Samuel Minkowicz’s research while affiliated with Northwestern University and other places

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Publications (9)


Figure 1. 3D tracking and characterization of the structure of mouse spontaneous grooming. A. Schematic of experimental setup. Mice were placed into an equilateral triangular arena made of transparent acrylic (12-inch sides and height) and their behavior was captured with three side view cameras. Schematic is not to scale. B. Example video frames of mice during spontaneous grooming. C. Overview of our 4-step grooming identification approach. D. Distribution of the percent of each session that mice spent spontaneously grooming (6 mice, 63 sessions, 117 experiment hours, 1,475 grooming bouts, 304.8 total minutes of spontaneous grooming). E. Distribution of spontaneous grooming bout durations in seconds (for the same dataset as in D). F. Distribution of inter-bout intervals in minutes (for the same dataset as in D).
Figure 1-figure supplement 1.
Figure 2-figure supplement 1. Probe placement and firing rate characteristics. A. Schematic depicting how the four shanks within our recording electrodes were implanted along the anterior-posterior axis (shanks span 600 μm). B. Representative coronal slices of electrode placements. Coronal histological slices were obtained and registered to the Allen Common Coordinate Framework (CCFv3) using the WholeBrain software package. Blue arrows depict the most ventral electrode position for each mouse. Blue lines depict the region covered by sites in one shank of each electrode. Anterior-Posterior (A/P) coordinates from Bregma used for registration are depicted above each slice. (6 mice, scale bar DV and ML = 1 mm). C. Distribution of firing rates for all recorded units (3,124 units. 6 mice). D. Distribution of inter-spike intervals for all recorded units (3,124 units. 6 mice).
Figure 3.
Figure 4. Identification and characterization of striatal grooming ensembles. A. Heatmap of activity during grooming for all units in an example session (53 units, 6.55 minutes). Arrows point out a subset of synchronous events. Each unit's activity is normalized to [0, 1]. B. Correlation matrix for the activity shown in A. C. Distribution of statistical estimate for the number of ensembles within a given session (4 mice, 33 sessions). The estimate is obtained by computing the number of eigenvalues from the data that are above the 99 th percentile of the distribution of eigenvalues from 5,000 random shuffles of the data. D. Average cumulative distribution of pairwise unit distances for each pair of units that are within the same cluster (red) and each pair of units that are not within the same cluster (blue). Plot depicts the mean ± SEM of the cumulative distribution across mice (4 mice). E. Distribution of pairwise unit correlations during grooming for each pair of units that are within the same cluster (pink 2,747 pairs) and each pair of units that are not within the same cluster (blue 27,421 pairs). Left: histogram. Right: cumulative distribution.

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Striatal ensemble activity in an innate naturalistic behavior
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April 2023

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31 Reads

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1 Citation

Samuel Minkowicz

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Mychaela Alexandria Mathews

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Felicia Hoilam Mou

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[...]

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Self-grooming is an innate, naturalistic behavior found in a wide variety of organisms. The control of rodent grooming has been shown to be mediated by the dorsolateral striatum through lesion studies and in-vivo extracellular recordings. Yet, it is unclear how populations of neurons in the striatum encode grooming. We recorded single-unit extracellular activity from populations of neurons in freely moving mice and developed a semi-automated approach to detect self-grooming events from 117 hours of simultaneous multi-camera video recordings of mouse behavior. We first characterized the grooming transition-aligned response profiles of striatal projection neuron and fast spiking interneuron single units. We identified striatal ensembles whose units were more strongly correlated during grooming than during the entire session. These ensembles display varied grooming responses, including transient changes around grooming transitions or sustained changes in activity throughout the duration of grooming. Neural trajectories computed from the identified ensembles retain the grooming related dynamics present in trajectories computed from all units in the session. These results elaborate striatal function in rodent self-grooming and demonstrate that striatal grooming-related activity is organized within functional ensembles, improving our understanding of how the striatum guides action selection in a naturalistic behavior.

