A Review of the use of Transcranial Doppler Waveform Morphology for Acute Stroke Assessment.

Acute ischemic stroke is a source of long-term disability in the United States, of which a large portion of cases are a result of large vessel occlusion (LVO). LVO strokes have high rates of morbidity and mortality due to difficulty of treatments in achieving recanalization. Recently, however, results of randomized clinical trials have shown that treatment options are expanding in both availability and efficacy. As these methods of intervention become more optimal, so must the preceding methods of assessment. Transcranial Doppler (TCD) ultrasound is a non-invasive method of evaluating cerebral hemodynamics, and has a long history in stroke assessment. Despite the importance of information provided by a TCD exam, its utilization in the acute stroke workflow has remained low because of its dependence on expert analysis. Here, we review the evolution of morphological analysis of TCD waveforms for the indication, localization, and monitoring of acute LVO.

Review: pathophysiology of intracranial hypertension and noninvasive intracranial pressure monitoring.

Measurement of intracranial pressure (ICP) is crucial in the management of many neurological conditions. However, due to the invasiveness, high cost, and required expertise of available ICP monitoring techniques, many patients who could benefit from ICP monitoring do not receive it. As a result, there has been a substantial effort to explore and develop novel noninvasive ICP monitoring techniques to improve the overall clinical care of patients who may be suffering from ICP disorders. This review attempts to summarize the general pathophysiology of ICP, discuss the importance and current state of ICP monitoring, and describe the many methods that have been proposed for noninvasive ICP monitoring. These noninvasive methods can be broken down into four major categories: fluid dynamic, otic, ophthalmic, and electrophysiologic. Each category is discussed in detail along with its associated techniques and their advantages, disadvantages, and reported accuracy. A particular emphasis in this review will be dedicated to methods based on the use of transcranial Doppler ultrasound. At present, it appears that the available noninvasive methods are either not sufficiently accurate, reliable, or robust enough for widespread clinical adoption or require additional independent validation. However, several methods appear promising and through additional study and clinical validation, could eventually make their way into clinical practice.

Toward automated classification of pathological transcranial Doppler waveform morphology via spectral clustering.

Cerebral Blood Flow Velocity waveforms acquired via Transcranial Doppler (TCD) can provide evidence for cerebrovascular occlusion and stenosis. Thrombolysis in Brain Ischemia (TIBI) flow grades are widely used for this purpose, but require subjective assessment by expert evaluators to be reliable. In this work we seek to determine whether TCD morphology can be objectively assessed using an unsupervised machine learning approach to waveform categorization. TCD beat waveforms were recorded at multiple depths from the Middle Cerebral Arteries of 106 subjects; 33 with Large Vessel Occlusion (LVO). From each waveform, three morphological features were extracted, quantifying onset of maximal velocity, systolic canopy length, and the number/prominence of peaks/troughs. Spectral clustering identified groups implicit in the resultant three-dimensional feature space, with gap statistic criteria establishing the optimal cluster number. We found that gap statistic disparity was maximized at four clusters, referred to as flow types I, II, III, and IV. Types I and II were primarily composed of control subject waveforms, whereas types III and IV derived mainly from LVO patients. Cluster morphologies for types I and IV aligned clearly with Normal and Blunted TIBI flows, respectively. Types II and III represented commonly observed flow-types not delineated by TIBI, which nonetheless deviate from normal and blunted flows. We conclude that important morphological variability exists beyond that currently quantified by TIBI in populations experiencing or at-risk for acute ischemic stroke, and posit that the observed flow-types provide the foundation for objective methods of real-time automated flow type classification.

Objective Assessment of Beat Quality in Transcranial Doppler Measurement of Blood Flow Velocity in Cerebral Arteries.

OBJECTIVE: Transcranial Doppler (TCD) ultrasonography measures pulsatile cerebral blood flow velocity in the arteries and veins of the head and neck. Similar to other real-time measurement modalities, especially in healthcare, the identification of high-quality signals is essential for clinical interpretation. Our goal is to identify poor quality beats and remove them prior to further analysis of the TCD signal. METHODS: We selected objective features for this purpose including Euclidean distance between individual and average beat waveforms, cross-correlation between individual and average beat waveforms, ratio of the high-frequency power to the total beat power, beat length, and variance of the diastolic portion of the beat waveform. We developed an iterative outlier detection algorithm to identify and remove the beats that are different from others in a recording. Finally, we tested the algorithm on a dataset consisting of more than 15 h of TCD data recorded from 48 stroke and 34 in-hospital control subjects. RESULTS: We assessed the performance of the algorithm in the improvement of estimation of clinically important TCD parameters by comparing them to that of manual beat annotation. The results show that there is a strong correlation between the two, that demonstrates the algorithm has successfully recovered the clinically important features. We obtained significant improvement in estimating the TCD parameters using the algorithm accepted beats compared to using all beats. SIGNIFICANCE: Our algorithm provides a valuable tool to clinicians for automated detection of the reliable portion of the data. Moreover, it can be used as a pre-processing tool to improve the data quality for automated diagnosis of pathologic beat waveforms using machine learning.

