BACKGROUND AND PURPOSE: Spasmodic dysphonia (SD), or laryngeal dystonia, is a task-specific isolated focal dystonia of unknown causes and pathophysiology. Although functional and structural abnormalities have been described in this disorder, the influence of its different clinical phenotypes and genotypes remains scant, making it difficult to explain SD pathophysiology and to identify potential biomarkers. METHODS: We used a combination of independent component analysis and linear discriminant analysis of resting-state functional magnetic resonance imaging data to investigate brain organization in different SD phenotypes (abductor versus adductor type) and putative genotypes (familial versus sporadic cases) and to characterize neural markers for genotype/phenotype categorization. RESULTS: We found abnormal functional connectivity within sensorimotor and frontoparietal networks in patients with SD compared with healthy individuals as well as phenotype- and genotype-distinct alterations of these networks, involving primary somatosensory, premotor and parietal cortices. The linear discriminant analysis achieved 71% accuracy classifying SD and healthy individuals using connectivity measures in the left inferior parietal and sensorimotor cortices. When categorizing between different forms of SD, the combination of measures from the left inferior parietal, premotor and right sensorimotor cortices achieved 81% discriminatory power between familial and sporadic SD cases, whereas the combination of measures from the right superior parietal, primary somatosensory and premotor cortices led to 71% accuracy in the classification of adductor and abductor SD forms. CONCLUSIONS: Our findings present the first effort to identify and categorize isolated focal dystonia based on its brain functional connectivity profile, which may have a potential impact on the future development of biomarkers for this rare disorder.
Dystonia is a brain disorder causing involuntary, often painful movements. Apart from a role for dopamine deficiency in some forms, the cellular mechanisms underlying most dystonias are currently unknown. Here, we discover a role for deficient eIF2α signaling in DYT1 dystonia, a rare inherited generalized form, through a genome-wide RNAi screen. Subsequent experiments including patient-derived cells and a mouse model support both a pathogenic role and therapeutic potential for eIF2α pathway perturbations. We further find genetic and functional evidence supporting similar pathway impairment in patients with sporadic cervical dystonia, due to rare coding variation in the eIF2α effector ATF4. Considering also that another dystonia, DYT16, involves a gene upstream of the eIF2α pathway, these results mechanistically link multiple forms of dystonia and put forth a new overall cellular mechanism for dystonia pathogenesis, impairment of eIF2α signaling, a pathway known for its roles in cellular stress responses and synaptic plasticity.
OBJECTIVE: Cortical high-frequency oscillations (HFOs; 100-500 Hz) play a critical role in the pathogenesis of epilepsy; however, whether they represent a true epileptogenic process remains largely unknown. HFOs have been recorded in the human cortex but their network dynamics during the transitional period from interictal to ictal phase remain largely unknown. We sought to determine the high-frequency network dynamics of these oscillations in patients with epilepsy who were undergoing intracranial electroencephalographic recording for seizure localization. METHODS: We applied a graph theoretical analysis framework to high-resolution intracranial electroencephalographic recordings of 24 interictal and 24 seizure periods to identify the spatiotemporal evolution of community structure of high-frequency cortical networks at rest and during multiple seizure episodes in patients with intractable epilepsy. RESULTS: Cortical networks at all examined frequencies showed temporally stable community architecture in all 24 interictal periods. During seizure periods, high-frequency networks showed a significant breakdown of their community structure, which was characterized by the emergence of numerous small nodal communities, not limited to seizure foci and encompassing the entire recorded network. Such network disorganization was observed on average 225 s before the electrographic seizure onset and extended on average 190 s after termination of the seizure. Gamma networks were characterized by stable community dynamics during resting and seizure periods. SIGNIFICANCE: Our findings suggest that the modular breakdown of high-frequency cortical networks represents a distinct functional pathology that underlies epileptogenesis and corresponds to a cortical state of highest propensity to generate seizures.
