Beyond Broca: neural architecture and evolution of a dual motor speech coordination system.

Classical neural architecture models of speech production propose a single system centred on Broca's area coordinating all the vocal articulators from lips to larynx. Modern evidence has challenged both the idea that Broca's area is involved in motor speech coordination and that there is only one coordination network. Drawing on a wide range of evidence, here we propose a dual speech coordination model in which laryngeal control of pitch-related aspects of prosody and song are coordinated by a hierarchically organized dorsolateral system while supralaryngeal articulation at the phonetic/syllabic level is coordinated by a more ventral system posterior to Broca's area. We argue further that these two speech production subsystems have distinguishable evolutionary histories and discuss the implications for models of language evolution.

A Role for the Frontal Aslant Tract in Speech Planning: A Neurosurgical Case Study.

Frontal and temporal white matter pathways play key roles in language processing, but the specific computations supported by different tracts remain a matter of study. A role in speech planning has been proposed for a recently described pathway, the frontal aslant tract (FAT), which connects the posterior inferior frontal gyrus to the pre-SMA. Here, we use longitudinal functional and structural MRI and behavioral testing to evaluate the behavioral consequences of a lesion to the left FAT that was incurred during surgical resection of a frontal glioma in a 60-year-old woman, Patient AF. The pattern of performance in AF is compared, using the same measures, with that in a 37-year-old individual who underwent a left anterior temporal resection and hippocampectomy (Patient AG). AF and AG were both cognitively intact preoperatively but exhibited specific and doubly dissociable behavioral deficits postoperatively: AF had dysfluent speech but no word finding difficulty, whereas AG had word finding difficulty but otherwise fluent speech. Probabilistic tractography showed that the left FAT was lesioned postoperatively in AF (but not AG) whereas the inferior longitudinal fasciculus was lesioned in AG (but not AF). Those structural changes were supported by corresponding changes in functional connectivity to the posterior inferior frontal gyrus: decreased functional connectivity postoperatively between the posterior inferior frontal gyrus and pre-SMA in AF (but not AG) and decreased functional connectivity between the posterior inferior frontal gyrus and the middle temporal gyrus in AG (but not AF). We suggest from these findings that the left FAT serves as a key communicative link between sentence planning and lexical access processes.

A causal test of the motor theory of speech perception: a case of impaired speech production and spared speech perception.

The debate about the causal role of the motor system in speech perception has been reignited by demonstrations that motor processes are engaged during the processing of speech sounds. Here, we evaluate which aspects of auditory speech processing are affected, and which are not, in a stroke patient with dysfunction of the speech motor system. We found that the patient showed a normal phonemic categorical boundary when discriminating two non-words that differ by a minimal pair (e.g., ADA-AGA). However, using the same stimuli, the patient was unable to identify or label the non-word stimuli (using a button-press response). A control task showed that he could identify speech sounds by speaker gender, ruling out a general labelling impairment. These data suggest that while the motor system is not causally involved in perception of the speech signal, it may be used when other cues (e.g., meaning, context) are not available.

Distinct brain morphometry patterns revealed by deep learning improve prediction of post-stroke aphasia severity.

BACKGROUND: Emerging evidence suggests that post-stroke aphasia severity depends on the integrity of the brain beyond the lesion. While measures of lesion anatomy and brain integrity combine synergistically to explain aphasic symptoms, substantial interindividual variability remains unaccounted. One explanatory factor may be the spatial distribution of morphometry beyond the lesion (e.g., atrophy), including not just specific brain areas, but distinct three-dimensional patterns. METHODS: Here, we test whether deep learning with Convolutional Neural Networks (CNNs) on whole brain morphometry (i.e., segmented tissue volumes) and lesion anatomy better predicts chronic stroke individuals with severe aphasia (N = 231) than classical machine learning (Support Vector Machines; SVMs), evaluating whether encoding spatial dependencies identifies uniquely predictive patterns. RESULTS: CNNs achieve higher balanced accuracy and F1 scores, even when SVMs are nonlinear or integrate linear or nonlinear dimensionality reduction. Parity only occurs when SVMs access features learned by CNNs. Saliency maps demonstrate that CNNs leverage distributed morphometry patterns, whereas SVMs focus on the area around the lesion. Ensemble clustering of CNN saliencies reveals distinct morphometry patterns unrelated to lesion size, consistent across individuals, and which implicate unique networks associated with different cognitive processes as measured by the wider neuroimaging literature. Individualized predictions depend on both ipsilateral and contralateral features outside the lesion. CONCLUSIONS: Three-dimensional network distributions of morphometry are directly associated with aphasia severity, underscoring the potential for CNNs to improve outcome prognostication from neuroimaging data, and highlighting the prospective benefits of interrogating spatial dependence at different scales in multivariate feature space.