Intranasal oxytocin enhances EEG mu rhythm desynchronization during execution and observation of social action: An exploratory study.

Intranasal administration of oxytocin (OT) has been found to facilitate prosocial behaviors, emotion recognition and cooperation between individuals. Recent electroencephalography (EEG) investigations have reported enhanced mu rhythm (alpha: 8-13 Hz; beta: 15-25 Hz) desynchronization during the observation of biological motion and stimuli probing social synchrony after the administration of intranasal OT. This hormone may therefore target a network of cortical circuits involved in higher cognitive functions, including the mirror neuron system (MNS). Here, in a double-blind, placebo-controlled, between-subjects exploratory study, we investigated whether intranasal OT modulates the cortical activity from sensorimotor areas during the observation and the execution of social and non-social grasping actions. Participants underwent EEG testing after receiving a single dose (24 IU) of either intranasal OT or placebo. Results revealed an enhancement of alpha - but not beta - desynchronization during observation and execution of social grasps, especially over central and parietal electrodes, in participants who received OT (OT group). No differences between the social and non-social condition were found in the control group (CTRL group). Moreover, we found a significant difference over the cortical central-parietal region between the OT and CTRL group only within the social condition. These results suggest a possible action of intranasal OT on sensorimotor circuits involved in social perception and action understanding, which might contribute to facilitate the prosocial effects typically reported by behavioral studies.

Algorithm for Reliable Detection of Pulse Onsets in Cerebral Blood Flow Velocity Signals.

Transcranial Doppler (TCD) ultrasound has been demonstrated to be a valuable tool for assessing cerebral hemodynamics via measurement of cerebral blood flow velocity (CBFV), with a number of established clinical indications. However, CBFV waveform analysis depends on reliable pulse onset detection, an inherently difficult task for CBFV signals acquired via TCD. We study the application of a new algorithm for CBFV pulse segmentation, which locates pulse onsets in a sequential manner using a moving difference filter and adaptive thresholding. The test data set used in this study consists of 92,012 annotated CBFV pulses, whose quality is representative of real world data. On this test set, the algorithm achieves a true positive rate of 99.998% (2 false negatives), positive predictive value of 99.998% (2 false positives), and mean temporal offset error of 6.10 ± 4.75 ms. We do note that in this context, the way in which true positives, false positives, and false negatives are defined caries some nuance, so care should be taken when drawing comparisons to other algorithms. Additionally, we find that 97.8% and 99.5% of onsets are detected within 10 and 30 ms, respectively, of the true onsets. The algorithm's performance in spite of the large degree of variation in signal quality and waveform morphology present in the test data suggests that it may serve as a valuable tool for the accurate and reliable identification of CBFV pulse onsets in neurocritical care settings.

Velocity Curvature Index: a Novel Diagnostic Biomarker for Large Vessel Occlusion.

Despite being a conveniently portable technology for stroke assessment, Transcranial Doppler ultrasound (TCD) remains widely underutilized due to complex training requirements necessary to reliably obtain and interpret cerebral blood flow velocity (CBFV) waveforms. The validation of objective TCD metrics for large vessel occlusion (LVO) represents a first critical step toward enabling use by less formally trained personnel. In this work, we assess the diagnostic utility, relative to current standard CT angiography (CTA), of a novel TCD-derived biomarker for detecting LVO. Patients admitted to the hospital with stroke symptoms underwent TCD screening and were grouped into LVO and control groups based on the presence of CTA confirmed occlusion. Velocity curvature index (VCI) was computed from CBFV waveforms recorded at multiple depths from the middle cerebral arteries (MCA) of both cerebral hemispheres. VCI was assessed for 66 patients, 33 of which had occlusions of the MCA or internal carotid artery. Our results show that VCI was more informative when measured from the cerebral hemisphere ipsilateral to the site of occlusion relative to contralateral. Moreover, given any pair of bilateral recordings, VCI separated LVO patients from controls with average area under receiver operating characteristic curve of 92%, which improved to greater than 94% when pairs were selected by maximal velocity. We conclude that VCI is an analytically valid candidate biomarker for LVO diagnosis, possessing comparable accuracy, and several important advantages, relative to current TCD diagnostic methodologies.

Decision Criteria for Large Vessel Occlusion Using Transcranial Doppler Waveform Morphology.

