Professor of Medicine (Neurology) & Clinical Director, Pacific Parkinson's Research Centre Associate Member, Department of Electrical and Computer Engineering Brain Research Centre University of British Columbia (UBC)
Abstract: Recent animal studies have suggested that cortical areas may play a greater role in the modulation of abnormal oscillatory activity in Parkinson's disease (PD) than previously recognized. We investigated task and medication-dependent, EEG-based directional cortical connectivity in the θ (4â7 Hz), α (8â12 Hz), β (13â30 Hz) and low γ (31â50 Hz) frequency bands in 10 PD subjects and 10 age-matched controls. All subjects performed a visually guided task previously shown to modulate abnormal oscillatory activity in PD subjects. We examined the connectivity in the simultaneously-recorded EEG between 5 electrode regions of interest (fronto-central, left and right sensorimotor, central and occipital) using a sparse, multivariate, autoregressive-based partial directed coherence method. For comparison, we utilized traditional Fourier analysis to evaluate task-dependent frequency spectra modulation in these same regions. While the spectral analysis revealed some overall differences between PD and control subjects, it demonstrated relatively modest changes between regions. In contrast, the partial directed coherence-based analysis revealed multifaceted, regionally and directionally-dependent alterations of connectivity in PD subjects during both movement preparation and execution. Connectivity was particularly altered posteriorly, suggesting abnormalities in visual and visuo-motor processing in PD. Moreover, connectivity measures in the α, β and low γ frequency ranges correlated with motor Unified Parkinson's Disease Rating Scores in PD subjects withdrawn from medication. Levodopa administration only partially restored connectivity, and in some cases resulted in further exacerbation of abnormalities. Our results support the notion that PD is associated with significant alterations in connectivity between brain regions, and that these changes can be non-invasively detected in the EEG using partial directed coherence methods. Thus, the role of EEG to monitor PD may need to be further expanded.
Abstract: Quantitative assessment of motor performance is important for diseases of motor control, such as Parkinsonâs disease (PD). Manual tracking tasks are well suited for motor assessment, as they can be performed concomitantly with brain mapping techniques. Here we propose utilizing second-order linear dynamical systems to assess manual pursuit tracking performance. With the desired trajectory as the input, and the subjectâs actual motor response as the output, a linear model characterized by natural frequency and damping ratio is identified for each subject. We applied this method to 10 PD subjects (on and off l-dopa medication) and 10 normal subjects performing a multi-frequency sinusoidal tracking task. Model parameters were more sensitive than overall tracking errors in determining significant differences between groups. The effect of l-dopa medication was to reduce the damping ratio and make the range in natural frequency across individuals approach that of normal subjects. We interpret the changes in damping ratio and natural frequency as possibly related to suppression of compensatory cerebellar activity and/or improvement of motor program selection, and the changes in natural frequency as an implicit strategy to maintain settling time in the face of reduce damping ratio. Our results suggest that simple linear dynamical system models can be a powerful method to assess tracking performance in Parkinsonâs disease because of the additional insight they can provide into neurological processes.
Abstract: Motor performance is profoundly influenced by sensory information, yet sensory input can be noisy and uncertain. The basal ganglia and the cerebellum are important in processing sensory uncertainty, as the basal ganglia incorporate the uncertainty of predictive reward cues to reinforce motor programs, and the cerebellum and its connections mitigate the effect of ambiguous sensory input on motor performance through the use of forward models. Although Parkinsonâs disease (PD) is classically considered a primary disease of the basal ganglia, alterations in cerebellar activation are also observed, which may have consequences for the processing of sensory uncertainty. The aim of this study was to investigate the effect of visual uncertainty on motor performance in 15 PD patients and ten age-matched control subjects. Subjects performed a visually guided tracking task, requiring large-amplitude arm movements, by tracking with their index finger a moving target along a smooth trajectory. To induce visual uncertainty, the target position randomly jittered about the desired trajectory with increasing amplitudes. Tracking error was related to target ambiguity to a significantly greater degree in PD subjects off medication compared with control subjects, indicative of susceptibility to visual uncertainty in PD. l-Dopa partially ameliorated this deficit. We interpret our findings as suggesting an inability of PD subjects to create adequate forward models and/or de-weight less informative visual input. As these computations are normally associated with the cerebellum and connections, we suggest that alterations in normal cerebellar functioning may be a significant contributor to altered motor performance in PD.
Abstract: Switched nonlinear systems with delay in the switching instant could be destabilized, despite stable dynamics in each mode, if the delay is long enough. We identify a restriction on the switching scheme to assure stability despite a nite delay in switchinginstant. The restriction partitions the state-space in a time-varying manner for a known switching delay, and converges to a steady-state partition that can be determined from the intersection of Lyapunov functions in each mode. We apply this technique to experimental data from a manual pursuit tracking task performed by 14 subjects with Parkinson's disease, and 10 control subjects. Each subject manually tracks a moving target through a joystick-controlled cursor, with sudden changes in the tracking dynamics. The tracking task can be modeled as a 3-mode switched system. By calculating the maximal time delay for each mode pair and for each subject, we obtain a measure of relative stability that can be compared across groups and across tasks. Using the derived stability measure, subjects with Parkinson's disease were shown to be relatively less stable than control subjects.
Abstract: Variable selection is a topic of great importance in high-dimensional statistical modeling and has a wide range of real-world applications. Many variable selection techniques have been proposed in the context of linear regression, and the Lasso model is probably one of the most popular penalized regression techniques. In this paper, we propose a new, fully hierarchical, Bayesian version of the Lasso model by employing flexible sparsity promoting priors. To obtain the Bayesian Lasso estimate, a reversible-jump MCMC algorithm is developed for joint posterior inference over both discrete and continuous parameter spaces. Simulations demonstrate that the proposed RJ-MCMC-based Bayesian Lasso yields smaller estimation errors and more accurate sparsity pattern detection when compared with state-of-the-art optimization-based Lasso-type methods, a standard Gibbs sampler-based Bayesian Lasso and the Binomial-Gaussian prior model. To demonstrate the applicability and estimation stability of the proposed Bayesian Lasso, we examine a benchmark diabetes data set and real functional Magnetic Resonance Imaging data. As an extension of the proposed RJ-MCMC framework, we also develop an MCMC-based algorithm for the Binomial-Gaussian prior model and illustrate its improved performance over the non-Bayesian estimate via simulations.
