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Purpose: Stuttering is characterized by intermittent speech disfluencies, which are dramatically reduced when speakers synchronize their speech with a steady beat. The goal of this study was to characterize the neural underpinnings of this phenomenon using functional magnetic resonance imaging. Method: Data were collected from 16 adults who stutter and 17 adults who do not stutter while they read sentences aloud either in a normal, self-paced fashion or paced by the beat of a series of isochronous tones ('rhythmic'). Task activation and task-based functional connectivity analyses were carried out to compare neural responses between speaking conditions and groups after controlling for speaking rate. Results: Adults who stutter produced fewer disfluent trials in the rhythmic condition than in the normal condition. Adults who stutter did not have any significant changes in activation between the rhythmic condition and the normal condition, but when groups were collapsed, participants had greater activation in the rhythmic condition in regions associated with speech sequencing, sensory feedback control, and timing perception. Adults who stutter also demonstrated increased functional connectivity among cerebellar regions during rhythmic speech as compared to normal speech and decreased connectivity between the left inferior cerebellum and the left prefrontal cortex. Conclusions: Modulation of connectivity in the cerebellum and prefrontal cortex during rhythmic speech suggests that this fluency-inducing technique activates a compensatory timing system in the cerebellum and potentially modulates top-down motor control and attentional systems. These findings corroborate previous work associating the cerebellum with fluency in adults who stutter and indicate that the cerebellum may be targeted to enhance future therapeutic interventions. Supplemental Material: https://doi.org/10.23641/asha. 14417681, Stuttering is a speech disorder that impacts the production of smooth and timely articulations of planned utterances. Stuttering typically emerges early in childhood and persists over the life span for [...]

Purpose: Kinematic measurements of speech have demonstrated some success in automatic detection of early symptoms of amyotrophic lateral sclerosis (ALS). In this study, we examined how the region of symptom onset (bulbar vs. spinal) affects the ability of data-driven models to detect ALS. Method: We used a correlation structure of articulatory movements combined with a machine learning model (i.e., artificial neural network) to detect differences between people with ALS and healthy controls. The performance of this system was evaluated separately for participants with bulbar onset and spinal onset to examine how region of onset affects classification performance. We then performed a regression analysis to examine how different severity measures and region of onset affects model performance. Results: The proposed model was significantly more accurate in classifying the bulbar-onset participants, achieving an area under the curve of 0.809 relative to the 0.674 achieved for spinal-onset participants. The regression analysis, however, found that differences in classifier performance across participants were better explained by their speech performance (intelligible speaking rate), and no significant differences were observed based on region of onset when intelligible speaking rate was accounted for. Conclusions: Although we found a significant difference in the model's ability to detect ALS depending on the region of onset, this disparity can be primarily explained by observable differences in speech motor symptoms. Thus, when the severity of speech symptoms (e.g., intelligible speaking rate) was accounted for, symptom onset location did not affect the proposed computational model's ability to detect ALS., A myotrophic lateral sclerosis (ALS) is a fatal neurode-generative disease that inhibits the brain's ability to control muscle movements (Kiernan et al., 2011). The loss of motor control is initially [...]

