Acute perturbation of the corticohippocampal circuitry is a primary pathophysiological mechanism underlying transient global amnesia (TGA). With regard to memory, 2 distinct corticohippocampal circuitries potentially exist: the anterior temporal network and the posterior medial network. We used electroencephalography (EEG) spectral analysis to determine which network is disrupted during the acute stage of TGA. Patients with TGA who visited Seoul National University Bundang Hospital within 24 hours after symptom onset were retrospectively identified. Twenty patients underwent EEG twice, once in the acute stage (<24 hours after symptom onset) and once in the resolved stage (>2 months after symptom onset). A fast Fourier transform was applied to compute the spectral power of the 6 frequency bands: delta, theta, alpha, beta 1, beta 2, and gamma. We assumed that the frontocentral and temporal regions belonged to the anterior temporal network, whereas the parieto-occipital regions belonged to the posterior medial network. A paired Student's t test was used to evaluate the difference in the regional spectral powers in each frequency band between the acute and resolved TGA stages. Compared with the resolved stage, relative theta power in the left parieto-occipital region was increased and relative alpha power in the right parieto-occipital region was reduced during the acute stage of TGA, with a statistical significance of P<.05 (uncorrected). The cortical regions that belonged to the posterior medial network showed alterations of neuronal activity, which reflects disruption of the posterior medial network during the acute stage of TGA.
Acute perturbation of the hippocampus, one of the connector hubs in the brain, is a key step in the pathophysiological cascade of transient global amnesia (TGA). We tested the hypothesis that network efficiency, meaning the efficiency of information exchange over a network, is impaired during the acute stage of TGA. Graph theoretical analysis was applied to resting-state EEG data collected from 21 patients with TGA. The EEG data were obtained twice, once during the acute stage (< 24 hours after symptom onset) and once during the resolved stage (> 2 months after symptom onset) of TGA. Characteristic path lengths and clustering coefficients of functional networks constructed using phase-locking values were computed and normalized as a function of the degree in the delta, theta, alpha, beta 1, beta 2 and gamma frequency bands of the EEG. We investigated whether the normalized characteristic path length (nCPL) and normalized clustering coefficients (nCC) differed significantly between the acute and resolved stages of TGA at each frequency band using the Wilcoxon signed-rank test. For networks where the nCPL or nCC differed significantly between the two stages, we also evaluated changes in the connections of the brain networks. During the acute stage of TGA, the nCPL of the theta band networks with mean degrees of 8, 8.5, 9 and 9.5 significantly increased (P < 0.05). During the acute stage, the lost edges for these networks were mostly found between the anterior (frontal and anterior temporal) and posterior (parieto-occipital and posterior temporal) brain regions, whereas newly developed edges were primarily found between the left and right frontotemporal regions. The nCC of the theta band with a mean degree of 5.5 significantly decreased during the acute stage (P < 0.05). Our results indicate that TGA deteriorates the network efficiency of the theta frequency band. This effect might be related to the desynchronization between the anterior and posterior brain areas.
Default mode network (DMN) is a set of functional brain structures coherently activated when individuals are in resting-state. In this study, we constructed multi-frequency band resting-state EEG-based DMN functional network models for major psychiatric disorders to easily compare their pathophysiological characteristics. Phase-locking values (PLVs) were evaluated to quantify functional connectivity; global and nodal clustering coefficients (CCs) were evaluated to quantify global and local connectivity patterns of DMN nodes, respectively. DMNs of patients with post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD), panic disorder, major depressive disorder (MDD), bipolar disorder, schizophrenia (SZ), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) were constructed relative to their demographically-matched healthy control groups. Overall DMN patterns were then visualized and compared with each other. In global CCs, SZ and AD showed hyper-clustering in the theta band; OCD, MCI, and AD showed hypo-clustering in the low-alpha band; OCD and MDD showed hypo-clustering and hyper-clustering in low-beta, and high-beta bands, respectively. In local CCs, disease-specific patterns were observed. In the PLVs, lowered theta-band functional connectivity between the left lingual gyrus and the left hippocampus was frequently observed. Our comprehensive comparisons suggest EEG-based DMN as a useful vehicle for understanding altered brain networks of major psychiatric disorders.
Accurately estimating consumers' subjective preference towards a specific product using neuroimaging methods is an important area in neuromarketing research, because this approach can be used to establish strategies for product design and marketing. Although functional magnetic resonance imaging (fMRI) is the neuroimaging modality widely used in neuromarketing studies, this technology requires large equipment and is relatively expensive, thus hindering its use in practical neuromarketing applications. In contrast, functional near infrared spectroscopy (fNIRS) has many advantages over fMRI, including portable equipment, cost-effectiveness and resistance to artefacts. However, the feasibility of fNIRS-based neuromarketing has not yet been verified because this technique has been rarely used in neuromarketing studies. In the present study, participants were asked to decide which products (various kinds of foods) they preferred while changes in their oxy-haemoglobin (Oxy-Hb) concentrations were recorded using a 16-channel NIR spectroscopy system that covered the prefrontal cortex. Our analysis results showed significantly increased Oxy-Hb concentration in the right prefrontal area when less-preferred food images were presented compared to when preferred food images were presented. These preliminary experiments demonstrate that hemodynamic response change in the prefrontal cortex is a potential indicator of the subjective preference of a group of individuals, suggesting that fNIRS can be a promising neuroimaging tool for future neuromarketing studies.
Traditionally, identification of epileptogenic zones primarily relied on visual inspection of intracranial electroencephalography (iEEG) recordings by experienced epileptologists; however, removal of epileptogenic zones identified by iEEG does not always guarantee favorable surgical outcomes. To confirm visual inspection results, and assist in making decisions about surgical resection areas, computational iEEG analysis methods have recently been used for the localization of epileptogenic zones. In this study, we have proposed a new approach for the localization of epileptogenic zones in Lennox-Gastaut syndrome (LGS), and have investigated whether the proposed approach could confirm surgical resection areas and predict seizure outcome before surgery. The proposed approach simultaneously used results of 2 iEEG analysis methods, directed transfer function (DTF) and time delay estimation, to enhance localization accuracy. This new combined method was applied to patients who became seizure-free after resective epilepsy surgery, as well as those who had unsuccessful surgery. A quantitative metric was also introduced that can measure how well the localized epileptogenic zones coincided with the surgical resection areas, with the aim of verifying whether the approach could confirm surgical resection areas determined by epileptologists. The estimated epileptogenic zones more strongly coincided with surgical resection areas in patients with successful, compared to those with unsuccessful surgical outcomes. Both qualitative and quantitative analyses showed that the combined use of 2 iEEG analyses resulted in a more accurate estimate of epileptogenic zones in LGS than the use of a single method. A combination of multiple iEEG analyses could not only enhance overall accuracy of localizing epileptogenic zones in LGS, but also has the potential to predict outcomes before resective surgery.
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