Investigations of the excitability of neurons in the peri-infarct cortex during recovery from ischemic stroke have yielded a complex and at times contradictory data set. Peri-infarct hyper-excitability was suggested by data from anesthetized rats that exhibited elevated spontaneous multiunit firing within peri-infarct regions 3–7 days after stroke (Schiene et al., 1996). Similarly, downregulated GABAergic inhibition in peri-infarct cortex (Schiene et al., 1996; Mittmann et al., 1998; Neumann-Haefelin et al., 1998; Neumann-Haefelin et al., 1999; Qü et al., 1998a; Redecker et al., 2002), degeneration of parvalbumin-positive inhibitory interneurons (Neumann-Haefelin et al., 1998; Luhmann et al., 1995), decreased paired pulse inhibition (Buchkremer-Ratzmann and Witte, 1997; Domann et al., 1993; Fujioka et al., 2004), increased NMDA-receptor-mediated excitation (Qü et al., 1998b; Que et al., 1999), and a downregulation of KCC2 Jaenisch et al., 2016; Jin et al., 2005; Khirug et al., 2021; Martín-Aragón Baudel et al., 2017; Schulte et al., 2018 have suggested that the peri-infarct cortex is hyper-excitable during recovery, contributing to increased risk of epileptogenesis in the post stroke brain (Jaenisch et al., 2016; Witte and Freund, 1999). Conversely, several groups have reported that the sensory-evoked responsiveness of the peri-infarct cortex is diminished during recovery from focal photothombotic stroke (Brown et al., 2009; Chen et al., 2012; Lim et al., 2014; Sigler et al., 2009; Sweetnam and Brown, 2013; Sweetnam et al., 2012; Winship and Murphy, 2008). Even after months of recovery following forelimb (FL) representation targeted stroke, the remapped representation of the FL has been shown to display prolonged modes of activation with lower amplitude (Brown et al., 2009) and reduced temporal fidelity (Sweetnam and Brown, 2013).

Almost all studies that have examined post-stroke cortical responsiveness have used surgical preparations with anesthetized animals. Anesthesia disrupts cortical activity dynamics and functional connectivity between cortical areas (Cramer et al., 2019; Grandjean et al., 2014; Jonckers et al., 2014; Kalthoff et al., 2013; Nasrallah et al., 2014), and has been shown to reduce the magnitude and spread of cortical activation from sensory stimuli (Chapin and Lin, 1984; Chen et al., 2005; Devonshire et al., 2010; Lissek et al., 2016; Ebner and Kaas, 2015; Friedberg et al., 1999; Faggin et al., 1997; Moore and Nelson, 1998; Nicolelis and Chapin, 1994). It further reduces the potential contribution of corollary discharge and reafference due to voluntary action in modulating sensory input and cortical responses (Crapse and Sommer, 2008). It has also recently been shown that the propagation pattern of cortical activity differs between evoked and spontaneous activity, with spontaneous activity showing more complex trajectories and lower activity amplitudes (Afrashteh et al., 2021). Somatosensory responses are known to be modulated by the relevance of stimuli to behavior and task performance (Dionne et al., 2013; Johansen-Berg et al., 2000; Nelson et al., 2004; Staines et al., 2002b; Gomez-Ramirez et al., 2016; Scaglione et al., 2014), and by corollary feedback during (Yu et al., 2016), or even before (London and Miller, 2013; Nelson, 1987; Bensmaia and Helms Tillery, 2014), the onset of movement. Together, these studies point to a complex, interconnected system that modulates the activity of somatosensory networks in the awake behaving animal that is either not present, or is altered, in studies of evoked cortical activity under the anesthetized state. While monitoring cortical activity in awake mobile animals presents methodological complexity, it avoids confounds associated with the anesthetized state and provides unique insight into evolving patterns of network activity that may be unique to the awake state.

To investigate neural activity patterns during stroke recovery while avoiding the potential confounds of anesthesia, the present work used two photon Ca²⁺ imaging within and distal to the peri-infarct region of a focal photothrombotic stroke lesioning the FL primary somatosensory cortex in mice. We used awake, behaving but head-fixed mice in a mobile homecage to longitudinally measure cortical activity, then used computational methods to assess functional connectivity and neural assembly architecture at baseline and each week for 2 months following stroke. Behavioral recovery from stroke was measured in the same animals on a tapered beam task and string pull task to determine the time course of sensorimotor deficits related to changes in cortical activity and network architecture. Our goal was to use longitudinal imaging and advanced computational analyses to define network changes in somatosensory cortex associated with sensorimotor recovery. We show that despite disturbances to the widefield topology and amplitude of the sensory-evoked FL map (acquired under anesthetic), it is only within the immediate peri-infarct region after FL targeted stroke that neural activity, functional connectivity, and neural assembly architecture are disrupted. We demonstrate that the peri-infarct somatosensory cortex displays a marked reduction in neural assembly number, an increase in neural assembly membership, and persistent alterations in assembly-assembly correlations, and that this occurs alongside transient deficits in both functional connectivity and neural activity. Notably, we show that reduced neuronal spiking is strongly correlated with a deficit in motor performance on the tapered beam task. Surprisingly, we also show that significant alterations in neuronal activity (firing rate), functional connectivity, and neural assembly architecture are absent within more distal regions of cortex as little as 750 µm from the stroke border, even in areas identified by regional functional imaging (under anaesthesia) as ‘remapped’ locations of sensory-evoked FL activity 8 weeks post-stroke.

The experimental timeline is illustrated in Figure 1A. Adult Thy1-GCaMP6S mice were implanted with chronic cranial windows and habituated on the floating homecage, tapered beam and string pull task (Figure 1A–C). The cFL and cHL somatosensory areas were mapped on the cortex using widefield Ca²⁺ imaging of sensory-evoked activity in isoflurane anaesthetized mice (Figures 1D and 2A, see methods). Regions of interest for longitudinal awake two-photon Ca²⁺ imaging of cellular activity (Figure 1E) were chosen based on these pre-stroke widefield Ca²⁺ limb maps (Figure 2). The first imaging region of interest was located at the boundary of the pre-stroke cFL and cHL somatosensory maps (termed ‘peri-infarct’ region based on its proximal location to the photothrombotic infarct after stroke) and the second region as lateral to the pre-stroke cHL map (termed ‘distal’ region due to its distance from the stroke boundary). These regions were imaged at baseline, and each week following photothrombotic stroke. Notably, these regions of interest incorporated the predicted areas of remapping for the limb associated somatosensory representations after focal cFL cortex stroke based on previous studies (Winship and Murphy, 2008; Winship and Murphy, 2009). Photothrombosis was directed to the cFL somatosensory representation as identified by regional imaging. Resulting infarcts lesioned this region, and borders were delineated by a region of decreased light absorption 1 week post-stroke (Figure 1D, Top). Within the peri-infarct imaging region, cellular dysmorphia and swelling was visually apparent in some cells during two-photon imaging 1 week after stroke, but recovered over the 2-month post-stroke imaging timeframe (data not shown). These gross morphological changes were not visually apparent in the more distal imaging region lateral to the cHL.

Figure 1

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Figure 2

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Impaired performance on tapered beam but not string pull after focal somatosensory stroke

Post-stroke sensorimotor impairment was measured on the tapered beam task and string pull task. In the tapered beam, errors (slips off the beam), indicative of post-stroke impairment, were tracked as the number of left (affected) side slips, right (non-affected) side slips, and the distance to first slip (Figure 3A). We observed a significant interaction of group and time in the number of left side (contralesional) slips (Figure 3B), with post hoc comparisons confirming increased left slips at 1 week post-stroke compared to baseline and compared to 1 week in sham. Consistent with the unilateral nature of the photothrombotic damage to the cortex in this study, no main effects or interaction was seen in the number of right side slips (Figure 3C). No main effects or interaction was observed in the distance to first slip (Figure 3D). These results are consistent with a transient deficit in motor behavior on the tapered beam during the first week after stroke induced by focal cortical lesion to the sensorimotor cortex (Ardesch et al., 2017; Zhao et al., 2005), as opposed to more sustained deficits observed in models of middle cerebral artery occlusion (Lipsanen et al., 2011; Schallert et al., 2002). In contrast to previous studies investigating string pull after stroke (Blackwell et al., 2018c), we did not detect a significant effect of photothrombosis on any kinematic parameters during string pulling (Figure 3—figure supplement 1).

