Semantic memory involvement in the default mode network: A functional neuroimaging study using independent component analysis

The primacy of experience in language processing: Semantic priming is driven primarily by experiential similarity

The organization of semantic memory, including memory for word meanings, has long been a central question in cognitive science. Although there is general agreement that word meaning representations must make contact with sensory-motor and affective experiences in a non-arbitrary fashion, the nature of this relationship remains controversial. One prominent view proposes that word meanings are represented directly in terms of their experiential content (i.e., sensory-motor and affective representations). Opponents of this view argue that the representation of word meanings reflects primarily taxonomic structure, that is, their relationships to natural categories. In addition, the recent success of language models based on word co-occurrence (i.e., distributional) information in emulating human linguistic behavior has led to proposals that this kind of information may play an important role in the representation of lexical concepts. We used a semantic priming paradigm designed for representational similarity analysis (RSA) to quantitatively assess how well each of these theories explains the representational similarity pattern for a large set of words. Crucially, we used partial correlation RSA to account for intercorrelations between model predictions, which allowed us to assess, for the first time, the unique effect of each model. Semantic priming was driven primarily by experiential similarity between prime and target, with no evidence of an independent effect of distributional or taxonomic similarity. Furthermore, only the experiential models accounted for unique variance in priming after partialling out explicit similarity ratings. These results support experiential accounts of semantic representation and indicate that, despite their good performance at some linguistic tasks, the distributional models evaluated here do not encode the same kind of information used by the human semantic system.

Linguistic inputs must be syntactically parsable to fully engage the language network

Human language comprehension is remarkably robust to ill-formed inputs (e.g., word transpositions). This robustness has led some to argue that syntactic parsing is largely an illusion, and that incremental comprehension is more heuristic, shallow, and semantics-based than is often assumed. However, the available data are also consistent with the possibility that humans always perform rule-like symbolic parsing and simply deploy error correction mechanisms to reconstruct ill-formed inputs when needed. We put these hypotheses to a new stringent test by examining brain responses to a) stimuli that should pose a challenge for syntactic reconstruction but allow for complex meanings to be built within local contexts through associative/shallow processing (sentences presented in a backward word order), and b) grammatically well-formed but semantically implausible sentences that should impede semantics-based heuristic processing. Using a novel behavioral syntactic reconstruction paradigm, we demonstrate that backward-presented sentences indeed impede the recovery of grammatical structure during incremental comprehension. Critically, these backward-presented stimuli elicit a relatively low response in the language areas, as measured with fMRI. In contrast, semantically implausible but grammatically well-formed sentences elicit a response in the language areas similar in magnitude to naturalistic (plausible) sentences. In other words, the ability to build syntactic structures during incremental language processing is both necessary and sufficient to fully engage the language network. Taken together, these results provide strongest to date support for a generalized reliance of human language comprehension on syntactic parsing.

Verbal memory network mapping in individual patients predicts postoperative functional impairments

Verbal memory decline is a significant concern following temporal lobe surgeries in patients with epilepsy, emphasizing the need for precision presurgical verbal memory mapping to optimize functional outcomes. However, the inter-individual variability in functional networks and brain function-structural dissociations pose challenges when relying solely on group-level atlases or anatomical landmarks for surgical guidance. Here, we aimed to develop and validate a personalized functional mapping technique for verbal memory using precision resting-state functional MRI (rs-fMRI) and neurosurgery. A total of 38 patients with refractory epilepsy scheduled for surgical interventions were enrolled and 28 patients were analyzed in the study. Baseline 30-min rs-fMRI scanning, verbal memory and language assessments were collected for each patient before surgery. Personalized verbal memory networks (PVMN) were delineated based on preoperative rs-fMRI data for each patient. The accuracy of PVMN was assessed by comparing post-operative functional impairments and the overlapping extent between PVMN and surgical lesions. A total of 14 out of 28 patients experienced clinically meaningful declines in verbal memory after surgery. The personalized network and the group-level atlas exhibited 100% and 75.0% accuracy in predicting postoperative verbal memory declines, respectively. Moreover, six patients with extra-temporal lesions that overlapped with PVMN showed selective impairments in verbal memory. Furthermore, the lesioned ratio of the personalized network rather than the group-level atlas was significantly correlated with postoperative declines in verbal memory (personalized networks: r = -0.39, p = .038; group-level atlas: r = -0.19, p = .332). In conclusion, our personalized functional mapping technique, using precision rs-fMRI, offers valuable insights into individual variability in the verbal memory network and holds promise in precision verbal memory network mapping in individuals.

How does the "default mode" network contribute to semantic cognition?

This review examines whether and how the "default mode" network (DMN) contributes to semantic processing. We review evidence implicating the DMN in the processing of individual word meanings and in sentence- and discourse-level semantics. Next, we argue that the areas comprising the DMN contribute to semantic processing by coordinating and integrating the simultaneous activity of local neuronal ensembles across multiple unimodal and multimodal cortical regions, creating a transient, global neuronal ensemble. The resulting ensemble implements an integrated simulation of phenomenological experience - that is, an embodied situation model - constructed from various modalities of experiential memory traces. These situation models, we argue, are necessary not only for semantic processing but also for aspects of cognition that are not traditionally considered semantic. Although many aspects of this proposal remain provisional, we believe it provides new insights into the relationships between semantic and non-semantic cognition and into the functions of the DMN.

Structural connectome combining DTI features predicts postoperative language decline and its recovery in glioma patients

OBJECTIVES

A decline in language function is a common complication after glioma surgery, affecting patients' quality of life and survival. This study predicts the postoperative decline in language function and whether it can be recovered based on the preoperative white matter structural network.

MATERIALS AND METHODS

Eighty-one right-handed patients with glioma involving the left hemisphere were retrospectively included. Their language function was assessed using the Western Aphasia Battery before and 1 week and 3 months after surgery. Structural connectome combining DTI features was selected to predict postoperative language decline and recovery. Nested cross-validation was used to optimize the models, evaluate the prediction performance of the models, and identify the most predictive features.

RESULTS

Five, seven, and seven features were finally selected as the predictive features in each model and used to establish predictive models for postoperative language decline (1 week after surgery), long-term language decline (3 months after surgery), and language recovery, respectively. The overall accuracy of the three models in nested cross-validation and overall area under the receiver operating characteristic curve were 0.840, 0.790, and 0.867, and 0.841, 0.778, and 0.901, respectively.

CONCLUSION

We used machine learning algorithms to establish models to predict whether the language function of glioma patients will decline after surgery and whether postoperative language deficit can recover, which may help improve the development of treatment strategies. The difference in features in the non-language decline or the language recovery group may reflect the structural basis for the protection and compensation of language function in gliomas.

CLINICAL RELEVANCE STATEMENT

Models can predict the postoperative language decline and whether it can recover in glioma patients, possibly improving the development of treatment strategies. The difference in selected features may reflect the structural basis for the protection and compensation of language function.

KEY POINTS

• Structural connectome combining diffusion tensor imaging features predicted glioma patients' language decline after surgery. • Structural connectome combining diffusion tensor imaging features predicted language recovery of glioma patients with postoperative language disorder. • Diffusion tensor imaging and connectome features related to language function changes imply plastic brain regions and connections.

The angular gyrus serves as an interface between the non-lexical reading network and the semantic system: evidence from dynamic causal modeling

Understanding encoded language, such as written words, requires multiple cognitive processes that act in a parallel and interactive fashion. These processes and their interactions, however, are not fully understood. Various conceptual and methodical approaches including computational modeling and neuroimaging have been applied to better understand the neural underpinnings of these complex processes in the human brain. In this study, we tested different predictions of cortical interactions that derived from computational models for reading using dynamic causal modeling. Morse code was used as a model for non-lexical decoding followed by a lexical-decision during a functional magnetic resonance examination. Our results suggest that individual letters are first converted into phonemes within the left supramarginal gyrus, followed by a phoneme assembly to reconstruct word phonology, involving the left inferior frontal cortex. To allow the identification and comprehension of known words, the inferior frontal cortex then interacts with the semantic system via the left angular gyrus. As such, the left angular gyrus is likely to host phonological and semantic representations and serves as a bidirectional interface between the networks involved in language perception and word comprehension.

