Thalamocortical and intracortical laminar connectivity determines sleep spindle properties
Sleep marks a profound change of brain state as manifested by the spontaneous emergence of characteristic oscillatory activities ., In humans , sleep spindles consist of waxing-and-waning bursts of field potentials oscillating at 11–15 Hz lasting for 0 . 5–3 s and recurring every 5–15 s ., Experimental and computational studies have identified that both the thalamus and the cortex are involved in the generation and propagation of spindles ., Spindles are known to occur in isolated thalamus after decortication in vivo and in thalamic slice recordings in vitro 1 , 2 , demonstrating that the thalamus is sufficient for spindle generation ., In in-vivo conditions , the cortex has been shown to be actively involved in the initiation and termination of spindles 3 as well as the long-range synchronization of spindles 4 5 ., Multiple lines of evidence indicate that spindle oscillations are linked to memory consolidation during sleep ., Spindle density is known to increase following training in hippocampal-dependent 6 as well as procedural memory 7 memory tasks ., Spindle density also correlates with better memory retention following sleep in verbal tasks 8 , 9 ., More recently , it was shown that pharmacologically increasing spindle density leads to better post-sleep performance in hippocampal-dependent learning tasks 10 ., Furthermore , spindle activity metrics , including amplitude and duration , were predictive of learning performance 11–13 , suggesting that spindle event occurrence , amplitude , and duration influence memory consolidation ., In human recordings , spindle occurrence and synchronization vary based on the recording modality ., Spindles recorded with magnetoencephalography ( MEG ) are more frequent and less synchronized , as compared to those recorded with electroencephalography ( EEG ) 14 ., It has been proposed that the contrast between MEG and EEG spindles reflects the differential involvement of the core and matrix thalamocortical systems , respectively 15 ., Core projections are focal to layer IV , whereas matrix projections are widespread in upper layers 16 ., This hypothesis is supported by human laminar microelectrode data which demonstrated two spindle generators , one associated with middle cortical layers and the other superficial 17 ., Taken together , these studies suggest that there could be two systems of spindle generation within the cortex and that these correspond to the core and matrix anatomical networks ., However , the network and cellular mechanisms whereby the core and matrix systems interact to generate both independent and co-occurring spindles across cortical layers are not understood ., In this study , we developed a computational model of thalamus and cortex that replicates known features of spindle occurrence in MEG and EEG recordings ., While our previous efforts have been focused on the neural mechanisms involved in the generation of isolated spindles5 , in this study we identified the critical mechanisms underlying the spontaneous generation of spindles across different cortical layers and their interactions ., Histograms of EEG and MEG gradiometer inter-spindle intervals are shown in Fig 1C ., For neither channel type are ISIs distributed normally as determined by Lilliefors tests ( D2571 = 0 . 1062 , p = 1 . 0e-3 , D4802 = 0 . 1022 , p = 1 . 0e-3 ) , suggesting that traditional descriptive statistics are of limited utility ., However , the ISI at peak of the respective distributions is longer for EEG than it is the MEG ., In addition , a two-sample Kolmogorov-Smirnov test confirms that EEG and MEG ISIs are not drawn from the same distribution ( D2571 , 4802 = 0 . 079 , p = 1 . 5e-9 ) ., While the data where not found to be drawn from any parametric distribution with 95% confidence , an exponential fit ( MEG ) and lognormal fit ( EEG ) are shown in red overlay for illustrative purposes ., These data are consistent with previous empirical recordings 18 and suggest that sleep spindles have different properties across superficial vs . deep cortical layers ., To investigate the mechanisms behind distinct spindle properties across cortical locations as observed in EEG and MEG signals , we constructed a model of thalamus and cortex that incorporated the two characteristic thalamocortical systems: core and matrix ., These systems contained distinct thalamic populations that projected to the superficial ( matrix ) and middle ( core ) cortical layers ., Four cell types were used to model distinct cell populations: thalamocortical relay ( TC ) and reticular ( RE ) neurons in the thalamus , and excitatory pyramidal ( PY ) and inhibitory ( IN ) neurons in each of three layers of the cortical network ., A schematic representation of the synaptic connections and cortical geometry of the network model is shown in Fig 2 ., In the matrix system , both thalamocortical ( from matrix TCs to the apical