Subthalamic Nucleus Neurons Differentially Encode Early and Late Aspects of Speech Production.

Basal ganglia-thalamocortical loops mediate all motor behavior, yet little detail is known about the role of basal ganglia nuclei in speech production. Using intracranial recording during deep brain stimulation surgery in humans with Parkinson's disease, we tested the hypothesis that the firing rate of subthalamic nucleus neurons is modulated in sync with motor execution aspects of speech. Nearly half of 79 unit recordings exhibited firing-rate modulation during a syllable reading task across 12 subjects (male and female). Trial-to-trial timing of changes in subthalamic neuronal activity, relative to cue onset versus production onset, revealed that locking to cue presentation was associated more with units that decreased firing rate, whereas locking to speech onset was associated more with units that increased firing rate. These unique data indicate that subthalamic activity is dynamic during the production of speech, reflecting temporally-dependent inhibition and excitation of separate populations of subthalamic neurons.SIGNIFICANCE STATEMENT The basal ganglia are widely assumed to participate in speech production, yet no prior studies have reported detailed examination of speech-related activity in basal ganglia nuclei. Using microelectrode recordings from the subthalamic nucleus during a single-syllable reading task, in awake humans undergoing deep brain stimulation implantation surgery, we show that the firing rate of subthalamic nucleus neurons is modulated in response to motor execution aspects of speech. These results are the first to establish a role for subthalamic nucleus neurons in encoding of aspects of speech production, and they lay the groundwork for launching a modern subfield to explore basal ganglia function in human speech.

Modulation of intrinsic brain activity by electroconvulsive therapy in major depression.

INTRODUCTION: One of the most effective interventions for intractable major depressive episodes is electroconvulsive therapy (ECT). Because ECT is also relatively fast-acting, longitudinal study of its neurobiological effects offers critical insight into the mechanisms underlying depression and antidepressant response. Here we assessed modulation of intrinsic brain activity in corticolimbic networks associated with ECT and clinical response. METHODS: We measured resting-state functional connectivity (RSFC) in patients with treatment-resistant depression (n=30), using functional magnetic resonance imaging (fMRI) acquired before and after completing a treatment series with right-unilateral ECT. Using independent component analysis, we assessed changes in RSFC with 1) symptom improvement and 2) ECT regardless of treatment outcome in patients, with reference to healthy controls (n=33, also scanned twice). RESULTS: After ECT, consistent changes in RSFC within targeted depression-relevant functional networks were observed in the dorsal anterior cingulate (ACC), mediodorsal thalamus (mdTh), hippocampus, and right anterior temporal, medial parietal, and posterior cingulate cortex in all patients. In a separate analysis, changes in depressive symptoms were associated with RSFC changes in the dorsal ACC, mdTh, putamen, medial prefrontal, and lateral parietal cortex. RSFC of these regions did not change in healthy controls. CONCLUSIONS: Neuroplasticity underlying clinical change was in part separable from changes associated with the effects of ECT observed in all patients. However, both ECT and clinical change were associated with RSFC modulation in dorsal ACC, mdTh and hippocampus, which may indicate that these regions underlie the mechanisms of clinical outcome in ECT and may be effective targets for future neurostimulation therapies.

Structural Plasticity of the Hippocampus and Amygdala Induced by Electroconvulsive Therapy in Major Depression.

BACKGROUND: Electroconvulsive therapy (ECT) elicits a rapid and robust clinical response in patients with refractory depression. Neuroimaging measurements of structural plasticity relating to and predictive of ECT response may point to the mechanisms underlying rapid antidepressant effects and establish biomarkers to inform other treatments. Here, we determine the effects of diagnosis and of ECT on global and local variations of hippocampal and amygdala structures in major depression and predictors of ECT-related clinical response. METHODS: Longitudinal changes in hippocampal and amygdala structures were examined in patients with major depression (N = 43, scanned three times: prior to ECT, after the second ECT session, and within 1 week of completing the ECT treatment series), referred for ECT as part of their standard clinical care. Cross-sectional comparisons with demographically similar controls (N = 32, scanned twice) established effects of diagnosis. RESULTS: Patients showed smaller hippocampal volumes than controls at baseline (p < .04). Both the hippocampal and the amygdala volumes increased with ECT (p < .001) and in relation to symptom improvement (p < .01). Hippocampal volume at baseline predicted subsequent clinical response (p < .05). Shape analysis revealed pronounced morphometric changes in the anterior hippocampus and basolateral and centromedial amygdala. All structural measurements remained stable across time in controls. CONCLUSIONS: ECT-induced neuroplasticity in the hippocampus and amygdala relates to improved clinical response and is pronounced in regions with prominent connections to ventromedial prefrontal cortex and other limbic structures. Smaller hippocampal volumes at baseline predict a more robust clinical response. Neurotrophic processes including neurogenesis shown in preclinical studies may underlie these structural changes.

