A classic paradigm for probing brain plasticity is monocular deprivation. During development, patching one eye for several days weakens the cortical representation of the deprived eye producing a stable change of ocular dominance columns in primary visual cortex (Hensch and Quinlan, 2018; Wiesel and Hubel, 1963; Wiesel and Hubel, 1965). In adult humans, a much shorter period of eye patching (about 2 hr) produces a paradoxical enhancement of the deprived eye signal (Bai et al., 2017; Binda and Lunghi, 2017; Castaldi et al., 2020; Chadnova et al., 2017; Lunghi et al., 2015a; Lunghi et al., 2011; Lunghi et al., 2013; Lunghi et al., 2015b; Lunghi and Sale, 2015c; Lunghi et al., 2019; Lyu et al., 2020; Min et al., 2018; Schwenk et al., 2020; Wang et al., 2020; Zhou et al., 2015; Zhou et al., 2013; Zhou et al., 2014) that was interpreted as a form of homeostatic plasticity (Turrigiano, 2012). Recently, we explored the neural underpinnings of this effect using ultra-high field functional magnetic resonance imaging (fMRI). Although our technique did not directly measure ocular dominance columns, we were able to detect short-term plasticity effects in primary visual cortex V1 that were compatible with a change in ocular drive (Binda et al., 2018).

While ocular dominance plasticity has been thoroughly investigated in the visual cortex, less is known about its effects on subcortical visual processing.

Multiple nuclei in the thalamus are involved in processing visual signals and relaying them to the cortex. Lateral Geniculate Nucleus (LGN) is the main retinorecipient thalamic nucleus and the main source of feedforward signals to V1 (Blasdel and Lund, 1983; Hendrickson et al., 1978; Hubel and Wiesel, 1972). LGN cells are largely monocular (Casagrande and Boyd, 1996), organized in layers based on cell type (magno- parvo-cellular layers with intermixed konio-cells) and eye-of-origin (ipsi- and contralateral). However, there are indications that interocular interactions are possible in LGN (Dougherty et al., 2021; Zeater et al., 2015), either due to local interthalamic circuits (Dougherty et al., 2019) or to the large contingent of corticothalamic fibers that feedback into LGN from striate and extrastriate visual areas (Adusei et al., 2021; Briggs and Usrey, 2011; Fitzpatrick et al., 2009; Hendrickson et al., 1978; Lund et al., 1975). Whichever their origin, these interocular interactions could account for the oscillations of LGN BOLD responses during binocular rivalry, shown in seminal work by Wunderlich et al., 2005 and Haynes et al., 2005.

Adjacent to LGN, the pulvinar is the largest thalamic nucleus displaying visual responses. Although a small inferior portion of the pulvinar receives a contingent of fibers from the retina and the superior colliculus (Kwan et al., 2019), the bulk of pulvinar input is binocular (Bender, 1982) and comes from the cortex, with which it is bidirectionally connected. The resulting cortico-pulvinar-cortical loops could participate in visual information processing (Kaas and Lyon, 2007; Purushothaman et al., 2012; Saalmann and Kastner, 2011; Sherman and Guillery, 2002; Shipp, 2004; Zhou et al., 2016) by regulating key parameters of visual cortical function such as gain and intracortical competition (e.g., Saalmann et al., 2012). The pulvinar may be further subdivided in subnuclei, but their identification in the in vivo human anatomy is problematic and MR studies often simplify the internal organization of the pulvinar into few subdivisions along the dorsoventral axis and/or the mediolateral direction (Arcaro et al., 2015; DeSimone et al., 2015; Schneider, 2011). Functional connectivity analyses clearly distinguish two subregions within the pulvinar (Arcaro et al., 2018): ventral (vPulv) and dorsal (dPulv). The ventral region vPulv is primarily connected with the occipital cortex, particularly with areas in the ventral pathway – in line with results in other primates (Kaas and Lyon, 2007). Coherently, vPulv is reliably activated by visual stimulation and follows perceptual oscillations (e.g., during binocular rivalry) even when it is presented to a passive observer (Wilke et al., 2009). The dPulv instead, is more strongly linked with parietal and frontal cortex and its responses are strongly modulated by the cognitive and attentional demands of the task (Fiebelkorn et al., 2019; Kaas and Lyon, 2007). Like for vPulv, also the activity of dPulv follows the perceptual alternation during binocular rivalry, but only when an active reporting task is performed, not in passive-viewing conditions (Wilke et al., 2009).

