Network connectivity predicts language processing in healthy adults

Network and state specificity in connectivity-based predictions of individual behavior

Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.

Network and State Specificity in Connectivity-Based Predictions of Individual Behavior

Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.

Structural and functional neural substrates underlying the concreteness effect

The concreteness effect refers to the advantage in speed and accuracy of processing concrete words over abstract words. Previous studies have shown that the processing of the two types of words is mediated by distinct neural mechanisms, but these studies were mainly conducted with task-based functional magnetic resonance imaging. This study investigates the associations between the concreteness effect and grey matter volume (GMV) of brain regions as well as resting-state functional connectivity (rsFC) of these identified regions. The results show that the GMV of left inferior frontal gyrus (IFG), right middle temporal gyrus (MTG), right supplementary motor area and right anterior cingulate cortex (ACC) negatively correlates with the concreteness effect. The rsFC of the left IFG, the right MTG and the right ACC with the nodes, mainly in default mode network, frontoparietal network and dorsal attention network positively correlates with the concreteness effect. The GMV and rsFC jointly and respectively predict the concreteness effect in individuals. In conclusion, stronger connectivity amongst functional networks and higher coherent engagement of the right hemisphere predict a greater difference in the verbal memory of abstract and concrete words.

Using modular connectome-based predictive modeling to reveal brain-behavior relationships of individual differences in working memory

Working memory plays a crucial role in our daily lives, and brain imaging has been used to predict working memory performance. Here, we present an improved connectome-based predictive modeling approach for building a predictive model of individual working memory performance from whole-brain functional connectivity. The model was built using n-back task-based fMRI and resting-state fMRI data from the Human Connectome Project. Compared to prior models, our model was more interpretable, demonstrated a closer connection to the known anatomical and functional network. The model also demonstrates strong generalization on nine other cognitive behaviors from the HCP database and can well predict the working memory performance of healthy individuals in external datasets. By comparing the differences in prediction effects of different brain networks and anatomical feature analysis on n-back tasks, we found the essential role of some networks in differentiating between high and low working memory loads conditions.

A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome

The human brain flexibly controls different cognitive behaviors, such as memory and attention, to satisfy contextual demands. Much progress has been made to reveal task-induced modulations in the whole-brain functional connectome, but we still lack a way to model context-dependent changes. Here, we present a novel connectome-to-connectome (C2C) transformation framework that enables us to model the brain's functional reorganization from one connectome state to another in response to specific task goals. Using functional magnetic resonance imaging data from the Human Connectome Project, we demonstrate that the C2C model accurately generates an individual's task-related connectomes from their task-free (resting-state) connectome with a high degree of specificity across seven different cognitive states. Moreover, the C2C model amplifies behaviorally relevant individual differences in the task-free connectome, thereby improving behavioral predictions with increased power, achieving similar performance with just a third of the subjects needed when relying on resting-state data alone. Finally, the C2C model reveals how the brain reorganizes between cognitive states. Our observations support the existence of reliable state-specific subsystems in the brain and demonstrate that we can quantitatively model how the connectome reconfigures to different cognitive states, enabling more accurate predictions of behavior with fewer subjects.

Connectome-based predictive models using resting-state fMRI for studying brain aging

Changes in the brain with age can provide useful information regarding an individual's chronological age. studies have suggested that functional connectomes identified via resting-state functional magnetic resonance imaging (fMRI) could be a powerful feature for predicting an individual's age. We applied connectome-based predictive modeling (CPM) to investigate individual chronological age predictions via resting-state fMRI using open-source datasets. The significant feature for age prediction was confirmed in 168 subjects from the Southwest University Adult Lifespan Dataset. The higher contributing nodes for age production included a positive connection from the left inferior parietal sulcus and a negative connection from the right middle temporal sulcus. On the network scale, the subcortical-cerebellum network was the dominant network for age prediction. The generalizability of CPM, which was constructed using the identified features, was verified by applying this model to independent datasets that were randomly selected from the Autism Brain Imaging Data Exchange I and the Open Access Series of Imaging Studies 3. CPM via resting-state fMRI is a potential robust predictor for determining an individual's chronological age from changes in the brain.

