Our brains have an amazing ability to adapt to changes in our environment or bodies, sometimes even ‘rewiring’ themselves as a result. This ‘rewiring’ is called plasticity, and it is especially important when one or more of our senses – such as sight or hearing – do not work.

In people born with deafness, a condition termed ‘congenital deafness’, the part of the brain that normally processes sounds (called the auditory cortex) reorganizes itself to process information from other senses, especially vision. This cross-modal plasticity lets the auditory cortex compensate for the missing sense of hearing by forming new connections to different brain areas. However, it is still unclear if this reorganization of the auditory cortex differs across individuals born with deafness.

Amaral, Wang et al. wanted to investigate if different people’s brains have distinct ways of adapting to deafness. Specifically, they tested if congenital deafness influenced the way the auditory cortex in different people was connected to other parts of the brain. They also tested if exposure to sign language early in life affected those connections.

To do this, Amaral, Wang et al. used a brain imaging technique called fMRI to scan the brains of both congenitally deaf participants and people with healthy hearing. This showed that most of the hearing participants had similar connections between the auditory cortex and other parts of the brain.

In contrast, the connectivity of the auditory cortex – particularly to brain areas that process language – was much more diverse across deaf individuals. This diversity was even present in ‘native signer’ deaf participants exposed to sign language very early in life. However, comparing the native signers to deaf individuals who learned sign language much later showed that the native signers had much more consistent connections between the auditory cortex and two specific areas associated with sign language comprehension. These results indicate that both deafness and early exposure to language can shape individual differences in brain plasticity.

These findings shed new light on how sensory loss and language exposure can shape people’s brains in different ways. In the future, Amaral, Wang et al. hope that the knowledge gained will benefit people with deafness or hearing loss, for example, by helping develop better tools to restore hearing or contributing to more personalised approaches to language and education in general.

Neural plasticity, a fundamental property of the brain, refers to its ability to adapt and reorganize in response to sensory input and environmental demands. Meaningful plasticity is found in response to extreme environmental scenarios, such as missing the typical input to an entire sensory channel. Extensive research into neural plasticity in congenital deafness has shown that deafness induces neural reorganization (e.g. Allen et al., 2013; Almeida et al., 2015; Almeida et al., 2018; Amaral et al., 2016; Finney et al., 2003; Lomber et al., 2010; Ruttorf et al., 2023; Scott et al., 2014; for a review, see Alencar et al., 2019; Lomber et al., 2020). For instance, the auditory cortex (AC) in deafness becomes highly responsive to visual stimuli, reflecting a compensatory adaptation to sensory loss (e.g. Codina et al., 2017; Hauthal et al., 2013; Simon et al., 2020). Importantly, the reorganization of the AC in deaf individuals also plays a role in language processing, responding to sign language, which uses the visual rather than the auditory modality (Nishimura et al., 1999; Trumpp and Kiefer, 2018). Although most findings in congenital deafness that suggest visual processing in the AC are caused by hearing loss, as opposed to using sign language (Cardin et al., 2013; Cardin et al., 2016; Fine et al., 2005), sign language itself also affects cross-modal plasticity, e.g., in the processing of motion (Bavelier et al., 2001; Codina et al., 2017; McCullough et al., 2012). Therefore, both hearing loss and compensatory capacities are important factors when seeking to comprehend the plastic alterations in the AC in deafness.

Overall, hearing loss promotes cross-modal plasticity in the AC and beyond it; but do all individuals with deafness undergo the same level or even type of reorganization? Or can reorganization affect deaf people differently, shedding light on the nature of plasticity at the individual level? Recent evidence on blindness suggests that the variability between individuals may even be further increased due to sensory loss (Sen et al., 2022). In this study, we showed that people who were congenitally blind have significantly more individual differences in brain connectivity from their deprived visual cortex beyond what is found in sighted controls. This was especially true in areas where connectivity is reshaped by blindness (Sen et al., 2022). This suggests that plasticity may be more variable among people than previously thought. Further, it illustrates the role of postnatal experience, in driving individual differences in brain development. Is the expansion of individual differences due to plasticity a general principle of brain development? If so, we can expect to find increased individual differences in deafness as well.