Cortical thickness and brain volumetric analysis in body dysmorphic disorder.

Individuals with body dysmorphic disorder (BDD) suffer from preoccupations with perceived defects in physical appearance, causing severe distress and disability. Although BDD affects 1-2% of the population, the neurobiology is not understood. Discrepant results in previous volumetric studies may be due to small sample sizes, and no study has investigated cortical thickness in BDD. The current study is the largest neuroimaging analysis of BDD. Participants included 49 medication-free, right-handed individuals with DSM-IV BDD and 44 healthy controls matched by age, sex, and education. Using high-resolution T1-weighted magnetic resonance imaging, we computed vertex-wise gray matter (GM) thickness on the cortical surface and GM volume using voxel-based morphometry. We also computed volumes in cortical and subcortical regions of interest. In addition to group comparisons, we investigated associations with symptom severity, insight, and anxiety within the BDD group. In BDD, greater anxiety was significantly associated with thinner GM in the left superior temporal cortex and greater GM volume in the right caudate nucleus. There were no significant differences in cortical thickness, GM volume, or volumes in regions of interest between BDD and control subjects. Subtle associations with clinical symptoms may characterize brain morphometric patterns in BDD, rather than large group differences in brain structure.

Hippocampal dysfunction during declarative memory encoding in schizophrenia and effects of genetic liability.

Declarative memory (DM) impairments are reported in schizophrenia and in unaffected biological relatives of patients. However, the neural correlates of successful and unsuccessful encoding, mediated by the medial temporal lobe (MTL) memory system, and the influence of disease-related genetic liability remain under explored. This study employed an event-related functional MRI paradigm to compare activations for successfully and unsuccessfully encoded associative face-name stimuli between 26 schizophrenia patients (mean age: 33, 19m/7f), 30 controls (mean age: 29, 24m/6f), and 14 unaffected relatives of patients (mean age: 40, 5m/9f). Compared to controls or unaffected relatives, patients showed hyper-activations in ventral visual stream and temporo-parietal cortical association areas when contrasting successfully encoded events to fixation. Follow-up hippocampal regions-of-interest analysis revealed schizophrenia-related hyper-activations in the right anterior hippocampus during successful encoding; contrasting successful versus unsuccessful events produced schizophrenia-related hypo-activations in the left anterior hippocampus. Similar hippocampal hypo-activations were observed in unaffected relatives during successful versus unsuccessful encoding. Post hoc analyses of hippocampal volume showed reductions in patients, but not in unaffected relatives compared to controls. Findings suggest that DM encoding deficits are attributable to both disease-specific and genetic liability factors that impact different components of the MTL memory system. Hyper-activations in temporo-occipital and parietal regions observed only in patients suggest the influence of disease-related factors. Regional hyper- and hypo-activations attributable to successful encoding occurring in both patients and unaffected relatives suggest the influence of schizophrenia-related genetic liability factors.

Random Forest Classification of Depression Status Based On Subcortical Brain Morphometry Following Electroconvulsive Therapy.

Disorders of the central nervous system are often accompanied by brain abnormalities detectable with MRI. Advances in biomedical imaging and pattern detection algorithms have led to classification methods that may help diagnose and track the progression of a brain disorder and/or predict successful response to treatment. These classification systems often use high-dimensional signals or images, and must handle the computational challenges of high dimensionality as well as complex data types such as shape descriptors. Here, we used shape information from subcortical structures to test a recently developed feature-selection method based on regularized random forests to 1) classify depressed subjects versus controls, and 2) patients before and after treatment with electroconvulsive therapy. We subsequently compared the classification performance of high-dimensional shape features with traditional volumetric measures. Shape-based models outperformed simple volumetric predictors in several cases, highlighting their utility as potential automated alternatives for establishing diagnosis and predicting treatment response.