The Primacy of Experience in Language Processing: Semantic Priming Is Driven Primarily by Experiential Similarity

The organization of semantic memory, including memory for word meanings, has long been a central question in cognitive science. Although there is general agreement that word meaning representations must make contact with sensory-motor and affective experiences in a non-arbitrary fashion, the nature of this relationship remains controversial. One prominent view proposes that word meanings are represented directly in terms of their experiential content (i.e., sensory-motor and affective representations). Opponents of this view argue that the representation of word meanings reflects primarily taxonomic structure, that is, their relationships to natural categories. In addition, the recent success of language models based on word co-occurrence (i.e., distributional) information in emulating human linguistic behavior has led to proposals that this kind of information may play an important role in the representation of lexical concepts. We used a semantic priming paradigm designed for representational similarity analysis (RSA) to quantitatively assess how well each of these theories explains the representational similarity pattern for a large set of words. Crucially, we used partial correlation RSA to account for intercorrelations between model predictions, which allowed us to assess, for the first time, the unique effect of each model. Semantic priming was driven primarily by experiential similarity between prime and target, with no evidence of an independent effect of distributional or taxonomic similarity. Furthermore, only the experiential models accounted for unique variance in priming after partialling out explicit similarity ratings. These results support experiential accounts of semantic representation and indicate that, despite their good performance at some linguistic tasks, the distributional models evaluated here do not encode the same kind of information used by the human semantic system.

Cortical networks for recognition of speech with simultaneous talkers

The relative contributions of superior temporal vs. inferior frontal and parietal networks to recognition of speech in a background of competing speech remain unclear, although the contributions themselves are well established. Here, we use fMRI with spectrotemporal modulation transfer function (ST-MTF) modeling to examine the speech information represented in temporal vs. frontoparietal networks for two speech recognition tasks with and without a competing talker. Specifically, 31 listeners completed two versions of a three-alternative forced choice competing speech task: "Unison" and "Competing", in which a female (target) and a male (competing) talker uttered identical or different phrases, respectively. Spectrotemporal modulation filtering (i.e., acoustic distortion) was applied to the two-talker mixtures and ST-MTF models were generated to predict brain activation from differences in spectrotemporal-modulation distortion on each trial. Three cortical networks were identified based on differential patterns of ST-MTF predictions and the resultant ST-MTF weights across conditions (Unison, Competing): a bilateral superior temporal (S-T) network, a frontoparietal (F-P) network, and a network distributed across cortical midline regions and the angular gyrus (M-AG). The S-T network and the M-AG network responded primarily to spectrotemporal cues associated with speech intelligibility, regardless of condition, but the S-T network responded to a greater range of temporal modulations suggesting a more acoustically driven response. The F-P network responded to the absence of intelligibility-related cues in both conditions, but also to the absence (presence) of target-talker (competing-talker) vocal pitch in the Competing condition, suggesting a generalized response to signal degradation. Task performance was best predicted by activation in the S-T and F-P networks, but in opposite directions (S-T: more activation = better performance; F-P: vice versa). Moreover, S-T network predictions were entirely ST-MTF mediated while F-P network predictions were ST-MTF mediated only in the Unison condition, suggesting an influence from non-acoustic sources (e.g., informational masking) in the Competing condition. Activation in the M-AG network was weakly positively correlated with performance and this relation was entirely superseded by those in the S-T and F-P networks. Regarding contributions to speech recognition, we conclude: (a) superior temporal regions play a bottom-up, perceptual role that is not qualitatively dependent on the presence of competing speech; (b) frontoparietal regions play a top-down role that is modulated by competing speech and scales with listening effort; and (c) performance ultimately relies on dynamic interactions between these networks, with ancillary contributions from networks not involved in speech processing per se (e.g., the M-AG network).

Decoding reappraisal and suppression from neural circuits: A combined supervised and unsupervised machine learning approach

Emotion regulation is a core construct of mental health and deficits in emotion regulation abilities lead to psychological disorders. Reappraisal and suppression are two widely studied emotion regulation strategies but, possibly due to methodological limitations in previous studies, a consistent picture of the neural correlates related to the individual differences in their habitual use remains elusive. To address these issues, the present study applied a combination of unsupervised and supervised machine learning algorithms to the structural MRI scans of 128 individuals. First, unsupervised machine learning was used to separate the brain into naturally grouping grey matter circuits. Then, supervised machine learning was applied to predict individual differences in the use of different strategies of emotion regulation. Two predictive models, including structural brain features and psychological ones, were tested. Results showed that a temporo-parahippocampal-orbitofrontal network successfully predicted the individual differences in the use of reappraisal. Differently, insular and fronto-temporo-cerebellar networks successfully predicted suppression. In both predictive models, anxiety, the opposite strategy, and specific emotional intelligence factors played a role in predicting the use of reappraisal and suppression. This work provides new insights regarding the decoding of individual differences from structural features and other psychologically relevant variables while extending previous observations on the neural bases of emotion regulation strategies.

Comparing reappraisal and acceptance strategies to understand the neural architecture of emotion regulation: a meta-analytic approach

Introduction

In the emotion regulation literature, the amount of neuroimaging studies on cognitive reappraisal led the impression that the same top-down, control-related neural mechanisms characterize all emotion regulation strategies. However, top-down processes may coexist with more bottom-up and emotion-focused processes that partially bypass the recruitment of executive functions. A case in point is acceptance-based strategies.

Method

To better understand neural commonalities and differences behind different emotion regulation processes, in the present study, we applied the Activation Likelihood Estimation (ALE) method to perform a meta-analysis on fMRI studies investigating task-related activity of reappraisal and acceptance. Both increased and decreased brain activity was taken into account in the contrast and conjunction analysis between the two strategies.

Results

Results showed increased activity in left-inferior frontal gyrus and insula for both strategies, and decreased activity in the basal ganglia for reappraisal, and decreased activity in limbic regions for acceptance.

Discussion

These findings are discussed in the context of a model of common and specific neural mechanisms of emotion regulation that support and expand the previous dual-routes models. We suggest that emotion regulation may rely on a core inhibitory circuit, and on strategy-specific top-down and bottom-up processes distinct for different strategies.

Visuo-spatial attention and semantic memory competition in the parietal cortex

Neuroimaging studies associate specific functional roles to distinct brain regions investigating separate cognitive processes using dedicated tasks. For example, using both correlative (i.e., fMRI) and causal (i.e., TMS) approaches it has been shown the involvement of intra-parietal sulcus (IPS), as part of the dorsal attention network, in spatial attentional tasks as well as the importance of the angular gyrus (AG), as part of the default mode network, during the selection of relevant information in semantic memory. Nonetheless, in our daily life attention and semantic memory are rarely needed in isolation. In the present TMS study we investigate how the brain combines attentional and semantic memory demands in a single task. Results showed that, compared to a pseudo-TMS, stimulation of IPS, but not AG, affects behavioral performance, thus suggesting its preponderant role in such a combined task. Moreover, the lack of difference between the effect of IPS and AG stimulations seems to suggest that the two regions may be coactivated or that a third-party source might indirectly mediate the interaction between the two networks.

Characterizing different cognitive and neurobiological profiles in a community sample of children using a non-parametric approach: An fMRI study

Executive Functions (EF) is an umbrella term for a set of mental processes geared towards goal-directed behavior supporting academic skills such as reading abilities. One of the brain's functional networks implicated in EF is the Default Mode Network (DMN). The current study uses measures of inhibitory control, a main sub-function of EF, to create cognitive and neurobiological "inhibitory control profiles" and relate them to reading abilities in a large sample (N = 5055) of adolescents aged 9-10 from the Adolescent Brain Cognitive Development (ABCD) study. Using a Latent Profile Analysis (LPA) approach, data related to inhibitory control was divided into four inhibition classes. For each class, functional connectivity within the DMN was calculated from resting-state data, using a non-parametric algorithm for detecting group similarities. These inhibitory control profiles were then related to reading abilities. The four inhibitory control groups showed significantly different reading abilities, with neurobiologically different DMN segregation profiles for each class versus controls. The current study demonstrates that a community sample of children is not entirely homogeneous and is composed of different subgroups that can be differentiated both behaviorally/cognitively and neurobiologically, by focusing on inhibitory control and the DMN. Educational implications relating these results to reading abilities are noted.