Abstract: Parkinson's disease preferentially affects internally generated movements, e.g., movements recalled from memory, while externally cued movements are relatively preserved. However, L-dopa may have effects on visually guided movements as well as error-related processing. Fourteen Parkinson's disease (PD) subjects (on and off L-dopa medication) as well as ten normal controls performed a tracking task using a joystick. During discrete 30 s blocks, the visual feedback of the actual tracking errors were attenuated, amplified or unaltered. Second order dynamical system models, with the desired trajectory as the input and the actual motor performance as the output, were used to characterize the motor performance by the each subject under each condition. Although the overall root-mean-square tracking error did not significantly differ between groups, the nature of the motor performance differed significantly across groups. A clear dissociation was made between manipulations of error feedback--which altered the natural frequency of the models--and the effects of L-dopa, which affected damping. Compared to normal controls, PD subjects were significantly overdamped before medication and underdamped after medication. We interpret our results as being suggestive of L: -dopa normalization of compensatory overactive cerebellar activity in PD.
Abstract: BACKGROUND: Past studies have identified reorganization of brain activity in relation to motor outcome through standardized laboratory measures, which are quantifiable surrogates for arm use in real life. In contrast, accelerometers can provide a real-life estimate of arm and hand usage. METHODS: Ten persons with chronic, subcortical stroke and 10 healthy controls of similar age performed a squeeze motor task at 40% maximum voluntary contraction during functional magnetic resonance imaging (fMRI). Use of the upper extremity was quantified over 3 consecutive days using wrist accelerometers. Correlations were performed between arm use and peak percent signal change (PSC) during grasp force production in 6 regions of interest (ROIs): bilateral primary motor cortex (M1), supplementary motor area (SMA), and premotor cortex (PM). RESULTS: Results demonstrate that in healthy controls, PSC across all ROIs did not show a relationship between arm use and brain activation during force production. In contrast, after stroke, contralesional PM and M1 showed a significant (P <or= .05) correlation between increasing activation and decreasing paretic arm use, whereas ipsilesional PM showed a significant correlation ( P <or= .05) between increasing activation and decreasing nonparetic arm use. CONCLUSIONS: The results of this pilot study demonstrate a negative relationship between brain activation and actual arm use after stroke. Larger studies using accelerometers that can detect amount and types of movement may offer further insight into brain reorganization and rehabilitation interventions.
Abstract: People with Parkinsonâs disease (PD) have difficulty performing dual tasks or simultaneous movements, even if the same movements can be easily performed individually. This has particular significance clinically, as for example falling injuries may occur if care is not taken to perform tasks one at a time. We investigated whether this difficultyx results from impaired dopamine-modulated connectivity. We recorded the EEG in PD subjects off and on L-dopa medication performing simultaneous and unimanual tracking tasks. To deal with the inherent non-stationarity of the EEG during motor tasks, we segmented the data into task-related sections based on transient synchronisation between independent components of the data, before assessing the mutual information (MI) between each EEG channel pair. In both tasks, PD subjects off-medication demonstrated enhanced fronto-central and decreased occipital synchronisa- tion within theta and alpha bands, and widespread increased beta-band synchronisation, compared to controls. Synchronisation changes in theta and beta bands were partially normalised by L-dopa, but L-dopa had relatively little effect on alpha band synchronisation. When comparing simultaneous movements to unimanual tracking, PD subjects off-medication demonstrated synchronisation changes within theta and beta bands, however alpha connectivity was largely unchanged. These results suggest that downstream influences of impaired basal ganglia function on cortico-cortical connectivity may result in difficulties with dual task performance in PD.
Abstract: Purpose of review: The on-going quest for potentially disease-modifying therapies in Parkinson's disease has prompted the development of methods that can differentiate direct disease effects from compensatory processes.
Recent findings: PET studies have suggested a number of changes at the synaptic level to maintain integrity of dopaminergic systems. Functional MRI studies support the long-held belief that relatively intact cerebellar circuits may compensate for impaired basal ganglia function. Altered connectivity and increased spatial extent of activation also appear to be mechanisms through which motor and cognitive performance can be maintained.
Summary: Ascertaining which changes in brain activation in Parkinson's disease are, in fact, compensatory represents a serious challenge. Compensatory mechanisms have been demonstrated from the microscopic, synaptic level to the macroscopic, system level. Augmentation of compensatory mechanisms, in addition to ameliorating the loss of dopaminergic neurons, may represent a joint strategy for overall minimization of disability.
Abstract: Neuroimaging studies in Parkinson's disease (PD) have previously demonstrated several regions of hypo- and hyper-activation during voluntary movement. How these patterns of amplitude changes at multiple discrete foci relate to changes within functional networks recruited by a given task is unclear. Changes in both amplitude and connectivity have both been individually shown within the striato-thalamo-cortical (STC) loop in PD, as well as other regions, most consistently in the cerebellum and primary motor cortex. We have previously shown overactivation of the cerebellum and motor cortex in PD subjects off medication during a visuo-motor tracking task performed at three frequencies. Here, we show that this change in activation amplitude is also accompanied by significant changes in functional connectivity between regions of interest (ROIs), with enhanced connectivity within the cerebello-thalamo-cortical (CTC) loop as well as increased inter-hemispheric communication between several basal ganglia structures. Although changes in activation amplitude were influenced by the frequency of movement performed in the tracking task, functional connectivity changes were robustly present across all three task frequencies performed, suggesting that functional connectivity analysis in PD may be a more sensitive means of detecting plastic changes which are relatively invariant to the particulars of the experimental task. Additionally, we demonstrate amplitude and connectivity changes in structures that are typically active during the resting state, or "default-mode," in PD. Unlike in STC/CTC loops, where the direction of change was the same for amplitude and connectivity, default-mode regions showed increased amplitude but decreased connectivity. Our results further support that the CTC is recruited in PD to compensate for dysfunctional basal ganglia circuits, and that this recruitment involves both amplitude and connectivity changes. The differing relationship between amplitude and connectivity changes within individual loops highlights the importance of jointly examining them in order to fully elucidate functional changes in Parkinson's disease.