Centrality and interhemispheric coordination are related to different clinical/behavioral factors in attention deficit/hyperactivity disorder: a resting-state fMRI study

Eigenvector-Centrality (EC) has shown promising results in the field of Psychiatry, with early results also pertaining to ADHD. Parallel efforts have focused on the description of aberrant interhemispheric coordination in ADHD, as measured by Voxel-Mirrored-Homotopic-Connectivity (VMHC), with early evidence of altered Resting-State fMRI. A sample was collected from the ADHD200-NYU initiative: 86 neurotypicals and 89 participants with ADHD between 7 and 18 years old were included after quality control for motion. After preprocessing, voxel-wise EC and VMHC values between diagnostic groups were compared, and network-level values from 15 functional networks extracted. Age, ADHD severity (Connor's Parent Rating-Scale), IQ (Wechsler-Abbreviated-Scale), and right-hand dominance were correlated with EC/VMHC values in the whole sample and within groups, both at the voxel-wise and network-level. Motion was controlled by censoring time-points with Framewise-Displacement > 0.5 mm, as well as controlling for group differences in mean Framewise-Displacement values. EC was significantly higher in ADHD compared to neurotypicals in the left inferior Frontal lobe, Lingual gyri, Peri-Calcarine cortex, superior and middle Occipital lobes, right inferior Occipital lobe, right middle Temporal gyrus, Fusiform gyri, bilateral Cuneus, right Precuneus, and Cerebellum (FDR-corrected-p = 0.05). No differences were observed between groups in voxel-wise VMHC. EC was positively correlated with ADHD severity scores at the network level (at p-value < 0.01, Inattentive: Cerebellum rho = 0.273; Hyper/Impulsive: High-Visual Network rho = 0.242, Cerebellum rho = 0.273; Global Index Severity: High-Visual Network rho = 0.241, Cerebellum rho = 0.293). No differences were observed between groups for motion (p = 0.443). While EC was more related to ADHD psychopathology, VMHC was consistently and negatively correlated with age across all networks.

A brain-based general measure of attention

Attention is central to many aspects of cognition, but there is no singular neural measure of a person's overall attentional functioning across tasks. Here, using original data from 92 participants performing three different attention-demanding tasks during functional magnetic resonance imaging, we constructed a suite of whole-brain models that can predict a profile of multiple attentional components (sustained attention, divided attention and tracking, and working memory capacity) for novel individuals. Multiple brain regions across the salience, subcortical and frontoparietal networks drove accurate predictions, supporting a common (general) attention factor across tasks, distinguished from task-specific ones. Furthermore, connectome-to-connectome transformation modelling generated an individual's task-related connectomes from rest functional magnetic resonance imaging, substantially improving predictive power. Finally, combining the connectome transformation and general attention factor, we built a standardized measure that shows superior generalization across four independent datasets (total N = 495) of various attentional measures, suggesting broad utility for research and clinical applications.

Abnormal Dynamic Functional Connectivity in Patients With End-Stage Renal Disease

Dynamic functional connectivity (FC) analysis can capture time-varying properties of connectivity; however, studies focusing on dynamic FC in patients with end-stage renal disease (ESRD) are very limited. This is the first study to explore the dynamic aspects of whole-brain FC and topological properties in ESRD patients. Resting-state functional magnetic resonance imaging data were acquired from 100 ESRD patients [50 hemodialysis (HD) patients and 50 non-dialysis patients] and 64 healthy controls (HCs). Independent component analysis, a sliding-window approach and graph-theory methods were used to study the dynamic FC properties. The intrinsic brain FC were clustered into four configuration states. Compared with HCs, both patient groups spent longer time in State 3, in which decreased FC between subnetworks of the default mode network (DMN) and between the dorsal DMN and language network was observed in these patients, and a further reduction in FC between the DMN subnetworks was found in HD patients compared to non-dialysis patients. The number of transitions and the variability of global and local efficiency progressively decreased from that in HCs to that of non-dialysis patients to that of HD patients. The completion time of Trail Making Test A and Trail Making Test B positively correlated with the mean dwell time of State 3 and negatively correlated with the number of transitions in ESRD patients. Our findings suggest impaired functional flexibility of network connections and state-specific FC disruptions in patients with ESRD, which may underlie their cognitive deficits. HD may have an adverse effect on time-varying FC.

