Propagated infra-slow intrinsic brain activity reorganizes across wake and slow wave sleep
Sleep is a state during which interactions with the environment are greatly attenuated . The behavioral consequences of this state , namely , immobility and reduced responsiveness , carry obvious costs , such as compromised avoidance of predators . Nevertheless , nearly all animals sleep , suggesting that sleep is essential to normal physiology ( Cirelli and Tononi , 2008 ) . In most mammals , including humans , prolonged sleep deprivation leads to impaired performance , psychosis , and eventually death ( Brown et al . , 2012; Everson et al . , 1989; Rechtschaffen , 1998 ) . Sleep is attended by changes in gene expression ( Abel et al . , 2013; Cirelli and Tononi , 2000 ) , neuromodulator levels ( Brown et al . , 2012 ) , metabolism ( Boyle et al . , 1994; Braun et al . , 1997 ) , and markedly altered patterns of neural activity ( Dang-Vu , 2012; Loomis et al . , 1935; McCormick and Bal , 1997 ) . Yet , the fundamental functions of sleep remain elusive . Sleep conventionally is divided into stages , the deepest of which is slow wave sleep ( SWS; also known as N3 sleep ) ( Soeffing et al . , 2008; Rechtschaffen and Kales , 1968 ) . The electrophysiologic hallmark of SWS is the slow oscillation , which manifests as periodic alternations of membrane potential , also known as Up/Down states ( UDSs ) , characteristically at frequencies in the range of 0 . 5–1 . 5 Hz ( Achermann and Borbély , 1997; Steriade et al . , 1993 ) . As observed with extracellular local field potential ( LFP ) recordings , slow oscillations are locally synchronous but exhibit apparent propagation over the whole brain on a time scale of 100’s of milliseconds ( Hahn et al . , 2012; Nir et al . , 2011; Riedner et al . , 2007; Sheroziya and Timofeev , 2014 ) . Similar patterns of propagation have also been observed using electroencephalography ( EEG ) , in which SWS manifests as a predominance of high amplitude , 1–4 Hz ( delta ) rhythms ( Massimini et al . , 2004 ) . The observation of propagated slow electrophysiological activity during SWS raises the question of whether similar phenomenology might be observed by other means , specifically , resting state functional magnetic resonance imaging ( rs-fMRI ) . Infra-slow ( <0 . 1 Hz ) intrinsic ( equivalently , spontaneous ) activity recorded using rs-fMRI has been understood predominantly in terms of zero-lag temporal synchrony ( functional connectivity ) within systems known as resting state networks ( RSNs ) ( Beckmann et al . , 2005; Biswal et al . , 2010 ) . Prior rs-fMRI studies have found that RSNs are generally preserved across wake and SWS ( Horovitz et al . , 2009; Larson-Prior et al . , 2009; Picchioni et al . , 2013; Sämann et al . , 2011; Tagliazucchi et al . , 2013a ) . Importantly , conventional functional connectivity analyses assume temporal synchronicity and make no provision for the possibility that intrinsic activity may propagate between regions . We have recently described an alternative analysis technique which explicitly focuses on apparent propagation in rs-fMRI data ( Mitra et al . , 2014; 2015; Yuste and Fairhall , 2015 ) . Our methodology applies parabolic interpolation to lagged cross-covariance curves to detect temporal lags at a resolution finer than the temporal sampling density of rs-fMRI ( see Materials and methods; Figure 1 ) . Using this technique , we previously demonstrated , in awake , normal humans , that the blood oxygen level dependent ( BOLD ) signal exhibits highly reproducible temporal lag patterns on a time scale of ~1 s; some regions are systematically early with respect to the rest of brain , whereas other regions are systematically late ( Mitra et al . , 2014; 2015 ) . Moreover , temporal lags in BOLD signal activity are altered , with appropriate focality , by prior performance of motor tasks as well as by time of day ( Mitra et al . , 2014 ) . We operationally infer apparent propagation on the basis of measured temporal lags , assuming nothing regarding the path or mechanism by which BOLD signals ‘propagate’ between regions . In particular , the temporal scale of this phenomenology is much slower than axonal transmission via fiber tracts ( Caminiti et al . , 2009 ) . With this understanding , we omit ‘apparent’ in references to BOLD signal propagation . 10 . 7554/eLife . 10781 . 003Figure 1 . Calculation of lag structure using lagged cross-covariance functions and parabolic interpolation . Lags are defined by analysis of timeseries derived from two loci . ( A ) Two exemplar loci ( both in the default mode network ) . The time series were extracted from the illustrated loci over ~200 s . ( B ) The corresponding lagged cross-covariance function ( Materials and methods Equation 2 ) . Although the lagged cross-covariance is defined over the range ± T , where T is the run duration , the range of the plotted values is restricted to ± 8 . 32 s , which is equivalent to ± 4 frames ( red markers ) as the repetition time was 2 . 08 s . The lag between the time series is the value at which the absolute value of the cross-covariance function is maximal . ( C ) This extremum can be determined at a resolution finer than the temporal sampling density by parabolic interpolation ( green line ) through the computed values ( red markers ) . This extremum ( arrow , yellow marker ) defines both the lag between time series i and j ( τi , j ) and the corresponding amplitude ( ai , j ) . ( D ) Toy case illustration of a time-delay ( TD ) matrix ( Materials and methods Equation 3 ) representing 3 voxels . TD matrices encode the lag between every pair of analyzed voxels and are anti-symmetric by definition . The mean over each column of a TD matrix generates a lag projection map ( Methods Equation 4 ) . A TD matrix obtained with real rs-fMRI data and the corresponding lag projection map are shown in panels ( E ) and ( F ) , respectively . Panels A-D are adapted from Mitra et al . , 2014 . DOI: http://dx . doi . org/10 . 7554/eLife . 10781 . 003 Previous work has related propagated slow electrophysiological activity during SWS to several aspects of physiology . First , SWS is believed to represent an off-line state during which propagated slow activity plays a central role in the consolidation of memory ( Born et al . , 2006; Maquet , 2001; Marshall et al . , 2006; Stickgold , 2005 ) . In particular , propagation along the anterior-posterior axis of the brain has been linked to consolidation of declarative memory ( Marshall et al . , 2006 ) . Second , loss of environmental awareness during SWS is thought to be mediated by thalamic hyperpolarization , which is modulated by propagation of slow ( <1 Hz ) activity from cortex to thalamus ( Blethyn et al . , 2006; Hughes et al . , 2002 ) . Evidence of such propagation has never been observed in vivo . Finally , it has been theorized that reduced subjective awareness ( ‘unconsciousness’ ) during SWS results from loss of integration across networks ( Mashour , 2005; Tononi , 2004 ) . Here , we report whole-brain patterns of propagated rs-fMRI activity in humans , contrasting eyes-closed wake vs . SWS . Our results lend support to each of the aforementioned perspectives .
