Top-down and bottom-up neurodynamic evidence in patients with tinnitus

AbstractAlthough a peripheral auditory (bottom-up) deficit is an essential prerequisite for the generation of tinnitus, central cognitive (top-down) impairment has also been shown to be an inherent neuropathological mechanism. Using an auditory oddball paradigm (for top-down analyses) and a passive listening paradigm (for bottom-up analyses) while recording electroencephalograms (EEGs), we investigated whether top-down or bottom-up components were more critical in the neuropathology of tinnitus, independent of peripheral hearing loss. We observed significantly reduced P300 amplitudes (reflecting fundamental cognitive processes such as attention) and evoked theta power (reflecting top-down regulation in memory systems) for target stimuli at the tinnitus frequency of patients with tinnitus but without hearing loss. The contingent negative variation (reflecting top-down expectation of a subsequent event prior to stimulation) and N100 (reflecting auditory bottom-up selective attention) were different between the healthy and patient groups. Interestingly, when tinnitus patients were divided into two subgroups based on their P300 amplitudes, their P170 and N200 components, and annoyance and distress indices to their tinnitus sound were different. EEG theta-band power and its Granger causal neurodynamic results consistently support a double dissociation of these two groups in both top-down and bottom-up tasks. Directed cortical connectivity corroborates that the tinnitus network involves the anterior cingulate and the parahippocampal areas, where higher-order top-down control is generated. Together, our observations provide neurophysiological and neurodynamic evidence revealing a differential engagement of top-down impairment along with deficits in bottom-up processing in patients with tinnitus but without hearing loss

Focused ultrasound-mediated suppression of chemically-induced acute epileptic EEG activity

<p>Abstract</p> <p>Background</p> <p>Epilepsy is a common neurological disorder, which is attributed to uncontrollable abnormal hyper-excitability of neurons. We investigated the feasibility of using low-intensity, pulsed radiation of focused ultrasound (FUS) to non-invasively suppress epileptic activity in an animal model (rat), which was induced by the intraperitonial injection of pentylenetetrazol (PTZ).</p> <p>Results</p> <p>After the onset of induced seizures, FUS was transcranially administered to the brain twice for three minutes each while undergoing electroencephalographic (EEG) monitoring. An air-backed, spherical segment ultrasound transducer (diameter: 6 cm; radius-of-curvature: 7 cm) operating at a fundamental frequency of 690 KHz was used to deliver a train of 0.5 msec-long pulses of sonication at a repetitive rate of 100 Hz to the thalamic areas of the brain. The acoustic intensity (130 mW/cm²) used in the experiment was sufficiently within the range of safety guidelines for the clinical ultrasound imaging. The occurrence of epileptic EEG bursts from epilepsy-induced rats significantly decreased after sonication when it was compared to the pre-sonication epileptic state. The PTZ-induced control group that did not receive any sonication showed a sustained number of epileptic EEG signal bursts. The animals that underwent sonication also showed less severe epileptic behavior, as assessed by the Racine score. Histological analysis confirmed that the sonication did not cause any damage to the brain tissue.</p> <p>Conclusions</p> <p>These results revealed that low-intensity, pulsed FUS sonication suppressed the number of epileptic signal bursts using acute epilepsy model in animal. Due to its non-invasiveness and spatial selectivity, FUS may offer new perspectives for a possible non-invasive treatment of epilepsy.</p

Task-related modulation of anterior theta and posterior alpha EEG reflects top-down preparation

<p>Abstract</p> <p>Background</p> <p>Prestimulus EEG alpha activity in humans has been considered to reflect ongoing top-down preparation for the performance of subsequent tasks. Since theta oscillations may be related to poststimulus top-down processing, we investigated whether prestimulus EEG theta activity also reflects top-down cognitive preparation for a stimulus.</p> <p>Results</p> <p>We recorded EEG data from 15 healthy controls performing a color and shape discrimination task, and used the wavelet transformation to investigate the time course and power of oscillatory activity in the signals. We observed a relationship between both anterior theta and posterior alpha power in the prestimulus period and the type of subsequent task.</p> <p>Conclusions</p> <p>Since task-differences were reflected in both theta and alpha activities prior to stimulus onset, both prestimulus theta (particularly around the anterior region) and prestimulus alpha (particularly around the posterior region) activities may reflect prestimulus top-down preparation for the performance of subsequent tasks.</p

