Recurrent unilateral facial nerve palsy in acute lymphocytic leukaemia.

A man in his mid-20s developed three episodes of right facial weakness over 5 months. He had a history of B-cell acute lymphoblastic leukaemia (ALL) in remission following allogenic stem cell transplantation. MR scan of brain during the second presentation showed facial nerve enhancement; cerebrospinal fluid (CSF) cytology and flow cytometry were negative. Re-assessment at the third presentation identified CSF B-lymphoblasts, and he was subsequently treated for central nervous system relapse of leukaemia. This case highlights an infrequent presenting symptom of ALL relapse and a rare cause of recurrent facial nerve palsy.

Prevalence of Electrographic Seizures in Hospitalized Patients With Altered Mental Status With No Significant Seizure Risk Factors Who Underwent Continuous EEG Monitoring: A Retrospective Study.

OBJECTIVES: The objective of this study is to evaluate the prevalence of electrographic seizures in hospitalized patients with altered mental status and no significant risk factors for seizures. METHODS: We retrospectively reviewed over a six-year period (2013-2019) the medical records of all adults admitted at Ohio State University Wexner Medical Center (OSUWMC), who underwent continuous electroencephalography (cEEG) monitoring for > 48 hours. Our primary objective was to identify the prevalence of electrographic seizures in patients with altered mental status and no significant acute or remote risk factors for seizures. RESULTS: A total of 1966 patients were screened for the study, 1892 were excluded (96.2%) and 74 patients met inclusion criteria. Electrographic seizures were identified in seven of 74 patients (9.45%). We found a significant correlation between electrographic seizures and a history of hepatic cirrhosis, n= 4 (57%), (p=0.035), acute chronic hepatic failure during admission, 71% (n=5), (p=0.027), and hyperammonemia (p =0.009). CONCLUSION: In this retrospective study of patients with altered mental status and no significant acute or remote risk factors for seizures who underwent cEEG monitoring for > 48 hours, electrographic seizures were identified in 9.45%. Electrographic seizures were associated with hepatic dysfunction and hyperammonemia. Based on our results, cEEG monitoring should be considered in patients with altered mental status and hepatic dysfunction even in the absence of other seizure risk factors.

A case of new cognitive changes in a patient with seronegative paraneoplastic limbic encephalitis: encephalitis relapse or Wernicke's encephalopathy?

Wernicke's encephalopathy (WE) and paraneoplastic limbic encephalitis (PLE) can both present with acute-to-subacute memory impairment and cognitive dysfunction. Both can lead to significant morbidity and mortality without rapid identification and treatment. Often patients with WE may not have the typical clinical triad of ophthalmoplegia, gait ataxia, and altered mental status. Furthermore, both WE and PLE may share similar MRI findings. Here, we present a case of a patient with a history of seronegative PLE presenting with acute-to-subacute cognitive changes and gait imbalance. Initially, it was felt to be a relapse of PLE but upon further history and testing may potentially have represented WE in the setting of a recent dietary change.

Cerebellar Ischemia Presenting as Transient Global Amnesia.

Transient global amnesia (TGA) consists of acute-onset anterograde amnesia and typically resolves within 24 hours. Reported etiologies of TGA include transient ischemia to the hippocampus or thalamus, migraine, venous flow abnormalities, and epilepsy. There are no reports of cerebellar ischemia as an etiology of TGA. A 78-year-old woman with a medical history of diabetes presented to the Ohio State University ER after a period of anterograde amnesia lasting 3 hours. She was alert during the event, but asked the same questions repeatedly. Upon arrival to the ER, she was hypertensive but clinically back to baseline, with no recall of the 3-hour time period. An MRI of her brain revealed an isolated hyperintense signal on diffusion-weighted imaging (DWI) at the junction of the superior cerebellum and vermis, with apparent diffusion coefficient correlation. Vascular imaging of the brain and neck and a routine EEG were unremarkable. We diagnosed her with cerebellar ischemia presenting as TGA. She had no head injury, migraine, or history of epilepsy to suggest alternative etiologies of TGA. An increasing amount of literature has reported that the cerebellum is linked to the limbic system. A case series of SPECT imaging on individuals with TGA revealed transient cerebellar vermis hypoperfusion in addition to hippocampal DWI changes. We present what may be a novel report of isolated cerebellar ischemia presenting as TGA, and we add to the literature for clinicians to consider the possibility that damage to the cerebellum or its circuit to the cerebrum or thalamus can present as TGA.

