Language lateralization from task-based and resting state functional MRI in patients with epilepsy

Functional location of the language cortical areas in focal refractory epilepsy using the conventional, selective, and supraselective Wada test

In refractory focal epilepsy, resective surgery offers an alternative for seizure control. However, there is a risk of language deterioration when the epileptogenic zone involves an eloquent cortical region. The Wada test involves the insertion of a catheter through the internal carotid artery and the injection of a short-acting anesthetic, resulting in transient loss of hemisphere function. While its specificity is high, its sensitivity is reduced, despite its limited or absent spatial resolution. Additionally, the generalized action of the anesthetic may lead to misinterpretations due to global cognitive arrest, particularly in patients with baseline deficits. The aim of this report was to prove the refinement of the selective and supraselective protocols, as well as their contribution to overcoming these disadvantages. The procedure began by placing a microcatheter in progressively more distal irrigation sites, according to the required technique, gradually performing angiography with contrast medium. Tissue perfusion allowed the identification of the cerebral parenchyma where the anesthetic would act. After injection, the assessment of neurocognitive changes was conducted. The characterization of language patterns was performed, delineating indispensable eloquent zones and dispensable eloquent zones, irrespective of the patients' cognitive condition. There was concordance between the findings and post-surgical results. The selective and supraselective Wada test surpasses the disadvantages of the conventional method and proves decisive in surgical planning and decision-making.

A simple clustering approach to map the human brain's cortical semantic network organization during task

Constructing task-state large-scale brain networks can enhance our understanding of the organization of brain functions during cognitive tasks. The primary goal of brain network partitioning is to cluster functionally homogeneous brain regions. However, a brain region often serves multiple cognitive functions, complicating the partitioning process. This study proposes a novel clustering method for partitioning large-scale brain networks based on specific cognitive functions, selecting semantic representation as the target cognitive function to evaluate the validity of the proposed method. Specifically, we analyzed functional magnetic resonance imaging (fMRI) data from 11 subjects, each exposed to 672 concepts, and correlated this with semantic rating data related to these concepts. We identified distinct semantic networks based on the concept comprehension task and validated the robustness of our network partitioning through multiple methods. We found that the semantic networks derived from multidimensional semantic activation clustering exhibit high reliability and cross-semantic model consistency (semantic ratings and word embeddings extracted from GPT-2), particularly in networks associated with high semantic functions. Moreover, these semantic networks exhibits significant differences from the resting-state and task-based brain networks obtained using traditional methods. Further analysis revealed functional differences between semantic networks, including disparities in their multidimensional semantic representation capabilities, differences in the information modalities they rely on to acquire semantic information, and varying associations with general cognitive domains. This study introduces a novel approach for analyzing brain networks tailored to specific cognitive functions, establishing a standard semantic parcellation with seven networks for future research, potentially enriching our understanding of complex cognitive processes and their neural bases.

Evolutionary Strategies Enable Systematic and Reliable Uncertainty Quantification: A Proof-of-Concept Pilot Study on Resting-State Functional MRI Language Lateralization

Reliable and trustworthy artificial intelligence (AI), particularly in high-stake medical diagnoses, necessitates effective uncertainty quantification (UQ). Existing UQ methods using model ensembles often introduce invalid variability or computational complexity, rendering them impractical and ineffective in clinical workflow. We propose a UQ approach based on deep neuroevolution (DNE), a data-efficient optimization strategy. Our goal is to replicate trends observed in expert-based UQ. We focused on language lateralization maps from resting-state functional MRI (rs-fMRI). Fifty rs-fMRI maps were divided into training/testing (30:20) sets, representing two labels: "left-dominant" and "co-dominant." DNE facilitated acquiring an ensemble of 100 models with high training and testing set accuracy. Model uncertainty was derived from distribution entropies over the 100 model predictions. Expert reviewers provided user-based uncertainties for comparison. Model (epistemic) and user-based (aleatoric) uncertainties were consistent in the independently and identically distributed (IID) testing set, mainly indicating low uncertainty. In a mostly out-of-distribution (OOD) holdout set, both model and user-based entropies correlated but displayed a bimodal distribution, with one peak representing low and another high uncertainty. We also found a statistically significant positive correlation between epistemic and aleatoric uncertainties. DNE-based UQ effectively mirrored user-based uncertainties, particularly highlighting increased uncertainty in OOD images. We conclude that DNE-based UQ correlates with expert assessments, making it reliable for our use case and potentially for other radiology applications.

