Development of tube tetrodes and a multi-tetrode drive for deep structure electrophysiological recordings in the macaque brain

Occurrence of Hippocampal Ripples is Associated with Activity Suppression in the Mediodorsal Thalamic Nucleus

Forming reliable memories requires coordinated activity within distributed brain networks. At present, neural mechanisms underlying systems-level consolidation of declarative memory beyond the hippocampal-prefrontal interactions remain largely unexplored. The mediodorsal thalamic nucleus (MD) is reciprocally connected with the medial prefrontal cortex (mPFC) and also receives inputs from parahippocampal regions. The MD may thus modulate functional connectivity between the hippocampus and the mPFC at different stages of information processing. Here, we characterized, in freely behaving Sprague Dawley male rats, the MD neural activity around hippocampal ripples, indicators of memory replay and hippocampal-cortical information transfer. Overall, the MD firing rate was transiently (0.76 ± 0.06 s) decreased around ripples, with the MD activity suppression preceding the ripple onset for 0.41 ± 0.04 s (range, 0.01-0.95 s). The degree of MD modulation correlated with ripple amplitude, differed across behavioral states, and also depended on the dynamics of hippocampal-cortical population activity. The MD suppression was the strongest and the most consistent during awake ripples. During non-rapid eye movement sleep, MD firing rate decreased around spindle-uncoupled ripples, but increased around spindle-coupled ripples. Our results suggest a competitive interaction between the thalamocortical and hippocampal-cortical networks supporting "on-line" and "off-line" information processing, respectively. We hypothesize that thalamic activity suppression during spindle-uncoupled ripples is favorable for memory replay, as it reduces interference from sensory relay. In turn, the thalamic input during hippocampal-cortical communication, as indicated by spindle/ripple coupling, may contribute to selectivity and reliability of information transfer. Both predictions need to be tested in future experiments.SIGNIFICANCE STATEMENT Systems mechanisms of declarative memory consolidation beyond the hippocampal-prefrontal interactions remain largely unexplored. The connectivity of the mediodorsal thalamic nucleus (MD) with extrahippocampal regions and with medial prefrontal cortex underlies its role in execution of diverse cognitive functions. However, little is known about the MD involvement in "off-line" consolidation. We found that MD neural activity was transiently suppressed around hippocampal ripples, except for ripples co-occurring with sleep spindles, when the MD activity was elevated. The thalamic activity suppression at times of spindle-uncoupled ripples may be favorable for memory replay, as it reduces interference with sensory relay. In turn, the thalamic input during hippocampal-cortical communication, as indicated by spindle/ripple coupling, may contribute to selectivity and reliability of information transfer.

Assessing Granger Causality in Electrophysiological Data: Removing the Adverse Effects of Common Signals via Bipolar Derivations

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.