Testing this question in deafness opens an additional question. Deafness is frequently accompanied by a secondary deprivation. Deaf children born to hearing parents who are raised without direct contact to other deaf adults often suffer from delayed language acquisition as they cannot perceive spoken or sign language in their environment (Hall, 2017; Mayberry et al., 2002; Mayberry and Eichen, 1991). This early-onset deprivation has unique effects on brain organization as well (Cheng et al., 2023; Lyness et al., 2013; Twomey et al., 2020; Wang et al., 2023). Therefore, testing individual differences in deafness allows testing a secondary question: If the absence of experience increases individual variability, would language acquisition delay cause additional variation in the link between the auditory and language systems? Or does only early-onset and long-lasting input loss cause such diversification?

Last, individual variability in neural plasticity may also impact the restoration of hearing. In terms of auditory recovery, hearing aids and cochlear implantation are the main options in auditory rehabilitation. In congenital hearing loss, cochlear implants should be applied in younger rather than older children, as the efficacy of cochlear implants decreases if implemented later (Karltorp et al., 2020; Kral and Sharma, 2012; Lyness et al., 2013; Purcell et al., 2021; Sharma and Campbell, 2011). However, even then, the success of their application might be dependent on the level of the reorganization of the AC: an early work showed that in children prior to cochlear implantation, the level of metabolism in their cortex, including the AC, predicted their speech perception outcomes (Lee et al., 2001), suggesting a challenge posed by reorganization to intact sensory restoration. In contrast, more recently, it was shown that recruitment of the broad AC (including language areas) for visual speech in deaf adults positively correlates with auditory speech perception following implantation (Anderson et al., 2017). Therefore, understanding the nuances of brain reorganization and specifically how it may vary among deaf individuals may enable the implementation of more effective and individualized auditory rehabilitative interventions.

Therefore, the goal of the current study is to use brain connectivity to test if individual variability is modulated by sensory loss in deafness, and how it may be affected by delayed language acquisition. We examine whether the reorganization of the AC in congenital deafness results in connectivity that is particularly variable across individuals. We predict that higher variability will be observed in deafness, indicating a significant influence of postnatal sensory loss on brain organization across sensory systems. Alternatively, if increased individual variability is not observed for the deaf, this would challenge previous findings from the blind (Sen et al., 2022), arguing against the idea that sensory loss promotes individual variation in general, and suggesting instead that different sensory systems may promote more consistent or variable plasticity patterns. Last, testing the role of delayed language acquisition, we predict that deaf individuals with additional delayed language acquisition may show an additional increase in their individual connectivity differences, signifying that delayed language acquisition, as a form of short-term deprivation, can also affect brain variability across individuals.

This study demonstrates a link between neural plasticity and variability in the AC of deaf individuals. Our study has demonstrated that, in comparison to a hearing group, individuals in the deaf group display a greater degree of individual variability in their FC from the AC. These were not driven by differences in their hearing threshold or hearing aid use experience, but rather by congenital auditory loss. This shows consistency with previous findings of increased individual differences in blindness (Sen et al., 2022). Furthermore, there is a mild relationship between this heightened variability and the adaptive changes occurring within the deprived AC. Specifically, we found that overall, the spatial patterns of plasticity and increased individual differences are significantly correlated, and there is increased variability in many areas functionally connected to the AC that have undergone reorganization due to deafness. Further, some, although more modest, increased variability was found when comparing deaf individuals who had varying degrees of sign language acquisition, suggesting that language acquisition timing itself also plays an additional significant role in producing different AC functional connections. These findings suggest that although hearing loss in itself may be sufficient to increase individual differences, the variation of the FC patterns in the AC in response to deafness can increase further when considering a combination of auditory experience and timing of language acquisition. Together, these findings show how the interaction of auditory and language exposure may amplify the spectrum of FC diversity from the AC, allowing for a more comprehensive understanding of the complex factors shaping neural plasticity in response to deafness.

