Search

Research has shown that people who are instructed to volitionally respond to pitch-shifted feedback either produce responses that follow the shift direction with a short latency of 100–200 ms or oppose the shift direction with longer latencies of 300–400 ms. This difference in response latencies prompted a comparison of three groups of vocalists with differing abilities, non-trained English-speaking subjects, non-trained Mandarin-speaking subjects, and trained English-speaking singers. All subjects produced short latency following responses and long latency opposing responses, and in most cases the opposing responses were preceded by a shorter latency following response. Across groups, the magnitudes of the opposing and following responses were largest for the Mandarin speakers. Singers produced the smallest opposing response magnitudes, suggesting differences in the pitch goals of the two groups. Opposing response latencies were longest for the English and Mandarin speaking subjects and shortest for the trained singers, demonstrating that musical training increases the speed of producing the opposing responses. The presence of similar latencies of small following responses preceding larger opposing responses in all groups suggests that the tendency to mimic changes in sounds to which a person is attending are not influenced by vocal training or experience.Research has shown that people who are instructed to volitionally respond to pitch-shifted feedback either produce responses that follow the shift direction with a short latency of 100–200 ms or oppose the shift direction with longer latencies of 300–400 ms. This difference in response latencies prompted a comparison of three groups of vocalists with differing abilities, non-trained English-speaking subjects, non-trained Mandarin-speaking subjects, and trained English-speaking singers. All subjects produced short latency following responses and long latency opposing responses, and in most cases the opposing responses were preceded by a shorter latency following response. Across groups, the magnitudes of the opposing and following responses were largest for the Mandarin speakers. Singers produced the smallest opposing response magnitudes, suggesting differences in the pitch goals of the two groups. Opposing response latencies were longest for the English and Mandarin speaking subjects and shortest for the ...

Purpose Functional magnetic resonance imaging (fMRI) has promise for understanding neural mechanisms of neurogenic speech and voice disorders. However, performing vocal tasks within the fMRI environment may not always be analogous to performance outside of the scanner. Using a mock MRI scanner, this study examines the effects of a simulated scanning environment on vowel intensity in individuals with Parkinson's disease (PD) and hypophonia and older healthy control (OHC) participants. Method Thirty participants (15 PD, 15 OHC) performed a sustained /ɑ/ vowel production task in three conditions: 1) Upright, 2) Mock Scanner + No Noise, and 3) Mock Scanner + MRI noise. We used a linear mixed-effects (multi-level) model to evaluate the contributions of group and recording environment to vowel intensity. A second linear mixed-effects model was also used to evaluate the contributions of PD medication state (On vs. Off) to voice intensity. Results Vowel intensity was significantly lower for PD compared to the OHC group. The intensity of vowels produced in the Upright condition was significantly lower compared to the Mock Scanner + No Noise condition, while vowel intensity in the Mock Scanner + MRI Noise condition was significantly higher compared to the Mock Scanner + No Noise condition. A group by condition interaction also indicated that the addition of scanner noise had a greater impact on the PD group. A second analysis conducted within the PD group showed no effects of medication state on vowel intensity. Conclusion Our findings demonstrate that performance on voice production tasks is altered for PD and OHC groups when translated into the fMRI environment, even in the absence of acoustic scanner noise. For fMRI studies of voice in PD hypophonia, careful thought should be given to how the presence of acoustic noise may differentially affect PD and OHC, for both group and task comparisons.