Dynamics of working memory process revealed by independent component analysis in an fMRI study

Human memory is prone to errors in many everyday activities but also when cultivating hobbies such as traveling and/or learning a new language. For instance, while visiting foreign countries, people erroneously recall foreign language words that are meaningless to them. Our research simulated such errors in a modified Deese-Roediger-McDermott paradigm for short-term memory with phonologically related stimuli aimed at uncovering behavioral and neuronal indices of false memory formation with regard to time-of-day, a variable known to influence memory. Fifty-eight participants were tested in a magnetic resonance (MR) scanner twice. The results of an Independent Component Analysis revealed encoding-related activity of the medial visual network preceding correct recognition of positive probes and correct rejection of lure probes. The engagement of this network preceding false alarms was not observed. We also explored if diurnal rhythmicity influences working memory processes. Diurnal differences were seen in the default mode network and the medial visual network with lower deactivation in the evening hours. The GLM results showed greater activation of the right lingual gyrus, part of the visual cortex and the left cerebellum in the evening. The study offers new insight into the mechanisms associated with false memories, suggesting that deficient engagement of the medial visual network during the memorization phase of a task results in short-term memory distortions. The results shed new light on the dynamics of working memory processes by taking into account the effect of time-of-day on memory performance.

Mechanical Affective Touch Therapy for Anxiety Disorders: Effects on Resting State Functional Connectivity

OBJECTIVES

Mechanical Affective Touch Therapy (MATT) is a safe, novel form of noninvasive peripheral nerve stimulation. Although mechanical stimulation activates nerves, we know little about its impact on psychiatric symptoms and their underlying cortical mechanisms. We examined the effects of open-label MATT on resting state functional connectivity (RSFC) and its relationship with anxiety and affective symptomatology (clinical results in separate report).

MATERIALS AND METHODS

A total of 22 adults with an Axis I anxiety disorder were recruited from the community. After two initial sessions assisted by research staff, participants self-administered 20-minute sessions of MATT at home at least twice daily for four weeks. Self-report measures of mood and anxiety severity were collected at baseline, two weeks, and four weeks. Resting state functional magnetic resonance imaging was collected before the initial MATT session (n = 20), immediately after the first session (n = 18), and following four weeks of MATT (n = 14). Seed-based whole-brain functional connectivity analyses identified brain connectivity patterns correlated with responsiveness to MATT. Seeds were based on Neurosynth meta-analytic maps for "anxiety" and "pain" given MATT's hypothesized role in anxiety symptom amelioration and potential mechanism of action through C-tactile afferents, which play an important role in detecting pain and its affective components. Connectivity results were corrected for multiple comparisons (voxel p < 0.005, cluster p-FDR < 0.05).

RESULTS

Baseline RSFC is predictive of symptom improvement with chronic MATT. Acute increases in insula connectivity were observed between mid-cingulate cortex and postcentral motor regions following the first MATT session. Chronic MATT was associated with increased connectivity between pain and anxiety regions of interest (ROIs) and posterior default mode network (DMN) regions involved in memory and self-reflection; the connectivity changes correlated with decreases in stress and depression symptoms.

CONCLUSIONS

MATT is associated with alterations in RSFC in the DMN of anxiety disorder patients both acutely and after long-term administration, and baseline RSFC is predictive of post-treatment symptom improvement.

Functional connectivity differences in speech production networks in Chinese children with Rolandic epilepsy

Previous studies have demonstrated that language impairments are frequently observed in patients with benign epilepsy with centrotemporal spikes (BECTS). However, how BECTS affects language processing in the Chinese population remains unclear. With the use of functional magnetic resonance imaging (fMRI) in an overt picture-naming task, the present study examined functional connectivity in 27 children with BECTS and 26 healthy controls. The results indicated that children with BECTS showed altered functional connectivity associated with speech production between the left precuneus and the right cerebellum, between the right precuneus and the bilateral thalamus and the left superior temporal gyrus, between the right cuneus and the right postcentral gyrus and the right inferior parietal lobule, and between the right cerebellum and right middle frontal gyrus. Collectively, the findings in this study demonstrate the abnormal functional connectivity basis of speech production in Chinese children with BECTS, providing clues to understanding the brain mechanisms of language-related network in patients with BECTS.

Generalizable predictive modeling of semantic processing ability from functional brain connectivity

Semantic processing (SP) is one of the critical abilities of humans for representing and manipulating conceptual and meaningful information. Neuroimaging studies of SP typically collapse data from many subjects, but its neural organization and behavioral performance vary between individuals. It is not yet understood whether and how the individual variabilities in neural network organizations contribute to the individual differences in SP behaviors. We aim to identify the neural signatures underlying SP variabilities by analyzing functional connectivity (FC) patterns based on a large‐sample Human Connectome Project (HCP) dataset and rigorous predictive modeling. We used a two‐stage predictive modeling approach to build an internally cross‐validated model and to test the model's generalizability with unseen data from different HCP samples and other out‐of‐sample datasets. FC patterns within a putative semantic brain network were significantly predictive of individual SP scores summarized from five SP‐related behavioral tests. This cross‐validated model can be used to predict unseen HCP data. The model generalizability was enhanced in the language task compared with other tasks used during scanning and was better for females than males. The model constructed from the HCP dataset can be partially generalized to two independent cohorts that participated in different semantic tasks. FCs connecting to the Perisylvian language network show the most reliable contributions to predictive modeling and the out‐of‐sample generalization. These findings contribute to our understanding of the neural sources of individual differences in SP, which potentially lay the foundation for personalized education for healthy individuals and intervention for SP and language deficits patients.

Changes in the Intranetwork and Internetwork Connectivity of the Default Mode Network and Olfactory Network in Patients with COVID-19 and Olfactory Dysfunction

Olfactory dysfunction (OD) is a common symptom in coronavirus disease 2019 (COVID-19) patients. Moreover, many neurological manifestations have been reported in these patients, suggesting central nervous system involvement. The default mode network (DMN) is closely associated with olfactory processing. In this study, we investigated the internetwork and intranetwork connectivity of the DMN and the olfactory network (ON) in 13 healthy controls and 22 patients presenting with COVID-19-related OD using independent component analysis and region of interest functional magnetic resonance imaging (fMRI) analysis. There was a significant correlation between the butanol threshold test (BTT) and the intranetwork connectivity in ON. Meanwhile, the COVID-19 patients with OD showed significantly higher intranetwork connectivity in the DMN, as well as higher internetwork connectivity between ON and DMN. However, no significant difference was found between groups in the intranetwork connectivity within ON. We postulate that higher intranetwork functional connectivities compensate for the deficits in olfactory processing and general well-being in COVID-19 patients. Nevertheless, the compensation process in the ON may not be obvious at this stage. Our results suggest that resting-state fMRI is a potentially valuable tool to evaluate neurosensory dysfunction in COVID-19 patients.

