Impact of transient hypotension on regional cerebral blood flow in humans

Abstract We examined the impact of progressive hypotension with and without hypocapnia on regional extracranial cerebral blood flow (CBF) and intracranial velocities. Participants underwent progressive lower-body negative pressure (LBNP) until pre-syncope to inflict hypotension. End-tidal carbon dioxide was clamped at baseline levels (isocapnic trial) or uncontrolled (poikilocapnic trial). Middle cerebral artery (MCA) and posterior cerebral artery (PCA) blood velocities (transcranial Doppler; TCD), heart rate, blood pressure and end-tidal carbon dioxide were obtained continuously. Measurements of internal carotid artery (ICA) and vertebral artery (VA) blood flow (ICA BF and VA BF respectively) were also obtained. Overall, blood pressure was reduced by ∼20 % from baseline in both trials (P < 0.001). In the isocapnic trial, end-tidal carbon dioxide was successfully clamped at baseline with hypotension, whereas in the poikilocapnic trial it was reduced by 11.1 mmHg (P < 0.001) with hypotension. The decline in the ICA BF with hypotension was comparable between trials (−139 + − 82 ml; ∼30 %; P < 0.0001); however, the decline in the VA BF was −28 + − 22 ml/min (∼21 %) greater in the poikilocapnic trial compared with the isocapnic trial (P = 0.002). Regardless of trial, the blood flow reductions in ICA (−26 + − 14 %) and VA (−27 + − 14 %) were greater than the decline in MCA (−21 + − 15 %) and PCA (−19 + − 10 %) velocities respectively (P 0.01). Significant reductions in the diameter of both the ICA (∼5 %) and the VA (∼7 %) contributed to the decline in cerebral perfusion with systemic hypotension, independent of hypocapnia. In summary, our findings indicate that blood flow in the VA, unlike the ICA, is sensitive to changes hypotension and hypocapnia. We show for the first time that the decline in global CBF with hypotension is influenced by arterial constriction in the ICA and VA. Additionally, our findings suggest TCD measures of blood flow velocity may modestly underestimate changes in CBF during hypotension with and without hypocapnia, particularly in the posterior circulation

Resting State EEG Characteristics During Sedation With Midazolam or Propofol in Older Subjects

BACKGROUND: Despite widespread application, little is known about the neurophysiological effects of light sedation with midazolam or propofol, particularly in older subjects. The aim of this study was to assess the effects of light sedation with midazolam or propofol on a variety of EEG measures in older subjects. METHODS: In patients (≥60 years without neuropsychiatric disease such as delirium), 2 EEG recordings were performed, before and after administration of either midazolam (n = 22) or propofol (n = 26) to facilitate an endoscopic procedure. Power spectrum, functional connectivity, and network topology based on the minimum spanning tree (MST) were compared within subjects. RESULTS: Midazolam and propofol administration resulted in Richmond Agitation and Sedation Scale levels between 0 and -4 and between -2 and -4, respectively. Both agents altered the power spectra with increased delta (0.5-4 Hz) and decreased alpha (8-13 Hz) power. Only propofol was found to significantly reduce functional connectivity. In the beta frequency band, the MST was more integrated during midazolam sedation. Propofol sedation resulted in a less integrated network in the alpha frequency band. CONCLUSION: Despite the different levels of light sedation with midazolam and propofol, similar changes in power were found. Functional connectivity and network topology showed differences between midazolam and propofol sedation. Future research should establish if these differences are caused by the different levels of sedation or the mechanism of action of these agents

Resting State EEG Characteristics During Sedation With Midazolam or Propofol in Older Subjects

