Biology subjects

Krusienski, D. J.

Publications and source records attributed to Krusienski, D. J..

6 recordsLinked to original sources

bioRxiv2025

Pallidal Spectral and Phase-Amplitude Coupling Differences in Parkinsons Disease Locomotor States

BackgroundDeep brain stimulation of the subthalamic nucleus or Globus Pallidus internus is an established therapy for Parkinsons Disease that is sub-optimally managed with medication. While post-operative motor studies have defined neurophysiological signatures of the subthalamic nucleic local field potentials, comparable studies in the Globus pallidus internus are needed. Recent work shows that bandpower and phase-amplitude coupling in the subthalamic nucleus reliably distinguish locomotor states, motivating parallel investigation in the Globus pallidus internus. ObjectivesTo characterize pallidal bandpower and phase-amplitude coupling across locomotor states (sitting, standing, and overground walking) in Parkinsons, and relate to clinical motor scores. MethodsBandpower and phase-amplitude coupling from pallidal signals of six adults with Parkinsons were compared across locomotor states; inter-state differences were correlated with clinical scores. ResultsHigh beta and gamma power differentiated sitting from walking, while delta power distinguished standing from walking. Phase-amplitude coupling between lower phases and gamma amplitude decreased during walking. Standing-to-walking delta-power modulation strongly correlated with Unified Parkinsons Disease Rating Scale scores (R2 = 0.82, q < 0.05). Standing-to-walking beta-gamma phase-amplitude coupling modulation highly correlated with freezing-of-gait scores (R2 = 0.96, p < 0.05). ConclusionsThis study provides the first detailed characterization of Globus pallidus internus activity in these locomotor states in Parkinsons. The coupling between lower frequencies and gamma was lower during walking compared to sitting and standing, revealing an inverse relationship to what has been reported in the subthalamic nucleus. These findings suggest that movement coordination in the Globus pallidus internus may rely on a balance of single- and cross-frequency mechanisms.

bioengineering↗

bioRxiv2025

Time-series models can forecast long periods of human temporalEEG responses to randomly alternating visual stimuli

Visual stimuli with constant temporal frequency input is known to induce peaks in the driving frequency of the power spectrum of the electroencephalogram (EEG) over the visual cortex. While EEG responses with random temporal frequencies (m-sequences) have been studied, the underlying mechanisms that shape these responses are not fully understood. We analyze our new EEG data from a controlled experiment with m-sequence inputs and model the EEG using statistical time series models: an autoregressive (AR) model, adding exogenous input to AR (ARX), adding moving average terms (ARMAX), and finally adding a seasonality term (SARMAX). We implement computational methods to robustly handle model instabilities induced by this data, fitting these models with the Box-Jenkins methodology and assessing prediction accuracy for long periods of several seconds out-of-sample. We find in-sample fits are good in all models despite the complexities of the visual pathway, and that all models can predict aspects of EEG: including the distribution of point-wise values in time, the point-wise Pearsons correlation of EEG and model, and the frequency content. Surprisingly, we find little variation in the performance among these models, with the most sophisticated model (SARMAX) performing comparatively poorly in some instances. Our results suggest the simplest AR model is viable and can out perform more complicated models. Since these models are relatively simple and more transparent than contemporary models with numerous parameters, our study could inform future mechanistic studies of the temporal dynamics of human EEG responses to visual stimuli.

neuroscience↗

bioRxiv2022

The Nested Hierarchy of Overt, Mouthed, and Imagined Speech Activity Evident in Intracranial Recordings

Recent studies have demonstrated that it is possible to decode and synthesize various aspects of acoustic speech directly from intracranial measurements of electrophysiological brain activity. In order to continue progressing toward the development of a practical speech neuroprosthesis for the individuals with speech impairments, better understanding and modeling of imagined speech processes are required. The present study uses intracranial brain recordings from participants that performed a speaking task with trials consisting of overt, mouthed, and imagined speech, representing various degrees of decreasing behavioral output. Speech activity detection models are constructed using spatial, spectral, and temporal brain activity features, and the features and model performances are characterized and compared across the three degrees of behavioral output. The results indicate there is a hierarchy in which the relevant channels for the lower behavioral output modes form nested subsets of the relevant channels from the higher behavioral output modes. This provides important insights for the elusive goal of developing more effective imagined speech decoding models with respect to the better-established overt speech decoding counterparts.