dendrites of layer 5 pyramidal neurons ( PYs ) located in the layer 1 ) and corticothalamic synapses ( from layer 5 PYs back to the thalamus ) formed diffuse connections ., The core system had a focal connection pattern in both thalamocortical ( from core TCs to PYs in the layer III/IV ) and corticothalamic ( from layer VI PYs to the thalamus ) projections ., Because spindles recorded in EEG signal reflect the activity of superficial layers while MEG records spindles originating from deeper layers ( Fig 1 and 19 ) , we compared the activity of the model’s matrix system , which has projections to the superficial layers , to empirical EEG recordings and compared the activity in model layer 3/4 to empirical MEG recordings ., In agreement with our previous studies 3 , 5 , 20 , 21 , simulated stage 2 sleep consisted of multiple spindle events involving thalamic and cortical neuronal populations ( Fig 3 ) ., During one such typical spindle event ( highlighted by the box in Fig 3A and 3B ) , cortical and thalamic neurons in both the core and matrix system had elevated and synchronized firing ( Fig 3A bottom ) , consistent with previous in-vivo experimental recordings 22 ., In the model , spindles within each system were initiated from spontaneous activity within cortical layers and then spread to thalamic neurons , similar to our previous study5 ., The spontaneous activity due to miniature EPSPs in glutamergic cortical synapses led to fluctuations in membrane voltage and sparse firing ., At random times , the miniature EPSPs summed such that a small number of locally connected PY neurons spiked within a short window ( <100ms ) , which then induced spiking in thalamic cells through corticothalamic connections ., This initiated spindle oscillations in the thalamic population mediated by TC-RE interactions as described before 20 , 23 , 24 ., Thalamic spindles in turn propagated to the neocortex leading to joint thalamocortical spindle events whose features were shaped by the properties of thalamocortical and corticothalamic connections ., In this study , we examined how the process of spindle generation occurs in a thalamocortical network with mutually interacting core and matrix systems , wherein the thalamic network of each system is capable of generating spindles independently ., Based on the anatomical data 16 , the main difference between the modeled core and matrix systems was the radii or fanout of connections in thalamocortical and corticothalamic projections ( in the baseline model , the fanout was 10 times wider for the matrix compared to the core system ) ., Furthermore , the strength of each synaptic connection was scaled by the number of input connections to each neuron 25 , 26 , leading to weaker individual thalamocortical projections in the matrix as compared to the core ., These differences in the strength and fanout of thalamocortical connectivity resulted in distinctive core and matrix spindle properties ( see Fig 3A , right vs left ) ., First , both cortical and thalamic spindles were more spatially focal in the core system as only a small subset of neurons was involved in a typical spindle event at any given time ., In contrast , within the matrix system spindles were global ( involving the entire cell population ) and highly synchronous across all cell types ., These results are consistent with our previous studies 5 and suggest that the connectivity properties of thalamocortical projections determine the degree of synchronization in the cortical network ., Second , spindle density was higher in the core system compared to the matrix system ., At every spatial location in the cortical network of the core system , the characteristic time between spindles was shorter compared to that between spindles in the matrix system ( Fig 3A left vs right ) ., In order to quantify the spatial and temporal properties of spindles , we computed an estimated LFP as an average of the dendritic synaptic currents for every group of contiguous 100 cortical neurons ., LFPs of the core system were estimated from the currents generated in the dendrites of layer 3/4 neurons while the LFP of the matrix system was computed from the dendritic currents of layer 5 neurons , located in the superficial cortical layers ( Fig 2 ) ., After applying a bandpass filter ( 6–15 Hz ) , the spatial properties of estimated core and matrix LFP ( Fig 3C ) closely matched the MEG and EEG recordings , respectively ( Fig 1 ) ., In subsequent analyses , we used this estimated LFP to further examine the properties of the spindle oscillations in the core and matrix systems ., We identified spindles in the estimated LFP using an automated spindle detection algorithm similar to that used in experimental studies ( details are provided in the method section ) ., The spindle density , defined as the number of spindles occurring per minute of simulation time , was greater in the core compared to the matrix ( Fig 4A ) as confirmed by an independent-sample t-test ( t ( 18 ) = 7 . 