Fabien Scalzo

Ischemia-Reperfusion Injury in Stroke

Despite ongoing advances in stroke imaging and treatment, ischemic and hemorrhagic stroke continue to debilitate patients with devastating outcomes at both the personal and societal levels. While the ultimate goal of therapy in ischemic stroke is geared towards restoration of blood flow, even when mitigation of initial tissue hypoxia is successful, exacerbation of tissue injury may occur in the form of cell death, or alternatively, hemorrhagic transformation of reperfused tissue. Animal models have extensively demonstrated the concept of reperfusion injury at the molecular and cellular levels, yet no study has quantified this effect in stroke patients. These preclinical models have also demonstrated the success of a wide array of neuroprotective strategies at lessening the deleterious effects of reperfusion injury. Serial multimodal imaging may provide a framework for developing therapies for reperfusion injury.

Reducing backward masking through action game training

Action video game play enhances basic visual skills such as crowding acuity and contrast sensitivity (C. S. Green & D. Bavelier, 2007; R. Li, U. Polat, W. Makous, & D. Bavelier, 2009). Here, we ask whether the dynamics of perception may also be altered as a result of playing action games. A backward masking paradigm was used to test the hypothesis that action video game play also alters the temporal dynamics of vision. As predicted, action gamers showed reduced backward masking and an accompanying training study established the causal role of action game play in this enhancement. Implications of this result are discussed in the context of the faster reaction times and enhanced sensitivity also documented after action game play.

Morphological Clustering and Analysis of Continuous Intracranial Pressure

The continuous measurement of intracranial pressure (ICP) is an important and established clinical tool that is used in the management of many neurosurgical disorders such as traumatic brain injury. Only mean ICP information is used currently in the prevailing clinical practice, ignoring the useful information in ICP pulse waveform that can be continuously acquired and is potentially useful for forecasting intracranial and cerebrovascular pathophysiological changes. The present study introduces and validates an algorithm of performing automated analysis of continuous ICP pulse waveform. This algorithm is capable of enhancing ICP signal quality, recognizing non artifactual ICP pulses, and optimally designating the three well-established subcomponents in an ICP pulse. Validation of the proposed algorithm is done by comparing non artifactual pulse recognition and peak designation results from a human observer with those from automated analysis based on a large signal database built from 700 h of recordings from 66 neurosurgical patients. An accuracy of 97.84% is achieved in recognizing non artifactual ICP pulses. An accuracy of 90.17%, 87.56%, and 86.53% was obtained for designating each of the three established ICP subpeaks. These results show that the proposed algorithm can be reliably applied to process continuous ICP recordings from real clinical environment to extract useful morphological features of ICP pulses.

Intracranial pressure pulse morphological features improved detection of decreased cerebral blood flow

We investigated whether intracranial pressure (ICP) pulse morphological metrics could be used to realize continuous detection of low cerebral blood flow. Sixty-three acutely brain injured patients with ICP monitoring, daily (133)Xenon cerebral blood flow (CBF) and daily transcranial Doppler (TCD) assessments were studied. Their ICP recordings were time-aligned with the CBF and TCD measurements so that a 1 h ICP segment near the CBF and TCD measurements was obtained. Each of these recordings was processed by the Morphological Cluster and Analysis of Intracranial Pressure (MOCAIP) algorithm to extract pulse morphological metrics. Then the differential evolution algorithm was used to find the optimal combination of the metrics that provided, using the regularized linear discriminant analysis, the largest combined positive predictivity and sensitivity. At a CBF threshold of 20 ml/min/100 g, a sensitivity of 81.8 +/- 0.9% and a specificity of 50.1 +/- 0.2% were obtained using the optimal combination of conventional TCD and blood analysis metrics as input to a regularized linear classifier. However, using the optimal combination of the MOCAIP metrics alone we were able to achieve a sensitivity of 92.5 +/- 0.7% and a specificity of 84.8 +/- 0.8%. Searching the optimal combination of all available metrics, we achieved the best result that was marginally better than those from using MOCAIP alone. This study demonstrated that the potential role of ICP monitoring may be extended to provide an indicator of low global cerebral blood perfusion.

Postischemic hyperperfusion on arterial spin labeled perfusion MRI is linked to hemorrhagic transformation in stroke

The purpose of this study was to investigate the relationship between hyperperfusion and hemorrhagic transformation (HT) in acute ischemic stroke (AIS). Pseudo-continuous arterial spin labeling (ASL) with background suppressed 3D GRASE was performed during routine clinical magnetic resonance imaging (MRI) on AIS patients at various time points. Arterial spin labeling cerebral blood flow (CBF) maps were visually inspected for the presence of hyperperfusion. Hemorrhagic transformation was followed during hospitalization and was graded on gradient recalled echo (GRE) scans into hemorrhagic infarction (HI) and parenchymal hematoma (PH). A total of 361 ASL scans were collected from 221 consecutive patients with middle cerebral artery stroke from May 2010 to September 2013. Hyperperfusion was more frequently detected posttreatment (odds ratio (OR)=4.8, 95% confidence interval (CI) 2.5 to 8.9, P<0.001) and with high National Institutes of Health Stroke Scale (NIHSS) scores at admission (P<0.001). There was a significant association between having hyperperfusion at any time point and HT (OR=3.5, 95% CI 2.0 to 6.3, P<0.001). There was a positive relationship between the grade of HT and time—hyperperfusion with the Spearmans rank correlation of 0.44 (P=0.003). Arterial spin labeling hyperperfusion may provide an imaging marker of HT, which may guide the management of AIS patients post tissue-type plasminogen activator (tPA) and/or endovascular treatments. Late hyperperfusion should be given more attention to prevent high-grade HT.

