Habituation of Brain Activity with Repetition in Color and Picture-Word Stroop Tests.

As a widely used mental task for functional near-infrared spectroscopy (fNIRS), the original color-word Stroop task has the advantage of being difficult to habituate, but also the disadvantage of being difficult to understand, especially for children. While the introduction of derived Stroop tasks offers highly promising countermeasures, changes in brain activity during these tests have not been well tested. We investigated the degree of habituation between the original and a derived Stroop task by measuring brain activity to obtain a better fNIRS task design. Fourteen healthy adults participated in the study, and a 10-channel fNIRS device was used. A picture-word Stroop task with lower linguistic conflict than the original was conducted. The original and derived Stroop tests were repeated four times in a 1-week interval. We found that the original Stroop test did not show any significant changes in brain activity with repeated measures; however, brain activity decreased during the derived test. The differences in habituation between the original and derived tests may be due to the differences in the strength of the linguistic conflict. Our findings also highlight the need to consider the effects of habituation when using derived Stroop tasks in repeated measures.

Comparison of Bootstrap Methods for Estimating Causality in Linear Dynamic Systems: A Review.

In this study, we present a thorough comparison of the performance of four different bootstrap methods for assessing the significance of causal analysis in time series data. For this purpose, multivariate simulated data are generated by a linear feedback system. The methods investigated are uncorrelated Phase Randomization Bootstrap (uPRB), which generates surrogate data with no cross-correlation between variables by randomizing the phase in the frequency domain; Time Shift Bootstrap (TSB), which generates surrogate data by randomizing the phase in the time domain; Stationary Bootstrap (SB), which calculates standard errors and constructs confidence regions for weakly dependent stationary observations; and AR-Sieve Bootstrap (ARSB), a resampling method based on AutoRegressive (AR) models that approximates the underlying data-generating process. The uPRB method accurately identifies variable interactions but fails to detect self-feedback in some variables. The TSB method, despite performing worse than uPRB, is unable to detect feedback between certain variables. The SB method gives consistent causality results, although its ability to detect self-feedback decreases, as the mean block width increases. The ARSB method shows superior performance, accurately detecting both self-feedback and causality across all variables. Regarding the analysis of the Impulse Response Function (IRF), only the ARSB method succeeds in detecting both self-feedback and causality in all variables, aligning well with the connectivity diagram. Other methods, however, show considerable variations in detection performance, with some detecting false positives and others only detecting self-feedback.

Modeling the interaction between donor-derived regulatory T cells and effector T cells early after allogeneic hematopoietic stem cell transplantation.

While allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a potential curative therapy against hematological malignancies, modulation of donor T cell alloreactivity is required to enhance the graft-versus-leukemia (GVL) effect and control graft-versus-host-disease (GVHD) after allo-HSCT. Donor-derived regulatory CD4+CD25+Foxp3+ T cells (Tregs) play a central role in establishing of immune tolerance after allo-HSCT. They could be a key target to be modulated for increasing the GVL effect and control of GVHD. We constructed an ordinary differential equation model incorporating bidirectional interactions between Tregs and effector CD4+ T cells (Teffs) as a mechanism for control of Treg cell concentration. The goal is to elucidate how the interaction between Tregs and Teffs is modulated in order to get insights into fine tuning of alloreactivity after allo-HSCT. The model was calibrated with respect to published Treg and Teff recovery data after allo-HSCT. The calibrated model exhibits perfect or near-perfect adaptation to stepwise perturbations between Treg and Teff interactions, as seen in Treg cell populations when patients with relapsed malignancy were treated with anti-CTLA-4 (cytotoxic T lymphocyte-associated antigen 4). In addition, the model predicts observed shifts of Tregs and Teffs concentrations after co-stimulatory receptor IL-2R or TNFR2 blockade with allo-HSCT. The present results suggest simultaneous blockades of co-stimulatory and co-inhibitory receptors as a potential treatment for enhancing the GVL effect after allo-HSCT without developing GVHD.

Cell types and synchronous-activity patterns of inspiratory neurons in the preBötzinger complex of mouse medullary slices during early postnatal development.

