Tag: EEG

Machine learning algorithms for detection of visuomotor neural control differences in individuals with PASC and ME

Abstract:

The COVID-19 pandemic has affected millions worldwide, giving rise to long-term symptoms known as post-acute sequelae of SARS-CoV-2 (PASC) infection, colloquially referred to as long COVID. With an increasing number of people experiencing these symptoms, early intervention is crucial. In this study, we introduce a novel method to detect the likelihood of PASC or Myalgic Encephalomyelitis (ME) using a wearable four-channel headband that collects Electroencephalogram (EEG) data. The raw EEG signals are processed using Continuous Wavelet Transform (CWT) to form a spectrogram-like matrix, which serves as input for various machine learning and deep learning models. We employ models such as CONVLSTM (Convolutional Long Short-Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional Long short-term memory). Additionally, we test the dataset on traditional machine learning models for comparative analysis.

Our results show that the best-performing model, CNN-LSTM, achieved an accuracy of 83%. In addition to the original spectrogram data, we generated synthetic spectrograms using Wasserstein Generative Adversarial Networks (WGANs) to augment our dataset. These synthetic spectrograms contributed to the training phase, addressing challenges such as limited data volume and patient privacy. Impressively, the model trained on synthetic data achieved an average accuracy of 93%, significantly outperforming the original model.

These results demonstrate the feasibility and effectiveness of our proposed method in detecting the effects of PASC and ME, paving the way for early identification and management of the condition. The proposed approach holds significant potential for various practical applications, particularly in the clinical domain. It can be utilized for evaluating the current condition of individuals with PASC or ME, and monitoring the recovery process of those with PASC, or the efficacy of any interventions in the PASC and ME populations. By implementing this technique, healthcare professionals can facilitate more effective management of chronic PASC or ME effects, ensuring timely intervention and improving the quality of life for those experiencing these conditions.

Source: Harit Ahuja, Smriti Badhwar, Heather Edgell, Lauren E. Sergio, Marin Litoiu. Machine learning algorithms for detection of visuomotor neural control differences in individuals with PASC and ME. Front. Hum. Neurosci. Sec. Brain-Computer Interfaces, Volume 18 – 2024 | doi: 10.3389/fnhum.2024.1359162 https://www.frontiersin.org/articles/10.3389/fnhum.2024.1359162/full (Full text)

The role of clinical neurophysiology in the definition and assessment of fatigue and fatigability

Highlights:

  • Though a common symptom, fatigue is difficult to define and investigate, and occurs in a wide variety of disorders, with differing pathological causes.
  • This review aims to guide clinicians in how to approach fatigue and to suggest that neurophysiological tests may allow an understanding of its origin and severity.
  • The effectiveness of neurophysiological tests as cost-effective objective biomarkers for the assessment of fatigue has been summarised.

Abstract

Though a common symptom, fatigue is difficult to define and investigate, occurs in a wide variety of neurological and systemic disorders, with differing pathological causes. It is also often accompanied by a psychological component. As a symptom of long-term COVID-19 it has gained more attention.

In this review, we begin by differentiating fatigue, a perception, from fatigability, quantifiable through biomarkers. Central and peripheral nervous system and muscle disorders associated with these are summarised. We provide a comprehensive and objective framework to help identify potential causes of fatigue and fatigability in a given disease condition. It also considers the effectiveness of neurophysiological tests as objective biomarkers for its assessment. Among these, twitch interpolation, motor cortex stimulation, electroencephalography and magnetencephalography, and readiness potentials will be described for the assessment of central fatigability, and surface and needle electromyography (EMG), single fibre EMG and nerve conduction studies for the assessment of peripheral fatigability.

The purpose of this review is to guide clinicians in how to approach fatigue, and fatigability, and to suggest that neurophysiological tests may allow an understanding of their origin and interactions. In this way, their differing types and origins, and hence their possible differing treatments, may also be defined more clearly.

Source: Tankisi H, Versace V, Kuppuswamy A, Cole J. The role of clinical neurophysiology in the definition and assessment of fatigue and fatigability. Clin Neurophysiol Pract. 2023 Dec 18;9:39-50. doi: 10.1016/j.cnp.2023.12.004. PMID: 38274859; PMCID: PMC10808861. https://www.sciencedirect.com/science/article/pii/S2467981X23000367 (Full text)

Electroencephalographic Abnormalities in a Patient Suffering from Long-Term Neuropsychological Complications following SARS-CoV-2 Infection

Abstract:

Introduction: Emotional apathy has recently been identified as a common symptom of long COVID. While recent meta-analyses have demonstrated generalized EEG slowing with the emergence of delta rhythms in patients hospitalized for severe SARS-CoV-2 infection, no EEG study or dopamine transporter scintigraphy (DaTSCAN) has been performed in patients with long COVID presenting with apathy. The objective of this case report was to explore the pathophysiology of neuropsychological symptoms in long COVID.

