Dynamic network reorganization underlying neuroplasticity: the deficits-severity-related language network dynamics in patients with left hemispheric gliomas involving language network.

Brain network dynamics not only endow the brain with flexible coordination for various cognitive processes but also with a huge potential of neuroplasticity for development, skill learning, and after cerebral injury. Diffusive and progressive glioma infiltration triggers the neuroplasticity for functional compensation, which is an outstanding pathophysiological model for the investigation of network reorganization underlying neuroplasticity. In this study, we employed dynamic conditional correlation to construct framewise language networks and investigated dynamic reorganizations in 83 patients with left hemispheric gliomas involving language networks (40 patients without aphasia and 43 patients with aphasia). We found that, in healthy controls (HCs) and patients, the language network dynamics in resting state clustered into 4 temporal-reoccurring states. Language deficits-severity-dependent topological abnormalities of dFCs were observed. Compared with HCs, suboptimal language network dynamics were observed for those patients without aphasia, while more severe network disruptions were observed for those patients with aphasia. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the 4 states significantly predicted individual patients' language scores. These findings shed light on our understanding of metaplasticity in glioma. Glioma-induced language network reorganizations were investigated under a dynamic "meta-networking" (network of networks) framework. In healthy controls and patients with glioma, the framewise language network dynamics in resting-state robustly clustered into 4 temporal-reoccurring states. The spatial but not temporal language deficits-severity-dependent abnormalities of dFCs were observed in patients with left hemispheric gliomas involving language network. Language network dynamics significantly predicted individual patients' language scores.

The domain-separation language network dynamics in resting state support its flexible functional segregation and integration during language and speech processing.

Modern linguistic theories and network science propose that language and speech processing are organized into hierarchical, segregated large-scale subnetworks, with a core of dorsal (phonological) stream and ventral (semantic) stream. The two streams are asymmetrically recruited in receptive and expressive language or speech tasks, which showed flexible functional segregation and integration. We hypothesized that the functional segregation of the two streams was supported by the underlying network segregation. A dynamic conditional correlation approach was employed to construct framewise time-varying language networks and k-means clustering was employed to investigate the temporal-reoccurring patterns. We found that the framewise language network dynamics in resting state were robustly clustered into four states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of speech perception and lexical-phonological processing, speech production, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and was regarded as a baseline state. Temporally, the first three states appeared exclusively in limited time bins (∼15%), and most of the time (> 55%), state 4 was dominant. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the four states significantly predicted individual linguistic performance. These findings suggest a domain-separation manner of language network dynamics in resting state, which forms a dynamic "meta-network" framework to support flexible functional segregation and integration during language and speech processing.

[Functional connectivity in ischemia stroke motor aphasia patients during resting state].

OBJECTIVE: To investigate the changes of Broca's area functional connectivity in ischemia stroke patients with motor aphasia during resting state using functional magnetic resonance imaging (fMRI). METHODS: The functional connectivity of Broca's area was analyzed by observing the correlation between low frequency signal fluctuations in Broca's area and those in all brain regions. RESULTS: In the normal controls group, there was multiple brain area positively correlated with Broca's area during resting state. The patients group compared with controls group, the functional connectivity between Broca's area and adjacent brain regions around its is most significant, and its controlateral brain area correlated with Broca's area reduced, but some cerebellum, occipital lobe, middle temporal gyrus and corpus callosum spenium correlated with Broca's area strengthened. CONCLUSION: There is a wide range of motor function of language network during resting state. The right anterior cingulate gyrus, knee of corpus callosum and hemisphere play an important part in motor language function network. The enhancement functional connectivity between the adjacent brain regions surrounding Broca's area, the right cerebellum, occipital lobe, middle temporal gyrus and spenium of corpus callosum and Broca's area may be one compensatory mechanism remodeling for the language recover of ischemia stroke patients with motor aphasia.

Intersubject Dynamic Conditional Correlation: A Novel Method to Track the Framewise Network Implication during Naturalistic Stimuli.

