Brain language networks and cognitive outcomes in children with frontotemporal lobe epilepsy

Introduction

Pediatric frontal and temporal lobe epilepsies (FLE, TLE) have been associated with language impairments and structural and functional brain alterations. However, there is no clear consensus regarding the specific patterns of cerebral reorganization of language networks in these patients. The current study aims at characterizing the cerebral language networks in children with FLE or TLE, and the association between brain network characteristics and cognitive abilities.

Methods

Twenty (20) children with FLE or TLE aged between 6 and 18 years and 29 age- and sex-matched healthy controls underwent a neuropsychological evaluation and a simultaneous functional near-infrared spectroscopy and electroencephalography (fNIRS-EEG) recording at rest and during a receptive language task. EEG was used to identify potential subclinical seizures in patients. We removed these time intervals from the fNIRS signal to investigate language brain networks and not epileptogenic networks. Functional connectivity matrices on fNIRS oxy-hemoglobin concentration changes were computed using cross-correlations between all channels.

Results and discussion

Group comparisons of residual matrices (=individual task-based matrix minus individual resting-state matrix) revealed significantly reduced connectivity within the left and between hemispheres, increased connectivity within the right hemisphere and higher right hemispheric local efficiency for the epilepsy group compared to the control group. The epilepsy group had significantly lower cognitive performance in all domains compared to their healthy peers. Epilepsy patients' local network efficiency in the left hemisphere was negatively associated with the estimated IQ (p = 0.014), suggesting that brain reorganization in response to FLE and TLE does not allow for an optimal cognitive development.

Dissociating the functional roles of arcuate fasciculus subtracts in speech production

Recent tractography and microdissection studies have shown that the left arcuate fasciculus (AF)-a fiber tract thought to be crucial for speech production-consists of a minimum of 2 subtracts directly connecting the temporal and frontal cortex. These subtracts link the posterior superior temporal gyrus (STG) and middle temporal gyrus (MTG) to the inferior frontal gyrus. Although they have been hypothesized to mediate different functions in speech production, direct evidence for this hypothesis is lacking. To functionally segregate the 2 AF segments, we combined functional magnetic resonance imaging with diffusion-weighted imaging and probabilistic tractography using 2 prototypical speech production tasks, namely spoken pseudoword repetition (tapping sublexical phonological mapping) and verb generation (tapping lexical-semantic mapping). We observed that the repetition of spoken pseudowords is mediated by the subtract of STG, while generating an appropriate verb to a spoken noun is mediated by the subtract of MTG. Our findings provide strong evidence for a functional dissociation between the AF subtracts, namely a sublexical phonological mapping by the STG subtract and a lexical-semantic mapping by the MTG subtract. Our results contribute to the unraveling of a century-old controversy concerning the functional role in speech production of a major fiber tract involved in language.

Connections between spatially distant primary language regions strengthen with age during infancy, as revealed by resting-state fNIRS

Objective.Hearing is an important sensory function that plays a key role in how children learn to speak and develop language skills. Although previous neuroimaging studies have established that much of brain network maturation happens in early childhood, our understanding of the developmental trajectory of language areas is still very limited. We hypothesized that typical development trajectory of language areas in early childhood could be established by analyzing the changes of functional connectivity in normal hearing infants at different ages using functional near-infrared spectroscopy.Approach.Resting-state data were recorded from two bilateral temporal and prefrontal regions associated with language processing by measuring the relative changes of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) concentrations. Connectivity was calculated using magnitude-squared coherence of channel pairs located in (a) inter-hemispheric homologous and (b) intra-hemispheric brain regions to assess connectivity between homologous regions across hemispheres and two regions of interest in the same hemisphere, respectively.Main results.A linear regression model fitted to the age vs coherence of inter-hemispheric homologous test group revealed a significant coefficient of determination for both HbO (R²= 0.216,p= 0.0169) and HbR (R²= 0.206,p= 0.0198). A significant coefficient of determination was also found for intra-hemispheric test group for HbO (R²= 0.237,p= 0.0117) but not for HbR (R²= 0.111,p= 0.0956).Significance.The findings from HbO data suggest that both inter-hemispheric homologous and intra-hemispheric connectivity between primary language regions significantly strengthen with age in the first year of life. Mapping out the developmental trajectory of primary language areas of normal hearing infants as measured by functional connectivity could potentially allow us to better understand the altered connectivity and its effects on language delays in infants with hearing impairments.

Connectomics underlying motor functional outcomes in the acute period following stroke

Objective

Stroke remains the number one cause of morbidity in many developing countries, and while effective neurorehabilitation strategies exist, it remains difficult to predict the individual trajectories of patients in the acute period, making personalized therapies difficult. Sophisticated and data-driven methods are necessary to identify markers of functional outcomes.