Unveiling the hemispheric specialization of language: Organization and neuroplasticity

The advancements in understanding hemispheric specialization of language (HSL) have been following two primary avenues: the development of neuroimaging techniques and the study of its reorganizations in patients with various neuropathologic conditions. Hence, the objectives of this chapter are twofold. First, to provide an overview of the key neuroimaging techniques employed to investigate HSL, along with the notable findings derived from them in the healthy population. Second, it focuses on the reorganization of HSL in physiologic (healthy aging) and pathologic (poststroke aphasia and temporal lobe epilepsy) conditions. The chapter emphasizes the importance of employing multimodal methodologies to comprehend the complex relationship between underlying HSL mechanisms affected by disease and resulting language impairments. Combining the neuroimaging techniques can help us understand how different characteristics of language networks combine into general mechanisms that support their plasticity. Nevertheless, it highlights the need for standardized HSL metrics, as the absence of such metrics poses challenges in synthesizing findings across studies. Additionally, while HSL findings are being accumulated, albeit multimodal, there is a lack of integration within a robust theoretical framework. In conclusion, there is a need for novel models acknowledging multimodal aspects of HSL while positioning it within the context of other cognitive functions.

Rest2Task: Modeling task-specific components in resting-state functional connectivity and applications

The networks observed in the brain during resting-state activity are not entirely "task-free." Instead, they hint at a hierarchical structure prepared for adaptive cognitive functions. Recent studies have increasingly demonstrated the potential of resting-state fMRI to predict local activations or global connectomes during task performance. However, uncertainties remain regarding the unique and shared task-specific components within resting-state brain networks, elucidating local activations and global connectome patterns. A coherent framework is also required to integrate these task-specific components to predict local activations and global connectome patterns. In this work, we introduce the Rest2Task model based on the partial least squares-based multivariate regression algorithm, which effectively integrates mappings from resting-state connectivity to local activations and global connectome patterns. By analyzing the coefficients of the regression model, we extracted task-specific resting-state components corresponding to brain local activation or global connectome of various tasks and applied them to the brain lateralization prediction and psychiatric disorders diagnostic. Our model effectively substitutes traditional whole-brain functional connectivity (FC) in predicting functional lateralization and diagnosing brain disorders. Our research represents the inaugural effort to quantify the contribution of patterns (components) within resting-state FC to different tasks, endowing these components with specific task-related contextual information. The task-specific resting-state components offer new insights into brain lateralization processing and disease diagnosis, potentially providing fresh perspectives on the adaptive transformation of brain networks in response to tasks.

Applications of Functional Magnetic Resonance Imaging to the Study of Functional Connectivity and Activation in Neurological Disease: A Scoping Review of the Literature

BACKGROUND

Functional magnetic resonance imaging (fMRI) has transformed our understanding of brain's functional architecture, providing critical insights into neurological diseases. This scoping review synthesizes the current landscape of fMRI applications across various neurological domains, elucidating the evolving role of both task-based and resting-state fMRI in different settings.

METHODS

We conducted a comprehensive scoping review following the Preferred Reporting Items for Systematic Review and Meta-Analyses Extension for Scoping Reviews guidelines. Extensive searches in Medline/PubMed, Embase, and Web of Science were performed, focusing on studies published between 2003 and 2023 that utilized fMRI to explore functional connectivity and regional activation in adult patients with neurological conditions. Studies were selected based on predefined inclusion and exclusion criteria, with data extracted.

RESULTS

We identified 211 studies, covering a broad spectrum of neurological disorders including mental health, movement disorders, epilepsy, neurodegeneration, traumatic brain injury, cerebrovascular accidents, vascular abnormalities, neurorehabilitation, neuro-critical care, and brain tumors. The majority of studies utilized resting-state fMRI, underscoring its prominence in identifying disease-specific connectivity patterns. Results highlight the potential of fMRI to reveal the underlying pathophysiological mechanisms of various neurological conditions, facilitate diagnostic processes, and potentially guide therapeutic interventions.

CONCLUSIONS

fMRI serves as a powerful tool for elucidating complex neural dynamics and pathologies associated with neurological diseases. Despite the breadth of applications, further research is required to standardize fMRI protocols, improve interpretative methodologies, and enhance the translation of imaging findings to clinical practice. Advances in fMRI technology and analytics hold promise for improving the precision of neurological assessments and interventions.