Some stroke survivors experience difficulties understanding and producing language. We performed brain imaging to capture information about brain structure in stroke survivors and used it to predict which survivors have more severe language problems. We found that a type of artificial intelligence (AI) specifically designed to find patterns in spatial data was more accurate at this task than more traditional methods. AI found more complex patterns of brain structure that distinguish stroke survivors with severe language problems by analyzing the brain's spatial properties. Our findings demonstrate that AI tools can provide new information about brain structure and function following stroke. With further developments, these models may be able to help clinicians understand the extent to which language problems can be improved after a stroke.

Progressive lesion necrosis is related to increasing aphasia severity in chronic stroke.

BACKGROUND: Volumetric investigations of cortical damage resulting from stroke indicate that lesion size and shape continue to change even in the chronic stage of recovery. However, the potential clinical relevance of continued lesion growth has yet to be examined. In the present study, we investigated the prevalence of lesion expansion and the relationship between expansion and changes in aphasia severity in a large sample of individuals in the chronic stage of aphasia recovery. METHODS: Retrospective structural MRI scans from 104 S survivors with at least 2 observations (k = 301 observations; mean time between scans = 31 months) were included. Lesion demarcation was performed using an automated lesion segmentation software and lesion volumes at each timepoint were subsequently calculated. A linear mixed effects model was conducted to investigate the effect of days between scan on lesion expansion. Finally, we investigated the association between lesion expansion and changes on the Western Aphasia Battery (WAB) in a group of participants assessed and scanned at 2 timepoints (N = 54) using a GLM. RESULTS: Most participants (81 %) showed evidence of lesion expansion. The mixed effects model revealed lesion volumes significantly increase, on average, by 0.02 cc each day (7.3 cc per year) following a scan (p < 0.0001). Change on language performance was significantly associated with change in lesion volume (p = 0.025) and age at stroke (p = 0.031). The results suggest that with every 10 cc increase in lesion size, language performance decreases by 0.9 points, and for every 10-year increase in age at stroke, language performance decreases by 1.9 points. CONCLUSIONS: The present study confirms and extends prior reports that lesion expansion occurs well into the chronic stage of stroke. For the first time, we present evidence that expansion is predictive of longitudinal changes in language performance in individuals with aphasia. Future research should focus on the potential mechanisms that may lead to necrosis in areas surrounding the chronic stroke lesion.

Regional brain aging: premature aging of the domain general system predicts aphasia severity.

Premature brain aging is associated with poorer cognitive reserve and lower resilience to injury. When there are focal brain lesions, brain regions may age at different rates within the same individual. Therefore, we hypothesize that reduced gray matter volume within specific brain systems commonly associated with language recovery may be important for long-term aphasia severity. Here we show that individuals with stroke aphasia have a premature brain aging in intact regions of the lesioned hemisphere. In left domain-general regions, premature brain aging, gray matter volume, lesion volume and age were all significant predictors of aphasia severity. Increased brain age following a stroke is driven by the lesioned hemisphere. The relationship between brain age in left domain-general regions and aphasia severity suggests that degradation is possible to specific brain regions and isolated aging matters for behavior.

Predictive Coding and Internal Error Correction in Speech Production.

Speech production involves the careful orchestration of sophisticated systems, yet overt speech errors rarely occur under naturalistic conditions. The present functional magnetic resonance imaging study sought neural evidence for internal error detection and correction by leveraging a tongue twister paradigm that induces the potential for speech errors while excluding any overt errors from analysis. Previous work using the same paradigm in the context of silently articulated and imagined speech production tasks has demonstrated forward predictive signals in auditory cortex during speech and presented suggestive evidence of internal error correction in left posterior middle temporal gyrus (pMTG) on the basis that this area tended toward showing a stronger response when potential speech errors are biased toward nonwords compared to words (Okada et al., 2018). The present study built on this prior work by attempting to replicate the forward prediction and lexicality effects in nearly twice as many participants but introduced novel stimuli designed to further tax internal error correction and detection mechanisms by biasing speech errors toward taboo words. The forward prediction effect was replicated. While no evidence was found for a significant difference in brain response as a function of lexical status of the potential speech error, biasing potential errors toward taboo words elicited significantly greater response in left pMTG than biasing errors toward (neutral) words. Other brain areas showed preferential response for taboo words as well but responded below baseline and were less likely to reflect language processing as indicated by a decoding analysis, implicating left pMTG in internal error correction.

Distinct brain morphometry patterns revealed by deep learning improve prediction of aphasia severity.