To examine whether and how the inspiratory neuronal network in the preBötzinger complex (preBötC) develops during the early postnatal period, we quantified the composition of the population of inspiratory neurons between postnatal day 1 (p1) and p10 by applying calcium imaging to medullary transverse slices in double-transgenic mice expressing fluorescent marker proteins. We found that putative excitatory and glycinergic neurons formed a majority of the population of inspiratory neurons, and the composition rates of these two inspiratory neurons inverted at p5-6. We also found that the activity patterns of these two types of inspiratory neurons became significantly well-synchronized with the inspiratory rhythmic bursting pattern in the preBötC within the first postnatal week. GABAergic and GABA-glycine cotransmitting inspiratory neurons formed only a small population just after birth, which almost disappeared until p10. In conclusion, the inspiratory neuronal network in the preBötC matures at the level of both neuronal population and neuronal activities during early postnatal development.

A computational model to explore how temporal stimulation patterns affect synapse plasticity.

Plasticity-related proteins (PRPs), which are synthesized in a synapse activation-dependent manner, are shared by multiple synapses to a limited spatial extent for a specific period. In addition, stimulated synapses can utilize shared PRPs through synaptic tagging and capture (STC). In particular, the phenomenon by which short-lived early long-term potentiation is transformed into long-lived late long-term potentiation using shared PRPs is called "late-associativity," which is the underlying principle of "cluster plasticity." We hypothesized that the competitive capture of PRPs by multiple synapses modulates late-associativity and affects the fate of each synapse in terms of whether it is integrated into a synapse cluster. We tested our hypothesis by developing a computational model to simulate STC, late-associativity, and the competitive capture of PRPs. The experimental results obtained using the model revealed that the number of competing synapses, timing of stimulation to each synapse, and basal PRP level in the dendritic compartment altered the effective temporal window of STC and influenced the conditions under which late-associativity occurs. Furthermore, it is suggested that the competitive capture of PRPs results in the selection of synapses to be integrated into a synapse cluster via late-associativity.

Technical note: Calving prediction in dairy cattle based on continuous measurements of ventral tail base skin temperature using supervised machine learning.

In this study, we developed a calving prediction model based on continuous measurements of ventral tail base skin temperature (ST) with supervised machine learning and evaluated the predictive ability of the model in 2 dairy farms with distinct cattle management practices. The ST data were collected at 2- or 10-min intervals from 105 and 33 pregnant cattle (mean ± standard deviation: 2.2 ± 1.8 parities) reared in farms A (freestall barn, in a temperate climate) and B (tiestall barn, in a subarctic climate), respectively. After extracting maximum hourly ST, the change in values was expressed as residual ST (rST = actual hourly ST - mean ST for the same hour on the previous 3 d) and analyzed. In both farms, rST decreased in a biphasic manner before calving. Briefly, an ambient temperature-independent gradual decrease occurred from around 36 to 16 h before calving, and an ambient temperature-dependent sharp decrease occurred from around 6 h before until calving. To make a universal calving prediction model, training data were prepared from pregnant cattle under different ambient temperatures (10 data sets were randomly selected from each of the 3 ambient temperature groups: <15°C, ≥15°C to <25°C, and ≥25°C in farm A). An hourly calving prediction model was then constructed with the training data by support vector machine based on 15 features extracted from sensing data (indicative of pre-calving rST changes) and 1 feature from non-sensor-based data (days to expected calving date). When the prediction model was applied to the data that were not part of the training process, calving within the next 24 h was predicted with sensitivities and precisions of 85.3% and 71.9% in farm A (n = 75), and 81.8% and 67.5% in farm B (n = 33), respectively. No differences were observed in means and variances of intervals from the calving alerts to actual calving between farms (12.7 ± 5.8 and 13.0 ± 5.6 h in farms A and B, respectively). Above all, a calving prediction model based on continuous measurement of ST with supervised machine learning has the potential to achieve effective calving prediction, irrespective of the rearing condition in dairy cattle.

Estrous detection by continuous measurements of vaginal temperature and conductivity with supervised machine learning in cattle.