Background: Naturalistic stimuli have become increasingly popular in modern cognitive neuroscience. These stimuli have high ecological validity due to their rich and multilayered features. However, their complexity also presents methodological challenges for uncovering neural network reconfiguration. Dynamic functional connectivity using the sliding-window technique is commonly used but has several limitations. In this study, we introduce a new method called intersubject dynamic conditional correlation (ISDCC). Method: ISDCC uses intersubject analysis to remove intrinsic and non-neuronal signals, retaining only intersubject-consistent stimuli-induced signals. It then applies dynamic conditional correlation (DCC) based on the generalized autoregressive conditional heteroskedasticity to calculate the framewise functional connectivity. To validate ISDCC, we analyzed simulation data with known network reconfiguration patterns and two publicly available narrative functional Magnetic Resonance Imaging (fMRI) datasets. Results: (1) ISDCC accurately unveiled the underlying network reconfiguration patterns in simulation data, demonstrating greater sensitivity than DCC; (2) ISDCC identified synchronized network reconfiguration patterns across listeners; (3) ISDCC effectively differentiated between stimulus types with varying temporal coherence; and (4) network reconfigurations unveiled by ISDCC were significantly correlated with listener engagement during narrative comprehension. Conclusion: ISDCC is a precise and dynamic method for tracking network implications in response to naturalistic stimuli.

Reading anxiety modulates the functional connectivity of the reading-related network during adult reading.

Researchers have studied cognitive and linguistic skills in predicting reading abilities, but the impact of affective factors such as anxiety on reading at the neurobiological level is not well understood. Here, we used functional magnetic resonance imaging to investigate the neural correlates of reading anxiety in adult readers performing a semantic judgment task. The results showed that reading anxiety was significantly correlated with response time but not with accuracy. Neurobiologically, functional connectivity strength rather than activation level of semantic-related areas significantly predicted reading anxiety. Activation of regions (i.e., the right putamen and right precentral gyrus) external to the semantic-related areas positively correlated with reading anxiety levels. These findings suggest that reading anxiety influences adult reading by modulating functional connections of semantic-related areas and brain activation of semantic-unrelated areas. This study provides insights into the neural mechanisms underlying reading anxiety experienced by adult readers.

Longitudinal assessment of network reorganizations and language recovery in postoperative patients with glioma.

For patients with glioma located in or adjacent to the linguistic eloquent cortex, awake surgery with an emphasis on the preservation of language function is preferred. However, the brain network basis of postoperative linguistic functional outcomes remains largely unknown. In this work, 34 patients with left cerebral gliomas who underwent awake surgery were assessed for language function and resting-state network properties before and after surgery. We found that there were 28 patients whose language function returned to at least 80% of the baseline scores within 3 months after surgery or to 85% within 6 months after surgery. For these patients, the spontaneous recovery of language function synchronized with changes within the language and cognitive control networks, but not with other networks. Specifically, compared with baseline values, language functions and global network properties were the worst within 1 month after surgery and gradually recovered within 6 months after surgery. The recovery of connections was tumour location dependent and was attributed to both ipsihemispheric and interhemispheric connections. In contrast, for six patients whose language function did not recover well, severe network disruptions were observed before surgery and persisted into the chronic phase. This study suggests the synchronization of functional network normalization and spontaneous language recovery in postoperative patients with glioma.

Multivariate machine learning-based language mapping in glioma patients based on lesion topography.

Diffusive and progressive tumor infiltration within language-related areas of the brain induces functional reorganization. However, the macrostructural basis of subsequent language deficits is less clear. To address this issue, lesion topography data from 137 preoperative patients with left cerebral language-network gliomas (81 low-grade gliomas and 56 high-grade gliomas), were adopted for multivariate machine-learning-based lesion-language mapping analysis. We found that tumor location in the left posterior middle temporal gyrus-a bottleneck where both dorsal and ventral language pathways travel-predicted deficits of spontaneous speech (cluster size = 1356 mm3, false discovery rate corrected P < 0.05) and naming scores (cluster size = 1491 mm3, false discovery rate corrected P < 0.05) in the high-grade glioma group. In contrast, no significant lesion-language mapping results were observed in the low-grade glioma group, suggesting a large functional reorganization. These findings suggest that in patients with gliomas, the macrostructural plasticity mechanisms that modulate brain-behavior relationships depend on glioma grade.

Individual differences in first-pass fixation duration in reading are related to resting-state functional connectivity.

Although there are considerable individual differences in eye movements during text reading, their neural correlates remain unclear. In this study, we investigated the relationship between the first-pass fixation duration (FPFD) in natural reading and resting-state functional connectivity (RSFC) in the brain. We defined the brain regions associated with early visual processing, word identification, attention shifts, and oculomotor control as seed regions. The results showed that individual FPFDs were positively correlated with individual RSFCs between the early visual network, visual word form area, and eye movement control/dorsal attention network. Our findings provide new evidence on the neural correlates of eye movements in text reading and indicate that individual differences in fixation time may shape the RSFC differences in the brain through the time-on-task effect and the mechanism of Hebbian learning.

