current study was to explore relationships between the domains of mood, emotional processing and coping and to analyse how anxiety affects coping, emotional processing, emotional balance and depression in people with MS.MethodsA cross-sectional questionnaire study involving 189 people with MS with a confirmed diagnosis of MS recruited from three French hospitals. Study participants completed a battery of questionnaires encompassing the following domains: i. anxiety and depression (Hospital Anxiety and Depression Scale (HADS)); ii. emotional processing (Emotional Processing Scale (EPS-25)); iii. positive and negative emotions (Positive and Negative Emotionality Scale (EPN-31)); iv. alexithymia (Bermond-Vorst Alexithymia Questionnaire) and v. coping (Coping with Health Injuries and Problems-Neuro (CHIP-Neuro) questionnaire. Relationships between these domains were explored using path analysis.ResultsAnxiety was a strong predictor of depression, in both a direct and indirect way, and our model explained 48% of the variance of depression. Gender and functional status (measured by the Expanded Disability Status Scale) played a modest role. Non-depressed people with MS reported high levels of negative emotions and low levels of positive emotions. Anxiety also had an indirect impact on depression via one of the subscales of the Emotional Processing Scale (“Unregulated Emotion”) and via negative emotions (EPN-31).ConclusionsThis research confirms that anxiety is a vulnerability factor for depression via both direct and indirect pathways. Anxiety symptoms should therefore be assessed systematically and treated in order to lessen the likelihood of depression symptoms.

Dear editors of the special issue of International Psychogeriatric on Social Health

Dementia can interfere with the maintenance of social interactions. The ability to participate in social interactions is one of the elements that enables good social health (Hubert et al., 2011), and having dementia does not automatically eliminates the persons opportunity to have good social health (Vernooij-Dassen and Jeon, 2016). We highlighted in a previous study that people with dementia who did not know each other interacted spontaneously when they were in a stimulating social interaction setting (Mabire et al., 2016). However, a lack of activity and social interaction in nursing homes is still a widespread issue (Harper Ice, 2002). Stimulation of social interactions is rarely used as an intervention and social interactions are seldomly used as social health related outcomes.

Use of fMRI and Structural Equation Modeling for Studying Interconnected Brain Areas within a Hypothetical Network

Functional neuroimaging first allowed researchers to describe the functional segregation of regionally activated areas during a variety of experimental tasks. More recently, functional integration studies have described how these functionally specialized areas (i.e. areas whose activity is temporally modified) interact within a highly distributed neural network. When applied to the field of functional magnetic resonance imaging (fMRI), structural equation modeling (SEM) uses theoretical and/or empirical hypotheses to estimate the effects (path coefficients) of an experimental task within a putative network. Structural equation modeling represents a linear technique for multivariate analysis of fMRI data and has been developed to simultaneously examine ratios of multiple causality in an experimental design; the method attempts to explain a covariance structure within an anatomical (constrained) model. This method, when combined with the concept of effective connectivity, can provide information on the strength and direction of the functional interactions which take place between identified nodes of a putative network. After having provided a brief reminder of the principle of the blood oxygen level-dependent (BOLD) contrast effect, the physiological bases of brain activity and the concepts of functional integration and effective connectivity, we specify the various steps in the SEM analysis and the use of fMRI data to explore putative networks of interconnected active areas.