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Purpose Firstly, to evaluate the validity of a neuropsychological test battery in epilepsy patients, i.e. whether its tests sufficiently allow the assessment of the required cognitive domains in this specific group. Secondly, to examine its ability to differentiate between cognitive profiles of different subgroups of focal epilepsy. Methods The test battery suggested by the German ILAE Chapter was performed on 207 epilepsy patients, and its factor structure was investigated by principal component analysis (PCA). To further examine its accuracy in two matched subgroups of patients with temporal lobe epilepsy (TLE, n = 35) and frontal lobe epilepsy (FLE, n = 35), a discriminant function analysis (DFA) was used. Results PCA revealed eleven interpretable factors, accounting for 69.1% of total variance: Divided Attention, Reaction Time, Verbal Learning, Verbal Memory, Contextual Memory, Short-term- and Working Memory, Visuospatial Functioning, Space Perception, Verbal Fluency, Response Monitoring and Cognitive Flexibility. DFA identified six test to be most appropriate to discern TLE from FLE: WMS-IV Logical Memory, recognition; WMS-R Digit Span, backwards; VLMT, repetitions; VOSP Silhouettes; VLMT, delayed recall; and RWT Phonemic verbal fluency. Group membership was correctly predicted for 78.6% of patients using cross-validation. Conclusions As neuropsychological assessments are central in clinical decision-making in presurgical work-up of epilepsy patients, the appropriateness of the test battery in use is essential. The majority of cognitive domains are sufficiently measurable by the test battery and it is highly sensitive to differentiate between the cognitive profiles of TLE and FLE. However, the selection of tests assessing nonverbal memory functions requires further improvement.

Neurofeedback (NFB) became a popular tool during the last decades, and seems to prove the brain’s ability to influence its own activity level when internal processes are made accessible. A rich literature indicates that humans and animals are able to learn to influence diverse brain parameters, oscillatory patterns as well as the BOLD signal. Even though there is much known about oscillatory processes, it is still unclear how the brain manages to influence its own oscillatory activity. We developed a novel MEG technique for the real-time analysis of MEG source data to investigate that question. Our method extracts the power of ongoing oscillatory activity from a region of interest at the source, not at the sensor level, using the weighted information of all 275 MEG sensors in a real-time Beamforming procedure. The extracted power of oscillations in a particular frequency range is then used as the feedback signal. To examine whether and how the brain can control the level of oscillatory activity in the high frequency band (>40 Hz, gamma band) we converted the gamma power into the pitch of a tone and trained subjects to modulate the pitch. Subsequent periods with and without feedback training were compared and the spectral and spatial parameters of oscillatory brain activity determined. Our pilot study includes 5 healthy subjects that all completed nine sessions of NFB training, each lasting about 50 minutes. The data suggests that 4 subjects were able to change high frequency oscillations in visual cortex in particular frequency bands. The distances between the region of interest and task related changes are less than 2 cm in the successful subjects. The task related changes vary between participants. Some frequency patterns could resemble those modulated under focused attention.