Intrinsic functional connectivity of medial prefrontal cortex predicts the individual moral bias in economic valuation partially through the moral sensitivity trait.

An individual's economic valuation of a given object is biased by the moral status of the persons to whom the object is attached. The neural basis for how such "moral bias" occurs, especially how it is maintained in the resting state, are largely unknown. In the current study, we explored this question by correlating the functional connectivity with participants' behavioral performance measured in a novel task which captured how the economic valuation was influenced by given moral information. Seed-based FC analysis showed that the functional connectivity between the mPFC and the orbital mPFC (omPFC), the mPFC and the precuneus, the mPFC and the left anterior cingulum, were significantly associated with the behavioral index of morality effect on economic valuation. Multivariate machine learning-based regression analysis showed that connections in the mPFC network, as well as in the putamen network could well predict the behavior performance, indicating that this mPFC network and the putamen network were crucial for this moral bias. Our results further revealed that the individuals' personal trait of moral sensitivity served as a mediator between the rsFC of mPFC network and the behavioral index of morality effect on economic valuation.

Tumor grade-related language and control network reorganization in patients with left cerebral glioma.

Language processing relies on both a functionally specialized language network and a domain-general cognitive control network. Yet, how the two networks reorganize after damage resulting from diffuse and progressive glioma remains largely unknown. To address this issue, 130 patients with left cerebral gliomas, including 77 patients with low-grade glioma (LGG, WHO grade Ⅰ/II), 53 patients with high-grade glioma (HGG, WHO grade III/IV) and 38 healthy controls (HC) were adopted. The changes in resting-state functional connectivity (rsFC) of the language network and the cingulo-opercular/fronto-parietal (CO-FP) network were examined using network-based statistics. We found that tumor grade negatively correlated with language scores and language network integrity. Compared with HCs, patients with LGGs exhibited slight language deficits, both decreased and increased changes in rsFC of language network, and nearly normal CO-FP network. Patients with HGGs had significantly lower language scores than those with LGG and exhibited more severe language and CO-FP network disruptions than HCs or patients with LGGs. Moreover, we found that in patients with HGGs, the decreased rsFCs of language network were positively correlated with language scores. Together, our findings suggest tumor grade-related network reorganization of both language and control networks underlie the different levels of language impairments observed in patients with gliomas.

Convergent syndromic atrophy of pain and emotional systems in patients with irritable bowel syndrome and depressive symptoms.

Irritable bowel syndrome (IBS) is a brain-gut disorder that is often accompanied by psychiatric comorbidities, particularly depression. However, the neuroanatomical substrates of IBS with depressive symptoms (DEP-IBS) and how depressive symptoms and brain morphology modulate IBS symptoms remain unknown. In this study, structural MRI data were processed using a voxel-based morphometry technique and one-way analysis of covariance (ANCOVA) and post-hoc t-tests were performed to compare gray matter volume (GMV) among 28 patients with DEP-IBS, 21 patients with IBS who lacked depressive symptoms (nDEP-IBS), and 36 healthy controls (HC). Correlation and mediation analyses were performed to evaluate the relationship between differing GMV in DEP-IBS and clinical variables. We found that GMV in the bilateral prefrontal, insular, and dorsal striatal areas, as well as the left temporal pole, were significantly lower in the DEP-IBS group than in the HC group. Moreover, compared with the nDEP-IBS group, the DEP-IBS group exhibited decreased GMV in the bilateral medial, dorsolateral prefrontal, and orbitofrontal cortices, bilateral dorsal striatum, and left insular cortices. Correlation analysis revealed that GMV in these atrophic brain areas of the DEP-IBS group was negatively correlated with depression, gastrointestinal symptoms, and disease duration. Our results further revealed that depressive symptoms served as a mediator between gastrointestinal symptoms and GMV in the left insula, right medial prefrontal cortex, and right middle frontal gyrus, while gastrointestinal symptoms served as a mediator between depression and GMV in these regions. Our results suggest convergent syndromic atrophy in the pain and emotional systems of patients with DEP-IBS.

Incremental prognostic value and underlying biological pathways of radiomics patterns in medulloblastoma.

