La remédiation cognitive des schizophrènes : le programme RECOS : [guide pour praticiens]

La remédiation cognitive est devenue en quelques années un outil thérapeutique important dans le domaine des maladies psychiatriques, et plus particulièrement dans celui des troubles schizophréniques. Parmi les programmes utilisés, RECOS est l'un des seuls à proposer un entraînement qui tient compte du profil cognitif individuel, permettant ainsi de répondre de manière ciblée à la grande hétérogénéité des déficits observés. Cet ouvrage constitue le support de base indispensable à la formation délivrée aux futurs thérapeutes RECOS. Il se divise en deux parties. La première partie présente les données scientifiques actuelles sur les troubles cognitifs de la schizophrénie et les moyens d'y remédier. Le lien entre les performances cognitives et les capacités fonctionnelles permet de comprendre comment et pourquoi la remédiation cognitive favorise la réinsertion sociale et professionnelle. La deuxième partie fait office de manuel d'utilisation pour tous les thérapeutes (psychologues, psychiatres, infirmiers, ergothérapeutes) souhaitant utiliser RECOS. L'ouvrage décrit les pathologies psychiatriques visées par le programme, la schizophrénie n'étant pas la seule concernée. Il aborde ensuite les différentes étapes du traitement, en consacrant une place importante à 1 'évaluation cognitive et clinique ainsi qu'aux exercices de remédiation. Des cas cliniques illustrent la manière d'adapter le travail thérapeutique au profil cognitif de chaque participant. Afin que le lecteur puisse bénéficier d'un maximum d'informations et de documents pratiques, plusieurs outils nécessaires à 1 'utilisation du programme figurent en annexe de l'ouvrage.

Troubles de la mémoire chez la personne agée: que faire au cabinet [Cognitive impairment in elderly patients: what to do in primary care?].

In Switzerland, each year there are about 25000 new patients suffering from dementia (one new case every 20 minutes!). Currently, recognition of cognitive impairment and dementia diagnoses remain essentially based on clinical features. For the primary care provider, a 4-step approach based on a) history, b) collateral information provided by a knowledgeable relative, c) a standardized brief cognitive screening instrument, and d) simple laboratory tests will identify most older persons suffering from dementia. Unclear situations benefit from an assessment in a memory clinic.

The switch from conventional to atypical antipsychotic treatment should not be based exclusively on the presence of cognitive deficits. A pilot study in individuals with schizophrenia.

Background: Atypical antipsychotics provide better control of the negative and affective symptoms of schizophrenia when compared with conventional neuroleptics; nevertheless, their heightened ability to improve cognitive dysfunction remains a matter of debate. This study aimed to examine the changes in cognition associated with long-term antipsychotic treatment and to evaluate the effect of the type of antipsychotic (conventional versus novel antipsychotic drugs) on cognitive performance over time. Methods: In this naturalistic study, we used a comprehensive neuropsychological battery of tests to assess a sample of schizophrenia patients taking either conventional (n = 13) or novel antipsychotics (n = 26) at baseline and at two years after. Results: Continuous antipsychotic treatment regardless of class was associated with improvement on verbal fluency, executive functions, and visual and verbal memory. Patients taking atypical antipsychotics did not show greater cognitive enhancement over two years than patients taking conventional antipsychotics. Conclusions Although long-term antipsychotic treatment slightly improved cognitive function, the switch from conventional to atypical antipsychotic treatment should not be based exclusively on the presence of these cognitive deficits.

The Beck Cognitive Insight Scale in outpatients with psychotic disorders: further evidence from a French-speaking sample.

OBJECTIVE: The Beck Cognitive Insight Scale (BCIS) evaluates patients' self-report of their ability to detect and correct misinterpretation. Our study aims to confirm the factor structure and the convergent validity of the original scale in a French-speaking environment. METHOD: Outpatients (n = 158) suffering from schizophrenia or schizoaffective disorders fulfilled the BCIS. The 51 patients in Montpellier were equally assessed with the Positive and Negative Syndrome Scale (PANSS) by a psychiatrist who was blind of the BCIS scores. RESULTS: The fit indices of the confirmatory factor analysis validated the 2-factor solution reported by the developers of the scale with inpatients, and in another study with middle-aged and older outpatients. The BCIS composite index was significantly negatively correlated with the clinical insight item of the PANSS. CONCLUSIONS: The French translation of the BCIS appears to have acceptable psychometric properties and gives additional support to the scale, as well as cross-cultural validity for its use with outpatients suffering from schizophrenia or schizoaffective disorders. The correlation between clinical and composite index of cognitive insight underlines the multidimensional nature of clinical insight. Cognitive insight does not recover clinical insight but is a potential target for developing psychological treatments that will improve clinical insight.

An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease.

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.