In previous studies, both LGN and pulvinar have been associated with plasticity. In humans, there are indications that LGN can shift function following sensory deprivation or restoration (Levine et al., 2020; Castaldi et al., 2016). In rodents, ocular dominance plasticity of LGN neurons was recently reported (Huh et al., 2020; Jaepel et al., 2017; Sommeijer et al., 2017), including a form of ocular dominance plasticity during development, leaving open the possibility that monocular deprivation effects may be present at the level of LGN even in the adult human. The pulvinar has also been implicated in developmental plasticity of the visual system (Bourne and Morrone, 2017; Bridge et al., 2016), particularly its inferior retinorecipient portion. Early in development, this region of the pulvinar relays retinal information to visual cortical area MT. During maturation, this connection is usually lost; however, it is preserved when LGN-V1 projections are lesioned (Warner et al., 2015), suggesting that this pulvinar subregion has a plasticity potential that may support residual visual abilities in these patients (Kiper et al., 2002; Tinelli et al., 2013). Thus, in principle, both LGN and pulvinar may support reorganization of visual processing; however, no previous study has tested the potential for short-term plasticity – specifically in response to short-term monocular deprivation – in the adult human thalamus.

One way in which short-term monocular deprivation could affect ocular dominance is by changing the automatic regulation of neuronal gain in monocular neurons and inducing an adaptation-like modulation (Başgöze et al., 2018); in this model, monocular deprivation effects could be seen in LGN.

We and others have disfavored this hypothesis and suggested that monocular deprivation effects could arise from interocular interactions (Lunghi et al., 2013; Lunghi et al., 2015b; Zhou et al., 2013). Even in this scenario, LGN could in principle show these effects, either because they arise through local interthalamic circuitry, or because they arise in the cortex and are inherited by LGN via cortical feedback pathways.

A similar reasoning could apply to responses in the pulvinar. As the majority of pulvinar cells receive binocular input (Bender, 1982), deprivation effects – if measurable at this level – would be most likely inherited from the visual cortex, but they could also arise within the pulvinar, in its small retinorecipient portion.

To test these hypotheses, we measured the plasticity response of LGN and pulvinar to short-term monocular deprivation in normally sighted human adults. We did so by gathering BOLD responses to passively viewed monocular stimuli, delivered before and after 2 hr eye patching. Mapping thalamic nuclei with MRI is notoriously difficult due to the low signal-to-noise ratio (SNR) and the small size of these structures. We overcame these limitations using ultra-high field (7 Tesla) fMRI and by extracting BOLD responses in selected regions of interest (ROIs), which were independently defined (based on the Natural Scenes Dataset [NSD], Allen et al., 2022).

In 18 adult participants with normal vision, we measured 7T BOLD responses to monocular visual stimulation (bandpass filtered noise, refreshing 8 times per second and presented in blocks of 9 s, followed by 12-s interstimulus intervals), delivered before and after 2 hr of eye patching (experimental design is shown in Figure 1A). We previously analyzed responses in visual cortical areas (Binda et al., 2018); here, we focused on responses in the visual thalamus. Pooling data across participants, after aligning them to the MNI template (Avants et al., 2008), we found that visual responses within the thalamus clustered in two foci (Figure 1B) that match two independently defined ROIs (green and blue outlines in Figure 1B): LGN and vPulv, obtained from the NSD (Guest et al., 2021). The third ROI, the mid-Pulv (magenta line in Figure 1B), failed to respond to our visual stimuli, which was expected since the stimuli were delivered passively and this region is primarily driven by images supporting execution of an active task (Wilke et al., 2009).