BACKGROUND: To develop a radiomics signature for predicting overall survival (OS)/progression-free survival (PFS) in patients with medulloblastoma (MB), and to investigate the incremental prognostic value and biological pathways of the radiomics patterns. METHODS: A radiomics signature was constructed based on magnetic resonance imaging (MRI) from a training cohort (n = 83), and evaluated on a testing cohort (n = 83). Key pathways associated with the signature were identified by RNA-seq (GSE151519). Prognostic value of pathway genes was assessed in a public GSE85218 cohort. FINDINGS: The radiomics-clinicomolecular signature predicted OS (C-index 0.762) and PFS (C-index 0.697) better than either the radiomics signature (C-index: OS: 0.649; PFS: 0.593) or the clinicomolecular signature (C-index: OS: 0.725; PFS: 0.691) alone, with a better calibration and classification accuracy (net reclassification improvement: OS: 0.298, P = 0.022; PFS: 0.252, P = 0.026). Nine pathways were significantly correlated with the radiomics signature. Average expression value of pathway genes achieved significant risk stratification in GSE85218 cohort (log-rank P = 0.016). INTERPRETATION: This study demonstrated radiomics signature, which associated with dysregulated pathways, was an independent parameter conferring incremental value over clinicomolecular factors in survival predictions for MB patients. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma.

The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n = 92) and evaluated on a testing cohort (n = 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.

Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network.

Language deficits following brain tumors should consider the dynamic interactions between different tumor growth kinetics and functional network reorganization. We measured the resting-state functional connectivity of 126 patients with left cerebral gliomas involving language network areas, including 77 patients with low-grade gliomas (LGG) and 49 patients with high-grade gliomas (HGG). Functional network mapping for language was performed by construction of a multivariate machine learning-based prediction model of individual aphasia quotient (AQ), a summary score that indicates overall severity of language impairment. We found that the AQ scores for HGG patients were significantly lower than those of LGG patients. The prediction accuracy of HGG patients (R2 = 0.27, permutation P = 0.007) was much higher than that of LGG patients (R2 = 0.09, permutation P = 0.032). The rsFC regions predictive of LGG's AQ involved the bilateral frontal, temporal, and parietal lobes, subcortical regions, and bilateral cerebro-cerebellar connections, mainly in regions belonging to the canonical language network. The functional network of language processing for HGG patients showed strong dependence on connections of the left cerebro-cerebellar connections, limbic system, and the temporal, occipital, and prefrontal lobes. Together, our findings suggested that individual language processing of glioma patients links large-scale, bilateral, cortico-subcortical, and cerebro-cerebellar functional networks with different network reorganizational mechanisms underlying the different levels of language impairments in LGG and HGG patients.

Brain hubs in lesion models: Predicting functional network topology with lesion patterns in patients.

Various important topological properties of healthy brain connectome have recently been identified. However, the manner in which brain lesion changes the functional network topology is unknown. We examined how critical specific brain areas are in the maintenance of network topology using multivariate support vector regression analysis on brain structural and resting-state functional imaging data in 96 patients with brain damages. Patients' cortical lesion distribution patterns could significantly predict the functional network topology and a set of regions with significant weights in the prediction models were identified as "lesion hubs". Intriguingly, we found two different types of lesion hubs, whose lesions associated with changes of network topology towards relatively different directions, being either more integrated (global) or more segregated (local), and correspond to hubs identified in healthy functional network in complex manners. Our results pose further important questions about the potential dynamics of the functional brain network after brain damage.

Influences of Head Motion Regression on High-Frequency Oscillation Amplitudes of Resting-State fMRI Signals.

High-frequency oscillations (HFOs, >0.1 Hz) of resting-state fMRI (rs-fMRI) signals have received much attention in recent years. Denoising is critical for HFO studies. Previous work indicated that head motion (HM) has remarkable influences on a variety of rs-fMRI metrics, but its influences on rs-fMRI HFOs are still unknown. In this study, we investigated the impacts of HM regression (HMR) on HFO results using a fast sampling rs-fMRI dataset. We demonstrated that apparent high-frequency (∼0.2-0.4 Hz) components existed in the HM trajectories in almost all subjects. In addition, we found that individual-level HMR could robustly reveal more between-condition (eye-open vs. eye-closed) amplitude differences in high-frequency bands. Although regression of mean framewise displacement (FD) at the group level had little impact on the results, mean FD could significantly account for inter-subject variance of HFOs even after individual-level HMR. Our findings suggest that HM artifacts should not be ignored in HFO studies, and HMR is necessary for detecting HFO between-condition differences.

Neural correlates of decision making after unfair treatment.

