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To determine how language is implemented in the brain, it is important to know which brain areas are primarily engaged in language processing and which are not. Existing protocols for localizing language are typically univariate, treating each small unit of brain volume as independent. One prominent example that focuses on the overall language network in functional magnetic resonance imaging (fMRI) uses a contrast between neural responses to sentences and sets of pseudowords (pronounceable nonwords). This contrast reliably activates peri-sylvian language areas but is less sensitive to extra-sylvian areas that are also known to support aspects of language such as word meanings (semantics). In this study, we assess areas where a multivariate, pattern-based approach shows high reproducibility across multiple measurements and participants, identifying these areas as multivariate regions of interest (mROI). We then perform a representational similarity analysis (RSA) of an fMRI dataset where participants made familiarity judgments on written words. We also compare those results to univariate regions of interest (uROI) taken from previous sentences > pseudowords contrasts. RSA with word stimuli defined in terms of their semantic distance showed greater correspondence with neural patterns in mROI than uROI. This was confirmed in two independent datasets, one involving single-word recognition, and the other focused on the meaning of noun-noun phrases by contrasting meaningful phrases > pseudowords. In all cases, areas of spatial overlap between mROI and uROI showed the greatest neural association. This suggests that ROIs defined in terms of multivariate reproducibility can help localize components of language such as semantics. The multivariate approach can also be extended to focus on other aspects of language such as phonology, and can be used along with the univariate approach for inclusively mapping language cortex., (© 2024. The Author(s).)

Working memory (WM) is often compromised after traumatic brain injury (TBI). A number of functional and effective connectivity studies investigated the interaction between brain regions during WM task performance. However, previously used WM tasks did not allow differentiation of WM subprocesses such as capacity and manipulation. We used a novel WM paradigm, CapMan, to investigate effective connectivity associated with the capacity and manipulation subprocesses of WM in individuals with TBI relative to healthy controls (HCs). CapMan allows independent investigation of brain regions associated with capacity and manipulation, while minimizing the influence of other WM-related subprocesses. Areas of the fronto-parietal WM network, previously identified in healthy individuals as engaged in capacity and manipulation during CapMan, were analyzed with the Independent Multiple-sample Greedy Equivalence Search (IMaGES) method to investigate the differences in information flow between healthy individuals and individuals with TBI. We predicted that diffuse axonal injury that often occurs after TBI might lead to changes in task-based effective connectivity and result in hyperconnectivity between the regions engaged in task performance. In accordance with this hypothesis, TBI participants showed greater inter-hemispheric connectivity and less coherent information flow from posterior to anterior brain regions compared with HC participants. Thus, this study provides much needed evidence about the potential mechanism of neurocognitive impairments in individuals affected by TBI.