Case presentation: A 47-year-old patient who developed a long COVID with prominent apathy following an initially clinically mild SARS-CoV-2 infection underwent neuropsychological assessment, cerebral MRI, DaTSCAN, and resting-state high-density EEG 7 months after SARS-CoV-2 infection. The EEG data were compared to those of 21 healthy participants. The patient presented with apathy, cognitive difficulties with dysexecutive syndrome, moderate attentional and verbal episodic memory disturbances, and resolution of premorbid mild gaming disorder, mild mood disturbances, and sleep disturbances. His MRI and DaTSCAN were unremarkable. EEG revealed a complex pattern of oscillatory abnormalities compared to the control group, with a strong increase in whole-scalp delta and beta band activity, as well as a decrease in alpha band activity. Overall, these effects were more prominent in the frontal-central-temporal region.

Conclusion: These results suggest widespread changes in EEG oscillatory patterns in a patient with long COVID characterized by neuropsychological complications with prominent apathy. Despite the inherent limitations of a case report, these results suggest dysfunction in the cortical networks involved in motivation and emotion.

Source: Benis D, Voruz P, Chiuve SC, Garibotto V, Assal F, Krack P, Péron J, Fleury V. Electroencephalographic Abnormalities in a Patient Suffering from Long-Term Neuropsychological Complications following SARS-CoV-2 Infection. Case Rep Neurol. 2023 Dec 5;16(1):6-17. doi: 10.1159/000535241. PMID: 38179211; PMCID: PMC10764086. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10764086/ (Full text)

High-density EEG sleep correlates of cognitive and affective impairment at 12-month follow-up after COVID-19

Abstract:

Objective: To disentangle the pathophysiology of cognitive/affective impairment in Coronavirus Disease-2019 (COVID-19), we studied long-term cognitive and affective sequelae and sleep high-density electroencephalography (EEG) at 12-month follow-up in people with a previous hospital admission for acute COVID-19.

Methods: People discharged from an intensive care unit (ICU) and a sub-intensive ward (nonICU) between March and May 2020 were contacted between March and June 2021. Participants underwent cognitive, psychological, and sleep assessment. High-density EEG recording was acquired during a nap. Slow and fast spindles density/amplitude/frequency and source reconstruction in brain gray matter were extracted. The relationship between psychological and cognitive findings was explored with Pearson correlation.

Results: We enrolled 33 participants ( 17 nonICU) and 12 controls. We observed a lower Physical Quality of Life index, higher post-traumatic stress disorder (PTSD) score, and a worse executive function performance in nonICU participants. Higher PTSD and Beck Depression Inventory scores correlated with lower executive performance. The same group showed a reorganization of spindle cortical generators.

Conclusions: Our results show executive and psycho-affective deficits and spindle alterations in COVID-19 survivors – especially in nonICU participants – after 12 months from discharge.

Significance: These findings may be suggestive of a crucial contribution of stress experienced during hospital admission on long-term cognitive functioning.

Source: Rubega M, Ciringione L, Bertuccelli M, Paramento M, Sparacino G, Vianello A, Masiero S, Vallesi A, Formaggio E, Del Felice A. High-density EEG sleep correlates of cognitive and affective impairment at 12-month follow-up after COVID-19. Clin Neurophysiol. 2022 Jun 15;140:126-135. doi: 10.1016/j.clinph.2022.05.017. Epub ahead of print. PMID: 35763985; PMCID: PMC9292469. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9292469/ (Full text)

A systematic review of neurological impairments in myalgic encephalomyelitis/ chronic fatigue syndrome using neuroimaging techniques

Abstract:

BACKGROUND: Myalgic encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS) is a multi-system illness characterised by a diverse range of debilitating symptoms including autonomic and cognitive dysfunction. The pathomechanism remains elusive, however, neurological and cognitive aberrations are consistently described. This systematic review is the first to collect and appraise the literature related to the structural and functional neurological changes in ME/CFS patients as measured by neuroimaging techniques and to investigate how these changes may influence onset, symptom presentation and severity of the illness.

METHODS: A systematic search of databases Pubmed, Embase, MEDLINE (via EBSCOhost) and Web of Science (via Clarivate Analytics) was performed for articles dating between December 1994 and August 2019. Included publications report on neurological differences in ME/CFS patients compared with healthy controls identified using neuroimaging techniques such as magnetic resonance imaging, positron emission tomography and electroencephalography. Article selection was further refined based on specific inclusion and exclusion criteria. A quality assessment of included publications was completed using the Joanna Briggs Institute checklist.

RESULTS: A total of 55 studies were included in this review. All papers assessed neurological or cognitive differences in adult ME/CFS patients compared with healthy controls using neuroimaging techniques. The outcomes from the articles include changes in gray and white matter volumes, cerebral blood flow, brain structure, sleep, EEG activity, functional connectivity and cognitive function. Secondary measures including symptom severity were also reported in most studies.

