Comprehensive characterization of oscillatory signatures in a model circuit with PV- and SOM-expressing interneurons

Neural circuits contain a wide variety of interneuron types, which differ in their biophysical properties and connectivity patterns. The two most common interneuron types, parvalbumin-expressing and somatostatin-expressing cells, have been shown to be differentially involved in many cognitive functions. These cell types also show different relationships with the power and phase of oscillations in local field potentials. The mechanisms that underlie the emergence of different oscillatory rhythms in neural circuits with more than one interneuron subtype, and the roles specific interneurons play in those mechanisms, are not fully understood. Here, we present a comprehensive analysis of all possible circuit motifs and input regimes that can be achieved in circuits comprised of excitatory cells, PV-like fast-spiking interneurons and SOM-like low-threshold spiking interneurons. We identify 18 unique motifs and simulate their dynamics over a range of input strengths. Using several characteristics, such as oscillation frequency, firing rates, phase of firing and burst fraction, we cluster the resulting circuit dynamics across motifs in order to identify patterns of activity and compare these patterns to behaviors that were generated in circuits with one interneuron type. In addition to the well-known PING and ING gamma oscillations and an asynchronous state, our analysis identified three oscillatory behaviors that were generated by the three-cell-type motifs only: theta-nested gamma oscillations, stable beta oscillations and theta-locked bursting behavior, which have also been observed in experiments. Our characterization provides a map to interpret experimental activity patterns and suggests pharmacological manipulations or optogenetics approaches to validate these conclusions.

Towards optimization of oscillatory stimulation during sleep

Background: Oscillatory rhythms during sleep such as slow oscillations (SO) and spindles, and most importantly their coupling, are thought to underlie processes of memory consolidation. External slow oscillatory transcranial direct current stimulation (so-tDCS) with a frequency of 0.75 Hz has been shown to improve this coupling and memory consolidation, however, effects varied quite markedly between individuals, studies and species. Objective: Here, we aimed to determine how precisely the frequency of stimulation has to match the naturally occurring SO frequency in individuals to optimally improve SO-spindle coupling. Moreover, we systematically tested stimulation durations necessary to induce changes. Methods: We addressed these questions by comparing so-tDCS with individually adapted SO frequency to standardized frequency of 0.75Hz in a cross-over design with 28 healthy older participants during napping while systematically varying stimulation train durations between 30s, 2min and 5min. Results: Stimulation trains as short as 30s were sufficient to modulate the coupling between SOs and spindle activity. Contrary to our expectations, so-tDCS with standardized frequency indicated stronger aftereffects with regard to SO-spindle coupling in comparison to individualized frequency. Angle and variance of spindle maxima occurrence during the SO cycle were similarly modulated. Conclusion: Short stimulation trains were sufficient to induce significant changes in sleep physiology allowing for more trains of stimulation, which provides methodological advantages and possibly even larger effects in future studies. With regard to individualized stimulation frequency, further options of optimization need to be investigated, such as closed-loop stimulation to calibrate stimulation frequency to the SO frequency at time of stimulation onset.

Functional protection in J20/VLW mice: a model of non-demented with Alzheimer’s disease neuropathology

Alzheimer’s disease comprises amyloid-β and hyperphosphorylated Tau accumulation, imbalanced neuronal activity, aberrant oscillatory rhythms, and cognitive deficits. Non-Demented with Alzheimer’s disease Neuropathology (NDAN) defines a novel clinical entity with amyloid-β and Tau pathologies but preserved cognition. The mechanisms underlying such neuroprotection remain undetermined and animal models of NDAN are currently unavailable. We demonstrate that J20/VLW mice (accumulating amyloid-β and hyperphosphorylated Tau) exhibit preserved hippocampal rhythmic activity and cognition, as opposed to J20 and VLW animals, which show significant alterations. Furthermore, we show that the overexpression of mutant human Tau in coexistence with amyloid-β accumulation renders a particular hyperphosphorylated Tau signature in hippocampal interneurons. The GABAergic septohippocampal pathway, responsible for hippocampal rhythmic activity, is preserved in J20/VLW mice, in contrast to single mutants. Our data highlight J20/VLW mice as a suitable animal model in which to explore the mechanisms driving cognitive preservation in NDAN. Moreover, they suggest that a differential Tau phosphorylation pattern in hippocampal interneurons prevents the loss of GABAergic septohippocampal innervation and alterations in local field potentials, thereby avoiding cognitive deficits.