We characterize lag structure ( e . g . , apparent propagation ) using three major approaches . First , we begin by computing lags for all pairs of voxels in gray matter in rs-fMRI data ( Figure 1; Mitra et al . , 2014 ) . These results are assembled into time-delay ( TD ) matrices , which have dimensions voxels × voxels and entries in units of seconds ( Materials and methods Equation 3 ) . TD matrices represent the lag between all pairs of voxels in gray matter . Second , computing the mean over all columns of the TD matrix yields a lag projection map ( Materials and methods Equation 4; Figure 1 ) . Lag projection maps topographically represent the mean lag between each voxel and the rest of the brain . Third , computing lags over the whole brain with respect to a particular region yields a seed-based lag map . Seed-based lag maps topographically represent the degree to which each voxel is , on average , early vs . late with respect to the selected seed . The present results are reported in terms of lag projection maps ( Figure 2 ) , seed-based lag maps ( Figure 3 and 4 ) , and TD matrices ( Figure 5 ) . Additionally , it is possible to decompose lag structure into multiple temporal sequences ( ‘lag threads’ ) by applying spatial principal components analysis ( PCA ) to the TD matrix ( Mitra et al . , 2015 ) ( see Figure 7 caption for details ) . Lag thread results are presented in Figure 7 . 10 . 7554/eLife . 10781 . 004Figure 2 . Lag projection maps in wake and slow wave sleep . Lag projection maps depict the mean lag between each voxel and the rest of the brain ( Mitra et al . , 2014; Nikolić , 2007 ) . Panels A-B display lag projection maps , in units of seconds , derived from wake ( A ) and SWS ( B ) . These lag projection maps demonstrate changes in lag structure as a function of state . For example , thalamus is early ( blue ) during wake but late ( red ) during SWS; the opposite shift is evident in visual cortex . Panel C shows voxels exhibiting cluster-wise statistical significance in the wake vs . SWS comparison ( z > 4 . 5 , p<0 . 05 corrected ) . These clusters are centered on putamen bilaterally , thalamus , paracentral lobule ( PCL ) , visual cortex , medial prefrontal cortex ( mPFC ) , and brainstem . Axial slices: Z = +69 , +57 , +9 , -3 , -27 , -39 . Sagittal slice: X = +3 . DOI: http://dx . doi . org/10 . 7554/eLife . 10781 . 00410 . 7554/eLife . 10781 . 005Figure 3 . Seed-based lag maps in wake and SWS corresponding to the subcortical regions identified in Figure 2C: thalamus ( A ) , putamen ( B ) , and brainstem ( C ) . Also shown are lag difference maps ( SWS minus wake ) thresholded for cluster-wise statistical significance ( Z > 4 . 5 , p<0 . 05 corrected; as in Figure 2C ) . During wake , the cerebral cortex is generally late ( yellow/red hues ) with respect to subcortical regions . The cerebral cortex becomes early ( blue/green hues ) with respect to subcortical areas during SWS . All significant lag differences are negative ( blue ) , and predominantly found in cortex . Pre-frontal cortex becomes markedly early with respect to the posterior parts of the brain ( pink arrows in A and B ) . This feature suggests a ‘front-to-back’ propagation of slow waves in SWS ( Massimini et al . , 2004 ) . Lag structure in the brainstem and thalamus is relatively constant ( pink ovals in A and C ) . Lag structure is present within the thalamus in panel A even though the whole thalamus was used as the reference seed . This effect is observed because voxels within large seed-regions , e . g . , thalamus , can exhibit non-zero lags with the mean timecourse computed over the entire seed . Axial slices: Z = +45 , +9 . Sagittal slice: X = +3 . An expanded view of these results is shown in Figure 3—figure supplement 1 and 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 10781 . 00510 . 7554/eLife . 10781 . 006Figure 3—figure supplement 1 . Expanded view of seed-based lag maps derived using thalamus , putamen , and brainstem regions of interest defined in Figure 2C . Axial and Sagittal slices as in Figure 1 . Coronal slice: Y = +12 . DOI: http://dx . doi . org/10 . 7554/eLife . 10781 . 00610 . 7554/eLife . 10781 . 007Figure 3—figure supplement 2 . Seed-based lag difference maps thresholded for cluster-wise statistical significance of p<0 . 05 ( as in Figure 2 ) . All significant lag differences are negative ( blue ) , and predominantly found in cortex , indicating that many areas of cortex become significantly earlier than subcortical structures during SWS as compared to wake . Each of the difference maps also exhibits focality . In particular , thalamus seed lag differences are prominent in anterior cingulate cortex and insula; putamen seed lag differences highlight somatosensory regions and insula; brainstem seed lag differences show that visual cortex becomes markedly earlier than brainstem during SWS . DOI: http://dx . doi . org/10 . 7554/eLife . 10781 . 00710 . 7554/eLife . 10781 . 008Figure 4 . Seed-based lag maps in wake and SWS corresponding to the cortical regions identified in Figure 2C: visual cortex ( A ) , medial prefrontal cortex ( B ) , and paracentral lobule ( C ) . Also shown are lag difference maps ( SWS minus wake ) , thresholded for statistical significance , as in Figure 2 . Panel A shows that , whereas the visual seed is neither wholly late nor early in wake , nearly the entire cortex is late with respect to visual cortex during SWS . Panel B shows that medial prefrontal cortex ( mPFC ) exhibits both early and late lag shifts between wake and SWS . The mPFC lag map in SWS also exhibits the ‘front-to-back’ propagation pattern highlighted in Figure 3 ( pink arrow ) . Panel C shows that many of the lag relations of the paracentral lobule seed are reversed during SWS relative to wake . For example , in wake , the seed-region leads lateral sensory-motor cortex and posterior insula . These relations reverse in SWS and nearly the entire cerebral cortex becomes early with respect to the paracentral lobule . An expanded view of these results is shown in Figure 4—figure supplement 1 and 2 . Slice coordinates identical to Figure 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 10781 . 00810 . 7554/eLife . 10781 . 009Figure 4—figure supplement 1 . Expanded view of seed-based lag maps derived using visual , medial prefrontal cortex , and paracentral lobule regions of interest defined in Figure 2C . Slice coordinates identical to Figure 3—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 10781 . 00910 . 7554/eLife . 10781 . 