Asymmetrical lateral ventricular enlargement in Parkinson's disease

BACKGROUND

A recent case report suggested the presence of asymmetrical lateral ventricular enlargement associated with motor asymmetry in Parkinson's disease (PD). The current study explored these associations further.

METHODS

Magnetic resonance imaging (3T) scans were obtained on 17 PD and 15 healthy control subjects at baseline and 12-43 months later. Baseline and longitudinal lateral ventricular volumetric changes were compared between contralateral and ipsilateral ventricles in PD subjects relative to symptom onset side and in controls relative to their dominant hand. Correlations between changes in ventricular volume and United Parkinson's disease rating scale motor scores (UPDRS-III) whilst on medication were determined.

RESULTS

The lateral ventricle contralateral to symptom onset side displayed a faster rate of enlargement compared to the ipsilateral (P = 0.004) in PD subjects, with no such asymmetry detected (P = 0.312) in controls. There was a positive correlation between ventricular enlargement and worsening motor function assessed by UPDRS-III scores (r = 0.96, P < 0.001).

DISCUSSION

There is asymmetrical lateral ventricular enlargement that is associated with PD motor asymmetry and progression. Further studies are warranted to investigate the underlying mechanism(s), as well as the potential of using volumetric measurements as a marker for PD progression.

Partial directed coherence-based information flow in Parkinson's disease patients performing a visually-guided motor task

We propose a partial directed coherence (PCD) method based on a sparse multivariate autoregressive (mAR) model to investigate patterns of information flow in electroencephalography (EEG) recordings in Parkinson's disease (PD) patients performing a visually-guided motor task. The use of a sparsity constraint on the mAR matrix addresses issues such as sample size, model order selection and number of parameters to be estimated, particularly when the number of EEG channels used is large and the window size is small in order to capture dynamic changes. The proposed PDC-based information flow analysis demonstrated distinctly altered patterns of connectivity between PD patients off medication and healthy subjects, particularly with respect to net information outflow from the left sensorimotor (L Sm) region, which might indicate excessive spreading of activity in the diseased state. Disrupted patterns of connectivity in PD were partially restored by levodopa medication. In addition, PDC-based analysis proved to be more sensitive to temporally-dynamic connectivity changes as compared to traditional spectral analysis, which might be influenced primarily by large-scale changes. We suggest that the proposed sparse-PDC method is a suitable technique to investigate altered connectivity in Parkinson's disease.

Levodopa-sensitive, dynamic changes in effective connectivity during simultaneous movements in Parkinson's disease

Changes in effective connectivity during the performance of a motor task appear important for the pathogenesis of motor symptoms in Parkinson's disease (PD). One type of task that is typically difficult for individuals with PD is simultaneous or bimanual movement, and here we investigate the changes in effective connectivity as a potential mechanism. Eight PD subjects off and on l-DOPA medication and 10 age-matched healthy control subjects performed both simultaneous and unimanual motor tasks in an fMRI scanner. Changes in effective connectivity between regions of interest (ROIs) during simultaneous and unimanual task performance were determined with structural equation modeling (SEM), and changes in the temporal dynamics of task performance were determined with multivariate autoregressive modeling (MAR). PD subjects demonstrated alterations in both effective connectivity and temporal dynamics compared with control subjects during the performance of a simultaneous task. l-DOPA treatment was able to partially normalize effective connectivity and temporal patterns of activity in PD, although some connections remained altered in PD even after medication. Our results suggest that difficulty performing simultaneous movements in PD is at least in part mediated by a disruption of effective communication between widespread cortical and subcortical areas, and l-DOPA assists in normalizing this disruption. These results suggest that even when the site of neurodegeneration is relatively localized, study of how disruption in a single region affects connectivity throughout the brain can lead to important advances in the understanding of the functional deficits caused by neurodegenerative disease.

Cortical muscle coupling in Parkinson's disease (PD) bradykinesia

OBJECTIVES

To determine if novel methods establishing patterns in EEG-EMG coupling can infer subcortical influences on the motor cortex, and the relationship between these subcortical rhythms and bradykinesia.