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Firing rate of neurons in the peri-infarct cortex correlates with performance on the tapered beam task

The firing rate of cortical somatosensory neurons in our awake behaving animals (Figure 4A) was calculated using a custom Matlab algorithm to identify ‘significant’ calcium transients (Romano et al., 2015; Romano et al., 2017) and the mean firing rate of the sampled neural population during periods of movement and periods of rest in the mobile homecage. The average firing rate in the peri-infarct region during recovery in movement and rest is illustrated in Figure 4B and C, respectively. A significant main effect of group on firing rate was detected at rest (Figure 4C). Post hoc comparisons confirmed significantly reduced average firing rate when moving and at rest at 1 week in the stroke group compared to pre-stroke (Figure 4B and C, respectively). Notably, firing rate at 1 week post-stroke during movement and at rest were significantly negatively correlated with the number of left slips in the tapered beam at the same 1 week timepoint (Figure 4D and E). No main effects or significant interaction was observed in the distal imaging region for either movement or at rest (Figure 4F and G, respectively). Firing rate during movement and at rest at 1 week within the distal region was also not significantly correlated with left slips on the tapered beam at the same timepoint (Figure 4H and I, respectively). These results indicate decreased neuronal spiking 1 week after stroke in regions immediately adjacent to the infarct, but not in distal regions, that is strongly related to sensorimotor impairment. This finding runs contrary to a previous report of increased spontaneous multi-unit activity as early as 3–7 days after focal photothrombotic stroke in the peri-infarct cortex (Schiene et al., 1996), but is in agreement with recent single-cell calcium imaging data demonstrating reduced sensory-evoked activity in neurons within the peri-infarct cortex after stroke (Zeiger et al., 2021; Motaharinia et al., 2021). While the majority of neuronal activity occurred during movement, correlations between firing rate and animal movement were low and did not vary between stroke and sham groups (Figure 4—figure supplement 1).

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Altered functional connectivity in the peri-infarct somatosensory cortex

Studies in humans and animals using mesoscale imaging methods indicate that stroke disrupts functional connectivity between widespread cortical regions (Cramer et al., 2019; Bauer et al., 2014; Blaschke et al., 2021; Carter et al., 2010; Carter et al., 2012; Hakon et al., 2018; Longo et al., 2022; Olafson et al., 2021; Rehme and Grefkes, 2013; Siegel et al., 2016; Silasi and Murphy, 2014; Urbin et al., 2014). However, few studies have examined functional connectivity at the level of local neural populations after focal stroke (Latifi et al., 2020; Bechay et al., 2022), and none have examined the primary somatosensory cortex. To define functional connectivity in local neural networks within the peri-infarct somatosensory cortex, we plotted the functional connectivity of the neural population in terms of the strength of their cell-cell correlations (Figure 5) and quantified the properties of these connections (Figure 6). We observed a significant loss of functional connectivity 1 week after stroke within the peri-infarct region. This was visually apparent in the stroke group functional connectivity plots at the 1 week timepoint (Figure 5A and B) compared to the same location in sham animals (Figure 5E and F). Notably, no profound loss of functional connectivity is visually apparent in the distal region in the stroke (Figure 5C and D) or sham (Figure 5G and H) groups. Within the peri-infarct region, we observed a statistical trend of a main effect of time in the average number of ‘significant’ functional connections per neuron (Figure 6A; see Materials and methods). Post hoc comparisons confirmed a significantly reduced average number of significant connections per neuron at 1 week post-stroke compared to pre-stroke and to 1 week sham. We observed a significant main effect of time on the average number of strong connections (r>.3) per neuron (Figure 6B). Post hoc comparisons confirmed strong connections were significantly reduced 1 week post-stroke relative to pre-stroke. In the distal region, no main effects or interaction was identified in the average number of significant connections per neuron (Figure 6D), highlighting regionally specific connectivity changes in individual neurons after stroke. However, we observed a significant main effect of time on the average number of strong (r>.3) connections per neuron in the distal region (Figure 6E), with post-hoc comparisons confirming that strong connections were significantly reduced 8 weeks post-stroke relative to pre-stroke. The deficit in significant connections between neurons for the peri-infarct region is consistent with a persistent deficit in functional connectivity lasting up to 30 days that was identified within the primary and supplementary motor cortex of mice using in vivo imaging after focal motor cortex stroke (Latifi et al., 2020; Bechay et al., 2022). However, network density, defined as the ratio of measured significant functional network connections to the number of total possible network connections if all neurons were interconnected, was not decreased in either peri-infarct or distal regions in our study (Figure 6C and F, respectively). This is in contrast with previous research in the primary and supplementary motor cortex after focal motor cortex stroke that has demonstrated deficits in network density lasting up to 3 weeks post-stroke (Bechay et al., 2022).

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Figure 6

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Neural assembly structure is disrupted in the peri-infarct region after stroke

The neural assembly hypothesis defines coordinated activity in groups of neurons as the basis for the representation of external and internal stimuli within the brain (Buzsáki, 2010; Buzsáki and Watson, 2012; Palm et al., 2014). Likewise, perturbations within the structure and function of assemblies may signify aberrant processing of stimuli within neural networks, and is believed to play a significant role in the dysfunction observed in brain damage and neuropsychiatric diseases (Buzsáki and Watson, 2012). Although the use of sophisticated methods for detecting neural assemblies in large populations of neurons has gained increasing popularity with the advent of large population recordings using Ca²⁺ imaging (Mölter et al., 2018), no previous studies have determined the longitudinal effects of stroke on the architecture of neural assemblies in cortex. To determine if focal stroke to the cFL cortex affects the properties of neural assemblies, we used a PCA-Promax procedure (Romano et al., 2015; Romano et al., 2017; Mölter et al., 2018) for neural assembly detection to determine assembly size, population membership, assembly quantity, and overlap in assembly membership. The PCA-Promax method for determining neural assemblies allows neurons to participate in more than a single assembly (Romano et al., 2015; Romano et al., 2017), a property necessary to examine the ability of neural assemblies to dynamically form with varying members of the population for only brief periods of time (Romano et al., 2015; Romano et al., 2017; Mölter et al., 2018). Figure 7 shows representative neuronal Ca²⁺ fluorescence images with neural assemblies color coded and overlaid for each timepoint for the peri-infarct imaging region and for the distal imaging region for an example stroke and sham animal (Figure 7A and B, respectively). We observed a significant main effect of group on the number of assemblies in the peri-infarct imaging region (Figure 8A). Post hoc comparisons confirmed significantly reduced number of assemblies 1- and 4 weeks after stroke relative to sham controls (Figure 8A). Post hoc comparisons also indicated a significant increase in the average number of neurons per assembly at 1- and 2 weeks post-stroke relative to pre-stroke (Figure 8B). We further observed a statistical trend of a main effect of group in the average percent of the population per assembly (Figure 8C), with post hoc tests indicating a statistical trend for 1 week post-stroke compared with pre-stroke. These data suggest that after stroke, neurons in peri-infarct cortex are functionally grouped into fewer distinct assemblies, with more neurons per assembly and more of the local network in the same assembly. For the distal imaging region, no differences in neural assembly properties were observed within and between the stroke and sham groups (Figure 8D–E).

Figure 7

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Figure 8

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Correlation between neural assembly activations is increased after stroke

If differential activation of neural assemblies in the naïve brain relates to representation of different aspects of the external world (Romano et al., 2015; Buzsáki, 2010; Palm et al., 2014; Deolindo et al., 2017) then changes in the relationship between the activation patterns of assemblies may predict disrupted processing of sensory information within neural networks and how these networks differentially represent external stimuli. An increase in assembly size and reduction in assembly number might reflect a mechanism to replace lost function and increase somatic sensitivity, but may come at the cost of reduced stimulus specificity/fidelity within the network. The activation magnitude of assemblies can be correlated between assemblies over the recording period (Figure 9A). A significant main effect of group was detected in the assembly-assembly correlation coefficient (Figure 9B) in the peri-infarct cortex, with post-hoc tests indicating an increase in the correlation at 1 week post-stroke relative to pre-stroke and relative to 1 week in sham animals. No main effects or interaction was observed for the assembly-assembly correlation coefficient in the distal region (Figure 9C), suggesting that changes to assembly-assembly correlation were confined to the peri-infarct cortex. This increased tendency for functionally connected assemblies to be co-active after stroke may reduce the signal to noise ratio for stimulus representation within the network and may reduce stimulus specificity within peri-infarct cortex. While assembly activations were often correlated with movement or rest periods (Figure 9—figure supplement 1), and 60% of assemblies in both the peri-infarct and distal regions were significantly correlated with animal movement at all time points (Figure 9—figure supplement 1C and E, respectively), stroke did not change the average correlation between assembly activation and animal movement in either the peri-infarct or distal region (Figure 9—figure supplement 1B and D, respectively).

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Here, we used longitudinal two photon calcium imaging of awake, head-fixed mice in a mobile homecage to examine how focal photothrombotic stroke to the forelimb sensorimotor cortex alters the activity and connectivity of neurons adjacent and distal to the infarct. Consistent with previous studies using intrinsic optical signal imaging, mesoscale imaging of regional calcium responses (reflecting bulk neuronal spiking in that region) showed that targeted stroke to the cFL somatosensory area disrupts the sensory-evoked forelimb representation in the infarcted region. Consistent with previous studies, this functional representation exhibited a posterior shift 8 weeks after injury, with activation in a region lateral to the cHL representation. Notably, sensory-evoked cFL representations exhibited reduced amplitudes of activity relative to pre-stroke activation measured in the cFL representation and in the region lateral the cHL representation. Longitudinal two-photon calcium imaging in awake animals was used to probe single neuron and local network changes adjacent the infarct and in a distal region that corresponded to the shifted region of cFL activation. This imaging revealed a decrease in firing rate at 1 week post-stroke in the peri-infarct region that was significantly negatively correlated with the number of errors made with the stroke-affected limbs on the tapered beam task. Peri-infarct cortical networks also exhibited a reduction in the number of functional connections per neuron and a sustained disruption in neural assembly structure, including a reduction in the number of assemblies and an increased recruitment of neurons into functional assemblies. Elevated correlation between assemblies within the peri-infarct region peaked 1 week after stroke and was sustained throughout recovery. Surprisingly, distal networks, even in the region associated with the shifted cFL functional map in anaesthetized preparations, were largely undisturbed.

