Relationships among Parvalbumin-Immunoreactive Neuron Density, Phase-Locked Gamma Oscillations, and Autistic/Schizophrenic Symptoms in PDGFR-β Knock-Out and Control Mice

<div><p>Cognitive deficits and negative symptoms are important therapeutic targets for schizophrenia and autism disorders. Although reduction of phase-locked gamma oscillation has been suggested to be a result of reduced parvalbumin-immunoreactive (putatively, GABAergic) neurons, no direct correlations between these have been established in these disorders. In the present study, we investigated such relationships during pharmacological treatment with a newly synthesized drug, T-817MA, which displays neuroprotective and neurotrophic effects. In this study, we used platelet-derived growth factor receptor-β gene knockout (PDGFR-β KO) mice as an animal model of schizophrenia and autism. These mutant mice display a reduction in social behaviors; deficits in prepulse inhibition (PPI); reduced levels of parvalbumin-immunoreactive neurons in the medical prefrontal cortex, hippocampus, amygdala, and superior colliculus; and a deficit in of auditory phase-locked gamma oscillations. We found that oral administration of T-817MA ameliorated all these symptoms in the PDGFR-β KO mice. Furthermore, phase-locked gamma oscillations were significantly correlated with the density of parvalbumin-immunoreactive neurons, which was, in turn, correlated with PPI and behavioral parameters. These findings suggest that recovery of parvalbumin-immunoreactive neurons by pharmacological intervention relieved the reduction of phase-locked gamma oscillations and, consequently, ameliorated PPI and social behavioral deficits. Thus, our findings suggest that phase-locked gamma oscillations could be a useful physiological biomarker for abnormality of parvalbumin-immunoreactive neurons that may induce cognitive deficits and negative symptoms of schizophrenia and autism, as well as of effective pharmacological interventions in both humans and experimental animals.</p></div

T-817MA ameliorated PPI deficits in PDGFR-β KO mice.

<p>(A) Acoustic startle amplitudes measured in trials without a prepulse. No significant differences were observed in the acoustic startle amplitudes among the four groups of mice. Values indicate the mean ± SE. (B) PPI (% inhibition) at five different prepulse intensities (70, 72, 74, 78, and 82 dB). Statistical results by three-way ANOVAs indicated significant main effects of genotype and treatment. Cont-DW, control mice with distilled water (DW); Cont-T817, control mice with T-817MA; KO-DW, PDGFR-β KO mice with DW; KO-T817, PDGFR-β KO mice with T-817MA.</p

Comparison of peak-ITC (30.4–67.1 Hz) among the four groups.

<p>Peak-ITC was significantly lower in PDGFR-β KO mice administered with distilled water (KO-DW) compared to control mice administered with distilled water (Cont-DW) and PDGFR-β KO mice administered with T-817MA (KO-T817). aa, p<0.01 (Bonferroni test).</p

Photomicrographs of the medial prefrontal cortex (mPFC, A), CA3 subfield of the hippocampus (B), basolateral amygdala (BLA, C), and superior colliculus (SC, D) of the four groups of mice.

<p>Number of parvalbumin-immunoreactive neurons was decreased in PDGFR-<b>β</b> KO mice (KO-DW). Scale bar = 100 μm.</p

T-817MA ameliorated the decrease in density of parvalbumin-immunoreactive neurons in the mPFC (A), hippocampus (B), basolateral amygdala (BLA) (C), and superior colliculus (SC) (D) of PDGFR-β KO mice.

<p>**, p<0.01 (main effect of genotype); #, p<0.05 (main effect of treatment); a, significant difference between Cont-DW and KO-T817 in the BLA (Bonferroni test, p<0.01); b, significant difference between Cont-DW (Bonferroni test, p<0.01) and KO-T817 (Bonferroni test, p<0.05) in the SC.</p

T-817MA ameliorated deficits of phase-locked gamma oscillations (peak-ITC, 26.4–67.1 Hz) in PDGFR-β KO mice.