Contributions of sex, depression, and cognition on brain connectivity dynamics in Parkinson's disease

Alterations in time-varying functional connectivity (FC) have been found in Parkinson's disease (PD) patients. To date, very little is known about the influence of sex on brain FC in PD patients and how this could be related to disease severity. The first objective was to evaluate the influence of sex on dynamic FC characteristics in PD patients and healthy controls (HC), while the second aim was to investigate the temporal patterns of dynamic connectivity related to PD motor and non-motor symptoms. Ninety-nine PD patients and sixty-two HC underwent a neuropsychological and clinical assessment. Rs-fMRI and T1-weighted MRI were also acquired. Dynamic FC analyses were performed in the GIFT toolbox. Dynamic FC analyses identified two States: State I, characterized by within-network positive coupling; and State II that showed between-network connectivity, mostly involving somatomotor and visual networks. Sex differences were found in dynamic indexes in HC but these differences were not observed in PD. Hierarchical clustering analysis identified three phenotypically distinct PD subgroups: (1) Subgroup A was characterized by mild motor symptoms; (2) Subgroup B was characterized by depressive and motor symptoms; (3) Subgroup C was characterized by cognitive and motor symptoms. Results revealed that changes in the temporal properties of connectivity were related to the motor/non-motor outcomes of PD severity. Findings suggest that while in HC sex differences may play a certain role in dynamic connectivity patterns, in PD patients, these effects may be overcome by the neurodegenerative process. Changes in the temporal properties of connectivity in PD were mainly related to the clinical markers of PD severity.

Separate neural networks of implicit emotional processing between pictures and words: A coordinate-based meta-analysis of brain imaging studies

Both pictures and words are frequently employed as experimental stimuli to investigate the neurocognitive mechanisms of emotional processing. However, it remains unclear whether emotional picture processing and emotional word processing share neural underpinnings. To address this issue, we focus on neuroimaging studies examining the implicit processing of affective words and pictures, which require participants to meet cognitive task demands under the implicit influence of emotional pictorial or verbal stimuli. A coordinate-based activation likelihood estimation meta-analysis was conducted on these studies, which revealed no common activation maximum between the picture and word conditions. Specifically, implicit negative picture processing (35 experiments, 393 foci, and 932 subjects) engages the bilateral amygdala, left hippocampus, fusiform gyri, and right insula, which are mainly located in the subcortical network and visual network associated with bottom-up emotional responses. In contrast, implicit negative word processing (34 experiments, 316 foci, and 799 subjects) engages the default mode network and fronto-parietal network including the ventrolateral prefrontal cortex, dorsolateral prefrontal cortex, and dorsomedial prefrontal cortex, indicating the involvement of top-down semantic processing and emotion regulation. Our findings indicate that affective pictures (that intrinsically have an affective valence) and affective words (that inherit the affective valence from their object) modulate implicit emotional processing in different ways, and therefore recruit distinct brain systems.

Age-Dependent Contribution of Domain-General Networks to Semantic Cognition

Aging is characterized by a decline of cognitive control. In semantic cognition, this leads to the paradox that older adults usually show poorer task performance than young adults despite their greater semantic knowledge. So far, the underlying neural changes of these behavioral differences are poorly understood. In the current neuroimaging study, we investigated the interaction of domain-specific and domain-general networks during verbal semantic fluency in young and older adults. Across age groups, task processing was characterized by a strong positive integration within the multiple-demand as well as between the multiple-demand and the default mode network during semantic fluency. However, the behavioral relevance of strengthened connectivity differed between groups: While within-network functional connectivity in both networks predicted greater efficiency in semantic fluency in young adults, it was associated with slower performance in older adults. Moreover, only young adults profited from connectivity between networks for their semantic memory performance. Our results suggest that the functional coupling of usually anticorrelated networks is critical for successful task processing, independent of age, when access to semantic memory is required. Furthermore, our findings lend novel support to the notion of reduced efficiency in the aging brain due to neural dedifferentiation in semantic cognition.

Professional chess expertise modulates whole brain functional connectivity pattern homogeneity and couplings

Previous studies have revealed changed functional connectivity patterns between brain areas in chess players using resting-state functional magnetic resonance imaging (rs-fMRI). However, how to exactly characterize the voxel-wise whole brain functional connectivity pattern changes in chess players remains unclear. It could provide more convincing evidence for establishing the relationship between long-term chess practice and brain function changes. In this study, we employed newly developed whole brain functional connectivity pattern homogeneity (FcHo) method to identify the voxel-wise changes of functional connectivity patterns in 28 chess master players and 27 healthy novices. Seed-based functional connectivity analysis was used to identify the alteration of corresponding functional couplings. FcHo analysis revealed significantly increased whole brain functional connectivity pattern similarity in anterior cingulate cortex (ACC), anterior middle temporal gyrus (aMTG), primary visual cortex (V1), and decreased FcHo in thalamus and precentral gyrus in chess players. Resting-state functional connectivity analyses identified chess players showing decreased functional connections between V1 and precentral gyrus. Besides, a linear support vector machine (SVM) based classification achieved an accuracy of 85.45%, a sensitivity of 85.71% and a specificity of 85.19% to differentiate chess players from novices by leave-one-out cross-validation. Finally, correlation analyses revealed that the mean FcHo values of thalamus were significantly negatively correlated with the training time. Our findings provide new evidences for the important roles of ACC, aMTG, V1, thalamus and precentral gyrus in chess players. The findings also indicate that long-term professional chess training may enhance the semantic and episodic processing, efficiency of visual-motor transformation, and cognitive ability.

Memory recovery in relation to default mode network impairment and neurite density during brain tumor treatment

OBJECTIVE

The aim of this study was to test brain tumor interactions with brain networks, thereby identifying protective features and risk factors for memory recovery after resection.

METHODS

Seventeen patients with diffuse nonenhancing glioma (ages 22-56 years) underwent longitudinal MRI before and after surgery, and during a 12-month recovery period (47 MRI scans in total after exclusion). After each scanning session, a battery of memory tests was performed using a tablet-based screening tool, including free verbal memory, overall verbal memory, episodic memory, orientation, forward digit span, and backward digit span. Using structural MRI and neurite orientation dispersion and density imaging (NODDI) derived from diffusion-weighted images, the authors estimated lesion overlap and neurite density, respectively, with brain networks derived from normative data in healthy participants (somatomotor, dorsal attention, ventral attention, frontoparietal, and default mode network [DMN]). Linear mixed-effect models (LMMs) that regressed out the effect of age, gender, tumor grade, type of treatment, total lesion volume, and total neurite density were used to test the potential longitudinal associations between imaging markers and memory recovery.

RESULTS

Memory recovery was not significantly associated with either the tumor location based on traditional lobe classification or the type of treatment received by patients (i.e., surgery alone or surgery with adjuvant chemoradiotherapy). Nonlocal effects of tumors were evident on neurite density, which was reduced not only within the tumor but also beyond the tumor boundary. In contrast, high preoperative neurite density outside the tumor but within the DMN was associated with better memory recovery (LMM, p value after false discovery rate correction [Pfdr] < 10-3). Furthermore, postoperative and follow-up neurite density within the DMN and frontoparietal network were also associated with memory recovery (LMM, Pfdr = 0.014 and Pfdr = 0.001, respectively). Preoperative tumor and postoperative lesion overlap with the DMN showed a significant negative association with memory recovery (LMM, Pfdr = 0.002 and Pfdr < 10-4, respectively).

CONCLUSIONS

Imaging biomarkers of cognitive recovery and decline can be identified using NODDI and resting-state networks. Brain tumors and their corresponding treatment affecting brain networks that are fundamental for memory functioning such as the DMN can have a major impact on patients' memory recovery.

Strategies and cognitive reserve to preserve lexical production in aging

In the absence of any neuropsychiatric condition, older adults may show declining performance in several cognitive processes and among them, in retrieving and producing words, reflected in slower responses and even reduced accuracy compared to younger adults. To overcome this difficulty, healthy older adults implement compensatory strategies, which are the focus of this paper. We provide a review of mainstream findings on deficient mechanisms and possible neurocognitive strategies used by older adults to overcome the deleterious effects of age on lexical production. Moreover, we present findings on genetic and lifestyle factors that might either be protective or risk factors of cognitive impairment in advanced age. We propose that "aging-modulating factors" (AMF) can be modified, offering prevention opportunities against aging effects. Based on our review and this proposition, we introduce an integrative neurocognitive model of mechanisms and compensatory strategies for lexical production in older adults (entitled Lexical Access and Retrieval in Aging, LARA). The main hypothesis defended in LARA is that cognitive aging evolves heterogeneously and involves complementary domain-general and domain-specific mechanisms, with substantial inter-individual variability, reflected at behavioral, cognitive, and brain levels. Furthermore, we argue that the ability to compensate for the effect of cognitive aging depends on the amount of reserve specific to each individual which is, in turn, modulated by the AMF. Our conclusion is that a variety of mechanisms and compensatory strategies coexist in the same individual to oppose the effect of age. The role of reserve is pivotal for a successful coping with age-related changes and future research should continue to explore the modulating role of AMF.