Purpose: This study investigated phonological and speech motor neural networks in children with residual speech sound disorder (RSSD) during an overt Syllable Repetition Task (SRT). Method: Sixteen children with RSSD with /[??]/ errors (6F [female]; ages 8;0-12;6 [years;months]) and 16 children with typically developing speech (TD; 8F; ages 8;5-13;7) completed a functional magnetic resonance imaging experiment. Children performed the SRT ('SRT-Early Sounds') with the phonemes/b, d, m, n, a/and an adapted version ('SRT-Late Sounds') with the phonemes /[??], s, l, t[??], a/. We compared the functional activation and transcribed production accuracy of the RSSD and TD groups during both conditions. Expected errors were not scored as inaccurate. Results: No between-group or within-group differences in repetition accuracy were found on the SRT-Early Sounds or SRT-Late Sounds tasks at any syllable sequence length. On a first-level analysis of the tasks, the TD group showed expected patterns of activation for both the SRT-Early Sounds and SRT-Late Sounds, including activation in the left primary motor cortex, left premotor cortex, bilateral anterior cingulate, bilateral primary auditory cortex, bilateral superior temporal gyrus, and bilateral insula. The RSSD group showed similar activation when correcting for multiple comparisons. In further exploratory analyses, we observed the following subthreshold patterns: (a) On the SRT-Early Sounds, greater activation was found in the left premotor cortex for the RSSD group, while greater activation was found in the left cerebellum for the TD group; (b) on the SRT-Late Sounds, a small area of greater activation was found in the right cerebellum for the RSSD group. No within-group functional differences were observed (SRT-Early Sounds vs. SRT-Late Sounds) for either group. Conclusions: Performance was similar between groups, and likewise, we found that functional activation did not differ. Observed functional differences in previous studies may reflect differences in task performance, rather than fundamental differences in neural mechanisms for syllable repetition., Speech sound disorders (SSDs) comprise a significant fraction (24%-41%; Black et al., 2015) of communication disorders in children. Residual SSD (also variously termed persistent or resistant in the literature) is [...]

Edema is an important target for clinical intervention after traumatic brain injury (TBI). We used in vivo cellular resolution imaging and electrophysiological recording to examine the ionic mechanisms underlying neuronal edema and their effects on neuronal and network excitability after controlled cortical impact (CCI) in mice. Unexpectedly, we found that neuronal edema 48 hours after CCI was associated with reduced cellular and network excitability, concurrent with an increase in the expression ratio of the cation-chloride cotransporters (CCCs) NKCC1 and KCC2. Treatment with the CCC blocker bumetanide prevented neuronal swelling via a reversal in the NKCC1/KCC2 expression ratio, identifying altered chloride flux as the mechanism of neuronal edema. Importantly, bumetanide treatment was associated with increased neuronal and network excitability after injury, including increased susceptibility to spreading depolarizations (SDs) and seizures, known agents of clinical worsening after TBI. Treatment with mannitol, a first-line edema treatment in clinical practice, was also associated with increased susceptibility to SDs and seizures after CCI, showing that neuronal volume reduction, regardless of mechanism, was associated with an excitability increase. Finally, we observed an increase in excitability when neuronal edema normalized by 1 week after CCI. We conclude that neuronal swelling may exert protective effects against damaging excitability in the aftermath of TBI and that treatment of edema has the potential to reverse these effects., Introduction Traumatic brain injury (TBI) is a major cause of death and morbidity in both military and civilian populations (1). Because of the high incidence and the disability associated with [...]

Microglia, the brain's resident macrophages, help to regulate brain function by removing dying neurons, pruning non-functional synapses, and producing ligands that support neuronal survival.sup.1. Here we show that microglia are also critical modulators of neuronal activity and associated behavioural responses in mice. Microglia respond to neuronal activation by suppressing neuronal activity, and ablation of microglia amplifies and synchronizes the activity of neurons, leading to seizures. Suppression of neuronal activation by microglia occurs in a highly region-specific fashion and depends on the ability of microglia to sense and catabolize extracellular ATP, which is released upon neuronal activation by neurons and astrocytes. ATP triggers the recruitment of microglial protrusions and is converted by the microglial ATP/ADP hydrolysing ectoenzyme CD39 into AMP; AMP is then converted into adenosine by CD73, which is expressed on microglia as well as other brain cells. Microglial sensing of ATP, the ensuing microglia-dependent production of adenosine, and the adenosine-mediated suppression of neuronal responses via the adenosine receptor A.sub.1R are essential for the regulation of neuronal activity and animal behaviour. Our findings suggest that this microglia-driven negative feedback mechanism operates similarly to inhibitory neurons and is essential for protecting the brain from excessive activation in health and disease. Microglia, the brain's immune cells, suppress neuronal activity in response to synaptic ATP release and alter behavioural responses in mice., Author(s): Ana Badimon [sup.1] [sup.2] [sup.3] , Hayley J. Strasburger [sup.1] [sup.2] [sup.3] , Pinar Ayata [sup.1] [sup.2] [sup.3] [sup.4] , Xinhong Chen [sup.5] , Aditya Nair [sup.5] , Ako [...]