010Figure 4—figure supplement 2 . Seed-based lag difference maps thresholded for cluster-wise statistical significance ( as in Figure 2 ) . For the visual cortex seed , nearly the entire cortex becomes late ( red ) with respect to visual cortex in SWS compared to wake . These effects are especially prominent in paracentral lobule and dorsolateral prefrontal cortex . For the medial prefrontal cortex ( mPFC ) seed , mPFC exhibits both early and late lag shifts as a function of sleep stage . Occipital and prefrontal areas become earlier ( blue ) than mPFC during SWS while medial motor structures become later ( red ) . Finally , for the paracentral lobule seed , nearly the entire cortex becomes early ( blue ) with respect to the paracentral lobule during SWS . DOI: http://dx . doi . org/10 . 7554/eLife . 10781 . 01010 . 7554/eLife . 10781 . 011Figure 5 . Time delay ( TD ) matrices . Panels A-B display TD matrices ( in units of seconds ) in wake and SWS , respectively . Each pixel represents the lag between two voxels . TD matrices are , by definition , anti-symmetric . Hence , all relevant information is contained in the displayed upper triangular values . The matrices displayed in ( A-B ) have been masked to include only cortical ( 6 mm ) 3 voxels with a ≥90% chance of belonging to one of 7 resting state networks ( RSNs: dorsal attention network ( DAN ) , ventral attention network ( VAN ) , sensory motor network ( SMN ) , visual network ( VIS ) , frontoparietal control network ( FPC ) , language network ( LAN ) , default mode network ( DMN ) ) ( ( Hacker et al . , 2013 ) , see Figure 5—figure supplement 2 ) . The same RSN definitions are applied in wake and SWS . After sorting voxels by RSN membership , voxels were sorted from early to late within RSN in the wake state . The wake ordering was applied to the SWS TD matrix . Panel C shows the Spearman rank order correlation between wake and SWS lag values in each of the 28 intra- and inter- RSN blocks in panels ( A-B ) . The diagonal blocks in panel C exhibit high correlation values , indicating that intra-RSN propagation is relatively preserved across wake and SWS . The correlation values in the off-diagonal blocks in panel C are low , demonstrating that inter-RSN propagation is strongly altered during SWS . White asterisks indicate significant ( p<0 . 05 ) effects computed by permutation resampling , including correction for multiple ( N = 28 ) comparisons . Panel D plots the mean within-block values of the wake TD matrix shown in panel A . The values in panel D ( in units of sec ) are very near zero , implying that no RSN is entirely leads or follows other RSNs . Panel E plots the mean within-block values of the SWS TD matrix shown in panel B . Note significant visual network earliness with respect to other networks ( p<0 . 05 by permutation resampling , multiple comparisons corrected for 21 upper diagonal blocks ) . Owing to anti-symmetry , the horizontal blue and vertical orange blocks both represent visual earliness . Diagonal blocks in panels D and E are colored gray to symbolize that they are constrained to be zero-mean . TD matrices with alternate voxel orderings are shown in Figure 5—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 10781 . 01110 . 7554/eLife . 10781 . 012Figure 5—figure supplement 1 . Time delay ( TD ) matrices as a function of sleep stage , alternate voxel orderings . ( A ) Wake . ( B-C ) N2 sleep . ( D-E ) N3 sleep ( SWS ) . Panels A and D duplicate panels A and B in Figure 5 . Panels C and E provide an alternative view of cross-RSN lag structure disorganization during sleep . In both Figure 5 and here , voxels are ordered first by RSN membership and , within RSN , by lag computed using the projection method . In Figure 5 , voxel ordering was determined according to the wake lag structure . Here , in panels C and E , voxel ordering was determined by the stage-specific lag structure , i . e . , N2 and N3 , respectively . Panels C and E demonstrate that the results shown in main text Figure 5 are robust to the alternative voxel ordering . Specifically , ( 1 ) intra-RSN lag structure ( diagonal blocks ) is generally preserved in all TD matrices , as illustrated by the DMN block highlighted in green; ( 2 ) Most inter-RSN ( off-diagonal ) blocks exhibit increasingly disorganized lag structure in N2 and N3 sleep , as in the DAN:DMN block , highlighted in yellow; ( 3 ) The visual network , highlighted in pink , assumes an ‘all early’ organization with respect to other cortical RSNs . DOI: http://dx . doi . org/10 . 7554/eLife . 10781 . 01210 . 7554/eLife . 10781 . 013Figure 5—figure supplement 2 . Resting state network ( RSN ) assignments for gray matter voxels used to compute the TD matrix and functional connectivity matrix results shown in main Figures 5 , 6 . This figure illustrates the 1065 ( 6 mm ) 3 gray matter voxels satisfying a criterion of ≥90% probability of belonging to one of the 7 cortical resting state networks defined in ( Hacker et al . , 2013 ) . The color code in this figure is unrelated to latency . The lag maps shown in main text Figures 2–4 are based on a parcellation of the entire gray matter into 6526 ( 6 mm ) 3 voxels . DOI: http://dx . doi . org/10 . 7554/eLife . 10781 . 01310 . 7554/eLife . 10781 . 014Figure 6 . Zero-lag correlation ( conventional functional connectivity; FC ) matrices . ( A ) : wake . ( B ) : slow wave sleep . Voxels shown in the correlation matrices correspond to Figure 5A , B ( see also Figure 5—figure supplement 2 ) , and matrix values are Fisher-z transformed Pearson correlations averaged over subjects . Note relatively preserved RSN organization across states , in line with previous analyses of these data ( Tagliazucchi et al . , 2013a ) . To assess the topography of pair-wise correlation changes , we computed the difference between the SWS and wake correlation matrices ( wake minus SWS ) , and applied spatial principal components analysis ( PCA ) to the difference matrix . The resulting eigenspectrum ( panel C ) shows that there are 2 statistically significant ( p<0 . 05; red line ) PCs ( threshold computed by permutation re-sampling ) . The topographies of these PC's are shown in panels D and E . These topographies reflect modest FC reductions in visual/somatosensory networks in wake vs . SWS , and modest FC increases in the DMN and thalamus in wake vs . SWS . These results are in accordance with previous findings comparing wake vs . NREM sleep ( Horovitz et al . , 2008; Picchioni et al . , 2013 ) . Importantly , zero-lag correlation structure ( panels A-B ) is much more preserved across wake and SWS than is lag structure ( Figure 5 ) . Axial slices: Z = +69 , +57 , +45 , +33 , +9 , -3 , -27 , -39 . Sagittal slices: X = +3 , +12 , -12 . DOI: http://dx . doi . org/10 . 7554/eLife . 10781 . 01410 . 7554/eLife . 10781 . 015Figure 7 . Lag structure dimensionality in wake and SWS . We have previously shown that multiple temporal sequences can be extracted from a TD matrix by applying spatial principal components analysis ( PCA ) to the TD matrix after zero-centering each column ( Mitra et al . , 2015 ) . Here we show PCA-derived eigenvalues ( scree plot ) obtained in wake ( pink ) and SWS ( blue ) . The maximum likelihood dimensionality estimates derived using the method of ( Minka , 2001 ) are 4 and 3 , respectively , in wake and SWS . The corresponding topographies ( ‘lag threads’ ) are shown in Figure 7—figure supplement 1 and 2 . N . B . : We previously obtained a maximum likelihood TD dimensionality estimate of 8 in a much larger ( N = 688 ) awake dataset ( Mitra et al . , 2015 ) . The lower presently obtained figure ( 4 ) reflects less statistical power owing to a smaller subject sample ( N = 39 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10781 . 