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Striatal ensemble activity in an innate naturalistic behavior

April 2023

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8 Reads

Self-grooming is an innate, naturalistic behavior found in a wide variety of organisms. The control of rodent grooming has been shown to be mediated by the dorsolateral striatum through lesion studies and in-vivo extracellular recordings. Yet, it is unclear how populations of neurons in the striatum encode grooming. We recorded single-unit extracellular activity from populations of neurons in freely moving mice and developed a semi-automated approach to detect self-grooming events from 117 hours of simultaneous multi-camera video recordings of mouse behavior. We first characterized the grooming transition-aligned response profiles of striatal projection neuron and fast spiking interneuron single units. We identified striatal ensembles whose units were more strongly correlated during grooming than during the entire session. These ensembles display varied grooming responses, including transient changes around grooming transitions or sustained changes in activity throughout the duration of grooming. Neural trajectories computed from the identified ensembles retain the grooming related dynamics present in trajectories computed from all units in the session. These results elaborate striatal function in rodent self-grooming and demonstrate that striatal grooming-related activity is organized within functional ensembles, improving our understanding of how the striatum guides action selection in a naturalistic behavior.


Figure 1. 3D tracking and characterization of the structure of mouse spontaneous grooming. A. Schematic of experimental setup. Mice were placed into an equilateral triangular arena made of transparent acrylic (12-inch sides and height) and their behavior was captured with three side view cameras. Schematic is not to scale. B. Example video frames of mice during spontaneous grooming. C. Overview of our 4-step grooming identification approach. D. Distribution of the percent of each session that mice spent spontaneously grooming (6 mice, 63 sessions, 117 experiment hours, 1,475 grooming bouts, 304.8 total minutes of spontaneous grooming). E. Distribution of spontaneous grooming bout durations in seconds (for the same dataset as in D). F. Distribution of inter-bout intervals in minutes (for the same dataset as in D).
Figure 3. Emergent motifs in SPN and FSI activity around grooming transitions. A. First principal component from decomposing 10 seconds of SPN activity centered around grooming bout start times (1,632 units, explains 9.3% of variance). B. Activity around grooming start for units that had the largest magnitude weight for the first
Figure 4. Identification and characterization of striatal grooming ensembles. A. Heatmap of activity during grooming for all units in an example session (53 units, 6.55 minutes). Arrows point out a subset of synchronous events. Each unit's activity is normalized to [0, 1]. B. Correlation matrix for the activity shown in A. C. Distribution
Figure 4-figure supplement 1. Statistics of identified clusters and activity patterns. A. Heatmap of activity during grooming with shuffled time bins for all units in the example session shown in 4A (53 units, 6.55 minutes). Arrows are in the same position as in Figure 4A highlighting the absence of synchrony after shuffling. Each unit's activity is normalized to the range from zero to one. B. Correlation matrix for the shuffled activity shown in A. C. Total number of SPNs and FSIs that were clustered (pink) and unclustered (blue) (4 mice, 33 sessions, 243 clusters). D. Total number of clusters that comprised of only SPNs (purple), only FSIs (green), and of both SPNs and FSIs
Figure 5-figure supplement 1. Patterns of cluster engagement. A. Heatmap depicting the percent of units within each ensemble that are active during grooming averaged across all grooming bouts in a given session. Prior to averaging across grooming bouts, ensemble activity during each grooming bout was interpolated to a fixed duration, excluding the 5 seconds before and after grooming. Ensembles are in the same order as in Figure 5A and data are normalized to the range from zero to one (112 ensembles, 4 mice, 33 sessions). B. Distribution of times when the percent of active units in an ensemble peaked (data as in A). C. Representative example of an ensemble with decreased average activity throughout the duration of grooming (grey region as in Figure 5C). D. Neural trajectories traced out in factor space by the population of unclustered units during all grooming bouts in an example session (16 units, 22 grooming bouts). Visualization elements as in Figure 5G.
Striatal ensemble activity in an innate naturalistic behavior