06 , p<0 . 001 for across estimated LFP channels and t ( 2060 ) = 19 . 2 , p<0 . 001 across all spindles ) ., The results of this analysis agree with the experimental observation that MEG spindles occur more frequently than EEG spindles ., While the average spindle density was significantly different between the core and matrix , in both systems the distribution of inter-spindle intervals peaks below 4 seconds and has a long tail ( Fig 4B ) ., A two sample KS test comparing the distributions of inter-spindle intervals confirmed that the intervals were derived from different distributions ( D1128 , 932 = 0 . 427 , p<0 . 001 ) ., The peak ISI of the core was shorter than that of the matrix system , suggesting that the core network experiences shorter and more frequent quiescence periods than the matrix population ., Furthermore , maximum-likelihood fits of the probability distributions ( red line in Fig 4B ) confirmed that the intervals of spindle occurrence cannot be described by a normal distribution ., The long tails of the distributions suggest that a Poisson like process , as oppose to a periodic process , is responsible for spindle generation ., This observation is consistent with previous experimental results 18 , 27 and suggests that our computational model replicates essential statistical properties of spindles observed in in vivo experiments ., Several other features of simulated core and matrix spindles were similar to those found in experimental recordings ., The average spindle duration was significantly higher in the core compared to the matrix system ( Fig 4C ) as confirmed by independent-sample t-test ( t ( 2060 ) = 16 . 3 , p<0 . 001 ) ., To quantify the difference in the spatial synchrony of spindles between the core and matrix systems , we computed the spatial correlation 28 between LFP groups at different distances ( measured by the location of a neuron group in the network ) ., The correlation strength decreased with distance for both systems ( Fig 4D ) ., However , the spindles in the core system were less spatially correlated overall when compared to spindles in the matrix system ., Simultaneous EEG and MEG measurements have found that about 50% of MEG spindles co-occur with EEG spindles , while about 85% of EEG spindles co-occur with MEG spindles 29 ., Further , a spindle detected in the EEG signal is found to co-occur with about 66% more MEG channels than a spindle detected in MEG ., Our model generates spindling patterns consistent with these features ., The co-occurrence probability revealed that during periods of spindles in the matrix system , there was about 80% probability that core was also generating spindles ( Fig 4E ) ., In contrast , there was only a 40% probability of observing a matrix spindle during a core system spindle ., An independent-sample t-test confirmed this difference between the systems across estimated LFP channels ( t ( 14 ) = 31 . 4 , p<0 . 001 ) ., Furthermore , we observed that the number of LFP channels that were simultaneously activated during a spindle event in the core system was higher when a spindle co-occurred in the matrix versus times when the spindles only occurred in the core ( Fig 4F , t ( 14 ) = 67 . 2 , p<0 . 001 ) ., This suggests that the co-occurrences of spindles in both systems are rare events but lead to the wide spread activation in both the core and matrix when they take place ., Finally , we examined the delay between spindles in the core and matrix systems ( Fig 4G ) ., We observed that on average ( red line in Fig 4G ) , the spindle originated from the core system then spread to the matrix system with a mean delay of about 300 ms ( delay was measured as the difference in onset times between co-occurring spindles within a window of 2 , 500 ms; negative delay values indicate spindles in which the core preceded matrix ) ., The peak at -750 ms corresponds to spindles originating from the core system , while the peak at +750 ms suggests that at some network sites , spindles originated in the matrix system and then spread to the core system ., While there were almost no events in which the matrix preceded the core by more than 1 sec ( right of Fig 4G ) , many events occurred in which the core preceded the matrix by more than 1 sec ( left of Fig 4G ) ., In sum , these results suggest that spindles were frequently initiated locally in the core system , then propagate to and spread throughout the matrix system ., This can trigger spindles at the other locations of the core , so eventually , even regions in the core system that were not previously involved become recruited ., These findings explain the experimental result that spindles are observed in more MEG channels when they also co-occur in the EEG 29 ., We leveraged our model to examine factors that may influence spindle occurrence across cortical layers ., The main difference between the core and matrix systems in the model was the breadth or fanout of the thalamic projections