Fast oscillations display sharper orientation tuning than slower components of the same recordings in striate cortex of the awake monkey

We wanted to know whether fast oscillations ( approximately 30-80 Hz) in striate cortex of awake monkeys show sharper orientation selectivity than (i) slower components, including spike rate modulations, and (ii) broad-band signals of the same recordings. As fast oscillations are probably of cortical origin this may further clarify whether cortical network mechanisms are substantially involved in generating orientation selectivity. We recorded multi unit activity (MUA) and local field potentials (LFP, 1-140 Hz) by the same microelectrodes from upper layers of macaque striate cortex during visual stimulation with grating textures of different orientations. An orientation index (OI) was derived from the cortical responses in three frequency ranges (low, 0-11.7 Hz; medium, 11.7-31.3 Hz; and fast oscillations, 31.3-62.5 Hz) and for the broad-band LFP and MUA power. (i) Both LFP and MUA fast oscillations reveal a higher orientation index than signal components in the low and medium frequency ranges. (ii) For MUA the orientation index was significantly higher with fast oscillations than for the lower frequency ranges and the initial broad-band transient responses. (iii) LFPs show a significantly higher orientation index only for the fast oscillations during sustained activation compared with their broad-band power during the transient responses. Thus, our main result is the sharper orientation tuning of fast oscillations in spike activities of local populations compared with slower components of the same broad-band recordings. As fast oscillations occur synchronized in the awake monkey's striate cortex we assume that they have enhanced probability of activating successive stages of visual processing and hence contribute to the perception of orientation.     

Distinct directional couplings between slow and fast gamma power to the phase of theta oscillations in the rat hippocampus

It is well‐established that theta (~4–10 Hz) and gamma (~25–100 Hz) oscillations interact in the rat hippocampus. This cross‐frequency coupling might facilitate neuronal coordination both within and between brain areas. However, it remains unclear whether the phase of theta oscillations controls the power of slow and fast gamma activity or vice versa. We here applied spectral Granger causality, phase slope index and a newly developed cross‐frequency directionality (CFD) measure to investigate directional interactions between local field potentials recorded within and across hippocampal subregions of CA1 and CA3 of freely exploring rats. Given the well‐known CA3 to CA1 anatomical connection, we hypothesized that interregional directional interactions were constrained by anatomical connection, and within‐frequency and cross‐frequency directional interactions were always from CA3 to CA1. As expected, we found that CA3 drove CA1 in the theta band, and theta phase‐to‐gamma power coupling was prominent both within and between CA3 and CA1 regions. The CFD measure further demonstrated that distinct directional couplings with respect to theta phase was different between slow and fast gamma activity. Importantly, CA3 slow gamma power phase‐adjusted CA1 theta oscillations, suggesting that slow gamma activity in CA3 entrains theta oscillations in CA1. In contrast, CA3 theta phase controls CA1 fast gamma activity, indicating that communication at CA1 fast gamma is coordinated by CA3 theta phase. Overall, these findings demonstrate dynamic directional interactions between theta and slow/fast gamma oscillations in the hippocampal network, suggesting that anatomical connections constrain the directional interactions.     

Synchronization of intrinsic 0.1‐Hz blood‐oxygen‐level‐dependent oscillations in amygdala and prefrontal cortex in subjects with increased state anxiety

Low‐frequency oscillations with a dominant frequency at 0.1 Hz are one of the most influential intrinsic blood‐oxygen‐level‐dependent (BOLD) signals. This raises the question if vascular BOLD oscillations (originating from blood flow in the brain) and intrinsic slow neural activity fluctuations (neural BOLD oscillations) can be differentiated. In this study, we report on two different approaches: first, on computing the phase‐locking value in the frequency band 0.07–0.13 Hz between heart beat‐to‐beat interval (RRI) and BOLD oscillations and second, between multiple BOLD oscillations (functional connectivity) in four resting states in 23 scanner‐naïve, anxious healthy subjects. The first method revealed that vascular 0.1‐Hz BOLD oscillations preceded those in RRI signals by 1.7 ± 0.6 s and neural BOLD oscillations lagged RRI oscillations by 0.8 ± 0.5 s. Together, vascular BOLD oscillations preceded neural BOLD oscillations by ~90° or ~2.5 s. To verify this discrimination, connectivity patterns of neural and vascular 0.1‐Hz BOLD oscillations were compared in 26 regions involved in processing of emotions. Neural BOLD oscillations revealed significant phase‐coupling between amygdala and medial frontal cortex, while vascular BOLD oscillations showed highly significant phase‐coupling between amygdala and multiple regions in the supply areas of the anterior and medial cerebral arteries. This suggests that not only slow neural and vascular BOLD oscillations can be dissociated but also that two strategies may exist to optimize regulation of anxiety, that is increased functional connectivity between amygdala and medial frontal cortex, and increased cerebral blood flow in amygdala and related structures.