February 2023

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71 Reads

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1 Citation

Self-grooming is an innate, naturalistic behavior found in a wide variety of organisms. The control of rodent grooming has been shown to be mediated by the dorsolateral striatum through lesion studies and in-vivo extracellular recordings. Yet, it is unclear how populations of neurons in the striatum encode grooming. We recorded single-unit extracellular activity from populations of neurons in freely moving mice and developed a semi-automated approach to detect self-grooming events from 117 hours of simultaneous multi-camera video recordings of mouse behavior. We first characterized the grooming transition-aligned response profiles of striatal projection neuron and fast spiking interneuron single units. We identified striatal ensembles whose units were more correlated during grooming than during the whole session. These ensembles display varied grooming responses including transient changes around grooming transitions or sustained changes in activity throughout the duration of grooming. Neural trajectories computed from the identified ensembles retain the grooming related dynamics present in trajectories computed from all units in the session. These results confirm the striatal role in rodent self-grooming and demonstrate that striatal grooming-related activity is organized within functional ensembles improving our understanding of how the striatum mediates action selection in a naturalistic behavior.



Device layout and implantation
a, Layered schematic illustration of an HM, subdermal device for optogenetic research in untethered animals with dynamically programmable operation. PI, polyimide. b, Photograph of the HM device. c, CT image of an HM device (b) in a mouse model. d, Layered schematic illustration of a similar device with a BM, subdermal design. e, Photograph of the BM device. f, CT image of a BM device (e) in a mouse model.
Characterization of mechanical properties and effects on animal locomotor behavior
a, Photograph of the curvature of the body of a mouse and changes in this curvature during natural movements. The colored dots mark points on the head, neck, back and tail of the mouse for characterizing these changes as the mouse moves in an enclosure. b, Probability distribution of tensile strains (that is, compressing and stretching) associated with routine activities and benchtop tests of changes in resistance of the stretchable interconnects used for the BM-device platform after 10,000 cycles of stretching and compressing (black line) at 1 Hz. c, Corresponding probability distribution of bending deformations (that is, radius of curvature). d, FEA simulations of a stretched and bent BM device. e, Photographs of a stretched and bent device. f–h, Results of motion tracking (5 min in a circular arena) for independent, freely moving mice (surgical control), for mice implanted with bilateral optical fibers (tethered) and for mice implanted with BM devices (BM wireless). i,j, The same cohorts of biologically independent animals (control, n = 8; tethered, n = 7; BM wireless, n = 7) were used to derive these results over three independent experiments. i, Average distance traveled over 5 min in the circular arena (one-way ANOVA, Tukey’s multiple-comparisons test; control versus tethered, P = 0.01; tethered versus BM wireless, P = 0.02; control versus BM wireless, P = 0.99). NS, not significant. j, Average movement speed over 5 min in the circular arena (one-way ANOVA, Tukey’s multiple-comparisons test; control versus tethered, P = 0.02; tethered versus BM wireless, P = 0.02; control versus BM wireless, P = 0.99). k, Average revolutions per minute when running on a wheel over a 60-min period (one-way ANOVA, Tukey’s multiple-comparisons test; control versus tethered, P < 0.0001; tethered versus BM wireless, P = 0.0014; control versus BM wireless, P = 0.40). Biologically independent animals (control, n = 6; tethered, n = 4; BM wireless, n = 4) were used to derive these results over three independent experiments. All data are represented as mean ± s.e.m. *P < 0.05, **P < 0.01, ****P < 0.0001.
Source data
Designs, operational features and use cases for dynamically programmable NFC electronics
a, Block diagram of an NFC-enabled platform for independent, programmable control over operational parameters. Microcontroller firmware coupled with this electronic module allows multichannel selection and control over the period (T), duty cycle and intensity of the μ-ILEDs. PC, personal computer; µC, microcontroller. b, Photograph of HM and BM devices implemented with dynamically programmable NFC electronics, operating after implantation in mice. c, Filter output voltage as a function of duty cycle of the intensity-encoding carrier waveform (Extended Data Fig. 5b). d, Time dependence of output waveforms with a period of 100 ms and a duty cycle of 20% at various peak voltage magnitudes. The carrier frequency is 2 kHz, and the duty cycle varies from 20% to 100%. e, Sequence of photographs of an HM device operated at different duty cycles of the carrier waveform, to control the intensity of one of the two μ-ILEDs. f, Sequence of photographs of a BM device showing multichannel operation. g, Schematic illustrations of several potential applications of this technology in optogenetic studies, including tunable modulation of bilateral brain regions (left), bidirectional modulation of distal regions (middle) and multi-brain synchrony manipulation (right).
Source data
Characterization of optical and thermal properties