External validation of the epidemiology-based mortality score in status epilepticus in an American intensive care population.

INTRODUCTION: Although overall mortality of status epilepticus is high, baseline patient characteristics and co-morbidities may be helpful to predict outcomes and shape treatment decisions. Two previously published scoring systems exist to predict outcomes: the Status Epilepticus Severity Score (STESS) and the Epidemiology-based Mortality Score in Status Epilepticus (EMSE). However, a comparison of the two scores has not previously been completed in an American intensive care unit. We hypothesize that both scores will adequately predict the primary outcome of in-hospital death, but that the EMSE may more accurately predict functional outcomes, and significantly impact treatment decisions for both clinicians and families. METHODS: We performed a retrospective analysis of all cases of status epilepticus admitted to the Neuro-Critical Care Unit (NCCU) at the Ohio State University Wexner Medical Center from 6/1/2014 - 8/31/2015. We collected data on age, comorbidities, EEG findings, and seizure history. The primary outcome measured was in-hospital death; secondary outcomes included length of stay in the NCCU, placement of a tracheostomy and/or a percutaneous endoscopic gastrostomy upon discharge, and discharge location were used as surrogate markers for outcome severity. A sensitivity and specificity analysis was carried out, in addition to a student's t-test for a comparison of the two scores. ANOVA was completed to compare secondary outcomes RESULTS: Forty-six patients were admitted to the NCCU for management of status epilepticus during June 2014 and January 2016, thirteen of which experienced in-hospital death. The median age of the sample was 60, with approximately half of the sample (52.63%) having 3 or more comorbidities. The sensitivity of both EMSE and STESS were very high (100% and 90% respectively); however, the specificities were very low (28.6% and 42.9% respectively). A student's t-test between those who experienced in-hospital death and those who did not was only significant for EMSE at the p < 0.1 level (p = 0.055). Additionally, mean EMSE scores but not STESS scores, were significantly higher (p < 0.001) for those patients who were discharged to skilled nursing facilities or with hospice than compared to those who were discharged to home or to acute inpatient rehabilitation. CONCLUSIONS: The EMSE and STESS may be useful to predict outcomes of status epilepticus in populations with few comorbid conditions, but are less helpful when patients have multiple medical problems. Secondly, while neither score may be specific enough to differentiate for the primary outcome of death, their utility may be helpful to predict secondary outcomes that strongly affect clinical decisions. Based on these results, we believe a prospective trial of EMSE and STESS should be carried out to obtain more information on their utility, especially in American patients who may have more relevant comorbidities than in other countries.

A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals.

Depression is a mental disorder characterized by persistent occurrences of lower mood states in the affected person. The electroencephalogram (EEG) signals are highly complex, nonlinear, and nonstationary in nature. The characteristics of the signal vary with the age and mental state of the subject. The signs of abnormality may be invisible to the naked eyes. Even when they are visible, deciphering the minute changes indicating abnormality is tedious and time consuming for the clinicians. This paper presents a novel method for automated EEG-based diagnosis of depression using nonlinear methods: fractal dimension, largest Lyapunov exponent, sample entropy, detrended fluctuation analysis, Hurst's exponent, higher order spectra, and recurrence quantification analysis. A novel Depression Diagnosis Index (DDI) is presented through judicious combination of the nonlinear features. The DDI calculated automatically based on the EEG recordings can be used to diagnose depression objectively using just one numeric value. Also, these features extracted from nonlinear methods are ranked using the t value and fed to the support vector machine (SVM) classifier. The SVM classifier yielded the highest classification performance with an average accuracy of about 98%, sensitivity of about 97%, and specificity of about 98.5%.