Abstract: Previous fMRI motor studies in Parkinson's disease (PD) have suggested that L-dopa may "normalize" areas of hypo- and hyperactivity. However, results from these studies, which were largely based on analyzing BOLD signal amplitude, have been conflicting. Examining only amplitude changes at distinct loci may thus be inadequate in fully capturing the activation changes induced by L-dopa. In this article, we extended prior analyses on the effects of L-dopa by investigating both amplitude and spatial changes of brain activation before and after L-dopa. Ten subjects with PD, both on and off medication, and ten healthy, age-matched controls performed a visuo-motor tracking task in which they sinusoidally squeezed a bulb at 0.25, 0.5, and 0.75 Hz. This task was contrasted with static squeezing to generate fMRI activation maps. To investigate the effects of L-dopa, we examined the amplitude and spatial variance of the BOLD response within anatomically-defined regions of interest (ROIs). L-dopa had significant main effects on the amplitude of BOLD signal in bilateral primary motor cortex and left SMA. In contrast, L-dopa-mediated spatial changes were apparent in bilateral cerebellar hemispheres, M1, SMA, and right prefrontal cortex. Moreover, L-dopa appeared to normalize the spatial distribution of ROI activation in PD to that of the controls. Specifically, L-dopa had a "focusing" effect on activity-an effect more pronounced than the typically-measured fMRI amplitude changes. This observation is consistent with modeling studies, which demonstrated that dopamine increases the signal-to-noise ratio at the neuronal level with a resultant focusing of representations at the macroscopic level.
Abstract: In the context of linear regression, the least absolute shrinkage and selection operator (LASSO) is probably the most popular supervised-learning technique proposed to recover sparse signals from high-dimensional measurements. Prior literature has mainly concerned itself with independent, identically distributed noise with moderate variance. In many real applications, however, the measurement errors may have heavy-tailed distributions or suffer from severe outliers, making the LASSO poorly estimate the coefficients due to its sensitivity to large error variance. To address this concern, a robust version of the LASSO is proposed, and the limiting distribution of its estimator is derived. Model selection consistency is established for the proposed robust LASSO under an adaptation procedure of the penalty weight. A parallel asymptotic analysis is derived for the Huberized LASSO, a previously proposed robust LASSO, and it is shown that the Huberized LASSO estimator preserves similar asymptotics even with a Cauchy error distribution. We show that asymptotic variances of the two robust LASSO estimators are stabilized in the presence of large variance noise, compared with the unbounded asymptotic variance of the ordinary LASSO estimator. The asymptotic analysis from the nonstochastic design is extended to the case of random design. Simulations further confirm our theoretical results.
Abstract: A novel approach is proposed for quantitatively characterizing the spatial patterns of activation statistics in functional magnetic resonance imaging (fMRI) activation maps. Specifically, we propose using 3-D invariant moment descriptors, as opposed to the traditionally-employed magnitude-based features such as mean voxel statistics or percentage of activated voxels, to characterize the task-specific spatial distribution of activation statistics within a given region of interest (ROI). The proposed method is applied to real fMRI data collected from 21 healthy subjects performing previously-learned right-handed finger tapping sequences that are either externally guided (EG) by a cue or internally guided (IG)--tasks expected to incur subtle differences in motor-related cortical and subcortical ROIs. Voxel-based activation statistics contrasting EG versus rest and IG versus rest are examined in multiple manually-drawn ROIs on unwarped brain images. Analyzing the activation statistics within each ROI using the proposed 3-D invariant moment descriptors detected significant group differences between the two tasks, thus quantitatively demonstrating that the spatial distribution of activation statistics within an ROI represent an important task-related attribute of brain activation. In contrast, conventional methods that solely rely on activation statistic magnitudes and disregard spatial information showed reduced discriminability. Normally, incorporating spatial information would merely increase inter-subject variability partly due to differences in brain size and subject's orientation in the scanner. Yet, our results suggest that the proposed spatial features, which are invariant to similarity transformations, can effectively account for such inter-subject variability, while enhancing the sensitivity in detecting task-specific activation. Thus, we argue that this novel quantitative description of the "3-D texture" of activation maps provides new directions to explore for ROI-based fMRI analysis.
Abstract: 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.
Abstract: BACKGROUND: Monitoring the functional connectivity between brain regions is becoming increasingly important in elucidating brain functionality in normal and disease states. Current methods of detecting networks in the recorded electroencephalogram (EEG) such as correlation and coherence are limited by the fact that they assume stationarity of the relationship between channels, and rely on linear dependencies. In contrast to diseases of the brain cortex (e.g. Alzheimer's disease), with motor disorders such as Parkinson's disease (PD) the EEG abnormalities are most apparent during performance of dynamic motor tasks, but this makes the stationarity assumption untenable. METHODS: We therefore propose a novel EEG segmentation method based on the temporal dynamics of the cross-spectrogram of the computed Independent Components (ICs). We then utilize mutual information (MI) as the metric for determining also nonlinear statistical dependencies between EEG channels. Graphical theoretical analysis is then applied to the derived MI networks. The method was applied to EEG data recorded from six normal subjects and seven PD subjects off medication. One-way analysis of variance (ANOVA) tests demonstrated statistically significant difference in the connectivity patterns between groups. RESULTS: The results suggested that PD subjects are unable to independently recruit different areas of the brain while performing simultaneous tasks compared to individual tasks, but instead they attempt to recruit disparate clusters of synchronous activity to maintain behavioral performance. CONCLUSION: The proposed segmentation/MI network method appears to be a promising approach for analyzing the EEG recorded during dynamic behaviors.
Abstract: Motor symptoms of Parkinson's disease (PD) do not appear until the majority of dopaminergic cells in the substantia nigra pars compacta are lost, suggesting significant redundancy or compensation in the motor systems affected by PD. Using functional magnetic resonance imaging, we examined whether compensation in PD is manifested by changes in amplitude and/or spatial extent of activity within normal networks (active motor reserve) and/or newly recruited regions [novel area recruitment (NAR)]. Ten PD subjects off and on medication and 10 age-matched controls performed a visually guided sinusoidal force task at 0.25, 0.5 and 0.75 Hz. Regression was used to determine the combination of regions where activation amplitude scaled linearly with movement speed in controls. We then determined the activation of PD subjects in this network, as well as the corresponding PD network. To measure the spatial variance of activation, we used an invariant spatial feature approach. Control subjects monotonically increased activity within striato-thalamo-cortical and cerebello-thalamo-cortical regions with increasing movement speed. In PD subjects, the activity of this network at low speeds was similar to that in controls at higher speeds. Additionally, PD subjects off medication demonstrated NARs of the bilateral cerebellum and primary motor cortex, which were incompletely normalized by levodopa. Our results suggest that PD subjects tap into motor reserve, increase the spatial extent of activation and demonstrate NAR to maintain near-normal motor output.
