Missing links: the functional unification of language and memory (L∪M)

Abstract

The field of neurocognition is currently undergoing a significant change of perspective. Traditional neurocognitive models evolved into an integrative and dynamic vision of cognitive functioning. Dynamic integration assumes an interaction between cognitive domains traditionally considered to be distinct. Language and declarative memory are regarded as separate functions supported by different neural systems. However, they also share anatomical structures (notably, the inferior frontal gyrus, the supplementary motor area, the superior and middle temporal gyrus, and the hippocampal complex) and cognitive processes (such as semantic and working memory) that merge to endorse our quintessential daily lives. We propose a new model, "L∪M" (i.e., Language/union/Memory), that considers these two functions interactively. We fractionated language and declarative memory into three cognitive dimensions, Embodiment-Formulation-Internalization, that communicate reciprocally. We formalized their interactions at the brain level with a connectivity-based approach. This new taxonomy overcomes the modular view of cognitive functioning and reconciles functional specialization with plasticity in neurological disorders.

Dynamics of White Matter Architecture in Lexical Production among Middle-Aged Adults

“…Temporally, the WM networks most affected by age (i.e., Net 16, 5, 11, 4) showed the largest microstructural alterations occurring from age 53 onwards. This aligns with previous studies concluding that discontinuities (i.e., nonlinear trajectory) in brain structure are likely to onset at middle age (Beck et al, 2021;Park & Festini, 2016) and further reinforces the idea that midlife is a critical period of major brain structural reorganization in line with reports on brain function and language performance (Deery et al, 2023;Hennessee et al, 2022;Park & Festini, 2016;Roger et al, 2023;. Overall, our study supports the current evidence that dorso-ventral association (SLF II/III, MLF) and anterior cortico-subcortical pathways suffer the largest age-related microstructural alterations (Gunning Dixon et al, 2009;Park & Festini, 2016;Yeatman et al, 2014).…”

Unraveling the functional attributes of the language connectome: crucial subnetworks, flexibility and variability

“…Task-based studies have shown its involvement in natural language processing (Simony et al, 2016). As a foundation of the episodic-semantic memory spectrum (and more broadly language-memory; Roger et al 2022), the DMN is a multimodal experiential system (Xu et al, 2017) that fosters resonance and binding between environmental features and those derived from similar prior knowledge and states (Binder & Desai, 2011; Constantinescu et al, 2016). For these reasons, Net3 has been referred to here as the “Abstract-Knowledge system” of language.…”

“…Our analyses to determine the functional role of brain regions within networks have identified them all as “connector” hubs, which is consistent with previous observations (e.g., IFG/TPJ/STG: Goucha, Zaccarella, et Friederici 2017; Hagoort 2016; PF/SMG: Braga et al 2013; pMFG, 55b: Hazem et al 2021). Connector hubs are most likely to be located at the contact points of several white matter (WM) fascicles, actively supporting long-distance information transport and processing (e.g., at the AF/SLF convergence areas for the IFG and TPJ; Roger et al 2022). We have indeed observed a strong matching with the AF endpoints (whose involvement in language has been widely and more directly reported; e.g., Forkel et al 2022 for a meta-analysis) and the lateral perisylvian part of Net1.…”

“…The great benefit of a partitioning emerging directly from data is to pinpoint latent mechanisms that transcend our classical cognitive descriptions (see interesting discussions on the current problem of brain-behavior concordance or the blurriness and ambiguity associated with terminology and definition of psychological constructs: Anderson 2011; Buzsáki 2020). Data-driven ontology provide an independent view, here from a neuro-centric perspective (Roger et al, 2022) and can serve as “ lingua franca across disciplines and theoretical gaps ” (Eisenberg et al, 2019).…”

“…Integrative areas (sometimes referred to as connector hubs), at the interface between local systems, are particularly important for the multimodal neural integration of information (Cocchi et al, 2013; Fornito et al, 2015; van den Heuvel & Sporns, 2013). With regard to the language circuitry, integration/segregation subsystems and specific connector hubs have been previously identified for both language production and comprehension (e.g., Friederici, 2012; Hagoort, 2016; Hertrich et al, 2020; Roger et al, 2022). However, language is multi-faceted and a comprehensive analysis of the functional properties of the language connectome in a broader framework would contribute to a more accurate description.…”