28 research outputs found
Altered Prefrontal Theta and Gamma Activity during an Emotional Face Processing Task in Parkinson Disease.
Patients with Parkinson disease (PD) often experience nonmotor symptoms including cognitive deficits, depression, and anxiety. Cognitive and affective processes are thought to be mediated by prefrontal cortico-basal ganglia circuitry. However, the topography and neurophysiology of prefrontal cortical activity during complex tasks are not well characterized. We used high-resolution electrocorticography in pFC of patients with PD and essential tremor, during implantation of deep brain stimulator leads in the awake state, to understand disease-specific changes in prefrontal activity during an emotional face processing task. We found that patients with PD had less task-related theta-alpha power and greater task-related gamma power in the dorsolateral pFC, inferior frontal cortex, and lateral OFC. These findings support a model of prefrontal neurophysiological changes in the dopamine-depleted state, in which focal areas of hyperactivity in prefrontal cortical regions may compensate for impaired long-range interactions mediated by low-frequency rhythms. These distinct neurophysiological changes suggest that nonmotor circuits undergo characteristic changes in PD
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Cortical encoding and decoding models of speech production
To speak is to dynamically orchestrate the movements of the articulators (jaw, tongue, lips, and larynx), which in turn generate speech sounds. It is an amazing mental and motor feat that is controlled by the brain and is fundamental for communication. Technology that could translate brain signals into speech would be transformative for people who are unable to communicate as a result of neurological impairments. This work first investigates how articulator movements that underlie natural speech production are represented in the brain. Building upon this, this work also presents a neural decoder that can synthesize audible speech from brain signals. Data to support these results were from direct cortical recordings of the human sensorimotor cortex while participants spoke natural sentences. Neural activity at individual electrodes encoded a diversity of articulatory kinematic trajectories (AKTs), each revealing coordinated articulator movements towards specific vocal tract shapes. The neural decoder was designed to leverage the kinematic trajectories encoded in the sensorimotor cortex which enhanced performance even with limited data. In closed vocabulary tests, listeners could readily identify and transcribe speech synthesized from cortical activity. These findings advance the clinical viability of using speech neuroprosthetic technology to restore spoken communication
Quantitative assessment of structural and functional changes in temporal lobe epilepsy with hippocampal sclerosis
Background: Magnetic resonance imaging (MRI) changes in hippocampal sclerosis (HS) could be subtle in a significant proportion of mesial temporal lobe epilepsy (mTLE) patients. In this study, we aimed to document the structural and functional changes in the hippocampus and amygdala seen in HS patients.
Methods: Quantitative features of the hippocampus and amygdala were extracted from structural MRI data in 66 mTLE patients and 28 controls. Structural covariance analysis was undertaken using volumetric data from the amygdala and hippocampus. Functional connectivity (FC) measured using resting intracranial electroencephalography (EEG) was analyzed in 22 HS patients and 16 non-HS disease controls.
Results: Hippocampal atrophy was present in both MRI-positive and MRI-negative HS groups (Mann-Whitney U: 7.61, P<0.01; Mann-Whitney U: 6.51, P<0.01). Amygdala volumes were decreased in the patient group (Mann-Whitney U: 2.92, P<0.05), especially in MRI-negative HS patients (Mann-Whitney U: 2.75, P<0.05). The structural covariance analysis showed the normalized volumes of the amygdala and hippocampus were tightly coupled in both controls and HS patients (ρSpearman =0.72, P<0.01). FC analysis indicated that HS patients had significantly increased connectivity (Student’s t: 2.58, P=0.03) within the hippocampus but decreased connectivity between the hippocampus and amygdala (Student’s t: 3.33, P=0.01), particularly for MRI-negative HS patients.
Conclusions: Quantitative structural changes, including hippocampal atrophy and temporal pole blurring, are present in both MRI-positive and MRI-negative HS patients, suggesting the potential usefulness of incorporating quantitative analyses into clinical practice. HS is characterized by increased intra-hippocampal EEG synchronization and decreased coupling between the hippocampus and amygdala
Normative brain mapping of interictal intracranial EEG to localize epileptogenic tissue
The identification of abnormal electrographic activity is important in a wide range of neurological disorders, including epilepsy for localising epileptogenic tissue. However, this identification may be challenging during non-seizure (interictal) periods, especially if abnormalities are subtle compared to the repertoire of possible healthy brain dynamics. Here, we investigate if such interictal abnormalities become more salient by quantitatively accounting for the range of healthy brain dynamics in a location-specific manner.
To this end, we constructed a normative map of brain dynamics, in terms of relative band power, from interictal intracranial recordings from 234 subjects (21,598 electrode contacts). We then compared interictal recordings from 62 patients with epilepsy to the normative map to identify abnormal regions. We hypothesised that if the most abnormal regions were spared by surgery, then patients would be more likely to experience continued seizures post-operatively.