Abstract: 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.
Abstract: OBJECTIVE: Functional imaging techniques represent useful tools to assess in vivo the neurochemical alterations and functional connectivity in Parkinson disease (PD). Here, the authors review the various approaches and potential application of these imaging techniques to the study of PD. METHOD: Radiotracer imaging using dopaminergic markers facilitates assessment of pre- and postsynaptic nigrostriatal integrity, while imaging with other appropriate radiotracers explores nondopaminergic neurotransmitter function, local metabolism, blood flow, and mechanisms potentially related to disease progression and pathogenesis. Activation studies using functional MRI detect blood oxygen level dependent signal, as an indirect marker of neuronal activity. RESULT: Functional imaging techniques have been applied to infer the potential role of inflammation and other factors in etiopathogenesis as well as to study compensatory and regulatory mechanisms in early PD and subclinical disease in genetic forms of PD. Imaging studies also help to understand the neurobiological basis of motor and nonmotor complications. Recent reports suggest a role for striatal dopaminergic transmission in modulating neurobehavioral processes including the placebo effect in PD. Although functional imaging has been employed to monitor disease progression, the discordance between clinical outcome and imaging measures after therapeutic interventions precludes their use as surrogate end points in clinical trials. Beyond these limitations and potential challenges, imaging techniques continue to find wide application in the study of PD. CONCLUSION: Functional imaging can provide meaningful insights into mechanisms underlying various aspects of motor and nonmotor dysfunction in Parkinson disease and the role of striatal dopaminergic transmission in behavioral processes beyond motor control. These modalities hold promise to study the preclinical phase and to elucidate further the benefits and complications of surgical interventions and the utility of neuroprotective strategies.
Abstract: The coordinated activities of muscles during reaching movements can be characterized by appropriate analysis of simultaneously-recorded surface electromyograms (sEMGs). Many recent sEMG studies have analyzed muscle synergies using statistical methods such as Independent Component Analysis, which commonly assume a small set of influences upstream of the muscles (e.g., originating from the motor cortex) produce the sEMG signals. Traditionally only the amplitude of the sEMG signal was investigated. Here, we present a fundamentally different approach and model sEMG signals after the effects of amplitude have been minimized. We develop the framework of Bayesian networks (BNs) for modeling muscle activities and for analyzing the overall muscle network structure. Instead of assuming that synergies may be independently activated, we assume that neuronal activity driving a given muscle may be conditionally dependent upon neurons driving other muscles. We call the resulting interactions between muscle activity patterns "dependent synergies". The learned BN networks were explored for the purpose of classification across subjects based on hand dominance or affliction by stroke. Network structure features were investigated as classification input features and it was determined that specific edge connection patterns of 3-node subnetworks were selectively recruited during reaching movements and were differentially recruited after stroke compared to normal control subjects. The resulting classification was robust to inter-subject and within-group variability and yielded excellent classification performance. The proposed framework extends muscle synergy analysis and provides a framework for thinking about muscle activity interactions in motor control.
Abstract: Bayesian network (BN) modeling has recently been introduced as a tool for determining the dependencies between brain regions from functional-magnetic-resonance-imaging (fMRI) data. However, studies to date have yet to explore the optimum way for meaningfully combining individually determined BN models to make group inferences. We contrasted the results from three broad approaches: the "virtual-typical- subject" (VTS) approach which pools or averages group data as if they are sampled from a single, hypothetical virtual typical subject; the "individual-structure" (IS) approach that learns a separate BN for each subject, and then finds commonality across the individual structures, and the "common-structure" (CS) approach that imposes the same network structure on the BN of every subject, but allows the parameters to differ across subjects. To explore the effects of these three approaches, we applied them to an fMRI study exploring the motor effect of L-dopa medication on ten subjects with Parkinson's disease (PD), as the profound clinical effects of this medication suggest that fMRI activation in PD subjects after medication should start approaching that of age-matched controls. We found that none of these approaches is generally superior over the others, according to Bayesian-information-criterion (BIC) scores, and that they led to considerably different group-level results. The IS approach was more sensitive to the normalization effect of the L-dopa medication on brain connectivity. However, for the more homogeneous control population, the VTS approach was superior. Group-analysis approaches should be selected carefully with consideration of both statistical and biomedical evidence.
Abstract: BACKGROUND: Recent pathological studies have suggested that thalamic degeneration may represent a site of non-dopaminergic degeneration in Parkinson's Disease (PD). Our objective was to determine if changes in the thalami could be non-invasively detected in structural MRI images obtained from subjects with Parkinson disease (PD), compared to age-matched controls. RESULTS: No significant differences in volume were detected in the thalami between eighteen normal subjects and eighteen PD subjects groups. However significant (p < 0.03) shape differences were detected between the Left vs. Right thalami in PD, between the left thalami in PD and controls, and between the right thalami in PD and controls using a recently-developed, spherical harmonic-based representation. CONCLUSION: Systematic changes in thalamic shape can be non-invasively assessed in PD in vivo. Shape changes, in addition to volume changes, may represent a new avenue to assess the progress of neurodegenerative processes. Although not directly discernable at the resolution of standard MRI, previous pathological studies would suggest that the shape changes detected in this study represent degeneration in the centre median-parafascicular (CM-Pf) complex, an area known to represent selective non-dopaminergic degeneration in PD.
Abstract: A post-processing method for group discriminant analysis of fMRI is proposed. It assumes that the fMRI data have been pre-processed and analyzed so that each voxel is given a statistic specifying task-related activation(s), and that individually specific regions of interest (ROIs) have been drawn for each subject. The method then utilizes Local Linear Discriminant Analysis (LLDA) to jointly optimize the individually-specific and group linear combinations of ROIs that maximally discriminates between groups (or between tasks, if using the same subjects). LLDA tries to linearly transform each subject's voxel-based activation statistics within ROIs to a common vector space of ROI combinations, enabling the relative similarity of different subjects' activation to be assessed. We applied the method to data recorded from 10 normal subjects during a motor task expected to activate both cortical and subcortical structures. The proposed method detected activation in multiple cortical and subcortical structures that were not present when the data were analyzed by warping the data to a common space. We suggest that the method be applied to group fMRI data when warping to a common space may be ill-advised, such as examining activation in small subcortical structures susceptible to mis-registration, or examining older or neurological patient populations.