Cortico-basal ganglia connections are involved in a range of behaviors within motor, cognitive, and emotional domains; however, the whole-brain functional connections of individual nuclei are poorly understood in humans. The first aim of this study was to characterize and compare the connectivity of the subthalamic nucleus (STN) and globus pallidus pars interna (GPi) using meta-analytic connectivity modeling. Structure-based activation likelihood estimation meta-analyses were performed for STN and GPi seeds using archived functional imaging coordinates from the BrainMap database. Both regions coactivated with caudate, putamen, thalamus, STN, GPi, and GPe, SMA, IFG, and insula. Contrast analyses also revealed coactivation differences within SMA, IFG, insula, and premotor cortex. The second aim of this study was to examine the degree of overlap between the connectivity maps derived for STN and GPi and a functional activation map representing the speech network. To do this, we examined the intersection of coactivation maps and their respective contrasts (STN > GPi and GPi > STN) with a coordinate-based meta-analysis of speech function. In conjunction with the speech map, both STN and GPi coactivation maps revealed overlap in the anterior insula with GPi map additionally showing overlap in the supplementary motor area (SMA). Among cortical regions activated by speech tasks, STN was found to have stronger connectivity than GPi with regions involved in cognitive linguistic processes (pre-SMA, dorsal anterior insula, and inferior frontal gyrus), while GPi demonstrated stronger connectivity to regions involved in motor speech processes (middle insula, SMA, and premotor cortex).

has addressed the critical issue of optimizing intervention intensity in the field of speech-language pathology. While this provides a useful framework from which to examine treatment intensities a...

Introduction Speech impairment in Parkinson's disease (PD) is pervasive, with life‐impacting consequences. Yet, little is known about how functional connections between the basal ganglia and cortex relate to PD speech impairment (PDSI). Whole‐brain resting‐state connectivity analyses of basal ganglia nuclei can expand the understanding of PDSI pathophysiology. Methods Resting‐state data from 89 right‐handed subjects were downloaded from the Parkinson's Progression Markers Initiative database. Subjects included 12 older healthy controls (“OHC”), 42 PD patients without speech impairment (“PDN”), and 35 PD subjects with speech impairment (“PDSI”). Subjects were assigned to PDN and PDSI groups based on the Movement Disorders Society Unified Parkinson's Disease Rating Scale (MDS‐UPDRS) Part III speech item scores (“0” vs. “1–4”). Whole‐brain functional connectivity was calculated for four basal ganglia seeds in each hemisphere: putamen, caudate, external globus pallidus (GPe), and internal globus pallidus (GPi). For each seed region, group‐averaged connectivity maps were compared among OHC, PDN, and PDSI groups using a multivariate ANCOVA controlling for the effects of age and sex. Subsequent planned pairwise t‐tests were performed to determine differences between the three groups using a voxel‐wise threshold of p

Cortico‐basal ganglia connections are involved in a range of behaviors within motor, cognitive, and emotional domains; however, the whole‐brain functional connections of individual nuclei are poorly understood in humans. The first aim of this study was to characterize and compare the connectivity of the subthalamic nucleus (STN) and globus pallidus pars interna (GPi) using meta‐analytic connectivity modeling. Structure‐based activation likelihood estimation meta‐analyses were performed for STN and GPi seeds using archived functional imaging coordinates from the BrainMap database. Both regions coactivated with caudate, putamen, thalamus, STN, GPi, and GPe, SMA, IFG, and insula. Contrast analyses also revealed coactivation differences within SMA, IFG, insula, and premotor cortex. The second aim of this study was to examine the degree of overlap between the connectivity maps derived for STN and GPi and a functional activation map representing the speech network. To do this, we examined the intersection of coactivation maps and their respective contrasts (STN > GPi and GPi > STN) with a coordinate‐based meta‐analysis of speech function. In conjunction with the speech map, both STN and GPi coactivation maps revealed overlap in the anterior insula with GPi map additionally showing overlap in the supplementary motor area (SMA). Among cortical regions activated by speech tasks, STN was found to have stronger connectivity than GPi with regions involved in cognitive linguistic processes (pre‐SMA, dorsal anterior insula, and inferior frontal gyrus), while GPi demonstrated stronger connectivity to regions involved in motor speech processes (middle insula, SMA, and premotor cortex). Hum Brain Mapp 35:3499–3516, 2014. © 2013 Wiley Periodicals, Inc.