BACKGROUND

Previous work has suggested that bradykinesia may be a result of inappropriate oscillatory drive to the muscles. Typically, the signal processing method of coherence is used to infer coupling between a single channel of EEG and a single channel of rectified EMG, which demonstrates 2 peaks during sustained contraction: one, approximately 10 Hz, which is pathologically increased in PD, and a approximately 30 Hz peak which is decreased in PD, and influenced by pharmacological manipulation of GABAA receptors in normal subjects.

MATERIALS AND METHODS

We employed a novel multiperiodic squeezing paradigm which also required simultaneous movements. Seven PD subjects (on and off L-Dopa) and five normal subjects were recruited. Extent of bradykinesia was inferred by reduced relative performance of the higher frequencies of the squeezing paradigm and UPDRS scores. We employed Independent Component Analysis (ICA) and Empirical Mode Decomposition (EMD) to determine EEG/EMG coupling.

RESULTS

Corticomuscular coupling was detected during the continually changing force levels. Different components included those over the primary motor cortex (ipsilaterally and contralaterally) and over the midline. Subjects with greater bradykinesia had a tendency towards increased approximately 10 Hz coupling and reduced approximately 30 Hz coupling that was erratically reversed with L-dopa.

CONCLUSIONS

These results suggest that lower approximately 10 Hz peak may represent pathological oscillations within the basal ganglia which may be a contributing factor to bradykinesia in PD.

Cortical activation related to arm-movement combinations

Recent studies support the long-standing hypothesis that continuous arm movements consist of overlapping, discrete submovements. However, the cortical activation associated with these submovements is unclear. We tested the hypothesis that electroencephalography (EEG) activity would more strongly correspond to the particular combinations of muscle electrical activity, the independent components (ICs) of surface electromyography (EMG), than the surface EMG from individual muscles alone. We examined data recorded from two normal subjects performing sustained submaximal contractions or continual, unpaced repetitive movements of the arm. Independent component analysis (ICA) was used to determine the ICs of the multichannel EMG recordings (EMGICs). ICA was also used to calculate the coupling between the simultaneously recorded EEG and the EMG from a single muscle (Subject 1) or the EMGICs (Subject 2). The EMGICs were either tonic or phasic. The significant couplings between the EEG and the EMGICs were different for each EMGIC. The distribution on the scalp of the coupling between the EEG and tonic EMGICs and those of the single-muscle EMG were similar and followed topographic patterns in sensorimotor regions. Couplings between the EEG and phasic EMGICs were bifrontal, lateral, and bioccipital and were significantly stronger than the coupling between a single muscle's EMG and the EEG (p < 2 x 10(-5)) or another EMG combination derived from principal component analysis. These preliminary results support the notion that electrophysiological cortical activations are more significantly related to the ICs of muscle activations than to the activations of individual muscles alone.

Phasic and tonic coupling between EEG and EMG demonstrated with independent component analysis

The authors describe a method for demonstrating the tonic and phasic couplings between suitably time-aligned surface eletromyographs (sEMGs) and the simultaneously recorded EEGs. The method, based on independent component analysis, was applied to data recorded from two normal subjects performing sustained submaximal contractions or continual repetitive movements of the arm. Augmented datasets, consisting of the EEG and either the sEMG from a single muscle (subject 1) or a combination of sEMGs from several muscles (subject 2), were analyzed with independent component analysis to determine the EEG/sEMG coupling. Each derived coupling consisted of a spatial distribution on the scalp and a waveform representing an EEG channel combination coactivating with the sEMG. The combinations of sEMGs, derived by applying independent component analysis to the simultaneous sEMG recordings from several muscles to create sEMG independent components (ICs), were either tonic or phasic with differing periods of activation. The topographic distributions on the scalp of the couplings between the EEG and sEMG ICs were different for each sEMG IC. The spatial distributions of the couplings between tonic sEMG ICs or single-muscle sEMGs and the EEG followed topographic patterns in sensorimotor regions. Phasic couplings were bifrontal, lateral, and bioccipital. Calculation of coherence between the sEMG ICs and calculated EEG combinations agreed well with the frequency spectra of the independent component analysis-derived coupling waveforms. These preliminary results demonstrate that detection of both the tonic and phasic coupling between the sEMG and the EEG is possible when monitoring unpaced proximal arm movement. This may thus be a practical means of exploring the dynamic cortical/muscle relationships in subjects unable to perform fine finger movements, such as patients recovering from stroke.