Computer-Aided Diagnosis of Depression Using EEG Signals.

The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very tedious to interpret visually and highly difficult to extract the significant features from them. The linear and nonlinear methods are effective in identifying the changes in EEG signals for the detection of depression. Linear methods do not exhibit the complex dynamical variations in the EEG signals. Hence, chaos theory and nonlinear dynamic methods are widely used in extracting the EEG signal features for computer-aided diagnosis (CAD) of depression. Hence, this article presents the recent efforts on CAD of depression using EEG signals with a focus on using nonlinear methods. Such a CAD system is simple to use and may be used by the clinicians as a tool to confirm their diagnosis. It should be of a particular value to enable the early detection of depression.

The role of magnetic resonance imaging in management of patients with nonlobar hypertensive intracerebral hemorrhage.

BACKGROUND AND PURPOSE: Despite the increased use and availability of magnetic resonance imaging (MRI), its role in hypertensive intracerebral hemorrhage (ICH) remains uncertain. In this retrospective study, we assessed the utility of MRI in diagnosis and management of patients with hypertensive ICH. METHODS: We retrospectively reviewed the charts of patients with ICH presenting to our hospital over an 18-month period. We included patients who presented with hypertensive ICH in typical locations and excluded lobar hemorrhages. We further isolated cases that had undergone MRI. Collected data included mean age, gender, location of hematoma, neuroradiologist's interpretative report of the MRI, and management steps taken in response to the results of the MRI. Logistic regression was used to determine whether the overall yield of MRI in these patients was significant. RESULTS: We found 222 patients with ICH in our database. Forty-eight patients met our inclusion criteria, of which 24 had brain MRI done as a part of their hospital workup. Brain MRI obtained in 2 (8%) of the 24 patients revealed abnormalities that led to a change in management. The diagnostic yield of MRI and the management decisions that followed were both insignificant. CONCLUSIONS: The diagnostic yield of brain MRI in patients with nonlobar hypertensive ICH is low and does not result in significant changes in management.

Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis.

Electroencephalography (EEG) is an important tool for studying the human brain activity and epileptic processes in particular. EEG signals provide important information about epileptogenic networks that must be analyzed and understood before the initiation of therapeutic procedures. Very small variations in EEG signals depict a definite type of brain abnormality. The challenge is to design and develop signal processing algorithms which extract this subtle information and use it for diagnosis, monitoring and treatment of patients with epilepsy. This paper presents a review of wavelet techniques for computer-aided seizure detection and epilepsy diagnosis with an emphasis on research reported during the past decade. A multiparadigm approach based on the integration of wavelets, nonlinear dynamics and chaos theory, and neural networks advanced by Adeli and associates is the most effective method for automated EEG-based diagnosis of epilepsy.

Computer-aided diagnosis of alcoholism-related EEG signals.

Alcoholism is a severe disorder that affects the functionality of neurons in the central nervous system (CNS) and alters the behavior of the affected person. Electroencephalogram (EEG) signals can be used as a diagnostic tool in the evaluation of subjects with alcoholism. The neurophysiological interpretation of EEG signals in persons with alcoholism (PWA) is based on observation and interpretation of the frequency and power in their EEGs compared to EEG signals from persons without alcoholism. This paper presents a review of the known features of EEGs obtained from PWA and proposes that the impact of alcoholism on the brain can be determined by computer-aided analysis of EEGs through extracting the minute variations in the EEG signals that can differentiate the EEGs of PWA from those of nonaffected persons. The authors advance the idea of automated computer-aided diagnosis (CAD) of alcoholism by employing the EEG signals. This is achieved through judicious combination of signal processing techniques such as wavelet, nonlinear dynamics, and chaos theory and pattern recognition and classification techniques. A CAD system is cost-effective and efficient and can be used as a decision support system by physicians in the diagnosis and treatment of alcoholism especially those who do not specialize in alcoholism or neurophysiology. It can also be of great value to rehabilitation centers to assess PWA over time and to monitor the impact of treatment aimed at minimizing or reversing the effects of the disease on the brain. A CAD system can be used to determine the extent of alcoholism-related changes in EEG signals (low, medium, high) and the effectiveness of therapeutic plans.