The auditory system, like the visual system, undergoes a critical phase during which its organization is fine-tuned by sensory experience (Knudsen, 2004). Given the fundamental role of auditory input for the AC organization, hearing loss leads to significant reorganization of this brain region. Importantly, while hearing loss induces cross-modal plasticity in the AC, we show that the extent of reorganization may vary among individuals with deafness. Recent evidence on blindness (Sen et al., 2022) suggests that the absence of visual experience increases individual differences in brain connectivity. Our study extends this inquiry to a different population experiencing a distinct type of sensory loss. Our findings demonstrate how hearing loss influences the neural connectivity profile of the deprived AC, introducing variability in the network outcomes. Much of the increased individual differences are found in areas that belong to the language system, including Broca’s area (notably bilaterally) and the left STG and MTG (Figure 1A and B). This aligns with prior research demonstrating significant FC alterations between the AC and the language network in deaf infants and children (Shi et al., 2016; Wang et al., 2019). It also aligns with animal studies showing how deafness can impact top-down connectivity (Yusuf et al., 2020) and cortico-cortical interactions (Yusuf et al., 2017). Therefore, it appears that connectivity between the early AC and many language regions is stabilized or else affected by the use of audition for language in early life and becomes less consistent in its absence. Notably, while our findings mostly point to increased AC-FC variability within the deaf group, a single cluster in the left early visual cortex exhibits higher variability in the hearing group. This suggests a potential stabilizing impact of hearing loss on the interaction between the auditory and visual cortices, possibly due to the prevalent use of vision for adaptation. Overall, these results, coupled with those of Sen et al., 2022, highlight the impact of postnatal sensory experience in promoting consistency in brain organization, suggesting a general principle of brain development of the sensory systems.

The results of this study also provide evidence for the role of neural plasticity in generating diverse individual patterns of brain connectivity. Our finding that the exhibited heightened AC-FC variability by the deaf group corresponds spatially to regions that reorganize in deafness, even if only moderately (r=0.24), suggests that the increased variability may be partly attributed to the reorganization and adaptation of neural circuits in response to hearing loss. Although not all areas that changed their mean FC to the AC showed increased variability in the deaf group, we found higher AC-FC variability in regions such as the inferior frontal cortex and pre-supplementary motor area. These same brain regions exhibited functional reorganization in response to deafness, as illustrated in Figure 2B, consistent with prior resting-state fMRI research emphasizing functional changes following hearing loss (e.g. Andin and Holmer, 2022; Ding et al., 2016). Importantly, these changes in the mean connectivity of the AC likely reflect the change in its function in deafness. Studies have shown that the AC can be activated in deaf individuals when performing parallel visual tasks, indicating a shift in functional activation from auditory to visual processing (e.g. Almeida et al., 2015; Benetti et al., 2017; Benetti et al., 2021; Bola et al., 2017; Bottari et al., 2014; Butler et al., 2017; Finney et al., 2001; Lomber et al., 2010; Meredith and Lomber, 2011; Petitto et al., 2016; Scott et al., 2014), albeit typically for the same type of functional computation (Cardin et al., 2020; Heimler et al., 2015; Lomber, 2017; Pascual-Leone and Hamilton, 2001). Our results suggest that such functional responses may vary across individuals in accordance with their variable levels of FC alterations. Interestingly, in addition to finding increased variability in connectivity for areas that increase their connectivity in deafness, we also found higher AC-FC variability in regions that show a decreased FC to the temporal lobe in deafness, specifically the somatosensory cortex (e.g. Andin and Holmer, 2022; Bonna et al., 2021; Ding et al., 2016). Here too, previous research has identified differences in somatosensory involvement between deaf and hearing individuals, which has been linked to sign language and visual processing (Bonna et al., 2021; Okada et al., 2016). Therefore, it appears that any type of plasticity in the connectivity of the AC, regardless of its direction (increased or decreased FC), may manifest variably across individuals.