Methods

Baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 79 patients following stroke. Sixteen models were constructed to predict performance across six tests of motor impairment, spasticity, and activities of daily living, using either whole-brain structural or functional connectivity. Feature importance analysis was also performed to identify brain regions and networks associated with performance in each test.

Results

The area under the receiver operating characteristic curve ranged from 0.650 to 0.868. Models utilizing functional connectivity tended to have better performance than those utilizing structural connectivity. The Dorsal and Ventral Attention Networks were among the top three features in several structural and functional models, while the Language and Accessory Language Networks were most commonly implicated in structural models.

Conclusions

Our study highlights the potential of machine learning methods combined with connectivity analysis in predicting outcomes in neurorehabilitation and disentangling the neural correlates of functional impairments, though further longitudinal studies are necessary.

Atypical structural connectivity of language networks in autism spectrum disorder: A meta-analysis of diffusion tensor imaging studies

Patients with autism spectrum disorder (ASD) often show pervasive and complex language impairments that are closely associated with aberrant structural connectivity of language networks. However, the characteristics of white matter connectivity in ASD have remained inconclusive in previous diffusion tensor imaging (DTI) studies. The current meta‐analysis aimed to comprehensively elucidate the abnormality in language‐related white matter connectivity in individuals with ASD. We searched PubMed, Web of Science, Scopus, and Medline databases to identify relevant studies. The standardized mean difference was calculated to measure the pooled difference in DTI metrics in each tract between the ASD and typically developing (TD) groups. The moderating effects of age, sex, language ability, and symptom severity were investigated using subgroup and meta‐regression analysis. Thirty‐three DTI studies involving 831 individuals with ASD and 836 TD controls were included in the meta‐analysis. ASD subjects showed significantly lower fractional anisotropy or higher mean diffusivity across language‐associated tracts than TD controls. These abnormalities tended to be more prominent in the left language networks than in the right. In addition, children with ASD exhibit more pronounced and pervasive disturbances in white matter connectivity than adults. These results support the under‐connectivity hypothesis and demonstrate the widespread abnormal microstructure of language‐related tracts in patients with ASD. Otherwise, white matter abnormalities in the autistic brain could vary depending on the developmental stage and hemisphere.

Investigating the Influence of Inverse Preferential Attachment on Network Development

Recent work investigating the development of the phonological lexicon, where edges between words represent phonological similarity, have suggested that phonological network growth may be partly driven by a process that favors the acquisition of new words that are phonologically similar to several existing words in the lexicon. To explore this growth mechanism, we conducted a simulation study to examine the properties of networks grown by inverse preferential attachment, where new nodes added to the network tend to connect to existing nodes with fewer edges. Specifically, we analyzed the network structure and degree distributions of artificial networks generated via either preferential attachment, an inverse variant of preferential attachment, or combinations of both network growth mechanisms. The simulations showed that network growth initially driven by preferential attachment followed by inverse preferential attachment led to densely-connected network structures (i.e., smaller diameters and average shortest path lengths), as well as degree distributions that could be characterized by non-power law distributions, analogous to the features of real-world phonological networks. These results provide converging evidence that inverse preferential attachment may play a role in the development of the phonological lexicon and reflect processing costs associated with a mature lexicon structure.

Dynamics of language reorganization after left temporo-parietal and frontal stroke

The loss and recovery of language functions are still incompletely understood. This longitudinal functional MRI study investigated the neural mechanisms underlying language recovery in patients with post-stroke aphasia putting particular emphasis on the impact of lesion site. To identify patterns of language-related activation, an auditory functional MRI sentence comprehension paradigm was administered to patients with circumscribed lesions of either left frontal (n = 17) or temporo-parietal (n = 17) cortex. Patients were examined repeatedly during the acute (≤1 week, t1), subacute (1-2 weeks, t2) and chronic phase (>6 months, t3) post-stroke; healthy age-matched control subjects (n = 17) were tested once. The separation into two patient groups with circumscribed lesions allowed for a direct comparison of the contributions of distinct lesion-dependent network components to language reorganization between both groups. We hypothesized that activation of left hemisphere spared and perilesional cortex as well as lesion-homologue cortex in the right hemisphere varies between patient groups and across time. In addition, we expected that domain-general networks serving cognitive control independently contribute to language recovery. First, we found a global network disturbance in the acute phase that is characterized by reduced functional MRI language activation including areas distant to the lesion (i.e. diaschisis) and subsequent subacute network reactivation (i.e. resolution of diaschisis). These phenomena were driven by temporo-parietal lesions. Second, we identified a lesion-independent sequential activation pattern with increased activity of perilesional cortex and bilateral domain-general networks in the subacute phase followed by reorganization of left temporal language areas in the chronic phase. Third, we observed involvement of lesion-homologue cortex only in patients with frontal but not temporo-parietal lesions. Fourth, irrespective of lesion location, language reorganization predominantly occurred in pre-existing networks showing comparable activation in healthy controls. Finally, we detected different relationships of performance and activation in language and domain-general networks demonstrating the functional relevance for language recovery. Our findings highlight that the dynamics of language reorganization clearly depend on lesion location and hence open new perspectives for neurobiologically motivated strategies of language rehabilitation, such as individually-tailored targeted application of neuro-stimulation.