Autism: cause factors, early diagnosis and therapies.

Autism spectrum disorder (ASD) is a complex neurobiological disorder characterized by neuropsychological and behavioral deficits. Cognitive impairment, lack of social skills, and stereotyped behavior are the major autistic symptoms, visible after a certain age. It is one of the fastest growing disabilities. Its current prevalence rate in the U.S. estimated by the Centers for Disease Control and Prevention is 1 in 68 births. The genetic and physiological structure of the brain is studied to determine the pathology of autism, but diagnosis of autism at an early age is challenging due to the existing phenotypic and etiological heterogeneity among ASD individuals. Volumetric and neuroimaging techniques are explored to elucidate the neuroanatomy of the ASD brain. Nuroanatomical, neurochemical, and neuroimaging biomarkers can help in the early diagnosis and treatment of ASD. This paper presents a review of the types of autism, etiologies, early detection, and treatment of ASD.

Automated diagnosis of autism: in search of a mathematical marker.

Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-the-art review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEG-based diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder.

Spatiotemporal analysis of relative convergence of EEGs reveals differences between brain dynamics of depressive women and men.

A new nonlinear technique for analysis of brain dynamics called spatiotemporal analysis of relative convergence (STARC) of electroencephalograms (EEGs) is introduced, based on the relative convergence of EEGs of different loci. This technique shows how many times EEGs of each loci pair converge together, which in turn is used as an indicator to determine the different neuronal regions involved in performing the same task. A higher STARC value indicates that more regions are recruited to perform the same task. The STARC methodology was used to reveal sex difference pathophysiology and brain dynamics, using EEG data from 11 male and 11 female adults with major depressive disorder (MDD). The results show significant differences in relative convergences of EEGs of intraleft temporal and frontoleft temporal lobes at δ band, between male and female patients.

Fuzzy Synchronization Likelihood-wavelet methodology for diagnosis of autism spectrum disorder.

This paper presents a methodology for investigation of functional connectivity in patients with autism spectrum disorder (ASD) using Fuzzy Synchronization Likelihood (Fuzzy SL). Fuzzy SLs between and within brain regions are calculated in all EEG sub-bands produced by the wavelet decomposition as well as in the full-band EEG. Then, discriminative Fuzzy SLs between and within different regions and different EEG sub-bands or full-band EEG for distinguishing autistic children from healthy control children are determined based on Analysis of Variation (ANOVA). Finally, the selected features are used as input to an Enhanced Probabilistic Neural Network classifier to make an accurate diagnosis of ASD based on the detected differences in the regional functional connectivity of autistic and healthy EEGs. The methodology is validated using EEG data obtained from 9 autistic and 9 healthy children. The ANOVA test showed high ability of the regional Fuzzy SLs in low frequency bands, delta and theta, as well as alpha band for discriminating the two groups. A high classification accuracy of 95.5% was achieved for distinguishing autistic EEGs from healthy EEGs. It is concluded that the methodology presented in this paper can be used as an effective tool for diagnosis of the autism. Further, the regional Fuzzy SLs discovered in this research can be used as reliable markers in neurofeedback treatments to improve neuronal plasticity and connectivity in autistic patients.

Fractality analysis of frontal brain in major depressive disorder.

EEGs of the frontal brain of patients diagnosed with major depressive disorder (MDD) have been investigated in recent years using linear methods but not based on nonlinear methods. This paper presents an investigation of the frontal brain of MDD patients using the wavelet-chaos methodology and Katz's and Higuchi's fractal dimensions (KFD and HFD) as measures of nonlinearity and complexity. EEGs of the frontal brain of healthy adults and MDD patients are decomposed into 5 EEG sub-bands employing a wavelet filter bank, and the FDs of the band-limited as well as those of their 5 sub-bands are computed. Then, using the ANOVA statistical test, HFDs and KFDs of the left and right frontal lobes in EEG full-band and sub-bands of MDD and healthy groups are compared in order to discover the FDs showing the most meaningful differences between the two groups. Finally, the discovered FDs are used as input to a classifier, enhanced probabilistic neural network (EPNN), to discriminate the MDD from healthy EEGs. The results of HFD show higher complexity of left, right and overall frontal lobes of the brain of MDD compared with non-MDD in beta and gamma sub-bands. Moreover, it is observed that HFD of the beta band is more discriminative than HFD of the gamma band for discriminating MDD and non-MDD participants, while the KFD did not show any meaningful difference. A high accuracy of 91.3% is achieved for classification of MDD and non-MDD EEGs based on HFDs of left, right, and overall frontal brain beta sub-band. The findings of this research, however, should be considered tentative because of limited data available to the authors.