This study aimed to evaluate the effectiveness of estrous detection technique based on continuous measurements of vaginal temperature (VT) and conductivity (VC) with supervised machine learning in cattle. The VT and VC of 17 cows in tie-stalls were measured using our developed wearable vaginal sensor from Day 11 (Day 0 = ovulation day) to Day 11 of the subsequent estrous cycle at 15-min interval. After the maximum VT and VC were extracted hourly, their changes were expressed as residual VT (rVT = actual VT - mean VT for the same time on the previous 3 days) and as VC ratio (VCr = actual VC/mean VC for the same time on Day 11-13), respectively, and were used for analysis. Trans-rectal ultrasonography was performed to monitor ovarian structure changes. The plasma concentrations of reproductive hormones (progesterone: P4, estradiol-17ß: E2, and LH) were measured in the experimental period. Standing estrus was confirmed by testing with herd mates at 3-h interval. The rVT decreased transiently, which coincided with decreasing P4 a few days before estrus, and a sharp increase was associated with LH surge during estrus. The VCr increased as estrus approached, corresponding with decreasing P4 and increasing E2 and LH. After noise reduction, features, possible to follow-up estrus-associated changes in rVT and VCr, were extracted and used for developing estrous detection models; 9 models were developed with 3 feature sets (features extracted from rVT alone, VCr alone, and combination of rVT and VCr) and 3 machine learning algorithms (decision tree: DT, support vector machine: SVM, and artificial neural network: ANN). Cross-validation showed that models using the features from the combination of rVT and VCr showed better performance in terms of sensitivity and precision than those using features from VCr alone, and precision than those of using features from rVT alone. Within the models using the features from the combination of rVT and VCr, sensitivity and precision of the model generated by ANN were numerically, but not statistically, higher than those generated by DT and SVM. Of 17 estruses, 16 were detected, with one false positive, when the best model was used. Furthermore, both mean and variance of the interval from the beginning of the estrous detection alert to ovulation (27.3 ±â€¯6.7 h, mean ±â€¯SD of 16 estruses) were not significantly different to those from the onset of standing estrus to ovulation (30.8 ±â€¯5.8 h, n = 17), indicating that the estrus can be detected real-time by the present technique. Hence, the estrous detection technique based on continuous measurements of VT and VC with supervised machine learning has a potential for efficient and accurate estrous detection in cattle.

Structural and functional identification of two distinct inspiratory neuronal populations at the level of the phrenic nucleus in the rat cervical spinal cord.

The diaphragm is driven by phrenic motoneurons that are located in the cervical spinal cord. Although the anatomical location of the phrenic nucleus and the function of phrenic motoneurons at a single cellular level have been extensively analyzed, the spatiotemporal dynamics of phrenic motoneuron group activity have not been fully elucidated. In the present study, we analyzed the functional and structural characteristics of respiratory neuron population in the cervical spinal cord at the level of the phrenic nucleus by voltage imaging, together with histological analysis of neuronal and astrocytic distribution in the cervical spinal cord. We found spatially distinct two cellular populations that exhibited synchronized inspiratory activity on the transversely cut plane at C4-C5 levels and on the ventral surface of the mid cervical spinal cord in the isolated brainstem-spinal cord preparation of the neonatal rat. Inspiratory activity of one group emerged in the central portion of the ventral horn that corresponded to the central motor column, and the other appeared in the medial portion of the ventral horn that corresponded to the medial motor column. We identified by retrogradely labeling study that the anatomical distributions of phrenic and scalene motoneurons coincided with optically detected central and medial motor regions, respectively. Furthermore, we anatomically demonstrated closely located features of putative motoneurons, interneurons and astrocytes in these regions. Collectively, we report that phrenic and scalene motoneuron populations show synchronized inspiratory activities with distinct anatomical locations in the mid cervical spinal cord.

Corrigendum: Cell Type-Dependent Activation Sequence During Rhythmic Bursting in the PreBötzinger Complex in Respiratory Rhythmic Slices From Mice.

[This corrects the article DOI: 10.3389/fphys.2018.01219.].

Cell Type-Dependent Activation Sequence During Rhythmic Bursting in the PreBötzinger Complex in Respiratory Rhythmic Slices From Mice.

Spontaneous respiratory rhythmic burst activity can be preserved in the preBötzinger Complex (preBötC) of rodent medullary transverse slices. It is known, that the activation sequence of inspiratory neurons in the preBötC stochastically varies from cycle to cycle. To test whether the activation timing of an inspiratory neuron depends on its neurotransmitter, we performed calcium imaging of preBötC neurons using double-transgenic mice expressing EGFP in GlyT2+ neurons and tdTomato in GAD65+ neurons. Five types of inspiratory neurons were identified using the fluorescence protein expression and the maximum cross-correlation coefficient between neuronal calcium fluctuation and field potential. Regarding the activation sequence, irregular type putative excitatory (GlyT2-/GAD65-) neurons and irregular type glycinergic (GlyT2+/GAD65-) neurons tended to be activated early, while regular type putative excitatory neurons, regular type glycinergic neurons tended to be activated later. In conclusion, the different cell types define a general framework for the stochastically changing activation sequence of inspiratory neurons in the preBötC.