What underlying mechanisms drive these changes, and what is their significance? Although the term ‘reorganization’ is debated, and some concerns exist about the functionality of neural reorganization (Makin and Krakauer, 2023), functional recruitment of AC for nonauditory tasks is supported by evidence from animal studies demonstrating causal links (Lomber et al., 2010; Meredith and Lomber, 2011; for a review, Alencar et al., 2019; Lomber et al., 2020). Such functional changes appear to arise from a combination of unmasking and re-weighting of nondominant inputs, rather than extensive anatomical alterations (Alencar et al., 2019; Kral and Sharma, 2012; Kral and Sharma, 2023; Kupers et al., 2010; Pascual-Leone and Hamilton, 2001). Our findings of increased variability in FC from the AC in the deaf suggest more than mere unmasking, as this variability cannot easily be explained without assuming some degree of reorganization.

Evidence for reorganization is found in research using congenitally deaf cats and other animal models, which have shown that auditory deprivation leads to significant disruptions in the development and maintenance of brain connectivity patterns. Although broadly anatomical connectivity pathways are maintained in deafness (Butler et al., 2016; Butler et al., 2018; Kok and Lomber, 2017), changes in connectivity are found, namely within and between sensory networks (Sacco et al., 2024). These changes map to the disruption of neural communication within and between the auditory system (Kral et al., 2017). For instance, the decoupling of supragranular and infragranular layers of cat’s primary AC (Yusuf et al., 2022) and the disruption of top-down cortico-cortical connectivity in congenitally deaf cats (Yusuf et al., 2020) suggest that neural communication within and between cortical regions is highly sensitive to sensory input. These disruptions likely contribute to heterogeneity in how neural networks in individuals with hearing loss are reorganized. Anatomical changes may also help explain brain connectivity variability in deafness, as congenital auditory deprivation leads to reduced thickness in both primary and secondary auditory regions, particularly in deep layers (e.g. infragranular layers) (Berger et al., 2017). Additionally, auditory deprivation induces synaptic reduction in the same infragranular cortical layers, further disrupting the structural and functional integrity of these regions (Kral et al., 2000). Together, these findings align with the idea that anatomical and functional constraints interact with sensory experience to drive cortical plasticity, and that these may vary between individuals. Importantly, while group-level animal studies have provided valuable insights into the effects of deafness, they rarely address individual variability, an aspect that has been increasingly explored in human studies. In animal research, the typically small sample sizes make it even more likely that interindividual variability could obscure group-level findings or lead to null results. Investigating variability within individual animals could reveal important patterns or mechanisms masked by averaging across groups, offering a more nuanced understanding of deafness and its effects on brain reorganization.

Our study does not determine the origins of the individual differences observed, but we expect that they may arise from a blend of genetic and environmental factors. Auditory experiences seem to stabilize FC patterns, implying that experience-dependent pruning in the auditory system consolidates a consistent pattern of connectivity optimized for hearing. In its absence, AC connectivity may become more variable, depending on random and inherited individual differences in patterns of AC connectivity at birth. Further, diverse compensatory experiences of deaf individuals could enhance the variability of AC-FC. Our analysis showed that factors such as hearing threshold and the age at which hearing aids are first used can influence AC-FC. However, these factors do not fully account for the observed variability, suggesting that AC connectivity is susceptible to a complex interplay of environmental influences. Future studies involving deaf infants will be crucial to determining the balance in which AC-FC variability is driven by environmental influences and inherited connectivity patterns.

