Incoordination between spikes and LFPs in Aβ1-42-mediated memory deficits in rats

Model-based signal processing enables bidirectional inferring between local field potential and spikes evoked by noxious stimulation

BACKGROUND

Recording spontaneous and evoked activities by means of unitary extracellular recordings and local field potential (LFP) are key understanding the mechanisms of neural coding. The LFP is one of the most popular and easy methods to measure the activity of a population of neurons. LFP is also a composite signal known to be difficult to interpret and model. There is a growing need to highlight the relationship between spiking activity and LFP. Here, we hypothesized that LFP could be inferred from spikes under evoked noxious conditions.

METHOD

Recording was performed from the medullary dorsal horn (MDH) in deeply anesthetized rats. We detail a process to highlight the C-fiber (nociceptive) evoked activity, by removing the A-fiber evoked activity using a model-based approach. Then, we applied the convolution kernel theory and optimization algorithms to infer the C-fiber LFP from the single cell spikes. Finally, we used a probability density function and an optimization algorithm to infer the spikes distribution from the LFP.

RESULTS

We successfully extracted C-fiber LFP in all data recordings. We observed that C-fibers spikes preceded the C-fiber LFP and were rather correlated to the LFP derivative. Finally, we inferred LFP from spikes with excellent correlation coefficient (r = 0.9) and reverse generated the spikes distribution from LFP with good correlation coefficients (r = 0.7) on spikes number.

CONCLUSION

We introduced the kernel convolution theory to successfully infer the LFP from spikes, and we demonstrated that we could generate the spikes distribution from the LFP.

The dynamic properties of a brain network during working memory based on the algorithm of cross-frequency coupling

Working memory (WM) refers to a memory system with limited energy for short-term maintenance and plays an important role in cognitive functions. At present, research regarding WM mostly focuses on the coordination between neural signals in the signal microelectrode channel. However, how neural signals coordinate the coding of WM at the network level is rarely studied. Cross-frequency coupling (CFC) reflects the coordinated effect between different frequency components (e.g., theta and gamma) of local field potentials (LFPs) during WM. In this study, we try to map the changes that occur in the brain networks during WM at the level of CFC between theta-gamma of LFPs. First, a 16-channel brain network by using the CFC between theta-gamma of LFPs during WM was constructed. Then, the dynamic properties of the brain network during WM were analyzed based on graph theory. Experimental results show that the LFPs power increased at the WM state than at resting stat, but decreased across learning; the CFC between theta-gamma increased with learning days and phase-amplitude coupling (PAC) in the WM state was higher than that in free choice state and rest state; the changes of average degree, average shortest path length and global efficiency had significant difference on learning days. We can indicate that the CFC between theta-gamma in the network plays an important role in the WM formation. Furthermore, correct storage of WM information will not change local information transmission and the small-world attribute, while, it can increase the network connection and efficiency of information transmission.

Amyloid β Peptide-Induced Changes in Prefrontal Cortex Activity and Its Response to Hippocampal Input

Alterations in prefrontal cortex (PFC) function and abnormalities in its interactions with other brain areas (i.e., the hippocampus) have been related to Alzheimer Disease (AD). Considering that these malfunctions correlate with the increase in the brain's amyloid beta (Aβ) peptide production, here we looked for a causal relationship between these pathognomonic signs of AD. Thus, we tested whether or not Aβ affects the activity of the PFC network and the activation of this cortex by hippocampal input stimulation in vitro. We found that Aβ application to brain slices inhibits PFC spontaneous network activity as well as PFC activation, both at the population and at the single-cell level, when the hippocampal input is stimulated. Our data suggest that Aβ can contribute to AD by disrupting PFC activity and its long-range interactions throughout the brain.