01510 . 7554/eLife . 10781 . 016Figure 7—figure supplement 1 . Lag thread topographies in wake corresponding to the eigenvalues shown in Figure 7 . Four lag threads are shown in accordance with the maximum likelihood dimensionality estimate . The illustrated topographies are comparable to the first 4 lag threads shown in Figure 2 of Mitra et al . , 2015 . DOI: http://dx . doi . org/10 . 7554/eLife . 10781 . 01610 . 7554/eLife . 10781 . 017Figure 7—figure supplement 2 . Lag threads topographies in SWS . Note pronounced differences in comparison to lag threads obtained in wake ( Figure 7—figure supplement 1 ) . SWS lag threads 1 and 3 show visual earliness and paracentral lobule lateness , as in Figure 2 and 4 . Lag thread 2 shows a front-to-back propagation pattern , in line with main Figures 3 , 4 as well as prior electrophysiological results ( Massimini et al . , 2004 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10781 . 017 Figure 2A , B exhibit lag projection maps computed during wake and SWS . State-dependent shifts are evident in the lag projection maps , for example in occipital cortex and thalamus ( Figure 2A , B ) . Figure 2C shows all statistically significant spatial clusters ( Z > 4 . 5 , p < 0 . 05 corrected; see Materials and methods ) in the wake vs . SWS comparison . These clusters include: thalamus , bilateral putamen , brainstem , visual cortex , medial prefrontal cortex ( mPFC ) , and paracentral lobule ( PCL ) ( Figure 2C ) . Visual cortex was later ( more positive lag values ) during wake as compared to SWS , whereas the remaining clusters were earlier ( more negative lag values ) . Having found voxel clusters exhibiting statistically significant changes in lag structure in the whole-brain wake vs . SWS contrast , we next computed seed-based lag maps using the clusters shown in Figure 2C as seeds ( see Materials and methods ) . Seed-based lag maps represent temporal lags between each voxel and the average timecourse computed over the seed-region of interest . Seed-based lag maps obtained with the subcortical clusters , specifically , thalamus , putamen , and brainstem , are shown in Figure 3 , which illustrates altered lags during SWS compared to wake . Three principal findings are evident . First , whereas cortex is generally late with respect to the subcortical seeds during wake , cortex becomes earlier than subcortical structures during SWS ( see significant differences in Figure 3 ) . Second , a ‘front-to-back’ propagation pattern appears in SWS; this phenomenon is best seen in the sagittal views of the thalamus and putamen lag maps ( pink arrows , Figure 3A , B ) . This pattern may correspond to previous reports of slow wave propagation along the anterior-posterior axis of the brain ( Massimini et al . , 2004; Murphy et al . , 2009 ) . Third , the lag structure within the thalamus and brainstem ( Figure 3A , C , pink ovals ) remains largely constant across states . An early-to-late sequence extending from lower brainstem to rostral thalamus is evident in each of the seed-based lag maps shown in Figure 3A , C ( pink ovals ) . Hence , the general pattern of BOLD signal propagation between cortex and subcortical structures reverses during SWS , but propagation within the brainstem-thalamus axis is largely preserved across wake vs . SWS . A broader view of these results is shown in Figure 3—figure supplement 1 and 2 . Figure 4 displays seed-based lag maps obtained with the cortical clusters shown in Figure 2C . State-contrasts in lag structure differ by seed . Specifically , visual cortex ( Figure 4A ) is neither wholly late nor early during wake , but nearly the entire cerebral cortex becomes late with respect to visual cortex during SWS . Two especially prominent foci of lateness in SWS are dorsolateral prefrontal cortex and the paracentral lobule ( Figure 4A ) . A variety of lag shifts are evident in the results obtained with the medial prefrontal seed ( Figure 4B ) . For example , subgenual prefrontal cortex ( red arrow ) shifts from mid-latency ( lag values near zero ) during wake to very early during SWS , and the paracentral lobule shifts from mid-latency during wake to late during SWS . A ‘front-to-back’ propagation pattern ( also highlighted in Figure 3 ) is clearly evident in the sagittal view in Figure 4B during SWS . Figure 4C illustrates dramatic changes in the lag relations of the paracentral lobule . In wake , paracentral lobule leads both lateral sensory-motor cortex and posterior insula . These relations are reversed in SWS , during which nearly the entire cortex becomes markedly early with respect to the paracentral lobule . A broader view of these results is shown in Figure 4—figure supplement 1 and 2 . The results presented so far highlight topographic features of apparent propagation that change between wake and SWS . The next set of results considers pair-wise lag relations defined over pairs of 6mm3 isotropic cortical gray matter voxels . The results are presented as time-delay ( TD ) matrices . Voxels in the TD matrices are ordered first by resting state network affiliation ( see Figure 5—figure supplement 2 ) . Then , within RSNs , the voxels are ordered from earliest ( most negative ) to latest ( most positive ) according to mean latency determined in wake ( Figure 5A ) . The ordering computed during wake was applied to the SWS TD results ( Figure 5B ) . A key algebraic feature of TD matrices is that they are exactly anti-symmetric ( τi , j=−τj , i; Materials and methods Equation 3 ) . Thus , if the lag between voxels i and j is τ seconds , then the lag between voxels j and i must be exactly −τ seconds ( see Materials and methods ) . As TD matrices are anti-symmetric , each diagonal block , which represents intra-RSN lag structure , is anti-symmetric as well . However , the algebra does not impose any relation between lag and RSN membership . Thus , the structure evident in the wake TD matrix ( Figure 5A ) is informative , and recapitulates previous findings obtained in a separate , large data set ( Mitra et al . , 2014 ) . The diagonal blocks show a wide range and well-ordered distribution of lags . This organization reflects propagation within RSNs; the DMN block , highlighted in green in Figure 5A , is an example of intra-network lag organization . The off-diagonal blocks , which represent lags across RSNs , also contain well-ordered early , middle , and late components , much like the diagonal blocks . This feature is not algebraically imposed . An important implication of the early-to-late organization of the inter-RSN blocks in Figure 5A is that each RSN is neither early nor late with respect to the others during the wake state , a feature which we have previously reported ( Mitra et al . , 2014 ) . To illustrate this point , consider the off-diagonal block corresponding to the DMN paired with the dorsal attention network ( DAN ) , outlined in yellow in Figure 5A . A well-ordered progression from early ( blue ) to late ( red ) is evident , indicating that parts of the DMN lead the DAN and vice versa; neither the DMN nor the DAN leads or follows the other as a whole . We next compared the TD matrix in SWS ( Figure 5B ) to the TD matrix in wake . Three features emerged from this comparison . First , the range of lag values in SWS is larger than in the wake data , as if the speed of BOLD signal propagation has slowed . We quantitatively represent this effect as the standard deviation ( SD ) , computed over the upper triangular ( hence unique ) lag values in wake and SWS . SD during wake was 0 . 