<p>(A) Examples of ITCs (Gamma ITC), non-averaged evoked potentials in one trial (Raw EP), non-averaged gamma-filtered evoked potentials (Gamma-filtered EP) and averaged evoke potentials (Averaged EP) in the four groups of mice. (B) Power spectral density of the auditory evoked potentials during 50 ms before (Pre, black line) and after (Post, red line) the tone onset in the Cont-DW mouse shown in A. (C) Changes in mean total gamma power (26.4–67.1 Hz) of the evoked potentials during 50 ms before (Pre-tone) and after (Post-tone) the tone onset in Cont-Dw (blue line), Cont-T817 (orange line), KO-DW (grey line) and KO-T817 (yellow line). (D) Time course of mean ITC between 26.4–67.1 Hz for the Cont-DW mouse shown in Aa. The arrow indicates peak-ITC. (E) Comparison of peak-ITC among the four groups. Peak-ITC was significantly lower in PDGFR-β KO mice administered with distilled water (KO-DW) compared to control mice administered with distilled water (Cont-DW) and PDGFR-β KO mice administered with T-817MA (KO-T817). a, p<0.05 (Bonferroni test).</p

Significant correlations among neurophysiological, immunohistological, and behavioral parameters.

<p>(A) Significant correlation was observed between peak-ITC and parvalbumin-immunoreactive neuron density in the mPFC. (B-E) Significant relationships were observed between parvalbumin-immunoreactive neuron density in the mPFC and the duration of approaching (B), parvalbumin-immunoreactive neuron density in the mPFC and the duration of social sniffing (C), parvalbumin-immunoreactive neuron density in the mPFC and the duration of passive contact (D), parvalbumin-immunoreactive neuron density in the hippocampus (Hip) and the duration of approaching (E) (p<0.05). (F, G) Significant relationships were observed between parvalbumin-immunoreactive neuron density in the mPFC and % inhibition at 74 dB in the PPI test (F) and parvalbumin-immunoreactive neuron density in the hippocampus (Hip) and % inhibition at 74 dB in the PPI test (G) (p<0.05). Circles, Cont-DW; squares, Cont-T817; triangles, KO-DW; Crosses, KO-T817.</p

T-817MA ameliorated social interaction deficits in PDGFR-β KO mice.

<p>(A, B) Durations of each social behavior are indicated. a, p<0.05; aa, p<0.01 (Bonferroni test); *, p<0.05 (main effect of genotype); #, p<0.05 (main effect of treatment).</p

Markerless MCS for monkeys.

<p>A: Experimental setup consisting of a monkey cage with four depth cameras. B: Schematic illustration of processing steps of the present MCS. A monkey was captured by four depth cameras (Cam1-4) (a, b), and the images were merged to make a 3D image of the monkey represented by 3D points on the entire surface of the monkey (b). Simultaneously captured color images were mapped onto the 3D points (c). Finally, a skeleton model of the monkey was fitted onto the 3D image (d). C: A skeletal model of a monkey used in the present study. The model consisted of spheres connected by joints. Centers of the spheres, where lines are connected, indicates joints. Number of degrees of freedom (DOF) in each joint is shown by color. D: Attraction force from the points. Small squares represent captured 3D points. Gray spheres represent spheres in the model. The red points attract the sphere <i>i</i>. E: Repulsive force from the points. Arrows indicate the surface normal at the points. The blue points push the sphere <i>i</i> away. Other descriptions are same as D.</p

A Markerless 3D Computerized Motion Capture System Incorporating a Skeleton Model for Monkeys

<div><p>In this study, we propose a novel markerless motion capture system (MCS) for monkeys, in which 3D surface images of monkeys were reconstructed by integrating data from four depth cameras, and a skeleton model of the monkey was fitted onto 3D images of monkeys in each frame of the video. To validate the MCS, first, estimated 3D positions of body parts were compared between the 3D MCS-assisted estimation and manual estimation based on visual inspection when a monkey performed a shuttling behavior in which it had to avoid obstacles in various positions. The mean estimation error of the positions of body parts (3–14 cm) and of head rotation (35–43°) between the 3D MCS-assisted and manual estimation were comparable to the errors between two different experimenters performing manual estimation. Furthermore, the MCS could identify specific monkey actions, and there was no false positive nor false negative detection of actions compared with those in manual estimation. Second, to check the reproducibility of MCS-assisted estimation, the same analyses of the above experiments were repeated by a different user. The estimation errors of positions of most body parts between the two experimenters were significantly smaller in the MCS-assisted estimation than in the manual estimation. Third, effects of methamphetamine (MAP) administration on the spontaneous behaviors of four monkeys were analyzed using the MCS. MAP significantly increased head movements, tended to decrease locomotion speed, and had no significant effect on total path length. The results were comparable to previous human clinical data. Furthermore, estimated data following MAP injection (total path length, walking speed, and speed of head rotation) correlated significantly between the two experimenters in the MCS-assisted estimation (r = 0.863 to 0.999). The results suggest that the presented MCS in monkeys is useful in investigating neural mechanisms underlying various psychiatric disorders and developing pharmacological interventions.</p></div