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Figure 1C shows the temporal dynamics of the average BOLD responses extracted from these independently defined ROIs. Responses in dPulv were almost absent, showing only a weakly negative modulation during stimulus presentation, whereas reliable BOLD responses were observed in both LGN and vPulv. Although clearly weaker than previously measured in V1 (were signals peaked at about 2.5% at 9 s from stimulus onset; Binda et al., 2018), these were reliably larger than 0 at all points between 3 s after stimulus onset to 3 s after its offset (all t(17) > 4.30 and p < 0.01). Response dynamics was faster than in V1 (the peak response in LGN and vPulv occurred around 6 s from stimulus onset, 1TR earlier than in V1), as previously reported (Lewis et al., 2018). It was also slightly faster in LGN than in vPulv.

Given these differences in the response profile, we opted to quantify BOLD response amplitudes with an approach that makes minimal assumptions on temporal dynamics. Since the visual stimulus was a periodic alternation of stimulus contrast ON/OFF (ignoring variations in stimulus spatial scale that is not relevant here, see methods), visually evoked responses could be studied by Fourier analyses of the fMRI time series, taking the amplitude and phase at the stimulus fundamental frequency to estimate response magnitude and its delay (Figure 1—figure supplement 1; note that analyses based on general linear modeling (GLM) and event-related averaging produced the same pattern of results, as detailed below).

Figure 1D shows a polar plot of these measures, separately for each participant and ROI (see also Figure 1—figure supplement 1C for averages across participants), confirming the similar though slightly faster responses in LGN and vPulv and the very small responses in dPulv.

With this approach, we compared the amplitude of responses to stimuli delivered to the two eyes.

Before monocular deprivation, no systematic differences in eye dominance were expected; therefore, we used BOLD responses to stimuli in the two eyes for estimating the internal consistency of our results. We found that responses to the two eyes were correlated across participants in all thalamic regions (Pearson’s correlation coefficients were vPulv: r(18) = 0.58, p = 0.011; LGN: r(18) = 0.66, p = 0.003; dPulv: r(18) = 0.74, p < 0.001) indicating good test–retest reliability of our measurements and allowing us to examine their variations after monocular deprivation.

Figure 2 compares response amplitudes before and after deprivation, for stimuli in the deprived and nondeprived eye. vPulv showed a significant eye by time interaction (Figure 2A: F(1,17) = 14.75, p = 0.001), similar as that seen in V1 (Binda et al., 2018). This is the hallmark of a significant short-term plasticity effect. In contrast, neither LGN nor dPulv showed any significant effect (Figure 2B, LGN, F(1,17) = 0.18, p = 0.675; Figure 2C, dPulv, F(1,17) = 0.70, p = 0.415).

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We also examined response phase estimates, which did not vary across eyes or times, for any of the regions (eye by time interaction in vPulv: F(1,17) = 0.07, p = 0.801; LGN: F(1,17) = 1.08, p = 0.313; dPulv: F(1,17) = 0.19, p = 0.669).

These results indicate that monocular deprivation selectively affected response amplitudes in the vPulv, but it did not reliably affect the dorsal part of the pulvinar or the LGN. The three-way interaction of factors eye, time and ROI was significant (F(2,17) = 3.45, p = 0.039) and so was the post hoc comparison of vPulv and LGN (p = 0.035, after Tukey–Kramer correction), implying that these thalamic regions were systematically different in their response to monocular deprivation and suggesting that the lack of significant modulations in LGN is not merely explained by lack of statistical power in this ROI.

As previously seen in V1 (Binda et al., 2018), we found that the interindividual variability of the plasticity effect size in vPulv was physiologically meaningful, as it correlated with the size of the behavioral effect (Figure 2C; r(18) = 0.47, p = 0.048); on the contrary, no significant correlation was found for the effect in LGN (r(18) = −0.12, p = 0.645, not shown) or dPulv (r(18) = −0.04, p = 0.867, not shown).