Effects of emotional maltreatment on semantic network activity during cognitive reappraisal

Maltreatment experiences alter brain development associated with emotion processing, and dysregulation of emotion may trigger mental health problems in maltreated people. However, studies revealing alterations in brain networks during cognitive reappraisal in victims of maltreatment are strikingly insufficient. In this study, 27 healthy subjects were recruited. The maltreatment experiences and positive reappraisal abilities were measured using the Childhood Trauma Questionnaire-Short Form (CTQ-SF) and Cognitive Emotion Regulation Questionnaire (CERQ), respectively. A cognitive reappraisal task using the International Affective Picture System (IAPS) was designed for functional magnetic resonance imaging (fMRI) experiments. Cognitive reappraisal induced more activities in the bilateral inferior parietal lobes and bilateral middle temporal gyri compared to the condition of "look" (false discovery rate (FDR) corrected p < 0.05). Furthermore, the left inferior parietal lobe and right middle temporal gyrus functionally interacted with components of the default mode network, including the precuneus and the posterior cingulate cortex. In residual analyses after controlling for age and depressive symptoms, the bilateral inferior parietal and middle temporal activities exhibited positive correlations with cognitive reappraisal abilities (all ps < 0.05), and emotional maltreatment experiences were negatively correlated with the left inferior parietal cortex, bilateral middle temporal cortex activities, and left inferior parietal lobe-posterior cingulate cortex connectivity (all ps < 0.05). We found that semantic networks were significant to cognitive reappraisal, especially reinterpretation, and negative effects of emotional maltreatment experiences on semantic network activities.

The Unique Fiber Anatomy of Middle Temporal Gyrus Default Mode Connectivity

BACKGROUND

The middle temporal gyrus (MTG) is understood to play a role in language-related tasks such as lexical comprehension and semantic cognition. However, a more specific understanding of its key white matter connections could promote the preservation of these functions during neurosurgery.

OBJECTIVE

To provide a detailed description of the underlying white matter tracts associated with the MTG to improve semantic preservation during neurosurgery.

METHODS

Tractography was performed using diffusion imaging obtained from 10 healthy adults from the Human Connectome Project. All tracts were mapped between cerebral hemispheres with a subsequent laterality index calculated based on resultant tract volumes. Ten postmortem dissections were performed for ex vivo validation of the tractography based on qualitative visual agreement.

RESULTS

We identified 2 major white matter bundles leaving the MTG: the inferior longitudinal fasciculus and superior longitudinal fasciculus. In addition to long association fibers, a unique linear sequence of U-shaped fibers was identified, possibly representing a form of visual semantic transfer down the temporal lobe.

CONCLUSION

We elucidate the underlying fiber-bundle anatomy of the MTG, an area highly involved in the brain's language network. Improved understanding of the unique, underlying white matter connections in and around this area may augment our overall understanding of language processing as well as the involvement of higher order cerebral networks like the default mode network in these functions.

rTMS affects EEG microstates dynamic during evoked activity

Electrophysiological (EEG) correlates both at time (i.e., event-related potentials, ERP) and frequency (i.e., event-related desynchronization, ERD) domains have been shown to be modulated by external magnetic interference. Parallel studies reported a similar interference also for the EEG microstate at rest and in the period that anticipates a task. Here we investigated whether such interference was prolonged during the evoked activity in the framework of the semantic decision task. To this aim, rTMS was delivered over a core region of both the Default mode network and the language network (i.e., left angular gyrus, AG), previously associated to the current task, and as active control we stimulated the left IPS. When subjects received a non-active stimulation (i.e., Sham), in the period that follows the target onset (i.e., 2 sec after the rTMS) we found an interesting alternation of two dominant microstates (MS1, MS3), previously associated to the phonological network and the Cingulo-Opercular Network (CON), respectively. This dynamic was not altered when TMS was delivered over the left IPS. On the contrary, rTMS over left AG selectively suppressed the phonological-related microstate. These findings provide the first causal evidence of region specificity of the EEG microstates topography during the evoked activity corroborating the idea of a crucial role of AG in the semantic memory. Moreover, the present results might provide insight for understanding the neurophysiological correlates of language disorders e.g., aphasia as well as for planning non-invasive brain stimulation protocols for the rehabilitation.

Altered functional connectivity of cortical networks in semantic variant Primary Progressive Aphasia

As their illness progresses, patients with the semantic variant of Primary Progressive Aphasia (svPPA) frequently exhibit peculiar behaviors indicative of altered visual attention or an increased interest in artistic endeavors. In the present study, we examined changes within and between large-scale functional brain networks that may explain this altered visual behavior. We first examined the connectivity of the visual association network, the dorsal attention network, and the default mode network in healthy young adults (n = 89) to understand the typical architecture of these networks in the healthy brain. We then compared the large-scale functional connectivity of these networks in a group of svPPA patients (n = 12) to a group of age-matched cognitively normal controls (n = 30). Our results showed that the between-network connectivity of the dorsal attention and visual association networks was elevated in svPPA patients relative to controls. We further showed that this heightened between-network connectivity was associated with a decrease in the within-network connectivity of the default mode network, possibly due to progressive degeneration of the anterior temporal lobes in svPPA. These results suggest that focal neurodegeneration can lead to the reorganization of large-scale cognitive networks beyond the primarily affected network(s), possibly contributing to cognitive or behavioral changes that are commonly present as part of the clinical phenotype of svPPA.

Functional Connectivity of Successful Picture-Naming: Age-Specific Organization and the Effect of Engaging in Stimulating Activities

Aging is a lifelong process that starts at birth. Throughout the course of their life, individuals are exposed to various levels of stimulating activities. A higher level of engagement in such activities is suspected to protect against the normal course of cognitive aging or the cognitive manifestations of age-related brain diseases. However, the exact mechanism underlying such protective action remains unclear. The concept of the neurocognitive reserve was introduced to refer to the hypothesis that engagement in stimulating activities shapes brain structure and function, thus indirectly allowing for better preserved cognitive abilities. Although it is known that word production is among the best-preserved cognitive abilities in aging, the underlying neurofunctional mechanisms that allow this relative preservation are still unknown, and it is still unclear how engagement in stimulating activities affects these processes. The objective of this study is to describe the brain functional connectivity patterns associated with picture-naming abilities in younger and older adults with varying levels of engagement in stimulating activities, as a proxy for neurocognitive reserve. A mediation analysis was applied to determine whether the association between reserve proxies and naming accuracy is dependent on task FC. Results show that naming accuracy depends on the posterior cingulate cortex (PCC) functional decoupling in both younger and older adults but through different pathways. While high-performing older adults rely on the asynchronization of this area from motor speech regions’ activity, the best-performing younger adults rely on the functional decoupling with language-related regions. Mediation analysis reveals that the PCC decoupling mediates the relationship between the level of engagement in stimulating activities and naming accuracy in younger adults, but not in older adults. These findings suggest that reserve-related mechanisms may be more critical for naming in early adult life, while older adults’ neurofunctional organization may benefit more from a lifetime of acquired knowledge.

Transition From Sublexical to Lexico-Semantic Stimulus Processing

Resembling letter-by-letter translation, Morse code can be used to investigate various linguistic components by slowing down the cognitive process of language decoding. Using fMRI and Morse code, we investigated patterns of brain activation associated with decoding three-letter words or non-words and making a lexical decision. Our data suggest that early sublexical processing is associated with activation in brain regions that are involved in sound-patterns to phoneme conversion (inferior parietal lobule), phonological output buffer (inferior frontal cortex: pars opercularis) as well as phonological and semantic top-down predictions (inferior frontal cortex: pars triangularis). In addition, later lexico-semantic processing of meaningful stimuli is associated with activation of the phonological lexicon (angular gyrus) and the semantic system (default mode network). Overall, our data indicate that sublexical and lexico-semantic analyses comprise two cognitive processes that rely on neighboring networks in the left frontal cortex and parietal lobule.