Predicting response time variability from task and resting-state functional connectivity in the aging brain

Aging is associated with declines in a host of cognitive functions, including attentional control, inhibitory control, episodic memory, processing speed, and executive functioning. Theoretical models attribute the age-related decline in cognitive functioning to deficits in goal maintenance and attentional inhibition. Despite these well-documented declines in executive control resources, older adults endorse fewer episodes of mind-wandering when assessed using task-embedded thought probes. Furthermore, previous work on the neural basis of mind-wandering has mostly focused on young adults with studies predominantly focusing on the activity and connectivity of a select few canonical networks. However, whole-brain functional networks associated with mind-wandering in aging have not yet been characterized. In this study, using response time variability-the trial-to-trial fluctuations in behavioral responses-as an indirect marker of mind-wandering or an "out-of-the-zone" attentional state representing suboptimal behavioral performance, we show that brain-based predictive models of response time variability can be derived from whole-brain task functional connectivity. In contrast, models derived from resting-state functional connectivity alone did not predict individual response time variability. Finally, we show that despite successful within-sample prediction of response time variability, our models did not generalize to predict response time variability in independent cohorts of older adults with resting-state connectivity. Overall, our findings provide evidence for the utility of task-based functional connectivity in predicting individual response time variability in aging. Future research is needed to derive more robust and generalizable models.

Functional Connectivity Signatures Underlying Simultaneous Language Translation in Interpreters and Non-Interpreters of Mandarin and English: An fNIRS Study

Recent neuroimaging research has suggested that interpreters and non-interpreters elicit different brain activation patterns during simultaneous language translation. However, whether these two groups have different functional connectivity during such a task, and how the neural coupling is among brain subregions, are still not well understood. In this study, we recruited Mandarin (L1)/English (L2) interpreters and non-interpreter bilinguals, whom we asked to perform simultaneous language translation and reading tasks. Functional near-infrared spectroscopy (fNIRS) was used to collect cortical brain data for participants during each task, using 68 channels that covered the prefrontal cortex and the bilateral perisylvian regions. Our findings revealed both interpreter and non-interpreter groups recruited the right dorsolateral prefrontal hub when completing the simultaneous language translation tasks. We also found different functional connectivity between the groups. The interpreter group was characterized by information exchange between the frontal cortex and Wernicke’s area. In comparison, the non-interpreter group revealed neural coupling between the frontal cortex and Broca’s area. These findings indicate expertise modulates functional connectivity, possibly because of more developed cognitive skills associated with executive functions in interpreters.

Predicting multilingual effects on executive function and individual connectomes in children: An ABCD study

While there is a substantial amount of work studying multilingualism's effect on cognitive functions, little is known about how the multilingual experience modulates the brain as a whole. In this study, we analyzed data of over 1,000 children from the Adolescent Brain Cognitive Development (ABCD) Study to examine whether monolinguals and multilinguals differ in executive function, functional brain connectivity, and brain-behavior associations. We observed significantly better performance from multilingual children than monolinguals in working-memory tasks. In one finding, we were able to classify multilinguals from monolinguals using only their whole-brain functional connectome at rest and during an emotional n-back task. Compared to monolinguals, the multilingual group had different functional connectivity mainly in the occipital lobe and subcortical areas during the emotional n-back task and in the occipital lobe and prefrontal cortex at rest. In contrast, we did not find any differences in behavioral performance and functional connectivity when performing a stop-signal task. As a second finding, we investigated the degree to which behavior is reflected in the brain by implementing a connectome-based behavior prediction approach. The multilingual group showed a significant correlation between observed and connectome-predicted individual working-memory performance scores, while the monolingual group did not show any correlations. Overall, our observations suggest that multilingualism enhances executive function and reliably modulates the corresponding brain functional connectome, distinguishing multilinguals from monolinguals even at the developmental stage.

