Electroencephalographic functional connectivity is altered in persons with Down syndrome and Alzheimer's disease

Type-2 Diabetes Alters Hippocampal Neural Oscillations and Disrupts Synchrony between the Hippocampus and Cortex

Type 2 diabetes mellitus (T2DM) increases the risk of neurological diseases, yet how brain oscillations change as age and T2DM interact is not well characterized. To delineate the age and diabetic effect on neurophysiology, we recorded local field potentials with multichannel electrodes spanning the somatosensory cortex and hippocampus (HPC) under urethane anesthesia in diabetic and normoglycemic control mice, at 200 and 400 days of age. We analyzed the signal power of brain oscillations, brain state, sharp wave associate ripples (SPW-Rs), and functional connectivity between the cortex and HPC. We found that while both age and T2DM were correlated with a breakdown in long-range functional connectivity and reduced neurogenesis in the dentate gyrus and subventricular zone, T2DM further slowed brain oscillations and reduced theta-gamma coupling. Age and T2DM also prolonged the duration of SPW-Rs and increased gamma power during SPW-R phase. Our results have identified potential electrophysiological substrates of hippocampal changes associated with T2DM and age. The perturbed brain oscillation features and diminished neurogenesis may underlie T2DM-accelerated cognitive impairment.

Type-2 diabetes alters hippocampal neural oscillations and disrupts synchrony between hippocampus and cortex

Type 2 diabetes mellitus (T2DM) increases the risk of neurological diseases, yet how brain oscillations change as age and T2DM interact is not well characterized. To delineate the age and diabetic effect on neurophysiology, we recorded local field potentials with multichannel electrodes spanning the somatosensory cortex and hippocampus (HPC) under urethane anesthesia in diabetic and normoglycemic control mice, at 200 and 400 days of age. We analyzed the signal power of brain oscillations, brain state, sharp wave associate ripples (SPW-Rs), and functional connectivity between the cortex and HPC. We found that while both age and T2DM were correlated with a breakdown in long-range functional connectivity and reduced neurogenesis in the dentate gyrus and subventricular zone, T2DM further slowed brain oscillations and reduced theta-gamma coupling. Age and T2DM also prolonged the duration of SPW-Rs and increased gamma power during SPW-R phase. Our results have identified potential electrophysiological substrates of hippocampal changes associated with T2DM and age. The perturbed brain oscillation features and diminished neurogenesis may underlie T2DM-accelerated cognitive impairment.

Power and connectivity changes on electroencephalogram in postoperative cerebellar mutism

INTRODUCTION

Cerebellar mutism syndrome is a debilitating postoperative neurological complication following posterior fossa surgery in children. It is characterized by a significant lack or loss of speech. Injury to the dentato-thalamo-cortical pathway is thought to be the main anatomical substrate of cerebellar mutism syndrome; however, few studies have investigated the physiological changes using computed electroencephalogram.

CASE REPORT

Herein, we report a case of a nine-year-old girl who developed cerebellar mutism syndrome after excision of an ependymoma of the fourth ventricle and was followed up with evaluation of aphasia, gross motor function, and scalp electroencephalograms. Her language, dysmetria and gait ataxia gradually improved until day 605 after onset. Computed electroencephalogram analyses were performed for the relative power spectrum and connectivity at each frequency band. On the three electroencephalograms at days 109, 299, and 605 after onset, the relative power spectrum at the delta band transiently decreased and then increased, and the relative power spectrums at theta, beta, and gamma bands transiently increased and then decreased. Only the relative power spectrum in the alpha band continuously increased in the occipital area. Additionally, brain connectivity in the delta, beta, and gamma bands increased continuously.

CONCLUSION

We report a case of cerebellar mutism syndrome with recovery of language, dysmetria and gait ataxia in 20 months. Electroencephalogram analyses indicated transient changes in the powers of brain activity and continuous improvements in connectivity during the long follow-up, reflecting the plasticity and remodeling of brain function after cerebellar mutism syndrome. Power and connectivity analyses for EEG might be a tool to investigate underlying pathophysiology of cerebellar mutism syndrome.

Brain circuit pathology in Down syndrome: from neurons to neural networks

Down syndrome (DS), a genetic pathology caused by triplication of chromosome 21, is characterized by brain hypotrophy and impairment of cognition starting from infancy. While studies in mouse models of DS have elucidated the major neuroanatomical and neurochemical defects of DS, comparatively fewer investigations have focused on the electrophysiology of the DS brain. Electrical activity is at the basis of brain functioning. Therefore, knowledge of the way in which brain circuits operate in DS is fundamental to understand the causes of behavioral impairment and devise targeted interventions. This review summarizes the state of the art regarding the electrical properties of the DS brain, starting from individual neurons and culminating in signal processing in whole neuronal networks. The reported evidence derives from mouse models of DS and from brain tissues and neurons derived from individuals with DS. EEG data recorded in individuals with DS are also provided as a key tool to understand the impact of brain circuit alterations on global brain activity.

Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021

BACKGROUND

With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis.

METHODS

QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis.

RESULTS

A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends.

CONCLUSION

The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.