All data generated or analyzed during this study has been made publicly available on OSF (https://doi.org/10.17605/OSF.IO/WAM9J).

1. 1. Bandet MV
   2. Winship IR

   (2024) Open Science Framework

   Aberrant cortical activity, functional connectivity, and neural assembly architecture after photothrombotic stroke in mice.

   https://doi.org/10.17605/OSF.IO/WAM9J

1. 1. Afrashteh N
   2. Inayat S
   3. Bermudez-Contreras E
   4. Luczak A
   5. McNaughton BL
   6. Mohajerani MH

   (2021) Spatiotemporal structure of sensory-evoked and spontaneous activity revealed by mesoscale imaging in anesthetized and awake mice

   Cell Reports 37:110081.

   https://doi.org/10.1016/j.celrep.2021.110081

   • PubMed
   • Google Scholar
2. 1. Akif Topcuoglu M
   2. Aydın H
   3. Saka E

   (2009) Occipital cortex activation studied with simultaneous recordings of functional transcranial Doppler ultrasound (fTCD) and visual evoked potential (VEP) in cognitively normal human subjects: Effect of healthy aging

   Neuroscience Letters 452:17–22.

   https://doi.org/10.1016/j.neulet.2009.01.030

   • Google Scholar
3. 1. Ardesch DJ
   2. Balbi M
   3. Murphy TH

   (2017) Automated touch sensing in the mouse tapered beam test using Raspberry Pi

   Journal of Neuroscience Methods 291:221–226.

   https://doi.org/10.1016/j.jneumeth.2017.08.030

   • PubMed
   • Google Scholar
4. 1. Avitan L
   2. Pujic Z
   3. Mölter J
   4. Van De Poll M
   5. Sun B
   6. Teng H
   7. Amor R
   8. Scott EK
   9. Goodhill GJ

   (2017) Spontaneous activity in the zebrafish tectum reorganizes over development and is influenced by visual experience

   Current Biology 27:2407–2419.

   https://doi.org/10.1016/j.cub.2017.06.056

   • Google Scholar
5. 1. Ay H
   2. Koroshetz WJ
   3. Vangel M
   4. Benner T
   5. Melinosky C
   6. Zhu M
   7. Menezes N
   8. Lopez CJ
   9. Sorensen AG

   (2005) Conversion of ischemic brain tissue into infarction increases with age

   Stroke 36:2632–2636.

   https://doi.org/10.1161/01.STR.0000189991.23918.01

   • PubMed
   • Google Scholar
6. 1. Bandet MV
   2. Dong B
   3. Winship IR

   (2021) Distinct patterns of activity in individual cortical neurons and local networks in primary somatosensory cortex of mice evoked by square-wave mechanical limb stimulation

   PLOS ONE 16:e0236684.

   https://doi.org/10.1371/journal.pone.0236684

   • Google Scholar
7. 1. Barlow H

   (2001)

   Redundancy reduction revisited

   Network 12:241–253.

   • PubMed
   • Google Scholar
8. 1. Bauer AQ
   2. Kraft AW
   3. Wright PW
   4. Snyder AZ
   5. Lee JM
   6. Culver JP

   (2014) Optical imaging of disrupted functional connectivity following ischemic stroke in mice

   NeuroImage 99:388–401.

   https://doi.org/10.1016/j.neuroimage.2014.05.051

   • PubMed
   • Google Scholar
9. 1. Bechay KR
   2. Abduljawad N
   3. Latifi S
   4. Suzuki K
   5. Iwashita H
   6. Carmichael ST

   (2022) PDE2A inhibition enhances axonal sprouting, functional connectivity, and recovery after stroke

   The Journal of Neuroscience 42:8225–8236.

   https://doi.org/10.1523/JNEUROSCI.0730-22.2022

   • PubMed
   • Google Scholar
10. Book

    1. Bensmaia S
    2. Helms Tillery SI

    (2014) Tactile feedback from the hand

    In: Balasubramanian R, Santos VJ, editors. The Human Hand as an Inspiration for Robot Hand Development. Cham: Springer International Publishing. pp. 143–157.

    https://doi.org/10.1007/978-3-319-03017-3

    • Google Scholar
11. 1. Berkes P
    2. Orbán G
    3. Lengyel M
    4. Fiser J

    (2011) Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment

    Science 331:83–87.

    https://doi.org/10.1126/science.1195870

    • Google Scholar
12. 1. Biernaskie J
    2. Chernenko G
    3. Corbett D

    (2004) Efficacy of rehabilitative experience declines with time after focal ischemic brain injury

    The Journal of Neuroscience 24:1245–1254.

    https://doi.org/10.1523/JNEUROSCI.3834-03.2004

    • Google Scholar
13. 1. Blackwell AA
    2. Banovetz MT
    3. Whishaw IQ
    4. Wallace DG

    (2018a) The structure of arm and hand movements in a spontaneous and food rewarded on-line string-pulling task by the mouse

    Behavioural Brain Research 345:49–58.

    https://doi.org/10.1016/j.bbr.2018.02.025

    • PubMed
    • Google Scholar
14. 1. Blackwell AA
    2. Köppen JR
    3. Whishaw IQ
    4. Wallace DG

    (2018b) String-pulling for food by the rat: Assessment of movement, topography and kinematics of a bilaterally skilled forelimb act

    Learning and Motivation 61:63–73.

    https://doi.org/10.1016/j.lmot.2017.03.010

    • Google Scholar
15. 1. Blackwell AA
    2. Widick WL
    3. Cheatwood JL
    4. Whishaw IQ
    5. Wallace DG

    (2018c) Unilateral forelimb sensorimotor cortex devascularization disrupts the topographic and kinematic characteristics of hand movements while string-pulling for food in the rat

    Behavioural Brain Research 338:88–100.

    https://doi.org/10.1016/j.bbr.2017.10.014

    • PubMed
    • Google Scholar
16. 1. Blaschke SJ
    2. Hensel L
    3. Minassian A
    4. Vlachakis S
    5. Tscherpel C
    6. Vay SU
    7. Rabenstein M
    8. Schroeter M
    9. Fink GR
    10. Hoehn M
    11. Grefkes C
    12. Rueger MA

    (2021) Translating functional connectivity after stroke: functional magnetic resonance imaging detects comparable network changes in mice and humans

    Stroke 52:2948–2960.

    https://doi.org/10.1161/STROKEAHA.120.032511

    • PubMed
    • Google Scholar
17. 1. Brecht M
    2. Schneider M
    3. Sakmann B
    4. Margrie TW

    (2004) Whisker movements evoked by stimulation of single pyramidal cells in rat motor cortex

    Nature 427:704–710.

    https://doi.org/10.1038/nature02266

    • PubMed
    • Google Scholar
18. 1. Brown CE
    2. Li P
    3. Boyd JD
    4. Delaney KR
    5. Murphy TH

    (2007) Extensive turnover of dendritic spines and vascular remodeling in cortical tissues recovering from stroke

    The Journal of Neuroscience 27:4101–4109.

    https://doi.org/10.1523/JNEUROSCI.4295-06.2007

    • Google Scholar
19. 1. Brown CE
    2. Murphy TH

    (2008) Livin’ on the edge: imaging dendritic spine turnover in the peri-infarct zone during ischemic stroke and recovery

    The Neuroscientist 14:139–146.

    https://doi.org/10.1177/1073858407309854

    • PubMed
    • Google Scholar
20. 1. Brown CE
    2. Aminoltejari K
    3. Erb H
    4. Winship IR
    5. Murphy TH

    (2009) In vivo voltage-sensitive dye imaging in adult mice reveals that somatosensory maps lost to stroke are replaced over weeks by new structural and functional circuits with prolonged modes of activation within both the peri-infarct zone and distant sites

    The Journal of Neuroscience 29:1719–1734.

    https://doi.org/10.1523/JNEUROSCI.4249-08.2009

    • PubMed
    • Google Scholar
21. 1. Buchkremer-Ratzmann I
    2. Witte OW

    (1997) Extended brain disinhibition following small photothrombotic lesions in rat frontal cortex

    Neuroreport 8:519–522.

    https://doi.org/10.1097/00001756-199701200-00028

    • PubMed
    • Google Scholar
22. 1. Bullmore E
    2. Sporns O

    (2009) Complex brain networks: graph theoretical analysis of structural and functional systems

    Nature Reviews. Neuroscience 10:186–198.

    https://doi.org/10.1038/nrn2575

    • PubMed
    • Google Scholar
23. 1. Buzsáki G

    (2010) Neural syntax: cell assemblies, synapsembles, and readers

    Neuron 68:362–385.