Abstract: The motor deficits in Parkinson's disease (PD) have been primarily associated with internally guided (IG), but not externally guided (EG), tasks. This study investigated the functional mechanisms underlying this phenomenon using genetically-matched twins. Functional magnetic resonance images were obtained from a monozygotic twin pair discordant for clinical PD. Single-photon emission computed tomography neuroimaging using [(123)I](-)-2-beta-carboxymethoxy-3-beta-(4-iodophenyl)tropane confirmed their disease-discordant status by demonstrating a severe loss of transporter binding in the PD-twin, whereas the non-PD-twin was normal. Six runs of functional magnetic resonance imaging (fMRI) data were acquired from each twin performing EG and IG right-hand finger sequential tasks. The percentage of voxels activated in each of several regions of interest (ROI) was calculated. Multiple analysis of variance was used to compare each twin's activity in ROIs constituting the striato-thalamo-cortical motor circuits [basal ganglia (BG)-cortical circuitry, but including the globus pallidus/putamen, thalamus, supplementary motor area, and primary motor cortex] and cerebello-thalamo-cortical circuits (cerebellar-cortical circuitry, including the cerebellum, thalamus, somatosensory cortex, and lateral premotor cortex). During the EG task, there were no significant differences between the twins in bilateral BG-cortical pathways, either basally or after levodopa, whereas the PD-twin had relatively increased activity in the cerebellar-cortical pathways basally that was normalized by levodopa. During the IG task, the PD-twin had less activation than the non-PD-twin in ROIs of the bilateral BG-cortical and cerebellar-cortical pathways. Levodopa normalized the hypoactivation in the contralateral BG-cortical pathway, but "over-corrected" the activation in the ipsilateral BG-cortical and bilateral cerebellar-cortical pathways. In this first fMRI study of twins discordant for PD, the data support the hypothesis that BG-cortical and cerebellar-cortical pathways are task-specifically influenced by PD. The levodopa-induced "over-activation" of BG-cortical and cerebellar-cortical pathways, and its relevance to both compensatory changes in PD and the long-term effects of levodopa in PD, merit further exploration.
Abstract: 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.
Abstract: Task-related head movement during acquisition of fMRI data represents a serious confound for both motion correction and estimates of task-related activation. Cost functions implemented in most conventional motion-correction algorithms compare two volumes for similarity but fail to account for signal variability that is not due to motion (e.g., brain activation). We therefore recently proposed the theoretical basis for a novel method for fMRI motion correction, termed motion-corrected independent component analysis (MCICA), that allows for brain activation present in an fMRI time-series to be implicitly modeled and mitigates motion-induced signal changes without having to directly estimate the motion parameters (Liao et al., IEEE Transactions on Medical Imaging 2005;25:29-44). To explore the effects of non-movement-related signal changes on registration error, we performed several previously proposed test simulations (Freire et al., IEEE Transactions on Medical Imaging 2002;21:470-484) to evaluate the performance of MCICA and compare it with the conventional square-of-difference-based measures such as LS-SPM and LS-AIR. We demonstrate that for both simulated data and real fMRI images, the proposed MCICA method performs favorably. Specifically, in simulations MCICA was more robust to the addition of simulated activation, and did not lead to the detection of false activations after correction for simulated task-correlated motion. With actual data from a motor fMRI experiment, the time course of the derived continually task-related ICA component became more correlated with the underlying behavioral task after preprocessing with MCICA compared to other methods, and the associated activation map was more clustered in the primary motor and supplementary motor cortices without spurious activation at the brain edge. We conclude that assessing the statistical properties of a motion-corrupted volume in relation to other volumes in the series, as is done with MCICA, is an accurate means of differentiating between motion-induced signal changes and other sources of variability in fMRI data.
Abstract: A three-dimensional image registration method for motion correction of functional magnetic resonance imaging (fMRI) time-series, based on independent component analysis (ICA), is described. We argue that movement during fMRI data acquisition results in a simultaneous increase in the joint entropy of the observed time-series and a decrease in the joint entropy of a nonlinear function of the derived spatially independent components calculated by ICA. We propose this entropy difference as a reliable criterion for motion correction and refer to a method that maximizes this as motion-corrected ICA (MCICA). Specifically, a given motion-corrupted volume may be corrected by determining the linear combination of spatial transformations of the motion-corrupted volume that maximizes the proposed criterion. In essence, MCICA consists of designing an adaptive spatial resampling filter which maintains maximum temporal independence among the recovered components. In contrast with conventional registration methods, MCICA does not require registration of motion-corrupted volumes to a single reference volume which can introduce artifacts because corrections are applied without accounting for variability due to the task-related activation. Simulations demonstrate that MCICA is robust to activation level, additive noise, random motion in the reference volumes and the exact number of independent components extracted. When the method was applied to real data with minimal estimated motion, the method had little effect and, hence, did not introduce spurious changes in the data. However, in a data series from a motor fMRI experiment with larger motion, preprocessing the data with the proposed method resulted in the emergence of activation in primary motor and supplementary motor cortices. Although mutual information (MI) was not explicitly optimized, the MI between all subsequent volumes and the first one was consistently increased for all volumes after preprocessing the data with MCICA. We suggest MCICA represents a robust and reliable method for preprocessing of fMRI time-series corrupted with motion.
Abstract: OBJECTIVE: To determine whether, like the paretic arm, movement of the ipsilesional arm after middle cerebral artery (MCA) stroke is associated with widespread neural activation changes in areas anatomically and functionally connected to the lesion. METHODS: In this fMRI experiment, seven patients with right MCA stroke and seven healthy control subjects performed a series of movements with their (nonparetic) right hand. Subjects either mimicked a visual display (visually guided) or generated the same motor task after a visual start signal (self-monitored). A multivariate linear discriminant analysis was used to determine the combinations of brain regions of interest (ROIs) that demonstrated maximum differences in activation between healthy and stroke subjects. The analysis was repeated within subject groups to differentiate self-monitored and visually guided movement. RESULTS: There was a significantly different network of neural regions recruited for movement with the nonparetic, ipsilesional arm in patients with stroke vs healthy control subjects. The anterior cingulate cortex was significantly more active when patients execute self-monitored movement than visually guided movement, suggesting changes in attentional processing required for the two tasks. The lesioned hemisphere was significantly more active in patients with stroke using the nonparetic arm than in control subjects during visually guided movement. CONCLUSIONS: These results support a model of widespread bihemispheric reorganization in the motor system after a focal right hemisphere lesion. Attentional demands of self-monitored movement may be much greater than visually guided movement in patients, possibly impacting rehabilitation protocols for these patients.