Independent component analysis of fMRI data: examining the assumptions

Independent component analysis (ICA), which separates fMRI data into spatially independent patterns of activity, has recently been shown to be a suitable method for exploratory fMRI analysis. The validity of the assumptions of ICA, mainly that the underlying components are spatially independent and add linearly, was explored with a representative fMRI data set by calculating the log-likelihood of observing each voxel's time course conditioned on the ICA model. The probability of observing the time courses from white-matter voxels was higher compared to other observed brain regions. Regions containing blood vessels had the lowest probabilities. The statistical distribution of probabilities over all voxels did not resemble that expected for a small number of independent components mixed with Gaussian noise. These results suggest the ICA model may more accurately represent the data in specific regions of the brain, and that both the activity-dependent sources of blood flow and noise are non-Gaussian.

Analysis of fMRI data by blind separation into independent spatial components

Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson work/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations (a component "map"), and a unique associated time course of activation. Given data from 144 time points collected during a 6-min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40-sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task-related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task-related, quasiperiodic, or slowly varying. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth-order decomposition technique (Comon [1994]: Signal Processing 36:11-20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. For each subject, the time courses and active regions of the task-related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks.

Removing electroencephalographic artifacts by blind source separation

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.

Spatially fixed patterns account for the spike and wave features in absence seizures

Despite genetic, morphological and experimental in vivo, data implying fixed abnormalities in patients with absence seizures, attempts to find highly consistent features in the 3-Hz spike-and-wave pattern recorded during sequential seizures from the same subject have been largely unsuccessful. We used a new data decomposition technique called Independent Component Analysis (ICA) to separate multiple spike-and-wave episodes in the EEG recorded from five subjects with absence seizures into multiple consistent components. Each component corresponded to a temporally-independent waveform and a fixed spatial distribution. Almost all components separated by the ICA algorithm had overlapping, largely frontal spatial distributions. The analysis unmasked 5-8 components from each subject that were consistently activated across all seizures, with no components detected that were selectively activated by one seizure and not another. The "spike" and "wave" features noted in the EEG of every subject were each separated by the ICA algorithm into two or more components. Other components were active only at the beginning of each seizure or were related to ongoing brain activity not directly related to the 3Hz spike-and-wave pattern. By contrast randomly selected spatial patterns used for data decomposition resulted in components that were uninformative, similar to simply changing the montage for viewing the EEG. Our results suggest that despite previously described variability in the raw EEG, certain highly specific spatial distributions of activation are reproducible across seizures. These may reflect ictal and non-ictal brain activity consistently activating the same group of neurons.

Detection of consistently task-related activations in fMRI data with hybrid independent component analysis

fMRI data are commonly analyzed by testing the time course from each voxel against specific hypothesized waveforms, despite the fact that many components of fMRI signals are difficult to specify explicitly. In contrast, purely data-driven techniques, by focusing on the intrinsic structure of the data, lack a direct means to test hypotheses of interest to the examiner. Between these two extremes, there is a role for hybrid methods that use powerful data-driven techniques to fully characterize the data, but also use some a priori hypotheses to guide the analysis. Here we describe such a hybrid technique, HYBICA, which uses the initial characterization of the fMRI data from Independent Component Analysis and allows the experimenter to sequentially combine assumed task-related components so that one can gracefully navigate from a fully data-derived approach to a fully hypothesis-driven approach. We describe the results of testing the method with two artificial and two real data sets. A metric based on the diagnostic Predicted Sum of Squares statistic was used to select the best number of spatially independent components to combine and utilize in a standard regressional framework. The proposed metric provided an objective method to determine whether a more data-driven or a more hypothesis-driven approach was appropriate, depending on the degree of mismatch between the hypothesized reference function and the features in the data. HYBICA provides a robust way to combine the data-derived independent components into a data-derived activation waveform and suitable confounds so that standard statistical analysis can be performed.