    https://doi.org/10.1016/j.neuron.2010.09.023

    • PubMed
    • Google Scholar
24. 1. Buzsáki G
    2. Watson BO

    (2012) Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease

    Dialogues in Clinical Neuroscience 14:345–367.

    https://doi.org/10.31887/DCNS.2012.14.4/gbuzsaki

    • PubMed
    • Google Scholar
25. 1. Carey LM
    2. Matyas TA

    (2011) Frequency of discriminative sensory loss in the hand after stroke in a rehabilitation setting

    Journal of Rehabilitation Medicine 43:257–263.

    https://doi.org/10.2340/16501977-0662

    • Google Scholar
26. 1. Carmichael ST

    (2003a) Gene expression changes after focal stroke, traumatic brain and spinal cord injuries

    Current Opinion in Neurology 16:699–704.

    https://doi.org/10.1097/01.wco.0000102621.38669.77

    • PubMed
    • Google Scholar
27. 1. Carmichael ST

    (2003b) Plasticity of cortical projections after stroke

    The Neuroscientist 9:64–75.

    https://doi.org/10.1177/1073858402239592

    • PubMed
    • Google Scholar
28. 1. Carmichael ST
    2. Archibeque I
    3. Luke L
    4. Nolan T
    5. Momiy J
    6. Li S

    (2005) Growth-associated gene expression after stroke: evidence for a growth-promoting region in peri-infarct cortex

    Experimental Neurology 193:291–311.

    https://doi.org/10.1016/j.expneurol.2005.01.004

    • Google Scholar
29. 1. Carmichael ST

    (2006) Cellular and molecular mechanisms of neural repair after stroke: making waves

    Annals of Neurology 59:735–742.

    https://doi.org/10.1002/ana.20845

    • PubMed
    • Google Scholar
30. 1. Carter AR
    2. Astafiev SV
    3. Lang CE
    4. Connor LT
    5. Rengachary J
    6. Strube MJ
    7. Pope DLW
    8. Shulman GL
    9. Corbetta M

    (2010) Resting interhemispheric functional magnetic resonance imaging connectivity predicts performance after stroke

    Annals of Neurology 67:365–375.

    https://doi.org/10.1002/ana.21905

    • PubMed
    • Google Scholar
31. 1. Carter AR
    2. Shulman GL
    3. Corbetta M

    (2012) Why use a connectivity-based approach to study stroke and recovery of function?

    NeuroImage 62:2271–2280.

    https://doi.org/10.1016/j.neuroimage.2012.02.070

    • PubMed
    • Google Scholar
32. 1. Chapin JK
    2. Lin CS

    (1984) Mapping the body representation in the SI cortex of anesthetized and awake rats

    The Journal of Comparative Neurology 229:199–213.

    https://doi.org/10.1002/cne.902290206

    • PubMed
    • Google Scholar
33. 1. Cheatwood JL
    2. Emerick AJ
    3. Kartje GL

    (2008) Neuronal plasticity and functional recovery after ischemic stroke

    Topics in Stroke Rehabilitation 15:42–50.

    https://doi.org/10.1310/tsr1501-42

    • PubMed
    • Google Scholar
34. 1. Chen LM
    2. Friedman RM
    3. Roe AW

    (2005) Optical imaging of si topography in anesthetized and awake squirrel monkeys

    The Journal of Neuroscience 25:7648–7659.

    https://doi.org/10.1523/JNEUROSCI.1990-05.2005

    • Google Scholar
35. 1. Chen S
    2. Mohajerani MH
    3. Xie Y
    4. Murphy TH

    (2012) Optogenetic analysis of neuronal excitability during global ischemia reveals selective deficits in sensory processing following reperfusion in mouse cortex

    The Journal of Neuroscience 32:13510–13519.

    https://doi.org/10.1523/JNEUROSCI.1439-12.2012

    • Google Scholar
36. 1. Chen T-W
    2. Wardill TJ
    3. Sun Y
    4. Pulver SR
    5. Renninger SL
    6. Baohan A
    7. Schreiter ER
    8. Kerr RA
    9. Orger MB
    10. Jayaraman V
    11. Looger LL
    12. Svoboda K
    13. Kim DS

    (2013) Ultrasensitive fluorescent proteins for imaging neuronal activity

    Nature 499:295–300.

    https://doi.org/10.1038/nature12354

    • Google Scholar
37. 1. Connell LA
    2. Lincoln NB
    3. Radford KA

    (2008) Somatosensory impairment after stroke: frequency of different deficits and their recovery

    Clinical Rehabilitation 22:758–767.

    https://doi.org/10.1177/0269215508090674

    • Google Scholar
38. 1. Cossart R
    2. Aronov D
    3. Yuste R

    (2003) Attractor dynamics of network UP states in the neocortex

    Nature 423:283–288.

    https://doi.org/10.1038/nature01614

    • PubMed
    • Google Scholar
39. 1. Cramer JV
    2. Gesierich B
    3. Roth S
    4. Dichgans M
    5. Düring M
    6. Liesz A

    (2019) In vivo widefield calcium imaging of the mouse cortex for analysis of network connectivity in health and brain disease

    NeuroImage 199:570–584.

    https://doi.org/10.1016/j.neuroimage.2019.06.014

    • PubMed
    • Google Scholar
40. 1. Crapse TB
    2. Sommer MA

    (2008) Corollary discharge across the animal kingdom

    Nature Reviews. Neuroscience 9:587–600.

    https://doi.org/10.1038/nrn2457

    • PubMed
    • Google Scholar
41. 1. Dana H
    2. Sun Y
    3. Mohar B
    4. Hulse BK
    5. Kerlin AM
    6. Hasseman JP
    7. Tsegaye G
    8. Tsang A
    9. Wong A
    10. Patel R
    11. Macklin JJ
    12. Chen Y
    13. Konnerth A
    14. Jayaraman V
    15. Looger LL
    16. Schreiter ER
    17. Svoboda K
    18. Kim DS

    (2019) High-performance calcium sensors for imaging activity in neuronal populations and microcompartments

    Nature Methods 16:649–657.

    https://doi.org/10.1038/s41592-019-0435-6

    • Google Scholar
42. 1. Dancause N
    2. Barbay S
    3. Frost SB
    4. Plautz EJ
    5. Chen D
    6. Zoubina EV
    7. Stowe AM
    8. Nudo RJ

    (2005) Extensive cortical rewiring after brain injury

    The Journal of Neuroscience 25:10167–10179.

    https://doi.org/10.1523/JNEUROSCI.3256-05.2005

    • PubMed
    • Google Scholar
43. 1. Deolindo CS
    2. Kunicki ACB
    3. da Silva MI
    4. Lima Brasil F
    5. Moioli RC

    (2017) Neuronal assemblies evidence distributed interactions within a tactile discrimination task in rats

    Frontiers in Neural Circuits 11:114.

    https://doi.org/10.3389/fncir.2017.00114

    • PubMed
    • Google Scholar
44. 1. Devonshire IM
    2. Grandy TH
    3. Dommett EJ
    4. Greenfield SA

    (2010) Effects of urethane anaesthesia on sensory processing in the rat barrel cortex revealed by combined optical imaging and electrophysiology

    The European Journal of Neuroscience 32:786–797.

    https://doi.org/10.1111/j.1460-9568.2010.07322.x

    • PubMed
    • Google Scholar
45. 1. Dionne JK
    2. Legon W
    3. Staines WR

    (2013) Crossmodal influences on early somatosensory processing: interaction of vision, touch, and task-relevance

    Experimental Brain Research 226:503–512.

    https://doi.org/10.1007/s00221-013-3462-z

    • PubMed
    • Google Scholar
46. 1. Domann R
    2. Hagemann G
    3. Kraemer M
    4. Freund HJ
    5. Witte OW

    (1993) Electrophysiological changes in the surrounding brain tissue of photochemically induced cortical infarcts in the rat

    Neuroscience Letters 155:69–72.

    https://doi.org/10.1016/0304-3940(93)90675-b

    • PubMed
    • Google Scholar
47. Book

    1. Ebner FF
    2. Kaas JH

    (2015) Chapter 24 - Somatosensory system

    In: Paxinos G, editors. The Rat Nervous System. Academic Press. pp. 675–701.

    https://doi.org/10.1016/B978-0-12-374245-2.00024-3

    • Google Scholar
48. 1. Ergul A
    2. Alhusban A
    3. Fagan SC

    (2012) Angiogenesis: a harmonized target for recovery after stroke

    Stroke 43:2270–2274.

    https://doi.org/10.1161/STROKEAHA.111.642710

    • PubMed
    • Google Scholar
49. 1. Faber JE
    2. Zhang H
    3. Lassance-Soares RM
    4. Prabhakar P
    5. Najafi AH
    6. Burnett MS
    7. Epstein SE

    (2011) Aging causes collateral rarefaction and increased severity of ischemic injury in multiple tissues

    Arteriosclerosis, Thrombosis, and Vascular Biology 31:1748–1756.