Emerging evidence suggests that post-stroke aphasia severity depends on the integrity of the brain beyond the stroke lesion. While measures of lesion anatomy and brain integrity combine synergistically to explain aphasic symptoms, significant interindividual variability remains unaccounted for. A possible explanatory factor may be the spatial distribution of brain atrophy beyond the lesion. This includes not just the specific brain areas showing atrophy, but also distinct three-dimensional patterns of atrophy. Here, we tested whether deep learning with Convolutional Neural Networks (CNN) on whole brain morphometry (i.e., segmented tissue volumes) and lesion anatomy can better predict which individuals with chronic stroke (N=231) have severe aphasia, and whether encoding spatial dependencies in the data might be capable of improving predictions by identifying unique individualized spatial patterns. We observed that CNN achieves significantly higher accuracy and F1 scores than Support Vector Machine (SVM), even when the SVM is nonlinear or integrates linear and nonlinear dimensionality reduction techniques. Performance parity was only achieved when the SVM was directly trained on the latent features learned by the CNN. Saliency maps demonstrated that the CNN leveraged widely distributed patterns of brain atrophy predictive of aphasia severity, whereas the SVM focused almost exclusively on the area around the lesion. Ensemble clustering of CNN saliency maps revealed distinct morphometry patterns that were unrelated to lesion size, highly consistent across individuals, and implicated unique brain networks associated with different cognitive processes as measured by the wider neuroimaging literature. Individualized predictions of severity depended on both ipsilateral and contralateral features outside of the location of stroke. Our findings illustrate that three-dimensional network distributions of atrophy in individuals with aphasia are directly associated with aphasia severity, underscoring the potential for deep learning to improve prognostication of behavioral outcomes from neuroimaging data, and highlighting the prospective benefits of interrogating spatial dependence at different scales in multivariate feature space.

Reorganized language network connectivity after left arcuate fasciculus resection: A case study.

Understanding the neural mechanisms that support spontaneous recovery of cognitive abilities can place important constraints on mechanistic theories of brain organization and function, and holds potential to inform clinical interventions. Connectivity-based MRI measures have emerged as a way to study how recovery from brain injury is modulated by changes in intra- and inter-hemispheric connectivity. Here we report a detailed and multi-modal case study of a 26 year-old male who presented with a left inferior parietal glioma infiltrating the left arcuate fasciculus. The patient underwent pre- and post-operative functional MRI and Diffusion Tensor Imaging, as well as behavioral assessments of language, motor, vision and praxis. The surgery for removal of the tumor was carried out with the patient awake, and direct electrical stimulation mapping was used to evaluate cortical language centers. The patient developed a specific difficulty with repeating sentences toward the end of the surgery, after resection of the tumor and partial transection of the arcuate fasciculus. The patient recovered from the sentence repetition impairments over several months after the operation. Coincident with the patient's cognitive recovery, we document a pattern whereby intra-hemispheric functional connectivity was reduced in the left hemisphere, while inter-hemispheric connectivity increased between classic left hemisphere language regions and their right hemisphere homologues. These findings suggest that increased synchrony between the two hemispheres, in the setting of focal transection of the left arcuate fasciculus, can facilitate functional recovery.

Disrupted functional connectivity affects resting state based language lateralization.

Pre-operative assessment of language localization and lateralization is critical to preserving brain function after lesion or epileptogenic tissue resection. Task fMRI (t-fMRI) has been extensively and reliably used to this end, but resting state fMRI (rs-fMRI) is emerging as an alternative pre-operative brain mapping method that is independent of a patient's ability to comply with a task. We sought to evaluate if language lateralization obtained from rs-fMRI can replace standard assessment using t-fMRI. In a group of 43 patients scheduled for pre-operative fMRI brain mapping and 17 healthy controls, we found that existing methods of determining rs-fMRI lateralization by considering interhemispheric and intrahemispheric functional connectivity are inadequate compared to t-fMRI when applied to the language network. We determined that this was attributable to widespread but nuanced disturbances in the functional connectivity of the language network in patients. We found changes in interhemispheric and intrahemispheric functional connectivity that were dependent on lesion location, and particularly impacted patients with lesions in the left temporal lobe. We then tested whether a simpler measure of functional connectivity to the language network has a better relation to t-fMRI based language lateralization. Remarkably, we found that functional connectivity between the language network and the frontal pole, and superior frontal gyrus, as well as the supramarginal gyrus, significantly correlated to task based language lateralization indices in both patients and healthy controls. These findings are consistent with prior work with epilepsy patients, and provide a framework for evaluating language lateralization at rest.