Single-channel qEEG characteristics distinguish delirium from no delirium, but not postoperative from non-postoperative delirium.

OBJECTIVE: This exploratory study examined quantitative electroencephalography (qEEG) changes in delirium and the use of qEEG features to distinguish postoperative from non-postoperative delirium. METHODS: This project was part of the DeltaStudy, a cross-sectional,multicenterstudy in Intensive Care Units (ICUs) and non-ICU wards. Single-channel (Fp2-Pz) four-minutes resting-state EEG was analyzed in 456 patients. After calculating 98 qEEG features per epoch, random forest (RF) classification was used to analyze qEEG changes in delirium and to test whether postoperative and non-postoperative delirium could be distinguished. RESULTS: An area under the receiver operatingcharacteristic curve (AUC) of 0.76 (95% Confidence Interval (CI) 0.71-0.80) was found when classifying delirium with a sensitivity of 0.77 and a specificity of 0.63 at the optimal operating point. The classification of postoperative versus non-postoperative delirium resulted in an AUC of 0.50 (95%CI 0.38-0.61). CONCLUSIONS: RF classification was able to discriminate delirium from no delirium with reasonable accuracy, while also identifying new delirium qEEG markers like autocorrelation and theta peak frequency. RF classification could not distinguish postoperative from non-postoperative delirium. SIGNIFICANCE: Single-channel EEG differentiates between delirium and no delirium with reasonable accuracy. We found no distinct EEG profile for postoperative delirium, which may suggest that delirium is one entity, whether it develops postoperatively or not.

fMRI connectivity as a biomarker of antipsychotic treatment response: A systematic review.

BACKGROUND: Antipsychotic drugs are the first-choice therapy for psychotic episodes, but antipsychotic treatment response (AP-R) is unpredictable and only becomes clear after weeks of therapy. A biomarker for AP-R is currently unavailable. We reviewed the evidence for the hypothesis that functional magnetic resonance imaging functional connectivity (fMRI-FC) is a predictor of AP-R or could serve as a biomarker for AP-R in psychosis. METHOD: A systematic review of longitudinal fMRI studies examining the predictive performance and relationship between FC and AP-R was performed following PRISMA guidelines. Technical and clinical aspects were critically assessed for the retrieved studies. We addressed three questions: Q1) is baseline fMRI-FC related to subsequent AP-R; Q2) is AP-R related to a change in fMRI-FC; and Q3) can baseline fMRI-FC predict subsequent AP-R? RESULTS: In total, 28 articles were included. Most studies were of good quality. fMRI-FC analysis pipelines included seed-based-, independent component- / canonical correlation analysis, network-based statistics, and graph-theoretical approaches. We found high heterogeneity in methodological approaches and results. For Q1 (N = 17) and Q2 (N = 18), the most consistent evidence was found for FC between the striatum and ventral attention network as a potential biomarker of AP-R. For Q3 (N = 9) accuracy's varied form 50 till 93%, and prediction models were based on FC between various brain regions. CONCLUSION: The current fMRI-FC literature on AP-R is hampered by heterogeneity of methodological approaches. Methodological uniformity and further improvement of the reliability and validity of fMRI connectivity analysis is needed before fMRI-FC analysis can have a place in clinical applications of antipsychotic treatment.

Gastrointestinal and eating problems in SCN1A-related seizure disorders.

OBJECTIVE: Our study aimed to describe the prevalence and characteristics of gastrointestinal and eating problems in Dravet syndrome (DS) and other SCN1A-related seizure disorders and to determine the association between the occurrence of gastrointestinal and eating problems and core features of DS. METHODS: Gastrointestinal and eating problems were assessed with a questionnaire in a Dutch cohort of participants with an SCN1A-related seizure disorder. Associations between the number of gastrointestinal and eating problems and core features of DS, seizure severity, level of intellectual disability, impaired mobility, behavioral problems, and use of anti-seizure medication, were explored by multivariate ordinal regression analyses. Symptoms were divided into the categories dysphagia-related, behavioral, and gastrointestinal, and were assessed separately. RESULTS: One hundred sixty-nine participants with an SCN1A-related seizure disorder, of whom 118 (69.8%) with DS and 51 (30.2%) with Generalized Epilepsy with Febrile Seizures Plus / Febrile Seizures (GEFS+/FS), the non-DS phenotype, were evaluated. Gastrointestinal and eating problems were highly prevalent in DS participants, 50.8% had more than three symptoms compared to 3.9% of non-DS participants. Of participants with DS, 17.8% were fully or partly fed by a gastric tube. Within the three different symptom categories, the most prevalent dysphagia-related symptom was drooling (60.7%), distraction during mealtimes (61.4%) the most prevalent behavioral symptom, and constipation and loss of appetite (both 50.4%) the most prevalent gastrointestinal symptoms. DS participants who use a wheelchair (odds ratio (OR) 4.9 95%CI (1.9-12.8) compared to walking without aid), who use ≥3 anti-seizure medications (ASM) (OR 5.9 95%CI (1.9-18.2) compared to <3 ASM) and who have behavioral problems (OR 3.0 95%CI (1.1-8.1) compared to no behavioral problems) had more gastrointestinal and eating problems. CONCLUSION: Gastrointestinal and eating problems are frequently reported symptoms in DS. Distinguishing between symptom categories will lead to tailored management of patients at risk, will improve early detection, and enable a timely referral to a dietitian, behavioral expert, and/or speech therapist, ultimately aiming to improve the quality of life of both patients and caregivers.

Macroscale EEG characteristics in antipsychotic-naïve patients with first-episode psychosis and healthy controls.