a, Simulated magnetic-field-intensity distribution at the central plane of a cage (dimensions, 30 cm (length) × 30 cm (width) × 15 cm (height)) surrounded by a double-loop antenna at heights of 3 cm and 9 cm. AU, arbitrary units. b, Electrical power supplied to HM and BM coils and different μ-ILEDs (460 nm, 535 nm, 595 nm, 630 nm) as a function of internal working impedance at 8 W of RF power applied to the transmission antenna. c, Maximum harvested power as a function of RF power applied to the transmission antenna for HM and BM coils. d, Maximum electrical and optical power for a single blue μ-ILED (460 nm) as a function of RF power applied to the transmission antenna for HM and BM devices. e, Illumination volume and penetration depth as a function of the output irradiance of the blue μ-ILED (460 nm) (cutoff intensity, 0.1 mW mm⁻²). f, Temperature change (ΔT (°C)) at the interface between the μ-ILED and brain tissue as a function of operational irradiance of the blue μ-ILED (460 nm) and its duty cycle at a frequency of 20 Hz.
Source data
Bilateral stimulation and subject-specific programmability for wireless optogenetic control of social behavior
a, Left: schematic of the arena for the social preference task and the stimulation area. Right: burst pattern of wireless light stimulation (460 nm). b, Example traces show position tracks in baseline and bilateral stimulation conditions from one animal. c, Left: summary data for total time spent in the social-interaction zone in baseline and stimulation conditions. Two-way ANOVA, Sidak’s multiple-comparisons test (baseline versus stim), ChR2, P = 0.0016; fluorophore, P = 0.7763. Right: total time spent in the object interaction zone. Two-way ANOVA, Sidak’s multiple-comparisons test (baseline versus stim), ChR2, P = 0.6208; fluorophore, P = 0.8363. n = 6 animals per group. d, Left: schematic of viral transduction and probe implantation in the mPFC. Right: the arena and experimental design for the free social-interaction assay. Sync, synchronized; desync, desynchronized. e, Left: stimulation patterns used to induce synchronized and desynchronized activity. Right: example traces from a current-clamp recording of a ChR2–mCherry⁺ mPFC pyramidal neuron during optogenetic activation with blue light, as noted. f, Photograph of two mice interacting during synchronized wireless light stimulation. g, Behavioral sequences recorded in individual mice receiving synchronized or desynchronized stimulation during free social interaction. h, Proportion of time spent engaging in non-locomotor behaviors for mice expressing ChR2 or fluorophore controls, receiving synchronized or desynchronized stimulation. Proportions of time spent engaging in social behavior: ChR2 synchronized, 46.5%; ChR2 desynchronized, 25.9%; fluorophore synchronized, 23.5%; fluorophore desynchronized, 18.4%. n = 10–12 pairs per group. i, Left: summary data show the total time spent in social interaction for paired mice during synchronized and desynchronized stimulation. Two-way ANOVA, Sidak’s multiple-comparisons test (synchronized pair versus desynchronized pair), ChR2, P < 0.0001; fluorophore, P = 0.6198. Right: total time spent engaging in non-social behaviors, exclusive of locomotion. Two-way ANOVA, Sidak’s multiple-comparisons test (synchronized pair versus desynchronized pair), ChR2, P = 0.9526; fluorophore, P = 0.6608. n = 10–12 pairs per group. j, Left: summary data for social event durations for paired ChR2-expressing mice during synchronized and desynchronized stimulation. Right: same as left but for fluorophore control mouse pairs. Two-way ANOVA, Holm–Sidak’s multiple-comparisons test (synchronized pair versus desynchronized pair). ChR2: allogroom, P = 0.0027; approach, P = 0.9517; sniff, P < 0.0001; pursue, P = 0.0939; escape, P = 0.9517; attack, P = 0.9517. Fluorophore: allogroom, P = 0.8903; approach, P = 0.9541; sniff, P = 0.9541; pursue, P = 0.8996; escape, P = 0.9092; attack, P = 0.788. n = 12 pairs for ChR2 and n = 10 pairs for the fluorophore. k, Left: summary data for non-social event durations for paired ChR2-expressing mice during synchronized and desynchronized stimulation. Right: same as left but for fluorophore control mouse pairs. Two-way ANOVA, Holm–Sidak’s multiple-comparisons test (synchronized pair versus desynchronized pair). ChR2: dig, P = 0.1941; rear, P = 0.6816; self-groom, P = 0.1941. Fluorophore: dig, P = 0.822; rear, P = 0.5568; self-groom, P = 0.822. n = 12 pairs for ChR2 and n = 10 pairs for the fluorophore. l, Photograph of three mice receiving 5-Hz or 25-Hz stimulation in the social-interaction arena, forming three synchronized or desynchronized pairs. m, Example social behavior sequences in synchronized or desynchronized pairs during free social interaction. n, Left: the proportion of social interactions for a focal animal in a triad with the synchronously stimulated one, over the total social-interaction time, in animals receiving 5-Hz optogenetic stimulation. Two-way ANOVA, Sidak’s multiple-comparisons test, ChR2 versus fluorophore, animal (A)1, P = 0.0145; A2, P = 0.0198. One-sample t-test, ChR2 versus random chance (0.5, dotted line), A1, P = 0.0066; A2, P = 0.0003. Right: the proportion of social interactions for the focal animal receiving 25-Hz stimulation in a triad, desynchronized from both other present conspecifics, over the total social-interaction time. Unpaired two-tailed t-test, ChR2 versus fluorophore, P = 0.7207. n = 8–10 experiments per group. Data represent mean ± s.e.m. in bar graphs; box-and-whisker plots show quantiles and medians. *P < 0.05, **P < 0.01, ****P < 0.0001.
Source data
Wireless multilateral devices for optogenetic studies of individual and social behaviors

