Standardized reporting for Head CT Scans in patients suspected of traumatic brain injury (TBI): An international expert endeavor.

BACKGROUND AND PURPOSE: Traumatic brain injury (TBI) is a major source of health loss and disability worldwide. Accurate and timely diagnosis of TBI is critical for appropriate treatment and management of the condition. Neuroimaging plays a crucial role in the diagnosis and characterization of TBI. Computed tomography (CT) is the first-line diagnostic imaging modality typically utilized in patients with suspected acute mild, moderate and severe TBI. Radiology reports play a crucial role in the diagnostic process, providing critical information about the location and extent of brain injury, as well as factors that could prevent secondary injury. However, the complexity and variability of radiology reports can make it challenging for healthcare providers to extract the necessary information for diagnosis and treatment planning. METHODS/RESULTS/CONCLUSION: In this article, we report the efforts of an international group of TBI imaging experts to develop a clinical radiology report template for CT scans obtained in patients suspected of TBI and consisting of fourteen different subdivisions (CT technique, mechanism of injury or clinical history, presence of scalp injuries, fractures, potential vascular injuries, potential injuries involving the extra-axial spaces, brain parenchymal injuries, potential injuries involving the cerebrospinal fluid spaces and the ventricular system, mass effect, secondary injuries, prior or coexisting pathology).

Cognitive impairment after focal brain lesions is better predicted by damage to structural than functional network hubs.

Hubs are highly connected brain regions important for coordinating processing in brain networks. It is unclear, however, which measures of network "hubness" are most useful in identifying brain regions critical to human cognition. We tested how closely two measures of hubness-edge density and participation coefficient, derived from white and gray matter, respectively-were associated with general cognitive impairment after brain damage in two large cohorts of patients with focal brain lesions (N = 402 and 102, respectively) using cognitive tests spanning multiple cognitive domains. Lesions disrupting white matter regions with high edge density were associated with cognitive impairment, whereas lesions damaging gray matter regions with high participation coefficient had a weaker, less consistent association with cognitive outcomes. Similar results were observed with six other gray matter hubness measures. This suggests that damage to densely connected white matter regions is more cognitively impairing than similar damage to gray matter hubs, helping to explain interindividual differences in cognitive outcomes after brain damage.

Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Neuroimaging.

BACKGROUND: Over the past 5 decades, advances in neuroimaging have yielded insights into the pathophysiologic mechanisms that cause disorders of consciousness (DoC) in patients with severe brain injuries. Structural, functional, metabolic, and perfusion imaging studies have revealed specific neuroanatomic regions, such as the brainstem tegmentum, thalamus, posterior cingulate cortex, medial prefrontal cortex, and occipital cortex, where lesions correlate with the current or future state of consciousness. Advanced imaging modalities, such as diffusion tensor imaging, resting-state functional magnetic resonance imaging (fMRI), and task-based fMRI, have been used to improve the accuracy of diagnosis and long-term prognosis, culminating in the endorsement of fMRI for the clinical evaluation of patients with DoC in the 2018 US (task-based fMRI) and 2020 European (task-based and resting-state fMRI) guidelines. As diverse neuroimaging techniques are increasingly used for patients with DoC in research and clinical settings, the need for a standardized approach to reporting results is clear. The success of future multicenter collaborations and international trials fundamentally depends on the implementation of a shared nomenclature and infrastructure. METHODS: To address this need, the Neurocritical Care Society's Curing Coma Campaign convened an international panel of DoC neuroimaging experts to propose common data elements (CDEs) for data collection and reporting in this field. RESULTS: We report the recommendations of this CDE development panel and disseminate CDEs to be used in neuroimaging studies of patients with DoC. CONCLUSIONS: These CDEs will support progress in the field of DoC neuroimaging and facilitate international collaboration.

Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning.