Abstract: The anterior cingulate cortex (ACC) has been implicated in both sustained attention (SA) and pain perception. Nonetheless, only a small body of literature has examined the relationship between SA and pain perception. This study utilized fMRI to examine activation patterns that emerged in the ACC in healthy participants and participants with chronic pain (due to osteoarthritis (OA) of the knee) while completing a sustained attention task with and without exposure to an acute painful stimulus. Independent component analysis (ICA) was used to determine groups of voxels within the ACC that covaried with performance on the SA task completed with and without exposure to a painful stimulus. In all participants, two distinct spatial patterns within the ACC were isolated that reflected (1) disrupted ACC activity when a painful stimulus was applied, or (2) emergent ACC activity when a painful stimulus was applied. In the healthy group, there were broadly distributed clusters of voxels within the ACC that were modulated by painful stimulation. But in the chronic pain group, a discrete focal region of the ACC was modulated by pain. These results demonstrate that ACC activity is modulated differently during tasks of SA and pain, and that acute pain in healthy participants and participants with chronic pain result in significantly different ACC activation patterns.
Abstract: To combine functional neuroimaging studies across subjects, anatomical and functional data are typically either transformed to a common space or averaged across regions of interest (ROIs). However, if there are (1) anatomical variations within the subject pool (as in clinical or aging populations), (2) non-Gaussian distributions of task-related activity within a typical ROI or, (3) more ROIs than subjects, neither spatial transformation of the data to a common space nor averaging across all subjects' ROIs is suitable for standard discriminant analysis. To solve these problems, we describe a post-processing method that uses voxel-based statistics representing task-related activity (pooled within ROIs) to establish combinations of ROIs that maximally differentiate tasks across all subjects. The method involves randomized resampling from multiple ROIs within each subject, multivariate linear discriminant analysis across all subjects and validation with bootstrapping techniques. When applied to experimental data from healthy subjects performing two motor tasks, the method detected some brain regions, including the supplementary motor area (SMA), that participated in a distributed network differentially active between tasks. However there was not a significant difference in SMA activity when this region was examined in isolation. We suggest this method is a practical means to combine voxel-based statistics within anatomically defined ROIs across subjects.
Abstract: We investigated the relation between electrophysiological and hemodynamic measures of brain activity through comparison of intracranially recorded event-related local field potentials (ERPs) and blood-oxygenation level dependent functional magnetic resonance imaging (BOLD fMRI). We manipulated the duration of visual checkerboard stimuli across trials and measured stimulus-duration-related changes in ERP and BOLD activity in three brain regions: peri-calcarine cortex, the fusiform gyrus and lateral temporal-occipital (LTO) cortex. ERPs were recorded from patients who had indwelling subdural electrodes as part of presurgical testing, while BOLD responses were measured in similar brain regions in a second set of subjects. Similar BOLD responses were measured in peri-calcarine and fusiform regions, with both showing monotonic but non-linear increases in hemodynamic amplitude with stimulus duration. In sharp contrast, very different ERP responses were observed in these same regions, such that calcarine electrodes exhibited onset potentials, sustained activity over the course of stimulus duration and prominent offset potentials, while fusiform electrodes only exhibited onset potentials that did not vary with stimulus duration. No duration-related ERP or BOLD changes were observed in LTO. Additional analyses revealed no consistent changes in the EEG spectrum across different brain sites that correlated with duration-related changes in the BOLD response. We conclude that the relation between ERPs and fMRI differs across brain regions.
Abstract: Many socially significant biological stimuli are polymodal, and information processing is enhanced for polymodal over unimodal stimuli. The human superior temporal sulcus (STS) region has been implicated in processing socially relevant stimuli--particularly those derived from biological motion such as mouth movements. Single unit studies in monkeys have demonstrated that regions of STS are polysensory--responding to visual, auditory and somato-sensory stimuli, and human neuroimaging studies have shown that lip-reading activates auditory regions of the lateral temporal lobe. We evaluated whether concurrent speech sounds and mouth movements were more potent activators of STS than either speech sounds or mouth movements alone. In an event-related fMRI study, subjects observed an animated character that produced audiovisual speech and the audio and visual components of speech alone. Strong activation of the STS region was evoked in all three conditions, with greatest levels of activity elicited by audiovisual speech. Subsets of activated voxels within the STS region demonstrated overadditivity (audiovisual > audio + visual) and underadditivity (audiovisual < audio + visual). These results confirm the polysensory nature of STS region and demonstrate for the first time that polymodal interactions may both potentiate and inhibit activation.
Abstract: Many sources of fluctuation contribute to the functional magnetic resonance imaging (fMRI) signal, complicating attempts to infer those changes that are truly related to brain activation. Unlike methods of analysis of fMRI data that test the time course of each voxel against a hypothesized waveform, data-driven methods, such as independent component analysis and clustering, attempt to find common features within the data. This exploratory approach can be revealing when the brain activation is difficult to predict beforehand, such as with complex stimuli and internal shifts of activation that are not time-locked to an easily specified sensory or motor event. These methods can be further improved by incorporating prior knowledge regarding the temporal and spatial extent of brain activation.
Abstract: Many functional neuroimaging studies of biological motion have used as stimuli point-light displays of walking figures and compared the resulting activations with those evoked by the same display elements moving in a random or noncoherent manner. Although these studies have established that biological motion activates the superior temporal sulcus (STS), the use of random motion controls has left open the possibility that coordinated and meaningful nonbiological motion might activate these same brain regions and thus call into question their specificity for processing biological motion. Here we used functional magnetic resonance imaging and an anatomical region-of-interest approach to test a hierarchy of three questions regarding activity within the STS. First, by comparing responses in the STS with animations of human and robot walking figures, we determined (1) that the STS is sensitive to biological motion itself, not merely to the superficial characteristics of the stimulus. Then we determined that the STS responds more strongly to biological motion (as conveyed by the walking robot) than to (2) a nonmeaningful but complex nonbiological motion (a disjointed mechanical figure) and (3) a complex and meaningful nonbiological motion (the movements of a grandfather clock). In subsequent whole-brain voxel-based analyses, we confirmed robust STS activity that was strongly right lateralized. In addition, we observed significant deactivations in the STS that differentiated biological and nonbiological motion. These voxel-based analyses also revealed regions of motion-related positive activity in other brain regions, including MT or V5, fusiform gyri, right premotor cortex, and the intraparietal sulci.