Together, these results suggest that the plasticity effect in the visual thalamus was selective for the vPulv region, where it correlated with the perceptual outcome of monocular deprivation.

We performed several control analyses to support these conclusions (since dPulv responded poorly to the visual stimulation, we excluded this ROI from further investigation).

First, we confirmed all our results using two alternative analyses approaches of the fMRI time series: GLM and event-related averaging. Both these methods require assumptions on the temporal dynamics of the BOLD response. GLM relies on choosing an appropriate hemodynamic response function (HRF). Using the canonical HRF previously applied to BOLD data from subcortical regions (Koizumi et al., 2019; McFadyen et al., 2019), we confirmed a reliable time by eye interaction in vPulv (F(1,17) = 11.07, p = 0.004), not in LGN (F(1,17) = 0.05, p = 0.822), the two being significantly different as testified by the significant three-way time by eye by ROI interaction (F(1,17) = 6.89, p = 0.018); we also confirmed that the vPulv effect correlated with the behavioral deprivation index (r(18) = 0.51, p = 0.031). Results were again similar when we quantified BOLD response amplitude from the event-related average curve, which we averaged in the interval between 3 and 12 s (essentially: integrating the response in Figure 1C over the 3–12 s interval and dividing by the duration of this interval). Again we found a reliable time by eye interaction in vPulv (F(1,17) = 16.07, p = 0.001), not in LGN (F(1,17) = 0.00, p = 0.964), with a significant three-way interaction (F(1,17) = 4.52, p = 0.048) and a significant correlation between the vPulv effect and the behavioral deprivation index (r(18) = 0.49, p = 0.039).

Second, we checked that our results were not dependent upon the specific definitions of LGN and vPulv regions that we elected to use. To this end, we redefined ROIs based on two different anatomical templates, intersected with functional activations from a separate experiment. We defined an alternative pulvinar ROI based on Najdenovska et al.’s atlas (Najdenovska et al., 2018), which was obtained from diffusion-weighted imaging. This label does not separate visual and nonvisual subregions of the pulvinar; we used data from an independent experiment involving a subset of our participants (N = 9) to isolate the visually responsive subregion. Selecting the 200 most active 1 mm³ voxels from Najdenovska et al.’s pulvinar, we identified a ventral cluster that largely overlapped the vPulv region used for our main analyses (Guest et al., 2021; Arcaro et al., 2015; Guest et al., 2021) further validating it (Figure 1—figure supplement 2).

Using this alternative ROI definition, we still found a significant time by eye interaction (F(1,17) = 11.66, p = 0.003, Figure 2—figure supplement 1, panel A), confirming the reliable monocular deprivation effect in the ventral (or visual) Pulvinar.

We followed a similar strategy to obtain an alternative definition of LGN. We located it based on the histological FSL atlas (Bürgel et al., 2006; Bürgel et al., 1999) and then again analyzed the 200 most active 1 mm³ voxels (Figure 1—figure supplement 1), thereby equating ROI size between LGN and vPulv. With this alternative definition of LGN, we still found no significant time × eye interaction in LGN (F(1,17) = 0.00, p = 0.979, Figure 2—figure supplement 1, panel B).

To further understand the lack of deprivation effects in LGN, we separately analyzed the parvo- and magnocellular divisions based on a third labeling system. Previous evidence suggests that this separation is possible with high-resolution fMRI (Denison et al., 2014; Müller-Axt et al., 2021; Qian et al., 2020; Zhang et al., 2015). We applied the separation drawn from Müller-Axt et al., 2021, which provided yet another definition of the LGN region (as a whole, largely overlapping the other definitions used above as shown in Figure 2—figure supplement 2A). Figure 2—figure supplement 2B, C show that there was no reliable effect of deprivation in either the parvocellular division (F(1,17) = 1.02, p = 0.327), nor in the smaller magnocellular division (F(1,17) = 2.97, p = 0.103), suggesting that the two subdivisions behaved similarly and both failed to show a systematic effect of monocular deprivation.