Aberrant sensory processing plays a fundamental role in the pathophysiology of dystonia; however, its underpinning neural mechanisms in relation to dystonia phenotype and genotype remain unclear. We examined temporal and spatial discrimination thresholds in patients with isolated laryngeal form of dystonia (LD), who exhibited different clinical phenotypes (adductor vs. abductor forms) and potentially different genotypes (sporadic vs. familial forms). We correlated our behavioral findings with the brain gray matter volume and functional activity during resting and symptomatic speech production. We found that temporal but not spatial discrimination was significantly altered across all forms of LD, with higher frequency of abnormalities seen in familial than sporadic patients. Common neural correlates of abnormal temporal discrimination across all forms were found with structural and functional changes in the middle frontal and primary somatosensory cortices. In addition, patients with familial LD had greater cerebellar involvement in processing of altered temporal discrimination, whereas sporadic LD patients had greater recruitment of the putamen and sensorimotor cortex. Based on the clinical phenotype, adductor form-specific correlations between abnormal discrimination and brain changes were found in the frontal cortex, whereas abductor form-specific correlations were observed in the cerebellum and putamen. Our behavioral and neuroimaging findings outline the relationship of abnormal sensory discrimination with the phenotype and genotype of isolated LD, suggesting the presence of potentially divergent pathophysiological pathways underlying different manifestations of this disorder.
Speech is one of the most unique features of human communication. Our ability to articulate our thoughts by means of speech production depends critically on the integrity of the motor cortex. Long thought to be a low-order brain region, exciting work in the past years is overturning this notion. Here, we highlight some of major experimental advances in speech motor control research and discuss the emerging findings about the complexity of speech motocortical organization and its large-scale networks. This review summarizes the talks presented at a symposium at the Annual Meeting of the Society of Neuroscience; it does not represent a comprehensive review of contemporary literature in the broader field of speech motor control.
The analysis of the community architecture in functional brain networks has revealed important relations between specific behavioral patterns and characteristic features of the associated functional organization. Numerous studies have assessed changes in functional communities during different states of awareness, learning, information processing, and various behavioral patterns. The robustness of detected communities within a network has been an often-discussed topic in complex systems research. However, our knowledge regarding the intersubject stability of functional communities in the human brain while performing different tasks is still lacking. In this study, we examined the variability of functional communities in weighted undirected graphs based on fMRI recordings of healthy participants across three conditions: the resting state, syllable production as a simple vocal motor task, and meaningful speech production representing a complex behavioral pattern with cognitive involvement. On the basis of the constructed empirical networks, we simulated a large cohort of artificial graphs and performed a leave-one-out stability analysis to assess the sensitivity of communities in the group-averaged networks with respect to perturbations in the averaging cohort. We found that the stability of partitions derived from group-averaged networks depended on task complexity. The determined community architecture in mean networks reflected within-behavior network stability and between-behavior flexibility of the human functional connectome. The sensitivity of functional communities increased from rest to syllable production to speaking, which suggests that the approximation quality of the community structure in the average network to reflect individual per-participant partitions depends on task complexity.
UNLABELLED: The laryngeal motor cortex (LMC) is essential for the production of learned vocal behaviors because bilateral damage to this area renders humans unable to speak but has no apparent effect on innate vocalizations such as human laughing and crying or monkey calls. Several hypotheses have been put forward attempting to explain the evolutionary changes from monkeys to humans that potentially led to enhanced LMC functionality for finer motor control of speech production. These views, however, remain limited to the position of the larynx area within the motor cortex, as well as its connections with the phonatory brainstem regions responsible for the direct control of laryngeal muscles. Using probabilistic diffusion tractography in healthy humans and rhesus monkeys, we show that, whereas the LMC structural network is largely comparable in both species, the LMC establishes nearly 7-fold stronger connectivity with the somatosensory and inferior parietal cortices in humans than in macaques. These findings suggest that important "hard-wired" components of the human LMC network controlling the laryngeal component of speech motor output evolved from an already existing, similar network in nonhuman primates. However, the evolution of enhanced LMC-parietal connections likely allowed for more complex synchrony of higher-order sensorimotor coordination, proprioceptive and tactile feedback, and modulation of learned voice for speech production. SIGNIFICANCE STATEMENT: The role of the primary motor cortex in the formation of a comprehensive network controlling speech and language has been long underestimated and poorly studied. Here, we provide comparative and quantitative evidence for the significance of this region in the control of a highly learned and uniquely human behavior: speech production. From the viewpoint of structural network organization, we discuss potential evolutionary advances of enhanced temporoparietal cortical connections with the laryngeal motor cortex in humans compared with nonhuman primates that may have contributed to the development of finer vocal motor control necessary for speech production.