Jonathan Weyhenmeyer

Non-linear dynamical analysis of EEG time series distinguishes patients with Parkinson's disease from healthy individuals

The pathophysiology of Parkinson’s disease (PD) is known to involve altered patterns of neuronal firing and synchronization in cortical-basal ganglia circuits. One window into the nature of the aberrant temporal dynamics in the cerebral cortex of PD patients can come from analysis of the patients electroencephalography (EEG). Rather than using spectral-based methods, we used data models based on delay differential equations (DDE) as non-linear time-domain classification tools to analyze EEG recordings from PD patients on and off dopaminergic therapy and healthy individuals. Two sets of 50 1-s segments of 64-channel EEG activity were recorded from nine PD patients on and off medication and nine age-matched controls. The 64 EEG channels were grouped into 10 clusters covering frontal, central, parietal, and occipital brain regions for analysis. DDE models were fitted to individual trials, and model coefficients and error were used as features for classification. The best models were selected using repeated random sub-sampling validation and classification performance was measured using the area under the ROC curve A′. In a companion paper, we show that DDEs can uncover hidden dynamical structure from short segments of simulated time series of known dynamical systems in high noise regimes. Using the same method for finding the best models, we found here that even short segments of EEG data in PD patients and controls contained dynamical structure, and moreover, that PD patients exhibited a greater dynamic range than controls. DDE model output on the means from one set of 50 trials provided nearly complete separation of PD patients off medication from controls: across brain regions, the area under the receiver-operating characteristic curves, A′, varied from 0.95 to 1.0. For distinguishing PD patients on vs. off medication, classification performance A′ ranged from 0.86 to 1.0 across brain regions. Moreover, the generalizability of the model to the second set of 50 trials was excellent, with A′ ranging from 0.81 to 0.94 across brain regions for controls vs. PD off medication, and from 0.62 to 0.82 for PD on medication vs. off. Finally, model features significantly predicted individual patients’ motor severity, as assessed with standard clinical rating scales.

Non-linear dynamical classification of short time series of the rössler system in high noise regimes.

Time series analysis with delay differential equations (DDEs) reveals non-linear properties of the underlying dynamical system and can serve as a non-linear time-domain classification tool. Here global DDE models were used to analyze short segments of simulated time series from a known dynamical system, the Rössler system, in high noise regimes. In a companion paper, we apply the DDE model developed here to classify short segments of encephalographic (EEG) data recorded from patients with Parkinson’s disease and healthy subjects. Nine simulated subjects in each of two distinct classes were generated by varying the bifurcation parameter b and keeping the other two parameters (a and c) of the Rössler system fixed. All choices of b were in the chaotic parameter range. We diluted the simulated data using white noise ranging from 10 to −30 dB signal-to-noise ratios (SNR). Structure selection was supervised by selecting the number of terms, delays, and order of non-linearity of the model DDE model that best linearly separated the two classes of data. The distances d from the linear dividing hyperplane was then used to assess the classification performance by computing the area A′ under the ROC curve. The selected model was tested on untrained data using repeated random sub-sampling validation. DDEs were able to accurately distinguish the two dynamical conditions, and moreover, to quantify the changes in the dynamics. There was a significant correlation between the dynamical bifurcation parameter b of the simulated data and the classification parameter d from our analysis. This correlation still held for new simulated subjects with new dynamical parameters selected from each of the two dynamical regimes. Furthermore, the correlation was robust to added noise, being significant even when the noise was greater than the signal. We conclude that DDE models may be used as a generalizable and reliable classification tool for even small segments of noisy data.

Muscle artifacts in single trial EEG data distinguish patients with Parkinson's disease from healthy individuals

Parkinsons disease (PD) is known to lead to marked alterations in cortical-basal ganglia activity that may be amenable to serve as a biomarker for PD diagnosis. Using non-linear delay differential equations (DDE) for classification of PD patients on and off dopaminergic therapy (PD-on, PD-off, respectively) from healthy age-matched controls (CO), we show that 1 second of quasi-resting state clean and raw electroencephalogram (EEG) data can be used to classify CO from PD-on/off based on the area under the receiver operating characteristic curve (AROC). Raw EEG is shown to classify more robustly (AROC=0.59-0.86) than clean EEG data (AROC=0.57-0.72). Decomposition of the raw data into stereotypical and non-stereotypical artifacts provides evidence that increased classification of raw EEG time series originates from muscle artifacts. Thus, non-linear feature extraction and classification of raw EEG data in a low dimensional feature space is a potential biomarker for Parkinsons disease.

Subarachnoid-to-Subarachnoid Shunt for Correction of Nonfunctioning Baclofen Pump in a Severe Case of Chronic Debilitating Post–Spinal Cord Injury Spasticity

BACKGROUND Perhaps the most disabling condition seen in patients with spinal cord injury (SCI) is spasticity. Spasticity is characterized as hyperreflexia and hypertonicity as a result of damage to the supraspinal tracts in the aftermath of SCI. Intrathecal baclofen (ITB) is the mainstay therapy for spasticity unresponsive to oral baclofen. One of the problems associated with post-SCI spasticity unresponsive to ITB is the development of scar tissue that prevents the diffusion of baclofen in the desired spinal cord area. This case offers a unique strategy to deal with multilevel scar tissue. CLINICAL PRESENTATION This 46-year-old paraplegic male with a T8 SCI whose spasticity had been well managed with ITB therapy for many years recently suffered intractable spasticity necessitating multiple reoperations for a nonfunctioning ITB catheter secondary to extensive scar tissue and intrathecal adhesions. Placement of a subarachnoid-to-subarachnoid shunt eliminated the problem of extensive scar tissue preventing adequate baclofen therapy. CONCLUSIONS After undergoing multilevel thoracic and lumbar laminectomies with subarachnoid-to-subarachnoid spinal shunt, the patients spasticity was finally brought under control with adequate daily baclofen infusion. This case demonstrates a creative way to address ITB catheter failure before considering other measures, such as neuroablative procedures (e.g., rhizotomy, myelotomy). This case reinforces the recommendation that ablative procedures, which have far greater complications, should be reserved for patients who have failed medical or other nonablative therapies.

Transcranial Approaches to the Sellar and Parasellar Areas

The pathology of the parasellar space is composed of a large number of potential diagnoses owing to the vast morphology of structures comprising the region. Often, parasellar lesions extend superiorly and/or laterally from the sella, e.g., pituitary adenoma or carcinoma, and engulf adjacent structures such as the cavernous carotid laterally or the optic chiasm superiorly. Still other pathologies stem from the embryonic Rathke’s cleft such as the craniopharyngioma and Rathke’s cleft cyst. The differential diagnosis for a space-occupying lesion includes, but is not limited to, metastasis, infection, intraaxial brain tumor with extension and meningioma, epidermoid cyst, dermoid cyst, teratoma, germinoma, and neurocysticercosis. While each of these pathologies is unique, the options for surgical resection can be grouped into two overarching surgical approaches: transsphenoidal surgery and transcranial surgery.