Long considered a structure dedicated primarily to motor control, the cerebellum is now known to contain regions that respond selectively to language. However, how cerebellar language specialization emerges during development remains unknown. The prevailing proposal is that cerebellar functional specialization critically depends on inputs from the contralateral neocortex, received through well-established reciprocal cortico-cerebellar connections. Here, we test this hypothesis in a right-handed woman (EG) who lacks most of her left temporal lobe (presumably, from birth) and whose neocortical language network resides in her right hemisphere. Using precision functional MRI in EG and a cohort of 74 typically developing adults, we find that EG’s cerebellar language network shows a strong left-hemispheric bias, mirroring the atypical lateralization of language in her cerebral cortex, while preserving canonical topography and response profiles of the language-dominant cerebellar regions. Critically, however, EG’s right cerebellar hemisphere also responds to language and even contains a language-selective region despite the absence of language regions in the neocortical left hemisphere. These findings challenge the view that cerebellar specialization critically requires contralateral neocortical inputs, and point instead to some degree of intrinsic neocortex-independent cerebellar organization.
Bangjie Wang, Greta Tuckute, Hope H. Kean et al.· bioRxiv· 0 citations
Healthy aging is associated with structural and functional brain changes. However, cognitive abilities vary in how they change with age: executive functions decline, while aspects of linguistic processing remain relatively preserved. This heterogeneity predicts differences among brain networks in whether and how they change with age. To evaluate this prediction, we used precision fMRI to examine the language-selective network and the Multiple Demand (MD) network, which supports executive functions, in older adults (N = 64) relative to young controls (N = 483). The MD network of older adults shows weaker, less spatially extensive, and more topographically variable activations during an executive function task and reduced within-network functional connectivity. In contrast, we find remarkable preservation of the language network in older adults: it responds during language comprehension as strongly and selectively as in younger adults, with similar left-hemispheric lateralization and within-network functional connectivity. These findings align with behavioral preservation of language comprehension in healthy aging. Here, the authors show that aging affects brain networks differently, with neural changes mirroring the preservation or decline of the cognitive functions they support. The multiple demand network exhibits age-related decline while the language network remains stable with age.
A. Billot, Niharika Jhingan, M. Varkanitsa et al.· Nature Communications· 0 citations
Human language processing can be studied through both behavior and brain activity, yet it remains unclear whether these two data types reflect sensitivity to the same information. One influential view holds that both behavioral and neural responses are largely determined by processing effort, often estimated by word surprisal together with the context-independent properties of word frequency and length. At the same time, neural responses have been shown to encode richer aspects of linguistic content, including meaning. Here, we use neural network language models to operationalize these alternatives and systematically compare, within the same analytic computational framework, the predictive power of low-dimensional effort-based predictors and high-dimensional embedding representations that encode contextualized linguistic content, including meaning. Across 8 behavioral datasets and 5 neural datasets (4 fMRI and 1 ERP), we find that processing effort captures substantial variance in both behavioral and neural measures of language processing, in line with much previous work. However, for brain responses—but not for behavioral measures—embedding representations carry substantial predictive power beyond the estimates of processing effort. These results therefore suggest that neural data provide access to rich, high-dimensional dynamics of language comprehension, whereas behavioral data reflect a bottlenecking of these dynamics into a small set of theoretically motivated properties of contextualized linguistic input. Significance Statement Two research communities study language comprehension as it unfolds in real time: psycholinguists use behavioral measures, such as eye movements during reading, and neuroscientists measure brain activity. The two are rarely studied together, but evidence from both must be integrated into a unified theory of language processing. Here we analyze both brain and behavioral responses within a single framework based on language models, comparing two long-standing accounts of what drives responses to language: processing effort versus meaning and other features not reducible to effort. We find that behavior is dominated by effort, whereas brain responses also reflect meaning. Developing a unified theory requires both kinds of data, but with a clear understanding of which levels of representation each measure reflects.
Andrea Gregor de Varda, Yevgeni Berzak, Evelina Fedorenko et al.· bioRxiv· 0 citations