    https://doi.org/10.1161/ATVBAHA.111.227314

    • Google Scholar
50. 1. Fabiani M
    2. Gordon BA
    3. Maclin EL
    4. Pearson MA
    5. Brumback-Peltz CR
    6. Low KA
    7. McAuley E
    8. Sutton BP
    9. Kramer AF
    10. Gratton G

    (2014) Neurovascular coupling in normal aging: A combined optical, ERP and fMRI study

    NeuroImage 85:592–607.

    https://doi.org/10.1016/j.neuroimage.2013.04.113

    • Google Scholar
51. 1. Faggin BM
    2. Nguyen KT
    3. Nicolelis MA

    (1997) Immediate and simultaneous sensory reorganization at cortical and subcortical levels of the somatosensory system

    PNAS 94:9428–9433.

    https://doi.org/10.1073/pnas.94.17.9428

    • PubMed
    • Google Scholar
52. 1. Friedberg MH
    2. Lee SM
    3. Ebner FF

    (1999) Modulation of receptive field properties of thalamic somatosensory neurons by the depth of anesthesia

    Journal of Neurophysiology 81:2243–2252.

    https://doi.org/10.1152/jn.1999.81.5.2243

    • PubMed
    • Google Scholar
53. 1. Fujioka H
    2. Kaneko H
    3. Suzuki SS
    4. Mabuchi K

    (2004) Hyperexcitability-associated rapid plasticity after a focal cerebral ischemia

    Stroke 35:e346–e348.

    https://doi.org/10.1161/01.STR.0000130990.28734.9c

    • PubMed
    • Google Scholar
54. 1. Gomez-Ramirez M
    2. Hysaj K
    3. Niebur E

    (2016) Neural mechanisms of selective attention in the somatosensory system

    Journal of Neurophysiology 116:1218–1231.

    https://doi.org/10.1152/jn.00637.2015

    • PubMed
    • Google Scholar
55. 1. Grandjean J
    2. Schroeter A
    3. Batata I
    4. Rudin M

    (2014) Optimization of anesthesia protocol for resting-state fMRI in mice based on differential effects of anesthetics on functional connectivity patterns

    NeuroImage 102 Pt 2:838–847.

    https://doi.org/10.1016/j.neuroimage.2014.08.043

    • PubMed
    • Google Scholar
56. 1. Greenberg DA

    (2014) Cerebral angiogenesis: a realistic therapy for ischemic disease?

    Methods in Molecular Biology 1135:21–24.

    https://doi.org/10.1007/978-1-4939-0320-7_2

    • PubMed
    • Google Scholar
57. 1. Hakon J
    2. Quattromani MJ
    3. Sjölund C
    4. Tomasevic G
    5. Carey L
    6. Lee J-M
    7. Ruscher K
    8. Wieloch T
    9. Bauer AQ

    (2018) Multisensory stimulation improves functional recovery and resting-state functional connectivity in the mouse brain after stroke

    NeuroImage. Clinical 17:717–730.

    https://doi.org/10.1016/j.nicl.2017.11.022

    • PubMed
    • Google Scholar
58. 1. Harris KD
    2. Csicsvari J
    3. Hirase H
    4. Dragoi G
    5. Buzsáki G

    (2003) Organization of cell assemblies in the hippocampus

    Nature 424:552–556.

    https://doi.org/10.1038/nature01834

    • Google Scholar
59. 1. Hatakeyama M
    2. Ninomiya I
    3. Kanazawa M

    (2020) Angiogenesis and neuronal remodeling after ischemic stroke

    Neural Regeneration Research 15:16.

    https://doi.org/10.4103/1673-5374.264442

    • Google Scholar
60. 1. Hendrickson AE
    2. White PO

    (1964) Promax: a quick method for rotation to oblique simple structure

    British Journal of Statistical Psychology 17:65–70.

    https://doi.org/10.1111/j.2044-8317.1964.tb00244.x

    • Google Scholar
61. Book

    1. Hilgetag CC
    2. Kötter R
    3. Stephan KE
    4. Sporns O

    (2002) Computational methods for the analysis of brain Connectivity

    In: Ascoli GA, editors. Computational Neuroanatomy: Principles and Methods. Totowa, NJ: Humana Press. pp. 295–335.

    https://doi.org/10.1385/1592592759

    • Google Scholar
62. 1. Holtmaat A
    2. Bonhoeffer T
    3. Chow DK
    4. Chuckowree J
    5. De Paola V
    6. Hofer SB
    7. Hübener M
    8. Keck T
    9. Knott G
    10. Lee W-CA
    11. Mostany R
    12. Mrsic-Flogel TD
    13. Nedivi E
    14. Portera-Cailliau C
    15. Svoboda K
    16. Trachtenberg JT
    17. Wilbrecht L

    (2009) Long-term, high-resolution imaging in the mouse neocortex through a chronic cranial window

    Nature Protocols 4:1128–1144.

    https://doi.org/10.1038/nprot.2009.89

    • PubMed
    • Google Scholar
63. 1. Houweling AR
    2. Brecht M

    (2008) Behavioural report of single neuron stimulation in somatosensory cortex

    Nature 451:65–68.

    https://doi.org/10.1038/nature06447

    • PubMed
    • Google Scholar
64. 1. Hyman JM
    2. Whitman J
    3. Emberly E
    4. Woodward TS
    5. Seamans JK

    (2013) Action and outcome activity state patterns in the anterior cingulate cortex

    Cerebral Cortex 23:1257–1268.

    https://doi.org/10.1093/cercor/bhs104

    • Google Scholar
65. 1. Inayat S
    2. Singh S
    3. Ghasroddashti A
    4. Egodage P
    5. Whishaw IQ
    6. Mohajerani MH

    (2020) A Matlab-based toolbox for characterizing behavior of rodents engaged in string-pulling

    eLife 9:e54540.

    https://doi.org/10.7554/eLife.54540

    • Google Scholar
66. 1. Jaenisch N
    2. Liebmann L
    3. Guenther M
    4. Hübner CA
    5. Frahm C
    6. Witte OW

    (2016) Reduced tonic inhibition after stroke promotes motor performance and epileptic seizures

    Scientific Reports 6:26173.

    https://doi.org/10.1038/srep26173

    • PubMed
    • Google Scholar
67. 1. Jin X
    2. Huguenard JR
    3. Prince DA

    (2005) Impaired Cl ⁻ extrusion in layer V pyramidal neurons of chronically injured epileptogenic neocortex

    Journal of Neurophysiology 93:2117–2126.

    https://doi.org/10.1152/jn.00728.2004

    • Google Scholar
68. 1. Johansen-Berg H
    2. Christensen V
    3. Woolrich M
    4. Matthews PM

    (2000) Attention to touch modulates activity in both primary and secondary somatosensory areas

    Neuroreport 11:1237–1241.

    https://doi.org/10.1097/00001756-200004270-00019

    • PubMed
    • Google Scholar
69. 1. Jonckers E
    2. Delgado y Palacios R
    3. Shah D
    4. Guglielmetti C
    5. Verhoye M
    6. Van der Linden A

    (2014) Different anesthesia regimes modulate the functional connectivity outcome in mice

    Magnetic Resonance in Medicine 72:1103–1112.

    https://doi.org/10.1002/mrm.24990

    • Google Scholar
70. 1. Kalthoff D
    2. Po C
    3. Wiedermann D
    4. Hoehn M

    (2013) Reliability and spatial specificity of rat brain sensorimotor functional connectivity networks are superior under sedation compared with general anesthesia

    NMR in Biomedicine 26:638–650.

    https://doi.org/10.1002/nbm.2908

    • PubMed
    • Google Scholar
71. 1. Khirug S
    2. Soni S
    3. Saez Garcia M
    4. Tessier M
    5. Zhou L
    6. Kulesskaya N
    7. Rauvala H
    8. Lindholm D
    9. Ludwig A
    10. Molinari F
    11. Rivera C

    (2021) Protective role of low ethanol administration following ischemic stroke via recovery of KCC2 and p75^NTR expression

    Molecular Neurobiology 58:1145–1161.

    https://doi.org/10.1007/s12035-020-02176-x

    • PubMed
    • Google Scholar
72. 1. Kislin M
    2. Mugantseva E
    3. Molotkov D
    4. Kulesskaya N
    5. Khirug S
    6. Kirilkin I
    7. Pryazhnikov E
    8. Kolikova J
    9. Toptunov D
    10. Yuryev M
    11. Giniatullin R
    12. Voikar V
    13. Rivera C
    14. Rauvala H
    15. Khiroug L

    (2014) Flat-floored air-lifted platform: a new method for combining behavior with microscopy or electrophysiology on awake freely moving rodents

    Journal of Visualized Experiments 1:e51869.

    https://doi.org/10.3791/51869

    • Google Scholar
73. 1. Kwakkel G
    2. Kollen B
    3. Lindeman E

    (2004)

    Understanding the pattern of functional recovery after stroke: facts and theories

    Restorative Neurology and Neuroscience 22:281–299.