Evaluating the concordance of functional MRI-based language lateralization and Wada testing in epilepsy patients: A single-center analysis

BACKGROUND

For patients with drug-resistant epilepsy, surgery may be effective in controlling their disease. Surgical evaluation may involve localization of the language areas using functional magnetic resonance imaging (fMRI) or Wada testing. We evaluated the accuracy of task-based fMRI versus Wada-based language lateralization in a cohort of our epilepsy patients.

METHODS

In a single-center, retrospective analysis, we identified patients with medically intractable epilepsy who participated in presurgical language mapping (n = 35) with fMRI and Wada testing. Demographic variables and imaging metrics were obtained. We calculated the laterality index (LI) from task-evoked fMRI activation maps across language areas during auditory and reading tasks to determine lateralization. Possible scores for LI range from -1 (strongly left-hemisphere dominant) to 1 (strongly right-hemisphere dominant). Concordance between fMRI and Wada was estimated using Cohen's Kappa coefficient. Association between the LI scores from the auditory and reading tasks was tested using Spearman's rank correlation coefficient.

RESULTS

The fMRI-based laterality indices were concordant with results from Wada testing in 91.4% of patients during the reading task (κ = .55) and 96.9% of patients during the auditory task (κ = .79). The mean LIs for the reading and auditory tasks were -0.52 ± 0.43 and -0.68 ± 0.42, respectively. The LI scores for the language and reading tasks were strongly correlated, r(30) = 0.57 (p = 0.001).

CONCLUSION

Our findings suggest that fMRI is generally an accurate, low-risk alternative to Wada testing for language lateralization. However, when fMRI indicates atypical language lateralization (e.g., bilateral dominance), patients may benefit from subsequent Wada testing or intraoperative language mapping.

Power spectral analysis can determine language laterality from resting-state functional MRI data in healthy controls

BACKGROUND AND PURPOSE

Resting-state functional magnetic resonance imaging (rsfMRI) has been proposed as an alternative to task-based fMRI including clinical situations such as preoperative brain tumor planning, due to advantages including ease of performance and time savings. However, one of its drawbacks is the limited ability to accurately lateralize language function.

METHODS

Using the rsfMRI data of healthy controls, we carried out a power spectra analysis on three regions of interest (ROIs): Broca's area (BA) in the frontal cortex for language, hand motor (HM) area in the primary motor cortex, and the primary visual cortex (V1). Spike removal, motion correction, linear trend removal, and spatial smoothing were applied. Spontaneous low-frequency fluctuations (0.01-0.1 Hz) were filtered to enable functional integration.

RESULTS

BA showed greater power on the left hemisphere relative to the right (p = .0055), while HM (p = .1563) and V1 (p = .4681) were not statistically significant. A novel index, termed the power laterality index (PLI), computed to estimate the degree of power lateralization for each brain region, revealed a statistically significant difference between BA and V1 (p < .00001), where V1 was used as a control since the primary visual cortex does not lateralize. Validation studies used to compare PLI to a laterality index computed using phonemic fluency, a task-based, language fMRI paradigm, demonstrated good correlation.

CONCLUSIONS

The power spectra for BA revealed left language lateralization, which was not replicated in HM or V1. This work demonstrates the feasibility and validity of an ROI-based power spectra analysis on rsfMRI data for language lateralization.

Occupation-modulated language networks and its lateralization: A resting-state fMRI study of seafarers

Introduction

Studies have revealed that the language network of Broca’s area and Wernicke’s area is modulated by factors such as disease, gender, aging, and handedness. However, how occupational factors modulate the language network remains unclear.

Methods

In this study, taking professional seafarers as an example, we explored the resting-state functional connectivity (RSFC) of the language network with seeds (the original and flipped Broca’s area and Wernicke’s area).

Results

The results showed seafarers had weakened RSFC of Broca’s area with the left superior/middle frontal gyrus and left precentral gyrus, and enhanced RSFC of Wernicke’s area with the cingulate and precuneus. Further, seafarers had a less right-lateralized RSFC with Broca’s area in the left inferior frontal gyrus, while the controls showed a left-lateralized RSFC pattern in Broca’s area and a right-lateralized one in Wernicke’s area. Moreover, seafarers displayed stronger RSFC with the left seeds of Broca’s area and Wernicke’s area.

Discussion

These findings suggest that years of working experience significantly modulates the RSFC of language networks and their lateralization, providing rich insights into language networks and occupational neuroplasticity.