Background: The current lack of effective tools for prehospital identification of Large Vessel Occlusion (LVO) represents a significant barrier to efficient triage of stroke patients and detriment to treatment efficacy. The validation of objective Transcranial Doppler (TCD) metrics for LVO detection could provide first responders with requisite tools for informing stroke transfer decisions, dramatically improving patient care. Objective: To compare the diagnostic efficacy of two such candidate metrics: Velocity Asymmetry Index (VAI), which quantifies disparity of blood flow velocity across the cerebral hemispheres, and Velocity Curvature Index (VCI), a recently proposed TCD morphological biomarker. Additionally, we investigate a simple decision tree combining both metrics. Methods: We retrospectively compare accuracy/sensitivity/specificity (ACC/SEN/SPE) of each method (relative to standard CT-Angiography) in detecting LVO in a population of 66 subjects presenting with stroke symptoms (33 with CTA-confirmed LVO), enrolled consecutively at Erlanger Southeast Regional Stroke Center in Chattanooga, TN. Results: Individual VCI and VAI metrics demonstrated robust performance, with area under receiver operating characteristic curve (ROC-AUC) of 94% and 88%, respectively. Additionally, leave-one-out cross-validation at optimal identified thresholds resulted in 88% ACC (88% SEN) for VCI, vs. 79% ACC (76% SEN) for VAI. When combined, the resultant decision tree achieved 91% ACC (94% SEN). Discussion: We conclude VCI to be superior to VAI for LVO detection, and provide evidence that simple decision criteria incorporating both metrics may further optimize. Performance: Our results suggest that machine-learning approaches to TCD morphological analysis may soon enable robust prehospital LVO identification. Registration: Was not required for this feasibility study.

Action mechanisms for social cognition: behavioral and neural correlates of developing Theory of Mind.

Many psychological theories posit foundational links between two fundamental constructs: (1) our ability to produce, perceive, and represent action; and (2) our ability to understand the meaning and motivation behind the action (i.e. Theory of Mind; ToM). This position is contentious, however, and long-standing competing theories of social-cognitive development debate roles for basic action-processing in ToM. Developmental research is key to investigating these hypotheses, but whether individual differences in neural and behavioral measures of motor action relate to social-cognitive development is unknown. We examined 3- to 5-year-old children's (N = 26) EEG mu-desynchronization during production of object-directed action, and explored associations between mu-desynchronization and children's behavioral motor skills, behavioral action-representation abilities, and behavioral ToM. For children with high (but not low) mu-desynchronization, motor skill related to action-representation abilities, and action-representation mediated relations between motor skill and ToM. Results demonstrate novel foundational links between action-processing and ToM, suggesting that basic motor action may be a key mechanism for social-cognitive development, thus shedding light on the origins and emergence of higher social cognition.

Motor System Activation Predicts Goal Imitation in 7-Month-Old Infants.

The current study harnessed the variability in infants' neural and behavioral responses as a novel method for evaluating the potential relations between motor system activation and social behavior. We used electroencephalography (EEG) to record neural activity as 7-month-old infants observed and responded to the actions of an experimenter. To determine whether motor system activation predicted subsequent imitation behavior, we assessed event-related desynchronization (ERD) at central sites during action observation as a function of subsequent behavior. Greater mu desynchronization over central sites was observed when infants subsequently reproduced the experimenter's goal than when they did not reproduce the goal and instead selected the nongoal object. We also found that mu desynchronization during action execution predicted the infants' later propensity to reproduce the experimenter's goal-directed behavior. These results provide the first evidence that motor system activation predicts the imitation of other individuals' goals during infancy.

Desynchronization in EEG during perception of means-end actions and relations with infants' grasping skill.

The current study examined age-related differences in electroencephalogram (EEG) activity during perception of means-end actions and production of grasps, and how EEG activity may relate to infants' motor competence. We collected data from 9- and 12-month-old infants during perception of means-end actions made with a tool and during execution of their own grasps. We computed event-related desynchronization (ERD) during perception and production events and assessed infants' reach-grasp competence by looking at their latency to complete grasps. Although we found greater ERD during perception of means-end actions in 9-month-olds compared with 12-month-olds, we found the relation between ERD during perception and emerging reach-grasp competence to be specific for 12-month-olds and not for 9-month-olds. These results provide evidence for an emerging neural system that supports the coupling of action and perception with infants' emerging motor competence in the first year of life.

Spectral and source structural development of mu and alpha rhythms from infancy through adulthood.

OBJECTIVE: To assess the developmental trajectory of spectral, topographic, and source structural properties of functional mu desynchronization (characterized during voluntary reaching/grasping movement), and investigate its spectral/topographic relation to spontaneous EEG in the developing alpha band. METHODS: Event related desynchronization (ERD) and power spectral density spectra/topography are analyzed in 12 month-old infants, 4 year-old children, and adults. Age-matched head models derived from structural MRI are used to obtain current density reconstructions of mu desynchronization across the cortical surface. RESULTS: Infant/child EEG contains spectral peaks evident in both the upper and lower developing alpha band, and spectral/topographic properties of functionally identified mu rhythm strongly reflect those of upper alpha in all subject groups. Source reconstructions show distributed frontoparietal patterns of cortical mu desynchronization concentrated in specific central and parietal regions which are consistent across age groups. CONCLUSIONS: Peak frequencies of mu desynchronization and spontaneous alpha band EEG increase with age, and characteristic mu topography/source-structure is evident in development at least as early as 12 months. SIGNIFICANCE: Results provide evidence for a cortically distributed functional mu network, with spontaneous activity measurable in the upper alpha band throughout development.