Regression analysis for peak designation in pulsatile pressure signals

Following recent studies, the automatic analysis of intracranial pressure (ICP) pulses appears to be a promising tool for forecasting critical intracranial and cerebrovascular pathophysiological variations during the management of many disorders. A pulse analysis framework has been recently developed to automatically extract morphological features of ICP pulses. The algorithm is able to enhance the quality of ICP signals, to segment ICP pulses, and to designate the locations of the three ICP sub-peaks in a pulse. This paper extends this algorithm by utilizing machine learning techniques to replace Gaussian priors used in the peak designation process with more versatile regression models. The experimental evaluations are conducted on a database of ICP signals built from 700 h of recordings from 64 neurosurgical patients. A comparative analysis of different state-of-the-art regression analysis methods is conducted and the best approach is then compared to the original pulse analysis algorithm. The results demonstrate a significant improvement in terms of accuracy in favor of our regression-based recognition framework. It reaches an average peak designation accuracy of 99% using a kernel spectral regression against 93% for the original algorithm.

Noninvasive Fractional Flow on MRA Predicts Stroke Risk of Intracranial Stenosis

Fractional flow may identify hemodynamic effects and ischemic risk beyond percent stenosis of an artery. We hypothesized that diminished TOF‐MRA signal intensity distal to an intracranial stenosis predicts stroke risk.

Statistical Learning of Visual Feature Hierarchies

We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method combines these primitives into high-level abstractions. Our appearance-based learning method uses local statistical analysis between features and Expectation- Maximization (EM) to identify and code spatial correlations. Spatial correlation is asserted when two features tend to occur at the same relative position of each other. This learning scheme results in a graphical model that allows a probabilistic representation of a flexible visual feature hierarchy. For feature detection, evidence is propagated using Nonparametric Belief Propagation (NBP), a recent generalization of particle filtering. In experiments, the proposed approach demonstrates efficient learning and robust detection of object models in the presence of clutter and occlusion and under view point changes.

Robust Peak Recognition in Intracranial Pressure Signals

BackgroundThe waveform morphology of intracranial pressure pulses (ICP) is an essential indicator for monitoring, and forecasting critical intracranial and cerebrovascular pathophysiological variations. While current ICP pulse analysis frameworks offer satisfying results on most of the pulses, we observed that the performance of several of them deteriorates significantly on abnormal, or simply more challenging pulses.MethodsThis paper provides two contributions to this problem. First, it introduces MOCAIP++, a generic ICP pulse processing framework that generalizes MOCAIP (Morphological Clustering and Analysis of ICP Pulse). Its strength is to integrate several peak recognition methods to describe ICP morphology, and to exploit different ICP features to improve peak recognition. Second, it investigates the effect of incorporating, automatically identified, challenging pulses into the training set of peak recognition models.ResultsExperiments on a large dataset of ICP signals, as well as on a representative collection of sampled challenging ICP pulses, demonstrate that both contributions are complementary and significantly improve peak recognition performance in clinical conditions.ConclusionThe proposed framework allows to extract more reliable statistics about the ICP waveform morphology on challenging pulses to investigate the predictive power of these pulses on the condition of the patient.

Computational fluid dynamics modeling of symptomatic intracranial atherosclerosis may predict risk of stroke recurrence.

Background Patients with symptomatic intracranial atherosclerosis (ICAS) of ≥70% luminal stenosis are at high risk of stroke recurrence. We aimed to evaluate the relationships between hemodynamics of ICAS revealed by computational fluid dynamics (CFD) models and risk of stroke recurrence in this patient subset. Methods Patients with a symptomatic ICAS lesion of 70–99% luminal stenosis were screened and enrolled in this study. CFD models were reconstructed based on baseline computed tomographic angiography (CTA) source images, to reveal hemodynamics of the qualifying symptomatic ICAS lesions. Change of pressures across a lesion was represented by the ratio of post- and pre-stenotic pressures. Change of shear strain rates (SSR) across a lesion was represented by the ratio of SSRs at the stenotic throat and proximal normal vessel segment, similar for the change of flow velocities. Patients were followed up for 1 year. Results Overall, 32 patients (median age 65; 59.4% males) were recruited. The median pressure, SSR and velocity ratios for the ICAS lesions were 0.40 (−2.46–0.79), 4.5 (2.2–20.6), and 7.4 (5.2–12.5), respectively. SSR ratio (hazard ratio [HR] 1.027; 95% confidence interval [CI], 1.004–1.051; P = 0.023) and velocity ratio (HR 1.029; 95% CI, 1.002–1.056; P = 0.035) were significantly related to recurrent territorial ischemic stroke within 1 year by univariate Cox regression, respectively with the c-statistics of 0.776 (95% CI, 0.594–0.903; P = 0.014) and 0.776 (95% CI, 0.594–0.903; P = 0.002) in receiver operating characteristic analysis. Conclusions Hemodynamics of ICAS on CFD models reconstructed from routinely obtained CTA images may predict subsequent stroke recurrence in patients with a symptomatic ICAS lesion of 70–99% luminal stenosis.