Both activation and deactivation of functional networks support increased sentence processing costs

The research on the neural correlates underlying the language system has gradually moved away from the traditional Broca-Wernicke framework to a network perspective in the past 15 years. Language processing is found to be supported by the co-activation of both core and peripheral brain regions. However, the dynamic co-activation patterns of these brain regions serving different language functions remain to be fully revealed. The present functional magnetic resonance imaging (fMRI) study focused on sentence processing at different syntactic complexity levels to examine how the co-activation of different brain networks will be modulated by increased processing costs. Chinese relative clauses were used to probe the two dimensions of syntactic complexity: embeddedness (left-branching vs. center-embedded) and gap-filler dependency (subject-gap vs. object-gap) using the general linear model (GLM) approach, independent component analysis (ICA) and graph theoretical analysis. In contrast to localized activation revealed by the GLM approach, ICA identified more extensive networks both positively and negatively correlated with the task. We found that the posterior default mode network was anti-correlated to the gap-filler integration costs with increased deactivation for the left-branching object relative clauses compared to subject relative clauses, suggesting the involvement of this network in leveraging the cognitive resources based on the complexity level of the language task. Concurrent activation and deactivation of networks were found to be associated with the higher costs induced by center-embedding and its interaction with gap-filler integration. The graph theoretical analysis further unveiled that center-embeddedness imposed more attentional demand on the subject relative clause, as characterized by its higher degree and strength in the ventral attention network, and higher processing costs of syntactic reanalysis on the object relative clause, as characterized by increased intermodular connections of the language network with other networks. The results suggest that network activation and deactivation profiles are modulated by different dimensions of syntactic complexity to serve the higher demand of creating a coherent semantic representation.

A gradient from long-term memory to novel cognition: Transitions through default mode and executive cortex

Human cognition flexibly guides decision-making in familiar and novel situations. Although these decisions are often treated as dichotomous, in reality, situations are neither completely familiar, nor entirely new. Contemporary accounts of brain organization suggest that neural function is organized along a connectivity gradient from unimodal regions of sensorimotor cortex, through executive regions to transmodal default mode network. We examined whether this graded view of neural organization helps to explain how decision-making changes across situations that vary in their alignment with long-term knowledge. We used a semantic judgment task, which parametrically varied the global semantic similarity of items within a feature matching task to create a 'task gradient', from conceptual combinations that were highly overlapping in long-term memory to trials that only shared the goal-relevant feature. We found the brain's response to the task gradient varied systematically along the connectivity gradient, with the strongest response in default mode network when the probe and target items were highly overlapping conceptually. This graded functional change was seen in multiple brain regions and within individual brains, and was not readily explained by task difficulty. Moreover, the gradient captured the spatial layout of networks involved in semantic processing, providing an organizational principle for controlled semantic cognition across the cortex. In this way, the cortex is organized to support semantic decision-making in both highly familiar and less familiar situations.

Revealing the neural networks that extract conceptual gestalts from continuously evolving or changing semantic contexts

Reading a book, understanding the news reports or any other behaviour involving the processing of meaningful stimuli requires the semantic system to have two main features: being active during an extended period of time and flexibly adapting the internal representation according to the changing environment. Despite being key features of many everyday tasks, formation and updating of the semantic “gestalt” are still poorly understood. In this fMRI study we used naturalistic stimuli and task manipulations to identify the neural network that forms and updates conceptual gestalts during time-extended integration of meaningful stimuli. Univariate and multivariate techniques allowed us to draw a distinction between networks that are crucial for the formation of a semantic gestalt (meaning integration) and those that instead are important for linking incoming cues about the current context (e.g., time and space cues) into a schema representation. Specifically, we revealed that time-extended formation of the conceptual gestalt was reflected in the neuro-computations of the anterior temporal lobe accompanied by multi-demand areas and hippocampus, with a key role of brain structures in the right hemisphere. This “semantic gestalt network” was strongly recruited when an update of the current semantic representation was required during narrative processing. A distinct fronto-parietal network, instead, was recruited for context integration, independently from the meaning associations between words (semantic coherence). Finally, in contrast with accounts positing that the default mode network (DMN) may have a crucial role in semantic cognition, our findings revealed that DMN activity was sensitive to task difficulty, but not to semantic integration. The implications of these findings for neurocognitive models of semantic cognition and the literature on narrative processing are discussed.

•

fMRI revealed areas and networks for semantic integration during narrative reading.

•

ATL has a key role in the formation of the conceptual gestalt.

•

IFG, pMTG and dAG support the update of the conceptual gestalt.

•

Left AG (mid-PGp) has a key role in context integration.

Imaging Biomarkers for Neurodegeneration in Presymptomatic Familial Frontotemporal Lobar Degeneration

Frontotemporal lobar degeneration (FTLD) is a neurodegenerative disorder characterized by behavioral changes, language abnormality, as well as executive function deficits and motor impairment. In about 30-50% of FTLD patients, an autosomal dominant pattern of inheritance was found with major mutations in the MAPT, GRN, and the C9orf72 repeat expansion. These mutations could lead to neurodegenerative pathology years before clinical symptoms onset. With potential disease-modifying treatments that are under development, non-invasive biomarkers that help determine the early brain changes in presymptomatic FTLD patients will be critical for tracking disease progression and enrolling the right participants into the clinical trials at the right time during the disease course. In recent years, there is increasing evidence that a number of imaging biomarkers show the abnormalities during the presymptomatic stage. Imaging biomarkers of presymptomatic familial FTLD may provide insight into the underlying neurodegenerative process years before symptom onset. Structural magnetic resonance imaging (MRI) has demonstrated cortical degeneration with a mutation-specific neurodegeneration pattern years before onset of clinical symptoms in presymptomatic familial FTLD mutation carriers. In addition, diffusion tensor imaging (DTI) has shown the loss of white matter microstructural integrity in the presymptomatic stage of familial FTLD. Furthermore, proton magnetic resonance spectroscopy (¹H MRS), which provides a non-invasive measurement of brain biochemistry, has identified early neurochemical abnormalities in presymptomatic MAPT mutation carriers. Positron emission tomography (PET) imaging with [¹⁸F]-fluorodeoxyglucose (FDG) has demonstrated the glucose hypometabolism in the presymptomatic stage of familial FTLD. Also, a novel PET ligand, ¹⁸F-AV-1451, has been used in this group to evaluate tau deposition in the brain. Promising imaging biomarkers for presymptomatic familial FTLD have been identified and assessed for specificity and sensitivity for accurate prediction of symptom onset and tracking disease progression during the presymptomatic stage when clinical measures are not useful. Furthermore, identifying imaging biomarkers for the presymptomatic stage is important for the design of disease-modifying trials. We review the recent progress in imaging biomarkers of the presymptomatic phase of familial FTLD and discuss the imaging techniques and analysis methods, with a focus on the potential implication of these imaging techniques and their utility in specific mutation types.

Altered Intrinsic Functional Connectivity of the Primary Visual Cortex in Patients with Neovascular Glaucoma: A Resting-State Functional Magnetic Resonance Imaging Study

PURPOSE

The purpose was to investigate the differences in spontaneous functional connectivity (FC) of the primary visual cortex (V1) between patients with neovascular glaucoma (NVG) and healthy controls (HCs) using resting-state functional magnetic resonance imaging data.

METHODS

A total of 18 patients with NVG (nine males and nine females) and 18 HCs with similar age and sex background were enrolled in the study and inspected using resting-state functional magnetic resonance imaging. The differences in FC of the V1 between the two groups were compared using the independent samples t-test. We used the receiver operating characteristic (ROC) curve to compare the average FC values of NVG subjects with those of HCs.

