Restoring the Sense of Touch Using a Sensorimotor Demultiplexing Neural Interface.

Paralyzed muscles can be reanimated following spinal cord injury (SCI) using a brain-computer interface (BCI) to enhance motor function alone. Importantly, the sense of touch is a key component of motor function. Here, we demonstrate that a human participant with a clinically complete SCI can use a BCI to simultaneously reanimate both motor function and the sense of touch, leveraging residual touch signaling from his own hand. In the primary motor cortex (M1), residual subperceptual hand touch signals are simultaneously demultiplexed from ongoing efferent motor intention, enabling intracortically controlled closed-loop sensory feedback. Using the closed-loop demultiplexing BCI almost fully restored the ability to detect object touch and significantly improved several sensorimotor functions. Afferent grip-intensity levels are also decoded from M1, enabling grip reanimation regulated by touch signaling. These results demonstrate that subperceptual neural signals can be decoded from the cortex and transformed into conscious perception, significantly augmenting function.

Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation.

OBJECTIVE: To demonstrate naturalistic motor control speed, coordinated grasp, and carryover from trained to novel objects by an individual with tetraplegia using a brain-computer interface (BCI)-controlled neuroprosthetic. DESIGN: Phase I trial for an intracortical BCI integrated with forearm functional electrical stimulation (FES). Data reported span postimplant days 137 to 1478. SETTING: Tertiary care outpatient rehabilitation center. PARTICIPANT: A 27-year-old man with C5 class A (on the American Spinal Injury Association Impairment Scale) traumatic spinal cord injury INTERVENTIONS: After array implantation in his left (dominant) motor cortex, the participant trained with BCI-FES to control dynamic, coordinated forearm, wrist, and hand movements. MAIN OUTCOME MEASURES: Performance on standardized tests of arm motor ability (Graded Redefined Assessment of Strength, Sensibility, and Prehension [GRASSP], Action Research Arm Test [ARAT], Grasp and Release Test [GRT], Box and Block Test), grip myometry, and functional activity measures (Capabilities of Upper Extremity Test [CUE-T], Quadriplegia Index of Function-Short Form [QIF-SF], Spinal Cord Independence Measure-Self-Report [SCIM-SR]) with and without the BCI-FES. RESULTS: With BCI-FES, scores improved from baseline on the following: Grip force (2.9 kg); ARAT cup, cylinders, ball, bar, and blocks; GRT can, fork, peg, weight, and tape; GRASSP strength and prehension (unscrewing lids, pouring from a bottle, transferring pegs); and CUE-T wrist and hand skills. QIF-SF and SCIM-SR eating, grooming, and toileting activities were expected to improve with home use of BCI-FES. Pincer grips and mobility were unaffected. BCI-FES grip skills enabled the participant to play an adapted "Battleship" game and manipulate household objects. CONCLUSIONS: Using BCI-FES, the participant performed skillful and coordinated grasps and made clinically significant gains in tests of upper limb function. Practice generalized from training objects to household items and leisure activities. Motor ability improved for palmar, lateral, and tip-to-tip grips. The expects eventual home use to confer greater independence for activities of daily living, consistent with observed neurologic level gains from C5-6 to C7-T1. This marks a critical translational step toward clinical viability for BCI neuroprosthetics.

A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent.

Laboratory demonstrations of brain-computer interface (BCI) systems show promise for reducing disability associated with paralysis by directly linking neural activity to the control of assistive devices. Surveys of potential users have revealed several key BCI performance criteria for clinical translation of such a system. Of these criteria, high accuracy, short response latencies, and multi-functionality are three key characteristics directly impacted by the neural decoding component of the BCI system, the algorithm that translates neural activity into control signals. Building a decoder that simultaneously addresses these three criteria is complicated because optimizing for one criterion may lead to undesirable changes in the other criteria. Unfortunately, there has been little work to date to quantify how decoder design simultaneously affects these performance characteristics. Here, we systematically explore the trade-off between accuracy, response latency, and multi-functionality for discrete movement classification using two different decoding strategies-a support vector machine (SVM) classifier which represents the current state-of-the-art for discrete movement classification in laboratory demonstrations and a proposed deep neural network (DNN) framework. We utilized historical intracortical recordings from a human tetraplegic study participant, who imagined performing several different hand and finger movements. For both decoders, we found that response time increases (i.e., slower reaction) and accuracy decreases as the number of functions increases. However, we also found that both the increase of response times and the decline in accuracy with additional functions is less for the DNN than the SVM. We also show that data preprocessing steps can affect the performance characteristics of the two decoders in drastically different ways. Finally, we evaluated the performance of our tetraplegic participant using the DNN decoder in real-time to control functional electrical stimulation (FES) of his paralyzed forearm. We compared his performance to that of able-bodied participants performing the same task, establishing a quantitative target for ideal BCI-FES performance on this task. Cumulatively, these results help quantify BCI decoder performance characteristics relevant to potential users and the complex interactions between them.