May 2021

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1,477 Reads

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136 Citations

Nature Neuroscience

Advanced technologies for controlled delivery of light to targeted locations in biological tissues are essential to neuroscience research that applies optogenetics in animal models. Fully implantable, miniaturized devices with wireless control and power-harvesting strategies offer an appealing set of attributes in this context, particularly for studies that are incompatible with conventional fiber-optic approaches or battery-powered head stages. Limited programmable control and narrow options in illumination profiles constrain the use of existing devices. The results reported here overcome these drawbacks via two platforms, both with real-time user programmability over multiple independent light sources, in head-mounted and back-mounted designs. Engineering studies of the optoelectronic and thermal properties of these systems define their capabilities and key design considerations. Neuroscience applications demonstrate that induction of interbrain neuronal synchrony in the medial prefrontal cortex shapes social interaction within groups of mice, highlighting the power of real-time subject-specific programmability of the wireless optogenetic platforms introduced here.


Attenuated dopamine signaling after aversive learning is restored by ketamine to rescue escape actions

April 2021

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122 Reads

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38 Citations

eLife

Escaping aversive stimuli is essential for complex organisms, but prolonged exposure to stress leads to maladaptive learning. Stress alters neuronal activity and neuromodulatory signaling in distributed networks, modifying behavior. Here we describe changes in dopaminergic neuron activity and signaling following aversive learning in a learned helplessness paradigm in mice. A single dose of ketamine suffices to restore escape behavior after aversive learning. Dopaminergic neuron activity in the ventral tegmental area (VTA) systematically varies across learning, correlating with future sensitivity to ketamine treatment. Ketamine's effects are blocked by chemogenetic inhibition of dopamine signaling. Rather than directly altering the activity of dopaminergic neurons, ketamine appears to rescue dopamine dynamics through actions in the medial prefrontal cortex (mPFC). Chemogenetic activation of Drd1 receptor positive mPFC neurons mimics ketamine's effects on behavior. Together, our data link neuromodulatory dynamics in mPFC-VTA circuits, aversive learning, and the effects of ketamine.