Electroencephalography in patients with a first episode of psychosis (FEP) may contribute to the diagnosis and treatment response prediction. Findings in the literature vary due to small sample sizes, medication effects, and variable illness duration. We studied macroscale resting-state EEG characteristics of antipsychotic naïve patients with FEP. We tested (1) for differences between FEP patients and controls, (2) if EEG could be used to classify patients as FEP, and (3) if EEG could be used to predict treatment response to antipsychotic medication. In total, we studied EEG recordings of 62 antipsychotic-naïve patients with FEP and 106 healthy controls. Spectral power, phase-based and amplitude-based functional connectivity, and macroscale network characteristics were analyzed, resulting in 60 EEG variables across four frequency bands. Positive and Negative Symptom Scale (PANSS) were assessed at baseline and 4-6 weeks follow-up after treatment with amisulpride or aripiprazole. Mann-Whitney U tests, a random forest (RF) classifier and RF regression were used for statistical analysis. Our study found that at baseline, FEP patients did not differ from controls in any of the EEG characteristics. A random forest classifier showed chance-level discrimination between patients and controls. The random forest regression explained 23% variance in positive symptom reduction after treatment in the patient group. In conclusion, in this largest antipsychotic- naïve EEG sample to date in FEP patients, we found no differences in macroscale EEG characteristics between patients with FEP and healthy controls. However, these EEG characteristics did show predictive value for positive symptom reduction following treatment with antipsychotic medication.

Minimum spanning tree analysis of brain networks: A systematic review of network size effects, sensitivity for neuropsychiatric pathology, and disorder specificity.

Brain network characteristics' potential to serve as a neurological and psychiatric pathology biomarker has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. It is yet unknown whether this approach leads to more consistent findings across studies and converging outcomes of either disease-specific biomarkers or transdiagnostic effects. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies (N = 43) to study consistency of MST metrics between different network sizes and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. Analysis of data from control groups (12 studies) showed that MST leaf fraction but not diameter decreased with increasing network size. Studies showed a broad range in metric values, suggesting that specific processing pipelines affect MST topology. Contradicting findings remain in the inconclusive literature of MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders across pathologies, and is associated with symptom severity and disease progression; (2) neurophysiological studies in epilepsy show frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology in alpha band is found across disorders associated with attention impairments.

High frequency oscillations and high frequency functional network characteristics in the intraoperative electrocorticogram in epilepsy.

OBJECTIVE: High frequency oscillations (HFOs; > 80 Hz), especially fast ripples (FRs, 250-500 Hz), are novel biomarkers for epileptogenic tissue. The pathophysiology suggests enhanced functional connectivity within FR generating tissue. Our aim was to determine the relation between brain areas showing FRs and 'baseline' functional connectivity within EEG networks, especially in the high frequency bands. METHODS: We marked FRs, ripples (80-250 Hz) and spikes in the electrocorticogram of 14 patients with refractory temporal lobe epilepsy. We assessed 'baseline' functional connectivity in epochs free of epileptiform events within these recordings, using the phase lag index. We computed the Eigenvector Centrality (EC) per channel in the FR and gamma band network. We compared EC between channels that did or did not show events at other moments in time. RESULTS: FR-band EC was higher in channels with than without spikes. Gamma-band EC was lower in channels with ripples and FRs. CONCLUSIONS: We confirmed previous findings of functional isolation in the gamma-band and found a first proof of functional integration in the FR-band network of channels covering presumed epileptogenic tissue. SIGNIFICANCE: 'Baseline' high-frequency network parameters might help intra-operative recognition of epileptogenic tissue without the need for waiting for events. These findings can increase our understanding of the 'architecture' of epileptogenic networks and help unravel the pathophysiology of HFOs.

Longitudinal assessment of blood-brain barrier leakage during epileptogenesis in rats. A quantitative MRI study.

The blood-brain barrier (BBB) plays an important role in the homeostasis of the brain. BBB dysfunction has been implicated in the pathophysiology of various neurological disorders, including epilepsy in which it may contribute to disease progression. Precise understanding of BBB dynamics during epileptogenesis may be of importance for the assessment of future therapies, including BBB leakage blocking-agents. Longitudinal changes in BBB integrity can be studied with in vivo magnetic resonance imaging (MRI) in combination with paramagnetic contrast agents. Although this approach has shown to be suitable to detect major BBB leakage during the acute phase in experimental epilepsy models, so far no studies have provided information on dynamics of the extent of BBB leakage towards later phases. Therefore a sensitive and quantitative approach was used in the present study, involving fast T1 mapping (dynamic approach) during a steady-state infusion of gadobutrol, as well as pre- and post-contrast T1-weighted MRI (post-pre approach). This was applied in an experimental epilepsy model in which previous MRI studies failed to detect BBB leakage during epileptogenesis. Adult male Sprague-Dawley rats were injected with kainic acid to induce status epilepticus (SE). MRI experiments were performed before SE (control) and during the acute (1 day) and chronic epileptic phases (6 weeks after SE). BBB leakage was quantified by fast T1 mapping (Look-Locker gradient echo MRI) with a time resolution of 48 s from 5 min before up to 45 min after 20 min step-down infusion of 0.2M gadobutrol. In addition, T1-weighted MRI was acquired before and 45 min after infusion. MRI data were compared to post-mortem microscopic analysis using the BBB tracer fluorescein. Our MRI data showed BBB leakage, which was evident at 1 day and 6 weeks after SE in the hippocampus, entorhinal cortex, amygdala and piriform cortex. These findings were confirmed by microscopic analysis of fluorescein leakage. Furthermore, our MRI data revealed non-uniform BBB leakage throughout epileptogenesis. This study demonstrates BBB leakage in specific brain regions during epileptogenesis, which can be quantified using MRI. Therefore, MRI may be a valuable tool for experimental or clinical studies to elucidate the role of the BBB in epileptogenesis.