Background. Despite widespread application, little is known about the neurophysiological effects of light sedation with midazolam or propofol, particularly in older subjects. The aim of this study was to assess the effects of light sedation with midazolam or propofol on a variety of EEG measures in older subjects. Methods. In patients (≥60 years without neuropsychiatric disease such as delirium), 2 EEG recordings were performed, before and after administration of either midazolam (n = 22) or propofol (n = 26) to facilitate an endoscopic procedure. Power spectrum, functional connectivity, and network topology based on the minimum spanning tree (MST) were compared within subjects. Results. Midazolam and propofol administration resulted in Richmond Agitation and Sedation Scale levels between 0 and −4 and between −2 and −4, respectively. Both agents altered the power spectra with increased delta (0.5-4 Hz) and decreased alpha (8-13 Hz) power. Only propofol was found to significantly reduce functional connectivity. In the beta frequency band, the MST was more integrated during midazolam sedation. Propofol sedation resulted in a less integrated network in the alpha frequency band. Conclusion. Despite the different levels of light sedation with midazolam and propofol, similar changes in power were found. Functional connectivity and network topology showed differences between midazolam and propofol sedation. Future research should establish if these differences are caused by the different levels of sedation or the mechanism of action of these agents

Postoperatief delirium herkennen bij ouderen

Objective: To determine the degree of agreement between delirium experts on the diagnosis of delirium based on exactly the same information, and to assess the sensitivity of delirium screening methods used by clinical nurses. Design: Prospective observational longitudinal study. Method: Older patients (≥ 60 years) who underwent major surgery were included. During the first three days after surgery they had a standardised cognitive screening test which was recorded on video. Two delirium experts independently evaluated these videos and the information from the patient records. They classified the patients as having 'no delirium', 'possible delirium' or 'delirium'. If there was disagreement, a third expert was consulted. The final classification, based on consensus of two or three delirium experts, was compared with the result of the delirium screening carried out by the clinical nurses. Results: A total of 167 patients were included and 424 postoperative classifications were obtained. The agreement between the experts was 0.61(95% confidence interval (CI):0.53-0.68), based on Cohen's kappa. In 89 (21.0%) of the postoperative classifications there was no agreement between the experts and a third expert was consulted. The nurses using the delirium screening tools recognised 32% of the cases that had been classified as delirium by the experts. Conclusion: There was considerable disagreement between the classifications of individual delirium experts, based on exactly the same information, indicating the difficulty of the diagnosis. Furthermore, the sensitivity of the delirium screening tools used by the clinical nurses was poor. Further research should focus on the development of objective methods for recognising delirium

Postoperatief delirium herkennen bij ouderen

Objective: To determine the degree of agreement between delirium experts on the diagnosis of delirium based on exactly the same information, and to assess the sensitivity of delirium screening methods used by clinical nurses. Design: Prospective observational longitudinal study. Method: Older patients (≥ 60 years) who underwent major surgery were included. During the first three days after surgery they had a standardised cognitive screening test which was recorded on video. Two delirium experts independently evaluated these videos and the information from the patient records. They classified the patients as having 'no delirium', 'possible delirium' or 'delirium'. If there was disagreement, a third expert was consulted. The final classification, based on consensus of two or three delirium experts, was compared with the result of the delirium screening carried out by the clinical nurses. Results: A total of 167 patients were included and 424 postoperative classifications were obtained. The agreement between the experts was 0.61(95% confidence interval (CI):0.53-0.68), based on Cohen's kappa. In 89 (21.0%) of the postoperative classifications there was no agreement between the experts and a third expert was consulted. The nurses using the delirium screening tools recognised 32% of the cases that had been classified as delirium by the experts. Conclusion: There was considerable disagreement between the classifications of individual delirium experts, based on exactly the same information, indicating the difficulty of the diagnosis. Furthermore, the sensitivity of the delirium screening tools used by the clinical nurses was poor. Further research should focus on the development of objective methods for recognising delirium

Functional connectivity and network analysis during hypoactive delirium and recovery from anesthesia