to the cortical network ., Neuroanatomical studies suggest that the core system has focused projections while matrix system projects widely 16 ., Here , we assessed the impacts of this characteristic by systematically varying the connection footprint of the thalamic matrix to superficial cortical regions , while holding the fanout of the thalamic core to layer 3/4 projections constant ., We also modulated the corticothalamic projections in proportion to the thalamocortical projections ., Using the estimated LFP from the cortical layers corresponding to core and matrix system , respectively , we quantified various spindle properties as the fanout was modulated ., Spindle density ( the number of spindles per minute ) in both layers was sensitive to the matrix system’s fanout ., ANOVA confirmed significant effects of fanout and layer location , as well as an interaction between layer and fanout ( fanout: F ( 6 , 112 ) = 66 . 4; p<0 . 01 , Layer: F ( 1 , 112 ) = 65 . 18; p<0 . 01 and interaction F ( 6 , 112 ) = 22 . 8; p<0 . 01 ) ., When the matrix and core thalamus had similar fanouts ( ratio 1 and 2 . 5 in Fig 5B ) , we observed a slightly higher density of spindles in the matrix than in the core system ., This observation is consistent with the properties of these circuits ( see Fig 2 ) , wherein the matrix system contains direct reciprocal projections connecting cortical and thalamic subpopulations and the core system routes indirect projections from cortical ( layer III/IV ) neurons through layer VI to the thalamic nucleus ., When the thalamocortical fanout of the matrix system was increased to above ~5 times the size of the core system , the density of spindles in the matrix system was reduced to around 4 spindles per minute ., Interestingly , the density of spindles in the core system was also reduced when the thalamocortical fanout of the matrix system was further increased to above ~10 times of that in the core system ( ratio above 10 in Fig 5B ) ., This suggests that spindle density in both systems is determined not only by the radius of thalamocortical vs . corticothalamic projections , but also by interactions between the systems among the cortical layers ., We further expound on the role of these cortical connections in the next section ., We also examined the effect of thalamocortical fanout on the distribution of inter-spindle intervals ( Fig 5C ) ., Although the mean value was largely independent of the projection radius , a long tailed distribution was observed for all values of fanout in the core ., Contrastingly , in the matrix system the mean and peak of the inter-spindle interval shifted to the right ( longer intervals ) with increased fanout ., With large fanouts , the majority of matrix system spindles had very long periods of silence ( 10-15s ) between them ., This suggests that thalamocortical fanout determines the peak of the inter-spindle interval distribution , but does not alter the stochastic nature of spindle occurrence ., The degree of thalamocortical fanout also influenced the co-occurrence of spindles in the core and matrix systems ( Fig 5D ) ., Increasing the fanout of the matrix system reduced spindle co-occurrence between two systems ., This reduction resulted mainly from lower spindle density in both layers ., However , the co-occurrence of core spindles during matrix spindles was higher for all values of fanout when matrix thalamocortical projections were at least 5 times broader than core projections ., This suggests that the difference in spindle co-occurrence between EEG and MEG as observed in experiments 14 depends mainly on the difference in the radius of thalamocortical projections between the core and matrix systems , while overall level of co-occurrence is determined by the interaction between cortical layers ., We examined how spatial correlations during periods of spindles vary depending on the fanout of thalamocortical projections ., The spatial correlation quantifies the degree of synchronization in the estimated LFP signals of network locations as a function of the distance between them ., As expected , increasing the distance reduced the spatial correlation ( Fig 4D ) ., We next measured the mean value of the spatial correlation for each fanout condition ., The mean correlation increased as a function of the fanout in the matrix system ( Fig 5A ) ., However , the spatial correlation within the core , and between the core and matrix systems , did not change with increases in the fanout , suggesting that the spatial synchronization of core spindles is largely influenced by thalamocortical fanout but not by interactions between the core and matrix systems as was observed for spindle density ., Does intra-cortical excitatory connectivity between layer 3/4 of the core system and layer 5 of the matrix system affect spindle occurrence ?, To answer this question , we first varied the strength of excitatory connections ( AMPA and NMDA ) from the core to matrix pyramidal neurons ( Fig 6A and 6B ) ., Here the reference point ( or 100% ) corresponds to the strength used in previous simulations , i . e . half the strength of a within-layer connection ., The spindle density varied with the strength of the interlaminar connections ( Fig 6A ) ., For low connectivity strengths ( below 100% ) , the spindle density of the matrix system was reduced significantly , while at high strengths ( above 140% ) the matrix system spindle density exceeded that of the control ( 100% ) ., There were significant effects of connection strength and layer on the spindle density , as well as an interaction between the two factors ( connection strength: F ( 5 , 96 ) = 24 . 