Mild cognitive impairment has frequently been reported for patients in the early stages of multiple sclerosis. The aim of the present study was to measure whether altered cortical activation during a sustained attention task occurs along with limited extent of neuropsychological problems. Expanded brain activation of multiple sclerosis patients with normal motor function compared with healthy controls during a finger tapping paradigm has previously been reported. Compensatory brain activation in patients with multiple sclerosis compared with normal controls may also be observed when the subjects are performing cognitive functions. In 21 patients with clinically definite relapsing-remitting multiple sclerosis, a psychometric assessment was performed using the Wechsler Memory Scale (WMS) and the Multiple Sclerosis Functional Composite Score (MSFC). In addition, functional MRI was performed during a Paced Visual Serial Addition Task (PVSAT), a visual analogue of the Paced Auditory Serial Addition Task (PASAT). All patients were within 3 years of diagnosis and were not suffering from a relapse at the time of investigation. The multiple sclerosis patients were compared with a control group of 21 healthy volunteers matched for handedness, age, years of education and sex. With regard to psychometric results, the WMS general memory score showed statistically significant differences between patients and controls. We did not find differences for either the MSFC or the PASAT scores. A group analysis of the functional imaging data during the PVSAT revealed different activation patterns for patients compared with control subjects. In healthy volunteers, the main activation was found in the frontal part of the right gyrus cinguli (Brodmann area 32). In patients, the main activation was detected at the right hemispheric frontal cortex (Brodmann areas 6, 8 and 9). In addition, the left hemispheric Brodmann area 39 was activated. We interpret the different patterns of activation, accompanied with intact performance in a sustained attention task of our multiple sclerosis sample compared with healthy controls, as the consequence of compensatory mechanisms. This is an expression of neuronal plasticity during early stages of a chronic disease.

Automated analysis of HMPAO-SPECT data from MCI patients showed significant perfusion deficits in regions also involved in DAT patients, but ROC analysis demonstrated only moderate sensitivity and specificity for differentiating DAT patients from controls and DCI patients. Frontal hypoperfusion seems to correspond with conversion from MCI to DAT. Finally, the results in DCI patients again raise the question of depression as an early symptom of neurodegeneration.

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Due to the increasing importance of early recognition and differential diagnosis of dementias, cerebral perfusion scans using "single photon emission computed tomography" (SPECT) are increasingly integrated into the examination routine. The goal of this study was to check the diagnostic validity of SPECT scans of MCI- and DAT-patients, two subgroups out of 369 persons with etiologically unclear cognitive dysfunction, which underwent an assessment program for probable dementia including cognitive testing, cranial computed tomography, ultrasound, routine laboratory testing including vascular risk factors. After exclusion of patients with no or other forms of dementia we analyzed SPECT data of patients with mild cognitive impairment (MCI; n = 85) and dementia of the Alzheimer type (DAT; n = 78) in comparison with a healthy control group (n = 34).Visual assessment as well as a manual "regions of interest" (ROI) regionalization of the cortex were performed, whereby a ROI/cerebellum ratio was calculated as a semi-quantitative value. Association cortex areas were assessed regarding frontal, temporal, and parietal lobes of both hemispheres. When comparing the ratios of patients with DAT and controls, we found a statistically significant reduction of the cerebral perfusion in all measured cortex areas (p < 0.001). The comparison of patients with MCI with the selected control group also established a statistically significant difference in the cerebral perfusion for the evaluated cortex areas with the exception of the left hemispheric frontal and parietal cortex.A considerable number of the MCI patients showed an MMSE-score within the normal range, but with regard to the perfusion in the right hemispheric association cortex these patients also could be distinguished unambiguously from controls. Sensitivity levels found by visual assessment were at least as high as those found by the ROI method (pathological assessment: visual 49.4% vs. ROI 47.1% for MCI; visual 75.6% vs. ROI 73.1% for DAT). High experienced visual assessment of cerebral perfusion scans using SPECT provides an useful additional tool in diagnosis of cognitive impairment. The used semiquantitative ROI-method is nearly equivalent and does not depend on the experience of the investigator.

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.