We first confirmed that the spatial variations of band power in the normative map across brain regions were consistent with healthy variations reported in the literature. Second, when accounting for the normative variations, regions which were spared by surgery were more abnormal than those resected only in patients with persistent post-operative seizures (t=-3.6, p = 0.0003), confirming our hypothesis. Third, we found that this effect discriminated patient outcomes (AUC = 0.75 p = 0.0003).
Normative mapping is a well-established practice in neuroscientific research. Our study suggests that this approach is feasible to detect interictal abnormalities in intracranial EEG, and of potential clinical value to identify pathological tissue in epilepsy. Finally, we make our normative intracranial map publicly available to facilitate future investigations in epilepsy and beyon
Synthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder Framework
Speech Neuroprostheses have the potential to enable communication for people
with dysarthria or anarthria. Recent advances have demonstrated high-quality
text decoding and speech synthesis from electrocorticographic grids placed on
the cortical surface. Here, we investigate a less invasive measurement modality
in three participants, namely stereotactic EEG (sEEG) that provides sparse
sampling from multiple brain regions, including subcortical regions. To
evaluate whether sEEG can also be used to synthesize high-quality audio from
neural recordings, we employ a recurrent encoder-decoder model based on modern
deep learning methods. We find that speech can indeed be reconstructed with
correlations up to 0.8 from these minimally invasive recordings, despite
limited amounts of training data
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Prefrontal Cortical Modulation of Motor and Non-Motor Functions in Parkinson's Disease
The prefrontal cortex is involved in various cognitive and affective functions. In neuropsychiatric conditions in which these functions are perturbed, such as Parkinson’s disease, prefrontal activity is not well characterized, in part due to methodological constraints that limit the ability to assess neural activity with high spatial and temporal resolution in humans. We utilized high resolution invasive neurophysiology in Parkinson’s patients undergoing awake, deep brain stimulation surgery to study the prefrontal cortex in Parkinson’s disease. In Chapter 1, we introduce Parkinson’s disease and circuit models of disease informed by invasive recordings. Next, we discuss two studies in which we used invasive recordings to characterize prefrontal activity during movement inhibition and during emotional face processing. In Chapter 2, we introduce evidence of a prefrontal hyperdirect pathway in humans, and we characterize its topography. We found broad prefrontal innervation, with a preferential localization of fast fibers in the inferior frontal cortex. We also show inferior frontal-subthalamic co-modulation during movement inhibition, providing evidence for human hyperdirect involvement in stopping. Finally, we discuss an exploratory study of prefrontal activity during emotional face processing, where we found that Parkinson’s disease is characterized by prefrontal hyperactivity (Chapter 3). These two studies expand our understanding of the prefrontal mechanisms of cognitive and affective functions in Parkinson’s disease
Understanding and Decoding Imagined Speech using Electrocorticographic Recordings in Humans
Certain brain disorders, resulting from brainstem infarcts, traumatic brain injury, stroke and amyotrophic lateral sclerosis, limit verbal communication despite the patient being fully aware. People that cannot communicate due to neurological disorders would benefit from a system that can infer internal speech directly from brain signals. Investigating how the human cortex encodes imagined speech remains a difficult challenge, due to the lack of behavioral and observable measures. As a consequence, the fine temporal properties of speech cannot be synchronized precisely with brain signals during internal subjective experiences, like imagined speech. This thesis aims at understanding and decoding the neural correlates of imagined speech (also called internal speech or covert speech), for targeting speech neuroprostheses. In this exploratory work, various imagined speech features, such as acoustic sound features, phonetic representations, and individual words were investigated and decoded from electrocorticographic signals recorded in epileptic patients in three different studies. This recording technique provides high spatiotemporal resolution, via electrodes placed beneath the skull, but without penetrating the cortex In the first study, we reconstructed continuous spectrotemporal acoustic features from brain signals recorded during imagined speech using cross-condition linear regression. Using this technique, we showed that significant acoustic features of imagined speech could be reconstructed in seven patients. In the second study, we decoded continuous phoneme sequences from brain signals recorded during imagined speech using hidden Markov models. This technique allowed incorporating a language model that defined phoneme transitions probabilities. In this preliminary study, decoding accuracy was significant across eight phonemes in one patients. In the third study, we classified individual words from brain signals recorded during an imagined speech word repetition task, using support-vector machines. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the classification framework. Classification accuracy was significant across five patients. In order to compare speech representations across conditions and integrate imagined speech into the general speech network, we investigated imagined speech in parallel with overt speech production and/or speech perception. Results shared across the three studies showed partial overlapping between imagined speech and speech perception/production in speech areas, such as superior temporal lobe, anterior frontal gyrus and sensorimotor cortex. In an attempt to understanding higher-level cognitive processing of auditory processes, we also investigated the neural encoding of acoustic features during music imagery using linear regression. Despite this study was not directly related to speech representations, it provided a unique opportunity to quantitatively study features of inner subjective experiences, similar to speech imagery. These studies demonstrated the potential of using predictive models for basic decoding of speech features. Despite low performance, results show the feasibility for direct decoding of natural speech. In this respect, we highlighted numerous challenges that were encountered, and suggested new avenues to improve performances