42 ± 0 . 05 s , whereas SD during SWS was 0 . 65 ± 0 . 07 s . This effect was significant ( p<0 . 01 , by permutation resampling with 10 , 000 trials ) . Second , inter-RSN lag structure is altered in SWS . This effect appears as a loss of early ( blue ) to late ( red ) organization in off-diagonal blocks , e . g . , as in the DAN:DMN block ( yellow outline in Figure 5B ) . To quantitatively assess this change , we computed the rank correlation ( Spearman’s ρ ) between wake and SWS lag values over all voxel pairs in each of the 8 ·7/2 = 28 unique intra- and inter-RSN blocks ( Figure 5A , B ) . Blocks exhibiting a significantly low correlation ( p<0 . 05 , corrected for 28 multiple comparisons ) comparing wake vs . SWS are marked with a white asterisk in Figure 5C; these effects represent a significant change in lag structure between resting state networks . Importantly , these effects were observed only in off-diagonal ( cross-RSN ) blocks . In other words , intra-network lag structure was relatively preserved ( ρ>0 . 6 ) across states , e . g . , within the DMN ( green outline Figure 5A , B ) . In contrast , inter-network lag structure was markedly altered ( ρ<0 . 3 ) in the majority of off-diagonal blocks . Thus , Figure 5C demonstrates that , whereas propagation within RSNs is relatively preserved during sleep , propagation across RSNs is significantly altered . During SWS , most cross-RSN blocks ( excluding visual network pairs ) develop a ‘disorganized’ structure , in which well-ordered propagation appears to be lost . This effect is not attributable to voxel ordering , as the voxels are ordered identically in the wake and SWS TD matrices ( Figure 5A ) . The same disorganized appearance persists even if the SWS TD matrix ROIs are ordered according to their own stage-specific lag structure ( Figure 5—figure supplement 1 ) . A third effect evident in Figure 5A , B is the appearance of an ‘all early’ organization in the visual network ( ‘VIS’ , pink box ) in SWS . That is , in half the TD matrix , all cross-RSN voxel pairs involving the visual network exhibit negative ( early; blue ) lag values in SWS , indicating that voxels in the visual network are earlier than nearly all other cortical voxels . Thus , the principle , which applies in wake , that no RSN leads or follows any other ( Mitra et al . , 2014 ) , does not hold during SWS . To quantitatively investigate changes in lag structure between the visual network and the rest of the brain , we computed the mean lag value for each off-diagonal ( inter-RSN ) block in wake and SWS ( Figure 5D and E respectively ) . Anti-symmetry forces a mean lag value of zero within diagonal blocks . Thus , 21 unique blocks are considered in this analysis . If no RSN leads or follows any other in aggregate , the average lag value in each inter-RSN block should be zero . Figure 5D illustrates that this is the case during wake: the mean lag in each off-diagonal block is very nearly zero , as previously reported in a completely independent dataset ( Mitra et al . , 2014 ) . Figure 5E shows that this principle approximately applies also in SWS except in the visual network ( white asterisks ) . During SWS , voxels in the visual network become early with respect to other cortical voxels ( Figure 5E ) . An analogous , but opposite feature also appears in a subset of the cross-RSN sensorimotor ( SMN ) block , which assumes an ‘all late’ ( red ) configuration . In other words , this set of voxels becomes late with respect to cortical BOLD signal activity in nearly all other cortical voxels during SWS . The voxels in question correspond to the paracentral lobule ( Figure 2C ) . That the paracentral lobule becomes late in SWS is also evident in Figure 4C . This effect is not significant in Figure 5E because the paracentral lobule is only part of the a priori defined SMN .
The implications of our findings critically depend on how spontaneous BOLD fMRI signal fluctuations relate to electrophysiology . This question currently remains a topic of active investigation ( Florin et al . , 2015 ) . Several studies using invasive recordings have found that slow ( 0 . 5–4 Hz ) and infra-slow ( <0 . 1 Hz ) local field potentials correspond most closely to BOLD signal fluctuations and correlation structure . This correspondence has been demonstrated in wake ( He et al . , 2008; Hiltunen et al . , 2014; Nir et al . , 2008 ) , sleep ( He et al . , 2008 ) , and anesthesia ( Pan et al . , 2013 ) . Thus , a natural hypothesis is that temporal lags in the BOLD signal reflect propagation of slow and infra-slow electrophysiological activity . However , as far as we are aware , temporal lags in infra-slow electrophysiology have not been investigated at the systems level . On the other hand , propagation of slow activity has been widely reported . The spectral content of the slow oscillation at the low end , approximately 0 . 5 Hz , approaches the infra-slow range . Hence , in the following , we consider correspondence between reports of propagated slow electrophysiology and the present results , with the understanding that future investigations specifically of infra-slow propagation are needed . Slow electrophysiological activity has been most studied in the context of SWS and anesthesia , during which the slow oscillation , or UP/DOWN states ( UDSs ) , ( Steriade et al . , 1993 ) is a characteristic feature . UDSs classically are described as periodic at frequencies in the 0 . 5–1 . 