The intrinsic resting state voice network in Parkinson's disease.

Over 90 percent of patients with Parkinson's disease experience speech-motor impairment, namely, hypokinetic dysarthria characterized by reduced pitch and loudness. Resting-state functional connectivity analysis of blood oxygen level-dependent functional magnetic resonance imaging is a useful measure of intrinsic neural functioning. We utilized resting-state functional connectivity modeling to analyze the intrinsic connectivity in patients with Parkinson's disease within a vocalization network defined by a previous meta-analysis of speech (Brown et al., 2009). Functional connectivity of this network was assessed in 56 patients with Parkinson's disease and 56 gender-, age-, and movement-matched healthy controls. We also had item 5 and 18 of the UPDRS, and the PDQ-39 Communication subscale available for correlation with the voice network connectivity strength in patients. The within-group analyses of connectivity patterns demonstrated a lack of subcortical-cortical connectivity in patients with Parkinson's disease. At the cortical level, we found robust (homotopic) interhemispheric connectivity but only inconsistent evidence for many intrahemispheric connections. When directly contrasted to the control group, we found a significant reduction of connections between the left thalamus and putamen, and cortical motor areas, as well as reduced right superior temporal gyrus connectivity. Furthermore, most symptom measures correlated with right putamen, left cerebellum, left superior temporal gyrus, right premotor, and left Rolandic operculum connectivity in the voice network. The results reflect the importance of (right) subcortical nodes and the superior temporal gyrus in Parkinson's disease, enhancing our understanding of the neurobiological underpinnings of vocalization impairment in Parkinson's disease.

Connectivity of the subthalamic nucleus and globus pallidus pars interna to regions within the speech network: a meta-analytic connectivity study.

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).

Understanding the neural mechanisms involved in sensory control of voice production.

Auditory feedback is important for the control of voice fundamental frequency (F0). In the present study we used neuroimaging to identify regions of the brain responsible for sensory control of the voice. We used a pitch-shift paradigm where subjects respond to an alteration, or shift, of voice pitch auditory feedback with a reflexive change in F0. To determine the neural substrates involved in these audio-vocal responses, subjects underwent fMRI scanning while vocalizing with or without pitch-shifted feedback. The comparison of shifted and unshifted vocalization revealed activation bilaterally in the superior temporal gyrus (STG) in response to the pitch shifted feedback. We hypothesize that the STG activity is related to error detection by auditory error cells located in the superior temporal cortex and efference copy mechanisms whereby this region is responsible for the coding of a mismatch between actual and predicted voice F0.

Altered resting-state network connectivity in stroke patients with and without apraxia of speech.

Motor speech disorders, including apraxia of speech (AOS), account for over 50% of the communication disorders following stroke. Given its prevalence and impact, and the need to understand its neural mechanisms, we used resting state functional MRI to examine functional connectivity within a network of regions previously hypothesized as being associated with AOS (bilateral anterior insula (aINS), inferior frontal gyrus (IFG), and ventral premotor cortex (PM)) in a group of 32 left hemisphere stroke patients and 18 healthy, age-matched controls. Two expert clinicians rated severity of AOS, dysarthria and nonverbal oral apraxia of the patients. Fifteen individuals were categorized as AOS and 17 were AOS-absent. Comparison of connectivity in patients with and without AOS demonstrated that AOS patients had reduced connectivity between bilateral PM, and this reduction correlated with the severity of AOS impairment. In addition, AOS patients had negative connectivity between the left PM and right aINS and this effect decreased with increasing severity of non-verbal oral apraxia. These results highlight left PM involvement in AOS, begin to differentiate its neural mechanisms from those of other motor impairments following stroke, and help inform us of the neural mechanisms driving differences in speech motor planning and programming impairment following stroke.

Effective connectivity associated with auditory error detection in musicians with absolute pitch.

