EEG background delta activity in temporal lobe epilepsy: correlation with volumetric and spectroscopic imaging

Limited clinical validity of univariate resting-state EEG markers for classifying seizure disorders

Differentiating between epilepsy and psychogenic non-epileptic seizures presents a considerable challenge in clinical practice, resulting in frequent misdiagnosis, unnecessary treatment and long diagnostic delays. Quantitative markers extracted from resting-state EEG may reveal subtle neurophysiological differences that are diagnostically relevant. Two observational, retrospective diagnostic accuracy studies were performed to test the clinical validity of univariate resting-state EEG markers for the differential diagnosis of epilepsy and psychogenic non-epileptic seizures. Clinical EEG data were collected for 179 quasi-consecutive patients (age > 18) with a suspected diagnosis of epilepsy or psychogenic non-epileptic seizures who were medication-naïve at the time of EEG; 148 age- and gender-matched patients subsequently received a diagnosis from specialist clinicians and were included in the analyses. Study 1 is a hypothesis-driven study testing the ability of theta power and peak alpha frequency to classify people with epilepsy and people with psychogenic non-epileptic seizures, with an advanced machine learning pipeline. The next study (Study 2) is data-driven; a high number of quantitative EEG features are extracted and a similar machine learning approach as Study 1 assesses whether previously unexplored univariate EEG measures show promise as diagnostic markers. The results of Study 1 suggest that EEG markers that were previously identified as promising diagnostic indicators (i.e. theta power and peak alpha frequency) have limited clinical validity for the classification of epilepsy and psychogenic non-epileptic seizures (mean accuracy: 48%). The results of Study 2 indicate that identifying univariate markers that show good correlation with a categorical diagnostic label is challenging (mean accuracy: 45-60%). This is due to a considerable overlap in neurophysiological features between the diagnostic classes considered in this study, and to the presence of more dominant EEG dynamics such as alterations due to temporal proximity to epileptiform discharges. Markers that were identified in the context of previous epilepsy research using visually normal resting-state EEG were found to have limited clinical validity for the classification task of distinguishing between people with epilepsy and people with psychogenic non-epileptic seizures. A search for alternative diagnostic markers uncovered the challenges involved and generated recommendations for further research.

Heterogeneity of resting-state EEG features in juvenile myoclonic epilepsy and controls

Abnormal EEG features are a hallmark of epilepsy, and abnormal frequency and network features are apparent in EEGs from people with idiopathic generalized epilepsy in both ictal and interictal states. Here, we characterize differences in the resting-state EEG of individuals with juvenile myoclonic epilepsy and assess factors influencing the heterogeneity of EEG features. We collected EEG data from 147 participants with juvenile myoclonic epilepsy through the Biology of Juvenile Myoclonic Epilepsy study. Ninety-five control EEGs were acquired from two independent studies [Chowdhury et al. (2014) and EU-AIMS Longitudinal European Autism Project]. We extracted frequency and functional network-based features from 10 to 20 s epochs of resting-state EEG, including relative power spectral density, peak alpha frequency, network topology measures and brain network ictogenicity: a computational measure of the propensity of networks to generate seizure dynamics. We tested for differences between epilepsy and control EEGs using univariate, multivariable and receiver operating curve analysis. In addition, we explored the heterogeneity of EEG features within and between cohorts by testing for associations with potentially influential factors such as age, sex, epoch length and time, as well as testing for associations with clinical phenotypes including anti-seizure medication, and seizure characteristics in the epilepsy cohort. P-values were corrected for multiple comparisons. Univariate analysis showed significant differences in power spectral density in delta (2-5 Hz) (P = 0.0007, hedges' g = 0.55) and low-alpha (6-9 Hz) (P = 2.9 × 10-8, g = 0.80) frequency bands, peak alpha frequency (P = 0.000007, g = 0.66), functional network mean degree (P = 0.0006, g = 0.48) and brain network ictogenicity (P = 0.00006, g = 0.56) between epilepsy and controls. Since age (P = 0.009) and epoch length (P = 1.7 × 10-8) differed between the two groups and were potential confounders, we controlled for these covariates in multivariable analysis where disparities in EEG features between epilepsy and controls remained. Receiver operating curve analysis showed low-alpha power spectral density was optimal at distinguishing epilepsy from controls, with an area under the curve of 0.72. Lower average normalized clustering coefficient and shorter average normalized path length were associated with poorer seizure control in epilepsy patients. To conclude, individuals with juvenile myoclonic epilepsy have increased power of neural oscillatory activity at low-alpha frequencies, and increased brain network ictogenicity compared with controls, supporting evidence from studies in other epilepsies with considerable external validity. In addition, the impact of confounders on different frequency-based and network-based EEG features observed in this study highlights the need for careful consideration and control of these factors in future EEG research in idiopathic generalized epilepsy particularly for their use as biomarkers.

Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review

Quantitative markers extracted from resting-state electroencephalogram (EEG) reveal subtle neurophysiological dynamics which may provide useful information to support the diagnosis of seizure disorders. We performed a systematic review to summarize evidence on markers extracted from interictal, visually normal resting-state EEG in adults with idiopathic epilepsy or psychogenic nonepileptic seizures (PNES). Studies were selected from 5 databases and evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2. 26 studies were identified, 19 focusing on people with epilepsy, 6 on people with PNES, and one comparing epilepsy and PNES directly. Results suggest that oscillations along the theta frequency (4-8 Hz) may have a relevant role in idiopathic epilepsy, whereas in PNES there was no evident trend. However, studies were subject to a number of methodological limitations potentially introducing bias. There was often a lack of appropriate reporting and high heterogeneity. Results were not appropriate for quantitative synthesis. We identify and discuss the challenges that must be addressed for valid resting-state EEG markers of epilepsy and PNES to be developed.

Quantitative EEG biomarkers for epilepsy and their relation to chemical biomarkers

The electroencephalogram (EEG) is the most important method to diagnose epilepsy. In clinical settings, it is evaluated by experts who identify patterns visually. Quantitative EEG is the application of digital signal processing to clinical recordings in order to automatize diagnostic procedures, and to make patterns visible that are hidden to the human eye. The EEG is related to chemical biomarkers, as electrical activity is based on chemical signals. The most well-known chemical biomarkers are blood laboratory tests to identify seizures after they have happened. However, research on chemical biomarkers is much less extensive than research on quantitative EEG, and combined studies are rarely published, but highly warranted. Quantitative EEG is as old as the EEG itself, but still, the methods are not yet standard in clinical practice. The most evident application is an automation of manual work, but also a quantitative description and localization of interictal epileptiform events as well as seizures can reveal important hints for diagnosis and contribute to presurgical evaluation. In addition, the assessment of network characteristics and entropy measures were found to reveal important insights into epileptic brain activity. Application scenarios of quantitative EEG in epilepsy include seizure prediction, pharmaco-EEG, treatment monitoring, evaluation of cognition, and neurofeedback. The main challenges to quantitative EEG are poor reliability and poor generalizability of measures, as well as the need for individualization of procedures. A main hindrance for quantitative EEG to enter clinical routine is also that training is not yet part of standard curricula for clinical neurophysiologists.

Subcortical and cerebellar atrophy in mesial temporal lobe epilepsy revealed by automatic segmentation

PURPOSE

To determine the validity and utility of using automated subcortical segmentation to identify atrophy of the hippocampus and other subcortical and cerebellar structures in patients with mesial temporal lobe epilepsy (MTLE).

METHODS

Volumetric MRIs were obtained on 21 patients with MTLE (11 right, 10 left) and 21 age- and gender-matched healthy controls. Labeling of subcortical and cerebellar structures was accomplished using automated reconstruction software (FreeSurfer). Multivariate analysis of covariance (MANCOVA) was used to explore group differences in intracranial-normalized, age-adjusted volumes and structural asymmetries. Step-wise discriminant function analysis was used to identify the linear combination of volumes that optimized classification of individual subjects.

RESULTS

Results revealed the expected reduction in hippocampal volume on the side ipsilateral to the seizure focus, as well as bilateral reductions in thalamic and cerebellar gray matter volume. Analysis of structural asymmetries revealed significant asymmetry in the hippocampus and putamen in patients compared to controls. The discriminant function analysis revealed that patients with right and left MTLE were best distinguished from one another using a combination of subcortical volumes that included the right and left hippocampus and left thalamus (91-100% correct classification using cross-validation).

DISCUSSION

Volumetric data obtained with automated segmentation of subcortical and cerebellar structures approximate data from previous studies based on manual tracings. Our data suggest that automated segmentation can provide a clinically useful means of evaluating the nature and extent of structural damage in patients with MTLE and may increase diagnostic classification of patients, especially when hippocampal atrophy is mild.