CONCLUSIONS: The results suggest widespread disruption of the autonomic nervous system network including morphological changes, white matter abnormalities and aberrations in functional connectivity. However, these findings are not consistent across studies and the origins of these anomalies remain unknown. Future studies are required confirm the potential neurological contribution to the pathology of ME/CFS.

Source: Maksoud R, du Preez S, Eaton-Fitch N, Thapaliya K, Barnden L, Cabanas H, Staines D, Marshall-Gradisnik S. A systematic review of neurological impairments in myalgic encephalomyelitis/ chronic fatigue syndrome using neuroimaging techniques. PLoS One. 2020 Apr 30;15(4):e0232475. doi: 10.1371/journal.pone.0232475. eCollection 2020. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0232475

Small-World Network Analysis of Cortical Connectivity in Chronic Fatigue Syndrome Using Quantitative EEG

Abstract:

The aim of this study was to explore the relationship between complex brain networks in people with Chronic Fatigue Syndrome (CFS) and neurocognitive impairment. Quantitative EEG (qEEG) recordings were taken from 14 people with CFS and 15 healthy controls (HCs) during an eye-closed resting condition.

Exact low resolution electromagnetic tomography (eLORETA) was used to estimate cortical sources and perform a functional connectivity analysis. The graph theory approach was used to characterize network representations for each participant and derive the “small-worldness” index, a measure of the overall homeostatic balance between local and long-distance connectedness.

Results showed that small-worldness for the delta band was significantly lower for patients with CFS compared to HCs. In addition, delta small-worldness was negatively associated with neurocognitive impairment scores on the DePaul Symptom Questionnaire (DSQ). Finally, delta small-worldness indicated a greater risk of complex brain network inefficiency for the CFS group.

These results suggest that CFS pathology may be functionally disruptive to small-world networks. In turn, small-world characteristics might serve as a neurophysiological indicator for confirming a biological basis of cognitive symptoms, treatment outcome, and neurophysiological status of people with CFS.

Source: Citation: Zinn, M. A., Zinn, M. L., & Jason, L. A. (2017). Small-world network analysis of cortical connectivity in Chronic Fatigue Syndrome using quantitative EEG. NeuroRegulation, 4(3–4), 125–137. http://dx.doi.org/10.15540/nr.4.3-4.125 http://www.neuroregulation.org/article/view/17838/11670 (Full article)

Electroencephalogram characteristics in patients with chronic fatigue syndrome

Abstract:

OBJECTIVE: To explore the electroencephalogram (EEG) characteristics in patients with chronic fatigue syndrome (CFS) using brain electrical activity mapping (BEAM) and EEG nonlinear dynamical analysis.

METHODS: Forty-seven outpatients were selected over a 3-month period and divided into an observation group (24 outpatients) and a control group (23 outpatients) by using the non-probability sampling method. All the patients were given a routine EEG. The BEAM and the correlation dimension changes were analyzed to characterize the EEG features.

RESULTS: 1) BEAM results indicated that the energy values of δ, θ, and α1 waves significantly increased in the observation group, compared with the control group (P<0.05, P<0.01, respectively), which suggests that the brain electrical activities in CFS patients were significantly reduced and stayed in an inhibitory state; 2) the increase of δ, θ, and α1 energy values in the right frontal and left occipital regions was more significant than other encephalic regions in CFS patients, indicating the region-specific encephalic distribution; 3) the correlation dimension in the observation group was significantly lower than the control group, suggesting decreased EEG complexity in CFS patients.

CONCLUSION: The spontaneous brain electrical activities in CFS patients were significantly reduced. The abnormal changes in the cerebral functions were localized at the right frontal and left occipital regions in CFS patients.

 

Source: Wu T, Qi X, Su Y, Teng J, Xu X. Electroencephalogram characteristics in patients with chronic fatigue syndrome. Neuropsychiatr Dis Treat. 2016 Jan 28;12:241-9. doi: 10.2147/NDT.S92911. ECollection 2016. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4734796/ (Full article)

 

EEG spectral coherence data distinguish chronic fatigue syndrome patients from healthy controls and depressed patients–a case control study

Abstract:

BACKGROUND: Previous studies suggest central nervous system involvement in chronic fatigue syndrome (CFS), yet there are no established diagnostic criteria. CFS may be difficult to differentiate from clinical depression. The study’s objective was to determine if spectral coherence, a computational derivative of spectral analysis of the electroencephalogram (EEG), could distinguish patients with CFS from healthy control subjects and not erroneously classify depressed patients as having CFS.