Night duty and decreased brain activity of medical residents: a wearable optical topography study.

BACKGROUND: Overwork, fatigue, and sleep deprivation due to night duty are likely to be detrimental to the performance of medical residents and can consequently affect patient safety. OBJECTIVE: The aim of this study was to determine the possibility of deterioration of cerebral function of sleep-deprived, fatigued residents using neuroimaging techniques. DESIGN: Six medical residents were instructed to draw blood from artificial vessels installed on the arm of a normal cooperator. Blood was drawn at a similar time of the day, before and after night duty. To assess sleep conditions during night duty, the participants wore actigraphy units throughout the period of night duty. Changes in cerebral hemodynamics, during the course of drawing blood, were measured using a wearable optical topography system. RESULTS: The visual analogue scale scores after night duty correlated negatively with sleep efficiency during the night duty (ρ = -0.812, p = 0.050). The right prefrontal cortex activity was significantly decreased in the second trial after night duty compared with the first (p = 0.028). The extent of [oxy-Hb] decrease, indicating decreased activity, in the right dorsolateral prefrontal cortex correlated negatively with the Epworth sleepiness score after night duty (ρ = -0.841, p = 0.036). CONCLUSIONS: Sleep deprivation and fatigue after night duty, caused a decrease in the activity of the right dorsolateral prefrontal cortex of the residents, even with a relatively easy routine. This result implies that the brain activity of medical residents exposed to stress on night duty, although not substantially sleep-deprived, was impaired after the night duty, even though they apparently performed a simple medical technique appropriately. Reconsideration of the shift assignments of medical residents is strongly advised. ABBREVIATIONS: DLPFC: Dorsolateral prefrontal cortex; ESS: Epworth sleepiness scale; PSQI: Pittsburgh sleep quality index; ROI: Regions of interest; VAS: Visual analogue scale; WOT: Wearable optical topography.

Emergent Network Topology within the Respiratory Rhythm-Generating Kernel Evolved In Silico.

We hypothesize that the network topology within the pre-Bötzinger Complex (preBötC), the mammalian respiratory rhythm generating kernel, is not random, but is optimized in the course of ontogeny/phylogeny so that the network produces respiratory rhythm efficiently and robustly. In the present study, we attempted to identify topology of synaptic connections among constituent neurons of the preBötC based on this hypothesis. To do this, we first developed an effective evolutionary algorithm for optimizing network topology of a neuronal network to exhibit a 'desired characteristic'. Using this evolutionary algorithm, we iteratively evolved an in silico preBötC 'model' network with initial random connectivity to a network exhibiting optimized synchronous population bursts. The evolved 'idealized' network was then analyzed to gain insight into: (1) optimal network connectivity among different kinds of neurons-excitatory as well as inhibitory pacemakers, non-pacemakers and tonic neurons-within the preBötC, and (2) possible functional roles of inhibitory neurons within the preBötC in rhythm generation. Obtained results indicate that (1) synaptic distribution within excitatory subnetwork of the evolved model network illustrates skewed/heavy-tailed degree distribution, and (2) inhibitory subnetwork influences excitatory subnetwork primarily through non-tonic pacemaker inhibitory neurons. Further, since small-world (SW) network is generally associated with network synchronization phenomena and is suggested as a possible network structure within the preBötC, we compared the performance of SW network with that of the evolved model network. Results show that evolved network is better than SW network at exhibiting synchronous bursts.

Respiratory calcium fluctuations in low-frequency oscillating astrocytes in the pre-Bötzinger complex.

Astrocytes have been found to modulate neuronal activity through calcium-dependent signaling in various brain regions. However, whether astrocytes of the pre-Bötzinger complex (preBötC) exhibit respiratory rhythmic fluctuations is still controversial. Here we evaluated calcium-imaging experiments within preBötC in rhythmically active medullary slices from TgN(hGFAP-EGFP) mice using advanced analyses. 13.8% of EGFP-negative cells, putative neurons, showed rhythmic fluorescent changes that were highly correlated to the respiratory rhythmic fluctuation (cross-correlation coefficient>0.5 and dF/F>0.2%). In contrast, a considerable number of astrocyte somata exhibited synchronized low-frequency (<0.03Hz) calcium oscillations. After band-pass filtering, signals that irregularly preceded the calcium signal of EGFP-negative cells were observed in 10.2% of astrocytes, indicating a functional coupling between astrocytes and neurons in preBötC. A model simulation confirmed that such preinspiratory astrocytic signals can arise from coupled neuronal and astrocytic oscillators, supporting a concept that slow oscillatory changes of astrocytic functions modulate neighboring neuronal activity to add variability in respiratory rhythm.