7; p<0 . 01 , layer: F ( 5 , 96 ) = 386 . 6; p<0 . 01 and interaction F ( 5 , 96 ) = 36 . 9; p<0 . 01 ) that suggests a layer-specific effect of modulating excitatory interlaminar connection strength ., Similar to the spindle density , spindle co-occurrence between the core and matrix systems also increased as a function of interlaminar connection strength , reaching 80% for the both core and matrix at 150% connectivity ., In contrast , changing the strength of excitatory connections from layer 5 to layer 3/4 had little effect on the spindle density , ( Fig 6C ) ., Taken together , these results suggest that the strength of the cortical core-to-matrix excitatory connections is one of the critical factors in determining spindle density and co-occurrence among spindles across both cortical lamina and the core/matrix systems ., Using computational modeling and data from EEG/MEG recordings in humans we found that the properties of sleep spindles vary across cortical layers and are influenced by thalamocortical , corticothalamic and cortico-laminar connections ., This study was motivated by empirical findings demonstrating that spindles measured in EEG have different synchronization properties from those measured in MEG 14 , 29 ., EEG spindles occur less frequently and more synchronously in comparison to MEG spindles ., Our new study confirms the speculation that anatomical differences between the matrix thalamocortical system , which has broader projections that target the cortex superficially , and the core system , which consists of focal projections which target the middle layers , can account for the differences between EEG and MEG signals ., Furthermore , we discovered that the strength of corticocortical feedforward excitatory connections from the core to matrix neurons determines the spindle density in the matrix system , which predicts a specific neural mechanism for the interactions observed between MEG and EEG spindles ., There were several novel findings in this study ., First , we developed a novel computational model of sleep spindling in which spindles manifested as a rare but global synchronous occurrence in the matrix pathway and a frequent but local occurrence in the core pathway ., In other words , many spontaneous spindles occurred locally in the core system but only occasionally did this lead to globally organized spindles appearing in the matrix system ., As a result , only a fraction of spindles co-occurred between the pathways ( about 80% in matrix and 40% in core pathway ) ., This is consistent with data reported for EEG vs MEG in vivo ( Fig 1 ) ., In contrast , in our previous models 3 , 5 , spindles were induced by external stimulation and always occurred simultaneously in the core and matrix systems , but with different degrees of internal synchrony ., In addition , these studies did not examine how the core and matrix pathways interact during spontaneously occurring spindles ., Second , in this study we found that the distribution of the inter-spindle intervals between spontaneously occurring spindles in both the core and matrix pathways had long tails similar to a log-normal distribution ., This result is consistent with analyses of MEG and EEG data reported in this study and in our prior study 18 ., In our previous models 3 , 5 , spindles were induced by external stimulation and the statistics of spontaneously occurring spindles could not be explored ., Third , we demonstrated that the strength of thalamocortical and corticothalamic connections determined the density and occurrence of spontaneously generated spindles ., The spindle density was higher in the core pathway as compared to the matrix pathway with high co-occurrence of core spindles with matrix spindles ., These findings were corroborated with experimental evidence from EEG/MEG recordings ., Finally , we reported that laminar connections between the core and matrix could be a significant factor in determining spindle density , suggesting a possible mechanism of learning ., When the strength of these connections was increased in the model , there was a significant increase in spindle occurrence , similar to the experimentally observed increase in spindle density following recent learning 10 ., The origin of sleep spindle activity has been linked to thalamic