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Distinctive and Complementary Roles of Default Mode Network Subsystems in Semantic Cognition The default mode network (DMN) typically deactivates to external tasks, yet supports semantic cognition. It comprises medial temporal (MT), core, and frontotemporal (FT) subsystems, but its functional organization is unclear: the requirement for perceptual coupling versus decoupling, input modality (visual/verbal), type of information (social/spatial), and control demands all potentially affect its recruitment. We examined the effect of these factors on activation and deactivation of DMN subsystems during semantic cognition, across four task-based human functional magnetic resonance imaging (fMRI) datasets, and localized these responses in whole-brain state space defined by gradients of intrinsic connectivity. FT showed activation consistent with a central role across domains, tasks, and modalities, although it was most responsive to abstract, verbal tasks; this subsystem uniquely showed more “tuned” states characterized by increases in both activation and deactivation when semantic retrieval demands were higher. MT also activated to both perceptually coupled (scenes) and decoupled (autobiographical memory) tasks and showed stronger responses to picture associations, consistent with a role in scene construction. Core DMN consistently showed deactivation, especially to externally oriented tasks. These diverse contributions of DMN subsystems to semantic cognition were related to their location on intrinsic connectivity gradients: activation was closer to the sensory-motor cortex than deactivation, particularly for FT and MT, while activation for core DMN was distant from both visual cortex and cognitive control. These results reveal distinctive yet complementary DMN responses: MT and FT support different memory-based representations that are accessed externally and internally, while deactivation in core DMN is associated with demanding, external semantic tasks.

“What” and “when” predictions jointly modulate speech processing Adaptive behavior rests on forming predictions based on previous statistical regularities encountered in the environment. Such regularities pertain not only to the contents of the stimuli (“what”) but also their timing (“when”), and both interactively modulate sensory processing. In speech streams, predictions can be formed at multiple hierarchical levels, both in terms of contents (e.g. single syllables vs. words) and timing (e.g., faster vs. slower time scales). Whether and how these hierarchies map onto each other in terms of integrating “what” and “when” predictions remains unknown. Under one hypothesis neural hierarchies may link “what” and “when” predictions within sensory processing areas: with lower cortical regions mediating interactions for smaller units e.g., syllables, and higher cortical areas mediating interactions for larger units e.g., words. Alternatively, interactions between “what” and “when” predictions might rest on a generic, sensory-independent mechanism, mediated by common attention-related (e.g., frontoparietal) networks. To address those questions, we manipulated “what” and “when” predictions at two levels – single syllables and disyllabic pseudowords – while recording neural activity using magnetoencephalography (MEG) in healthy volunteers (N=22). We studied how syllable and/or word deviants are modulated by “when” predictability, both analyzing event-related fields and using source reconstruction and dynamic causal modeling to explain the observed effects in terms of the underlying effective connectivity. “When” predictions modulated “what” mismatch responses in a specific way with regards to speech hierarchy, such that mismatch responses to deviant words (vs. syllables) were amplified by temporal predictions at a slower (vs. faster) time scale. However, these modulations were source-localized to a shared network of cortical regions, including frontal and parietal sources. Effective connectivity analysis showed that, while mismatch responses to violations of “what” predictions modulated connectivity between regions, the integration of “what” and “when” predictions selectively modulated connectivity within regions, consistent with gain effects. These results suggest that the brain integrates “what” and “when” predictions that are congruent with respect to their hierarchical level, but this integration is mediated by a shared and distributed cortical network. This contrasts with recent studies indicating separable networks for different levels of hierarchical speech processing.