Computed tomography (CT) of the head is used worldwide to diagnose neurologic emergencies. However, expertise is required to interpret these scans, and even highly trained experts may miss subtle life-threatening findings. For head CT, a unique challenge is to identify, with perfect or near-perfect sensitivity and very high specificity, often small subtle abnormalities on a multislice cross-sectional (three-dimensional [3D]) imaging modality that is characterized by poor soft tissue contrast, low signal-to-noise using current low radiation-dose protocols, and a high incidence of artifacts. We trained a fully convolutional neural network with 4,396 head CT scans performed at the University of California at San Francisco and affiliated hospitals and compared the algorithm's performance to that of 4 American Board of Radiology (ABR) certified radiologists on an independent test set of 200 randomly selected head CT scans. Our algorithm demonstrated the highest accuracy to date for this clinical application, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.991 ± 0.006 for identification of examinations positive for acute intracranial hemorrhage, and also exceeded the performance of 2 of 4 radiologists. We demonstrate an end-to-end network that performs joint classification and segmentation with examination-level classification comparable to experts, in addition to robust localization of abnormalities, including some that are missed by radiologists, both of which are critically important elements for this application.

A quantitative approach for measuring laterality in clinical fMRI for preoperative language mapping.

PURPOSE: fMRI is increasingly used for presurgical language mapping, but lack of standard methodology has made it difficult to combine/compare data across institutions or determine the relative efficacy of different approaches. Here, we describe a quantitative analytic framework for determining language laterality in clinical fMRI that addresses these concerns. METHODS: We retrospectively analyzed fMRI data from 59 patients who underwent presurgical language mapping at our institution with identical imaging and behavioral protocols. First, we compared the efficacy of different regional masks in capturing language activations. Then, we systematically explored how laterality indices (LIs) computed from these masks vary as a function of task and activation threshold. Finally, we determined the percentile threshold that maximized the correlation between the results of our LI approach and the laterality assessments from the original clinical radiology reports. RESULTS: First, we found that a regional mask derived from a meta-analysis of the fMRI literature better captured language task activations than masks based on anatomically defined language areas. Then, we showed that an LI approach based on this functional mask and percentile thresholding of subject activation can quantify the relative ability of different language tasks to lateralize language function at the population level. Finally, we determined that the 92nd percentile of subject-level activation provides the optimal LI threshold with which to reproduce the original clinical reports. CONCLUSION: A quantitative framework for determining language laterality that uses a functionally-derived language mask and percentile thresholding of subject activation can combine/compare results across tasks and patients and reproduce clinical assessments of language laterality.

Prognostic Value of Hemorrhagic Brainstem Injury on Early Computed Tomography: A TRACK-TBI Study.

BACKGROUND: Traumatic brainstem injury has yet to be incorporated into widely used imaging classification systems for traumatic brain injury (TBI), and questions remain regarding prognostic implications for this TBI subgroup. To address this, retrospective data on patients from the multicenter prospective Transforming Research and Clinical Knowledge in TBI study were studied. METHODS: Patients with brainstem and cerebrum injury (BSI+) were matched by age, sex, and admission Glasgow Coma Scale (GCS) score to patients with cerebrum injuries only. All patients had an interpretable head computed tomography (CT) scan from the first 48 hours after injury and a 6-month Glasgow Outcome Scale Extended (GOSE) score. CT scans were reviewed for brainstem lesions and, when present, characterized by location, size, and type (traumatic axonal injury, contusion, or Duret hemorrhage). Clinical, demographic, and outcome data were then compared between the two groups. RESULTS: Mann-Whitney U-tests showed no significant difference in 6-month GOSE scores in patients with BSI+ (mean 2.7) compared with patients with similar but only cerebrum injuries (mean 3.9), although there is a trend (p = 0.10). However, subclassification by brainstem lesion type, traumatic axonal injury (mean 4.0) versus Duret hemorrhage or contusion (mean 1.4), did identify a proportion of BSI+ with significantly less favorable outcome (p = 0.002). The incorporation of brainstem lesion type (traumatic axonal injury vs. contusion/Duret), along with GCS into a multivariate logistic regression model of favorable outcome (GOSE score 4-8) did show a significant contribution to the prognostication of this brainstem injury subgroup (odds ratio 0.08, 95% confidence interval 0.00-0.67, p = 0.01). CONCLUSIONS: These findings suggest two groups of patients with brainstem injuries may exist with divergent recovery potential after TBI. These data support the notion that newer CT imaging classification systems may augment traditional clinical measures, such as GCS in identifying those patients with TBI and brainstem injuries that stand a higher chance of favorable outcome.

Sensorimotor Cortical Oscillations during Movement Preparation in 16p11.2 Deletion Carriers.