Figure 1. KET regulates mPFC plasticity through a DA-dependent mechanism. (A) Schematic illustrating glutamate-evoked de novo spinogenesis platform. Top: viral transduction and an example EGFP 1 pyramidal neuron in the mPFC. Bottom: MNI-glutamate uncaging parameters for the induction of new dendritic spines. Scale bar = 50 mm. (B) Example two-photon laser-scanning microscopy images of successful and unsuccessful induction trials of de novo spinogenesis. Red circles show uncaging sites. Black rectangles show close-up images of local dendritic segments before and after glutamate uncaging. Scale bar = 2 mm. (C) Left: Schematic illustrating time course of KET treatments and experiments. Middle: Time course of evoked spinogenesis probability on deep layer mPFC neurons in mice treated with either saline or KET (10 mg/kg, intraperitoneal, acute slice preparation 2-72 hours after treatment). Each small circle shows aggregate probability of evoked spinogenesis from a single animal. Large circle shows group data. n = 6-7 animals/time point, 15-25 trials/animal, oneway ANOVA, F 5,35 = 9.895, p , .0001, Sidak's multiple comparison test vs. saline, 2 hours, p = .0076, 4 hours, p , .0001, 12 hours, p = .0532, 24/72 hours, p . .90. Right: Same as left but for dendritic spine density. n = 7-8 animals/time point, one-way ANOVA, F 5,37 = 6.319, p = .0002, Sidak's multiple comparison test vs. saline, 2/4 hours, p . .80, 12 hours, p = .0056, 24 hours, p = .0011, 72 hours, p = .1271. Inset: Normalized time course of changes in evoked spinogenesis (orange) and dendritic spine density (blue). (D) Left: Viral transduction and percentage of Drd1a 1 Egfp 1 /Egfp 1 cells in layer 5 mPFC. Right: Probability of glutamate-evoked spinogenesis on deep layer mPFC neurons in mice treated with saline, KET (10 mg/kg), KET 1 SKF 83566 (10 mg/kg), or SKF 83566 alone. Each small circle shows aggregate probability of evoked spinogenesis from a single animal. Large circle shows group data. One-way ANOVA, p , .0001, F 3,16 = 20.29, Sidak's multiple comparison test, saline vs. KET, p , .0001, KET vs. KET 1 SKF 83566, p = .0002, saline vs. SKF 83566, p = .8574. (E) Left: Schematic illustrating triple viral transduction strategy for evoked spinogenesis with DA neuron inhibition. Right: Probability of spinogenesis on deep layer mPFC neurons in DAT iCre1 and DAT iCre2 animals treated with CNO (3 mg/kg) across conditions (baseline and KET). n = 4 animals/condition as shown in plots, two-way ANOVA, Sidak's multiple comparison test, Cre 2 vs. Cre 1 , CNO, p = .8686, CNO 1 KET, p = .0042. (F) Left: Example confocal images of EGFP expression in dendrites of deep layer mPFC pyramidal neurons in response to CNO and KET treatment, as noted. Scale = 2 mm. Right: same as (E) but for dendritic spine density. n = 5-6 animals/condition as shown in plots, two-way ANOVA, Sidak's multiple comparison test, Cre 2 vs. Cre 1 , CNO, p = .5005, CNO 1 KET, p , .0001. Scale bar = 2 mm. **p , .01, ***p , .001, ****p , .0001. Error bars reflect SEM. ANOVA, analysis of variance; CNO, clozapine N-oxide; DA, dopaminergic; EGFP, enhanced green fluorescent protein; FISH, fluorescence in situ hybridization; KET, ketamine; mPFC, medial prefrontal cortex; ns, nonsignificant; t, time; VTA, ventral tegmental area.
Figure 2. KET rescues mPFC plasticity after stressful experience through dopmanine Drd1 receptor (Drd1). (A) Schematic illustrating glutamate-evoked spinogenesis assay in baseline, LH, and LH 1 KET conditions. (B) Summary data showing the percentage of failures to escape an escapable aversive shock, one-way ANOVA, F 2,18 = 20.26, p , .0001, Sidak's multiple comparison test, baseline vs. LH, p , .0001, LH vs. LH 1 KET, p = .0041. (C) Probability of glutamate-evoked spinogenesis on deep layer mPFC neurons in distinct stages of aversive learning (baseline, LH, and LH 1 KET). n = 9-12 animals/condition as shown in plots, one-way ANOVA, F 2,28 = 7.146, p = .0031, Sidak's multiple comparison test, baseline vs. LH, p = .0496, LH vs. LH 1 KET, p = .0016. (D) Left: Schematic illustrating dual viral transduction strategy with sparse genetic manipulation of Drd1 expression in Drd1 ff mice. Middle: Fluorescence in situ hybridization image confirming the absence of Drd1a mRNA expression (purple) in Egfp mRNA-expressing mPFC cells (green) in Drd1 ff mice. Inset: Close-up of a single neuron. Scale bar = 50 mm. Right: Quantification of the percentage of Drd1a 1 cells among Egfp 1 cells in mPFC. 5% Drd1a 1 and 95% Drd1a 2 among 151 Egfp 1 cells from 2 animals. (E) Probability of glutamate-evoked spinogenesis on deep layer mPFC neurons in distinct stages of aversive learning (baseline, LH, LH 1 KET, and LH 1 saline) in WT and Drd1 ff mice. Two-way ANOVA, Sidak's multiple comparison test, WT vs. Drd1 ff , LH 1 KET, p = .0043, baseline, LH and LH 1 saline, p . .90, n = 5 animals. *p , .05, **p , .01, ****p , .0001. Error bars reflect SEM. ANOVA, analysis of variance; EGFP, enhanced green fluorescent protein; KET, ketamine; LH, learned helplessness; mPFC, medial prefrontal cortex; mRNA, messenger RNA; ns, nonsignificant; WT, wild-type.
Ketamine Rapidly Enhances Glutamate-Evoked Dendritic Spinogenesis in Medial Prefrontal Cortex Through Dopaminergic Mechanisms