oscillators based on a broad range of experimental studies 2 , 30 , 31 ., The excitatory and inhibitory connections between thalamic relay and reticular neurons are critical in generating spindles 20 , 23 , 32 , 33 ., However , in intact brain , the properties of sleep spindles are also shaped by cortical networks ., Indeed , the onset of a spindle oscillation and its termination are both dependent on cortical input to the thalamus 3 , 34 , 35 ., In model studies , spindle oscillations in the thalamus are initiated when sufficiently strong activity in the cortex activates the thalamic network , and spindle termination is partially mediated by the desynchronization of corticothalamic input towards the end of spindles 3 , 32 ., However , in simultaneous cortical and thalamic studies in humans , thalamic spindles were found to be tightly coupled to a preceding downstate , which in turn was triggered by converging cortical downstates 36 ., Further modeling is required to reconcile these experimental results ., In addition , thalamocortical interactions are known to be integral to the synchronization of spindles 5 , 33 ., In our new study , the core thalamocortical system revealed relatively high spindle density produced by focal and strong thalamocortical and corticothalamic projections ., Such a pattern of connectivity between core thalamus and middle cortical layers allowed input from a small region of the cortex to initiate and maintain focal spindles in the core system ., In contrast , the matrix system had relatively weak and broad thalamocortical connections requiring synchronized activity in broader cortical regions in order to initiate spindles in the thalamus ., We previously reported 5 that ( 1 ) within a single spindle event the synchrony of the neuronal firing is higher in the matrix than in the core system; ( 2 ) spindle are initiated in the core and with some delay in the matrix system ., The overal density of core and matrix spindle events was , however , the same in these earlier models ., In the new study we extended these previous results by explaining differences in the global spatio-temporal structure of spindle activity between the core and matrix systems ., Our new model predicts that the focal nature of the core thalamocortical connectivity can explain the more frequent occurrence of spindles in the core system as observed in vivo ., The strength of core-to-matrix intracortical connections determined the probability of core spindles to “propagate” to the matrix system ., In our new model core spindles remained localized and have never involved the entire network , again in agreement with in vivo data ., We observed that the distribution of inter-spindle intervals reflects a non-periodic stochastic process such as a Poisson process , which is consistent with previous data 18 , 27 ., The state of the thalamocortical network , determined by the level of the intrinsic and synaptic conductances , contributed to the stochastic nature of spindle occurrence ., Building off our previous work 21 , we chose the intrinsic and synaptic properties in the model that match those in stage 2 sleep , a brain state when concentrations of acetylcholine and monoamines are reduced 37–39 ., As a consequence , the K-leak currents and excitatory intracortical connections were set higher than in an awake-like state due to the reduction of acetylcholine and norepinephrine 40 ., The high K-leak currents resulted in sparse spontaneous cortical firing during periods between spindles with occasional surges of local synchrony sustained by recurrent excitation within the cortex that could trigger spindle oscillations in the thalamus ., Note that this mechanism may be different from spindle initiation during slow oscillation , when spindle activity appears to be initiated during Down state in thalamus 35 ., Furthermore , the release of miniature EPSPs and IPSPs in the cortex was implemented as a Poission process that contributed to the stochastic nature of the baseline activity ., All these factors led to a variable inter-spindle interval with long periods of silence when activity in the cortex was not sufficient to induce spindles ., While it is known that an excitable medium with noise has a Poisson event distribution in reduced systems 41 , here we show that a detailed biophysical model of spindle generation may lead to a Poission process due to specific intrinsic and network properties ., Layer IV excitatory neurons have a smaller dendritic structure compared to Layer V excitatory neurons 42 ., Direct recordings and detailed dendritic reconstructions have shown large post-synaptic potentials in layer IV due to core thalamic input 42 , 43 ., We examined the role of thalamocortical and corticothalamic connections in a thalamocortical network with only one cortical layer ( S1 Fig ) ., We found that increasing the synaptic strength of thalamocortical and corticothalamic connections both increased the density and duration of spindles , however it did not influence their synchronization ( S1A Fig ) ., In contrast , changing fanout led to an increase in spindle density , duration , and synchronization ., Furthermore , we examined the impact of thalamocortical and corticothalamic connections individually without applying a synaptic normalization rule ( see Methods ) ., We observed that the thalamocortical connections had a higher impact on spindle properties than corticothalamic connections ( S1B Fig ) ., In our full model with multiple layers , which included a weight normalization rule and wider fanout of the matrix pathway ( based on experimental findings16 ) , the synaptic strength of each thalamocortical synapse in the core pathway was higher than that in the matrix pathway ., The exact value of the synaptic strength was chosen from the reduced model to match experimentally observed spindle durations , as observed in EEG/MEG and laminar recordings 17 ., The simultaneous EEG and MEG recordings reported here and in our previous publications 14 , 29 revealed that, ( a ) MEG spindles occur earlier compared to the EEG spindles and, ( b ) EEG spindles are seen in a higher number of the MEG sensors compared to the spindles occurring only in the MEG recordings ., This resembles our current findings , in which the number of regions that were spindling in the core system during a matrix spindle was higher than when there was no spindle in the matrix system ., Further , the distribution of spindle onset delays between the systems indicates that during matrix spindles some neurons of the core system fired early , and presumably contributed to the initiation of the matrix spindle , while others fired late and were recruited ., Taken together , all the evidence suggests a characteristic and complex spatiotemporal evolution of spindle activity during co-occurring spindles , where spindles in the core spread to the matrix and in turn activate wider regions in the core leading to synchronized activation across cortical layers that is reflected by strong activity in both EEG and MEG ., Thus , the model predicts that co-occurring spindles could lead to the recruitment of the large cortical areas , which indeed has been reported in previous studies 28 , 44 ., At the same time , local spindles occurring in the model within deep cortical l
Sleep spindles are brief oscillatory events during non-rapid eye movement ( NREM ) sleep ., Spindle density and synchronization properties are different in MEG versus EEG recordings in humans and also vary with learning performance , suggesting spindle involvement in memory consolidation ., Here , using computational models , we identified network mechanisms that may explain differences in spindle properties across cortical structures ., First , we report that differences in spindle occurrence between MEG and EEG data may arise from the contrasting properties of the core and matrix thalamocortical systems ., The matrix system , projecting superficially , has wider thalamocortical fanout compared to the core system , which projects to middle layers , and requires the recruitment of a larger population of neurons to initiate a spindle ., This property was sufficient to explain lower spindle density and higher spatial synchrony of spindles in the superficial cortical layers , as observed in the EEG signal ., In contrast , spindles in the core system occurred more frequently but less synchronously , as observed in the MEG recordings ., Furthermore , consistent with human recordings , in the model , spindles occurred independently in the core system but the matrix system spindles commonly co-occurred with core spindles ., We also found that the intracortical excitatory connections from layer III/IV to layer V promote spindle propagation from the core to the matrix system , leading to widespread spindle activity ., Our study predicts that plasticity of intra- and inter-cortical connectivity can potentially be a mechanism for increased spindle density as has been observed during learning .
The density of sleep spindles has been shown to correlate with memory consolidation ., Sleep spindles occur more often in human MEG than EEG recordings ., We developed a thalamocortical network model that is capable of spontaneous generation of spindles across cortical layers and that captures the essential statistical features of spindles observed empirically ., Our study predicts that differences in thalamocortical connectivity , known from anatomical studies , are sufficient to explain the differences in the spindle properties between EEG and MEG which are observed in human recordings ., Furthermore , our model predicts that intracortical connectivity between cortical layers , a property influenced by sleep preceding learning , increases spindle density ., Results from our study highlight the role of intracortical and thalamocortical projections on the occurrence and properties of spindles .
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