Scaling up to meet new challenges of brain and behaviour: Deep brain electrophysiology in freely moving sheep

While rodents are arguably the easiest animals to use for studying brain function, relying on them as model species for translational research comes with its own sets of limitations. Here, we propose sheep as a practical large animal species for in vivo brain function studies performed in naturalistic settings. To demonstrate their experimental usefulness, we performed proof-of-principle deep brain electrophysiological recording experiments from unrestrained sheep. Recordings were made from cortex and hippocampus both whilst sheep performed goal-directed behaviours (two-choice discrimination tasks), and across states of vigilance that included natural sleep. Hippocampal and cortical oscillatory rhythms were consistent with those seen in rodents and non-human primates, and included cortical alpha oscillations during immobility, hippocampal theta oscillations (5-6Hz) during locomotion and hippocampal sharp wave ripple oscillations (~150 Hz) during immobility. Moreover, we found clear examples of neurons whose activity was modulated by task, speed of locomotion, spatial position, reward and vigilance states. Recordings were conducted over a period of many months. Due to the exceptional stability of individual electrodes we were able to record from some neurons continuously for more than 1 month. Together these experiments demonstrate that sheep are an excellent experimental animal model to use in longitudinal electrophysiological and imaging studies, particularly those requiring a large brained mammal, large scale recordings across distributed neuronal networks, experimentation outside the confounds of the traditional laboratory, or all the above concomitantly.

Nested oscillations and brain connectivity during sequential stages of feature-based attention

Brain mechanisms of visual selective attention involve both local and network-level activity changes at specific oscillatory rhythms, but their interplay remains poorly explored. Here, we investigate anticipatory and reactive effects of feature-based attention using separate fMRI and EEG recordings, while participants attended to one of two spatially overlapping visual features (motion and orientation). We focused on EEG source analysis of local nested oscillations and on graph analysis of connectivity changes in a network of fMRI-defined regions of interest, and characterized a cascade of attentional effects and their interplay at multiple spatial scales. We discuss how the results may reconcile several theories of selective attention, by showing how β rhythms support anticipatory information routing through increased network efficiency and β-γ coupling in functionally specialized regions (V1 for orientation, V5 for motion), while reactive α-band desynchronization patterns and increased α-γ coupling in V1 and V5 mediate stimulus-evoked processing of task-relevant signals.

Drums of Doubt: On the Rhythmical Origins of Poetic and Scientific Exploration

In his article “Drums of Doubt: On the Rhythmical Origins of Poetic and Scientific Exploration” Caius Dobrescu argues that even though the sciences and arts of doubt have never been connected to the notion of rhythm, doubt is a form of energy, and more specifically, a form of vibration. It implies an exploratory movement that constantly expands and recoils in a space essentially experienced as uncharted territory. Poetry acquires cognitive attributes through oscillatory rhythmic patterns that are explorative and adaptive. In order to test this hypothesis, the essay focuses on the nature and functioning of free verse. This modern prosodic mutation brings about a dovetailing of the rhythmic spectrum, but also, and more significantly, a change in the very manner of understanding and experiencing rhythm. Oscillatory rhythms are broadly associable with entrainment indexes that point to the adaptation of inner physiological and behavioral rhythms to oscillatory environment stimuli. Free verse emerges from the experience of regaining an original explorative, adaptive, and orientation-oriented condition of consciousness.

Broadband Signal Rather than Frequency-Specific Rhythms Underlies Prediction Error in the Primate Auditory Cortex

SummaryDetection of statistical irregularities, measured as a prediction error response, is fundamental to the perceptual monitoring of the environment. We studied whether prediction error response is generated by neural oscillations or asynchronous neuronal firing. Electrocorticography (ECoG) was carried out in three monkeys, who passively listened to the auditory roving oddball stimuli. Local field potentials (LFP) recorded over the auditory cortex underwent spectral principal component analysis, which decoupled broadband and rhythmic components of LFP signal. We found that broadband component generated prediction error response, whereas none of the rhythmic components encoded statistical irregularities of sounds. The broadband component displayed more stochastic, asymmetrical multifractal properties than the rhythmic components, which revealed more self-similar dynamics. We thus conclude that the prediction error response is encoded by asynchronous neuronal populations, defined by irregular dynamical states which, unlike oscillatory rhythms, appear to enable the neural representation of auditory prediction error response.