A thalamic reticular networking model of consciousness

<p>Abstract</p> <p>[Background]</p> <p>It is reasonable to consider the thalamus a primary candidate for the location of consciousness, given that the thalamus has been referred to as the gateway of nearly all sensory inputs to the corresponding cortical areas. Interestingly, in an early stage of brain development, communicative innervations between the dorsal thalamus and telencephalon must pass through the ventral thalamus, the major derivative of which is the thalamic reticular nucleus (TRN). The TRN occupies a striking control position in the brain, sending inhibitory axons back to the thalamus, roughly to the same region where they receive afferents.</p> <p>[Hypotheses]</p> <p>The present study hypothesizes that the TRN plays a pivotal role in dynamic attention by controlling thalamocortical synchronization. The TRN is thus viewed as a functional networking filter to regulate conscious perception, which is possibly embedded in thalamocortical networks. Based on the anatomical structures and connections, modality-specific sectors of the TRN and the thalamus appear to be responsible for modality-specific perceptual representation. Furthermore, the coarsely overlapped topographic maps of the TRN appear to be associated with cross-modal or unitary conscious awareness. Throughout the latticework structure of the TRN, conscious perception could be accomplished and elaborated through accumulating intercommunicative processing across the first-order input signal and the higher-order signals from its functionally associated cortices. As the higher-order relay signals run cumulatively through the relevant thalamocortical loops, conscious awareness becomes more refined and sophisticated.</p> <p>[Conclusions]</p> <p>I propose that the thalamocortical integrative communication across first- and higher-order information circuits and repeated feedback looping may account for our conscious awareness. This TRN-modulation hypothesis for conscious awareness provides a comprehensive rationale regarding previously reported psychological phenomena and neurological symptoms such as blindsight, neglect, the priming effect, the threshold/duration problem, and TRN-impairment resembling coma. This hypothesis can be tested by neurosurgical investigations of thalamocortical loops via the TRN, while simultaneously evaluating the degree to which conscious perception depends on the severity of impairment in a TRN-modulated network.</p

Harnessing prefrontal cognitive signals for brain-machine interfaces

Brain-machine interfaces (BMIs) enable humans to interact with devices by modulating their brain signals. Despite impressive technological advancements, several obstacles remain. The most commonly used BMI control signals are derived from the brain areas involved in primary sensory- or motor-related processing. However, these signals only reflect a limited range of human intentions. Therefore, additional sources of brain activity for controlling BMIs need to be explored. In particular, higher-order cognitive brain signals, specifically those encoding goal-directed intentions are natural candidates for enlarging the repertoire of BMI control signals and making them more efficient and intuitive. Thus, this paper identifies the prefrontal brain area as a key target region for future BMIs, given its involvement in higher-order, goal-oriented cognitive processes

Multilevel Feature Fusion with 3D Convolutional Neural Network for EEG-Based Workload Estimation

Mental workload is defined as the proportion of the information processing capability used to perform a task. High cognitive load requires additional resources to process information; this demand for additional resources may reduce the processing efficiency and performance. Therefore, the technique of workload estimation can ensure a proper working environment to promote the working efficiency of each person. In this paper, we propose a three-dimensional convolutional neural network (3D CNN) employing a multilevel feature fusion algorithm for mental workload estimation using electroencephalogram (EEG) signals. The 1D EEG signals are converted to 3D EEG images to enable the 3D CNN to learn the spectral and spatial information over the scalp. The multilevel feature fusion framework integrates local and global neuronal activities by workload tasks in the 3D CNN algorithm. Multilevel features are extracted in each layer of the 3D convolution operation and each multilevel feature is multiplied by a weighting factor, which determines the importance of the feature. The weighting factor is adaptively estimated for each EEG image by a backpropagation process. Furthermore, we generate subframes from each EEG image and propose a temporal attention technique based on the long short-term memory model (LSTM) to extract a significant subframe at each multilevel feature that is strongly correlated with task difficulty. To verify the performance of our network, we performed the Sternberg task to measure the mental workload of the participant, which was classified according to its difficulty as low or high workload condition. We showed that the difficulty of the workload was well designed, which was reflected in the behavior of the participant. Our network is trained on this dataset and the accuracy of our network is 90.8 %, which is better than that of conventional algorithms. We also evaluated our method using the public EEG dataset and achieved 93.9 % accuracy. © 2013 IEEE.11Ysciescopu

Electrophysiological Decoding of Spatial and Color Processing in Human Prefrontal Cortex

The prefrontal cortex (PFC) plays a pivotal role in goal-directed cognition, yet its representational code remains an open problem with decoding techniques ineffective in disentangling task-relevant variables from PFC. Here we applied regularized linear discriminant analysis to human scalp EEG data and were able to distinguish a mental-rotation task versus a color-perception task with 87% decoding accuracy. Dorsal and ventral areas in lateral PFC provided the dominant features dissociating the two tasks. Our findings show that EEG can reliably decode two independent task states from PFC and emphasize the PFC dorsal/ventral functional specificity in processing the where rotation task versus the what color task

Resting-state electroencephalographic characteristics related to mild cognitive impairments

Alzheimer's disease (AD) causes a rapid deterioration in cognitive and physical functions, including problem-solving, memory, language, and daily activities. Mild cognitive impairment (MCI) is considered a risk factor for AD, and early diagnosis and treatment of MCI may help slow the progression of AD. Electroencephalography (EEG) analysis has become an increasingly popular tool for developing biomarkers for MCI and AD diagnosis. Compared with healthy elderly, patients with AD showed very clear differences in EEG patterns, but it is inconclusive for MCI. This study aimed to investigate the resting-state EEG features of individuals with MCI (n = 12) and cognitively healthy controls (HC) (n = 13) with their eyes closed. EEG data were analyzed using spectral power, complexity, functional connectivity, and graph analysis. The results revealed no significant difference in EEG spectral power between the HC and MCI groups. However, we observed significant changes in brain complexity and networks in individuals with MCI compared with HC. Patients with MCI exhibited lower complexity in the middle temporal lobe, lower global efficiency in theta and alpha bands, higher local efficiency in the beta band, lower nodal efficiency in the frontal theta band, and less small-world network topology compared to the HC group. These observed differences may be related to underlying neuropathological alterations associated with MCI progression. The findings highlight the potential of network analysis as a promising tool for the diagnosis of MCI