Functional Connectivity during Encoding Predicts Individual Differences in Long-Term Memory

What is the neural basis of individual differences in the ability to hold information in long-term memory (LTM)? Here, we first characterize two whole-brain functional connectivity networks based on fMRI data acquired during an n-back task that robustly predict individual differences in two important forms of LTM, recognition and recollection. We then focus on the recognition memory model and contrast it with a working memory model. Although functional connectivity during the n-back task also predicts working memory performance and the two networks have some shared components, they are also largely distinct from each other: The recognition memory model performance remains robust when we control for working memory, and vice versa. Functional connectivity only within regions traditionally associated with LTM formation, such as the medial temporal lobe and those that show univariate subsequent memory effect, have little predictive power for both forms of LTM. Interestingly, the interactions between these regions and other brain regions play a more substantial role in predicting recollection memory than recognition memory. These results demonstrate that individual differences in LTM are dependent on the configuration of a whole-brain functional network including but not limited to regions associated with LTM during encoding and that such a network is separable from what supports the retention of information in working memory.

Accelerated Aging of the Amygdala in Alcohol Use Disorders: Relevance to the Dark Side of Addiction

Here we assessed changes in subcortical volumes in alcohol use disorder (AUD). A simple morphometry-based classifier (MC) was developed to identify subcortical volumes that distinguished 32 healthy controls (HCs) from 33 AUD patients, who were scanned twice, during early and later withdrawal, to assess the effect of abstinence on MC-features (Discovery cohort). We validated the novel classifier in an independent Validation cohort (19 AUD patients and 20 HCs). MC-accuracy reached 80% (Discovery) and 72% (Validation). MC features included the hippocampus, amygdala, cerebellum, putamen, corpus callosum, and brain stem, which were smaller and showed stronger age-related decreases in AUD than HCs, and the ventricles and cerebrospinal fluid, which were larger in AUD and older participants. The volume of the amygdala showed a positive association with anxiety and negative urgency in AUD. Repeated imaging during the third week of detoxification revealed slightly larger subcortical volumes in AUD patients, consistent with partial recovery during abstinence. The steeper age-associated volumetric reductions in stress- and reward-related subcortical regions in AUD are consistent with accelerated aging, whereas the amygdalar associations with negative urgency and anxiety in AUD patients support its involvement in the "dark side of addiction".

Brain Structure and Functional Connectivity Correlate with Psychosocial Development in Contemplative Practitioners and Controls

Jane Loevinger’s theory of adult development, termed ego development (1966) and more recently maturity development, provides a useful framework for understanding the development of the self throughout the lifespan. However, few studies have investigated its neural correlates. In the present study, we use structural and functional magnetic resonance imaging (MRI) to investigate the neural correlates of maturity development in contemplative practitioners and controls. Since traits possessed by individuals with higher levels of maturity development are similar to those attributed to individuals at advanced stages of contemplative practice, we chose to investigate levels of maturity development in meditation practitioners as well as matched controls. We used the Maturity Assessment Profile (MAP) to measure maturity development in a mixed sample of participants composed of 14 long-term meditators, 16 long-term yoga practitioners, and 16 demographically matched controls. We investigated the relationship between contemplative practice and maturity development with behavioral, seed-based resting state functional connectivity, and cortical thickness analyses. The results of this study indicate that contemplative practitioners possess higher maturity development compared to a matched control group, and in addition, maturity development correlates with cortical thickness in the posterior cingulate. Furthermore, we identify a brain network implicated in theory of mind, narrative, and self-referential processing, comprising the posterior cingulate cortex, dorsomedial prefrontal cortex, temporoparietal junction, and inferior frontal cortex, as a primary neural correlate.