Progressive Voxel-Wise Homotopic Connectivity from childhood to adulthood: Age-related functional asymmetry in resting-state functional magnetic resonance imaging

Homotopic connectivity during resting state has been proposed as a risk marker for neurologic and psychiatric conditions, but a precise characterization of its trajectory through development is currently lacking. Voxel-Mirrored Homotopic Connectivity (VMHC) was evaluated in a sample of 85 neurotypical individuals aged 7-18 years. VMHC associations with age, handedness, sex, and motion were explored at the voxel-wise level. VMHC correlates were also explored within 14 functional networks. Primary and secondary outcomes were repeated in a sample of 107 adults aged 21-50 years. In adults, VMHC was negatively correlated with age only in the posterior insula (false discovery rate p < .05, >30-voxel clusters), while a distributed effect among the medial axis was observed in minors. Four out of 14 considered networks showed significant negative correlations between VMHC and age in minors (basal ganglia r = -.280, p = .010; anterior salience r = -.245, p = .024; language r = -.222, p = .041; primary visual r = -.257, p = .017), but not adults. In minors, a positive effect of motion on VMHC was observed only in the putamen. Sex did not significantly influence age effects on VMHC. The current study showed a specific decrease in VMHC for minors as a function of age, but not adults, supporting the notion that interhemispheric interactions can shape late neurodevelopment.

Unraveling the functional attributes of the language connectome: crucial subnetworks, flexibility and variability

Language processing is a highly integrative function, intertwining linguistic operations (processing the language code intentionally used for communication) and extra-linguistic processes (e.g., attention monitoring, predictive inference, long-term memory). This synergetic cognitive architecture requires a distributed and specialized neural substrate. Brain systems have mainly been examined at rest. However, task-related functional connectivity provides additional and valuable information about how information is processed when various cognitive states are involved. We gathered thirteen language fMRI tasks in a unique database of one hundred and fifty neurotypical adults (InLang [Interactive networks of Language] database), providing the opportunity to assess language features across a wide range of linguistic processes. Using this database, we applied network theory as a computational tool to model the task-related functional connectome of language (LANG atlas). The organization of this data-driven neurocognitive atlas of language was examined at multiple levels, uncovering its major components (or crucial subnetworks), and its anatomical and functional correlates. In addition, we estimated its reconfiguration as a function of linguistic demand (flexibility) or several factors such as age or gender (variability). We observed that several discrete networks could be specifically shaped to promote key functional features of language: coding-decoding (Net1), control-executive (Net2), abstract-knowledge (Net3), and sensorimotor (Net4) functions. The architecture of these systems and the functional connectivity of the pivotal brain regions varied according to the nature of the linguistic process, gender, or age. By accounting for the multifaceted nature of language and modulating factors, this study can contribute to enriching and refining existing neurocognitive models of language. The LANG atlas can also be considered a reference for comparative or clinical studies involving various patients and conditions.

Roles of fMRI and Wada tests in the presurgical evaluation of language functions in temporal lobe epilepsy

Surgical treatment of pharmacoresistant temporal lobe epilepsy (TLE) carries risks for language function that can significantly affect the quality of life. Predicting the risks of decline in language functions before surgery is, consequently, just as important as predicting the chances of becoming seizure-free. The intracarotid amobarbital test, generally known as the Wada test (WT), has been traditionally used to determine language lateralization and to estimate their potential decline after surgery. However, the test is invasive and it does not localize the language functions. Therefore, other noninvasive methods have been proposed, of which functional magnetic resonance (fMRI) has the greatest potential. Functional MRI allows localization of language areas. It has good concordance with the WT for language lateralization, and it is of predictive value for postsurgical naming outcomes. Consequently, fMRI has progressively replaced WT for presurgical language evaluation. The objective of this manuscript is to review the most relevant aspects of language functions in TLE and the current role of fMRI and WT in the presurgical evaluation of language. First, we will provide context by revising the language network distribution and the effects of TLE on them. Then, we will assess the functional outcomes following various forms of TLE surgery and measures to reduce postoperative language decline. Finally, we will discuss the current indications for WT and fMRI and the potential usefulness of the resting-state fMRI technique.