Stochastic activation among inspiratory cells in the pre-Bötzinger complex of the rat medulla revealed by Ca(2+) imaging.

In the pre-Bötzinger complex of the ventral medulla (preBötC), a variable pattern of inspiratory neuronal output and synchronous activation of inspiratory cells can be observed. However, it is not well known whether cellular activation patterns among inspiratory cells are variable or fixed. Here, we evaluated the activation sequence of inspiratory cells during individual rhythmic bursts using calcium imaging. Onset timing and peak timing of calcium fluctuations during rhythmic bursts in individual inspiratory cells were used to evaluate the activation sequence. The sequences of both timings changed stochastically in individual rhythmic bursts, although the sequences differed between the two timings even within the same rhythmic burst. The weak correlation between these two timings might indicate that the two parameters reflect different physiological events. Furthermore, a subset of inspiratory cells was found to initially activate in the sequences of successive rhythmic bursts. These results suggest that rhythmic activation of inspiratory cells occurs with a degree of loose regularity but is not invariable with respect to the sequence of either onset or peak timing.

Pixel timing correction in time-lapsed calcium imaging using point scanning microscopy.

In point scanning imaging, data are acquired by sequentially scanning each pixel of a predetermined area. This way of scanning leads to time delays between pixels, especially for lower scanning speed or large scanned areas. Therefore, experiments are often performed at lower framerates in order to ensure a sufficient signal-to-noise ratio, even though framerates above 30 frames per second are technically feasible. For these framerates, we suggest that it becomes crucial to correct the time delay between image pixels prior to analyses. In this paper, we apply temporal interpolation (or pixel timing correction) for calcium imaging in two-photon microscopy as an example of fluorescence imaging. We present and compare three interpolation methods (linear, Lanczos and cubic B-spline). We test these methods on a simulated network of coupled bursting neurons at different framerates. In this network, we introduce a time delay to simulate a scanning by point scanning microscopy. We also assess these methods on actual microscopic calcium imaging movies recorded at usual framerates. Our numerical results suggest that point scanning microscopy imaging introduces statistically significant time delays between image pixels at low frequency. However, we demonstrate that pixel timing correction compensates for these time delays, regardless of the used interpolation method.

Standardization of size, shape and internal structure of spinal cord images: comparison of three transformation methods.

Functional fluorescence imaging has been widely applied to analyze spatio-temporal patterns of cellular dynamics in the brain and spinal cord. However, it is difficult to integrate spatial information obtained from imaging data in specific regions of interest across multiple samples, due to large variability in the size, shape and internal structure of samples. To solve this problem, we attempted to standardize transversely sectioned spinal cord images focusing on the laminar structure in the gray matter. We employed three standardization methods, the affine transformation (AT), the angle-dependent transformation (ADT) and the combination of these two methods (AT+ADT). The ADT is a novel non-linear transformation method developed in this study to adjust an individual image onto the template image in the polar coordinate system. We next compared the accuracy of these three standardization methods. We evaluated two indices, i.e., the spatial distribution of pixels that are not categorized to any layer and the error ratio by the leave-one-out cross validation method. In this study, we used neuron-specific marker (NeuN)-stained histological images of transversely sectioned cervical spinal cord slices (21 images obtained from 4 rats) to create the standard atlas and also to serve for benchmark tests. We found that the AT+ADT outperformed other two methods, though the accuracy of each method varied depending on the layer. This novel image standardization technique would be applicable to optical recording such as voltage-sensitive dye imaging, and will enable statistical evaluations of neural activation across multiple samples.

Relation between parametric change of the workload and prefrontal cortex activity during a modified version of the 'rock, paper, scissors' task.