In addition to the effect of deafness itself, we have also demonstrated how a particular additional factor, namely language experience, may moderately affect variations in the FC of the AC. Although our data may be somewhat underpowered to fully explore this question, deaf individuals who have had exposure to sign language from birth, for example, appear to exhibit more consistent connectivity between the AC and the left pMFG, as well as the right aIFG, compared to those who had experienced delayed language acquisition in early development (Figure 3B). Interestingly, the left pMFG/PreCG have been associated with sign language comprehension (Trettenbrein et al., 2021; Yang et al., 2024), and our results indicate that delayed language acquisition leads to higher FC variability in this area specifically. This provides some evidence that early experience with sign language consolidates this connectivity pattern. On the other hand, the aIFG, part of the inferior frontal cortex, is known to be involved in lexical comprehension and discourse semantics for sign language (Emmorey, 2021). The literature often highlights higher activation in the left hemisphere, typically accompanied by less extensive neural activity in the right hemisphere’s homologous region. Therefore, it is unclear why the right aIFG shows higher FC variability in deaf delayed signers, as opposed to the left hemisphere; this may be due to variable reliance on areas capable of compensating for deficits in typical language systems (Martin et al., 2023; Newport et al., 2022; Tuckute et al., 2022). Further research is needed to elucidate the precise role of the right hemisphere’s IFG in this context. In contrast, the AC connectivity to the left supramarginal gyrus and the left cuneus/precuneus is more consistent in people who experienced delayed language acquisition in addition to deafness. Although we only speculate why these areas show such interactions, these findings highlight the complex interplay between sensory experience, language acquisition, and neural plasticity in shaping the individual patterns of FC of the AC. However, these outcomes did not align with our initial hypothesis, which anticipated a more pronounced effect of increased individual differences in delayed signers, especially within the language network. This may be since the observed variations in brain connectivity from early AC are primarily attributed to hearing loss, rather than delayed language acquisition. In turn, this could be both due to the early AC function as primarily responsive to auditory stimulation and to the fairly early maturation of this region (Kral and O’Donoghue, 2010), which may make it more susceptible to hearing loss, rather than to delayed language acquisition itself. This conclusion is further reinforced by our analysis targeting the variability in deaf native signers: the findings showed similar patterns of increased individual variability for FC within this subgroup as compared to the hearing (Figure 1C and D) compared to the analysis involving both native and non-native signers (Figure 1A and B). It would appear that auditory loss in itself, regardless of language experience, is a larger driver of increased individual differences. Along the same lines, we have also tested if increased individual differences may then be found in the connectivity from Broca’s area in the case of delayed language acquisition, and did not find any significant effect. Though this may be due to insufficient power, this further emphasizes that the increase in individual differences in AC-FC, and possibly beyond it, during deafness are primarily attributed to hearing loss. An additional variable that may contribute to the relatively minor effect of language experience in our results is the relatively high language abilities within our cohort of delayed signers, which were comparable to those of the native signers. All deaf participants self-reported consistent levels of sign language proficiency, a factor that is typically affected following delayed language acquisition (Bogliotti et al., 2020; Caselli et al., 2021; Cheng and Mayberry, 2021; Tomaszewski et al., 2022). Furthermore, a subset of delayed deaf signers acquired sign language before the age of 6 (N=6, see also Supplementary file 2, table S2), potentially rendering them less susceptible to the impact of language deprivation. To further elucidate these findings, future investigations should include a larger and more diverse sample, specifically in terms of sign language acquisition age, in order to comprehensively address this aspect.