Frontoparietal microstructural damage mediates age-dependent working memory decline in face and body information processing: Evidence for dichotomic hemispheric bias mechanisms

Age-associated damage in the microstructure of frontally-based connections (e.g. genu of the corpus callosum and superior longitudinal fasciculus) is believed to lead to impairments in processing speed and executive function. Using mediation analysis, we tested the potential contribution of callosal and frontoparietal association tracts to age-dependent effects on cognition/executive function as measured with 1-back working memory tasks for visual stimulus categories (i.e. faces and non-emotional bodies) in a group of 55 healthy adults (age range 23-79 years). Constrained spherical deconvolution-based tractography was employed to reconstruct the genu/prefrontal section of the corpus callosum (GCC) and the central/second branch of the superior longitudinal fasciculus (CB-SLF). Age was associated with (i) reductions in fractional anisotropy (FA) in the GCC and in the right and left CB-SLF and (iii) decline in visual object category processing. Mediation analysis revealed that microstructural damage in right hemispheric CB-SLF is associated with age-dependent decline in face processing likely reflecting the stimulus-specific/holistic nature of face processing within dedicated/specialized frontoparietal routes. By contrast, microstructural damage in left hemispheric CB-SLF associated with age-dependent decline in non-emotional body processing, consistent with the more abstract nature of non-emotional body categories. In sum, our findings suggest that frontoparietal microstructural damage mediates age-dependent decline in face and body information processing in a manner that reflects the hemispheric bias of holistic vs. abstract nature of face and non-emotional body category processing.

The interaction between language and working memory: a systematic review of fMRI studies in the past two decades

Language processing involves other cognitive domains, including Working Memory (WM). Much detail about the neural correlates of language and WM interaction remains unclear. This review summarizes the evidence for the interaction between WM and language obtained via functional Magnetic Resonance Imaging (fMRI) in the past two decades. The search was limited to PubMed, Google Scholar, Science direct and Neurosynth for working memory, language, fMRI, neuroimaging, cognition, attention, network, connectome keywords. The exclusion criteria consisted of studies including children, older adults, bilingual or multilingual population, clinical cases, music, sign language, speech, motor processing, review papers, meta-analyses, electroencephalography/event-related potential, and positron emission tomography. A total of 20 articles were included and discussed in four categories: language comprehension, language production, syntax, and networks. Studies on neural correlates of WM and language interaction are rare. Language tasks that involve WM activate common neural systems. Activated areas can be associated with cognitive concepts proposed by Baddeley and Hitch (1974), including the phonological loop of WM (mainly Broca and Wernicke's areas), other prefrontal cortex and right hemispheric regions linked to the visuospatial sketchpad. There is a clear, dynamic interaction between language and WM, reflected in the involvement of subcortical structures, particularly the basal ganglia (caudate), and of widespread right hemispheric regions. WM involvement is levered by cognitive demand in response to task complexity. High WM capacity readers draw upon buffer memory systems in midline cortical areas to decrease the WM demands for efficiency. Different dynamic networks are involved in WM and language interaction in response to the task in hand for an ultimate brain function efficiency, modulated by language modality and attention.

Network connectivity predicts language processing in healthy adults

Brain imaging has been used to predict language skills during development and neuropathology but its accuracy in predicting language performance in healthy adults has been poorly investigated. To address this shortcoming, we studied the ability to predict reading accuracy and single‐word comprehension scores from rest‐ and task‐based functional magnetic resonance imaging (fMRI) datasets of 424 healthy adults. Using connectome‐based predictive modeling, we identified functional brain networks with >400 edges that predicted language scores and were reproducible in independent data sets. To simplify these complex models we identified the overlapping edges derived from the three task‐fMRI sessions (language, working memory, and motor tasks), and found 12 edges for reading recognition and 11 edges for vocabulary comprehension that accounted for 20% of the variance of these scores, both in the training sample and in the independent sample. The overlapping edges predominantly emanated from language areas within the frontoparietal and default‐mode networks, with a strong precuneus prominence. These findings identify a small subset of edges that accounted for a significant fraction of the variance in language performance that might serve as neuromarkers for neuromodulation interventions to improve language performance or for presurgical planning to minimize language impairments.