METHODS: This is a study, conducted in an academic medical center electroencephalography laboratory, of 632 subjects: 390 healthy normal controls, 70 patients with carefully defined CFS, 24 with major depression, and 148 with general fatigue. Aside from fatigue, all patients were medically healthy by history and examination. EEGs were obtained and spectral coherences calculated after extensive artifact removal. Principal Components Analysis identified coherence factors and corresponding factor loading patterns. Discriminant analysis determined whether spectral coherence factors could reliably discriminate CFS patients from healthy control subjects without misclassifying depression as CFS.

RESULTS: Analysis of EEG coherence data from a large sample (n = 632) of patients and healthy controls identified 40 factors explaining 55.6% total variance. Factors showed highly significant group differentiation (p < .0004) identifying 89.5% of unmedicated female CFS patients and 92.4% of healthy female controls. Recursive jackknifing showed predictions were stable. A conservative 10-factor discriminant function model was subsequently applied, and also showed highly significant group discrimination (p < .001), accurately classifying 88.9% unmedicated males with CFS, and 82.4% unmedicated male healthy controls. No patient with depression was classified as having CFS. The model was less accurate (73.9%) in identifying CFS patients taking psychoactive medications. Factors involving the temporal lobes were of primary importance.

CONCLUSIONS: EEG spectral coherence analysis identified unmedicated patients with CFS and healthy control subjects without misclassifying depressed patients as CFS, providing evidence that CFS patients demonstrate brain physiology that is not observed in healthy normals or patients with major depression. Studies of new CFS patients and comparison groups are required to determine the possible clinical utility of this test. The results concur with other studies finding neurological abnormalities in CFS, and implicate temporal lobe involvement in CFS pathophysiology.

 

Source: Duffy FH, McAnulty GB, McCreary MC, Cuchural GJ, Komaroff AL. EEG spectral coherence data distinguish chronic fatigue syndrome patients from healthy controls and depressed patients–a case control study. BMC Neurol. 2011 Jul 1;11:82. doi: 10.1186/1471-2377-11-82. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3146818/ (Full article)

 

Cognitive impairment in fatigue and sleepiness associated conditions

Abstract:

Although relating to very different concepts, sleepiness and fatigue are often confounded. However, both fatigue-associated conditions such as the chronic fatigue syndrome (CFS) and sleepiness-associated conditions such as the sleep apnea-hypopnea syndrome (SAHS) are associated with cognitive impairment with impaired attention, concentration and memory performances.

Fifteen pure CFS patients, without primary sleep disorders or clinically relevant sleepiness, were compared to 15 untreated SAHS patients, without clinically relevant fatigue, and to 16 healthy controls of similar age. The auditory verbal learning test (AVLT), digit span, digit symbol and finger tapping test (FTT) were used as cognitive and behavioural measures. In addition we assessed daytime EEG spectral power and P300 evoked potentials.

With exception for the digit span, all tests showed lower performances in patient groups. Recall on the AVLT did not differ between the two patient groups, but the digit and symbol spans showed more severe impairment in SAHS patients. Psychomotor performance on the FTT presented with slower hit rates in SAHS than in CFS. EEG theta power was highest in CFS patients. P300 latencies and amplitudes did not differ between groups.

Fatigue- and sleepiness-associated conditions can both present with significant and objective impairment of cognitive functioning and behavioural motor performance. In our sample cognitive impairment and psychomotor performance were worse when associated to sleepiness in SAHS than with fatigue in CFS.

Copyright © 2010 Elsevier Ltd. All rights reserved.

 

Source: Neu D, Kajosch H, Peigneux P, Verbanck P, Linkowski P, Le Bon O. Cognitive impairment in fatigue and sleepiness associated conditions. Psychiatry Res. 2011 Aug 30;189(1):128-34. doi: 10.1016/j.psychres.2010.12.005. Epub 2010 Dec 31. https://www.ncbi.nlm.nih.gov/pubmed/21196050

 

EEG findings in burnout patients

Abstract:

The concept of burnout remains enigmatic since it is only determined by behavioral characteristics. Moreover, the differential diagnosis with depression and chronic fatigue syndrome is difficult.

EEG-related variables in 13 patients diagnosed with burnout syndrome were compared with 13 healthy comparison subjects in order to explore the existence of neurobiological markers for burnout.

Burnout patients showed reduced P300 amplitude, a lower alpha peak frequency and reduced beta power. These EEG-related differences in burnout patients differ from those described in the literature in depression and chronic fatigue patients. Our preliminary findings suggest that burnout might be considered as a separate clinical syndrome.

 

Source: van Luijtelaar G, Verbraak M, van den Bunt M, Keijsers G, Arns M. EEG findings in burnout patients. J Neuropsychiatry Clin Neurosci. 2010 Spring;22(2):208-17. doi: 10.1176/appi.neuropsych.22.2.208. https://www.ncbi.nlm.nih.gov/pubmed/20463115