Diffusion magnetic resonance imaging connectome features are predictive of functional lateralization of semantic processing in the anterior temporal lobes

Assessment of regional language lateralization is crucial in many scenarios, but not all populations are suited for its evaluation via task-functional magnetic resonance imaging (fMRI). In this study, the utility of structural connectome features for the classification of language lateralization in the anterior temporal lobes (ATLs) was investigated. Laterality indices for semantic processing in the ATL were computed from task-fMRI in 1038 subjects from the Human Connectome Project who were labeled as stronger rightward lateralized (RL) or stronger leftward to bilaterally lateralized (LL) in a data-driven approach. Data of unrelated subjects (n = 432) were used for further analyses. Structural connectomes were generated from diffusion-MRI tractography, and graph theoretical metrics (node degree, betweenness centrality) were computed. A neural network (NN) and a random forest (RF) classifier were trained on these metrics to classify subjects as RL or LL. After classification, comparisons of network measures were conducted via permutation testing. Degree-based classifiers produced significant above-chance predictions both during cross-validation (NN: AUC-ROC[CI] = 0.68[0.64-0.73], accuracy[CI] = 68.34%[63-73.2%]; RF: AUC-ROC[CI] = 0.7[0.66-0.73], accuracy[CI] = 64.81%[60.9-68.5]) and testing (NN: AUC-ROC[CI] = 0.69[0.53-0.84], accuracy[CI] = 68.09[53.2-80.9]; RF: AUC-ROC[CI] = 0.68[0.53-0.84], accuracy[CI] = 68.09[55.3-80.9]). Comparison of network metrics revealed small effects of increased node degree within the right posterior middle temporal gyrus (pMTG) in subjects with RL, while degree was decreased in the right posterior cingulate cortex (PCC). Above-chance predictions of functional language lateralization in the ATL are possible based on diffusion-MRI connectomes alone. Increased degree within the right pMTG as a right-sided homologue of a known semantic hub, and decreased hubness of the right PCC may form a structural basis for rightward-lateralized semantic processing.

Right-hemispheric language reorganization in patients with brain arteriovenous malformations: A functional magnetic resonance imaging study

Brain arteriovenous malformation (AVM), a presumed congenital lesion, may involve traditional language areas but usually does not lead to language dysfunction unless it ruptures. The objective of this research was to study right-hemispheric language reorganization patterns in patients with brain AVMs using functional magnetic resonance imaging (fMRI). We prospectively enrolled 30 AVM patients with lesions involving language areas and 32 age- and sex-matched healthy controls. Each subject underwent fMRI during three language tasks: visual synonym judgment, oral word reading, and auditory sentence comprehension. The activation differences between the AVM and control groups were investigated by voxelwise analysis. Lateralization indices (LIs) for the frontal lobe, temporal lobe, and cerebellum were compared between the two groups, respectively. Results suggested that the language functions of AVM patients and controls were all normal. Voxelwise analysis showed no significantly different activations between the two groups in visual synonym judgment and oral word reading tasks. In auditory sentence comprehension task, AVM patients had significantly more activations in the right precentral gyrus (BA 6) and right cerebellar lobule VI (AAL 9042). According to the LI results, the frontal lobe in oral word reading task and the temporal lobe in auditory sentence comprehension task were significantly more right-lateralized in the AVM group. These findings suggest that for patients with AVMs involving language cortex, different language reorganization patterns may develop for different language functions. The recruitment of brain areas in the right cerebral and cerebellar hemispheres may play a compensatory role in the reorganized language network of AVM patients.

Resting-State Functional MRI for Determining Language Lateralization in Children with Drug-Resistant Epilepsy

BACKGROUND AND PURPOSE

Task-based fMRI is a noninvasive method of determining language dominance; however, not all children can complete language tasks due to age, cognitive/intellectual, or language barriers. Task-free approaches such as resting-state fMRI offer an alternative method. This study evaluated resting-state fMRI for predicting language laterality in children with drug-resistant epilepsy.

MATERIALS AND METHODS

A retrospective review of 43 children with drug-resistant epilepsy who had undergone resting-state fMRI and task-based fMRI during presurgical evaluation was conducted. Independent component analysis of resting-state fMRI was used to identify language networks by comparing the independent components with a language network template. Concordance rates in language laterality between resting-state fMRI and each of the 4 task-based fMRI language paradigms (auditory description decision, auditory category, verbal fluency, and silent word generation tasks) were calculated.

RESULTS

Concordance ranged from 0.64 (95% CI, 0.48-0.65) to 0.73 (95% CI, 0.58-0.87), depending on the language paradigm, with the highest concordance found for the auditory description decision task. Most (78%-83%) patients identified as left-lateralized on task-based fMRI were correctly classified as left-lateralized on resting-state fMRI. No patients classified as right-lateralized or bilateral on task-based fMRI were correctly classified by resting-state fMRI.

CONCLUSIONS

While resting-state fMRI correctly classified most patients who had typical (left) language dominance, its ability to correctly classify patients with atypical (right or bilateral) language dominance was poor. Further study is required before resting-state fMRI can be used clinically for language mapping in the context of epilepsy surgery evaluation in children with drug-resistant epilepsy.

