Multielectrode voltage data are usually recorded against a common reference. Such data are frequently used without further treatment to assess patterns of functional connectivity between neuronal populations and between brain areas. It is important to note from the outset that such an approach is valid only when the reference electrode is nearly electrically silent. In practice, however, the reference electrode is generally not electrically silent, thereby adding a common signal to the recorded data. Volume conduction further complicates the problem. In this study we demonstrate the adverse effects of common signals on the estimation of Granger causality, which is a statistical measure used to infer synaptic transmission and information flow in neural circuits from multielectrode data. We further test the hypothesis that the problem can be overcome by utilizing bipolar derivations where the difference between two nearby electrodes is taken and treated as a representation of local neural activity. Simulated data generated by a neuronal network model where the connectivity pattern is known were considered first. This was followed by analyzing data from three experimental preparations where a priori predictions regarding the patterns of causal interactions can be made: (1) laminar recordings from the hippocampus of an anesthetized rat during theta rhythm, (2) laminar recordings from V4 of an awake-behaving macaque monkey during alpha rhythm, and (3) ECoG recordings from electrode arrays implanted in the middle temporal lobe and prefrontal cortex of an epilepsy patient during fixation. For both simulation and experimental analysis the results show that bipolar derivations yield the expected connectivity patterns whereas the untreated data (referred to as unipolar signals) do not. In addition, current source density signals, where applicable, yield results that are close to the expected connectivity patterns, whereas the commonly practiced average re-reference method leads to erroneous results.

Phase-amplitude coupling (PAC) estimates the statistical dependence between the phase of a low-frequency component and the amplitude of a high-frequency component of local field potentials (LFP). To date PAC has been mainly applied to one signal. In this work, we introduce a new application of PAC to two LFPs and suggest that it can be used to infer the direction and strength of rhythmic neural transmission between distinct brain networks. This hypothesis is based on the accumulating evidence that transmembrane currents related to action potentials contribute a broad-band component to LFP in the high-gamma band, and PAC calculated between the amplitude of high-gamma (>60 Hz) in one LFP and the phase of a low-frequency oscillation (e.g., theta) in another would therefore relate the output (spiking) of one area to the input (somatic/dendritic postsynaptic potentials) of the other. We tested the hypothesis on theta-band long range communications between hippocampus and prefrontal cortex (PFC) and theta-band short range communications between dentate gyrus (DG) and the Ammon’s horn (CA1) within the hippocampus. The ground truth was provided by the known anatomical connections predicting hippocampus → PFC and DG → CA1, i.e., theta transmission is unidirectional in both cases: from hippocampus to PFC and from DG to CA1 along the tri-synaptic pathway within hippocampus. We found that (1) hippocampal high-gamma amplitude was significantly coupled to PFC theta phase, but not vice versa; (2) similarly, DG high-gamma amplitude was significantly coupled to CA1 theta phase, but not vice versa, and (3) the DG high-gamma-CA1 theta PAC was significantly correlated with DG → CA1 Granger causality, a well-established analytical measure of directional neural transmission. These results support the hypothesis that inter-regional PAC (ir-PAC) can be used to relate the output of a rhythmic “driver” network (i.e., high gamma) to the input of a rhythmic “receiver” network (i.e., theta) and thereby establish the direction and strength of rhythmic neural transmission.

Phase-amplitude coupling (PAC) estimates the statistical dependence between the phase of a low-frequency and the amplitude of a high-frequency component of local field potentials (LFP). Characterizing the relationship between nested oscillations in LFPs, PAC has become a powerful tool for understanding neural dynamics in both animals and humans. In this work, we introduce a new application for this measure to two LFPs to infer the direction and strength of rhythmic neural transmission between distinct networks. Based on recently accumulating evidence that transmembrane currents related to action potentials contribute a broad-band component to LFP in the highgamma band, we hypothesized that PAC calculated between high-gamma in one LFP and low-frequencyoscillations in another would relate the output (spiking) of one area to the input (soma/dendritic postsynaptic potentials) of the other. We tested this hypothesis on theta-band long range communications between hippocampus and prefrontal cortex (PFC) and theta-band short range communications between different regions within the hippocampus. The results were interpreted within the known anatomical connections predicting hippocampus→PFC and DG→CA3→CA1, i.e., theta transmission is unidirectional in both cases: from hippocampus to PFC and along the tri-synaptic pathway within hippocampus. We found that (1) hippocampal highgamma amplitude was significantly coupled to theta phase in PFC, but not vice versa; (2) similarly, high-gamma amplitude in DG was significantly coupled to CA1 theta phase, but not vice versa, and (3) the DG high-gamma-CA1 theta PAC was significantly correlated with DG→CA1 Granger causality, a well-established analytical measure of directional neural transmission. These results support the hypothesis that inter-regional PAC (ir-PAC) can be used to relate the output of a "driver" network (i.e., high gamma) to the input of a "receiver" network (i.e., theta) and thereby establish the direction and strength of rhythmic neural transmission.

The present work proposes a computer-aided normal and abnormal heart sound identification based on Discrete Wavelet Transform (DWT), it being useful for tele-diagnosis of heart diseases. Due to the presence of Cumulative Frequency components in the spectrogram, DWT is applied on the spectrogram up to n level to extract the features from the individual approximation components. One dimensional feature vector is obtained by evaluating the Row Mean of the approximation components of these spectrograms. For this present approach, the set of spectrograms has been considered as the database, rather than raw sound samples. Minimum Euclidean distance is computed between feature vector of the test sample and the feature vectors of the stored samples to identify the heart sound. By applying this algorithm, almost 82% of accuracy was achieved.

The top–down control of attention involves command signals arising chiefly in the dorsal attention network (DAN) in frontal and parietal cortex and propagating to sensory cortex to enable the selective processing of incoming stimuli based on their behavioral relevance. Consistent with this view, the DAN is active during preparatory (anticipatory) attention for relevant events and objects, which, in vision, may be defined by different stimulus attributes including their spatial location, color, motion, or form. How this network is organized to support different forms of preparatory attention to different stimulus attributes remains unclear. We propose that, within the DAN, there exist functional microstructures (patterns of activity) specific for controlling attention based on the specific information to be attended. To test this, we contrasted preparatory attention to stimulus location (spatial attention) and to stimulus color (feature attention), and used multivoxel pattern analysis to characterize the corresponding patterns of activity within the DAN. We observed different multivoxel patterns of BOLD activation within the DAN for the control of spatial attention (attending left vs. right) and feature attention (attending red vs. green). These patterns of activity for spatial and feature attentional control showed limited overlap with each other within the DAN. Our findings thus support a model in which the DAN has different functional microstructures for distinctive forms of top–down control of visual attention.