Empirical evidence indicates that people are inequity averse. However, it is unclear whether and how suffering unfairness impacts subsequent behavior. We investigated the consequences of unfair treatment in subsequent interactions with new interaction partners and the associated neural mechanisms. Participants were experimentally manipulated to experience fair or unfair treatment in the ultimatum game (UG), and subsequently, they were given the opportunity to retaliate in the dictator game (DG) in their interactions with players who had not played a role in the previous fair or unfair treatment. The results showed that participants dictated less money to unrelated partners after frequently receiving unfair offers in the previous UG (vs. frequently receiving fair offers in the previous UG), but only when they were first exposed to unfair UG/DG. Stronger activation in the right dorsal anterior insula was found during receiving unfair offers and during the subsequent offer-considering phase. The regional homogeneity (ReHo), a measure of the local synchronization of neighboring voxels in resting-state brain activity, in the left ventral anterior insula and left superior temporal pole was positively correlated with the behavior change. These findings suggest that unfair treatment may encourage a spread of unfairness, and that the anterior insula may be not only engaged in signaling social norm violations, but also recruited in guiding subsequent adaptive behaviors.

Detecting static and dynamic differences between eyes-closed and eyes-open resting states using ASL and BOLD fMRI.

Resting-state fMRI studies have increasingly focused on multi-contrast techniques, such as BOLD and ASL imaging. However, these techniques may reveal different aspects of brain activity (e.g., static vs. dynamic), and little is known about the similarity or disparity of these techniques in detecting resting-state brain activity. It is therefore important to assess the static and dynamic characteristics of these fMRI techniques to guide future applications. Here we acquired fMRI data while subjects were in eyes-closed (EC) and eyes-open (EO) states, using both ASL and BOLD techniques, at two research centers (NIDA and HNU). Static brain activity was calculated as voxel-wise mean cerebral blood flow (CBF) using ASL, i.e., CBF-mean, while dynamic activity was measured by the amplitude of low frequency fluctuations (ALFF) of BOLD, i.e., BOLD-ALFF, at both NIDA and HNU, and CBF, i.e., CBF-ALFF, at NIDA. We showed that mean CBF was lower under EC than EO in the primary visual cortex, while BOLD-ALFF was higher under EC in the primary somatosensory cortices extending to the primary auditory cortices and lower in the lateral occipital area. Interestingly, mean CBF and BOLD-ALFF results overlapped at the visual cortex to a very small degree. Importantly, these findings were largely replicated by the HNU dataset. State differences found by CBF-ALFF were located in the primary auditory cortices, which were generally a subset of BOLD-ALFF and showed no spatial overlap with CBF-mean. In conclusion, static brain activity measured by mean CBF and dynamic brain activity measured by BOLD- and CBF-ALFF may reflect different aspects of resting-state brain activity and a combination of ASL and BOLD may provide complementary information on the biophysical and physiological processes of the brain.

Amplitude differences in high-frequency fMRI signals between eyes open and eyes closed resting states.

Recent studies employing rapid sampling techniques have demonstrated that the resting state fMRI (rs-fMRI) signal exhibits synchronized activities at frequencies much higher than the conventional frequency range (<0.1 Hz). However, little work has investigated the changes in the high-frequency fluctuations between different resting states. Here, we acquired rs-fMRI data at a high sampling rate (TR = 400 ms) from subjects with both eyes open (EO) and eyes closed (EC), and compared the amplitude of fluctuation (AF) between EO and EC for both the low- and high-frequency components. In addition to robust AF differences in the conventional low frequency band (<0.1 Hz) in visual cortex, primary auditory cortex and primary sensorimotor cortex (PSMC), we also detected high-frequency (primarily in 0.1-0.35 Hz) differences. The high-frequency results without covariates regression exhibited noisy patterns. For the data with nuisance covariates regression, we found a significant and reproducible reduction in high-frequency AF between EO and EC in the bilateral PSMC and the supplementary motor area (SMA), and an increase in high-frequency AF in the left middle occipital gyrus (MOG). Furthermore, we investigated the effect of sampling rate by down-sampling the data to effective TR = 2 s. Briefly, by using the rapid sampling rate, we were able to detect more regions with significant differences while identifying fewer artifactual differences in the high-frequency bands as compared to the down-sampled dataset. We concluded that (1) high-frequency fluctuations of rs-fMRI signals can be modulated by different resting states and thus may be of physiological importance; and (2) the regression of covariates and the use of fast sampling rates are superior for revealing high-frequency differences in rs-fMRI signals.