neuroscience↗

bioRxiv2020

Real-time Synthesis of Imagined Speech Processes from Minimally Invasive Recordings of Neural Activity

Speech neuroprosthetics aim to provide a natural communication channel to individuals who are unable to speak due to physical or neurological impairments. Real-time synthesis of acoustic speech directly from measured neural activity could enable natural conversations and significantly improve quality of life, particularly for individuals who have severely limited means of communication. Recent advances in decoding approaches have led to high quality reconstructions of acoustic speech from invasively measured neural activity. However, most prior research utilizes data collected during open-loop experiments of articulated speech, which neglects the critical human-in-the-loop aspect of a practical speech neuroprosthetic. Here we present an approach that synthesizes audible speech in real-time for both imagined and whispered speech conditions. Using a participant implanted with stereotactic depth electrodes, we were able to reliably generate audible speech in real-time. The decoding models rely predominately on frontal activity suggesting that speech processes have similar representations when vocalized, whispered, or imagined. Our real-time synthesis approach represents an essential step towards investigating how patients will learn to operate a closed-loop speech neuroprosthesis, as well as the development of techniques that incorporate co-adaptation of the user and system for optimized performance.

neuroscience↗

bioRxiv2019

Prefrontal high-gamma in ECoG tags periodicity of musical rhythms in perception and imagination

Rhythmic auditory stimuli are known to elicit matching activity patterns in neural populations. Furthermore, recent research has established the particular importance of high-gamma brain activity in auditory processing by showing its involvement in auditory phrase segmentation and envelope-tracking. Here, we use electrocorticographic (ECoG) recordings from eight human listeners, to see whether periodicities in high-gamma activity track the periodicities in the envelope of musical rhythms during rhythm perception and imagination. Rhythm imagination was elicited by instructing participants to imagine the rhythm to continue during pauses of several repetitions. To identify electrodes whose periodicities in high-gamma activity track the periodicities in the musical rhythms, we compute the correlation between the autocorrelations (ACC) of both the musical rhythms and the neural signals. A condition in which participants listened to white noise was used to establish a baseline. High-gamma autocorrelations in auditory areas in the superior temporal gyrus and in frontal areas on both hemispheres significantly matched the autocorrelation of the musical rhythms. Overall, numerous significant electrodes are observed on the right hemisphere. Of particular interest is a large cluster of electrodes in the right prefrontal cortex that is active during both rhythm perception and imagination. This indicates conscious processing of the rhythms structure as opposed to mere auditory phenomena. The ACC approach clearly highlights that high-gamma activity measured from cortical electrodes tracks both attended and imagined rhythms.

neuroscience↗

bioRxiv2018

Speech Synthesis from ECoG using Densely Connected 3D Convolutional Neural Networks

ObjectiveDirect synthesis of speech from neural signals could provide a fast and natural way of communication to people with neurological diseases. Invasively-measured brain activity (electrocorticography; ECoG) supplies the necessary temporal and spatial resolution to decode fast and complex processes such as speech production. A number of impressive advances in speech decoding using neural signals have been achieved in recent years, but the complex dynamics are still not fully understood. However, it is unlikely that simple linear models can capture the relation between neural activity and continuous spoken speech.\n\nApproachHere we show that deep neural networks can be used to map ECoG from speech production areas onto an intermediate representation of speech (logMel spectrogram). The proposed method uses a densely connected convolutional neural network topology which is well-suited to work with the small amount of data available from each participant.\n\nMain resultsIn a study with six participants, we achieved correlations up to r = 0.69 between the reconstructed and original logMel spectrograms. We transfered our prediction back into an audible waveform by applying a Wavenet vocoder. The vocoder was conditioned on logMel features that harnessed a much larger, pre-existing data corpus to provide the most natural acoustic output.\n\nSignificanceTo the best of our knowledge, this is the first time that high-quality speech has been reconstructed from neural recordings during speech production using deep neural networks.

neuroscience↗