Deciphering neuronal circuitry is central to understanding brain function and dysfunction, yet it remains a daunting task. To facilitate the dissection of neuronal circuits, a process requiring functional analysis of synaptic connections and morphological identification of interconnected neurons, we present here a method for stable simultaneous octuple patch-clamp recordings. This method allows physiological analysis of synaptic interconnections among 4–8 simultaneously recorded neurons and/or 10–30 sequentially recorded neurons, and it allows anatomical identification of >85% of recorded interneurons and >99% of recorded principal neurons. We describe how to apply the method to rodent tissue slices; however, it can be used on other model organisms. We also describe the latest refinements and optimizations of mechanics, electronics, optics and software programs that are central to the realization of a combined single- and two-photon microscopy–based, optogenetics- and imaging-assisted, stable, simultaneous quadruple–viguple patch-clamp recording system. Setting up the system, from the beginning of instrument assembly and software installation to full operation, can be completed in 3–4 d.
Summary
Objective
To examine if mice with focal cortical dysplasia (FCD) develop spontaneous epileptic seizures and if so, what are the key EEG features.
Methods
Unilateral single freeze lesions to the S1 region (SFLS1R) were made in postnatal day 0–1 pups to induce a neocortical microgyrus in the right cortical hemisphere. Continuous 24-hour recordings with intracranial EEG electrodes and behavioral tests were performed in adult SFLS1R and sham-control mice to assess neurological status.
Results
A high percentage of adult SFLS1R animals (89%, 40/45) exhibited at least one or more spontaneous non-convulsive seizure events over the course of 24 hours. Of these animals, 60% (27/45) presented with a chronic seizure state that was persistent throughout the recording session, consisting of bursts of rhythmic high-amplitude spike-wave activities and primarily occurring during periods of slow-wave sleep. In comparison, none of the control, age-matched, mice (0/12) developed seizures. The epileptic discharge pattern closely resembled a pattern of continuous spike-waves during slow-wave sleep (CSWS) of the human syndrome described as an electrical status epilepticus during slow wave sleep (ESES). Key findings in the SFLS1R model indicated that the observed CSWS 1) were more prevalent in female (18/23) vs. male (9/22, p < 0.05), 2) were strongest in the right S1 region though generalized to other brain regions, 3) were associated with significant cognitive and behavioral deficits, 4) were temporarily alleviated by ethosuximide treatment or optogenetic activation of cortical GABAergic neurons, and 5) theta and alpha band rhythms may play a key role in the generalization of spike-wave activities.
Significance
This is the first report of an in vivo animal FCD model that induces chronic spontaneous electrographic brain seizures. Further characterization of the abnormal oscillations in this mouse model may lead to a better understanding of the mechanisms of CSWS/ESES.
Neurons in frontal cortex exhibit diverse selectivity representing sensory, motor and cognitive variables during decision-making. The neural circuit basis for this complex selectivity remains unclear. We examined activity mediating a tactile decision in mouse anterior lateral motor cortex in relation to the underlying circuits. Contrary to the notion of randomly mixed selectivity, an analysis of 20,000 neurons revealed organized activity coding behavior. Individual neurons exhibited prototypical response profiles that were repeatable across mice. Stimulus, choice and action were coded nonrandomly by distinct neuronal populations that could be delineated by their response profiles. We related distinct selectivity to long-range inputs from somatosensory cortex, contralateral anterior lateral motor cortex and thalamus. Each input connects to all functional populations but with differing strength. Task selectivity was more strongly dependent on thalamic inputs than cortico-cortical inputs. Our results suggest that the thalamus drives subnetworks within frontal cortex coding distinct features of decision-making.
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