Hepatic encephalopathy: a neurochemical, neuroanatomical, and neuropsychological study.

Hepatic encephalopathy (HE) is normally diagnosed by neuropsychological (NP) tests, which are not very specific and do not reveal the underlying pathology. Magnetic resonance imaging (MRI) and spectroscopy (MRS) of the brain offer alternative and possibly more specific markers for HE. These methods were applied in conjunction with NP testing in order to determine their usefulness in the identification of HE and to understand the pathogenesis of HE more clearly. MR imaging and spectroscopy examinations, in addition to a battery of 15 NP tests, were administered to investigate 31 patients awaiting liver transplantation and 23 healthy controls. MR image intensities from the globus pallidus region were calculated and normalized to those of the thalamus. Absolute concentrations and ratios with respect to creatine (Cr) of several metabolites were computed from MR spectra. The MR data were correlated with the results of NP tests. The patients showed impairment in NP tests of attention and visuospatial and verbal fluency. In T1-weighted MRI, the relative intensity of the globus pallidus with respect to that of the thalamus region was significantly elevated in patients and correlated(negatively) with three NP tests (Hooper, FAS, and Trails B). The absolute concentrations of myo-inositol (mI) and choline (Ch) were significantly reduced in three brain regions. In addition, the absolute concentrations of glutamine (Gln) and combined glutamate and glutamine (Glx) were increased in all three locations, with Gln increase being significant in all areas while that of Glx only in the occipital white matter. In summary, this study partially confirms a hypothesized mechanism of HE pathogenesis, an increased synthesis of glutamine by brain glutamate in astrocytes due to excessive blood ammonia, followed by a compensatory loss of myo-inositol to maintain astrocyte volume homeostasis. It also indicates that the hyperintensity observed in globus pallidus could be used as complementary to the NP test scores in evaluating the mental health of HE patients

Hepatic encephalopathy: a neurochemical, neuroanatomical, and neuropsychological study.

Hepatic encephalopathy (HE) is normally diagnosed by neuropsychological (NP) tests, which are not very specific and do not reveal the underlying pathology. Magnetic resonance imaging (MRI) and spectroscopy (MRS) of the brain offer alternative and possibly more specific markers for HE. These methods were applied in conjunction with NP testing in order to determine their usefulness in the identification of HE and to understand the pathogenesis of HE more clearly. MR imaging and spectroscopy examinations, in addition to a battery of 15 NP tests, were administered to investigate 31 patients awaiting liver transplantation and 23 healthy controls. MR image intensities from the globus pallidus region were calculated and normalized to those of the thalamus. Absolute concentrations and ratios with respect to creatine (Cr) of several metabolites were computed from MR spectra. The MR data were correlated with the results of NP tests. The patients showed impairment in NP tests of attention and visuospatial and verbal fluency. In T1-weighted MRI, the relative intensity of the globus pallidus with respect to that of the thalamus region was significantly elevated in patients and correlated(negatively) with three NP tests (Hooper, FAS, and Trails B). The absolute concentrations of myo-inositol (mI) and choline (Ch) were significantly reduced in three brain regions. In addition, the absolute concentrations of glutamine (Gln) and combined glutamate and glutamine (Glx) were increased in all three locations, with Gln increase being significant in all areas while that of Glx only in the occipital white matter. In summary, this study partially confirms a hypothesized mechanism of HE pathogenesis, an increased synthesis of glutamine by brain glutamate in astrocytes due to excessive blood ammonia, followed by a compensatory loss of myo-inositol to maintain astrocyte volume homeostasis. It also indicates that the hyperintensity observed in globus pallidus could be used as complementary to the NP test scores in evaluating the mental health of HE patients

Source-sink connectivity: A novel interictal EEG marker for seizure localization

Over 15 million epilepsy patients worldwide have drug-resistant epilepsy (DRE). Successful surgery is a standard of care treatment for DRE but can only be achieved through complete resection or disconnection of the epileptogenic zone (EZ), the brain region(s) where seizures originate. Surgical success rates vary between 20-80% because no clinically validated biological markers of the EZ exist. Localizing the EZ is a costly and time-consuming process beginning with non-invasive neuroimaging and often followed by days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity (e.g., low-voltage high frequency activity) on individual channels occurring immediately before seizures or spikes that occur on interictal iEEG (i.e., between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in EZ localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. Intracranial EEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aim to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the EZ. We hypothesize that when a patient is not clinically seizing, it is because the EZ is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighboring nodes ("sources") and the inhibited nodes themselves ("sinks"). Specifically, patient-specific dynamical network models (DNMs) were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics (SSMs). We validated the SSMs in a retrospective analysis of 65 patients by using the SSMs of the annotated EZ to predict surgical outcomes. The SSMs predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians' predictions (surgical success rate of this dataset). In failed outcomes, we identified regions of the brain with high SSMs that were untreated. When compared to high frequency oscillations, the most commonly proposed interictal iEEG feature for EZ localization, SSMs outperformed in predictive power (by a factor of 1.2) suggesting SSMs may be an interictal iEEG fingerprint of the EZ. Competing Interest Statement The authors have declared no competing interest

Source-sink connectivity: a novel interictal EEG marker for seizure localization

Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone, the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the epileptogenic zone exist. Localizing the epileptogenic zone is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in epileptogenic zone localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients.IEEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aimed to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the epileptogenic zone. We hypothesized that when a patient is not clinically seizing, it is because the epileptogenic zone is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighbouring nodes ('sources') and the inhibited nodes themselves ('sinks'). Specifically, patient-specific dynamical network models were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics. We validated the algorithm in a retrospective analysis of 65 patients. The source-sink metrics identified epileptogenic regions with 73% accuracy and clinicians agreed with the algorithm in 93% of seizure-free patients. The algorithm was further validated by using the metrics of the annotated epileptogenic zone to predict surgical outcomes. The source-sink metrics predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians' predictions (surgical success rate of this dataset). In failed outcomes, we identified brain regions with high metrics that were untreated. When compared with high frequency oscillations, the most commonly proposed interictal iEEG feature for epileptogenic zone localization, source-sink metrics outperformed in predictive power (by a factor of 1.2), suggesting they may be an interictal iEEG fingerprint of the epileptogenic zone