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

Roger, Elise; Banjac, Sonja; Schotten, Michel Thiebaut de; Baciu, Monica

doi:10.1016/j.neubiorev.2021.12.012

Cited by 34 publications

(39 citation statements)

References 179 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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).…”

Section: Satellite Centers Are Regions Whose Functional Communication...mentioning

confidence: 99%

“…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;. 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).…”

Section: Introductionmentioning

confidence: 99%

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

Roger

Almeida

Lœvenbruck

et al. 2022

Preprint

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Language processing is a highly integrative function, intertwining linguistic operations (processing the language code intentionally used for communication) and extra-linguistic processes (e.g., attention monitoring, predictive inference, long-term memory). This synergetic cognitive architecture requires a distributed and specialized neural substrate. Brain systems have mostly been examined at rest. However, task-related functional connectivity provides additional and valuable information about how information is processed when various cognitive states are involved. We gathered thirteen language fMRI tasks in a unique database of one hundred and fifty neurotypical adults (InLang database). The tasks were designed to assess a wide range of linguistic processes and subprocesses. From this database, we applied network theory as a computational tool to model the task-related functional connectome of language (LANG). The organization of this data-driven neurocognitive atlas of language is examined at multiple levels, uncovering its major components (or crucial subnetworks) and its anatomical and functional correlates. Furthermore, we estimate its reconfiguration as a function of linguistic demand (flexibility), or several factors such as age or gender (variability). By accounting for the multifaceted nature of language and modulating factors, this study can contribute to enrich and refine existing neurocognitive models of language. The LANG atlas can also be considered as a reference for comparative or clinical studies, involving a variety of patients and conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Satellite Centers Are Regions Whose Functional Communication...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Roger

Almeida

Lœvenbruck

et al. 2022

Preprint

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“…In our series, both affected and unaffected hippocampus and amygdala of patients with left bMTLE exhibited a decreased FC with bilateral inferior frontal areas, limbic structures, and temporal neocortices ( Figure 1 ). These regions belong to language-and-memory network (LMN), a complex brain system mainly located in the dominant hemisphere, which groups together cortical areas responsible for verbal memory and for basic language skills ( 36 – 38 ).…”

Section: Discussionmentioning

confidence: 99%

Distinct limbic connectivity in left and right benign mesial temporal lobe epilepsy: Evidence from a resting state functional MRI study

Pizzanelli

Pesaresi²,

Milano

et al. 2022

Front. Neurol.

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BackgroundFunctional connectivity (FC) studies showed that pharmaco-resistant mesial temporal lobe epilepsy (MTLE) affects not only the limbic system, but also several extra-limbic regions, including areas belonging to resting state networks. Less is known about FC in subjects with benign MTLE (i.e., sensitive to antiseizure medication, bMTLE).Aim and methodsWe evaluated FC of hippocampus and amygdala in subjects with bMTLE, distinguished based on the epileptic focus lateralization. We enrolled 19 patients (10 with left and 9 with right bMTLE) and 10 age-matched healthy subjects. Connectivity was investigated at rest by using a seed-based regression analyses approach with four regions of interest (left and right hippocampus, left and right amygdala). Patients were also tested with a neuropsychological battery and their scores were correlated with fMRI data.Results and conclusionsOur study documented an asymmetrical disruption of FC in bMTLE, in relation to the side of the focus. Right subjects only exhibited limited altered connections, while left subjects—who performed worse in verbal memory tests—showed a wide bilateral hypoconnectivity of hippocampus and amygdala with areas belonging to language and memory network. The strength of FC between left limbic areas and language and memory network correlated with better performances in verbal memory tests. Moreover, we observed an increased FC with areas of default mode network, more pronounced in left subjects, a possible attempt to compensate cognitive deficit but without effectiveness.We believe that these findings could help to better characterize bMTLE, in which a dysfunction of limbic connectivity is detectable despite well-controlled epilepsy.

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An explainable machine learning model of cognitive decline derived from speech

Chandler,

Diaz‐Asper,

Turner

et al. 2023

Alz & Dem Diag Ass & Dis Mo

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INTRODUCTIONTraditional Alzheimer's disease (AD) and mild cognitive impairment (MCI) screening lacks the sensitivity and timeliness required to detect subtle indicators of cognitive decline. Multimodal artificial intelligence technologies using only speech data promise improved detection of neurodegenerative disorders.METHODSSpeech collected over the telephone from 91 older participants who were cognitively healthy (n = 29) or had diagnoses of AD (n = 30) or amnestic MCI (aMCI; n = 32) was analyzed with multimodal natural language and speech processing methods. An explainable ensemble decision tree classifier for the multiclass prediction of cognitive decline was created.RESULTSThis approach was 75% accurate overall—an improvement over traditional speech‐based screening tools and a unimodal language‐based model. We include a dashboard for the examination of the results, allowing for novel ways of interpreting such data.DISCUSSIONThis work provides a foundation for a meaningful change in medicine as clinical translation, scalability, and user friendliness were core to the methodologies.Highlights Remote assessments and artificial intelligence (AI) models allow greater access to cognitive decline screening. Speech impairments differ significantly between mild AD, amnestic mild cognitive impairment (aMCI), and healthy controls. AI predictions of cognitive decline are more accurate than experts and standard tools. The AI model was 75% accurate in classifying mild AD, aMCI, and healthy controls.

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

Cited by 34 publications

References 179 publications

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

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

Distinct limbic connectivity in left and right benign mesial temporal lobe epilepsy: Evidence from a resting state functional MRI study

An explainable machine learning model of cognitive decline derived from speech

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