Sensorimotor deficits are prevalent in many neurodevelopmental disorders like autism, including one of its common genetic etiologies, a 600 kb reciprocal deletion/duplication at 16p11.2. We have previously shown that copy number variations of 16p11.2 impact regional brain volume, white matter integrity, and early sensory responses in auditory cortex. Here, we test the hypothesis that abnormal cortical neurophysiology is present when genes in the 16p11.2 region are haploinsufficient, and in humans that this in turn may account for behavioral deficits specific to deletion carriers. We examine sensorimotor cortical network activity in males and females with 16p11.2 deletions compared with both typically developing individuals, and those with duplications of 16p11.2, using magnetoencephalographic imaging during preparation of overt speech or hand movements in tasks designed to be easy for all participants. In deletion carriers, modulation of beta oscillations (12-30 Hz) were increased during both movement types over effector-specific regions of motor cortices compared with typically developing individuals or duplication carriers, with no task-related performance differences between cohorts, even when corrected for their own cognitive and sensorimotor deficits. Reduced left hemispheric language specialization was observed in deletion carriers but not in duplication carriers. Neural activity over sensorimotor cortices in deletion carriers was linearly related to clinical measures of speech and motor impairment. These findings link insufficient copy number repeats at 16p11.2 to excessive neural activity (e.g., increased beta oscillations) in motor cortical networks for speech and hand motor control. These results have significant implications for understanding the neural basis of autism and related neurodevelopmental disorders.SIGNIFICANCE STATEMENT The recurrent ∼600 kb deletion at 16p11.2 (BP4-BP5) is one of the most common genetic etiologies of ASD and, more generally, of neurodevelopmental disorders. Here, we use high-resolution magnetoencephalographic imaging (MEG-I) to define with millisecond precision the underlying neurophysiological signature of motor impairments for individuals with 16p11.2 deletions. We identify significant increases in beta (12-30 Hz) suppression in sensorimotor cortices related to performance during speech and hand movement tasks. These findings not only provide a neurophysiological phenotype for the clinical presentation of this genetic deletion, but also guide our understanding of how genetic variation encodes for neural oscillatory dynamics.

Spectral graph theory of brain oscillations.

The relationship between the brain's structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectome's Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. The resulting parsimonious and analytical solution stands in contrast to complex numerical simulations of high dimensional coupled nonlinear neural field models. This spectral graph model accurately predicts spatial and spectral features of neural oscillatory activity across the brain and was successful in simultaneously reproducing empirically observed spatial and spectral patterns of alpha-band (8-12 Hz) and beta-band (15-30 Hz) activity estimated from source localized magnetoencephalography (MEG). This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics. .

Connectome mapping with edge density imaging differentiates pediatric mild traumatic brain injury from typically developing controls: proof of concept.

BACKGROUND: Although acute neurologic impairment might be transient, other long-term effects can be observed with mild traumatic brain injury. However, when pediatric patients with mild traumatic brain injury present for medical care, conventional imaging with CT and MR imaging often does not reveal abnormalities. OBJECTIVE: To determine whether edge density imaging can separate pediatric mild traumatic brain injury from typically developing controls. MATERIALS AND METHODS: Subjects were recruited as part of the "Therapeutic Resources for Attention Improvement using Neuroimaging in Traumatic Brain Injury" (TRAIN-TBI) study. We included 24 adolescents (χ=14.1 years of age, σ=1.6 years, range 10-16 years), 14 with mild traumatic brain injury (TBI) and 10 typically developing controls. Neurocognitive assessments included the pediatric version of the California Verbal Learning Test (CVLT) and the Attention Network Task (ANT). Diffusion MR imaging was acquired on a 3-tesla (T) scanner. Edge density images were computed utilizing fiber tractography. Principal component analysis (PCA) and support vector machines (SVM) were used in an exploratory analysis to separate mild TBI and control groups. The diagnostic accuracy of edge density imaging, neurocognitive tests, and fractional anisotropy (FA) from diffusion tensor imaging (DTI) was computed with two-sample t-tests and receiver operating characteristic (ROC) metrics. RESULTS: Support vector machine-principal component analysis of edge density imaging maps identified three white matter regions distinguishing pediatric mild TBI from controls. The bilateral tapetum, sagittal stratum, and callosal splenium identified mild TBI subjects with sensitivity of 79% and specificity of 100%. Accuracy from the area under the ROC curve (AUC) was 94%. Neurocognitive testing provided an AUC of 61% (CVLT) and 71% (ANT). Fractional anisotropy yielded an AUC of 48%. CONCLUSION: In this proof-of-concept study, we show that edge density imaging is a new form of connectome mapping that provides better diagnostic delineation between pediatric mild TBI and healthy controls than DTI or neurocognitive assessments of memory or attention.