Graph theoretical analysis of organization of functional brain networks in ADHD.

This article presents a new methodology for investigation of the organization of the overall and hemispheric brain network of patients with attention-deficit hyperactivity disorder (ADHD) using theoretical analysis of a weighted graph with the goal of discovering how the brain topology is affected in such patients. The synchronization measure used is the nonlinear fuzzy synchronization likelihood (FSL) developed by the authors recently. Recent evidence indicates a normal neocortex has a small-world (SW) network with a balance between local structure and global structure characteristics. Such a network results in optimal balance between segregation and integration which is essential for high synchronizabilty and fast information transmission in a complex network. The SW network is characterized by the coexistence of dense clustering of connections (C) and short path lengths (L) among the network units. The results of investigation of C show the local structure of functional left-hemisphere brain networks of ADHD diverges from that of non-ADHD which is recognizable in the delta electroencephalograph (EEG) sub-band. Also, the results of investigation for L show the global structure of functional left-hemisphere brain networks of ADHD diverges from that of non-ADHD which is observable in the delta EEG sub-band. It is concluded that the changes in left-hemisphere brain's structure of ADHD from that of the non-ADHD are so much that L and C can distinguish the ADHD brain from the non-ADHD brain in the delta EEG sub-band.

Fractality and a wavelet-chaos-neural network methodology for EEG-based diagnosis of autistic spectrum disorder.

A method is presented for investigation of EEG of children with autistic spectrum disorder using complexity and chaos theory with the goal of discovering a nonlinear feature space. Fractal Dimension is proposed for investigation of complexity and dynamical changes in autistic spectrum disorder in brain. Two methods are investigated for computation of fractal dimension: Higuchi's Fractal Dimension and Katz's Fractal Dimension. A wavelet-chaos-neural network methodology is presented for automated EEG-based diagnosis of autistic spectrum disorder. The model is tested on a database of eyes-closed EEG data obtained from two groups: nine autistic spectrum disorder children, 6 to 13 years old, and eight non-autistic spectrum disorder children, 7 to 13 years old. Using a radial basis function classifier, an accuracy of 90% was achieved based on the most significant features discovered via analysis of variation statistical test, which are three Katz's Fractal Dimensions in delta (of loci Fp2 and C3) and gamma (of locus T6) EEG sub-bands with P < 0.001.

Intraoperative neurophysiologic monitoring in 80 patients with Chiari I malformation: role of duraplasty.

Neurophysiologic intraoperative monitoring of the brainstem auditory evoked potentials (BAEPs) is a widely used method to assess the functional integrity of the central auditory system during surgery involving the brainstem or the cranial nerves. The purpose of this study is to describe our experience with neurophysiologic intraoperative monitoring of BAEPs during posterior fossa decompression (PFD) surgery for the management of Chiari I malformation. Although suboccipital craniectomy is the standard surgical technique applied in all cases undergoing PFD, the role of dural patch grafting (duraplasty) remains controversial. In most cases, the PFD was supplemented by duraplasty only when the Chiari I malformation was complicated by the presence of syringomyelia. Our study reviewed the intraoperative BAEP changes during the different surgical stages of Chiari repair and correlated these with clinical and radiological findings present. Our data revealed that for both groups of patients, with or without associated syringomyelia, the predominant improvement in central conduction in most cases occurred during the period of bony decompression without significant additional improvement after the duraplasty procedure.