Resting-state hippocampal networks related to language processing reveal unique patterns in temporal lobe epilepsy

OBJECTIVE

Patients with temporal lobe epilepsy (TLE) commonly experience a broad range of language impairments. These deficits are thought to arise from repeated seizure activity that damages language regions. However, connectivity between the seizure onset region in the hippocampus and regions related to language processing has rarely been studied, and could also have a strong impact on language function. The purpose of this study was to use resting-state functional connectivity (FC) measures to assess hippocampal network patterns and their relation to language abilities in patients with right TLE (RLTE), left TLE (LTLE), and healthy controls.

METHODS

Presurgical resting-state 3T functional MRI data were acquired from 40 patients with mesial TLE (27 RTLE, 13 LTLE) and 54 controls. The regions of interest were the anterior and posterior bilateral hippocampi and eleven regions grouped by frontal or temporo-parietal locations, including large areas of language-related cortex. FC values were computed with the right/left anterior and posterior hippocampi as the seeds and frontal and temporo-parietal regions as targets. Resting-state lateralization indices were also calculated (LI-Rest), and all FC measures were correlated to neuropsychological language scores and measures related to manifestation of epilepsy including age of onset, duration of disease, monthly seizure frequency, and hippocampal volume.

RESULTS

We found significant group differences between the anterior hippocampi and temporo-parietal regions closest to the seizure focus, in which RTLE and LTLE showed stronger connectivity to their contralateral hippocampus, while controls showed similar connectivity to both hippocampi. In addition, LI-Rest demonstrated significantly more right lateralization in LTLE compared to RTLE for temporo-parietal regions only. In LTLE, we found significant associations between stronger hippocampal network resting-state FC and later age of onset and decreased left anterior hippocampal volume.

SIGNIFICANCE

The results of our study indicate that the presence of TLE impacts hippocampal-temporo-parietal networks relevant to language processing.

Language lateralization from task-based and resting state functional MRI in patients with epilepsy

We compared resting state (RS) functional connectivity and task‐based fMRI to lateralize language dominance in 30 epilepsy patients (mean age = 33; SD = 11; 12 female), a measure used for presurgical planning. Language laterality index (LI) was calculated from task fMRI in frontal, temporal, and frontal + temporal regional masks using LI bootstrap method from SPM12. RS language LI was assessed using two novel methods of calculating RS language LI from bilateral Broca's area seed based connectivity maps across regional masks and multiple thresholds (p < .05, p < .01, p < .001, top 10% connections). We compared LI from task and RS fMRI continuous values and dominance classifications. We found significant positive correlations between task LI and RS LI when functional connectivity thresholds were set to the top 10% of connections. Concordance of dominance classifications ranged from 20% to 30% for the intrahemispheric resting state LI method and 50% to 63% for the resting state LI intra‐ minus interhemispheric difference method. Approximately 40% of patients left dominant on task showed RS bilateral dominance. There was no difference in LI concordance between patients with right‐sided and left‐sided resections. Early seizure onset (<6 years old) was not associated with atypical language dominance during task‐based or RS fMRI. While a relationship between task LI and RS LI exists in patients with epilepsy, language dominance is less lateralized on RS than task fMRI. Concordance of language dominance classifications between task and resting state fMRI depends on brain regions surveyed and RS LI calculation method.

A Functional Magnetic Resonance Imaging Approach for Language Laterality Assessment in Young Children

Functional magnetic resonance imaging (fMRI) is a usable technique to determine hemispheric dominance of language function, but high-quality fMRI images are difficult to acquire in young children. Here we aimed to develop and validate an fMRI approach to reliably determine hemispheric language dominance in young children. We designed two new tasks (story, SR; Letter picture matching, LPM) that aimed to match the interests and the levels of cognitive development of young children. We studied 32 healthy children (6-10 years old, median age 8.7 years) and seven children with epilepsy (7-11 years old, median age 8.6 years) and compared the lateralization index of the new tasks with those of a well-validated task (verb generation, VG) and with clinical measures of hemispheric language dominance. A conclusive assessment of hemispheric dominance (lateralization index ≤-0.2 or ≥0.2) was obtained for 94% of the healthy participants who performed both new tasks. At least one new task provided conclusive language laterality assessment in six out of seven participants with epilepsy. The new tasks may contribute to assessing language laterality in young and preliterate children and may benefit children who are scheduled for surgical treatment of disorders such as epilepsy.