Our study is the first to show evidence for short-term plasticity in the adult human thalamus. We found that 2 hr of monocular deprivation, besides shifting ocular dominance assessed with binocular rivalry and V1 monocular BOLD responses (Binda et al., 2018), also affects ocular drive in a specific subregion of the visual thalamus: the vPulv.

With a series of controls, we obtained strong evidence against the possibility that this is an artifact of BOLD analyses or region labeling. We confirmed the results with three different approaches and we cross-checked them with two independent atlases, always concluding that the plasticity effect is clearly observable in vPulv.

In contrast, the adjacent dPulv and LGN regions were reliably unaffected by monocular deprivation. Considering that LGN is a complex structure with diverse morphofunctional subregions (Ichida et al., 2014; Nassi and Callaway, 2009; Shapley, 1990), and given prior indications that the parvocellular pathway may be preferentially subject to short-term plasticity (Begum and Tso, 2016; Lunghi et al., 2013), we also separately analyzed two LGN subdivisions corresponding to the magno- and parvocellular subregions (Denison et al., 2014). Both subregions lacked any detectable effect of monocular deprivation. Of course, this does not imply that LGN lacks short-term plasticity potential in humans, which may well emerge in other contexts (Castaldi et al., 2016; Levine et al., 2020), for example in pathology (Bhat et al., 2022; Mikellidou et al., 2019; Muckli et al., 2009) or during a stabilization of the short-term plasticity effect, as observed for repeated monocular deprivations in amblyopia (Lunghi et al., 2019). LGN and/or dPulv plasticity could have emerged in the context of a different task, requiring the allocation of attention to the stimuli. In the passive-viewing conditions used here, plasticity effects were strong and reliable in vPulv but undetectable in dPulv and LGN; this indicates that, if plasticity effects were present in dPulv and LGN, they were small and/or inconsistent.

Our results are coherent with neurophysiological measurements in the same three regions in behaving nonhuman primates, made in the context of a binocular rivalry task (Wilke et al., 2009). By measuring single cell activity, Wilke et al., 2009 found that vPulv tracked oscillations even when stimulation was passively delivered, while dPulv tracked perception only when the animal engaged in the perceptual task; in contrast, LGN activity never followed perceptual oscillations – in contrast with evidence from human fMRI studies, where LGN responses did follow perceptual oscillations (Haynes et al., 2005; Wunderlich et al., 2005). In line with the observations by Wilke et al., 2009, our results confirm the close relationship between changes in visual perception and changes in vPulv activity: the short-term plasticity effect showed interindividual variability that correlated with the behavioral boost of the deprived eye measured by binocular rivalry. However, our findings extend this association beyond the context of bistable perception: we observed modulations of vPulv responses during passive monocular stimulation, not during binocular rivalry perception. We only used binocular rivalry to index shifts in ocular dominance following deprivation, to be correlated with BOLD modulations; alternative psychophysical indices (e.g., binocular fusion; Sheynin et al., 2019) should in principle highlight similar correlations with the changes of ocular drive in vPulv.

The lack of a reliable monocular deprivation effect in LGN suggests that adaptation at the level of monocular cells (in LGN or in the retina) is not likely to mediate the boost of the deprived eye. This negative finding suggests that the short-term plasticity effects seen in visual cortex (in the same dataset, Binda et al., 2018) does not result from modulations in the feedforward input to V1.

We cannot exclude that the monocular deprivation effect in vPulv originates within the small inferior subregion of the pulvinar receiving direct input from the retina and the colliculus (Kwan et al., 2019). However, given the small size of this subregion compared to the extent of vPulv ROI and considering that vPulv is mainly driven by binocular cortical input (Bender, 1982), we suggest that monocular deprivation effects are more likely inherited from the visual cortex input, although intranuclear connectivity within vPulv and cortico-pulvinar-cortex loops (Sherman and Guillery, 2002) may contribute to sustaining and amplifying these effects.