Emergency Imaging in Pregnancy and Lactation [Formula: see text].

The use of diagnostic imaging studies in the emergency setting has increased dramatically over the past couple of decades. The emergency imaging of pregnant and lactating patients poses unique challenges and calls upon the crucial role of radiologists as consultants to the referring physician to guide appropriate use of imaging tests, minimize risk, ensure timely management, and occasionally alleviate unwarranted trepidation. A clear understanding of the risks and benefits involved with various imaging tests in this patient population is vital to achieve this. This review discusses the different safety and appropriateness issues that could arise with the use of ionizing radiation, iodinated-, and gadolinium-based contrast media and radiopharmaceuticals in pregnant and lactating patients. Special considerations such as trauma imaging, safety concerns with magnetic resonance imaging and ultrasound, management of claustrophobia, contrast extravasation, and allergic reactions are also reviewed. The consent process for these examinations has also been described.

Delineation of early brain development from fetuses to infants with diffusion MRI and beyond.

Dynamic macrostructural and microstructural changes take place from the mid-fetal stage to 2 years after birth. Delineating structural changes of the brain during early development provides new insights into the complicated processes of both typical development and the pathological mechanisms underlying various psychiatric and neurological disorders including autism, attention deficit hyperactivity disorder and schizophrenia. Decades of histological studies have identified strong spatial and functional maturation gradients in human brain gray and white matter. The recent improvements in magnetic resonance imaging (MRI) techniques, especially diffusion MRI (dMRI), relaxometry imaging, and magnetization transfer imaging (MTI) have provided unprecedented opportunities to non-invasively quantify and map the early developmental changes at whole brain and regional levels. Here, we review the recent advances in understanding early brain structural development during the second half of gestation and the first two postnatal years using modern MR techniques. Specifically, we review studies that delineate the emergence and microstructural maturation of white matter tracts, as well as dynamic mapping of inhomogeneous cortical microstructural organization unique to fetuses and infants. These imaging studies converge into maturational curves of MRI measurements that are distinctive across different white matter tracts and cortical regions. Furthermore, contemporary models offering biophysical interpretations of the dMRI-derived measurements are illustrated to infer the underlying microstructural changes. Collectively, this review summarizes findings that contribute to charting spatiotemporally heterogeneous gray and white matter structural development, offering MRI-based biomarkers of typical brain development and setting the stage for understanding aberrant brain development in neurodevelopmental disorders.

Replication and generalization in applied neuroimaging.

There is much interest in translating neuroimaging findings into meaningful clinical diagnostics. The goal of scientific discoveries differs from clinical diagnostics. Scientific discoveries must replicate under a specific set of conditions; to translate to the clinic we must show that findings using purpose-built scientific instruments will be observable in clinical populations and instruments. Here we describe and evaluate data and computational methods designed to translate a scientific observation to a clinical setting. Using diffusion weighted imaging (DWI), Wahl et al. (2010) observed that across subjects the mean fractional anisotropy (FA) of homologous pairs of tracts is highly correlated. We hypothesize that this is a fundamental biological trait that should be present in most healthy participants, and deviations from this assessment may be a useful diagnostic metric. Using this metric as an illustration of our methods, we analyzed six pairs of homologous white matter tracts in nine different DWI datasets with 44 subjects each. Considering the original FA measurement as a baseline, we show that the new metric is between 2 and 4 times more precise when used in a clinical context. Our framework to translate research findings into clinical practice can be applied, in principle, to other neuroimaging results.

Longitudinal increases in structural connectome segregation and functional connectome integration are associated with better recovery after mild TBI.