    • PubMed
    • Google Scholar
74. 1. Latifi S
    2. Mitchell S
    3. Habibey R
    4. Hosseini F
    5. Donzis E
    6. Estrada-Sánchez AM
    7. Nejad HR
    8. Levine M
    9. Golshani P
    10. Carmichael ST

    (2020) Neuronal network topology indicates distinct recovery processes after stroke

    Cerebral Cortex 30:6363–6375.

    https://doi.org/10.1093/cercor/bhaa191

    • Google Scholar
75. 1. Li S
    2. Carmichael ST

    (2006) Growth-associated gene and protein expression in the region of axonal sprouting in the aged brain after stroke

    Neurobiology of Disease 23:362–373.

    https://doi.org/10.1016/j.nbd.2006.03.011

    • Google Scholar
76. 1. Li S
    2. Overman JJ
    3. Katsman D
    4. Kozlov SV
    5. Donnelly CJ
    6. Twiss JL
    7. Giger RJ
    8. Coppola G
    9. Geschwind DH
    10. Carmichael ST

    (2010) An age-related sprouting transcriptome provides molecular control of axonal sprouting after stroke

    Nature Neuroscience 13:1496–1504.

    https://doi.org/10.1038/nn.2674

    • Google Scholar
77. 1. Lim DH
    2. LeDue JM
    3. Mohajerani MH
    4. Murphy TH

    (2014) Optogenetic mapping after stroke reveals network-wide scaling of functional connections and heterogeneous recovery of the peri-infarct

    The Journal of Neuroscience 34:16455–16466.

    https://doi.org/10.1523/JNEUROSCI.3384-14.2014

    • Google Scholar
78. 1. Lipsanen A
    2. Hiltunen M
    3. Jolkkonen J

    (2011) Chronic ibuprofen treatment does not affect the secondary pathology in the thalamus or improve behavioral outcome in middle cerebral artery occlusion rats

    Pharmacology, Biochemistry, and Behavior 99:468–474.

    https://doi.org/10.1016/j.pbb.2011.04.019

    • PubMed
    • Google Scholar
79. 1. Lissek T
    2. Obenhaus HA
    3. Ditzel DAW
    4. Nagai T
    5. Miyawaki A
    6. Sprengel R
    7. Hasan MT

    (2016) General anesthetic conditions induce network synchrony and disrupt sensory processing in the cortex

    Frontiers in Cellular Neuroscience 10:64.

    https://doi.org/10.3389/fncel.2016.00064

    • PubMed
    • Google Scholar
80. 1. London BM
    2. Miller LE

    (2013) Responses of somatosensory area 2 neurons to actively and passively generated limb movements

    Journal of Neurophysiology 109:1505–1513.

    https://doi.org/10.1152/jn.00372.2012

    • PubMed
    • Google Scholar
81. 1. Longo V
    2. Barbati SA
    3. Re A
    4. Paciello F
    5. Bolla M
    6. Rinaudo M
    7. Miraglia F
    8. Alù F
    9. Di Donna MG
    10. Vecchio F
    11. Rossini PM
    12. Podda MV
    13. Grassi C

    (2022) Transcranial direct current stimulation enhances neuroplasticity and accelerates motor recovery in a stroke mouse model

    Stroke 53:1746–1758.

    https://doi.org/10.1161/STROKEAHA.121.034200

    • PubMed
    • Google Scholar
82. 1. Luczak A
    2. Barthó P
    3. Harris KD

    (2009) Spontaneous events outline the realm of possible sensory responses in neocortical populations

    Neuron 62:413–425.

    https://doi.org/10.1016/j.neuron.2009.03.014

    • Google Scholar
83. 1. Luhmann HJ
    2. Mudrick-Donnon LA
    3. Mittmann T
    4. Heinemann U

    (1995) Ischaemia-induced long-term hyperexcitability in rat neocortex

    The European Journal of Neuroscience 7:180–191.

    https://doi.org/10.1111/j.1460-9568.1995.tb01054.x

    • PubMed
    • Google Scholar
84. 1. MacLean JN
    2. Watson BO
    3. Aaron GB
    4. Yuste R

    (2005) Internal dynamics determine the cortical response to thalamic stimulation

    Neuron 48:811–823.

    https://doi.org/10.1016/j.neuron.2005.09.035

    • Google Scholar
85. 1. Malvache A
    2. Reichinnek S
    3. Villette V
    4. Haimerl C
    5. Cossart R

    (2016) Awake hippocampal reactivations project onto orthogonal neuronal assemblies

    Science 353:1280–1283.

    https://doi.org/10.1126/science.aaf3319

    • Google Scholar
86. 1. Martín-Aragón Baudel MAS
    2. Poole AV
    3. Darlison MG

    (2017) Chloride co-transporters as possible therapeutic targets for stroke

    Journal of Neurochemistry 140:195–209.

    https://doi.org/10.1111/jnc.13901

    • PubMed
    • Google Scholar
87. 1. Menon BK
    2. Smith EE
    3. Coutts SB
    4. Welsh DG
    5. Faber JE
    6. Goyal M
    7. Hill MD
    8. Demchuk AM
    9. Damani Z
    10. Cho K-H
    11. Chang H-W
    12. Hong J-H
    13. Sohn SI

    (2013) Leptomeningeal collaterals are associated with modifiable metabolic risk factors

    Annals of Neurology 74:241–248.

    https://doi.org/10.1002/ana.23906

    • PubMed
    • Google Scholar
88. 1. Miller JK
    2. Ayzenshtat I
    3. Carrillo-Reid L
    4. Yuste R

    (2014) Visual stimuli recruit intrinsically generated cortical ensembles

    PNAS 111:E4053–E4061.

    https://doi.org/10.1073/pnas.1406077111

    • Google Scholar
89. 1. Mittmann T
    2. Qü M
    3. Zilles K
    4. Luhmann HJ

    (1998) Long-term cellular dysfunction after focal cerebral ischemia: in vitro analyses

    Neuroscience 85:15–27.

    https://doi.org/10.1016/s0306-4522(97)00638-6

    • PubMed
    • Google Scholar
90. 1. Mölter J
    2. Avitan L
    3. Goodhill GJ

    (2018) Detecting neural assemblies in calcium imaging data

    BMC Biology 16:143.

    https://doi.org/10.1186/s12915-018-0606-4

    • PubMed
    • Google Scholar
91. 1. Moore CI
    2. Nelson SB

    (1998) Spatio-temporal subthreshold receptive fields in the vibrissa representation of rat primary somatosensory cortex

    Journal of Neurophysiology 80:2882–2892.

    https://doi.org/10.1152/jn.1998.80.6.2882

    • PubMed
    • Google Scholar
92. 1. Motaharinia M
    2. Gerrow K
    3. Boghozian R
    4. White E
    5. Choi S-E
    6. Delaney KR
    7. Brown CE

    (2021) Longitudinal functional imaging of VIP interneurons reveals sup-population specific effects of stroke that are rescued with chemogenetic therapy

    Nature Communications 12:6112.

    https://doi.org/10.1038/s41467-021-26405-6

    • PubMed
    • Google Scholar
93. 1. Murphy TH
    2. Corbett D

    (2009) Plasticity during stroke recovery: from synapse to behaviour

    Nature Reviews. Neuroscience 10:861–872.

    https://doi.org/10.1038/nrn2735

    • PubMed
    • Google Scholar
94. 1. Nasrallah FA
    2. Tay HC
    3. Chuang KH

    (2014) Detection of functional connectivity in the resting mouse brain

    NeuroImage 86:417–424.

    https://doi.org/10.1016/j.neuroimage.2013.10.025

    • PubMed
    • Google Scholar
95. 1. Nelson RJ

    (1987) Activity of monkey primary somatosensory cortical neurons changes prior to active movement

    Brain Research 406:402–407.

    https://doi.org/10.1016/0006-8993(87)90815-8

    • PubMed
    • Google Scholar
96. 1. Nelson AJ
    2. Staines WR
    3. Graham SJ
    4. McIlroy WE

    (2004) Activation in SI and SII: the influence of vibrotactile amplitude during passive and task-relevant stimulation

    Brain Research. Cognitive Brain Research 19:174–184.

    https://doi.org/10.1016/j.cogbrainres.2003.11.013

    • PubMed
    • Google Scholar
97. 1. Neumann-Haefelin T
    2. Staiger JF
    3. Redecker C
    4. Zilles K
    5. Fritschy JM
    6. Möhler H
    7. Witte OW

    (1998) Immunohistochemical evidence for dysregulation of the GABAergic system ipsilateral to photochemically induced cortical infarcts in rats

    Neuroscience 87:871–879.

    https://doi.org/10.1016/s0306-4522(98)00124-9

    • PubMed
    • Google Scholar
98. 1. Neumann-Haefelin T
    2. Bosse F
    3. Redecker C
    4. Müller HW
    5. Witte OW

    (1999) Upregulation of GABAA-receptor alpha1- and alpha2-subunit mRNAs following ischemic cortical lesions in rats

    Brain Research 816:234–237.

    https://doi.org/10.1016/s0006-8993(98)01162-7

    • PubMed
    • Google Scholar
99. 1. Nicolelis MA
    2. Chapin JK

    (1994) Spatiotemporal structure of somatosensory responses of many-neuron ensembles in the rat ventral posterior medial nucleus of the thalamus