BACKGROUND/AIMS: Modified rock, paper, scissors (RPS) tasks have previously been used in neuroscience to investigate activity of the prefrontal cortex (PFC). In this study, we investigated hemodynamic changes in the PFC using near-infrared spectroscopy (NIRS) during a modified RPS task in which each subject's successful performance rate was equalized; the workload was increased parametrically in order to reveal the resulting pattern of PFC activation. METHODS: The subjects were 20 healthy adults. During RPS, the player uses hand gestures to represent rock, paper, and scissors. Rock beats scissors, paper beats rock, and scissors beats paper. In the modified RPS task, the player is instructed to lose intentionally against the computer hand; the computer goes first and the player follows. The interstimulus interval (ISI) level was adjusted with 11 steps. If the level rose, the ISI decreased and the workload increased parametrically. The maximal level (maxLv: the shortest ISI and the biggest workload) in which a subject could perform the task correctly was determined for every subject during rehearsal of the task prior to the experiment. Lowering the level from the maxLv made the task easier. Hemodynamic changes were measured by NIRS over 4 task levels (maxLv-3, maxLv-2, maxLv-1 and maxLv). RESULTS: The hemodynamic changes in the left lateral PFC and bilateral Brodmann area 6 rose significantly with the increase in workload and presented a linear trend. CONCLUSION: These results suggest that PFC activation may linearly increase with increased workload during a modified RPS task in which successful performance rates of subjects are equalized.

Detection and visualization method of dynamic state transition for biological spatio-temporal imaging data.

In the statistical analysis of functional brain imaging data, regression analysis and cross correlation analysis between time series data on each grid point have been widely used. The results can be graphically represented as an activation map on an anatomical image, but only activation signal, whose temporal pattern resembles the predefined reference function, can be detected. In the present study, we propose a fusion method comprising innovation approach in time series analysis and statistical test. Autoregressive (AR) models were fitted to time series data of each pixel for the range sufficiently before or after the state transition. Then, the remaining time series data were filtered using these AR parameters to obtain its innovation (filter output). The proposed method could extract brain neural activation as a phase transition of dynamics in the system without employing external information such as the reference function. The activation could be detected as temporal transitions of statistical test values. We evaluated this method by applying to optical imaging data obtained from the mammalian brain and the cardiac sino-atrial node (SAN), and demonstrated that our method can precisely detect spatio-temporal activation profiles in the brain or SAN.

Dual oscillator model of the respiratory neuronal network generating quantal slowing of respiratory rhythm.

We developed a dual oscillator model to facilitate the understanding of dynamic interactions between the parafacial respiratory group (pFRG) and the preBötzinger complex (preBötC) neurons in the respiratory rhythm generation. Both neuronal groups were modeled as groups of 81 interconnected pacemaker neurons; the bursting cell model described by Butera and others [model 1 in Butera et al. (J Neurophysiol 81:382-397, 1999a)] were used to model the pacemaker neurons. We assumed (1) both pFRG and preBötC networks are rhythm generators, (2) preBötC receives excitatory inputs from pFRG, and pFRG receives inhibitory inputs from preBötC, and (3) persistent Na(+) current conductance and synaptic current conductances are randomly distributed within each population. Our model could reproduce 1:1 coupling of bursting rhythms between pFRG and preBötC with the characteristic biphasic firing pattern of pFRG neurons, i.e., firings during pre-inspiratory and post-inspiratory phases. Compatible with experimental results, the model predicted the changes in firing pattern of pFRG neurons from biphasic expiratory to monophasic inspiratory, synchronous with preBötC neurons. Quantal slowing, a phenomena of prolonged respiratory period that jumps non-deterministically to integer multiples of the control period, was observed when the excitability of preBötC network decreased while strengths of synaptic connections between the two groups remained unchanged, suggesting that, in contrast to the earlier suggestions (Mellen et al., Neuron 37:821-826, 2003; Wittmeier et al., Proc Natl Acad Sci USA 105(46):18000-18005, 2008), quantal slowing could occur without suppressed or stochastic excitatory synaptic transmission. With a reduced excitability of preBötC network, the breakdown of synchronous bursting of preBötC neurons was predicted by simulation. We suggest that quantal slowing could result from a breakdown of synchronized bursting within the preBötC.

Anatomical changes of phrenic motoneurons during development.

Although the phrenic motoneurons are relatively well-developed at the time of birth as compared to non-respiratory motoneurons, they show distinct anatomical changes during postnatal development. In the present review we summarize anatomical changes of phrenic motoneurons during pre- and postnatal development. Cell bodies of phrenic motoneurons migrate into the ventromedial region of the ventral horn of C3-C6 by E13-E14 in the rat. During development the sizes and surface areas of phrenic motoneurons are increased with changes in dendritic morphology.