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Zaitsev, M.

Publications and source records attributed to Zaitsev, M..

2 recordsLinked to original sources

Prefrontal orchestration: a cortical network for rodent motor inhibition

Goal-directed action control and behavioral flexibility are prerequisites for effective, adaptive behavior. Both abilities rely on functional motor inhibition, which is linked to the prefrontal cortex (PFC), where distinct subsections collaborate in functional networks. How these PFC subsections interact and which roles they play during motor inhibition remains incompletely understood. In this study, we employed an action-preparation task in rats, combined with bidirectional optogenetic interventions, opto-fMRI, single unit electrophysiology and local field potential synchrony measurements across PFC subsections. Our findings support a clear and simple model of action inhibition within the prefrontal network. This model suggests prelimbic cortex (PL) as an input-dependent switch between motor inhibition and execution, modulated by an infralimbic cortex (IL)-dominated network. This distribution of tasks allows the PL to mediate goal-directed action while the IL ensures behavioral flexibility. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/618207v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@147ab98org.highwire.dtl.DTLVardef@52b6f9org.highwire.dtl.DTLVardef@6ab566org.highwire.dtl.DTLVardef@1ab5044_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract:C_FLOATNO Behavioral measurements were conducted alongside optogenetic modulation of PL, IL or VO. Inhibitory modulation led to varying effects on performance, while excitatory ChR2 stimulation of PL, IL or VO led to analogous effects on proactive motor inhibition. To identify shared nodes recruited by ChR2 stimulation of distinct PFC subareas, we performed whole-brain mapping with opto-fMRI. This revealed an overlapping activation volume spanning PFC, BF, Fr, Cg2, and M2. Notably, this common activation volume closely outlined the entirety of the IL-recruited regions; IL excitation also produced robust behavioral effects. Multisite recordings revealed task performance-dependent PL-IL delta synchrony. PCA of single-unit activity during behavior revealed varied neural patterns among PFC subsections, highlighting PL to have the most the homogenous input-driven activity. The findings can be interpreted as PL acting as an input-dependent switch between motor inhibition and execution, modulated by IL to maintain behavioral flexibility. C_FIG

neuroscience↗

Time-division multiplexing (TDM) sequence removes bias in T2 estimation and relaxation-diffusion measurements

PurposeTo compare the performance of multi-echo (ME) and time-division multiplexing (TDM) sequences for accelerated relaxation-diffusion MRI (rdMRI) acquisition and to examine their reliability in estimating accurate rdMRI microstructure measures. MethodThe ME, TDM, and the reference single-echo (SE) sequences with six echo times (TE) were implemented using Pulseq with single-band (SB-) and multi-band 2 (MB2-) acceleration factors. On a diffusion phantom, the image intensities of the three sequences were compared, and the differences were quantified using the normalized root mean squared error (NRMSE). For the in-vivo brain scan, besides the image intensity comparison and T2-estimates, different methods were used to assess sequence-related effects on microstructure estimation, including the relaxation diffusion imaging moment (REDIM) and the maximum-entropy relaxation diffusion distribution (MaxEnt-RDD). ResultsTDM performance was similar to the gold standard SE acquisition, whereas ME showed greater biases (3-4x larger NRMSEs for phantom, 2x for in-vivo). T2 values obtained from TDM closely matched SE, whereas ME sequences underestimated the T2 relaxation time. TDM provided similar diffusion and relaxation parameters as SE using REDIM, whereas SB-ME exhibited a 60% larger bias in the map and on average 3.5x larger bias in the covariance between relaxation-diffusion coefficients. ConclusionOur analysis demonstrates that TDM provides a more accurate estimation of relaxation-diffusion measurements while accelerating the acquisitions by a factor of 2 to 3.

neuroscience↗