Influence of levetiracetame on ictal and postictal EEG in patients with partial seizures

To investigate the influence of levetiracetame (LEV) treatment on the interhemispheric seizure pattern propagation and postictal recovery of electroencephalography (EEG) background activity. Twenty-three adult patients (age > 16 years) with pharmacoresistant focal epilepsies presenting at the Epilepsy Center Erlangen for pre-surgical evaluation were enrolled in the study. Those eligible patients receiving only one antiepileptic drugs were recruited to the 48-h baseline phase and, after at least two seizures, were randomized to the 7-day treatment phase with either LEV (n = 11) or placebo (n = 12). All participants were submitted to continuous day-and-night video-EEG monitoring. The daily dose of LEV was 1000 mg (500 mg bid.) on the first treatment day and was increased to 2000 mg (1000 mg bid.) from the second day onward. The EEG changes relating to the time delay of the interhemispheric seizure pattern propagation and to the postictal recovery of the background activity were analysed by computerized video-EEG recording and compared using the non-parameter Mann-Whitney U-exact test (alpha = 0.05). A prolonged latency of the contralateral seizure pattern propagation was observed in the LEV group, whereas a more rapid propagation was observed in the placebo group (P = 0.009). Postictal generalized slowing of the background activity was recorded in 21 patients during the baseline phase. More rapid postictal recovery of the EEG background activity was observed in the LEV, but not in the placebo group (P = 0.03). This study demonstrated that LEV not only prevented the seizure pattern propagation but also helped the speedy recovery of the postictal background activity in the EEG.

Clinical applications of MR spectroscopy in epilepsy

1H and 31P spectroscopy detects relevant metabolite changes in patients with TLE. Numerous studies confirm reduction in NAA and in the ratio of PCr/Pi. In his 1999 review, Kuzniecky concluded that proton MRS, using single-voxel or chemical shift imaging, lateralizes temporal lobe epilepsy in 65% to 96% of cases, with bilateral changes seen in 35% to 45% of cases, whereas phosphorus MRS shows a lateralizing PCr/Pi ratio in 65% to 75% of the TLE patients. There are indications that these changes are reversible with seizure treatment. Improvements in MRS technology, such as the ability to calculate absolute concentrations, to account for differences be-tween gray and white matter and to achieve better spectral resolution by use of a higher magnetic field strength, will now allow more extensive use of this technique for patients with epilepsy.

MR-based in vivo hippocampal volumetrics: 2. Findings in neuropsychiatric disorders

Magnetic resonance imaging (MRI) has opened a new window to the brain. Measuring hippocampal volume with MRI has provided important information about several neuropsychiatric disorders. We reviewed the literature and selected all English-language, human subject, data-driven papers on hippocampal volumetry, yielding a database of 423 records. Smaller hippocampal volumes have been reported in epilepsy, Alzheimer's disease, dementia, mild cognitive impairment, the aged, traumatic brain injury, cardiac arrest, Parkinson's disease, Huntington's disease, Cushing's disease, herpes simplex encephalitis, Turner's syndrome, Down's syndrome, survivors of low birth weight, schizophrenia, major depression, posttraumatic stress disorder, chronic alcoholism, borderline personality disorder, obsessive-compulsive disorder, and antisocial personality disorder. Significantly larger hippocampal volumes have been correlated with autism and children with fragile X syndrome. Preservation of hippocampal volume has been reported in congenital hyperplasia, children with fetal alcohol syndrome, anorexia nervosa, attention-deficit and hyperactivity disorder, bipolar disorder, and panic disorder. Possible mechanisms of hippocampal volume loss in neuropsychiatric disorders are discussed.

Amygdalar interhemispheric functional connectivity differs between the non-depressed and depressed human brain

