The hypothalamic-pituitary-adrenal axis (HPA) is highly relevant in depressive disorders. Some investigations suggest that the HPA axis is altered in depressive disorders as indicated by higher awakening cortisol levels. There are also some results that show relations between cortisol level, psychopathology and neuropsychological performance. However, a systematic investigation of this relationship with a large and matched sample of patients and controls is missing. We tested 59 patients with depressive disorders and 75 healthy controls with tasks from the neuropsychological CANTAB and NEUROBAT battery. Before and after these tests we collected salivary samples. The study ended followed with an extensive measurement of psychopathology (e. g. BDI, HAM-D) and mood (visual analog scales).

The study revealed a significant relationship between salivary cortisol and results in tasks to executive function in the neuropsychological assessment in the control group but not in the patient group. There was no relationship between salivary cortisol and other cognitive performances. While patients with higher salivary cortisol levels reported worse mood, higher salivary levels in healthy controls were associated with better mood. These results could be related to different stress levels and different expectations regarding the examination of the groups.

Neuropsychological impairment in depression is less concise compared to schizophrenia, dementia or other brain disorders. It is varying between patients and over time in the natural course of depression. Furthermore it depends on various co-variables. These characteristics make the detection of depressive patterns in neuropsychological performance very difficult for conventional statistics.

Artificial neural networks are highly parallel nonlinear teachable systems of information processing. They are used for pattern recognition and classification tasks in different fields and can be superior to conventional linear statistics in the analysis of complex data.

The results of 1100 neuropsychological examinations of psychiatric patients with varying diagnoses and healthy controls were used to train different kinds of neural networks. The neuropsychological battery (NEUROBAT) consists of usual test paradigms as optical reaction time, a go-nogo task, recognition and free memory recall, sensorimotor interference and a continuous performance task.

Trained multilayer perceptrons and radial basis function networks allowed a significant recognition of depressive patterns. Patients were classified correctly in up to 71% of cases, whereas up to 64% of depressive disorders were recognized correctly by linear artificial neural networks.

Recognition of depressive neuropsychological patterns seems to be possible by artificial neural networks. But sensitivity and specificity are too low for a possible support of clinical diagnostics. The superiority to linear classification models could not shown clearly. More complex hierarchical neural networks, as they are commonly used in picture recognition, should be tested in future studies in order to improve classification results.

Negative computer attitude has been shown to be a possible co-variable in computerized examinations of psychiatric patients, affecting patient-computer interaction as well as reliability and validity of assessment (Weber et al. 2002, Acta Psychiatr.Scand., 105, 126-130).

It remains still uncertain if the psychological construct of computer attitude can be dependably measured in acute psychiatric inpatients or whether it is impeded by the effects of mental illness. For that reason a German translation of the Groningen Computer Attitude Scale (GCAS) was evaluated in 160 acute psychiatric inpatients under naturalistic conditions.

General test criteria (internal structure, item analysis, internal consistency, split half reliability) to a large extent corresponded to those formerly found in healthy subjects and psychiatric outpatients. The mean GCAS score was calculated as 56.2 ± 10.8 points and a significantly better computer attitude was found in male, better educated and younger patients. Some diverging correlation patterns were found in diagnostic subgroups, indicating a possible minor impact of mental disorder on computer attitude.

Overall, the GCAS was found to be a suitable instrument for measuring computer attitude in acute psychiatric inpatients. It should be used in identifying patients with a negative attitude to computers in order to ensure reliability and validity of computerized assessment.

Impairment of memory function in depressive patients is discussed controversially. At least memory impairment might be expected in more complex and effortful memory tasks.

80 patients with recurrent depressive disorder (ICD-10: F33) were compared to healthy controls in two computerized memory tasks (NEUROBAT verbal recognition and nonverbal free recall). Psychopathology (HDRS, BDI, mood scales) and computer attitude as well as computer experience were controlled as possible co-variables. A correlation between performance in computerized neuropsychological assessment and computer attitude had been found in former studies (Weber et al. 2002, Acta Psychiatr.Scand., 105, 126-130).

Unexpectedly in older patients poorer memory performance could be shown in the simple recognition task and not in the more effortful free recall. No correlations were found to depressive psychopathology. Significant correlations between computer experience and recognition task performance indicate that computer operation might be regarded as a relevant additional executive demand. The additional executive demand seems to cause a relevant inhibition of memory function in patients with lower degree of automation in computer operation.

The results of the present study confirm the well known difficulties in interpretation of neuropsychological test results in depression. The impairment by computer operation demands predominantly concerns female and older patients. Computer experience and computer attitude should be measured routinely concomitant to computerized neuropsychological assessment. Non-computerized tests should be used additionally in order to confirm results if necessary.

Furthermore the inhibition of distinct cognitive functions by additional executive demands might be regarded as a neuropsychological dimension of depressive psychopathology.