Language network reorganization before and after temporal lobe epilepsy surgery

OBJECTIVE

Epilepsy surgery is the recommended treatment option for patients with drug-resistant temporal lobe epilepsy (TLE). This method offers a good chance of seizure freedom but carries a considerable risk of postoperative language impairment. The extremely variable neurocognitive profiles in surgical epilepsy patients cannot be fully explained by extent of resection, fiber integrity, or current task-based functional MRI (fMRI). In this study, the authors aimed to investigate pathology- and surgery-triggered language organization in TLE by using fMRI activation and network analysis as well as considering structural and neuropsychological measures.

METHODS

Twenty-eight patients with unilateral TLE (16 right, 12 left) underwent T1-weighted imaging, diffusion tensor imaging, and task-based language fMRI pre- and postoperatively (n = 15 anterior temporal lobectomy, n = 11 selective amygdalohippocampectomy, n = 2 focal resection). Twenty-two healthy subjects served as the control cohort. Functional connectivity, activation maps, and laterality indices for language dominance were analyzed from fMRI data. Postoperative fractional anisotropy values of 7 major tracts were calculated. Naming, semantic, and phonematic verbal fluency scores before and after surgery were correlated with imaging parameters.

RESULTS

fMRI network analysis revealed widespread, bihemispheric alterations in language architecture that were not captured by activation analysis. These network changes were found preoperatively and proceeded after surgery with characteristic patterns in the left and right TLEs. Ipsilesional fronto-temporal connectivity decreased in both left and right TLE. In left TLE specifically, preoperative atypical language dominance predicted better postoperative verbal fluency and naming function. In right TLE, left frontal language dominance correlated with good semantic verbal fluency before and after surgery, and left fronto-temporal language laterality predicted good naming outcome. Ongoing seizures after surgery (Engel classes ID-IV) were associated with naming deterioration irrespective of seizure side. Functional findings were not explained by the extent of resection or integrity of major white matter tracts.

CONCLUSIONS

Functional connectivity analysis contributes unique insight into bihemispheric remodeling processes of language networks after epilepsy surgery, with characteristic findings in left and right TLE. Presurgical contralateral language recruitment is associated with better postsurgical language outcome in left and right TLE.

Task-Free Functional Language Networks: Reproducibility and Clinical Application

Intrinsic connectivity networks (ICNs) identified through task-free fMRI (tf-fMRI) offer the opportunity to investigate human brain circuits involved in language processes without requiring participants to perform challenging cognitive tasks. In this study, we assessed the ability of tf-fMRI to isolate reproducible networks critical for specific language functions and often damaged in primary progressive aphasia (PPA). First, we performed whole-brain seed-based correlation analyses on tf-fMRI data to identify ICNs anchored in regions known for articulatory, phonological, and semantic processes in healthy male and female controls (HCs). We then evaluated the reproducibility of these ICNs in an independent cohort of HCs, and recapitulated their functional relevance with a post hoc meta-analysis on task-based fMRI. Last, we investigated whether atrophy in these ICNs could inform the differential diagnosis of nonfluent/agrammatic, semantic, and logopenic PPA variants. The identified ICNs included a dorsal articulatory-phonological network involving inferior frontal and supramarginal regions; a ventral semantic network involving anterior middle temporal and angular gyri; a speech perception network involving superior temporal and sensorimotor regions; and a network between posterior inferior temporal and intraparietal regions likely linking visual, phonological, and attentional processes for written language. These ICNs were highly reproducible across independent groups and revealed areas consistent with those emerging from task-based meta-analysis. By comparing ICNs' spatial distribution in HCs with patients' atrophy patterns, we identified ICNs associated with each PPA variant. Our findings demonstrate the potential use of tf-fMRI to investigate the functional status of language networks in patients for whom activation studies can be methodologically challenging.SIGNIFICANCE STATEMENT We showed that a single, short, task-free fMRI acquisition is able to identify four reproducible and relatively segregated intrinsic left-dominant networks associated with articulatory, phonological, semantic, and multimodal orthography-to-phonology processes, in HCs. We also showed that these intrinsic networks relate to syndrome-specific atrophy patterns in primary progressive aphasia. Collectively, our results support the application of task-free fMRI in future research to study functionality of language circuits in patients for whom tasked-based activation studies might be methodologically challenging.