Meeting brain-computer interface user performance expectations using a deep neural network decoding framework.

Brain-computer interface (BCI) neurotechnology has the potential to reduce disability associated with paralysis by translating neural activity into control of assistive devices1-9. Surveys of potential end-users have identified key BCI system features10-14, including high accuracy, minimal daily setup, rapid response times, and multifunctionality. These performance characteristics are primarily influenced by the BCI's neural decoding algorithm1,15, which is trained to associate neural activation patterns with intended user actions. Here, we introduce a new deep neural network16 decoding framework for BCI systems enabling discrete movements that addresses these four key performance characteristics. Using intracortical data from a participant with tetraplegia, we provide offline results demonstrating that our decoder is highly accurate, sustains this performance beyond a year without explicit daily retraining by combining it with an unsupervised updating procedure3,17-20, responds faster than competing methods8, and can increase functionality with minimal retraining by using a technique known as transfer learning21. We then show that our participant can use the decoder in real-time to reanimate his paralyzed forearm with functional electrical stimulation (FES), enabling accurate manipulation of three objects from the grasp and release test (GRT)22. These results demonstrate that deep neural network decoders can advance the clinical translation of BCI technology.

Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia.

Individuals with tetraplegia identify restoration of hand function as a critical, unmet need to regain their independence and improve quality of life. Brain-Computer Interface (BCI)-controlled Functional Electrical Stimulation (FES) technology addresses this need by reconnecting the brain with paralyzed limbs to restore function. In this study, we quantified performance of an intuitive, cortically-controlled, transcutaneous FES system on standardized object manipulation tasks from the Grasp and Release Test (GRT). We found that a tetraplegic individual could use the system to control up to seven functional hand movements, each with >95% individual accuracy. He was able to select one movement from the possible seven movements available to him and use it to appropriately manipulate all GRT objects in real-time using naturalistic grasps. With the use of the system, the participant not only improved his GRT performance over his baseline, demonstrating an increase in number of transfers for all objects except the Block, but also significantly improved transfer times for the heaviest objects (videocassette (VHS), Can). Analysis of underlying motor cortex neural representations associated with the hand grasp states revealed an overlap or non-separability in neural activation patterns for similarly shaped objects that affected BCI-FES performance. These results suggest that motor cortex neural representations for functional grips are likely more related to hand shape and force required to hold objects, rather than to the objects themselves. These results, demonstrating multiple, naturalistic functional hand movements with the BCI-FES, constitute a further step toward translating BCI-FES technologies from research devices to clinical neuroprosthetics.

Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications.

BACKGROUND: Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain. METHODS: In this study, we show the applicability of wavelet decomposition method to extract and demonstrate the utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor cortex of a human with tetraplegia. Wavelet decomposition was applied to the raw voltage data to generate mean wavelet power (MWP) features, which were further divided into three sub-frequency bands, low-frequency MWP (lf-MWP, 0-234 Hz), mid-frequency MWP (mf-MWP, 234 Hz-3.75 kHz) and high-frequency MWP (hf-MWP, >3.75 kHz). We analyzed these features using data collected from two experiments that were repeated over the course of about 3 years and compared their signal stability and decoding performance with the more standard threshold crossings, local field potentials (LFP), multi-unit activity (MUA) features obtained from the raw voltage recordings. RESULTS: All neural features could stably track neural information for over 3 years post-implantation and were less prone to signal degradation compared to threshold crossings. Furthermore, when used as an input to support vector machine based decoding algorithms, the mf-MWP and MUA demonstrated significantly better performance, respectively, in classifying imagined motor tasks than using the lf-MWP, hf-MWP, LFP, or threshold crossings. CONCLUSIONS: Our results suggest that using MWP features in the appropriate frequency bands can provide an effective neural feature for brain computer interface intended for chronic applications. TRIAL REGISTRATION: This study was approved by the U.S. Food and Drug Administration (Investigational Device Exemption) and the Ohio State University Medical Center Institutional Review Board (Columbus, Ohio). The study conformed to institutional requirements for the conduct of human subjects and was filed on ClinicalTrials.gov (Identifier NCT01997125).

