Electric Stimulation Therapy

One commonly used approach to image fluorescently reported neuronal dynamics is 2-photon microscopy^{30 (link)}. This technique utilizes low energy near infrared (IR) photons to penetrate highly light-scattering brain tissue up to 600–700 μm below the surface of the brain^{31 (link)}. A significant advantage of 2-photon microscopy is the ability to selectively excite fluorophores within a well-defined focal plane, resulting in a spatial resolution capable of resolving cellular activity within precisely defined anatomical sub-regions of neurons, such as dendrites and axonal boutons^{30 (link)}. Notably, although this imaging modality provides superior spatial resolution, it requires the head fixation of animals and, in the absence of a microendoscope or optical cannula, 2-photon imaging is limited to superficial layers of the brain^{32 (link),33 (link)}. Together, these behavioral and optical limitations greatly reduce the scope of scientific questions that can be examined with 2-photon microscopy.
Implantation of small, lightweight fiber optics above a region of interest, such as with fiber photometry, circumvents optical and behavioral limitations posed by 2-photon microscopy^{34 (link)}. However, unlike 2-photon microscopy, fiber photometry lacks cellular level resolution and provides only aggregate activity within the field of view (i.e., bulk changes in fluorescent signal)²² . Thus, this method is better suited for monitoring dynamic activity within neural projection fields^{35 (link)}. In addition to limitations in optical resolution, fiber photometry requires the test subject to be secured to a rigid fiber optic bundle, which can be difficult for small mammals, such as mice, to maneuver^{34 (link)}. Thus, while fiber photometry increases the depth in which neural activity can be monitored, it presents significant limitations in optical resolution, restricts the natural behavioral repertoire of an animal, and limits the animal models that can be optimally utilized.
Large-scale recordings of neural activity within freely behaving mammals^{36 (link)} can also be conducted with techniques that do not rely on the use of fluorescence indicators of neural activity, such as in vivo electrophysiological recordings^{2 (link)}. Importantly, compared to in vivo Ca²⁺ imaging, electrophysiology provides superior temporal resolution, allowing for more accurate spike timing estimations^{17 (link),37 (link),38 (link)} as well as the correlation of neural activity with precisely defined temporal events. In addition, in vivo electrophysiology can be combined with optogenetic perturbations of genetically defined neuronal populations to permit the identification (although not unequivocally) and manipulation of defined neuronal populations^{39 –41 (link)}. The ability to monitor and subsequently manipulate a circuit is particularly important to the study of brain function as it allows the causal role of identified computations to be elucidated. Thus, compared to freely behaving in vivo optical imaging methods, in vivo electrophysiology methods offer advantages in the domain of temporal resolution as well as technological integration. One notable limitation of this method is that the spatial location of monitored cells cannot be visualized, making it difficult to assert that an identified cell is similar or unique across recording sessions^{1 (link)}. Moreover, because in vivo electrophysiology relies on waveform shapes to differentiate individual cells from each other, it can be challenging to detect cells with sparse firing patterns or that are located within densely populated networks. Finally, the number of cells that can be detected with in vivo electrophysiology methods is often far less than the number of cells that can be monitored with the optical imaging methods described in this protocol^{29 (link),42 (link)}. Taken together, these limitations in cell identification and statistical power pose a significant disadvantage for studies that require chronic monitoring of neural activity.

Cellular [Ca²⁺]_i and electrophysiological methods are described in the Online Supplement and were used to tune our model and for validation. Table 1 shows key changes made in our new human atrial model vs. our ventricular myocyte model, ^{12 (link)} to account for ionic remodeling in cAF, and to simulate the effects of β-adrenergic and cholinergic stimulation. Further details are in Online Supplement, including formulation of I_Kur block by AVE0118.
Model differential equations were implemented in Matlab (Mathworks Inc., Natick, MA, USA) and solved numerically using a variable order solver (ode15s). APDs were obtained after pacing digital cells at indicated frequencies at steady-state. APD was measured as the interval between AP upstroke and 90% repolarization level (APD₉₀).

Mice were anesthetized with isoflurane and held in place with a head post cemented to the skull. All incisions were infiltrated with lido-caine. A small craniotomy was made over barrel cortex approximately 200 μm anterior to the virus injection site. Extracellular single-unit and LFP recordings were made with tetrodes or stereotrodes. Intracellular recordings were conducted by whole-cell in vivo recording in current clamp mode. Stimulus control and data acquisition was performed using software custom-written in LabView (National Instruments) and Matlab (The Mathworks). Further electrophysiology methods and a description of the reversal potential calculation are given in Supplementary Methods.
Light stimulation was generated by a 473 nm laser (Shanghai Dream Lasers) controlled by a Grass stimulator (Grass Technologies) or computer. Light pulses were given via a 200-μm diameter, unjacketed optical fibre (Ocean Optics) positioned at the cortical surface 75–200 μm from the recording electrodes. For experiments using the broad range of light-stimulation frequencies (8, 16, 24, 32, 40, 48, 80, 100 and 200 Hz), we stimulated in bouts of 3 s of 1-ms pulses at 46 mW mm⁻² at each frequency in a random order. In a subset of these experiments, we stimulated at 31, 46 and 68 mW mm⁻².
Vibrissae were stimulated by computer-controlled movements of piezoelectric wafers (Piezo Systems). Vibrissa stimulations were single high-velocity deflections in the dorsal and then in the ventral direction (~6 ms duration). In most cases, adjacent vibrissae that yielded indistinguishable amplitude responses during hand mapping were deflected simultaneously. Vibrissa stimulations evoked layer 4 RS spike responses with an onset latency of 9.1 ± 0.08 ms. For RS cell response suppression experiments, light pulses were given on randomly interleaved trials. For gamma-phase experiments, we gave a series of trials each consisting of a 1-s series of 1-ms light pulses at 40 Hz, with a single whisker deflection after the thirtieth light pulse. The precise timing of the whisker deflection relative to the light pulses was varied across five phase points. Each of the five phase points was included in a random order across a minimum of 250 total trials.
Unit and LFP analysis used software custom-written in Igor Pro (Wavemetrics). For each stimulation frequency, we measured the relative power in an 8-Hz band centred on that frequency. For each recording site, we measured power from 5–10 LFP traces under each condition. Example power spectra are averages of the power spectra from 5–10 traces of unfiltered LFPs from individual experiments. Relative power was calculated by measuring the ratio of power within the band of interest to total power in the power spectrum of the unfiltered LFP. We also measured the power ratio: P_light/P_baseline, where P_light is the relative power in a frequency band in the presence of light stimulation and P_baseline is the power in that band in the absence of light stimulation. All numbers are given as mean ± s.e.m., except where otherwise noted.