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Single photon emission computed tomography (SPECT) and Electroencephalography (EEG) have become established tools in routine diagnostics of dementia. We aimed to increase the diagnostic power by combining quantitative markers from SPECT and EEG for differential diagnosis of disorders with amnestic symptoms. We hypothesize that the combination of SPECT with measures of interaction (connectivity) in the EEG yields higher diagnostic accuracy than the single modalities. We examined 39 patients with Alzheimer's dementia (AD), 69 patients with depressive cognitive impairment (DCI), 71 patients with amnestic mild cognitive impairment (aMCI), and 41 patients with amnestic subjective cognitive complaints (aSCC). We calculated 14 measures of interaction from a standard clinical EEG-recording and derived graph-theoretic network measures. From regional brain perfusion measured by 99mTc-hexamethyl-propylene-aminoxime (HMPAO)-SPECT in 46 regions, we calculated relative cerebral perfusion in these patients. Patient groups were classified pairwise with a linear support vector machine. Classification was conducted separately for each biomarker, and then again for each EEG- biomarker combined with SPECT. Combination of SPECT with EEG-biomarkers outperformed single use of SPECT or EEG when classifying aSCC vs. AD (90%), aMCI vs. AD (70%), and AD vs. DCI (100%), while a selection of EEG measures performed best when classifying aSCC vs. aMCI (82%) and aMCI vs. DCI (90%). Only the contrast between aSCC and DCI did not result in above-chance classification accuracy (60%). In general, accuracies were higher when measures of interaction (i.e., connectivity measures) were applied directly than when graph-theoretical measures were derived. We suggest that quantitative analysis of EEG and machine-learning techniques can support differentiating AD, aMCI, aSCC, and DCC, especially when being combined with imaging methods such as SPECT. Quantitative analysis of EEG connectivity could become an integral part for early differential diagnosis of cognitive impairment.

The active oddball paradigm is a candidate task for voluntary brain activation. Previous research has focused on group effects, and has largely overlooked the potential problem of interindividual differences. Interindividual variance causes problems with the interpretation of group-level results. In this study we want to demonstrate the degree of consistency in the active oddball task across subjects, in order to answer the question of whether this task is able to reliably detect conscious target processing in unresponsive patients. We asked 18 subjects to count rare targets and to ignore frequent standards and rare distractors in an auditory active oddball task. Event-related-potentials (ERPs) and time-frequency data were analyzed with permutation-t-tests on a single subject level. We plotted the group-average ERPs and time-frequency data, and evaluated the numbers of subjects showing significant differences between targets and distractors in certain time-ranges. The distinction between targets/distractors and standards was found to be significant in the time-range of the P300 in all participants. In contrast, significant differences between targets and distractors in the time-range of the P3a/b were found in 8 subjects, only. By including effects in the N1 and in a late negative component there remained 2 subjects who did not show a distinction between targets and distractors in the ERP. While time-frequency data showed prominent effects for target/distractor vs. standard, significant differences between targets and distractors were found in 2 subjects, only. The results suggest that time-frequency- and ERP-analysis of the active oddball task may not be sensitive enough to detect voluntary brain activation in unresponsive patients. In addition, we found that time-frequency analysis was even less informative than ERPs about the subjects task performance. Despite suggesting the use of more sensitive paradigms and/or analysis techniques, the present results give further evidence that electroencephalographic research should rely more strongly on single-subject analysis because interpretations of group-effects may be misleading.

Available pharmacological treatments for Alzheimer disease (AD) have limited effectiveness, are expensive, and sometimes induce side effects. Therefore, alternative or complementary adjuvant therapeutic strategies have gained increasing attention. The development of novel noninvasive methods of brain stimulation has increased the interest in neuromodulatory techniques as potential therapeutic tool for cognitive rehabilitation in AD. In particular, repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) are noninvasive approaches that induce prolonged functional changes in the cerebral cortex. Several studies have begun to therapeutically use rTMS or tDCS to improve cognitive performances in patients with AD. However, most of them induced short-duration beneficial effects and were not adequately powered to establish evidence for therapeutic efficacy. Therefore, TMS and tDCS approaches, seeking to enhance cognitive function, have to be considered still very preliminary. In future studies, multiple rTMS or tDCS sessions might also interact, and metaplasticity effects could affect the outcome.