A new method for detecting state changes in the EEG: exploratory application to sleep data

A new statistical method is described for detecting state changes in the electroencephalogram (EEG), based on the ongoing relationships between electrode voltages at different scalp locations. An EEG sleep recording from one NREM-REM sleep cycle from a healthy subject was used for exploratory analysis. A dimensionless function defined at discrete times ti, u(ti), was calculated by determining the log-likelihood of observing all scalp electrode voltages under the assumption that the data can be modeled by linear combinations of stationary relationships between derivations. The u(ti), calculated by using independent component analysis, provided a sensitive, but non-specific measure of changes in the global pattern of the EEG. In stage 2, abrupt increases in u(ti) corresponded to sleep spindles. In stages 3 and 4, low frequency (approximately equal to 0.6 Hz) oscillations occurred in u(ti) which may correspond to slow oscillations described in cellular recordings and the EEG of sleeping cats. In stage 4 sleep, additional irregular very low frequency (approximately equal to 0.05-0.2 Hz) oscillations were observed in u(ti) consistent with possible cyclic changes in cerebral blood flow or changes of vigilance and muscle tone. These preliminary results suggest that the new method can detect subtle changes in the overall pattern of the EEG without the necessity of making tenuous assumptions about stationarity.

Electromyography of the diaphragm in neuromuscular disease

We compared the diaphragmatic electromyographic (EMG) recordings from 32 patients with known neuromuscular disease and respiratory symptoms (23 neuropathies, 9 myopathies) to recordings from 23 normal subjects. Turns analysis of 219-ms sections, or epochs, of the EMG demonstrated a significant overlap between diagnostic groups, although some epochs from neuromuscular patients were significantly different from normal. Empirical rules were derived to infer neuropathic and myopathic involvement of the diaphragmatic EMG.

Spatially independent activity patterns in functional MRI data during the stroop color-naming task

A method is given for determining the time course and spatial extent of consistently and transiently task-related activations from other physiological and artifactual components that contribute to functional MRI (fMRI) recordings. Independent component analysis (ICA) was used to analyze two fMRI data sets from a subject performing 6-min trials composed of alternating 40-sec Stroop color-naming and control task blocks. Each component consisted of a fixed three-dimensional spatial distribution of brain voxel values (a "map") and an associated time course of activation. For each trial, the algorithm detected, without a priori knowledge of their spatial or temporal structure, one consistently task-related component activated during each Stroop task block, plus several transiently task-related components activated at the onset of one or two of the Stroop task blocks only. Activation patterns occurring during only part of the fMRI trial are not observed with other techniques, because their time courses cannot easily be known in advance. Other ICA components were related to physiological pulsations, head movements, or machine noise. By using higher-order statistics to specify stricter criteria for spatial independence between component maps, ICA produced improved estimates of the temporal and spatial extent of task-related activation in our data compared with principal component analysis (PCA). ICA appears to be a promising tool for exploratory analysis of fMRI data, particularly when the time courses of activation are not known in advance.

Comparison between the alpha pattern in normal subjects and in alpha pattern coma

Alpha pattern coma (APC) is an uncommon clinical EEG pattern in comatose patients, most commonly in association with anoxic-ischemic encephalopathy after cardiac arrest. Despite the pattern's striking similarity to that of the normal awake EEG, there are theoretical and experimental reasons for believing that the two rhythms result from different processes. The analysis of quantitative differences in APC from normal rhythms requires computer analysis. Because most cases of this rare entity have been collected over the years on paper traces, computer analysis appears implausible. In a companion article, we describe a method to quantify sections of paper EEGs. We applied this method to EEGs of five APC patients and five normal controls and noted a significant difference in the coherence between the two hemispheres in the alpha range. This finding is in keeping with theoretical, experimental, and clinical observations suggesting that APC may result from significant thalamo-cortical disruption.

Digital conversion of paper electroencephalograms using a hand scanner

We report a method of quantifying the analog signal of paper-recorded EEGs that involves readily available technology, including a standard personal computer and a compatible hand scanner. Simulations assessed the effects of erroneously scanning the paper at a slight angle and estimating pen arc distortion; these effects were demonstrated to be insignificant. The method allows application of several quantitative techniques, including power spectral analysis and determination of the squared coherence between homologous regions. In a companion study, we applied this technique to compare the coherence in patients with alpha pattern coma to coherence in normal subjects.

