No abstract

In this paper, we explore the utility of resting-state EEG measures as potential biomarkers for the detection and assessment of cognitive decline in mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Neurophysiological biomarkers of AD derived from EEG and FDG-PET, once characterized and validated, would expand the set of existing diagnostic molecular biomarkers of AD pathology with associated biomarkers of disease progression and neural dysfunction. Since symptoms of AD often begin to appear later in life, successful identification of EEG-based biomarkers must account for age-related neurophysiological changes that occur even in healthy individuals. To this end, we collected EEG data from individuals with AD (n = 26), MCI (n = 53), and cognitively normal healthy controls stratified by age into three groups: 18–40 (n = 129), 40–60 (n = 62) and 60–90 (= 55) years old. For each participant, we computed power spectral density at each channel and spectral coherence between pairs of channels. Compared to age matched controls, in the AD group, we found increases in both spectral power and coherence at the slower frequencies (Delta, Theta). A smaller but significant increase in power of slow frequencies was observed for the MCI group, localized to temporal areas. These effects on slow frequency spectral power opposed that of normal aging observed by a decrease in the power of slow frequencies in our control groups. The AD group showed a significant decrease in the spectral power and coherence in the Alpha band consistent with the same effect in normal aging. However, the MCI group did not show any significant change in the Alpha band. Overall, Theta to Alpha ratio (TAR) provided the largest and most significant differences between the AD group and controls. However, differences in the MCI group remained small and localized. We proposed a novel method to quantify these small differences between Theta and Alpha bands’ power using empirically derived distributions of spectral power across the time domain as opposed to averaging power across time. We defined Power Distribution Distance Measure (PDDM) as a distance measure between probability distribution functions (pdf) of Theta and Alpha power. Compared to average TAR, using PDDF enhanced the statistical significance, the effect size, and the spatial distribution of significant effects in the MCI group. We designed classifiers for differentiating individual MCI and AD participants from age-matched controls. The classification performance measured by the area under ROC curve after cross-validation were AUC = 0.85 and AUC = 0.6, for AD and MCI classifiers, respectively. Posterior probability of AD, TAR, and the proposed PDDM measure were all significantly correlated with MMSE score and neuropsychological tests in the AD group.

Background-Associations between schizophrenia (SCZ) and polymorphisms at the regulator of G-protein signaling 4 (RGS4) gene have been reported (single nucleotide polymorphisms [SNPs] 1, 4, 7, and 18). Yet, similar to other SCZ candidate genes, studies have been inconsistent with respect to the associated alleles.

Background Spike-wave discharges (SWD) found in neuroelectrical recordings are pathognomonic to absence epilepsy. The characteristic spike-wave morphology of the spike-wave complex (SWC) constituents of SWDs can be mathematically described by a subset of possible spectral power and phase values. Morlet wavelet transform (MWT) generates time-frequency representations well-suited to identifying this SWC-associated subset. New method MWT decompositions of SWDs reveal spectral power concentrated at harmonic frequencies. The phase relationships underlying SWC morphology were identified by calculating the differences between phase values at SWD fundamental frequency and the 2nd, 3rd and 4th harmonics. The three phase differences were then used as coordinates to generate a density distribution in a {360° × 360° × 360°} phase difference space. Strain-specific density distributions were generated from SWDs of mice carrying the Gria4, Gabrg2 or Scn8a mutations to determine whether SWC morphological variants reliably mapped to the same regions of the distribution, and if distribution values could be used to detect SWD. Comparison with existing methods To the best of our knowledge, this algorithm is the first to employ spectral phase to quantify SWC morphology, making it possible to computationally distinguish SWC subtypes and detect SWDs. Results/conclusions Proof-of-concept testing of the SWDreader algorithm shows: (1) a major pattern of variation in SWC morphology maps to one axis of the phase difference distribution, (2) variability between the strain-specific distributions reflects differences in the proportion of SWC subtypes generated during SWD, and (3) regularities in the spectral power and phase profiles of SWCs can be used to detect waveforms possessing SWC-like morphology.