January 2021

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300 Reads

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73 Citations

Biological Psychiatry

Background Ketamine elicits rapid onset antidepressant effects in clinically depressed patients, through mechanisms hypothesized to involve the genesis of neocortical dendritic spines and synapses. Yet, the observed changes in dendritic spine morphology usually emerge well after ketamine clearance, raising questions about the link between rapid behavioral effects of ketamine and plasticity. Methods Here, we use 2-photon glutamate uncaging/imaging to focally induce spinogenesis in the medial prefrontal cortex (mPFC), directly interrogating baseline and ketamine-associated plasticity of deep layer pyramidal neurons in C57BL/6 mice. We combine pharmacological, genetic, optogenetic, and chemogenetic manipulations to interrogate dopaminergic mechanisms underlying ketamine-induced rapid enhancement in evoked plasticity and associated behavioral changes. Results We find that ketamine rapidly enhances glutamate-evoked spinogenesis in mPFC, with timing that matches the onset of its behavioral efficacy and precedes changes in dendritic spine density. Ketamine increases evoked cortical spinogenesis through Drd1 receptor activation that requires dopamine release, compensating blunted plasticity in a learned helplessness paradigm. The enhancement in evoked spinogenesis after Drd1 activation or ketamine treatment depends on postsynaptic Protein Kinase A (PKA) activity. Furthermore, ketamine’s behavioral effects are blocked by chemogenetic inhibition of dopamine release and mimicked by activating presynaptic dopaminergic terminals, or postsynaptic Gαs-coupled cascades in mPFC. Conclusions Our findings highlight dopaminergic mediation of rapid enhancement in activity-dependent dendritic spinogenesis and behavioral effects induced by ketamine.


Ketamine restores escape behavior by re-engaging dopamine systems to drive cortical spinogenesis

March 2020

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95 Reads

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2 Citations

Escaping aversive stimuli is essential for complex organisms, but prolonged exposure to stress leads to maladaptive learning. Stress alters neuronal activity in distributed networks, plasticity, and neuromodulatory signaling; yet, the field lacks a unifying framework for its variegated sequelae. Here we build this framework using learned helplessness paradigm, where ketamine restores escape behavior after aversive learning. Low-dimensional optical readout of dopamine (DA) neuron activity across learning predicts acute behavioral responses, transitions through learning phases, and future sensitivity to ketamine treatment. Ketamine effects are blocked by chemogenetic inhibition of DA signaling and mimicked by optogenetic activation. We use 2-photon glutamate uncaging/imaging to interrogate structural plasticity in medial prefrontal cortex, revealing that dendritic spinogenesis on pyramidal neurons is regulated by aversive experience and recovered by ketamine in a DA-dependent manner. Together, these data describe recurrent circuits that causally link neuromodulatory dynamics, aversive learning, and plasticity enhancements driven by a therapeutically promising anti-depressant.