Objective To gain insight in the underlying mechanism of reduced levels of consciousness due to hypoactive delirium versus recovery from anesthesia, we studied functional connectivity and network topology using electroencephalography (EEG). Methods EEG recordings were performed in age and sex-matched patients with hypoactive delirium (n = 18), patients recovering from anesthesia (n = 20), and non-delirious control patients (n = 20), all after cardiac surgery. Functional and directed connectivity were studied with phase lag index and directed phase transfer entropy. Network topology was characterized using the minimum spanning tree (MST). A random forest classifier was calculated based on all measures to obtain discriminative ability between the three groups. Results Non-delirious control subjects showed a back-to-front information flow, which was lost during hypoactive delirium (p = 0.01) and recovery from anesthesia (p < 0.01). The recovery from anesthesia group had more integrated network in the delta band compared to non-delirious controls. In contrast, hypoactive delirium showed a less integrated network in the alpha band. High accuracy for discrimination between hypoactive delirious patients and controls (86%) and recovery from anesthesia and controls (95%) were found. Accuracy for discrimination between hypoactive delirium and recovery from anesthesia was 73%. Conclusion Loss of functional and directed connectivity were observed in both hypoactive delirium and recovery from anesthesia, which might be related to the reduced level of consciousness in both states. These states could be distinguished in topology, which was a less integrated network during hypoactive delirium. Significance Functional and directed connectivity are similarly disturbed during a reduced level of consciousness due to hypoactive delirium and sedatives, however topology was differently affected

Functional connectivity and network analysis during hypoactive delirium and recovery from anesthesia

OBJECTIVE: To gain insight in the underlying mechanism of reduced levels of consciousness due to hypoactive delirium versus recovery from anesthesia, we studied functional connectivity and network topology using electroencephalography (EEG). METHODS: EEG recordings were performed in age and sex-matched patients with hypoactive delirium (n=18), patients recovering from anesthesia (n=20), and non-delirious control patients (n=20), all after cardiac surgery. Functional and directed connectivity were studied with phase lag index and directed phase transfer entropy. Network topology was characterized using the minimum spanning tree (MST). A random forest classifier was calculated based on all measures to obtain discriminative ability between the three groups. RESULTS: Non-delirious control subjects showed a back-to-front information flow, which was lost during hypoactive delirium (p=0.01) and recovery from anesthesia (p<0.01). The recovery from anesthesia group had more integrated network in the delta band compared to non-delirious controls. In contrast, hypoactive delirium showed a less integrated network in the alpha band. High accuracy for discrimination between hypoactive delirious patients and controls (86%) and recovery from anesthesia and controls (95%) were found. Accuracy for discrimination between hypoactive delirium and recovery from anesthesia was 73%. CONCLUSION: Loss of functional and directed connectivity were observed in both hypoactive delirium and recovery from anesthesia, which might be related to the reduced level of consciousness in both states. These states could be distinguished in topology, which was a less integrated network during hypoactive delirium. SIGNIFICANCE: Functional and directed connectivity are similarly disturbed during a reduced level of consciousness due to hypoactive delirium and sedatives, however topology was differently affected

Understanding global brain network alterations in glioma patients

INTRODUCTION: Glioma patients show increased global brain network clustering relating to poorer cognition and epilepsy. However, it is unclear whether this increase is spatially widespread, localized in the (peri)tumor region only, or decreases with distance from the tumor. MATERIALS AND METHODS: Weighted global and local brain network clustering was determined in 71 glioma patients and 53 controls using magnetoencephalography. Tumor clustering was determined by averaging local clustering of regions overlapping with the tumor, and vice versa for non-tumor regions. Euclidean distance was determined from the tumor centroid to the centroids of other regions. RESULTS: Patients showed higher global clustering compared to controls. Clustering of tumor and non-tumor regions did not differ and local clustering was not associated with distance from the tumor. Post-hoc analyses revealed that in the patient group, tumors were located more often in regions with higher clustering in controls, but it seemed that tumors of patients with high global clustering were located more often in regions with lower clustering in controls. CONCLUSIONS: Glioma patients show non-local network disturbances. Tumors of patients with high global clustering may have a preferred localization, namely regions with lower clustering in controls, suggesting that tumor localization relates to the extent of network disruption