5 Hz range , and are known to propagate on a time-scale of 100's of milliseconds ( Hahn et al . , 2006; McCormick et al . , 2015; Petersen et al . , 2003 ) . More recently , UDSs have also been described in awake , resting rodents ( Ferezou et al . , 2007 ) . Although UDS periodicity is a characteristic of single neuron membrane potential recordings ( Hahn et al . , 2006; Petersen et al . , 2003 ) , macro-electrode recordings , e . g . , electroencephalography ( EEG ) and electrocorticography ( ECoG ) , show aperiodic , 1/f-like spectral content during SWS ( He et al . , 2010; Nir et al . , 2011; Sirota et al . , 2003 ) . More generally , the spectral content of intrinsic electrophysiologic activity and rs-fMRI is 1/f-like in wake as well as SWS ( He et al . , 2008; Hiltunen et al . , 2014 ) . Moreover , macro-electrode recordings of slow activity also show apparent propagation on time-scales as long as 1–2 s during sleep and anesthesia ( Nir et al . , 2011; Sirota et al . , 2003 ) , as in the present rs-fMRI data . Propagated slow potentials during SWS share additional points of concordance with the present BOLD fMRI results . Electrophysiological studies in both humans and rodents suggest that slow activity tends to originate in medial prefrontal cortex and propagate to more posterior regions ( Massimini et al . , 2004; Nir et al . , 2011; Sheroziya and Timofeev , 2014 ) . We also observe a ‘front-to-back’ propagation pattern ( see Figures 4B and Figure 7—figure supplement 2 ) . However , this pattern should be understood as only one of many . In that regard , a noteworthy principle that has emerged on the basis of human high-density EEG and ECoG recordings is that slow waves originate in multiple locations and propagate through multiple routes ( Massimini et al . , 2004; Murphy et al . , 2009; Nir et al . , 2011 ) . A necessary algebraic consequence of this principle is that the lag structure of propagated slow activity exists in a multi-dimensional space ( Mitra et al . , 2015 ) . We have recently demonstrated precisely this point by analysis of a very large resting state fMRI dataset acquired in quietly resting awake adults ( Mitra et al . , 2015 ) . We confirm that the same principle applies in the current fMRI data , both during wake and SWS ( Figure 7 ) . Indeed , the spatial principal components ( ‘lag threads’ ) derived by analysis of the fMRI time-delay matrix provides a compact means of visualizing how the propagation of intrinsic activity becomes rearranged in wake vs . SWS ( Figure 7—figure supplement 1 and 2 ) . To the best of our knowledge , apparent propagation of either slow or infra-slow electrophysiological activity in the awake state has not been reported in either humans or animals . However , voltage sensitive dye ( VSD ) studies in quietly resting rodents have demonstrated multiple patterns of propagation of slow intrinsic activity over 100's of milliseconds ( Mohajerani et al . , 2013; Mohajerani et al . , 2010 ) . Moreover , propagation patterns assessed using VSD are bilaterally symmetric ( Mohajerani et al . , 2010 ) , as are BOLD fMRI patterns of propagation ( Mitra et al . , 2015; Mitra et al . , 2014 ) . In summary , the available evidence suggests that the BOLD signal and slow/infra-slow electrophysiological activity exhibit similarities in spectral content , correlation structure , and apparent propagation . The present results motivate future investigations to clarify the physiological links between propagated UDSs , propagated VSD patterns in rodents , and propagated BOLD fMRI signal fluctuations in humans , both during wakefulness and SWS . Direct electrophysiological studies of infra-slow propagation are especially needed . In the following , we discuss hypotheses regarding the possible functions of propagated infra-slow activity , observed using rs-fMRI , in relation to theories originally derived by observations of propagated slow potentials . Cortical re-arrangements in lag structure are complex and regionally dependent , as demonstrated by the seed-based analyses shown in Figure 4 . In the following , we present three hypotheses , informed by prior studies of SWS , suggesting how infra-slow propagation may relate to the physiological functions of sleep . Future work combining rs-fMRI sleep recordings with behavioral data will be required to investigate these hypotheses . First , slow wave sleep is believed to represent an off-line state during which slow activity enables consolidation of newly acquired memories ( Born et al . , 2006; Maquet , 2001; Marshall et al . , 2006; Stickgold , 2005 ) . Human imaging studies of word-pair association tasks have shown that the medial prefrontal cortex ( mPFC ) is implicated in consolidation of declarative memory ( Euston et al . , 2012; Gais et al . , 2007; Takashima et al . , 2006 ) . Consolidation is accompanied by increases in mPFC activity during successful later recall ( Gais et al . , 2007; Takashima et al . , 2006 ) . The locus of these previously reported effects is notably close to the mPFC region illustrated in Figure 2C ( cf . Figure 3 in Takashima et al . , 2006 and Figure 4 in Gais et al . , 2007 ) . Using the same task , Marshall and colleagues found that artificially initiating slow wave activity by applying current via frontal electrodes during SWS boosts subsequent recall ( Marshall et al . , 2006 ) . Thus , the available data suggest that the mPFC shift towards earliness during SWS may facilitate consolidation of declarative memory . This hypothesis could be tested by relating infra-slow , front-to-back propagation to consolidation of declarative memory . Second , SWS is also thought to improve procedural memory , e . g . , motor sequence learning and motor adaptation ( Stickgold , 2005 ) . We observed significant wake vs . SWS latency shifts in putamen and paracentral lobule ( PCL ) ( Figures 2–4 ) , both of which structures are strongly associated with procedural learning ( Graybiel , 2005; Halsband and Lange , 2006; Knowlton et al . , 1996 ) . The paracentral lobule is a functional component of the supplementary motor area ( SMA ) ( Lim et al . , 1994 ) . Sleep-dependent improvements in procedural tasks have been postulated to involve plasticity in cortico-striatal circuits ( Doyon and Benali , 2005 ) . Thus , the reversal in the temporal lag between putamen and cortex ( Figure 3B ) during SWS could be a correlate of off-line consolidation of procedural memory . Similarly , altered apparent propagation in PCL may relate to offline adjustment of motor programs . Alternatively , PCL lateness during SWS could represent a correlate of the abolition of motor behavior . These possibilities could be investigated by combining rs-fMRI studies of SWS with procedural learning paradigms . Third , we show that visual cortex is neither wholly late nor early during wake , but that nearly the entire cerebral cortex becomes late with respect to visual cortex during SWS ( Figure 4A , Figure 5 ) . This visual cortex finding may relate to the now widely recognized fact that dreaming also occurs in NREM sleep ( Hobson et al . , 2000 ) . Recent work has shown that BOLD fMRI activity in visual cortex predicts NREM dreaming ( Horikawa et al . , 2013 ) . Hence , there may exist a link between initiation of infra-slow activity in visual cortex and dreaming . The question of whether initiation of infra-slow activity in visual cortices relates to dreaming has not been studied thus far , but could be explicitly examined using the methodology of Horikawa and colleagues ( Horikawa et al . , 2013 ) . Wakefulness is a state during which environmental awareness is possible ( Seth et al . , 2005 ) . The thalamus plays a critical role in this state by relaying sensory signals to the cerebral cortex ( Alkire et al . , 2008 ) . Electrophysiological recordings