Neuroprosthetic-enabled control of graded arm muscle contraction in a paralyzed human.

Neuroprosthetics that combine a brain computer interface (BCI) with functional electrical stimulation (FES) can restore voluntary control of a patients' own paralyzed limbs. To date, human studies have demonstrated an "all-or-none" type of control for a fixed number of pre-determined states, like hand-open and hand-closed. To be practical for everyday use, a BCI-FES system should enable smooth control of limb movements through a continuum of states and generate situationally appropriate, graded muscle contractions. Crucially, this functionality will allow users of BCI-FES neuroprosthetics to manipulate objects of different sizes and weights without dropping or crushing them. In this study, we present the first evidence that using a BCI-FES system, a human with tetraplegia can regain volitional, graded control of muscle contraction in his paralyzed limb. In addition, we show the critical ability of the system to generalize beyond training states and accurately generate wrist flexion states that are intermediate to training levels. These innovations provide the groundwork for enabling enhanced and more natural fine motor control of paralyzed limbs by BCI-FES neuroprosthetics.

Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface.

Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. We present our methodology with examples of decoding human brain data in real-time to inform a BCI.

Effects of passive dendritic tree properties on the firing dynamics of a leaky-integrate-and-fire neuron.

We study the effects of dendritic tree topology and biophysical properties on the firing dynamics of a leaky-integrate-and-fire (LIF) neuron that explicitly includes spiking dynamics. We model the dendrites as a multi-compartment tree with passive dynamics. Owing to the simplicity of the system, we obtain the full analytical solution for the model which we use to derive a lower dimensional return map that captures the complete dynamics of the system. Using the map, we explore how biophysical properties and dendritic tree architecture affect firing dynamics. As was first reported in earlier work by one of the authors, we also find that the addition of the dendritic tree can induce bistability between periodic firing and quiescence. However, we go beyond their results by systematically examining how dendritic tree topology affects the appearance of this bistable behavior. We find that the structure of the dendritic tree can have significant quantitative effects on the bifurcation structure of the system, with branchier topologies tending to promote bistable behavior over unbranched chain topologies. We also show that this effect occurs even when the input conductance at the soma is held fixed, indicating that the topology of the dendritic tree is mainly responsible for this quantitative change in the bifurcation structure. Lastly, we demonstrate how our framework can be used to explore the effect of biophysical properties on the firing dynamics of a neuron with a more complex dendritic tree topology.

A Multi-Area Stochastic Model for a Covert Visual Search Task.

Decisions typically comprise several elements. For example, attention must be directed towards specific objects, their identities recognized, and a choice made among alternatives. Pairs of competing accumulators and drift-diffusion processes provide good models of evidence integration in two-alternative perceptual choices, but more complex tasks requiring the coordination of attention and decision making involve multistage processing and multiple brain areas. Here we consider a task in which a target is located among distractors and its identity reported by lever release. The data comprise reaction times, accuracies, and single unit recordings from two monkeys' lateral interparietal area (LIP) neurons. LIP firing rates distinguish between targets and distractors, exhibit stimulus set size effects, and show response-hemifield congruence effects. These data motivate our model, which uses coupled sets of leaky competing accumulators to represent processes hypothesized to occur in feature-selective areas and limb motor and pre-motor areas, together with the visual selection process occurring in LIP. Model simulations capture the electrophysiological and behavioral data, and fitted parameters suggest that different connection weights between LIP and the other cortical areas may account for the observed behavioral differences between the animals.

Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. SIGNIFICANCE STATEMENT: We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks including irregular, Poisson-like spike times, and a tight balance between excitation and inhibition. These results significantly increase the biological plausibility of the spike-based approach to network computation, and uncover how several components of biological networks may work together to efficiently carry out computation.

Effects of moderate noise on a limit cycle oscillator: counterrotation and bistability.

The effects of noise on the dynamics of nonlinear systems is known to lead to many counterintuitive behaviors. Using simple planar limit cycle oscillators, we show that the addition of moderate noise leads to qualitatively different dynamics. In particular, the system can appear bistable, rotate in the opposite direction of the deterministic limit cycle, or cease oscillating altogether. Utilizing standard techniques from stochastic calculus and recently developed stochastic phase reduction methods, we elucidate the mechanisms underlying the different dynamics and verify our analysis with the use of numerical simulations. Last, we show that similar bistable behavior is found when moderate noise is applied to the FitzHugh-Nagumo model, which is more commonly used in biological applications.

The robustness of phase-locking in neurons with dendro-dendritic electrical coupling.

We examine the effects of dendritic filtering on the existence, stability, and robustness of phase-locked states to heterogeneity and noise in a pair of electrically coupled ball-and-stick neurons with passive dendrites. We use the theory of weakly coupled oscillators and analytically derived filtering properties of the dendritic coupling to systematically explore how the electrotonic length and diameter of dendrites can alter phase-locking. In the case of a fixed value of the coupling conductance (gc) taken from the literature, we find that repeated exchanges in stability between the synchronous and anti-phase states can occur as the electrical coupling becomes more distally located on the dendrites. However, the robustness of the phase-locked states in this case decreases rapidly towards zero as the distance between the electrical coupling and the somata increases. Published estimates of gc are calculated from the experimentally measured coupling coefficient (CC) based on a single-compartment description of a neuron, and therefore may be severe underestimates of gc. With this in mind, we re-examine the stability and robustness of phase-locking using a fixed value of CC, which imposes a limit on the maximum distance the electrical coupling can be located away from the somata. In this case, although the phase-locked states remain robust over the entire range of possible coupling locations, no exchanges in stability with changing coupling position are observed except for a single exchange that occurs in the case of a high somatic firing frequency and a large dendritic radius. Thus, our analysis suggests that multiple exchanges in stability with changing coupling location are unlikely to be observed in real neural systems.

Can post-error dynamics explain sequential reaction time patterns?

We investigate human error dynamics in sequential two-alternative choice tasks. When subjects repeatedly discriminate between two stimuli, their error rates and reaction times (RTs) systematically depend on prior sequences of stimuli. We analyze these sequential effects on RTs, separating error and correct responses, and identify a sequential RT tradeoff: a sequence of stimuli which yields a relatively fast RT on error trials will produce a relatively slow RT on correct trials and vice versa. We reanalyze previous data and acquire and analyze new data in a choice task with stimulus sequences generated by a first-order Markov process having unequal probabilities of repetitions and alternations. We then show that relationships among these stimulus sequences and the corresponding RTs for correct trials, error trials, and averaged over all trials are significantly influenced by the probability of alternations; these relationships have not been captured by previous models. Finally, we show that simple, sequential updates to the initial condition and thresholds of a pure drift diffusion model can account for the trends in RT for correct and error trials. Our results suggest that error-based parameter adjustments are critical to modeling sequential effects.

Constraints on the use of lifespan-shortening Wolbachia to control dengue fever.

Dengue fever, a viral disease spread by the mosquito Aedes aegypti, affects 50-100 million people a year in many tropical countries. Because the virus must incubate within mosquito hosts for two weeks before being able to transmit the infection, shortening the lifespan of mosquitoes may curtail dengue transmission. We developed a continuous time reaction-diffusion model of the spatial spread of Wolbachia through a population of A. aegypti. This model incorporates the lifespan-shortening effects of Wolbachia on infected A. aegypti and the fitness advantage to infected females due to cytoplasmic incompatibility (CI). We found that local establishment of the Wolbachia infection can occur if the fitness advantage due to CI exceeds the fitness reduction due to lifespan-shortening effects, in accordance with earlier results concerning fecundity reduction. However, spatial spread is possible only if the fitness advantage due to CI is twice as great as the fitness reduction due to lifespan shortening effects. Moreover, lifespan-shortening and fecundity-reduction can have different effects on the speed of wave-retreat. Using data from the literature, we estimated all demographic parameters for infected and uninfected mosquitoes and computed the velocities of spread of infection. Our most optimistic estimates suggest that the spatial spread of lifespan-shortening Wolbachia may be so slow that efficient spatial spread would require a prohibitively large number of point releases. However, as these estimates of demographic parameters may not accurately reflect natural conditions, further research is necessary to corroborate these predictions.