Traumatic brain injury damages white matter pathways that connect brain regions, disrupting transmission of electrochemical signals and causing cognitive and emotional dysfunction. Connectome-level mechanisms for how the brain compensates for injury have not been fully characterized. Here, we collected serial MRI-based structural and functional connectome metrics and neuropsychological scores in 26 mild traumatic brain injury subjects (29.4 ± 8.0 years, 20 males) at 1 and 6 months postinjury. We quantified the relationship between functional and structural connectomes using network diffusion (ND) model propagation time, a measure that can be interpreted as how much of the structural connectome is being utilized for the spread of functional activation, as captured via the functional connectome. Overall cognition showed significant improvement from 1 to 6 months (t25 = -2.15, p = .04). None of the structural or functional global connectome metrics was significantly different between 1 and 6 months, or when compared to 34 age- and gender-matched controls (28.6 ± 8.8 years, 25 males). We predicted longitudinal changes in overall cognition from changes in global connectome measures using a partial least squares regression model (cross-validated R2 = .27). We observe that increased ND model propagation time, increased structural connectome segregation, and increased functional connectome integration were related to better cognitive recovery. We interpret these findings as suggesting two connectome-based postinjury recovery mechanisms: one of neuroplasticity that increases functional connectome integration and one of remote white matter degeneration that increases structural connectome segregation. We hypothesize that our inherently multimodal measure of ND model propagation time captures the interplay between these two mechanisms.

Recommendations towards standards for quantitative MRI (qMRI) and outstanding needs.

LEVEL OF EVIDENCE: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019.

Brain MR Imaging Findings and Associated Outcomes in Carriers of the Reciprocal Copy Number Variation at 16p11.2.

Purpose To identify developmental neuroradiologic findings in a large cohort of carriers who have deletion and duplication at 16p11.2 (one of the most common genetic causes of autism spectrum disorder [ASD]) and assess how these features are associated with behavioral and cognitive outcomes. Materials and Methods Seventy-nine carriers of a deletion at 16p11.2 (referred to as deletion carriers; age range, 1-48 years; mean age, 12.3 years; 42 male patients), 79 carriers of a duplication at 16p11.2 (referred to as duplication carriers; age range, 1-63 years; mean age, 24.8 years; 43 male patients), 64 unaffected family members (referred to as familial noncarriers; age range, 1-46 years; mean age, 11.7 years; 31 male participants), and 109 population control participants (age range, 6-64 years; mean age, 25.5 years; 64 male participants) were enrolled in this cross-sectional study. Participants underwent structural magnetic resonance (MR) imaging and completed cognitive and behavioral tests. MR images were reviewed for development-related abnormalities by neuroradiologists. Differences in frequency were assessed with a Fisher exact test corrected for multiple comparisons. Unsupervised machine learning was used to cluster radiologic features and an association between clusters and cognitive and behavioral scores from IQ testing, and parental measures of development were tested by using analysis of covariance. Volumetric analysis with automated segmentation was used to confirm radiologic interpretation. Results For deletion carriers, the most prominent features were dysmorphic and thicker corpora callosa compared with familial noncarriers and population control participants (16%; P < .001 and P < .001, respectively) and a greater likelihood of cerebellar tonsillar ectopia (30.7%; P < .002 and P < .001, respectively) and Chiari I malformations (9.3%; P < .299 and P < .002, respectively). For duplication carriers, the most salient findings compared with familial noncarriers and population control participants were reciprocally thinner corpora callosa (18.6%; P < .003 and P < .001, respectively), decreased white matter volume (22.9%; P < .001, and P < .001, respectively), and increased ventricular volume (24.3%; P < .001 and P < .001, respectively). By comparing cognitive assessments to imaging findings, the presence of any imaging feature associated with deletion carriers indicated worse daily living, communication, and social skills compared with deletion carriers without any radiologic abnormalities (P < .005, P < .002, and P < .004, respectively). For the duplication carriers, presence of decreased white matter, callosal volume, and/or increased ventricle size was associated with decreased full-scale and verbal IQ scores compared with duplication carriers without these findings (P < .007 and P < .004, respectively). Conclusion In two genetically related cohorts at high risk for ASD, reciprocal neuroanatomic abnormalities were found and determined to be associated with cognitive and behavioral impairments. © RSNA, 2017 Online supplemental material is available for this article.

Brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease.