have shown that the awake state is associated with depolarization of thalamocortical cells , which facilitates transmission of ascending input to the cortex ( Brown et al . , 2012; Franks , 2008; McCormick and Bal , 1997 ) . Conversely , during SWS , signals from the environment do not reliably reach the cortex owing to hyperpolarization of thalamocortical neurons ( Franks , 2008; Franks and Lieb , 1994; Hirsch et al . , 1983; Steriade and Timofeev , 2003 ) . Thus , the thalamus ‘gates’ transmission of environmental stimuli to the cortex . Moreover , in vitro work suggests that thalamic hyperpolarization in SWS is modulated by propagation of slow ( <1 Hz ) activity from cortex to thalamus ( Blethyn et al . , 2006; Hughes et al . , 2002 ) . Our analysis of BOLD signal fluctuations is consistent with this account: Cerebral cortex leads thalamus during SWS , but thalamus leads cortex during wake ( Figure 3A ) . Importantly , BOLD signal propagation from brainstem to thalamus is similar in wake and SWS . This result suggests that the thalamus is the site at which ascending signals are impeded during SWS , in accordance with the hypothesized role of the thalamus as a sensory gate . Figure 5 shows that cross-network lag structure is extensively altered in SWS as compared to wake . During wake , both diagonal- and off-diagonal blocks of the TD matrix exhibit well-ordered early , middle , and late components . Thus , cross-network and within-network propagation are comparable during wake . During SWS , apparent propagation within and across networks is no longer comparable ( Figure 5B , E ) . Cross-network ( off-diagonal ) blocks lose their ‘early-to-late’ structure , whereas within-network ( diagonal blocks ) lag structure is preserved ( Figure 5A-C ) . Moreover , most cross-network blocks ( excluding visual network pairs ) appear disorganized during SWS . Speculatively , the dissociation between preserved within-network propagation and disorganized cross-network propagation suggests that neural communication during SWS , as measured by propagation of BOLD signals , is largely intact within functional networks ( RSNs ) , but is disrupted across functional networks . These observations support theories postulating that reduced subjective awareness ( ‘unconsciousness’ ) during SWS results from loss of integration across networks ( Mashour , 2005; Tononi , 2004 ) . ‘Conscious’ here refers to the awake , or on-line , state . Accordingly , decreased subjective awareness during SWS theoretically results from loss of integration across networks , while within-network activity is generally preserved ( Boly et al . , 2012; Mashour , 2005; Tagliazucchi et al . , 2013a ) . A corollary is that , during SWS , neural communication is maintained within functional modules , but altered across functional modules , resulting in ‘network segregation’ ( Mashour , 2013; Tagliazucchi et al . , 2013a ) . Our TD matrix results are consistent with this principle , provided the assumption that BOLD signal propagation reflects neural communication at a broad spatio-temporal scale , We interpret our findings as follows . During wake , the brain is capable of responding to the environment and functional systems reciprocally communicate to sustain conscious content ( Alkire et al . , 2008; McCormick et al . , 2015 ) . Accordingly , we find ascending BOLD signal propagation from thalamus to cortex ( Figure 3A ) , as well as well-ordered propagation of activity between resting state networks ( Figure 5A ) . In SWS , off-line brain activity theoretically serves mechanisms concerned with synaptic homeostasis and memory consolidation ( McClelland et al . , 1995; McCormick et al . , 2015 ) . Features of the off-line state ( SWS ) include reduced responses to environmental stimuli , altered inter-network communication , but relatively intact intra-network communication ( McClelland et al . , 1995; McNaughton et al . 2003; Tagliazucchi et al . , 2013a ) . Accordingly , we observe reversal of thalamo-cortical propagation ( Figure 3A ) , altered cross-RSN propagation ( Figure 5B-C ) , and intact within-RSN propagation . Future study of BOLD signal propagation and infra-slow activity is necessary to obtain a better understanding of the physiology of spontaneous activity in wake and sleep .
Acquisition parameters and details for these data have been previously published ( Tagliazucchi et al . , 2013 ) . fMRI was acquired using a 3 T scanner ( Siemens Trio ) with optimized polysomnographic settings ( 1 , 505 volumes of T2*-weighted echo planar images , repetition time/echo time = 2 , 080 ms/30 ms , matrix = 64 × 64 , voxel size = 3 × 3 × 2 mm3 , distance factor = 50%; field of view = 192 mm2 ) . 30 EEG channels were simultaneously recorded using a modified cap ( EASYCAP ) with FCz as reference ( sampling rate = 5 kHz , low pass filter = 250 Hz , high pass filter = 0 . 016 Hz ) . MRI and pulse artifact correction were performed based on the average artifact subtraction method ( Allen et al . , 1998 ) as implemented in Vision Analyzer2 ( Brain Products ) followed by ICA-based rejection of residual artifact components ( CBC parameters; Vision Analyzer ) . EEG sleep staging was done by an expert according to the American Academy of Sleep Medicine ( AASM ) criteria ( Soeffing et al . , 2008 ) . 63 non-sleep-deprived subjects were scanned in the evening ( starting at ∼8:00 PM ) . Subjects were instructed to keep eyes closed during wakefulness . Hypnograms were inspected to identify epochs of contiguous sleep stages lasting at least 5 min ( 150 volumes ) . These criteria yielded 39 subjects contributing to the present analyses . Included are 70 epochs of wakefulness , 47 epochs of N2 sleep , and 38 epochs of N3 sleep ( SWS ) . Detailed sleep architectures of each participant have been previously published ( Tagliazucchi et al . , 2013 ) . fMRI preprocessing was as described in ( Mitra et al . , 2014 ) . Briefly , this included compensation for slice-dependent time shifts , elimination of systematic odd-even slice intensity differences due to interleaved acquisition , and rigid body correction of head movement within and across runs . Atlas transformation was achieved by composition of affine transforms connecting the fMRI volumes with the T2-weighted and T1-weighted structural images . Additional preprocessing in preparation for lags analysis included spatial smoothing ( 6 mm full width at half maximum ( FWHM ) Gaussian blur in each direction ) , voxel-wise removal of linear trends over each fMRI run , and temporal low-pass filtering retaining frequencies below 0 . 1 Hz . Spurious variance was reduced by regression of nuisance waveforms derived from head motion correction and timeseries extracted from regions ( of ‘non-interest’ ) in white matter and CSF as well the BOLD timeseries averaged over the brain ( Fox et al . , 2009 ) . Frame censoring was computed at a threshold of 0 . 5% root mean square frame-to-frame intensity change ( Power et al . , 2012 ) . Epochs containing fewer than 10 contiguous frames were excluded . These criteria removed 5 . 