Effects of dendritic load on the firing frequency of oscillating neurons.

We study the effects of passive dendritic properties on the dynamics of neuronal oscillators. We find that the addition of a passive dendrite can sometimes have counterintuitive effects on firing frequency. Specifically, the addition of a hyperpolarized passive dendritic load can either increase, decrease, or have negligible effects on firing frequency. We use the theory of weak coupling to derive phase equations for "ball-and-stick" model neurons and two-compartment model neurons. We then develop a framework for understanding how the addition of passive dendrites modulates the frequency of neuronal oscillators. We show that the average value of the neuronal oscillator's phase response curves measures the sensitivity of the neuron's firing rate to the dendritic load, including whether the addition of the dendrite causes an increase or decrease in firing frequency. We interpret this finding in terms of to the slope of the neuronal oscillator's frequency-applied current curve. We also show that equivalent results exist for constant and noisy point-source input to the dendrite. We note that the results are not specific to neurons but are applicable to any oscillator subject to a passive load.

Effects of lacidipine on vascular responses in patients with coronary artery disease.

BACKGROUND: To evaluate the effects of the calcium channel blocker lacidipine on vascular responses, such as endothelial function and carotid intima-media thickness (IMT), and on levels of high-sensitivity C-reactive protein (hs-CRP) in patients with coronary artery disease (CAD). METHODS: Endothelial function was assessed by measuring the flow-mediated vasodilation (FMD) of the brachial artery and IMT was measured in the common, bifurcating, and in the internal carotid artery by using high-resolution ultrasound. The study population consisted of 96 consecutive patients [mean age 60 years, male (n) = 70] who showed at least one coronary artery narrowed by more than 50% (coronary angiography). These patients were randomly assigned to lacidipine treatment (4 mg/day, n = 48) or to a placebo (n = 48). We measured FMD, IMT, and hs-CRP prior to and after 6 months of treatment and following coronary angiography in all patients. RESULTS: Clinical and medical history revealed no significant differences between the groups. IMT of the common carotid artery was significantly diminished from 0.92 +/- 0.15 to 0.87 +/- 0.15 mm 6 months after treatment with lacidipine (p < 0.005). However, IMT of any region in the carotid artery did not show any significant changes in the placebo group. Endothelial function and hs-CRP levels were slightly improved (insignificant) in the lacidipine group. In the placebo group, there were no significant changes. CONCLUSION: Lacidipine leads to a significant reduction of the common carotid artery IMT as well as to a decrease in markers of inflammation in patients with CAD during a relatively short period (6 months).

Effects of low-dose atorvastatin on vascular responses in patients undergoing percutaneous coronary intervention with stenting.

BACKGROUND: The primary endpoint of this study was to evaluate the effects of low-dose atorvastatin on carotid intima-media thickness (IMT) and endothelial function, and the secondary endpoint comprised restenosis and target lesion revascularization (TLR) in patients undergoing percutaneous coronary intervention (PCI) with stenting for the treatment of coronary artery disease. METHODS: Two hundred five consecutive patients (mean age, 60 years) undergoing PCI were prospectively randomized to usual therapy (control group, n = 100) or to 10 mg of atorvastatin daily plus usual therapy (statin group, n = 105). Carotid IMT, endothelial function (flow-mediated dilatation [FMD] of the brachial artery), and coronary angiograms were taken before the study and 6 months after randomization. The 6-month follow-up measurements of the above factors were obtained in 83 patients (83%) of the control group and in 97 patients (92%) of the statin group. RESULTS: No significant differences were noted in the baseline clinical and angiographic findings in either group. FMD was significantly improved during the 6 months in the statin group (4.38% +/- 1.7% vs 4.85% +/- 1.6%, P = .003), but did not change in the control group. Carotid IMT did not show any significant changes at 6 months in either group. There was a trend in favor of statin in terms of restenosis rate (26.8% vs 36.1%, P = .177) and TLR rate (18.6% vs 25.3%, P = .274). The changes of FMD were significantly correlated with the changes of total cholesterol and the changes of low-density lipoprotein, respectively (r = -0.336, P = .009, and r = -0.310, P = .046). CONCLUSION: Low-dose atorvastatin reduces endothelial dysfunction as measured by FMD, which coincides with the beneficial effects on lipid profiles, and can decrease restenosis and TLR rate in patients undergoing PCI with stenting.