Abstract: As the limits of stimuli presentation rates are explored in event-related fMRI design, there is a greater need to assess the implications of averaging raw fMRI data. Selective averaging assumes that the fMRI signal consists of task-dependent signal, random noise, and non-task dependent brain signal that can be modeled as random noise so that it tends to zero when averaged over a practical number of trials. We recorded a total of four fMRI data series from two normal subjects (subject 1, axially acquired; subject 2, coronally acquired) performing a simple visual event-related task and a water phantom with the same fMRI scanner imaging parameters. To determine which fraction of the fMRI data was deterministic as opposed to random, we created different data subsets by taking the odd or even time points of the full data sets. All data sets were first dimension-reduced with principal component analysis (PCA) and separated into 100 spatially independent components with independent component analysis (ICA). The mutual information between best-matching pairs of components selected from full data set-subset comparisons was plotted for each data set. Visual inspection suggested that 45-85 components were reproducible, and hence deterministic, accounting for 79-97% of the variance, respectively, in the raw data. The reproducible components exhibited much less trial-to-trial variability than the raw data from even the most activated voxel. Many (22-47) of reproducible components were significantly affected by stimulus presentation (P < 0.001). The most significantly-stimulus-correlated component was strongly time-locked to stimulus presentation and was directly stimulus correlated, corresponding to occipital brain regions. However, other spatially distinct task-related components demonstrated variable temporal relationships with the most significantly-stimulus-correlated component. Our results suggest that the majority of the variance in fMRI data is in fact deterministic, and support the notion that the data consist of differing components with differing temporal relationships to visual stimulation. They further suggest roles for restricting interpretations of the spatial extent of activation from event-related designs to a specific region of interest (ROI) and/or first separating the data into spatially independent components. Averaging the time courses of spatially independent components time-locked to stimulus presentation may prevent possible biases in the estimates of the spatial and temporal extent of stimulus-correlated activation and of trial-to-trial variability.
Abstract: OBJECTIVES: Dysphagia is an important consequence of many diseases. As some of the muscles of deglutition tend to be deep to the surface, needle electrodes are typically used, but this limits the number of muscles that can be simultaneously recorded. Since control of swallowing involves central pattern generators (CPGs) which distribute commands to several muscles, monitoring several muscles simultaneously is desirable. Here we describe a novel method, based on computing the independent components (ICs) of the simultaneous sEMG recordings (Muscle Nerve Suppl 9 (2000) 9) to detect the underlying functional muscle activations during swallowing using only surface EMG (sEMG) electrodes. METHODS: Seven normal subjects repeatedly swallowed liquids of varying consistency while sEMG was recorded from 15 electrodes from the face and throat. Active areas of EMG were excised from the recordings and the ICs of the sEMG were calculated. RESULTS: The ICs demonstrated less swallow-to-swallow variability than the raw sEMG. The ICs, each consisting of a unique temporal waveform and a spatial distribution, provided a means to segregate the complex sequence of muscle activation into rigorously defined separate functional units. The temporal profiles of the ICs and their spatial distribution were consistent with prior needle EMG studies of the cricopharyngeal, superior pharyngeal constrictor, submental and possibly arytenoid muscles. Other components appeared to correspond to EKG artifact contaminating the EMG recordings, laryngeal excursion, tongue movement and activation of the buccal and/or masseter musculature At least two of the components were affected by the consistency of the liquids swallowed. Re-testing one subject a week later demonstrated good intertrial reliability. CONCLUSIONS: We propose that the ICs of the sEMG provide a non-invasive means to assess the complex muscle sequence activation of deglutition.
Abstract: 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.
Abstract: 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.
Abstract: 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.
Abstract: 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.
Abstract: 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.
Abstract: 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.
Abstract: 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.
Abstract: 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.
Abstract: 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.
Abstract: 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.
Abstract: 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.
Abstract: 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.
Abstract: 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.
Abstract: 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.
Abstract: 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.
Abstract: 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.
Abstract: Functional magnetic resonance imaging (fMRI) has become increasingly used for studying functional integration of the brain. However, the large inter-subject variability in functional connectivity renders detection of representative group networks very difficult. In this paper, we propose a new iterative method that we refer to as "group replicator dynamics," for detecting sparse functional networks that are common across subjects within a group. The proposed method uses replicator dynamics, which we show to be equivalent to non-negative sparse PCA, and incorporates group information for identifying common networks across subjects with subject-specific weightings of the identified brain regions reflecting individual differences. Finding a separate network for each subject, as opposed to employing traditional averaging approaches, permits statistical testing of group significance. We validated our method on synthetic data, and applying it to real fMRI data detected task-specific group networks that conform well with prior neuroscience knowledge.
Abstract: We propose a novel method for functional segmentation of fMRI data that incorporates multiple functional attributes such as activation effects and functional connectivity, under a single framework. Similar to PCA, our method exploits the structure of the correlation matrix but with neighborhood information adaptively integrated to encourage detection of spatially contiguous clusters yet without falsely pooling non-active voxels near the functional boundaries. In addition, our method adaptively combines PCA and replicator dynamics, which we show to be equivalent to non-negative sparse PCA, based on the sparsity of the activation pattern. We validate our method quantitatively on synthetic data and demonstrate that it outperforms methods including replicator dynamics, PCA, Gaussian mixture models, and general linear models. Furthermore, when applied to real fMRI data, our method successfully segmented the Brodmann area 6 into its known functional sub-regions, whereas other conventional methods that we examined failed to attain such delineation.
Abstract: To investigate the effects of stroke and hand dominance on muscle association patterns during reaching movements, we applied the hidden Markov model, multivariate autoregressive (HMM-mAR) framework to real sEMG recordings from healthy and stroke subjects performing reaching tasks. Statistical analysis is performed to construct subject- and group-level muscle connectivity networks. Associating structural features are extracted for subsequent classification of reaching movements. The HMM-mAR framework is shown to be able to consistently segments each reaching movement into the initial phase and the full-movement phase. The inferred muscle networks illustrate that healthy and stroke subjects use distinguishably different muscle synergies during the initial phase. The classification results further confirm that structural features extracted from the initial phase are useful in classifying subjects with differing stroke condition and handedness.