3 ± 0 . 9% of frames per individual during wake , 6 . 1 ± 0 . 7% during N2 sleep , and 5 . 8 ± 0 . 7% during N3 sleep . There were no statistically significant differences in the amount of frame censoring by state . Our method for computing lags between time series has been previously published ( Mitra et al . , 2014 ) . Conventional seed-based correlation analysis involves computation of the Pearson correlation , r , between the time series , x1 ( t ) , extracted from a seed region , and a second time series , x2 ( t ) , extracted from some other locus ( single voxel or region of interest ) . Thus , ( 1 ) rx1x2=1σx1σx21T∫x1 ( t ) ·x2 ( t ) dt , ​ where σx1 and σx2 are the temporal standard deviations of signals x1 and x2 , and T is the interval of integration . Here , we generalize the assumption of exact temporal synchrony and compute lagged cross-covariance functions . Thus , ( 2 ) Cx1x2 ( τ ) =1T∫x1 ( t+τ ) ·x2 ( t ) dt , ​ where τ is the lag ( in units of time ) . The value of τ at which Cx1x2 ( τ ) exhibits an extremum defines the temporal lag ( equivalently , delay ) between signals x1 and x2 ( König , 1994 ) . Although cross-covariance functions can exhibit multiple extrema in the analysis of periodic signals , BOLD time series are aperiodic ( He et al . , 2010; Maxim et al . , 2005 ) , and almost always give rise to lagged cross-covariance functions with a single , well defined extremum , typically in the range ±1 s . We determine the extremum abscissa and ordinate using parabolic interpolation ( Mitra et al . , 2014 ) . Given a set of n time series , {x1 ( t ) , x2 ( t ) , ⋯ , xn ( t ) } , finding all τi , j corresponding to the extrema , ai , j , of Cxixj ( τ ) yields the anti-symmetric , time delay matrix: ( 3 ) TD =τ1 , 1⋯τ1 , n⋮⋱⋮−τ1 , n⋯τn , n The diagonal entries of TD are necessarily zero , as any time series has zero lag with itself . Moreover , τi , j=−τj , i , since time series xi ( t ) preceding xj ( t ) implies that xj ( t ) follows xi ( t ) by the same interval . Here , the timeseries were extracted from ( 6mm ) 3cubic voxels evenly distributed throughout gray matter in the whole brain ( Mitra et al . , 2015 ) . We projected the multivariate data represented in the TD matrix onto one-dimensional maps using the technique described by Nikolic and colleagues ( Nikolić , 2007; Schneider et al . , 2006 ) . We refer to these one-dimensional maps as lag projections . Operationally , the projection is done by taking the mean across the columns of TD ( Equation 3 ) , that is , ( 4 ) Tp=∑j=1n τ1 , j ⋯ ∑j=1n τn , j Seed-based lag maps were computed according to Equation 2 , except instead of computing lags between all voxels , we computed the lag between a reference timeseries extracted from the seed and timeseries extracted from all ( 6mm ) 3 voxels . This procedure produces a one-dimensional seed-based lag map . Group level TD matrices , lag projections , and seed-based lag maps were obtained in each state ( W , N2 sleep , and N3 sleep ) by computing each quantity at the individual subject level ( averaging across temporally contiguous epochs ) and then averaging . Statistical significance of wake versus SWS differences in lag projection maps was assessed on a cluster-wise basis using threshold-extent criteria computed by extensive permutation resampling ( Hacker et al . , 2012; Hayasaka and Nichols , 2003 ) . Figure 5C reports Spearman ρ correlations over entries in TD matrix blocks in wake and SWS . Statistical significance was assessed by permutation resampling of the matrix entries within blocks , with correction for multiple comparisons . Figure 5D and E report mean lag values averaged over entries in TD matrix blocks . Statistical significance of the difference in mean lag value across TD matrix blocks was also assessed through permutation resampling , with correction for multiple comparisons .
Propagation of slow intrinsic brain activity has been widely observed in electrophysiogical studies of slow wave sleep ( SWS ) . However , in human resting state fMRI ( rs-fMRI ) , intrinsic activity has been understood predominantly in terms of zero-lag temporal synchrony ( functional connectivity ) within systems known as resting state networks ( RSNs ) . Prior rs-fMRI studies have found that RSNs are generally preserved across wake and sleep . Here , we use a recently developed analysis technique to study propagation of infra-slow intrinsic blood oxygen level dependent ( BOLD ) signals in normal adults during wake and SWS . This analysis reveals marked changes in propagation patterns in SWS vs . wake . Broadly , ordered propagation is preserved within traditionally defined RSNs but lost between RSNs . Additionally , propagation between cerebral cortex and subcortical structures reverses directions , and intra-cortical propagation becomes reorganized , especially in visual and sensorimotor cortices . These findings show that propagated rs-fMRI activity informs theoretical accounts of the neural functions of sleep .
The brain shows spontaneous activity all the time , even when we are sleeping . A technique called functional magnetic resonance imaging ( fMRI ) has revealed that this spontaneous activity can occur in distinct groups of brain regions at roughly at the same time . Each group is referred to as a resting-state network and the brain regions that make up these networks are largely the same between individuals , and between the sleep and awake states . However , when spontaneous brain activity is measured in rodents and humans using electrodes , it appears that there are actually waves of electrical activity that spread both within and across resting-state networks . In other words , these studies suggest that brain regions tend to become active in turn rather than at the same time . This led Mitra et al . to question whether the techniques used to analyze fMRI scans of spontaneous brain activity might have overlooked differences in the timing of brain activity . Mitra et al . used a new technique to analyze fMRI data from healthy adult volunteers . The experiments show that brain regions are activated in a different order depending on whether the individuals are awake or asleep . Specifically , in conscious individuals information from the senses is first processed by a structure deep within the brain called the thalamus before it is passed to the brain’s outer layer , known as the cortex . During deep sleep , this flow of information is reversed and signals are instead sent from the cortex to the thalamus . This may contribute to our loss of sensory awareness during sleep , and even to the occurrence of dreaming . The exchange of informationbetween resting-state networks also becomes disorganized during sleep . This lends support to the idea that the coordinated transfer of information between networks in the awake state may contribute to consciousness . Future experiments should explore differences in spontaneous brain activity in different phases of sleep , and investigate how such activity is able to spread throughout the brain .