Shared Reading Quality and Brain Activation during Story Listening in Preschool-Age Children

OBJECTIVE

To explore the relationship between maternal shared reading quality (verbal interactivity and engagement) and brain function during story listening in at-risk, preschool-age children, in the context of behavioral evidence and American Academy of Pediatrics, recommendations.

STUDY DESIGN

In this cross-sectional study, 22 healthy, 4-year-old girls from low socioeconomic status households completed functional magnetic resonance imaging using an established story listening task, followed by videotaped observation of uncoached mother-daughter reading of the same, age-appropriate picture book. Shared reading quality was independently scored applying dialogic reading and other evidence-based criteria reflecting interactivity and engagement, and applied as a predictor of neural activation during the functional magnetic resonance imaging task, controlling for income and maternal education.

RESULTS

Shared reading quality scores were generally low and negatively correlated with maternal distraction by smartphones (P < .05). Scores were positively correlated with activation in left-sided brain areas supporting expressive and complex language, social-emotional integration, and working memory (P <.05, false discovery rate corrected).

CONCLUSIONS

Maternal shared reading quality is positively correlated with brain activation supporting complex language, executive function, and social-emotional processing in at-risk, preschool-age children. These findings represent novel neural biomarkers of how this modifiable aspect of home reading environment may influence foundational emergent literacy skills, reinforce behavioral evidence and American Academy of Pediatrics, recommendations, and underscore the potential of dialogic reading interventions to promote healthy brain development, especially in at-risk households.

Syntactic computations in the language network: characterizing dynamic network properties using representational similarity analysis

The core human capacity of syntactic analysis involves a left hemisphere network involving left inferior frontal gyrus (LIFG) and posterior middle temporal gyrus (LMTG) and the anatomical connections between them. Here we use magnetoencephalography (MEG) to determine the spatio-temporal properties of syntactic computations in this network. Listeners heard spoken sentences containing a local syntactic ambiguity (e.g., “… landing planes …”), at the offset of which they heard a disambiguating verb and decided whether it was an acceptable/unacceptable continuation of the sentence. We charted the time-course of processing and resolving syntactic ambiguity by measuring MEG responses from the onset of each word in the ambiguous phrase and the disambiguating word. We used representational similarity analysis (RSA) to characterize syntactic information represented in the LIFG and left posterior middle temporal gyrus (LpMTG) over time and to investigate their relationship to each other. Testing a variety of lexico-syntactic and ambiguity models against the MEG data, our results suggest early lexico-syntactic responses in the LpMTG and later effects of ambiguity in the LIFG, pointing to a clear differentiation in the functional roles of these two regions. Our results suggest the LpMTG represents and transmits lexical information to the LIFG, which responds to and resolves the ambiguity.

Classification of fMRI patterns--a study of the language network segregation in pediatric localization related epilepsy

This article describes a pattern classification algorithm for pediatric epilepsy using fMRI language-related activation maps. 122 fMRI datasets from a control group (64) and localization related epilepsy patients (58) provided by five children's hospitals were used. Each subject performed an auditory description decision task. Using the artificial data as training data, incremental Principal Component Analysis was used in order to generate the feature space while overcoming memory requirements of large datasets. The nearest-neighbor classifier (NNC) and the distance-based fuzzy classifier (DFC) were used to perform group separation into left dominant, right dominant, bilateral, and others. The results show no effect of age, age at seizure onset, seizure duration, or seizure etiology on group separation. Two sets of parameters were significant for group separation, the patient vs. control populations and handedness. Of the 122 real datasets, 90 subjects gave the same classification results across all the methods (three raters, LI, bootstrap LI, NNC, and DFC). For the remaining datasets, 18 cases for the IPCA-NNC and 21 cases for the IPCA-DFC agreed with the majority of the five classification results (three visual ratings and two LI results). Kappa values vary from 0.59 to 0.73 for NNC and 0.61 to 0.75 for DFC, which indicate good agreement between NNC or DFC with traditional methods. The proposed method as designed can serve as an alternative method to corroborate existing LI and visual rating classification methods and to resolve some of the cases near the boundaries in between categories.