RESULTS

FC in the left V1 and right fusiform gyrus, bilateral cuneus, and left precuneus was significantly decreased in the NVG group compared with that reported in the HC group. Meanwhile, patients with NVG presented increased FC between the right V1 and bilateral middle frontal gyrus. However, they also exhibited declining FC between the right V1 and left precuneus, and bilateral cuneus. The ROC curve analysis of each brain region indicated that the accuracy of the area under the ROC curves regarding NVG was excellent.

CONCLUSION

NVG involves aberrant FC in the V1 in different brain areas, including the visual-related and cognitive-related regions. These findings may assist in unveiling the underlying neural mechanisms of impaired visual function in NVG.

Delineating functional segregations of the human middle temporal gyrus with resting-state functional connectivity and coactivation patterns

Although the middle temporal gyrus (MTG) has been parcellated into subregions with distinguished anatomical connectivity patterns, whether the structural topography of MTG can inform functional segregations of this area remains largely unknown. Accumulating evidence suggests that the brain's underlying organization and function can be directly and effectively delineated with resting-state functional connectivity (RSFC) by identifying putative functional boundaries between cortical areas. Here, RSFC profiles were used to explore functional segregations of the MTG and defined four subregions from anterior to posterior in two independent datasets, which showed a similar pattern with MTG parcellation scheme obtained using anatomical connectivity. The functional segregations of MTG were further supported by whole brain RSFC, coactivation, and specific RFSC, and coactivation mapping. Furthermore, the fingerprint with predefined 10 networks and functional characterizations of each subregion using meta-analysis also identified functional distinction between subregions. The specific connectivity analysis and functional characterization indicated that the bilateral most anterior subregions mainly participated in social cognition and semantic processing; the ventral middle subregions were involved in social cognition in left hemisphere and auditory processing in right hemisphere; the bilateral ventro-posterior subregions participated in action observation, whereas the left subregion was also involved in semantic processing; both of the dorsal subregions in superior temporal sulcus were involved in language, social cognition, and auditory processing. Taken together, our findings demonstrated MTG sharing similar structural and functional topographies and provide more detailed information about the functional organization of the MTG, which may facilitate future clinical and cognitive research on this area.

A systematic review of brain functional connectivity patterns involved in episodic and semantic memory

The study of functional connectivity and declarative memory has lately been focused on finding biomarkers of neuropsychological diseases. However, little is known about its patterns in healthy brains. Thus, in this systematic review we analyze and integrate the findings of 81 publications regarding functional connectivity (measured by fMRI during both task and resting-state) and semantic and episodic memory in healthy adults. Moreover, we discriminate and analyze the main areas and links found in specific memory phases (encoding, storage or retrieval) based on several criteria, such as time length, depth of processing, rewarding value of the information, vividness and amount or kind of details retrieved. There is a certain degree of overlap between the networks of episodic and semantic memory and between the encoding and retrieval stages. Although several differences are pointed out during the article, this calls to attention the need for further empirical studies that actively compare both types of memory, particularly using other baseline conditions apart from the traditional resting state. Indeed, the active involvement of the default mode network in both declarative memory and resting condition suggests the possibility that during rest there is an on-going memory processing. We find support for the 'attention to memory' hypothesis, the memory differentiation model and the appropriate transfer hypothesis, but some evidence is inconsistent with the traditional hub-and-spoke model.

Distinct individual differences in default mode network connectivity relate to off-task thought and text memory during reading

Often, as we read, we find ourselves thinking about something other than the text; this tendency to mind-wander is linked to poor comprehension and reduced subsequent memory for texts. Contemporary accounts argue that periods of off-task thought are related to the tendency for attention to be decoupled from external input. We used fMRI to understand the neural processes that underpin this phenomenon. First, we found that individuals with poorer text-based memory tend to show reduced recruitment of left middle temporal gyrus in response to orthographic input, within a region located at the intersection of default mode, dorsal attention and frontoparietal networks. Voxels within these networks were taken as seeds in a subsequent resting-state study. The default mode network region (i) had greater connectivity with medial prefrontal cortex, falling within the same network, for individuals with better text-based memory, and (ii) was more decoupled from medial visual regions in participants who mind-wandered more frequently. These findings suggest that stronger intrinsic connectivity within the default mode network is linked to better text processing, while reductions in default mode network coupling to the visual system may underpin individual variation in the tendency for our attention to become disengaged from what we are reading.

Age and hippocampal volume predict distinct parts of default mode network activity

Group comparison studies have established that activity in the posterior part of the default-mode network (DMN) is down-regulated by both normal ageing and Alzheimer’s disease (AD). In this study linear regression models were used to disentangle distinctive DMN activity patterns that are more profoundly associated with either normal ageing or a structural marker of neurodegeneration. 312 datasets inclusive of healthy adults and patients were analysed. Days of life at scan (DOL) and hippocampal volume were used as predictors. Group comparisons confirmed a significant association between functional connectivity in the posterior cingulate/retrosplenial cortex and precuneus and both ageing and AD. Fully-corrected regression models revealed that DOL significantly predicted DMN strength in these regions. No such effect, however, was predicted by hippocampal volume. A significant positive association was found between hippocampal volumes and DMN connectivity in the right temporo-parietal junction (TPJ). These results indicate that postero-medial DMN down-regulation may not be specific to neurodegenerative processes but may be more an indication of brain vulnerability to degeneration. The DMN-TPJ disconnection is instead linked to the volumetric properties of the hippocampus, may reflect early-stage regional accumulation of pathology and might be of aid in the clinical detection of abnormal ageing.

The neural basis of semantic cognition in Mandarin Chinese: A combined fMRI and TMS study

While converging sources of evidence point to the possibility of a large‐scale distributed network for semantic cognition, a consensus regarding the underlying subregions and their specific function in this network has not been reached. In the current study, we combined functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) methodology to investigate the neural basis of semantic cognition in Mandarin Chinese. In the fMRI experiment, strong activations were observed in left inferior frontal gyrus (IFG) and left middle temporal gyrus (MTG) for semantic judgment task. Moreover, functional connectivity was found from seed region left IFG to left MTG. Meanwhile, negative correlation between performance and extracted parameter estimates from left IFG to left MTG was detected in semantic task. Subsequent TMS stimulation over left IFG resulted in performance deficits in semantic judgment task, in contrast to other three sites: left MTG, right intraparietal sulcus (IPS) and a control site. We concluded that the neural basis of semantic processing for Mandarin Chinese closely resembled that for alphabetic languages such as English, supporting a language‐universal view on semantic cognition.

Multimodal Magnetic Resonance Imaging reveals alterations of sensorimotor circuits in restless legs syndrome

Study Objectives

Integrated information on brain microstructural integrity and iron storage and its impact on the morphometric profile is not available in restless legs syndrome (RLS). We applied multimodal magnetic resonance imaging (MRI) including diffusion tensor imaging, the transverse relaxation rate (R2*), a marker for iron storage, as well as gray and white matter volume measures to characterize RLS-related MRI signal distribution patterns and to analyze their associations with clinical parameters.

Methods

Eighty-seven patients with RLS (mean age 51, range 20–72 years; disease duration, mean 13 years, range 1–46 years, of those untreated n = 30) and 87 healthy control subjects, individually matched for age and gender, were investigated with multimodal 3T MRI.

Results

Volume of the white matter compartment adjacent to the post- and precentral cortex and fractional anisotropy (FA) of the frontopontine tract were both significantly reduced in RLS compared to healthy controls, and these alterations were associated with disease duration (r = 0.25, p = 0.025 and r = 0.23, p = 0.037, respectively). Corresponding gray matter volume increases of the right primary motor cortex in RLS (p &lt; 0.001) were negatively correlated with the right FA signal of the frontopontine tract (r = −0.22; p &lt; 0.05). Iron content evaluated with R2* was reduced in the putamen as well as in temporal and occipital compartments of the RLS cohort compared to the control group (p &lt; 0.01).

