Background. Research on mood disorders has progressively focused on the study of seasons and on the mood in association with them during depressive or manic episodes yet few studies have focused on the seasonal fluctuation that characterizes the patient's clinical course both during an illness episode and during euthymic periods. Methods. 113 euthymic outpatients 46 affected by major recurrent depression and 67 affected by bipolar disorder were recruited. We evaluated the impact of clinical “rhythmical” factors: seasonality, sleep disturbance, and chronotype. Patients completed the SPAQ+ questionnaire, the MEQ questionnaire, and the medical outcomes study (MOS) sleep scale. We used t-test analyses to compare differences of clinical “rhythmical” and sociodemographic variables and of differences in the assessment scales among the diagnostic groups. Results. Patients reporting a family history for mood disorders have higher fluctuations throughout seasons. Sleep disturbance is more problematic in unipolars when compared to bipolars. Conclusions. Sleep, light, and seasonality seem to be three interconnected features that lie at the basis of chronobiology that, when altered, have an important effect both on the psychopathology and on the treatment of mood disorders.

Chronic management of schizophrenia and schizoaffective disorders is frequently complicated by symptomatic relapse. An open-label, randomized, active-controlled, 2-year trial evaluated 710 patients with schizophrenia or related disorders who were switched from stable treatment with oral risperidone, olanzapine, or conventional neuroleptics to risperidone long-acting injectable (RLAI) or oral quetiapine. Primary effectiveness evaluation was time-to-relapse. Safety evaluations included adverse events (AEs) reported for the duration of the study, Extrapyramidal Symptom Rating Scale (ESRS), clinical laboratory tests, and vital signs. A total of 666 patients (n=329 RLAI, n=337 quetiapine) were evaluable for effectiveness measures. Baseline demographics were similar between treatment groups. Kaplan–Meier estimate of time-to-relapse was significantly longer with RLAI (p<0.0001). Relapse occurred in 16.5% of patients with RLAI and 31.3% with quetiapine. RLAI and quetiapine were both safe and well tolerated. Weight gain affected 7% of patients with RLAI and 6% with quetiapine, with mean end point increases of 1.25±6.61 and 0±6.55 kg, respectively. There were no significant between-group differences in weight gain. ESRS total scores decreased similarly after randomization to either RLAI or quetiapine. Extrapyramidal AEs occurred in 10% of patients with RLAI and 6% with quetiapine. Treatment-emergent potentially prolactin-related AEs were reported in 15 (5%) patients with RLAI and 5 (2%) patients with quetiapine; hyperprolactinemia was reported in 43 (13.1%) patients with RLAI and 5 (1.5%) patients with quetiapine. Somnolence occurred in 2% of patients with RLAI and 11% with quetiapine. To our knowledge, this is the first report of a randomized clinical trial directly comparing relapse prevention with a second-generation long-acting injectable antipsychotic and oral therapy. Time-to-relapse in stable patients with schizophrenia or schizoaffective disorder was significantly longer in patients randomized to RLAI compared with those randomized to oral quetiapine. Both antipsychotics were generally well tolerated.

Background

A number of reports showed en encouraging remediation in some patients' executive deficits thanks to the use of 'information processing strategies'. Moreover the impact of antipsychotics on cognitive functions of the schizophrenics is an important issue, especially if an integrated psychosocial treatment is needed.

The aim of this paper is to evaluate different executive performance and response to verbalization, a strategy of the Wisconsin Card Sorting Test (WCST) remediation, in subjects on classical vs atypical antipsychotic (AP) treatment.

Methods

Sixty-three schizophrenic subjects undertook the WCST under standard and modified (verbalization) administration. Subjects were stratified by the kind of WCST response (i.e. good, poor and remediable) and AP treatment (i.e. atypical vs. classical).

Results

Subjects on atypical APs showed a better performance than those on classical ones. More poor performers who did not remediate were seen in the sample with classical Aps while subjects who remediated the performance were seen in the subgroup with atypical APs only. An increase of perseverative and total errors was seen in poor performers subjects on classical APs.

Conclusion

Subjects on atypicals showed a better cognitive pattern in terms of WCST performance. Since the naturalistic assignment of medication we cannot draw conclusions about its effect on cognitive performance and its interaction with cognitive remediation potential. However the data lead us to hypothesize that subjects with potential room for remediation did so with the atypical APs.

Background

The increasing number of available genotypes for genetic studies in humans requires more advanced techniques of analysis. We previously reported significant univariate associations between gene polymorphisms and antidepressant response in mood disorders. However the combined analysis of multiple gene polymorphisms and clinical variables requires the use of non linear methods.

Methods

In the present study we tested a neural network strategy for a combined analysis of two gene polymorphisms. A Multi Layer Perceptron model showed the best performance and was therefore selected over the other networks. One hundred and twenty one depressed inpatients treated with fluvoxamine in the context of previously reported pharmacogenetic studies were included. The polymorphism in the transcriptional control region upstream of the 5HTT coding sequence (SERTPR) and in the Tryptophan Hydroxylase (TPH) gene were analysed simultaneously.

Results

A multi layer perceptron network composed by 1 hidden layer with 7 nodes was chosen. 77.5 % of responders and 51.2% of non responders were correctly classified (ROC area = 0.731 – empirical p value = 0.0082). Finally, we performed a comparison with traditional techniques. A discriminant function analysis correctly classified 34.1 % of responders and 68.1 % of non responders (F = 8.16 p = 0.0005).

Conclusions

Overall, our findings suggest that neural networks may be a valid technique for the analysis of gene polymorphisms in pharmacogenetic studies. The complex interactions modelled through NN may be eventually applied at the clinical level for the individualized therapy.