Abstract: Discovering the connectivity networks in the brain, i.e. the neural influence that brain regions exert over one another, has attracted increasing research attention in studies on brain functions. An important error rate criterion on the discovered network is the false discovery rate (FDR), that is the expected ratio of the falsely 'discovered' connections to all those 'discovered'. Very recently, we have developed an algorithm that is able to control the FDR under a given level q at the limit of large sample size, and its modification that controlled the FDR accurately in simulations with moderate sample sizes [1]. However, the algorithms do not consider prior knowledge on the network structure, and can not be applied to models such as dynamic Bayesian networks. In this paper, we extend the algorithms to incorporate prior knowledge, and demonstrate how to apply the extended algorithm to learning the structure of dynamic Bayesian networks from continuous data. Its application to a real functional-Magnetic-Resonance-Imaging (fMRI) data set revealed that Parkinson's disease patients' brain connectivities are normalized by L-dopa medication. This result is consistent with the fact that L-dopa has dramatic effects against bradykinesia and rigidity.
Abstract: We present a new functional magnetic resonance imaging (fMRI) analysis method that incorporates both spatial and temporal dynamics of blood-oxygen-level dependent (BOLD) signals within a region of interest (ROI). 3D moment descriptors are used to characterize the spatial changes in BOLD signals over time. The method is tested on fMRI data collected from eight healthy subjects performing a bulb-squeezing motor task with their right-hand at various frequencies. Multiple brain regions including the left cerebellum, both primary motor cortices (MI), both supplementary motor areas (SMA), left prefrontal cortex (PFC), and left anterior cingulate cortex (ACC) demonstrate significant task-related changes. Furthermore, our method is able to discriminate differences in activation patterns at the various task frequencies, whereas using a traditional intensity based method, no significant activation difference is detected. This suggests that temporal dynamics of the spatial distribution of BOLD signal provide additional information regarding task-related activation thus complementing conventional intensity-based approaches.
Abstract: Surface electromyographic (sEMG) analysis is complicated by the fact that the data are inherently non-stationary. To deal with this and to determine muscle activity patterns during reaching movements, we proposed modeling sEMG with a hidden Markov model-multivariate autoregressive (HMM-mAR) framework. The classification between healthy and stroke subjects was performed using structural features extracted from HMM-mAR models. Both the raw and carrier data produced excellent classification performance. The proposed method represents a fundamental departure from most existing methods where only the amplitude is analyzed or the mAR coefficients are directly used for classification. In contrast, our analysis shows that structural features of the multivariate sEMG carrier data or the residuals after model fitting can enhance the classification of reaching movements.
Abstract: In earlier work, we have shown the importance of including 3D shape characteristics when analyzing regions of interest (ROIs) in magnetic resonance imaging (MRI) data. Spherical harmonics (SPHARM) based ROI shape descriptors were proposed and shown to provide important complementary information to traditionally used simple volumetric ROI measures. In this paper we extend our SPHARM shape parameterization technique by using functions defined on concentric spherical shells. We then propose the use of a novel radial transform to obtain unique features even under independent rotations of the constituting shells. These enhanced features enable the analysis of 3D ROIs with complex topologies including those with possible disconnections (e.g. ventricles). We validate the proposed 3D shape descriptors on synthetic data and demonstrate their sensitivity to subtle shape changes in the presence of inter-subject variability. We also apply our approach to real MRI data and detect significant shape changes in the left and right thalamus in Parkinson's disease (PD) patients when compared against normal volunteers, complementing the observed volumetric changes.
Abstract: In region of interest (ROI) based functional magnetic resonance imaging (fMRI) group analysis, errors in delineation of an ROI or inclusion of non-active voxels within an ROI can bias the statistical results. Addressing these concerns, this paper presents a new fMRI processing method that simultaneously refines ROI delineation and spatially denoises fMRI activation statistics within the ROI. The underlying assumption is that activation statistics within a small neighborhood are spatially correlated, thereby exhibit similar levels of influence on the overall ROI's response. Based on this assumption, we first identify outlier voxels as those having undue influence on an ROI's feature. Isolated outlier voxels at region boundaries are then removed, thereby refining the ROI delineation. The remaining outlier voxels are de-weighted based on their influence relative to their neighbors to reduce the effects of voxels deemed falsely active in later analysis. The proposed method was tested on real fMRI data collected from 8 healthy subjects performing a bulb-squeezing motor task at various frequencies. Using the proposed method, enhanced capability for detection of frequency-related activation map feature differences (AMFD) was demonstrated when compared to Gaussian spatial smoothing of ROI activation statistics. The validity of the proposed method is suggested by the fact that using one feature for denoising (e.g. spatial variance) results in greater effect size in another feature (e.g. average activation statistics magnitude). Our results demonstrate the importance of accurate ROI delineation in ROI-based fMRI analysis.
Abstract: Identifying active regions of the brain that are task-related is important in fMRI study. Current methods of determining functional Regions of Interest (ROIs) are unsatisfactory because they either reduce the effect size or bias the statistical results. We propose a spectral clustering method for assessing those voxels within an ROI that are suitable for further task-activation analysis. Different similarity functions are studied and the correlation index is chosen based on the simulation study. In real fMRI study, further group analysis employing regression is investigated to identify different brain activation patterns between groups in order to reveal the effects of disease and medicine. A real fMRI case study in Parkinson's disease suggests that the technique is promising, warranting further study.
Abstract: FMRI experiments are usually performed to make inferences about groups of subjects, but current group analysis methods for dynamic Bayesian networks (DBNs) do not easily allow incorporation of covariates of interest. In this paper, we propose a group-analysis method which uses multivariate analysis of variance (MANOVA) to address this issue. The method is performed in two stages: first, deriving a DBN connectivity network among brain regions for each subject separately; second, regressing the connectivity coefficients of DBNs to the factors of interest and performing MANOVA. A case study involving fMRI data from Parkinson's disease (PD) subjects yields promising results. Ten out of the thirteen potential connections between Regions of Interest (ROIs) which are associated with disease state are functionally improved after medication (Table I), consistent with clinical observations. The results confirm that improvement in PD symptoms after medications is in part mediated by enhanced functional brain connectivity between brain regions.