Conclusions

Multimodal MRI identified progressing white matter decline of key somatosensory circuits that may underlie the perception of sensory leg discomfort. Increases of gray matter volume of the premotor cortex are likely to be a consequence of functional neuronal reorganization.

Reduced semantic control in older adults is linked to intrinsic DMN connectivity

Ageing provides an interesting window into semantic cognition: while younger adults generally outperform older adults on many cognitive tasks, knowledge continues to accumulate over the lifespan and consequently, the semantic store (i.e., vocabulary size) remains stable (or even improves) during healthy ageing. Semantic cognition involves the interaction of at least two components - a semantic store and control processes that interact to ensure efficient and context-relevant use of representations. Given older adults perform less well on tasks measuring executive control, their ability to access the semantic store in a goal driven manner may be compromised. Older adults also consistently show reductions in intrinsic brain connectivity, and we examined how these brain changes relate to age-related changes in semantic performance. We found that while older participants outperformed their younger counterparts on tests of vocabulary size (i.e., NART), younger participants were faster and more accurate in tasks requiring semantic control, and these age differences correlated with measures of intrinsic connectivity between the anterior temporal lobe (ATL) and medial prefrontal cortex (mPFC), within the default mode network. Higher intrinsic connectivity from right ATL to mPFC at rest related to better performance on verbal (but not picture) semantic tasks, and older adults showed an exaggerated version of this pattern, suggesting that this within-DMN connectivity may become more important for conceptual access from words as we age. However, this appeared to be at the expense of control over semantic retrieval - there was little relationship between connectivity and performance for strong associations in either group, but older adults with stronger connectivity showed particularly inefficient retrieval of weak associations. Older adults may struggle to harness the default mode network to support demanding patterns of semantic retrieval, resulting in a performance cost.

Resting-state and Vocabulary Tasks Distinctively Inform On Age-Related Differences in the Functional Brain Connectome

Most of the current knowledge about age-related differences in brain neurofunctional organization stems from neuroimaging studies using either a "resting state" paradigm, or cognitive tasks for which performance decreases with age. However, it remains to be known if comparable age-related differences are found when participants engage in cognitive activities for which performance is maintained with age, such as vocabulary knowledge tasks. A functional connectivity analysis was performed on 286 adults ranging from 18 to 80 years old, based either on a resting state paradigm or when engaged in vocabulary tasks. Notable increases in connectivity of regions of the language network were observed during task completion. Conversely, only age-related decreases were observed across the whole connectome during resting-state. While vocabulary accuracy increased with age, no interaction was found between functional connectivity, age and task accuracy or proxies of cognitive reserve, suggesting that older individuals typically benefits from semantic knowledge accumulated throughout one's life trajectory, without the need for compensatory mechanisms.

Two critical brain networks for generation and combination of remote associations

Recent functional imaging findings in humans indicate that creativity relies on spontaneous and controlled processes, possibly supported by the default mode and the fronto-parietal control networks, respectively. Here, we examined the ability to generate and combine remote semantic associations, in relation to creative abilities, in patients with focal frontal lesions. Voxel-based lesion-deficit mapping, disconnection-deficit mapping and network-based lesion-deficit approaches revealed critical prefrontal nodes and connections for distinct mechanisms related to creative cognition. Damage to the right medial prefrontal region, or its potential disrupting effect on the default mode network, affected the ability to generate remote ideas, likely by altering the organization of semantic associations. Damage to the left rostrolateral prefrontal region and its connections, or its potential disrupting effect on the left fronto-parietal control network, spared the ability to generate remote ideas but impaired the ability to appropriately combine remote ideas. Hence, the current findings suggest that damage to specific nodes within the default mode and fronto-parietal control networks led to a critical loss of verbal creative abilities by altering distinct cognitive mechanisms.

Alterations of resting-state functional network centrality in patients with asthma: evidence from a voxel-wise degree centrality analysis

Several previous studies have reported that asthma patients have abnormal brain activities, whereas alterations in the resting-state network still remain unknown. The aim of this study was to investigate the changes in functional network centrality in asthma patients using voxel-wise degree centrality (DC) method. Asthma patients and healthy controls (HCs) were matched closely in age, sex, and education of participants. The DC method was used to evaluate the functional network centrality. The receiver operating characteristic curve was used to distinguish the asthma group from the HCs group. The Pearson correlation coefficient was used to explore the relationship between the observed mean values of DC in different brain areas and the behavioral performance. Compared with HCs, DC values were significantly decreased in the right middle temporal gyrus and the right putamen of asthma patients. In contrast, in asthma patients, DC values were markedly increased in the right posterior lobe of the cerebellum, right inferior temporal gyrus, left superior frontal gyrus, left postcentral gyrus and inferior parietal lobule, left middle frontal gyrus, and left postcentral gyrus. However, there was no relationship between the observed mean DC values in different brain areas and the behavioral performance. The results showed that the DC values were altered in various brain regions of asthma patients, which were related to default mode network, the cortex-basal ganglia network, the frontoparietal network, and the sensorimotor network, leading to some useful information for clinical studies in asthma patients.

Exploring distinct default mode and semantic networks using a systematic ICA approach

Resting-state networks (RSNs; groups of regions consistently co-activated without an explicit task) are hugely influential in modern brain research. Despite this popularity, the link between specific RSNs and their functions remains elusive, limiting the impact on cognitive neuroscience (where the goal is to link cognition to neural systems). Here we present a series of logical steps to formally test the relationship between a coherent RSN with a cognitive domain. This approach is applied to a challenging and significant test-case; extracting a recently-proposed semantic RSN, determining its relation with a well-known RSN, the default mode network (DMN), and assessing their roles in semantic cognition. Results showed the DMN and semantic network are two distinct coherent RSNs. Assessing the cognitive signature of these spatiotemporally coherent networks directly (and therefore accounting for overlapping networks) showed involvement of the proposed semantic network, but not the DMN, in task-based semantic cognition. Following the steps presented here, researchers could formally test specific hypotheses regarding the function of RSNs, including other possible functions of the DMN.

A robust dissociation among the language, multiple demand, and default mode networks: Evidence from inter-region correlations in effect size

Complex cognitive processes, including language, rely on multiple mental operations that are carried out by several large-scale functional networks in the frontal, temporal, and parietal association cortices of the human brain. The central division of cognitive labor is between two fronto-parietal bilateral networks: (a) the multiple demand (MD) network, which supports executive processes, such as working memory and cognitive control, and is engaged by diverse task domains, including language, especially when comprehension gets difficult; and (b) the default mode network (DMN), which supports introspective processes, such as mind wandering, and is active when we are not engaged in processing external stimuli. These two networks are strongly dissociated in both their functional profiles and their patterns of activity fluctuations during naturalistic cognition. Here, we focus on the functional relationship between these two networks and a third network: (c) the fronto-temporal left-lateralized "core" language network, which is selectively recruited by linguistic processing. Is the language network distinct and dissociated from both the MD network and the DMN, or is it synchronized and integrated with one or both of them? Recent work has provided evidence for a dissociation between the language network and the MD network. However, the relationship between the language network and the DMN is less clear, with some evidence for coordinated activity patterns and similar response profiles, perhaps due to the role of both in semantic processing. Here we use a novel fMRI approach to examine the relationship among the three networks: we measure the strength of activations in different language, MD, and DMN regions to functional contrasts typically used to identify each network, and then test which regions co-vary in their contrast effect sizes across 60 individuals. We find that effect sizes correlate strongly within each network (e.g., one language region and another language region, or one DMN region and another DMN region), but show little or no correlation for region pairs across networks (e.g., a language region and a DMN region). Thus, using our novel method, we replicate the language/MD network dissociation discovered previously with other approaches, and also show that the language network is robustly dissociated from the DMN, overall suggesting that these three networks contribute to high-level cognition in different ways and, perhaps, support distinct computations. Inter-individual differences in effect sizes therefore do not simply reflect general differences in vascularization or attention, but exhibit sensitivity to the functional architecture of the brain. The strength of activation in each network can thus be probed separately in studies that attempt to link neural variability to behavioral or genetic variability.