The role of emotion in acquisition of verb meaning Children’s earliest acquired words are often learned through sensorimotor experience, but it is less clear how children learn the meaning of concepts whose referents are less associated with sensorimotor experience. The Affective Embodiment Account postulates that children use emotional experience to learn more abstract word meanings. There is mixed evidence for this account; analyses using mega-study datasets suggest that negative or positively valenced abstract words are learned earlier than emotionally neutral abstract words, yet the relationship between sensorimotor experience and valence is inconsistent across different methods of operationalizing sensorimotor experience. In the present study, we tested the Affective Embodiment Account specifically in the context of verb acquisition. We tested two semantic dimensions of sensorimotor experience: concreteness and embodiment ratings. Our analyses showed that more positive and negative abstract verbs are acquired at an earlier age than neutral abstract verbs, consistent with the Affective Embodiment Account. When sensorimotor experience is operationalised as embodiment, high embodiment verbs are acquired at an earlier age than low embodiment verbs, and there is further benefit for high embodiment and positively valenced verbs. The findings further clarify the role of Affective Embodiment as a mechanism of language acquisition.

Representation of internal speech by single neurons in human supramarginal gyrus Speech brain–machine interfaces (BMIs) translate brain signals into words or audio outputs, enabling communication for people having lost their speech abilities due to diseases or injury. While important advances in vocalized, attempted and mimed speech decoding have been achieved, results for internal speech decoding are sparse and have yet to achieve high functionality. Notably, it is still unclear from which brain areas internal speech can be decoded. Here two participants with tetraplegia with implanted microelectrode arrays located in the supramarginal gyrus (SMG) and primary somatosensory cortex (S1) performed internal and vocalized speech of six words and two pseudowords. In both participants, we found significant neural representation of internal and vocalized speech, at the single neuron and population level in the SMG. From recorded population activity in the SMG, the internally spoken and vocalized words were significantly decodable. In an offline analysis, we achieved average decoding accuracies of 55% and 24% for each participant, respectively (chance level 12.5%), and during an online internal speech BMI task, we averaged 79% and 23% accuracy, respectively. Evidence of shared neural representations between internal speech, word reading and vocalized speech processes was found in participant 1. SMG represented words as well as pseudowords, providing evidence for phonetic encoding. Furthermore, our decoder achieved high classification with multiple internal speech strategies (auditory imagination/visual imagination). Activity in S1 was modulated by vocalized but not internal speech in both participants, suggesting no articulator movements of the vocal tract occurred during internal speech production. This work represents a proof-of-concept for a high-performance internal speech BMI.

Neural Signatures of Evidence Accumulation Encode Subjective Perceptual Confidence Independent of Performance Confidence is an adaptive computation when environmental feedback is absent, yet there is little consensus regarding how perceptual confidence is computed in the brain. Difficulty arises because confidence correlates with other factors, such as accuracy, response time (RT), or evidence quality. We investigated whether neural signatures of evidence accumulation during a perceptual choice predict subjective confidence independently of these factors. Using motion stimuli, a central-parietal positive-going electroencephalogram component (CPP) behaves as an accumulating decision variable that predicts evidence quality, RT, accuracy, and confidence (Experiment 1, N = 25 adults). When we psychophysically varied confidence while holding accuracy constant (Experiment 2, N = 25 adults), the CPP still predicted confidence. Statistically controlling for RT, accuracy, and evidence quality (Experiment 3, N = 24 adults), the CPP still explained unique variance in confidence. The results indicate that a predecision neural signature of evidence accumulation, the CPP, encodes subjective perceptual confidence in decision-making independent of task performance.

Distinct feedforward and feedback pathways for cell-type specific attention effects Selective attention is thought to depend on enhanced firing activity in extrastriate areas. Theories suggest that this enhancement depends on selective inter-areal communication via gamma (30–80 Hz) phase-locking. To test this, we simultaneously recorded from different cell types and cortical layers of macaque V1 and V4. We find that while V1-V4 gamma phase-locking between local field potentials increases with attention, the V1 gamma rhythm does not engage V4 excitatory-neurons, but only fast-spiking interneurons in L4 of V4. By contrast, attention enhances V4 spike-rates in both excitatory and inhibitory cells, most strongly in L2/3. The rate increase in L2/3 of V4 precedes V1 in time. These findings suggest enhanced signal transmission with attention does not depend on inter-areal gamma phase-locking and show that the endogenous gamma rhythm has cell-type- and layer-specific effects on downstream target areas. Similar findings were made in the mouse visual system, based on opto-tagging of identified interneurons.