Finally, hearing aids and cochlear implants represent the primary approaches in auditory rehabilitation, and individual differences could be considered with respect to these interventions. The effectiveness of these treatments, especially cochlear implantation, is intricately linked to the extent of reorganization within the AC (Feng et al., 2018; Heimler et al., 2014; Heimler et al., 2015; Kral et al., 2019; Lee et al., 2001; Lee et al., 2007). The ability to regain a lost sense (i.e. hearing) is likely influenced by the preservation of the auditory system, as cross-modal reorganization for a different function may hinder its capacity to process information from the original modality and computation. Although this link is nuanced, given that some portions of the AC appear to reorganize for parallel functions to those they typically perform (Cardin et al., 2020; Heimler et al., 2015; Lomber, 2017; Pascual-Leone and Hamilton, 2001), reorganization appears to affect the ability to restore auditory function to AC. Although future research would need to establish a direct link between the individual brain connectivity patterns reported here and their functional utility, the diverse reorganization levels shown in this study hold potential clinical relevance for auditory rehabilitation. This is particularly true when considering the larger individual differences in how strongly the AC connects to the language system (Figure 1B), where a disconnect may form between the reorganized role in visual language and auditory feed-forward roles. Additionally, this study highlights the imperative of acknowledging and considering differences between hearing and deaf individuals, particularly when employing normative data in clinical contexts (e.g. neurosurgery). The recognition of variability in brain organization among diverse populations underscores the necessity for tailored approaches in clinical practices, ensuring more accurate and effective interventions for deaf individuals.

It is worth noting that we assessed individual differences based on FC during task performance and not at rest. Although it would be prudent for future research to explore this aspect, we expect that individual patterns of plasticity in the AC connectivity remain relatively consistent across different time periods and states. FC patterns of hearing individuals are primarily shaped by common system and stable individual features, and not by time, state, or task (Finn et al., 2015; Gratton et al., 2018; Tavor et al., 2016). While the task may impact FC variability, we have recently shown that individual FC patterns are stable across time and state even in the context of plasticity due to visual deprivation (Amaral et al., 2024). Therefore, we expect that in deafness as well there should not be meaningful differences between resting-state and task FC networks, in terms of FC individual differences.

In conclusion, this study demonstrates that the lack of auditory experience results in increased individual differences in brain organization. Notably, this increased variability is prominent in language areas and regions undergoing reorganization in response to deafness, highlighting the relationship between brain plasticity and individual differences. Furthermore, our findings indicate that this variability is not solely influenced by sensory loss due to deafness; deprivation from language during early life also plays a role in shaping this variability. Ultimately, these outcomes underscore the significance of postnatal experience in generating individual differences. Additionally, they support tailoring rehabilitation strategies to match the unique patterns of plasticity seen in individuals with sensory impairments, including those with deafness.

Seed-based connectivity maps, code, and the source maps for the figures, have been deposited in OSF at the link https://doi.org/10.17605/OSF.IO/YVKMF. Deidentified raw nifti files are not publicly available due to ethical constraints, however, they are available from the corresponding authors upon reasonable request. Researchers who are interested in the raw neuroimaging data for scientific purposes could contact the corresponding author (ybi@bnu.edu.cn) by submitting a brief project proposal. The proposal will be further assessed by the Human Subject Review Committee at Peking University. Any commercial research is not allowed to be performed on the data.

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Author details

1. Lenia Amaral

   Department of Neuroscience, Georgetown University Medical Center, Washington DC, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Writing - original draft

   Contributed equally with

   Xiaosha Wang

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0631-7944

2. Xiaosha Wang

   1. State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
   2. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China

   Contribution

   Data curation, Funding acquisition, Investigation, Writing – review and editing

   Contributed equally with

   Lenia Amaral

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2133-8161

3. Yanchao Bi

   1. State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
   2. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
   3. Chinese Institute for Brain Research, Beijing, China

   Contribution

   Supervision, Funding acquisition, Investigation, Writing – review and editing

   For correspondence

   ybi@bnu.edu.cn

   Competing interests

   Senior Editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0002-0522-3372

4. Ella Striem-Amit

   Department of Neuroscience, Georgetown University Medical Center, Washington DC, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing – review and editing

   For correspondence

   striemit@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5900-3455

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