    The Journal of Neuroscience 14:3511–3532.

    https://doi.org/10.1523/JNEUROSCI.14-06-03511.1994

    • PubMed
    • Google Scholar
100. 1. Olafson ER
     2. Jamison KW
     3. Sweeney EM
     4. Liu H
     5. Wang D
     6. Bruss JE
     7. Boes AD
     8. Kuceyeski A

     (2021) Functional connectome reorganization relates to post-stroke motor recovery and structural and functional disconnection

     NeuroImage 245:118642.

     https://doi.org/10.1016/j.neuroimage.2021.118642

     • PubMed
     • Google Scholar
101. 1. Palm G
     2. Knoblauch A
     3. Hauser F
     4. Schüz A

     (2014) Cell assemblies in the cerebral cortex

     Biological Cybernetics 108:559–572.

     https://doi.org/10.1007/s00422-014-0596-4

     • PubMed
     • Google Scholar
102. 1. Park L
     2. Anrather J
     3. Girouard H
     4. Zhou P
     5. Iadecola C

     (2007) Nox2-derived reactive oxygen species mediate neurovascular dysregulation in the aging mouse brain

     Journal of Cerebral Blood Flow & Metabolism 27:1908–1918.

     https://doi.org/10.1038/sj.jcbfm.9600491

     • Google Scholar
103. 1. Patel AT
     2. Duncan PW
     3. Lai SM
     4. Studenski S

     (2000) The relation between impairments and functional outcomes poststroke

     Archives of Physical Medicine and Rehabilitation 81:1357–1363.

     https://doi.org/10.1053/apmr.2000.9397

     • Google Scholar
104. 1. Paz JT
     2. Christian CA
     3. Parada I
     4. Prince DA
     5. Huguenard JR

     (2010) Focal cortical infarcts alter intrinsic excitability and synaptic excitation in the reticular thalamic nucleus

     The Journal of Neuroscience 30:5465–5479.

     https://doi.org/10.1523/JNEUROSCI.5083-09.2010

     • Google Scholar
105. 1. Peron S
     2. Chen TW
     3. Svoboda K

     (2015) Comprehensive imaging of cortical networks

     Current Opinion in Neurobiology 32:115–123.

     https://doi.org/10.1016/j.conb.2015.03.016

     • PubMed
     • Google Scholar
106. 1. Pietri T
     2. Romano SA
     3. Pérez-Schuster V
     4. Boulanger-Weill J
     5. Candat V
     6. Sumbre G

     (2017) The emergence of the spatial structure of tectal spontaneous activity is independent of visual inputs

     Cell Reports 19:939–948.

     https://doi.org/10.1016/j.celrep.2017.04.015

     • PubMed
     • Google Scholar
107. 1. Ploughman M
     2. Windle V
     3. MacLellan CL
     4. White N
     5. Doré JJ
     6. Corbett D

     (2009) Brain-derived neurotrophic factor contributes to recovery of skilled reaching after focal ischemia in rats

     Stroke 40:1490–1495.

     https://doi.org/10.1161/STROKEAHA.108.531806

     • PubMed
     • Google Scholar
108. 1. Qü M
     2. Buchkremer-Ratzmann I
     3. Schiene K
     4. Schroeter M
     5. Witte OW
     6. Zilles K

     (1998a) Bihemispheric reduction of GABAA receptor binding following focal cortical photothrombotic lesions in the rat brain

     Brain Research 813:374–380.

     https://doi.org/10.1016/s0006-8993(98)01063-4

     • PubMed
     • Google Scholar
109. 1. Qü M
     2. Mittmann T
     3. Luhmann HJ
     4. Schleicher A
     5. Zilles K

     (1998b) Long-term changes of ionotropic glutamate and GABA receptors after unilateral permanent focal cerebral ischemia in the mouse brain

     Neuroscience 85:29–43.

     https://doi.org/10.1016/s0306-4522(97)00656-8

     • PubMed
     • Google Scholar
110. 1. Que M
     2. Schiene K
     3. Witte OW
     4. Zilles K

     (1999) Widespread up-regulation of N-methyl-D-aspartate receptors after focal photothrombotic lesion in rat brain

     Neuroscience Letters 273:77–80.

     https://doi.org/10.1016/s0304-3940(99)00598-4

     • PubMed
     • Google Scholar
111. 1. Redecker C
     2. Wang W
     3. Fritschy JM
     4. Witte OW

     (2002) Widespread and long-lasting alterations in GABA(A)-receptor subtypes after focal cortical infarcts in rats: mediation by NMDA-dependent processes

     Journal of Cerebral Blood Flow and Metabolism 22:1463–1475.

     https://doi.org/10.1097/01.WCB.0000034149.72481.BD

     • PubMed
     • Google Scholar
112. 1. Rehme AK
     2. Grefkes C

     (2013) Cerebral network disorders after stroke: evidence from imaging-based connectivity analyses of active and resting brain states in humans

     The Journal of Physiology 591:17–31.

     https://doi.org/10.1113/jphysiol.2012.243469

     • PubMed
     • Google Scholar
113. 1. Romano SA
     2. Pietri T
     3. Pérez-Schuster V
     4. Jouary A
     5. Haudrechy M
     6. Sumbre G

     (2015) Spontaneous neuronal network dynamics reveal circuit’s functional adaptations for behavior

     Neuron 85:1070–1085.

     https://doi.org/10.1016/j.neuron.2015.01.027

     • PubMed
     • Google Scholar
114. 1. Romano SA
     2. Pérez-Schuster V
     3. Jouary A
     4. Boulanger-Weill J
     5. Candeo A
     6. Pietri T
     7. Sumbre G

     (2017) An integrated calcium imaging processing toolbox for the analysis of neuronal population dynamics

     PLOS Computational Biology 13:e1005526.

     https://doi.org/10.1371/journal.pcbi.1005526

     • PubMed
     • Google Scholar
115. 1. Sakata S
     2. Harris KD

     (2009) Laminar structure of spontaneous and sensory-evoked population activity in auditory cortex

     Neuron 64:404–418.

     https://doi.org/10.1016/j.neuron.2009.09.020

     • Google Scholar
116. 1. Scaglione A
     2. Foffani G
     3. Moxon KA

     (2014) Spike count, spike timing and temporal information in the cortex of awake, freely moving rats

     Journal of Neural Engineering 11:046022.

     https://doi.org/10.1088/1741-2560/11/4/046022

     • PubMed
     • Google Scholar
117. 1. Schallert T
     2. Woodlee MT
     3. Fleming SM

     (2002)

     Disentangling multiple types of recovery from brain injury

     Pharmacol Cereb Ischemia 1:201–216.

     • Google Scholar
118. 1. Schiene K
     2. Bruehl C
     3. Zilles K
     4. Qu M
     5. Hagemann G
     6. Kraemer M
     7. Witte OW

     (1996) Neuronal hyperexcitability and reduction of GABA _A -receptor expression in the surround of cerebral photothrombosis

     Journal of Cerebral Blood Flow & Metabolism 16:906–914.

     https://doi.org/10.1097/00004647-199609000-00014

     • Google Scholar
119. 1. Schindelin J
     2. Arganda-Carreras I
     3. Frise E
     4. Kaynig V
     5. Longair M
     6. Pietzsch T
     7. Preibisch S
     8. Rueden C
     9. Saalfeld S
     10. Schmid B
     11. Tinevez J-Y
     12. White DJ
     13. Hartenstein V
     14. Eliceiri K
     15. Tomancak P
     16. Cardona A

     (2012) Fiji: an open-source platform for biological-image analysis

     Nature Methods 9:676–682.

     https://doi.org/10.1038/nmeth.2019

     • PubMed
     • Google Scholar
120. 1. Schulte JT
     2. Wierenga CJ
     3. Bruining H

     (2018) Chloride transporters and GABA polarity in developmental, neurological and psychiatric conditions

     Neuroscience and Biobehavioral Reviews 90:260–271.

     https://doi.org/10.1016/j.neubiorev.2018.05.001

     • PubMed
     • Google Scholar
121. 1. See JZ
     2. Atencio CA
     3. Sohal VS
     4. Schreiner CE

     (2018) Coordinated neuronal ensembles in primary auditory cortical columns

     eLife 7:e35587.

     https://doi.org/10.7554/eLife.35587

     • PubMed
     • Google Scholar
122. 1. Siegel JS
     2. Ramsey LE
     3. Snyder AZ
     4. Metcalf NV
     5. Chacko RV
     6. Weinberger K
     7. Baldassarre A
     8. Hacker CD
     9. Shulman GL
     10. Corbetta M

     (2016) Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke

     PNAS 113:E4367–E4376.

     https://doi.org/10.1073/pnas.1521083113

     • PubMed
     • Google Scholar
123. 1. Sigler A
     2. Mohajerani MH
     3. Murphy TH

     (2009) Imaging rapid redistribution of sensory-evoked depolarization through existing cortical pathways after targeted stroke in mice

     PNAS 106:11759–11764.