It is advantageous to study a wide range of vocal abilities in order to fully understand how vocal control measures vary across the full spectrum. Individuals with absolute pitch (AP) are able to assign a verbal label to musical notes and have enhanced abilities in pitch identification without reliance on an external referent. In this study we used dynamic causal modeling (DCM) to model effective connectivity of ERP responses to pitch perturbation in voice auditory feedback in musicians with relative pitch (RP), AP, and non-musician controls. We identified a network compromising left and right hemisphere superior temporal gyrus (STG), primary motor cortex (M1), and premotor cortex (PM). We specified nine models and compared two main factors examining various combinations of STG involvement in feedback pitch error detection/correction process. Our results suggest that modulation of left to right STG connections are important in the identification of self-voice error and sensory motor integration in AP musicians. We also identify reduced connectivity of left hemisphere PM to STG connections in AP and RP groups during the error detection and corrections process relative to non-musicians. We suggest that this suppression may allow for enhanced connectivity relating to pitch identification in the right hemisphere in those with more precise pitch matching abilities. Musicians with enhanced pitch identification abilities likely have an improved auditory error detection and correction system involving connectivity of STG regions. Our findings here also suggest that individuals with AP are more adept at using feedback related to pitch from the right hemisphere.

The neural changes in connectivity of the voice network during voice pitch perturbation.

Voice control is critical to communication. To date, studies have used behavioral, electrophysiological and functional data to investigate the neural correlates of voice control using perturbation tasks, but have yet to examine the interactions of these neural regions. The goal of this study was to use structural equation modeling of functional neuroimaging data to examine network properties of voice with and without perturbation. Results showed that the presence of a pitch shift, which was processed as an error in vocalization, altered connections between right STG and left STG. Other regions that revealed differences in connectivity during error detection and correction included bilateral inferior frontal gyrus, and the primary and pre motor cortices. Results indicated that STG plays a critical role in voice control, specifically, during error detection and correction. Additionally, pitch perturbation elicits changes in the voice network that suggest the right hemisphere is critical to pitch modulation.

Modulation of effective connectivity during vocalization with perturbed auditory feedback.

The integration of auditory feedback with vocal motor output is important for the control of voice fundamental frequency (F0). We used a pitch-shift paradigm where subjects respond to an alteration, or shift, of voice pitch auditory feedback with a reflexive change in F0. We presented varying magnitudes of pitch shifted auditory feedback to subjects during vocalization and passive listening and measured event related potentials (ERPs) to the feedback shifts. Shifts were delivered at +100 and +400 cents (200 ms duration). The ERP data were modeled with dynamic causal modeling (DCM) techniques where the effective connectivity between the superior temporal gyrus (STG), inferior frontal gyrus and premotor areas were tested. We compared three main factors: the effect of intrinsic STG connectivity, STG modulation across hemispheres and the specific effect of hemisphere. A Bayesian model selection procedure was used to make inference about model families. Results suggest that both intrinsic STG and left to right STG connections are important in the identification of self-voice error and sensory motor integration. We identified differences in left-to-right STG connections between 100 cent and 400 cent shift conditions suggesting that self- and non-self-voice error are processed differently in the left and right hemisphere. These results also highlight the potential of DCM modeling of ERP responses to characterize specific network properties of forward models of voice control.

Structural brain anomalies and chronic pain: a quantitative meta-analysis of gray matter volume.

UNLABELLED: The diversity of chronic pain syndromes and the methods employed to study them make integrating experimental findings challenging. This study performed coordinate-based meta-analyses using voxel-based morphometry imaging results to examine gray matter volume (GMV) differences between chronic pain patients and healthy controls. There were 12 clusters where GMV was decreased in patients compared with controls, including many regions thought to be part of the "pain matrix" of regions involved in pain perception, but also including many other regions that are not commonly regarded as pain-processing areas. The right hippocampus and parahippocampal gyrus were the only regions noted to have increased GMV in patients. Functional characterizations were implemented using the BrainMap database to determine which behavioral domains were significantly represented in these regions. The most common behavioral domains associated with these regions were cognitive, affective, and perceptual domains. Because many of these regions are not classically connected with pain and because there was such significance in functionality outside of perception, it is proposed that many of these regions are related to the constellation of comorbidities of chronic pain, such as fatigue and cognitive and emotional impairments. Further research into the mechanisms of GMV changes could provide a perspective on these findings. PERSPECTIVE: Quantitative meta-analyses revealed structural differences between brains of individuals with chronic pain and healthy controls. These differences may be related to comorbidities of chronic pain.