Subacute uremic and diabetic polyneuropathy

We present 4 patients who had a subacute, predominantly motor polyneuropathy associated with diabetes mellitus and end-stage renal disease. Electrophysiological studies and muscle biopsy indicated a primary axonal degeneration of nerve with secondary segmental demyelination, and mild to moderate, acute and chronic denervation of muscle. A relative absence of denervation potentials on needle electromyography was an unusual feature. Three of our patients improved with a switch from conventional to high-flux hemodialysis. We speculate on possible mechanisms.

Use of the Internet for obstetricians and gynecologists

The Internet is such a large unstructured body of information that it must be used to be understood. This article limits itself to the World Wide Web for information retrieval. Equipment and access methods are discussed but not in depth. Search engines and their use are discussed in greater detail and examples are given. Repetitive personal use is emphasized as the best possible method of learning. Liberal use of bookmarks is emphasized to build one's own map of this immense knowledge structure.

Subacute uremic and diabetic polyneuropathy

We present 4 patients who had a subacute, predominantly motor polyneuropathy associated with diabetes mellitus and end-stage renal disease. Electrophysiological studies and muscle biopsy indicated a primary axonal degeneration of nerve with secondary segmental demyelination, and mild to moderate, acute and chronic denervation of muscle. A relative absence of denervation potentials on needle electromyography was an unusual feature. Three of our patients improved with a switch from conventional to high-flux hemodialysis. We speculate on possible mechanisms.

Classification of astrocytomas and malignant astrocytomas by principal components analysis and a neural net

The classification of astrocytomas, astrocytomas with anaplastic foci and glioblastoma multiformes is not always straightforward because the tumors form a histological continuum. The use of principal component analysis (PCA) and neural nets in the classification of these tumors is explored. PCA was performed on 14 histological features recorded from 52 gliomas classified by the Radiation Therapy Oncology Group method (17 astrocytomas, 18 astrocytomas with anaplastic foci, 17 glioblastoma multiformes). Four of the 14 possible 'scores' derived from this analysis were selected to summarize the histological variability seen in all the tumors. These scores were mostly significantly different between tumor types and were thus used to successfully train a neural net to correctly classify these tumors. The first principal component (score) supported the use of increasing cellularity, mitoses, endothelial proliferation, and necrosis in differentiating between the tumor categories, but accounted for only 39% of the variability seen. Other histological features that were significant components of the other scores included the presence of multinucleated or giant cells, gemistocytes, atypical mitoses and changes in nuclear chromatin. Computer programs derived from the methodology described provide a way of standardizing glioma diagnosis and may be extended to assist with management decisions.

Alpha, theta and alpha-theta coma: a clinical outcome study utilizing serial recordings

Alpha coma (AC), theta coma (TC) and alpha-theta coma (ATC) are transient clinical-electroencephalographic phenomena which do not differ from each other in etiology or outcome and are indicative of a severe disturbance in thalamo-cortical physiology. Although most patients do poorly, these patterns are not reliably predictive of outcome, regardless of etiology. We found that AC, TC or ATC usually change to a more definitive pattern by 5 days from coma onset. EEG reactivity in subsequent patterns is relatively favorable, while a burst-suppression pattern without reactivity is unfavorable in anoxic-ischemic encephalopathy.

Defective monocyte accessory function due to surface sulphydryl (SH) oxidation in rheumatoid arthritis

Low serum sulphydryl (SH) levels are a feature of active rheumatoid arthritis (RA). We have investigated whether a similar blockade of membrane SH groups on mononuclear cells modifies the function of these cells in this disease. Using pokeweed mitogen stimulated IgG synthesis as the assay system, we have found that the accessory cell function of peripheral blood monocytes is totally dependent on free SH groups on the cell surface. Monocytes from patients with active RA display poor accessory cell function when compared with healthy monocytes or with cells from patients treated with D-penicillamine. The poor function of the rheumatoid accessory cells may be corrected in vitro by 2-mercaptoethanol (2-ME). Addition of 2-ME to the culture system also enhances IgG synthesis by rheumatoid mononuclear cells to normal levels. We suggest that surface SH-dependent mechanisms are responsible, at least in part, for the depressed mononuclear cell functions of rheumatoid cells in vitro and may explain some effects of D-penicillamine therapy in rheumatoid patients.