     https://doi.org/10.1073/pnas.0812695106

     • PubMed
     • Google Scholar
124. 1. Silasi G
     2. Murphy TH

     (2014) Stroke and the connectome: how connectivity guides therapeutic intervention

     Neuron 83:1354–1368.

     https://doi.org/10.1016/j.neuron.2014.08.052

     • PubMed
     • Google Scholar
125. 1. Simoncelli EP
     2. Olshausen BA

     (2001) Natural image statistics and neural representation

     Annual Review of Neuroscience 24:1193–1216.

     https://doi.org/10.1146/annurev.neuro.24.1.1193

     • PubMed
     • Google Scholar
126. 1. Sorond FA
     2. Hurwitz S
     3. Salat DH
     4. Greve DN
     5. Fisher NDL

     (2013) Neurovascular coupling, cerebral white matter integrity, and response to cocoa in older people

     Neurology 81:904–909.

     https://doi.org/10.1212/WNL.0b013e3182a351aa

     • Google Scholar
127. 1. Sporns O
     2. Honey CJ
     3. Kötter R

     (2007) Identification and classification of hubs in brain networks

     PLOS ONE 2:e1049.

     https://doi.org/10.1371/journal.pone.0001049

     • PubMed
     • Google Scholar
128. 1. Sporns O

     (2011) The non-random brain: efficiency, economy, and complex dynamics

     Frontiers in Computational Neuroscience 5:5.

     https://doi.org/10.3389/fncom.2011.00005

     • Google Scholar
129. 1. Staines WR
     2. Black SE
     3. Graham SJ
     4. McIlroy WE

     (2002a) Somatosensory gating and recovery from stroke involving the thalamus

     Stroke 33:2642–2651.

     https://doi.org/10.1161/01.str.0000032552.40405.40

     • PubMed
     • Google Scholar
130. 1. Staines WR
     2. Graham SJ
     3. Black SE
     4. McIlroy WE

     (2002b) Task-relevant modulation of contralateral and ipsilateral primary somatosensory cortex and the role of a prefrontal-cortical sensory gating system

     NeuroImage 15:190–199.

     https://doi.org/10.1006/nimg.2001.0953

     • PubMed
     • Google Scholar
131. 1. Stam CJ

     (2014) Modern network science of neurological disorders

     Nature Reviews Neuroscience 15:683–695.

     https://doi.org/10.1038/nrn3801

     • Google Scholar
132. 1. Stefanova I
     2. Stephan T
     3. Becker-Bense S
     4. Dera T
     5. Brandt T
     6. Dieterich M

     (2013) Age-related changes of blood-oxygen-level-dependent signal dynamics during optokinetic stimulation

     Neurobiology of Aging 34:2277–2286.

     https://doi.org/10.1016/j.neurobiolaging.2013.03.031

     • PubMed
     • Google Scholar
133. 1. Strandgaard S

     (1991) Cerebral blood flow in the elderly: impact of hypertension and antihypertensive treatment

     Cardiovascular Drugs and Therapy 4 Suppl 6:1217–1221.

     https://doi.org/10.1007/BF00114223

     • PubMed
     • Google Scholar
134. 1. Sweetnam D
     2. Holmes A
     3. Tennant KA
     4. Zamani A
     5. Walle M
     6. Jones P
     7. Wong C
     8. Brown CE

     (2012) Diabetes impairs cortical plasticity and functional recovery following ischemic stroke

     The Journal of Neuroscience 32:5132–5143.

     https://doi.org/10.1523/JNEUROSCI.5075-11.2012

     • Google Scholar
135. 1. Sweetnam DA
     2. Brown CE

     (2013) Stroke induces long-lasting deficits in the temporal fidelity of sensory processing in the somatosensory cortex

     Journal of Cerebral Blood Flow & Metabolism 33:91–96.

     https://doi.org/10.1038/jcbfm.2012.135

     • Google Scholar
136. 1. Tennant KA
     2. Taylor SL
     3. White ER
     4. Brown CE

     (2017) Optogenetic rewiring of thalamocortical circuits to restore function in the stroke injured brain

     Nature Communications 8:15879.

     https://doi.org/10.1038/ncomms15879

     • Google Scholar
137. 1. Thomas Carmichael S
     2. Wei L
     3. Rovainen CM
     4. Woolsey TA

     (2001) New patterns of intracortical projections after focal cortical stroke

     Neurobiology of Disease 8:910–922.

     https://doi.org/10.1006/nbdi.2001.0425

     • Google Scholar
138. 1. Thompson AW
     2. Scott EK

     (2016) Characterisation of sensitivity and orientation tuning for visually responsive ensembles in the zebrafish tectum

     Scientific Reports 6:34887.

     https://doi.org/10.1038/srep34887

     • Google Scholar
139. 1. Truccolo W
     2. Hochberg LR
     3. Donoghue JP

     (2010) Collective dynamics in human and monkey sensorimotor cortex: predicting single neuron spikes

     Nature Neuroscience 13:105–111.

     https://doi.org/10.1038/nn.2455

     • PubMed
     • Google Scholar
140. 1. Tyson SF
     2. Hanley M
     3. Chillala J
     4. Selley AB
     5. Tallis RC

     (2008) Sensory loss in hospital-admitted people with stroke: characteristics, associated factors, and relationship with function

     Neurorehabilitation and Neural Repair 22:166–172.

     https://doi.org/10.1177/1545968307305523

     • PubMed
     • Google Scholar
141. 1. Urbin MA
     2. Hong X
     3. Lang CE
     4. Carter AR

     (2014) Resting-state functional connectivity and its association with multiple domains of upper-extremity function in chronic stroke

     Neurorehabilitation and Neural Repair 28:761–769.

     https://doi.org/10.1177/1545968314522349

     • PubMed
     • Google Scholar
142. 1. Villette V
     2. Malvache A
     3. Tressard T
     4. Dupuy N
     5. Cossart R

     (2015) Internally recurring hippocampal sequences as a population template of spatiotemporal information

     Neuron 88:357–366.

     https://doi.org/10.1016/j.neuron.2015.09.052

     • Google Scholar
143. 1. Winship IR
     2. Murphy TH

     (2008) In vivo calcium imaging reveals functional rewiring of single somatosensory neurons after stroke

     Journal of Neuroscience 28:6592–6606.

     https://doi.org/10.1523/JNEUROSCI.0622-08.2008

     • Google Scholar
144. 1. Winship IR
     2. Murphy TH

     (2009) Remapping the somatosensory cortex after stroke: insight from imaging the synapse to network

     The Neuroscientist 15:507–524.

     https://doi.org/10.1177/1073858409333076

     • Google Scholar
145. 1. Witte OW
     2. Freund HJ

     (1999)

     Neuronal dysfunction, epilepsy, and postlesional brain plasticity

     Advances in Neurology 81:25–36.

     • PubMed
     • Google Scholar
146. 1. Xu X
     2. Ye L
     3. Ruan Q

     (2009) Environmental enrichment induces synaptic structural modification after transient focal cerebral ischemia in rats

     Experimental Biology and Medicine 234:296–305.

     https://doi.org/10.3181/0804-RM-128

     • Google Scholar
147. 1. Yu J
     2. Gutnisky DA
     3. Hires SA
     4. Svoboda K

     (2016) Layer 4 fast-spiking interneurons filter thalamocortical signals during active somatosensation

     Nature Neuroscience 19:1647–1657.

     https://doi.org/10.1038/nn.4412

     • PubMed
     • Google Scholar
148. 1. Zaletel M
     2. Strucl M
     3. Pretnar-Oblak J
     4. Zvan B

     (2005)

     Age-related changes in the relationship between visual evoked potentials and visually evoked cerebral blood flow velocity response

     Functional Neurology 20:115–120.

     • PubMed
     • Google Scholar
149. 1. Zeiger WA
     2. Marosi M
     3. Saggi S
     4. Noble N
     5. Samad I
     6. Portera-Cailliau C

     (2021) Barrel cortex plasticity after photothrombotic stroke involves potentiating responses of pre-existing circuits but not functional remapping to new circuits

     Nature Communications 12:3972.

     https://doi.org/10.1038/s41467-021-24211-8

     • PubMed
     • Google Scholar
150. 1. Zhao C
     2. Puurunen K
     3. Schallert T
     4. Sivenius J
     5. Jolkkonen J

     (2005) Effect of cholinergic medication, before and after focal photothrombotic ischemic cortical injury, on histological and functional outcome in aged and young adult rats

     Behavioural Brain Research 156:85–94.

     https://doi.org/10.1016/j.bbr.2004.05.011

     • PubMed
     • Google Scholar

Author details

1. Mischa Vance Bandet

   1. Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
   2. Neurochemical Research Unit, University of Alberta, Edmonton, Canada
   3. Department of Psychiatry, University of Alberta, Edmonton, Canada

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3744-441X

2. Ian Robert Winship

   1. Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
   2. Neurochemical Research Unit, University of Alberta, Edmonton, Canada
   3. Department of Psychiatry, University of Alberta, Edmonton, Canada

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Methodology, Project administration, Writing - review and editing

   For correspondence

   iwinship@ualberta.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8574-4855

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