This hypothesis opens the question why we selectively observed the monocular deprivation effect in vPulv and not in LGN, given that both regions receive massive corticofugal input. We see two possibilities. First, it is known that corticofugal connections to LGN and vPulv generally have independent origin (Sherman and Guillery, 2002) that might be differently affected by the monocular deprivation. Second, in LGN, direct retinal input is expected to account for a large portion of the BOLD responses, and this could mask the modulation of cortical input following monocular deprivation. In contrast, vPulv serves as a hub for converging signals from vast portions of the occipital and temporal cortex (Arcaro et al., 2018; Kaas and Lyon, 2007), where short-term plasticity is the strongest (Binda et al., 2018). If short-term plasticity effects depend on corticothalamic signals, it is reasonable to assume that these will be stronger, more stable, and ultimately easier to detect in vPulv than in LGN.

Our observation of short-term plasticity in vPulv is in line with growing evidence on the importance of the thalamus in active vision (Saalmann and Kastner, 2011). The traditional view of the thalamus as a passive relay of peripheral information has been overruled by evidence that thalamic neurons actively regulate information transmission to the cortex and between cortical areas (Saalmann and Kastner, 2011). This is particularly true for the pulvinar, which has been involved in a variety of mechanisms, including the modulation of response magnitude through gain control (Fiebelkorn and Kastner, 2019; Purushothaman et al., 2012) and synchrony of neurons (Saalmann et al., 2012) according to behavioral demands. These may be implemented through loops of cortico-pulvinar-cortical pathways (Jaramillo et al., 2019), which allow for filtering or gating incoming information. These functions have been often studied in the context of attention (Zhou et al., 2016) and primarily associated with dorsal subregions of the pulvinar. However, gain control of cortical responses is likely to participate in setting ocular dominance and regulating its short-term changes (Lunghi et al., 2011; Spiegel et al., 2017) and vPulv could be involved in these regulations, which could explain the correlation between BOLD modulations in this subcortical area and perceptual modulations. Interestingly, the concept that the visual pulvinar plays a fundamental role in short-term plasticity is also supported by a recent human neuroimaging study, where pulvinar was suggested to gate GABAergic inhibition in the cortex and the associated short-term learning effect (Ziminski et al., 2021).

In conclusion, the present study showed that short-term monocular deprivation effects, which are widespread in cortical visual areas, also extend to subcortical regions. Within the thalamus, plasticity mainly affects the ventral portion of the pulvinar – the portion of this nucleus that shows the most obvious visual functions and the strongest recurrent connections with visual cortex. Although our fMRI data do not allow to ascertain the origin of the plasticity effect, our observations open the possibility that vPulv plays a role in setting ocular dominance and maintaining its plasticity in adulthood.

The data analysed for this study are available online at the following https://doi.org/10.5281/zenodo.6457759.

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Author details

1. Jan W Kurzawski

   1. University of Pisa, Pisa, Italy
   2. IRCCS Stella Maris, Pisa, Italy

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2781-1236

2. Claudia Lunghi

   1. University of Pisa, Pisa, Italy
   2. Laboratoire des systèmes perceptifs, Département d’études cognitives, École normale supérieure, PSL Research University, Paris, France

   Contribution

   Conceptualization, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3811-5404

3. Laura Biagi

   IRCCS Stella Maris, Pisa, Italy

   Contribution

   Conceptualization, Data curation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2159-439X

4. Michela Tosetti

   1. IRCCS Stella Maris, Pisa, Italy
   2. IMAGO7 Foundation, Pisa, Italy

   Contribution

   Resources, Supervision

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2515-7560

5. Maria Concetta Morrone

   1. University of Pisa, Pisa, Italy
   2. IRCCS Stella Maris, Pisa, Italy

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1025-0316

6. Paola Binda

   University of Pisa, Pisa, Italy

   Contribution

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

   For correspondence

   paola1binda@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7200-353X

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