Impact of left ventricular ejection fraction on endothelial function in patients with coronary artery disease.

BACKGROUND: Endothelial dysfunction is present in patients with coronary artery disease (CAD) or with congestive heart failure. HYPOTHESIS: This study was performed to evaluate the impact of systolic heart function on endothelial function in patients with CAD. METHODS: The study population consisted of 283 consecutive patients (mean age 59 years, 176 men) undergoing coronary angiography. Endothelial function was assessed by measuring flow-mediated vasodilation (FMD) of the brachial artery. RESULTS: Patients (n = 236) with an ejection fraction (EF) > or = 55% on routine echocardiogram were younger (mean age 58 vs. 62 years), showed a lower prevalence of diabetes (15 vs. 38%) and myocardial infarction (13 vs. 66%), and showed a higher FMD (4.8 +/- 2.4 vs. 4.0 +/- 2.0%, p < 0.05) than patients (n = 47) with an EF < 55%. The correlation coefficient between FMD/endothelial function and EF/systolic heart function was 0.149 (p < 0.02) in the overall study population. Multivariate analysis showed that of age, gender, frequency of diabetes mellitus, myocardial infarction, and CAD extent, EF was the only significant independent parameter correlating with FMD in patients with CAD. CONCLUSIONS: Compared with the other tested risk factors, EF surprisingly was the only significant independent parameter correlating with endothelial function in patients with CAD. Our results support the view that endothelial function is an independent prognostic factor in patients with CAD.

Significance of the intima-media thickness of the thoracic aorta in patients with coronary atherosclerosis.

BACKGROUND: The prevalence and clinical significance of atherosclerotic aortic disease have now been documented in a variety of patient populations by use of transesophageal echocardiography (TEE). There are many reports that atherosclerotic aortic plaques detected by TEE are a marker for coronary artery disease (CAD). HYPOTHESIS: The study was undertaken to evaluate the significance of the intima-media thickness (IMT) and formation of atherosclerotic plaques of the thoracic aorta (TA) in patients with CAD, especially in terms of a correlation between the IMT of the TA and the extent of coronary atherosclerosis. METHODS: The IMT of the TA was measured using TEE. The study population comprised 100 patients (68 men, mean age 59 years). The extent of coronary atherosclerosis was divided into four groups (0, 1, 2, 3) according to the number of coronary arteries narrowed > or = 50%. RESULTS: There was no significant difference in the IMT of the ascending TA according to the presence of significant (> 50% narrowed) coronary stenosis, but there was a significant difference in the IMT of the descending TA (1.39 vs. 1.88 mm, p = 0.005). There was a significant correlation between the extent of coronary atherosclerosis and the IMT of the ascending and descending TA (r = 0.24, p < 0.05; r = 0.352, p < 0.001, respectively). The plaques in the TA were seen in 7, 41, 52, and 65% of patients in Groups 0, 1, 2, and 3, respectively. Among atherosclerosis risk factors, hyperlipidemia was the only factor analyzed that affected the IMT of the descending TA (2.11 vs. 1.78 mm, p < 0.05). CONCLUSION: The IMT of the TA correlates significantly with coronary atherosclerosis, and correlation of the descending TA IMT with coronary atherosclerosis is better than that of ascending TA IMT. Age is associated with coronary atherosclerosis, and TA IMT and hyperlipidemia are associated with descending TA IMT. Therefore, although TEE is not recommended for measuring TA IMT or for evaluating aortic plaques in patients with CAD, measurement of TA IMT as well as carotid artery IMT is very helpful for understanding the extent of coronary atherosclerosis.