Citations (6)


... However, it remained unclear how the neurons within the striatum work together to produce the sequence of self-grooming. Now, in eLife, Ann Kennedy and Yevgenia Kozorovitskiy of Northwestern University and colleagues -including Samuel Minkowicz as first author -report a new method for identifying grooming episodes in video footage, and then show that discrete groups of striatal neurons show activity at the beginning and end of such episodes in mice, as well as throughout episodes (Minkowicz et al., 2023). ...

Reference:

A groom with a view
Striatal ensemble activity in an innate naturalistic behavior

... Initially, we focused on grooming behaviors ( Fig 3E) as they are explicitly forelimb-dependent, just like the learned behavior in the timed-lever pressing task. Additionally, grooming sequences have been studied extensively in terms of striatal function, with studies suggesting a role for DLS in their sequencing 27,30,[60][61][62] . To probe the degree to which their kinematics are affected by DLS lesions, we identified a set of pre-lesion grooming templates using the egocentrically-defined 3D positions of the left and right wrists during MotionMapper-identified grooming bouts (n = 50 ± 23 templates per animal, mean ± std, see Methods for details on template identification). ...

Striatal ensemble activity in an innate naturalistic behavior

... The development of compact and power efficient computing devices have evolved optogenetic techniques toward wireless technology, allowing to study animal behavior with minimal physical constraints Park et al. 2015;Montgomery et al. 2015;Jeong et al. 2015). Furthermore, wireless neuronal manipulations expanded the range of behavioral studies with freely moving animals, enabling the exploration of complex three-dimensional environments (S.-G. Park et al. 2018) and social settings (Yang et al. 2021;Li et al. 2022). For these situations the use of traditional tethered optical fiber setups would be challenging, as they restrict the animal's range of movement or can get tangled with the tethers of other animals or with structures found in a natural setting. ...

Wireless multilateral devices for optogenetic studies of individual and social behaviors

Nature Neuroscience

... Tg(Adora2a-cre)KG139Gsat and Tg(Drd1-Cre)FK150Gsat mice with WT (C57BL6/J) in-house, 66 were used for Core experiments. To distinguish Sox6+ and Sox6-dopamine axons, DAT-2A-67 ...

Attenuated dopamine signaling after aversive learning is restored by ketamine to rescue escape actions

eLife

... This timing sequence where ketamine first enhances plasticity potential implies that alterations in cellular properties, influencing the propensity to form new spines, play a role in the gradual rise of spine density following ketamine treatment. In an uncontrollable stress paradigm the probability of glutamate evoking spinogenesis decreased relative to the baseline, with ketamine treatment restoring the baseline potential for plasticity (Wu et al., 2021). Interestingly, these effects were found in Drd1-expressing mPFC neurons to be dependent upon the activation by DA neurons of the ventral tegmental area (VTA) (Wu et al., 2021). ...

Ketamine Rapidly Enhances Glutamate-Evoked Dendritic Spinogenesis in Medial Prefrontal Cortex Through Dopaminergic Mechanisms

Biological Psychiatry

... Moreover, several studies have found that somatodendritic DA release in the midbrain is less dependent on calcium as compared to axonal release in the striatum, making GCaMPs a less appropriate tool to study such somatodendritic release mechanisms. Nonetheless, combined monitoring of calcium transients (GCaMPs) and DA release (dLight/GRAB-DA) can represent a powerful platform to understand how neural activity shapes release dynamics, as demonstrated in recent preprints for acetylcholine [97] or DA [98]. ...

Ketamine restores escape behavior by re-engaging dopamine systems to drive cortical spinogenesis
  • Citing Preprint
  • March 2020