Recent research has demonstrated the use of the structural connectome as a powerful tool to characterize the network architecture of the brain and potentially generate biomarkers for neurologic and psychiatric disorders. In particular, the anatomic embedding of the edges of the cerebral graph have been postulated to elucidate the relative importance of white matter tracts to the overall network connectivity, explaining the varying effects of localized white matter pathology on cognition and behavior. Here, we demonstrate the use of a linear diffusion model to quantify the impact of these perturbations on brain connectivity. We show that the eigenmodes governing the dynamics of this model are strongly conserved between healthy subjects regardless of cortical and sub-cortical parcellations, but show significant, interpretable deviations in improperly developed brains. More specifically, we investigated the effect of agenesis of the corpus callosum (AgCC), one of the most common brain malformations to identify differences in the effect of virtual corpus callosotomies and the neurodevelopmental disorder itself. These findings, including the strong correspondence between regions of highest importance from graph eigenmodes of network diffusion and nexus regions of white matter from edge density imaging, show converging evidence toward understanding the relationship between white matter anatomy and the structural connectome.

Concordance of common data elements for assessment of subjective cognitive complaints after mild-traumatic brain injury: a TRACK-TBI Pilot Study.

OBJECTIVE: To determine characteristics and concordance of subjective cognitive complaints (SCCs) 6 months following mild-traumatic brain injury (mTBI) as assessed by two different TBI common data elements (CDEs). RESEARCH DESIGN: The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot Study was a prospective observational study that utilized the NIH TBI CDEs, Version 1.0. We examined variables associated with SCC, performance on objective cognitive tests (Wechsler Adult Intelligence Scale, California Verbal Learning Test, and Trail Making Tests A and B), and agreement on self-report of SCCs as assessed by the acute concussion evaluation (ACE) versus the Rivermead Post Concussion Symptoms Questionnaire (RPQ). RESULTS: In total, 68% of 227 participants endorsed SCCs at 6 months. Factors associated with SCC included less education, psychiatric history, and being assaulted. Compared to participants without SCC, those with SCC defined by RPQ performed significantly worse on all cognitive tests. There was moderate agreement between the two measures of SCCs (kappa = 0.567 to 0.680). CONCLUSION: We show that the symptom questionnaires ACE and RPQ show good, but not excellent, agreement for SCCs in an mTBI study population. Our results support the retention of RPQ as a basic CDE for mTBI research. ABBREVIATIONS: BSI-18: Brief Symptom Inventory; 18CDEs: common data elements; CT: computed tomography; CVLT: California Verbal Learning Test; ED: emergency department; GCS: Glasgow coma scale; LOC: loss of consciousnessm; TBI: mild-traumatic brain injury; PTA: post-traumatic amnesia; SCC: subjective cognitive complaints; TBI: traumatic brain injury; TRACK-TBI: Transforming Research and Clinical Knowledge in Traumatic Brain Injury; TMT: Trail Making Test; WAIS-PSI: Wechsler Adult Intelligence Scale, Fourth Edition, Processing Speed Index.

The Contribution of the Corpus Callosum to Language Lateralization.

The development of hemispheric lateralization for language is poorly understood. In one hypothesis, early asymmetric gene expression assigns language to the left hemisphere. In an alternate view, language is represented a priori in both hemispheres and lateralization emerges via cross-hemispheric communication through the corpus callosum. To address this second hypothesis, we capitalized on the high temporal and spatial resolution of magnetoencephalographic imaging to measure cortical activity during language processing, speech preparation, and speech execution in 25 participants with agenesis of the corpus callosum (AgCC) and 21 matched neurotypical individuals. In contrast to strongly lateralized left hemisphere activations for language in neurotypical controls, participants with complete or partial AgCC exhibited bilateral hemispheric activations in both auditory or visually driven language tasks, with complete AgCC participants showing significantly more right hemisphere activations than controls or than individuals with partial AgCC. In AgCC individuals, language laterality positively correlated with verbal IQ. These findings suggest that the corpus callosum helps to drive language lateralization. SIGNIFICANCE STATEMENT: The role that corpus callosum development has on the hemispheric specialization of language is poorly understood. Here, we used magnetoencephalographic imaging during linguistic tests (verb generation, picture naming) to test for hemispheric dominance in patients with agenesis of the corpus callosum (AgCC) and found reduced laterality (i.e., greater likelihood of bilaterality or right hemisphere dominance) in this cohort compared with controls, especially in patients with complete agenesis. Laterality was positively correlated with behavioral measures of verbal intelligence. These findings provide support for the hypothesis that the callosum aids in functional specialization throughout neural development and that the loss of this mechanism correlates with impairments in verbal performance.