When Load is Low, Working Memory is Shielded From Long-Term Memory’s Influence Previous studies found that episodic long-term memory (eLTM) enhances working memory (WM) performance when both novel and previously learnt word pairs must be retained on a short-term basis. However, there is uncertainty regarding how and when WM draws on eLTM. Three possibilities are (a) that people draw on eLTM only if WM capacity is exceeded; (b) that there is always a contribution of eLTM to WM performance, irrespective of whether prior knowledge is helpful or not; or (c) benefits of prior knowledge are specific to comparisons between conditions which are similarly ambiguous concerning whether LTM may be useful. We built on the assumption that under conditions of a contribution from LTM, these LTM traces of memoranda could benefit or hamper performance in WM tasks depending on the match between the traces stored in LTM and the ones to-be stored in WM in the current trial, yielding proactive facilitation (PF) and proactive interference (PI), respectively. Across four experiments, we familiarized participants with some items before they completed a separate WM task. In accordance with possibility (a) we show that there are indeed conditions in which only WM contributes to performance. Performance deteriorated with the addition of stimuli from eLTM when WM load was low, but not when it was high; and an exchange of information between LTM and WM occurred only when WM capacity was exceeded, with PI and PF effects affecting immediate memory performance in verbal and visual tasks only at higher set sizes.

Cortical beta oscillations map to shared brain networks modulated by dopamine Brain rhythms can facilitate neural communication for the maintenance of brain function. Beta rhythms (13–35 Hz) have been proposed to serve multiple domains of human ability, including motor control, cognition, memory and emotion, but the overarching organisational principles remain unknown. To uncover the circuit architecture of beta oscillations, we leverage normative brain data, analysing over 30 hours of invasive brain signals from 1772 cortical areas in epilepsy patients, to demonstrate that beta is the most distributed cortical brain rhythm. Next, we identify a shared brain network from beta dominant areas with deeper brain structures, like the basal ganglia, by mapping parametrised oscillatory peaks to whole-brain functional and structural MRI connectomes. Finally, we show that these networks share significant overlap with dopamine uptake as indicated by positron emission tomography. Our study suggests that beta oscillations emerge in cortico-subcortical brain networks that are modulated by dopamine. It provides the foundation for a unifying circuit-based conceptualization of the functional role of beta activity beyond the motor domain and may inspire an extended investigation of beta activity as a feedback signal for closed-loop neurotherapies for dopaminergic disorders.

The effects of intermittent theta burst stimulation over dorsal premotor cortex on primary motor cortex plasticity in young and older adults One of the universal effects of ageing is widespread deficits in motor function. Although these deficits occur at all levels of the motor system, the structural, functional and biochemical changes within the brain are important (Seidler et al., 2010). In particular, alterations to the ability of the brain’s motor system to continuously modify its structure and function are a critical factor. Termed neuroplasticity, this process is initially mediated by changes in the strength of synaptic communication with long-term potentiation (LTP) and depression (LTD) and underpins the ability to learn new motor skills (Buonomano & Merzenich, 1998; Sanes & Donoghue, 2000). While the capacity for neuroplastic change is present across the lifespan, some studies using non-invasive brain stimulation (NIBS) show reduced plasticity in older adults (Fathi et al., 2010; Freitas et al., 2011; Müller-Dahlhaus et al., 2008; Todd et al., 2010). This reduced plasticity may contribute to the motor deficits that limit the ability of older adults to learn or modify motor skills that may be essential for daily life. However, the neurophysiological mechanisms underpinning these changes with advancing age remain unclear.