The amygdalae are important, if not critical, brain regions for many affective, attentional and memorial processes, and dysfunction of the amygdalae has been a consistent finding in the study of clinical depression. Theoretical models of the functional neuroanatomy of both normal and psychopathological affective processes which posit cortical hemispheric specialization of functions have been supported by both lesion and functional neuroimaging studies in humans. Results from human neuroimaging studies in support of amygdalar hemispheric specialization are inconsistent. However, recent results from human lesion studies are consistent with hemispheric specialization. An important, yet largely ignored, feature of the amygdalae in the primate brain--derived from both neuroanatomical and electrophysiological data--is that there are virtually no direct interhemispheric connections via the anterior commissure (AC). This feature stands in stark contrast to that of the rodent brain wherein virtually all amygdalar nuclei have direct interhemispheric connections. We propose this feature of the primate brain, in particular the human brain, is a result of influences from frontocortical hemispheric specialization which have developed over the course of primate brain evolution. Results consistent with this notion were obtained by examining the nature of human amygdalar interhemispheric connectivity using both functional magnetic resonance imaging (FMRI) and positron emission tomography (PET). We found modest evidence of amygdalar interhemispheric functional connectivity in the non-depressed brain, whereas there was strong evidence of functional connectivity in the depressed brain. We interpret and discuss the nature of this connectivity in the depressed brain in the context of dysfunctional frontocortical-amygdalar interactions which accompany clinical depression.

The human amygdala: a systematic review and meta-analysis of volumetric magnetic resonance imaging

The structure and function of the human amygdala is attracting increasing attention in the scientific literature, particularly since the advent of high resolution magnetic resonance imaging (MRI). We carried out a systematic review of the published literature reporting left and right amygdala volumes from MRI in non-clinical subjects. Our aim was to estimate the normal range of the volume of the amygdala and to account for heterogeneity of the measures. The factors we considered included the detail given regarding various subject factors, the plane of scan acquisition, slice thickness and contiguity, magnet strength, positional and volume correction, and the reliability of measurement. Thirty-nine studies with 51 data sets fulfilled selection criteria. The mean+/-95% confidence interval for the left amygdala volume was 1726.7 mm(3)+/-35.1, and right was 1691.7 mm(3)+/-37.2. The left-right difference did not reach statistical significance. The overall range of reported volumes was 1050 mm(3) to 3880 mm(3). The amygdala is significantly larger in men and shows an inverse correlation with age. The main methodological factor found to influence amygdala measurement was anatomical definition. Studies using 'Watson's criteria' (Neurology 42 (1992) 1743) produced significantly larger volumes than the remainder. An index of study quality revealed an inverse relationship with volume-the higher the quality the smaller the volume. This reflected such factors as slice thickness, correction for brain volume, positional correction and number of subjects. We conclude by putting forward a detailed operationalized anatomical delineation of the amygdala, based on Watson's criteria. This work should guide future research in obtaining accurate and reliable amygdala volume measures which in turn will aid comparisons with clinical groups and the specification of structural-functional relationships.

Detection and significance of focal, interictal, slow-wave activity visualized by magnetoencephalography for localization of a primary epileptogenic region

OBJECT

Magnetoencephalography (MEG) is a novel noninvasive diagnostic tool used to determine preoperatively the location of the epileptogenic zone in patients with epilepsy. The presence of focal slowing of activity recorded by electroencephalography (EEG) is an additional indicator of an underlying pathological condition in cases of intractable mesial temporal lobe epilepsy (MTLE). In the present study the authors examined the significance of focal, slow-wave and interictal spike activity detected using MEG in 29 patients who suffered from MTLE that was not associated with structural brain lesions.

METHODS

All patients underwent resective surgery after MEG and EEG monitoring. Equivalent single-dipole modeling was applied to focal low-frequency magnetic activity (LFMA) and interictal paroxysmal activity. Lateralized LFMA was defined as trains of rhythmic activity over the temporal area, with frequencies lower than 7 Hz, which were easily distinguished from background activity. Lateralized LFMA was found in 17 patients (58.6%); it always occurred on the side ipsilateral to the side of resection and displayed a maximum amplitude over the temporal area. Dipolar sources of magnetic flux computed during slow-wave trains were found in the majority of cases in the posterior superior temporal region and, occasionally, in mesial temporal structures that were subsequently resected. With respect to lateralization there was never disagreement between LFMA and MEG interictal spike sources. Thus, in patients with MTLE that is not associated with a mass lesion LFMA is topographically related to the epileptogenic area and, therefore, has value for reliable determination of the side and, possibly, the location of this area.

CONCLUSIONS

Although focal slowing of EEG background activity is generally considered to be a nonspecific sign of functional disturbance, interictal LFMA in patients with MTLE should be conceptualized as a distinct electrographic phenomenon that is directly related to the epileptogenic abnormality. Analyzing the interictal MEG distribution of LFMA and sharp activity improves the diagnostic utility of MEG in patients with suspected TLE who are undergoing surgical evaluation.