Early change in maladaptive defense style and development of the therapeutic alliance

Résumé Cette étude examine les changements précoces dans le Style Défensif Maladaptatif (SDM), le développement de l'alliance thérapeutique et la relation entre le SDM et l'alliance au cours d'une psychothérapie psychodynamique ultra-brève. Soixante-huit patients ambulatoires du centre de consultation psychiatrique et psychothérapique ont bénéficié d'une intervention psychodynamique en quatre séances. Les mesures des défenses et de l'alliance étaient effectuées à la première et à la dernière séance. Les patients qui ont débuté l'intervention avec une alliance faible et qui l'ont terminée avec une alliance haute (groupe de patients avec une alliance de croissance linéaire) ont diminué leur utilisation de défenses maladaptatives de manière significative au cours de la thérapie, alors que ce n'a pas été le cas pour les patients des groupes à alliances haute-stable et basse-stable. Les résultats ont montré qu'à la fin de l'intervention, le SDM et l'alliance étaient corrélés pour tous les patients. Cette corrélation intéressait plus particulièrement le groupe avec une alliance de croissance linéaire. Ces résultats suggèrent, que le développement de l'alliance thérapeutique reflètent le travail de collaboration entre le patient et son thérapeute alors qu'ils essayent de mieux comprendre les causes de la crise du patient. Cette compréhension peut aider à réduire les défenses initialement activées pour permettre au patient de se défendre de l'anxiété et d'un sentiment de détresse. Abstract This study examined the early change in Maladaptive Defense Style (MDS), the development of the Therapeutic Alliance, and the relationship between MDS and alliance, in a short psychodynamic intervention. Sixty-eight outpatients from a psychiatric clinic completed a four-session psychodynamic intervention. Defense and alliance measures were collected at the intake and the final session. Patients who began the intervention with a poor alliance but ended with a good alliance (linear growth therapeutic alliance group) significantly decreased their use of maladaptive defenses over the course of therapy, while patients in the high and low alliance groups did not. Results showed that at the end of the intervention, MDS and alliance were related across all patients. This relation concerned particularly the linear growth therapeutic alliance profile. These results suggest that the developing therapeutic alliance might reflect the collaborative work between the patient and the therapist as they try to understand the causes of the crisis. This understanding might help reduce maladaptive defenses that were initially activated to ward off anxiety and distress.

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.